






'''simple docstring'''







import argparse

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


logging.set_verbosity_info()





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Tuple:
							# Initialise PyTorch model
							lowerCamelCase__   :      Union[str, Any]			    =			BertConfig.from_pretrained(
							    """bert-large-cased"""							,     vocab_size=UpperCamelCase							,     max_position_embeddings=512							,     is_decoder=UpperCamelCase							,     add_cross_attention=UpperCamelCase							,     )
							lowerCamelCase__   :      str			    =			bert_config.to_dict()
							del bert_config_dict["type_vocab_size"]
							lowerCamelCase__   :      Tuple			    =			BertGenerationConfig(**UpperCamelCase )
							if is_encoder:
														lowerCamelCase__   :      Any			    =			BertGenerationEncoder(UpperCamelCase )
							else:
														lowerCamelCase__   :      Tuple			    =			BertGenerationDecoder(UpperCamelCase )
							print(f'''Building PyTorch model from configuration: {config}''' )

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

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


if __name__ == "__main__":
			_A      :     List[Any]       	=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=50_358, type=int, help='''Vocab size of model''')
			_A      :     Optional[int]       	=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,
			)









'''simple docstring'''







from __future__ import annotations





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Dict:
							lowerCamelCase__   :      List[str]			    =			0
							lowerCamelCase__   :      Tuple			    =			len(UpperCamelCase ) - 1
							while left <= right:
														lowerCamelCase__   :      List[str]			    =			(left + right) // 2
														if left == right:
																					if sorted_list[middle] < item:
																												lowerCamelCase__   :      Any			    =			middle + 1
																					break
														elif sorted_list[middle] < item:
																					lowerCamelCase__   :      Optional[Any]			    =			middle + 1
														else:
																					lowerCamelCase__   :      Dict			    =			middle - 1
							sorted_list.insert(UpperCamelCase							,     UpperCamelCase )
							return sorted_list





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Tuple:
							lowerCamelCase__   :      Any			    =			[]
							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 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			Any:
							lowerCamelCase__   :      Optional[Any]			    =			len(UpperCamelCase )
							if length <= 1:
														return list_ad
							lowerCamelCase__   :      Tuple			    =			length // 2
							return merge(sortlist_ad(list_ad[:middle] )							,     sortlist_ad(list_ad[middle:] ) )





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			list[int]:

							if len(UpperCamelCase ) <= 1:
														return collection
							lowerCamelCase__   :      int			    =			[]
							lowerCamelCase__   :      int			    =			False
							for i in range(0							,     len(UpperCamelCase )							,     2 ):
														if i == len(UpperCamelCase ) - 1:
																					lowerCamelCase__   :      List[Any]			    =			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]] )
							lowerCamelCase__   :      List[Any]			    =			sortlist_ad(UpperCamelCase )
							lowerCamelCase__   :      List[Any]			    =			[i[0] for i in sorted_list_ad]
							result.append(sorted_list_ad[-1][1] )
							if has_last_odd_item:
														lowerCamelCase__   :      int			    =			collection[-1]
														lowerCamelCase__   :      str			    =			binary_search_insertion(UpperCamelCase							,     UpperCamelCase )
							lowerCamelCase__   :      Any			    =			False
							for i in range(len(UpperCamelCase ) - 1 ):
														if result[i] == collection[-1] and has_last_odd_item:
																					lowerCamelCase__   :      Any			    =			True
														lowerCamelCase__   :      Dict			    =			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:
																					lowerCamelCase__   :      List[str]			    =			result[: i + 2] + binary_search_insertion(result[i + 2 :]							,     UpperCamelCase )
														else:
																					lowerCamelCase__   :      Dict			    =			result[: i + 1] + binary_search_insertion(result[i + 1 :]							,     UpperCamelCase )

							return result


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

