From 651408a077f842e76e75bfc7d02b8ac38eeb6480 Mon Sep 17 00:00:00 2001 From: Arthur <48595927+ArthurZucker@users.noreply.github.com> Date: Thu, 16 Nov 2023 17:43:19 +0100 Subject: [PATCH] [`Styling`] stylify using ruff (#27144) * try to stylify using ruff * might need to remove these changes? * use ruf format andruff check * use isinstance instead of type comparision * use # fmt: skip * use # fmt: skip * nits * soem styling changes * update ci job * nits isinstance * more files update * nits * more nits * small nits * check and format * revert wrong changes * actually use formatter instead of checker * nits * well docbuilder is overwriting this commit * revert notebook changes * try to nuke docbuilder * style * fix feature exrtaction test * remve `indent-width = 4` * fixup * more nits * update the ruff version that we use * style * nuke docbuilder styling * leve the print for detected changes * nits * Remove file I/O Co-authored-by: charliermarsh * style * nits * revert notebook changes * Add # fmt skip when possible * Add # fmt skip when possible * Fix * More ` # fmt: skip` usage * More ` # fmt: skip` usage * More ` # fmt: skip` usage * NIts * more fixes * fix tapas * Another way to skip * Recommended way * Fix two more fiels * Remove asynch Remove asynch --------- Co-authored-by: charliermarsh --- .circleci/config.yml | 5 +- .circleci/create_circleci_config.py | 5 +- Makefile | 14 +++--- docs/source/_config.py | 2 +- docs/source/en/_config.py | 2 +- docs/source/en/tasks/semantic_segmentation.md | 2 +- docs/source/ko/_config.py | 2 +- docs/source/ko/tasks/semantic_segmentation.md | 2 +- docs/source/pt/_config.py | 2 +- .../flax/text-classification/run_flax_glue.py | 2 +- examples/legacy/pytorch-lightning/run_glue.py | 2 +- examples/legacy/pytorch-lightning/run_ner.py | 2 +- .../deebert/src/modeling_highway_bert.py | 6 +-- .../research_projects/longform-qa/eli5_app.py | 4 +- .../lxmert/modeling_frcnn.py | 4 +- .../emmental/modeling_bert_masked.py | 4 +- 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.../test_modeling_chinese_clip.py | 6 +-- tests/models/clip/test_processor_clip.py | 4 +- tests/models/clip/test_tokenization_clip.py | 4 +- .../models/clipseg/test_processor_clipseg.py | 4 +- .../test_tokenization_code_llama.py | 8 ++- .../test_tokenization_deberta_v2.py | 4 +- .../test_modeling_tf_encoder_decoder.py | 2 +- .../ernie_m/test_tokenization_ernie_m.py | 4 +- tests/models/esm/test_tokenization_esm.py | 4 +- tests/models/flava/test_processor_flava.py | 4 +- tests/models/fnet/test_modeling_fnet.py | 4 +- tests/models/fnet/test_tokenization_fnet.py | 16 +++--- tests/models/fuyu/test_modeling_fuyu.py | 4 +- tests/models/gpt2/test_modeling_gpt2.py | 6 +-- tests/models/gpt_neo/test_modeling_gpt_neo.py | 4 +- .../test_modeling_gpt_neox_japanese.py | 2 +- .../gpt_sw3/test_tokenization_gpt_sw3.py | 4 +- tests/models/gptj/test_modeling_gptj.py | 4 +- tests/models/gptj/test_modeling_tf_gptj.py | 4 +- .../test_modeling_gptsan_japanese.py | 9 ++-- .../test_tokenization_gptsan_japanese.py | 4 +- .../test_modeling_instructblip.py | 8 +-- .../layoutlmv2/test_processor_layoutlmv2.py | 16 ++---- .../test_tokenization_layoutlmv2.py | 24 +++------ .../layoutlmv3/test_processor_layoutlmv3.py | 16 ++---- .../test_tokenization_layoutlmv3.py | 24 +++------ .../layoutxlm/test_processor_layoutxlm.py | 16 ++---- .../layoutxlm/test_tokenization_layoutxlm.py | 25 +++------- tests/models/llama/test_modeling_llama.py | 16 ++---- tests/models/llama/test_tokenization_llama.py | 8 ++- tests/models/lxmert/test_modeling_lxmert.py | 2 +- .../models/lxmert/test_modeling_tf_lxmert.py | 2 +- .../m2m_100/test_tokenization_m2m_100.py | 12 ++--- .../models/marian/test_tokenization_marian.py | 5 +- .../markuplm/test_processor_markuplm.py | 24 +++------ .../markuplm/test_tokenization_markuplm.py | 26 +++------- .../test_image_processing_mask2former.py | 8 +-- .../test_image_processing_maskformer.py | 8 +-- .../mbart50/test_tokenization_mbart50.py | 18 ++----- .../models/mgp_str/test_processor_mgp_str.py | 4 +- .../mgp_str/test_tokenization_mgp_str.py | 4 +- tests/models/mistral/test_modeling_mistral.py | 4 +- tests/models/mvp/test_modeling_mvp.py | 4 +- tests/models/nllb/test_tokenization_nllb.py | 4 +- .../models/nllb_moe/test_modeling_nllb_moe.py | 9 ++-- .../oneformer/test_processor_oneformer.py | 24 +++------ tests/models/owlv2/test_modeling_owlv2.py | 4 +- tests/models/owlvit/test_processor_owlvit.py | 4 +- .../pegasus/test_tokenization_pegasus.py | 4 +- .../perceiver/test_tokenization_perceiver.py | 4 +- tests/models/phi/test_modeling_phi.py | 8 +-- .../reformer/test_tokenization_reformer.py | 4 +- .../test_modeling_roberta_prelayernorm.py | 8 +-- tests/models/sam/test_modeling_sam.py | 4 +- tests/models/sam/test_modeling_tf_sam.py | 4 +- .../test_modeling_seamless_m4t.py | 28 +++-------- .../test_tokenization_seamless_m4t.py | 8 +-- .../test_tokenization_speech_to_text.py | 18 ++----- .../speecht5/test_tokenization_speecht5.py | 22 ++------ .../test_modeling_switch_transformers.py | 1 + tests/models/t5/test_tokenization_t5.py | 4 +- tests/models/tapas/test_tokenization_tapas.py | 8 +-- .../transfo_xl/test_modeling_tf_transfo_xl.py | 34 ++++++------- .../transfo_xl/test_modeling_transfo_xl.py | 8 +-- ...test_modeling_tf_vision_encoder_decoder.py | 2 +- .../test_modeling_vision_encoder_decoder.py | 4 +- ...test_processor_vision_text_dual_encoder.py | 4 +- .../wav2vec2/test_modeling_tf_wav2vec2.py | 29 ++++++----- .../whisper/test_modeling_flax_whisper.py | 4 +- tests/models/whisper/test_modeling_whisper.py | 4 +- .../whisper/test_tokenization_whisper.py | 20 +++----- tests/models/xglm/test_modeling_tf_xglm.py | 4 +- tests/models/xglm/test_modeling_xglm.py | 4 +- tests/models/xglm/test_tokenization_xglm.py | 4 +- .../test_modeling_xlm_prophetnet.py | 4 +- .../test_tokenization_xlm_prophetnet.py | 4 +- .../test_tokenization_xlm_roberta.py | 4 +- tests/models/xlnet/test_tokenization_xlnet.py | 4 +- ..._pipelines_automatic_speech_recognition.py | 16 ++---- .../test_pipelines_conversational.py | 4 +- .../test_pipelines_question_answering.py | 9 ++-- .../test_pipelines_token_classification.py | 20 +++----- tests/test_modeling_common.py | 2 +- tests/test_modeling_tf_common.py | 2 +- tests/test_tokenization_common.py | 16 ++++-- tests/tokenization/test_tokenization_fast.py | 8 +-- tests/trainer/test_trainer.py | 4 +- utils/check_copies.py | 25 ++++++---- utils/tests_fetcher.py | 4 +- 480 files changed, 867 insertions(+), 1059 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 4e6718005ab..1b3a57c76f7 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -157,11 +157,10 @@ jobs: command: pip freeze | tee installed.txt - store_artifacts: path: ~/transformers/installed.txt - - run: black --check examples tests src utils - - run: ruff examples tests src utils + - run: ruff check examples tests src utils + - run: ruff format tests src utils --check - run: python utils/custom_init_isort.py --check_only - run: python utils/sort_auto_mappings.py --check_only - - run: doc-builder style src/transformers docs/source --max_len 119 --check_only --path_to_docs docs/source - run: python utils/check_doc_toc.py check_repository_consistency: diff --git a/.circleci/create_circleci_config.py b/.circleci/create_circleci_config.py index e326e324b65..41e83d87438 100644 --- a/.circleci/create_circleci_config.py +++ b/.circleci/create_circleci_config.py @@ -15,7 +15,6 @@ import argparse import copy -import glob import os import random from dataclasses import dataclass @@ -239,7 +238,7 @@ class CircleCIJob: py_command = f'import os; fp = open("reports/{self.job_name}/summary_short.txt"); failed = os.linesep.join([x for x in fp.read().split(os.linesep) if x.startswith("ERROR ")]); fp.close(); fp = open("summary_short.txt", "w"); fp.write(failed); fp.close()' check_test_command += f"$(python3 -c '{py_command}'); " - check_test_command += f'cat summary_short.txt; echo ""; exit -1; ' + check_test_command += 'cat summary_short.txt; echo ""; exit -1; ' # Deeal with failed tests check_test_command += f'elif [ -s reports/{self.job_name}/failures_short.txt ]; ' @@ -249,7 +248,7 @@ class CircleCIJob: py_command = f'import os; fp = open("reports/{self.job_name}/summary_short.txt"); failed = os.linesep.join([x for x in fp.read().split(os.linesep) if x.startswith("FAILED ")]); fp.close(); fp = open("summary_short.txt", "w"); fp.write(failed); fp.close()' check_test_command += f"$(python3 -c '{py_command}'); " - check_test_command += f'cat summary_short.txt; echo ""; exit -1; ' + check_test_command += 'cat summary_short.txt; echo ""; exit -1; ' check_test_command += f'elif [ -s reports/{self.job_name}/stats.txt ]; then echo "All tests pass!"; ' diff --git a/Makefile b/Makefile index 0c51598594c..befdce08a26 100644 --- a/Makefile +++ b/Makefile @@ -9,8 +9,8 @@ modified_only_fixup: $(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs))) @if test -n "$(modified_py_files)"; then \ echo "Checking/fixing $(modified_py_files)"; \ - black $(modified_py_files); \ - ruff $(modified_py_files) --fix; \ + ruff check $(modified_py_files) --fix; \ + ruff format $(modified_py_files);\ else \ echo "No library .py files were modified"; \ fi @@ -48,11 +48,10 @@ repo-consistency: # this target runs checks on all files quality: - black --check $(check_dirs) setup.py conftest.py + ruff check $(check_dirs) setup.py conftest.py + ruff format --check $(check_dirs) setup.py conftest.py python utils/custom_init_isort.py --check_only python utils/sort_auto_mappings.py --check_only - ruff $(check_dirs) setup.py conftest.py - doc-builder style src/transformers docs/source --max_len 119 --check_only --path_to_docs docs/source python utils/check_doc_toc.py # Format source code automatically and check is there are any problems left that need manual fixing @@ -60,14 +59,13 @@ quality: extra_style_checks: python utils/custom_init_isort.py python utils/sort_auto_mappings.py - doc-builder style src/transformers docs/source --max_len 119 --path_to_docs docs/source python utils/check_doc_toc.py --fix_and_overwrite # this target runs checks on all files and potentially modifies some of them style: - black $(check_dirs) setup.py conftest.py - ruff $(check_dirs) setup.py conftest.py --fix + ruff check $(check_dirs) setup.py conftest.py --fix + ruff format $(check_dirs) setup.py conftest.py ${MAKE} autogenerate_code ${MAKE} extra_style_checks diff --git a/docs/source/_config.py b/docs/source/_config.py index 4a7a86cc23d..d26d908aa29 100644 --- a/docs/source/_config.py +++ b/docs/source/_config.py @@ -10,5 +10,5 @@ notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}] black_avoid_patterns = { "{processor_class}": "FakeProcessorClass", "{model_class}": "FakeModelClass", - "{object_class}": "FakeObjectClass", + "{object_class}": "FakeObjectClass", } diff --git a/docs/source/en/_config.py b/docs/source/en/_config.py index cd76263e9a5..a6d75853f57 100644 --- a/docs/source/en/_config.py +++ b/docs/source/en/_config.py @@ -10,5 +10,5 @@ notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}] black_avoid_patterns = { "{processor_class}": "FakeProcessorClass", "{model_class}": "FakeModelClass", - "{object_class}": "FakeObjectClass", + "{object_class}": "FakeObjectClass", } diff --git a/docs/source/en/tasks/semantic_segmentation.md b/docs/source/en/tasks/semantic_segmentation.md index c3ad3e00f61..2895a197772 100644 --- a/docs/source/en/tasks/semantic_segmentation.md +++ b/docs/source/en/tasks/semantic_segmentation.md @@ -245,7 +245,7 @@ logits first, and then reshaped to match the size of the labels before you can c ... reduce_labels=False, ... ) ... for key, value in metrics.items(): -... if type(value) is np.ndarray: +... if isinstance(value, np.ndarray): ... metrics[key] = value.tolist() ... return metrics ``` diff --git a/docs/source/ko/_config.py b/docs/source/ko/_config.py index 5d966e8c40f..9bdfef7af94 100644 --- a/docs/source/ko/_config.py +++ b/docs/source/ko/_config.py @@ -10,5 +10,5 @@ notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}] black_avoid_patterns = { "{processor_class}": "FakeProcessorClass", "{model_class}": "FakeModelClass", - "{object_class}": "FakeObjectClass", + "{object_class}": "FakeObjectClass", } diff --git a/docs/source/ko/tasks/semantic_segmentation.md b/docs/source/ko/tasks/semantic_segmentation.md index fe27b8f0ba8..4b6109d692b 100644 --- a/docs/source/ko/tasks/semantic_segmentation.md +++ b/docs/source/ko/tasks/semantic_segmentation.md @@ -242,7 +242,7 @@ pip install -q datasets transformers evaluate ... reduce_labels=False, ... ) ... for key, value in metrics.items(): -... if type(value) is np.ndarray: +... if isinstance(value, np.ndarray): ... metrics[key] = value.tolist() ... return metrics ``` diff --git a/docs/source/pt/_config.py b/docs/source/pt/_config.py index cd76263e9a5..a6d75853f57 100644 --- a/docs/source/pt/_config.py +++ b/docs/source/pt/_config.py @@ -10,5 +10,5 @@ notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}] black_avoid_patterns = { "{processor_class}": "FakeProcessorClass", "{model_class}": "FakeModelClass", - "{object_class}": "FakeObjectClass", + "{object_class}": "FakeObjectClass", } diff --git a/examples/flax/text-classification/run_flax_glue.py b/examples/flax/text-classification/run_flax_glue.py index 37f16459d3b..632a66841b3 100755 --- a/examples/flax/text-classification/run_flax_glue.py +++ b/examples/flax/text-classification/run_flax_glue.py @@ -212,7 +212,7 @@ class DataTrainingArguments: if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." - self.task_name = self.task_name.lower() if type(self.task_name) == str else self.task_name + self.task_name = self.task_name.lower() if isinstance(self.task_name, str) else self.task_name def create_train_state( diff --git a/examples/legacy/pytorch-lightning/run_glue.py b/examples/legacy/pytorch-lightning/run_glue.py index 5f22e2fc7a1..681f633fcd6 100644 --- a/examples/legacy/pytorch-lightning/run_glue.py +++ b/examples/legacy/pytorch-lightning/run_glue.py @@ -23,7 +23,7 @@ class GLUETransformer(BaseTransformer): mode = "sequence-classification" def __init__(self, hparams): - if type(hparams) == dict: + if isinstance(hparams, dict): hparams = Namespace(**hparams) hparams.glue_output_mode = glue_output_modes[hparams.task] num_labels = glue_tasks_num_labels[hparams.task] diff --git a/examples/legacy/pytorch-lightning/run_ner.py b/examples/legacy/pytorch-lightning/run_ner.py index 7f6b00854d9..fc6f812275e 100644 --- a/examples/legacy/pytorch-lightning/run_ner.py +++ b/examples/legacy/pytorch-lightning/run_ner.py @@ -25,7 +25,7 @@ class NERTransformer(BaseTransformer): mode = "token-classification" def __init__(self, hparams): - if type(hparams) == dict: + if isinstance(hparams, dict): hparams = Namespace(**hparams) module = import_module("tasks") try: diff --git a/examples/research_projects/deebert/src/modeling_highway_bert.py b/examples/research_projects/deebert/src/modeling_highway_bert.py index 2a881decbbd..b866ef0869c 100644 --- a/examples/research_projects/deebert/src/modeling_highway_bert.py +++ b/examples/research_projects/deebert/src/modeling_highway_bert.py @@ -32,7 +32,7 @@ class DeeBertEncoder(nn.Module): self.early_exit_entropy = [-1 for _ in range(config.num_hidden_layers)] def set_early_exit_entropy(self, x): - if (type(x) is float) or (type(x) is int): + if isinstance(x, (float, int)): for i in range(len(self.early_exit_entropy)): self.early_exit_entropy[i] = x else: @@ -232,9 +232,7 @@ class DeeBertModel(BertPreTrainedModel): outputs = ( sequence_output, pooled_output, - ) + encoder_outputs[ - 1: - ] # add hidden_states and attentions if they are here + ) + encoder_outputs[1:] # add hidden_states and attentions if they are here return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits diff --git a/examples/research_projects/longform-qa/eli5_app.py b/examples/research_projects/longform-qa/eli5_app.py index 1bcb6fd20d2..ae8d8f91568 100644 --- a/examples/research_projects/longform-qa/eli5_app.py +++ b/examples/research_projects/longform-qa/eli5_app.py @@ -158,9 +158,7 @@ header_full = """ -""" % ( - header_html, -) +""" % (header_html,) st.sidebar.markdown( header_full, unsafe_allow_html=True, diff --git a/examples/research_projects/lxmert/modeling_frcnn.py b/examples/research_projects/lxmert/modeling_frcnn.py index 943588a5ed8..499de532070 100644 --- a/examples/research_projects/lxmert/modeling_frcnn.py +++ b/examples/research_projects/lxmert/modeling_frcnn.py @@ -1706,9 +1706,7 @@ class GeneralizedRCNN(nn.Module): elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path): archive_file = pretrained_model_name_or_path elif os.path.isfile(pretrained_model_name_or_path + ".index"): - assert ( - from_tf - ), "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint".format( + assert from_tf, "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint".format( pretrained_model_name_or_path + ".index" ) archive_file = pretrained_model_name_or_path + ".index" diff --git a/examples/research_projects/movement-pruning/emmental/modeling_bert_masked.py b/examples/research_projects/movement-pruning/emmental/modeling_bert_masked.py index d404bf49aaa..f47395bb000 100644 --- a/examples/research_projects/movement-pruning/emmental/modeling_bert_masked.py +++ b/examples/research_projects/movement-pruning/emmental/modeling_bert_masked.py @@ -652,9 +652,7 @@ class MaskedBertModel(MaskedBertPreTrainedModel): outputs = ( sequence_output, pooled_output, - ) + encoder_outputs[ - 1: - ] # add hidden_states and attentions if they are here + ) + encoder_outputs[1:] # add hidden_states and attentions if they are here return outputs # sequence_output, pooled_output, (hidden_states), (attentions) diff --git a/examples/research_projects/movement-pruning/masked_run_glue.py b/examples/research_projects/movement-pruning/masked_run_glue.py index f440e627bbe..e2090c431e3 100644 --- a/examples/research_projects/movement-pruning/masked_run_glue.py +++ b/examples/research_projects/movement-pruning/masked_run_glue.py @@ -311,8 +311,7 @@ def train(args, train_dataset, model, tokenizer, teacher=None): tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0 or ( # last step in epoch but step is always smaller than gradient_accumulation_steps - len(epoch_iterator) <= args.gradient_accumulation_steps - and (step + 1) == len(epoch_iterator) + len(epoch_iterator) <= args.gradient_accumulation_steps and (step + 1) == len(epoch_iterator) ): if args.fp16: nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) diff --git a/examples/research_projects/quantization-qdqbert/quant_trainer.py b/examples/research_projects/quantization-qdqbert/quant_trainer.py index 73e880ebf67..09bac19e921 100755 --- a/examples/research_projects/quantization-qdqbert/quant_trainer.py +++ b/examples/research_projects/quantization-qdqbert/quant_trainer.py @@ -239,7 +239,7 @@ def print_model_summary(model, name_width=25, line_width=180, ignore=None): continue if type(mod) in ignore: continue - if [True for s in ignore if type(s) is str and s in name]: + if [True for s in ignore if isinstance(s, str) and s in name]: continue act_str = f"Act:{input_q.extra_repr()}" wgt_str = f"Wgt:{weight_q.extra_repr()}" diff --git a/examples/research_projects/visual_bert/modeling_frcnn.py b/examples/research_projects/visual_bert/modeling_frcnn.py index 943588a5ed8..499de532070 100644 --- a/examples/research_projects/visual_bert/modeling_frcnn.py +++ b/examples/research_projects/visual_bert/modeling_frcnn.py @@ -1706,9 +1706,7 @@ class GeneralizedRCNN(nn.Module): elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path): archive_file = pretrained_model_name_or_path elif os.path.isfile(pretrained_model_name_or_path + ".index"): - assert ( - from_tf - ), "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint".format( + assert from_tf, "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint".format( pretrained_model_name_or_path + ".index" ) archive_file = pretrained_model_name_or_path + ".index" diff --git a/hubconf.py b/hubconf.py index 6c60cd4213d..f2ef70b73db 100644 --- a/hubconf.py +++ b/hubconf.py @@ -15,6 +15,7 @@ import os import sys + SRC_DIR = os.path.join(os.path.dirname(__file__), "src") sys.path.append(SRC_DIR) diff --git a/pyproject.toml b/pyproject.toml index d13ee54afcf..a7e17200221 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,10 +1,6 @@ -[tool.black] -line-length = 119 -target-version = ['py37'] - [tool.ruff] # Never enforce `E501` (line length violations). -ignore = ["C901", "E501", "E741"] +ignore = ["C901", "E501", "E741", "F402", "F823" ] select = ["C", "E", "F", "I", "W"] line-length = 119 @@ -18,6 +14,19 @@ line-length = 119 lines-after-imports = 2 known-first-party = ["transformers"] +[tool.ruff.format] +# Like Black, use double quotes for strings. +quote-style = "double" + +# Like Black, indent with spaces, rather than tabs. +indent-style = "space" + +# Like Black, respect magic trailing commas. +skip-magic-trailing-comma = false + +# Like Black, automatically detect the appropriate line ending. +line-ending = "auto" + [tool.pytest.ini_options] doctest_optionflags="NUMBER NORMALIZE_WHITESPACE ELLIPSIS" doctest_glob="**/*.md" diff --git a/scripts/check_tokenizers.py b/scripts/check_tokenizers.py index cfd0a7f3a1d..ea0d0bc2185 100644 --- a/scripts/check_tokenizers.py +++ b/scripts/check_tokenizers.py @@ -1,10 +1,12 @@ from collections import Counter + import datasets + import transformers from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS - from transformers.utils import logging + logging.set_verbosity_info() TOKENIZER_CLASSES = { @@ -101,8 +103,8 @@ def check_details(line, spm_ids, tok_ids, slow, fast): except Exception: pass - ok_start = fast.decode(spm_ids[:first]) - ok_end = fast.decode(spm_ids[last:]) + fast.decode(spm_ids[:first]) + fast.decode(spm_ids[last:]) wrong = fast.decode(spm_ids[first:last]) print() print(wrong) diff --git a/scripts/fsmt/fsmt-make-super-tiny-model.py b/scripts/fsmt/fsmt-make-super-tiny-model.py index 4a6b8e0c1b4..a70f40ee6ca 100755 --- a/scripts/fsmt/fsmt-make-super-tiny-model.py +++ b/scripts/fsmt/fsmt-make-super-tiny-model.py @@ -24,18 +24,19 @@ # # It will be used then as "stas/tiny-wmt19-en-ru" -from pathlib import Path import json import tempfile +from pathlib import Path -from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration +from transformers import FSMTConfig, FSMTForConditionalGeneration, FSMTTokenizer from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES + mname_tiny = "tiny-wmt19-en-ru" # Build -# borrowed from a test +# borrowed from a test vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "w", "r", "t", "lo", "low", "er", "low", "lowest", "newer", "wider", "", ] vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["l o 123", "lo w 1456", "e r 1789", ""] @@ -57,7 +58,7 @@ with tempfile.TemporaryDirectory() as tmpdirname: tgt_vocab_file=tgt_vocab_file, merges_file=merges_file, ) - + config = FSMTConfig( langs=['ru', 'en'], src_vocab_size=1000, tgt_vocab_size=1000, diff --git a/scripts/fsmt/fsmt-make-tiny-model.py b/scripts/fsmt/fsmt-make-tiny-model.py index 431942c05dd..b737cc61cea 100755 --- a/scripts/fsmt/fsmt-make-tiny-model.py +++ b/scripts/fsmt/fsmt-make-tiny-model.py @@ -27,16 +27,18 @@ # It will be used then as "stas/tiny-wmt19-en-de" # Build -from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration +from transformers import FSMTConfig, FSMTForConditionalGeneration, FSMTTokenizer + + mname = "facebook/wmt19-en-de" tokenizer = FSMTTokenizer.from_pretrained(mname) # get the correct vocab sizes, etc. from the master model config = FSMTConfig.from_pretrained(mname) -config.update(dict( - d_model=4, - encoder_layers=1, decoder_layers=1, - encoder_ffn_dim=4, decoder_ffn_dim=4, - encoder_attention_heads=1, decoder_attention_heads=1)) +config.update({ + "d_model": 4, + "encoder_layers": 1, "decoder_layers": 1, + "encoder_ffn_dim": 4, "decoder_ffn_dim": 4, + "encoder_attention_heads": 1, "decoder_attention_heads": 1}) tiny_model = FSMTForConditionalGeneration(config) print(f"num of params {tiny_model.num_parameters()}") diff --git a/scripts/fsmt/gen-card-allenai-wmt16.py b/scripts/fsmt/gen-card-allenai-wmt16.py index b910cb05b1b..1b5fe1cda8b 100755 --- a/scripts/fsmt/gen-card-allenai-wmt16.py +++ b/scripts/fsmt/gen-card-allenai-wmt16.py @@ -19,6 +19,7 @@ import os from pathlib import Path + def write_model_card(model_card_dir, src_lang, tgt_lang, model_name): texts = { diff --git a/scripts/fsmt/gen-card-allenai-wmt19.py b/scripts/fsmt/gen-card-allenai-wmt19.py index df0f5851c82..b7d727ff2a1 100755 --- a/scripts/fsmt/gen-card-allenai-wmt19.py +++ b/scripts/fsmt/gen-card-allenai-wmt19.py @@ -19,6 +19,7 @@ import os from pathlib import Path + def write_model_card(model_card_dir, src_lang, tgt_lang, model_name): texts = { diff --git a/scripts/fsmt/gen-card-facebook-wmt19.py b/scripts/fsmt/gen-card-facebook-wmt19.py index e75406b261d..58df676cbc9 100755 --- a/scripts/fsmt/gen-card-facebook-wmt19.py +++ b/scripts/fsmt/gen-card-facebook-wmt19.py @@ -19,6 +19,7 @@ import os from pathlib import Path + def write_model_card(model_card_dir, src_lang, tgt_lang): texts = { @@ -39,7 +40,7 @@ def write_model_card(model_card_dir, src_lang, tgt_lang): readme = f""" --- -language: +language: - {src_lang} - {tgt_lang} thumbnail: diff --git a/scripts/pegasus/build_test_sample_spm_no_bos.py b/scripts/pegasus/build_test_sample_spm_no_bos.py index 324db02ef71..f223304a771 100755 --- a/scripts/pegasus/build_test_sample_spm_no_bos.py +++ b/scripts/pegasus/build_test_sample_spm_no_bos.py @@ -13,15 +13,16 @@ # See the License for the specific language governing permissions and # limitations under the License. -# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus +# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus # 1. pip install sentencepiece -# +# # 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt # 3. build import sentencepiece as spm + # pegasus: # 1. no bos # 2. eos_id is 1 diff --git a/scripts/stale.py b/scripts/stale.py index c7fc6aa221a..bf7c6670c43 100644 --- a/scripts/stale.py +++ b/scripts/stale.py @@ -15,8 +15,8 @@ Script to close stale issue. Taken in part from the AllenNLP repository. https://github.com/allenai/allennlp. """ -from datetime import datetime as dt import os +from datetime import datetime as dt import github.GithubException from github import Github @@ -39,7 +39,7 @@ def main(): for i, issue in enumerate(open_issues): print(i, issue) - comments = sorted([comment for comment in issue.get_comments()], key=lambda i: i.created_at, reverse=True) + comments = sorted(list(issue.get_comments()), key=lambda i: i.created_at, reverse=True) last_comment = comments[0] if len(comments) > 0 else None if ( last_comment is not None and last_comment.user.login == "github-actions[bot]" diff --git a/setup.py b/setup.py index 86c8a8a5bff..deccac468a8 100644 --- a/setup.py +++ b/setup.py @@ -99,7 +99,6 @@ _deps = [ "accelerate>=0.20.3", "av==9.2.0", # Latest version of PyAV (10.0.0) has issues with audio stream. "beautifulsoup4", - "black~=23.1", "codecarbon==1.2.0", "cookiecutter==1.7.3", "dataclasses", @@ -156,7 +155,7 @@ _deps = [ "rhoknp>=1.1.0,<1.3.1", "rjieba", "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1", - "ruff>=0.0.241,<=0.0.259", + "ruff>=0.1.5,<=0.2", "sacrebleu>=1.4.12,<2.0.0", "sacremoses", "safetensors>=0.3.1", @@ -310,7 +309,7 @@ extras["testing"] = ( "dill", "evaluate", "pytest-timeout", - "black", + "ruff", "sacrebleu", "rouge-score", "nltk", @@ -329,7 +328,7 @@ extras["testing"] = ( extras["deepspeed-testing"] = extras["deepspeed"] + extras["testing"] + extras["optuna"] + extras["sentencepiece"] -extras["quality"] = deps_list("black", "datasets", "isort", "ruff", "GitPython", "hf-doc-builder", "urllib3") +extras["quality"] = deps_list("datasets", "isort", "ruff", "GitPython", "hf-doc-builder", "urllib3") extras["all"] = ( extras["tf"] diff --git a/src/transformers/configuration_utils.py b/src/transformers/configuration_utils.py index d4f59020434..22ea0abbd60 100755 --- a/src/transformers/configuration_utils.py +++ b/src/transformers/configuration_utils.py @@ -246,6 +246,7 @@ class PretrainedConfig(PushToHubMixin): not be XLA-compatible. This option is here for backward compatibility and will be removed in Transformers v5. """ + model_type: str = "" is_composition: bool = False attribute_map: Dict[str, str] = {} diff --git a/src/transformers/convert_slow_tokenizer.py b/src/transformers/convert_slow_tokenizer.py index 04f3e7e1c58..76ac66ceb9e 100644 --- a/src/transformers/convert_slow_tokenizer.py +++ b/src/transformers/convert_slow_tokenizer.py @@ -724,9 +724,7 @@ class MBart50Converter(SpmConverter): ("", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] - # fmt: off - vocab += [("ar_AR", 0.0), ("cs_CZ", 0.0), ("de_DE", 0.0), ("en_XX", 0.0), ("es_XX", 0.0), ("et_EE", 0.0), ("fi_FI", 0.0), ("fr_XX", 0.0), ("gu_IN", 0.0), ("hi_IN", 0.0), ("it_IT", 0.0), ("ja_XX", 0.0), ("kk_KZ", 0.0), ("ko_KR", 0.0), ("lt_LT", 0.0), ("lv_LV", 0.0), ("my_MM", 0.0), ("ne_NP", 0.0), ("nl_XX", 0.0), ("ro_RO", 0.0), ("ru_RU", 0.0), ("si_LK", 0.0), ("tr_TR", 0.0), ("vi_VN", 0.0), ("zh_CN", 0.0), ("af_ZA", 0.0), ("az_AZ", 0.0), ("bn_IN", 0.0), ("fa_IR", 0.0), ("he_IL", 0.0), ("hr_HR", 0.0), ("id_ID", 0.0), ("ka_GE", 0.0), ("km_KH", 0.0), ("mk_MK", 0.0), ("ml_IN", 0.0), ("mn_MN", 0.0), ("mr_IN", 0.0), ("pl_PL", 0.0), ("ps_AF", 0.0), ("pt_XX", 0.0), ("sv_SE", 0.0), ("sw_KE", 0.0), ("ta_IN", 0.0), ("te_IN", 0.0), ("th_TH", 0.0), ("tl_XX", 0.0), ("uk_UA", 0.0), ("ur_PK", 0.0), ("xh_ZA", 0.0), ("gl_ES", 0.0), ("sl_SI", 0.0)] - # fmt: on + vocab += [("ar_AR", 0.0), ("cs_CZ", 0.0), ("de_DE", 0.0), ("en_XX", 0.0), ("es_XX", 0.0), ("et_EE", 0.0), ("fi_FI", 0.0), ("fr_XX", 0.0), ("gu_IN", 0.0), ("hi_IN", 0.0), ("it_IT", 0.0), ("ja_XX", 0.0), ("kk_KZ", 0.0), ("ko_KR", 0.0), ("lt_LT", 0.0), ("lv_LV", 0.0), ("my_MM", 0.0), ("ne_NP", 0.0), ("nl_XX", 0.0), ("ro_RO", 0.0), ("ru_RU", 0.0), ("si_LK", 0.0), ("tr_TR", 0.0), ("vi_VN", 0.0), ("zh_CN", 0.0), ("af_ZA", 0.0), ("az_AZ", 0.0), ("bn_IN", 0.0), ("fa_IR", 0.0), ("he_IL", 0.0), ("hr_HR", 0.0), ("id_ID", 0.0), ("ka_GE", 0.0), ("km_KH", 0.0), ("mk_MK", 0.0), ("ml_IN", 0.0), ("mn_MN", 0.0), ("mr_IN", 0.0), ("pl_PL", 0.0), ("ps_AF", 0.0), ("pt_XX", 0.0), ("sv_SE", 0.0), ("sw_KE", 0.0), ("ta_IN", 0.0), ("te_IN", 0.0), ("th_TH", 0.0), ("tl_XX", 0.0), ("uk_UA", 0.0), ("ur_PK", 0.0), ("xh_ZA", 0.0), ("gl_ES", 0.0), ("sl_SI", 0.0)] # fmt: skip vocab += [("", 0.0)] return vocab @@ -753,11 +751,7 @@ class NllbConverter(SpmConverter): ("", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] - vocab += [ - # fmt: off - ('ace_Arab', 0.0), ('ace_Latn', 0.0), ('acm_Arab', 0.0), ('acq_Arab', 0.0), ('aeb_Arab', 0.0), ('afr_Latn', 0.0), ('ajp_Arab', 0.0), ('aka_Latn', 0.0), ('amh_Ethi', 0.0), ('apc_Arab', 0.0), ('arb_Arab', 0.0), ('ars_Arab', 0.0), ('ary_Arab', 0.0), ('arz_Arab', 0.0), ('asm_Beng', 0.0), ('ast_Latn', 0.0), ('awa_Deva', 0.0), ('ayr_Latn', 0.0), ('azb_Arab', 0.0), ('azj_Latn', 0.0), ('bak_Cyrl', 0.0), ('bam_Latn', 0.0), ('ban_Latn', 0.0), ('bel_Cyrl', 0.0), ('bem_Latn', 0.0), ('ben_Beng', 0.0), ('bho_Deva', 0.0), ('bjn_Arab', 0.0), ('bjn_Latn', 0.0), ('bod_Tibt', 0.0), ('bos_Latn', 0.0), ('bug_Latn', 0.0), ('bul_Cyrl', 0.0), ('cat_Latn', 0.0), ('ceb_Latn', 0.0), ('ces_Latn', 0.0), ('cjk_Latn', 0.0), ('ckb_Arab', 0.0), ('crh_Latn', 0.0), ('cym_Latn', 0.0), ('dan_Latn', 0.0), ('deu_Latn', 0.0), ('dik_Latn', 0.0), ('dyu_Latn', 0.0), ('dzo_Tibt', 0.0), ('ell_Grek', 0.0), ('eng_Latn', 0.0), ('epo_Latn', 0.0), ('est_Latn', 0.0), ('eus_Latn', 0.0), ('ewe_Latn', 0.0), ('fao_Latn', 0.0), ('pes_Arab', 0.0), ('fij_Latn', 0.0), ('fin_Latn', 0.0), ('fon_Latn', 0.0), ('fra_Latn', 0.0), ('fur_Latn', 0.0), ('fuv_Latn', 0.0), ('gla_Latn', 0.0), ('gle_Latn', 0.0), ('glg_Latn', 0.0), ('grn_Latn', 0.0), ('guj_Gujr', 0.0), ('hat_Latn', 0.0), ('hau_Latn', 0.0), ('heb_Hebr', 0.0), ('hin_Deva', 0.0), ('hne_Deva', 0.0), ('hrv_Latn', 0.0), ('hun_Latn', 0.0), ('hye_Armn', 0.0), ('ibo_Latn', 0.0), ('ilo_Latn', 0.0), ('ind_Latn', 0.0), ('isl_Latn', 0.0), ('ita_Latn', 0.0), ('jav_Latn', 0.0), ('jpn_Jpan', 0.0), ('kab_Latn', 0.0), ('kac_Latn', 0.0), ('kam_Latn', 0.0), ('kan_Knda', 0.0), ('kas_Arab', 0.0), ('kas_Deva', 0.0), ('kat_Geor', 0.0), ('knc_Arab', 0.0), ('knc_Latn', 0.0), ('kaz_Cyrl', 0.0), ('kbp_Latn', 0.0), ('kea_Latn', 0.0), ('khm_Khmr', 0.0), ('kik_Latn', 0.0), ('kin_Latn', 0.0), ('kir_Cyrl', 0.0), ('kmb_Latn', 0.0), ('kon_Latn', 0.0), ('kor_Hang', 0.0), ('kmr_Latn', 0.0), ('lao_Laoo', 0.0), ('lvs_Latn', 0.0), ('lij_Latn', 0.0), ('lim_Latn', 0.0), ('lin_Latn', 0.0), ('lit_Latn', 0.0), ('lmo_Latn', 0.0), ('ltg_Latn', 0.0), ('ltz_Latn', 0.0), ('lua_Latn', 0.0), ('lug_Latn', 0.0), ('luo_Latn', 0.0), ('lus_Latn', 0.0), ('mag_Deva', 0.0), ('mai_Deva', 0.0), ('mal_Mlym', 0.0), ('mar_Deva', 0.0), ('min_Latn', 0.0), ('mkd_Cyrl', 0.0), ('plt_Latn', 0.0), ('mlt_Latn', 0.0), ('mni_Beng', 0.0), ('khk_Cyrl', 0.0), ('mos_Latn', 0.0), ('mri_Latn', 0.0), ('zsm_Latn', 0.0), ('mya_Mymr', 0.0), ('nld_Latn', 0.0), ('nno_Latn', 0.0), ('nob_Latn', 0.0), ('npi_Deva', 0.0), ('nso_Latn', 0.0), ('nus_Latn', 0.0), ('nya_Latn', 0.0), ('oci_Latn', 0.0), ('gaz_Latn', 0.0), ('ory_Orya', 0.0), ('pag_Latn', 0.0), ('pan_Guru', 0.0), ('pap_Latn', 0.0), ('pol_Latn', 0.0), ('por_Latn', 0.0), ('prs_Arab', 0.0), ('pbt_Arab', 0.0), ('quy_Latn', 0.0), ('ron_Latn', 0.0), ('run_Latn', 0.0), ('rus_Cyrl', 0.0), ('sag_Latn', 0.0), ('san_Deva', 0.0), ('sat_Beng', 0.0), ('scn_Latn', 0.0), ('shn_Mymr', 0.0), ('sin_Sinh', 0.0), ('slk_Latn', 0.0), ('slv_Latn', 0.0), ('smo_Latn', 0.0), ('sna_Latn', 0.0), ('snd_Arab', 0.0), ('som_Latn', 0.0), ('sot_Latn', 0.0), ('spa_Latn', 0.0), ('als_Latn', 0.0), ('srd_Latn', 0.0), ('srp_Cyrl', 0.0), ('ssw_Latn', 0.0), ('sun_Latn', 0.0), ('swe_Latn', 0.0), ('swh_Latn', 0.0), ('szl_Latn', 0.0), ('tam_Taml', 0.0), ('tat_Cyrl', 0.0), ('tel_Telu', 0.0), ('tgk_Cyrl', 0.0), ('tgl_Latn', 0.0), ('tha_Thai', 0.0), ('tir_Ethi', 0.0), ('taq_Latn', 0.0), ('taq_Tfng', 0.0), ('tpi_Latn', 0.0), ('tsn_Latn', 0.0), ('tso_Latn', 0.0), ('tuk_Latn', 0.0), ('tum_Latn', 0.0), ('tur_Latn', 0.0), ('twi_Latn', 0.0), ('tzm_Tfng', 0.0), ('uig_Arab', 0.0), ('ukr_Cyrl', 0.0), ('umb_Latn', 0.0), ('urd_Arab', 0.0), ('uzn_Latn', 0.0), ('vec_Latn', 0.0), ('vie_Latn', 0.0), ('war_Latn', 0.0), ('wol_Latn', 0.0), ('xho_Latn', 0.0), ('ydd_Hebr', 0.0), ('yor_Latn', 0.0), ('yue_Hant', 0.0), ('zho_Hans', 0.0), ('zho_Hant', 0.0), ('zul_Latn', 0.0) - # fmt: on - ] + vocab += [('ace_Arab', 0.0), ('ace_Latn', 0.0), ('acm_Arab', 0.0), ('acq_Arab', 0.0), ('aeb_Arab', 0.0), ('afr_Latn', 0.0), ('ajp_Arab', 0.0), ('aka_Latn', 0.0), ('amh_Ethi', 0.0), ('apc_Arab', 0.0), ('arb_Arab', 0.0), ('ars_Arab', 0.0), ('ary_Arab', 0.0), ('arz_Arab', 0.0), ('asm_Beng', 0.0), ('ast_Latn', 0.0), ('awa_Deva', 0.0), ('ayr_Latn', 0.0), ('azb_Arab', 0.0), ('azj_Latn', 0.0), ('bak_Cyrl', 0.0), ('bam_Latn', 0.0), ('ban_Latn', 0.0), ('bel_Cyrl', 0.0), ('bem_Latn', 0.0), ('ben_Beng', 0.0), ('bho_Deva', 0.0), ('bjn_Arab', 0.0), ('bjn_Latn', 0.0), ('bod_Tibt', 0.0), ('bos_Latn', 0.0), ('bug_Latn', 0.0), ('bul_Cyrl', 0.0), ('cat_Latn', 0.0), ('ceb_Latn', 0.0), ('ces_Latn', 0.0), ('cjk_Latn', 0.0), ('ckb_Arab', 0.0), ('crh_Latn', 0.0), ('cym_Latn', 0.0), ('dan_Latn', 0.0), ('deu_Latn', 0.0), ('dik_Latn', 0.0), ('dyu_Latn', 0.0), ('dzo_Tibt', 0.0), ('ell_Grek', 0.0), ('eng_Latn', 0.0), ('epo_Latn', 0.0), ('est_Latn', 0.0), ('eus_Latn', 0.0), ('ewe_Latn', 0.0), ('fao_Latn', 0.0), ('pes_Arab', 0.0), ('fij_Latn', 0.0), ('fin_Latn', 0.0), ('fon_Latn', 0.0), ('fra_Latn', 0.0), ('fur_Latn', 0.0), ('fuv_Latn', 0.0), ('gla_Latn', 0.0), ('gle_Latn', 0.0), ('glg_Latn', 0.0), ('grn_Latn', 0.0), ('guj_Gujr', 0.0), ('hat_Latn', 0.0), ('hau_Latn', 0.0), ('heb_Hebr', 0.0), ('hin_Deva', 0.0), ('hne_Deva', 0.0), ('hrv_Latn', 0.0), ('hun_Latn', 0.0), ('hye_Armn', 0.0), ('ibo_Latn', 0.0), ('ilo_Latn', 0.0), ('ind_Latn', 0.0), ('isl_Latn', 0.0), ('ita_Latn', 0.0), ('jav_Latn', 0.0), ('jpn_Jpan', 0.0), ('kab_Latn', 0.0), ('kac_Latn', 0.0), ('kam_Latn', 0.0), ('kan_Knda', 0.0), ('kas_Arab', 0.0), ('kas_Deva', 0.0), ('kat_Geor', 0.0), ('knc_Arab', 0.0), ('knc_Latn', 0.0), ('kaz_Cyrl', 0.0), ('kbp_Latn', 0.0), ('kea_Latn', 0.0), ('khm_Khmr', 0.0), ('kik_Latn', 0.0), ('kin_Latn', 0.0), ('kir_Cyrl', 0.0), ('kmb_Latn', 0.0), ('kon_Latn', 0.0), ('kor_Hang', 0.0), ('kmr_Latn', 0.0), ('lao_Laoo', 0.0), ('lvs_Latn', 0.0), ('lij_Latn', 0.0), ('lim_Latn', 0.0), ('lin_Latn', 0.0), ('lit_Latn', 0.0), ('lmo_Latn', 0.0), ('ltg_Latn', 0.0), ('ltz_Latn', 0.0), ('lua_Latn', 0.0), ('lug_Latn', 0.0), ('luo_Latn', 0.0), ('lus_Latn', 0.0), ('mag_Deva', 0.0), ('mai_Deva', 0.0), ('mal_Mlym', 0.0), ('mar_Deva', 0.0), ('min_Latn', 0.0), ('mkd_Cyrl', 0.0), ('plt_Latn', 0.0), ('mlt_Latn', 0.0), ('mni_Beng', 0.0), ('khk_Cyrl', 0.0), ('mos_Latn', 0.0), ('mri_Latn', 0.0), ('zsm_Latn', 0.0), ('mya_Mymr', 0.0), ('nld_Latn', 0.0), ('nno_Latn', 0.0), ('nob_Latn', 0.0), ('npi_Deva', 0.0), ('nso_Latn', 0.0), ('nus_Latn', 0.0), ('nya_Latn', 0.0), ('oci_Latn', 0.0), ('gaz_Latn', 0.0), ('ory_Orya', 0.0), ('pag_Latn', 0.0), ('pan_Guru', 0.0), ('pap_Latn', 0.0), ('pol_Latn', 0.0), ('por_Latn', 0.0), ('prs_Arab', 0.0), ('pbt_Arab', 0.0), ('quy_Latn', 0.0), ('ron_Latn', 0.0), ('run_Latn', 0.0), ('rus_Cyrl', 0.0), ('sag_Latn', 0.0), ('san_Deva', 0.0), ('sat_Beng', 0.0), ('scn_Latn', 0.0), ('shn_Mymr', 0.0), ('sin_Sinh', 0.0), ('slk_Latn', 0.0), ('slv_Latn', 0.0), ('smo_Latn', 0.0), ('sna_Latn', 0.0), ('snd_Arab', 0.0), ('som_Latn', 0.0), ('sot_Latn', 0.0), ('spa_Latn', 0.0), ('als_Latn', 0.0), ('srd_Latn', 0.0), ('srp_Cyrl', 0.0), ('ssw_Latn', 0.0), ('sun_Latn', 0.0), ('swe_Latn', 0.0), ('swh_Latn', 0.0), ('szl_Latn', 0.0), ('tam_Taml', 0.0), ('tat_Cyrl', 0.0), ('tel_Telu', 0.0), ('tgk_Cyrl', 0.0), ('tgl_Latn', 0.0), ('tha_Thai', 0.0), ('tir_Ethi', 0.0), ('taq_Latn', 0.0), ('taq_Tfng', 0.0), ('tpi_Latn', 0.0), ('tsn_Latn', 0.0), ('tso_Latn', 0.0), ('tuk_Latn', 0.0), ('tum_Latn', 0.0), ('tur_Latn', 0.0), ('twi_Latn', 0.0), ('tzm_Tfng', 0.0), ('uig_Arab', 0.0), ('ukr_Cyrl', 0.0), ('umb_Latn', 0.0), ('urd_Arab', 0.0), ('uzn_Latn', 0.0), ('vec_Latn', 0.0), ('vie_Latn', 0.0), ('war_Latn', 0.0), ('wol_Latn', 0.0), ('xho_Latn', 0.0), ('ydd_Hebr', 0.0), ('yor_Latn', 0.0), ('yue_Hant', 0.0), ('zho_Hans', 0.0), ('zho_Hant', 0.0), ('zul_Latn', 0.0)] # fmt: skip vocab += [("", 0.0)] return vocab @@ -1128,9 +1122,7 @@ class XGLMConverter(SpmConverter): ("", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] - # fmt: off - vocab += [("", 0.0), ("", 0.0), ("", 0.0), ("", 0.0), ("", 0.0), ("", 0.0), ("", 0.0)] - # fmt: on + vocab += [("", 0.0), ("", 0.0), ("", 0.0), ("", 0.0), ("", 0.0), ("", 0.0), ("", 0.0)] # fmt: skip return vocab def unk_id(self, proto): diff --git a/src/transformers/data/data_collator.py b/src/transformers/data/data_collator.py index 57879ec3e2d..7954c828fee 100644 --- a/src/transformers/data/data_collator.py +++ b/src/transformers/data/data_collator.py @@ -121,7 +121,7 @@ def torch_default_data_collator(features: List[InputDataClass]) -> Dict[str, Any if isinstance(first["label_ids"], torch.Tensor): batch["labels"] = torch.stack([f["label_ids"] for f in features]) else: - dtype = torch.long if type(first["label_ids"][0]) is int else torch.float + dtype = torch.long if isinstance(first["label_ids"][0], int) else torch.float batch["labels"] = torch.tensor([f["label_ids"] for f in features], dtype=dtype) # Handling of all other possible keys. @@ -196,7 +196,7 @@ def numpy_default_data_collator(features: List[InputDataClass]) -> Dict[str, Any if isinstance(first["label_ids"], np.ndarray): batch["labels"] = np.stack([f["label_ids"] for f in features]) else: - dtype = np.int64 if type(first["label_ids"][0]) is int else np.float32 + dtype = np.int64 if isinstance(first["label_ids"][0], int) else np.float32 batch["labels"] = np.array([f["label_ids"] for f in features], dtype=dtype) # Handling of all other possible keys. diff --git a/src/transformers/dependency_versions_table.py b/src/transformers/dependency_versions_table.py index 30e902d6989..93e21ab2d3e 100644 --- a/src/transformers/dependency_versions_table.py +++ b/src/transformers/dependency_versions_table.py @@ -6,7 +6,6 @@ deps = { "accelerate": "accelerate>=0.20.3", "av": "av==9.2.0", "beautifulsoup4": "beautifulsoup4", - "black": "black~=23.1", "codecarbon": "codecarbon==1.2.0", "cookiecutter": "cookiecutter==1.7.3", "dataclasses": "dataclasses", @@ -62,7 +61,7 @@ deps = { "rhoknp": "rhoknp>=1.1.0,<1.3.1", "rjieba": "rjieba", "rouge-score": "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1", - "ruff": "ruff>=0.0.241,<=0.0.259", + "ruff": "ruff>=0.1.5,<=0.2", "sacrebleu": "sacrebleu>=1.4.12,<2.0.0", "sacremoses": "sacremoses", "safetensors": "safetensors>=0.3.1", diff --git a/src/transformers/image_utils.py b/src/transformers/image_utils.py index 628fe5dea72..bfb88d03d3f 100644 --- a/src/transformers/image_utils.py +++ b/src/transformers/image_utils.py @@ -245,8 +245,7 @@ def is_valid_annotation_coco_detection(annotation: Dict[str, Union[List, Tuple]] and isinstance(annotation["annotations"], (list, tuple)) and ( # an image can have no annotations - len(annotation["annotations"]) == 0 - or isinstance(annotation["annotations"][0], dict) + len(annotation["annotations"]) == 0 or isinstance(annotation["annotations"][0], dict) ) ): return True @@ -262,8 +261,7 @@ def is_valid_annotation_coco_panoptic(annotation: Dict[str, Union[List, Tuple]]) and isinstance(annotation["segments_info"], (list, tuple)) and ( # an image can have no segments - len(annotation["segments_info"]) == 0 - or isinstance(annotation["segments_info"][0], dict) + len(annotation["segments_info"]) == 0 or isinstance(annotation["segments_info"][0], dict) ) ): return True diff --git a/src/transformers/modeling_flax_utils.py b/src/transformers/modeling_flax_utils.py index 37567d3d843..d4617c111bb 100644 --- a/src/transformers/modeling_flax_utils.py +++ b/src/transformers/modeling_flax_utils.py @@ -179,6 +179,7 @@ class FlaxPreTrainedModel(PushToHubMixin, FlaxGenerationMixin): - **main_input_name** (`str`) -- The name of the principal input to the model (often `input_ids` for NLP models, `pixel_values` for vision models and `input_values` for speech models). """ + config_class = None base_model_prefix = "" main_input_name = "input_ids" diff --git a/src/transformers/modeling_tf_utils.py b/src/transformers/modeling_tf_utils.py index c342b5059c4..bfd928a9011 100644 --- a/src/transformers/modeling_tf_utils.py +++ b/src/transformers/modeling_tf_utils.py @@ -1075,6 +1075,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu - **main_input_name** (`str`) -- The name of the principal input to the model (often `input_ids` for NLP models, `pixel_values` for vision models and `input_values` for speech models). """ + config_class = None base_model_prefix = "" main_input_name = "input_ids" @@ -3242,6 +3243,7 @@ class TFSharedEmbeddings(tf.keras.layers.Layer): kwargs (`Dict[str, Any]`, *optional*): Additional keyword arguments passed along to the `__init__` of `tf.keras.layers.Layer`. """ + # TODO (joao): flagged for delection due to embeddings refactor def __init__(self, vocab_size: int, hidden_size: int, initializer_range: Optional[float] = None, **kwargs): diff --git a/src/transformers/modeling_utils.py b/src/transformers/modeling_utils.py index 6d3a4d34dfb..d7e0580e435 100644 --- a/src/transformers/modeling_utils.py +++ b/src/transformers/modeling_utils.py @@ -1095,6 +1095,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix - **main_input_name** (`str`) -- The name of the principal input to the model (often `input_ids` for NLP models, `pixel_values` for vision models and `input_values` for speech models). """ + config_class = None base_model_prefix = "" main_input_name = "input_ids" diff --git a/src/transformers/models/align/configuration_align.py b/src/transformers/models/align/configuration_align.py index 74cfbfbe338..b7f377d4813 100644 --- a/src/transformers/models/align/configuration_align.py +++ b/src/transformers/models/align/configuration_align.py @@ -97,6 +97,7 @@ class AlignTextConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "align_text_model" def __init__( diff --git a/src/transformers/models/altclip/configuration_altclip.py b/src/transformers/models/altclip/configuration_altclip.py index 03200645209..e557d270dfb 100755 --- a/src/transformers/models/altclip/configuration_altclip.py +++ b/src/transformers/models/altclip/configuration_altclip.py @@ -100,6 +100,7 @@ class AltCLIPTextConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "altclip_text_model" def __init__( diff --git a/src/transformers/models/altclip/modeling_altclip.py b/src/transformers/models/altclip/modeling_altclip.py index 048c18edcc8..2f511bace5f 100755 --- a/src/transformers/models/altclip/modeling_altclip.py +++ b/src/transformers/models/altclip/modeling_altclip.py @@ -174,8 +174,7 @@ class AltCLIPOutput(ModelOutput): text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`AltCLIPTextModel`]. image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): - The image embeddings obtained by applying the projection layer to the pooled output of - [`AltCLIPVisionModel`]. + The image embeddings obtained by applying the projection layer to the pooled output of [`AltCLIPVisionModel`]. text_model_output(`BaseModelOutputWithPooling`): The output of the [`AltCLIPTextModel`]. vision_model_output(`BaseModelOutputWithPooling`): @@ -1049,9 +1048,7 @@ class AltCLIPPreTrainedModel(PreTrainedModel): nn.init.normal_(module.out_proj.weight, std=out_proj_std) elif isinstance(module, AltCLIPMLP): factor = self.config.initializer_factor - in_proj_std = ( - (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor - ) + in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) diff --git a/src/transformers/models/altclip/processing_altclip.py b/src/transformers/models/altclip/processing_altclip.py index 102535bc5b0..e9b4f45269c 100644 --- a/src/transformers/models/altclip/processing_altclip.py +++ b/src/transformers/models/altclip/processing_altclip.py @@ -35,6 +35,7 @@ class AltCLIPProcessor(ProcessorMixin): tokenizer ([`XLMRobertaTokenizerFast`], *optional*): The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "CLIPImageProcessor" tokenizer_class = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast") diff --git a/src/transformers/models/audio_spectrogram_transformer/configuration_audio_spectrogram_transformer.py b/src/transformers/models/audio_spectrogram_transformer/configuration_audio_spectrogram_transformer.py index 23a2d83e78a..81a087f07f6 100644 --- a/src/transformers/models/audio_spectrogram_transformer/configuration_audio_spectrogram_transformer.py +++ b/src/transformers/models/audio_spectrogram_transformer/configuration_audio_spectrogram_transformer.py @@ -86,6 +86,7 @@ class ASTConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "audio-spectrogram-transformer" def __init__( diff --git a/src/transformers/models/autoformer/configuration_autoformer.py b/src/transformers/models/autoformer/configuration_autoformer.py index ced76448cd1..7604233e327 100644 --- a/src/transformers/models/autoformer/configuration_autoformer.py +++ b/src/transformers/models/autoformer/configuration_autoformer.py @@ -131,6 +131,7 @@ class AutoformerConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "autoformer" attribute_map = { "hidden_size": "d_model", diff --git a/src/transformers/models/bark/processing_bark.py b/src/transformers/models/bark/processing_bark.py index ef8ed03224b..b322615ae23 100644 --- a/src/transformers/models/bark/processing_bark.py +++ b/src/transformers/models/bark/processing_bark.py @@ -46,6 +46,7 @@ class BarkProcessor(ProcessorMixin): a list of `voice_preset_names`. """ + tokenizer_class = "AutoTokenizer" attributes = ["tokenizer"] diff --git a/src/transformers/models/bart/configuration_bart.py b/src/transformers/models/bart/configuration_bart.py index 2a04657f419..8c03be9a620 100644 --- a/src/transformers/models/bart/configuration_bart.py +++ b/src/transformers/models/bart/configuration_bart.py @@ -107,6 +107,7 @@ class BartConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "bart" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} diff --git a/src/transformers/models/bart/tokenization_bart_fast.py b/src/transformers/models/bart/tokenization_bart_fast.py index dfbf493af26..850c9636833 100644 --- a/src/transformers/models/bart/tokenization_bart_fast.py +++ b/src/transformers/models/bart/tokenization_bart_fast.py @@ -147,6 +147,7 @@ class BartTokenizerFast(PreTrainedTokenizerFast): trim_offsets (`bool`, *optional*, defaults to `True`): Whether the post processing step should trim offsets to avoid including whitespaces. """ + vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES diff --git a/src/transformers/models/beit/configuration_beit.py b/src/transformers/models/beit/configuration_beit.py index e554f45f791..102718e17ff 100644 --- a/src/transformers/models/beit/configuration_beit.py +++ b/src/transformers/models/beit/configuration_beit.py @@ -115,6 +115,7 @@ class BeitConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "beit" def __init__( diff --git a/src/transformers/models/bert/configuration_bert.py b/src/transformers/models/bert/configuration_bert.py index 589c2b02618..e0db2c9f1bb 100644 --- a/src/transformers/models/bert/configuration_bert.py +++ b/src/transformers/models/bert/configuration_bert.py @@ -136,6 +136,7 @@ class BertConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "bert" def __init__( diff --git a/src/transformers/models/bert_generation/configuration_bert_generation.py b/src/transformers/models/bert_generation/configuration_bert_generation.py index e3e1b30e56f..841aec5c0fb 100644 --- a/src/transformers/models/bert_generation/configuration_bert_generation.py +++ b/src/transformers/models/bert_generation/configuration_bert_generation.py @@ -84,6 +84,7 @@ class BertGenerationConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "bert-generation" def __init__( diff --git a/src/transformers/models/big_bird/configuration_big_bird.py b/src/transformers/models/big_bird/configuration_big_bird.py index 53bf1ee6f44..e71d3ea4446 100644 --- a/src/transformers/models/big_bird/configuration_big_bird.py +++ b/src/transformers/models/big_bird/configuration_big_bird.py @@ -104,6 +104,7 @@ class BigBirdConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "big_bird" def __init__( diff --git a/src/transformers/models/big_bird/modeling_big_bird.py b/src/transformers/models/big_bird/modeling_big_bird.py index 4383d210cd8..d90aa443dad 100755 --- a/src/transformers/models/big_bird/modeling_big_bird.py +++ b/src/transformers/models/big_bird/modeling_big_bird.py @@ -896,15 +896,11 @@ class BigBirdBlockSparseAttention(nn.Module): # global keys (corresponding to 1st key block) attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, :to_block_size] = attn_weights[ :, :, :, :, :to_block_size - ].view( - bsz, n_heads, -1, to_block_size - ) # first_band_product + ].view(bsz, n_heads, -1, to_block_size) # first_band_product # global keys (corresponding to last key block) attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, -to_block_size:] = attn_weights[ :, :, :, :, -to_block_size: - ].view( - bsz, n_heads, -1, to_block_size - ) # last_band_product + ].view(bsz, n_heads, -1, to_block_size) # last_band_product # random keys for p1, i1, w1 in zip(range(bsz), rand_attn, attn_weights): # p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch diff --git a/src/transformers/models/bigbird_pegasus/configuration_bigbird_pegasus.py b/src/transformers/models/bigbird_pegasus/configuration_bigbird_pegasus.py index a7f198a735b..1c78803c4b1 100644 --- a/src/transformers/models/bigbird_pegasus/configuration_bigbird_pegasus.py +++ b/src/transformers/models/bigbird_pegasus/configuration_bigbird_pegasus.py @@ -120,6 +120,7 @@ class BigBirdPegasusConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "bigbird_pegasus" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { diff --git a/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py b/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py index 222873ac852..bc968278694 100755 --- a/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py +++ b/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py @@ -683,15 +683,11 @@ class BigBirdPegasusBlockSparseAttention(nn.Module): # global keys (corresponding to 1st key block) attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, :to_block_size] = attn_weights[ :, :, :, :, :to_block_size - ].view( - bsz, n_heads, -1, to_block_size - ) # first_band_product + ].view(bsz, n_heads, -1, to_block_size) # first_band_product # global keys (corresponding to last key block) attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, -to_block_size:] = attn_weights[ :, :, :, :, -to_block_size: - ].view( - bsz, n_heads, -1, to_block_size - ) # last_band_product + ].view(bsz, n_heads, -1, to_block_size) # last_band_product # random keys for p1, i1, w1 in zip(range(bsz), rand_attn, attn_weights): # p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch diff --git a/src/transformers/models/biogpt/configuration_biogpt.py b/src/transformers/models/biogpt/configuration_biogpt.py index b6911e2ef90..e8635490bf3 100644 --- a/src/transformers/models/biogpt/configuration_biogpt.py +++ b/src/transformers/models/biogpt/configuration_biogpt.py @@ -93,6 +93,7 @@ class BioGptConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "biogpt" def __init__( diff --git a/src/transformers/models/bit/configuration_bit.py b/src/transformers/models/bit/configuration_bit.py index 1e5ded1e191..495d348f053 100644 --- a/src/transformers/models/bit/configuration_bit.py +++ b/src/transformers/models/bit/configuration_bit.py @@ -85,6 +85,7 @@ class BitConfig(BackboneConfigMixin, PretrainedConfig): >>> configuration = model.config ``` """ + model_type = "bit" layer_types = ["preactivation", "bottleneck"] supported_padding = ["SAME", "VALID"] diff --git a/src/transformers/models/blenderbot/configuration_blenderbot.py b/src/transformers/models/blenderbot/configuration_blenderbot.py index 93ee9281364..4f55a96bf62 100644 --- a/src/transformers/models/blenderbot/configuration_blenderbot.py +++ b/src/transformers/models/blenderbot/configuration_blenderbot.py @@ -104,6 +104,7 @@ class BlenderbotConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "blenderbot" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} diff --git a/src/transformers/models/blenderbot/modeling_blenderbot.py b/src/transformers/models/blenderbot/modeling_blenderbot.py index f49f90f794f..2fbd4621361 100755 --- a/src/transformers/models/blenderbot/modeling_blenderbot.py +++ b/src/transformers/models/blenderbot/modeling_blenderbot.py @@ -1511,9 +1511,7 @@ class BlenderbotForCausalLM(BlenderbotPreTrainedModel): >>> from transformers import AutoTokenizer, BlenderbotForCausalLM >>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill") - >>> model = BlenderbotForCausalLM.from_pretrained( - ... "facebook/blenderbot-400M-distill", add_cross_attention=False - ... ) + >>> model = BlenderbotForCausalLM.from_pretrained("facebook/blenderbot-400M-distill", add_cross_attention=False) >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder." >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) diff --git a/src/transformers/models/blenderbot/tokenization_blenderbot.py b/src/transformers/models/blenderbot/tokenization_blenderbot.py index 7c1ef43bccb..29386c1233a 100644 --- a/src/transformers/models/blenderbot/tokenization_blenderbot.py +++ b/src/transformers/models/blenderbot/tokenization_blenderbot.py @@ -376,8 +376,8 @@ class BlenderbotTokenizer(PreTrainedTokenizer): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. Blenderbot does - not make use of token type ids, therefore a list of zeros is returned. + Create a mask from the two sequences passed to be used in a sequence-pair classification