






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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case												=  logging.get_logger(__name__)


__snake_case												=  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all BART models at https://huggingface.co/models?filter=bart
__snake_case												=  {
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
}

__snake_case												=  {
    """facebook/bart-base""": 10_24,
    """facebook/bart-large""": 10_24,
    """facebook/bart-large-mnli""": 10_24,
    """facebook/bart-large-cnn""": 10_24,
    """facebook/bart-large-xsum""": 10_24,
    """yjernite/bart_eli5""": 10_24,
}


@lru_cache()
def   _A   (				):
    UpperCamelCase       :str								=   (
        list(range(ord('''!'''       )     ,  ord('''~'''       ) + 1       )       ) + list(range(ord('''¡'''       )     ,  ord('''¬'''       ) + 1       )       ) + list(range(ord('''®'''       )     ,  ord('''ÿ'''       ) + 1       )       )
    )
    UpperCamelCase       :Union[str, Any]								=   bs[:]
    UpperCamelCase       :int								=   0
    for b in range(2**8       ):
        if b not in bs:
            bs.append(_UpperCAmelCase       )
            cs.append(2**8 + n       )
            n += 1
    UpperCamelCase       :Dict								=   [chr(_UpperCAmelCase       ) for n in cs]
    return dict(zip(_UpperCAmelCase     ,  _UpperCAmelCase       )       )


def   _A   (				SCREAMING_SNAKE_CASE__  :	List[str]       ):
    UpperCamelCase       :Optional[int]								=   set()
    UpperCamelCase       :Tuple								=   word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)       )
        UpperCamelCase       :str								=   char
    return pairs







class 			UpperCAmelCase_						(					snake_case__ ):



      """simple docstring"""
      UpperCamelCase_				:					int           =VOCAB_FILES_NAMES
      UpperCamelCase_				:					Optional[Any]           =PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_				:					str           =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_				:					List[str]           =["input_ids", "attention_mask"]
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_="replace"    ,				SCREAMING_SNAKE_CASE_="<s>"    ,				SCREAMING_SNAKE_CASE_="</s>"    ,				SCREAMING_SNAKE_CASE_="</s>"    ,				SCREAMING_SNAKE_CASE_="<s>"    ,				SCREAMING_SNAKE_CASE_="<unk>"    ,				SCREAMING_SNAKE_CASE_="<pad>"    ,				SCREAMING_SNAKE_CASE_="<mask>"    ,				SCREAMING_SNAKE_CASE_=False    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      List[Any]:
          UpperCamelCase       :Dict								=   AddedToken(SCREAMING_SNAKE_CASE_    ,				lstrip=SCREAMING_SNAKE_CASE_    ,				rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ ) else bos_token
          UpperCamelCase       :Optional[Any]								=   AddedToken(SCREAMING_SNAKE_CASE_    ,				lstrip=SCREAMING_SNAKE_CASE_    ,				rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ ) else eos_token
          UpperCamelCase       :Any								=   AddedToken(SCREAMING_SNAKE_CASE_    ,				lstrip=SCREAMING_SNAKE_CASE_    ,				rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ ) else sep_token
          UpperCamelCase       :str								=   AddedToken(SCREAMING_SNAKE_CASE_    ,				lstrip=SCREAMING_SNAKE_CASE_    ,				rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ ) else cls_token
          UpperCamelCase       :List[Any]								=   AddedToken(SCREAMING_SNAKE_CASE_    ,				lstrip=SCREAMING_SNAKE_CASE_    ,				rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ ) else unk_token
          UpperCamelCase       :List[Any]								=   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       :Dict								=   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       :Any								=   json.load(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Dict								=   {v: k for k, v in self.encoder.items()}
          UpperCamelCase       :List[str]								=   errors  # how to handle errors in decoding
          UpperCamelCase       :List[str]								=   bytes_to_unicode()
          UpperCamelCase       :Optional[int]								=   {v: k for k, v in self.byte_encoder.items()}
          with open(SCREAMING_SNAKE_CASE_    ,				encoding='''utf-8''' ) as merges_handle:
              UpperCamelCase       :Dict								=   merges_handle.read().split('''\n''' )[1:-1]
          UpperCamelCase       :int								=   [tuple(merge.split() ) for merge in bpe_merges]
          UpperCamelCase       :Dict								=   dict(zip(SCREAMING_SNAKE_CASE_    ,				range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
          UpperCamelCase       :Tuple								=   {}
          UpperCamelCase       :Any								=   add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          UpperCamelCase       :List[str]								=   re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
      @property
      def     UpperCAmelCase						(				self )				->      str:
          return len(self.encoder )
      def     UpperCAmelCase						(				self )				->      Dict:
          return dict(self.encoder    ,				**self.added_tokens_encoder )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      List[str]:
          if token in self.cache:
              return self.cache[token]
          UpperCamelCase       :Optional[Any]								=   tuple(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :List[Any]								=   get_pairs(SCREAMING_SNAKE_CASE_ )

