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


__lowerCamelCase    :				Any							     =	logging.get_logger(__name__)


__lowerCamelCase    :				Dict							     =	'''▁'''

__lowerCamelCase    :				Optional[Any]							     =	{'''vocab_file''': '''spiece.model'''}

__lowerCamelCase    :				str							     =	{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

__lowerCamelCase    :				Optional[int]							     =	{
    '''google/reformer-crime-and-punishment''': 52_4288,
}







class __snake_case       (		__UpperCAmelCase	):
   lowerCAmelCase_														=							VOCAB_FILES_NAMES
   lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_														=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase_														=							['''input_ids''', '''attention_mask''']




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         eos_token=__SCREAMING_SNAKE_CASE ,			unk_token=__SCREAMING_SNAKE_CASE ,			additional_special_tokens=__SCREAMING_SNAKE_CASE ,			sp_model_kwargs=self.sp_model_kwargs ,			**__SCREAMING_SNAKE_CASE ,			)

     SCREAMING_SNAKE_CASE__    							=   vocab_file
     SCREAMING_SNAKE_CASE__    							=   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(__SCREAMING_SNAKE_CASE   )




   @property
   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     return self.sp_model.get_piece_size()




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   {self.convert_ids_to_tokens(__SCREAMING_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"""






     SCREAMING_SNAKE_CASE__    							=   self.__dict__.copy()
     SCREAMING_SNAKE_CASE__    							=   None
     return state




   def __setstate__(					self    :       str ,			_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   d

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

     SCREAMING_SNAKE_CASE__    							=   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(self.vocab_file   )




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       str   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE   )




   def 			__a		(					self    :       str ,			_lowercase    :       Dict   ):



     """simple docstring"""






     if index < self.sp_model.get_piece_size():
       SCREAMING_SNAKE_CASE__    							=   self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE   )
     return token




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   []
     SCREAMING_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(__SCREAMING_SNAKE_CASE   ) + token
         SCREAMING_SNAKE_CASE__    							=   []
       else:
         current_sub_tokens.append(__SCREAMING_SNAKE_CASE   )
     out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE   )
     return out_string.strip()




   def 			__a		(					self    :       Tuple ,			_lowercase    :       str ,			_lowercase    :       Optional[str] = None   ):



     """simple docstring"""






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

     if os.path.abspath(self.vocab_file   ) != os.path.abspath(__SCREAMING_SNAKE_CASE   ) and os.path.isfile(self.vocab_file   ):
       copyfile(self.vocab_file ,			__SCREAMING_SNAKE_CASE   )
     elif not os.path.isfile(self.vocab_file   ):
       with open(__SCREAMING_SNAKE_CASE ,			"""wb"""   ) as fi:
         SCREAMING_SNAKE_CASE__    							=   self.sp_model.serialized_model_proto()
         fi.write(__SCREAMING_SNAKE_CASE   )

     return (out_vocab_file,)

from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"EncodecFeatureExtractor"
   lowerCAmelCase_														=							("T5Tokenizer", "T5TokenizerFast")




   def __init__(					self    :       List[Any] ,			_lowercase    :       int ,			_lowercase    :       Any   ):



     """simple docstring"""






     super().__init__(_lowercase ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.feature_extractor
     SCREAMING_SNAKE_CASE__    							=   False




   def 			__a		(					self    :       Tuple ,			_lowercase    :       str=None ,			_lowercase    :       int=None ,			_lowercase    :       Any=True   ):



     """simple docstring"""






     return self.tokenizer.get_decoder_prompt_ids(task=_lowercase ,			language=_lowercase ,			no_timestamps=_lowercase   )




   def __call__(					self    :       Tuple ,			*_lowercase    :       Tuple ,			**_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     if self._in_target_context_manager:
       return self.current_processor(*_lowercase ,			**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""sampling_rate""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""text""" ,			_lowercase   )
     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   args[1:]

     if audio is None and text is None:
       raise ValueError("""You need to specify either an `audio` or `text` input to process."""   )

     if text is not None:
       SCREAMING_SNAKE_CASE__    							=   self.tokenizer(_lowercase ,			**_lowercase   )

     if audio is not None:
       SCREAMING_SNAKE_CASE__    							=   self.feature_extractor(_lowercase ,			*_lowercase ,			sampling_rate=_lowercase ,			**_lowercase   )

     if audio is None:
       return inputs

     elif text is None:
       return audio_inputs

     else:
       SCREAMING_SNAKE_CASE__    							=   audio_inputs["""input_values"""]
       if "padding_mask" in audio_inputs:
         SCREAMING_SNAKE_CASE__    							=   audio_inputs["""padding_mask"""]
       return inputs




   def 			__a		(					self    :       Dict ,			*_lowercase    :       Any ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""padding_mask""" ,			_lowercase   )

     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   args[1:]

     if audio_values is not None:
       return self._decode_audio(_lowercase ,			padding_mask=_lowercase   )
     else:
       return self.tokenizer.batch_decode(*_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       List[str] ,			*_lowercase    :       Tuple ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     return self.tokenizer.decode(*_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional = None   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   to_numpy(_lowercase   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   audio_values.shape

     if padding_mask is None:
       return list(_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   to_numpy(_lowercase   )

     # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
     # token (so that the generated audio values are **not** treated as padded tokens)
     SCREAMING_SNAKE_CASE__    							=   seq_len - padding_mask.shape[-1]
     SCREAMING_SNAKE_CASE__    							=   1 - self.feature_extractor.padding_value
     SCREAMING_SNAKE_CASE__    							=   np.pad(_lowercase ,			((0, 0), (0, difference)) ,			"""constant""" ,			constant_values=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   audio_values.tolist()
     for i in range(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   np.asarray(audio_values[i]   )[
           padding_mask[i][None, :] != self.feature_extractor.padding_value
       ]
       SCREAMING_SNAKE_CASE__    							=   sliced_audio.reshape(_lowercase ,			-1   )

     return audio_values