task. Blenderbot does not + make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): diff --git a/src/transformers/models/blenderbot/tokenization_blenderbot_fast.py b/src/transformers/models/blenderbot/tokenization_blenderbot_fast.py index 1c0d8f3fab7..6245025b503 100644 --- a/src/transformers/models/blenderbot/tokenization_blenderbot_fast.py +++ b/src/transformers/models/blenderbot/tokenization_blenderbot_fast.py @@ -212,8 +212,8 @@ class BlenderbotTokenizerFast(PreTrainedTokenizerFast): `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not having been set. - Blenderbot tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will - greedily comprise the space before the **. + Blenderbot tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily + comprise the space before the **. """ if self._mask_token is None: if self.verbose: @@ -264,8 +264,8 @@ class BlenderbotTokenizerFast(PreTrainedTokenizerFast): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. Blenderbot does - not make use of token type ids, therefore a list of zeros is returned. + Create a mask from the two sequences passed to be used in a sequence-pair classification task. Blenderbot does not + make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): diff --git a/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py b/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py index fbc23435d66..b41330656d3 100644 --- a/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py +++ b/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py @@ -104,6 +104,7 @@ class BlenderbotSmallConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "blenderbot-small" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} diff --git a/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py b/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py index 292b5a8c6e8..1669602832d 100755 --- a/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py +++ b/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py @@ -1478,9 +1478,7 @@ class BlenderbotSmallForCausalLM(BlenderbotSmallPreTrainedModel): >>> from transformers import AutoTokenizer, BlenderbotSmallForCausalLM >>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M") - >>> model = BlenderbotSmallForCausalLM.from_pretrained( - ... "facebook/blenderbot_small-90M", add_cross_attention=False - ... ) + >>> model = BlenderbotSmallForCausalLM.from_pretrained("facebook/blenderbot_small-90M", add_cross_attention=False) >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder." >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) diff --git a/src/transformers/models/blip/configuration_blip.py b/src/transformers/models/blip/configuration_blip.py index 2482fcaf9ec..0b3dfb4a121 100644 --- a/src/transformers/models/blip/configuration_blip.py +++ b/src/transformers/models/blip/configuration_blip.py @@ -109,6 +109,7 @@ class BlipTextConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "blip_text_model" def __init__( diff --git a/src/transformers/models/blip/modeling_blip_text.py b/src/transformers/models/blip/modeling_blip_text.py index 00c6a85ee61..353c0f486a5 100644 --- a/src/transformers/models/blip/modeling_blip_text.py +++ b/src/transformers/models/blip/modeling_blip_text.py @@ -742,13 +742,13 @@ class BlipTextModel(BlipTextPreTrainedModel): # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if encoder_hidden_states is not None: - if type(encoder_hidden_states) == list: + if isinstance(encoder_hidden_states, list): encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() else: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) - if type(encoder_attention_mask) == list: + if isinstance(encoder_attention_mask, list): encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] elif encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) diff --git a/src/transformers/models/blip/modeling_tf_blip_text.py b/src/transformers/models/blip/modeling_tf_blip_text.py index 9873c292b7a..b7307c062f7 100644 --- a/src/transformers/models/blip/modeling_tf_blip_text.py +++ b/src/transformers/models/blip/modeling_tf_blip_text.py @@ -741,13 +741,13 @@ class TFBlipTextModel(TFBlipTextPreTrainedModel): # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if encoder_hidden_states is not None: - if type(encoder_hidden_states) == list: + if isinstance(encoder_hidden_states, list): encoder_batch_size, encoder_sequence_length, _ = shape_list(encoder_hidden_states[0]) else: encoder_batch_size, encoder_sequence_length, _ = shape_list(encoder_hidden_states) encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) - if type(encoder_attention_mask) == list: + if isinstance(encoder_attention_mask, list): encoder_extended_attention_mask = [invert_attention_mask(mask) for mask in encoder_attention_mask] elif encoder_attention_mask is None: encoder_attention_mask = tf.ones(encoder_hidden_shape) diff --git a/src/transformers/models/blip/processing_blip.py b/src/transformers/models/blip/processing_blip.py index c4df8ddffab..3b9d5c369a4 100644 --- a/src/transformers/models/blip/processing_blip.py +++ b/src/transformers/models/blip/processing_blip.py @@ -37,6 +37,7 @@ class BlipProcessor(ProcessorMixin): tokenizer (`BertTokenizerFast`): An instance of ['BertTokenizerFast`]. The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "BlipImageProcessor" tokenizer_class = ("BertTokenizer", "BertTokenizerFast") diff --git a/src/transformers/models/blip_2/configuration_blip_2.py b/src/transformers/models/blip_2/configuration_blip_2.py index 1b375e147f7..85749888a54 100644 --- a/src/transformers/models/blip_2/configuration_blip_2.py +++ b/src/transformers/models/blip_2/configuration_blip_2.py @@ -190,6 +190,7 @@ class Blip2QFormerConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "blip_2_qformer" def __init__( diff --git a/src/transformers/models/blip_2/modeling_blip_2.py b/src/transformers/models/blip_2/modeling_blip_2.py index 10a37c79b86..00433f3ea34 100644 --- a/src/transformers/models/blip_2/modeling_blip_2.py +++ b/src/transformers/models/blip_2/modeling_blip_2.py @@ -1123,13 +1123,13 @@ class Blip2QFormerModel(Blip2PreTrainedModel): # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if encoder_hidden_states is not None: - if type(encoder_hidden_states) == list: + if isinstance(encoder_hidden_states, list): encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() else: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) - if type(encoder_attention_mask) == list: + if isinstance(encoder_attention_mask, list): encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] elif encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) diff --git a/src/transformers/models/blip_2/processing_blip_2.py b/src/transformers/models/blip_2/processing_blip_2.py index 837056f8889..ff7044c82ae 100644 --- a/src/transformers/models/blip_2/processing_blip_2.py +++ b/src/transformers/models/blip_2/processing_blip_2.py @@ -37,6 +37,7 @@ class Blip2Processor(ProcessorMixin): tokenizer (`AutoTokenizer`): An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "BlipImageProcessor" tokenizer_class = "AutoTokenizer" @@ -141,8 +142,8 @@ class Blip2Processor(ProcessorMixin): # Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer def decode(self, *args, **kwargs): """ - This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer - to the docstring of this method for more information. + This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to + the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) diff --git a/src/transformers/models/bridgetower/configuration_bridgetower.py b/src/transformers/models/bridgetower/configuration_bridgetower.py index e44373cba59..c12c1600e9b 100644 --- a/src/transformers/models/bridgetower/configuration_bridgetower.py +++ b/src/transformers/models/bridgetower/configuration_bridgetower.py @@ -73,6 +73,7 @@ class BridgeTowerVisionConfig(PretrainedConfig): >>> # Accessing the configuration >>> configuration ```""" + model_type = "bridgetower_vision_model" def __init__( @@ -179,6 +180,7 @@ class BridgeTowerTextConfig(PretrainedConfig): >>> # Accessing the configuration >>> configuration ```""" + model_type = "bridgetower_text_model" def __init__( @@ -291,6 +293,7 @@ class BridgeTowerConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "bridgetower" def __init__( diff --git a/src/transformers/models/bridgetower/modeling_bridgetower.py b/src/transformers/models/bridgetower/modeling_bridgetower.py index 89655db7f04..f5822070db6 100644 --- a/src/transformers/models/bridgetower/modeling_bridgetower.py +++ b/src/transformers/models/bridgetower/modeling_bridgetower.py @@ -46,7 +46,7 @@ _TOKENIZER_FOR_DOC = "RobertaTokenizer" BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST = [ "BridgeTower/bridgetower-base", - "BridgeTower/bridgetower-base-itm-mlm" + "BridgeTower/bridgetower-base-itm-mlm", # See all bridgetower models at https://huggingface.co/BridgeTower ] diff --git a/src/transformers/models/bridgetower/processing_bridgetower.py b/src/transformers/models/bridgetower/processing_bridgetower.py index c268d7c26f4..7718c3bf833 100644 --- a/src/transformers/models/bridgetower/processing_bridgetower.py +++ b/src/transformers/models/bridgetower/processing_bridgetower.py @@ -38,6 +38,7 @@ class BridgeTowerProcessor(ProcessorMixin): tokenizer (`RobertaTokenizerFast`): An instance of ['RobertaTokenizerFast`]. The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "BridgeTowerImageProcessor" tokenizer_class = ("RobertaTokenizer", "RobertaTokenizerFast") diff --git a/src/transformers/models/bros/configuration_bros.py b/src/transformers/models/bros/configuration_bros.py index f0a5dbff86e..4384810a55a 100644 --- a/src/transformers/models/bros/configuration_bros.py +++ b/src/transformers/models/bros/configuration_bros.py @@ -90,6 +90,7 @@ class BrosConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "bros" def __init__( diff --git a/src/transformers/models/bros/processing_bros.py b/src/transformers/models/bros/processing_bros.py index 77b73e48b90..9c2e0642d8c 100644 --- a/src/transformers/models/bros/processing_bros.py +++ b/src/transformers/models/bros/processing_bros.py @@ -34,6 +34,7 @@ class BrosProcessor(ProcessorMixin): tokenizer (`BertTokenizerFast`, *optional*): An instance of ['BertTokenizerFast`]. The tokenizer is a required input. """ + attributes = ["tokenizer"] tokenizer_class = ("BertTokenizer", "BertTokenizerFast") diff --git a/src/transformers/models/canine/configuration_canine.py b/src/transformers/models/canine/configuration_canine.py index e218044b553..9cd86c6ac0e 100644 --- a/src/transformers/models/canine/configuration_canine.py +++ b/src/transformers/models/canine/configuration_canine.py @@ -95,6 +95,7 @@ class CanineConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "canine" def __init__( diff --git a/src/transformers/models/canine/modeling_canine.py b/src/transformers/models/canine/modeling_canine.py index ead9619d926..378a5775256 100644 --- a/src/transformers/models/canine/modeling_canine.py +++ b/src/transformers/models/canine/modeling_canine.py @@ -54,7 +54,7 @@ _CONFIG_FOR_DOC = "CanineConfig" CANINE_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/canine-s", - "google/canine-r" + "google/canine-r", # See all CANINE models at https://huggingface.co/models?filter=canine ] diff --git a/src/transformers/models/chinese_clip/configuration_chinese_clip.py b/src/transformers/models/chinese_clip/configuration_chinese_clip.py index 0e91200ce60..e0007739911 100644 --- a/src/transformers/models/chinese_clip/configuration_chinese_clip.py +++ b/src/transformers/models/chinese_clip/configuration_chinese_clip.py @@ -106,6 +106,7 @@ class ChineseCLIPTextConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "chinese_clip_text_model" def __init__( diff --git a/src/transformers/models/chinese_clip/modeling_chinese_clip.py b/src/transformers/models/chinese_clip/modeling_chinese_clip.py index ec2086bf67c..a16fb081b19 100644 --- a/src/transformers/models/chinese_clip/modeling_chinese_clip.py +++ b/src/transformers/models/chinese_clip/modeling_chinese_clip.py @@ -718,9 +718,7 @@ class ChineseCLIPPreTrainedModel(PreTrainedModel): nn.init.normal_(module.out_proj.weight, std=out_proj_std) elif isinstance(module, ChineseCLIPVisionMLP): factor = self.config.initializer_factor - in_proj_std = ( - (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor - ) + in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) diff --git a/src/transformers/models/chinese_clip/processing_chinese_clip.py b/src/transformers/models/chinese_clip/processing_chinese_clip.py index fbd4d579df9..832f44102ab 100644 --- a/src/transformers/models/chinese_clip/processing_chinese_clip.py +++ b/src/transformers/models/chinese_clip/processing_chinese_clip.py @@ -36,6 +36,7 @@ class ChineseCLIPProcessor(ProcessorMixin): tokenizer ([`BertTokenizerFast`], *optional*): The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "ChineseCLIPImageProcessor" tokenizer_class = ("BertTokenizer", "BertTokenizerFast") diff --git a/src/transformers/models/clap/configuration_clap.py b/src/transformers/models/clap/configuration_clap.py index fca9b0087c8..f940ee15f97 100644 --- a/src/transformers/models/clap/configuration_clap.py +++ b/src/transformers/models/clap/configuration_clap.py @@ -97,6 +97,7 @@ class ClapTextConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "clap_text_model" def __init__( diff --git a/src/transformers/models/clap/processing_clap.py b/src/transformers/models/clap/processing_clap.py index 7492f102b4b..87799899945 100644 --- a/src/transformers/models/clap/processing_clap.py +++ b/src/transformers/models/clap/processing_clap.py @@ -33,6 +33,7 @@ class ClapProcessor(ProcessorMixin): tokenizer ([`RobertaTokenizerFast`]): The tokenizer is a required input. """ + feature_extractor_class = "ClapFeatureExtractor" tokenizer_class = ("RobertaTokenizer", "RobertaTokenizerFast") diff --git a/src/transformers/models/clip/configuration_clip.py b/src/transformers/models/clip/configuration_clip.py index f9ecf5f7d46..e9d2b132b81 100644 --- a/src/transformers/models/clip/configuration_clip.py +++ b/src/transformers/models/clip/configuration_clip.py @@ -96,6 +96,7 @@ class CLIPTextConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "clip_text_model" def __init__( diff --git a/src/transformers/models/clip/modeling_clip.py b/src/transformers/models/clip/modeling_clip.py index ccc253cc98d..9e708e51056 100644 --- a/src/transformers/models/clip/modeling_clip.py +++ b/src/transformers/models/clip/modeling_clip.py @@ -421,9 +421,7 @@ class CLIPPreTrainedModel(PreTrainedModel): nn.init.normal_(module.out_proj.weight, std=out_proj_std) elif isinstance(module, CLIPMLP): factor = self.config.initializer_factor - in_proj_std = ( - (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor - ) + in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) diff --git a/src/transformers/models/clip/processing_clip.py b/src/transformers/models/clip/processing_clip.py index f083380e6ad..31351f31efc 100644 --- a/src/transformers/models/clip/processing_clip.py +++ b/src/transformers/models/clip/processing_clip.py @@ -35,6 +35,7 @@ class CLIPProcessor(ProcessorMixin): tokenizer ([`CLIPTokenizerFast`], *optional*): The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "CLIPImageProcessor" tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast") diff --git a/src/transformers/models/clipseg/configuration_clipseg.py b/src/transformers/models/clipseg/configuration_clipseg.py index cb178514b29..5f8ba12723a 100644 --- a/src/transformers/models/clipseg/configuration_clipseg.py +++ b/src/transformers/models/clipseg/configuration_clipseg.py @@ -86,6 +86,7 @@ class CLIPSegTextConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "clipseg_text_model" def __init__( diff --git a/src/transformers/models/clipseg/modeling_clipseg.py b/src/transformers/models/clipseg/modeling_clipseg.py index 4ef9395eb25..c0cf6b3b165 100644 --- a/src/transformers/models/clipseg/modeling_clipseg.py +++ b/src/transformers/models/clipseg/modeling_clipseg.py @@ -77,8 +77,7 @@ class CLIPSegOutput(ModelOutput): text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`CLIPSegTextModel`]. image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): - The image embeddings obtained by applying the projection layer to the pooled output of - [`CLIPSegVisionModel`]. + The image embeddings obtained by applying the projection layer to the pooled output of [`CLIPSegVisionModel`]. text_model_output(`BaseModelOutputWithPooling`): The output of the [`CLIPSegTextModel`]. vision_model_output(`BaseModelOutputWithPooling`): @@ -443,9 +442,7 @@ class CLIPSegPreTrainedModel(PreTrainedModel): nn.init.normal_(module.out_proj.weight, std=out_proj_std) elif isinstance(module, CLIPSegMLP): factor = self.config.initializer_factor - in_proj_std = ( - (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor - ) + in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) diff --git a/src/transformers/models/clipseg/processing_clipseg.py b/src/transformers/models/clipseg/processing_clipseg.py index bc1d36a1c66..e57021f213a 100644 --- a/src/transformers/models/clipseg/processing_clipseg.py +++ b/src/transformers/models/clipseg/processing_clipseg.py @@ -35,6 +35,7 @@ class CLIPSegProcessor(ProcessorMixin): tokenizer ([`CLIPTokenizerFast`], *optional*): The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "ViTImageProcessor" tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast") diff --git a/src/transformers/models/clvp/modeling_clvp.py b/src/transformers/models/clvp/modeling_clvp.py index 045ac33ffb8..db2bbe3f00e 100644 --- a/src/transformers/models/clvp/modeling_clvp.py +++ b/src/transformers/models/clvp/modeling_clvp.py @@ -684,9 +684,7 @@ class ClvpPreTrainedModel(PreTrainedModel): module.bias.data.zero_() elif isinstance(module, ClvpEncoderMLP): factor = self.config.initializer_factor - in_proj_std = ( - (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor - ) + in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.proj.weight if getattr(module.fc1, "proj") else module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) diff --git a/src/transformers/models/clvp/processing_clvp.py b/src/transformers/models/clvp/processing_clvp.py index cf4bd4de8f7..0723986db97 100644 --- a/src/transformers/models/clvp/processing_clvp.py +++ b/src/transformers/models/clvp/processing_clvp.py @@ -34,6 +34,7 @@ class ClvpProcessor(ProcessorMixin): tokenizer (`ClvpTokenizer`): An instance of [`ClvpTokenizer`]. The tokenizer is a required input. """ + feature_extractor_class = "ClvpFeatureExtractor" tokenizer_class = "ClvpTokenizer" model_input_names = [ @@ -76,15 +77,15 @@ class ClvpProcessor(ProcessorMixin): # Copied from transformers.models.whisper.processing_whisper.WhisperProcessor.batch_decode with Whisper->Clvp def batch_decode(self, *args, **kwargs): """ - This method forwards all its arguments to ClvpTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer - to the docstring of this method for more information. + This method forwards all its arguments to ClvpTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please + refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs) # Copied from transformers.models.whisper.processing_whisper.WhisperProcessor.decode with Whisper->Clvp def decode(self, *args, **kwargs): """ - This method forwards all its arguments to ClvpTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the - docstring of this method for more information. + This method forwards all its arguments to ClvpTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to + the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) diff --git a/src/transformers/models/codegen/configuration_codegen.py b/src/transformers/models/codegen/configuration_codegen.py index d960b21b6af..73c019870f1 100644 --- a/src/transformers/models/codegen/configuration_codegen.py +++ b/src/transformers/models/codegen/configuration_codegen.py @@ -105,6 +105,7 @@ class CodeGenConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "codegen" attribute_map = { "max_position_embeddings": "n_positions", diff --git a/src/transformers/models/conditional_detr/configuration_conditional_detr.py b/src/transformers/models/conditional_detr/configuration_conditional_detr.py index 356e5c0a574..5f9e49db6cc 100644 --- a/src/transformers/models/conditional_detr/configuration_conditional_detr.py +++ b/src/transformers/models/conditional_detr/configuration_conditional_detr.py @@ -134,6 +134,7 @@ class ConditionalDetrConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "conditional_detr" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { diff --git a/src/transformers/models/conditional_detr/image_processing_conditional_detr.py b/src/transformers/models/conditional_detr/image_processing_conditional_detr.py index 998cb741917..c46527971f1 100644 --- a/src/transformers/models/conditional_detr/image_processing_conditional_detr.py +++ b/src/transformers/models/conditional_detr/image_processing_conditional_detr.py @@ -478,8 +478,7 @@ def post_process_panoptic_sample( threshold=0.85, ) -> Dict: """ - Converts the output of [`ConditionalDetrForSegmentation`] into panoptic segmentation predictions for a single - sample. + Converts the output of [`ConditionalDetrForSegmentation`] into panoptic segmentation predictions for a single sample. Args: out_logits (`torch.Tensor`): @@ -1454,8 +1453,7 @@ class ConditionalDetrImageProcessor(BaseImageProcessor): # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process_semantic_segmentation with Detr->ConditionalDetr def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple[int, int]] = None): """ - Converts the output of [`ConditionalDetrForSegmentation`] into semantic segmentation maps. Only supports - PyTorch. + Converts the output of [`ConditionalDetrForSegmentation`] into semantic segmentation maps. Only supports PyTorch. Args: outputs ([`ConditionalDetrForSegmentation`]): @@ -1511,8 +1509,7 @@ class ConditionalDetrImageProcessor(BaseImageProcessor): return_coco_annotation: Optional[bool] = False, ) -> List[Dict]: """ - Converts the output of [`ConditionalDetrForSegmentation`] into instance segmentation predictions. Only supports - PyTorch. + Converts the output of [`ConditionalDetrForSegmentation`] into instance segmentation predictions. Only supports PyTorch. Args: outputs ([`ConditionalDetrForSegmentation`]): @@ -1596,8 +1593,8 @@ class ConditionalDetrImageProcessor(BaseImageProcessor): target_sizes: Optional[List[Tuple[int, int]]] = None, ) -> List[Dict]: """ - Converts the output of [`ConditionalDetrForSegmentation`] into image panoptic segmentation predictions. Only - supports PyTorch. + Converts the output of [`ConditionalDetrForSegmentation`] into image panoptic segmentation predictions. Only supports + PyTorch. Args: outputs ([`ConditionalDetrForSegmentation`]): diff --git a/src/transformers/models/conditional_detr/modeling_conditional_detr.py b/src/transformers/models/conditional_detr/modeling_conditional_detr.py index 961febc480b..fd9a9d7e6f2 100644 --- a/src/transformers/models/conditional_detr/modeling_conditional_detr.py +++ b/src/transformers/models/conditional_detr/modeling_conditional_detr.py @@ -153,8 +153,8 @@ class ConditionalDetrObjectDetectionOutput(ModelOutput): pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding - possible padding). You can use [`~ConditionalDetrImageProcessor.post_process_object_detection`] to retrieve - the unnormalized bounding boxes. + possible padding). You can use [`~ConditionalDetrImageProcessor.post_process_object_detection`] to retrieve the + unnormalized bounding boxes. auxiliary_outputs (`list[Dict]`, *optional*): Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and @@ -217,14 +217,14 @@ class ConditionalDetrSegmentationOutput(ModelOutput): pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding - possible padding). You can use [`~ConditionalDetrImageProcessor.post_process_object_detection`] to retrieve - the unnormalized bounding boxes. + possible padding). You can use [`~ConditionalDetrImageProcessor.post_process_object_detection`] to retrieve the + unnormalized bounding boxes. pred_masks (`torch.FloatTensor` of shape `(batch_size, num_queries, height/4, width/4)`): Segmentation masks logits for all queries. See also [`~ConditionalDetrImageProcessor.post_process_semantic_segmentation`] or [`~ConditionalDetrImageProcessor.post_process_instance_segmentation`] - [`~ConditionalDetrImageProcessor.post_process_panoptic_segmentation`] to evaluate semantic, instance and - panoptic segmentation masks respectively. + [`~ConditionalDetrImageProcessor.post_process_panoptic_segmentation`] to evaluate semantic, instance and panoptic + segmentation masks respectively. auxiliary_outputs (`list[Dict]`, *optional*): Optional, only returned when auxiliary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and diff --git a/src/transformers/models/convbert/configuration_convbert.py b/src/transformers/models/convbert/configuration_convbert.py index 4c1032f4ffa..bbdbd26349b 100644 --- a/src/transformers/models/convbert/configuration_convbert.py +++ b/src/transformers/models/convbert/configuration_convbert.py @@ -96,6 +96,7 @@ class ConvBertConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "convbert" def __init__( diff --git a/src/transformers/models/convbert/tokenization_convbert.py b/src/transformers/models/convbert/tokenization_convbert.py index 439beb7abb4..8c359886cf7 100644 --- a/src/transformers/models/convbert/tokenization_convbert.py +++ b/src/transformers/models/convbert/tokenization_convbert.py @@ -263,8 +263,8 @@ class ConvBertTokenizer(PreTrainedTokenizer): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. A ConvBERT - sequence pair mask has the following format: + Create a mask from the two sequences passed to be used in a sequence-pair classification task. A ConvBERT sequence + pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 diff --git a/src/transformers/models/convbert/tokenization_convbert_fast.py b/src/transformers/models/convbert/tokenization_convbert_fast.py index 7ccc21b3e05..14909876ded 100644 --- a/src/transformers/models/convbert/tokenization_convbert_fast.py +++ b/src/transformers/models/convbert/tokenization_convbert_fast.py @@ -168,8 +168,8 @@ class ConvBertTokenizerFast(PreTrainedTokenizerFast): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. A ConvBERT - sequence pair mask has the following format: + Create a mask from the two sequences passed to be used in a sequence-pair classification task. A ConvBERT sequence + pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 diff --git a/src/transformers/models/convnext/configuration_convnext.py b/src/transformers/models/convnext/configuration_convnext.py index 0cba7804057..24fb822b846 100644 --- a/src/transformers/models/convnext/configuration_convnext.py +++ b/src/transformers/models/convnext/configuration_convnext.py @@ -87,6 +87,7 @@ class ConvNextConfig(BackboneConfigMixin, PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "convnext" def __init__( diff --git a/src/transformers/models/convnextv2/configuration_convnextv2.py b/src/transformers/models/convnextv2/configuration_convnextv2.py index 14dfcf85124..952760eb9a2 100644 --- a/src/transformers/models/convnextv2/configuration_convnextv2.py +++ b/src/transformers/models/convnextv2/configuration_convnextv2.py @@ -79,6 +79,7 @@ class ConvNextV2Config(BackboneConfigMixin, PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "convnextv2" def __init__( diff --git a/src/transformers/models/cpmant/configuration_cpmant.py b/src/transformers/models/cpmant/configuration_cpmant.py index bd85244c81f..7013b8dde73 100644 --- a/src/transformers/models/cpmant/configuration_cpmant.py +++ b/src/transformers/models/cpmant/configuration_cpmant.py @@ -84,6 +84,7 @@ class CpmAntConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "cpmant" def __init__( diff --git a/src/transformers/models/cvt/configuration_cvt.py b/src/transformers/models/cvt/configuration_cvt.py index a540c0f4807..f1d96fc17ea 100644 --- a/src/transformers/models/cvt/configuration_cvt.py +++ b/src/transformers/models/cvt/configuration_cvt.py @@ -96,6 +96,7 @@ class CvtConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "cvt" def __init__( diff --git a/src/transformers/models/data2vec/configuration_data2vec_audio.py b/src/transformers/models/data2vec/configuration_data2vec_audio.py index 6c24f3effba..e37def379fb 100644 --- a/src/transformers/models/data2vec/configuration_data2vec_audio.py +++ b/src/transformers/models/data2vec/configuration_data2vec_audio.py @@ -168,6 +168,7 @@ class Data2VecAudioConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "data2vec-audio" def __init__( diff --git a/src/transformers/models/data2vec/configuration_data2vec_text.py b/src/transformers/models/data2vec/configuration_data2vec_text.py index 305a3ea5e4f..01a81e95b41 100644 --- a/src/transformers/models/data2vec/configuration_data2vec_text.py +++ b/src/transformers/models/data2vec/configuration_data2vec_text.py @@ -95,6 +95,7 @@ class Data2VecTextConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "data2vec-text" def __init__( diff --git a/src/transformers/models/data2vec/configuration_data2vec_vision.py b/src/transformers/models/data2vec/configuration_data2vec_vision.py index b45f8420ca0..5d8e4a252a7 100644 --- a/src/transformers/models/data2vec/configuration_data2vec_vision.py +++ b/src/transformers/models/data2vec/configuration_data2vec_vision.py @@ -111,6 +111,7 @@ class Data2VecVisionConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "data2vec-vision" def __init__( diff --git a/src/transformers/models/data2vec/modeling_data2vec_vision.py b/src/transformers/models/data2vec/modeling_data2vec_vision.py index 49f8c411c33..b749b7bf154 100644 --- a/src/transformers/models/data2vec/modeling_data2vec_vision.py +++ b/src/transformers/models/data2vec/modeling_data2vec_vision.py @@ -289,8 +289,8 @@ class Data2VecVisionSelfAttention(nn.Module): # Copied from transformers.models.beit.modeling_beit.BeitSelfOutput with Beit->Data2VecVision class Data2VecVisionSelfOutput(nn.Module): """ - The residual connection is defined in Data2VecVisionLayer instead of here (as is the case with other models), due - to the layernorm applied before each block. + The residual connection is defined in Data2VecVisionLayer instead of here (as is the case with other models), due to the + layernorm applied before each block. """ def __init__(self, config: Data2VecVisionConfig) -> None: diff --git a/src/transformers/models/deberta/configuration_deberta.py b/src/transformers/models/deberta/configuration_deberta.py index 94ea91cd3a0..f6db66f0d8d 100644 --- a/src/transformers/models/deberta/configuration_deberta.py +++ b/src/transformers/models/deberta/configuration_deberta.py @@ -105,6 +105,7 @@ class DebertaConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "deberta" def __init__( @@ -148,7 +149,7 @@ class DebertaConfig(PretrainedConfig): self.position_biased_input = position_biased_input # Backwards compatibility - if type(pos_att_type) == str: + if isinstance(pos_att_type, str): pos_att_type = [x.strip() for x in pos_att_type.lower().split("|")] self.pos_att_type = pos_att_type diff --git a/src/transformers/models/deberta_v2/configuration_deberta_v2.py b/src/transformers/models/deberta_v2/configuration_deberta_v2.py index d55486cd563..68f2112754a 100644 --- a/src/transformers/models/deberta_v2/configuration_deberta_v2.py +++ b/src/transformers/models/deberta_v2/configuration_deberta_v2.py @@ -107,6 +107,7 @@ class DebertaV2Config(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "deberta-v2" def __init__( @@ -150,7 +151,7 @@ class DebertaV2Config(PretrainedConfig): self.position_biased_input = position_biased_input # Backwards compatibility - if type(pos_att_type) == str: + if isinstance(pos_att_type, str): pos_att_type = [x.strip() for x in pos_att_type.lower().split("|")] self.pos_att_type = pos_att_type diff --git a/src/transformers/models/deberta_v2/modeling_deberta_v2.py b/src/transformers/models/deberta_v2/modeling_deberta_v2.py index 0d3ed94aeab..a8f06436926 100644 --- a/src/transformers/models/deberta_v2/modeling_deberta_v2.py +++ b/src/transformers/models/deberta_v2/modeling_deberta_v2.py @@ -780,15 +780,11 @@ class DisentangledSelfAttention(nn.Module): if "c2p" in self.pos_att_type: pos_key_layer = self.transpose_for_scores( self.pos_key_proj(rel_embeddings), self.num_attention_heads - ).repeat( - query_layer.size(0) // self.num_attention_heads, 1, 1 - ) # .split(self.all_head_size, dim=-1) + ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) # .split(self.all_head_size, dim=-1) if "p2c" in self.pos_att_type: pos_query_layer = self.transpose_for_scores( self.pos_query_proj(rel_embeddings), self.num_attention_heads - ).repeat( - query_layer.size(0) // self.num_attention_heads, 1, 1 - ) # .split(self.all_head_size, dim=-1) + ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) # .split(self.all_head_size, dim=-1) score = 0 # content->position diff --git a/src/transformers/models/deformable_detr/configuration_deformable_detr.py b/src/transformers/models/deformable_detr/configuration_deformable_detr.py index dbe5fd7f0a7..a6161061d9a 100644 --- a/src/transformers/models/deformable_detr/configuration_deformable_detr.py +++ b/src/transformers/models/deformable_detr/configuration_deformable_detr.py @@ -143,6 +143,7 @@ class DeformableDetrConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "deformable_detr" attribute_map = { "hidden_size": "d_model", diff --git a/src/transformers/models/deit/configuration_deit.py b/src/transformers/models/deit/configuration_deit.py index 905473c13eb..ef346637ba7 100644 --- a/src/transformers/models/deit/configuration_deit.py +++ b/src/transformers/models/deit/configuration_deit.py @@ -91,6 +91,7 @@ class DeiTConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "deit" def __init__( diff --git a/src/transformers/models/deprecated/mctct/configuration_mctct.py b/src/transformers/models/deprecated/mctct/configuration_mctct.py index e91104112b6..aea085cc5a6 100644 --- a/src/transformers/models/deprecated/mctct/configuration_mctct.py +++ b/src/transformers/models/deprecated/mctct/configuration_mctct.py @@ -114,6 +114,7 @@ class MCTCTConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "mctct" def __init__( diff --git a/src/transformers/models/deprecated/mctct/processing_mctct.py b/src/transformers/models/deprecated/mctct/processing_mctct.py index 764ed8d3db5..4e0cbe27dd9 100644 --- a/src/transformers/models/deprecated/mctct/processing_mctct.py +++ b/src/transformers/models/deprecated/mctct/processing_mctct.py @@ -34,6 +34,7 @@ class MCTCTProcessor(ProcessorMixin): tokenizer (`AutoTokenizer`): An instance of [`AutoTokenizer`]. The tokenizer is a required input. """ + feature_extractor_class = "MCTCTFeatureExtractor" tokenizer_class = "AutoTokenizer" diff --git a/src/transformers/models/deprecated/open_llama/configuration_open_llama.py b/src/transformers/models/deprecated/open_llama/configuration_open_llama.py index 1d5756cd38a..5786abac850 100644 --- a/src/transformers/models/deprecated/open_llama/configuration_open_llama.py +++ b/src/transformers/models/deprecated/open_llama/configuration_open_llama.py @@ -90,6 +90,7 @@ class OpenLlamaConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "open-llama" def __init__( diff --git a/src/transformers/models/deprecated/retribert/configuration_retribert.py b/src/transformers/models/deprecated/retribert/configuration_retribert.py index 11d19193b36..3861b9c90f3 100644 --- a/src/transformers/models/deprecated/retribert/configuration_retribert.py +++ b/src/transformers/models/deprecated/retribert/configuration_retribert.py @@ -72,6 +72,7 @@ class RetriBertConfig(PretrainedConfig): projection_dim (`int`, *optional*, defaults to 128): Final dimension of the query and document representation after projection """ + model_type = "retribert" def __init__( diff --git a/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py b/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py index a64a0cbd89e..cfad075c6ae 100644 --- a/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py +++ b/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py @@ -100,6 +100,7 @@ class TrajectoryTransformerConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "trajectory_transformer" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { diff --git a/src/transformers/models/deprecated/van/configuration_van.py b/src/transformers/models/deprecated/van/configuration_van.py index 70942ad645b..85f228193c4 100644 --- a/src/transformers/models/deprecated/van/configuration_van.py +++ b/src/transformers/models/deprecated/van/configuration_van.py @@ -77,6 +77,7 @@ class VanConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "van" def __init__( diff --git a/src/transformers/models/deta/configuration_deta.py b/src/transformers/models/deta/configuration_deta.py index 8abe077ae12..0d8e59e9608 100644 --- a/src/transformers/models/deta/configuration_deta.py +++ b/src/transformers/models/deta/configuration_deta.py @@ -124,6 +124,7 @@ class DetaConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "deta" attribute_map = { "hidden_size": "d_model", diff --git a/src/transformers/models/deta/modeling_deta.py b/src/transformers/models/deta/modeling_deta.py index db607b183b7..0f245f2a305 100644 --- a/src/transformers/models/deta/modeling_deta.py +++ b/src/transformers/models/deta/modeling_deta.py @@ -70,8 +70,8 @@ DETA_PRETRAINED_MODEL_ARCHIVE_LIST = [ # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrDecoderOutput with DeformableDetr->Deta class DetaDecoderOutput(ModelOutput): """ - Base class for outputs of the DetaDecoder. This class adds two attributes to BaseModelOutputWithCrossAttentions, - namely: + Base class for outputs of the DetaDecoder. This class adds two attributes to + BaseModelOutputWithCrossAttentions, namely: - a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer) - a stacked tensor of intermediate reference points. diff --git a/src/transformers/models/detr/configuration_detr.py b/src/transformers/models/detr/configuration_detr.py index 3b6ac3624f1..fadd9ce0872 100644 --- a/src/transformers/models/detr/configuration_detr.py +++ b/src/transformers/models/detr/configuration_detr.py @@ -132,6 +132,7 @@ class DetrConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "detr" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { diff --git a/src/transformers/models/dinat/configuration_dinat.py b/src/transformers/models/dinat/configuration_dinat.py index b70797b55c3..4baa92f469f 100644 --- a/src/transformers/models/dinat/configuration_dinat.py +++ b/src/transformers/models/dinat/configuration_dinat.py @@ -94,6 +94,7 @@ class DinatConfig(BackboneConfigMixin, PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "dinat" attribute_map = { diff --git a/src/transformers/models/dinov2/configuration_dinov2.py b/src/transformers/models/dinov2/configuration_dinov2.py index 4c3c26623a3..741007f3076 100644 --- a/src/transformers/models/dinov2/configuration_dinov2.py +++ b/src/transformers/models/dinov2/configuration_dinov2.py @@ -105,6 +105,7 @@ class Dinov2Config(BackboneConfigMixin, PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "dinov2" def __init__( diff --git a/src/transformers/models/distilbert/configuration_distilbert.py b/src/transformers/models/distilbert/configuration_distilbert.py index 3dabb3d3e23..97b5b7c8690 100644 --- a/src/transformers/models/distilbert/configuration_distilbert.py +++ b/src/transformers/models/distilbert/configuration_distilbert.py @@ -98,6 +98,7 @@ class DistilBertConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "distilbert" attribute_map = { "hidden_size": "dim", diff --git a/src/transformers/models/donut/configuration_donut_swin.py b/src/transformers/models/donut/configuration_donut_swin.py index c7d6792467f..9de3181b55b 100644 --- a/src/transformers/models/donut/configuration_donut_swin.py +++ b/src/transformers/models/donut/configuration_donut_swin.py @@ -85,6 +85,7 @@ class DonutSwinConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "donut-swin" attribute_map = { diff --git a/src/transformers/models/donut/processing_donut.py b/src/transformers/models/donut/processing_donut.py index f797aec18ed..5636ecb9435 100644 --- a/src/transformers/models/donut/processing_donut.py +++ b/src/transformers/models/donut/processing_donut.py @@ -37,6 +37,7 @@ class DonutProcessor(ProcessorMixin): tokenizer ([`XLMRobertaTokenizer`/`XLMRobertaTokenizerFast`], *optional*): An instance of [`XLMRobertaTokenizer`/`XLMRobertaTokenizerFast`]. The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "AutoImageProcessor" tokenizer_class = "AutoTokenizer" diff --git a/src/transformers/models/dpr/configuration_dpr.py b/src/transformers/models/dpr/configuration_dpr.py index 06433f57daa..3b6785c6b54 100644 --- a/src/transformers/models/dpr/configuration_dpr.py +++ b/src/transformers/models/dpr/configuration_dpr.py @@ -109,6 +109,7 @@ class DPRConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "dpr" def __init__( diff --git a/src/transformers/models/dpt/configuration_dpt.py b/src/transformers/models/dpt/configuration_dpt.py index 99994c5cb2d..e668bb7f021 100644 --- a/src/transformers/models/dpt/configuration_dpt.py +++ b/src/transformers/models/dpt/configuration_dpt.py @@ -126,6 +126,7 @@ class DPTConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "dpt" def __init__( diff --git a/src/transformers/models/efficientnet/configuration_efficientnet.py b/src/transformers/models/efficientnet/configuration_efficientnet.py index e6b6a1c261c..49e50a45e11 100644 --- a/src/transformers/models/efficientnet/configuration_efficientnet.py +++ b/src/transformers/models/efficientnet/configuration_efficientnet.py @@ -100,6 +100,7 @@ class EfficientNetConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "efficientnet" def __init__( diff --git a/src/transformers/models/electra/configuration_electra.py b/src/transformers/models/electra/configuration_electra.py index d8e1de0fc97..d45f6293021 100644 --- a/src/transformers/models/electra/configuration_electra.py +++ b/src/transformers/models/electra/configuration_electra.py @@ -130,6 +130,7 @@ class ElectraConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "electra" def __init__( diff --git a/src/transformers/models/electra/modeling_flax_electra.py b/src/transformers/models/electra/modeling_flax_electra.py index 8fced6ff1ea..64d49eb17a4 100644 --- a/src/transformers/models/electra/modeling_flax_electra.py +++ b/src/transformers/models/electra/modeling_flax_electra.py @@ -1196,6 +1196,7 @@ class FlaxElectraSequenceSummary(nn.Module): - **summary_first_dropout** (`float`) -- Optional dropout probability before the projection and activation. - **summary_last_dropout** (`float`)-- Optional dropout probability after the projection and activation. """ + config: ElectraConfig dtype: jnp.dtype = jnp.float32 diff --git a/src/transformers/models/electra/tokenization_electra.py b/src/transformers/models/electra/tokenization_electra.py index fb9bf9dfa13..6ea9a600a6e 100644 --- a/src/transformers/models/electra/tokenization_electra.py +++ b/src/transformers/models/electra/tokenization_electra.py @@ -280,8 +280,8 @@ class ElectraTokenizer(PreTrainedTokenizer): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. A Electra - sequence pair mask has the following format: + Create a mask from the two sequences passed to be used in a sequence-pair classification task. A Electra sequence + pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 diff --git a/src/transformers/models/electra/tokenization_electra_fast.py b/src/transformers/models/electra/tokenization_electra_fast.py index 81704317f86..e76082de174 100644 --- a/src/transformers/models/electra/tokenization_electra_fast.py +++ b/src/transformers/models/electra/tokenization_electra_fast.py @@ -201,8 +201,8 @@ class ElectraTokenizerFast(PreTrainedTokenizerFast): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. A ELECTRA - sequence pair mask has the following format: + Create a mask from the two sequences passed to be used in a sequence-pair classification task. A ELECTRA sequence + pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 diff --git a/src/transformers/models/encodec/configuration_encodec.py b/src/transformers/models/encodec/configuration_encodec.py index e75711d9264..af493c325be 100644 --- a/src/transformers/models/encodec/configuration_encodec.py +++ b/src/transformers/models/encodec/configuration_encodec.py @@ -108,6 +108,7 @@ class EncodecConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "encodec" def __init__( diff --git a/src/transformers/models/encoder_decoder/configuration_encoder_decoder.py b/src/transformers/models/encoder_decoder/configuration_encoder_decoder.py index 15fed4dbd1b..9f373ea4544 100644 --- a/src/transformers/models/encoder_decoder/configuration_encoder_decoder.py +++ b/src/transformers/models/encoder_decoder/configuration_encoder_decoder.py @@ -68,6 +68,7 @@ class EncoderDecoderConfig(PretrainedConfig): >>> encoder_decoder_config = EncoderDecoderConfig.from_pretrained("my-model") >>> model = EncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config) ```""" + model_type = "encoder-decoder" is_composition = True diff --git a/src/transformers/models/encoder_decoder/modeling_encoder_decoder.py b/src/transformers/models/encoder_decoder/modeling_encoder_decoder.py index 27a213707c7..12959f8f200 100644 --- a/src/transformers/models/encoder_decoder/modeling_encoder_decoder.py +++ b/src/transformers/models/encoder_decoder/modeling_encoder_decoder.py @@ -174,6 +174,7 @@ class EncoderDecoderModel(PreTrainedModel): :meth*~transformers.AutoModel.from_pretrained* class method for the encoder and :meth*~transformers.AutoModelForCausalLM.from_pretrained* class method for the decoder. """ + config_class = EncoderDecoderConfig base_model_prefix = "encoder_decoder" main_input_name = "input_ids" diff --git a/src/transformers/models/encoder_decoder/modeling_flax_encoder_decoder.py b/src/transformers/models/encoder_decoder/modeling_flax_encoder_decoder.py index 3d9679f26a1..93cac0b3f65 100644 --- a/src/transformers/models/encoder_decoder/modeling_flax_encoder_decoder.py +++ b/src/transformers/models/encoder_decoder/modeling_flax_encoder_decoder.py @@ -306,6 +306,7 @@ class FlaxEncoderDecoderModel(FlaxPreTrainedModel): decoder module when created with the :meth*~transformers.FlaxAutoModel.from_pretrained* class method for the encoder and :meth*~transformers.FlaxAutoModelForCausalLM.from_pretrained* class method for the decoder. """ + config_class = EncoderDecoderConfig base_model_prefix = "encoder_decoder" module_class = FlaxEncoderDecoderModule diff --git a/src/transformers/models/encoder_decoder/modeling_tf_encoder_decoder.py b/src/transformers/models/encoder_decoder/modeling_tf_encoder_decoder.py index 14653410b02..5b4fc5884c1 100644 --- a/src/transformers/models/encoder_decoder/modeling_tf_encoder_decoder.py +++ b/src/transformers/models/encoder_decoder/modeling_tf_encoder_decoder.py @@ -197,6 +197,7 @@ class TFEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLoss): [`~TFAutoModel.from_pretrained`] class method for the encoder and [`~TFAutoModelForCausalLM.from_pretrained`] class method for the decoder. """ + config_class = EncoderDecoderConfig base_model_prefix = "encoder_decoder" load_weight_prefix = "tf_encoder_decoder_model" diff --git a/src/transformers/models/ernie/configuration_ernie.py b/src/transformers/models/ernie/configuration_ernie.py index 91253ab1384..143fb8cc587 100644 --- a/src/transformers/models/ernie/configuration_ernie.py +++ b/src/transformers/models/ernie/configuration_ernie.py @@ -109,6 +109,7 @@ class ErnieConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "ernie" def __init__( diff --git a/src/transformers/models/ernie_m/configuration_ernie_m.py b/src/transformers/models/ernie_m/configuration_ernie_m.py index d23d616b819..eb7eaad8372 100644 --- a/src/transformers/models/ernie_m/configuration_ernie_m.py +++ b/src/transformers/models/ernie_m/configuration_ernie_m.py @@ -78,6 +78,7 @@ class ErnieMConfig(PretrainedConfig): A normal_initializer initializes weight matrices as normal distributions. See `ErnieMPretrainedModel._init_weights()` for how weights are initialized in `ErnieMModel`. """ + model_type = "ernie_m" attribute_map: Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"} diff --git a/src/transformers/models/esm/configuration_esm.py b/src/transformers/models/esm/configuration_esm.py index e51c5d01f15..75f8609ab0f 100644 --- a/src/transformers/models/esm/configuration_esm.py +++ b/src/transformers/models/esm/configuration_esm.py @@ -97,6 +97,7 @@ class EsmConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "esm" def __init__( diff --git a/src/transformers/models/esm/modeling_esmfold.py b/src/transformers/models/esm/modeling_esmfold.py index a0e9ebcc2d6..4843d688dab 100644 --- a/src/transformers/models/esm/modeling_esmfold.py +++ b/src/transformers/models/esm/modeling_esmfold.py @@ -229,7 +229,7 @@ def dict_multimap(fn, dicts): new_dict = {} for k, v in first.items(): all_v = [d[k] for d in dicts] - if type(v) is dict: + if isinstance(v, dict): new_dict[k] = dict_multimap(fn, all_v) else: new_dict[k] = fn(all_v) @@ -1060,7 +1060,7 @@ class EsmFoldDropout(nn.Module): super().