


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

import sentencepiece as spm

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


_SCREAMING_SNAKE_CASE      :			str      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   {"vocab_file": "spm_char.model"}

_SCREAMING_SNAKE_CASE      :			Any      				=   {
    "vocab_file": {
        "microsoft/speecht5_asr": "https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model",
        "microsoft/speecht5_tts": "https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model",
        "microsoft/speecht5_vc": "https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model",
    }
}

_SCREAMING_SNAKE_CASE      :			str      				=   {
    "microsoft/speecht5_asr": 10_24,
    "microsoft/speecht5_tts": 10_24,
    "microsoft/speecht5_vc": 10_24,
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		VOCAB_FILES_NAMES
    __magic_name__        =		PRETRAINED_VOCAB_FILES_MAP
    __magic_name__        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __magic_name__        =		['input_ids', 'attention_mask']
    def __init__(	self      , __snake_case      , __snake_case="<s>"      , __snake_case="</s>"      , __snake_case="<unk>"      , __snake_case="<pad>"      , __snake_case = None      , **__snake_case      , ):
         snake_case					=    {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=__snake_case      , eos_token=__snake_case      , unk_token=__snake_case      , pad_token=__snake_case      , sp_model_kwargs=self.sp_model_kwargs      , **__snake_case      , )

         snake_case					=    vocab_file

         snake_case					=    spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(__snake_case   )
    @property
    def 		a_      (	self   ):
         return self.sp_model.get_piece_size()
    def 		a_      (	self   ):
         snake_case					=    {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   ):
         snake_case					=    self.__dict__.copy()
         snake_case					=    None
         return state
    def __setstate__(	self      , __snake_case   ):
         snake_case					=    d

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

         snake_case					=    spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(self.vocab_file   )
    def 		a_      (	self      , __snake_case   ):
         return self.sp_model.encode(__snake_case      , out_type=__snake_case   )
    def 		a_      (	self      , __snake_case   ):
         return self.sp_model.piece_to_id(__snake_case   )
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    self.sp_model.IdToPiece(__snake_case   )
         return token
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    []
         snake_case					=    ''''''
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(__snake_case   ) + token
                   snake_case					=    []
              else:
                   current_sub_tokens.append(__snake_case   )
         out_string += self.sp_model.decode(__snake_case   )
         return out_string.strip()
    def 		a_      (	self      , __snake_case      , __snake_case=None   ):
         if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]
    def 		a_      (	self      , __snake_case      , __snake_case = None      , __snake_case = False   ):
         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   )

         snake_case					=    [1]
         if token_ids_a is None:
              return ([0] * len(__snake_case   )) + suffix_ones
         return ([0] * len(__snake_case   )) + ([0] * len(__snake_case   )) + suffix_ones




    def 		a_      (	self      , __snake_case      , __snake_case = None   ):
         if not os.path.isdir(__snake_case   ):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
              return
         snake_case					=    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:
                   snake_case					=    self.sp_model.serialized_model_proto()
                   fi.write(__snake_case   )

         return (out_vocab_file,)








def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    len(UpperCamelCase_    )
     snake_case					=    [[0] * n for i in range(UpperCamelCase_    )]
     for i in range(UpperCamelCase_    ):
          snake_case					=    y_points[i]

     for i in range(2							,UpperCamelCase_    ):
          for j in range(UpperCamelCase_							,UpperCamelCase_    ):
               snake_case					=    (
                   (xa - x_points[j - i + 1]) * q[j][i - 1]
                   - (xa - x_points[j]) * q[j - 1][i - 1]
               ) / (x_points[j] - x_points[j - i + 1])

     return [q[n - 1][n - 1], q]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




