






import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union

from .. import config
from .filelock import FileLock
from .logging import get_logger


__snake_case      :      str             =       get_logger(__name__)


class       __SCREAMING_SNAKE_CASE   :






						def __init__(		self       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												lowerCAmelCase__        =  (
												    os.path.join(_UpperCamelCase       ,  config.EXTRACTED_DATASETS_DIR				) if cache_dir else config.EXTRACTED_DATASETS_PATH
												)
												lowerCAmelCase__        =  Extractor






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												from .file_utils import hash_url_to_filename

												# Path where we extract compressed archives
												# We extract in the cache dir, and get the extracted path name by hashing the original path"
												lowerCAmelCase__        =  os.path.abspath(_UpperCamelCase				)
												return os.path.join(self.extract_dir       ,  hash_url_to_filename(_UpperCamelCase				)				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												return force_extract or (
												    not os.path.isfile(_UpperCamelCase				) and not (os.path.isdir(_UpperCamelCase				) and os.listdir(_UpperCamelCase				))
												)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = False				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.extractor.infer_extractor_format(_UpperCamelCase				)
												if not extractor_format:
																		return input_path
												lowerCAmelCase__        =  self._get_output_path(_UpperCamelCase				)
												if self._do_extract(_UpperCamelCase       ,  _UpperCamelCase				):
																		self.extractor.extract(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				)
												return output_path




class       __SCREAMING_SNAKE_CASE   (				__lowercase):






						@classmethod
						@abstractmethod
						def  UpperCamelCase__ (		cls       ,  _UpperCamelCase       ,  **_UpperCamelCase				):






												"""simple docstring"""
												...







						@staticmethod
						@abstractmethod
						def  UpperCamelCase__ (		_UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												...




class       __SCREAMING_SNAKE_CASE   (				__lowercase						,   __lowercase):
						_SCREAMING_SNAKE_CASE		:    List[bytes]						=   []






						@staticmethod
						def  UpperCamelCase__ (		_UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												with open(_UpperCamelCase       ,  'rb'				) as f:
																		return f.read(_UpperCamelCase				)







						@classmethod
						def  UpperCamelCase__ (		cls       ,  _UpperCamelCase       ,  _UpperCamelCase = b""				):






												"""simple docstring"""
												if not magic_number:
																		lowerCAmelCase__        =  max(len(_UpperCamelCase				) for cls_magic_number in cls.magic_numbers				)
																		try:
																								lowerCAmelCase__        =  cls.read_magic_number(_UpperCamelCase       ,  _UpperCamelCase				)
																		except OSError:
																								return False
												return any(magic_number.startswith(_UpperCamelCase				) for cls_magic_number in cls.magic_numbers				)




class       __SCREAMING_SNAKE_CASE   (				__lowercase):






						@classmethod
						def  UpperCamelCase__ (		cls       ,  _UpperCamelCase       ,  **_UpperCamelCase				):






												"""simple docstring"""
												return tarfile.is_tarfile(_UpperCamelCase				)






						@staticmethod
						def  UpperCamelCase__ (		_UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""

												def resolved(_UpperCamelCase				) -> str:
																		return os.path.realpath(os.path.abspath(_UpperCamelCase				)				)

												def badpath(_UpperCamelCase       ,  _UpperCamelCase				) -> bool:
																		# joinpath will ignore base if path is absolute
																		return not resolved(os.path.join(_UpperCamelCase       ,  _UpperCamelCase				)				).startswith(_UpperCamelCase				)

												def badlink(_UpperCamelCase       ,  _UpperCamelCase				) -> bool:
																		# Links are interpreted relative to the directory containing the link
																		lowerCAmelCase__        =  resolved(os.path.join(_UpperCamelCase       ,  os.path.dirname(info.name				)				)				)
																		return badpath(info.linkname       ,  base=_UpperCamelCase				)

												lowerCAmelCase__        =  resolved(_UpperCamelCase				)

												for finfo in members:
																		if badpath(finfo.name       ,  _UpperCamelCase				):
																								logger.error(F"Extraction of {finfo.name} is blocked (illegal path)"				)
																		elif finfo.issym() and badlink(_UpperCamelCase       ,  _UpperCamelCase				):
																								logger.error(F"Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}"				)
																		elif finfo.islnk() and badlink(_UpperCamelCase       ,  _UpperCamelCase				):
																								logger.error(F"Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}"				)
																		else:
																								yield finfo







