import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
					import torch

if is_vision_available():
					import PIL


__UpperCamelCase	: int   		=  logging.get_logger(__name__)
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase		):
						def constraint_to_multiple_of(_lowercase	,						_lowercase	,						_lowercase=0	,						_lowercase=None		):
												SCREAMING_SNAKE_CASE							:					int           =							round(val / multiple		) * multiple

												if max_val is not None and x > max_val:
																		SCREAMING_SNAKE_CASE							:					Dict           =							math.floor(val / multiple		) * multiple

												if x < min_val:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							math.ceil(val / multiple		) * multiple

												return x

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(output_size, output_size) if isinstance(_lowercase	,						_lowercase		) else output_size

						SCREAMING_SNAKE_CASE							:					Optional[int]           =							get_image_size(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							output_size

						# determine new height and width
						SCREAMING_SNAKE_CASE							:					Dict           =							output_height / input_height
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							output_width / input_width

						if keep_aspect_ratio:
												# scale as little as possible
												if abs(1 - scale_width		) < abs(1 - scale_height		):
																		# fit width
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							scale_width
												else:
																		# fit height
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							scale_height

						SCREAMING_SNAKE_CASE							:					List[str]           =							constraint_to_multiple_of(scale_height * input_height	,						multiple=_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							constraint_to_multiple_of(scale_width * input_width	,						multiple=_lowercase		)

						return (new_height, new_width)





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	["""pixel_values"""]


						def __init__( self			:       int					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Dict[str, int] = None					,	UpperCamelCase__			:       PILImageResampling = PILImageResampling.BILINEAR					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       int = 1					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Union[int, float] = 1 / 255					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							size if size is not None else {'''height''': 384, '''width''': 384}
												SCREAMING_SNAKE_CASE							:					Any           =							get_size_dict(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Dict           =							do_resize
												SCREAMING_SNAKE_CASE							:					Any           =							size
												SCREAMING_SNAKE_CASE							:					str           =							keep_aspect_ratio
												SCREAMING_SNAKE_CASE							:					List[str]           =							ensure_multiple_of
												SCREAMING_SNAKE_CASE							:					int           =							resample
												SCREAMING_SNAKE_CASE							:					Any           =							do_rescale
												SCREAMING_SNAKE_CASE							:					List[Any]           =							rescale_factor
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							do_normalize
												SCREAMING_SNAKE_CASE							:					str           =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							image_std if image_std is not None else IMAGENET_STANDARD_STD


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Dict[str, int]					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       int = 1					,	UpperCamelCase__			:       PILImageResampling = PILImageResampling.BICUBIC					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       Union[str, Any]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							get_size_dict(UpperCamelCase__		)
												if "height" not in size or "width" not in size:
																		raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""		)
												SCREAMING_SNAKE_CASE							:					Any           =							get_resize_output_image_size(
												    UpperCamelCase__					,	output_size=(size['''height'''], size['''width'''])					,	keep_aspect_ratio=UpperCamelCase__					,	multiple=UpperCamelCase__					,	)
												return resize(UpperCamelCase__					,	size=UpperCamelCase__					,	resample=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Union[int, float]					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       str					,	):





												'''simple docstring'''





												return rescale(UpperCamelCase__					,	scale=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Any					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Union[float, List[float]]					,	UpperCamelCase__			:       Union[float, List[float]]					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       List[str]					,	):





												'''simple docstring'''





												return normalize(UpperCamelCase__					,	mean=UpperCamelCase__					,	std=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       ImageInput					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       PILImageResampling = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       float = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[str, TensorType]] = None					,	UpperCamelCase__			:       ChannelDimension = ChannelDimension.FIRST					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							size if size is not None else self.size
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							get_size_dict(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												SCREAMING_SNAKE_CASE							:					List[str]           =							ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												SCREAMING_SNAKE_CASE							:					Tuple           =							resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE							:					str           =							do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE							:					List[Any]           =							image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE							:					List[Any]           =							image_std if image_std is not None else self.image_std

												SCREAMING_SNAKE_CASE							:					Dict           =							make_list_of_images(UpperCamelCase__		)

												if not valid_images(UpperCamelCase__		):
																		raise ValueError(
																		    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																		    '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

												if do_resize and size is None or resample is None:
																		raise ValueError('''Size and resample must be specified if do_resize is True.'''		)

												if do_rescale and rescale_factor is None:
																		raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

												if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('''Image mean and std must be specified if do_normalize is True.'''		)

												# All transformations expect numpy arrays.
												SCREAMING_SNAKE_CASE							:					Tuple           =							[to_numpy_array(UpperCamelCase__		) for image in images]

												if do_resize:
																		SCREAMING_SNAKE_CASE							:					Dict           =							[self.resize(image=UpperCamelCase__					,	size=UpperCamelCase__					,	resample=UpperCamelCase__		) for image in images]

												if do_rescale:
																		SCREAMING_SNAKE_CASE							:					Any           =							[self.rescale(image=UpperCamelCase__					,	scale=UpperCamelCase__		) for image in images]

												if do_normalize:
																		SCREAMING_SNAKE_CASE							:					Any           =							[self.normalize(image=UpperCamelCase__					,	mean=UpperCamelCase__					,	std=UpperCamelCase__		) for image in images]

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[to_channel_dimension_format(UpperCamelCase__					,	UpperCamelCase__		) for image in images]