          if not pairs:
              return token

          while True:
              UpperCamelCase       :Tuple								=   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       :Optional[Any]								=   bigram
              UpperCamelCase       :int								=   []
              UpperCamelCase       :Optional[int]								=   0
              while i < len(SCREAMING_SNAKE_CASE_ ):
                  try:
                      UpperCamelCase       :List[Any]								=   word.index(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ )
                  except ValueError:
                      new_word.extend(word[i:] )
                      break
                  else:
                      new_word.extend(word[i:j] )
                      UpperCamelCase       :List[Any]								=   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       :Optional[int]								=   tuple(SCREAMING_SNAKE_CASE_ )
              UpperCamelCase       :List[Any]								=   new_word
              if len(SCREAMING_SNAKE_CASE_ ) == 1:
                  break
              else:
                  UpperCamelCase       :Union[str, Any]								=   get_pairs(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Optional[Any]								=   " ".join(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Any								=   word
          return word
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      str:
          UpperCamelCase       :List[str]								=   []
          for token in re.findall(self.pat    ,				SCREAMING_SNAKE_CASE_ ):
              UpperCamelCase       :Dict								=   "".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     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      List[Any]:
          return self.encoder.get(SCREAMING_SNAKE_CASE_    ,				self.encoder.get(self.unk_token ) )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      Any:
          return self.decoder.get(SCREAMING_SNAKE_CASE_ )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      List[Any]:
          UpperCamelCase       :Dict								=   "".join(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Optional[int]								=   bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8'''    ,				errors=self.errors )
          return text
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None )				->      Optional[Any]:
          if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
              return
          UpperCamelCase       :Tuple								=   os.path.join(
              SCREAMING_SNAKE_CASE_    ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
          UpperCamelCase       :List[str]								=   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       :Union[str, Any]								=   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       :Union[str, Any]								=   token_index
                  writer.write(''' '''.join(SCREAMING_SNAKE_CASE_ ) + '''\n''' )
                  index += 1

          return vocab_file, merge_file
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None )				->      Dict:
          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          UpperCamelCase       :List[Any]								=   [self.cls_token_id]
          UpperCamelCase       :List[str]								=   [self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = False )				->      Dict:
          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     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None )				->      Optional[Any]:
          UpperCamelCase       :List[Any]								=   [self.sep_token_id]
          UpperCamelCase       :str								=   [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     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_=False    ,				**SCREAMING_SNAKE_CASE_ )				->      List[str]:
          UpperCamelCase       :Dict								=   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       :List[Any]								=   " " + text
          return (text, kwargs)






'''simple docstring'''




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

import numpy as np
import torch

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


__SCREAMING_SNAKE_CASE       :     List[str]     =  logging.get_logger(__name__)

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						["input_features", "is_longer"]




      def __init__(       self						:				str						,					A						:				int=64						,					A						:				Dict=48000						,					A						:				str=480						,					A						:				List[Any]=10						,					A						:				Optional[Any]=1024						,					A						:				Tuple=0.0						,					A						:				List[Any]=False						,					A						:				float = 0						,					A						:				float = 14000						,					A						:				int = None						,					A						:				str = "fusion"						,					A						:				str = "repeatpad"						,					**A						:				Dict						,					):
             super().__init__(
                 feature_size=A						,					sampling_rate=A						,					padding_value=A						,					return_attention_mask=A						,					**A						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    top_db
             _UpperCAmelCase     :  Dict							      =    truncation
             _UpperCAmelCase     :  List[Any]							      =    padding
             _UpperCAmelCase     :  Optional[Any]							      =    fft_window_size
             _UpperCAmelCase     :  Dict							      =    (fft_window_size >> 1) + 1
             _UpperCAmelCase     :  Any							      =    hop_length
             _UpperCAmelCase     :  Tuple							      =    max_length_s
             _UpperCAmelCase     :  str							      =    max_length_s * sampling_rate
             _UpperCAmelCase     :  Any							      =    sampling_rate
             _UpperCAmelCase     :  Optional[int]							      =    frequency_min
             _UpperCAmelCase     :  str							      =    frequency_max
             _UpperCAmelCase     :  Union[str, Any]							      =    mel_filter_bank(
                 num_frequency_bins=self.nb_frequency_bins						,					num_mel_filters=A						,					min_frequency=A						,					max_frequency=A						,					sampling_rate=A						,					norm=A						,					mel_scale="htk"						,					)
             _UpperCAmelCase     :  Tuple							      =    mel_filter_bank(
                 num_frequency_bins=self.nb_frequency_bins						,					num_mel_filters=A						,					min_frequency=A						,					max_frequency=A						,					sampling_rate=A						,					norm="slaney"						,					mel_scale="slaney"						,					)