						@staticmethod
						def  UpperCamelCase__ (		_UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												os.makedirs(_UpperCamelCase       ,  exist_ok=_UpperCamelCase				)
												lowerCAmelCase__        =  tarfile.open(_UpperCamelCase				)
												tar_file.extractall(_UpperCamelCase       ,  members=TarExtractor.safemembers(_UpperCamelCase       ,  _UpperCamelCase				)				)
												tar_file.close()




class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Any						=   [B'''\x1F\x8B''']






						@staticmethod
						def  UpperCamelCase__ (		_UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												with gzip.open(_UpperCamelCase       ,  'rb'				) as gzip_file:
																		with open(_UpperCamelCase       ,  'wb'				) as extracted_file:
																								shutil.copyfileobj(_UpperCamelCase       ,  _UpperCamelCase				)




class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Union[str, Any]						=   [
						    B'''PK\x03\x04''',
						    B'''PK\x05\x06''',  # empty archive
						    B'''PK\x07\x08''',  # spanned archive
						]






						@classmethod
						def  UpperCamelCase__ (		cls       ,  _UpperCamelCase       ,  _UpperCamelCase = b""				):






												"""simple docstring"""
												if super().is_extractable(_UpperCamelCase       ,  magic_number=_UpperCamelCase				):
																		return True
												try:
																		# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
																		# From: https://github.com/python/cpython/pull/5053
																		from zipfile import (
																		    _CD_SIGNATURE,
																		    _ECD_DISK_NUMBER,
																		    _ECD_DISK_START,
																		    _ECD_ENTRIES_TOTAL,
																		    _ECD_OFFSET,
																		    _ECD_SIZE,
																		    _EndRecData,
																		    sizeCentralDir,
																		    stringCentralDir,
																		    structCentralDir,
																		)

																		with open(_UpperCamelCase       ,  'rb'				) as fp:
																								lowerCAmelCase__        =  _EndRecData(_UpperCamelCase				)
																								if endrec:
																														if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
																																				return True  # Empty zipfiles are still zipfiles
																														elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
																																				fp.seek(endrec[_ECD_OFFSET]				)  # Central directory is on the same disk
																																				if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
																																										lowerCAmelCase__        =  fp.read(_UpperCamelCase				)  # CD is where we expect it to be
																																										if len(_UpperCamelCase				) == sizeCentralDir:
																																																lowerCAmelCase__        =  struct.unpack(_UpperCamelCase       ,  _UpperCamelCase				)  # CD is the right size
																																																if centdir[_CD_SIGNATURE] == stringCentralDir:
																																																						return True  # First central directory entry  has correct magic number
																		return False
												except Exception:  # catch all errors in case future python versions change the zipfile internals
																		return False







						@staticmethod
						def  UpperCamelCase__ (		_UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												os.makedirs(_UpperCamelCase       ,  exist_ok=_UpperCamelCase				)
												with zipfile.ZipFile(_UpperCamelCase       ,  'r'				) as zip_file:
																		zip_file.extractall(_UpperCamelCase				)
																		zip_file.close()




class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Tuple						=   [B'''\xFD\x37\x7A\x58\x5A\x00''']






						@staticmethod
						def  UpperCamelCase__ (		_UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												with lzma.open(_UpperCamelCase				) as compressed_file:
																		with open(_UpperCamelCase       ,  'wb'				) as extracted_file:
																								shutil.copyfileobj(_UpperCamelCase       ,  _UpperCamelCase				)




class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Tuple						=   [B'''Rar!\x1a\x07\x00''', B'''Rar!\x1a\x07\x01\x00''']  # RAR_ID  # RAR5_ID






						@staticmethod
						def  UpperCamelCase__ (		_UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												if not config.RARFILE_AVAILABLE:
																		raise ImportError('Please pip install rarfile'				)
												import rarfile

												os.makedirs(_UpperCamelCase       ,  exist_ok=_UpperCamelCase				)
												lowerCAmelCase__        =  rarfile.RarFile(_UpperCamelCase				)
												rf.extractall(_UpperCamelCase				)
												rf.close()




class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Tuple						=   [B'''\x28\xb5\x2F\xFD''']






