






import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__magic_name__   :  Dict        =							logging.get_logger(__name__)


__magic_name__   :  List[Any]        =							{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
__magic_name__   :  Dict        =							{
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__magic_name__   :  Tuple        =							{
    """allenai/led-base-16384""": 1_6384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 		lowercase__  (				) ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  (
          list(range(ord('!')       ,							ord('~') + 1)) + list(range(ord('¡')       ,							ord('¬') + 1)) + list(range(ord('®')       ,							ord('ÿ') + 1))
      )
      UpperCamelCase					       =  bs[:]
      UpperCamelCase					       =  0
      for b in range(2**8):
            if b not in bs:
                  bs.append(_UpperCAmelCase)
                  cs.append(2**8 + n)
                  n += 1
      UpperCamelCase					       =  [chr(_UpperCAmelCase) for n in cs]
      return dict(zip(_UpperCAmelCase       ,							_UpperCAmelCase))
def 		lowercase__  (				_UpperCamelCase) ->			Optional[Any]:
      """simple docstring"""

      UpperCamelCase					       =  set()
      UpperCamelCase					       =  word[0]
      for char in word[1:]:
            pairs.add((prev_char, char))
            UpperCamelCase					       =  char
      return pairs





class 					A__     (     __snake_case ):

     '''simple docstring'''






     snake_case__					=					VOCAB_FILES_NAMES
     snake_case__					=					PRETRAINED_VOCAB_FILES_MAP
     snake_case__					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case__					=					["""input_ids""", """attention_mask"""]







     def __init__( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Any="replace"   ,						_SCREAMING_SNAKE_CASE      :    Dict="<s>"   ,						_SCREAMING_SNAKE_CASE      :    str="</s>"   ,						_SCREAMING_SNAKE_CASE      :    Optional[int]="</s>"   ,						_SCREAMING_SNAKE_CASE      :    Dict="<s>"   ,						_SCREAMING_SNAKE_CASE      :    List[str]="<unk>"   ,						_SCREAMING_SNAKE_CASE      :    List[Any]="<pad>"   ,						_SCREAMING_SNAKE_CASE      :    List[Any]="<mask>"   ,						_SCREAMING_SNAKE_CASE      :    Tuple=False   ,						**_SCREAMING_SNAKE_CASE      :    List[str]   ,						):






           """simple docstring"""




           UpperCamelCase					       =  AddedToken(_SCREAMING_SNAKE_CASE   ,						lstrip=_SCREAMING_SNAKE_CASE   ,						rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) else bos_token
           UpperCamelCase					       =  AddedToken(_SCREAMING_SNAKE_CASE   ,						lstrip=_SCREAMING_SNAKE_CASE   ,						rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) else eos_token
           UpperCamelCase					       =  AddedToken(_SCREAMING_SNAKE_CASE   ,						lstrip=_SCREAMING_SNAKE_CASE   ,						rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) else sep_token
           UpperCamelCase					       =  AddedToken(_SCREAMING_SNAKE_CASE   ,						lstrip=_SCREAMING_SNAKE_CASE   ,						rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) else cls_token
           UpperCamelCase					       =  AddedToken(_SCREAMING_SNAKE_CASE   ,						lstrip=_SCREAMING_SNAKE_CASE   ,						rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) else unk_token
           UpperCamelCase					       =  AddedToken(_SCREAMING_SNAKE_CASE   ,						lstrip=_SCREAMING_SNAKE_CASE   ,						rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) else pad_token

           # Mask token behave like a normal word, i.e. include the space before it
           UpperCamelCase					       =  AddedToken(_SCREAMING_SNAKE_CASE   ,						lstrip=_SCREAMING_SNAKE_CASE   ,						rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) else mask_token

           super().__init__(
               errors=_SCREAMING_SNAKE_CASE   ,						bos_token=_SCREAMING_SNAKE_CASE   ,						eos_token=_SCREAMING_SNAKE_CASE   ,						unk_token=_SCREAMING_SNAKE_CASE   ,						sep_token=_SCREAMING_SNAKE_CASE   ,						cls_token=_SCREAMING_SNAKE_CASE   ,						pad_token=_SCREAMING_SNAKE_CASE   ,						mask_token=_SCREAMING_SNAKE_CASE   ,						add_prefix_space=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE   ,						)