      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    copy.deepcopy(self.__dict__       )
             _UpperCAmelCase     :  Dict							      =    self.__class__.__name__
             if "mel_filters" in output:
                    del output["mel_filters"]
             if "mel_filters_slaney" in output:
                    del output["mel_filters_slaney"]
             return output




      def      _A	(       self						:				Optional[Any]						,					A						:				np.array						,					A						:				Optional[np.array] = None       ):
             _UpperCAmelCase     :  Dict							      =    spectrogram(
                 A						,					window_function(self.fft_window_size						,					"hann"       )						,					frame_length=self.fft_window_size						,					hop_length=self.hop_length						,					power=2.0						,					mel_filters=A						,					log_mel="dB"						,					)
             return log_mel_spectrogram.T




      def      _A	(       self						:				str						,					A						:				str						,					A						:				List[str]						,					A						:				List[Any]       ):
             _UpperCAmelCase     :  List[str]							      =    np.array_split(list(range(0						,					total_frames - chunk_frames + 1       )       )						,					3       )
             if len(ranges[1]       ) == 0:
                    # if the audio is too short, we just use the first chunk
                    _UpperCAmelCase     :  Optional[Any]							      =    [0]
             if len(ranges[2]       ) == 0:
                    # if the audio is too short, we just use the first chunk
                    _UpperCAmelCase     :  Tuple							      =    [0]
             # randomly choose index for each part
             _UpperCAmelCase     :  Dict							      =    np.random.choice(ranges[0]       )
             _UpperCAmelCase     :  str							      =    np.random.choice(ranges[1]       )
             _UpperCAmelCase     :  Tuple							      =    np.random.choice(ranges[2]       )

             _UpperCAmelCase     :  str							      =    mel[idx_front : idx_front + chunk_frames, :]
             _UpperCAmelCase     :  str							      =    mel[idx_middle : idx_middle + chunk_frames, :]
             _UpperCAmelCase     :  List[Any]							      =    mel[idx_back : idx_back + chunk_frames, :]

             _UpperCAmelCase     :  Dict							      =    torch.tensor(mel[None, None, :]       )
             _UpperCAmelCase     :  Optional[Any]							      =    torch.nn.functional.interpolate(
                 A						,					size=[chunk_frames, 64]						,					mode="bilinear"						,					align_corners=A       )
             _UpperCAmelCase     :  List[str]							      =    mel_shrink[0][0].numpy()
             _UpperCAmelCase     :  str							      =    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]						,					axis=0       )
             return mel_fusion




      def      _A	(       self						:				List[Any]						,					A						:				np.array						,					A						:				List[str]						,					A						:				Any						,					A						:				Optional[int]       ):
             if waveform.shape[0] > max_length:
                    if truncation == "rand_trunc":
                           _UpperCAmelCase     :  int							      =    True
                           # random crop to max_length (for compatibility) -> this should be handled by self.pad
                           _UpperCAmelCase     :  str							      =    len(A       ) - max_length
                           _UpperCAmelCase     :  str							      =    np.random.randint(0						,					overflow + 1       )
                           _UpperCAmelCase     :  int							      =    waveform[idx : idx + max_length]
                           _UpperCAmelCase     :  Any							      =    self._np_extract_fbank_features(A						,					self.mel_filters_slaney       )[None, :]
                    elif truncation == "fusion":
                           _UpperCAmelCase     :  Tuple							      =    self._np_extract_fbank_features(A						,					self.mel_filters       )
                           _UpperCAmelCase     :  List[str]							      =    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                           _UpperCAmelCase     :  Optional[Any]							      =    mel.shape[0]
                           if chunk_frames == total_frames:
                                  # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                                  # In this case, we just use the whole audio.
                                  _UpperCAmelCase     :  Any							      =    np.stack([mel, mel, mel, mel]						,					axis=0       )
                                  _UpperCAmelCase     :  int							      =    False
                           else:
                                  _UpperCAmelCase     :  Tuple							      =    self._random_mel_fusion(A						,					A						,					A       )
                                  _UpperCAmelCase     :  Any							      =    True
                    else:
                           raise NotImplementedError(F"""data_truncating {truncation} not implemented"""       )

