



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


A__								=						logging.get_logger(__name__)


if is_vision_available():
				import PIL


class       a							(			__lowerCamelCase				):
		__lowerCAmelCase	:    Optional[Any]											=					["""pixel_values"""]



		def __init__(     self     :Tuple   ,__lowercase     :bool = True   ,__lowercase     :Dict[str, int] = None   ,__lowercase     :PILImageResampling = PILImageResampling.BICUBIC   ,__lowercase     :bool = True   ,__lowercase     :Dict[str, int] = None   ,__lowercase     :bool = True   ,__lowercase     :Union[int, float] = 1 / 2_5_5   ,__lowercase     :bool = True   ,__lowercase     :Optional[Union[float, List[float]]] = None   ,__lowercase     :Optional[Union[float, List[float]]] = None   ,__lowercase     :bool = True   ,**__lowercase     :Dict   ,):
						super().__init__(**__lowercase  )
						snake_case__	:      Any     		=	size if size is not None else {'''shortest_edge''': 2_2_4}
						snake_case__	:      List[Any]     		=	get_size_dict(__lowercase   ,default_to_square=__lowercase  )
						snake_case__	:      Any     		=	crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						snake_case__	:      Tuple     		=	get_size_dict(__lowercase   ,default_to_square=__lowercase   ,param_name='''crop_size'''  )

						snake_case__	:      Dict     		=	do_resize
						snake_case__	:      Tuple     		=	size
						snake_case__	:      Optional[int]     		=	resample
						snake_case__	:      Tuple     		=	do_center_crop
						snake_case__	:      Any     		=	crop_size
						snake_case__	:      Optional[int]     		=	do_rescale
						snake_case__	:      Optional[int]     		=	rescale_factor
						snake_case__	:      int     		=	do_normalize
						snake_case__	:      Tuple     		=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						snake_case__	:      str     		=	image_std if image_std is not None else OPENAI_CLIP_STD
						snake_case__	:      List[Any]     		=	do_convert_rgb



		def 	__lowerCamelCase			(     self     :int   ,__lowercase     :np.ndarray   ,__lowercase     :Dict[str, int]   ,__lowercase     :PILImageResampling = PILImageResampling.BICUBIC   ,__lowercase     :Optional[Union[str, ChannelDimension]] = None   ,**__lowercase     :str   ,):
						snake_case__	:      List[str]     		=	get_size_dict(__lowercase   ,default_to_square=__lowercase  )
						if "shortest_edge" not in size:
										raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""  )
						snake_case__	:      Optional[int]     		=	get_resize_output_image_size(__lowercase   ,size=size['''shortest_edge''']   ,default_to_square=__lowercase  )
						return resize(__lowercase   ,size=__lowercase   ,resample=__lowercase   ,data_format=__lowercase   ,**__lowercase  )



		def 	__lowerCamelCase			(     self     :List[str]   ,__lowercase     :np.ndarray   ,__lowercase     :Dict[str, int]   ,__lowercase     :Optional[Union[str, ChannelDimension]] = None   ,**__lowercase     :int   ,):
						snake_case__	:      Optional[int]     		=	get_size_dict(__lowercase  )
						if "height" not in size or "width" not in size:
										raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""  )
						return center_crop(__lowercase   ,size=(size['''height'''], size['''width'''])   ,data_format=__lowercase   ,**__lowercase  )



		def 	__lowerCamelCase			(     self     :Any   ,__lowercase     :np.ndarray   ,__lowercase     :Union[int, float]   ,__lowercase     :Optional[Union[str, ChannelDimension]] = None   ,**__lowercase     :Tuple   ,):
						return rescale(__lowercase   ,scale=__lowercase   ,data_format=__lowercase   ,**__lowercase  )



		def 	__lowerCamelCase			(     self     :str   ,__lowercase     :np.ndarray   ,__lowercase     :Union[float, List[float]]   ,__lowercase     :Union[float, List[float]]   ,__lowercase     :Optional[Union[str, ChannelDimension]] = None   ,**__lowercase     :Optional[int]   ,):
						return normalize(__lowercase   ,mean=__lowercase   ,std=__lowercase   ,data_format=__lowercase   ,**__lowercase  )






