


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					:		Dict      =    logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
_snake_case					:		Tuple      =    {
    "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					:		Union[str, Any]      =    {
    "facebook/bart-base": 1_024,
    "facebook/bart-large": 1_024,
    "facebook/bart-large-mnli": 1_024,
    "facebook/bart-large-cnn": 1_024,
    "facebook/bart-large-xsum": 1_024,
    "yjernite/bart_eli5": 1_024,
}







@lru_cache()
def     lowerCAmelCase_					( ):
							__snake_case		:      Any										=       (
							    list(range(ord("!"     )							,     ord("~"     ) + 1     )     ) + list(range(ord("¡"     )							,     ord("¬"     ) + 1     )     ) + list(range(ord("®"     )							,     ord("ÿ"     ) + 1     )     )
							)
							__snake_case		:      Union[str, Any]										=       bs[:]
							__snake_case		:      Union[str, Any]										=       0
							for b in range(2**8     ):
														if b not in bs:
																					bs.append(__lowerCamelCase     )
																					cs.append(2**8 + n     )
																					n += 1
							__snake_case		:      List[Any]										=       [chr(__lowerCamelCase     ) for n in cs]
							return dict(zip(__lowerCamelCase							,     __lowerCamelCase     )     )







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Any										=       set()
							__snake_case		:      List[str]										=       word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)     )
														__snake_case		:      Dict										=       char
							return pairs

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Any					  =  VOCAB_FILES_NAMES
							__UpperCAmelCase   :							Optional[Any]					  =  PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase   :							List[Any]					  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase   :							Union[str, Any]					  =  ["input_ids", "attention_mask"]

							def __init__(							self	:  Optional[int]   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Optional[Any]   ,     lowerCamelCase	:  Dict="replace"   ,     lowerCamelCase	:  Union[str, Any]="<s>"   ,     lowerCamelCase	:  str="</s>"   ,     lowerCamelCase	:  str="</s>"   ,     lowerCamelCase	:  str="<s>"   ,     lowerCamelCase	:  Union[str, Any]="<unk>"   ,     lowerCamelCase	:  Tuple="<pad>"   ,     lowerCamelCase	:  Optional[Any]="<mask>"   ,     lowerCamelCase	:  Tuple=False   ,     **lowerCamelCase	:  Optional[int]   ,     )     ->   Union[str, Any]:
														__snake_case		:      str										=       AddedToken(lowerCamelCase   ,     lstrip=lowerCamelCase   ,     rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase   ,     lowerCamelCase    ) else bos_token
														__snake_case		:      Optional[Any]										=       AddedToken(lowerCamelCase   ,     lstrip=lowerCamelCase   ,     rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase   ,     lowerCamelCase    ) else eos_token
														__snake_case		:      int										=       AddedToken(lowerCamelCase   ,     lstrip=lowerCamelCase   ,     rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase   ,     lowerCamelCase    ) else sep_token
														__snake_case		:      Optional[Any]										=       AddedToken(lowerCamelCase   ,     lstrip=lowerCamelCase   ,     rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase   ,     lowerCamelCase    ) else cls_token
														__snake_case		:      str										=       AddedToken(lowerCamelCase   ,     lstrip=lowerCamelCase   ,     rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase   ,     lowerCamelCase    ) else unk_token
														__snake_case		:      Dict										=       AddedToken(lowerCamelCase   ,     lstrip=lowerCamelCase   ,     rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase   ,     lowerCamelCase    ) else pad_token

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

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

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

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

							@property
							def    __snake_case (							self	:  Optional[int]    )     ->   int:
														return len(self.encoder    )

							def    __snake_case (							self	:  List[str]    )     ->   Tuple:
														return dict(self.encoder   ,     **self.added_tokens_encoder    )

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  str    )     ->   Union[str, Any]:
														if token in self.cache:
																					return self.cache[token]
														__snake_case		:      Optional[Any]										=       tuple(lowerCamelCase    )
														__snake_case		:      Tuple										=       get_pairs(lowerCamelCase    )

														if not pairs:
																					return token

														while True:
																					__snake_case		:      Tuple										=       min(lowerCamelCase   ,     key=lambda lowerCamelCase    : self.bpe_ranks.get(lowerCamelCase   ,     float("inf"    )    )    )
																					if bigram not in self.bpe_ranks:
																												break
																					__snake_case			,      __snake_case		:      Optional[Any]										=       bigram
																					__snake_case		:      Union[str, Any]										=       []
																					__snake_case		:      Dict										=       0
																					while i < len(lowerCamelCase    ):
																												try:
																																			__snake_case		:      Optional[Any]										=       word.index(lowerCamelCase   ,     lowerCamelCase    )
																												except ValueError:
																																			new_word.extend(word[i:]    )
																																			break
																												else:
																																			new_word.extend(word[i:j]    )
																																			__snake_case		:      int										=       j