           with open(_SCREAMING_SNAKE_CASE   ,						encoding='utf-8' ) as vocab_handle:
                 UpperCamelCase					       =  json.load(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  {v: k for k, v in self.encoder.items()}
           UpperCamelCase					       =  errors  # how to handle errors in decoding
           UpperCamelCase					       =  bytes_to_unicode()
           UpperCamelCase					       =  {v: k for k, v in self.byte_encoder.items()}
           with open(_SCREAMING_SNAKE_CASE   ,						encoding='utf-8' ) as merges_handle:
                 UpperCamelCase					       =  merges_handle.read().split('\n' )[1:-1]
           UpperCamelCase					       =  [tuple(merge.split() ) for merge in bpe_merges]
           UpperCamelCase					       =  dict(zip(_SCREAMING_SNAKE_CASE   ,						range(len(_SCREAMING_SNAKE_CASE ) ) ) )
           UpperCamelCase					       =  {}
           UpperCamelCase					       =  add_prefix_space

           # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
           UpperCamelCase					       =  re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )







     @property
     # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           return len(self.encoder )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           return dict(self.encoder   ,						**self.added_tokens_encoder )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Dict ):






           """simple docstring"""




           if token in self.cache:
                 return self.cache[token]
           UpperCamelCase					       =  tuple(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  get_pairs(_SCREAMING_SNAKE_CASE )

           if not pairs:
                 return token

           while True:
                 UpperCamelCase					       =  min(_SCREAMING_SNAKE_CASE   ,						key=lambda _SCREAMING_SNAKE_CASE : self.bpe_ranks.get(_SCREAMING_SNAKE_CASE   ,						float('inf' ) ) )
                 if bigram not in self.bpe_ranks:
                       break
                 UpperCamelCase					       =  bigram
                 UpperCamelCase					       =  []
                 UpperCamelCase					       =  0
                 while i < len(_SCREAMING_SNAKE_CASE ):
                       try:
                             UpperCamelCase					       =  word.index(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
                       except ValueError:
                             new_word.extend(word[i:] )
                             break
                       else:
                             new_word.extend(word[i:j] )
                             UpperCamelCase					       =  j

                       if word[i] == first and i < len(_SCREAMING_SNAKE_CASE ) - 1 and word[i + 1] == second:
                             new_word.append(first + second )
                             i += 2
                       else:
                             new_word.append(word[i] )
                             i += 1
                 UpperCamelCase					       =  tuple(_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  new_word
                 if len(_SCREAMING_SNAKE_CASE ) == 1:
                       break
                 else:
                       UpperCamelCase					       =  get_pairs(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  " ".join(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  word
           return word







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  []
           for token in re.findall(self.pat   ,						_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  "".join(
                     self.byte_encoder[b] for b in token.encode('utf-8' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                 bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_SCREAMING_SNAKE_CASE ).split(' ' ) )
           return bpe_tokens







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any] ):






           """simple docstring"""




           return self.encoder.get(_SCREAMING_SNAKE_CASE   ,						self.encoder.get(self.unk_token ) )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    List[str] ):






           """simple docstring"""




           return self.decoder.get(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  "".join(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'   ,						errors=self.errors )
           return text







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    str   ,						_SCREAMING_SNAKE_CASE      :    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
           UpperCamelCase					       =  os.path.join(
               _SCREAMING_SNAKE_CASE   ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
           UpperCamelCase					       =  os.path.join(
               _SCREAMING_SNAKE_CASE   ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

           with open(_SCREAMING_SNAKE_CASE   ,						'w'   ,						encoding='utf-8' ) as f:
                 f.write(json.dumps(self.encoder   ,						indent=2   ,						sort_keys=_SCREAMING_SNAKE_CASE   ,						ensure_ascii=_SCREAMING_SNAKE_CASE ) + '\n' )

           UpperCamelCase					       =  0
           with open(_SCREAMING_SNAKE_CASE   ,						'w'   ,						encoding='utf-8' ) as writer:
                 writer.write('#version: 0.2\n' )
                 for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,						key=lambda _SCREAMING_SNAKE_CASE : kv[1] ):
                       if index != token_index:
                             logger.warning(
                                 f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                                 ' Please check that the tokenizer is not corrupted!' )
                             UpperCamelCase					       =  token_index
                       writer.write(' '.join(_SCREAMING_SNAKE_CASE ) + '\n' )
                       index += 1

           return vocab_file, merge_file







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    List[int]   ,						_SCREAMING_SNAKE_CASE      :    Optional[List[int]] = None ):






