




import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_						=			logging.get_logger(__name__)

lowerCAmelCase_						=			'▁'

lowerCAmelCase_						=			{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

lowerCAmelCase_						=			{
    'vocab_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
        ),
    },
    'spm_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
        )
    },
}

lowerCAmelCase_						=			{
    'facebook/s2t-small-librispeech-asr': 10_24,
}

lowerCAmelCase_						=			['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

lowerCAmelCase_						=			{'mustc': MUSTC_LANGS}
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      List[str]			=      VOCAB_FILES_NAMES
  _UpperCamelCase       :      List[Any]			=      PRETRAINED_VOCAB_FILES_MAP
  _UpperCamelCase       :      Optional[int]			=      MAX_MODEL_INPUT_SIZES
  _UpperCamelCase       :      Optional[int]			=      ['''input_ids''', '''attention_mask''']

  _UpperCamelCase       :      List[int]			=      []






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






       """simple docstring"""







       lowercase :       List[str]       =			{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=_A				,						eos_token=_A				,						unk_token=_A				,						pad_token=_A				,						do_upper_case=_A				,						do_lower_case=_A				,						tgt_lang=_A				,						lang_codes=_A				,						sp_model_kwargs=self.sp_model_kwargs				,						**_A				,						)
       lowercase :       List[str]       =			do_upper_case
       lowercase :       List[Any]       =			do_lower_case

       lowercase :       Union[str, Any]       =			load_json(_A				)
       lowercase :       List[Any]       =			{v: k for k, v in self.encoder.items()}
       lowercase :       Optional[int]       =			spm_file
       lowercase :       Optional[int]       =			load_spm(_A				,						self.sp_model_kwargs				)

       if lang_codes is not None:
            lowercase :       Dict       =			lang_codes
            lowercase :       List[Any]       =			LANGUAGES[lang_codes]
            lowercase :       Union[str, Any]       =			[f"""<lang:{lang}>""" for lang in self.langs]
            lowercase :       List[str]       =			{lang: self.sp_model.PieceToId(f"""<lang:{lang}>"""				) for lang in self.langs}

            lowercase :       List[Any]       =			self.lang_tokens
            lowercase :       Tuple       =			tgt_lang if tgt_lang is not None else self.langs[0]

            self.set_tgt_lang_special_tokens(self._tgt_lang				)
       else:
            lowercase :       Union[str, Any]       =			{}






  @property
  def  __a      (  self		:     List[str]				)		->   int:






       """simple docstring"""







       return len(self.encoder				)






  @property
  def  __a      (  self		:     Tuple				)		->   str:






       """simple docstring"""







       return self._tgt_lang






  @tgt_lang.setter
  def  __a      (  self		:     List[str]				,						_A		:     Any				)		->   None:






       """simple docstring"""







       lowercase :       Optional[int]       =			new_tgt_lang
       self.set_tgt_lang_special_tokens(_A				)






  def  __a      (  self		:     List[Any]				,						_A		:     str				)		->   None:






       """simple docstring"""







       lowercase :       List[str]       =			self.lang_code_to_id[tgt_lang]
       lowercase :       List[str]       =			[lang_code_id]






  def  __a      (  self		:     Optional[int]				,						_A		:     str				)		->   List[str]:






       """simple docstring"""







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






  def  __a      (  self		:     Tuple				,						_A		:     Dict				)		->   int:






       """simple docstring"""







       return self.encoder.get(_A				,						self.encoder[self.unk_token]				)






  def  __a      (  self		:     str				,						_A		:     int				)		->   str:






       """simple docstring"""







       return self.decoder.get(_A				,						self.unk_token				)






  def  __a      (  self		:     Tuple				,						_A		:     List[str]				)		->   str:






       """simple docstring"""







       lowercase :       Tuple       =			[]
       lowercase :       int       =			''''''
       for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                 lowercase :       Dict       =			self.sp_model.decode(_A				)
                 out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                 lowercase :       Tuple       =			[]
            else:
                 current_sub_tokens.append(_A				)
       lowercase :       List[Any]       =			self.sp_model.decode(_A				)
       out_string += decoded.upper() if self.do_upper_case else decoded
       return out_string.strip()






  def  __a      (  self		:     List[str]				,						_A		:     List[str]				,						_A		:     List[str]=None				)		->   List[int]:






