


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

import black


UpperCamelCase__          = 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__          = '    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 ):






      def lowercase_				(   self							:       int					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir		,		'''models/bert/'''					)					)
          UpperCAmelCase__     :						List[Any]						=							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 lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							'''src/transformers'''
          shutil.rmtree(self.transformer_dir					)






      def lowercase_				(   self							:       List[str]		,		_A							:       Optional[Any]		,		_A							:       int		,		_A							:       List[Any]		,		_A							:       str=None					):


          '''simple docstring'''


          UpperCAmelCase__     :						Tuple						=							comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
          if overwrite_result is not None:
              UpperCAmelCase__     :						List[Any]						=							comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
          UpperCAmelCase__     :						int						=							black.Mode(target_versions={black.TargetVersion.PYaa}		,		line_length=119					)
          UpperCAmelCase__     :						Optional[Any]						=							black.format_str(__snake_case		,		mode=__snake_case					)
          UpperCAmelCase__     :						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 lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''					)
          self.assertEqual(__snake_case		,		__snake_case					)






      def lowercase_				(   self							:       Tuple					):


          '''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__     :						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'''		,		__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 lowercase_				(   self							:       str					):


          '''simple docstring'''


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

          UpperCAmelCase__     :						Optional[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.'''
          )
          UpperCAmelCase__     :						Optional[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'''
          )
          UpperCAmelCase__     :						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。\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__     :						Union[str, Any]						=							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__		,		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__     :						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.'''
          )
          UpperCAmelCase__     :						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'''
          )
          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__		,		UpperCAmelCase__     :						Optional[int]						=							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					)







def      UpperCAmelCase_    (    _UpperCAmelCase							:list				)	->				list:
     '''simple docstring'''


     if len(_UpperCAmelCase				) < 2:
          return collection

     def circle_sort_util(_UpperCAmelCase							:list  ,						_UpperCAmelCase							:int  ,						_UpperCAmelCase							:int				) -> bool:
          A_          =       False

          if low == high:
               return swapped

          A_          =       low
          A_          =       high

          while left < right:
               if collection[left] > collection[right]:
                    A_   ,    A_          =       (
                        collection[right],
                        collection[left],
                    )
                    A_          =       True

               left += 1
               right -= 1

          if left == right and collection[left] > collection[right + 1]:
               A_   ,    A_          =       (
                   collection[right + 1],
                   collection[left],
               )

               A_          =       True

          A_          =       low + int((high - low) / 2				)
          A_          =       circle_sort_util(_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)
          A_          =       circle_sort_util(_UpperCAmelCase  ,						mid + 1  ,						_UpperCAmelCase				)

          return swapped or left_swap or right_swap

     A_          =       True

     while is_not_sorted is True:
          A_          =       circle_sort_util(_UpperCAmelCase  ,						0  ,						len(_UpperCAmelCase				) - 1				)

     return collection


if __name__ == "__main__":
      a__      :     Optional[Any]         =		input('Enter numbers separated by a comma:\n').strip()
      a__      :     List[str]         =		[int(item) for item in user_input.split(',')]
      print(circle_sort(unsorted))