																												if word[i] == first and i < len(lowerCamelCase    ) - 1 and word[i + 1] == second:
																																			new_word.append(first + second    )
																																			i += 2
																												else:
																																			new_word.append(word[i]    )
																																			i += 1
																					__snake_case		:      List[Any]										=       tuple(lowerCamelCase    )
																					__snake_case		:      Optional[Any]										=       new_word
																					if len(lowerCamelCase    ) == 1:
																												break
																					else:
																												__snake_case		:      Union[str, Any]										=       get_pairs(lowerCamelCase    )
														__snake_case		:      Union[str, Any]										=       " ".join(lowerCamelCase    )
														__snake_case		:      Optional[Any]										=       word
														return word

							def    __snake_case (							self	:  str   ,     lowerCamelCase	:  List[str]    )     ->   List[Any]:
														__snake_case		:      List[str]										=       []
														for token in re.findall(self.pat   ,     lowerCamelCase    ):
																					__snake_case		:      Union[str, Any]										=       "".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(lowerCamelCase    ).split(" "    )    )
														return bpe_tokens

							def    __snake_case (							self	:  Optional[int]   ,     lowerCamelCase	:  Optional[int]    )     ->   Dict:
														return self.encoder.get(lowerCamelCase   ,     self.encoder.get(self.unk_token    )    )

							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  Union[str, Any]    )     ->   Optional[int]:
														return self.decoder.get(lowerCamelCase    )

							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  Any    )     ->   int:
														__snake_case		:      Optional[int]										=       "".join(lowerCamelCase    )
														__snake_case		:      List[str]										=       bytearray([self.byte_decoder[c] for c in text]    ).decode("utf-8"   ,     errors=self.errors    )
														return text

							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  str   ,     lowerCamelCase	:  Optional[str] = None    )     ->   Tuple[str]:
														if not os.path.isdir(lowerCamelCase    ):
																					logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
																					return
														__snake_case		:      Optional[Any]										=       os.path.join(
														    lowerCamelCase   ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )
														__snake_case		:      Optional[int]										=       os.path.join(
														    lowerCamelCase   ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]    )

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

														__snake_case		:      Tuple										=       0
														with open(lowerCamelCase   ,     "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 lowerCamelCase    : 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!"    )
																																			__snake_case		:      List[str]										=       token_index
																												writer.write(" ".join(lowerCamelCase    ) + "\n"    )
																												index += 1

														return vocab_file, merge_file

							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  List[int]   ,     lowerCamelCase	:  Optional[List[int]] = None    )     ->   List[int]:
														if token_ids_a is None:
																					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
														__snake_case		:      str										=       [self.cls_token_id]
														__snake_case		:      Tuple										=       [self.sep_token_id]
														return cls + token_ids_a + sep + sep + token_ids_a + sep

							def    __snake_case (							self	:  Optional[int]   ,     lowerCamelCase	:  List[int]   ,     lowerCamelCase	:  Optional[List[int]] = None   ,     lowerCamelCase	:  bool = False    )     ->   List[int]:
														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=lowerCamelCase   ,     token_ids_a=lowerCamelCase   ,     already_has_special_tokens=lowerCamelCase    )

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

							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  List[int]   ,     lowerCamelCase	:  Optional[List[int]] = None    )     ->   List[int]:
														__snake_case		:      Union[str, Any]										=       [self.sep_token_id]
														__snake_case		:      Tuple										=       [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    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  Optional[int]=False   ,     **lowerCamelCase	:  Union[str, Any]    )     ->   Dict:
														__snake_case		:      List[str]										=       kwargs.pop("add_prefix_space"   ,     self.add_prefix_space    )
														if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase    ) > 0 and not text[0].isspace()):
																					__snake_case		:      int										=       " " + text
														return (text, kwargs)





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


_snake_case					:		List[Any]      =    logging.get_logger(__name__)

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  ["input_features", "is_longer"]

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

							def    __snake_case (							self	:  str    )     ->   Dict[str, Any]:
														__snake_case		:      List[str]										=       copy.deepcopy(self.__dict__    )
														__snake_case		:      List[Any]										=       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    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  np.array   ,     lowerCamelCase	:  Optional[np.array] = None    )     ->   np.ndarray:
														__snake_case		:      List[Any]										=       spectrogram(
														    lowerCamelCase   ,     window_function(self.fft_window_size   ,     "hann"    )   ,     frame_length=self.fft_window_size   ,     hop_length=self.hop_length   ,     power=2.0   ,     mel_filters=lowerCamelCase   ,     log_mel="dB"   ,     )
														return log_mel_spectrogram.T

							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  Any    )     ->   str:
														__snake_case		:      Tuple										=       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
																					__snake_case		:      Tuple										=       [0]
														if len(ranges[2]    ) == 0:
																					# if the audio is too short, we just use the first chunk
																					__snake_case		:      Tuple										=       [0]
														# randomly choose index for each part
														__snake_case		:      List[Any]										=       np.random.choice(ranges[0]    )
														__snake_case		:      int										=       np.random.choice(ranges[1]    )
														__snake_case		:      List[str]										=       np.random.choice(ranges[2]    )