           """simple docstring"""




           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           UpperCamelCase					       =  [self.cls_token_id]
           UpperCamelCase					       =  [self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    List[int]   ,						_SCREAMING_SNAKE_CASE      :    Optional[List[int]] = None   ,						_SCREAMING_SNAKE_CASE      :    bool = False ):






           """simple docstring"""




           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 )

           if token_ids_a is None:
                 return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
           return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    List[int]   ,						_SCREAMING_SNAKE_CASE      :    Optional[List[int]] = None ):






           """simple docstring"""




           UpperCamelCase					       =  [self.sep_token_id]
           UpperCamelCase					       =  [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 + sep + token_ids_a + sep ) * [0]







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    List[str]=False   ,						**_SCREAMING_SNAKE_CASE      :    List[str] ):






           """simple docstring"""




           UpperCamelCase					       =  kwargs.pop('add_prefix_space'   ,						self.add_prefix_space )
           if (is_split_into_words or add_prefix_space) and (len(_SCREAMING_SNAKE_CASE ) > 0 and not text[0].isspace()):
                 UpperCamelCase					       =  " " + text
           return (text, kwargs)







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    Union[Dict[str, EncodedInput], BatchEncoding]   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] = None   ,						_SCREAMING_SNAKE_CASE      :    PaddingStrategy = PaddingStrategy.DO_NOT_PAD   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[bool] = None   ,						):






           """simple docstring"""




           UpperCamelCase					       =  super()._pad(
               encoded_inputs=_SCREAMING_SNAKE_CASE   ,						max_length=_SCREAMING_SNAKE_CASE   ,						padding_strategy=_SCREAMING_SNAKE_CASE   ,						pad_to_multiple_of=_SCREAMING_SNAKE_CASE   ,						return_attention_mask=_SCREAMING_SNAKE_CASE   ,						)

           # Load from model defaults
           if return_attention_mask is None:
                 UpperCamelCase					       =  "attention_mask" in self.model_input_names

           if return_attention_mask and "global_attention_mask" in encoded_inputs:
                 UpperCamelCase					       =  encoded_inputs[self.model_input_names[0]]
                 # `global_attention_mask` need to have the same length as other (sequential) inputs.
                 UpperCamelCase					       =  len(encoded_inputs['global_attention_mask'] ) != len(_SCREAMING_SNAKE_CASE )

                 if needs_to_be_padded:
                       UpperCamelCase					       =  len(_SCREAMING_SNAKE_CASE ) - len(encoded_inputs['global_attention_mask'] )

                       if self.padding_side == "right":
                             # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                             UpperCamelCase					       =  (
                                 encoded_inputs["global_attention_mask"] + [-1] * difference
                             )
                       elif self.padding_side == "left":
                             UpperCamelCase					       =  [-1] * difference + encoded_inputs[
                                 "global_attention_mask"
                             ]
                       else:
                             raise ValueError('Invalid padding strategy:' + str(self.padding_side ) )

           return encoded_inputs





import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


UpperCamelCase_						:     Union[str, Any]	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   ["input_values", "attention_mask"]






	def __init__(self				: int      ,	snake_case				: int = 1      ,	snake_case				: int = 1_6000      ,	snake_case				: float = 0.0      ,	snake_case				: bool = False      ,	snake_case				: int = 80      ,	snake_case				: int = 16      ,	snake_case				: int = 64      ,	snake_case				: str = "hann_window"      ,	snake_case				: float = 1.0      ,	snake_case				: float = 80      ,	snake_case				: float = 7600      ,	snake_case				: float = 1e-10      ,	snake_case				: int = 2      ,	snake_case				: bool = True      ,	**snake_case				: Any      ,	) ->			str:
							super().__init__(feature_size=snake_case      ,	sampling_rate=snake_case      ,	padding_value=snake_case      ,	**snake_case    )
							_lowercase       :	int           =   do_normalize
							_lowercase       :	Any           =   return_attention_mask

