


'''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  __magic_name__							(		unittest.TestCase    ):






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir  ,							'models/bert/'))
          _UpperCAmelCase       :       Optional[int]							   =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       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   ='src/transformers'
          shutil.rmtree(self.transformer_dir)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case=None)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =comment + f"\nclass {class_name}(nn.Module):\n" + class_code
          if overwrite_result is not None:
             _UpperCAmelCase       :       List[str]							   =comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
          _UpperCAmelCase       :       Optional[Any]							   =black.Mode(target_versions={black.TargetVersion.PYaa}  ,							line_length=1_1_9)
          _UpperCAmelCase       :       Any							   =black.format_str(snake_case  ,							mode=snake_case)
          _UpperCAmelCase       :       Union[str, Any]							   =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       lowerCAmelCase		(      self)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   =check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead')
          self.assertEqual(snake_case  ,							snake_case)






       def       lowerCAmelCase		(      self)						->  int:




          '''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       :       List[str]							   ='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       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =check_copies.LOCALIZED_READMES['README_zh-hans.md']

          _UpperCAmelCase       :       List[str]							   =(
              '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       :       Any							   =(
              '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       :       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'
          )

          _UpperCAmelCase       :       str							   =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       :       Any							   =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       :       Optional[int]							   =(
              '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       :       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'
          )
          _UpperCAmelCase       :       Any							   =(
              '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       :       str							   =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 warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


lowercase							=logging.get_logger(__name__)






class  __magic_name__							(		lowerCAmelCase    ):






       def __init__(      self  ,							*snake_case  ,							**snake_case)						->  None:




          '''simple docstring'''
          warnings.warn(
              'The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
              ' Please use DeformableDetrImageProcessor instead.'  ,							snake_case  ,							)
          super().__init__(*snake_case  ,							**snake_case)