             else:
                    _UpperCAmelCase     :  Optional[Any]							      =    False
                    # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
                    if waveform.shape[0] < max_length:
                           if padding == "repeat":
                                  _UpperCAmelCase     :  str							      =    int(max_length / len(A       )       )
                                  _UpperCAmelCase     :  Dict							      =    np.stack(np.tile(A						,					n_repeat + 1       )       )[:max_length]
                           if padding == "repeatpad":
                                  _UpperCAmelCase     :  Dict							      =    int(max_length / len(A       )       )
                                  _UpperCAmelCase     :  List[str]							      =    np.stack(np.tile(A						,					A       )       )
                           _UpperCAmelCase     :  Optional[Any]							      =    np.pad(A						,					(0, max_length - waveform.shape[0])						,					mode="constant"						,					constant_values=0       )

                    if truncation == "fusion":
                           _UpperCAmelCase     :  str							      =    self._np_extract_fbank_features(A						,					self.mel_filters       )
                           _UpperCAmelCase     :  Optional[int]							      =    np.stack([input_mel, input_mel, input_mel, input_mel]						,					axis=0       )
                    else:
                           _UpperCAmelCase     :  List[str]							      =    self._np_extract_fbank_features(A						,					self.mel_filters_slaney       )[None, :]

             return input_mel, longer





      def __call__(       self						:				Union[str, Any]						,					A						:				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]						,					A						:				str = None						,					A						:				Optional[str] = None						,					A						:				Optional[int] = None						,					A						:				Optional[int] = None						,					A						:				Optional[Union[str, TensorType]] = None						,					**A						:				List[str]						,					):
             _UpperCAmelCase     :  int							      =    truncation if truncation is not None else self.truncation
             _UpperCAmelCase     :  Optional[int]							      =    padding if padding else self.padding

             if sampling_rate is not None:
                    if sampling_rate != self.sampling_rate:
                           raise ValueError(
                               F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
                               F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
                               F""" was sampled 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."       )

             _UpperCAmelCase     :  Any							      =    isinstance(A						,					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}"""       )
             _UpperCAmelCase     :  Optional[Any]							      =    is_batched_numpy or (
                 isinstance(A						,					(list, tuple)       ) and (isinstance(raw_speech[0]						,					(np.ndarray, tuple, list)       ))
             )

             if is_batched:
                    _UpperCAmelCase     :  int							      =    [np.asarray(A						,					dtype=np.floataa       ) for speech in raw_speech]
             elif not is_batched and not isinstance(A						,					np.ndarray       ):
                    _UpperCAmelCase     :  List[str]							      =    np.asarray(A						,					dtype=np.floataa       )
             elif isinstance(A						,					np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
                    _UpperCAmelCase     :  Any							      =    raw_speech.astype(np.floataa       )

             # always return batch
             if not is_batched:
                    _UpperCAmelCase     :  List[str]							      =    [np.asarray(A       )]

             # convert to mel spectrogram, truncate and pad if needed.
             _UpperCAmelCase     :  Dict							      =    [
                 self._get_input_mel(A						,					max_length if max_length else self.nb_max_samples						,					A						,					A       )
                 for waveform in raw_speech
             ]

             _UpperCAmelCase     :  int							      =    []
             _UpperCAmelCase     :  Optional[Any]							      =    []
             for mel, longer in padded_inputs:
                    input_mel.append(A       )
                    is_longer.append(A       )

             if truncation == "fusion" and sum(A       ) == 0:
                    # if no audio is longer than 10s, then randomly select one audio to be longer
                    _UpperCAmelCase     :  Union[str, Any]							      =    np.random.randint(0						,					len(A       )       )
                    _UpperCAmelCase     :  Optional[Any]							      =    True

             if isinstance(input_mel[0]						,					A       ):
                    _UpperCAmelCase     :  List[str]							      =    [np.asarray(A						,					dtype=np.floataa       ) for feature in input_mel]

             # is_longer is a list of bool
             _UpperCAmelCase     :  Tuple							      =    [[longer] for longer in is_longer]

             _UpperCAmelCase     :  Optional[Any]							      =    {"input_features": input_mel, "is_longer": is_longer}
             _UpperCAmelCase     :  Tuple							      =    BatchFeature(A       )

             if return_tensors is not None:
                    _UpperCAmelCase     :  List[Any]							      =    input_features.convert_to_tensors(A       )

             return input_features

