
"""simple docstring"""


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


SCREAMING_SNAKE_CASE      =     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
SCREAMING_SNAKE_CASE      =     """    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""



class   __a  (      unittest.TestCase	):






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		tempfile.mkdtemp()
              os.makedirs(os.path.join(self.transformer_dir       ,      "models/bert/"			)			)
              UpperCamelCase					 =		self.transformer_dir
              shutil.copy(
                  os.path.join(UpperCAmelCase_       ,      "src/transformers/models/bert/modeling_bert.py"			)       ,      os.path.join(self.transformer_dir       ,      "models/bert/modeling_bert.py"			)       ,      )






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  List[str]:







              """simple docstring"""
              UpperCamelCase					 =		"src/transformers"
              shutil.rmtree(self.transformer_dir			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      UpperCAmelCase_ :							Tuple       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							Tuple       ,      UpperCAmelCase_ :							Dict=None			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		comment + f"\nclass {class_name}(nn.Module):\n" + class_code
              if overwrite_result is not None:
                     UpperCamelCase					 =		comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
              UpperCamelCase					 =		black.Mode(target_versions={black.TargetVersion.PYaa}       ,      line_length=119			)
              UpperCamelCase					 =		black.format_str(UpperCAmelCase_       ,      mode=UpperCAmelCase_			)
              UpperCamelCase					 =		os.path.join(self.transformer_dir       ,      "new_code.py"			)
              with open(UpperCAmelCase_       ,      "w"       ,      newline="\n"			) as f:
                     f.write(UpperCAmelCase_			)
              if overwrite_result is None:
                     self.assertTrue(len(check_copies.is_copy_consistent(UpperCAmelCase_			)			) == 0			)
              else:
                     check_copies.is_copy_consistent(f.name       ,      overwrite=UpperCAmelCase_			)
                     with open(UpperCAmelCase_       ,      "r"			) as f:
                            self.assertTrue(f.read()       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Any:







              """simple docstring"""
              UpperCamelCase					 =		check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"			)
              self.assertEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Any:







              """simple docstring"""
              # Base copy consistency
              self.check_copy_consistency(
                  "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"       ,      "BertLMPredictionHead"       ,      REFERENCE_CODE + "\n"       ,      )

              # With no empty line at the end
              self.check_copy_consistency(
                  "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"       ,      "BertLMPredictionHead"       ,      UpperCAmelCase_       ,      )

              # Copy consistency with rename
              self.check_copy_consistency(
                  "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"       ,      "TestModelLMPredictionHead"       ,      re.sub("Bert"       ,      "TestModel"       ,      UpperCAmelCase_			)       ,      )

              # Copy consistency with a really long name
              UpperCamelCase					 =		"TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
              self.check_copy_consistency(
                  f"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"       ,      f"{long_class_name}LMPredictionHead"       ,      re.sub("Bert"       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)       ,      )

              # Copy consistency with overwrite
              self.check_copy_consistency(
                  "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"       ,      "TestModelLMPredictionHead"       ,      UpperCAmelCase_       ,      overwrite_result=re.sub("Bert"       ,      "TestModel"       ,      UpperCAmelCase_			)       ,      )






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		check_copies.LOCALIZED_READMES["README_zh-hans.md"]

              UpperCamelCase					 =		(
                  "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
                  " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
                  " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
                  " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
                  " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
                  " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
                  " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
                  " method has been applied to compress GPT2 into"
                  " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
                  " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
                  " Multilingual BERT into"
                  " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
                  " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
                  " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
                  " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
                  " Luong, Quoc V. Le, Christopher D. Manning."
              )
              UpperCamelCase					 =		(
                  "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
                  " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
                  " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
                  " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
              )
              UpperCamelCase					 =		(
                  "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
                  " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
                  " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
                  " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
                  " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
                  " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
                  " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
                  " method has been applied to compress GPT2 into"
                  " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
                  " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
                  " Multilingual BERT into"
                  " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
                  " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
                  " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
                  " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
                  " Christopher D. Manning 发布。\n"
              )

