From 183af58b1151d26c1ac4f56d3018851e0015bda4 Mon Sep 17 00:00:00 2001 From: IMvision12 <88665786+IMvision12@users.noreply.github.com> Date: Fri, 9 Dec 2022 01:25:22 +0530 Subject: [PATCH] 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 --- tests/models/led/test_tokenization_led.py | 184 ++++++++++++++++++++++ 1 file changed, 184 insertions(+) create mode 100644 tests/models/led/test_tokenization_led.py diff --git a/tests/models/led/test_tokenization_led.py b/tests/models/led/test_tokenization_led.py new file mode 100644 index 0000000000..2c761ad17a --- /dev/null +++ b/tests/models/led/test_tokenization_led.py @@ -0,0 +1,184 @@ +# Copyright 2020 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import json +import os +import unittest + +from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast +from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES +from transformers.testing_utils import require_tokenizers, require_torch +from transformers.utils import cached_property + +from ...test_tokenization_common import TokenizerTesterMixin + + +@require_tokenizers +class TestTokenizationLED(TokenizerTesterMixin, unittest.TestCase): + tokenizer_class = LEDTokenizer + rust_tokenizer_class = LEDTokenizerFast + test_rust_tokenizer = True + + def setUp(self): + super().setUp() + vocab = [ + "l", + "o", + "w", + "e", + "r", + "s", + "t", + "i", + "d", + "n", + "\u0120", + "\u0120l", + "\u0120n", + "\u0120lo", + "\u0120low", + "er", + "\u0120lowest", + "\u0120newer", + "\u0120wider", + "", + ] + vocab_tokens = dict(zip(vocab, range(len(vocab)))) + merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] + self.special_tokens_map = {"unk_token": ""} + + self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) + self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) + with open(self.vocab_file, "w", encoding="utf-8") as fp: + fp.write(json.dumps(vocab_tokens) + "\n") + with open(self.merges_file, "w", encoding="utf-8") as fp: + fp.write("\n".join(merges)) + + def get_tokenizer(self, **kwargs): + kwargs.update(self.special_tokens_map) + return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) + + def get_rust_tokenizer(self, **kwargs): + kwargs.update(self.special_tokens_map) + return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) + + def get_input_output_texts(self, tokenizer): + return "lower newer", "lower newer" + + @cached_property + def default_tokenizer(self): + return LEDTokenizer.from_pretrained("allenai/led-base-16384") + + @cached_property + def default_tokenizer_fast(self): + return LEDTokenizerFast.from_pretrained("allenai/led-base-16384") + + @require_torch + def test_prepare_batch(self): + src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."] + expected_src_tokens = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2] + + for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: + batch = tokenizer(src_text, max_length=len(expected_src_tokens), padding=True, return_tensors="pt") + self.assertIsInstance(batch, BatchEncoding) + + self.assertEqual((2, 9), batch.input_ids.shape) + self.assertEqual((2, 9), batch.attention_mask.shape) + result = batch.input_ids.tolist()[0] + self.assertListEqual(expected_src_tokens, result) + + @require_torch + def test_prepare_batch_empty_target_text(self): + src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."] + for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: + batch = tokenizer(src_text, padding=True, return_tensors="pt") + self.assertIn("input_ids", batch) + self.assertIn("attention_mask", batch) + self.assertNotIn("labels", batch) + self.assertNotIn("decoder_attention_mask", batch) + + @require_torch + def test_tokenizer_as_target_length(self): + tgt_text = [ + "Summary of the text.", + "Another summary.", + ] + for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: + targets = tokenizer(text_target=tgt_text, max_length=32, padding="max_length", return_tensors="pt") + self.assertEqual(32, targets["input_ids"].shape[1]) + + @require_torch + def test_prepare_batch_not_longer_than_maxlen(self): + for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: + batch = tokenizer( + ["I am a small frog" * 1024, "I am a small frog"], padding=True, truncation=True, return_tensors="pt" + ) + self.assertIsInstance(batch, BatchEncoding) + self.assertEqual(batch.input_ids.shape, (2, 5122)) + + @require_torch + def test_special_tokens(self): + + src_text = ["A long paragraph for summarization."] + tgt_text = [ + "Summary of the text.", + ] + for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: + inputs = tokenizer(src_text, return_tensors="pt") + targets = tokenizer(text_target=tgt_text, return_tensors="pt") + input_ids = inputs["input_ids"] + labels = targets["input_ids"] + self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()) + self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()) + self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()) + self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()) + + @require_torch + def test_global_attention_mask(self): + for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: + src_text = ["Summary of the text.", "Another summary."] + expected_global_attention_mask = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]] + + encoded_output = tokenizer(src_text, padding=False) + encoded_output["global_attention_mask"] = [[0] * len(x) for x in encoded_output["input_ids"]] + outputs = tokenizer.pad(encoded_output) + self.assertSequenceEqual(outputs["global_attention_mask"], expected_global_attention_mask) + + def test_pretokenized_inputs(self): + pass + + def test_embeded_special_tokens(self): + for tokenizer, pretrained_name, kwargs in self.tokenizers_list: + with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): + tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) + tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) + sentence = "A, AllenNLP sentence." + tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True) + tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True) + self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"])) + self.assertEqual( + sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"]), + sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]), + ) + + tokens_r_str = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]) + tokens_p_str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]) + self.assertSequenceEqual(tokens_p["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]) + self.assertSequenceEqual(tokens_r["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]) + + self.assertSequenceEqual( + tokens_p_str, ["", "A", ",", "", "ĠAllen", "N", "LP", "Ġsentence", ".", ""] + ) + self.assertSequenceEqual( + tokens_r_str, ["", "A", ",", "", "ĠAllen", "N", "LP", "Ġsentence", ".", ""] + )