


'''simple docstring'''






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

import black


a_   :      str				    =      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.
a_   :      Tuple				    =      "    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    ):
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							tempfile.mkdtemp()
      os.makedirs(os.path.join(self.transformer_dir ,			'models/bert/'  )  )
      _a      =							self.transformer_dir
      shutil.copy(
          os.path.join(__magic_name__ ,			'src/transformers/models/bert/modeling_bert.py'  ) ,			os.path.join(self.transformer_dir ,			'models/bert/modeling_bert.py'  ) ,			)
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							'src/transformers'
      shutil.rmtree(self.transformer_dir  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__=None  )   ->							Optional[Any]:
      _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=1_19  )
      _a      =							black.format_str(__magic_name__ ,			mode=__magic_name__  )
      _a      =							os.path.join(self.transformer_dir ,			'new_code.py'  )
      with open(__magic_name__ ,			'w' ,			newline='\n'  ) as f:
           f.write(__magic_name__  )
      if overwrite_result is None:
           self.assertTrue(len(check_copies.is_copy_consistent(__magic_name__  )  ) == 0  )
      else:
           check_copies.is_copy_consistent(f.name ,			overwrite=__magic_name__  )
           with open(__magic_name__ ,			'r'  ) as f:
                self.assertTrue(f.read() ,			__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'  )
      self.assertEqual(__magic_name__ ,			__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      # 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' ,			__magic_name__ ,			)

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

      # 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' ,			__magic_name__ ,			__magic_name__  ) ,			)

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




 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _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(
          __magic_name__ ,			__magic_name__ ,			localized_readme['format_model_list']  )

      self.assertFalse(__magic_name__  )
      self.assertEqual(__magic_name__ ,			__magic_name__  )

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

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

      _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(
          __magic_name__ ,			__magic_name__ ,			localized_readme['format_model_list']  )

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








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_   :      Optional[int]				    =      {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Dict				    =      ["DeiTFeatureExtractor"]
 a_   :      List[Any]				    =      ["DeiTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DeiTForImageClassification",
     "DeiTForImageClassificationWithTeacher",
     "DeiTForMaskedImageModeling",
     "DeiTModel",
     "DeiTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Optional[Any]				    =      [
     "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDeiTForImageClassification",
     "TFDeiTForImageClassificationWithTeacher",
     "TFDeiTForMaskedImageModeling",
     "TFDeiTModel",
     "TFDeiTPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_deit import DeiTFeatureExtractor
  from .image_processing_deit import DeiTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deit import (
      DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DeiTForImageClassification,
      DeiTForImageClassificationWithTeacher,
      DeiTForMaskedImageModeling,
      DeiTModel,
      DeiTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deit import (
      TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDeiTForImageClassification,
      TFDeiTForImageClassificationWithTeacher,
      TFDeiTForMaskedImageModeling,
      TFDeiTModel,
      TFDeiTPreTrainedModel,
  )


else:
 import sys

 a_   :      List[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

