

from __future__ import annotations


def 							__lowerCAmelCase     (							_UpperCamelCase		:	list		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	int						)		->				list:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      input_list[low:mid], input_list[mid : high + 1]
 while left and right:
  result.append((left if left[0] <= right[0] else right).pop(0						)						)
 SCREAMING_SNAKE_CASE		    =      result + left + right
 return input_list


def 							__lowerCAmelCase     (							_UpperCamelCase		:	list						)		->				list:
 '''simple docstring'''


 if len(_UpperCamelCase						) <= 1:
  return input_list
 SCREAMING_SNAKE_CASE		    =      list(_UpperCamelCase						)

 # iteration for two-way merging
 SCREAMING_SNAKE_CASE		    =      2
 while p <= len(_UpperCamelCase						):
  # getting low, high and middle value for merge-sort of single list
  for i in range(0		,				len(_UpperCamelCase						)		,				_UpperCamelCase						):
   SCREAMING_SNAKE_CASE		    =      i
   SCREAMING_SNAKE_CASE		    =      i + p - 1
   SCREAMING_SNAKE_CASE		    =      (low + high + 1) // 2
   SCREAMING_SNAKE_CASE		    =      merge(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
  # final merge of last two parts
  if p * 2 >= len(_UpperCamelCase						):
   SCREAMING_SNAKE_CASE		    =      i
   SCREAMING_SNAKE_CASE		    =      merge(_UpperCamelCase		,				0		,				_UpperCamelCase		,				len(_UpperCamelCase						) - 1						)
   break
  p *= 2

 return input_list


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




import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	Tuple						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      BigBirdConfig.from_json_file(_UpperCamelCase						)
 print(f"""Building PyTorch model from configuration: {config}"""						)

 if is_trivia_qa:
  SCREAMING_SNAKE_CASE		    =      BigBirdForQuestionAnswering(_UpperCamelCase						)
 else:
  SCREAMING_SNAKE_CASE		    =      BigBirdForPreTraining(_UpperCamelCase						)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_UpperCamelCase		,				_UpperCamelCase		,				is_trivia_qa=_UpperCamelCase						)

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""						)
 model.save_pretrained(_UpperCamelCase						)


if __name__ == "__main__":
      a_     :    int     		=   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(
          "--big_bird_config_file",
          default=None,
          type=str,
          required=True,
          help=(
              "The config json file corresponding to the pre-trained BERT model. \n"
              "This specifies the model architecture."
          ),
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head."
      )
      a_     :    Any     		=   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
      )

