






import argparse

import torch

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


logging.set_verbosity_info()


def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Tuple						    =     AlbertConfig.from_json_file(lowercase					)
  print(F'''Building PyTorch model from configuration: {config}'''					)
  SCREAMING_SNAKE_CASE							:   Optional[int]						    =     AlbertForPreTraining(lowercase					)

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

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


if __name__ == "__main__":
   snake_case     					=		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."""
   )
   snake_case     					=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)









from itertools import count





def a(lowercase__ = 50						):





	'''simple docstring'''
	snake_case_   		=			[1] * min_block_length

	for n in count(lowercase__						):
		fill_count_functions.append(1						)

		for block_length in range(lowercase__      ,			n + 1						):
			for block_start in range(n - block_length						):
				fill_count_functions[n] += fill_count_functions[
				    n - block_start - block_length - 1
				]

			fill_count_functions[n] += 1

		if fill_count_functions[n] > 1000000:
			break

	return n


if __name__ == "__main__":
		print(f"""{solution() = }""")
