






def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				if len(snake_case			) != len(snake_case			):
								raise ValueError('''The length of profit and weight must be same.'''			)
				if max_weight <= 0:
								raise ValueError('''max_weight must greater than zero.'''			)
				if any(p < 0 for p in profit			):
								raise ValueError('''Profit can not be negative.'''			)
				if any(w < 0 for w in weight			):
								raise ValueError('''Weight can not be negative.'''			)

				# List created to store profit gained for the 1kg in case of each weight
				# respectively.  Calculate and append profit/weight for each element.
				__magic_name__		:List[Any]   =   [p / w for p, w in zip(snake_case,	snake_case			)]

				# Creating a copy of the list and sorting profit/weight in ascending order
				__magic_name__		:Tuple   =   sorted(snake_case			)

				# declaring useful variables
				__magic_name__		:str   =   len(snake_case			)
				__magic_name__		:List[str]   =   0
				__magic_name__		:List[str]   =   0
				__magic_name__		:Union[str, Any]   =   0

				# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
				while limit <= max_weight and i < length:
								# flag value for encountered greatest element in sorted_profit_by_weight
								__magic_name__		:List[Any]   =   sorted_profit_by_weight[length - i - 1]
								__magic_name__		:int   =   profit_by_weight.index(snake_case			)
								__magic_name__		:Any   =   -1

								# check if the weight encountered is less than the total weight
								# encountered before.
								if max_weight - limit >= weight[index]:
												limit += weight[index]
												# Adding profit gained for the given weight 1 ===
												# weight[index]/weight[index]
												gain += 1 * profit[index]
								else:
												# Since the weight encountered is greater than limit, therefore take the
												# required number of remaining kgs and calculate profit for it.
												# weight remaining / weight[index]
												gain += (max_weight - limit) / weight[index] * profit[index]
												break
								i += 1
				return gain


if __name__ == "__main__":
					print(
					    """Input profits, weights, and then max_weight (all positive ints) separated by """
					    """spaces."""
					)

					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		[int(x) for x in input("""Input profits separated by spaces: """).split()]
					SCREAMING_SNAKE_CASE__   :      Tuple				=		[int(x) for x in input("""Input weights separated by spaces: """).split()]
					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		int(input("""Max weight allowed: """))

					# Function Call
					calc_profit(profit, weight, max_weight)







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 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	torch.load(_A				,					map_location="cpu"       )

 __a							=	chkpt["model"]

 # We have the base model one level deeper than the original XLM repository
 __a							=	{}
 for k, v in state_dict.items():
  if "pred_layer" in k:
   __a							=	v
  else:
   __a							=	v

 __a							=	chkpt["params"]
 __a							=	{n: v for n, v in config.items() if not isinstance(_A				,					(torch.FloatTensor, numpy.ndarray)       )}

 __a							=	chkpt["dico_word2id"]
 __a							=	{s + "</w>" if s.find("@@"       ) == -1 and i > 13 else s.replace("@@"				,					""       ): i for s, i in vocab.items()}

 # Save pytorch-model
 __a							=	pytorch_dump_folder_path + "/" + WEIGHTS_NAME
 __a							=	pytorch_dump_folder_path + "/" + CONFIG_NAME
 __a							=	pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]

 print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""       )
 torch.save(_A				,					_A       )

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

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		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."""
  )
  SCREAMING_SNAKE_CASE			:						Any				  =		parser.parse_args()
  convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)


