





"""simple docstring"""





from __future__ import annotations




def 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							str		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			str												=       0
 __SCREAMING_SNAKE_CASE :			str												=       len(lowercase_		) - 1
 while left <= right:
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       (left + right) // 2
  if left == right:
   if sorted_list[middle] < item:
    __SCREAMING_SNAKE_CASE :			Optional[int]												=       middle + 1
   break
  elif sorted_list[middle] < item:
   __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       middle + 1
  else:
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       middle - 1
 sorted_list.insert(lowercase_ ,      lowercase_		)
 return sorted_list




def 		lowerCAmelCase_	(  lowercase_   :							Dict ,      lowercase_   :							Any		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			List[str]												=       []
 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_	(  lowercase_   :							Tuple		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       len(lowercase_		)
 if length <= 1:
  return list_ad
 __SCREAMING_SNAKE_CASE :			Any												=       length // 2
 return merge(sortlist_ad(list_ad[:middle]		) ,      sortlist_ad(list_ad[middle:]		)		)




def 		lowerCAmelCase_	(  lowercase_   :							list[int]		):

 '''simple docstring'''






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

 return result


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








"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							List[str] ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Tuple												=       TaConfig.from_json_file(lowercase_		)
 print(F'''Building PyTorch model from configuration: {config}'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       TaForConditionalGeneration(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(lowercase_ ,      lowercase_ ,      lowercase_		)

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


if __name__ == "__main__":
    _lowerCamelCase       =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    _lowerCamelCase       =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