		def 	__lowerCamelCase			(     self     :Tuple   ,__lowercase     :ImageInput   ,__lowercase     :bool = None   ,__lowercase     :Dict[str, int] = None   ,__lowercase     :PILImageResampling = None   ,__lowercase     :bool = None   ,__lowercase     :int = None   ,__lowercase     :bool = None   ,__lowercase     :float = None   ,__lowercase     :bool = None   ,__lowercase     :Optional[Union[float, List[float]]] = None   ,__lowercase     :Optional[Union[float, List[float]]] = None   ,__lowercase     :bool = None   ,__lowercase     :Optional[Union[str, TensorType]] = None   ,__lowercase     :Optional[ChannelDimension] = ChannelDimension.FIRST   ,**__lowercase     :Any   ,):
						snake_case__	:      int     		=	do_resize if do_resize is not None else self.do_resize
						snake_case__	:      Tuple     		=	size if size is not None else self.size
						snake_case__	:      str     		=	get_size_dict(__lowercase   ,param_name='''size'''   ,default_to_square=__lowercase  )
						snake_case__	:      Dict     		=	resample if resample is not None else self.resample
						snake_case__	:      Optional[Any]     		=	do_center_crop if do_center_crop is not None else self.do_center_crop
						snake_case__	:      Optional[Any]     		=	crop_size if crop_size is not None else self.crop_size
						snake_case__	:      List[Any]     		=	get_size_dict(__lowercase   ,param_name='''crop_size'''   ,default_to_square=__lowercase  )
						snake_case__	:      Tuple     		=	do_rescale if do_rescale is not None else self.do_rescale
						snake_case__	:      Any     		=	rescale_factor if rescale_factor is not None else self.rescale_factor
						snake_case__	:      int     		=	do_normalize if do_normalize is not None else self.do_normalize
						snake_case__	:      List[str]     		=	image_mean if image_mean is not None else self.image_mean
						snake_case__	:      Union[str, Any]     		=	image_std if image_std is not None else self.image_std
						snake_case__	:      Optional[int]     		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						snake_case__	:      Tuple     		=	make_list_of_images(__lowercase  )

						if not valid_images(__lowercase  ):
										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:
										raise ValueError('''Size must be specified if do_resize is True.'''  )

						if do_center_crop and crop_size is None:
										raise ValueError('''Crop size must be specified if do_center_crop 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.'''  )

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
										snake_case__	:      Optional[int]     		=	[convert_to_rgb(__lowercase  ) for image in images]

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

						if do_resize:
										snake_case__	:      List[str]     		=	[self.resize(image=__lowercase   ,size=__lowercase   ,resample=__lowercase  ) for image in images]

						if do_center_crop:
										snake_case__	:      Optional[Any]     		=	[self.center_crop(image=__lowercase   ,size=__lowercase  ) for image in images]

						if do_rescale:
										snake_case__	:      Union[str, Any]     		=	[self.rescale(image=__lowercase   ,scale=__lowercase  ) for image in images]

						if do_normalize:
										snake_case__	:      List[Any]     		=	[self.normalize(image=__lowercase   ,mean=__lowercase   ,std=__lowercase  ) for image in images]

						snake_case__	:      Tuple     		=	[to_channel_dimension_format(__lowercase   ,__lowercase  ) for image in images]

						snake_case__	:      str     		=	{'''pixel_values''': images}
						return BatchFeature(data=__lowercase   ,tensor_type=__lowercase  )






