


'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest

import black


UpperCAmelCase_			:  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.
UpperCAmelCase_			:  Dict										=   '    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     _lowerCamelCase       (     unittest.TestCase					):




 '''simple docstring'''


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      tempfile.mkdtemp()
  os.makedirs(os.path.join(self.transformer_dir   ,   'models/bert/'    )    )
  __A	:		Optional[int]	  =      self.transformer_dir
  shutil.copy(
      os.path.join(__lowercase   ,   'src/transformers/models/bert/modeling_bert.py'    )   ,   os.path.join(self.transformer_dir   ,   'models/bert/modeling_bert.py'    )   ,   )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      'src/transformers'
  shutil.rmtree(self.transformer_dir    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase=None    ):

  """simple docstring"""







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


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		int	  =      check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'    )
  self.assertEqual(__lowercase   ,   __lowercase    )


 def 			snake_case__ (   self    ):

  """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'   ,   __lowercase   ,   )

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

  # Copy consistency with a really long name
  __A	:		Union[str, Any]	  =      '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'   ,   __lowercase   ,   __lowercase    )   ,   )

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







 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      check_copies.LOCALIZED_READMES['README_zh-hans.md']

  __A	:		Any	  =      (
      '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	:		List[str]	  =      (
      '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	:		Tuple	  =      (
      '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	:		Union[str, Any]	  =      check_copies.convert_to_localized_md(
      __lowercase   ,   __lowercase   ,   localized_readme['format_model_list']    )

  self.assertFalse(__lowercase    )
  self.assertEqual(__lowercase   ,   __lowercase    )

  __A  ,__A	:		List[str]	  =      check_copies.convert_to_localized_md(
      __lowercase   ,   __lowercase   ,   localized_readme['format_model_list']    )

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

  __A	:		Dict	  =      (
      '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	:		List[Any]	  =      (
      '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	:		int	  =      (
      '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	:		Any	  =      check_copies.convert_to_localized_md(
      __lowercase   ,   __lowercase   ,   localized_readme['format_model_list']    )

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










'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_			:  int										=   logging.get_logger(__name__)

UpperCAmelCase_			:  List[Any]										=   {
    'facebook/deit-base-distilled-patch16-224': (
        'https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json'
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Union[str, Any]     				=    '''deit'''


 def __init__(   self   ,   __lowercase=768   ,   __lowercase=12   ,   __lowercase=12   ,   __lowercase=3_072   ,   __lowercase="gelu"   ,   __lowercase=0.0   ,   __lowercase=0.0   ,   __lowercase=0.0_2   ,   __lowercase=1E-12   ,   __lowercase=224   ,   __lowercase=16   ,   __lowercase=3   ,   __lowercase=True   ,   __lowercase=16   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(**__lowercase    )

  __A	:		Union[str, Any]	  =      hidden_size
  __A	:		int	  =      num_hidden_layers
  __A	:		str	  =      num_attention_heads
  __A	:		Optional[int]	  =      intermediate_size
  __A	:		List[str]	  =      hidden_act
  __A	:		str	  =      hidden_dropout_prob
  __A	:		str	  =      attention_probs_dropout_prob
  __A	:		int	  =      initializer_range
  __A	:		Tuple	  =      layer_norm_eps
  __A	:		List[Any]	  =      image_size
  __A	:		Dict	  =      patch_size
  __A	:		Optional[Any]	  =      num_channels
  __A	:		List[Any]	  =      qkv_bias
  __A	:		str	  =      encoder_stride


class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Optional[int]     				=    version.parse('''1.11'''					)


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return OrderedDict(
      [
          ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
      ]    )







 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return 1E-4






