





import functools

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):

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

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

				if len(__lowerCAmelCase ) == 0:
								return 0

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

				if max(__lowerCAmelCase ) >= 3_66:
								raise ValueError('All days elements should be less than 366' )

				_snake_case :			Optional[int]  						=   set(__lowerCAmelCase )

				@functools.cache
				def dynamic_programming(__lowerCAmelCase ) -> int:
								if index > 3_65:
												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 + 30 )	,			)

				return dynamic_programming(1 )


if __name__ == "__main__":
		import doctest

		doctest.testmod()













import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				# Initialise PyTorch model
				_snake_case :			Optional[int]  						=   BertConfig.from_json_file(__lowerCAmelCase )
				print(F'''Building PyTorch model from configuration: {config}''' )
				_snake_case :			List[str]  						=   BertForPreTraining(__lowerCAmelCase )

				# Load weights from tf checkpoint
				load_tf_weights_in_bert(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )

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


if __name__ == "__main__":
		lowercase_		:				Any				=						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(
		    '''--bert_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.'''
		)
		lowercase_		:				List[str]				=						parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