__init__() self.r = r - if type(batch_dim) == int: + if isinstance(batch_dim, int): batch_dim = [batch_dim] self.batch_dim = batch_dim self.dropout = nn.Dropout(self.r) @@ -2254,7 +2254,7 @@ class EsmForProteinFolding(EsmPreTrainedModel): seqs: Union[str, List[str]], position_ids=None, ): - if type(seqs) is str: + if isinstance(seqs, str): lst = [seqs] else: lst = seqs @@ -2312,7 +2312,7 @@ class EsmForProteinFolding(EsmPreTrainedModel): def infer_pdb(self, seqs, *args, **kwargs) -> str: """Returns the pdb (file) string from the model given an input sequence.""" - assert type(seqs) is str + assert isinstance(seqs, str) output = self.infer(seqs, *args, **kwargs) return self.output_to_pdb(output)[0] diff --git a/src/transformers/models/falcon/configuration_falcon.py b/src/transformers/models/falcon/configuration_falcon.py index 251dd7f2d57..fe0a450a24e 100644 --- a/src/transformers/models/falcon/configuration_falcon.py +++ b/src/transformers/models/falcon/configuration_falcon.py @@ -104,6 +104,7 @@ class FalconConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "falcon" keys_to_ignore_at_inference = ["past_key_values"] diff --git a/src/transformers/models/flaubert/modeling_tf_flaubert.py b/src/transformers/models/flaubert/modeling_tf_flaubert.py index 068119d35f1..375e19360f2 100644 --- a/src/transformers/models/flaubert/modeling_tf_flaubert.py +++ b/src/transformers/models/flaubert/modeling_tf_flaubert.py @@ -518,9 +518,10 @@ class TFFlaubertMainLayer(tf.keras.layers.Layer): # check inputs # assert shape_list(lengths)[0] == bs - tf.debugging.assert_equal( - shape_list(lengths)[0], bs - ), f"Expected batch size {shape_list(lengths)[0]} and received batch size {bs} mismatched" + ( + tf.debugging.assert_equal(shape_list(lengths)[0], bs), + f"Expected batch size {shape_list(lengths)[0]} and received batch size {bs} mismatched", + ) # assert lengths.max().item() <= slen # input_ids = input_ids.transpose(0, 1) # batch size as dimension 0 # assert (src_enc is None) == (src_len is None) @@ -539,17 +540,19 @@ class TFFlaubertMainLayer(tf.keras.layers.Layer): position_ids = tf.tile(position_ids, (bs, 1)) # assert shape_list(position_ids) == [bs, slen] # (slen, bs) - tf.debugging.assert_equal( - shape_list(position_ids), [bs, slen] - ), f"Position id shape {shape_list(position_ids)} and input shape {[bs, slen]} mismatched" + ( + tf.debugging.assert_equal(shape_list(position_ids), [bs, slen]), + f"Position id shape {shape_list(position_ids)} and input shape {[bs, slen]} mismatched", + ) # position_ids = position_ids.transpose(0, 1) # langs if langs is not None: # assert shape_list(langs) == [bs, slen] # (slen, bs) - tf.debugging.assert_equal( - shape_list(langs), [bs, slen] - ), f"Lang shape {shape_list(langs)} and input shape {[bs, slen]} mismatched" + ( + tf.debugging.assert_equal(shape_list(langs), [bs, slen]), + f"Lang shape {shape_list(langs)} and input shape {[bs, slen]} mismatched", + ) # langs = langs.transpose(0, 1) # Prepare head mask if needed diff --git a/src/transformers/models/flava/configuration_flava.py b/src/transformers/models/flava/configuration_flava.py index 4125d912622..fee98ecfa9c 100644 --- a/src/transformers/models/flava/configuration_flava.py +++ b/src/transformers/models/flava/configuration_flava.py @@ -218,6 +218,7 @@ class FlavaTextConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "flava_text_model" def __init__( diff --git a/src/transformers/models/flava/modeling_flava.py b/src/transformers/models/flava/modeling_flava.py index de5ec177ae4..64ede9c89ed 100644 --- a/src/transformers/models/flava/modeling_flava.py +++ b/src/transformers/models/flava/modeling_flava.py @@ -1254,9 +1254,7 @@ class FlavaModel(FlavaPreTrainedModel): ... text=["a photo of a cat", "a photo of a dog"], max_length=77, padding="max_length", return_tensors="pt" ... ) >>> text_features = model.get_text_features(**inputs) - ```""".format( - _CHECKPOINT_FOR_DOC - ) + ```""".format(_CHECKPOINT_FOR_DOC) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, @@ -1305,9 +1303,7 @@ class FlavaModel(FlavaPreTrainedModel): >>> inputs = processor(images=image, return_tensors="pt") >>> image_features = model.get_image_features(**inputs) - ```""".format( - _CHECKPOINT_FOR_DOC - ) + ```""".format(_CHECKPOINT_FOR_DOC) image_outputs = self.image_model( pixel_values=pixel_values, bool_masked_pos=bool_masked_pos, @@ -1583,9 +1579,7 @@ class FlavaImageCodebook(FlavaPreTrainedModel): >>> outputs = model.get_codebook_indices(**inputs) ``` - """.format( - _CHECKPOINT_FOR_CODEBOOK_DOC - ) + """.format(_CHECKPOINT_FOR_CODEBOOK_DOC) z_logits = self.blocks(pixel_values) return torch.argmax(z_logits, axis=1) @@ -1620,9 +1614,7 @@ class FlavaImageCodebook(FlavaPreTrainedModel): >>> print(outputs.shape) (1, 196) ``` - """.format( - _CHECKPOINT_FOR_CODEBOOK_DOC - ) + """.format(_CHECKPOINT_FOR_CODEBOOK_DOC) if len(pixel_values.shape) != 4: raise ValueError(f"input shape {pixel_values.shape} is not 4d") if pixel_values.shape[1] != self.input_channels: diff --git a/src/transformers/models/flava/processing_flava.py b/src/transformers/models/flava/processing_flava.py index 1736257a355..7f439b040a8 100644 --- a/src/transformers/models/flava/processing_flava.py +++ b/src/transformers/models/flava/processing_flava.py @@ -36,6 +36,7 @@ class FlavaProcessor(ProcessorMixin): image_processor ([`FlavaImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`BertTokenizerFast`], *optional*): The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "FlavaImageProcessor" tokenizer_class = ("BertTokenizer", "BertTokenizerFast") diff --git a/src/transformers/models/fnet/configuration_fnet.py b/src/transformers/models/fnet/configuration_fnet.py index 9efa0648775..c2cf25615bb 100644 --- a/src/transformers/models/fnet/configuration_fnet.py +++ b/src/transformers/models/fnet/configuration_fnet.py @@ -22,7 +22,7 @@ logger = logging.get_logger(__name__) FNET_PRETRAINED_CONFIG_ARCHIVE_MAP = { "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json", - "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json" + "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json", # See all FNet models at https://huggingface.co/models?filter=fnet } @@ -84,6 +84,7 @@ class FNetConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "fnet" def __init__( diff --git a/src/transformers/models/fnet/modeling_fnet.py b/src/transformers/models/fnet/modeling_fnet.py index 2784880f3c7..dac75178d5f 100755 --- a/src/transformers/models/fnet/modeling_fnet.py +++ b/src/transformers/models/fnet/modeling_fnet.py @@ -61,7 +61,7 @@ _CONFIG_FOR_DOC = "FNetConfig" FNET_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/fnet-base", - "google/fnet-large" + "google/fnet-large", # See all FNet models at https://huggingface.co/models?filter=fnet ] diff --git a/src/transformers/models/focalnet/configuration_focalnet.py b/src/transformers/models/focalnet/configuration_focalnet.py index 83540c0f349..95ee92de1bf 100644 --- a/src/transformers/models/focalnet/configuration_focalnet.py +++ b/src/transformers/models/focalnet/configuration_focalnet.py @@ -104,6 +104,7 @@ class FocalNetConfig(BackboneConfigMixin, PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "focalnet" def __init__( diff --git a/src/transformers/models/fsmt/configuration_fsmt.py b/src/transformers/models/fsmt/configuration_fsmt.py index afd97f137dc..493e6b6bf5d 100644 --- a/src/transformers/models/fsmt/configuration_fsmt.py +++ b/src/transformers/models/fsmt/configuration_fsmt.py @@ -28,6 +28,7 @@ class DecoderConfig(PretrainedConfig): r""" Configuration class for FSMT's decoder specific things. note: this is a private helper class """ + model_type = "fsmt_decoder" def __init__(self, vocab_size=0, bos_token_id=0): @@ -132,6 +133,7 @@ class FSMTConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "fsmt" attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} diff --git a/src/transformers/models/fsmt/modeling_fsmt.py b/src/transformers/models/fsmt/modeling_fsmt.py index 1e566b150f7..b0dd427a681 100644 --- a/src/transformers/models/fsmt/modeling_fsmt.py +++ b/src/transformers/models/fsmt/modeling_fsmt.py @@ -472,9 +472,7 @@ class FSMTEncoder(nn.Module): self.embed_positions = SinusoidalPositionalEmbedding( config.max_position_embeddings + self.padding_idx + 1, embed_dim, self.padding_idx ) - self.layers = nn.ModuleList( - [EncoderLayer(config) for _ in range(config.encoder_layers)] - ) # type: List[EncoderLayer] + self.layers = nn.ModuleList([EncoderLayer(config) for _ in range(config.encoder_layers)]) # type: List[EncoderLayer] def forward( self, @@ -682,9 +680,7 @@ class FSMTDecoder(nn.Module): self.embed_positions = SinusoidalPositionalEmbedding( config.max_position_embeddings + self.padding_idx + 1, embed_dim, self.padding_idx ) - self.layers = nn.ModuleList( - [DecoderLayer(config) for _ in range(config.decoder_layers)] - ) # type: List[DecoderLayer] + self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.decoder_layers)]) # type: List[DecoderLayer] if is_deepspeed_zero3_enabled(): import deepspeed diff --git a/src/transformers/models/funnel/configuration_funnel.py b/src/transformers/models/funnel/configuration_funnel.py index d049b15911b..228216163b2 100644 --- a/src/transformers/models/funnel/configuration_funnel.py +++ b/src/transformers/models/funnel/configuration_funnel.py @@ -96,6 +96,7 @@ class FunnelConfig(PretrainedConfig): pool_q_only (`bool`, *optional*, defaults to `True`): Whether or not to apply the pooling only to the query or to query, key and values for the attention layers. """ + model_type = "funnel" attribute_map = { "hidden_size": "d_model", diff --git a/src/transformers/models/fuyu/configuration_fuyu.py b/src/transformers/models/fuyu/configuration_fuyu.py index c385ad06130..9376ccb5ef4 100644 --- a/src/transformers/models/fuyu/configuration_fuyu.py +++ b/src/transformers/models/fuyu/configuration_fuyu.py @@ -102,6 +102,7 @@ class FuyuConfig(PretrainedConfig): >>> # Initializing a Fuyu fuyu-7b style configuration >>> configuration = FuyuConfig() ```""" + model_type = "fuyu" keys_to_ignore_at_inference = ["past_key_values"] diff --git a/src/transformers/models/fuyu/processing_fuyu.py b/src/transformers/models/fuyu/processing_fuyu.py index e0f362a6c87..f7078554cbc 100644 --- a/src/transformers/models/fuyu/processing_fuyu.py +++ b/src/transformers/models/fuyu/processing_fuyu.py @@ -319,6 +319,7 @@ class FuyuProcessor(ProcessorMixin): tokenizer ([`LlamaTokenizerFast`]): The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "FuyuImageProcessor" tokenizer_class = "AutoTokenizer" diff --git a/src/transformers/models/git/configuration_git.py b/src/transformers/models/git/configuration_git.py index 41f54612afd..bfc2b4bf745 100644 --- a/src/transformers/models/git/configuration_git.py +++ b/src/transformers/models/git/configuration_git.py @@ -188,6 +188,7 @@ class GitConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "git" def __init__( diff --git a/src/transformers/models/git/processing_git.py b/src/transformers/models/git/processing_git.py index 3e11be322b4..2f0851c0627 100644 --- a/src/transformers/models/git/processing_git.py +++ b/src/transformers/models/git/processing_git.py @@ -33,6 +33,7 @@ class GitProcessor(ProcessorMixin): tokenizer ([`AutoTokenizer`]): The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "AutoImageProcessor" tokenizer_class = "AutoTokenizer" diff --git a/src/transformers/models/glpn/configuration_glpn.py b/src/transformers/models/glpn/configuration_glpn.py index 63056c4c04f..5408ee94a8a 100644 --- a/src/transformers/models/glpn/configuration_glpn.py +++ b/src/transformers/models/glpn/configuration_glpn.py @@ -90,6 +90,7 @@ class GLPNConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "glpn" def __init__( diff --git a/src/transformers/models/gpt2/modeling_gpt2.py b/src/transformers/models/gpt2/modeling_gpt2.py index b5adf39ecee..bc95c774039 100644 --- a/src/transformers/models/gpt2/modeling_gpt2.py +++ b/src/transformers/models/gpt2/modeling_gpt2.py @@ -1534,7 +1534,20 @@ class GPT2ForTokenClassification(GPT2PreTrainedModel): output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_loss=0.25, - expected_output=["Lead", "Lead", "Lead", "Position", "Lead", "Lead", "Lead", "Lead", "Lead", "Lead", "Lead", "Lead"], + expected_output=[ + "Lead", + "Lead", + "Lead", + "Position", + "Lead", + "Lead", + "Lead", + "Lead", + "Lead", + "Lead", + "Lead", + "Lead", + ], ) # fmt: on def forward( diff --git a/src/transformers/models/gpt_neo/configuration_gpt_neo.py b/src/transformers/models/gpt_neo/configuration_gpt_neo.py index 9b84b18e26c..96c04cb8752 100644 --- a/src/transformers/models/gpt_neo/configuration_gpt_neo.py +++ b/src/transformers/models/gpt_neo/configuration_gpt_neo.py @@ -102,6 +102,7 @@ class GPTNeoConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "gpt_neo" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"} diff --git a/src/transformers/models/gpt_neox/configuration_gpt_neox.py b/src/transformers/models/gpt_neox/configuration_gpt_neox.py index e263a8a9fee..f24f185b683 100644 --- a/src/transformers/models/gpt_neox/configuration_gpt_neox.py +++ b/src/transformers/models/gpt_neox/configuration_gpt_neox.py @@ -101,6 +101,7 @@ class GPTNeoXConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config # doctest: +SKIP ```""" + model_type = "gpt_neox" def __init__( diff --git a/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py b/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py index 8d8519b9eae..ddf3d4dec8b 100644 --- a/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py +++ b/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py @@ -81,6 +81,7 @@ class GPTNeoXJapaneseConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "gpt_neox_japanese" def __init__( diff --git a/src/transformers/models/gptj/configuration_gptj.py b/src/transformers/models/gptj/configuration_gptj.py index b40861c354b..47b12242793 100644 --- a/src/transformers/models/gptj/configuration_gptj.py +++ b/src/transformers/models/gptj/configuration_gptj.py @@ -85,6 +85,7 @@ class GPTJConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "gptj" attribute_map = { "max_position_embeddings": "n_positions", diff --git a/src/transformers/models/gptsan_japanese/configuration_gptsan_japanese.py b/src/transformers/models/gptsan_japanese/configuration_gptsan_japanese.py index d20b79daacf..c25e4b0e1ea 100644 --- a/src/transformers/models/gptsan_japanese/configuration_gptsan_japanese.py +++ b/src/transformers/models/gptsan_japanese/configuration_gptsan_japanese.py @@ -87,6 +87,7 @@ class GPTSanJapaneseConfig(PretrainedConfig): use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models) """ + model_type = "gptsan-japanese" keys_to_ignore_at_inference = [ "past_key_values", diff --git a/src/transformers/models/gptsan_japanese/modeling_gptsan_japanese.py b/src/transformers/models/gptsan_japanese/modeling_gptsan_japanese.py index dcbda1d7384..2ac76be8d25 100644 --- a/src/transformers/models/gptsan_japanese/modeling_gptsan_japanese.py +++ b/src/transformers/models/gptsan_japanese/modeling_gptsan_japanese.py @@ -1288,7 +1288,7 @@ class GPTSanJapaneseForConditionalGeneration(GPTSanJapanesePreTrainedModel): past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, **kwargs, ): - if type(spout) is list: + if isinstance(spout, list): spout = torch.tensor(spout).float() if input_ids is not None: spout = spout.to(input_ids.device) diff --git a/src/transformers/models/gptsan_japanese/tokenization_gptsan_japanese.py b/src/transformers/models/gptsan_japanese/tokenization_gptsan_japanese.py index 3c3132edb44..df3f94dc1e8 100644 --- a/src/transformers/models/gptsan_japanese/tokenization_gptsan_japanese.py +++ b/src/transformers/models/gptsan_japanese/tokenization_gptsan_japanese.py @@ -375,7 +375,7 @@ class GPTSanJapaneseTokenizer(PreTrainedTokenizer): verbose: bool = True, ) -> BatchEncoding: # This tokenizer converts input text pairs into Prefix input and subsequent input - if type(batch_text_or_text_pairs[0]) is tuple or type(batch_text_or_text_pairs[0]) is list: + if isinstance(batch_text_or_text_pairs[0], tuple) or isinstance(tuple(batch_text_or_text_pairs[0]), list): # As a single text with an explicit un-prefix position batch_prefix_texts = [] for pref, txt in batch_text_or_text_pairs: diff --git a/src/transformers/models/graphormer/configuration_graphormer.py b/src/transformers/models/graphormer/configuration_graphormer.py index 7f270f94343..9d49fbea294 100644 --- a/src/transformers/models/graphormer/configuration_graphormer.py +++ b/src/transformers/models/graphormer/configuration_graphormer.py @@ -129,6 +129,7 @@ class GraphormerConfig(PretrainedConfig): >>> configuration = model.config ``` """ + model_type = "graphormer" keys_to_ignore_at_inference = ["past_key_values"] diff --git a/src/transformers/models/groupvit/configuration_groupvit.py b/src/transformers/models/groupvit/configuration_groupvit.py index 8acf0d1c4e3..3638ea06923 100644 --- a/src/transformers/models/groupvit/configuration_groupvit.py +++ b/src/transformers/models/groupvit/configuration_groupvit.py @@ -88,6 +88,7 @@ class GroupViTTextConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "groupvit_text_model" def __init__( diff --git a/src/transformers/models/groupvit/modeling_groupvit.py b/src/transformers/models/groupvit/modeling_groupvit.py index 3ed41574194..c99c96ec87f 100644 --- a/src/transformers/models/groupvit/modeling_groupvit.py +++ b/src/transformers/models/groupvit/modeling_groupvit.py @@ -783,9 +783,7 @@ class GroupViTPreTrainedModel(PreTrainedModel): nn.init.normal_(module.out_proj.weight, std=out_proj_std) elif isinstance(module, GroupViTMLP): factor = self.config.initializer_factor - in_proj_std = ( - (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor - ) + in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) diff --git a/src/transformers/models/hubert/configuration_hubert.py b/src/transformers/models/hubert/configuration_hubert.py index 8c9f3d6929e..7e9f1d9f904 100644 --- a/src/transformers/models/hubert/configuration_hubert.py +++ b/src/transformers/models/hubert/configuration_hubert.py @@ -159,6 +159,7 @@ class HubertConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "hubert" def __init__( diff --git a/src/transformers/models/idefics/configuration_idefics.py b/src/transformers/models/idefics/configuration_idefics.py index 12d710d726d..a61c96e0a41 100644 --- a/src/transformers/models/idefics/configuration_idefics.py +++ b/src/transformers/models/idefics/configuration_idefics.py @@ -72,6 +72,7 @@ class IdeficsVisionConfig(PretrainedConfig): initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. """ + model_type = "idefics" attribute_map = { "hidden_size": "embed_dim", @@ -134,6 +135,7 @@ class IdeficsPerceiverConfig(PretrainedConfig): qk_layer_norms_perceiver (`bool`, *optional*, defaults to `False`): Whether or not to use qk layer norms in perceiver """ + model_type = "idefics" def __init__( @@ -236,6 +238,7 @@ class IdeficsConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "idefics" is_composition = False diff --git a/src/transformers/models/idefics/processing_idefics.py b/src/transformers/models/idefics/processing_idefics.py index e6e0a9254aa..590e2475ca6 100644 --- a/src/transformers/models/idefics/processing_idefics.py +++ b/src/transformers/models/idefics/processing_idefics.py @@ -119,6 +119,7 @@ class IdeficsProcessor(ProcessorMixin): An instance of [`LlamaTokenizerFast`]. The tokenizer is a required input. image_size (`int`, *optional*, defaults to 224): Image size (assuming a square image) """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "IdeficsImageProcessor" tokenizer_class = "LlamaTokenizerFast" diff --git a/src/transformers/models/informer/configuration_informer.py b/src/transformers/models/informer/configuration_informer.py index d8af8c793cd..dedf09bb2bb 100644 --- a/src/transformers/models/informer/configuration_informer.py +++ b/src/transformers/models/informer/configuration_informer.py @@ -133,6 +133,7 @@ class InformerConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "informer" attribute_map = { "hidden_size": "d_model", diff --git a/src/transformers/models/informer/modeling_informer.py b/src/transformers/models/informer/modeling_informer.py index c0a5a205950..3abf48eaec0 100644 --- a/src/transformers/models/informer/modeling_informer.py +++ b/src/transformers/models/informer/modeling_informer.py @@ -1222,7 +1222,8 @@ class InformerEncoder(InformerPreTrainedModel): # Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesTransformerDecoder with TimeSeriesTransformer->Informer,TimeSeriesTransformerConfig->InformerConfig,time-series-transformer->informer,Transformer->Informer,TimeSeries->Informer class InformerDecoder(InformerPreTrainedModel): """ - Informer decoder consisting of *config.decoder_layers* layers. Each layer is a [`InformerDecoderLayer`] + Informer decoder consisting of *config.decoder_layers* layers. Each layer is a + [`InformerDecoderLayer`] Args: config: InformerConfig @@ -1781,7 +1782,9 @@ class InformerForPrediction(InformerPreTrainedModel): ... ) >>> batch = torch.load(file) - >>> model = InformerForPrediction.from_pretrained("huggingface/informer-tourism-monthly") + >>> model = InformerForPrediction.from_pretrained( + ... "huggingface/informer-tourism-monthly" + ... ) >>> # during training, one provides both past and future values >>> # as well as possible additional features diff --git a/src/transformers/models/instructblip/configuration_instructblip.py b/src/transformers/models/instructblip/configuration_instructblip.py index 78c7e4e8b65..98c06d2fe89 100644 --- a/src/transformers/models/instructblip/configuration_instructblip.py +++ b/src/transformers/models/instructblip/configuration_instructblip.py @@ -192,6 +192,7 @@ class InstructBlipQFormerConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "instructblip_qformer" def __init__( diff --git a/src/transformers/models/instructblip/modeling_instructblip.py b/src/transformers/models/instructblip/modeling_instructblip.py index 4e0173bd997..e175cd57285 100644 --- a/src/transformers/models/instructblip/modeling_instructblip.py +++ b/src/transformers/models/instructblip/modeling_instructblip.py @@ -1176,13 +1176,13 @@ class InstructBlipQFormerModel(InstructBlipPreTrainedModel): # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if encoder_hidden_states is not None: - if type(encoder_hidden_states) == list: + if isinstance(encoder_hidden_states, list): encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() else: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) - if type(encoder_attention_mask) == list: + if isinstance(encoder_attention_mask, list): encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] elif encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) diff --git a/src/transformers/models/instructblip/processing_instructblip.py b/src/transformers/models/instructblip/processing_instructblip.py index ab4fa0f6753..4d266d8b98e 100644 --- a/src/transformers/models/instructblip/processing_instructblip.py +++ b/src/transformers/models/instructblip/processing_instructblip.py @@ -43,6 +43,7 @@ class InstructBlipProcessor(ProcessorMixin): qformer_tokenizer (`AutoTokenizer`): An instance of ['PreTrainedTokenizer`]. The Q-Former tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "BlipImageProcessor" tokenizer_class = "AutoTokenizer" @@ -142,8 +143,8 @@ class InstructBlipProcessor(ProcessorMixin): # Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer def decode(self, *args, **kwargs): """ - This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer - to the docstring of this method for more information. + This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to + the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) diff --git a/src/transformers/models/kosmos2/configuration_kosmos2.py b/src/transformers/models/kosmos2/configuration_kosmos2.py index d97269733ff..198016a9287 100644 --- a/src/transformers/models/kosmos2/configuration_kosmos2.py +++ b/src/transformers/models/kosmos2/configuration_kosmos2.py @@ -77,6 +77,7 @@ class Kosmos2TextConfig(PretrainedConfig): use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). ```""" + model_type = "kosmos_2_text_model" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { @@ -271,6 +272,7 @@ class Kosmos2Config(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "kosmos-2" is_composition = True diff --git a/src/transformers/models/kosmos2/modeling_kosmos2.py b/src/transformers/models/kosmos2/modeling_kosmos2.py index 797523493eb..e99be059f86 100644 --- a/src/transformers/models/kosmos2/modeling_kosmos2.py +++ b/src/transformers/models/kosmos2/modeling_kosmos2.py @@ -1374,9 +1374,7 @@ class Kosmos2PreTrainedModel(PreTrainedModel): if module.out_proj.bias is not None: module.out_proj.bias.data.zero_() elif isinstance(module, Kosmos2VisionMLP): - in_proj_std = ( - (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor - ) + in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) diff --git a/src/transformers/models/kosmos2/processing_kosmos2.py b/src/transformers/models/kosmos2/processing_kosmos2.py index 6943c12c487..a203ee4c506 100644 --- a/src/transformers/models/kosmos2/processing_kosmos2.py +++ b/src/transformers/models/kosmos2/processing_kosmos2.py @@ -52,6 +52,7 @@ class Kosmos2Processor(ProcessorMixin): num_patch_index_tokens (`int`, *optional*, defaults to 1024): The number of tokens that represent patch indices. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "CLIPImageProcessor" tokenizer_class = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast") @@ -390,8 +391,8 @@ class Kosmos2Processor(ProcessorMixin): # Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer def decode(self, *args, **kwargs): """ - This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer - to the docstring of this method for more information. + This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to + the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) diff --git a/src/transformers/models/layoutlm/configuration_layoutlm.py b/src/transformers/models/layoutlm/configuration_layoutlm.py index 0ca51e6d579..77d62ded403 100644 --- a/src/transformers/models/layoutlm/configuration_layoutlm.py +++ b/src/transformers/models/layoutlm/configuration_layoutlm.py @@ -101,6 +101,7 @@ class LayoutLMConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "layoutlm" def __init__( diff --git a/src/transformers/models/layoutlm/tokenization_layoutlm.py b/src/transformers/models/layoutlm/tokenization_layoutlm.py index de6bc4de953..6105d5d77c1 100644 --- a/src/transformers/models/layoutlm/tokenization_layoutlm.py +++ b/src/transformers/models/layoutlm/tokenization_layoutlm.py @@ -262,8 +262,8 @@ class LayoutLMTokenizer(PreTrainedTokenizer): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. A LayoutLM - sequence pair mask has the following format: + Create a mask from the two sequences passed to be used in a sequence-pair classification task. A LayoutLM sequence + pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 diff --git a/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py b/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py index afa92abaf87..c0bc1072f7f 100644 --- a/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py +++ b/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py @@ -175,8 +175,8 @@ class LayoutLMTokenizerFast(PreTrainedTokenizerFast): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. A LayoutLM - sequence pair mask has the following format: + Create a mask from the two sequences passed to be used in a sequence-pair classification task. A LayoutLM sequence + pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 diff --git a/src/transformers/models/layoutlmv2/configuration_layoutlmv2.py b/src/transformers/models/layoutlmv2/configuration_layoutlmv2.py index 3cc8027c1dd..1a8e94c2334 100644 --- a/src/transformers/models/layoutlmv2/configuration_layoutlmv2.py +++ b/src/transformers/models/layoutlmv2/configuration_layoutlmv2.py @@ -114,6 +114,7 @@ class LayoutLMv2Config(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "layoutlmv2" def __init__( diff --git a/src/transformers/models/layoutlmv2/processing_layoutlmv2.py b/src/transformers/models/layoutlmv2/processing_layoutlmv2.py index fe52c16fd25..1edf87465bb 100644 --- a/src/transformers/models/layoutlmv2/processing_layoutlmv2.py +++ b/src/transformers/models/layoutlmv2/processing_layoutlmv2.py @@ -43,6 +43,7 @@ class LayoutLMv2Processor(ProcessorMixin): tokenizer (`LayoutLMv2Tokenizer` or `LayoutLMv2TokenizerFast`, *optional*): An instance of [`LayoutLMv2Tokenizer`] or [`LayoutLMv2TokenizerFast`]. The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "LayoutLMv2ImageProcessor" tokenizer_class = ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast") diff --git a/src/transformers/models/layoutlmv3/configuration_layoutlmv3.py b/src/transformers/models/layoutlmv3/configuration_layoutlmv3.py index 31ca2e00e47..1dfee1f29d7 100644 --- a/src/transformers/models/layoutlmv3/configuration_layoutlmv3.py +++ b/src/transformers/models/layoutlmv3/configuration_layoutlmv3.py @@ -119,6 +119,7 @@ class LayoutLMv3Config(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "layoutlmv3" def __init__( diff --git a/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py b/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py index fe1cbcc2c5c..3148155a435 100644 --- a/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py +++ b/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py @@ -904,8 +904,9 @@ class LayoutLMv3Model(LayoutLMv3PreTrainedModel): final_bbox = final_position_ids = None patch_height = patch_width = None if pixel_values is not None: - patch_height, patch_width = int(pixel_values.shape[2] / self.config.patch_size), int( - pixel_values.shape[3] / self.config.patch_size + patch_height, patch_width = ( + int(pixel_values.shape[2] / self.config.patch_size), + int(pixel_values.shape[3] / self.config.patch_size), ) visual_embeddings = self.forward_image(pixel_values) visual_attention_mask = torch.ones( diff --git a/src/transformers/models/layoutlmv3/processing_layoutlmv3.py b/src/transformers/models/layoutlmv3/processing_layoutlmv3.py index 31d0c5e60a5..369bd51bec2 100644 --- a/src/transformers/models/layoutlmv3/processing_layoutlmv3.py +++ b/src/transformers/models/layoutlmv3/processing_layoutlmv3.py @@ -43,6 +43,7 @@ class LayoutLMv3Processor(ProcessorMixin): tokenizer (`LayoutLMv3Tokenizer` or `LayoutLMv3TokenizerFast`, *optional*): An instance of [`LayoutLMv3Tokenizer`] or [`LayoutLMv3TokenizerFast`]. The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "LayoutLMv3ImageProcessor" tokenizer_class = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast") diff --git a/src/transformers/models/layoutlmv3/tokenization_layoutlmv3.py b/src/transformers/models/layoutlmv3/tokenization_layoutlmv3.py index 4d3d1078db6..351e811b814 100644 --- a/src/transformers/models/layoutlmv3/tokenization_layoutlmv3.py +++ b/src/transformers/models/layoutlmv3/tokenization_layoutlmv3.py @@ -268,6 +268,7 @@ class LayoutLMv3Tokenizer(PreTrainedTokenizer): only_label_first_subword (`bool`, *optional*, defaults to `True`): Whether or not to only label the first subword, in case word labels are provided. """ + vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES diff --git a/src/transformers/models/led/configuration_led.py b/src/transformers/models/led/configuration_led.py index 34c286ce189..d9efc308fec 100644 --- a/src/transformers/models/led/configuration_led.py +++ b/src/transformers/models/led/configuration_led.py @@ -97,6 +97,7 @@ class LEDConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "led" attribute_map = { "num_attention_heads": "encoder_attention_heads", diff --git a/src/transformers/models/levit/configuration_levit.py b/src/transformers/models/levit/configuration_levit.py index 06c7925a8f3..3a9546a652e 100644 --- a/src/transformers/models/levit/configuration_levit.py +++ b/src/transformers/models/levit/configuration_levit.py @@ -87,6 +87,7 @@ class LevitConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "levit" def __init__( diff --git a/src/transformers/models/lilt/configuration_lilt.py b/src/transformers/models/lilt/configuration_lilt.py index d11899c9431..3db595e86e1 100644 --- a/src/transformers/models/lilt/configuration_lilt.py +++ b/src/transformers/models/lilt/configuration_lilt.py @@ -90,6 +90,7 @@ class LiltConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "lilt" def __init__( diff --git a/src/transformers/models/llama/configuration_llama.py b/src/transformers/models/llama/configuration_llama.py index 354bcb5b2e8..cd16ec72811 100644 --- a/src/transformers/models/llama/configuration_llama.py +++ b/src/transformers/models/llama/configuration_llama.py @@ -110,6 +110,7 @@ class LlamaConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "llama" keys_to_ignore_at_inference = ["past_key_values"] diff --git a/src/transformers/models/longformer/configuration_longformer.py b/src/transformers/models/longformer/configuration_longformer.py index 1542c497989..2935dd4aaaa 100644 --- a/src/transformers/models/longformer/configuration_longformer.py +++ b/src/transformers/models/longformer/configuration_longformer.py @@ -104,6 +104,7 @@ class LongformerConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "longformer" def __init__( diff --git a/src/transformers/models/longformer/tokenization_longformer.py b/src/transformers/models/longformer/tokenization_longformer.py index 7661634a000..4f76f16d518 100644 --- a/src/transformers/models/longformer/tokenization_longformer.py +++ b/src/transformers/models/longformer/tokenization_longformer.py @@ -416,8 +416,8 @@ class LongformerTokenizer(PreTrainedTokenizer): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. Longformer does - not make use of token type ids, therefore a list of zeros is returned. + Create a mask from the two sequences passed to be used in a sequence-pair classification task. Longformer does not + make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): diff --git a/src/transformers/models/longformer/tokenization_longformer_fast.py b/src/transformers/models/longformer/tokenization_longformer_fast.py index 32c6f6c2dee..fb35a8b67bb 100644 --- a/src/transformers/models/longformer/tokenization_longformer_fast.py +++ b/src/transformers/models/longformer/tokenization_longformer_fast.py @@ -254,8 +254,8 @@ class LongformerTokenizerFast(PreTrainedTokenizerFast): `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not having been set. - Longformer tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will - greedily comprise the space before the **. + Longformer tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily + comprise the space before the **. """ if self._mask_token is None: if self.verbose: @@ -309,8 +309,8 @@ class LongformerTokenizerFast(PreTrainedTokenizerFast): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. Longformer does - not make use of token type ids, therefore a list of zeros is returned. + Create a mask from the two sequences passed to be used in a sequence-pair classification task. Longformer does not + make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): diff --git a/src/transformers/models/longt5/configuration_longt5.py b/src/transformers/models/longt5/configuration_longt5.py index b9a67a970b1..0095af0e246 100644 --- a/src/transformers/models/longt5/configuration_longt5.py +++ b/src/transformers/models/longt5/configuration_longt5.py @@ -82,6 +82,7 @@ class LongT5Config(PretrainedConfig): use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). """ + model_type = "longt5" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"} diff --git a/src/transformers/models/luke/configuration_luke.py b/src/transformers/models/luke/configuration_luke.py index 099a5cf6d9b..53ab1a35280 100644 --- a/src/transformers/models/luke/configuration_luke.py +++ b/src/transformers/models/luke/configuration_luke.py @@ -97,6 +97,7 @@ class LukeConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "luke" def __init__( diff --git a/src/transformers/models/lxmert/tokenization_lxmert.py b/src/transformers/models/lxmert/tokenization_lxmert.py index 17ff0ff8e7f..1557be1add6 100644 --- a/src/transformers/models/lxmert/tokenization_lxmert.py +++ b/src/transformers/models/lxmert/tokenization_lxmert.py @@ -254,8 +254,8 @@ class LxmertTokenizer(PreTrainedTokenizer): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. A Lxmert - sequence pair mask has the following format: + Create a mask from the two sequences passed to be used in a sequence-pair classification task. A Lxmert sequence + pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 diff --git a/src/transformers/models/lxmert/tokenization_lxmert_fast.py b/src/transformers/models/lxmert/tokenization_lxmert_fast.py index 0584f1fe83c..7d9758a601b 100644 --- a/src/transformers/models/lxmert/tokenization_lxmert_fast.py +++ b/src/transformers/models/lxmert/tokenization_lxmert_fast.py @@ -161,8 +161,8 @@ class LxmertTokenizerFast(PreTrainedTokenizerFast): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. A Lxmert - sequence pair mask has the following format: + Create a mask from the two sequences passed to be used in a sequence-pair classification task. A Lxmert sequence + pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 diff --git a/src/transformers/models/m2m_100/configuration_m2m_100.py b/src/transformers/models/m2m_100/configuration_m2m_100.py index 07414c1b822..1b15658c03d 100644 --- a/src/transformers/models/m2m_100/configuration_m2m_100.py +++ b/src/transformers/models/m2m_100/configuration_m2m_100.py @@ -99,6 +99,7 @@ class M2M100Config(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "m2m_100" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} diff --git a/src/transformers/models/marian/configuration_marian.py b/src/transformers/models/marian/configuration_marian.py index a2fdd41d744..201788673e6 100644 --- a/src/transformers/models/marian/configuration_marian.py +++ b/src/transformers/models/marian/configuration_marian.py @@ -102,6 +102,7 @@ class MarianConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "marian" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} diff --git a/src/transformers/models/markuplm/configuration_markuplm.py b/src/transformers/models/markuplm/configuration_markuplm.py index 1455150598a..ff0ab969198 100644 --- a/src/transformers/models/markuplm/configuration_markuplm.py +++ b/src/transformers/models/markuplm/configuration_markuplm.py @@ -97,6 +97,7 @@ class MarkupLMConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "markuplm" def __init__( diff --git a/src/transformers/models/markuplm/feature_extraction_markuplm.py b/src/transformers/models/markuplm/feature_extraction_markuplm.py index b20349fafb0..73c16bad302 100644 --- a/src/transformers/models/markuplm/feature_extraction_markuplm.py +++ b/src/transformers/models/markuplm/feature_extraction_markuplm.py @@ -67,7 +67,7 @@ class MarkupLMFeatureExtractor(FeatureExtractionMixin): string2xsubs_seq = [] for element in html_code.descendants: - if type(element) == bs4.element.NavigableString: + if isinstance(element, bs4.element.NavigableString): if type(element.parent) != bs4.element.Tag: continue diff --git a/src/transformers/models/markuplm/processing_markuplm.py b/src/transformers/models/markuplm/processing_markuplm.py index 51307d20eb5..81aaca9e5cc 100644 --- a/src/transformers/models/markuplm/processing_markuplm.py +++ b/src/transformers/models/markuplm/processing_markuplm.py @@ -41,6 +41,7 @@ class MarkupLMProcessor(ProcessorMixin): parse_html (`bool`, *optional*, defaults to `True`): Whether or not to use `MarkupLMFeatureExtractor` to parse HTML strings into nodes and corresponding xpaths. """ + feature_extractor_class = "MarkupLMFeatureExtractor" tokenizer_class = ("MarkupLMTokenizer", "MarkupLMTokenizerFast") parse_html = True diff --git a/src/transformers/models/mask2former/configuration_mask2former.py b/src/transformers/models/mask2former/configuration_mask2former.py index 1fe24147393..a7ca3dbc506 100644 --- a/src/transformers/models/mask2former/configuration_mask2former.py +++ b/src/transformers/models/mask2former/configuration_mask2former.py @@ -119,6 +119,7 @@ class Mask2FormerConfig(PretrainedConfig): ``` """ + model_type = "mask2former" backbones_supported = ["swin"] attribute_map = {"hidden_size": "hidden_dim"} diff --git a/src/transformers/models/mask2former/modeling_mask2former.py b/src/transformers/models/mask2former/modeling_mask2former.py index c62e3a04c13..08d43bd2960 100644 --- a/src/transformers/models/mask2former/modeling_mask2former.py +++ b/src/transformers/models/mask2former/modeling_mask2former.py @@ -2537,5 +2537,5 @@ class Mask2FormerForUniversalSegmentation(Mask2FormerPreTrainedModel): if not return_dict: output = tuple(v for v in output.values() if v is not None) if loss is not None: - output = ((loss)) + output + output = (loss) + output return output diff --git a/src/transformers/models/maskformer/configuration_maskformer.py b/src/transformers/models/maskformer/configuration_maskformer.py index baf907ee53c..f3d83a0bbf5 100644 --- a/src/transformers/models/maskformer/configuration_maskformer.py +++ b/src/transformers/models/maskformer/configuration_maskformer.py @@ -94,6 +94,7 @@ class MaskFormerConfig(PretrainedConfig): ``` """ + model_type = "maskformer" attribute_map = {"hidden_size": "mask_feature_size"} backbones_supported = ["resnet", "swin"] diff --git a/src/transformers/models/maskformer/configuration_maskformer_swin.py b/src/transformers/models/maskformer/configuration_maskformer_swin.py index 7c3ac54bd80..6a4d9c2c0a4 100644 --- a/src/transformers/models/maskformer/configuration_maskformer_swin.py +++ b/src/transformers/models/maskformer/configuration_maskformer_swin.py @@ -90,6 +90,7 @@ class MaskFormerSwinConfig(BackboneConfigMixin, PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "maskformer-swin" attribute_map = { diff --git a/src/transformers/models/mbart/configuration_mbart.py b/src/transformers/models/mbart/configuration_mbart.py index 1a775f57fdf..176ce52dbfa 100644 --- a/src/transformers/models/mbart/configuration_mbart.py +++ b/src/transformers/models/mbart/configuration_mbart.py @@ -104,6 +104,7 @@ class MBartConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "mbart" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} diff --git a/src/transformers/models/mbart/tokenization_mbart.py b/src/transformers/models/mbart/tokenization_mbart.py index 9c090449698..37f4c849ab9 100644 --- a/src/transformers/models/mbart/tokenization_mbart.py +++ b/src/transformers/models/mbart/tokenization_mbart.py @@ -45,9 +45,7 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "facebook/mbart-large-cc25": 1024, } -# fmt: off -FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"] -# fmt: on +FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"] # fmt: skip class MBartTokenizer(PreTrainedTokenizer): diff --git a/src/transformers/models/mbart/tokenization_mbart_fast.py b/src/transformers/models/mbart/tokenization_mbart_fast.py index ed0d0de9c86..8638ab974e2 100644 --- a/src/transformers/models/mbart/tokenization_mbart_fast.py +++ b/src/transformers/models/mbart/tokenization_mbart_fast.py @@ -55,9 +55,7 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "facebook/mbart-large-cc25": 1024, } -# fmt: off -FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"] -# fmt: on +FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"] # fmt: skip class MBartTokenizerFast(PreTrainedTokenizerFast): diff --git a/src/transformers/models/mbart50/tokenization_mbart50.py b/src/transformers/models/mbart50/tokenization_mbart50.py index 39986851b05..5fbeb678674 100644 --- a/src/transformers/models/mbart50/tokenization_mbart50.py +++ b/src/transformers/models/mbart50/tokenization_mbart50.py @@ -41,9 +41,7 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "facebook/mbart-large-50-one-to-many-mmt": 1024, } -# fmt: off -FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"] -# fmt: on +FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"] # fmt: skip class MBart50Tokenizer(PreTrainedTokenizer): diff --git a/src/transformers/models/mbart50/tokenization_mbart50_fast.py b/src/transformers/models/mbart50/tokenization_mbart50_fast.py index 7bd302ee8c8..701e30d916d 100644 --- a/src/transformers/models/mbart50/tokenization_mbart50_fast.py +++ b/src/transformers/models/mbart50/tokenization_mbart50_fast.py @@ -51,9 +51,7 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "facebook/mbart-large-50-one-to-many-mmt": 1024, } -# fmt: off -FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"] -# fmt: on +FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"] # fmt: skip class MBart50TokenizerFast(PreTrainedTokenizerFast): diff --git a/src/transformers/models/mega/configuration_mega.py b/src/transformers/models/mega/configuration_mega.py index cade307c84e..34f858569cd 100644 --- a/src/transformers/models/mega/configuration_mega.py +++ b/src/transformers/models/mega/configuration_mega.py @@ -145,6 +145,7 @@ class MegaConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "mega" def __init__( diff --git a/src/transformers/models/mega/modeling_mega.py b/src/transformers/models/mega/modeling_mega.py index 45ce5242428..60628fb5df8 100644 --- a/src/transformers/models/mega/modeling_mega.py +++ b/src/transformers/models/mega/modeling_mega.py @@ -539,9 +539,7 @@ class MegaGatedCrossAttention(nn.Module): self.config = config self.activation = ACT2FN[self.config.activation] self.attention_activation = self.config.attention_activation - self.scaling = ( - self.config.shared_representation_size**-0.5 if self.attention_activation == "softmax" else None - ) + self.scaling = self.config.shared_representation_size**-0.5 if self.attention_activation == "softmax" else None self.dropout = MegaDropout(self.config.dropout_prob, is_featurewise=self.config.use_feature_dropout) self.hidden_dropout = MegaDropout( diff --git a/src/transformers/models/megatron_bert/configuration_megatron_bert.py b/src/transformers/models/megatron_bert/configuration_megatron_bert.py index db9b67090ac..874aaa331d7 100644 --- a/src/transformers/models/megatron_bert/configuration_megatron_bert.py +++ b/src/transformers/models/megatron_bert/configuration_megatron_bert.py @@ -90,6 +90,7 @@ class MegatronBertConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "megatron-bert" def __init__( diff --git a/src/transformers/models/mgp_str/configuration_mgp_str.py b/src/transformers/models/mgp_str/configuration_mgp_str.py index b553c6a0ff6..4644b4f0cc1 100644 --- a/src/transformers/models/mgp_str/configuration_mgp_str.py +++ b/src/transformers/models/mgp_str/configuration_mgp_str.py @@ -89,6 +89,7 @@ class MgpstrConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "mgp-str" def __init__( diff --git a/src/transformers/models/mgp_str/processing_mgp_str.py b/src/transformers/models/mgp_str/processing_mgp_str.py index 6e18e2dd485..71422e844d0 100644 --- a/src/transformers/models/mgp_str/processing_mgp_str.py +++ b/src/transformers/models/mgp_str/processing_mgp_str.py @@ -49,6 +49,7 @@ class MgpstrProcessor(ProcessorMixin): tokenizer ([`MgpstrTokenizer`], *optional*): The tokenizer is a required input. """ + attributes = ["image_processor", "char_tokenizer"] image_processor_class = "ViTImageProcessor" char_tokenizer_class = "MgpstrTokenizer" diff --git a/src/transformers/models/mobilebert/configuration_mobilebert.py b/src/transformers/models/mobilebert/configuration_mobilebert.py index afe6c3b3d92..b14d25ea9ed 100644 --- a/src/transformers/models/mobilebert/configuration_mobilebert.py +++ b/src/transformers/models/mobilebert/configuration_mobilebert.py @@ -108,6 +108,7 @@ class MobileBertConfig(PretrainedConfig): Attributes: pretrained_config_archive_map (Dict[str, str]): A dictionary containing all the available pre-trained checkpoints. """ + pretrained_config_archive_map = MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP model_type = "mobilebert" diff --git a/src/transformers/models/mobilebert/tokenization_mobilebert.py b/src/transformers/models/mobilebert/tokenization_mobilebert.py index 398f054a992..f27873e92fc 100644 --- a/src/transformers/models/mobilebert/tokenization_mobilebert.py +++ b/src/transformers/models/mobilebert/tokenization_mobilebert.py @@ -252,8 +252,8 @@ class MobileBertTokenizer(PreTrainedTokenizer): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. A MobileBERT - sequence pair mask has the following format: + Create a mask from the two sequences passed to be used in a sequence-pair classification task. A MobileBERT sequence + pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 diff --git a/src/transformers/models/mobilebert/tokenization_mobilebert_fast.py b/src/transformers/models/mobilebert/tokenization_mobilebert_fast.py index f8d62158b22..2b137d2ed60 100644 --- a/src/transformers/models/mobilebert/tokenization_mobilebert_fast.py +++ b/src/transformers/models/mobilebert/tokenization_mobilebert_fast.py @@ -159,8 +159,8 @@ class MobileBertTokenizerFast(PreTrainedTokenizerFast): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. A MobileBERT - sequence pair mask has the following format: + Create a mask from the two sequences passed to be used in a sequence-pair classification task. A MobileBERT sequence + pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 diff --git a/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py b/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py index 2ee20cd2baf..59f025c621d 100644 --- a/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py +++ b/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py @@ -78,6 +78,7 @@ class MobileNetV1Config(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "mobilenet_v1" def __init__( diff --git a/src/transformers/models/mobilenet_v2/configuration_mobilenet_v2.py b/src/transformers/models/mobilenet_v2/configuration_mobilenet_v2.py index ab4eef23cfb..161f0e6d8ff 100644 --- a/src/transformers/models/mobilenet_v2/configuration_mobilenet_v2.py +++ b/src/transformers/models/mobilenet_v2/configuration_mobilenet_v2.py @@ -96,6 +96,7 @@ class MobileNetV2Config(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "mobilenet_v2" def __init__( diff --git a/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py b/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py index 8f19869e47f..99791efe04c 100644 --- a/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py +++ b/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py @@ -312,8 +312,7 @@ class MobileNetV2ImageProcessor(BaseImageProcessor): # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.post_process_semantic_segmentation with Beit->MobileNetV2 def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None): """ - Converts the output of [`MobileNetV2ForSemanticSegmentation`] into semantic segmentation maps. Only supports - PyTorch. + Converts the output of [`MobileNetV2ForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch. Args: outputs ([`MobileNetV2ForSemanticSegmentation`]): diff --git a/src/transformers/models/mobilevit/configuration_mobilevit.py b/src/transformers/models/mobilevit/configuration_mobilevit.py index a4aafe997eb..48811c28ba0 100644 --- a/src/transformers/models/mobilevit/configuration_mobilevit.py +++ b/src/transformers/models/mobilevit/configuration_mobilevit.py @@ -111,6 +111,7 @@ class MobileViTConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "mobilevit" def __init__( diff --git a/src/transformers/models/mobilevit/image_processing_mobilevit.py b/src/transformers/models/mobilevit/image_processing_mobilevit.py index ecd8cac9e95..79eaeac9007 100644 --- a/src/transformers/models/mobilevit/image_processing_mobilevit.py +++ b/src/transformers/models/mobilevit/image_processing_mobilevit.py @@ -311,8 +311,7 @@ class MobileViTImageProcessor(BaseImageProcessor): # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.post_process_semantic_segmentation with Beit->MobileViT def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None): """ - Converts the output of [`MobileViTForSemanticSegmentation`] into semantic segmentation maps. Only supports - PyTorch. + Converts the output of [`MobileViTForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch. Args: outputs ([`MobileViTForSemanticSegmentation`]): diff --git a/src/transformers/models/mobilevitv2/configuration_mobilevitv2.py b/src/transformers/models/mobilevitv2/configuration_mobilevitv2.py index 0181d17c351..c3bc44f38e0 100644 --- a/src/transformers/models/mobilevitv2/configuration_mobilevitv2.py +++ b/src/transformers/models/mobilevitv2/configuration_mobilevitv2.py @@ -97,6 +97,7 @@ class MobileViTV2Config(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "mobilevitv2" def __init__( diff --git a/src/transformers/models/mpnet/configuration_mpnet.py b/src/transformers/models/mpnet/configuration_mpnet.py index 5a11a390503..fe492a963e5 100644 --- a/src/transformers/models/mpnet/configuration_mpnet.py +++ b/src/transformers/models/mpnet/configuration_mpnet.py @@ -79,6 +79,7 @@ class MPNetConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "mpnet" def __init__( diff --git a/src/transformers/models/mpt/modeling_mpt.py b/src/transformers/models/mpt/modeling_mpt.py index 2477b906592..e2d12f0e777 100644 --- a/src/transformers/models/mpt/modeling_mpt.py +++ b/src/transformers/models/mpt/modeling_mpt.py @@ -51,7 +51,7 @@ MPT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "mosaicml/mpt-7b-8k-chat", "mosaicml/mpt-30b", "mosaicml/mpt-30b-instruct", - "mosaicml/mpt-30b-chat" + "mosaicml/mpt-30b-chat", # See all MPT models at https://huggingface.co/models?filter=mpt ] diff --git a/src/transformers/models/mra/configuration_mra.py b/src/transformers/models/mra/configuration_mra.py index bc6aeebc907..17b0f21ff4c 100644 --- a/src/transformers/models/mra/configuration_mra.py +++ b/src/transformers/models/mra/configuration_mra.py @@ -90,6 +90,7 @@ class MraConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "mra" def __init__( diff --git a/src/transformers/models/mt5/configuration_mt5.py b/src/transformers/models/mt5/configuration_mt5.py index 93b7c42d345..aab93711dfc 100644 --- a/src/transformers/models/mt5/configuration_mt5.py +++ b/src/transformers/models/mt5/configuration_mt5.py @@ -68,6 +68,7 @@ class MT5Config(PretrainedConfig): use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). """ + model_type = "mt5" keys_to_ignore_at_inference = ["past_key_values"] diff --git a/src/transformers/models/mt5/modeling_flax_mt5.py b/src/transformers/models/mt5/modeling_flax_mt5.py index 0046e02ca73..98406439dfb 100644 --- a/src/transformers/models/mt5/modeling_flax_mt5.py +++ b/src/transformers/models/mt5/modeling_flax_mt5.py @@ -61,6 +61,7 @@ class FlaxMT5Model(FlaxT5Model): >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=decoder_input_ids) >>> hidden_states = outputs.last_hidden_state ```""" + model_type = "mt5" config_class = MT5Config @@ -87,6 +88,7 @@ class FlaxMT5EncoderModel(FlaxT5EncoderModel): >>> outputs = model(input_ids=inputs["input_ids"]) >>> hidden_states = outputs.last_hidden_state ```""" + model_type = "mt5" config_class = MT5Config diff --git a/src/transformers/models/mt5/modeling_mt5.py b/src/transformers/models/mt5/modeling_mt5.py index 799b4c54bff..e4a217196a6 100644 --- a/src/transformers/models/mt5/modeling_mt5.py +++ b/src/transformers/models/mt5/modeling_mt5.py @@ -1331,6 +1331,7 @@ class MT5Model(MT5PreTrainedModel): >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"]) >>> hidden_states = outputs.last_hidden_state ```""" + model_type = "mt5" config_class = MT5Config _keys_to_ignore_on_load_missing = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"] diff --git a/src/transformers/models/mt5/modeling_tf_mt5.py b/src/transformers/models/mt5/modeling_tf_mt5.py index ba7bd33c344..f8350eb1979 100644 --- a/src/transformers/models/mt5/modeling_tf_mt5.py +++ b/src/transformers/models/mt5/modeling_tf_mt5.py @@ -44,6 +44,7 @@ class TFMT5Model(TFT5Model): >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"]) >>> hidden_states = outputs.last_hidden_state ```""" + model_type = "mt5" config_class = MT5Config diff --git a/src/transformers/models/musicgen/configuration_musicgen.py b/src/transformers/models/musicgen/configuration_musicgen.py index e954181242e..c0f56626409 100644 --- a/src/transformers/models/musicgen/configuration_musicgen.py +++ b/src/transformers/models/musicgen/configuration_musicgen.py @@ -79,6 +79,7 @@ class MusicgenDecoderConfig(PretrainedConfig): Number of channels in the audio data. Either 1 for mono or 2 for stereo. Stereo models generate a separate audio stream for the left/right output channels. Mono models generate a single audio stream output. """ + model_type = "musicgen_decoder" keys_to_ignore_at_inference = ["past_key_values"] diff --git a/src/transformers/models/musicgen/processing_musicgen.py b/src/transformers/models/musicgen/processing_musicgen.py index ed8d1277f2f..847c542a601 100644 --- a/src/transformers/models/musicgen/processing_musicgen.py +++ b/src/transformers/models/musicgen/processing_musicgen.py @@ -37,6 +37,7 @@ class MusicgenProcessor(ProcessorMixin): tokenizer (`T5Tokenizer`): An instance of [`T5Tokenizer`]. The tokenizer is a required input. """ + feature_extractor_class = "EncodecFeatureExtractor" tokenizer_class = ("T5Tokenizer", "T5TokenizerFast") diff --git a/src/transformers/models/mvp/configuration_mvp.py b/src/transformers/models/mvp/configuration_mvp.py index 0880985b793..9f60c79efa6 100644 --- a/src/transformers/models/mvp/configuration_mvp.py +++ b/src/transformers/models/mvp/configuration_mvp.py @@ -104,6 +104,7 @@ class MvpConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "mvp" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} diff --git a/src/transformers/models/mvp/tokenization_mvp_fast.py b/src/transformers/models/mvp/tokenization_mvp_fast.py index afe2a0a89e2..a6ff13c0898 100644 --- a/src/transformers/models/mvp/tokenization_mvp_fast.py +++ b/src/transformers/models/mvp/tokenization_mvp_fast.py @@ -130,6 +130,7 @@ class MvpTokenizerFast(PreTrainedTokenizerFast): trim_offsets (`bool`, *optional*, defaults to `True`): Whether the post processing step should trim offsets to avoid including whitespaces. """ + vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES diff --git a/src/transformers/models/nat/configuration_nat.py b/src/transformers/models/nat/configuration_nat.py index e24ad679995..2afaa064668 100644 --- a/src/transformers/models/nat/configuration_nat.py +++ b/src/transformers/models/nat/configuration_nat.py @@ -92,6 +92,7 @@ class NatConfig(BackboneConfigMixin, PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "nat" attribute_map = { diff --git a/src/transformers/models/nezha/configuration_nezha.py b/src/transformers/models/nezha/configuration_nezha.py index f41a9b2bf89..e47f6e721f6 100644 --- a/src/transformers/models/nezha/configuration_nezha.py +++ b/src/transformers/models/nezha/configuration_nezha.py @@ -63,6 +63,7 @@ class NezhaConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + pretrained_config_archive_map = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP model_type = "nezha" diff --git a/src/transformers/models/nllb/tokenization_nllb.py b/src/transformers/models/nllb/tokenization_nllb.py index f37eb69cc9e..7daf729c132 100644 --- a/src/transformers/models/nllb/tokenization_nllb.py +++ b/src/transformers/models/nllb/tokenization_nllb.py @@ -41,9 +41,7 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "facebook/nllb-200-distilled-600M": 1024, } -# fmt: off -FAIRSEQ_LANGUAGE_CODES = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn'] -# fmt: on +FAIRSEQ_LANGUAGE_CODES = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn'] # fmt: skip class NllbTokenizer(PreTrainedTokenizer): diff --git a/src/transformers/models/nllb/tokenization_nllb_fast.py b/src/transformers/models/nllb/tokenization_nllb_fast.py index 2b4b09da830..7240133e1d9 100644 --- a/src/transformers/models/nllb/tokenization_nllb_fast.py +++ b/src/transformers/models/nllb/tokenization_nllb_fast.py @@ -53,9 +53,7 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "facebook/nllb-200-distilled-600M": 1024, } -# fmt: off -FAIRSEQ_LANGUAGE_CODES = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn'] -# fmt: on +FAIRSEQ_LANGUAGE_CODES = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn'] # fmt: skip class NllbTokenizerFast(PreTrainedTokenizerFast): diff --git a/src/transformers/models/nllb_moe/configuration_nllb_moe.py b/src/transformers/models/nllb_moe/configuration_nllb_moe.py index f2701e3781b..435d7caa17c 100644 --- a/src/transformers/models/nllb_moe/configuration_nllb_moe.py +++ b/src/transformers/models/nllb_moe/configuration_nllb_moe.py @@ -125,6 +125,7 @@ class NllbMoeConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "nllb-moe" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} diff --git a/src/transformers/models/nougat/processing_nougat.py b/src/transformers/models/nougat/processing_nougat.py index b63639e2dd1..8f94c6718ba 100644 --- a/src/transformers/models/nougat/processing_nougat.py +++ b/src/transformers/models/nougat/processing_nougat.py @@ -37,6 +37,7 @@ class NougatProcessor(ProcessorMixin): tokenizer ([`NougatTokenizerFast`]): An instance of [`NougatTokenizerFast`]. The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "AutoImageProcessor" tokenizer_class = "AutoTokenizer" diff --git a/src/transformers/models/nougat/tokenization_nougat_fast.py b/src/transformers/models/nougat/tokenization_nougat_fast.py index 9d95940875e..d02aec75752 100644 --- a/src/transformers/models/nougat/tokenization_nougat_fast.py +++ b/src/transformers/models/nougat/tokenization_nougat_fast.py @@ -251,7 +251,7 @@ def remove_numbers(lines): def _clean(s): return re.sub(r"(?:[\d_]|\*\*)", "", s).strip() - if type(lines) is str: + if isinstance(lines, str): return _clean(lines) out = [] for l in lines: diff --git a/src/transformers/models/nystromformer/configuration_nystromformer.py b/src/transformers/models/nystromformer/configuration_nystromformer.py index 98b3e511ac0..eeba112ebb4 100644 --- a/src/transformers/models/nystromformer/configuration_nystromformer.py +++ b/src/transformers/models/nystromformer/configuration_nystromformer.py @@ -89,6 +89,7 @@ class NystromformerConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "nystromformer" def __init__( diff --git a/src/transformers/models/oneformer/configuration_oneformer.py b/src/transformers/models/oneformer/configuration_oneformer.py index 06c75b92b1c..672f1dcba8d 100644 --- a/src/transformers/models/oneformer/configuration_oneformer.py +++ b/src/transformers/models/oneformer/configuration_oneformer.py @@ -137,6 +137,7 @@ class OneFormerConfig(PretrainedConfig): >>> configuration = model.config ``` """ + model_type = "oneformer" attribute_map = {"hidden_size": "hidden_dim"} diff --git a/src/transformers/models/oneformer/modeling_oneformer.py b/src/transformers/models/oneformer/modeling_oneformer.py index 33095e53e2f..d0c0d405502 100644 --- a/src/transformers/models/oneformer/modeling_oneformer.py +++ b/src/transformers/models/oneformer/modeling_oneformer.py @@ -3247,5 +3247,5 @@ class OneFormerForUniversalSegmentation(OneFormerPreTrainedModel): if not return_dict: output = tuple(v for v in output.values()) if loss is not None: - output = ((loss)) + output + output = (loss) + output return output diff --git a/src/transformers/models/oneformer/processing_oneformer.py b/src/transformers/models/oneformer/processing_oneformer.py index c4479110ae7..dc20f48f68b 100644 --- a/src/transformers/models/oneformer/processing_oneformer.py +++ b/src/transformers/models/oneformer/processing_oneformer.py @@ -42,6 +42,7 @@ class OneFormerProcessor(ProcessorMixin): task_seq_len (`int`, *optional*, defaults to 77): Sequence length for input task token. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "OneFormerImageProcessor" tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast") diff --git a/src/transformers/models/opt/configuration_opt.py b/src/transformers/models/opt/configuration_opt.py index d2b7a4347ea..2918ee269ae 100644 --- a/src/transformers/models/opt/configuration_opt.py +++ b/src/transformers/models/opt/configuration_opt.py @@ -93,6 +93,7 @@ class OPTConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "opt" keys_to_ignore_at_inference = ["past_key_values"] diff --git a/src/transformers/models/owlv2/configuration_owlv2.py b/src/transformers/models/owlv2/configuration_owlv2.py index 4b96cf1f142..fd15c0e7972 100644 --- a/src/transformers/models/owlv2/configuration_owlv2.py +++ b/src/transformers/models/owlv2/configuration_owlv2.py @@ -46,8 +46,8 @@ class Owlv2TextConfig(PretrainedConfig): Args: vocab_size (`int`, *optional*, defaults to 49408): - Vocabulary size of the OWLv2 text model. Defines the number of different tokens that can be represented by - the `inputs_ids` passed when calling [`Owlv2TextModel`]. + Vocabulary size of the OWLv2 text model. Defines the number of different tokens that can be represented + by the `inputs_ids` passed when calling [`Owlv2TextModel`]. hidden_size (`int`, *optional*, defaults to 512): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 2048): @@ -92,6 +92,7 @@ class Owlv2TextConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "owlv2_text_model" def __init__( @@ -148,8 +149,8 @@ class Owlv2TextConfig(PretrainedConfig): # Copied from transformers.models.owlvit.configuration_owlvit.OwlViTVisionConfig with OwlViT->Owlv2, owlvit-base-patch32->owlv2-base-patch16, owlvit->owlv2, OWL-ViT->OWLv2, 32->16 class Owlv2VisionConfig(PretrainedConfig): r""" - This is the configuration class to store the configuration of an [`Owlv2VisionModel`]. It is used to instantiate an - OWLv2 image encoder according to the specified arguments, defining the model architecture. Instantiating a + This is the configuration class to store the configuration of an [`Owlv2VisionModel`]. It is used to instantiate + an OWLv2 image encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the OWLv2 [google/owlv2-base-patch16](https://huggingface.co/google/owlv2-base-patch16) architecture. @@ -255,8 +256,8 @@ class Owlv2VisionConfig(PretrainedConfig): class Owlv2Config(PretrainedConfig): r""" [`Owlv2Config`] is the configuration class to store the configuration of an [`Owlv2Model`]. It is used to - instantiate an OWLv2 model according to the specified arguments, defining the text model and vision model configs. - Instantiating a configuration with the defaults will yield a similar configuration to that of the OWLv2 + instantiate an OWLv2 model according to the specified arguments, defining the text model and vision model + configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the OWLv2 [google/owlv2-base-patch16](https://huggingface.co/google/owlv2-base-patch16) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the @@ -270,7 +271,8 @@ class Owlv2Config(PretrainedConfig): projection_dim (`int`, *optional*, defaults to 512): Dimensionality of text and vision projection layers. logit_scale_init_value (`float`, *optional*, defaults to 2.6592): - The inital value of the *logit_scale* parameter. Default is used as per the original OWLv2 implementation. + The inital value of the *logit_scale* parameter. Default is used as per the original OWLv2 + implementation. return_dict (`bool`, *optional*, defaults to `True`): Whether or not the model should return a dictionary. If `False`, returns a tuple. kwargs (*optional*): @@ -323,8 +325,8 @@ class Owlv2Config(PretrainedConfig): @classmethod def from_text_vision_configs(cls, text_config: Dict, vision_config: Dict, **kwargs): r""" - Instantiate a [`Owlv2Config`] (or a derived class) from owlv2 text model configuration and owlv2 vision model - configuration. + Instantiate a [`Owlv2Config`] (or a derived class) from owlv2 text model configuration and owlv2 vision + model configuration. Returns: [`Owlv2Config`]: An instance of a configuration object diff --git a/src/transformers/models/owlv2/modeling_owlv2.py b/src/transformers/models/owlv2/modeling_owlv2.py index 0ee5372afbe..9c7cede8fbf 100644 --- a/src/transformers/models/owlv2/modeling_owlv2.py +++ b/src/transformers/models/owlv2/modeling_owlv2.py @@ -245,11 +245,11 @@ class Owlv2ImageGuidedObjectDetectionOutput(ModelOutput): (disregarding possible padding). You can use [`~Owlv2ImageProcessor.post_process_object_detection`] to retrieve the unnormalized bounding boxes. image_embeds (`torch.FloatTensor` of shape `(batch_size, patch_size, patch_size, output_dim`): - Pooled output of [`Owlv2VisionModel`]. OWLv2 represents images as a set of image patches and computes image - embeddings for each patch. + Pooled output of [`Owlv2VisionModel`]. OWLv2 represents images as a set of image patches and computes + image embeddings for each patch. query_image_embeds (`torch.FloatTensor` of shape `(batch_size, patch_size, patch_size, output_dim`): - Pooled output of [`Owlv2VisionModel`]. OWLv2 represents images as a set of image patches and computes image - embeddings for each patch. + Pooled output of [`Owlv2VisionModel`]. OWLv2 represents images as a set of image patches and computes + image embeddings for each patch. class_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`): Class embeddings of all image patches. OWLv2 represents images as a set of image patches where the total number of patches is (image_size / patch_size)**2. @@ -548,9 +548,7 @@ class Owlv2PreTrainedModel(PreTrainedModel): nn.init.normal_(module.out_proj.weight, std=out_proj_std) elif isinstance(module, Owlv2MLP): factor = self.config.initializer_factor - in_proj_std = ( - (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor - ) + in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) diff --git a/src/transformers/models/owlv2/processing_owlv2.py b/src/transformers/models/owlv2/processing_owlv2.py index 23fd14835b9..77493f6cb2d 100644 --- a/src/transformers/models/owlv2/processing_owlv2.py +++ b/src/transformers/models/owlv2/processing_owlv2.py @@ -37,6 +37,7 @@ class Owlv2Processor(ProcessorMixin): tokenizer ([`CLIPTokenizer`, `CLIPTokenizerFast`]): The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "Owlv2ImageProcessor" tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast") diff --git a/src/transformers/models/owlvit/configuration_owlvit.py b/src/transformers/models/owlvit/configuration_owlvit.py index f9d3914ed8b..254619cccd1 100644 --- a/src/transformers/models/owlvit/configuration_owlvit.py +++ b/src/transformers/models/owlvit/configuration_owlvit.py @@ -96,6 +96,7 @@ class OwlViTTextConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "owlvit_text_model" def __init__( diff --git a/src/transformers/models/owlvit/image_processing_owlvit.py b/src/transformers/models/owlvit/image_processing_owlvit.py index 4242356cabd..d190bc1d636 100644 --- a/src/transformers/models/owlvit/image_processing_owlvit.py +++ b/src/transformers/models/owlvit/image_processing_owlvit.py @@ -124,6 +124,7 @@ class OwlViTImageProcessor(BaseImageProcessor): image_std (`List[int]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`): The sequence of standard deviations for each channel, to be used when normalizing images. """ + model_input_names = ["pixel_values"] def __init__( diff --git a/src/transformers/models/owlvit/modeling_owlvit.py b/src/transformers/models/owlvit/modeling_owlvit.py index 77446f6512a..8c502c410d3 100644 --- a/src/transformers/models/owlvit/modeling_owlvit.py +++ b/src/transformers/models/owlvit/modeling_owlvit.py @@ -540,9 +540,7 @@ class OwlViTPreTrainedModel(PreTrainedModel): nn.init.normal_(module.out_proj.weight, std=out_proj_std) elif isinstance(module, OwlViTMLP): factor = self.config.initializer_factor - in_proj_std = ( - (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor - ) + in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) diff --git a/src/transformers/models/owlvit/processing_owlvit.py b/src/transformers/models/owlvit/processing_owlvit.py index 088693a057f..670f7206fd8 100644 --- a/src/transformers/models/owlvit/processing_owlvit.py +++ b/src/transformers/models/owlvit/processing_owlvit.py @@ -38,6 +38,7 @@ class OwlViTProcessor(ProcessorMixin): tokenizer ([`CLIPTokenizer`, `CLIPTokenizerFast`], *optional*): The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "OwlViTImageProcessor" tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast") diff --git a/src/transformers/models/pegasus/configuration_pegasus.py b/src/transformers/models/pegasus/configuration_pegasus.py index fd7de9a1a49..51b506c4e03 100644 --- a/src/transformers/models/pegasus/configuration_pegasus.py +++ b/src/transformers/models/pegasus/configuration_pegasus.py @@ -97,6 +97,7 @@ class PegasusConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "pegasus" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} diff --git a/src/transformers/models/pegasus/tokenization_pegasus.py b/src/transformers/models/pegasus/tokenization_pegasus.py index 9e2fd0d979a..e1c8f6933ff 100644 --- a/src/transformers/models/pegasus/tokenization_pegasus.py +++ b/src/transformers/models/pegasus/tokenization_pegasus.py @@ -96,6 +96,7 @@ class PegasusTokenizer(PreTrainedTokenizer): - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. """ + vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES diff --git a/src/transformers/models/pegasus/tokenization_pegasus_fast.py b/src/transformers/models/pegasus/tokenization_pegasus_fast.py index aadd3c32271..3faeccd2500 100644 --- a/src/transformers/models/pegasus/tokenization_pegasus_fast.py +++ b/src/transformers/models/pegasus/tokenization_pegasus_fast.py @@ -91,6 +91,7 @@ class PegasusTokenizerFast(PreTrainedTokenizerFast): tokenizer](https://github.com/google-research/pegasus/blob/939830367bcf411193d2b5eca2f2f90f3f9260ca/pegasus/ops/pretrain_parsing_ops.cc#L66) that uses the tokens 2 - 104 only for pretraining """ + vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES diff --git a/src/transformers/models/pegasus_x/configuration_pegasus_x.py b/src/transformers/models/pegasus_x/configuration_pegasus_x.py index f48e19bdcbc..be092c018a4 100644 --- a/src/transformers/models/pegasus_x/configuration_pegasus_x.py +++ b/src/transformers/models/pegasus_x/configuration_pegasus_x.py @@ -103,6 +103,7 @@ class PegasusXConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "pegasus_x" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} diff --git a/src/transformers/models/perceiver/configuration_perceiver.py b/src/transformers/models/perceiver/configuration_perceiver.py index 72b13a11e11..d741b287e5d 100644 --- a/src/transformers/models/perceiver/configuration_perceiver.py +++ b/src/transformers/models/perceiver/configuration_perceiver.py @@ -117,6 +117,7 @@ class PerceiverConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "perceiver" def __init__( diff --git a/src/transformers/models/persimmon/configuration_persimmon.py b/src/transformers/models/persimmon/configuration_persimmon.py index c48070a9da0..6997e159d52 100644 --- a/src/transformers/models/persimmon/configuration_persimmon.py +++ b/src/transformers/models/persimmon/configuration_persimmon.py @@ -88,6 +88,7 @@ class PersimmonConfig(PretrainedConfig): >>> # Initializing a Persimmon persimmon-7b style configuration >>> configuration = PersimmonConfig() ```""" + model_type = "persimmon" keys_to_ignore_at_inference = ["past_key_values"] diff --git a/src/transformers/models/phi/configuration_phi.py b/src/transformers/models/phi/configuration_phi.py index aec31054ce1..5025ef798ff 100644 --- a/src/transformers/models/phi/configuration_phi.py +++ b/src/transformers/models/phi/configuration_phi.py @@ -103,6 +103,7 @@ class PhiConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "phi" keys_to_ignore_at_inference = ["past_key_values"] diff --git a/src/transformers/models/pix2struct/configuration_pix2struct.py b/src/transformers/models/pix2struct/configuration_pix2struct.py index c23d2cdcfe6..d0b81d105bd 100644 --- a/src/transformers/models/pix2struct/configuration_pix2struct.py +++ b/src/transformers/models/pix2struct/configuration_pix2struct.py @@ -90,6 +90,7 @@ class Pix2StructTextConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "pix2struct_text_model" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { diff --git a/src/transformers/models/pix2struct/processing_pix2struct.py b/src/transformers/models/pix2struct/processing_pix2struct.py index bc54e14604f..269fa8c62fb 100644 --- a/src/transformers/models/pix2struct/processing_pix2struct.py +++ b/src/transformers/models/pix2struct/processing_pix2struct.py @@ -37,6 +37,7 @@ class Pix2StructProcessor(ProcessorMixin): tokenizer (Union[`T5TokenizerFast`, `T5Tokenizer`]): An instance of ['T5TokenizerFast`] or ['T5Tokenizer`]. The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "Pix2StructImageProcessor" tokenizer_class = ("T5Tokenizer", "T5TokenizerFast") diff --git a/src/transformers/models/plbart/configuration_plbart.py b/src/transformers/models/plbart/configuration_plbart.py index 25f4c31c577..836cf5900c8 100644 --- a/src/transformers/models/plbart/configuration_plbart.py +++ b/src/transformers/models/plbart/configuration_plbart.py @@ -102,6 +102,7 @@ class PLBartConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "plbart" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} diff --git a/src/transformers/models/poolformer/configuration_poolformer.py b/src/transformers/models/poolformer/configuration_poolformer.py index 7444de8ec2b..d859cefc90e 100644 --- a/src/transformers/models/poolformer/configuration_poolformer.py +++ b/src/transformers/models/poolformer/configuration_poolformer.py @@ -91,6 +91,7 @@ class PoolFormerConfig(PretrainedConfig): >>> configuration = model.config ``` """ + model_type = "poolformer" def __init__( diff --git a/src/transformers/models/pop2piano/feature_extraction_pop2piano.py b/src/transformers/models/pop2piano/feature_extraction_pop2piano.py index 0568fb74efb..9bf5326c0b6 100644 --- a/src/transformers/models/pop2piano/feature_extraction_pop2piano.py +++ b/src/transformers/models/pop2piano/feature_extraction_pop2piano.py @@ -76,6 +76,7 @@ class Pop2PianoFeatureExtractor(SequenceFeatureExtractor): num_bars (`int`, *optional*, defaults to 2): Determines interval between each sequence. """ + model_input_names = ["input_features", "beatsteps", "extrapolated_beatstep"] def __init__( diff --git a/src/transformers/models/pop2piano/processing_pop2piano.py b/src/transformers/models/pop2piano/processing_pop2piano.py index 5ea579111dd..639d2e7aea4 100644 --- a/src/transformers/models/pop2piano/processing_pop2piano.py +++ b/src/transformers/models/pop2piano/processing_pop2piano.py @@ -39,6 +39,7 @@ class Pop2PianoProcessor(ProcessorMixin): tokenizer (`Pop2PianoTokenizer`): An instance of ['Pop2PianoTokenizer`]. The tokenizer is a required input. """ + attributes = ["feature_extractor", "tokenizer"] feature_extractor_class = "Pop2PianoFeatureExtractor" tokenizer_class = "Pop2PianoTokenizer" diff --git a/src/transformers/models/prophetnet/configuration_prophetnet.py b/src/transformers/models/prophetnet/configuration_prophetnet.py index 35988eaa132..4072709af96 100644 --- a/src/transformers/models/prophetnet/configuration_prophetnet.py +++ b/src/transformers/models/prophetnet/configuration_prophetnet.py @@ -98,6 +98,7 @@ class ProphetNetConfig(PretrainedConfig): use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). """ + model_type = "prophetnet" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { diff --git a/src/transformers/models/pvt/configuration_pvt.py b/src/transformers/models/pvt/configuration_pvt.py index 12fb3a5b9a9..ac7d5add7f5 100644 --- a/src/transformers/models/pvt/configuration_pvt.py +++ b/src/transformers/models/pvt/configuration_pvt.py @@ -97,6 +97,7 @@ class PvtConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "pvt" def __init__( diff --git a/src/transformers/models/qdqbert/configuration_qdqbert.py b/src/transformers/models/qdqbert/configuration_qdqbert.py index c4f8c1559e6..eaa8af4af28 100644 --- a/src/transformers/models/qdqbert/configuration_qdqbert.py +++ b/src/transformers/models/qdqbert/configuration_qdqbert.py @@ -85,6 +85,7 @@ class QDQBertConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "qdqbert" def __init__( diff --git a/src/transformers/models/rag/modeling_rag.py b/src/transformers/models/rag/modeling_rag.py index 7048168a064..df7c68cef07 100644 --- a/src/transformers/models/rag/modeling_rag.py +++ b/src/transformers/models/rag/modeling_rag.py @@ -229,6 +229,7 @@ class RagPreTrainedModel(PreTrainedModel): generator, the encoder and generator are trainable while the retriever is just an indexed dataset. """ + config_class = RagConfig base_model_prefix = "rag" diff --git a/src/transformers/models/rag/modeling_tf_rag.py b/src/transformers/models/rag/modeling_tf_rag.py index a58bdb6e753..d1151bcd5a6 100644 --- a/src/transformers/models/rag/modeling_tf_rag.py +++ b/src/transformers/models/rag/modeling_tf_rag.py @@ -224,6 +224,7 @@ class TFRagPreTrainedModel(TFPreTrainedModel): generator, the encoder and generator are trainable while the retriever is just an indexed dataset. """ + config_class = RagConfig base_model_prefix = "rag" _keys_to_ignore_on_load_missing = [r"position_ids"] diff --git a/src/transformers/models/realm/configuration_realm.py b/src/transformers/models/realm/configuration_realm.py index bef2baf05f2..d7003302149 100644 --- a/src/transformers/models/realm/configuration_realm.py +++ b/src/transformers/models/realm/configuration_realm.py @@ -125,6 +125,7 @@ class RealmConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "realm" def __init__( diff --git a/src/transformers/models/reformer/configuration_reformer.py b/src/transformers/models/reformer/configuration_reformer.py index af712ced1ee..e01f25a5fbf 100755 --- a/src/transformers/models/reformer/configuration_reformer.py +++ b/src/transformers/models/reformer/configuration_reformer.py @@ -158,6 +158,7 @@ class ReformerConfig(PretrainedConfig): >>> configuration = model.config ``` """ + model_type = "reformer" keys_to_ignore_at_inference = ["past_buckets_states"] attribute_map = {} diff --git a/src/transformers/models/regnet/configuration_regnet.py b/src/transformers/models/regnet/configuration_regnet.py index 201354d1553..4969e426bcb 100644 --- a/src/transformers/models/regnet/configuration_regnet.py +++ b/src/transformers/models/regnet/configuration_regnet.py @@ -66,6 +66,7 @@ class RegNetConfig(PretrainedConfig): >>> configuration = model.config ``` """ + model_type = "regnet" layer_types = ["x", "y"] diff --git a/src/transformers/models/regnet/convert_regnet_to_pytorch.py b/src/transformers/models/regnet/convert_regnet_to_pytorch.py index 14d01ae4452..d29077c1a72 100644 --- a/src/transformers/models/regnet/convert_regnet_to_pytorch.py +++ b/src/transformers/models/regnet/convert_regnet_to_pytorch.py @@ -192,7 +192,7 @@ def convert_weight_and_push( ) from_output = from_model(x) - from_output = from_output[-1] if type(from_output) is list else from_output + from_output = from_output[-1] if isinstance(from_output, list) else from_output # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state if "seer" in name and "in1k" in name: diff --git a/src/transformers/models/rembert/configuration_rembert.py b/src/transformers/models/rembert/configuration_rembert.py index 792a6dbcfad..9dfa8cc6b24 100644 --- a/src/transformers/models/rembert/configuration_rembert.py +++ b/src/transformers/models/rembert/configuration_rembert.py @@ -96,6 +96,7 @@ class RemBertConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "rembert" def __init__( diff --git a/src/transformers/models/resnet/configuration_resnet.py b/src/transformers/models/resnet/configuration_resnet.py index da465f5d039..69117ff07ab 100644 --- a/src/transformers/models/resnet/configuration_resnet.py +++ b/src/transformers/models/resnet/configuration_resnet.py @@ -84,6 +84,7 @@ class ResNetConfig(BackboneConfigMixin, PretrainedConfig): >>> configuration = model.config ``` """ + model_type = "resnet" layer_types = ["basic", "bottleneck"] diff --git a/src/transformers/models/roberta/configuration_roberta.py b/src/transformers/models/roberta/configuration_roberta.py index f82033f4588..86334f0a224 100644 --- a/src/transformers/models/roberta/configuration_roberta.py +++ b/src/transformers/models/roberta/configuration_roberta.py @@ -101,6 +101,7 @@ class RobertaConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "roberta" def __init__( diff --git a/src/transformers/models/roberta_prelayernorm/configuration_roberta_prelayernorm.py b/src/transformers/models/roberta_prelayernorm/configuration_roberta_prelayernorm.py index fca6763f274..1957a30f41b 100644 --- a/src/transformers/models/roberta_prelayernorm/configuration_roberta_prelayernorm.py +++ b/src/transformers/models/roberta_prelayernorm/configuration_roberta_prelayernorm.py @@ -34,10 +34,9 @@ ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP = { # Copied from transformers.models.roberta.configuration_roberta.RobertaConfig with roberta-base->andreasmadsen/efficient_mlm_m0.40,RoBERTa->RoBERTa-PreLayerNorm,Roberta->RobertaPreLayerNorm,roberta->roberta-prelayernorm class RobertaPreLayerNormConfig(PretrainedConfig): r""" - This is the configuration class to store the configuration of a [`RobertaPreLayerNormModel`] or a - [`TFRobertaPreLayerNormModel`]. It is used to instantiate a RoBERTa-PreLayerNorm model according to the specified - arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar - configuration to that of the RoBERTa-PreLayerNorm + This is the configuration class to store the configuration of a [`RobertaPreLayerNormModel`] or a [`TFRobertaPreLayerNormModel`]. It is + used to instantiate a RoBERTa-PreLayerNorm model according to the specified arguments, defining the model architecture. + Instantiating a configuration with the defaults will yield a similar configuration to that of the RoBERTa-PreLayerNorm [andreasmadsen/efficient_mlm_m0.40](https://huggingface.co/andreasmadsen/efficient_mlm_m0.40) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the @@ -46,9 +45,8 @@ class RobertaPreLayerNormConfig(PretrainedConfig): Args: vocab_size (`int`, *optional*, defaults to 50265): - Vocabulary size of the RoBERTa-PreLayerNorm model. Defines the number of different tokens that can be - represented by the `inputs_ids` passed when calling [`RobertaPreLayerNormModel`] or - [`TFRobertaPreLayerNormModel`]. + Vocabulary size of the RoBERTa-PreLayerNorm model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`RobertaPreLayerNormModel`] or [`TFRobertaPreLayerNormModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): @@ -68,8 +66,7 @@ class RobertaPreLayerNormConfig(PretrainedConfig): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 2): - The vocabulary size of the `token_type_ids` passed when calling [`RobertaPreLayerNormModel`] or - [`TFRobertaPreLayerNormModel`]. + The vocabulary size of the `token_type_ids` passed when calling [`RobertaPreLayerNormModel`] or [`TFRobertaPreLayerNormModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): @@ -102,6 +99,7 @@ class RobertaPreLayerNormConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "roberta-prelayernorm" def __init__( diff --git a/src/transformers/models/roc_bert/configuration_roc_bert.py b/src/transformers/models/roc_bert/configuration_roc_bert.py index 2f0a0dd0e0f..23a9e01be77 100644 --- a/src/transformers/models/roc_bert/configuration_roc_bert.py +++ b/src/transformers/models/roc_bert/configuration_roc_bert.py @@ -109,6 +109,7 @@ class RoCBertConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "roc_bert" def __init__( diff --git a/src/transformers/models/roc_bert/modeling_roc_bert.py b/src/transformers/models/roc_bert/modeling_roc_bert.py index ff2900774fa..f3de92fed38 100644 --- a/src/transformers/models/roc_bert/modeling_roc_bert.py +++ b/src/transformers/models/roc_bert/modeling_roc_bert.py @@ -56,9 +56,7 @@ _EXPECTED_OUTPUT_SHAPE = [1, 8, 768] # Token Classification output _CHECKPOINT_FOR_TOKEN_CLASSIFICATION = "ArthurZ/dummy-rocbert-ner" -# fmt: off -_TOKEN_CLASS_EXPECTED_OUTPUT = ["S-EVENT", "S-FAC", "I-ORDINAL", "I-ORDINAL", "E-ORG", "E-LANGUAGE", "E-ORG", "E-ORG", "E-ORG", "E-ORG", "I-EVENT", "S-TIME", "S-TIME", "E-LANGUAGE", "S-TIME", "E-DATE", "I-ORDINAL", "E-QUANTITY", "E-LANGUAGE", "S-TIME", "B-ORDINAL", "S-PRODUCT", "E-LANGUAGE", "E-LANGUAGE", "E-ORG", "E-LOC", "S-TIME", "I-ORDINAL", "S-FAC", "O", "S-GPE", "I-EVENT", "S-GPE", "E-LANGUAGE", "E-ORG", "S-EVENT", "S-FAC", "S-FAC", "S-FAC", "E-ORG", "S-FAC", "E-ORG", "S-GPE"] -# fmt: on +_TOKEN_CLASS_EXPECTED_OUTPUT = ["S-EVENT", "S-FAC", "I-ORDINAL", "I-ORDINAL", "E-ORG", "E-LANGUAGE", "E-ORG", "E-ORG", "E-ORG", "E-ORG", "I-EVENT", "S-TIME", "S-TIME", "E-LANGUAGE", "S-TIME", "E-DATE", "I-ORDINAL", "E-QUANTITY", "E-LANGUAGE", "S-TIME", "B-ORDINAL", "S-PRODUCT", "E-LANGUAGE", "E-LANGUAGE", "E-ORG", "E-LOC", "S-TIME", "I-ORDINAL", "S-FAC", "O", "S-GPE", "I-EVENT", "S-GPE", "E-LANGUAGE", "E-ORG", "S-EVENT", "S-FAC", "S-FAC", "S-FAC", "E-ORG", "S-FAC", "E-ORG", "S-GPE"] # fmt: skip _TOKEN_CLASS_EXPECTED_LOSS = 3.62 # SequenceClassification docstring diff --git a/src/transformers/models/roformer/configuration_roformer.py b/src/transformers/models/roformer/configuration_roformer.py index a5af26aa5d6..5d8f9919b10 100644 --- a/src/transformers/models/roformer/configuration_roformer.py +++ b/src/transformers/models/roformer/configuration_roformer.py @@ -106,6 +106,7 @@ class RoFormerConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "roformer" def __init__( diff --git a/src/transformers/models/roformer/modeling_flax_roformer.py b/src/transformers/models/roformer/modeling_flax_roformer.py index cb7c2e4bb31..10a9bdece68 100644 --- a/src/transformers/models/roformer/modeling_flax_roformer.py +++ b/src/transformers/models/roformer/modeling_flax_roformer.py @@ -49,7 +49,7 @@ FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [ "junnyu/roformer_chinese_char_small", "junnyu/roformer_chinese_char_base", "junnyu/roformer_small_discriminator", - "junnyu/roformer_small_generator" + "junnyu/roformer_small_generator", # See all RoFormer models at https://huggingface.co/models?filter=roformer ] diff --git a/src/transformers/models/roformer/modeling_roformer.py b/src/transformers/models/roformer/modeling_roformer.py index 95dc0c99394..7aa9a0b12d7 100644 --- a/src/transformers/models/roformer/modeling_roformer.py +++ b/src/transformers/models/roformer/modeling_roformer.py @@ -58,7 +58,7 @@ ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [ "junnyu/roformer_chinese_char_small", "junnyu/roformer_chinese_char_base", "junnyu/roformer_small_discriminator", - "junnyu/roformer_small_generator" + "junnyu/roformer_small_generator", # See all RoFormer models at https://huggingface.co/models?filter=roformer ] diff --git a/src/transformers/models/roformer/modeling_tf_roformer.py b/src/transformers/models/roformer/modeling_tf_roformer.py index f6067f9237f..cea286c828b 100644 --- a/src/transformers/models/roformer/modeling_tf_roformer.py +++ b/src/transformers/models/roformer/modeling_tf_roformer.py @@ -69,7 +69,7 @@ TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [ "junnyu/roformer_chinese_char_small", "junnyu/roformer_chinese_char_base", "junnyu/roformer_small_discriminator", - "junnyu/roformer_small_generator" + "junnyu/roformer_small_generator", # See all RoFormer models at https://huggingface.co/models?filter=roformer ] diff --git a/src/transformers/models/roformer/tokenization_roformer.py b/src/transformers/models/roformer/tokenization_roformer.py index 88c0f398b30..27a7281600a 100644 --- a/src/transformers/models/roformer/tokenization_roformer.py +++ b/src/transformers/models/roformer/tokenization_roformer.py @@ -358,6 +358,7 @@ class RoFormerTokenizer(PreTrainedTokenizer): >>> tokenizer.tokenize("今天天气非常好。") ['今', '天', '天', '气', '非常', '好', '。'] ```""" + vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES diff --git a/src/transformers/models/sam/processing_sam.py b/src/transformers/models/sam/processing_sam.py index 0ec47a995af..7c632b2151c 100644 --- a/src/transformers/models/sam/processing_sam.py +++ b/src/transformers/models/sam/processing_sam.py @@ -44,6 +44,7 @@ class SamProcessor(ProcessorMixin): image_processor (`SamImageProcessor`): An instance of [`SamImageProcessor`]. The image processor is a required input. """ + attributes = ["image_processor"] image_processor_class = "SamImageProcessor" diff --git a/src/transformers/models/seamless_m4t/configuration_seamless_m4t.py b/src/transformers/models/seamless_m4t/configuration_seamless_m4t.py index c3296cfabc7..e1fc44b492d 100644 --- a/src/transformers/models/seamless_m4t/configuration_seamless_m4t.py +++ b/src/transformers/models/seamless_m4t/configuration_seamless_m4t.py @@ -239,6 +239,7 @@ class SeamlessM4TConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "seamless_m4t" def __init__( diff --git a/src/transformers/models/seamless_m4t/convert_fairseq2_to_hf.py b/src/transformers/models/seamless_m4t/convert_fairseq2_to_hf.py index e97ca3046a9..a90a30f5795 100644 --- a/src/transformers/models/seamless_m4t/convert_fairseq2_to_hf.py +++ b/src/transformers/models/seamless_m4t/convert_fairseq2_to_hf.py @@ -33,23 +33,10 @@ from transformers import ( from transformers.utils import logging -# fmt: off -UNIT_SUPPORTED_LANGUAGES = ["__arb__", "__ben__", "__cat__", "__ces__", "__cmn__", "__cym__", "__dan__", "__deu__", "__eng__", "__est__", "__fin__", "__fra__", "__hin__", "__ind__", "__ita__", "__jpn__", "__kan__", "__kor__", "__mlt__", "__nld__", "__pes__", "__pol__", "__por__", "__ron__", "__rus__", "__slk__", "__spa__", "__swe__", "__swh__", "__tam__", "__tel__", "__tgl__", "__tha__", "__tur__", "__ukr__", "__urd__", "__uzn__", "__vie__", ] -# fmt: on - -# fmt: off -VOCODER_SUPPORTED_LANGUAGES = ["__arb__", "__ben__", "__cat__", "__ces__", "__cmn__", "__cym__", "__dan__", "__deu__", "__eng__", "__est__", "__fin__", "__fra__", "__hin__", "__ind__", "__ita__", "__jpn__", "__kor__", "__mlt__", "__nld__", "__pes__", "__pol__", "__por__", "__ron__", "__rus__", "__slk__", "__spa__", "__swe__", "__swh__", "__tel__", "__tgl__", "__tha__", "__tur__", "__ukr__", "__urd__", "__uzn__", "__vie__",] -# fmt: on - - -# fmt: off -MEDIUM_SUPPORTED_LANGUAGES = ["ace","ace_Latn","acm","acq","aeb","afr","ajp","aka","amh","apc","arb","ars","ary","arz","asm","ast","awa","ayr","azb","azj","bak","bam","ban","bel","bem","ben","bho","bjn","bjn_Latn","bod","bos","bug","bul","cat","ceb","ces","cjk","ckb","crh","cym","dan","deu","dik","dyu","dzo","ell","eng","epo","est","eus","ewe","fao","pes","fij","fin","fon","fra","fur","fuv","gla","gle","glg","grn","guj","hat","hau","heb","hin","hne","hrv","hun","hye","ibo","ilo","ind","isl","ita","jav","jpn","kab","kac","kam","kan","kas","kas_Deva","kat","knc","knc_Latn","kaz","kbp","kea","khm","kik","kin","kir","kmb","kon","kor","kmr","lao","lvs","lij","lim","lin","lit","lmo","ltg","ltz","lua","lug","luo","lus","mag","mai","mal","mar","min","mkd","plt","mlt","mni","khk","mos","mri","zsm","mya","nld","nno","nob","npi","nso","nus","nya","oci","gaz","ory","pag","pan","pap","pol","por","prs","pbt","quy","ron","run","rus","sag","san","sat","scn","shn","sin","slk","slv","smo","sna","snd","som","sot","spa","als","srd","srp","ssw","sun","swe","swh","szl","tam","tat","tel","tgk","tgl","tha","tir","taq","taq_Tfng","tpi","tsn","tso","tuk","tum","tur","twi","tzm","uig","ukr","umb","urd","uzn","vec","vie","war","wol","xho","ydd","yor","yue","cmn","cmn_Hant","zul",] -# fmt: on - - -# fmt: off -LARGE_SUPPORTED_LANGUAGES = ["afr","amh","arb","ary","arz","asm","azj","bel","ben","bos","bul","cat","ceb","ces","ckb","cmn","cmn_Hant","cym","dan","deu","ell","eng","est","eus","fin","fra","fuv","gaz","gle","glg","guj","heb","hin","hrv","hun","hye","ibo","ind","isl","ita","jav","jpn","kan","kat","kaz","khk","khm","kir","kor","lao","lit","lug","luo","lvs","mai","mal","mar","mkd","mlt","mni","mya","nld","nno","nob","npi","nya","ory","pan","pbt","pes","pol","por","ron","rus","sat","slk","slv","sna","snd","som","spa","srp","swe","swh","tam","tel","tgk","tgl","tha","tur","ukr","urd","uzn","vie","yor","yue","zlm","zul",] -# fmt: on +UNIT_SUPPORTED_LANGUAGES = ["__arb__", "__ben__", "__cat__", "__ces__", "__cmn__", "__cym__", "__dan__", "__deu__", "__eng__", "__est__", "__fin__", "__fra__", "__hin__", "__ind__", "__ita__", "__jpn__", "__kan__", "__kor__", "__mlt__", "__nld__", "__pes__", "__pol__", "__por__", "__ron__", "__rus__", "__slk__", "__spa__", "__swe__", "__swh__", "__tam__", "__tel__", "__tgl__", "__tha__", "__tur__", "__ukr__", "__urd__", "__uzn__", "__vie__", ] # fmt: skip +VOCODER_SUPPORTED_LANGUAGES = ["__arb__", "__ben__", "__cat__", "__ces__", "__cmn__", "__cym__", "__dan__", "__deu__", "__eng__", "__est__", "__fin__", "__fra__", "__hin__", "__ind__", "__ita__", "__jpn__", "__kor__", "__mlt__", "__nld__", "__pes__", "__pol__", "__por__", "__ron__", "__rus__", "__slk__", "__spa__", "__swe__", "__swh__", "__tel__", "__tgl__", "__tha__", "__tur__", "__ukr__", "__urd__", "__uzn__", "__vie__",] # fmt: skip +MEDIUM_SUPPORTED_LANGUAGES = ["ace","ace_Latn","acm","acq","aeb","afr","ajp","aka","amh","apc","arb","ars","ary","arz","asm","ast","awa","ayr","azb","azj","bak","bam","ban","bel","bem","ben","bho","bjn","bjn_Latn","bod","bos","bug","bul","cat","ceb","ces","cjk","ckb","crh","cym","dan","deu","dik","dyu","dzo","ell","eng","epo","est","eus","ewe","fao","pes","fij","fin","fon","fra","fur","fuv","gla","gle","glg","grn","guj","hat","hau","heb","hin","hne","hrv","hun","hye","ibo","ilo","ind","isl","ita","jav","jpn","kab","kac","kam","kan","kas","kas_Deva","kat","knc","knc_Latn","kaz","kbp","kea","khm","kik","kin","kir","kmb","kon","kor","kmr","lao","lvs","lij","lim","lin","lit","lmo","ltg","ltz","lua","lug","luo","lus","mag","mai","mal","mar","min","mkd","plt","mlt","mni","khk","mos","mri","zsm","mya","nld","nno","nob","npi","nso","nus","nya","oci","gaz","ory","pag","pan","pap","pol","por","prs","pbt","quy","ron","run","rus","sag","san","sat","scn","shn","sin","slk","slv","smo","sna","snd","som","sot","spa","als","srd","srp","ssw","sun","swe","swh","szl","tam","tat","tel","tgk","tgl","tha","tir","taq","taq_Tfng","tpi","tsn","tso","tuk","tum","tur","twi","tzm","uig","ukr","umb","urd","uzn","vec","vie","war","wol","xho","ydd","yor","yue","cmn","cmn_Hant","zul",] # fmt: skip +LARGE_SUPPORTED_LANGUAGES = ["afr","amh","arb","ary","arz","asm","azj","bel","ben","bos","bul","cat","ceb","ces","ckb","cmn","cmn_Hant","cym","dan","deu","ell","eng","est","eus","fin","fra","fuv","gaz","gle","glg","guj","heb","hin","hrv","hun","hye","ibo","ind","isl","ita","jav","jpn","kan","kat","kaz","khk","khm","kir","kor","lao","lit","lug","luo","lvs","mai","mal","mar","mkd","mlt","mni","mya","nld","nno","nob","npi","nya","ory","pan","pbt","pes","pol","por","ron","rus","sat","slk","slv","sna","snd","som","spa","srp","swe","swh","tam","tel","tgk","tgl","tha","tur","ukr","urd","uzn","vie","yor","yue","zlm","zul",] # fmt: skip def assert_param_count(model_1, model_2): diff --git a/src/transformers/models/seamless_m4t/processing_seamless_m4t.py b/src/transformers/models/seamless_m4t/processing_seamless_m4t.py index 4f22e9e33d0..7e838913ca1 100644 --- a/src/transformers/models/seamless_m4t/processing_seamless_m4t.py +++ b/src/transformers/models/seamless_m4t/processing_seamless_m4t.py @@ -34,6 +34,7 @@ class SeamlessM4TProcessor(ProcessorMixin): tokenizer ([`SeamlessM4TTokenizerFast`]): The tokenizer is a required input. """ + feature_extractor_class = "SeamlessM4TFeatureExtractor" tokenizer_class = ("SeamlessM4TTokenizer", "SeamlessM4TTokenizerFast") diff --git a/src/transformers/models/segformer/configuration_segformer.py b/src/transformers/models/segformer/configuration_segformer.py index 7f95657e197..ad1c2053295 100644 --- a/src/transformers/models/segformer/configuration_segformer.py +++ b/src/transformers/models/segformer/configuration_segformer.py @@ -100,6 +100,7 @@ class SegformerConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "segformer" def __init__( diff --git a/src/transformers/models/segformer/image_processing_segformer.py b/src/transformers/models/segformer/image_processing_segformer.py index 27687fde03f..57f2628a9cd 100644 --- a/src/transformers/models/segformer/image_processing_segformer.py +++ b/src/transformers/models/segformer/image_processing_segformer.py @@ -440,8 +440,7 @@ class SegformerImageProcessor(BaseImageProcessor): # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.post_process_semantic_segmentation with Beit->Segformer def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None): """ - Converts the output of [`SegformerForSemanticSegmentation`] into semantic segmentation maps. Only supports - PyTorch. + Converts the output of [`SegformerForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch. Args: outputs ([`SegformerForSemanticSegmentation`]): diff --git a/src/transformers/models/sew/configuration_sew.py b/src/transformers/models/sew/configuration_sew.py index 662eaff7f26..f5db6fd2c10 100644 --- a/src/transformers/models/sew/configuration_sew.py +++ b/src/transformers/models/sew/configuration_sew.py @@ -154,6 +154,7 @@ class SEWConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "sew" def __init__( diff --git a/src/transformers/models/sew_d/configuration_sew_d.py b/src/transformers/models/sew_d/configuration_sew_d.py index 78c0f2a7125..2f08ff81f50 100644 --- a/src/transformers/models/sew_d/configuration_sew_d.py +++ b/src/transformers/models/sew_d/configuration_sew_d.py @@ -169,6 +169,7 @@ class SEWDConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "sew-d" def __init__( diff --git a/src/transformers/models/sew_d/modeling_sew_d.py b/src/transformers/models/sew_d/modeling_sew_d.py index cbb74dcfa24..8e890f207d4 100644 --- a/src/transformers/models/sew_d/modeling_sew_d.py +++ b/src/transformers/models/sew_d/modeling_sew_d.py @@ -847,15 +847,11 @@ class DisentangledSelfAttention(nn.Module): if "c2p" in self.pos_att_type: pos_key_layer = self.transpose_for_scores( self.pos_key_proj(rel_embeddings), self.num_attention_heads - ).repeat( - query_layer.size(0) // self.num_attention_heads, 1, 1 - ) # .split(self.all_head_size, dim=-1) + ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) # .split(self.all_head_size, dim=-1) if "p2c" in self.pos_att_type: pos_query_layer = self.transpose_for_scores( self.pos_query_proj(rel_embeddings), self.num_attention_heads - ).repeat( - query_layer.size(0) // self.num_attention_heads, 1, 1 - ) # .split(self.all_head_size, dim=-1) + ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) # .split(self.all_head_size, dim=-1) score = 0 # content->position diff --git a/src/transformers/models/speech_encoder_decoder/configuration_speech_encoder_decoder.py b/src/transformers/models/speech_encoder_decoder/configuration_speech_encoder_decoder.py index 4a144514fd3..378f082e4b9 100644 --- a/src/transformers/models/speech_encoder_decoder/configuration_speech_encoder_decoder.py +++ b/src/transformers/models/speech_encoder_decoder/configuration_speech_encoder_decoder.py @@ -69,6 +69,7 @@ class SpeechEncoderDecoderConfig(PretrainedConfig): >>> encoder_decoder_config = SpeechEncoderDecoderConfig.from_pretrained("my-model") >>> model = SpeechEncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config) ```""" + model_type = "speech-encoder-decoder" is_composition = True diff --git a/src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py b/src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py index 78a652e91d0..5028e30344c 100644 --- a/src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py +++ b/src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py @@ -177,6 +177,7 @@ class SpeechEncoderDecoderModel(PreTrainedModel): :meth*~transformers.AutoModel.from_pretrained* class method for the encoder and :meth*~transformers.AutoModelForCausalLM.from_pretrained* class method for the decoder. """ + config_class = SpeechEncoderDecoderConfig base_model_prefix = "speech_encoder_decoder" main_input_name = "inputs" diff --git a/src/transformers/models/speech_to_text/configuration_speech_to_text.py b/src/transformers/models/speech_to_text/configuration_speech_to_text.py index 89d8e9a9105..fb1a8e1b5ac 100644 --- a/src/transformers/models/speech_to_text/configuration_speech_to_text.py +++ b/src/transformers/models/speech_to_text/configuration_speech_to_text.py @@ -121,6 +121,7 @@ class Speech2TextConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "speech_to_text" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} diff --git a/src/transformers/models/speech_to_text/processing_speech_to_text.py b/src/transformers/models/speech_to_text/processing_speech_to_text.py index 29af8ae6b90..42e90063386 100644 --- a/src/transformers/models/speech_to_text/processing_speech_to_text.py +++ b/src/transformers/models/speech_to_text/processing_speech_to_text.py @@ -36,6 +36,7 @@ class Speech2TextProcessor(ProcessorMixin): tokenizer (`Speech2TextTokenizer`): An instance of [`Speech2TextTokenizer`]. The tokenizer is a required input. """ + feature_extractor_class = "Speech2TextFeatureExtractor" tokenizer_class = "Speech2TextTokenizer" diff --git a/src/transformers/models/speech_to_text_2/configuration_speech_to_text_2.py b/src/transformers/models/speech_to_text_2/configuration_speech_to_text_2.py index 596f6bea0bb..5dd34cb86ba 100644 --- a/src/transformers/models/speech_to_text_2/configuration_speech_to_text_2.py +++ b/src/transformers/models/speech_to_text_2/configuration_speech_to_text_2.py @@ -86,6 +86,7 @@ class Speech2Text2Config(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "speech_to_text_2" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"} diff --git a/src/transformers/models/speech_to_text_2/processing_speech_to_text_2.py b/src/transformers/models/speech_to_text_2/processing_speech_to_text_2.py index 1472eb70be5..47a45d700f7 100644 --- a/src/transformers/models/speech_to_text_2/processing_speech_to_text_2.py +++ b/src/transformers/models/speech_to_text_2/processing_speech_to_text_2.py @@ -35,6 +35,7 @@ class Speech2Text2Processor(ProcessorMixin): tokenizer (`Speech2Text2Tokenizer`): An instance of [`Speech2Text2Tokenizer`]. The tokenizer is a required input. """ + feature_extractor_class = "AutoFeatureExtractor" tokenizer_class = "Speech2Text2Tokenizer" diff --git a/src/transformers/models/speecht5/configuration_speecht5.py b/src/transformers/models/speecht5/configuration_speecht5.py index 8d6a61023c7..c7cd7d2f62f 100644 --- a/src/transformers/models/speecht5/configuration_speecht5.py +++ b/src/transformers/models/speecht5/configuration_speecht5.py @@ -194,6 +194,7 @@ class SpeechT5Config(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "speecht5" attribute_map = {"num_attention_heads": "encoder_attention_heads", "num_hidden_layers": "encoder_layers"} @@ -398,6 +399,7 @@ class SpeechT5HifiGanConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "hifigan" def __init__( diff --git a/src/transformers/models/speecht5/processing_speecht5.py b/src/transformers/models/speecht5/processing_speecht5.py index 27353b4702b..468a0c1d89a 100644 --- a/src/transformers/models/speecht5/processing_speecht5.py +++ b/src/transformers/models/speecht5/processing_speecht5.py @@ -30,6 +30,7 @@ class SpeechT5Processor(ProcessorMixin): tokenizer (`SpeechT5Tokenizer`): An instance of [`SpeechT5Tokenizer`]. The tokenizer is a required input. """ + feature_extractor_class = "SpeechT5FeatureExtractor" tokenizer_class = "SpeechT5Tokenizer" diff --git a/src/transformers/models/splinter/configuration_splinter.py b/src/transformers/models/splinter/configuration_splinter.py index bdbe5f01314..38b33c45cba 100644 --- a/src/transformers/models/splinter/configuration_splinter.py +++ b/src/transformers/models/splinter/configuration_splinter.py @@ -88,6 +88,7 @@ class SplinterConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "splinter" def __init__( diff --git a/src/transformers/models/squeezebert/configuration_squeezebert.py b/src/transformers/models/squeezebert/configuration_squeezebert.py index 5757b9410fc..4926a731776 100644 --- a/src/transformers/models/squeezebert/configuration_squeezebert.py +++ b/src/transformers/models/squeezebert/configuration_squeezebert.py @@ -109,6 +109,7 @@ class SqueezeBertConfig(PretrainedConfig): Attributes: pretrained_config_archive_map (Dict[str, str]): A dictionary containing all the available pre-trained checkpoints. """ + pretrained_config_archive_map = SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP model_type = "squeezebert" diff --git a/src/transformers/models/squeezebert/tokenization_squeezebert.py b/src/transformers/models/squeezebert/tokenization_squeezebert.py index 0cefa03edf3..c655ba8ddaa 100644 --- a/src/transformers/models/squeezebert/tokenization_squeezebert.py +++ b/src/transformers/models/squeezebert/tokenization_squeezebert.py @@ -266,8 +266,8 @@ class SqueezeBertTokenizer(PreTrainedTokenizer): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. A SqueezeBERT - sequence pair mask has the following format: + Create a mask from the two sequences passed to be used in a sequence-pair classification task. A SqueezeBERT sequence + pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 diff --git a/src/transformers/models/squeezebert/tokenization_squeezebert_fast.py b/src/transformers/models/squeezebert/tokenization_squeezebert_fast.py index 23faab71349..a06aaf615e1 100644 --- a/src/transformers/models/squeezebert/tokenization_squeezebert_fast.py +++ b/src/transformers/models/squeezebert/tokenization_squeezebert_fast.py @@ -182,8 +182,8 @@ class SqueezeBertTokenizerFast(PreTrainedTokenizerFast): self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ - Create a mask from the two sequences passed to be used in a sequence-pair classification task. A SqueezeBERT - sequence pair mask has the following format: + Create a mask from the two sequences passed to be used in a sequence-pair classification task. A SqueezeBERT sequence + pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 diff --git a/src/transformers/models/swiftformer/configuration_swiftformer.py b/src/transformers/models/swiftformer/configuration_swiftformer.py index 21dfe4cd8c5..3e06b2feab2 100644 --- a/src/transformers/models/swiftformer/configuration_swiftformer.py +++ b/src/transformers/models/swiftformer/configuration_swiftformer.py @@ -85,6 +85,7 @@ class SwiftFormerConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "swiftformer" def __init__( diff --git a/src/transformers/models/swin/configuration_swin.py b/src/transformers/models/swin/configuration_swin.py index 1b0efca1c47..29f38f72557 100644 --- a/src/transformers/models/swin/configuration_swin.py +++ b/src/transformers/models/swin/configuration_swin.py @@ -105,6 +105,7 @@ class SwinConfig(BackboneConfigMixin, PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "swin" attribute_map = { diff --git a/src/transformers/models/swin2sr/configuration_swin2sr.py b/src/transformers/models/swin2sr/configuration_swin2sr.py index 622001f29fc..81c6af31e27 100644 --- a/src/transformers/models/swin2sr/configuration_swin2sr.py +++ b/src/transformers/models/swin2sr/configuration_swin2sr.py @@ -97,6 +97,7 @@ class Swin2SRConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "swin2sr" attribute_map = { diff --git a/src/transformers/models/swinv2/configuration_swinv2.py b/src/transformers/models/swinv2/configuration_swinv2.py index 595d920c6b5..1dac62583ca 100644 --- a/src/transformers/models/swinv2/configuration_swinv2.py +++ b/src/transformers/models/swinv2/configuration_swinv2.py @@ -89,6 +89,7 @@ class Swinv2Config(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "swinv2" attribute_map = { diff --git a/src/transformers/models/switch_transformers/configuration_switch_transformers.py b/src/transformers/models/switch_transformers/configuration_switch_transformers.py index 291a9f1f3ab..9d8bfe8ba32 100644 --- a/src/transformers/models/switch_transformers/configuration_switch_transformers.py +++ b/src/transformers/models/switch_transformers/configuration_switch_transformers.py @@ -94,6 +94,7 @@ class SwitchTransformersConfig(PretrainedConfig): use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). """ + model_type = "switch_transformers" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"} diff --git a/src/transformers/models/t5/configuration_t5.py b/src/transformers/models/t5/configuration_t5.py index 10afdea0c1e..05d737d035a 100644 --- a/src/transformers/models/t5/configuration_t5.py +++ b/src/transformers/models/t5/configuration_t5.py @@ -77,6 +77,7 @@ class T5Config(PretrainedConfig): use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). """ + model_type = "t5" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"} diff --git a/src/transformers/models/table_transformer/configuration_table_transformer.py b/src/transformers/models/table_transformer/configuration_table_transformer.py index 9cc903656a4..d79734b383c 100644 --- a/src/transformers/models/table_transformer/configuration_table_transformer.py +++ b/src/transformers/models/table_transformer/configuration_table_transformer.py @@ -132,6 +132,7 @@ class TableTransformerConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "table-transformer" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { diff --git a/src/transformers/models/table_transformer/modeling_table_transformer.py b/src/transformers/models/table_transformer/modeling_table_transformer.py index dbff6476a7c..92aac58e745 100644 --- a/src/transformers/models/table_transformer/modeling_table_transformer.py +++ b/src/transformers/models/table_transformer/modeling_table_transformer.py @@ -65,10 +65,9 @@ TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [ # Copied from transformers.models.detr.modeling_detr.DetrDecoderOutput with DETR->TABLE_TRANSFORMER,Detr->TableTransformer class TableTransformerDecoderOutput(BaseModelOutputWithCrossAttentions): """ - Base class for outputs of the TABLE_TRANSFORMER decoder. This class adds one attribute to - BaseModelOutputWithCrossAttentions, namely an optional stack of intermediate decoder activations, i.e. the output - of each decoder layer, each of them gone through a layernorm. This is useful when training the model with auxiliary - decoding losses. + Base class for outputs of the TABLE_TRANSFORMER decoder. This class adds one attribute to BaseModelOutputWithCrossAttentions, + namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them + gone through a layernorm. This is useful when training the model with auxiliary decoding losses. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): @@ -97,10 +96,9 @@ class TableTransformerDecoderOutput(BaseModelOutputWithCrossAttentions): # Copied from transformers.models.detr.modeling_detr.DetrModelOutput with DETR->TABLE_TRANSFORMER,Detr->TableTransformer class TableTransformerModelOutput(Seq2SeqModelOutput): """ - Base class for outputs of the TABLE_TRANSFORMER encoder-decoder model. This class adds one attribute to - Seq2SeqModelOutput, namely an optional stack of intermediate decoder activations, i.e. the output of each decoder - layer, each of them gone through a layernorm. This is useful when training the model with auxiliary decoding - losses. + Base class for outputs of the TABLE_TRANSFORMER encoder-decoder model. This class adds one attribute to Seq2SeqModelOutput, + namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them + gone through a layernorm. This is useful when training the model with auxiliary decoding losses. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): @@ -153,8 +151,8 @@ class TableTransformerObjectDetectionOutput(ModelOutput): pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding - possible padding). You can use [`~TableTransformerImageProcessor.post_process_object_detection`] to - retrieve the unnormalized bounding boxes. + possible padding). You can use [`~TableTransformerImageProcessor.post_process_object_detection`] to retrieve the + unnormalized bounding boxes. auxiliary_outputs (`list[Dict]`, *optional*): Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and @@ -1583,15 +1581,15 @@ def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: f # Copied from transformers.models.detr.modeling_detr.DetrLoss with Detr->TableTransformer,detr->table_transformer class TableTransformerLoss(nn.Module): """ - This class computes the losses for TableTransformerForObjectDetection/TableTransformerForSegmentation. The process - happens in two steps: 1) we compute hungarian assignment between ground truth boxes and the outputs of the model 2) - we supervise each pair of matched ground-truth / prediction (supervise class and box). + This class computes the losses for TableTransformerForObjectDetection/TableTransformerForSegmentation. The process happens in two steps: 1) + we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair + of matched ground-truth / prediction (supervise class and box). - A note on the `num_classes` argument (copied from original repo in table_transformer.py): "the naming of the - `num_classes` parameter of the criterion is somewhat misleading. It indeed corresponds to `max_obj_id` + 1, where - `max_obj_id` is the maximum id for a class in your dataset. For example, COCO has a `max_obj_id` of 90, so we pass - `num_classes` to be 91. As another example, for a dataset that has a single class with `id` 1, you should pass - `num_classes` to be 2 (`max_obj_id` + 1). For more details on this, check the following discussion + A note on the `num_classes` argument (copied from original repo in table_transformer.py): "the naming of the `num_classes` + parameter of the criterion is somewhat misleading. It indeed corresponds to `max_obj_id` + 1, where `max_obj_id` is + the maximum id for a class in your dataset. For example, COCO has a `max_obj_id` of 90, so we pass `num_classes` to + be 91. As another example, for a dataset that has a single class with `id` 1, you should pass `num_classes` to be 2 + (`max_obj_id` + 1). For more details on this, check the following discussion https://github.com/facebookresearch/table_transformer/issues/108#issuecomment-650269223" diff --git a/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py b/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py index 9676b50ed0b..a2e31ba48d3 100644 --- a/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py +++ b/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py @@ -127,6 +127,7 @@ class TimeSeriesTransformerConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "time_series_transformer" attribute_map = { "hidden_size": "d_model", diff --git a/src/transformers/models/timesformer/configuration_timesformer.py b/src/transformers/models/timesformer/configuration_timesformer.py index dcd7b2a518a..cb743ee2908 100644 --- a/src/transformers/models/timesformer/configuration_timesformer.py +++ b/src/transformers/models/timesformer/configuration_timesformer.py @@ -85,6 +85,7 @@ class TimesformerConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "timesformer" def __init__( diff --git a/src/transformers/models/timm_backbone/configuration_timm_backbone.py b/src/transformers/models/timm_backbone/configuration_timm_backbone.py index 23d2aa22370..0f2f1b0b6c3 100644 --- a/src/transformers/models/timm_backbone/configuration_timm_backbone.py +++ b/src/transformers/models/timm_backbone/configuration_timm_backbone.py @@ -60,6 +60,7 @@ class TimmBackboneConfig(PretrainedConfig): >>> configuration = model.config ``` """ + model_type = "timm_backbone" def __init__( diff --git a/src/transformers/models/transfo_xl/tokenization_transfo_xl.py b/src/transformers/models/transfo_xl/tokenization_transfo_xl.py index 91f3d78aae7..8a2aba92f7a 100644 --- a/src/transformers/models/transfo_xl/tokenization_transfo_xl.py +++ b/src/transformers/models/transfo_xl/tokenization_transfo_xl.py @@ -212,7 +212,7 @@ class TransfoXLTokenizer(PreTrainedTokenizer): # Loading a torch-saved transfo-xl vocab dict with pickle results in an integer # Entering this if statement means that we tried to load a torch-saved file with pickle, and we failed. # We therefore load it with torch, if it's available. - if type(vocab_dict) == int: + if isinstance(vocab_dict, int): if not is_torch_available(): raise ImportError( "Not trying to load dict with PyTorch as you need to install pytorch to load " diff --git a/src/transformers/models/trocr/configuration_trocr.py b/src/transformers/models/trocr/configuration_trocr.py index b3f03373618..4964ab27acb 100644 --- a/src/transformers/models/trocr/configuration_trocr.py +++ b/src/transformers/models/trocr/configuration_trocr.py @@ -91,6 +91,7 @@ class TrOCRConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "trocr" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { diff --git a/src/transformers/models/trocr/processing_trocr.py b/src/transformers/models/trocr/processing_trocr.py index 6b7723a975b..e7ce7362d49 100644 --- a/src/transformers/models/trocr/processing_trocr.py +++ b/src/transformers/models/trocr/processing_trocr.py @@ -35,6 +35,7 @@ class TrOCRProcessor(ProcessorMixin): tokenizer ([`RobertaTokenizer`/`XLMRobertaTokenizer`], *optional*): An instance of [`RobertaTokenizer`/`XLMRobertaTokenizer`]. The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "AutoImageProcessor" tokenizer_class = "AutoTokenizer" diff --git a/src/transformers/models/tvlt/configuration_tvlt.py b/src/transformers/models/tvlt/configuration_tvlt.py index 013952dbb1b..e37fd20912f 100644 --- a/src/transformers/models/tvlt/configuration_tvlt.py +++ b/src/transformers/models/tvlt/configuration_tvlt.py @@ -110,6 +110,7 @@ class TvltConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "tvlt" def __init__( diff --git a/src/transformers/models/tvlt/processing_tvlt.py b/src/transformers/models/tvlt/processing_tvlt.py index b14a3437c28..c67a3a8c6d6 100644 --- a/src/transformers/models/tvlt/processing_tvlt.py +++ b/src/transformers/models/tvlt/processing_tvlt.py @@ -32,6 +32,7 @@ class TvltProcessor(ProcessorMixin): feature_extractor (`TvltFeatureExtractor`): An instance of [`TvltFeatureExtractor`]. The feature extractor is a required input. """ + attributes = ["image_processor", "feature_extractor"] image_processor_class = "TvltImageProcessor" feature_extractor_class = "TvltFeatureExtractor" diff --git a/src/transformers/models/umt5/configuration_umt5.py b/src/transformers/models/umt5/configuration_umt5.py index 980686da6e7..93025c5bc4a 100644 --- a/src/transformers/models/umt5/configuration_umt5.py +++ b/src/transformers/models/umt5/configuration_umt5.py @@ -73,6 +73,7 @@ class UMT5Config(PretrainedConfig): use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). """ + model_type = "umt5" keys_to_ignore_at_inference = ["past_key_values"] diff --git a/src/transformers/models/umt5/modeling_umt5.py b/src/transformers/models/umt5/modeling_umt5.py index 220aff273bc..be13cee193d 100644 --- a/src/transformers/models/umt5/modeling_umt5.py +++ b/src/transformers/models/umt5/modeling_umt5.py @@ -940,6 +940,7 @@ class UMT5Model(UMT5PreTrainedModel): >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"]) >>> hidden_states = outputs.last_hidden_state ```""" + model_type = "uumt5" config_class = UMT5Config _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] diff --git a/src/transformers/models/unispeech/configuration_unispeech.py b/src/transformers/models/unispeech/configuration_unispeech.py index 0cf270d1fa1..ef8da01e325 100644 --- a/src/transformers/models/unispeech/configuration_unispeech.py +++ b/src/transformers/models/unispeech/configuration_unispeech.py @@ -187,6 +187,7 @@ class UniSpeechConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "unispeech" def __init__( diff --git a/src/transformers/models/unispeech_sat/configuration_unispeech_sat.py b/src/transformers/models/unispeech_sat/configuration_unispeech_sat.py index 9d0a3061158..f0aa57141f5 100644 --- a/src/transformers/models/unispeech_sat/configuration_unispeech_sat.py +++ b/src/transformers/models/unispeech_sat/configuration_unispeech_sat.py @@ -197,6 +197,7 @@ class UniSpeechSatConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "unispeech-sat" def __init__( diff --git a/src/transformers/models/upernet/configuration_upernet.py b/src/transformers/models/upernet/configuration_upernet.py index f7ad5d04652..ba4afad10ff 100644 --- a/src/transformers/models/upernet/configuration_upernet.py +++ b/src/transformers/models/upernet/configuration_upernet.py @@ -69,6 +69,7 @@ class UperNetConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "upernet" def __init__( diff --git a/src/transformers/models/videomae/configuration_videomae.py b/src/transformers/models/videomae/configuration_videomae.py index 8120bb23fc2..61bfe1d6a89 100644 --- a/src/transformers/models/videomae/configuration_videomae.py +++ b/src/transformers/models/videomae/configuration_videomae.py @@ -94,6 +94,7 @@ class VideoMAEConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "videomae" def __init__( diff --git a/src/transformers/models/vilt/configuration_vilt.py b/src/transformers/models/vilt/configuration_vilt.py index 3db6535e5f0..1fc7aa58195 100644 --- a/src/transformers/models/vilt/configuration_vilt.py +++ b/src/transformers/models/vilt/configuration_vilt.py @@ -96,6 +96,7 @@ class ViltConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "vilt" def __init__( diff --git a/src/transformers/models/vilt/processing_vilt.py b/src/transformers/models/vilt/processing_vilt.py index e86aa34c099..0ccb884ea00 100644 --- a/src/transformers/models/vilt/processing_vilt.py +++ b/src/transformers/models/vilt/processing_vilt.py @@ -37,6 +37,7 @@ class ViltProcessor(ProcessorMixin): tokenizer (`BertTokenizerFast`, *optional*): An instance of ['BertTokenizerFast`]. The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "ViltImageProcessor" tokenizer_class = ("BertTokenizer", "BertTokenizerFast") diff --git a/src/transformers/models/vision_encoder_decoder/configuration_vision_encoder_decoder.py b/src/transformers/models/vision_encoder_decoder/configuration_vision_encoder_decoder.py index 8a8fd2f0f63..ba380ed3ea3 100644 --- a/src/transformers/models/vision_encoder_decoder/configuration_vision_encoder_decoder.py +++ b/src/transformers/models/vision_encoder_decoder/configuration_vision_encoder_decoder.py @@ -76,6 +76,7 @@ class VisionEncoderDecoderConfig(PretrainedConfig): >>> encoder_decoder_config = VisionEncoderDecoderConfig.from_pretrained("my-model") >>> model = VisionEncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config) ```""" + model_type = "vision-encoder-decoder" is_composition = True diff --git a/src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py b/src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py index 3d914c9658d..899acd10703 100644 --- a/src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py +++ b/src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py @@ -272,6 +272,7 @@ class FlaxVisionEncoderDecoderModel(FlaxPreTrainedModel): another one as decoder module when created with the :meth*~transformers.FlaxAutoModel.from_pretrained* class method for the encoder and :meth*~transformers.FlaxAutoModelForCausalLM.from_pretrained* class method for the decoder. """ + config_class = VisionEncoderDecoderConfig base_model_prefix = "vision_encoder_decoder" main_input_name = "pixel_values" diff --git a/src/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py b/src/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py index dea1aaaf59c..65f55d55e82 100644 --- a/src/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py +++ b/src/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py @@ -180,6 +180,7 @@ class TFVisionEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLos decoder when created with the [`~TFAutoModel.from_pretrained`] class method for the encoder and [`~TFAutoModelForCausalLM.from_pretrained`] class method for the decoder. """ + config_class = VisionEncoderDecoderConfig base_model_prefix = "vision_encoder_decoder" load_weight_prefix = "tf_vision_encoder_decoder_model" diff --git a/src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py b/src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py index f9c6c25cd8d..f7134c94ff0 100644 --- a/src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py +++ b/src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py @@ -155,6 +155,7 @@ class VisionEncoderDecoderModel(PreTrainedModel): :meth*~transformers.AutoModel.from_pretrained* class method for the encoder and :meth*~transformers.AutoModelForCausalLM.from_pretrained* class method for the decoder. """ + config_class = VisionEncoderDecoderConfig base_model_prefix = "vision_encoder_decoder" main_input_name = "pixel_values" diff --git a/src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py b/src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py index e6449914680..322c13aadcc 100644 --- a/src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py +++ b/src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py @@ -37,6 +37,7 @@ class VisionTextDualEncoderProcessor(ProcessorMixin): tokenizer ([`PreTrainedTokenizer`], *optional*): The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "AutoImageProcessor" tokenizer_class = "AutoTokenizer" diff --git a/src/transformers/models/visual_bert/configuration_visual_bert.py b/src/transformers/models/visual_bert/configuration_visual_bert.py index a7282ef2bb5..85020ba9ac9 100644 --- a/src/transformers/models/visual_bert/configuration_visual_bert.py +++ b/src/transformers/models/visual_bert/configuration_visual_bert.py @@ -35,7 +35,7 @@ VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json", "uclanlp/visualbert-nlvr2-coco-pre": ( "https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json" - ) + ), # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert } diff --git a/src/transformers/models/visual_bert/modeling_visual_bert.py b/src/transformers/models/visual_bert/modeling_visual_bert.py index 30fe60ef7a1..f8a146ed2c4 100755 --- a/src/transformers/models/visual_bert/modeling_visual_bert.py +++ b/src/transformers/models/visual_bert/modeling_visual_bert.py @@ -57,7 +57,7 @@ VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "uclanlp/visualbert-vcr-coco-pre", "uclanlp/visualbert-nlvr2", "uclanlp/visualbert-nlvr2-pre", - "uclanlp/visualbert-nlvr2-coco-pre" + "uclanlp/visualbert-nlvr2-coco-pre", # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert ] diff --git a/src/transformers/models/vit/configuration_vit.py b/src/transformers/models/vit/configuration_vit.py index cbf67a01093..5eda0385c30 100644 --- a/src/transformers/models/vit/configuration_vit.py +++ b/src/transformers/models/vit/configuration_vit.py @@ -88,6 +88,7 @@ class ViTConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "vit" def __init__( diff --git a/src/transformers/models/vit_hybrid/configuration_vit_hybrid.py b/src/transformers/models/vit_hybrid/configuration_vit_hybrid.py index 5e5db3600d7..0b8a0da75ff 100644 --- a/src/transformers/models/vit_hybrid/configuration_vit_hybrid.py +++ b/src/transformers/models/vit_hybrid/configuration_vit_hybrid.py @@ -86,6 +86,7 @@ class ViTHybridConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "vit-hybrid" def __init__( diff --git a/src/transformers/models/vit_mae/configuration_vit_mae.py b/src/transformers/models/vit_mae/configuration_vit_mae.py index aed808d7325..fa57fbe4fb0 100644 --- a/src/transformers/models/vit_mae/configuration_vit_mae.py +++ b/src/transformers/models/vit_mae/configuration_vit_mae.py @@ -93,6 +93,7 @@ class ViTMAEConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "vit_mae" def __init__( diff --git a/src/transformers/models/vit_msn/configuration_vit_msn.py b/src/transformers/models/vit_msn/configuration_vit_msn.py index 87d9a37a68e..4ee05e3c393 100644 --- a/src/transformers/models/vit_msn/configuration_vit_msn.py +++ b/src/transformers/models/vit_msn/configuration_vit_msn.py @@ -81,6 +81,7 @@ class ViTMSNConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "vit_msn" def __init__( diff --git a/src/transformers/models/vitdet/configuration_vitdet.py b/src/transformers/models/vitdet/configuration_vitdet.py index 45dc9e9296f..c2e7aff6d99 100644 --- a/src/transformers/models/vitdet/configuration_vitdet.py +++ b/src/transformers/models/vitdet/configuration_vitdet.py @@ -100,6 +100,7 @@ class VitDetConfig(BackboneConfigMixin, PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "vitdet" def __init__( diff --git a/src/transformers/models/vitmatte/configuration_vitmatte.py b/src/transformers/models/vitmatte/configuration_vitmatte.py index aee3463dd90..562abbe5e5a 100644 --- a/src/transformers/models/vitmatte/configuration_vitmatte.py +++ b/src/transformers/models/vitmatte/configuration_vitmatte.py @@ -67,6 +67,7 @@ class VitMatteConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "vitmatte" def __init__( diff --git a/src/transformers/models/vits/configuration_vits.py b/src/transformers/models/vits/configuration_vits.py index 2cadd39792b..72f69e75a51 100644 --- a/src/transformers/models/vits/configuration_vits.py +++ b/src/transformers/models/vits/configuration_vits.py @@ -150,6 +150,7 @@ class VitsConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "vits" def __init__( diff --git a/src/transformers/models/vits/modeling_vits.py b/src/transformers/models/vits/modeling_vits.py index f347e900c38..33e02abe868 100644 --- a/src/transformers/models/vits/modeling_vits.py +++ b/src/transformers/models/vits/modeling_vits.py @@ -809,9 +809,7 @@ class VitsStochasticDurationPredictor(nn.Module): latents = torch.flip(latents, [1]) log_determinant_sum += log_determinant - nll = ( - torch.sum(0.5 * (math.log(2 * math.pi) + (latents**2)) * padding_mask, [1, 2]) - log_determinant_sum - ) + nll = torch.sum(0.5 * (math.log(2 * math.pi) + (latents**2)) * padding_mask, [1, 2]) - log_determinant_sum return nll + logq else: flows = list(reversed(self.flows)) diff --git a/src/transformers/models/vivit/configuration_vivit.py b/src/transformers/models/vivit/configuration_vivit.py index c554999b906..0e367fcb9b7 100644 --- a/src/transformers/models/vivit/configuration_vivit.py +++ b/src/transformers/models/vivit/configuration_vivit.py @@ -83,6 +83,7 @@ class VivitConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "vivit" def __init__( diff --git a/src/transformers/models/vivit/convert_vivit_flax_to_pytorch.py b/src/transformers/models/vivit/convert_vivit_flax_to_pytorch.py index bcd2e37c0a6..571dfe896ef 100644 --- a/src/transformers/models/vivit/convert_vivit_flax_to_pytorch.py +++ b/src/transformers/models/vivit/convert_vivit_flax_to_pytorch.py @@ -100,31 +100,26 @@ def transform_state_encoder_block(state_dict, i): prefix + "layernorm_before.weight": state["LayerNorm_0"]["scale"], prefix + "layernorm_after.bias": state["LayerNorm_1"]["bias"], prefix + "layernorm_after.weight": state["LayerNorm_1"]["scale"], - prefix - + "attention.attention.query.bias": transform_attention( + prefix + "attention.attention.query.bias": transform_attention( state["MultiHeadDotProductAttention_0"]["query"]["bias"] ), - prefix - + "attention.attention.query.weight": transform_attention( + prefix + "attention.attention.query.weight": transform_attention( state["MultiHeadDotProductAttention_0"]["query"]["kernel"] ), - prefix - + "attention.attention.key.bias": transform_attention(state["MultiHeadDotProductAttention_0"]["key"]["bias"]), - prefix - + "attention.attention.key.weight": transform_attention( + prefix + "attention.attention.key.bias": transform_attention( + state["MultiHeadDotProductAttention_0"]["key"]["bias"] + ), + prefix + "attention.attention.key.weight": transform_attention( state["MultiHeadDotProductAttention_0"]["key"]["kernel"] ), - prefix - + "attention.attention.value.bias": transform_attention( + prefix + "attention.attention.value.bias": transform_attention( state["MultiHeadDotProductAttention_0"]["value"]["bias"] ), - prefix - + "attention.attention.value.weight": transform_attention( + prefix + "attention.attention.value.weight": transform_attention( state["MultiHeadDotProductAttention_0"]["value"]["kernel"] ), prefix + "attention.output.dense.bias": state["MultiHeadDotProductAttention_0"]["out"]["bias"], - prefix - + "attention.output.dense.weight": transform_attention_output_weight( + prefix + "attention.output.dense.weight": transform_attention_output_weight( state["MultiHeadDotProductAttention_0"]["out"]["kernel"] ), } diff --git a/src/transformers/models/wav2vec2/configuration_wav2vec2.py b/src/transformers/models/wav2vec2/configuration_wav2vec2.py index 91be7cf85b6..32cdaa29d96 100644 --- a/src/transformers/models/wav2vec2/configuration_wav2vec2.py +++ b/src/transformers/models/wav2vec2/configuration_wav2vec2.py @@ -203,6 +203,7 @@ class Wav2Vec2Config(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "wav2vec2" def __init__( diff --git a/src/transformers/models/wav2vec2/processing_wav2vec2.py b/src/transformers/models/wav2vec2/processing_wav2vec2.py index d6585a4f4dd..dc6e9d14ee6 100644 --- a/src/transformers/models/wav2vec2/processing_wav2vec2.py +++ b/src/transformers/models/wav2vec2/processing_wav2vec2.py @@ -37,6 +37,7 @@ class Wav2Vec2Processor(ProcessorMixin): tokenizer ([`PreTrainedTokenizer`]): An instance of [`PreTrainedTokenizer`]. The tokenizer is a required input. """ + feature_extractor_class = "Wav2Vec2FeatureExtractor" tokenizer_class = "AutoTokenizer" diff --git a/src/transformers/models/wav2vec2_conformer/configuration_wav2vec2_conformer.py b/src/transformers/models/wav2vec2_conformer/configuration_wav2vec2_conformer.py index f408338b457..7e78d8d85e4 100644 --- a/src/transformers/models/wav2vec2_conformer/configuration_wav2vec2_conformer.py +++ b/src/transformers/models/wav2vec2_conformer/configuration_wav2vec2_conformer.py @@ -209,6 +209,7 @@ class Wav2Vec2ConformerConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "wav2vec2-conformer" def __init__( diff --git a/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py b/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py index f7b519185b7..324cbf0a71e 100644 --- a/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py +++ b/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py @@ -1445,10 +1445,7 @@ class Wav2Vec2ConformerForPreTraining(Wav2Vec2ConformerPreTrainedModel): ```python >>> import torch >>> from transformers import AutoFeatureExtractor, Wav2Vec2ConformerForPreTraining - >>> from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import ( - ... _compute_mask_indices, - ... _sample_negative_indices, - ... ) + >>> from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import _compute_mask_indices, _sample_negative_indices >>> from datasets import load_dataset >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large") diff --git a/src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py b/src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py index fdc06a80688..916cca51a98 100644 --- a/src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py +++ b/src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py @@ -77,6 +77,7 @@ class Wav2Vec2ProcessorWithLM(ProcessorMixin): decoder (`pyctcdecode.BeamSearchDecoderCTC`): An instance of [`pyctcdecode.BeamSearchDecoderCTC`]. The decoder is a required input. """ + feature_extractor_class = "Wav2Vec2FeatureExtractor" tokenizer_class = "Wav2Vec2CTCTokenizer" diff --git a/src/transformers/models/wavlm/configuration_wavlm.py b/src/transformers/models/wavlm/configuration_wavlm.py index 831b85f24c6..589741c520f 100644 --- a/src/transformers/models/wavlm/configuration_wavlm.py +++ b/src/transformers/models/wavlm/configuration_wavlm.py @@ -192,6 +192,7 @@ class WavLMConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "wavlm" def __init__( diff --git a/src/transformers/models/whisper/configuration_whisper.py b/src/transformers/models/whisper/configuration_whisper.py index 6ff5e529b19..5af32421418 100644 --- a/src/transformers/models/whisper/configuration_whisper.py +++ b/src/transformers/models/whisper/configuration_whisper.py @@ -189,6 +189,7 @@ class WhisperConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "whisper" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} diff --git a/src/transformers/models/whisper/processing_whisper.py b/src/transformers/models/whisper/processing_whisper.py index b0d0d6c9545..cae72aa0e13 100644 --- a/src/transformers/models/whisper/processing_whisper.py +++ b/src/transformers/models/whisper/processing_whisper.py @@ -34,6 +34,7 @@ class WhisperProcessor(ProcessorMixin): tokenizer (`WhisperTokenizer`): An instance of [`WhisperTokenizer`]. The tokenizer is a required input. """ + feature_extractor_class = "WhisperFeatureExtractor" tokenizer_class = "WhisperTokenizer" diff --git a/src/transformers/models/x_clip/configuration_x_clip.py b/src/transformers/models/x_clip/configuration_x_clip.py index fbccdf1167d..f95bc0a5a0b 100644 --- a/src/transformers/models/x_clip/configuration_x_clip.py +++ b/src/transformers/models/x_clip/configuration_x_clip.py @@ -81,6 +81,7 @@ class XCLIPTextConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "xclip_text_model" def __init__( diff --git a/src/transformers/models/x_clip/modeling_x_clip.py b/src/transformers/models/x_clip/modeling_x_clip.py index 754358b352f..e341b9639d8 100644 --- a/src/transformers/models/x_clip/modeling_x_clip.py +++ b/src/transformers/models/x_clip/modeling_x_clip.py @@ -492,9 +492,7 @@ class XCLIPPreTrainedModel(PreTrainedModel): nn.init.normal_(module.out_proj.weight, std=out_proj_std) elif isinstance(module, XCLIPMLP): factor = self.config.initializer_factor - in_proj_std = ( - (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor - ) + in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) diff --git a/src/transformers/models/x_clip/processing_x_clip.py b/src/transformers/models/x_clip/processing_x_clip.py index 6e54c9e7876..a11aeb18dc4 100644 --- a/src/transformers/models/x_clip/processing_x_clip.py +++ b/src/transformers/models/x_clip/processing_x_clip.py @@ -35,6 +35,7 @@ class XCLIPProcessor(ProcessorMixin): tokenizer ([`CLIPTokenizerFast`], *optional*): The tokenizer is a required input. """ + attributes = ["image_processor", "tokenizer"] image_processor_class = "VideoMAEImageProcessor" tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast") diff --git a/src/transformers/models/xglm/configuration_xglm.py b/src/transformers/models/xglm/configuration_xglm.py index 8a59ee6682d..9377bbce6f0 100644 --- a/src/transformers/models/xglm/configuration_xglm.py +++ b/src/transformers/models/xglm/configuration_xglm.py @@ -85,6 +85,7 @@ class XGLMConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "xglm" keys_to_ignore_at_inference = ["past_key_values"] diff --git a/src/transformers/models/xlm/modeling_tf_xlm.py b/src/transformers/models/xlm/modeling_tf_xlm.py index 63d214da0c5..9343f6cb524 100644 --- a/src/transformers/models/xlm/modeling_tf_xlm.py +++ b/src/transformers/models/xlm/modeling_tf_xlm.py @@ -387,9 +387,10 @@ class TFXLMMainLayer(tf.keras.layers.Layer): # check inputs # assert shape_list(lengths)[0] == bs - tf.debugging.assert_equal( - shape_list(lengths)[0], bs - ), f"Expected batch size {shape_list(lengths)[0]} and received batch size {bs} mismatched" + ( + tf.debugging.assert_equal(shape_list(lengths)[0], bs), + f"Expected batch size {shape_list(lengths)[0]} and received batch size {bs} mismatched", + ) # assert lengths.max().item() <= slen # input_ids = input_ids.transpose(0, 1) # batch size as dimension 0 # assert (src_enc is None) == (src_len is None) @@ -408,17 +409,19 @@ class TFXLMMainLayer(tf.keras.layers.Layer): position_ids = tf.tile(position_ids, (bs, 1)) # assert shape_list(position_ids) == [bs, slen] # (slen, bs) - tf.debugging.assert_equal( - shape_list(position_ids), [bs, slen] - ), f"Position id shape {shape_list(position_ids)} and input shape {[bs, slen]} mismatched" + ( + tf.debugging.assert_equal(shape_list(position_ids), [bs, slen]), + f"Position id shape {shape_list(position_ids)} and input shape {[bs, slen]} mismatched", + ) # position_ids = position_ids.transpose(0, 1) # langs if langs is not None: # assert shape_list(langs) == [bs, slen] # (slen, bs) - tf.debugging.assert_equal( - shape_list(langs), [bs, slen] - ), f"Lang shape {shape_list(langs)} and input shape {[bs, slen]} mismatched" + ( + tf.debugging.assert_equal(shape_list(langs), [bs, slen]), + f"Lang shape {shape_list(langs)} and input shape {[bs, slen]} mismatched", + ) # langs = langs.transpose(0, 1) # Prepare head mask if needed diff --git a/src/transformers/models/xlm_prophetnet/configuration_xlm_prophetnet.py b/src/transformers/models/xlm_prophetnet/configuration_xlm_prophetnet.py index 29c8678f279..88ca83a7322 100644 --- a/src/transformers/models/xlm_prophetnet/configuration_xlm_prophetnet.py +++ b/src/transformers/models/xlm_prophetnet/configuration_xlm_prophetnet.py @@ -100,6 +100,7 @@ class XLMProphetNetConfig(PretrainedConfig): use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). """ + model_type = "xlm-prophetnet" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { diff --git a/src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py b/src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py index f99cd4549a4..37bd32186af 100644 --- a/src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py +++ b/src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py @@ -1469,9 +1469,7 @@ class XLMProphetNetDecoder(XLMProphetNetPreTrainedModel): >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone") - >>> model = XLMProphetNetDecoder.from_pretrained( - ... "patrickvonplaten/xprophetnet-large-uncased-standalone", add_cross_attention=False - ... ) + >>> model = XLMProphetNetDecoder.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone", add_cross_attention=False) >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) @@ -1948,9 +1946,7 @@ class XLMProphetNetForConditionalGeneration(XLMProphetNetPreTrainedModel): >>> from transformers import AutoTokenizer, XLMProphetNetForConditionalGeneration >>> tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone") - >>> model = XLMProphetNetForConditionalGeneration.from_pretrained( - ... "patrickvonplaten/xprophetnet-large-uncased-standalone" - ... ) + >>> model = XLMProphetNetForConditionalGeneration.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone") >>> input_ids = tokenizer( ... "Studies have been shown that owning a dog is good for you", return_tensors="pt" diff --git a/src/transformers/models/xlm_roberta/configuration_xlm_roberta.py b/src/transformers/models/xlm_roberta/configuration_xlm_roberta.py index 98e12d07826..517b751f422 100644 --- a/src/transformers/models/xlm_roberta/configuration_xlm_roberta.py +++ b/src/transformers/models/xlm_roberta/configuration_xlm_roberta.py @@ -110,6 +110,7 @@ class XLMRobertaConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "xlm-roberta" def __init__( diff --git a/src/transformers/models/xlm_roberta_xl/configuration_xlm_roberta_xl.py b/src/transformers/models/xlm_roberta_xl/configuration_xlm_roberta_xl.py index acf30bf3878..e2dee1cbe4e 100644 --- a/src/transformers/models/xlm_roberta_xl/configuration_xlm_roberta_xl.py +++ b/src/transformers/models/xlm_roberta_xl/configuration_xlm_roberta_xl.py @@ -97,6 +97,7 @@ class XLMRobertaXLConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "xlm-roberta-xl" def __init__( diff --git a/src/transformers/models/xmod/configuration_xmod.py b/src/transformers/models/xmod/configuration_xmod.py index 012b7446c4c..abf7a3275c5 100644 --- a/src/transformers/models/xmod/configuration_xmod.py +++ b/src/transformers/models/xmod/configuration_xmod.py @@ -119,6 +119,7 @@ class XmodConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "xmod" def __init__( diff --git a/src/transformers/models/yolos/configuration_yolos.py b/src/transformers/models/yolos/configuration_yolos.py index 77a036f5adb..8b969bdd8b1 100644 --- a/src/transformers/models/yolos/configuration_yolos.py +++ b/src/transformers/models/yolos/configuration_yolos.py @@ -103,6 +103,7 @@ class YolosConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "yolos" def __init__( diff --git a/src/transformers/models/yolos/modeling_yolos.py b/src/transformers/models/yolos/modeling_yolos.py index 08e7f0777c6..b2f0ca0db53 100755 --- a/src/transformers/models/yolos/modeling_yolos.py +++ b/src/transformers/models/yolos/modeling_yolos.py @@ -904,9 +904,9 @@ def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: f # Copied from transformers.models.detr.modeling_detr.DetrLoss with Detr->Yolos class YolosLoss(nn.Module): """ - This class computes the losses for YolosForObjectDetection/YolosForSegmentation. The process happens in two steps: - 1) we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each - pair of matched ground-truth / prediction (supervise class and box). + This class computes the losses for YolosForObjectDetection/YolosForSegmentation. The process happens in two steps: 1) + we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair + of matched ground-truth / prediction (supervise class and box). A note on the `num_classes` argument (copied from original repo in detr.py): "the naming of the `num_classes` parameter of the criterion is somewhat misleading. It indeed corresponds to `max_obj_id` + 1, where `max_obj_id` is diff --git a/src/transformers/models/yoso/configuration_yoso.py b/src/transformers/models/yoso/configuration_yoso.py index c6d2b176ef9..85501ac9d08 100644 --- a/src/transformers/models/yoso/configuration_yoso.py +++ b/src/transformers/models/yoso/configuration_yoso.py @@ -94,6 +94,7 @@ class YosoConfig(PretrainedConfig): >>> # Accessing the model configuration >>> configuration = model.config ```""" + model_type = "yoso" def __init__( diff --git a/src/transformers/models/yoso/modeling_yoso.py b/src/transformers/models/yoso/modeling_yoso.py index 6666c0a5aa8..4e08b999ad3 100644 --- a/src/transformers/models/yoso/modeling_yoso.py +++ b/src/transformers/models/yoso/modeling_yoso.py @@ -88,7 +88,7 @@ def to_contiguous(input_tensors): def normalize(input_tensors): - if type(input_tensors) is list: + if isinstance(input_tensors, list): out = [] for tensor in input_tensors: out.append(nn.functional.normalize(tensor, p=2, dim=-1)) diff --git a/src/transformers/tokenization_utils_base.py b/src/transformers/tokenization_utils_base.py index ea730bc00d0..723de720a5f 100644 --- a/src/transformers/tokenization_utils_base.py +++ b/src/transformers/tokenization_utils_base.py @@ -1593,9 +1593,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin): # By default, do not split special tokens for both fast and slow tokenizers self.split_special_tokens = kwargs.pop("split_special_tokens", False) - self.deprecation_warnings = ( - {} - ) # Use to store when we have already noticed a deprecation warning (avoid overlogging). + self.deprecation_warnings = {} # Use to store when we have already noticed a deprecation warning (avoid overlogging). self._in_target_context_manager = False # Stores a Jinja template that formats chat histories into tokenizable strings diff --git a/src/transformers/trainer_pt_utils.py b/src/transformers/trainer_pt_utils.py index cb6249f19a9..b8dfb3124c5 100644 --- a/src/transformers/trainer_pt_utils.py +++ b/src/transformers/trainer_pt_utils.py @@ -896,7 +896,7 @@ def metrics_format(self, metrics: Dict[str, float]) -> Dict[str, float]: metrics_copy[k] = _secs2timedelta(v) elif k == "total_flos": metrics_copy[k] = f"{ int(v) >> 30 }GF" - elif type(metrics_copy[k]) == float: + elif isinstance(metrics_copy[k], float): metrics_copy[k] = round(v, 4) return metrics_copy diff --git a/src/transformers/utils/fx.py b/src/transformers/utils/fx.py index 0eba32d2593..50320dabb70 100755 --- a/src/transformers/utils/fx.py +++ b/src/transformers/utils/fx.py @@ -174,7 +174,7 @@ _SPECIAL_SUPPORTED_MODELS = [ "Speech2Text2Decoder", "TrOCRDecoder", "PeftModelForCausalLM", - "PeftModelForSeq2SeqLM" + "PeftModelForSeq2SeqLM", # TODO: add support for them as it should be quite easy to do so (small blocking issues). # XLNetForQuestionAnswering, ] diff --git a/src/transformers/utils/sentencepiece_model_pb2.py b/src/transformers/utils/sentencepiece_model_pb2.py index 458fe913d63..b4b2992a630 100644 --- a/src/transformers/utils/sentencepiece_model_pb2.py +++ b/src/transformers/utils/sentencepiece_model_pb2.py @@ -1445,7 +1445,7 @@ TrainerSpec = _reflection.GeneratedProtocolMessageType( (_message.Message,), { "DESCRIPTOR": _TRAINERSPEC, - "__module__": "sentencepiece_model_pb2" + "__module__": "sentencepiece_model_pb2", # @@protoc_insertion_point(class_scope:sentencepiece.TrainerSpec) }, ) @@ -1456,7 +1456,7 @@ NormalizerSpec = _reflection.GeneratedProtocolMessageType( (_message.Message,), { "DESCRIPTOR": _NORMALIZERSPEC, - "__module__": "sentencepiece_model_pb2" + "__module__": "sentencepiece_model_pb2", # @@protoc_insertion_point(class_scope:sentencepiece.NormalizerSpec) }, ) @@ -1471,12 +1471,12 @@ SelfTestData = _reflection.GeneratedProtocolMessageType( (_message.Message,), { "DESCRIPTOR": _SELFTESTDATA_SAMPLE, - "__module__": "sentencepiece_model_pb2" + "__module__": "sentencepiece_model_pb2", # @@protoc_insertion_point(class_scope:sentencepiece.SelfTestData.Sample) }, ), "DESCRIPTOR": _SELFTESTDATA, - "__module__": "sentencepiece_model_pb2" + "__module__": "sentencepiece_model_pb2", # @@protoc_insertion_point(class_scope:sentencepiece.SelfTestData) }, ) @@ -1492,12 +1492,12 @@ ModelProto = _reflection.GeneratedProtocolMessageType( (_message.Message,), { "DESCRIPTOR": _MODELPROTO_SENTENCEPIECE, - "__module__": "sentencepiece_model_pb2" + "__module__": "sentencepiece_model_pb2", # @@protoc_insertion_point(class_scope:sentencepiece.ModelProto.SentencePiece) }, ), "DESCRIPTOR": _MODELPROTO, - "__module__": "sentencepiece_model_pb2" + "__module__": "sentencepiece_model_pb2", # @@protoc_insertion_point(class_scope:sentencepiece.ModelProto) }, ) diff --git a/templates/adding_a_missing_tokenization_test/cookiecutter-template-{{cookiecutter.modelname}}/test_tokenization_{{cookiecutter.lowercase_modelname}}.py b/templates/adding_a_missing_tokenization_test/cookiecutter-template-{{cookiecutter.modelname}}/test_tokenization_{{cookiecutter.lowercase_modelname}}.py index 36e35c04ed3..6cbe8bd481a 100644 --- a/templates/adding_a_missing_tokenization_test/cookiecutter-template-{{cookiecutter.modelname}}/test_tokenization_{{cookiecutter.lowercase_modelname}}.py +++ b/templates/adding_a_missing_tokenization_test/cookiecutter-template-{{cookiecutter.modelname}}/test_tokenization_{{cookiecutter.lowercase_modelname}}.py @@ -75,4 +75,4 @@ class {{cookiecutter.camelcase_modelname}}TokenizationTest(TokenizerTesterMixin, "`self.tmpdirname`." ) - # TODO: add tests with hard-coded target values \ No newline at end of file + # TODO: add tests with hard-coded target values diff --git a/templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py b/templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py index e447246da2c..acdfe49090e 100755 --- a/templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py +++ b/templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py @@ -502,7 +502,7 @@ def main(): trainer.log_metrics("predict", metrics) trainer.save_metrics("predict", metrics) - + # write custom code for saving predictions according to task def _mp_fn(index): @@ -900,7 +900,7 @@ def main(): model.eval() for step, batch in enumerate(eval_dataloader): - with torch.no_grad(): + with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) metric.add_batch( diff --git a/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/configuration_{{cookiecutter.lowercase_modelname}}.py b/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/configuration_{{cookiecutter.lowercase_modelname}}.py index 2898b5cf6f8..3f9b5d1fb67 100644 --- a/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/configuration_{{cookiecutter.lowercase_modelname}}.py +++ b/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/configuration_{{cookiecutter.lowercase_modelname}}.py @@ -137,7 +137,7 @@ class {{cookiecutter.camelcase_modelname}}Config(PretrainedConfig): {% else -%} keys_to_ignore_at_inference = ["past_key_values"] {% endif -%} - + {% if cookiecutter.is_encoder_decoder_model == "False" %} {%- else %} attribute_map = { @@ -238,4 +238,3 @@ class {{cookiecutter.camelcase_modelname}}Config(PretrainedConfig): **kwargs ) - \ No newline at end of file diff --git a/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_flax_{{cookiecutter.lowercase_modelname}}.py b/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_flax_{{cookiecutter.lowercase_modelname}}.py index 37b22a75c3e..a01ab3e19ad 100644 --- a/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_flax_{{cookiecutter.lowercase_modelname}}.py +++ b/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_flax_{{cookiecutter.lowercase_modelname}}.py @@ -541,7 +541,7 @@ def prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict( class Flax{{cookiecutter.camelcase_modelname}}ModelTest(FlaxModelTesterMixin, unittest.TestCase): all_model_classes = ( ( - Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, + Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification, Flax{{cookiecutter.camelcase_modelname}}Model, diff --git a/tests/models/albert/test_tokenization_albert.py b/tests/models/albert/test_tokenization_albert.py index c25cfaec77b..d9bb86bf299 100644 --- a/tests/models/albert/test_tokenization_albert.py +++ b/tests/models/albert/test_tokenization_albert.py @@ -123,9 +123,7 @@ class AlbertTokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'input_ids': [[2, 21970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 12051, 18, 17, 7103, 2153, 673, 8, 3515, 18684, 8, 4461, 6, 1927, 297, 8, 12060, 2607, 18, 13, 5, 4461, 15, 10538, 38, 8, 135, 15, 822, 58, 15, 993, 10363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 10641, 6, 29, 84, 2512, 2430, 782, 18684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 11712, 15, 7103, 2153, 673, 17, 24883, 9990, 9, 3], [2, 11502, 25, 1006, 20, 782, 8, 11809, 855, 1732, 19393, 18667, 37, 367, 21018, 69, 1854, 34, 11860, 19124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 17659, 84, 14, 16792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 - # fmt: on + expected_encoding = {'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'input_ids': [[2, 21970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 12051, 18, 17, 7103, 2153, 673, 8, 3515, 18684, 8, 4461, 6, 1927, 297, 8, 12060, 2607, 18, 13, 5, 4461, 15, 10538, 38, 8, 135, 15, 822, 58, 15, 993, 10363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 10641, 6, 29, 84, 2512, 2430, 782, 18684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 11712, 15, 7103, 2153, 673, 17, 24883, 9990, 9, 3], [2, 11502, 25, 1006, 20, 782, 8, 11809, 855, 1732, 19393, 18667, 37, 367, 21018, 69, 1854, 34, 11860, 19124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 17659, 84, 14, 16792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, diff --git a/tests/models/altclip/test_modeling_altclip.py b/tests/models/altclip/test_modeling_altclip.py index f4f7a8fd523..610a66f8ae3 100755 --- a/tests/models/altclip/test_modeling_altclip.py +++ b/tests/models/altclip/test_modeling_altclip.py @@ -574,7 +574,7 @@ class AltCLIPModelIntegrationTest(unittest.TestCase): processor = AltCLIPProcessor.from_pretrained(model_name) image = prepare_img() - inputs = processor(text=["一张猫的照片", "一张狗的照片"], images=image, padding=True, return_tensors="pt").to(torch_device) + inputs = processor(text=["一张猫的照片", "一张狗的照片"], images=image, padding=True, return_tensors="pt").to(torch_device) # fmt: skip # forward pass with torch.no_grad(): diff --git a/tests/models/bark/test_modeling_bark.py b/tests/models/bark/test_modeling_bark.py index 4a71f9f723c..713fb6c3ee7 100644 --- a/tests/models/bark/test_modeling_bark.py +++ b/tests/models/bark/test_modeling_bark.py @@ -1029,10 +1029,8 @@ class BarkModelIntegrationTests(unittest.TestCase): def test_generate_semantic(self): input_ids = self.inputs - # fmt: off # check first ids - expected_output_ids = [7363, 321, 41, 1461, 6915, 952, 326, 41, 41, 927,] - # fmt: on + expected_output_ids = [7363, 321, 41, 1461, 6915, 952, 326, 41, 41, 927,] # fmt: skip # greedy decoding with torch.no_grad(): @@ -1049,10 +1047,8 @@ class BarkModelIntegrationTests(unittest.TestCase): input_ids = self.inputs min_eos_p = 0.01 - # fmt: off # check first ids - expected_output_ids = [7363, 321, 41, 1461, 6915, 952, 326, 41, 41, 927,] - # fmt: on + expected_output_ids = [7363, 321, 41, 1461, 6915, 952, 326, 41, 41, 927,] # fmt: skip # Should be able to read min_eos_p from kwargs with torch.no_grad(): @@ -1095,10 +1091,8 @@ class BarkModelIntegrationTests(unittest.TestCase): history_prompt = input_ids["history_prompt"] - # fmt: off # check first ids - expected_output_ids = [11018, 11391, 10651, 11418, 10857, 11620, 10642, 11366, 10312, 11528, 10531, 11516, 10474, 11051, 10524, 11051, ] - # fmt: on + expected_output_ids = [11018, 11391, 10651, 11418, 10857, 11620, 10642, 11366, 10312, 11528, 10531, 11516, 10474, 11051, 10524, 11051, ] # fmt: skip with torch.no_grad(): output_ids = self.model.semantic.generate( diff --git a/tests/models/barthez/test_tokenization_barthez.py b/tests/models/barthez/test_tokenization_barthez.py index fa128f5091b..7759d3560de 100644 --- a/tests/models/barthez/test_tokenization_barthez.py +++ b/tests/models/barthez/test_tokenization_barthez.py @@ -96,9 +96,7 @@ class BarthezTokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # fmt: skip # moussaKam/mbarthez is a french model. So we also use french texts. sequences = [ diff --git a/tests/models/bert_generation/test_tokenization_bert_generation.py b/tests/models/bert_generation/test_tokenization_bert_generation.py index 12be95d53eb..41d99288351 100644 --- a/tests/models/bert_generation/test_tokenization_bert_generation.py +++ b/tests/models/bert_generation/test_tokenization_bert_generation.py @@ -233,9 +233,7 @@ class BertGenerationTokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, diff --git a/tests/models/bert_japanese/test_tokenization_bert_japanese.py b/tests/models/bert_japanese/test_tokenization_bert_japanese.py index 3e840018bdc..bc780069797 100644 --- a/tests/models/bert_japanese/test_tokenization_bert_japanese.py +++ b/tests/models/bert_japanese/test_tokenization_bert_japanese.py @@ -198,12 +198,12 @@ class BertJapaneseTokenizationTest(TokenizerTesterMixin, unittest.TestCase): def test_sudachi_tokenizer_core(self): tokenizer = SudachiTokenizer(sudachi_dict_type="core") + # fmt: off self.assertListEqual( tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された　。 "), - # fmt: off - [" ", "\t", "アップル", "ストア", "で", "iPhone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "], - # fmt: on + [" ", "\t", "アップル", "ストア", "で", "iPhone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "], ) + # fmt: on @require_sudachi def test_sudachi_tokenizer_split_mode_A(self): @@ -227,23 +227,13 @@ class BertJapaneseTokenizationTest(TokenizerTesterMixin, unittest.TestCase): def test_sudachi_tokenizer_lower(self): tokenizer = SudachiTokenizer(do_lower_case=True, sudachi_dict_type="core") - self.assertListEqual( - tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された　。 "), - # fmt: off - [" ", "\t", "アップル", "ストア", "で", "iphone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "], - # fmt: on - ) + self.assertListEqual(tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された　。 "),[" ", "\t", "アップル", "ストア", "で", "iphone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "]) # fmt: skip @require_sudachi def test_sudachi_tokenizer_no_normalize(self): tokenizer = SudachiTokenizer(normalize_text=False, sudachi_dict_type="core") - self.assertListEqual( - tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された　。 "), - # fmt: off - [" ", "\t", "ｱｯﾌﾟﾙ", "ストア", "で", "iPhone", "８", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", "\u3000", "。", " ", " "], - # fmt: on - ) + self.assertListEqual(tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された　。 "),[" ", "\t", "ｱｯﾌﾟﾙ", "ストア", "で", "iPhone", "８", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", "\u3000", "。", " ", " "]) # fmt: skip @require_sudachi def test_sudachi_tokenizer_trim_whitespace(self): @@ -280,33 +270,19 @@ class BertJapaneseTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer = JumanppTokenizer() self.assertListEqual( - tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された　。 "), - # fmt: off - ["アップル", "ストア", "で", "iPhone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"], - # fmt: on - ) + tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された　。 "),["アップル", "ストア", "で", "iPhone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"]) # fmt: skip @require_jumanpp def test_jumanpp_tokenizer_lower(self): tokenizer = JumanppTokenizer(do_lower_case=True) - self.assertListEqual( - tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された　。 "), - # fmt: off - ["アップル", "ストア", "で", "iphone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"], - # fmt: on - ) + self.assertListEqual(tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された　。 "),["アップル", "ストア", "で", "iphone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"],) # fmt: skip @require_jumanpp def test_jumanpp_tokenizer_no_normalize(self): tokenizer = JumanppTokenizer(normalize_text=False) - self.assertListEqual( - tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された　。 "), - # fmt: off - ["ｱ", "ｯ", "ﾌ", "ﾟ", "ﾙ", "ストア", "で", "iPhone", "８", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"], - # fmt: on - ) + self.assertListEqual(tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された　。 "),["ｱ", "ｯ", "ﾌ", "ﾟ", "ﾙ", "ストア", "で", "iPhone", "８", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"],) # fmt: skip @require_jumanpp def test_jumanpp_tokenizer_trim_whitespace(self): @@ -327,7 +303,7 @@ class BertJapaneseTokenizationTest(TokenizerTesterMixin, unittest.TestCase): ) def test_wordpiece_tokenizer(self): - vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "こんにちは", "こん", "にちは", "ばんは", "##こん", "##にちは", "##ばんは"] + vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "こんにちは", "こん", "にちは", "ばんは", "##こん", "##にちは", "##ばんは"] # fmt: skip vocab = {} for i, token in enumerate(vocab_tokens): @@ -340,14 +316,14 @@ class BertJapaneseTokenizationTest(TokenizerTesterMixin, unittest.TestCase): self.assertListEqual(tokenizer.tokenize("こんばんは"), ["こん", "##ばんは"]) - self.assertListEqual(tokenizer.tokenize("こんばんは こんばんにちは こんにちは"), ["こん", "##ばんは", "[UNK]", "こんにちは"]) + self.assertListEqual(tokenizer.tokenize("こんばんは こんばんにちは こんにちは"), ["こん", "##ばんは", "[UNK]", "こんにちは"]) # fmt: skip def test_sentencepiece_tokenizer(self): tokenizer = BertJapaneseTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese-with-auto-jumanpp") subword_tokenizer = tokenizer.subword_tokenizer tokens = subword_tokenizer.tokenize("国境 の 長い トンネル を 抜ける と 雪国 であった 。") - self.assertListEqual(tokens, ["▁国境", "▁の", "▁長い", "▁トンネル", "▁を", "▁抜ける", "▁と", "▁雪", "国", "▁であった", "▁。"]) + self.assertListEqual(tokens, ["▁国境", "▁の", "▁長い", "▁トンネル", "▁を", "▁抜ける", "▁と", "▁雪", "国", "▁であった", "▁。"]) # fmt: skip tokens = subword_tokenizer.tokenize("こんばんは こんばん にち は こんにちは") self.assertListEqual(tokens, ["▁こん", "ばん", "は", "▁こん", "ばん", "▁に", "ち", "▁は", "▁こんにちは"]) @@ -401,9 +377,7 @@ class BertJapaneseCharacterTokenizationTest(TokenizerTesterMixin, unittest.TestC tokenizer = self.tokenizer_class(self.vocab_file, subword_tokenizer_type="character") tokens = tokenizer.tokenize("こんにちは、世界。 \nこんばんは、世界。") - self.assertListEqual( - tokens, ["こ", "ん", "に", "ち", "は", "、", "世", "界", "。", "こ", "ん", "ば", "ん", "は", "、", "世", "界", "。"] - ) + self.assertListEqual(tokens, ["こ", "ん", "に", "ち", "は", "、", "世", "界", "。", "こ", "ん", "ば", "ん", "は", "、", "世", "界", "。"]) # fmt: skip self.assertListEqual( tokenizer.convert_tokens_to_ids(tokens), [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] ) diff --git a/tests/models/big_bird/test_tokenization_big_bird.py b/tests/models/big_bird/test_tokenization_big_bird.py index fd4323cb0f5..23b25e40294 100644 --- a/tests/models/big_bird/test_tokenization_big_bird.py +++ b/tests/models/big_bird/test_tokenization_big_bird.py @@ -171,9 +171,7 @@ class BigBirdTokenizationTest(TokenizerTesterMixin, unittest.TestCase): 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will' " add words that should not exsist and be tokenized to , such as saoneuhaoesuth" ) - # fmt: off - original_tokenizer_encodings = [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66] # noqa: E231 - # fmt: on + original_tokenizer_encodings = [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66] # fmt: skip self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols)) @require_torch @@ -225,9 +223,7 @@ class BigBirdTokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, diff --git a/tests/models/bloom/test_modeling_bloom.py b/tests/models/bloom/test_modeling_bloom.py index 5d090c5785f..95160179c20 100644 --- a/tests/models/bloom/test_modeling_bloom.py +++ b/tests/models/bloom/test_modeling_bloom.py @@ -699,9 +699,7 @@ class BloomEmbeddingTest(unittest.TestCase): }, } - # fmt: off - EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478] - # fmt: on + EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478] # fmt: skip EMBEDDINGS_DS_AFTER_LN_MEAN = { 3478: -6.580352783203125e-05, @@ -782,9 +780,7 @@ class BloomEmbeddingTest(unittest.TestCase): ) model.eval() - # fmt: off - EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478] - # fmt: on + EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478] # fmt: skip MEAN_VALUE_LAST_LM = -4.3392181396484375e-05 MIN_MAX_DICT = {"min": -2.0625, "max": 2.75} @@ -812,9 +808,7 @@ class BloomEmbeddingTest(unittest.TestCase): ) # load in bf16 model.eval() - # fmt: off - EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478] - # fmt: on + EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478] # fmt: skip MEAN_LOGITS_GPU_1 = -1.823902130126953e-05 MEAN_LOGITS_GPU_2 = 1.9431114196777344e-05 diff --git a/tests/models/byt5/test_tokenization_byt5.py b/tests/models/byt5/test_tokenization_byt5.py index 486f9d1747f..dcda3e3bf7a 100644 --- a/tests/models/byt5/test_tokenization_byt5.py +++ b/tests/models/byt5/test_tokenization_byt5.py @@ -119,9 +119,7 @@ class ByT5TokenizationTest(TokenizerTesterMixin, unittest.TestCase): def test_prepare_batch_integration(self): tokenizer = self.t5_base_tokenizer src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."] - # fmt: off - expected_src_tokens = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0] - # fmt: on + expected_src_tokens = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0] # fmt: skip batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK) self.assertIsInstance(batch, BatchEncoding) @@ -160,10 +158,8 @@ class ByT5TokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer = self.t5_base_tokenizer src_text = ["A long paragraph for summarization. "] tgt_text = ["Summary of the text. "] - # fmt: off - expected_src_tokens = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1] - expected_tgt_tokens = [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1] - # fmt: on + expected_src_tokens = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1] # fmt: skip + expected_tgt_tokens = [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1] # fmt: skip batch = tokenizer(src_text, text_target=tgt_text) diff --git a/tests/models/camembert/test_tokenization_camembert.py b/tests/models/camembert/test_tokenization_camembert.py index 8ece3b04f49..7f72d304d5c 100644 --- a/tests/models/camembert/test_tokenization_camembert.py +++ b/tests/models/camembert/test_tokenization_camembert.py @@ -115,9 +115,7 @@ class CamembertTokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # fmt: skip # camembert is a french model. So we also use french texts. sequences = [ diff --git a/tests/models/canine/test_tokenization_canine.py b/tests/models/canine/test_tokenization_canine.py index bfa5ae28aaa..2d9ffa79716 100644 --- a/tests/models/canine/test_tokenization_canine.py +++ b/tests/models/canine/test_tokenization_canine.py @@ -49,9 +49,7 @@ class CanineTokenizationTest(TokenizerTesterMixin, unittest.TestCase): def test_prepare_batch_integration(self): tokenizer = self.canine_tokenizer src_text = ["Life is like a box of chocolates.", "You never know what you're gonna get."] - # fmt: off - expected_src_tokens = [57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0] - # fmt: on + expected_src_tokens = [57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0] # fmt: skip batch = tokenizer(src_text, padding=True, return_tensors="pt") self.assertIsInstance(batch, BatchEncoding) diff --git a/tests/models/chinese_clip/test_modeling_chinese_clip.py b/tests/models/chinese_clip/test_modeling_chinese_clip.py index c2030ebec12..8d0eb131e23 100644 --- a/tests/models/chinese_clip/test_modeling_chinese_clip.py +++ b/tests/models/chinese_clip/test_modeling_chinese_clip.py @@ -714,9 +714,9 @@ class ChineseCLIPModelIntegrationTest(unittest.TestCase): processor = ChineseCLIPProcessor.from_pretrained(model_name) image = prepare_img() - inputs = processor(text=["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"], images=image, padding=True, return_tensors="pt").to( - torch_device - ) + inputs = processor( + text=["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"], images=image, padding=True, return_tensors="pt" + ).to(torch_device) # forward pass with torch.no_grad(): diff --git a/tests/models/clip/test_processor_clip.py b/tests/models/clip/test_processor_clip.py index 59e1f135462..fb88ef27053 100644 --- a/tests/models/clip/test_processor_clip.py +++ b/tests/models/clip/test_processor_clip.py @@ -38,9 +38,7 @@ class CLIPProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() - # fmt: off - vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l", "w", "r", "t", "low", "er", "lowest", "newer", "wider", "", "<|startoftext|>", "<|endoftext|>"] - # fmt: on + vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l", "w", "r", "t", "low", "er", "lowest", "newer", "wider", "", "<|startoftext|>", "<|endoftext|>"] # fmt: skip vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "l o", "lo w", "e r", ""] self.special_tokens_map = {"unk_token": ""} diff --git a/tests/models/clip/test_tokenization_clip.py b/tests/models/clip/test_tokenization_clip.py index 27387be42ba..4f1d9a73ef0 100644 --- a/tests/models/clip/test_tokenization_clip.py +++ b/tests/models/clip/test_tokenization_clip.py @@ -36,9 +36,7 @@ class CLIPTokenizationTest(TokenizerTesterMixin, unittest.TestCase): def setUp(self): super().setUp() - # fmt: off - vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l", "w", "r", "t", "low", "er", "lowest", "newer", "wider", "", "<|startoftext|>", "<|endoftext|>"] - # fmt: on + vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l", "w", "r", "t", "low", "er", "lowest", "newer", "wider", "", "<|startoftext|>", "<|endoftext|>"] # fmt: skip vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "l o", "lo w", "e r"] self.special_tokens_map = {"unk_token": ""} diff --git a/tests/models/clipseg/test_processor_clipseg.py b/tests/models/clipseg/test_processor_clipseg.py index 2bc82dd022c..e33049b2768 100644 --- a/tests/models/clipseg/test_processor_clipseg.py +++ b/tests/models/clipseg/test_processor_clipseg.py @@ -38,9 +38,7 @@ class CLIPSegProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() - # fmt: off - vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l", "w", "r", "t", "low", "er", "lowest", "newer", "wider", "", "<|startoftext|>", "<|endoftext|>"] - # fmt: on + vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l", "w", "r", "t", "low", "er", "lowest", "newer", "wider", "", "<|startoftext|>", "<|endoftext|>"] # fmt: skip vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "l o", "lo w", "e r", ""] self.special_tokens_map = {"unk_token": ""} diff --git a/tests/models/code_llama/test_tokenization_code_llama.py b/tests/models/code_llama/test_tokenization_code_llama.py index 7452990ba75..a7232280239 100644 --- a/tests/models/code_llama/test_tokenization_code_llama.py +++ b/tests/models/code_llama/test_tokenization_code_llama.py @@ -289,9 +289,7 @@ class CodeLlamaTokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[1, 4103, 689, 414, 313, 24784, 368, 2998, 408, 282, 3637, 25350, 29899, 9067, 414, 322, 282, 3637, 25350, 29899, 1457, 3018, 1312, 29899, 2151, 29897, 8128, 2498, 29899, 15503, 4220, 6956, 1973, 313, 13635, 29911, 29892, 402, 7982, 29899, 29906, 29892, 1528, 13635, 29911, 29874, 29892, 1060, 26369, 29892, 6652, 309, 29933, 814, 29892, 1060, 29931, 6779, 11410, 363, 18385, 17088, 7634, 11235, 313, 25103, 29965, 29897, 322, 18385, 17088, 28203, 313, 25103, 29954, 29897, 411, 975, 29871, 29941, 29906, 29974, 758, 3018, 1312, 4733, 297, 29871, 29896, 29900, 29900, 29974, 10276, 322, 6483, 1006, 3372, 3097, 1546, 435, 1165, 29892, 10772, 29911, 25350, 322, 323, 6073, 17907, 29889], [1, 350, 20161, 338, 8688, 304, 758, 29899, 14968, 6483, 21000, 8684, 284, 22540, 515, 443, 29880, 24025, 1426, 491, 14002, 368, 4195, 292, 373, 1716, 2175, 322, 1492, 3030, 297, 599, 15359, 29889], [1, 450, 4996, 17354, 1701, 29916, 432, 17204, 975, 278, 17366, 11203, 29889]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} - # fmt: on + expected_encoding = {'input_ids': [[1, 4103, 689, 414, 313, 24784, 368, 2998, 408, 282, 3637, 25350, 29899, 9067, 414, 322, 282, 3637, 25350, 29899, 1457, 3018, 1312, 29899, 2151, 29897, 8128, 2498, 29899, 15503, 4220, 6956, 1973, 313, 13635, 29911, 29892, 402, 7982, 29899, 29906, 29892, 1528, 13635, 29911, 29874, 29892, 1060, 26369, 29892, 6652, 309, 29933, 814, 29892, 1060, 29931, 6779, 11410, 363, 18385, 17088, 7634, 11235, 313, 25103, 29965, 29897, 322, 18385, 17088, 28203, 313, 25103, 29954, 29897, 411, 975, 29871, 29941, 29906, 29974, 758, 3018, 1312, 4733, 297, 29871, 29896, 29900, 29900, 29974, 10276, 322, 6483, 1006, 3372, 3097, 1546, 435, 1165, 29892, 10772, 29911, 25350, 322, 323, 6073, 17907, 29889], [1, 350, 20161, 338, 8688, 304, 758, 29899, 14968, 6483, 21000, 8684, 284, 22540, 515, 443, 29880, 24025, 1426, 491, 14002, 368, 4195, 292, 373, 1716, 2175, 322, 1492, 3030, 297, 599, 15359, 29889], [1, 450, 4996, 17354, 1701, 29916, 432, 17204, 975, 278, 17366, 11203, 29889]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, @@ -409,8 +407,8 @@ class LlamaIntegrationTest(unittest.TestCase): self.assertEqual(rust_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test") # bytefallback showcase - self.assertEqual(pyth_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392]) - self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392]) + self.assertEqual(pyth_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392]) # fmt: skip + self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392]) # fmt: skip self.assertEqual( pyth_tokenizer.decode( [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True diff --git a/tests/models/deberta_v2/test_tokenization_deberta_v2.py b/tests/models/deberta_v2/test_tokenization_deberta_v2.py index 404aaa9e7e1..c75f45bfe8d 100644 --- a/tests/models/deberta_v2/test_tokenization_deberta_v2.py +++ b/tests/models/deberta_v2/test_tokenization_deberta_v2.py @@ -252,9 +252,7 @@ class DebertaV2TokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[1, 39867, 36, 19390, 486, 27, 35052, 81436, 18, 60685, 1225, 7, 35052, 81436, 18, 9367, 16899, 18, 15937, 53, 594, 773, 18, 16287, 30465, 36, 15937, 6, 41139, 38, 36979, 60763, 191, 6, 34132, 99, 6, 50538, 390, 43230, 6, 34132, 2779, 20850, 14, 699, 1072, 1194, 36, 382, 10901, 53, 7, 699, 1072, 2084, 36, 20422, 630, 53, 19, 105, 3049, 1896, 1053, 16899, 1506, 11, 37978, 4243, 7, 1237, 31869, 200, 16566, 654, 6, 35052, 81436, 7, 55630, 13593, 4, 2], [1, 26, 15011, 13, 667, 8, 1053, 18, 23611, 1237, 72356, 12820, 34, 104134, 1209, 35, 13313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 15785, 14951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[1, 39867, 36, 19390, 486, 27, 35052, 81436, 18, 60685, 1225, 7, 35052, 81436, 18, 9367, 16899, 18, 15937, 53, 594, 773, 18, 16287, 30465, 36, 15937, 6, 41139, 38, 36979, 60763, 191, 6, 34132, 99, 6, 50538, 390, 43230, 6, 34132, 2779, 20850, 14, 699, 1072, 1194, 36, 382, 10901, 53, 7, 699, 1072, 2084, 36, 20422, 630, 53, 19, 105, 3049, 1896, 1053, 16899, 1506, 11, 37978, 4243, 7, 1237, 31869, 200, 16566, 654, 6, 35052, 81436, 7, 55630, 13593, 4, 2], [1, 26, 15011, 13, 667, 8, 1053, 18, 23611, 1237, 72356, 12820, 34, 104134, 1209, 35, 13313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 15785, 14951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, diff --git a/tests/models/encoder_decoder/test_modeling_tf_encoder_decoder.py b/tests/models/encoder_decoder/test_modeling_tf_encoder_decoder.py index 1d8d4e985b6..48d9a03e578 100644 --- a/tests/models/encoder_decoder/test_modeling_tf_encoder_decoder.py +++ b/tests/models/encoder_decoder/test_modeling_tf_encoder_decoder.py @@ -474,7 +474,7 @@ class TFEncoderDecoderMixin: def prepare_pt_inputs_from_tf_inputs(self, tf_inputs_dict): pt_inputs_dict = {} for name, key in tf_inputs_dict.items(): - if type(key) == bool: + if isinstance(key, bool): pt_inputs_dict[name] = key elif name == "input_values": pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) diff --git a/tests/models/ernie_m/test_tokenization_ernie_m.py b/tests/models/ernie_m/test_tokenization_ernie_m.py index 2e06bb20c10..19f144df453 100644 --- a/tests/models/ernie_m/test_tokenization_ernie_m.py +++ b/tests/models/ernie_m/test_tokenization_ernie_m.py @@ -125,9 +125,7 @@ class ErnieMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} - # fmt: on + expected_encoding = {'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, diff --git a/tests/models/esm/test_tokenization_esm.py b/tests/models/esm/test_tokenization_esm.py index 242f6d77081..539baaf3415 100644 --- a/tests/models/esm/test_tokenization_esm.py +++ b/tests/models/esm/test_tokenization_esm.py @@ -32,9 +32,7 @@ class ESMTokenizationTest(unittest.TestCase): def setUp(self): super().setUp() self.tmpdirname = tempfile.mkdtemp() - # fmt: off - vocab_tokens: List[str] = ["", "", "", "", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "", ""] # noqa: E501 - # fmt: on + vocab_tokens: List[str] = ["", "", "", "", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "", ""] # fmt: skip self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) diff --git a/tests/models/flava/test_processor_flava.py b/tests/models/flava/test_processor_flava.py index f89d7edfaa9..a83e459153d 100644 --- a/tests/models/flava/test_processor_flava.py +++ b/tests/models/flava/test_processor_flava.py @@ -45,9 +45,7 @@ class FlavaProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() - # fmt: off - vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"] - # fmt: on + vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"] # fmt: skip self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as fp: diff --git a/tests/models/fnet/test_modeling_fnet.py b/tests/models/fnet/test_modeling_fnet.py index ba89542ee66..83b84edddcc 100644 --- a/tests/models/fnet/test_modeling_fnet.py +++ b/tests/models/fnet/test_modeling_fnet.py @@ -560,9 +560,7 @@ class FNetModelIntegrationTest(unittest.TestCase): max_length=512, ) - # fmt: off - torch.testing.assert_allclose(inputs["input_ids"], torch.tensor([[4, 13, 283, 2479, 106, 8, 6, 845, 5, 168, 65, 367, 6, 845, 5, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3]])) - # fmt: on + torch.testing.assert_allclose(inputs["input_ids"], torch.tensor([[4, 13, 283, 2479, 106, 8, 6, 845, 5, 168, 65, 367, 6, 845, 5, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3]])) # fmt: skip inputs = {k: v.to(torch_device) for k, v in inputs.items()} diff --git a/tests/models/fnet/test_tokenization_fnet.py b/tests/models/fnet/test_tokenization_fnet.py index 17fe3e0dd30..85080efc3e5 100644 --- a/tests/models/fnet/test_tokenization_fnet.py +++ b/tests/models/fnet/test_tokenization_fnet.py @@ -425,11 +425,15 @@ class FNetTokenizationTest(TokenizerTesterMixin, unittest.TestCase): model_main_input_name: str = "input_ids", ): for i_r in input_r.values(): - self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual( - len(i_r[1]), max_length + ( + self.assertEqual(len(i_r), 2), + self.assertEqual(len(i_r[0]), max_length), + self.assertEqual(len(i_r[1]), max_length), ) - self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual( - len(i_r[1]), max_length + ( + self.assertEqual(len(i_r), 2), + self.assertEqual(len(i_r[0]), max_length), + self.assertEqual(len(i_r[1]), max_length), ) for i_r, i_p in zip(input_r[model_main_input_name], input_p[model_main_input_name]): @@ -437,9 +441,7 @@ class FNetTokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[4, 4616, 107, 163, 328, 14, 63, 1726, 106, 11954, 16659, 23, 83, 16688, 11427, 328, 107, 36, 11954, 16659, 23, 83, 16688, 6153, 82, 961, 16688, 3474, 16710, 1696, 2306, 16688, 10854, 2524, 3827, 561, 163, 3474, 16680, 62, 226, 2092, 16680, 379, 3474, 16660, 16680, 2436, 16667, 16671, 16680, 999, 87, 3474, 16680, 2436, 16667, 5208, 800, 16710, 68, 2018, 2959, 3037, 163, 16663, 11617, 16710, 36, 2018, 2959, 4737, 163, 16663, 16667, 16674, 16710, 91, 372, 5087, 16745, 2205, 82, 961, 3608, 38, 1770, 16745, 7984, 36, 2565, 751, 9017, 1204, 864, 218, 1244, 16680, 11954, 16659, 23, 83, 36, 14686, 23, 7619, 16678, 5], [4, 28, 532, 65, 1929, 33, 391, 16688, 3979, 9, 2565, 7849, 299, 225, 34, 2040, 305, 167, 289, 16667, 16078, 32, 1966, 181, 4626, 63, 10575, 71, 851, 1491, 36, 624, 4757, 38, 208, 8038, 16678, 5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [4, 13, 1467, 5187, 26, 2521, 4567, 16664, 372, 13, 16209, 3314, 16678, 5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[4, 4616, 107, 163, 328, 14, 63, 1726, 106, 11954, 16659, 23, 83, 16688, 11427, 328, 107, 36, 11954, 16659, 23, 83, 16688, 6153, 82, 961, 16688, 3474, 16710, 1696, 2306, 16688, 10854, 2524, 3827, 561, 163, 3474, 16680, 62, 226, 2092, 16680, 379, 3474, 16660, 16680, 2436, 16667, 16671, 16680, 999, 87, 3474, 16680, 2436, 16667, 5208, 800, 16710, 68, 2018, 2959, 3037, 163, 16663, 11617, 16710, 36, 2018, 2959, 4737, 163, 16663, 16667, 16674, 16710, 91, 372, 5087, 16745, 2205, 82, 961, 3608, 38, 1770, 16745, 7984, 36, 2565, 751, 9017, 1204, 864, 218, 1244, 16680, 11954, 16659, 23, 83, 36, 14686, 23, 7619, 16678, 5], [4, 28, 532, 65, 1929, 33, 391, 16688, 3979, 9, 2565, 7849, 299, 225, 34, 2040, 305, 167, 289, 16667, 16078, 32, 1966, 181, 4626, 63, 10575, 71, 851, 1491, 36, 624, 4757, 38, 208, 8038, 16678, 5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [4, 13, 1467, 5187, 26, 2521, 4567, 16664, 372, 13, 16209, 3314, 16678, 5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, diff --git a/tests/models/fuyu/test_modeling_fuyu.py b/tests/models/fuyu/test_modeling_fuyu.py index aaed47a2aea..62d58127973 100644 --- a/tests/models/fuyu/test_modeling_fuyu.py +++ b/tests/models/fuyu/test_modeling_fuyu.py @@ -343,9 +343,7 @@ class FuyuModelIntegrationTest(unittest.TestCase): @slow @require_torch_accelerator def test_model_8b_chat_greedy_generation_chart_vqa(self): - # fmt: off - EXPECTED_TEXT_TOKENS = ["The","life expectancy","at","birth","of male","s in","","20","18","is","","80",".","7",".","\n","|ENDOFTEXT|",] - # fmt: on + EXPECTED_TEXT_TOKENS = ["The","life expectancy","at","birth","of male","s in","","20","18","is","","80",".","7",".","\n","|ENDOFTEXT|",] # fmt: skip expected_text_completion = " ".join(EXPECTED_TEXT_TOKENS) # TODO make sure the end string matches text_prompt_chart_vqa = "What is the highest life expectancy at birth of male?\n" diff --git a/tests/models/gpt2/test_modeling_gpt2.py b/tests/models/gpt2/test_modeling_gpt2.py index 4c49c0b746d..245b29d56a6 100644 --- a/tests/models/gpt2/test_modeling_gpt2.py +++ b/tests/models/gpt2/test_modeling_gpt2.py @@ -736,11 +736,7 @@ class GPT2ModelLanguageGenerationTest(unittest.TestCase): input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device) # The dog was found in a field near the intersection of West and West Streets.\n\nThe dog - # fmt: off - expected_output_ids = [ - 464, 3290, 373, 1043, 287, 257, 2214, 1474, 262, 16246, 286, 2688, 290, 2688, 27262, 13, 198, 198, 464, 3290, - ] - # fmt: on + expected_output_ids = [464, 3290, 373, 1043, 287, 257, 2214, 1474, 262, 16246, 286, 2688, 290, 2688, 27262, 13, 198, 198, 464, 3290,] # fmt: skip output_ids = model.generate(input_ids, do_sample=False) if verify_outputs: self.assertListEqual(output_ids[0].tolist(), expected_output_ids) diff --git a/tests/models/gpt_neo/test_modeling_gpt_neo.py b/tests/models/gpt_neo/test_modeling_gpt_neo.py index 075b9a26633..d4a9dd90eb8 100644 --- a/tests/models/gpt_neo/test_modeling_gpt_neo.py +++ b/tests/models/gpt_neo/test_modeling_gpt_neo.py @@ -537,10 +537,8 @@ class GPTNeoModelLanguageGenerationTest(unittest.TestCase): else: model.gradient_checkpointing_disable() input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device) # The dog - # fmt: off # The dog-eared copy of the book, which is a collection of essays by the late author, - expected_output_ids = [464, 3290, 12, 3380, 4866, 286, 262, 1492, 11, 543, 318, 257, 4947, 286, 27126, 416, 262, 2739, 1772, 11] - # fmt: on + expected_output_ids = [464, 3290, 12, 3380, 4866, 286, 262, 1492, 11, 543, 318, 257, 4947, 286, 27126, 416, 262, 2739, 1772, 11] # fmt: skip output_ids = model.generate(input_ids, do_sample=False) self.assertListEqual(output_ids[0].tolist(), expected_output_ids) diff --git a/tests/models/gpt_neox_japanese/test_modeling_gpt_neox_japanese.py b/tests/models/gpt_neox_japanese/test_modeling_gpt_neox_japanese.py index fc78b8bdd42..752b7a0b8d5 100644 --- a/tests/models/gpt_neox_japanese/test_modeling_gpt_neox_japanese.py +++ b/tests/models/gpt_neox_japanese/test_modeling_gpt_neox_japanese.py @@ -238,7 +238,7 @@ class GPTNeoXModelJapaneseTest(ModelTesterMixin, PipelineTesterMixin, unittest.T def test_generation(self): model_id = "abeja/gpt-neox-japanese-2.7b" - prompts = ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"] + prompts = ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"] # fmt: skip EXPECTED_OUTPUTS = [ "データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。", diff --git a/tests/models/gpt_sw3/test_tokenization_gpt_sw3.py b/tests/models/gpt_sw3/test_tokenization_gpt_sw3.py index 040f6c77117..6f9fe0dfa95 100644 --- a/tests/models/gpt_sw3/test_tokenization_gpt_sw3.py +++ b/tests/models/gpt_sw3/test_tokenization_gpt_sw3.py @@ -120,9 +120,7 @@ class GPTSw3TokenizationTest(TokenizerTesterMixin, unittest.TestCase): "Det är inget fel på Mr. Cool", ] - # fmt: off - expected_encoding = {"input_ids": [[63423, 5, 6811, 14954, 282, 816, 3821, 63466, 63425, 63462, 18, 63978, 678, 301, 1320, 63423, 63455, 63458, 18, 63982, 4246, 3940, 1901, 47789, 5547, 18994], [19630, 1100, 63446, 1342, 633, 544, 4488, 593, 5102, 2416, 63495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 58593, 22413, 9106, 546, 268, 33213, 63979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55130, 63450, 924, 63449, 2249, 4062, 1558, 318, 63504, 21498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 63443, 26801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} - # fmt: on + expected_encoding = {"input_ids": [[63423, 5, 6811, 14954, 282, 816, 3821, 63466, 63425, 63462, 18, 63978, 678, 301, 1320, 63423, 63455, 63458, 18, 63982, 4246, 3940, 1901, 47789, 5547, 18994], [19630, 1100, 63446, 1342, 633, 544, 4488, 593, 5102, 2416, 63495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 58593, 22413, 9106, 546, 268, 33213, 63979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55130, 63450, 924, 63449, 2249, 4062, 1558, 318, 63504, 21498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 63443, 26801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="AI-Sweden/gpt-sw3-126m", diff --git a/tests/models/gptj/test_modeling_gptj.py b/tests/models/gptj/test_modeling_gptj.py index 4fc41ba6d31..42ded9c81ae 100644 --- a/tests/models/gptj/test_modeling_gptj.py +++ b/tests/models/gptj/test_modeling_gptj.py @@ -534,10 +534,8 @@ class GPTJModelLanguageGenerationTest(unittest.TestCase): model.gradient_checkpointing_disable() model.to(torch_device) input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device) # The dog - # fmt: off # The dog is a man's best friend. It is a loyal companion, and it is a friend - expected_output_ids = [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545] - # fmt: on + expected_output_ids = [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545] # fmt: skip output_ids = model.generate(input_ids, do_sample=False) self.assertListEqual(output_ids[0].tolist(), expected_output_ids) diff --git a/tests/models/gptj/test_modeling_tf_gptj.py b/tests/models/gptj/test_modeling_tf_gptj.py index 896df148058..166bd3a45a9 100644 --- a/tests/models/gptj/test_modeling_tf_gptj.py +++ b/tests/models/gptj/test_modeling_tf_gptj.py @@ -384,10 +384,8 @@ class TFGPTJModelLanguageGenerationTest(unittest.TestCase): def test_lm_generate_gptj(self): model = TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", from_pt=True) input_ids = tf.convert_to_tensor([[464, 3290]], dtype=tf.int32) # The dog - # fmt: off # The dog is a man's best friend. It is a loyal companion, and it is a friend - expected_output_ids = [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545] - # fmt: on + expected_output_ids = [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545] # fmt: skip output_ids = model.generate(input_ids, do_sample=False) self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids) diff --git a/tests/models/gptsan_japanese/test_modeling_gptsan_japanese.py b/tests/models/gptsan_japanese/test_modeling_gptsan_japanese.py index 1a86e23fdcc..716b8b9fc6d 100644 --- a/tests/models/gptsan_japanese/test_modeling_gptsan_japanese.py +++ b/tests/models/gptsan_japanese/test_modeling_gptsan_japanese.py @@ -220,8 +220,8 @@ class GPTSanJapaneseForConditionalGenerationTest(ModelTesterMixin, GenerationTes outputs = model(input_ids) output_logits = outputs.logits.detach().cpu().numpy() # Output of original model created with mesh-tensoflow + # fmt: off target = [ - # fmt: off [-12.037839889526367, -12.433061599731445, -14.333840370178223, -12.450345993041992, -11.1661376953125, -11.930137634277344, -10.659740447998047, -12.909574508666992, -13.241043090820312, -13.398579597473145, -11.107524871826172, -12.3685941696167, -22.97943115234375, -10.481067657470703, -12.484030723571777, @@ -242,8 +242,8 @@ class GPTSanJapaneseForConditionalGenerationTest(ModelTesterMixin, GenerationTes -10.113405227661133, -10.546867370605469, -10.04369068145752, -10.907809257507324, -10.504216194152832, -11.129199028015137, -10.151124000549316, -21.96586799621582, -9.086349487304688, -11.730339050292969, -10.460667610168457, -10.298049926757812, -10.784148216247559, -10.840693473815918, -22.03152847290039], - # fmt: on ] + # fmt: on target = np.array(target).flatten() predict = output_logits[0, :, :20].flatten() @@ -341,8 +341,8 @@ class GPTSanJapaneseForConditionalGenerationTest(ModelTesterMixin, GenerationTes input_ids_batch = tokenizer([input_text, input_text], return_tensors="pt").input_ids.to(torch_device) # spout from uniform and one-hot + spouts = [ - # fmt: off [0.87882208, 0.38426396, 0.33220248, 0.43890406, 0.16562252, 0.04803985, 0.211572 , 0.23188473, 0.37153068, 0.7836377 , 0.02160172, 0.38761719, 0.75290772, 0.90198857, 0.34365777, @@ -378,8 +378,7 @@ class GPTSanJapaneseForConditionalGenerationTest(ModelTesterMixin, GenerationTes 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], - # fmt: on - ] + ] # fmt: skip output1 = model.generate( input_ids=input_ids, diff --git a/tests/models/gptsan_japanese/test_tokenization_gptsan_japanese.py b/tests/models/gptsan_japanese/test_tokenization_gptsan_japanese.py index 2c6fd962edb..6d656b2d0ff 100644 --- a/tests/models/gptsan_japanese/test_tokenization_gptsan_japanese.py +++ b/tests/models/gptsan_japanese/test_tokenization_gptsan_japanese.py @@ -36,9 +36,7 @@ class GPTSanJapaneseTokenizationTest(TokenizerTesterMixin, unittest.TestCase): def setUp(self): super().setUp() - # fmt: off - vocab_tokens = ["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "
", "", "", "", "", "", "", "", "", "", "", "<|emoji1|>", "", "<|bagoftoken|>", "<|endoftext|>"] - # fmt: on + vocab_tokens = ["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "
", "", "", "", "", "", "", "", "", "", "", "<|emoji1|>", "", "<|bagoftoken|>", "<|endoftext|>"] # fmt: skip emoji_tokens = {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}} # 😀 self.special_tokens_map = {"unk_token": ""} diff --git a/tests/models/instructblip/test_modeling_instructblip.py b/tests/models/instructblip/test_modeling_instructblip.py index 3b7dc002aff..0af427c3586 100644 --- a/tests/models/instructblip/test_modeling_instructblip.py +++ b/tests/models/instructblip/test_modeling_instructblip.py @@ -567,9 +567,7 @@ class InstructBlipModelIntegrationTest(unittest.TestCase): outputs = model.generate(**inputs, max_new_tokens=30) generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip() - # fmt: off - expected_outputs = [2, 450, 22910, 9565, 310, 445, 1967, 338, 393, 263, 767, 338, 13977, 292, 22095, 373, 278, 1250, 310, 263, 13328, 20134, 29963, 1550, 19500, 1623, 263, 19587, 4272, 11952, 29889] - # fmt: on + expected_outputs = [2, 450, 22910, 9565, 310, 445, 1967, 338, 393, 263, 767, 338, 13977, 292, 22095, 373, 278, 1250, 310, 263, 13328, 20134, 29963, 1550, 19500, 1623, 263, 19587, 4272, 11952, 29889] # fmt: skip self.assertEqual(outputs[0].tolist(), expected_outputs) self.assertEqual( generated_text, @@ -606,9 +604,7 @@ class InstructBlipModelIntegrationTest(unittest.TestCase): ) generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0] - # fmt: off - expected_outputs = [0, 37, 1023, 9850, 7, 3, 9, 388, 3575, 53, 4954, 30, 8, 223, 13, 3, 9, 4459, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 37, 388, 19, 5119, 3, 9, 4459, 8677, 28, 3, 9, 2756, 4459, 6177, 6, 11, 3, 88, 19, 338, 46, 3575, 53, 1476, 12, 743, 112, 2491, 5, 37, 1023, 19, 7225, 788, 12, 8, 685, 24, 34, 1267, 3, 9, 388, 3575, 53, 4954, 30, 8, 223, 13, 3, 9, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 94, 19, 487, 24, 8, 388, 19, 1119, 12, 1097, 540, 57, 692, 112, 10428, 30, 8, 223, 13, 8, 4049, 6, 68, 34, 19, 92, 487, 24, 3, 88, 19, 1119, 12, 1097, 97, 57, 692, 112, 10428, 30, 8, 223, 13, 8, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 3, 13865, 13, 8, 1053, 21, 8, 388, 31, 7, 2874, 6, 34, 19, 964, 24, 3, 88, 19, 1119, 12, 1097, 97, 57, 692, 112, 10428, 30, 8, 223, 13, 8, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 1] - # fmt: on + expected_outputs = [0, 37, 1023, 9850, 7, 3, 9, 388, 3575, 53, 4954, 30, 8, 223, 13, 3, 9, 4459, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 37, 388, 19, 5119, 3, 9, 4459, 8677, 28, 3, 9, 2756, 4459, 6177, 6, 11, 3, 88, 19, 338, 46, 3575, 53, 1476, 12, 743, 112, 2491, 5, 37, 1023, 19, 7225, 788, 12, 8, 685, 24, 34, 1267, 3, 9, 388, 3575, 53, 4954, 30, 8, 223, 13, 3, 9, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 94, 19, 487, 24, 8, 388, 19, 1119, 12, 1097, 540, 57, 692, 112, 10428, 30, 8, 223, 13, 8, 4049, 6, 68, 34, 19, 92, 487, 24, 3, 88, 19, 1119, 12, 1097, 97, 57, 692, 112, 10428, 30, 8, 223, 13, 8, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 3, 13865, 13, 8, 1053, 21, 8, 388, 31, 7, 2874, 6, 34, 19, 964, 24, 3, 88, 19, 1119, 12, 1097, 97, 57, 692, 112, 10428, 30, 8, 223, 13, 8, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 1] # fmt: skip self.assertEqual(outputs[0].tolist(), expected_outputs) self.assertEqual( generated_text, diff --git a/tests/models/layoutlmv2/test_processor_layoutlmv2.py b/tests/models/layoutlmv2/test_processor_layoutlmv2.py index 91a8da9cafb..61d8e2e195d 100644 --- a/tests/models/layoutlmv2/test_processor_layoutlmv2.py +++ b/tests/models/layoutlmv2/test_processor_layoutlmv2.py @@ -241,9 +241,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase): # verify input_ids # this was obtained with Tesseract 4.1.1 - # fmt: off - expected_decoding = "[CLS] 11 : 14 to 11 : 39 a. m 11 : 39 to 11 : 44 a. m. 11 : 44 a. m. to 12 : 25 p. m. 12 : 25 to 12 : 58 p. m. 12 : 58 to 4 : 00 p. m. 2 : 00 to 5 : 00 p. m. coffee break coffee will be served for men and women in the lobby adjacent to exhibit area. please move into exhibit area. ( exhibits open ) trrf general session ( part | ) presiding : lee a. waller trrf vice president “ introductory remarks ” lee a. waller, trrf vice presi - dent individual interviews with trrf public board members and sci - entific advisory council mem - bers conducted by trrf treasurer philip g. kuehn to get answers which the public refrigerated warehousing industry is looking for. plus questions from the floor. dr. emil m. mrak, university of cal - ifornia, chairman, trrf board ; sam r. cecil, university of georgia college of agriculture ; dr. stanley charm, tufts university school of medicine ; dr. robert h. cotton, itt continental baking company ; dr. owen fennema, university of wis - consin ; dr. robert e. hardenburg, usda. questions and answers exhibits open capt. jack stoney room trrf scientific advisory council meeting ballroom foyer [SEP]" # noqa: E231 - # fmt: on + expected_decoding = "[CLS] 11 : 14 to 11 : 39 a. m 11 : 39 to 11 : 44 a. m. 11 : 44 a. m. to 12 : 25 p. m. 12 : 25 to 12 : 58 p. m. 12 : 58 to 4 : 00 p. m. 2 : 00 to 5 : 00 p. m. coffee break coffee will be served for men and women in the lobby adjacent to exhibit area. please move into exhibit area. ( exhibits open ) trrf general session ( part | ) presiding : lee a. waller trrf vice president “ introductory remarks ” lee a. waller, trrf vice presi - dent individual interviews with trrf public board members and sci - entific advisory council mem - bers conducted by trrf treasurer philip g. kuehn to get answers which the public refrigerated warehousing industry is looking for. plus questions from the floor. dr. emil m. mrak, university of cal - ifornia, chairman, trrf board ; sam r. cecil, university of georgia college of agriculture ; dr. stanley charm, tufts university school of medicine ; dr. robert h. cotton, itt continental baking company ; dr. owen fennema, university of wis - consin ; dr. robert e. hardenburg, usda. questions and answers exhibits open capt. jack stoney room trrf scientific advisory council meeting ballroom foyer [SEP]" # fmt: skip decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) @@ -261,9 +259,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase): # verify input_ids # this was obtained with Tesseract 4.1.1 - # fmt: off - expected_decoding = "[CLS] 7 itc limited report and accounts 2013 itc ’ s brands : an asset for the nation the consumer needs and aspirations they fulfil, the benefit they generate for millions across itc ’ s value chains, the future - ready capabilities that support them, and the value that they create for the country, have made itc ’ s brands national assets, adding to india ’ s competitiveness. it is itc ’ s aspiration to be the no 1 fmcg player in the country, driven by its new fmcg businesses. a recent nielsen report has highlighted that itc's new fmcg businesses are the fastest growing among the top consumer goods companies operating in india. itc takes justifiable pride that, along with generating economic value, these celebrated indian brands also drive the creation of larger societal capital through the virtuous cycle of sustainable and inclusive growth. di wills * ; love delightfully soft skin? aia ans source : https : / / www. industrydocuments. ucsf. edu / docs / snbx0223 [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]" # noqa: E231 - # fmt: on + expected_decoding = "[CLS] 7 itc limited report and accounts 2013 itc ’ s brands : an asset for the nation the consumer needs and aspirations they fulfil, the benefit they generate for millions across itc ’ s value chains, the future - ready capabilities that support them, and the value that they create for the country, have made itc ’ s brands national assets, adding to india ’ s competitiveness. it is itc ’ s aspiration to be the no 1 fmcg player in the country, driven by its new fmcg businesses. a recent nielsen report has highlighted that itc's new fmcg businesses are the fastest growing among the top consumer goods companies operating in india. itc takes justifiable pride that, along with generating economic value, these celebrated indian brands also drive the creation of larger societal capital through the virtuous cycle of sustainable and inclusive growth. di wills * ; love delightfully soft skin? aia ans source : https : / / www. industrydocuments. ucsf. edu / docs / snbx0223 [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]" # fmt: skip decoding = processor.decode(input_processor.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) @@ -408,9 +404,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase): # verify input_ids # this was obtained with Tesseract 4.1.1 - # fmt: off - expected_decoding = "[CLS] what's his name? [SEP] 11 : 14 to 11 : 39 a. m 11 : 39 to 11 : 44 a. m. 11 : 44 a. m. to 12 : 25 p. m. 12 : 25 to 12 : 58 p. m. 12 : 58 to 4 : 00 p. m. 2 : 00 to 5 : 00 p. m. coffee break coffee will be served for men and women in the lobby adjacent to exhibit area. please move into exhibit area. ( exhibits open ) trrf general session ( part | ) presiding : lee a. waller trrf vice president “ introductory remarks ” lee a. waller, trrf vice presi - dent individual interviews with trrf public board members and sci - entific advisory council mem - bers conducted by trrf treasurer philip g. kuehn to get answers which the public refrigerated warehousing industry is looking for. plus questions from the floor. dr. emil m. mrak, university of cal - ifornia, chairman, trrf board ; sam r. cecil, university of georgia college of agriculture ; dr. stanley charm, tufts university school of medicine ; dr. robert h. cotton, itt continental baking company ; dr. owen fennema, university of wis - consin ; dr. robert e. hardenburg, usda. questions and answers exhibits open capt. jack stoney room trrf scientific advisory council meeting ballroom foyer [SEP]" # noqa: E231 - # fmt: on + expected_decoding = "[CLS] what's his name? [SEP] 11 : 14 to 11 : 39 a. m 11 : 39 to 11 : 44 a. m. 11 : 44 a. m. to 12 : 25 p. m. 12 : 25 to 12 : 58 p. m. 12 : 58 to 4 : 00 p. m. 2 : 00 to 5 : 00 p. m. coffee break coffee will be served for men and women in the lobby adjacent to exhibit area. please move into exhibit area. ( exhibits open ) trrf general session ( part | ) presiding : lee a. waller trrf vice president “ introductory remarks ” lee a. waller, trrf vice presi - dent individual interviews with trrf public board members and sci - entific advisory council mem - bers conducted by trrf treasurer philip g. kuehn to get answers which the public refrigerated warehousing industry is looking for. plus questions from the floor. dr. emil m. mrak, university of cal - ifornia, chairman, trrf board ; sam r. cecil, university of georgia college of agriculture ; dr. stanley charm, tufts university school of medicine ; dr. robert h. cotton, itt continental baking company ; dr. owen fennema, university of wis - consin ; dr. robert e. hardenburg, usda. questions and answers exhibits open capt. jack stoney room trrf scientific advisory council meeting ballroom foyer [SEP]" # fmt: skip decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) @@ -432,9 +426,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase): self.assertSequenceEqual(decoding, expected_decoding) # verify bbox - # fmt: off - expected_bbox = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [0, 45, 67, 80], [72, 56, 109, 67], [72, 56, 109, 67], [116, 56, 189, 67], [198, 59, 253, 66], [257, 59, 285, 66], [289, 59, 365, 66], [372, 59, 407, 66], [74, 136, 161, 158], [74, 136, 161, 158], [74, 136, 161, 158], [74, 136, 161, 158], [1000, 1000, 1000, 1000]] # noqa: E231 - # fmt: on + expected_bbox = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [0, 45, 67, 80], [72, 56, 109, 67], [72, 56, 109, 67], [116, 56, 189, 67], [198, 59, 253, 66], [257, 59, 285, 66], [289, 59, 365, 66], [372, 59, 407, 66], [74, 136, 161, 158], [74, 136, 161, 158], [74, 136, 161, 158], [74, 136, 161, 158], [1000, 1000, 1000, 1000]] # fmt: skip self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox) @slow diff --git a/tests/models/layoutlmv2/test_tokenization_layoutlmv2.py b/tests/models/layoutlmv2/test_tokenization_layoutlmv2.py index ca90eb9641e..3360933be67 100644 --- a/tests/models/layoutlmv2/test_tokenization_layoutlmv2.py +++ b/tests/models/layoutlmv2/test_tokenization_layoutlmv2.py @@ -2412,9 +2412,7 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase): # CASE 1: not batched words, boxes = self.get_words_and_boxes() - # fmt: off - expected_results = {'input_ids': [101, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [101, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, padding="max_length", max_length=20) encoding_r = tokenizer_r(words, boxes=boxes, padding="max_length", max_length=20) @@ -2424,9 +2422,7 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase): # CASE 1: batched words, boxes = self.get_words_and_boxes_batch() - # fmt: off - expected_results = {'input_ids': [[101, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 7592, 2026, 2171, 2003, 3960, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [961, 885, 992, 912], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [34, 42, 66, 69], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [[101, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 7592, 2026, 2171, 2003, 3960, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [961, 885, 992, 912], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [34, 42, 66, 69], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, padding="max_length", max_length=20) encoding_r = tokenizer_r(words, boxes=boxes, padding="max_length", max_length=20) @@ -2437,9 +2433,7 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase): words, boxes = self.get_words_and_boxes() word_labels = [1, 2, 3] - # fmt: off - expected_results = {'input_ids': [101, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'labels': [-100, 1, 2, -100, 3, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], 'attention_mask': [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [101, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'labels': [-100, 1, 2, -100, 3, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], 'attention_mask': [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) encoding_r = tokenizer_r(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) @@ -2450,9 +2444,7 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase): words, boxes = self.get_words_and_boxes_batch() word_labels = [[1, 2, 3], [2, 46, 17, 22, 3]] - # fmt: off - expected_results = {'input_ids': [[101, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 7592, 2026, 2171, 2003, 3960, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [961, 885, 992, 912], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [34, 42, 66, 69], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'labels': [[-100, 1, 2, -100, 3, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], [-100, 2, 46, 17, 22, 3, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [[101, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 7592, 2026, 2171, 2003, 3960, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [961, 885, 992, 912], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [34, 42, 66, 69], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'labels': [[-100, 1, 2, -100, 3, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], [-100, 2, 46, 17, 22, 3, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) encoding_r = tokenizer_r(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) @@ -2462,9 +2454,7 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase): # CASE 3: not batched question, words, boxes = self.get_question_words_and_boxes() - # fmt: off - expected_results = {'input_ids': [101, 2054, 1005, 1055, 2010, 2171, 1029, 102, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0], 'bbox': [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [101, 2054, 1005, 1055, 2010, 2171, 1029, 102, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0], 'bbox': [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(question, words, boxes, padding="max_length", max_length=20) encoding_r = tokenizer_r(question, words, boxes, padding="max_length", max_length=20) @@ -2474,9 +2464,7 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase): # CASE 3: batched questions, words, boxes = self.get_question_words_and_boxes_batch() - # fmt: off - expected_results = {'input_ids': [[101, 2054, 1005, 1055, 2010, 2171, 1029, 102, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0], [101, 2129, 2003, 2002, 2170, 1029, 102, 2054, 1037, 21110, 2546, 3806, 2102, 2078, 102, 0, 0, 0, 0, 0]], 'bbox': [[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [336, 42, 353, 57], [34, 42, 66, 69], [34, 42, 66, 69], [34, 42, 66, 69], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [[101, 2054, 1005, 1055, 2010, 2171, 1029, 102, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0], [101, 2129, 2003, 2002, 2170, 1029, 102, 2054, 1037, 21110, 2546, 3806, 2102, 2078, 102, 0, 0, 0, 0, 0]], 'bbox': [[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [336, 42, 353, 57], [34, 42, 66, 69], [34, 42, 66, 69], [34, 42, 66, 69], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(questions, words, boxes, padding="max_length", max_length=20) encoding_r = tokenizer_r(questions, words, boxes, padding="max_length", max_length=20) diff --git a/tests/models/layoutlmv3/test_processor_layoutlmv3.py b/tests/models/layoutlmv3/test_processor_layoutlmv3.py index f649e0c275a..0c7e0d666d8 100644 --- a/tests/models/layoutlmv3/test_processor_layoutlmv3.py +++ b/tests/models/layoutlmv3/test_processor_layoutlmv3.py @@ -223,9 +223,7 @@ class LayoutLMv3ProcessorIntegrationTests(unittest.TestCase): # verify input_ids # this was obtained with Tesseract 4.1.1 - # fmt: off - expected_decoding = " 11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer" # noqa: E231 - # fmt: on + expected_decoding = " 11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer" # fmt: skip decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) @@ -245,9 +243,7 @@ class LayoutLMv3ProcessorIntegrationTests(unittest.TestCase): # verify input_ids # this was obtained with Tesseract 4.1.1 - # fmt: off - expected_decoding = " 7 ITC Limited REPORT AND ACCOUNTS 2013 ITC’s Brands: An Asset for the Nation The consumer needs and aspirations they fulfil, the benefit they generate for millions across ITC’s value chains, the future-ready capabilities that support them, and the value that they create for the country, have made ITC’s brands national assets, adding to India’s competitiveness. It is ITC’s aspiration to be the No 1 FMCG player in the country, driven by its new FMCG businesses. A recent Nielsen report has highlighted that ITC's new FMCG businesses are the fastest growing among the top consumer goods companies operating in India. ITC takes justifiable pride that, along with generating economic value, these celebrated Indian brands also drive the creation of larger societal capital through the virtuous cycle of sustainable and inclusive growth. DI WILLS * ; LOVE DELIGHTFULLY SOFT SKIN? aia Ans Source: https://www.industrydocuments.ucsf.edu/docs/snbx0223" # noqa: E231 - # fmt: on + expected_decoding = " 7 ITC Limited REPORT AND ACCOUNTS 2013 ITC’s Brands: An Asset for the Nation The consumer needs and aspirations they fulfil, the benefit they generate for millions across ITC’s value chains, the future-ready capabilities that support them, and the value that they create for the country, have made ITC’s brands national assets, adding to India’s competitiveness. It is ITC’s aspiration to be the No 1 FMCG player in the country, driven by its new FMCG businesses. A recent Nielsen report has highlighted that ITC's new FMCG businesses are the fastest growing among the top consumer goods companies operating in India. ITC takes justifiable pride that, along with generating economic value, these celebrated Indian brands also drive the creation of larger societal capital through the virtuous cycle of sustainable and inclusive growth. DI WILLS * ; LOVE DELIGHTFULLY SOFT SKIN? aia Ans Source: https://www.industrydocuments.ucsf.edu/docs/snbx0223" # fmt: skip decoding = processor.decode(input_processor.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) @@ -392,9 +388,7 @@ class LayoutLMv3ProcessorIntegrationTests(unittest.TestCase): # verify input_ids # this was obtained with Tesseract 4.1.1 - # fmt: off - expected_decoding = " What's his name? 11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer" # noqa: E231 - # fmt: on + expected_decoding = " What's his name? 11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer" # fmt: skip decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) @@ -416,9 +410,7 @@ class LayoutLMv3ProcessorIntegrationTests(unittest.TestCase): self.assertSequenceEqual(decoding, expected_decoding) # verify bbox - # fmt: off - expected_bbox = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 45, 67, 80], [72, 56, 109, 67], [72, 56, 109, 67], [116, 56, 189, 67], [198, 59, 253, 66], [257, 59, 285, 66], [289, 59, 365, 66], [289, 59, 365, 66], [289, 59, 365, 66], [372, 59, 407, 66], [74, 136, 161, 158], [74, 136, 161, 158], [0, 0, 0, 0]] # noqa: E231 - # fmt: on + expected_bbox = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 45, 67, 80], [72, 56, 109, 67], [72, 56, 109, 67], [116, 56, 189, 67], [198, 59, 253, 66], [257, 59, 285, 66], [289, 59, 365, 66], [289, 59, 365, 66], [289, 59, 365, 66], [372, 59, 407, 66], [74, 136, 161, 158], [74, 136, 161, 158], [0, 0, 0, 0]] # fmt: skip self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox) @slow diff --git a/tests/models/layoutlmv3/test_tokenization_layoutlmv3.py b/tests/models/layoutlmv3/test_tokenization_layoutlmv3.py index 1e2bb6610e3..db02dc65d65 100644 --- a/tests/models/layoutlmv3/test_tokenization_layoutlmv3.py +++ b/tests/models/layoutlmv3/test_tokenization_layoutlmv3.py @@ -2329,9 +2329,7 @@ class LayoutLMv3TokenizationTest(TokenizerTesterMixin, unittest.TestCase): # CASE 1: not batched words, boxes = self.get_words_and_boxes() - # fmt: off - expected_results = {'input_ids': [0, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [0, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, padding="max_length", max_length=20) encoding_r = tokenizer_r(words, boxes=boxes, padding="max_length", max_length=20) @@ -2341,9 +2339,7 @@ class LayoutLMv3TokenizationTest(TokenizerTesterMixin, unittest.TestCase): # CASE 1: batched words, boxes = self.get_words_and_boxes_batch() - # fmt: off - expected_results = {'input_ids': [[0, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 92, 614, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [961, 885, 992, 912], [256, 38, 330, 58], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'attention_mask': [[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [[0, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 92, 614, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [961, 885, 992, 912], [256, 38, 330, 58], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'attention_mask': [[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, padding="max_length", max_length=20) encoding_r = tokenizer_r(words, boxes=boxes, padding="max_length", max_length=20) @@ -2354,9 +2350,7 @@ class LayoutLMv3TokenizationTest(TokenizerTesterMixin, unittest.TestCase): words, boxes = self.get_words_and_boxes() word_labels = [1, 2] - # fmt: off - expected_results = {'input_ids': [0, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'labels': [-100, 1, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [0, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'labels': [-100, 1, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) encoding_r = tokenizer_r(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) @@ -2367,9 +2361,7 @@ class LayoutLMv3TokenizationTest(TokenizerTesterMixin, unittest.TestCase): words, boxes = self.get_words_and_boxes_batch() word_labels = [[1, 2], [2, 46]] - # fmt: off - expected_results = {'input_ids': [[0, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 92, 614, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [961, 885, 992, 912], [256, 38, 330, 58], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'labels': [[-100, 1, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], [-100, 2, 46, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]], 'attention_mask': [[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [[0, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 92, 614, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [961, 885, 992, 912], [256, 38, 330, 58], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'labels': [[-100, 1, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], [-100, 2, 46, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]], 'attention_mask': [[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) encoding_r = tokenizer_r(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) @@ -2379,9 +2371,7 @@ class LayoutLMv3TokenizationTest(TokenizerTesterMixin, unittest.TestCase): # # CASE 3: not batched question, words, boxes = self.get_question_words_and_boxes() - # fmt: off - expected_results = {'input_ids': [0, 99, 18, 39, 766, 116, 2, 2, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [0, 99, 18, 39, 766, 116, 2, 2, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(question, words, boxes, padding="max_length", max_length=20) encoding_r = tokenizer_r(question, words, boxes, padding="max_length", max_length=20) @@ -2391,9 +2381,7 @@ class LayoutLMv3TokenizationTest(TokenizerTesterMixin, unittest.TestCase): # # CASE 3: batched questions, words, boxes = self.get_question_words_and_boxes_batch() - # fmt: off - expected_results = {'input_ids': [[0, 99, 18, 39, 766, 116, 2, 2, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 141, 16, 37, 373, 116, 2, 2, 13964, 795, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [256, 38, 330, 58], [256, 38, 330, 58], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [[0, 99, 18, 39, 766, 116, 2, 2, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 141, 16, 37, 373, 116, 2, 2, 13964, 795, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [256, 38, 330, 58], [256, 38, 330, 58], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(questions, words, boxes, padding="max_length", max_length=20) encoding_r = tokenizer_r(questions, words, boxes, padding="max_length", max_length=20) diff --git a/tests/models/layoutxlm/test_processor_layoutxlm.py b/tests/models/layoutxlm/test_processor_layoutxlm.py index 57c349b26b7..240c2ae05c2 100644 --- a/tests/models/layoutxlm/test_processor_layoutxlm.py +++ b/tests/models/layoutxlm/test_processor_layoutxlm.py @@ -238,9 +238,7 @@ class LayoutXLMProcessorIntegrationTests(unittest.TestCase): # verify input_ids # this was obtained with Tesseract 4.1.1 - # fmt: off - expected_decoding = " 11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer" # noqa: E231 - # fmt: on + expected_decoding = " 11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer" # fmt: skip decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) @@ -260,9 +258,7 @@ class LayoutXLMProcessorIntegrationTests(unittest.TestCase): # verify input_ids # this was obtained with Tesseract 4.1.1 - # fmt: off - expected_decoding = " 7 ITC Limited REPORT AND ACCOUNTS 2013 ITC’s Brands: An Asset for the Nation The consumer needs and aspirations they fulfil, the benefit they generate for millions across ITC’s value chains, the future-ready capabilities that support them, and the value that they create for the country, have made ITC’s brands national assets, adding to India’s competitiveness. It is ITC’s aspiration to be the No 1 FMCG player in the country, driven by its new FMCG businesses. A recent Nielsen report has highlighted that ITC's new FMCG businesses are the fastest growing among the top consumer goods companies operating in India. ITC takes justifiable pride that, along with generating economic value, these celebrated Indian brands also drive the creation of larger societal capital through the virtuous cycle of sustainable and inclusive growth. DI WILLS * ; LOVE DELIGHTFULLY SOFT SKIN? aia Ans Source: https://www.industrydocuments.ucsf.edu/docs/snbx0223" # noqa: E231 - # fmt: on + expected_decoding = " 7 ITC Limited REPORT AND ACCOUNTS 2013 ITC’s Brands: An Asset for the Nation The consumer needs and aspirations they fulfil, the benefit they generate for millions across ITC’s value chains, the future-ready capabilities that support them, and the value that they create for the country, have made ITC’s brands national assets, adding to India’s competitiveness. It is ITC’s aspiration to be the No 1 FMCG player in the country, driven by its new FMCG businesses. A recent Nielsen report has highlighted that ITC's new FMCG businesses are the fastest growing among the top consumer goods companies operating in India. ITC takes justifiable pride that, along with generating economic value, these celebrated Indian brands also drive the creation of larger societal capital through the virtuous cycle of sustainable and inclusive growth. DI WILLS * ; LOVE DELIGHTFULLY SOFT SKIN? aia Ans Source: https://www.industrydocuments.ucsf.edu/docs/snbx0223" # fmt: skip decoding = processor.decode(input_processor.