							_lowercase       :	str           =   num_mel_bins
							_lowercase       :	str           =   hop_length
							_lowercase       :	List[Any]           =   win_length
							_lowercase       :	Tuple           =   win_function
							_lowercase       :	Any           =   frame_signal_scale
							_lowercase       :	Any           =   fmin
							_lowercase       :	Any           =   fmax
							_lowercase       :	List[str]           =   mel_floor
							_lowercase       :	str           =   reduction_factor

							_lowercase       :	str           =   win_length * sampling_rate // 1000
							_lowercase       :	Optional[int]           =   hop_length * sampling_rate // 1000
							_lowercase       :	Tuple           =   optimal_fft_length(self.sample_size    )
							_lowercase       :	int           =   (self.n_fft // 2) + 1

							_lowercase       :	Tuple           =   window_function(window_length=self.sample_size      ,	name=self.win_function      ,	periodic=snake_case    )

							_lowercase       :	Optional[int]           =   mel_filter_bank(
							    num_frequency_bins=self.n_freqs      ,	num_mel_filters=self.num_mel_bins      ,	min_frequency=self.fmin      ,	max_frequency=self.fmax      ,	sampling_rate=self.sampling_rate      ,	norm="slaney"      ,	mel_scale="slaney"      ,	)

							if frame_signal_scale != 1.0:
													warnings.warn(
													    "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"      ,	snake_case      ,	)
							if reduction_factor != 2.0:
													warnings.warn(
													    "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"      ,	snake_case      ,	)






	@staticmethod
	# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
	def       _a(snake_case				: List[np.ndarray]      ,	snake_case				: List[np.ndarray]      ,	snake_case				: float = 0.0    ) ->			List[np.ndarray]:
							if attention_mask is not None:
													_lowercase       :	List[str]           =   np.array(snake_case      ,	np.intaa    )
													_lowercase       :	Dict           =   []

													for vector, length in zip(snake_case      ,	attention_mask.sum(-1    )    ):
																			_lowercase       :	Tuple           =   (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7    )
																			if length < normed_slice.shape[0]:
																									_lowercase       :	List[str]           =   padding_value

																			normed_input_values.append(snake_case    )
							else:
													_lowercase       :	Dict           =   [(x - x.mean()) / np.sqrt(x.var() + 1e-7    ) for x in input_values]

							return normed_input_values






	def       _a(self				: Optional[Any]      ,	snake_case				: np.ndarray      ,	) ->			np.ndarray:
							_lowercase       :	Tuple           =   spectrogram(
							    snake_case      ,	window=self.window      ,	frame_length=self.sample_size      ,	hop_length=self.sample_stride      ,	fft_length=self.n_fft      ,	mel_filters=self.mel_filters      ,	mel_floor=self.mel_floor      ,	log_mel="log10"      ,	)
							return log_mel_spec.T






	def __call__(self				: Tuple      ,	snake_case				: Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None      ,	snake_case				: Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None      ,	snake_case				: Union[bool, str, PaddingStrategy] = False      ,	snake_case				: Optional[int] = None      ,	snake_case				: bool = False      ,	snake_case				: Optional[int] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Union[str, TensorType]] = None      ,	snake_case				: Optional[int] = None      ,	**snake_case				: Union[str, Any]      ,	) ->			BatchFeature:
							if audio is None and audio_target is None:
													raise ValueError("You must provide either `audio` or `audio_target` values."    )

							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 audio is not None:
													_lowercase       :	Union[str, Any]           =   self._process_audio(
													    snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	**snake_case      ,	)
							else:
													_lowercase       :	str           =   None

							if audio_target is not None:
													_lowercase       :	Union[str, Any]           =   self._process_audio(
													    snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	**snake_case      ,	)

													if inputs is None:
																			return inputs_target
													else:
																			_lowercase       :	int           =   inputs_target["input_values"]
																			_lowercase       :	Optional[int]           =   inputs_target.get("attention_mask"    )
																			if decoder_attention_mask is not None:
																									_lowercase       :	List[str]           =   decoder_attention_mask