												SCREAMING_SNAKE_CASE							:					Tuple           =							{'''pixel_values''': images}
												return BatchFeature(data=UpperCamelCase__					,	tensor_type=UpperCamelCase__		)



						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       List[Tuple] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							outputs.logits

												# Resize logits and compute semantic segmentation maps
												if target_sizes is not None:
																		if len(UpperCamelCase__		) != len(UpperCamelCase__		):
																								raise ValueError(
																								    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''		)

																		if is_torch_tensor(UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							target_sizes.numpy()

																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[]

																		for idx in range(len(UpperCamelCase__		)		):
																								SCREAMING_SNAKE_CASE							:					List[str]           =							torch.nn.functional.interpolate(
																								    logits[idx].unsqueeze(dim=0		)					,	size=target_sizes[idx]					,	mode='''bilinear'''					,	align_corners=UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Dict           =							resized_logits[0].argmax(dim=0		)
																								semantic_segmentation.append(UpperCamelCase__		)
												else:
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							logits.argmax(dim=1		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

												return semantic_segmentation






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


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





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	["""input_features""", """is_longer"""]


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Dict=64					,	UpperCamelCase__			:       Optional[Any]=4_8000					,	UpperCamelCase__			:       Tuple=480					,	UpperCamelCase__			:       Union[str, Any]=10					,	UpperCamelCase__			:       List[Any]=1024					,	UpperCamelCase__			:       Tuple=0.0					,	UpperCamelCase__			:       int=False					,	UpperCamelCase__			:       float = 0					,	UpperCamelCase__			:       float = 1_4000					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       str = "fusion"					,	UpperCamelCase__			:       str = "repeatpad"					,	**UpperCamelCase__			:       Union[str, Any]					,	):





												'''simple docstring'''





												super().__init__(
												    feature_size=UpperCamelCase__					,	sampling_rate=UpperCamelCase__					,	padding_value=UpperCamelCase__					,	return_attention_mask=UpperCamelCase__					,	**UpperCamelCase__					,	)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							top_db
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							truncation
												SCREAMING_SNAKE_CASE							:					str           =							padding
												SCREAMING_SNAKE_CASE							:					List[Any]           =							fft_window_size
												SCREAMING_SNAKE_CASE							:					Tuple           =							(fft_window_size >> 1) + 1
												SCREAMING_SNAKE_CASE							:					List[str]           =							hop_length
												SCREAMING_SNAKE_CASE							:					List[Any]           =							max_length_s
												SCREAMING_SNAKE_CASE							:					Tuple           =							max_length_s * sampling_rate
												SCREAMING_SNAKE_CASE							:					List[Any]           =							sampling_rate
												SCREAMING_SNAKE_CASE							:					List[str]           =							frequency_min
												SCREAMING_SNAKE_CASE							:					Any           =							frequency_max
												SCREAMING_SNAKE_CASE							:					List[Any]           =							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'''					,	)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							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  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							copy.deepcopy(self.__dict__		)
												SCREAMING_SNAKE_CASE							:					Optional[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  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       np.array					,	UpperCamelCase__			:       Optional[np.array] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							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  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							mel[idx_front : idx_front + chunk_frames, :]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							mel[idx_middle : idx_middle + chunk_frames, :]
												SCREAMING_SNAKE_CASE							:					Tuple           =							mel[idx_back : idx_back + chunk_frames, :]

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


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       np.array					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





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

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

																		if truncation == "fusion":
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							self._np_extract_fbank_features(UpperCamelCase__					,	self.mel_filters		)
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.stack([input_mel, input_mel, input_mel, input_mel]					,	axis=0		)
																		else:
																								SCREAMING_SNAKE_CASE							:					List[str]           =							self._np_extract_fbank_features(UpperCamelCase__					,	self.mel_filters_slaney		)[None, :]

												return input_mel, longer



						def __call__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,	UpperCamelCase__			:       str = None					,	UpperCamelCase__			:       Optional[str] = None					,	UpperCamelCase__			:       Optional[int] = None					,	UpperCamelCase__			:       Optional[int] = None					,	UpperCamelCase__			:       Optional[Union[str, TensorType]] = None					,	**UpperCamelCase__			:       Any					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							truncation if truncation is not None else self.truncation
												SCREAMING_SNAKE_CASE							:					List[str]           =							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.'''		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							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}"""		)
												SCREAMING_SNAKE_CASE							:					int           =							is_batched_numpy or (
												    isinstance(UpperCamelCase__					,	(list, tuple)		) and (isinstance(raw_speech[0]					,	(np.ndarray, tuple, list)		))
												)

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

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

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

												SCREAMING_SNAKE_CASE							:					str           =							[]
												SCREAMING_SNAKE_CASE							:					List[str]           =							[]
												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
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.random.randint(0					,	len(UpperCamelCase__		)		)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							True

												if isinstance(input_mel[0]					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[np.asarray(UpperCamelCase__					,	dtype=np.floataa		) for feature in input_mel]

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

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{'''input_features''': input_mel, '''is_longer''': is_longer}
												SCREAMING_SNAKE_CASE							:					int           =							BatchFeature(UpperCamelCase__		)

												if return_tensors is not None:
																		SCREAMING_SNAKE_CASE							:					int           =							input_features.convert_to_tensors(UpperCamelCase__		)

												return input_features