						@staticmethod
						def  UpperCamelCase__ (		_UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												if not config.ZSTANDARD_AVAILABLE:
																		raise ImportError('Please pip install zstandard'				)
												import zstandard as zstd

												lowerCAmelCase__        =  zstd.ZstdDecompressor()
												with open(_UpperCamelCase       ,  'rb'				) as ifh, open(_UpperCamelCase       ,  'wb'				) as ofh:
																		dctx.copy_stream(_UpperCamelCase       ,  _UpperCamelCase				)




class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Union[str, Any]						=   [B'''\x42\x5A\x68''']






						@staticmethod
						def  UpperCamelCase__ (		_UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												with bza.open(_UpperCamelCase       ,  'rb'				) as compressed_file:
																		with open(_UpperCamelCase       ,  'wb'				) as extracted_file:
																								shutil.copyfileobj(_UpperCamelCase       ,  _UpperCamelCase				)




class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   [B'''\x37\x7A\xBC\xAF\x27\x1C''']






						@staticmethod
						def  UpperCamelCase__ (		_UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												if not config.PY7ZR_AVAILABLE:
																		raise ImportError('Please pip install py7zr'				)
												import pyazr

												os.makedirs(_UpperCamelCase       ,  exist_ok=_UpperCamelCase				)
												with pyazr.SevenZipFile(_UpperCamelCase       ,  'r'				) as archive:
																		archive.extractall(_UpperCamelCase				)




class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   [B'''\x04\x22\x4D\x18''']






						@staticmethod
						def  UpperCamelCase__ (		_UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												if not config.LZ4_AVAILABLE:
																		raise ImportError('Please pip install lz4'				)
												import lza.frame

												with lza.frame.open(_UpperCamelCase       ,  'rb'				) as compressed_file:
																		with open(_UpperCamelCase       ,  'wb'				) as extracted_file:
																								shutil.copyfileobj(_UpperCamelCase       ,  _UpperCamelCase				)




class       __SCREAMING_SNAKE_CASE   :
						#  Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
						_SCREAMING_SNAKE_CASE		:    Dict[str, Type[BaseExtractor]]						=   {
						    "tar": TarExtractor,
						    "gzip": GzipExtractor,
						    "zip": ZipExtractor,
						    "xz": XzExtractor,
						    "rar": RarExtractor,
						    "zstd": ZstdExtractor,
						    "bz2": BzipaExtractor,
						    "7z": SevenZipExtractor,  # <Added version="2.4.0"/>
						    "lz4": LzaExtractor,  # <Added version="2.4.0"/>
						}






						@classmethod
						def  UpperCamelCase__ (		cls				):






												"""simple docstring"""
												return max(
												    len(_UpperCamelCase				)
												    for extractor in cls.extractors.values()
												    if issubclass(_UpperCamelCase       ,  _UpperCamelCase				)
												    for extractor_magic_number in extractor.magic_numbers				)






						@staticmethod
						def  UpperCamelCase__ (		_UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												try:
																		return MagicNumberBaseExtractor.read_magic_number(_UpperCamelCase       ,  magic_number_length=_UpperCamelCase				)
												except OSError:
																		return b""






						@classmethod
						def  UpperCamelCase__ (		cls       ,  _UpperCamelCase       ,  _UpperCamelCase = False				):






												"""simple docstring"""
												warnings.warn(
												    'Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '
												    'Use \'infer_extractor_format\' instead.'       ,  category=_UpperCamelCase       ,  )
												lowerCAmelCase__        =  cls.infer_extractor_format(_UpperCamelCase				)
												if extractor_format:
																		return True if not return_extractor else (True, cls.extractors[extractor_format])
												return False if not return_extractor else (False, None)






						@classmethod
						def  UpperCamelCase__ (		cls       ,  _UpperCamelCase				):  # <Added version="2.4.0"/>






												"""simple docstring"""
												lowerCAmelCase__        =  cls._get_magic_number_max_length()
												lowerCAmelCase__        =  cls._read_magic_number(_UpperCamelCase       ,  _UpperCamelCase				)
												for extractor_format, extractor in cls.extractors.items():
																		if extractor.is_extractable(_UpperCamelCase       ,  magic_number=_UpperCamelCase				):
																								return extractor_format







