



import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()







def       lowerCamelCase_  (    _a       :						Union[str, Any]   ,		_a       :						Union[str, Any]   ,		_a       :						str   ,		_a       :						Tuple   ,		_a       :						Any    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					BertConfig.from_pretrained(
        """bert-large-cased"""   ,		vocab_size=_a   ,		max_position_embeddings=512   ,		is_decoder=_a   ,		add_cross_attention=_a   ,		)
    UpperCAmelCase_     :       Any  		=					bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    UpperCAmelCase_     :       str  		=					BertGenerationConfig(**_a    )
    if is_encoder:
        UpperCAmelCase_     :       List[str]  		=					BertGenerationEncoder(_a    )
    else:
        UpperCAmelCase_     :       Any  		=					BertGenerationDecoder(_a    )
    print(F'''Building PyTorch model from configuration: {config}'''    )

    # Load weights from tf checkpoint
    load_tf_weights_in_bert_generation(
        _a   ,		_a   ,		model_class="""bert"""   ,		is_encoder_named_decoder=_a   ,		is_encoder=_a   ,		)

    # Save pytorch-model
    print(F'''Save PyTorch model and config to {pytorch_dump_path}'''    )
    model.save_pretrained(_a    )


if __name__ == "__main__":
      UpperCamelCase_									=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      parser.add_argument(
          '''--is_encoder_named_decoder''',
          action='''store_true''',
          help='''If decoder has to be renamed to encoder in PyTorch model.''',
      )
      parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
      parser.add_argument('''--vocab_size''', default=50358, type=int, help='''Vocab size of model''')
      UpperCamelCase_									=   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )








from __future__ import annotations

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    0
						_SCREAMING_SNAKE_CASE       =    len(snake_case__						) - 1
						while left <= right:
												_SCREAMING_SNAKE_CASE       =    (left + right) // 2
												if left == right:
																		if sorted_list[middle] < item:
																								_SCREAMING_SNAKE_CASE       =    middle + 1
																		break
												elif sorted_list[middle] < item:
																		_SCREAMING_SNAKE_CASE       =    middle + 1
												else:
																		_SCREAMING_SNAKE_CASE       =    middle - 1
						sorted_list.insert(snake_case__			,snake_case__						)
						return sorted_list

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Optional[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    []
						while left and right:
												if left[0][0] < right[0][0]:
																		result.append(left.pop(0						)						)
												else:
																		result.append(right.pop(0						)						)
						return result + left + right

def 			__lowerCamelCase     (						snake_case__						)				->    List[str]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    len(snake_case__						)
						if length <= 1:
												return list_ad
						_SCREAMING_SNAKE_CASE       =    length // 2
						return merge(sortlist_ad(list_ad[:middle]						)			,sortlist_ad(list_ad[middle:]						)						)

def 			__lowerCamelCase     (						snake_case__						)				->    list[int]:






						"""simple docstring"""





						if len(snake_case__						) <= 1:
												return collection
						_SCREAMING_SNAKE_CASE       =    []
						_SCREAMING_SNAKE_CASE       =    False
						for i in range(0			,len(snake_case__						)			,2						):
												if i == len(snake_case__						) - 1:
																		_SCREAMING_SNAKE_CASE       =    True
												else:
																		if collection[i] < collection[i + 1]:
																								two_paired_list.append([collection[i], collection[i + 1]]						)
																		else:
																								two_paired_list.append([collection[i + 1], collection[i]]						)
						_SCREAMING_SNAKE_CASE       =    sortlist_ad(snake_case__						)
						_SCREAMING_SNAKE_CASE       =    [i[0] for i in sorted_list_ad]
						result.append(sorted_list_ad[-1][1]						)
						if has_last_odd_item:
												_SCREAMING_SNAKE_CASE       =    collection[-1]
												_SCREAMING_SNAKE_CASE       =    binary_search_insertion(snake_case__			,snake_case__						)
						_SCREAMING_SNAKE_CASE       =    False
						for i in range(len(snake_case__						) - 1						):
												if result[i] == collection[-1] and has_last_odd_item:
																		_SCREAMING_SNAKE_CASE       =    True
												_SCREAMING_SNAKE_CASE       =    sorted_list_ad[i][1]
												# If last_odd_item is inserted before the item's index,
												# you should forward index one more.
												if is_last_odd_item_inserted_before_this_index:
																		_SCREAMING_SNAKE_CASE       =    result[: i + 2] + binary_search_insertion(result[i + 2 :]			,snake_case__						)
												else:
																		_SCREAMING_SNAKE_CASE       =    result[: i + 1] + binary_search_insertion(result[i + 1 :]			,snake_case__						)

						return result


if __name__ == "__main__":
		UpperCamelCase												=      input('''Enter numbers separated by a comma:\n''').strip()
		UpperCamelCase												=      [int(item) for item in user_input.split(''',''')]
		print(merge_insertion_sort(unsorted))

