


"""simple docstring"""






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

import sentencepiece as spm

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


__snake_case		=       logging.get_logger(__name__)

__snake_case		=       {'vocab_file': 'sentencepiece.model'}

__snake_case		=       {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

__snake_case		=       {
    'google/rembert': 256,
}


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Any    =		VOCAB_FILES_NAMES
     A_       :	str    =		PRETRAINED_VOCAB_FILES_MAP
     A_       :	Dict    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase="[CLS]"    ,				__UpperCAmelCase="[SEP]"    ,				__UpperCAmelCase="[UNK]"    ,				__UpperCAmelCase="[SEP]"    ,				__UpperCAmelCase="[PAD]"    ,				__UpperCAmelCase="[CLS]"    ,				__UpperCAmelCase="[MASK]"    ,				**__UpperCAmelCase    ,				)       ->							int:
          super().__init__(
              do_lower_case=UpperCAmelCase__    ,				remove_space=UpperCAmelCase__    ,				keep_accents=UpperCAmelCase__    ,				bos_token=UpperCAmelCase__    ,				eos_token=UpperCAmelCase__    ,				unk_token=UpperCAmelCase__    ,				sep_token=UpperCAmelCase__    ,				pad_token=UpperCAmelCase__    ,				cls_token=UpperCAmelCase__    ,				mask_token=UpperCAmelCase__    ,				**UpperCAmelCase__    ,				)

          _a    	=				do_lower_case
          _a    	=				remove_space
          _a    	=				keep_accents
          _a    	=				vocab_file

          _a    	=				spm.SentencePieceProcessor()
          self.sp_model.Load(UpperCAmelCase__							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          return len(self.sp_model							)


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				{self.convert_ids_to_tokens(UpperCAmelCase__							): i for i in range(self.vocab_size							)}
          vocab.update(self.added_tokens_encoder							)
          return vocab


     def __getstate__(      self							)       ->							List[str]:
          _a    	=				self.__dict__.copy()
          _a    	=				None
          return state


     def __setstate__(      self    ,				__UpperCAmelCase							)       ->							int:
          _a    	=				d
          _a    	=				spm.SentencePieceProcessor()
          self.sp_model.Load(self.vocab_file							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=False							)       ->							Dict:
          _a    	=				self.sp_model.EncodeAsPieces(UpperCAmelCase__							)
          return pieces


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Any:
          return self.sp_model.PieceToId(UpperCAmelCase__							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Tuple:
          return self.sp_model.IdToPiece(UpperCAmelCase__							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Any:
          _a    	=				self.sp_model.decode_pieces(UpperCAmelCase__							)
          return out_string


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							Any:
          _a    	=				[self.sep_token_id]
          _a    	=				[self.cls_token_id]
          if token_ids_a is None:
               return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False							)       ->							str:

          if already_has_special_tokens:
               if token_ids_a is not None:
                    raise ValueError(
                        '''You should not supply a second sequence if the provided sequence of '''
                        '''ids is already formatted with special tokens for the model.'''							)
               return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

          if token_ids_a is not None:
               return [1] + ([0] * len(UpperCAmelCase__							)) + [1] + ([0] * len(UpperCAmelCase__							)) + [1]
          return [1] + ([0] * len(UpperCAmelCase__							)) + [1]


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							Optional[Any]:
          _a    	=				[self.sep_token_id]
          _a    	=				[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							) * [0] + len(token_ids_a + sep							) * [1]





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							Any:
          if not os.path.isdir(UpperCAmelCase__							):
               logger.error('''Vocabulary path ({}) should be a directory'''.format(UpperCAmelCase__							)							)
               return
          _a    	=				os.path.join(
              UpperCAmelCase__    ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

          if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCAmelCase__							):
               copyfile(self.vocab_file    ,				UpperCAmelCase__							)

          return (out_vocab_file,)



"""simple docstring"""






from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__snake_case		=       logging.get_logger(__name__)


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Union[str, Any]    =		['input_values', 'padding_mask']


     def __init__(      self    ,				__UpperCAmelCase = 1    ,				__UpperCAmelCase = 24000    ,				__UpperCAmelCase = 0.0    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							List[str]:
          super().__init__(feature_size=__UpperCAmelCase    ,				sampling_rate=__UpperCAmelCase    ,				padding_value=__UpperCAmelCase    ,				**__UpperCAmelCase							)
          _a    	=				chunk_length_s
          _a    	=				overlap


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          if self.chunk_length_s is None:
               return None
          else:
               return int(self.chunk_length_s * self.sampling_rate							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          if self.chunk_length_s is None or self.overlap is None:
               return None
          else:
               return max(1    ,				int((1.0 - self.overlap) * self.chunk_length							)							)





     def __call__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				)       ->							BatchFeature:
          if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                    raise ValueError(
                        F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                        F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
                        F' {self.sampling_rate} and not {sampling_rate}.'							)
          else:
               logger.warning(
                   '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                   '''Failing to do so can result in silent errors that might be hard to debug.'''							)

          if padding and truncation:
               raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''							)
          elif padding is None:
               # by default let's pad the inputs
               _a    	=				True

          _a    	=				bool(
              isinstance(__UpperCAmelCase    ,				(list, tuple)							) and (isinstance(raw_audio[0]    ,				(np.ndarray, tuple, list)							))							)

          if is_batched:
               _a    	=				[np.asarray(__UpperCAmelCase    ,				dtype=np.floataa							).T for audio in raw_audio]
          elif not is_batched and not isinstance(__UpperCAmelCase    ,				np.ndarray							):
               _a    	=				np.asarray(__UpperCAmelCase    ,				dtype=np.floataa							)
          elif isinstance(__UpperCAmelCase    ,				np.ndarray							) and raw_audio.dtype is np.dtype(np.floataa							):
               _a    	=				raw_audio.astype(np.floataa							)

          # always return batch
          if not is_batched:
               _a    	=				[np.asarray(__UpperCAmelCase							).T]

          # verify inputs are valid
          for idx, example in enumerate(__UpperCAmelCase							):
               if example.ndim > 2:
                    raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'							)
               if self.feature_size == 1 and example.ndim != 1:
                    raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'							)
               if self.feature_size == 2 and example.shape[-1] != 2:
                    raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'							)

          _a    	=				None
          _a    	=				BatchFeature({'''input_values''': raw_audio}							)
          if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
               if truncation:
                    _a    	=				min(array.shape[0] for array in raw_audio							)
                    _a    	=				int(np.floor(max_length / self.chunk_stride							)							)
                    _a    	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
               elif padding:
                    _a    	=				max(array.shape[0] for array in raw_audio							)
                    _a    	=				int(np.ceil(max_length / self.chunk_stride							)							)
                    _a    	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
                    _a    	=				'''max_length'''
               else:
                    _a    	=				input_values

        # normal padding on batch
          if padded_inputs is None:
               _a    	=				self.pad(
                   __UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				padding=__UpperCAmelCase    ,				return_attention_mask=__UpperCAmelCase    ,				)
               if padding:
                    _a    	=				padded_inputs.pop('''attention_mask'''							)

          _a    	=				[]
          for example in padded_inputs.pop('''input_values'''							):
               if self.feature_size == 1:
                    _a    	=				example[..., None]
               input_values.append(example.T							)

          _a    	=				input_values
          if return_tensors is not None:
               _a    	=				padded_inputs.convert_to_tensors(__UpperCAmelCase							)

          return padded_inputs