						@classmethod
						def  UpperCamelCase__ (		cls       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = "deprecated"       ,  ):






												"""simple docstring"""
												os.makedirs(os.path.dirname(_UpperCamelCase				)       ,  exist_ok=_UpperCamelCase				)
												# Prevent parallel extractions
												lowerCAmelCase__        =  str(Path(_UpperCamelCase				).with_suffix('.lock'				)				)
												with FileLock(_UpperCamelCase				):
																		shutil.rmtree(_UpperCamelCase       ,  ignore_errors=_UpperCamelCase				)
																		if extractor_format or extractor != "deprecated":
																								if extractor != "deprecated" or not isinstance(_UpperCamelCase       ,  _UpperCamelCase				):  # passed as positional arg
																														warnings.warn(
																														    'Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '
																														    'Use \'extractor_format\' instead.'       ,  category=_UpperCamelCase       ,  )
																														lowerCAmelCase__        =  extractor if extractor != 'deprecated' else extractor_format
																								else:
																														lowerCAmelCase__        =  cls.extractors[extractor_format]
																								return extractor.extract(_UpperCamelCase       ,  _UpperCamelCase				)
																		else:
																								warnings.warn(
																								    'Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '
																								    'exception in 3.0.0.'       ,  category=_UpperCamelCase       ,  )
																								for extractor in cls.extractors.values():
																														if extractor.is_extractable(_UpperCamelCase				):
																																				return extractor.extract(_UpperCamelCase       ,  _UpperCamelCase				)













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      :      Any             =       logging.get_logger(__name__)


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Tuple						=   ['''input_features''', '''is_longer''']






						def __init__(		self       ,  _UpperCamelCase=64       ,  _UpperCamelCase=4_80_00       ,  _UpperCamelCase=4_80       ,  _UpperCamelCase=10       ,  _UpperCamelCase=10_24       ,  _UpperCamelCase=0.0       ,  _UpperCamelCase=False       ,  _UpperCamelCase = 0       ,  _UpperCamelCase = 1_40_00       ,  _UpperCamelCase = None       ,  _UpperCamelCase = "fusion"       ,  _UpperCamelCase = "repeatpad"       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												super().__init__(
												    feature_size=_UpperCamelCase       ,  sampling_rate=_UpperCamelCase       ,  padding_value=_UpperCamelCase       ,  return_attention_mask=_UpperCamelCase       ,  **_UpperCamelCase       ,  )
												lowerCAmelCase__        =  top_db
												lowerCAmelCase__        =  truncation
												lowerCAmelCase__        =  padding
												lowerCAmelCase__        =  fft_window_size
												lowerCAmelCase__        =  (fft_window_size >> 1) + 1
												lowerCAmelCase__        =  hop_length
												lowerCAmelCase__        =  max_length_s
												lowerCAmelCase__        =  max_length_s * sampling_rate
												lowerCAmelCase__        =  sampling_rate
												lowerCAmelCase__        =  frequency_min
												lowerCAmelCase__        =  frequency_max
												lowerCAmelCase__        =  mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins       ,  num_mel_filters=_UpperCamelCase       ,  min_frequency=_UpperCamelCase       ,  max_frequency=_UpperCamelCase       ,  sampling_rate=_UpperCamelCase       ,  norm=_UpperCamelCase       ,  mel_scale='htk'       ,  )
												lowerCAmelCase__        =  mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins       ,  num_mel_filters=_UpperCamelCase       ,  min_frequency=_UpperCamelCase       ,  max_frequency=_UpperCamelCase       ,  sampling_rate=_UpperCamelCase       ,  norm='slaney'       ,  mel_scale='slaney'       ,  )






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  copy.deepcopy(self.__dict__				)
												lowerCAmelCase__        =  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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												lowerCAmelCase__        =  spectrogram(
												    _UpperCamelCase       ,  window_function(self.fft_window_size       ,  'hann'				)       ,  frame_length=self.fft_window_size       ,  hop_length=self.hop_length       ,  power=2.0       ,  mel_filters=_UpperCamelCase       ,  log_mel='dB'       ,  )
												return log_mel_spectrogram.T