       """simple docstring"""







       if token_ids_a is None:
            return self.prefix_tokens + token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]






  def  __a      (  self		:     List[str]				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				,						_A		:     bool = False				)		->   List[int]:






       """simple docstring"""







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

       lowercase :       Dict       =			[1] * len(self.prefix_tokens				)
       lowercase :       Union[str, Any]       =			[1]
       if token_ids_a is None:
            return prefix_ones + ([0] * len(_A				)) + suffix_ones
       return prefix_ones + ([0] * len(_A				)) + ([0] * len(_A				)) + suffix_ones






  def  __a      (  self		:     Optional[int]				)		->   Dict:






       """simple docstring"""







       lowercase :       Optional[Any]       =			self.encoder.copy()
       vocab.update(self.added_tokens_encoder				)
       return vocab






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






       """simple docstring"""







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






  def __setstate__(  self		:     List[Any]				,						_A		:     Dict				)		->   None:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			d

       # for backward compatibility
       if not hasattr(self				,						'''sp_model_kwargs'''				):
            lowercase :       Optional[Any]       =			{}

       lowercase :       List[Any]       =			load_spm(self.spm_file				,						self.sp_model_kwargs				)






  def  __a      (  self		:     int				,						_A		:     str				,						_A		:     Optional[str] = None				)		->   Tuple[str]:






       """simple docstring"""







       lowercase :       Optional[Any]       =			Path(_A				)
       assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
       lowercase :       Union[str, Any]       =			save_dir / (
           (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
       )
       lowercase :       Dict       =			save_dir / (
           (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
       )

       save_json(self.encoder				,						_A				)

       if os.path.abspath(self.spm_file				) != os.path.abspath(_A				) and os.path.isfile(self.spm_file				):
            copyfile(self.spm_file				,						_A				)
       elif not os.path.isfile(self.spm_file				):
            with open(_A				,						'''wb'''				) as fi:
                 lowercase :       Dict       =			self.sp_model.serialized_model_proto()
                 fi.write(_A				)

       return (str(_A				), str(_A				))






def  snake_case(    __magic_name__			,		__magic_name__   )			->							sentencepiece.SentencePieceProcessor:





     '''simple docstring'''


     lowercase :       List[Any]       =			sentencepiece.SentencePieceProcessor(**__magic_name__   )
     spm.Load(str(__magic_name__   )   )
     return spm






def  snake_case(    __magic_name__   )			->							Union[Dict, List]:





     '''simple docstring'''


     with open(__magic_name__			,		'''r'''   ) as f:
          return json.load(__magic_name__   )






def  snake_case(    __magic_name__			,		__magic_name__   )			->							None:





     '''simple docstring'''


     with open(__magic_name__			,		'''w'''   ) as f:
          json.dump(__magic_name__			,		__magic_name__			,		indent=2   )









def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							int:





     '''simple docstring'''


     if len(__magic_name__   ) != len(__magic_name__   ):
          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.
     lowercase :       str       =			[p / w for p, w in zip(__magic_name__			,		__magic_name__   )]

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

     # declaring useful variables
     lowercase :       Union[str, Any]       =			len(__magic_name__   )
     lowercase :       Optional[int]       =			0
     lowercase :       Optional[int]       =			0
     lowercase :       int       =			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
          lowercase :       Optional[int]       =			sorted_profit_by_weight[length - i - 1]
          lowercase :       Union[str, Any]       =			profit_by_weight.index(__magic_name__   )
          lowercase :       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(x) for x in input('Input profits separated by spaces: ').split()]
 lowerCAmelCase_						=			[int(x) for x in input('Input weights separated by spaces: ').split()]
 lowerCAmelCase_						=			int(input('Max weight allowed: '))

 # Function Call
 calc_profit(profit, weight, max_weight)