'''simple docstring'''






import functools





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			list[int]   ,		__snake_case							:			list[int]				):

     # Validation
     if not isinstance(__snake_case   ,		__snake_case				) or not all(isinstance(__snake_case   ,		__snake_case				) for day in days				):
          raise ValueError('The parameter days should be a list of integers'				)

     if len(__snake_case				) != 3 or not all(isinstance(__snake_case   ,		__snake_case				) for cost in costs				):
          raise ValueError('The parameter costs should be a list of three integers'				)

     if len(__snake_case				) == 0:
          return 0

     if min(__snake_case				) <= 0:
          raise ValueError('All days elements should be greater than 0'				)

     if max(__snake_case				) >= 3_6_6:
          raise ValueError('All days elements should be less than 366'				)

     _A    		=  set(__snake_case				)

     @functools.cache
     def dynamic_programming(__snake_case							:			int				) -> int:
          if index > 3_6_5:
               return 0

          if index not in days_set:
               return dynamic_programming(index + 1				)

          return min(
              costs[0] + dynamic_programming(index + 1				)   ,		costs[1] + dynamic_programming(index + 7				)   ,		costs[2] + dynamic_programming(index + 3_0				)   ,		)

     return dynamic_programming(1				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()







"""simple docstring"""



import argparse

import torch

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


logging.set_verbosity_info()







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    Dict		,				lowercase    :    str					)      ->		Optional[int]:


    """simple docstring"""





    snake_case       : Tuple    =       AlbertConfig.from_json_file(lowercase					)
    print(F'Building PyTorch model from configuration: {config}'					)
    snake_case       : 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)