              UpperCamelCase ,       UpperCamelCase					 =		check_copies.convert_to_localized_md(
                  UpperCAmelCase_       ,      UpperCAmelCase_       ,      localized_readme["format_model_list"]			)

              self.assertFalse(UpperCAmelCase_			)
              self.assertEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase ,       UpperCamelCase					 =		check_copies.convert_to_localized_md(
                  UpperCAmelCase_       ,      UpperCAmelCase_       ,      localized_readme["format_model_list"]			)

              # Check whether the number of models is equal to README.md after conversion.
              self.assertTrue(UpperCAmelCase_			)

              UpperCamelCase					 =		(
                  "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
                  " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
                  " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
                  " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
              )
              UpperCamelCase					 =		(
                  "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
                  " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
                  " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
                  " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
              )
              UpperCamelCase					 =		(
                  "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
                  " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
                  " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
                  " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
              )

              UpperCamelCase ,       UpperCamelCase					 =		check_copies.convert_to_localized_md(
                  UpperCAmelCase_       ,      UpperCAmelCase_       ,      localized_readme["format_model_list"]			)

              # Check if the model link is synchronized.
              self.assertEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)







"""simple docstring"""


import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					Any   =			BarthezTokenizer
       UpperCamelCase_		:					Tuple   =			BarthezTokenizerFast
       UpperCamelCase_		:					str   =			True
       UpperCamelCase_		:					Dict   =			True






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  int:







              """simple docstring"""
              super().setUp()

              UpperCamelCase					 =		BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"			)
              tokenizer.save_pretrained(self.tmpdirname			)
              tokenizer.save_pretrained(self.tmpdirname       ,      legacy_format=UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		"<pad>"
              UpperCamelCase					 =		1

              self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_			)       ,      UpperCAmelCase_			)
              self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_			)       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		list(self.get_tokenizer().get_vocab().keys()			)

              self.assertEqual(vocab_keys[0]       ,      "<s>"			)
              self.assertEqual(vocab_keys[1]       ,      "<pad>"			)
              self.assertEqual(vocab_keys[-1]       ,      "<mask>"			)
              self.assertEqual(len(UpperCAmelCase_			)       ,      101_122			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  List[str]:







              """simple docstring"""
              self.assertEqual(self.get_tokenizer().vocab_size       ,      101_122			)






       @require_torch
       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		["A long paragraph for summarization.", "Another paragraph for summarization."]
              UpperCamelCase					 =		[0, 57, 3_018, 70_307, 91, 2]

              UpperCamelCase					 =		self.tokenizer(
                  UpperCAmelCase_       ,      max_length=len(UpperCAmelCase_			)       ,      padding=UpperCAmelCase_       ,      truncation=UpperCAmelCase_       ,      return_tensors="pt"			)
              self.assertIsInstance(UpperCAmelCase_       ,      UpperCAmelCase_			)

              self.assertEqual((2, 6)       ,      batch.input_ids.shape			)
              self.assertEqual((2, 6)       ,      batch.attention_mask.shape			)
              UpperCamelCase					 =		batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Optional[int]:







              """simple docstring"""
              if not self.test_rust_tokenizer:
                     return

              UpperCamelCase					 =		self.get_tokenizer()
              UpperCamelCase					 =		self.get_rust_tokenizer()

              UpperCamelCase					 =		"I was born in 92000, and this is falsé."

              UpperCamelCase					 =		tokenizer.tokenize(UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.tokenize(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		self.get_rust_tokenizer()
              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Union[str, Any]:







              """simple docstring"""
              # fmt: off
              UpperCamelCase					 =		{"input_ids": [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 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, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 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

              # moussaKam/mbarthez is a french model. So we also use french texts.
              UpperCamelCase					 =		[
                  "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
                  "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
                  "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
                  "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
                  "telles que la traduction et la synthèse de texte.",
              ]

              self.tokenizer_integration_test_util(
                  expected_encoding=UpperCAmelCase_       ,      model_name="moussaKam/mbarthez"       ,      revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"       ,      sequences=UpperCAmelCase_       ,      )





