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

import black


SCREAMING_SNAKE_CASE_    					=						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.
SCREAMING_SNAKE_CASE_    					=						'''    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     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir   ,       'models/bert/'							)							)
          UpperCAmelCase									=					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     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					'src/transformers'
          shutil.rmtree(self.transformer_dir							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_=None							)						->					Union[str, Any]:
          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_1_9							)
          UpperCAmelCase									=					black.format_str(lowercase_   ,       mode=lowercase_							)
          UpperCAmelCase									=					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     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'							)
          self.assertEqual(lowercase_   ,       lowercase_							)




      def     a_      (							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'   ,       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
          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'   ,       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     a_      (							self							)						->					Union[str, Any]:
          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(
              lowercase_   ,       lowercase_   ,       localized_readme['format_model_list']							)

          self.assertFalse(lowercase_							)
          self.assertEqual(lowercase_   ,       lowercase_							)

          UpperCAmelCase			,							UpperCAmelCase									=					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_							)

          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(
              lowercase_   ,       lowercase_   ,       localized_readme['format_model_list']							)

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





"""simple docstring"""

from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE_    					=						Lock()




def     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Optional[Any] ,				lowerCAmelCase				:					str ,				lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					Optional[Any] ) ->      Optional[int]:






    """simple docstring"""




    global process_lock

    # we perform n swaps since after n swaps we know we are sorted
    # we *could* stop early if we are sorted already, but it takes as long to
    # find out we are sorted as it does to sort the list with this algorithm
    for i in range(0 ,				10 ):
        if (i + position) % 2 == 0 and r_send is not None:
            # send your value to your right neighbor
            process_lock.acquire()
            r_send[1].send(lowerCAmelCase )
            process_lock.release()

            # receive your right neighbor's value
            process_lock.acquire()
            UpperCAmelCase									=					rr_cv[0].recv()
            process_lock.release()

            # take the lower value since you are on the left
            UpperCAmelCase									=					min(lowerCAmelCase ,				lowerCAmelCase )
        elif (i + position) % 2 != 0 and l_send is not None:
            # send your value to your left neighbor
            process_lock.acquire()
            l_send[1].send(lowerCAmelCase )
            process_lock.release()

            # receive your left neighbor's value
            process_lock.acquire()
            UpperCAmelCase									=					lr_cv[0].recv()
            process_lock.release()

            # take the higher value since you are on the right
            UpperCAmelCase									=					max(lowerCAmelCase ,				lowerCAmelCase )
    # after all swaps are performed, send the values back to main
    result_pipe[1].send(lowerCAmelCase )




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ) ->      List[str]:






    """simple docstring"""




    UpperCAmelCase									=					[]
    UpperCAmelCase									=					[]
    # initialize the list of pipes where the values will be retrieved
    for _ in arr:
        result_pipe.append(Pipe() )
    # creates the processes
    # the first and last process only have one neighbor so they are made outside
    # of the loop
    UpperCAmelCase									=					Pipe()
    UpperCAmelCase									=					Pipe()
    process_array_.append(
        Process(
            target=lowerCAmelCase ,				args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) ,				) )
    UpperCAmelCase									=					temp_rs
    UpperCAmelCase									=					temp_rr

    for i in range(1 ,				len(lowerCAmelCase ) - 1 ):
        UpperCAmelCase									=					Pipe()
        UpperCAmelCase									=					Pipe()
        process_array_.append(
            Process(
                target=lowerCAmelCase ,				args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) ,				) )
        UpperCAmelCase									=					temp_rs
        UpperCAmelCase									=					temp_rr

    process_array_.append(
        Process(
            target=lowerCAmelCase ,				args=(
                len(lowerCAmelCase ) - 1,
                arr[len(lowerCAmelCase ) - 1],
                temp_ls,
                None,
                temp_lr,
                None,
                result_pipe[len(lowerCAmelCase ) - 1],
            ) ,				) )

    # start the processes
    for p in process_array_:
        p.start()

    # wait for the processes to end and write their values to the list
    for p in range(0 ,				len(lowerCAmelCase ) ):
        UpperCAmelCase									=					result_pipe[p][0].recv()
        process_array_[p].join()
    return arr




def     lowercase__						(					) ->      Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase									=					list(range(10 ,				0 ,				-1 ) )
    print('Initial List' )
    print(*lowerCAmelCase )
    UpperCAmelCase									=					odd_even_transposition(lowerCAmelCase )
    print('Sorted List\n' )
    print(*lowerCAmelCase )


if __name__ == "__main__":
   main()



