





'''simple docstring'''




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

import black


__SCREAMING_SNAKE_CASE     :Optional[int]       =				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     :Any       =				'''    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      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   tempfile.mkdtemp()
      os.makedirs(os.path.join(self.transformer_dir	,			"models/bert/"    )    )
      _UpperCAmelCase     							=   self.transformer_dir
      shutil.copy(
          os.path.join(snake_case_	,			"src/transformers/models/bert/modeling_bert.py"    )	,			os.path.join(self.transformer_dir	,			"models/bert/modeling_bert.py"    )	,			)




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   "src/transformers"
      shutil.rmtree(self.transformer_dir    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    int	,			snake_case_       :    Any	,			snake_case_       :    Any	,			snake_case_       :    Union[str, Any]=None    ):
      _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=1_1_9    )
      _UpperCAmelCase     							=   black.format_str(snake_case_	,			mode=snake_case_    )
      _UpperCAmelCase     							=   os.path.join(self.transformer_dir	,			"new_code.py"    )
      with open(snake_case_	,			"w"	,			newline="\n"    ) as f:
           f.write(snake_case_    )
      if overwrite_result is None:
           self.assertTrue(len(check_copies.is_copy_consistent(snake_case_    )    ) == 0    )
      else:
           check_copies.is_copy_consistent(f.name	,			overwrite=snake_case_    )
           with open(snake_case_	,			"r"    ) as f:
                self.assertTrue(f.read()	,			snake_case_    )




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"    )
      self.assertEqual(snake_case_	,			snake_case_    )




 def      lowercase	(					self       :    Any    ):
      # 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"	,			snake_case_	,			)

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

      # 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"	,			snake_case_	,			snake_case_    )	,			)

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




 def      lowercase	(					self       :    List[Any]    ):
      _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(
          snake_case_	,			snake_case_	,			localized_readme["format_model_list"]    )

      self.assertFalse(snake_case_    )
      self.assertEqual(snake_case_	,			snake_case_    )

      _UpperCAmelCase							,  _UpperCAmelCase     							=   check_copies.convert_to_localized_md(
          snake_case_	,			snake_case_	,			localized_readme["format_model_list"]    )

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

      _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(
          snake_case_	,			snake_case_	,			localized_readme["format_model_list"]    )

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











'''simple docstring'''




import re

from filelock import FileLock


try:
  import nltk

  __SCREAMING_SNAKE_CASE     :Optional[int]       =				True
except (ImportError, ModuleNotFoundError):
  __SCREAMING_SNAKE_CASE     :str       =				False

if NLTK_AVAILABLE:
  with FileLock('''.lock''') as lock:
    nltk.download('''punkt''', quiet=True)
def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       str:


     '''simple docstring'''



     re.sub("<n>"			,     ""			,     __lowercase       )  # remove pegasus newline char
     assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
     return "\n".join(nltk.sent_tokenize(__lowercase       )       )




