




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase      :						Dict							=  logging.get_logger(__name__)

_lowerCamelCase      :						Dict							=  {"""vocab_file""": """sentencepiece.bpe.model"""}

_lowerCamelCase      :						Any							=  {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

_lowerCamelCase      :						List[Any]							=  {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

_lowerCamelCase      :						Union[str, Any]							=  """▁"""


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = VOCAB_FILES_NAMES
    UpperCAmelCase__				    = PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__				    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__				    = ['''input_ids''', '''attention_mask''']


    def __init__(     self :      int   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      int="<s>"   ,					UpperCAmelCase__ :      str="</s>"   ,					UpperCAmelCase__ :      Dict="</s>"   ,					UpperCAmelCase__ :      Optional[int]="<s>"   ,					UpperCAmelCase__ :      List[str]="<unk>"   ,					UpperCAmelCase__ :      str="<pad>"   ,					UpperCAmelCase__ :      str="<mask>"   ,					UpperCAmelCase__ :      Optional[Dict[str, Any]] = None   ,					**UpperCAmelCase__ :      List[str]   ,					)		->None:





           '''simple docstring'''

           A__ 					=       AddedToken(UpperCAmelCase__   ,					lstrip=UpperCAmelCase__   ,					rstrip=UpperCAmelCase__) if isinstance(UpperCAmelCase__   ,					UpperCAmelCase__) else mask_token

           A__ 					=       {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               bos_token=UpperCAmelCase__   ,					eos_token=UpperCAmelCase__   ,					unk_token=UpperCAmelCase__   ,					sep_token=UpperCAmelCase__   ,					cls_token=UpperCAmelCase__   ,					pad_token=UpperCAmelCase__   ,					mask_token=UpperCAmelCase__   ,					sp_model_kwargs=self.sp_model_kwargs   ,					**UpperCAmelCase__   ,					)

           A__ 					=       vocab_file
           A__ 					=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(str(UpperCAmelCase__))

           A__ 					=       {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

           A__ 					=       len(self.sp_model) - 1
           A__ 					=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any]   ,					UpperCAmelCase__ :      List[int]   ,					UpperCAmelCase__ :      Optional[List[int]] = None)		->List[int]:





           '''simple docstring'''

           if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           A__ 					=       [self.cls_token_id]
           A__ 					=       [self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      List[int]   ,					UpperCAmelCase__ :      Optional[List[int]] = None   ,					UpperCAmelCase__ :      bool = False)		->List[int]:





           '''simple docstring'''

           if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCAmelCase__   ,					token_ids_a=UpperCAmelCase__   ,					already_has_special_tokens=UpperCAmelCase__)

           if token_ids_a is None:
                  return [1] + ([0] * len(UpperCAmelCase__)) + [1]
           return [1] + ([0] * len(UpperCAmelCase__)) + [1, 1] + ([0] * len(UpperCAmelCase__)) + [1]


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      List[int]   ,					UpperCAmelCase__ :      Optional[List[int]] = None)		->List[int]:





           '''simple docstring'''

           A__ 					=       [self.sep_token_id]
           A__ 					=       [self.cls_token_id]

           if token_ids_a is None:
                  return len(cls + token_ids_a + sep) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any])		->Any:





           '''simple docstring'''

           return len(self.sp_model)


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->List[Any]:





           '''simple docstring'''

           A__ 					=       {self.convert_ids_to_tokens(UpperCAmelCase__): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int]   ,					UpperCAmelCase__ :      str)		->List[str]:





           '''simple docstring'''

           return self.sp_model.encode(UpperCAmelCase__   ,					out_type=UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      str   ,					UpperCAmelCase__ :      Optional[int])		->str:





           '''simple docstring'''

           if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
           A__ 					=       self.sp_model.PieceToId(UpperCAmelCase__)

           return spm_id if spm_id else self.unk_token_id


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any]   ,					UpperCAmelCase__ :      List[str])		->Optional[Any]:





           '''simple docstring'''

           if index in self.fairseq_ids_to_tokens:
                  return self.fairseq_ids_to_tokens[index]
           return self.sp_model.IdToPiece(UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      int)		->Union[str, Any]:





           '''simple docstring'''

           A__ 					=       []
           A__ 					=       ''''''
           A__ 					=       False
           for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                         if not prev_is_special:
                                out_string += " "
                         out_string += self.sp_model.decode(UpperCAmelCase__) + token
                         A__ 					=       True
                         A__ 					=       []
                  else:
                         current_sub_tokens.append(UpperCAmelCase__)
                         A__ 					=       False
           out_string += self.sp_model.decode(UpperCAmelCase__)
           return out_string.strip()


    def __getstate__(     self :      Optional[Any])		->Optional[Any]:





           '''simple docstring'''

           A__ 					=       self.__dict__.copy()
           A__ 					=       None
           return state


    def __setstate__(     self :      List[Any]   ,					UpperCAmelCase__ :      int)		->Optional[int]:





           '''simple docstring'''

           A__ 					=       d

           # for backward compatibility
           if not hasattr(self   ,					'''sp_model_kwargs'''):
                  A__ 					=       {}

           A__ 					=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(self.vocab_file)





    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      Optional[str] = None)		->Tuple[str]:





           '''simple docstring'''

           if not os.path.isdir(UpperCAmelCase__):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
                  return
           A__ 					=       os.path.join(
               UpperCAmelCase__   ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

           if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCAmelCase__) and os.path.isfile(self.vocab_file):
                  copyfile(self.vocab_file   ,					UpperCAmelCase__)
           elif not os.path.isfile(self.vocab_file):
                  with open(UpperCAmelCase__   ,					'''wb''') as fi:
                         A__ 					=       self.sp_model.serialized_model_proto()
                         fi.write(UpperCAmelCase__)

           return (out_vocab_file,)




'''simple docstring'''
def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	if len(A__						) != len(A__						):
		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.
	a_      				=						[p / w for p, w in zip(A__  ,      A__						)]

	# Creating a copy of the list and sorting profit/weight in ascending order
	a_      				=						sorted(A__						)

	# declaring useful variables
	a_      				=						len(A__						)
	a_      				=						0
	a_      				=						0
	a_      				=						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
		a_      				=						sorted_profit_by_weight[length - i - 1]
		a_      				=						profit_by_weight.index(A__						)
		a_      				=						-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.'
		)

		lowercase__											=[int(x) for x in input('Input profits separated by spaces: ').split()]
		lowercase__											=[int(x) for x in input('Input weights separated by spaces: ').split()]
		lowercase__											=int(input('Max weight allowed: '))

		# Function Call
		calc_profit(profit, weight, max_weight)