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) @@ -407,9 +403,7 @@ class LayoutXLMProcessorIntegrationTests(unittest.TestCase): # verify input_ids # this was obtained with Tesseract 4.1.1 - # fmt: off - expected_decoding = " What's his name? 11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer" # noqa: E231 - # fmt: on + expected_decoding = " What's his name? 11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer" # fmt: skip decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) @@ -431,9 +425,7 @@ class LayoutXLMProcessorIntegrationTests(unittest.TestCase): self.assertSequenceEqual(decoding, expected_decoding) # verify bbox - # fmt: off - expected_bbox = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [1000, 1000, 1000, 1000], [0, 45, 67, 80], [72, 56, 109, 67], [72, 56, 109, 67], [116, 56, 189, 67], [198, 59, 253, 66], [257, 59, 285, 66], [289, 59, 365, 66], [289, 59, 365, 66], [289, 59, 365, 66], [289, 59, 365, 66], [372, 59, 407, 66], [1000, 1000, 1000, 1000]] # noqa: E231 - # fmt: on + expected_bbox = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [1000, 1000, 1000, 1000], [0, 45, 67, 80], [72, 56, 109, 67], [72, 56, 109, 67], [116, 56, 189, 67], [198, 59, 253, 66], [257, 59, 285, 66], [289, 59, 365, 66], [289, 59, 365, 66], [289, 59, 365, 66], [289, 59, 365, 66], [372, 59, 407, 66], [1000, 1000, 1000, 1000]] # fmt: skip self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox) @slow diff --git a/tests/models/layoutxlm/test_tokenization_layoutxlm.py b/tests/models/layoutxlm/test_tokenization_layoutxlm.py index 0b502748d13..086bbc6ba0b 100644 --- a/tests/models/layoutxlm/test_tokenization_layoutxlm.py +++ b/tests/models/layoutxlm/test_tokenization_layoutxlm.py @@ -1876,9 +1876,7 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): # CASE 1: not batched words, boxes = self.get_words_and_boxes() - # fmt: off - expected_results = {'input_ids': [0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'attention_mask': [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'attention_mask': [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, padding="max_length", max_length=20) encoding_r = tokenizer_r(words, boxes=boxes, padding="max_length", max_length=20) @@ -1888,9 +1886,7 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): # CASE 1: batched words, boxes = self.get_words_and_boxes_batch() - # fmt: off - expected_results = {'input_ids': [[0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 33600, 31, 759, 9351, 83, 21895, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [961, 885, 992, 912], [961, 885, 992, 912], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [34, 42, 66, 69], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [[0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 33600, 31, 759, 9351, 83, 21895, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [961, 885, 992, 912], [961, 885, 992, 912], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [34, 42, 66, 69], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, padding="max_length", max_length=20) encoding_r = tokenizer_r(words, boxes=boxes, padding="max_length", max_length=20) @@ -1901,9 +1897,7 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): words, boxes = self.get_words_and_boxes() word_labels = [1, 2, 3] - # fmt: off - expected_results = {'input_ids': [0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'labels': [-100, 1, 2, -100, 3, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], 'attention_mask': [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'labels': [-100, 1, 2, -100, 3, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], 'attention_mask': [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) encoding_r = tokenizer_r(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) @@ -1914,9 +1908,7 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): words, boxes = self.get_words_and_boxes_batch() word_labels = [[1, 2, 3], [2, 46, 17, 22, 3]] - # fmt: off - expected_results = {'input_ids': [[0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 33600, 31, 759, 9351, 83, 21895, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [961, 885, 992, 912], [961, 885, 992, 912], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [34, 42, 66, 69], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'labels': [[-100, 1, 2, -100, 3, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], [-100, 2, -100, 46, 17, 22, 3, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [[0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 33600, 31, 759, 9351, 83, 21895, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [961, 885, 992, 912], [961, 885, 992, 912], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [34, 42, 66, 69], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], 'labels': [[-100, 1, 2, -100, 3, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], [-100, 2, -100, 46, 17, 22, 3, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) encoding_r = tokenizer_r(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) @@ -1926,9 +1918,7 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): # CASE 3: not batched question, words, boxes = self.get_question_words_and_boxes() - # fmt: off - expected_results = {'input_ids': [0, 2367, 25, 7, 1919, 9351, 32, 2, 2, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], 'bbox': [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [1000, 1000, 1000, 1000], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]} # noqa: E231 - # fmt: on + expected_results = {'input_ids': [0, 2367, 25, 7, 1919, 9351, 32, 2, 2, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], 'bbox': [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [1000, 1000, 1000, 1000], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(question, words, boxes, padding="max_length", max_length=20) encoding_r = tokenizer_r(question, words, boxes, padding="max_length", max_length=20) @@ -1938,10 +1928,7 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): # CASE 3: batched questions, words, boxes = self.get_question_words_and_boxes_batch() - # fmt: off - expected_results = {'input_ids': [[0, 2367, 25, 7, 1919, 9351, 32, 2, 2, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1], [0, 3642, 83, 764, 35839, 32, 2, 2, 2367, 10, 21, 3190, 53496, 19, 2, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]], 'bbox': [[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [1000, 1000, 1000, 1000], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [1000, 1000, 1000, 1000], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [336, 42, 353, 57], [34, 42, 66, 69], [34, 42, 66, 69], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]} # noqa: E231 - # fmt: on - + expected_results = {'input_ids': [[0, 2367, 25, 7, 1919, 9351, 32, 2, 2, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1], [0, 3642, 83, 764, 35839, 32, 2, 2, 2367, 10, 21, 3190, 53496, 19, 2, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]], 'bbox': [[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [1000, 1000, 1000, 1000], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [1000, 1000, 1000, 1000], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [336, 42, 353, 57], [34, 42, 66, 69], [34, 42, 66, 69], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]} # fmt: skip encoding_p = tokenizer_p(questions, words, boxes, padding="max_length", max_length=20) encoding_r = tokenizer_r(questions, words, boxes, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_p), expected_results) diff --git a/tests/models/llama/test_modeling_llama.py b/tests/models/llama/test_modeling_llama.py index a646e5ab7a5..21fb4f44d2b 100644 --- a/tests/models/llama/test_modeling_llama.py +++ b/tests/models/llama/test_modeling_llama.py @@ -431,9 +431,7 @@ class LlamaIntegrationTest(unittest.TestCase): EXPECTED_MEAN = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]) torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2) # slicing logits[0, 0, 0:30] - # fmt: off - EXPECTED_SLICE = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]) - # fmt: on + EXPECTED_SLICE = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]) # fmt: skip torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5) @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!") @@ -446,9 +444,7 @@ class LlamaIntegrationTest(unittest.TestCase): EXPECTED_MEAN = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]) torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2) # slicing logits[0, 0, 0:30] - # fmt: off - EXPECTED_SLICE = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]) - # fmt: on + EXPECTED_SLICE = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]) # fmt: skip torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5) @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!") @@ -461,9 +457,7 @@ class LlamaIntegrationTest(unittest.TestCase): EXPECTED_MEAN = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]) torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2) # slicing logits[0, 0, 0:30] - # fmt: off - EXPECTED_SLICE = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]) - # fmt: on + EXPECTED_SLICE = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]) # fmt: skip torch.testing.assert_close(out.mean(-1), EXPECTED_SLICE, atol=1e-2, rtol=1e-2) @unittest.skip( @@ -479,9 +473,7 @@ class LlamaIntegrationTest(unittest.TestCase): [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]], dtype=torch.float32 ) torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2) - # fmt: off - EXPECTED_SLICE = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]) - # fmt: on + EXPECTED_SLICE = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]) # fmt: skip torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5) @unittest.skip("Model is curently gated") diff --git a/tests/models/llama/test_tokenization_llama.py b/tests/models/llama/test_tokenization_llama.py index e45cf253e68..d77e56ed7d6 100644 --- a/tests/models/llama/test_tokenization_llama.py +++ b/tests/models/llama/test_tokenization_llama.py @@ -282,9 +282,7 @@ class LlamaTokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[1, 4103, 689, 414, 313, 24784, 368, 2998, 408, 282, 3637, 25350, 29899, 9067, 414, 322, 282, 3637, 25350, 29899, 1457, 3018, 1312, 29899, 2151, 29897, 8128, 2498, 29899, 15503, 4220, 6956, 1973, 313, 13635, 29911, 29892, 402, 7982, 29899, 29906, 29892, 1528, 13635, 29911, 29874, 29892, 1060, 26369, 29892, 6652, 309, 29933, 814, 29892, 1060, 29931, 6779, 11410, 363, 18385, 17088, 7634, 11235, 313, 25103, 29965, 29897, 322, 18385, 17088, 28203, 313, 25103, 29954, 29897, 411, 975, 29871, 29941, 29906, 29974, 758, 3018, 1312, 4733, 297, 29871, 29896, 29900, 29900, 29974, 10276, 322, 6483, 1006, 3372, 3097, 1546, 435, 1165, 29892, 10772, 29911, 25350, 322, 323, 6073, 17907, 29889], [1, 350, 20161, 338, 8688, 304, 758, 29899, 14968, 6483, 21000, 8684, 284, 22540, 515, 443, 29880, 24025, 1426, 491, 14002, 368, 4195, 292, 373, 1716, 2175, 322, 1492, 3030, 297, 599, 15359, 29889], [1, 450, 4996, 17354, 1701, 29916, 432, 17204, 975, 278, 17366, 11203, 29889]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} - # fmt: on + expected_encoding = {'input_ids': [[1, 4103, 689, 414, 313, 24784, 368, 2998, 408, 282, 3637, 25350, 29899, 9067, 414, 322, 282, 3637, 25350, 29899, 1457, 3018, 1312, 29899, 2151, 29897, 8128, 2498, 29899, 15503, 4220, 6956, 1973, 313, 13635, 29911, 29892, 402, 7982, 29899, 29906, 29892, 1528, 13635, 29911, 29874, 29892, 1060, 26369, 29892, 6652, 309, 29933, 814, 29892, 1060, 29931, 6779, 11410, 363, 18385, 17088, 7634, 11235, 313, 25103, 29965, 29897, 322, 18385, 17088, 28203, 313, 25103, 29954, 29897, 411, 975, 29871, 29941, 29906, 29974, 758, 3018, 1312, 4733, 297, 29871, 29896, 29900, 29900, 29974, 10276, 322, 6483, 1006, 3372, 3097, 1546, 435, 1165, 29892, 10772, 29911, 25350, 322, 323, 6073, 17907, 29889], [1, 350, 20161, 338, 8688, 304, 758, 29899, 14968, 6483, 21000, 8684, 284, 22540, 515, 443, 29880, 24025, 1426, 491, 14002, 368, 4195, 292, 373, 1716, 2175, 322, 1492, 3030, 297, 599, 15359, 29889], [1, 450, 4996, 17354, 1701, 29916, 432, 17204, 975, 278, 17366, 11203, 29889]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, @@ -402,8 +400,8 @@ class LlamaIntegrationTest(unittest.TestCase): self.assertEqual(rust_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test") # bytefallback showcase - self.assertEqual(pyth_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392]) - self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392]) + self.assertEqual(pyth_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392]) # fmt: skip + self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392]) # fmt: skip self.assertEqual( pyth_tokenizer.decode( [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True diff --git a/tests/models/lxmert/test_modeling_lxmert.py b/tests/models/lxmert/test_modeling_lxmert.py index 489d22d92ef..63d83de36b7 100644 --- a/tests/models/lxmert/test_modeling_lxmert.py +++ b/tests/models/lxmert/test_modeling_lxmert.py @@ -751,7 +751,7 @@ class LxmertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): tf_inputs_dict[key] = self.prepare_pt_inputs_from_tf_inputs(value) elif isinstance(value, (list, tuple)): tf_inputs_dict[key] = (self.prepare_pt_inputs_from_tf_inputs(iter_value) for iter_value in value) - elif type(value) == bool: + elif isinstance(value, bool): tf_inputs_dict[key] = value elif key == "input_values": tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.float32) diff --git a/tests/models/lxmert/test_modeling_tf_lxmert.py b/tests/models/lxmert/test_modeling_tf_lxmert.py index 52f64d61442..d40ffbb3b96 100644 --- a/tests/models/lxmert/test_modeling_tf_lxmert.py +++ b/tests/models/lxmert/test_modeling_tf_lxmert.py @@ -499,7 +499,7 @@ class TFLxmertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCa pt_inputs_dict[key] = self.prepare_pt_inputs_from_tf_inputs(value) elif isinstance(value, (list, tuple)): pt_inputs_dict[key] = (self.prepare_pt_inputs_from_tf_inputs(iter_value) for iter_value in value) - elif type(key) == bool: + elif isinstance(key, bool): pt_inputs_dict[key] = value elif key == "input_values": pt_inputs_dict[key] = torch.from_numpy(value.numpy()).to(torch.float32) diff --git a/tests/models/m2m_100/test_tokenization_m2m_100.py b/tests/models/m2m_100/test_tokenization_m2m_100.py index 13345a899f6..50087a7d9d2 100644 --- a/tests/models/m2m_100/test_tokenization_m2m_100.py +++ b/tests/models/m2m_100/test_tokenization_m2m_100.py @@ -116,9 +116,7 @@ class M2M100TokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, @@ -141,9 +139,7 @@ class M2M100TokenizerIntegrationTest(unittest.TestCase): "L'affaire NSA souligne l'absence totale de débat sur le renseignement", ] - # fmt: off - expected_src_tokens = [EN_CODE, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2] - # fmt: on + expected_src_tokens = [EN_CODE, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2] # fmt: skip @classmethod def setUpClass(cls): @@ -172,9 +168,7 @@ class M2M100TokenizerIntegrationTest(unittest.TestCase): def test_tokenizer_decode_ignores_language_codes(self): self.assertIn(FR_CODE, self.tokenizer.all_special_ids) - # fmt: off - generated_ids = [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2] - # fmt: on + generated_ids = [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2] # fmt: skip result = self.tokenizer.decode(generated_ids, skip_special_tokens=True) expected_french = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True) self.assertEqual(result, expected_french) diff --git a/tests/models/marian/test_tokenization_marian.py b/tests/models/marian/test_tokenization_marian.py index f32026be1a0..6fb3c9a85d0 100644 --- a/tests/models/marian/test_tokenization_marian.py +++ b/tests/models/marian/test_tokenization_marian.py @@ -121,10 +121,7 @@ class MarianTokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 - # fmt: on - + expected_encoding = {'input_ids': [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="Helsinki-NLP/opus-mt-en-de", diff --git a/tests/models/markuplm/test_processor_markuplm.py b/tests/models/markuplm/test_processor_markuplm.py index 3959b231ff7..aa5cb6253fb 100644 --- a/tests/models/markuplm/test_processor_markuplm.py +++ b/tests/models/markuplm/test_processor_markuplm.py @@ -46,9 +46,7 @@ class MarkupLMProcessorTest(unittest.TestCase): def setUp(self): # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt - # fmt: off - vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "\u0120hello", "\u0120world", "",] # noqa - # fmt: on + vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "\u0120hello", "\u0120world", "",] # fmt: skip self.tmpdirname = tempfile.mkdtemp() vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] @@ -339,15 +337,11 @@ class MarkupLMProcessorIntegrationTests(unittest.TestCase): self.assertSequenceEqual(inputs.input_ids[1].tolist(), expected_ids) # verify xpath_tags_seq - # fmt: off - expected_xpaths_tags_seq = [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]] # noqa: - # fmt: on + expected_xpaths_tags_seq = [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]] # fmt: skip self.assertSequenceEqual(inputs.xpath_tags_seq[1].tolist(), expected_xpaths_tags_seq) # verify labels - # fmt: off - expected_labels = [-100, 6, 3, 10, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100] - # fmt: on + expected_labels = [-100, 6, 3, 10, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100] # fmt: skip self.assertListEqual(inputs.labels[1].tolist(), expected_labels) @slow @@ -371,9 +365,7 @@ class MarkupLMProcessorIntegrationTests(unittest.TestCase): self.assertListEqual(actual_keys, expected_keys) # verify input_ids - # fmt: off - expected_decoding = "What's his name?Hello worldWelcomeHere is my website." # noqa: E231 - # fmt: on + expected_decoding = "What's his name?Hello worldWelcomeHere is my website." # fmt: skip decoding = processor.decode(inputs.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) @@ -401,9 +393,7 @@ class MarkupLMProcessorIntegrationTests(unittest.TestCase): self.assertSequenceEqual(decoding, expected_decoding) # verify xpath_subs_seq - # fmt: off - expected_xpath_subs_seq = [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]] - # fmt: on + expected_xpath_subs_seq = [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]] # fmt: skip self.assertListEqual(inputs.xpath_subs_seq[1].tolist(), expected_xpath_subs_seq) @slow @@ -457,7 +447,5 @@ class MarkupLMProcessorIntegrationTests(unittest.TestCase): self.assertSequenceEqual(decoding, expected_decoding) # verify xpath_subs_seq - # fmt: off - expected_xpath_subs_seq = [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]] - # fmt: on + expected_xpath_subs_seq = [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]] # fmt: skip self.assertListEqual(inputs.xpath_subs_seq[1].tolist()[-5:], expected_xpath_subs_seq) diff --git a/tests/models/markuplm/test_tokenization_markuplm.py b/tests/models/markuplm/test_tokenization_markuplm.py index d795aa2b2b9..9d2af513e1a 100644 --- a/tests/models/markuplm/test_tokenization_markuplm.py +++ b/tests/models/markuplm/test_tokenization_markuplm.py @@ -51,9 +51,7 @@ class MarkupLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt - # fmt: off - vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "\u0120hello", "\u0120world", "",] # noqa - # fmt: on + vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "\u0120hello", "\u0120world", "",] # fmt: skip vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] self.tags_dict = {"a": 0, "abbr": 1, "acronym": 2, "address": 3} @@ -2202,8 +2200,7 @@ class MarkupLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): # CASE 1: not batched nodes, xpaths = self.get_nodes_and_xpaths() - # fmt: off - expected_results = {'input_ids': [0, 42891, 8331, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 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1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: on + expected_results = {'input_ids': [0, 42891, 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1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(nodes, xpaths=xpaths, padding="max_length", max_length=20) encoding_r = tokenizer_r(nodes, xpaths=xpaths, padding="max_length", max_length=20) @@ -2213,9 +2210,7 @@ class MarkupLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): # CASE 1: batched nodes, xpaths = self.get_nodes_and_xpaths_batch() - # fmt: off - expected_results = {'input_ids': [[0, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 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1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} - # fmt: on + expected_results = {'input_ids': [[0, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 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1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(nodes, xpaths=xpaths, padding="max_length", max_length=20) encoding_r = tokenizer_r(nodes, xpaths=xpaths, padding="max_length", max_length=20) @@ -2226,9 +2221,7 @@ class MarkupLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): nodes, xpaths = self.get_nodes_and_xpaths() node_labels = [1, 2, 3] - # fmt: off - expected_results = {'input_ids': [0, 42891, 8331, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'labels': [-100, 1, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} - # fmt: on + expected_results = {'input_ids': [0, 42891, 8331, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 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1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'labels': [-100, 1, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20) encoding_r = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20) @@ -2239,8 +2232,7 @@ class MarkupLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): nodes, xpaths = self.get_nodes_and_xpaths_batch() node_labels = [[1, 2, 3], [2, 46, 17, 22, 3]] - # fmt: off - expected_results = {'input_ids': [[0, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'labels': [[-100, 1, -100, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], [-100, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]], 'attention_mask': [[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: on + expected_results = {'input_ids': [[0, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 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1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'labels': [[-100, 1, -100, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], [-100, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]], 'attention_mask': [[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20) encoding_r = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20) @@ -2250,9 +2242,7 @@ class MarkupLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): # CASE 3: not batched question, nodes, xpaths = self.get_question_nodes_and_xpaths() - # fmt: off - expected_results = {'input_ids': [0, 12196, 18, 39, 766, 116, 2, 42891, 232, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 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1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} - # fmt: on + expected_results = {'input_ids': [0, 12196, 18, 39, 766, 116, 2, 42891, 232, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 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1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(question, nodes, xpaths, padding="max_length", max_length=20) encoding_r = tokenizer_r(question, nodes, xpaths, padding="max_length", max_length=20) @@ -2262,9 +2252,7 @@ class MarkupLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): # CASE 3: batched questions, nodes, xpaths = self.get_question_nodes_and_xpaths_batch() - # fmt: off - expected_results = {'input_ids': [[0, 12196, 18, 39, 766, 116, 2, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 9178, 16, 37, 373, 116, 2, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]]], 'xpath_subs_seq': [[[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 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1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]]} - # fmt: on + expected_results = {'input_ids': [[0, 12196, 18, 39, 766, 116, 2, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 9178, 16, 37, 373, 116, 2, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 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1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(questions, nodes, xpaths, padding="max_length", max_length=20) encoding_r = tokenizer_r(questions, nodes, xpaths, padding="max_length", max_length=20) diff --git a/tests/models/mask2former/test_image_processing_mask2former.py b/tests/models/mask2former/test_image_processing_mask2former.py index b3fe50164e5..24d5b8cf89e 100644 --- a/tests/models/mask2former/test_image_processing_mask2former.py +++ b/tests/models/mask2former/test_image_processing_mask2former.py @@ -391,13 +391,9 @@ class Mask2FormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase # verify the class labels self.assertEqual(len(inputs["class_labels"]), 2) - # fmt: off - expected_class_labels = torch.tensor([4, 17, 32, 42, 42, 42, 42, 42, 42, 42, 32, 12, 12, 12, 12, 12, 42, 42, 12, 12, 12, 42, 12, 12, 12, 12, 12, 3, 12, 12, 12, 12, 42, 42, 42, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 5, 12, 12, 12, 12, 12, 12, 12, 0, 43, 43, 43, 96, 43, 104, 43, 31, 125, 31, 125, 138, 87, 125, 149, 138, 125, 87, 87]) # noqa: E231 - # fmt: on + expected_class_labels = torch.tensor([4, 17, 32, 42, 42, 42, 42, 42, 42, 42, 32, 12, 12, 12, 12, 12, 42, 42, 12, 12, 12, 42, 12, 12, 12, 12, 12, 3, 12, 12, 12, 12, 42, 42, 42, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 5, 12, 12, 12, 12, 12, 12, 12, 0, 43, 43, 43, 96, 43, 104, 43, 31, 125, 31, 125, 138, 87, 125, 149, 138, 125, 87, 87]) # fmt: skip self.assertTrue(torch.allclose(inputs["class_labels"][0], torch.tensor(expected_class_labels))) - # fmt: off - expected_class_labels = torch.tensor([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 67, 82, 19, 19, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 12, 12, 42, 12, 12, 12, 12, 3, 14, 12, 12, 12, 12, 12, 12, 12, 12, 14, 5, 12, 12, 0, 115, 43, 43, 115, 43, 43, 43, 8, 8, 8, 138, 138, 125, 143]) # noqa: E231 - # fmt: on + expected_class_labels = torch.tensor([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 67, 82, 19, 19, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 12, 12, 42, 12, 12, 12, 12, 3, 14, 12, 12, 12, 12, 12, 12, 12, 12, 14, 5, 12, 12, 0, 115, 43, 43, 115, 43, 43, 43, 8, 8, 8, 138, 138, 125, 143]) # fmt: skip self.assertTrue(torch.allclose(inputs["class_labels"][1], expected_class_labels)) # verify the mask labels diff --git a/tests/models/maskformer/test_image_processing_maskformer.py b/tests/models/maskformer/test_image_processing_maskformer.py index e7dc0077765..e4779f896a7 100644 --- a/tests/models/maskformer/test_image_processing_maskformer.py +++ b/tests/models/maskformer/test_image_processing_maskformer.py @@ -391,13 +391,9 @@ class MaskFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase) # verify the class labels self.assertEqual(len(inputs["class_labels"]), 2) - # fmt: off - expected_class_labels = torch.tensor([4, 17, 32, 42, 42, 42, 42, 42, 42, 42, 32, 12, 12, 12, 12, 12, 42, 42, 12, 12, 12, 42, 12, 12, 12, 12, 12, 3, 12, 12, 12, 12, 42, 42, 42, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 5, 12, 12, 12, 12, 12, 12, 12, 0, 43, 43, 43, 96, 43, 104, 43, 31, 125, 31, 125, 138, 87, 125, 149, 138, 125, 87, 87]) # noqa: E231 - # fmt: on + expected_class_labels = torch.tensor([4, 17, 32, 42, 42, 42, 42, 42, 42, 42, 32, 12, 12, 12, 12, 12, 42, 42, 12, 12, 12, 42, 12, 12, 12, 12, 12, 3, 12, 12, 12, 12, 42, 42, 42, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 5, 12, 12, 12, 12, 12, 12, 12, 0, 43, 43, 43, 96, 43, 104, 43, 31, 125, 31, 125, 138, 87, 125, 149, 138, 125, 87, 87]) # fmt: skip self.assertTrue(torch.allclose(inputs["class_labels"][0], torch.tensor(expected_class_labels))) - # fmt: off - expected_class_labels = torch.tensor([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 67, 82, 19, 19, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 12, 12, 42, 12, 12, 12, 12, 3, 14, 12, 12, 12, 12, 12, 12, 12, 12, 14, 5, 12, 12, 0, 115, 43, 43, 115, 43, 43, 43, 8, 8, 8, 138, 138, 125, 143]) # noqa: E231 - # fmt: on + expected_class_labels = torch.tensor([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 67, 82, 19, 19, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 12, 12, 42, 12, 12, 12, 12, 3, 14, 12, 12, 12, 12, 12, 12, 12, 12, 14, 5, 12, 12, 0, 115, 43, 43, 115, 43, 43, 43, 8, 8, 8, 138, 138, 125, 143]) # fmt: skip self.assertTrue(torch.allclose(inputs["class_labels"][1], expected_class_labels)) # verify the mask labels diff --git a/tests/models/mbart50/test_tokenization_mbart50.py b/tests/models/mbart50/test_tokenization_mbart50.py index d10d51df907..a5ba802b6c3 100644 --- a/tests/models/mbart50/test_tokenization_mbart50.py +++ b/tests/models/mbart50/test_tokenization_mbart50.py @@ -84,12 +84,7 @@ class MBart50TokenizationTest(TokenizerTesterMixin, unittest.TestCase): ) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") - self.assertListEqual( - tokens, - # fmt: off - [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."], - # fmt: on - ) + self.assertListEqual(tokens,[SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."]) # fmt: skip ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual( ids, @@ -100,18 +95,11 @@ class MBart50TokenizationTest(TokenizerTesterMixin, unittest.TestCase): ) back_tokens = tokenizer.convert_ids_to_tokens(ids) - self.assertListEqual( - back_tokens, - # fmt: off - [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "", "."], - # fmt: on - ) + self.assertListEqual(back_tokens,[SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "", "."],) # fmt: skip @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, diff --git a/tests/models/mgp_str/test_processor_mgp_str.py b/tests/models/mgp_str/test_processor_mgp_str.py index 7c373b44736..3fd7d835348 100644 --- a/tests/models/mgp_str/test_processor_mgp_str.py +++ b/tests/models/mgp_str/test_processor_mgp_str.py @@ -52,9 +52,7 @@ class MgpstrProcessorTest(unittest.TestCase): self.image_size = (3, 32, 128) self.tmpdirname = tempfile.mkdtemp() - # fmt: off - vocab = ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'] - # fmt: on + vocab = ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'] # fmt: skip vocab_tokens = dict(zip(vocab, range(len(vocab)))) self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) diff --git a/tests/models/mgp_str/test_tokenization_mgp_str.py b/tests/models/mgp_str/test_tokenization_mgp_str.py index a05d7f3cbf9..0d0e6bb0bf1 100644 --- a/tests/models/mgp_str/test_tokenization_mgp_str.py +++ b/tests/models/mgp_str/test_tokenization_mgp_str.py @@ -35,9 +35,7 @@ class MgpstrTokenizationTest(TokenizerTesterMixin, unittest.TestCase): def setUp(self): super().setUp() - # fmt: off - vocab = ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'] - # fmt: on + vocab = ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'] # fmt: skip vocab_tokens = dict(zip(vocab, range(len(vocab)))) self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) diff --git a/tests/models/mistral/test_modeling_mistral.py b/tests/models/mistral/test_modeling_mistral.py index 2989f403046..b30e70ba71f 100644 --- a/tests/models/mistral/test_modeling_mistral.py +++ b/tests/models/mistral/test_modeling_mistral.py @@ -450,9 +450,7 @@ class MistralIntegrationTest(unittest.TestCase): EXPECTED_MEAN = torch.tensor([[-2.5548, -2.5737, -3.0600, -2.5906, -2.8478, -2.8118, -2.9325, -2.7694]]) torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2) # slicing logits[0, 0, 0:30] - # fmt: off - EXPECTED_SLICE = torch.tensor([-5.8781, -5.8616, -0.1052, -4.7200, -5.8781, -5.8774, -5.8773, -5.8777, -5.8781, -5.8780, -5.8781, -5.8779, -1.0787, 1.7583, -5.8779, -5.8780, -5.8783, -5.8778, -5.8776, -5.8781, -5.8784, -5.8778, -5.8778, -5.8777, -5.8779, -5.8778, -5.8776, -5.8780, -5.8779, -5.8781]) - # fmt: on + EXPECTED_SLICE = torch.tensor([-5.8781, -5.8616, -0.1052, -4.7200, -5.8781, -5.8774, -5.8773, -5.8777, -5.8781, -5.8780, -5.8781, -5.8779, -1.0787, 1.7583, -5.8779, -5.8780, -5.8783, -5.8778, -5.8776, -5.8781, -5.8784, -5.8778, -5.8778, -5.8777, -5.8779, -5.8778, -5.8776, -5.8780, -5.8779, -5.8781]) # fmt: skip print(out[0, 0, :30]) torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-4, rtol=1e-4) diff --git a/tests/models/mvp/test_modeling_mvp.py b/tests/models/mvp/test_modeling_mvp.py index ab9dbd95f75..3e0a4802371 100644 --- a/tests/models/mvp/test_modeling_mvp.py +++ b/tests/models/mvp/test_modeling_mvp.py @@ -572,9 +572,7 @@ class MvpModelIntegrationTests(unittest.TestCase): def test_summarization_inference(self): model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp").to(torch_device) tok = self.default_tokenizer - # fmt: off - PGE_ARTICLE = """ Listen to local radio broadcasts for advertisements that reference casinos in your area.\nIf none are in your area, listen to national radio broadcasts for advertisements of casinos in other areas.\nNote the location that is mentioned in each advertisement that involves a casino.\nIf no locations are mentioned, note any additional contact information, such as a website or phone number. Use that information to find out where the casinos are.;\n,\n\nIf you learn about more than 1 casino on the radio, use the Internet to search the distance between your location and each casino. Sites such as maps.google.com or mapquest.com will help you in this search.'""" - # fmt: on + PGE_ARTICLE = """ Listen to local radio broadcasts for advertisements that reference casinos in your area.\nIf none are in your area, listen to national radio broadcasts for advertisements of casinos in other areas.\nNote the location that is mentioned in each advertisement that involves a casino.\nIf no locations are mentioned, note any additional contact information, such as a website or phone number. Use that information to find out where the casinos are.;\n,\n\nIf you learn about more than 1 casino on the radio, use the Internet to search the distance between your location and each casino. Sites such as maps.google.com or mapquest.com will help you in this search.'""" # fmt: skip EXPECTED_SUMMARY = "Listen to the radio.\nUse the Internet." dct = tok.batch_encode_plus( [PGE_ARTICLE], diff --git a/tests/models/nllb/test_tokenization_nllb.py b/tests/models/nllb/test_tokenization_nllb.py index 8b6605204cb..10e2a47be8d 100644 --- a/tests/models/nllb/test_tokenization_nllb.py +++ b/tests/models/nllb/test_tokenization_nllb.py @@ -345,9 +345,7 @@ class NllbDistilledIntegrationTest(unittest.TestCase): def test_enro_tokenizer_decode_ignores_language_codes(self): self.assertIn(RO_CODE, self.tokenizer.all_special_ids) - # fmt: off - generated_ids = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047] - # fmt: on + generated_ids = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047] # fmt: skip result = self.tokenizer.decode(generated_ids, skip_special_tokens=True) expected_romanian = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True) diff --git a/tests/models/nllb_moe/test_modeling_nllb_moe.py b/tests/models/nllb_moe/test_modeling_nllb_moe.py index c3bf17ed8e6..1109948e0e7 100644 --- a/tests/models/nllb_moe/test_modeling_nllb_moe.py +++ b/tests/models/nllb_moe/test_modeling_nllb_moe.py @@ -408,9 +408,7 @@ class NllbMoeModelIntegrationTests(unittest.TestCase): with torch.no_grad(): output = model(**self.model_inputs) - # fmt: off - EXPECTED_LOGTIS = torch.Tensor([-0.3059, 0.0000, 9.3029, 0.6456, -0.9148, 1.7836, 0.6478, 0.9438, -0.5272, -0.6617, -1.2717, 0.4564, 0.1345, -0.2301, -1.0140, 1.1427, -1.5535, 0.1337, 0.2082, -0.8112, -0.3842, -0.3377, 0.1256, 0.6450, -0.0452, 0.0219, 1.4274, -0.4991, -0.2063, -0.4409,]) - # fmt: on + EXPECTED_LOGTIS = torch.Tensor([-0.3059, 0.0000, 9.3029, 0.6456, -0.9148, 1.7836, 0.6478, 0.9438, -0.5272, -0.6617, -1.2717, 0.4564, 0.1345, -0.2301, -1.0140, 1.1427, -1.5535, 0.1337, 0.2082, -0.8112, -0.3842, -0.3377, 0.1256, 0.6450, -0.0452, 0.0219, 1.4274, -0.4991, -0.2063, -0.4409,]) # fmt: skip torch.testing.assert_allclose(output.logits[1, 0, :30], EXPECTED_LOGTIS, rtol=6e-3, atol=9e-3) @unittest.skip("This requires 300GB of RAM") @@ -474,6 +472,7 @@ class NllbMoeRouterTest(unittest.TestCase): Original implementation of the routers here: """ + config = NllbMoeConfig( num_experts=4, hidden_size=32, @@ -516,9 +515,7 @@ class NllbMoeRouterTest(unittest.TestCase): masked_hidden_states[idx, token_indices] = torch.einsum("b,be->be", combining_weights, expert_output) hidden_states = masked_hidden_states.sum(dim=0).reshape(self.batch_size, self.sequence_length, hidden_dim) - # fmt: off - EXPECTED_MEAN_FAIRSEQ_HIDDEN_STATES = torch.Tensor([[ 7.0340e-04, 2.7997e-03, -1.3351e-02, -7.6705e-03, -3.5089e-03,3.9773e-03, 7.4593e-03, 1.2566e-02, 3.5860e-03, -2.7448e-02,-1.3731e-02, -1.0534e-02, -1.3606e-02, -1.5048e-02, -2.8914e-03,-5.0371e-03, -1.3963e-03, 6.0076e-03, -1.1380e-02, -1.4620e-02, 5.2401e-03, 8.4660e-04, -1.5319e-03, -1.6735e-02, 1.1302e-02, 3.6119e-03, 4.6084e-03, -1.3458e-02, 7.7792e-05, 1.4312e-02, 4.9107e-03, -5.0936e-03], [-4.4538e-03, 3.1026e-03, 1.4121e-04, -4.8121e-03, -5.6279e-03, 7.2493e-03, 3.9769e-03, 1.1114e-02, -1.5666e-03, -2.3477e-02, 8.7268e-03, 1.3446e-02, -2.8845e-05, -1.7287e-02, 8.7619e-03, -4.5316e-03, -1.2164e-02, 5.7461e-03, -4.5861e-03, -9.3907e-03, 2.9808e-02, 8.9206e-04, -7.6232e-04, -1.4173e-02, 3.0208e-03, 1.5310e-02, 9.7717e-03, 3.1014e-03, 7.8042e-03, 8.0197e-03, 3.4784e-03, -7.1728e-03]]) - # fmt: on + EXPECTED_MEAN_FAIRSEQ_HIDDEN_STATES = torch.Tensor([[ 7.0340e-04, 2.7997e-03, -1.3351e-02, -7.6705e-03, -3.5089e-03,3.9773e-03, 7.4593e-03, 1.2566e-02, 3.5860e-03, -2.7448e-02,-1.3731e-02, -1.0534e-02, -1.3606e-02, -1.5048e-02, -2.8914e-03,-5.0371e-03, -1.3963e-03, 6.0076e-03, -1.1380e-02, -1.4620e-02, 5.2401e-03, 8.4660e-04, -1.5319e-03, -1.6735e-02, 1.1302e-02, 3.6119e-03, 4.6084e-03, -1.3458e-02, 7.7792e-05, 1.4312e-02, 4.9107e-03, -5.0936e-03], [-4.4538e-03, 3.1026e-03, 1.4121e-04, -4.8121e-03, -5.6279e-03, 7.2493e-03, 3.9769e-03, 1.1114e-02, -1.5666e-03, -2.3477e-02, 8.7268e-03, 1.3446e-02, -2.8845e-05, -1.7287e-02, 8.7619e-03, -4.5316e-03, -1.2164e-02, 5.7461e-03, -4.5861e-03, -9.3907e-03, 2.9808e-02, 8.9206e-04, -7.6232e-04, -1.4173e-02, 3.0208e-03, 1.5310e-02, 9.7717e-03, 3.1014e-03, 7.8042e-03, 8.0197e-03, 3.4784e-03, -7.1728e-03]]) # fmt: skip self.assertTrue(torch.allclose(hidden_states.mean(1), EXPECTED_MEAN_FAIRSEQ_HIDDEN_STATES, 1e-4)) def test_batch_prioritized_routing(self): diff --git a/tests/models/oneformer/test_processor_oneformer.py b/tests/models/oneformer/test_processor_oneformer.py index f6d97643810..6c30ee94d46 100644 --- a/tests/models/oneformer/test_processor_oneformer.py +++ b/tests/models/oneformer/test_processor_oneformer.py @@ -499,13 +499,9 @@ class OneFormerProcessingTest(unittest.TestCase): # verify the class labels self.assertEqual(len(inputs["class_labels"]), 2) - # fmt: off - expected_class_labels = torch.tensor([4, 17, 32, 42, 12, 3, 5, 0, 43, 96, 104, 31, 125, 138, 87, 149]) # noqa: E231 - # fmt: on + expected_class_labels = torch.tensor([4, 17, 32, 42, 12, 3, 5, 0, 43, 96, 104, 31, 125, 138, 87, 149]) # noqa: E231 # fmt: skip self.assertTrue(torch.allclose(inputs["class_labels"][0], expected_class_labels)) - # fmt: off - expected_class_labels = torch.tensor([19, 67, 82, 17, 12, 42, 3, 14, 5, 0, 115, 43, 8, 138, 125, 143]) # noqa: E231 - # fmt: on + expected_class_labels = torch.tensor([19, 67, 82, 17, 12, 42, 3, 14, 5, 0, 115, 43, 8, 138, 125, 143]) # noqa: E231 # fmt: skip self.assertTrue(torch.allclose(inputs["class_labels"][1], expected_class_labels)) # verify the task inputs @@ -591,13 +587,9 @@ class OneFormerProcessingTest(unittest.TestCase): # verify the class labels self.assertEqual(len(inputs["class_labels"]), 2) - # fmt: off - expected_class_labels = torch.tensor([32, 42, 42, 42, 42, 42, 42, 42, 32, 12, 12, 12, 12, 12, 42, 42, 12, 12, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 42, 42, 42, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 43, 43, 43, 43, 104, 43, 31, 125, 31, 125, 138, 87, 125, 149, 138, 125, 87, 87]) # noqa: E231 - # fmt: on + expected_class_labels = torch.tensor([32, 42, 42, 42, 42, 42, 42, 42, 32, 12, 12, 12, 12, 12, 42, 42, 12, 12, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 42, 42, 42, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 43, 43, 43, 43, 104, 43, 31, 125, 31, 125, 138, 87, 125, 149, 138, 125, 87, 87]) # fmt: skip self.assertTrue(torch.allclose(inputs["class_labels"][0], expected_class_labels)) - # fmt: off - expected_class_labels = torch.tensor([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 67, 82, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 12, 12, 42, 12, 12, 12, 12, 14, 12, 12, 12, 12, 12, 12, 12, 12, 14, 12, 12, 115, 43, 43, 115, 43, 43, 43, 8, 8, 8, 138, 138, 125, 143]) # noqa: E231 - # fmt: on + expected_class_labels = torch.tensor([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 67, 82, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 12, 12, 42, 12, 12, 12, 12, 14, 12, 12, 12, 12, 12, 12, 12, 12, 14, 12, 12, 115, 43, 43, 115, 43, 43, 43, 8, 8, 8, 138, 138, 125, 143]) # fmt: skip self.assertTrue(torch.allclose(inputs["class_labels"][1], expected_class_labels)) # verify the task inputs @@ -683,13 +675,9 @@ class OneFormerProcessingTest(unittest.TestCase): # verify the class labels self.assertEqual(len(inputs["class_labels"]), 2) - # fmt: off - expected_class_labels = torch.tensor([4, 17, 32, 42, 42, 42, 42, 42, 42, 42, 32, 12, 12, 12, 12, 12, 42, 42, 12, 12, 12, 42, 12, 12, 12, 12, 12, 3, 12, 12, 12, 12, 42, 42, 42, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 5, 12, 12, 12, 12, 12, 12, 12, 0, 43, 43, 43, 96, 43, 104, 43, 31, 125, 31, 125, 138, 87, 125, 149, 138, 125, 87, 87]) # noqa: E231 - # fmt: on + expected_class_labels = torch.tensor([4, 17, 32, 42, 42, 42, 42, 42, 42, 42, 32, 12, 12, 12, 12, 12, 42, 42, 12, 12, 12, 42, 12, 12, 12, 12, 12, 3, 12, 12, 12, 12, 42, 42, 42, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 5, 12, 12, 12, 12, 12, 12, 12, 0, 43, 43, 43, 96, 43, 104, 43, 31, 125, 31, 125, 138, 87, 125, 149, 138, 125, 87, 87]) # fmt: skip self.assertTrue(torch.allclose(inputs["class_labels"][0], expected_class_labels)) - # fmt: off - expected_class_labels = torch.tensor([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 67, 82, 19, 19, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 12, 12, 42, 12, 12, 12, 12, 3, 14, 12, 12, 12, 12, 12, 12, 12, 12, 14, 5, 12, 12, 0, 115, 43, 43, 115, 43, 43, 43, 8, 8, 8, 138, 138, 125, 143]) # noqa: E231 - # fmt: on + expected_class_labels = torch.tensor([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 67, 82, 19, 19, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 12, 12, 42, 12, 12, 12, 12, 3, 14, 12, 12, 12, 12, 12, 12, 12, 12, 14, 5, 12, 12, 0, 115, 43, 43, 115, 43, 43, 43, 8, 8, 8, 138, 138, 125, 143]) # fmt: skip self.assertTrue(torch.allclose(inputs["class_labels"][1], expected_class_labels)) # verify the task inputs diff --git a/tests/models/owlv2/test_modeling_owlv2.py b/tests/models/owlv2/test_modeling_owlv2.py index d9913596ac5..51d9e537840 100644 --- a/tests/models/owlv2/test_modeling_owlv2.py +++ b/tests/models/owlv2/test_modeling_owlv2.py @@ -141,8 +141,8 @@ class Owlv2VisionModelTester: # Copied from tests.models.owlvit.test_modeling_owlvit.OwlViTVisionModelTest with OwlViT->Owlv2, OWL-ViT->OwlV2, OWLVIT->OWLV2 class Owlv2VisionModelTest(ModelTesterMixin, unittest.TestCase): """ - Here we also overwrite some of the tests of test_modeling_common.py, as OWLV2 does not use input_ids, - inputs_embeds, attention_mask and seq_length. + Here we also overwrite some of the tests of test_modeling_common.py, as OWLV2 does not use input_ids, inputs_embeds, + attention_mask and seq_length. """ all_model_classes = (Owlv2VisionModel,) if is_torch_available() else () diff --git a/tests/models/owlvit/test_processor_owlvit.py b/tests/models/owlvit/test_processor_owlvit.py index b8bd17e027c..b271c8880bf 100644 --- a/tests/models/owlvit/test_processor_owlvit.py +++ b/tests/models/owlvit/test_processor_owlvit.py @@ -38,9 +38,7 @@ class OwlViTProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() - # fmt: off - vocab = ["", "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l", "w", "r", "t", "low", "er", "lowest", "newer", "wider", "", "<|startoftext|>", "<|endoftext|>"] - # fmt: on + vocab = ["", "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l", "w", "r", "t", "low", "er", "lowest", "newer", "wider", "", "<|startoftext|>", "<|endoftext|>"] # fmt: skip vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "l o", "lo w", "e r", ""] self.special_tokens_map = {"unk_token": ""} diff --git a/tests/models/pegasus/test_tokenization_pegasus.py b/tests/models/pegasus/test_tokenization_pegasus.py index 9a40854e86c..3abe36d1183 100644 --- a/tests/models/pegasus/test_tokenization_pegasus.py +++ b/tests/models/pegasus/test_tokenization_pegasus.py @@ -119,9 +119,7 @@ class PegasusTokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, diff --git a/tests/models/perceiver/test_tokenization_perceiver.py b/tests/models/perceiver/test_tokenization_perceiver.py index e08f2e4c5c7..f2366120097 100644 --- a/tests/models/perceiver/test_tokenization_perceiver.py +++ b/tests/models/perceiver/test_tokenization_perceiver.py @@ -113,9 +113,7 @@ class PerceiverTokenizationTest(TokenizerTesterMixin, unittest.TestCase): def test_prepare_batch_integration(self): tokenizer = self.perceiver_tokenizer src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."] - # fmt: off - expected_src_tokens = [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0] - # fmt: on + expected_src_tokens = [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0] # fmt: skip batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK) self.assertIsInstance(batch, BatchEncoding) diff --git a/tests/models/phi/test_modeling_phi.py b/tests/models/phi/test_modeling_phi.py index 200fac25907..93c5ca85e9a 100644 --- a/tests/models/phi/test_modeling_phi.py +++ b/tests/models/phi/test_modeling_phi.py @@ -361,9 +361,7 @@ class PhiIntegrationTest(unittest.TestCase): output = model(**input_ids).logits - # fmt: off - EXPECTED_OUTPUT = torch.tensor([[2.2671, 6.7684, -2.0107, -1.2440, -1.5335, -2.3828, 6.9186, 6.4245, 3.1548, 0.9998, 0.0760, 4.4653, 4.9857, 4.2956, 1.2308, -1.4178, 0.1361, 0.5191, -0.5699, -2.2201, -3.0750, -3.9600, -4.5936, -3.7394, -2.7777, 6.1874, -0.4148, -1.5684, -0.5967, 0.2395], [1.7004, 4.0383, 0.0546, 0.4530, -0.3619, -0.9021, 1.8355, 1.3587, 1.2406, 2.5775, -0.8834, 5.1910, 4.2565, 4.1406, 3.0752, -0.9099, 1.1595, 0.0264, 0.3243, -1.1803, -1.3945, -2.1406, -3.9939, -1.4438, -2.9546, 3.9204, 1.0851, -1.0598, -1.7819, -0.4827]]).to(torch_device) - # fmt: on + EXPECTED_OUTPUT = torch.tensor([[2.2671, 6.7684, -2.0107, -1.2440, -1.5335, -2.3828, 6.9186, 6.4245, 3.1548, 0.9998, 0.0760, 4.4653, 4.9857, 4.2956, 1.2308, -1.4178, 0.1361, 0.5191, -0.5699, -2.2201, -3.0750, -3.9600, -4.5936, -3.7394, -2.7777, 6.1874, -0.4148, -1.5684, -0.5967, 0.2395], [1.7004, 4.0383, 0.0546, 0.4530, -0.3619, -0.9021, 1.8355, 1.3587, 1.2406, 2.5775, -0.8834, 5.1910, 4.2565, 4.1406, 3.0752, -0.9099, 1.1595, 0.0264, 0.3243, -1.1803, -1.3945, -2.1406, -3.9939, -1.4438, -2.9546, 3.9204, 1.0851, -1.0598, -1.7819, -0.4827]]).to(torch_device) # fmt: skip self.assertTrue(torch.allclose(EXPECTED_OUTPUT, output[0, :2, :30], atol=1e-4, rtol=1e-4)) @@ -379,8 +377,6 @@ class PhiIntegrationTest(unittest.TestCase): output = model(**input_ids).logits - # fmt: off - EXPECTED_OUTPUT = torch.tensor([[12.2922, 13.3507, 8.6963, 9.1355, 9.3502, 9.2667, 14.2027, 13.1363, 13.5446, 11.1337, 9.9279, 16.7195, 13.0768, 14.9141, 11.9965, 8.0233, 10.3129, 10.6118, 10.0204, 9.3827, 8.8344, 8.2806, 8.0153, 8.0540, 7.0964, 16.5743, 11.1256, 9.6987, 11.4770, 10.5440], [12.3323, 14.6050, 8.9986, 8.1580, 9.5654, 6.6728, 12.5966, 12.6662, 12.2784, 11.7522, 8.2039, 16.3102, 11.2203, 13.6088, 12.0125, 9.1021, 9.8216, 10.0987, 9.0926, 8.4260, 8.8009, 7.6547, 6.8075, 7.7881, 7.4501, 15.7451, 10.5053, 8.3129, 10.0027, 9.2612]]).to(torch_device) - # fmt: on + EXPECTED_OUTPUT = torch.tensor([[12.2922, 13.3507, 8.6963, 9.1355, 9.3502, 9.2667, 14.2027, 13.1363, 13.5446, 11.1337, 9.9279, 16.7195, 13.0768, 14.9141, 11.9965, 8.0233, 10.3129, 10.6118, 10.0204, 9.3827, 8.8344, 8.2806, 8.0153, 8.0540, 7.0964, 16.5743, 11.1256, 9.6987, 11.4770, 10.5440], [12.3323, 14.6050, 8.9986, 8.1580, 9.5654, 6.6728, 12.5966, 12.6662, 12.2784, 11.7522, 8.2039, 16.3102, 11.2203, 13.6088, 12.0125, 9.1021, 9.8216, 10.0987, 9.0926, 8.4260, 8.8009, 7.6547, 6.8075, 7.7881, 7.4501, 15.7451, 10.5053, 8.3129, 10.0027, 9.2612]]).to(torch_device) # fmt: skip self.assertTrue(torch.allclose(EXPECTED_OUTPUT, output[0, :2, :30], atol=1e-4, rtol=1e-4)) diff --git a/tests/models/reformer/test_tokenization_reformer.py b/tests/models/reformer/test_tokenization_reformer.py index a2a0db6c370..0f72bf311a2 100644 --- a/tests/models/reformer/test_tokenization_reformer.py +++ b/tests/models/reformer/test_tokenization_reformer.py @@ -351,9 +351,7 @@ class ReformerTokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # fmt: skip # This tokenizer does not know some characters like ")". # That is the reason why we use very simple texts here. diff --git a/tests/models/roberta_prelayernorm/test_modeling_roberta_prelayernorm.py b/tests/models/roberta_prelayernorm/test_modeling_roberta_prelayernorm.py index a2f56e31a09..c975718778d 100644 --- a/tests/models/roberta_prelayernorm/test_modeling_roberta_prelayernorm.py +++ b/tests/models/roberta_prelayernorm/test_modeling_roberta_prelayernorm.py @@ -493,8 +493,8 @@ class RobertaPreLayerNormModelTest(ModelTesterMixin, GenerationTesterMixin, Pipe """Ensure that the default position ids only assign a sequential . This is a regression test for https://github.com/huggingface/transformers/issues/1761 - The position ids should be masked with the embedding object's padding index. Therefore, the first available - non-padding position index is RobertaPreLayerNormEmbeddings.padding_idx + 1 + The position ids should be masked with the embedding object's padding index. Therefore, the + first available non-padding position index is RobertaPreLayerNormEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] model = RobertaPreLayerNormEmbeddings(config=config) @@ -513,8 +513,8 @@ class RobertaPreLayerNormModelTest(ModelTesterMixin, GenerationTesterMixin, Pipe """Ensure that the default position ids only assign a sequential . This is a regression test for https://github.com/huggingface/transformers/issues/1761 - The position ids should be masked with the embedding object's padding index. Therefore, the first available - non-padding position index is RobertaPreLayerNormEmbeddings.padding_idx + 1 + The position ids should be masked with the embedding object's padding index. Therefore, the + first available non-padding position index is RobertaPreLayerNormEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] embeddings = RobertaPreLayerNormEmbeddings(config=config) diff --git a/tests/models/sam/test_modeling_sam.py b/tests/models/sam/test_modeling_sam.py index eb8f265c294..a84e3695c13 100644 --- a/tests/models/sam/test_modeling_sam.py +++ b/tests/models/sam/test_modeling_sam.py @@ -728,9 +728,7 @@ class SamModelIntegrationTest(unittest.TestCase): raw_image = prepare_image() - # fmt: off - input_points = torch.Tensor([[[400, 650]], [[220, 470]]]).cpu() - # fmt: on + input_points = torch.Tensor([[[400, 650]], [[220, 470]]]).cpu() # fmt: skip input_points = input_points.unsqueeze(0) diff --git a/tests/models/sam/test_modeling_tf_sam.py b/tests/models/sam/test_modeling_tf_sam.py index a14b9912867..4478815e7ce 100644 --- a/tests/models/sam/test_modeling_tf_sam.py +++ b/tests/models/sam/test_modeling_tf_sam.py @@ -628,9 +628,7 @@ class TFSamModelIntegrationTest(unittest.TestCase): raw_image = prepare_image() - # fmt: off - input_points = tf.convert_to_tensor([[[400, 650]], [[220, 470]]]) - # fmt: on + input_points = tf.convert_to_tensor([[[400, 650]], [[220, 470]]]) # fmt: skip input_points = tf.expand_dims(input_points, 0) diff --git a/tests/models/seamless_m4t/test_modeling_seamless_m4t.py b/tests/models/seamless_m4t/test_modeling_seamless_m4t.py index 6963433e01b..ab7a48694d2 100644 --- a/tests/models/seamless_m4t/test_modeling_seamless_m4t.py +++ b/tests/models/seamless_m4t/test_modeling_seamless_m4t.py @@ -1001,9 +1001,7 @@ class SeamlessM4TModelIntegrationTest(unittest.TestCase): def input_text(self): # corresponds to "C'est un test." with seamlessM4T_medium checkpoint - # fmt: off - input_ids = torch.tensor([[256057, 152, 248116, 354, 159, 7356, 248075, 3]]) - # fmt: on + input_ids = torch.tensor([[256057, 152, 248116, 354, 159, 7356, 248075, 3]]) # fmt: skip input_ids = input_ids.to(torch_device) @@ -1049,9 +1047,7 @@ class SeamlessM4TModelIntegrationTest(unittest.TestCase): # test text - tgt lang: eng - # fmt: off - expected_text_tokens = [3, 256047, 3291, 248116, 248066, 9, 7356, 248075, 3] - # fmt: on + expected_text_tokens = [3, 256047, 3291, 248116, 248066, 9, 7356, 248075, 3] # fmt: skip # fmt: off expected_unit_tokens = [ @@ -1062,9 +1058,7 @@ class SeamlessM4TModelIntegrationTest(unittest.TestCase): ] # fmt: on - # fmt: off - expected_wav_slice = [-3e-05, -0.0004, -0.00037, -0.00013, -6e-05, 0.00012, -0.00016, 0.00025, 7e-05, -3e-05] - # fmt: on + expected_wav_slice = [-3e-05, -0.0004, -0.00037, -0.00013, -6e-05, 0.00012, -0.00016, 0.00025, 7e-05, -3e-05] # fmt: skip set_seed(0) output = model.generate(**self.input_text, num_beams=1, tgt_lang="eng", return_intermediate_token_ids=True) @@ -1081,9 +1075,7 @@ class SeamlessM4TModelIntegrationTest(unittest.TestCase): # test text - tgt lang: swh - # fmt: off - expected_text_tokens = [3, 256168, 1665, 188589, 7040, 248075, 3] - # fmt: on + expected_text_tokens = [3, 256168, 1665, 188589, 7040, 248075, 3] # fmt: skip # fmt: off expected_unit_tokens = [ @@ -1093,9 +1085,7 @@ class SeamlessM4TModelIntegrationTest(unittest.TestCase): ] # fmt: on - # fmt: off - expected_wav_slice = [1e-05, -7e-05, -4e-05, -4e-05, -6e-05, -9e-05, -0.0001, -2e-05, -7e-05, -2e-05] - # fmt: on + expected_wav_slice = [1e-05, -7e-05, -4e-05, -4e-05, -6e-05, -9e-05, -0.0001, -2e-05, -7e-05, -2e-05] # fmt: skip set_seed(0) output = model.generate(**self.input_text, num_beams=1, tgt_lang="swh", return_intermediate_token_ids=True) @@ -1111,9 +1101,7 @@ class SeamlessM4TModelIntegrationTest(unittest.TestCase): # test audio - tgt lang: rus - # fmt: off - expected_text_tokens = [3, 256147, 1197, 73565, 3413, 537, 233331, 248075, 3] - # fmt: on + expected_text_tokens = [3, 256147, 1197, 73565, 3413, 537, 233331, 248075, 3] # fmt: skip # fmt: off expected_unit_tokens = [ @@ -1124,9 +1112,7 @@ class SeamlessM4TModelIntegrationTest(unittest.TestCase): ] # fmt: on - # fmt: off - expected_wav_slice = [0.00013, 0.00012, 0.00014, 3e-05, 0.0, -6e-05, -0.00018, -0.00016, -0.00021, -0.00018] - # fmt: on + expected_wav_slice = [0.00013, 0.00012, 0.00014, 3e-05, 0.0, -6e-05, -0.00018, -0.00016, -0.00021, -0.00018] # fmt: skip set_seed(0) output = model.generate(**self.input_audio, num_beams=1, tgt_lang="rus", return_intermediate_token_ids=True) diff --git a/tests/models/seamless_m4t/test_tokenization_seamless_m4t.py b/tests/models/seamless_m4t/test_tokenization_seamless_m4t.py index 2cd9e8c56b5..7752156eab6 100644 --- a/tests/models/seamless_m4t/test_tokenization_seamless_m4t.py +++ b/tests/models/seamless_m4t/test_tokenization_seamless_m4t.py @@ -449,9 +449,7 @@ class SeamlessM4TDistilledIntegrationTest(unittest.TestCase): " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.", ] - # fmt: off - expected_src_tokens = [256047, 16297, 134408, 8165, 248066, 14734, 950, 1135, 105721, 3573, 83, 27352, 108, 49486, 3] - # fmt: on + expected_src_tokens = [256047, 16297, 134408, 8165, 248066, 14734, 950, 1135, 105721, 3573, 83, 27352, 108, 49486, 3] # fmt: skip @classmethod def setUpClass(cls): @@ -483,9 +481,7 @@ class SeamlessM4TDistilledIntegrationTest(unittest.TestCase): # Copied from tests.models.nllb.test_tokenization_nllb.NllbDistilledIntegrationTest.test_enro_tokenizer_decode_ignores_language_codes def test_enro_tokenizer_decode_ignores_language_codes(self): self.assertIn(RO_CODE, self.tokenizer.all_special_ids) - # fmt: off - generated_ids = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047] - # fmt: on + generated_ids = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047] # fmt: skip result = self.tokenizer.decode(generated_ids, skip_special_tokens=True) expected_romanian = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True) diff --git a/tests/models/speech_to_text/test_tokenization_speech_to_text.py b/tests/models/speech_to_text/test_tokenization_speech_to_text.py index 46c24279672..b0cb1acc856 100644 --- a/tests/models/speech_to_text/test_tokenization_speech_to_text.py +++ b/tests/models/speech_to_text/test_tokenization_speech_to_text.py @@ -90,28 +90,16 @@ class SpeechToTextTokenizerTest(TokenizerTesterMixin, unittest.TestCase): ) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") - self.assertListEqual( - tokens, - # fmt: off - [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."], - # fmt: on - ) + self.assertListEqual(tokens,[SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."]) # fmt: skip ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual(ids, [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8]) back_tokens = tokenizer.convert_ids_to_tokens(ids) - self.assertListEqual( - back_tokens, - # fmt: off - [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "", "."], - # fmt: on - ) + self.assertListEqual(back_tokens,[SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "", "."]) # fmt: skip @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[3791, 797, 31, 11, 64, 797, 31, 2429, 433, 12, 1176, 12, 20, 786, 915, 142, 2413, 240, 37, 3238, 797, 31, 11, 35, 93, 915, 142, 2413, 240, 37, 5540, 567, 1276, 93, 37, 610, 40, 62, 455, 657, 1042, 123, 780, 177, 37, 309, 241, 1298, 514, 20, 292, 2737, 114, 2469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3388, 511, 459, 4, 3555, 40, 321, 302, 705, 4, 3388, 511, 583, 326, 5, 5, 5, 62, 3310, 560, 177, 2680, 217, 1508, 32, 31, 853, 418, 64, 583, 511, 1605, 62, 35, 93, 560, 177, 2680, 217, 1508, 1521, 64, 583, 511, 519, 62, 20, 1515, 764, 20, 149, 261, 5625, 7972, 20, 5540, 567, 1276, 93, 3925, 1675, 11, 15, 802, 7972, 576, 217, 1508, 11, 35, 93, 1253, 2441, 15, 289, 652, 31, 416, 321, 3842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2681, 1153, 3434, 20, 5540, 37, 567, 126, 1253, 2441, 3376, 449, 210, 431, 1563, 177, 767, 5540, 11, 1203, 472, 11, 2953, 685, 285, 364, 706, 1153, 20, 6799, 20, 2869, 20, 4464, 126, 40, 2429, 20, 1040, 866, 2664, 418, 20, 318, 20, 1726, 186, 20, 265, 522, 35, 93, 2191, 4634, 20, 1040, 12, 6799, 15, 228, 2356, 142, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2575, 2666, 684, 1582, 1176, 12, 627, 149, 619, 20, 4902, 563, 11, 20, 149, 261, 3420, 2356, 174, 142, 4714, 131, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[3791, 797, 31, 11, 64, 797, 31, 2429, 433, 12, 1176, 12, 20, 786, 915, 142, 2413, 240, 37, 3238, 797, 31, 11, 35, 93, 915, 142, 2413, 240, 37, 5540, 567, 1276, 93, 37, 610, 40, 62, 455, 657, 1042, 123, 780, 177, 37, 309, 241, 1298, 514, 20, 292, 2737, 114, 2469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3388, 511, 459, 4, 3555, 40, 321, 302, 705, 4, 3388, 511, 583, 326, 5, 5, 5, 62, 3310, 560, 177, 2680, 217, 1508, 32, 31, 853, 418, 64, 583, 511, 1605, 62, 35, 93, 560, 177, 2680, 217, 1508, 1521, 64, 583, 511, 519, 62, 20, 1515, 764, 20, 149, 261, 5625, 7972, 20, 5540, 567, 1276, 93, 3925, 1675, 11, 15, 802, 7972, 576, 217, 1508, 11, 35, 93, 1253, 2441, 15, 289, 652, 31, 416, 321, 3842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2681, 1153, 3434, 20, 5540, 37, 567, 126, 1253, 2441, 3376, 449, 210, 431, 1563, 177, 767, 5540, 11, 1203, 472, 11, 2953, 685, 285, 364, 706, 1153, 20, 6799, 20, 2869, 20, 4464, 126, 40, 2429, 20, 1040, 866, 2664, 