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


A__								=						logging.get_logger(__name__)


class       a							(			__lowerCamelCase				):
		__lowerCAmelCase	:    str											=					["""input_features""", """is_longer"""]



		def __init__(     self     :Union[str, Any]   ,__lowercase     :str=6_4   ,__lowercase     :Any=4_8_0_0_0   ,__lowercase     :List[Any]=4_8_0   ,__lowercase     :Optional[int]=1_0   ,__lowercase     :Optional[int]=1_0_2_4   ,__lowercase     :int=0.0   ,__lowercase     :List[Any]=False   ,__lowercase     :float = 0   ,__lowercase     :float = 1_4_0_0_0   ,__lowercase     :int = None   ,__lowercase     :str = "fusion"   ,__lowercase     :str = "repeatpad"   ,**__lowercase     :List[Any]   ,):
						super().__init__(
						    feature_size=__lowercase   ,sampling_rate=__lowercase   ,padding_value=__lowercase   ,return_attention_mask=__lowercase   ,**__lowercase   ,)
						snake_case__	:      Optional[Any]     		=	top_db
						snake_case__	:      Tuple     		=	truncation
						snake_case__	:      Tuple     		=	padding
						snake_case__	:      List[Any]     		=	fft_window_size
						snake_case__	:      List[Any]     		=	(fft_window_size >> 1) + 1
						snake_case__	:      str     		=	hop_length
						snake_case__	:      Dict     		=	max_length_s
						snake_case__	:      List[str]     		=	max_length_s * sampling_rate
						snake_case__	:      List[Any]     		=	sampling_rate
						snake_case__	:      Any     		=	frequency_min
						snake_case__	:      Dict     		=	frequency_max
						snake_case__	:      Tuple     		=	mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins   ,num_mel_filters=__lowercase   ,min_frequency=__lowercase   ,max_frequency=__lowercase   ,sampling_rate=__lowercase   ,norm=__lowercase   ,mel_scale='''htk'''   ,)
						snake_case__	:      Dict     		=	mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins   ,num_mel_filters=__lowercase   ,min_frequency=__lowercase   ,max_frequency=__lowercase   ,sampling_rate=__lowercase   ,norm='''slaney'''   ,mel_scale='''slaney'''   ,)



		def 	__lowerCamelCase			(     self     :int  ):
						snake_case__	:      Dict     		=	copy.deepcopy(self.__dict__  )
						snake_case__	:      Optional[int]     		=	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 	__lowerCamelCase			(     self     :Union[str, Any]   ,__lowercase     :np.array   ,__lowercase     :Optional[np.array] = None  ):
						snake_case__	:      List[Any]     		=	spectrogram(
						    __lowercase   ,window_function(self.fft_window_size   ,'''hann'''  )   ,frame_length=self.fft_window_size   ,hop_length=self.hop_length   ,power=2.0   ,mel_filters=__lowercase   ,log_mel='''dB'''   ,)
						return log_mel_spectrogram.T



		def 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :List[str]   ,__lowercase     :Tuple   ,__lowercase     :List[str]  ):
						snake_case__	:      Dict     		=	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__	:      str     		=	[0]
						# randomly choose index for each part
						snake_case__	:      Dict     		=	np.random.choice(ranges[0]  )
						snake_case__	:      Any     		=	np.random.choice(ranges[1]  )
						snake_case__	:      Dict     		=	np.random.choice(ranges[2]  )

						snake_case__	:      Optional[int]     		=	mel[idx_front : idx_front + chunk_frames, :]
						snake_case__	:      Optional[Any]     		=	mel[idx_middle : idx_middle + chunk_frames, :]
						snake_case__	:      List[str]     		=	mel[idx_back : idx_back + chunk_frames, :]

