


'''simple docstring'''



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			# Load checkpoint
			UpperCAmelCase__					:   List[Any]       				=       torch.load(UpperCamelCase__	, map_location="""cpu"""   )

			UpperCAmelCase__					:   Tuple       				=       chkpt["""model"""]

			# We have the base model one level deeper than the original XLM repository
			UpperCAmelCase__					:   Any       				=       {}
			for k, v in state_dict.items():
						if "pred_layer" in k:
									UpperCAmelCase__					:   Tuple       				=       v
						else:
									UpperCAmelCase__					:   List[str]       				=       v

			UpperCAmelCase__					:   Optional[int]       				=       chkpt["""params"""]
			UpperCAmelCase__					:   Dict       				=       {n: v for n, v in config.items() if not isinstance(UpperCamelCase__	, (torch.FloatTensor, numpy.ndarray)   )}

			UpperCAmelCase__					:   Tuple       				=       chkpt["""dico_word2id"""]
			UpperCAmelCase__					:   Any       				=       {s + """</w>""" if s.find("""@@"""   ) == -1 and i > 1_3 else s.replace("""@@"""	, """"""   ): i for s, i in vocab.items()}

			# Save pytorch-model
			UpperCAmelCase__					:   Union[str, Any]       				=       pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
			UpperCAmelCase__					:   List[Any]       				=       pytorch_dump_folder_path + """/""" + CONFIG_NAME
			UpperCAmelCase__					:   Union[str, Any]       				=       pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]

			print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''   )
			torch.save(UpperCamelCase__	, UpperCamelCase__   )

			print(f'''Save configuration file to {pytorch_config_dump_path}'''   )
			with open(UpperCamelCase__	, """w"""	, encoding="""utf-8"""   ) as f:
						f.write(json.dumps(UpperCamelCase__	, indent=2   ) + """\n"""   )

			print(f'''Save vocab file to {pytorch_config_dump_path}'''   )
			with open(UpperCamelCase__	, """w"""	, encoding="""utf-8"""   ) as f:
						f.write(json.dumps(UpperCamelCase__	, indent=2   ) + """\n"""   )


if __name__ == "__main__":
		__A		=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__A		=parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)



'''simple docstring'''



import functools







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):

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

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

			if len(UpperCamelCase__   ) == 0:
						return 0

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

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

			UpperCAmelCase__					:   Union[str, Any]       				=       set(UpperCamelCase__   )

			@functools.cache
			def dynamic_programming(UpperCamelCase__   ) -> 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()