														__snake_case		:      Dict										=       mel[idx_front : idx_front + chunk_frames, :]
														__snake_case		:      Optional[Any]										=       mel[idx_middle : idx_middle + chunk_frames, :]
														__snake_case		:      Tuple										=       mel[idx_back : idx_back + chunk_frames, :]

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

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  np.array   ,     lowerCamelCase	:  str   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  Dict    )     ->   np.array:
														if waveform.shape[0] > max_length:
																					if truncation == "rand_trunc":
																												__snake_case		:      List[Any]										=       True
																												# random crop to max_length (for compatibility) -> this should be handled by self.pad
																												__snake_case		:      Tuple										=       len(lowerCamelCase    ) - max_length
																												__snake_case		:      List[Any]										=       np.random.randint(0   ,     overflow + 1    )
																												__snake_case		:      Dict										=       waveform[idx : idx + max_length]
																												__snake_case		:      Optional[int]										=       self._np_extract_fbank_features(lowerCamelCase   ,     self.mel_filters_slaney    )[None, :]
																					elif truncation == "fusion":
																												__snake_case		:      Any										=       self._np_extract_fbank_features(lowerCamelCase   ,     self.mel_filters    )
																												__snake_case		:      Optional[int]										=       max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																												__snake_case		:      str										=       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.
																																			__snake_case		:      str										=       np.stack([mel, mel, mel, mel]   ,     axis=0    )
																																			__snake_case		:      Optional[Any]										=       False
																												else:
																																			__snake_case		:      Any										=       self._random_mel_fusion(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    )
																																			__snake_case		:      Tuple										=       True
																					else:
																												raise NotImplementedError(F'data_truncating {truncation} not implemented'    )

														else:
																					__snake_case		:      Dict										=       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":
																																			__snake_case		:      List[str]										=       int(max_length / len(lowerCamelCase    )    )
																																			__snake_case		:      Any										=       np.stack(np.tile(lowerCamelCase   ,     n_repeat + 1    )    )[:max_length]
																												if padding == "repeatpad":
																																			__snake_case		:      str										=       int(max_length / len(lowerCamelCase    )    )
																																			__snake_case		:      List[str]										=       np.stack(np.tile(lowerCamelCase   ,     lowerCamelCase    )    )
																												__snake_case		:      str										=       np.pad(lowerCamelCase   ,     (0, max_length - waveform.shape[0])   ,     mode="constant"   ,     constant_values=0    )

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

														return input_mel, longer






							def __call__(							self	:  List[str]   ,     lowerCamelCase	:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,     lowerCamelCase	:  str = None   ,     lowerCamelCase	:  Optional[str] = None   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  Optional[Union[str, TensorType]] = None   ,     **lowerCamelCase	:  Any   ,     )     ->   BatchFeature:
														__snake_case		:      Union[str, Any]										=       truncation if truncation is not None else self.truncation
														__snake_case		:      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."    )

														__snake_case		:      Any										=       isinstance(lowerCamelCase   ,     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}'    )
														__snake_case		:      str										=       is_batched_numpy or (
														    isinstance(lowerCamelCase   ,     (list, tuple)    ) and (isinstance(raw_speech[0]   ,     (np.ndarray, tuple, list)    ))
														)

														if is_batched:
																					__snake_case		:      Tuple										=       [np.asarray(lowerCamelCase   ,     dtype=np.floataa    ) for speech in raw_speech]
														elif not is_batched and not isinstance(lowerCamelCase   ,     np.ndarray    ):
																					__snake_case		:      str										=       np.asarray(lowerCamelCase   ,     dtype=np.floataa    )
														elif isinstance(lowerCamelCase   ,     np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
																					__snake_case		:      int										=       raw_speech.astype(np.floataa    )

														# always return batch
														if not is_batched:
																					__snake_case		:      Union[str, Any]										=       [np.asarray(lowerCamelCase    )]

														# convert to mel spectrogram, truncate and pad if needed.
														__snake_case		:      Optional[int]										=       [
														    self._get_input_mel(lowerCamelCase   ,     max_length if max_length else self.nb_max_samples   ,     lowerCamelCase   ,     lowerCamelCase    )
														    for waveform in raw_speech
														]

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

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

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

														# is_longer is a list of bool
														__snake_case		:      Any										=       [[longer] for longer in is_longer]

														__snake_case		:      Tuple										=       {"input_features": input_mel, "is_longer": is_longer}
														__snake_case		:      List[str]										=       BatchFeature(lowerCamelCase    )

														if return_tensors is not None:
																					__snake_case		:      Any										=       input_features.convert_to_tensors(lowerCamelCase    )

														return input_features

