



'''simple docstring'''





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

import black


lowercase_	 =						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.
lowercase_	 =						"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"






class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						tempfile.mkdtemp()
      os.makedirs(os.path.join(self.transformer_dir				,     '''models/bert/''' ) )
      _a											=						self.transformer_dir
      shutil.copy(
          os.path.join(A				,     '''src/transformers/models/bert/modeling_bert.py''' )				,     os.path.join(self.transformer_dir				,     '''models/bert/modeling_bert.py''' )				,     )



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						'''src/transformers'''
      shutil.rmtree(self.transformer_dir )



 def       a__						(self				,     A				,     A				,     A				,     A=None )					->  List[str]:


      """simple docstring"""



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



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead''' )
      self.assertEqual(A				,     A )



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      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'''				,     A				,     )

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

      # Copy consistency with a really long name
      _a											=						'''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'''				,     A				,     A )				,     )

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



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

      _a											=						(
          '''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.'''
      )
      _a											=						(
          '''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'''
      )
      _a											=						(
          '''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'''
      )

      _a						,					_a											=						check_copies.convert_to_localized_md(
          A				,     A				,     localized_readme['''format_model_list'''] )

      self.assertFalse(A )
      self.assertEqual(A				,     A )

      _a						,					_a											=						check_copies.convert_to_localized_md(
          A				,     A				,     localized_readme['''format_model_list'''] )

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

      _a											=						(
          '''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.'''
      )
      _a											=						(
          '''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'''
      )
      _a											=						(
          '''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'''
      )

      _a						,					_a											=						check_copies.convert_to_localized_md(
          A				,     A				,     localized_readme['''format_model_list'''] )

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








'''simple docstring'''





import pytest

import datasets


# Import fixture modules as plugins
lowercase_	 =						["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     for item in items:
          if any(marker in item.keywords for marker in ['''integration''', '''unit''']):
               continue
          item.add_marker(pytest.mark.unit)




def lowerCAmelCase						(__A):



     """simple docstring"""



     config.addinivalue_line('''markers'''						,			'''torchaudio_latest: mark test to run with torchaudio>=0.12''')




@pytest.fixture(autouse=__A)
def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     _a											=						tmp_path_factory.getbasetemp() / '''cache'''
     _a											=						test_hf_cache_home / '''datasets'''
     _a											=						test_hf_cache_home / '''metrics'''
     _a											=						test_hf_cache_home / '''modules'''
     monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE'''						,			str(__A))
     monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE'''						,			str(__A))
     monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE'''						,			str(__A))
     _a											=						test_hf_datasets_cache / '''downloads'''
     monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH'''						,			str(__A))
     _a											=						test_hf_datasets_cache / '''downloads''' / '''extracted'''
     monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''						,			str(__A))




@pytest.fixture(autouse=__A						,			scope='''session''')
def lowerCAmelCase						():



     """simple docstring"""



     datasets.disable_progress_bar()




@pytest.fixture(autouse=__A)
def lowerCAmelCase						(__A):



     """simple docstring"""



     monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'''						,			__A)




@pytest.fixture
def lowerCAmelCase						(__A):



     """simple docstring"""



     monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'''						,			__A)



