


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


snake_case				:    List[Any]       				=     logging.get_logger(__name__)

snake_case				:    Dict       				=     '''▁'''

snake_case				:    List[Any]       				=     {'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

snake_case				:    Any       				=     {
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

snake_case				:    int       				=     {'''vinai/bartpho-syllable''': 10_24}







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__      =       PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE__      =       ['input_ids', 'attention_mask']




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase="<s>"					,  _lowerCamelCase="</s>"					,  _lowerCamelCase="</s>"					,  _lowerCamelCase="<s>"					,  _lowerCamelCase="<unk>"					,  _lowerCamelCase="<pad>"					,  _lowerCamelCase="<mask>"					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       # Mask token behave like a normal word, i.e. include the space before it
       a						:List[str] 							=       AddedToken(_lowerCamelCase					,  lstrip=_lowerCamelCase					,  rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase					,  _lowerCamelCase    ) else mask_token

       a						:Tuple 							=       {} if sp_model_kwargs is None else sp_model_kwargs

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

       a						:Optional[int] 							=       vocab_file
       a						:List[Any] 							=       monolingual_vocab_file
       a						:Tuple 							=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.Load(str(_lowerCamelCase    )    )

       # Load the reduced vocab

       # Keep order of special tokens for backward compatibility
       a						:List[str] 							=       {}
       a						:Optional[int] 							=       0
       for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
             if str(_lowerCamelCase    ) not in self.fairseq_tokens_to_ids:
                   a						:List[str] 							=       cnt
                   cnt += 1
       with open(_lowerCamelCase					,  '''r'''					,  encoding='''utf-8'''    ) as f:
             for line in f.readlines():
                   a						:Union[str, Any] 							=       line.strip().split()[0]
                   a						:Union[str, Any] 							=       len(self.fairseq_tokens_to_ids    )
       if str(_lowerCamelCase    ) not in self.fairseq_tokens_to_ids:
             a						:Optional[int] 							=       len(self.fairseq_tokens_to_ids    )

       a						:Dict 							=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}




 def __getstate__(		self    ):
       a						:Union[str, Any] 							=       self.__dict__.copy()
       a						:List[Any] 							=       None
       a						:Dict 							=       self.sp_model.serialized_model_proto()
       return state




 def __setstate__(		self					,  _lowerCamelCase    ):
       a						:Tuple 							=       d

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

       a						:str 							=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None    ):

       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       a						:Union[str, Any] 							=       [self.cls_token_id]
       a						:Dict 							=       [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None					,  _lowerCamelCase = False    ):

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

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




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None    ):
       a						:Tuple 							=       [self.sep_token_id]
       a						:Optional[int] 							=       [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    ):
       return len(self.fairseq_ids_to_tokens    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[Any] 							=       {self.convert_ids_to_tokens(_lowerCamelCase    ): i for i in range(self.vocab_size    )}
       vocab.update(self.added_tokens_encoder    )
       return vocab




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       return self.sp_model.encode(_lowerCamelCase					,  out_type=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       else:
             return self.unk_token_id




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       return self.fairseq_ids_to_tokens[index]




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:Union[str, Any] 							=       ''''''.join(_lowerCamelCase    ).replace(_lowerCamelCase					,  ''' '''    ).strip()
       return out_string




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None    ):
       if not os.path.isdir(_lowerCamelCase    ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
             return
       a						:int 							=       os.path.join(
           _lowerCamelCase					,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
       a						:Optional[int] 							=       os.path.join(
           _lowerCamelCase					,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']					,  )

       if os.path.abspath(self.vocab_file    ) != os.path.abspath(_lowerCamelCase    ) and os.path.isfile(self.vocab_file    ):
             copyfile(self.vocab_file					,  _lowerCamelCase    )
       elif not os.path.isfile(self.vocab_file    ):
             with open(_lowerCamelCase					,  '''wb'''    ) as fi:
                   a						:List[Any] 							=       self.sp_model.serialized_model_proto()
                   fi.write(_lowerCamelCase    )

       if os.path.abspath(self.monolingual_vocab_file    ) != os.path.abspath(
           _lowerCamelCase    ) and os.path.isfile(self.monolingual_vocab_file    ):
             copyfile(self.monolingual_vocab_file					,  _lowerCamelCase    )
       elif not os.path.isfile(self.monolingual_vocab_file    ):
             with open(_lowerCamelCase					,  '''w'''					,  encoding='''utf-8'''    ) as fp:
                   for token in self.fairseq_tokens_to_ids:
                         if token not in self.all_special_tokens:
                               fp.write(F'''{str(_lowerCamelCase    )} \n'''    )

       return out_vocab_file, out_monolingual_vocab_file









def 				__lowerCamelCase						( UpperCAmelCase_						:		list ,  UpperCAmelCase_						:		list ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      if len(UpperCAmelCase_						) != len(UpperCAmelCase_						):
            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						:Optional[int] 							=       [p / w for p, w in zip(UpperCAmelCase_ ,  UpperCAmelCase_						)]

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

      # declaring useful variables
      a						:Dict 							=       len(UpperCAmelCase_						)
      a						:Tuple 							=       0
      a						:List[Any] 							=       0
      a						:str 							=       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						:Optional[Any] 							=       profit_by_weight.index(UpperCAmelCase_						)
            a						:Optional[int] 							=       -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.'''
     )

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

     # Function Call
     calc_profit(profit, weight, max_weight)





