





import os
import unicodedata
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 SPIECE_UNDERLINE, logging


_lowerCAmelCase				:   int						       =						logging.get_logger(__name__)

_lowerCAmelCase				:   Union[str, Any]						       =						{'''vocab_file''': '''spiece.model'''}

_lowerCAmelCase				:   Optional[Any]						       =						{
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    }
}

_lowerCAmelCase				:   int						       =						{
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

# Segments (not really needed)
_lowerCAmelCase				:   List[Any]						       =						0
_lowerCAmelCase				:   int						       =						1
_lowerCAmelCase				:   List[str]						       =						2
_lowerCAmelCase				:   Tuple						       =						3
_lowerCAmelCase				:   int						       =						4


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  VOCAB_FILES_NAMES
     __UpperCamelCase            =  PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase            =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase            =  '''left'''



     def __init__(				self							:int      ,   snake_case							:Dict      ,   snake_case							:Tuple=False      ,   snake_case							:Optional[Any]=True      ,   snake_case							:Optional[Any]=False      ,   snake_case							:List[Any]="<s>"      ,   snake_case							:int="</s>"      ,   snake_case							:Dict="<unk>"      ,   snake_case							:str="<sep>"      ,   snake_case							:List[str]="<pad>"      ,   snake_case							:str="<cls>"      ,   snake_case							:Dict="<mask>"      ,   snake_case							:List[str]=["<eop>", "<eod>"]      ,   snake_case							:Optional[Dict[str, Any]] = None      ,   **snake_case							:int      ,   ):





        '''simple docstring'''




        A_       :					Tuple        =      AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case	) if isinstance(snake_case      ,   snake_case	) else mask_token

        A_       :					str        =      {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            do_lower_case=snake_case      ,   remove_space=snake_case      ,   keep_accents=snake_case      ,   bos_token=snake_case      ,   eos_token=snake_case      ,   unk_token=snake_case      ,   sep_token=snake_case      ,   pad_token=snake_case      ,   cls_token=snake_case      ,   mask_token=snake_case      ,   additional_special_tokens=snake_case      ,   sp_model_kwargs=self.sp_model_kwargs      ,   **snake_case      ,   )

        A_       :					str        =      3

        A_       :					str        =      do_lower_case
        A_       :					Dict        =      remove_space
        A_       :					Optional[Any]        =      keep_accents
        A_       :					Tuple        =      vocab_file

        A_       :					Optional[int]        =      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(snake_case	)



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        return len(self.sp_model	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        A_       :					Tuple        =      {self.convert_ids_to_tokens(snake_case	): i for i in range(self.vocab_size	)}
        vocab.update(self.added_tokens_encoder	)
        return vocab



     def __getstate__(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					int        =      self.__dict__.copy()
        A_       :					Any        =      None
        return state



     def __setstate__(				self							:str      ,   snake_case							:Dict	):





        '''simple docstring'''




        A_       :					Tuple        =      d

        # for backward compatibility
        if not hasattr(self      ,   "sp_model_kwargs"	):
           A_       :					Tuple        =      {}

        A_       :					str        =      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(self.vocab_file	)



     def     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:Tuple	):





        '''simple docstring'''




        if self.remove_space:
           A_       :					Dict        =      " ".join(inputs.strip().split()	)
        else:
           A_       :					int        =      inputs
        A_       :					Dict        =      outputs.replace("``"      ,   "\""	).replace("''"      ,   "\""	)

        if not self.keep_accents:
           A_       :					Optional[Any]        =      unicodedata.normalize("NFKD"      ,   snake_case	)
           A_       :					List[str]        =      "".join([c for c in outputs if not unicodedata.combining(snake_case	)]	)
        if self.do_lower_case:
           A_       :					Any        =      outputs.lower()

        return outputs



     def     SCREAMING_SNAKE_CASE		(				self							:Any      ,   snake_case							:str	):





        '''simple docstring'''




        A_       :					Dict        =      self.preprocess_text(snake_case	)
        A_       :					int        =      self.sp_model.encode(snake_case      ,   out_type=snake_case	)
        A_       :					List[Any]        =      []
        for piece in pieces:
           if len(snake_case	) > 1 and piece[-1] == str(","	) and piece[-2].isdigit():
              A_       :					Optional[Any]        =      self.sp_model.EncodeAsPieces(piece[:-1].replace(snake_case      ,   ""	)	)
              if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                 if len(cur_pieces[0]	) == 1:
                    A_       :					int        =      cur_pieces[1:]
                 else:
                    A_       :					Any        =      cur_pieces[0][1:]
              cur_pieces.append(piece[-1]	)
              new_pieces.extend(snake_case	)
           else:
              new_pieces.append(snake_case	)

        return new_pieces



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]      ,   snake_case							:Dict	):





