






"""simple docstring"""





from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE					:				Dict					    =						logging.get_logger(__name__)






class      a						(					_UpperCAmelCase		):
    SCREAMING_SNAKE_CASE						:     Tuple						= ['audio_values', 'audio_mask']






    def __init__(       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							str=2048       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=1       ,						__SCREAMING_SNAKE_CASE     :							Dict=[16, 16]       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=128       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=44100       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=86       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=2048       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=0.0       ,						**__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						)     ->		Union[str, Any]:
      super().__init__(
          feature_size=SCREAMING_SNAKE_CASE_       ,						sampling_rate=SCREAMING_SNAKE_CASE_       ,						padding_value=SCREAMING_SNAKE_CASE_       ,						**SCREAMING_SNAKE_CASE_       ,						)

      lowerCamelCase_         =						spectrogram_length
      lowerCamelCase_         =						num_channels
      lowerCamelCase_         =						patch_size
      lowerCamelCase_         =						feature_size // self.patch_size[1]
      lowerCamelCase_         =						n_fft
      lowerCamelCase_         =						sampling_rate // hop_length_to_sampling_rate
      lowerCamelCase_         =						sampling_rate
      lowerCamelCase_         =						padding_value
      lowerCamelCase_         =						mel_filter_bank(
          num_frequency_bins=1 + n_fft // 2       ,						num_mel_filters=SCREAMING_SNAKE_CASE_       ,						min_frequency=0.0       ,						max_frequency=22050.0       ,						sampling_rate=SCREAMING_SNAKE_CASE_       ,						norm='slaney'       ,						mel_scale='slaney'       ,						).T






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]					)     ->		np.ndarray:
      lowerCamelCase_         =						spectrogram(
          SCREAMING_SNAKE_CASE_       ,						window_function(self.n_fft       ,						'hann'					)       ,						frame_length=self.n_fft       ,						hop_length=self.hop_length       ,						power=2.0       ,						mel_filters=self.mel_filters.T       ,						log_mel='dB'       ,						db_range=80.0       ,						)
      lowerCamelCase_         =						log_spec[:, :-1]
      lowerCamelCase_         =						log_spec - 20.0
      lowerCamelCase_         =						np.clip(log_spec / 40.0       ,						-2.0       ,						0.0					) + 1.0
      return log_spec






    def __call__(       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							List[Any] = None       ,						__SCREAMING_SNAKE_CASE     :							List[Any] = True       ,						__SCREAMING_SNAKE_CASE     :							int = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = False       ,						__SCREAMING_SNAKE_CASE     :							Dict = False       ,						**__SCREAMING_SNAKE_CASE     :							Any       ,						)     ->		BatchFeature:

      if sampling_rate is not None:
        if sampling_rate != self.sampling_rate:
          raise ValueError(
              'This feature extractor is set to support sampling rate'
              F''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
              F''' with {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.'					)

      lowerCamelCase_         =						isinstance(SCREAMING_SNAKE_CASE_       ,						np.ndarray					) and len(raw_speech.shape					) > 1
      if is_batched_numpy and len(raw_speech.shape					) > 2:
        raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''					)
      lowerCamelCase_         =						is_batched_numpy or (
          isinstance(SCREAMING_SNAKE_CASE_       ,						(list, tuple)					) and (isinstance(raw_speech[0]       ,						(np.ndarray, tuple, list)					))
      )
      if is_batched:
        lowerCamelCase_         =						[np.asarray([speech]       ,						dtype=np.floataa					).T for speech in raw_speech]
      elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_       ,						np.ndarray					):
        lowerCamelCase_         =						np.asarray(SCREAMING_SNAKE_CASE_       ,						dtype=np.floataa					)
      elif isinstance(SCREAMING_SNAKE_CASE_       ,						np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
        lowerCamelCase_         =						raw_speech.astype(np.floataa					)
      # always return batch
      if not is_batched:
        lowerCamelCase_         =						[np.asarray([raw_speech]					).T]

      # Convert audio signals to log mel spectrograms, truncate by time axis
      lowerCamelCase_         =						[
          self._np_extract_fbank_features(waveform.squeeze()					).T[: self.spectrogram_length] for waveform in raw_speech
      ]
      if isinstance(audio_features[0]       ,						SCREAMING_SNAKE_CASE_					):
        lowerCamelCase_         =						[np.asarray(SCREAMING_SNAKE_CASE_       ,						dtype=np.floataa					) for feature in audio_features]

