


import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				Tuple		     =					AlbertConfig.from_json_file(SCREAMING_SNAKE_CASE       )
  print(f'''Building PyTorch model from configuration: {config}'''       )
  A_      :				Optional[int]		     =					AlbertForPreTraining(SCREAMING_SNAKE_CASE       )

  # Load weights from tf checkpoint
  load_tf_weights_in_albert(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

  # Save pytorch-model
  print(f'''Save PyTorch model to {pytorch_dump_path}'''       )
  torch.save(model.state_dict() ,			SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
     UpperCamelCase								=	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(
         """--albert_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained ALBERT 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."""
     )
     UpperCamelCase								=	parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)






def       A       (					lowercase )   ->  list:
		'''simple docstring'''

		UpperCamelCase		=     len(lowercase )
		for i in range(1    ,       lowercase ):
				UpperCamelCase		=     collection[i]
				UpperCamelCase		=     0
				UpperCamelCase		=     i - 1

				while low <= high:
						UpperCamelCase		=     (low + high) // 2
						if val < collection[mid]:
								UpperCamelCase		=     mid - 1
						else:
								UpperCamelCase		=     mid + 1
				for j in range(lowercase    ,       lowercase    ,       -1 ):
						UpperCamelCase		=     collection[j - 1]
				UpperCamelCase		=     val
		return collection


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