						snake_case__	:      Optional[Any]     		=	torch.tensor(mel[None, None, :]  )
						snake_case__	:      Any     		=	torch.nn.functional.interpolate(
						    __lowercase   ,size=[chunk_frames, 6_4]   ,mode='''bilinear'''   ,align_corners=__lowercase  )
						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 	__lowerCamelCase			(     self     :Any   ,__lowercase     :np.array   ,__lowercase     :str   ,__lowercase     :int   ,__lowercase     :List[str]  ):
						if waveform.shape[0] > max_length:
										if truncation == "rand_trunc":
														snake_case__	:      Tuple     		=	True
														# random crop to max_length (for compatibility) -> this should be handled by self.pad
														snake_case__	:      List[str]     		=	len(__lowercase  ) - max_length
														snake_case__	:      Any     		=	np.random.randint(0   ,overflow + 1  )
														snake_case__	:      Tuple     		=	waveform[idx : idx + max_length]
														snake_case__	:      Tuple     		=	self._np_extract_fbank_features(__lowercase   ,self.mel_filters_slaney  )[None, :]
										elif truncation == "fusion":
														snake_case__	:      str     		=	self._np_extract_fbank_features(__lowercase   ,self.mel_filters  )
														snake_case__	:      Union[str, Any]     		=	max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
														snake_case__	:      Union[str, 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.
																		snake_case__	:      Tuple     		=	np.stack([mel, mel, mel, mel]   ,axis=0  )
																		snake_case__	:      List[Any]     		=	False
														else:
																		snake_case__	:      List[Any]     		=	self._random_mel_fusion(__lowercase   ,__lowercase   ,__lowercase  )
																		snake_case__	:      Dict     		=	True
										else:
														raise NotImplementedError(F"""data_truncating {truncation} not implemented"""  )

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

										if truncation == "fusion":
														snake_case__	:      Tuple     		=	self._np_extract_fbank_features(__lowercase   ,self.mel_filters  )
														snake_case__	:      Optional[int]     		=	np.stack([input_mel, input_mel, input_mel, input_mel]   ,axis=0  )
										else:
														snake_case__	:      List[Any]     		=	self._np_extract_fbank_features(__lowercase   ,self.mel_filters_slaney  )[None, :]

						return input_mel, longer






		def __call__(     self     :Dict   ,__lowercase     :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,__lowercase     :str = None   ,__lowercase     :Optional[str] = None   ,__lowercase     :Optional[int] = None   ,__lowercase     :Optional[int] = None   ,__lowercase     :Optional[Union[str, TensorType]] = None   ,**__lowercase     :Optional[int]   ,):
						snake_case__	:      Optional[int]     		=	truncation if truncation is not None else self.truncation
						snake_case__	:      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.'''  )

						snake_case__	:      List[str]     		=	isinstance(__lowercase   ,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__	:      Optional[int]     		=	is_batched_numpy or (
						    isinstance(__lowercase   ,(list, tuple)  ) and (isinstance(raw_speech[0]   ,(np.ndarray, tuple, list)  ))
						)

						if is_batched:
										snake_case__	:      Optional[Any]     		=	[np.asarray(__lowercase   ,dtype=np.floataa  ) for speech in raw_speech]
						elif not is_batched and not isinstance(__lowercase   ,np.ndarray  ):
										snake_case__	:      Tuple     		=	np.asarray(__lowercase   ,dtype=np.floataa  )
						elif isinstance(__lowercase   ,np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
										snake_case__	:      Dict     		=	raw_speech.astype(np.floataa  )

						# always return batch
						if not is_batched:
										snake_case__	:      Dict     		=	[np.asarray(__lowercase  )]

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

						snake_case__	:      Any     		=	[]
						snake_case__	:      Tuple     		=	[]
						for mel, longer in padded_inputs:
										input_mel.append(__lowercase  )
										is_longer.append(__lowercase  )

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

						if isinstance(input_mel[0]   ,__lowercase  ):
										snake_case__	:      Optional[int]     		=	[np.asarray(__lowercase   ,dtype=np.floataa  ) for feature in input_mel]

						# is_longer is a list of bool
						snake_case__	:      Dict     		=	[[longer] for longer in is_longer]

						snake_case__	:      Dict     		=	{'''input_features''': input_mel, '''is_longer''': is_longer}
						snake_case__	:      str     		=	BatchFeature(__lowercase  )

						if return_tensors is not None:
										snake_case__	:      List[str]     		=	input_features.convert_to_tensors(__lowercase  )

						return input_features