      # Create audio attention mask
      lowerCamelCase_         =						max(
          [ceil(feature.shape[0] / self.patch_size[0]					) * self.freq_len for feature in audio_features]					)  # The maximum number of audio patches in a batch
      if return_attention_mask:
        lowerCamelCase_         =						[
            (ceil(feature.shape[0] / self.patch_size[0]					) * self.freq_len) * [1]
            + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]					) * self.freq_len) * [0]
            for feature in audio_features
        ]
        lowerCamelCase_         =						np.array(SCREAMING_SNAKE_CASE_					).astype(np.floataa					)

      # convert into correct format for padding
      lowerCamelCase_         =						max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
      lowerCamelCase_         =						np.ones([len(SCREAMING_SNAKE_CASE_					), 1, max_time_len, self.feature_size]					).astype(np.floataa					)
      lowerCamelCase_         =						padded_audio_features * self.padding_value
      for i in range(len(SCREAMING_SNAKE_CASE_					)					):
        lowerCamelCase_         =						audio_features[i]
        lowerCamelCase_         =						feature

      # return as BatchFeature
      if return_attention_mask:
        lowerCamelCase_         =						{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
      else:
        lowerCamelCase_         =						{'audio_values': padded_audio_features}

      lowerCamelCase_         =						BatchFeature(data=SCREAMING_SNAKE_CASE_       ,						tensor_type=SCREAMING_SNAKE_CASE_					)
      return encoded_inputs








'''simple docstring'''



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


A__		:					Tuple												=  logging.get_logger(__name__)

A__		:					List[str]												=  {"""vocab_file""": """spm_char.model"""}

A__		:					str												=  {
    """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""",
    }
}

A__		:					List[Any]												=  {
    """microsoft/speecht5_asr""": 1024,
    """microsoft/speecht5_tts""": 1024,
    """microsoft/speecht5_vc""": 1024,
}





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Optional[Any]							     =			VOCAB_FILES_NAMES
    lowerCamelCase  :					Any							     =			PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  :					Union[str, Any]							     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  :					int							     =			['input_ids', 'attention_mask']







    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_="<s>"			,     SCREAMING_SNAKE_CASE_="</s>"			,     SCREAMING_SNAKE_CASE_="<unk>"			,     SCREAMING_SNAKE_CASE_="<pad>"			,     SCREAMING_SNAKE_CASE_ = None			,     **SCREAMING_SNAKE_CASE_			,     )				->     None:
         __lowerCamelCase   :	Any								=		{} if sp_model_kwargs is None else sp_model_kwargs

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

         __lowerCamelCase   :	List[Any]								=		vocab_file

         __lowerCamelCase   :	str								=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(SCREAMING_SNAKE_CASE_	)







    @property
    def       lowercase_				(							self	)				->     List[str]:
         return self.sp_model.get_piece_size()







    def       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	str								=		{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	)				->     Optional[Any]:
         __lowerCamelCase   :	Dict								=		self.__dict__.copy()
         __lowerCamelCase   :	int								=		None
         return state







    def __setstate__(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         __lowerCamelCase   :	Union[str, Any]								=		d

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

         __lowerCamelCase   :	Dict								=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(self.vocab_file	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         return self.sp_model.encode(SCREAMING_SNAKE_CASE_			,     out_type=SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     str:
         return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     Dict:
         __lowerCamelCase   :	Any								=		self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_	)
         return token







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[Any]:
         __lowerCamelCase   :	Union[str, Any]								=		[]
         __lowerCamelCase   :	str								=		''
         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
                   __lowerCamelCase   :	int								=		[]
              else:
                   current_sub_tokens.append(SCREAMING_SNAKE_CASE_	)
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	)
         return out_string.strip()







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None	)				->     List[int]:
         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       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = False	)				->     List[int]:
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=SCREAMING_SNAKE_CASE_			,     token_ids_a=SCREAMING_SNAKE_CASE_			,     already_has_special_tokens=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		[1]
         if token_ids_a is None:
              return ([0] * len(SCREAMING_SNAKE_CASE_	)) + suffix_ones
         return ([0] * len(SCREAMING_SNAKE_CASE_	)) + ([0] * len(SCREAMING_SNAKE_CASE_	)) + suffix_ones







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None	)				->     Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
              return
         __lowerCamelCase   :	Tuple								=		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:
                   __lowerCamelCase   :	str								=		self.sp_model.serialized_model_proto()
                   fi.write(SCREAMING_SNAKE_CASE_	)

         return (out_vocab_file,)






