



'''simple docstring'''





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


A__    :   str           =					logging.get_logger(__name__)

A__    :   Any           =					'''▁'''

A__    :   Optional[int]           =					{
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

A__    :   Tuple           =					{
    '''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'''
        )
    },
}

A__    :   Tuple           =					{
    '''facebook/s2t-small-librispeech-asr''': 1_0_2_4,
}

A__    :   List[Any]           =					['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

A__    :   List[str]           =					{'''mustc''': MUSTC_LANGS}



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= VOCAB_FILES_NAMES
 A__				= PRETRAINED_VOCAB_FILES_MAP
 A__				= MAX_MODEL_INPUT_SIZES
 A__				= ['''input_ids''', '''attention_mask''']

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







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    {} 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      ,     )
       __snake_case :   Tuple													=    do_upper_case
       __snake_case :   Optional[Any]													=    do_lower_case

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

       if lang_codes is not None:
             __snake_case :   Tuple													=    lang_codes
             __snake_case :   int													=    LANGUAGES[lang_codes]
             __snake_case :   Union[str, Any]													=    [f'''<lang:{lang}>''' for lang in self.langs]
             __snake_case :   str													=    {lang: self.sp_model.PieceToId(f'''<lang:{lang}>'''  ) for lang in self.langs}

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

             self.set_tgt_lang_special_tokens(self._tgt_lang  )
       else:
             __snake_case :   Dict													=    {}
 @property
 def A_   (   self						:							Optional[int]  )      ->   int:







       '''simple docstring'''
       return len(self.encoder  )
 @property
 def A_   (   self						:							List[Any]  )      ->   str:







       '''simple docstring'''
       return self._tgt_lang
 @tgt_lang.setter
 def A_   (   self						:							Optional[int]      ,     __a						:							Optional[Any]  )      ->   None:







       '''simple docstring'''
       __snake_case :   Dict													=    new_tgt_lang
       self.set_tgt_lang_special_tokens(__a  )
 def A_   (   self						:							str      ,     __a						:							str  )      ->   None:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    self.lang_code_to_id[tgt_lang]
       __snake_case :   Dict													=    [lang_code_id]
 def A_   (   self						:							Dict      ,     __a						:							str  )      ->   List[str]:







       '''simple docstring'''
       return self.sp_model.encode(__a      ,     out_type=__a  )
 def A_   (   self						:							str      ,     __a						:							Union[str, Any]  )      ->   Tuple:







       '''simple docstring'''
       return self.encoder.get(__a      ,     self.encoder[self.unk_token]  )
 def A_   (   self						:							Tuple      ,     __a						:							int  )      ->   str:







       '''simple docstring'''
       return self.decoder.get(__a      ,     self.unk_token  )
 def A_   (   self						:							Optional[int]      ,     __a						:							List[str]  )      ->   str:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    []
       __snake_case :   Optional[int]													=    ''
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   __snake_case :   List[str]													=    self.sp_model.decode(__a  )
                   out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                   __snake_case :   int													=    []
             else:
                   current_sub_tokens.append(__a  )
       __snake_case :   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						:							Optional[int]      ,     __a						:							Union[str, Any]      ,     __a						:							Union[str, Any]=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						:							Dict      ,     __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  )

       __snake_case :   Tuple													=    [1] * len(self.prefix_tokens  )
       __snake_case :   Tuple													=    [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						:							int  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   int													=    self.encoder.copy()
       vocab.update(self.added_tokens_encoder  )
       return vocab
 def __getstate__(   self						:							Tuple  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   List[Any]													=    self.__dict__.copy()
       __snake_case :   Optional[Any]													=    None
       return state
 def __setstate__(   self						:							List[Any]      ,     __a						:							Dict  )      ->   None:







       '''simple docstring'''
       __snake_case :   List[str]													=    d

       # for backward compatibility
       if not hasattr(self      ,     'sp_model_kwargs'  ):
             __snake_case :   List[str]													=    {}

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





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







       '''simple docstring'''
       __snake_case :   Optional[int]													=    Path(__a  )
       assert save_dir.is_dir(), f'''{save_directory} should be a directory'''
       __snake_case :   List[str]													=    save_dir / (
           (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
       )
       __snake_case :   Union[str, Any]													=    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:
                   __snake_case :   Dict													=    self.sp_model.serialized_model_proto()
                   fi.write(__a  )

       return (str(__a  ), str(__a  ))





def   a_   (		_UpperCAmelCase    :      str ,_UpperCAmelCase    :      Dict[str, Any]				)    ->  sentencepiece.SentencePieceProcessor:
      __snake_case :   List[Any]													=    sentencepiece.SentencePieceProcessor(**_UpperCAmelCase				)
      spm.Load(str(_UpperCAmelCase				)				)
      return spm





def   a_   (		_UpperCAmelCase    :      str				)    ->  Union[Dict, List]:
      with open(_UpperCAmelCase ,'r'				) as f:
            return json.load(_UpperCAmelCase				)





def   a_   (		_UpperCAmelCase    :      Union[str, Any] ,_UpperCAmelCase    :      str				)    ->  None:
      with open(_UpperCAmelCase ,'w'				) as f:
            json.dump(_UpperCAmelCase ,_UpperCAmelCase ,indent=2				)








'''simple docstring'''





import functools





def   a_   (		_UpperCAmelCase    :      list[int] ,_UpperCAmelCase    :      list[int]				)    ->  int:

      # Validation
      if not isinstance(_UpperCAmelCase ,_UpperCAmelCase				) or not all(isinstance(_UpperCAmelCase ,_UpperCAmelCase				) for day in days				):
            raise ValueError('The parameter days should be a list of integers'				)

      if len(_UpperCAmelCase				) != 3 or not all(isinstance(_UpperCAmelCase ,_UpperCAmelCase				) for cost in costs				):
            raise ValueError('The parameter costs should be a list of three integers'				)

      if len(_UpperCAmelCase				) == 0:
            return 0

      if min(_UpperCAmelCase				) <= 0:
            raise ValueError('All days elements should be greater than 0'				)

      if max(_UpperCAmelCase				) >= 3_66:
            raise ValueError('All days elements should be less than 366'				)

      __snake_case :   str													=    set(_UpperCAmelCase				)

      @functools.cache
      def dynamic_programming(_UpperCAmelCase    :      int				) -> int:
            if index > 3_65:
                  return 0

            if index not in days_set:
                  return dynamic_programming(index + 1				)

            return min(
                costs[0] + dynamic_programming(index + 1				) ,costs[1] + dynamic_programming(index + 7				) ,costs[2] + dynamic_programming(index + 30				) ,)

      return dynamic_programming(1				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



