


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

import black


snake_case				:    Any       				=     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.
snake_case				:    int       				=     '''    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 		_snake_case  (      unittest.TestCase							):




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[Any] 							=       tempfile.mkdtemp()
       os.makedirs(os.path.join(self.transformer_dir					,  '''models/bert/'''    )    )
       a						:List[str] 							=       self.transformer_dir
       shutil.copy(
           os.path.join(_lowerCamelCase					,  '''src/transformers/models/bert/modeling_bert.py'''    )					,  os.path.join(self.transformer_dir					,  '''models/bert/modeling_bert.py'''    )					,  )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Tuple 							=       '''src/transformers'''
       shutil.rmtree(self.transformer_dir    )




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




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''    )
       self.assertEqual(_lowerCamelCase					,  _lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # 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'''					,  _lowerCamelCase					,  )

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

       # Copy consistency with a really long name
       a						: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'''					,  _lowerCamelCase					,  _lowerCamelCase    )					,  )

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




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

       a						: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.'''
       )
       a						:Union[str, 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'''
       )
       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。\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						:Optional[Any] 							=       check_copies.convert_to_localized_md(
           _lowerCamelCase					,  _lowerCamelCase					,  localized_readme['''format_model_list''']    )

       self.assertFalse(_lowerCamelCase    )
       self.assertEqual(_lowerCamelCase					,  _lowerCamelCase    )

       a							,				a						:Optional[int] 							=       check_copies.convert_to_localized_md(
           _lowerCamelCase					,  _lowerCamelCase					,  localized_readme['''format_model_list''']    )

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

       a						: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.'''
       )
       a						:Dict 							=       (
           '''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						: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							,				a						:int 							=       check_copies.convert_to_localized_md(
           _lowerCamelCase					,  _lowerCamelCase					,  localized_readme['''format_model_list''']    )

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






'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__ ,							lowercase__				:    Optional[int]  =					[], []
 while len(UpperCAmelCase     ) > 1:
  lowercase__ ,							lowercase__				:    List[str]  =					min(UpperCAmelCase     ), max(UpperCAmelCase     )
  start.append(UpperCAmelCase     )
  end.append(UpperCAmelCase     )
  collection.remove(UpperCAmelCase     )
  collection.remove(UpperCAmelCase     )
 end.reverse()
 return start + collection + end


if __name__ == "__main__":
  __a: Optional[int]          =   input("""Enter numbers separated by a comma:\n""").strip()
  __a: Tuple          =   [int(item) for item in user_input.split(""",""")]
  print(*merge_sort(unsorted), sep=""",""")

