






"""simple docstring"""







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

import black


lowerCAmelCase_	:    Union[str, Any]            =      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.
lowerCAmelCase_	:    int            =      '''    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   UpperCamelCase_ (       unittest.TestCase  ):



  def 		UpperCamelCase_				(      self				)			->			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(snake_case__ ,		"""src/transformers/models/bert/modeling_bert.py"""				) ,		os.path.join(self.transformer_dir ,		"""models/bert/modeling_bert.py"""				) ,		)



  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			"""src/transformers"""
         shutil.rmtree(self.transformer_dir				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__=None				)			->			Any:



         """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=1_19				)
         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 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         UpperCAmelCase	    =			check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""				)
         self.assertEqual(snake_case__ ,		snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			str:



         """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""" ,		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 		UpperCamelCase_				(      self				)			->			Any:



         """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(
             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"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_	:    Optional[int]            =      {
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_	:    str            =      [
     '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GraphormerForGraphClassification''',
     '''GraphormerModel''',
     '''GraphormerPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_	:    Optional[Any]            =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
