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Added missing test_tokenization_led (#20568)
* Create test_tokenization_led.py * Update test_tokenization_led.py * Update test_tokenization_led.py * Update test_tokenization_led.py * Update test_tokenization_led.py * Update test_tokenization_led.py * Update test_tokenization_led.py * Update test_tokenization_led.py * Update test_tokenization_led.py
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tests/models/led/test_tokenization_led.py
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184
tests/models/led/test_tokenization_led.py
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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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 json
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import os
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import unittest
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from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
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from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
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from transformers.testing_utils import require_tokenizers, require_torch
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from transformers.utils import cached_property
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from ...test_tokenization_common import TokenizerTesterMixin
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@require_tokenizers
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class TestTokenizationLED(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = LEDTokenizer
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rust_tokenizer_class = LEDTokenizerFast
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test_rust_tokenizer = True
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def setUp(self):
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super().setUp()
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vocab = [
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"l",
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"o",
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"w",
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"e",
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"r",
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"s",
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"t",
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"i",
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"d",
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"n",
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"\u0120",
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"\u0120l",
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"\u0120n",
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"\u0120lo",
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"\u0120low",
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"er",
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"\u0120lowest",
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"\u0120newer",
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"\u0120wider",
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"<unk>",
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]
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
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self.special_tokens_map = {"unk_token": "<unk>"}
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as fp:
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fp.write(json.dumps(vocab_tokens) + "\n")
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with open(self.merges_file, "w", encoding="utf-8") as fp:
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fp.write("\n".join(merges))
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def get_tokenizer(self, **kwargs):
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kwargs.update(self.special_tokens_map)
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return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
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def get_rust_tokenizer(self, **kwargs):
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kwargs.update(self.special_tokens_map)
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return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
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def get_input_output_texts(self, tokenizer):
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return "lower newer", "lower newer"
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@cached_property
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def default_tokenizer(self):
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return LEDTokenizer.from_pretrained("allenai/led-base-16384")
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@cached_property
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def default_tokenizer_fast(self):
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return LEDTokenizerFast.from_pretrained("allenai/led-base-16384")
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@require_torch
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def test_prepare_batch(self):
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src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
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expected_src_tokens = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]
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for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
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batch = tokenizer(src_text, max_length=len(expected_src_tokens), padding=True, return_tensors="pt")
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self.assertIsInstance(batch, BatchEncoding)
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self.assertEqual((2, 9), batch.input_ids.shape)
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self.assertEqual((2, 9), batch.attention_mask.shape)
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result = batch.input_ids.tolist()[0]
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self.assertListEqual(expected_src_tokens, result)
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@require_torch
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def test_prepare_batch_empty_target_text(self):
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src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
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for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
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batch = tokenizer(src_text, padding=True, return_tensors="pt")
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self.assertIn("input_ids", batch)
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self.assertIn("attention_mask", batch)
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self.assertNotIn("labels", batch)
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self.assertNotIn("decoder_attention_mask", batch)
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@require_torch
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def test_tokenizer_as_target_length(self):
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tgt_text = [
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"Summary of the text.",
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"Another summary.",
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]
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for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
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targets = tokenizer(text_target=tgt_text, max_length=32, padding="max_length", return_tensors="pt")
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self.assertEqual(32, targets["input_ids"].shape[1])
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@require_torch
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def test_prepare_batch_not_longer_than_maxlen(self):
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for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
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batch = tokenizer(
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["I am a small frog" * 1024, "I am a small frog"], padding=True, truncation=True, return_tensors="pt"
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)
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self.assertIsInstance(batch, BatchEncoding)
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self.assertEqual(batch.input_ids.shape, (2, 5122))
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@require_torch
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def test_special_tokens(self):
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src_text = ["A long paragraph for summarization."]
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tgt_text = [
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"Summary of the text.",
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]
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for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
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inputs = tokenizer(src_text, return_tensors="pt")
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targets = tokenizer(text_target=tgt_text, return_tensors="pt")
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input_ids = inputs["input_ids"]
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labels = targets["input_ids"]
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self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
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self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
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self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
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self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())
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@require_torch
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def test_global_attention_mask(self):
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for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
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src_text = ["Summary of the text.", "Another summary."]
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expected_global_attention_mask = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
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encoded_output = tokenizer(src_text, padding=False)
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encoded_output["global_attention_mask"] = [[0] * len(x) for x in encoded_output["input_ids"]]
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outputs = tokenizer.pad(encoded_output)
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self.assertSequenceEqual(outputs["global_attention_mask"], expected_global_attention_mask)
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def test_pretokenized_inputs(self):
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pass
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def test_embeded_special_tokens(self):
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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sentence = "A, <mask> AllenNLP sentence."
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tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
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tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
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self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))
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self.assertEqual(
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sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"]),
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sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]),
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)
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tokens_r_str = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
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tokens_p_str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])
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self.assertSequenceEqual(tokens_p["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
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self.assertSequenceEqual(tokens_r["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
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self.assertSequenceEqual(
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tokens_p_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
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)
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self.assertSequenceEqual(
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tokens_r_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
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)
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