							return inputs






	def       _a(self				: Optional[Any]      ,	snake_case				: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,	snake_case				: bool = False      ,	snake_case				: Union[bool, str, PaddingStrategy] = False      ,	snake_case				: Optional[int] = None      ,	snake_case				: bool = False      ,	snake_case				: Optional[int] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Union[str, TensorType]] = None      ,	**snake_case				: int      ,	) ->			BatchFeature:
							_lowercase       :	int           =   isinstance(snake_case      ,	np.ndarray    ) and len(speech.shape    ) > 1
							if is_batched_numpy and len(speech.shape    ) > 2:
													raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""    )
							_lowercase       :	List[str]           =   is_batched_numpy or (
							    isinstance(snake_case      ,	(list, tuple)    ) and (isinstance(speech[0]      ,	(np.ndarray, tuple, list)    ))
							)

							if is_batched:
													_lowercase       :	List[Any]           =   [np.asarray(snake_case      ,	dtype=np.floataa    ) for speech in speech]
							elif not is_batched and not isinstance(snake_case      ,	np.ndarray    ):
													_lowercase       :	Optional[Any]           =   np.asarray(snake_case      ,	dtype=np.floataa    )
							elif isinstance(snake_case      ,	np.ndarray    ) and speech.dtype is np.dtype(np.floataa    ):
													_lowercase       :	Optional[int]           =   speech.astype(np.floataa    )

							# always return batch
							if not is_batched:
													_lowercase       :	int           =   [speech]

							# needed to make pad() work on spectrogram inputs
							_lowercase       :	Tuple           =   self.feature_size

							# convert into correct format for padding
							if is_target:
													_lowercase       :	List[str]           =   [self._extract_mel_features(snake_case    ) for waveform in speech]
													_lowercase       :	int           =   BatchFeature({"input_values": features}    )
													_lowercase       :	str           =   self.num_mel_bins
							else:
													_lowercase       :	Optional[int]           =   BatchFeature({"input_values": speech}    )

							_lowercase       :	List[Any]           =   self.pad(
							    snake_case      ,	padding=snake_case      ,	max_length=snake_case      ,	truncation=snake_case      ,	pad_to_multiple_of=snake_case      ,	return_attention_mask=snake_case      ,	**snake_case      ,	)

							_lowercase       :	Optional[Any]           =   feature_size_hack

							# convert input values to correct format
							_lowercase       :	int           =   padded_inputs["input_values"]
							if not isinstance(input_values[0]      ,	np.ndarray    ):
													_lowercase       :	Any           =   [np.asarray(snake_case      ,	dtype=np.floataa    ) for array in input_values]
							elif (
							    not isinstance(snake_case      ,	np.ndarray    )
							    and isinstance(input_values[0]      ,	np.ndarray    )
							    and input_values[0].dtype is np.dtype(np.floataa    )
							):
													_lowercase       :	Union[str, Any]           =   [array.astype(np.floataa    ) for array in input_values]
							elif isinstance(snake_case      ,	np.ndarray    ) and input_values.dtype is np.dtype(np.floataa    ):
													_lowercase       :	Tuple           =   input_values.astype(np.floataa    )

							# convert attention_mask to correct format
							_lowercase       :	Union[str, Any]           =   padded_inputs.get("attention_mask"    )
							if attention_mask is not None:
													_lowercase       :	str           =   [np.asarray(snake_case      ,	dtype=np.intaa    ) for array in attention_mask]

							# zero-mean and unit-variance normalization
							if not is_target and self.do_normalize:
													_lowercase       :	Dict           =   (
													    attention_mask
													    if self._get_padding_strategies(snake_case      ,	max_length=snake_case    ) is not PaddingStrategy.DO_NOT_PAD
													    else None
													)
													_lowercase       :	List[Any]           =   self.zero_mean_unit_var_norm(
													    padded_inputs["input_values"]      ,	attention_mask=snake_case      ,	padding_value=self.padding_value    )

							if return_tensors is not None:
													_lowercase       :	Any           =   padded_inputs.convert_to_tensors(snake_case    )

							return padded_inputs






	def       _a(self				: Dict    ) ->			Dict[str, Any]:
							_lowercase       :	str           =   super().to_dict()

							# Don't serialize these as they are derived from the other properties.
							_lowercase       :	Optional[Any]           =   ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
							for name in names:
													if name in output:
																			del output[name]

							return output