418, 20, 318, 20, 1726, 186, 20, 265, 522, 35, 93, 2191, 4634, 20, 1040, 12, 6799, 15, 228, 2356, 142, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2575, 2666, 684, 1582, 1176, 12, 627, 149, 619, 20, 4902, 563, 11, 20, 149, 261, 3420, 2356, 174, 142, 4714, 131, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, diff --git a/tests/models/speecht5/test_tokenization_speecht5.py b/tests/models/speecht5/test_tokenization_speecht5.py index 32468abe6ad..f078402d505 100644 --- a/tests/models/speecht5/test_tokenization_speecht5.py +++ b/tests/models/speecht5/test_tokenization_speecht5.py @@ -152,9 +152,7 @@ class SpeechT5TokenizerTest(TokenizerTesterMixin, unittest.TestCase): tokenizer = self.get_tokenizer(normalize=True) tokens = tokenizer.tokenize("This is a test") - # fmt: off - self.assertListEqual(tokens, [SPIECE_UNDERLINE, 'T', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'a', SPIECE_UNDERLINE, 't', 'e', 's', 't']) - # fmt: on + self.assertListEqual(tokens, [SPIECE_UNDERLINE, 'T', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'a', SPIECE_UNDERLINE, 't', 'e', 's', 't']) # fmt: skip self.assertListEqual( tokenizer.convert_tokens_to_ids(tokens), @@ -162,25 +160,13 @@ class SpeechT5TokenizerTest(TokenizerTesterMixin, unittest.TestCase): ) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") - self.assertListEqual( - tokens, - # fmt: off - [SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, 'n', 'i', 'n', 'e', 't', 'y', SPIECE_UNDERLINE, 't', 'w', 'o', SPIECE_UNDERLINE, 't', 'h', 'o', 'u', 's', 'a', 'n', 'd', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.'] - # fmt: on - ) + self.assertListEqual(tokens,[SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, 'n', 'i', 'n', 'e', 't', 'y', SPIECE_UNDERLINE, 't', 'w', 'o', SPIECE_UNDERLINE, 't', 'h', 'o', 'u', 's', 'a', 'n', 'd', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.']) # fmt: skip ids = tokenizer.convert_tokens_to_ids(tokens) - # fmt: off - self.assertListEqual(ids, [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 9, 10, 9, 5, 6, 22, 4, 6, 20, 8, 4, 6, 11, 8, 16, 12, 7, 9, 14, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26]) - # fmt: on + self.assertListEqual(ids, [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 9, 10, 9, 5, 6, 22, 4, 6, 20, 8, 4, 6, 11, 8, 16, 12, 7, 9, 14, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26]) # fmt: skip back_tokens = tokenizer.convert_ids_to_tokens(ids) - self.assertListEqual( - back_tokens, - # fmt: off - [SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, 'n', 'i', 'n', 'e', 't', 'y', SPIECE_UNDERLINE, 't', 'w', 'o', SPIECE_UNDERLINE, 't', 'h', 'o', 'u', 's', 'a', 'n', 'd', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.'] - # fmt: on - ) + self.assertListEqual(back_tokens,[SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, 'n', 'i', 'n', 'e', 't', 'y', SPIECE_UNDERLINE, 't', 'w', 'o', SPIECE_UNDERLINE, 't', 'h', 'o', 'u', 's', 'a', 'n', 'd', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.']) # fmt: skip @slow def test_tokenizer_integration(self): diff --git a/tests/models/switch_transformers/test_modeling_switch_transformers.py b/tests/models/switch_transformers/test_modeling_switch_transformers.py index 6a2c3f5525e..5458b566667 100644 --- a/tests/models/switch_transformers/test_modeling_switch_transformers.py +++ b/tests/models/switch_transformers/test_modeling_switch_transformers.py @@ -905,6 +905,7 @@ class SwitchTransformerRouterTest(unittest.TestCase): Original implementation of the routers here: """ + config = SwitchTransformersConfig( num_experts=2, hidden_size=8, diff --git a/tests/models/t5/test_tokenization_t5.py b/tests/models/t5/test_tokenization_t5.py index 26cd20c74c1..a141dea86b7 100644 --- a/tests/models/t5/test_tokenization_t5.py +++ b/tests/models/t5/test_tokenization_t5.py @@ -369,9 +369,7 @@ class T5TokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[31220, 7, 41, 14034, 801, 38, 3, 102, 63, 17, 127, 524, 18, 7031, 2032, 277, 11, 3, 102, 63, 17, 127, 524, 18, 2026, 17, 10761, 18, 7041, 61, 795, 879, 18, 19681, 4648, 7, 41, 12920, 382, 6, 350, 6383, 4949, 6, 2158, 12920, 382, 9, 6, 3, 4, 11160, 6, 2043, 17153, 279, 49, 17, 6, 3, 4, 434, 9688, 11439, 21, 6869, 10509, 17725, 41, 567, 9138, 61, 11, 6869, 10509, 11946, 41, 18207, 517, 61, 28, 147, 3538, 1220, 7140, 10761, 2250, 16, 910, 1220, 8024, 11, 1659, 1413, 32, 883, 2020, 344, 2215, 226, 6, 12901, 382, 127, 524, 11, 4738, 7, 127, 15390, 5, 1], [272, 24203, 19, 876, 12, 554, 18, 9719, 1659, 2647, 26352, 6497, 7, 45, 73, 9339, 400, 26, 1499, 57, 22801, 10760, 30, 321, 646, 11, 269, 2625, 16, 66, 7500, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [37, 1704, 4216, 3, 20400, 4418, 7, 147, 8, 19743, 1782, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[31220, 7, 41, 14034, 801, 38, 3, 102, 63, 17, 127, 524, 18, 7031, 2032, 277, 11, 3, 102, 63, 17, 127, 524, 18, 2026, 17, 10761, 18, 7041, 61, 795, 879, 18, 19681, 4648, 7, 41, 12920, 382, 6, 350, 6383, 4949, 6, 2158, 12920, 382, 9, 6, 3, 4, 11160, 6, 2043, 17153, 279, 49, 17, 6, 3, 4, 434, 9688, 11439, 21, 6869, 10509, 17725, 41, 567, 9138, 61, 11, 6869, 10509, 11946, 41, 18207, 517, 61, 28, 147, 3538, 1220, 7140, 10761, 2250, 16, 910, 1220, 8024, 11, 1659, 1413, 32, 883, 2020, 344, 2215, 226, 6, 12901, 382, 127, 524, 11, 4738, 7, 127, 15390, 5, 1], [272, 24203, 19, 876, 12, 554, 18, 9719, 1659, 2647, 26352, 6497, 7, 45, 73, 9339, 400, 26, 1499, 57, 22801, 10760, 30, 321, 646, 11, 269, 2625, 16, 66, 7500, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [37, 1704, 4216, 3, 20400, 4418, 7, 147, 8, 19743, 1782, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, diff --git a/tests/models/tapas/test_tokenization_tapas.py b/tests/models/tapas/test_tokenization_tapas.py index 9d82c468aa3..692dc91b6d8 100644 --- a/tests/models/tapas/test_tokenization_tapas.py +++ b/tests/models/tapas/test_tokenization_tapas.py @@ -1214,9 +1214,7 @@ class TapasTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer = TapasTokenizer.from_pretrained("google/tapas-base-finetuned-wtq", model_max_length=512) - # fmt: off - expected_results = {'input_ids':[101,2043,2001,8226,15091,2141,1029,102,5889,2287,2193,1997,5691,3058,1997,4182,8226,15091,5179,6584,2324,2285,3699,14720,4487,6178,9488,3429,5187,2340,2281,3326,2577,18856,7828,3240,5354,6353,1020,2089,3777],'attention_mask':[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],'token_type_ids':[[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[1,1,0,0,0,0,0],[1,2,0,0,0,0,0],[1,3,0,0,0,0,0],[1,3,0,0,0,0,0],[1,3,0,0,0,0,0],[1,4,0,0,0,0,0],[1,4,0,0,0,0,0],[1,4,0,0,0,0,0],[1,1,1,0,0,0,0],[1,1,1,0,0,0,0],[1,2,1,0,2,2,0],[1,3,1,0,3,1,0],[1,4,1,0,2,2,0],[1,4,1,0,2,2,0],[1,4,1,0,2,2,0],[1,1,2,0,0,0,0],[1,1,2,0,0,0,0],[1,1,2,0,0,0,0],[1,1,2,0,0,0,0],[1,2,2,0,1,3,0],[1,3,2,0,1,3,0],[1,4,2,0,3,1,0],[1,4,2,0,3,1,0],[1,4,2,0,3,1,0],[1,1,3,0,0,0,0],[1,1,3,0,0,0,0],[1,1,3,0,0,0,0],[1,1,3,0,0,0,0],[1,2,3,0,3,1,0],[1,3,3,0,2,2,0],[1,4,3,0,1,3,0],[1,4,3,0,1,3,0],[1,4,3,0,1,3,0]]} # noqa: E231 - # fmt: on + expected_results = {'input_ids':[101,2043,2001,8226,15091,2141,1029,102,5889,2287,2193,1997,5691,3058,1997,4182,8226,15091,5179,6584,2324,2285,3699,14720,4487,6178,9488,3429,5187,2340,2281,3326,2577,18856,7828,3240,5354,6353,1020,2089,3777],'attention_mask':[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],'token_type_ids':[[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0],[1,1,0,0,0,0,0],[1,2,0,0,0,0,0],[1,3,0,0,0,0,0],[1,3,0,0,0,0,0],[1,3,0,0,0,0,0],[1,4,0,0,0,0,0],[1,4,0,0,0,0,0],[1,4,0,0,0,0,0],[1,1,1,0,0,0,0],[1,1,1,0,0,0,0],[1,2,1,0,2,2,0],[1,3,1,0,3,1,0],[1,4,1,0,2,2,0],[1,4,1,0,2,2,0],[1,4,1,0,2,2,0],[1,1,2,0,0,0,0],[1,1,2,0,0,0,0],[1,1,2,0,0,0,0],[1,1,2,0,0,0,0],[1,2,2,0,1,3,0],[1,3,2,0,1,3,0],[1,4,2,0,3,1,0],[1,4,2,0,3,1,0],[1,4,2,0,3,1,0],[1,1,3,0,0,0,0],[1,1,3,0,0,0,0],[1,1,3,0,0,0,0],[1,1,3,0,0,0,0],[1,2,3,0,3,1,0],[1,3,3,0,2,2,0],[1,4,3,0,1,3,0],[1,4,3,0,1,3,0],[1,4,3,0,1,3,0]]} # fmt: skip new_encoded_inputs = tokenizer.encode_plus(table=table, query=queries[0]) @@ -1258,9 +1256,7 @@ class TapasTokenizationTest(TokenizerTesterMixin, unittest.TestCase): column_ids = token_type_ids[:, 1] row_ids = token_type_ids[:, 2] - # fmt: off - expected_results = 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# noqa: E231 - # fmt: on + expected_results = {'input_ids':[101,2054,2020,1996,6853,3415,1029,102,13433,2015,2053,4062,2136,10876,2051,1013,3394,8370,2685,1015,3590,4754,29267,4765,3771,2136,2447,1005,1055,6584,1015,1024,4466,1024,2340,1012,6185,2509,1015,2570,1016,1015,10391,12022,4226,7895,10625,1013,22996,3868,6584,1009,1014,1012,1022,10819,2015,1016,2459,1017,1017,2703,10555,2136,2447,1005,1055,6584,1009,2654,1012,1020,10819,2015,1017,2403,1018,1023,8709,8183,3126,21351,2078,1010,3781,1012,2136,10958,8865,6584,1009,2871,1012,1022,10819,2015,2410,2260,1019,4090,7986,5292,5677,8151,2771,1011,2990,9187,3868,6584,1009,4413,1012,1015,10819,2015,1020,2184,1020,2322,2030,20282,14262,9035,4754,3868,6584,1009,1015,1024,4002,1012,1016,2184,1022,1021,4868,7918,12023,12023,3868,6584,1009,1015,1024,5890,1012,1018,1019,1020,1022,2260,5261,12436,18116,2137,4382,2136,26447,6584,1009,1015,1024,5890,1012,1022,1022,1019,1023,1021,27339,3995,10125,9711,4906,25101,24657,1011,22033,2386,3868,6564,1009,1015,5001,2321,1018,2184,4583,7986,14383,2075,29488,14906,9351,2971,6564,1009,1015,5001,2340,1017,2340,2676,8527,2014,2696,1052,2243,3868,6564,1009,1015,5001,2260,1016,2260,2861,4575,4477,1011,2128,4710,2137,4382,2136,26447,6564,1009,1015,5001,2459,1015,2410,2539,8963,11503,25457,3022,8512,2522,9654,3868,5594,1009,1016,10876,2324,1014,2403,3943,4074,6415,15204,2072,12496,25378,3868,5594,1009,1016,10876,2403,1014,2321,1018,10704,17921,14906,9351,2971,5594,1009,1016,10876,1023,1014,2385,2340,14915,5795,8512,2522,9654,3868,6640,6228,2539,1014,2459,1016,28328,8945,3126,21351,2015,10625,1013,22996,3868,6255,6228,1018,1014,2324,2321,12270,11956,5232,3868,2260,6228,1021,1014,2539,1019,8473,28027,2080,2474,6371,5232,3868,2184,6228,2385,1014,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],'column_ids':[0,0,0,0,0,0,0,0,1,1,2,3,4,5,6,6,6,7,8,1,2,3,3,3,3,4,4,4,4,5,6,6,6,6,6,6,6,6,7,8,1,2,3,3,3,3,4,4,4,4,5,6,6,6,6,6,6,7,8,1,2,3,3,4,4,4,4,5,6,6,6,6,6,6,7,8,1,2,3,3,3,3,3,3,3,3,4,4,4,5,6,6,6,6,6,6,7,8,1,2,3,3,3,3,4,4,4,4,4,5,6,6,6,6,6,6,7,8,1,2,3,3,3,3,4,4,5,6,6,6,6,6,6,7,8,1,2,3,3,4,4,5,6,6,6,6,6,6,7,8,1,2,3,3,3,4,4,4,4,5,6,6,6,6,6,6,7,8,1,2,3,3,3,3,4,4,4,4,4,4,4,5,6,6,6,7,8,1,2,3,3,3,3,4,4,4,5,6,6,6,7,8,1,2,3,3,3,4,4,4,5,6,6,6,7,8,1,2,3,3,3,3,3,4,4,4,4,5,6,6,6,7,8,1,2,3,3,3,3,4,4,4,4,5,6,6,6,7,8,1,2,3,3,3,3,4,4,4,5,6,6,6,7,8,1,2,3,3,4,4,4,5,6,6,6,7,8,1,2,3,3,4,4,4,4,5,6,7,8,1,2,3,3,3,3,3,4,4,4,4,5,6,7,8,1,2,3,3,4,4,5,6,7,8,1,2,3,3,3,3,3,4,4,5,6,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],'row_ids':[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,11,11,11,11,11,11,11,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,15,15,15,15,15,15,15,15,15,15,15,15,15,16,16,16,16,16,16,16,16,16,16,16,16,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,18,18,18,18,18,18,18,18,18,18,19,19,19,19,19,19,19,19,19,19,19,19,19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],'segment_ids':[0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0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# fmt: skip self.assertListEqual(input_ids, expected_results["input_ids"]) self.assertListEqual(segment_ids.tolist(), expected_results["segment_ids"]) diff --git a/tests/models/transfo_xl/test_modeling_tf_transfo_xl.py b/tests/models/transfo_xl/test_modeling_tf_transfo_xl.py index 88e759307ed..fdbff90b24b 100644 --- a/tests/models/transfo_xl/test_modeling_tf_transfo_xl.py +++ b/tests/models/transfo_xl/test_modeling_tf_transfo_xl.py @@ -251,9 +251,7 @@ class TFTransfoXLModelLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_transfo_xl_wt103(self): model = TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103") - # fmt: off - input_ids = tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]],dtype=tf.int32) # noqa: E231 - # fmt: on + input_ids = tf.convert_to_tensor([[33, 1297, 2, 1, 1009, 4, 1109, 11739, 4762, 358, 5, 25, 245, 22, 1706, 17, 20098, 5, 3215, 21, 37, 1110, 3, 13, 1041, 4, 24, 603, 490, 2, 71477, 20098, 104447, 2, 20961, 1, 2604, 4, 1, 329, 3, 6224, 831, 16002, 2, 8, 603, 78967, 29546, 23, 803, 20, 25, 416, 5, 8, 232, 4, 277, 6, 1855, 4601, 3, 29546, 54, 8, 3609, 5, 57211, 49, 4, 1, 277, 18, 8, 1755, 15691, 3, 341, 25, 416, 693, 42573, 71, 17, 401, 94, 31, 17919, 2, 29546, 7873, 18, 1, 435, 23, 11011, 755, 5, 5167, 3, 7983, 98, 84, 2, 29546, 3267, 8, 3609, 4, 1, 4865, 1075, 2, 6087, 71, 6, 346, 8, 5854, 3, 29546, 824, 1400, 1868, 2, 19, 160, 2, 311, 8, 5496, 2, 20920, 17, 25, 15097, 3, 24, 24, 0]]) # fmt: skip # In 1991 , the remains of Russian Tsar Nicholas II and his family # ( except for Alexei and Maria ) are discovered . # The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the @@ -265,22 +263,20 @@ class TFTransfoXLModelLanguageGenerationTest(unittest.TestCase): # the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous , # with people , even a bishop , begging for his blessing . - # fmt: off - expected_output_ids = [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0] # noqa: E231 - # fmt: on - # In 1991, the remains of Russian Tsar Nicholas II and his family ( - # except for Alexei and Maria ) are discovered. The voice of young son, - # Tsarevich Alexei Nikolaevich, narrates the remainder of the story. - # 1883 Western Siberia, a young Grigori Rasputin is asked by his father - # and a group of men to perform magic. Rasputin has a vision and - # denounces one of the men as a horse thief. Although his father initially - # slaps him for making such an accusation, Rasputin watches as the man - # is chased outside and beaten. Twenty years later, Rasputin sees a vision - # of the Virgin Mary, prompting him to become a priest. - # Rasputin quickly becomes famous, with people, even a bishop, begging for - # his blessing. In the 1990s, the remains of Russian Tsar - # Nicholas II and his family were discovered. The voice of young son, - # Tsarevich Alexei Nikolaevich, narrates the remainder of the story. + expected_output_ids = [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,142,1298,188,2,29546,113,8,3654,4,1,1109,7136,833,3,13,1645,4,29546,11,104,7,1,1109,532,7129,2,10,83507,2,1162,1123,2,6,7245,10,2,5,11,104,7,1,1109,532,7129,2,10,24,24,10,22,10,13,770,5863,4,7245,10] # fmt: skip + # In 1991, the remains of Russian Tsar Nicholas II and his family ( except for + # Alexei and Maria ) are discovered. The voice of young son, Tsarevich Alexei + # Nikolaevich, narrates the remainder of the story. 1883 Western Siberia, a young + # Grigori Rasputin is asked by his father and a group of men to perform magic. + # Rasputin has a vision and denounces one of the men as a horse thief. Although + # his father initially slaps him for making such an accusation, Rasputin watches + # as the man is chased outside and beaten. Twenty years later, Rasputin sees a + # vision of the Virgin Mary, prompting him to become a priest. Rasputin quickly + # becomes famous, with people, even a bishop, begging for his blessing. In the + # early 20th century, Rasputin became a symbol of the Russian Orthodox Church. + # The image of Rasputin was used in the Russian national anthem, " Nearer, My God, + # to Heaven ", and was used in the Russian national anthem, " " ( " The Great Spirit + # of Heaven " output_ids = model.generate(input_ids, max_length=200, do_sample=False) self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids) diff --git a/tests/models/transfo_xl/test_modeling_transfo_xl.py b/tests/models/transfo_xl/test_modeling_transfo_xl.py index d100ea381d6..9534b13c852 100644 --- a/tests/models/transfo_xl/test_modeling_transfo_xl.py +++ b/tests/models/transfo_xl/test_modeling_transfo_xl.py @@ -502,9 +502,7 @@ class TransfoXLModelLanguageGenerationTest(unittest.TestCase): model = TransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103") model.to(torch_device) - # fmt: off - input_ids = torch.tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]],dtype=torch.long,device=torch_device) # noqa: E231 - # fmt: on + input_ids = torch.tensor([[33, 1297, 2, 1, 1009, 4, 1109, 11739, 4762, 358, 5, 25, 245, 22, 1706, 17, 20098, 5, 3215, 21, 37, 1110, 3, 13, 1041, 4, 24, 603, 490, 2, 71477, 20098, 104447, 2, 20961, 1, 2604, 4, 1, 329, 3, 6224, 831, 16002, 2, 8, 603, 78967, 29546, 23, 803, 20, 25, 416, 5, 8, 232, 4, 277, 6, 1855, 4601, 3, 29546, 54, 8, 3609, 5, 57211, 49, 4, 1, 277, 18, 8, 1755, 15691, 3, 341, 25, 416, 693, 42573, 71, 17, 401, 94, 31, 17919, 2, 29546, 7873, 18, 1, 435, 23, 11011, 755, 5, 5167, 3, 7983, 98, 84, 2, 29546, 3267, 8, 3609, 4, 1, 4865, 1075, 2, 6087, 71, 6, 346, 8, 5854, 3, 29546, 824, 1400, 1868, 2, 19, 160, 2, 311, 8, 5496, 2, 20920, 17, 25, 15097, 3, 24, 24, 0]], dtype=torch.long,device=torch_device) # fmt: skip # In 1991 , the remains of Russian Tsar Nicholas II and his family # ( except for Alexei and Maria ) are discovered . # The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the @@ -516,9 +514,7 @@ class TransfoXLModelLanguageGenerationTest(unittest.TestCase): # the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous , # with people , even a bishop , begging for his blessing . - # fmt: off - expected_output_ids = [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,142,1298,188,2,29546,113,8,3654,4,1,1109,7136,833,3,13,1645,4,29546,11,104,7,1,1109,532,7129,2,10,83507,2,1162,1123,2,6,7245,10,2,5,11,104,7,1,1109,532,7129,2,10,24,24,10,22,10,13,770,5863,4,7245,10] # noqa: E231 - # fmt: on + expected_output_ids = [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,142,1298,188,2,29546,113,8,3654,4,1,1109,7136,833,3,13,1645,4,29546,11,104,7,1,1109,532,7129,2,10,83507,2,1162,1123,2,6,7245,10,2,5,11,104,7,1,1109,532,7129,2,10,24,24,10,22,10,13,770,5863,4,7245,10] # fmt: skip # In 1991, the remains of Russian Tsar Nicholas II and his family ( except for # Alexei and Maria ) are discovered. The voice of young son, Tsarevich Alexei # Nikolaevich, narrates the remainder of the story. 1883 Western Siberia, a young diff --git a/tests/models/vision_encoder_decoder/test_modeling_tf_vision_encoder_decoder.py b/tests/models/vision_encoder_decoder/test_modeling_tf_vision_encoder_decoder.py index 2cb5e446724..db38e4a9899 100644 --- a/tests/models/vision_encoder_decoder/test_modeling_tf_vision_encoder_decoder.py +++ b/tests/models/vision_encoder_decoder/test_modeling_tf_vision_encoder_decoder.py @@ -407,7 +407,7 @@ class TFVisionEncoderDecoderMixin: def prepare_pt_inputs_from_tf_inputs(self, tf_inputs_dict): pt_inputs_dict = {} for name, key in tf_inputs_dict.items(): - if type(key) == bool: + if isinstance(key, bool): pt_inputs_dict[name] = key elif name == "input_values": pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) diff --git a/tests/models/vision_encoder_decoder/test_modeling_vision_encoder_decoder.py b/tests/models/vision_encoder_decoder/test_modeling_vision_encoder_decoder.py index 755d59e71cf..8ac4449cf3c 100644 --- a/tests/models/vision_encoder_decoder/test_modeling_vision_encoder_decoder.py +++ b/tests/models/vision_encoder_decoder/test_modeling_vision_encoder_decoder.py @@ -928,9 +928,7 @@ class DonutModelIntegrationTest(unittest.TestCase): sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() # remove first task start token # verify generated sequence - # fmt: off - expected_sequence = " CINNAMON SUGAR 17,000 1 x 17,000 17,000 17,000 20,000 3,000" # noqa: E231 - # fmt: on + expected_sequence = " CINNAMON SUGAR 17,000 1 x 17,000 17,000 17,000 20,000 3,000" # noqa: E231 # fmt: skip self.assertEqual(sequence, expected_sequence) # verify scores diff --git a/tests/models/vision_text_dual_encoder/test_processor_vision_text_dual_encoder.py b/tests/models/vision_text_dual_encoder/test_processor_vision_text_dual_encoder.py index 7cd8b7645db..aebe723bd5f 100644 --- a/tests/models/vision_text_dual_encoder/test_processor_vision_text_dual_encoder.py +++ b/tests/models/vision_text_dual_encoder/test_processor_vision_text_dual_encoder.py @@ -38,9 +38,7 @@ class VisionTextDualEncoderProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() - # fmt: off - vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"] - # fmt: on + vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"] # fmt: skip self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) diff --git a/tests/models/wav2vec2/test_modeling_tf_wav2vec2.py b/tests/models/wav2vec2/test_modeling_tf_wav2vec2.py index 393e056b84d..7af3cd34e8a 100644 --- a/tests/models/wav2vec2/test_modeling_tf_wav2vec2.py +++ b/tests/models/wav2vec2/test_modeling_tf_wav2vec2.py @@ -850,8 +850,9 @@ class TFWav2Vec2ModelIntegrationTest(unittest.TestCase): input_values = inputs.input_values attention_mask = inputs.attention_mask outputs = model(input_values, attention_mask) - predicted_logits, predicted_ids = tf.math.reduce_max(outputs.logits, axis=-1), tf.argmax( - outputs.logits, axis=-1 + predicted_logits, predicted_ids = ( + tf.math.reduce_max(outputs.logits, axis=-1), + tf.argmax(outputs.logits, axis=-1), ) expected_labels = [7, 6, 10, 9] expected_logits = tf.convert_to_tensor([6.1186, 11.8961, 10.2931, 6.0898]) @@ -866,15 +867,18 @@ class TFWav2Vec2ModelIntegrationTest(unittest.TestCase): input_values = inputs.input_values attention_mask = inputs.attention_mask outputs = model(input_values, attention_mask=attention_mask) - predicted_logits_action, predicted_ids_action = tf.math.reduce_max(outputs.logits[:, :6], axis=-1), tf.argmax( - outputs.logits[:, :6], axis=-1 + predicted_logits_action, predicted_ids_action = ( + tf.math.reduce_max(outputs.logits[:, :6], axis=-1), + tf.argmax(outputs.logits[:, :6], axis=-1), + ) + predicted_logits_object, predicted_ids_object = ( + tf.math.reduce_max(outputs.logits[:, 6:20], axis=-1), + tf.argmax(outputs.logits[:, 6:20], axis=-1), + ) + predicted_logits_location, predicted_ids_location = ( + tf.math.reduce_max(outputs.logits[:, 20:24], axis=-1), + tf.argmax(outputs.logits[:, 20:24], axis=-1), ) - predicted_logits_object, predicted_ids_object = tf.math.reduce_max( - outputs.logits[:, 6:20], axis=-1 - ), tf.argmax(outputs.logits[:, 6:20], axis=-1) - predicted_logits_location, predicted_ids_location = tf.math.reduce_max( - outputs.logits[:, 20:24], axis=-1 - ), tf.argmax(outputs.logits[:, 20:24], axis=-1) expected_labels_action = [0, 0, 2, 3] expected_logits_action = tf.convert_to_tensor([0.4568, 11.0848, 1.6621, 9.3841]) expected_labels_object = [3, 10, 3, 4] @@ -915,8 +919,9 @@ class TFWav2Vec2ModelIntegrationTest(unittest.TestCase): input_values = inputs.input_values attention_mask = inputs.attention_mask outputs = model(input_values, attention_mask=attention_mask) - predicted_logits, predicted_ids = tf.math.reduce_max(outputs.logits, axis=-1), tf.argmax( - outputs.logits, axis=-1 + predicted_logits, predicted_ids = ( + tf.math.reduce_max(outputs.logits, axis=-1), + tf.argmax(outputs.logits, axis=-1), ) expected_labels = [1, 1, 2, 2] diff --git a/tests/models/whisper/test_modeling_flax_whisper.py b/tests/models/whisper/test_modeling_flax_whisper.py index 982dcb4827a..d0616637692 100644 --- a/tests/models/whisper/test_modeling_flax_whisper.py +++ b/tests/models/whisper/test_modeling_flax_whisper.py @@ -670,9 +670,7 @@ class FlaxWhisperModelIntegrationTest(unittest.TestCase): generated_ids = generate_fn(input_features) - # fmt: off - EXPECTED_OUTPUT = np.array([50258, 50259, 50359, 50364, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 11, 293, 321, 366, 5404, 281, 2928, 702, 14943, 13, 50692, 50692, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50926, 50926, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904, 9256, 450, 10539, 51208, 51208, 949, 505, 11, 14138, 10117, 490, 3936, 293, 1080, 3542, 5160, 881, 26336, 281, 264, 1575, 13, 51552, 51552, 634, 575, 12525, 22618, 1968, 6144, 35617, 7354, 1292, 6, 589, 307, 534, 10281, 934, 439, 11, 293, 51836, 51836, 50257]) - # fmt: on + EXPECTED_OUTPUT = np.array([50258, 50259, 50359, 50364, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 11, 293, 321, 366, 5404, 281, 2928, 702, 14943, 13, 50692, 50692, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50926, 50926, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904, 9256, 450, 10539, 51208, 51208, 949, 505, 11, 14138, 10117, 490, 3936, 293, 1080, 3542, 5160, 881, 26336, 281, 264, 1575, 13, 51552, 51552, 634, 575, 12525, 22618, 1968, 6144, 35617, 7354, 1292, 6, 589, 307, 534, 10281, 934, 439, 11, 293, 51836, 51836, 50257]) # fmt: skip self.assertTrue(np.allclose(generated_ids, EXPECTED_OUTPUT)) diff --git a/tests/models/whisper/test_modeling_whisper.py b/tests/models/whisper/test_modeling_whisper.py index c7d6fb69265..e26527267fc 100644 --- a/tests/models/whisper/test_modeling_whisper.py +++ b/tests/models/whisper/test_modeling_whisper.py @@ -1566,9 +1566,7 @@ class WhisperModelIntegrationTests(unittest.TestCase): generated_ids = model.generate(input_features, max_length=448, return_timestamps=True).to("cpu") - # fmt: off - EXPECTED_OUTPUT = torch.tensor([50258, 50259, 50359, 50364, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 11, 293, 321, 366, 5404, 281, 2928, 702, 14943, 13, 50692, 50692, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50926, 50926, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904, 9256, 450, 10539, 51208, 51208, 949, 505, 11, 14138, 10117, 490, 3936, 293, 1080, 3542, 5160, 881, 26336, 281, 264, 1575, 13, 51552, 51552, 634, 575, 12525, 22618, 1968, 6144, 35617, 7354, 1292, 6, 589, 307, 534, 10281, 934, 439, 11, 293, 51836, 51836, 50257]) - # fmt: on + EXPECTED_OUTPUT = torch.tensor([50258, 50259, 50359, 50364, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 11, 293, 321, 366, 5404, 281, 2928, 702, 14943, 13, 50692, 50692, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50926, 50926, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904, 9256, 450, 10539, 51208, 51208, 949, 505, 11, 14138, 10117, 490, 3936, 293, 1080, 3542, 5160, 881, 26336, 281, 264, 1575, 13, 51552, 51552, 634, 575, 12525, 22618, 1968, 6144, 35617, 7354, 1292, 6, 589, 307, 534, 10281, 934, 439, 11, 293, 51836, 51836, 50257]) # fmt: skip self.assertTrue(torch.allclose(generated_ids, EXPECTED_OUTPUT)) diff --git a/tests/models/whisper/test_tokenization_whisper.py b/tests/models/whisper/test_tokenization_whisper.py index fd1c135deb5..731abd3a283 100644 --- a/tests/models/whisper/test_tokenization_whisper.py +++ b/tests/models/whisper/test_tokenization_whisper.py @@ -77,20 +77,16 @@ class WhisperTokenizerTest(TokenizerTesterMixin, unittest.TestCase): tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") self.assertListEqual( tokens, - # fmt: off - ['I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ9', '2000', ',', 'Ġand', 'Ġ', 'this', 'Ġis', 'Ġfals', 'é', '.'], - # fmt: on - ) + ["I", "Ġwas", "Ġborn", "Ġin", "Ġ9", "2000", ",", "Ġand", "Ġ", "this", "Ġis", "Ġfals", "é", "."], # fmt: skip + ) # fmt: skip ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual(ids, [40, 390, 4232, 294, 1722, 25743, 11, 293, 220, 11176, 307, 16720, 526, 13]) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual( back_tokens, - # fmt: off - ['I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ9', '2000', ',', 'Ġand', 'Ġ', 'this', 'Ġis', 'Ġfals', 'é', '.'], - # fmt: on - ) + ["I", "Ġwas", "Ġborn", "Ġin", "Ġ9", "2000", ",", "Ġand", "Ġ", "this", "Ġis", "Ġfals", "é", "."], # fmt: skip + ) # fmt: skip def test_tokenizer_slow_store_full_signature(self): pass @@ -108,9 +104,7 @@ class WhisperTokenizerTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[50257, 50362, 41762, 364, 357, 36234, 1900, 355, 12972, 13165, 354, 12, 35636, 364, 290, 12972, 13165, 354, 12, 5310, 13363, 12, 4835, 8, 3769, 2276, 12, 29983, 45619, 357, 13246, 51, 11, 402, 11571, 12, 17, 11, 5564, 13246, 38586, 11, 16276, 44, 11, 4307, 346, 33, 861, 11, 16276, 7934, 23029, 329, 12068, 15417, 28491, 357, 32572, 52, 8, 290, 12068, 15417, 16588, 357, 32572, 38, 8, 351, 625, 3933, 10, 2181, 13363, 4981, 287, 1802, 10, 8950, 290, 2769, 48817, 1799, 1022, 449, 897, 11, 9485, 15884, 354, 290, 309, 22854, 37535, 13, 50256], [50257, 50362, 13246, 51, 318, 3562, 284, 662, 12, 27432, 2769, 8406, 4154, 282, 24612, 422, 9642, 9608, 276, 2420, 416, 26913, 21143, 319, 1111, 1364, 290, 826, 4732, 287, 477, 11685, 13, 50256], [50257, 50362, 464, 2068, 7586, 21831, 18045, 625, 262, 16931, 3290, 13, 50256]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[50257, 50362, 41762, 364, 357, 36234, 1900, 355, 12972, 13165, 354, 12, 35636, 364, 290, 12972, 13165, 354, 12, 5310, 13363, 12, 4835, 8, 3769, 2276, 12, 29983, 45619, 357, 13246, 51, 11, 402, 11571, 12, 17, 11, 5564, 13246, 38586, 11, 16276, 44, 11, 4307, 346, 33, 861, 11, 16276, 7934, 23029, 329, 12068, 15417, 28491, 357, 32572, 52, 8, 290, 12068, 15417, 16588, 357, 32572, 38, 8, 351, 625, 3933, 10, 2181, 13363, 4981, 287, 1802, 10, 8950, 290, 2769, 48817, 1799, 1022, 449, 897, 11, 9485, 15884, 354, 290, 309, 22854, 37535, 13, 50256], [50257, 50362, 13246, 51, 318, 3562, 284, 662, 12, 27432, 2769, 8406, 4154, 282, 24612, 422, 9642, 9608, 276, 2420, 416, 26913, 21143, 319, 1111, 1364, 290, 826, 4732, 287, 477, 11685, 13, 50256], [50257, 50362, 464, 2068, 7586, 21831, 18045, 625, 262, 16931, 3290, 13, 50256]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="openai/whisper-tiny.en", padding=False @@ -128,9 +122,7 @@ class WhisperTokenizerTest(TokenizerTesterMixin, unittest.TestCase): ) # Merge when the previous sequence is a suffix of the next sequence - # fmt: off - next_sequences_1 = [50364, 295, 6177, 3391, 11, 19817, 3337, 507, 307, 406, 3163, 1953, 466, 13, 50614, 50614, 2812, 9836, 14783, 390, 6263, 538, 257, 1359, 11, 8199, 6327, 1090, 322, 702, 7443, 13, 50834, 50257] - # fmt: on + next_sequences_1 = [50364, 295, 6177, 3391, 11, 19817, 3337, 507, 307, 406, 3163, 1953, 466, 13, 50614, 50614, 2812, 9836, 14783, 390, 6263, 538, 257, 1359, 11, 8199, 6327, 1090, 322, 702, 7443, 13, 50834, 50257] # fmt: skip self.assertEqual( tokenizer.decode(next_sequences_1, output_offsets=True), { diff --git a/tests/models/xglm/test_modeling_tf_xglm.py b/tests/models/xglm/test_modeling_tf_xglm.py index 54641693c77..3950ccf6524 100644 --- a/tests/models/xglm/test_modeling_tf_xglm.py +++ b/tests/models/xglm/test_modeling_tf_xglm.py @@ -177,9 +177,7 @@ class TFXGLMModelLanguageGenerationTest(unittest.TestCase): model = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M") input_ids = tf.convert_to_tensor([[2, 268, 9865]], dtype=tf.int32) # The dog # The dog is a very friendly dog. He is very affectionate and loves to play with other - # fmt: off - expected_output_ids = [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581] - # fmt: on + expected_output_ids = [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581] # fmt: skip output_ids = model.generate(input_ids, do_sample=False, num_beams=1) if verify_outputs: self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids) diff --git a/tests/models/xglm/test_modeling_xglm.py b/tests/models/xglm/test_modeling_xglm.py index 5fa92c8082c..457317f0783 100644 --- a/tests/models/xglm/test_modeling_xglm.py +++ b/tests/models/xglm/test_modeling_xglm.py @@ -383,9 +383,7 @@ class XGLMModelLanguageGenerationTest(unittest.TestCase): model.to(torch_device) input_ids = torch.tensor([[2, 268, 9865]], dtype=torch.long, device=torch_device) # The dog # The dog is a very friendly dog. He is very affectionate and loves to play with other - # fmt: off - expected_output_ids = [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581] - # fmt: on + expected_output_ids = [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581] # fmt: skip output_ids = model.generate(input_ids, do_sample=False, num_beams=1) if verify_outputs: self.assertListEqual(output_ids[0].tolist(), expected_output_ids) diff --git a/tests/models/xglm/test_tokenization_xglm.py b/tests/models/xglm/test_tokenization_xglm.py index 74dd4dab5e3..61674976a38 100644 --- a/tests/models/xglm/test_tokenization_xglm.py +++ b/tests/models/xglm/test_tokenization_xglm.py @@ -182,9 +182,7 @@ class XGLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will' " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth" ) - # fmt: off - original_tokenizer_encodings = [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 71630, 28085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 13675, 377, 652, 7580, 10341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 202277, 17892, 33, 60, 87, 4, 3234, 157, 61, 2667, 52376, 19, 88, 23, 735] - # fmt: on + original_tokenizer_encodings = [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 71630, 28085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 13675, 377, 652, 7580, 10341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 202277, 17892, 33, 60, 87, 4, 3234, 157, 61, 2667, 52376, 19, 88, 23, 735] # fmt: skip self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols)) diff --git a/tests/models/xlm_prophetnet/test_modeling_xlm_prophetnet.py b/tests/models/xlm_prophetnet/test_modeling_xlm_prophetnet.py index 1af9ecef6c1..614ccd6ebc2 100644 --- a/tests/models/xlm_prophetnet/test_modeling_xlm_prophetnet.py +++ b/tests/models/xlm_prophetnet/test_modeling_xlm_prophetnet.py @@ -114,9 +114,7 @@ class XLMProphetNetModelIntegrationTest(unittest.TestCase): " этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми" " к кибератакам." ) - ZH_SENTENCE = ( - "根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。" - ) + ZH_SENTENCE = "根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。" input_ids = tokenizer( [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE], padding=True, max_length=255, return_tensors="pt" diff --git a/tests/models/xlm_prophetnet/test_tokenization_xlm_prophetnet.py b/tests/models/xlm_prophetnet/test_tokenization_xlm_prophetnet.py index 13c02b5415f..679e808dc97 100644 --- a/tests/models/xlm_prophetnet/test_tokenization_xlm_prophetnet.py +++ b/tests/models/xlm_prophetnet/test_tokenization_xlm_prophetnet.py @@ -144,9 +144,7 @@ class XLMProphetNetTokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, diff --git a/tests/models/xlm_roberta/test_tokenization_xlm_roberta.py b/tests/models/xlm_roberta/test_tokenization_xlm_roberta.py index 0dde56481cc..1cba1c01d58 100644 --- a/tests/models/xlm_roberta/test_tokenization_xlm_roberta.py +++ b/tests/models/xlm_roberta/test_tokenization_xlm_roberta.py @@ -334,9 +334,7 @@ class XLMRobertaTokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, diff --git a/tests/models/xlnet/test_tokenization_xlnet.py b/tests/models/xlnet/test_tokenization_xlnet.py index 216eb0f637a..9fb28658aab 100644 --- a/tests/models/xlnet/test_tokenization_xlnet.py +++ b/tests/models/xlnet/test_tokenization_xlnet.py @@ -199,9 +199,7 @@ class XLNetTokenizationTest(TokenizerTesterMixin, unittest.TestCase): @slow def test_tokenizer_integration(self): - # fmt: off - expected_encoding = {'input_ids': [[17, 21442, 270, 17, 10, 14645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 22018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 14431, 13, 5500, 11, 1176, 580, 13, 16819, 4797, 23, 17, 10, 17135, 658, 19, 457, 7932, 13, 184, 19, 3154, 17135, 6468, 19, 1404, 12269, 19, 4229, 5356, 16264, 46, 19, 17, 20545, 10395, 9, 9, 9, 11, 28, 6421, 9531, 20729, 17, 10, 353, 17022, 11, 21, 6421, 9531, 16949, 17, 10, 11509, 753, 11, 33, 95, 2421, 7385, 956, 14431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 24738, 19, 13203, 658, 218, 787, 21, 430, 18482, 847, 2637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 22178, 27, 1064, 22, 956, 13, 11101, 1429, 5854, 24313, 18953, 40, 422, 24366, 68, 1758, 37, 10483, 14257, 31, 207, 263, 21, 203, 3773, 25, 71, 9735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2049, 3442, 17, 13894, 3380, 23, 95, 18, 17634, 2288, 9, 4, 3]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501 - # fmt: on + expected_encoding = {'input_ids': [[17, 21442, 270, 17, 10, 14645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 22018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 14431, 13, 5500, 11, 1176, 580, 13, 16819, 4797, 23, 17, 10, 17135, 658, 19, 457, 7932, 13, 184, 19, 3154, 17135, 6468, 19, 1404, 12269, 19, 4229, 5356, 16264, 46, 19, 17, 20545, 10395, 9, 9, 9, 11, 28, 6421, 9531, 20729, 17, 10, 353, 17022, 11, 21, 6421, 9531, 16949, 17, 10, 11509, 753, 11, 33, 95, 2421, 7385, 956, 14431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 24738, 19, 13203, 658, 218, 787, 21, 430, 18482, 847, 2637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 22178, 27, 1064, 22, 956, 13, 11101, 1429, 5854, 24313, 18953, 40, 422, 24366, 68, 1758, 37, 10483, 14257, 31, 207, 263, 21, 203, 3773, 25, 71, 9735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2049, 3442, 17, 13894, 3380, 23, 95, 18, 17634, 2288, 9, 4, 3]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, diff --git a/tests/pipelines/test_pipelines_automatic_speech_recognition.py b/tests/pipelines/test_pipelines_automatic_speech_recognition.py index df9b23b407f..b5dee1e00fc 100644 --- a/tests/pipelines/test_pipelines_automatic_speech_recognition.py +++ b/tests/pipelines/test_pipelines_automatic_speech_recognition.py @@ -517,9 +517,7 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase): ) # Merge when the previous sequence is not included in the current sequence - # fmt: off - next_sequences_3 = [[50364, 2812, 9836, 14783, 390, 6263, 538, 257, 1359, 11, 8199, 6327, 1090, 322, 702, 7443, 13, 50584, 50257]] - # fmt: on + next_sequences_3 = [[50364, 2812, 9836, 14783, 390, 6263, 538, 257, 1359, 11, 8199, 6327, 1090, 322, 702, 7443, 13, 50584, 50257]] # fmt: skip # {'text': ' His instant panic was followed by a small, sharp blow high on his chest.','timestamp': (0.0, 9.4)} merge = _find_timestamp_sequence( [[previous_sequence, (480_000, 0, 0)], [next_sequences_3, (480_000, 120_000, 0)]], @@ -527,12 +525,10 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase): processor.feature_extractor, max_source_positions, ) - # fmt: off self.assertEqual( merge, [51492, 406, 3163, 1953, 466, 13, 51612, 51612, 2812, 9836, 14783, 390, 6263, 538, 257, 1359, 11, 8199, 6327, 1090, 322, 702, 7443, 13, 51832], - ) - # fmt: on + ) # fmt: skip self.assertEqual( processor.decode(merge, output_offsets=True), { @@ -550,23 +546,19 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase): }, ) # last case is when the sequence is not in the first next predicted start and end of timestamp - # fmt: off next_sequences_3 = [ [50364, 2812, 9836, 14783, 390, 406, 3163, 1953, 466, 13, 50634, 50634, 2812, 9836, 14783, 390, 6263, 538, 257, 1359, 11, 8199, 6327, 1090, 322, 702, 7443, 13, 50934] - ] - # fmt: on + ] # fmt: skip merge = _find_timestamp_sequence( [[previous_sequence, (480_000, 0, 0)], [next_sequences_3, (480_000, 167_000, 0)]], processor.tokenizer, processor.feature_extractor, max_source_positions, ) - # fmt: off self.assertEqual( merge, [51492, 406, 3163, 1953, 466, 13, 51612, 51612, 2812, 9836, 14783, 390, 6263, 538, 257, 1359, 11, 8199, 6327, 1090, 322, 702, 7443, 13, 51912] - ) - # fmt: on + ) # fmt: skip self.assertEqual( processor.decode(merge, output_offsets=True), { diff --git a/tests/pipelines/test_pipelines_conversational.py b/tests/pipelines/test_pipelines_conversational.py index 6ba2d8379d2..e80d6a646f9 100644 --- a/tests/pipelines/test_pipelines_conversational.py +++ b/tests/pipelines/test_pipelines_conversational.py @@ -242,9 +242,7 @@ These are just a few of the many attractions that Paris has to offer. With so mu ], ) inputs = tokenizer._build_conversation_input_ids(conversation) - # fmt: off - EXPECTED_INPUTS_IDS = [ 1, 518, 25580, 29962, 3532, 14816, 29903, 6778, 13, 3492, 526, 263, 8444, 29892, 3390, 1319, 322, 15993, 20255, 29889, 29849, 1234, 408, 1371, 3730, 408, 1950, 29892, 1550, 1641, 9109, 29889, 29871, 3575, 6089, 881, 451, 3160, 738, 10311, 1319, 29892, 443, 621, 936, 29892, 11021, 391, 29892, 7916, 391, 29892, 304, 27375, 29892, 18215, 29892, 470, 27302, 2793, 29889, 3529, 9801, 393, 596, 20890, 526, 5374, 635, 443, 5365, 1463, 322, 6374, 297, 5469, 29889, 13, 13, 3644, 263, 1139, 947, 451, 1207, 738, 4060, 29892, 470, 338, 451, 2114, 1474, 16165, 261, 296, 29892, 5649, 2020, 2012, 310, 22862, 1554, 451, 1959, 29889, 960, 366, 1016, 29915, 29873, 1073, 278, 1234, 304, 263, 1139, 29892, 3113, 1016, 29915, 29873, 6232, 2089, 2472, 29889, 13, 29966, 829, 14816, 29903, 6778, 13, 13, 29902, 626, 2675, 304, 3681, 29892, 825, 881, 306, 1074, 29973, 518, 29914, 25580, 29962, 3681, 29892, 278, 7483, 310, 3444, 29892, 338, 2998, 363, 967, 380, 27389, 11258, 29892, 1616, 19133, 29879, 29892, 15839, 2982, 22848, 29892, 322, 6017, 7716, 25005, 29889, 2266, 526, 777, 310, 278, 2246, 19650, 1953, 304, 1074, 297, 3681, 29901, 13, 13, 29896, 29889, 450, 382, 2593, 295, 23615, 29901, 450, 9849, 293, 382, 2593, 295, 23615, 338, 697, 310, 278, 1556, 5936, 13902, 2982, 22848, 297, 278, 3186, 322, 16688, 2078, 271, 400, 5086, 8386, 310, 278, 4272, 29889, 13, 29906, 29889, 450, 4562, 12675, 6838, 29901, 450, 4562, 12675, 338, 697, 310, 278, 3186, 29915, 29879, 10150, 322, 1556, 13834, 19133, 29879, 29892, 27261, 385, 21210, 573, 4333, 310, 1616, 322, 24238, 29879, 29892, 3704, 278, 2598, 29874, 29420, 29889, 13, 29941, 29889, 24337, 29899, 29928, 420, 315, 21471, 29901, 910, 9560, 274, 21471, 338, 697, 310, 278, 1556, 13834, 2982, 22848, 297, 3681, 322, 338, 2998, 363, 967, 22883, 293, 11258, 322, 380, 27389, 380, 7114, 12917, 5417, 29889, 13, 13, 1349, 968, 526, 925, 263, 2846, 310, 278, 1784, 19650, 1953, 393, 3681, 756, 304, 5957, 29889, 2973, 577, 1568, 304, 1074, 322, 437, 29892, 372, 29915, 29879, 694, 4997, 393, 3681, 338, 697, 310, 278, 1556, 5972, 6282, 391, 15422, 800, 297, 278, 3186, 29889, 29871, 2, 1, 518, 25580, 29962, 1724, 338, 577, 2107, 1048, 396, 29896, 29973, 518, 29914, 25580, 29962] - # fmt: on + EXPECTED_INPUTS_IDS = [ 1, 518, 25580, 29962, 3532, 14816, 29903, 6778, 13, 3492, 526, 263, 8444, 29892, 3390, 1319, 322, 15993, 20255, 29889, 29849, 1234, 408, 1371, 3730, 408, 1950, 29892, 1550, 1641, 9109, 29889, 29871, 3575, 6089, 881, 451, 3160, 738, 10311, 1319, 29892, 443, 621, 936, 29892, 11021, 391, 29892, 7916, 391, 29892, 304, 27375, 29892, 18215, 29892, 470, 27302, 2793, 29889, 3529, 9801, 393, 596, 20890, 526, 5374, 635, 443, 5365, 1463, 322, 6374, 297, 5469, 29889, 13, 13, 3644, 263, 1139, 947, 451, 1207, 738, 4060, 29892, 470, 338, 451, 2114, 1474, 16165, 261, 296, 29892, 5649, 2020, 2012, 310, 22862, 1554, 451, 1959, 29889, 960, 366, 1016, 29915, 29873, 1073, 278, 1234, 304, 263, 1139, 29892, 3113, 1016, 29915, 29873, 6232, 2089, 2472, 29889, 13, 29966, 829, 14816, 29903, 6778, 13, 13, 29902, 626, 2675, 304, 3681, 29892, 825, 881, 306, 1074, 29973, 518, 29914, 25580, 29962, 3681, 29892, 278, 7483, 310, 3444, 29892, 338, 2998, 363, 967, 380, 27389, 11258, 29892, 1616, 19133, 29879, 29892, 15839, 2982, 22848, 29892, 322, 6017, 7716, 25005, 29889, 2266, 526, 777, 310, 278, 2246, 19650, 1953, 304, 1074, 297, 3681, 29901, 13, 13, 29896, 29889, 450, 382, 2593, 295, 23615, 29901, 450, 9849, 293, 382, 2593, 295, 23615, 338, 697, 310, 278, 1556, 5936, 13902, 2982, 22848, 297, 278, 3186, 322, 16688, 2078, 271, 400, 5086, 8386, 310, 278, 4272, 29889, 13, 29906, 29889, 450, 4562, 12675, 6838, 29901, 450, 4562, 12675, 338, 697, 310, 278, 3186, 29915, 29879, 10150, 322, 1556, 13834, 19133, 29879, 29892, 27261, 385, 21210, 573, 4333, 310, 1616, 322, 24238, 29879, 29892, 3704, 278, 2598, 29874, 29420, 29889, 13, 29941, 29889, 24337, 29899, 29928, 420, 315, 21471, 29901, 910, 9560, 274, 21471, 338, 697, 310, 278, 1556, 13834, 2982, 22848, 297, 3681, 322, 338, 2998, 363, 967, 22883, 293, 11258, 322, 380, 27389, 380, 7114, 12917, 5417, 29889, 13, 13, 1349, 968, 526, 925, 263, 2846, 310, 278, 1784, 19650, 1953, 393, 3681, 756, 304, 5957, 29889, 2973, 577, 1568, 304, 1074, 322, 437, 29892, 372, 29915, 29879, 694, 4997, 393, 3681, 338, 697, 310, 278, 1556, 5972, 6282, 391, 15422, 800, 297, 278, 3186, 29889, 29871, 2, 1, 518, 25580, 29962, 1724, 338, 577, 2107, 1048, 396, 29896, 29973, 518, 29914, 25580, 29962] # fmt: skip self.assertEqual(inputs, EXPECTED_INPUTS_IDS) model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf") diff --git a/tests/pipelines/test_pipelines_question_answering.py b/tests/pipelines/test_pipelines_question_answering.py index 0ab574f5f2c..f7683aec15c 100644 --- a/tests/pipelines/test_pipelines_question_answering.py +++ b/tests/pipelines/test_pipelines_question_answering.py @@ -198,17 +198,14 @@ class QAPipelineTests(unittest.TestCase): "question-answering", model="KoichiYasuoka/deberta-base-japanese-aozora-ud-head", ) - output = question_answerer(question="国語", context="全学年にわたって小学校の国語の教科書に挿し絵が用いられている") + output = question_answerer(question="国語", context="全学年にわたって小学校の国語の教科書に挿し絵が用いられている") # fmt: skip # Wrong answer, the whole text is identified as one "word" since the tokenizer does not include # a pretokenizer - self.assertEqual( - nested_simplify(output), - {"score": 1.0, "start": 0, "end": 30, "answer": "全学年にわたって小学校の国語の教科書に挿し絵が用いられている"}, - ) + self.assertEqual(nested_simplify(output),{"score": 1.0, "start": 0, "end": 30, "answer": "全学年にわたって小学校の国語の教科書に挿し絵が用いられている"}) # fmt: skip # Disable word alignment - output = question_answerer(question="国語", context="全学年にわたって小学校の国語の教科書に挿し絵が用いられている", align_to_words=False) + output = question_answerer(question="国語", context="全学年にわたって小学校の国語の教科書に挿し絵が用いられている", align_to_words=False) # fmt: skip self.assertEqual( nested_simplify(output), {"score": 1.0, "start": 15, "end": 18, "answer": "教科書"}, diff --git a/tests/pipelines/test_pipelines_token_classification.py b/tests/pipelines/test_pipelines_token_classification.py index f6f47accc2b..b139fbfd2f7 100644 --- a/tests/pipelines/test_pipelines_token_classification.py +++ b/tests/pipelines/test_pipelines_token_classification.py @@ -486,8 +486,7 @@ class TokenClassificationPipelineTests(unittest.TestCase): token_classifier.model.config.id2label = {0: "O", 1: "MISC", 2: "PER", 3: "ORG", 4: "LOC"} example = [ { - # fmt : off - "scores": np.array([0, 0, 0, 0, 0.9968166351318359]), + "scores": np.array([0, 0, 0, 0, 0.9968166351318359]), # fmt : skip "index": 1, "is_subword": False, "word": "En", @@ -495,8 +494,7 @@ class TokenClassificationPipelineTests(unittest.TestCase): "end": 2, }, { - # fmt : off - "scores": np.array([0, 0, 0, 0, 0.9957635998725891]), + "scores": np.array([0, 0, 0, 0, 0.9957635998725891]), # fmt : skip "index": 2, "is_subword": True, "word": "##zo", @@ -504,9 +502,7 @@ class TokenClassificationPipelineTests(unittest.TestCase): "end": 4, }, { - # fmt: off - "scores": np.array([0, 0, 0, 0.9986497163772583, 0]), - # fmt: on + "scores": np.array([0, 0, 0, 0.9986497163772583, 0]), # fmt : skip "index": 7, "word": "UN", "is_subword": False, @@ -542,8 +538,7 @@ class TokenClassificationPipelineTests(unittest.TestCase): ) example = [ { - # fmt : off - "scores": np.array([0, 0, 0, 0, 0.9968166351318359, 0, 0, 0]), + "scores": np.array([0, 0, 0, 0, 0.9968166351318359, 0, 0, 0]), # fmt : skip "index": 1, "is_subword": False, "word": "En", @@ -551,8 +546,7 @@ class TokenClassificationPipelineTests(unittest.TestCase): "end": 2, }, { - # fmt : off - "scores": np.array([0, 0, 0, 0, 0.9957635998725891, 0, 0, 0]), + "scores": np.array([0, 0, 0, 0, 0.9957635998725891, 0, 0, 0]), # fmt : skip "index": 2, "is_subword": True, "word": "##zo", @@ -560,9 +554,7 @@ class TokenClassificationPipelineTests(unittest.TestCase): "end": 4, }, { - # fmt: off - "scores": np.array([0, 0, 0, 0, 0, 0.9986497163772583, 0, 0, ]), - # fmt: on + "scores": np.array([0, 0, 0, 0, 0, 0.9986497163772583, 0, 0]), # fmt : skip "index": 7, "word": "UN", "is_subword": False, diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py index 31c3c7af030..0edc23c7af2 100755 --- a/tests/test_modeling_common.py +++ b/tests/test_modeling_common.py @@ -2039,7 +2039,7 @@ class ModelTesterMixin: tf_inputs_dict = {} for key, tensor in pt_inputs_dict.items(): # skip key that does not exist in tf - if type(tensor) == bool: + if isinstance(tensor, bool): tf_inputs_dict[key] = tensor elif key == "input_values": tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32) diff --git a/tests/test_modeling_tf_common.py b/tests/test_modeling_tf_common.py index 0d7bf373a4d..d7cd62b41a0 100644 --- a/tests/test_modeling_tf_common.py +++ b/tests/test_modeling_tf_common.py @@ -576,7 +576,7 @@ class TFModelTesterMixin: def prepare_pt_inputs_from_tf_inputs(self, tf_inputs_dict): pt_inputs_dict = {} for name, key in tf_inputs_dict.items(): - if type(key) == bool: + if isinstance(key, bool): pt_inputs_dict[name] = key elif name == "input_values": pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) diff --git a/tests/test_tokenization_common.py b/tests/test_tokenization_common.py index 25d4ab873e8..9b60b2f1867 100644 --- a/tests/test_tokenization_common.py +++ b/tests/test_tokenization_common.py @@ -362,11 +362,15 @@ class TokenizerTesterMixin: model_main_input_name: str = "input_ids", ): for i_r in input_r.values(): - self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual( - len(i_r[1]), max_length + ( + self.assertEqual(len(i_r), 2), + self.assertEqual(len(i_r[0]), max_length), + self.assertEqual(len(i_r[1]), max_length), ) - self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual( - len(i_r[1]), max_length + ( + self.assertEqual(len(i_r), 2), + self.assertEqual(len(i_r[0]), max_length), + self.assertEqual(len(i_r[1]), max_length), ) for i_r, i_p in zip(input_r[model_main_input_name], input_p[model_main_input_name]): @@ -1565,7 +1569,9 @@ class TokenizerTesterMixin: # Testing single inputs encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False) encoded_sequence_dict = tokenizer.encode_plus( - sequence_0, add_special_tokens=True, return_special_tokens_mask=True # , add_prefix_space=False + sequence_0, + add_special_tokens=True, + return_special_tokens_mask=True, # , add_prefix_space=False ) encoded_sequence_w_special = encoded_sequence_dict["input_ids"] special_tokens_mask = encoded_sequence_dict["special_tokens_mask"] diff --git a/tests/tokenization/test_tokenization_fast.py b/tests/tokenization/test_tokenization_fast.py index ad3b2e81841..48ac31b97c4 100644 --- a/tests/tokenization/test_tokenization_fast.py +++ b/tests/tokenization/test_tokenization_fast.py @@ -156,9 +156,7 @@ class PreTrainedTokenizationFastTest(TokenizerTesterMixin, unittest.TestCase): self.assertEqual(tok.pad_token, "") self.assertEqual(tok.init_kwargs["max_length"], 512) self.assertEqual(tok.init_kwargs["pad_to_multiple_of"], 8) - # fmt: off - self.assertEqual(tok(sentences, padding = True), {'input_ids': [[8774, 6, 3, 63, 31, 1748, 55, 1, 0, 0, 0, 0,0, 0, 0, 0],[ 571, 33, 25, 3, 2, 3, 58, 290, 225, 59, 36, 136, 962, 269, 58, 1]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}) - # fmt: on + self.assertEqual(tok(sentences, padding = True), {'input_ids': [[8774, 6, 3, 63, 31, 1748, 55, 1, 0, 0, 0, 0,0, 0, 0, 0],[ 571, 33, 25, 3, 2, 3, 58, 290, 225, 59, 36, 136, 962, 269, 58, 1]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}) # fmt: skip tokenizer.enable_truncation(8, stride=0, strategy="longest_first", direction="right") self.assertEqual( @@ -175,9 +173,7 @@ class PreTrainedTokenizationFastTest(TokenizerTesterMixin, unittest.TestCase): self.assertEqual(tok.init_kwargs["stride"], 0) # NOTE even if the model has a default max_length, it is not used... # thus tok(sentences, truncation = True) does nothing and does not warn either - # fmt: off - self.assertEqual(tok(sentences, truncation = True, max_length = 8), {'input_ids': [[8774, 6, 3, 63, 31, 1748, 55, 1],[ 571, 33, 25, 3, 2, 3, 58, 1]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0],[0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1]]}) - # fmt: on + self.assertEqual(tok(sentences, truncation = True, max_length = 8), {'input_ids': [[8774, 6, 3, 63, 31, 1748, 55, 1],[ 571, 33, 25, 3, 2, 3, 58, 1]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0],[0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1]]}) # fmt: skip @require_tokenizers diff --git a/tests/trainer/test_trainer.py b/tests/trainer/test_trainer.py index 9df5ac84d74..b9f801fabd7 100644 --- a/tests/trainer/test_trainer.py +++ b/tests/trainer/test_trainer.py @@ -2201,9 +2201,7 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon): model = nn.Sequential(TstLayer(128), nn.ModuleList([TstLayer(128), TstLayer(128)])) trainer = Trainer(model=model) trainer.create_optimizer_and_scheduler(10) - # fmt: off - wd_names = ['0.linear1.weight', '0.linear2.weight', '1.0.linear1.weight', '1.0.linear2.weight', '1.1.linear1.weight', '1.1.linear2.weight'] - # fmt: on + wd_names = ['0.linear1.weight', '0.linear2.weight', '1.0.linear1.weight', '1.0.linear2.weight', '1.1.linear1.weight', '1.1.linear2.weight'] # fmt: skip wd_params = [p for n, p in model.named_parameters() if n in wd_names] no_wd_params = [p for n, p in model.named_parameters() if n not in wd_names] self.assertListEqual(trainer.optimizer.param_groups[0]["params"], wd_params) diff --git a/utils/check_copies.py b/utils/check_copies.py index 667c11ec724..c536eb800ff 100644 --- a/utils/check_copies.py +++ b/utils/check_copies.py @@ -40,11 +40,9 @@ import argparse import glob import os import re +import subprocess from typing import List, Optional, Tuple -import black -from doc_builder.style_doc import style_docstrings_in_code - from transformers.utils import direct_transformers_import @@ -226,9 +224,17 @@ def get_indent(code: str) -> str: return "" -def blackify(code: str) -> str: +def run_ruff(code): + command = ["ruff", "format", "-", "--config", "pyproject.toml", "--silent"] + process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE) + stdout, _ = process.communicate(input=code.encode()) + return stdout.decode() + + +def stylify(code: str) -> str: """ - Applies the black part of our `make style` command to some code. + Applies the ruff part of our `make style` command to some code. This formats the code using `ruff format`. + As `ruff` does not provide a python api this cannot be done on the fly. Args: code (`str`): The code to format. @@ -239,10 +245,8 @@ def blackify(code: str) -> str: has_indent = len(get_indent(code)) > 0 if has_indent: code = f"class Bla:\n{code}" - mode = black.Mode(target_versions={black.TargetVersion.PY37}, line_length=119) - result = black.format_str(code, mode=mode) - result, _ = style_docstrings_in_code(result) - return result[len("class Bla:\n") :] if has_indent else result + formatted_code = run_ruff(code) + return formatted_code[len("class Bla:\n") :] if has_indent else formatted_code def check_codes_match(observed_code: str, theoretical_code: str) -> Optional[int]: @@ -351,8 +355,7 @@ def is_copy_consistent(filename: str, overwrite: bool = False) -> Optional[List[ if option.strip() == "all-casing": theoretical_code = re.sub(obj1.lower(), obj2.lower(), theoretical_code) theoretical_code = re.sub(obj1.upper(), obj2.upper(), theoretical_code) - - theoretical_code = blackify(theoretical_code) + theoretical_code = stylify(theoretical_code) # Test for a diff and act accordingly. diff_index = check_codes_match(observed_code, theoretical_code) diff --git a/utils/tests_fetcher.py b/utils/tests_fetcher.py index 728a76bde00..c7638a129a0 100644 --- a/utils/tests_fetcher.py +++ b/utils/tests_fetcher.py @@ -515,9 +515,7 @@ def extract_imports(module_fname: str, cache: Dict[str, List[str]] = None) -> Li # Filter out all docstrings to not get imports in code examples. As before we need to deactivate formatting to # keep this as escaped quotes and avoid this function failing on this file. - # fmt: off - splits = content.split('\"\"\"') - # fmt: on + splits = content.split('\"\"\"') # fmt: skip content = "".join(splits[::2]) module_parts = str(module_fname).split(os.path.sep)