
'''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_       :   List[str]   =       '    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  UpperCAmelCase__						(       unittest.TestCase ):



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Tuple								=   tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir,"models/bert/"     )     )
          _lowerCamelCase				:   Any								=   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   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   List[str]								=   "src/transformers"
          shutil.rmtree(self.transformer_dir     )



    def   lowerCamelCase_  (	self      :       Any,__A      :       Dict,__A      :       List[Any],__A      :       Union[str, Any],__A      :       Any=None     ):
          _lowerCamelCase				:   List[Any]								=   comment + f'\nclass {class_name}(nn.Module):\n' + class_code
          if overwrite_result is not None:
                _lowerCamelCase				:   Union[str, Any]								=   comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
          _lowerCamelCase				:   Tuple								=   black.Mode(target_versions={black.TargetVersion.PYaa},line_length=1_1_9     )
          _lowerCamelCase				:   Union[str, Any]								=   black.format_str(__A,mode=__A     )
          _lowerCamelCase				:   Dict								=   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   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   List[str]								=   check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"     )
          self.assertEqual(__A,__A     )



    def   lowerCamelCase_  (	self      :       Dict     ):
          # 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",__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
          _lowerCamelCase				:   Tuple								=   "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   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   Any								=   check_copies.LOCALIZED_READMES["README_zh-hans.md"]

          _lowerCamelCase				:   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.\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."
          )
          _lowerCamelCase				:   Optional[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"
          )
          _lowerCamelCase				:   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。\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"
          )

          _lowerCamelCase						,		_lowerCamelCase				:   Any								=   check_copies.convert_to_localized_md(
              __A,__A,localized_readme["format_model_list"]     )

          self.assertFalse(__A     )
          self.assertEqual(__A,__A     )

          _lowerCamelCase						,		_lowerCamelCase				:   Tuple								=   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     )

          _lowerCamelCase				:   Tuple								=   (
              "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."
          )
          _lowerCamelCase				:   Tuple								=   (
              "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"
          )
          _lowerCamelCase				:   Optional[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"
          )

          _lowerCamelCase						,		_lowerCamelCase				:   List[Any]								=   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 random
from typing import Any







def 		A_       (	_lowerCAmelCase   :    list							):



      """simple docstring"""




      for _ in range(len(_lowerCAmelCase							)							):
            _lowerCamelCase				:   Any								=   random.randint(0				,	len(_lowerCAmelCase							) - 1							)
            _lowerCamelCase				:   List[str]								=   random.randint(0				,	len(_lowerCAmelCase							) - 1							)
            _lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   data[b], data[a]
      return data


if __name__ == "__main__":
 UpperCAmelCase_       :   Any   =       [0, 1, 2, 3, 4, 5, 6, 7]
 UpperCAmelCase_       :   Dict   =       ['python', 'says', 'hello', '!']
 print('Fisher-Yates Shuffle:')
 print('List', integers, strings)
 print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))