mirror of
https://github.com/huggingface/transformers.git
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29d4992453
* Apply on BERT and ALBERT * Update TF Bart * Add input processing to TF BART * Add input processing for TF CTRL * Add input processing to TF Distilbert * Add input processing to TF DPR * Add input processing to TF Electra * Add input processing for TF Flaubert * Add deprecated arguments * Add input processing to TF XLM * remove unused imports * Add input processing to TF Funnel * Add input processing to TF GPT2 * Add input processing to TF Longformer * Add input processing to TF Lxmert * Apply style * Add input processing to TF Mobilebert * Add input processing to TF GPT * Add input processing to TF Roberta * Add input processing to TF T5 * Add input processing to TF TransfoXL * Apply style * Rebase on master * Bug fix * Retry to bugfix * Retry bug fix * Fix wrong model name * Try another fix * Fix BART * Fix input precessing * Apply style * Put the deprecated warnings in the input processing function * Remove the unused imports * Raise an error when len(kwargs)>0 * test ModelOutput instead of TFBaseModelOutput * Bug fix * Address Patrick's comments * Address Patrick's comments * Address Sylvain's comments * Add the new inputs in new Longformer models * Update the template with the new input processing * Remove useless assert * Apply style * Trigger CI
100 lines
3.6 KiB
Python
100 lines
3.6 KiB
Python
# coding=utf-8
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# Copyright 2020 HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from tests.test_configuration_common import ConfigTester
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from tests.test_modeling_tf_bart import TFBartModelTester
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from tests.test_modeling_tf_common import TFModelTesterMixin
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from transformers import (
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BlenderbotConfig,
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BlenderbotSmallTokenizer,
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TFAutoModelForSeq2SeqLM,
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TFBlenderbotForConditionalGeneration,
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is_tf_available,
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)
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from transformers.file_utils import cached_property
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from transformers.testing_utils import is_pt_tf_cross_test, require_tf, require_tokenizers, slow
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class TFBlenderbotModelTester(TFBartModelTester):
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config_updates = dict(
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normalize_before=True,
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static_position_embeddings=True,
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do_blenderbot_90_layernorm=True,
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normalize_embeddings=True,
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)
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config_cls = BlenderbotConfig
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@require_tf
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class TFBlenderbotModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
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all_generative_model_classes = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
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is_encoder_decoder = True
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test_pruning = False
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def setUp(self):
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self.model_tester = TFBlenderbotModelTester(self)
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self.config_tester = ConfigTester(self, config_class=BlenderbotConfig)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_inputs_embeds(self):
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# inputs_embeds not supported
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pass
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def test_saved_model_with_hidden_states_output(self):
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# Should be uncommented during patrick TF refactor
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pass
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def test_saved_model_with_attentions_output(self):
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# Should be uncommented during patrick TF refactor
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pass
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@is_pt_tf_cross_test
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@require_tokenizers
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class TFBlenderbot90MIntegrationTests(unittest.TestCase):
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src_text = [
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"Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like i'm going to throw up.\nand why is that?"
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]
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model_name = "facebook/blenderbot-90M"
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@cached_property
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def tokenizer(self):
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return BlenderbotSmallTokenizer.from_pretrained(self.model_name)
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@cached_property
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def model(self):
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model = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name, from_pt=True)
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return model
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@slow
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def test_90_generation_from_long_input(self):
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model_inputs = self.tokenizer(self.src_text, return_tensors="tf")
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generated_ids = self.model.generate(
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model_inputs.input_ids,
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attention_mask=model_inputs.attention_mask,
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num_beams=2,
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use_cache=True,
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)
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generated_words = self.tokenizer.batch_decode(generated_ids.numpy(), skip_special_tokens=True)[0]
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assert generated_words in (
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"i don't know. i just feel like i'm going to throw up. it's not fun.",
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"i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
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"i'm not sure. i just feel like i've been in a bad situation.",
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)
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