        '''simple docstring'''




        return self.sp_model.PieceToId(snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[str]      ,   snake_case							:Optional[Any]	):





        '''simple docstring'''




        return self.sp_model.IdToPiece(snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:Any	):





        '''simple docstring'''




        A_       :					str        =      "".join(snake_case	).replace(snake_case      ,   " "	).strip()
        return out_string



     def     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:List[int]      ,   snake_case							:bool = False      ,   snake_case							:bool = None      ,   snake_case							:bool = True      ,   **snake_case							:List[str]      ,   ):





        '''simple docstring'''




        A_       :					int        =      kwargs.pop("use_source_tokenizer"      ,   snake_case	)

        A_       :					Optional[Any]        =      self.convert_ids_to_tokens(snake_case      ,   skip_special_tokens=snake_case	)

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        A_       :					Optional[int]        =      []
        A_       :					Any        =      []
        for token in filtered_tokens:
           if skip_special_tokens and token in self.all_special_ids:
              continue
           if token in self.added_tokens_encoder:
              if current_sub_text:
                 sub_texts.append(self.convert_tokens_to_string(snake_case	)	)
                 A_       :					Optional[int]        =      []
              sub_texts.append(snake_case	)
           else:
              current_sub_text.append(snake_case	)
        if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(snake_case	)	)

        # Mimic the behavior of the Rust tokenizer:
        # By default, there are no spaces between special tokens
        A_       :					Optional[Any]        =      "".join(snake_case	)

        A_       :					Any        =      (
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
           A_       :					Union[str, Any]        =      self.clean_up_tokenization(snake_case	)
           return clean_text
        else:
           return text



     def     SCREAMING_SNAKE_CASE		(				self							:List[str]      ,   snake_case							:List[int]      ,   snake_case							:Optional[List[int]] = None	):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]      ,   snake_case							:List[int]      ,   snake_case							:Optional[List[int]] = None      ,   snake_case							:bool = False	):





        '''simple docstring'''




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

        if token_ids_a is not None:
           return ([0] * len(snake_case	)) + [1] + ([0] * len(snake_case	)) + [1, 1]
        return ([0] * len(snake_case	)) + [1, 1]



     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:List[int]      ,   snake_case							:Optional[List[int]] = None	):





        '''simple docstring'''




        A_       :					Tuple        =      [self.sep_token_id]
        A_       :					Union[str, Any]        =      [2]

        if token_ids_a is None:
           return len(token_ids_a + sep	) * [0] + cls_segment_id
        return len(token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1] + cls_segment_id





     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:str      ,   snake_case							:Optional[str] = None	):





        '''simple docstring'''




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

        if os.path.abspath(self.vocab_file	) != os.path.abspath(snake_case	) and os.path.isfile(self.vocab_file	):
           copyfile(self.vocab_file      ,   snake_case	)
        elif not os.path.isfile(self.vocab_file	):
           with open(snake_case      ,   "wb"	) as fi:
              A_       :					Union[str, Any]        =      self.sp_model.serialized_model_proto()
              fi.write(snake_case	)

        return (out_vocab_file,)







def   __snake_case    (       _lowerCAmelCase			:			list					,					_lowerCAmelCase			:			list					,					_lowerCAmelCase			:			int						)			->    int:
   if len(_lowerCAmelCase						) != len(_lowerCAmelCase						):
      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_       :					Dict        =      [p / w for p, w in zip(_lowerCAmelCase					,					_lowerCAmelCase						)]

   # Creating a copy of the list and sorting profit/weight in ascending order
   A_       :					Optional[Any]        =      sorted(_lowerCAmelCase						)

   # declaring useful variables
   A_       :					List[Any]        =      len(_lowerCAmelCase						)
   A_       :					List[Any]        =      0
   A_       :					Any        =      0
   A_       :					List[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
      A_       :					List[Any]        =      sorted_profit_by_weight[length - i - 1]
      A_       :					List[Any]        =      profit_by_weight.index(_lowerCAmelCase						)
      A_       :					List[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.'''
 )

 _lowerCAmelCase				:   int						       =						[int(x) for x in input('''Input profits separated by spaces: ''').split()]
 _lowerCAmelCase				:   Dict						       =						[int(x) for x in input('''Input weights separated by spaces: ''').split()]
 _lowerCAmelCase				:   Union[str, Any]						       =						int(input('''Max weight allowed: '''))

 # Function Call
 calc_profit(profit, weight, max_weight)