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  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
																		lowerCAmelCase__        =  [0]
												if len(ranges[2]				) == 0:
																		# if the audio is too short, we just use the first chunk
																		lowerCAmelCase__        =  [0]
												# randomly choose index for each part
												lowerCAmelCase__        =  np.random.choice(ranges[0]				)
												lowerCAmelCase__        =  np.random.choice(ranges[1]				)
												lowerCAmelCase__        =  np.random.choice(ranges[2]				)

												lowerCAmelCase__        =  mel[idx_front : idx_front + chunk_frames, :]
												lowerCAmelCase__        =  mel[idx_middle : idx_middle + chunk_frames, :]
												lowerCAmelCase__        =  mel[idx_back : idx_back + chunk_frames, :]

												lowerCAmelCase__        =  torch.tensor(mel[None, None, :]				)
												lowerCAmelCase__        =  torch.nn.functional.interpolate(
												    _UpperCamelCase       ,  size=[chunk_frames, 64]       ,  mode='bilinear'       ,  align_corners=_UpperCamelCase				)
												lowerCAmelCase__        =  mel_shrink[0][0].numpy()
												lowerCAmelCase__        =  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]       ,  axis=0				)
												return mel_fusion






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






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

												else:
																		lowerCAmelCase__        =  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":
																														lowerCAmelCase__        =  int(max_length / len(_UpperCamelCase				)				)
																														lowerCAmelCase__        =  np.stack(np.tile(_UpperCamelCase       ,  n_repeat + 1				)				)[:max_length]
																								if padding == "repeatpad":
																														lowerCAmelCase__        =  int(max_length / len(_UpperCamelCase				)				)
																														lowerCAmelCase__        =  np.stack(np.tile(_UpperCamelCase       ,  _UpperCamelCase				)				)
																								lowerCAmelCase__        =  np.pad(_UpperCamelCase       ,  (0, max_length - waveform.shape[0])       ,  mode='constant'       ,  constant_values=0				)

																		if truncation == "fusion":
																								lowerCAmelCase__        =  self._np_extract_fbank_features(_UpperCamelCase       ,  self.mel_filters				)
																								lowerCAmelCase__        =  np.stack([input_mel, input_mel, input_mel, input_mel]       ,  axis=0				)
																		else:
																								lowerCAmelCase__        =  self._np_extract_fbank_features(_UpperCamelCase       ,  self.mel_filters_slaney				)[None, :]

												return input_mel, longer







						def __call__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  truncation if truncation is not None else self.truncation
												lowerCAmelCase__        =  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.'				)

												lowerCAmelCase__        =  isinstance(_UpperCamelCase       ,  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(_UpperCamelCase       ,  (list, tuple)				) and (isinstance(raw_speech[0]       ,  (np.ndarray, tuple, list)				))
												)

												if is_batched:
																		lowerCAmelCase__        =  [np.asarray(_UpperCamelCase       ,  dtype=np.floataa				) for speech in raw_speech]
												elif not is_batched and not isinstance(_UpperCamelCase       ,  np.ndarray				):
																		lowerCAmelCase__        =  np.asarray(_UpperCamelCase       ,  dtype=np.floataa				)
												elif isinstance(_UpperCamelCase       ,  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(_UpperCamelCase				)]

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

												lowerCAmelCase__        =  []
												lowerCAmelCase__        =  []
												for mel, longer in padded_inputs:
																		input_mel.append(_UpperCamelCase				)
																		is_longer.append(_UpperCamelCase				)

												if truncation == "fusion" and sum(_UpperCamelCase				) == 0:
																		# if no audio is longer than 10s, then randomly select one audio to be longer
																		lowerCAmelCase__        =  np.random.randint(0       ,  len(_UpperCamelCase				)				)
																		lowerCAmelCase__        =  True

												if isinstance(input_mel[0]       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  [np.asarray(_UpperCamelCase       ,  dtype=np.floataa				) for feature in input_mel]

												# is_longer is a list of bool
												lowerCAmelCase__        =  [[longer] for longer in is_longer]

												lowerCAmelCase__        =  {'input_features': input_mel, 'is_longer': is_longer}
												lowerCAmelCase__        =  BatchFeature(_UpperCamelCase				)

												if return_tensors is not None:
																		lowerCAmelCase__        =  input_features.convert_to_tensors(_UpperCamelCase				)

												return input_features





