




"""simple docstring"""






import argparse

import torch

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


logging.set_verbosity_info()







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Any    =    AlbertConfig.from_json_file(snake_case_		)
				print(f'''Building PyTorch model from configuration: {config}'''		)
				_lowerCamelCase     :			str    =    AlbertForPreTraining(snake_case_		)

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

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


if __name__ == "__main__":
			lowercase__     						=						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."""
			)
			lowercase__     						=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
'''simple docstring'''





from __future__ import annotations





def 	lowerCAmelCase_				(					snake_case_       : list					,    snake_case_       : int | None = None					,    snake_case_       : int | None = None			)    ->      None:


     '''simple docstring'''




     if start is None:
          UpperCAmelCase_           =   0

     if end is None:
          UpperCAmelCase_           =   len(snake_case_			) - 1

     if start >= end:
          return

     UpperCAmelCase_           =   (start + end) // 2

     slowsort(snake_case_					,    snake_case_					,    snake_case_			)
     slowsort(snake_case_					,    mid + 1					,    snake_case_			)

     if sequence[end] < sequence[mid]:
          UpperCAmelCase_    , UpperCAmelCase_           =   sequence[mid], sequence[end]

     slowsort(snake_case_					,    snake_case_					,    end - 1			)


if __name__ == "__main__":
    from doctest import testmod

    testmod()




