


from typing import List, Optional, Union

import numpy as np

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


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







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Union[str, Any]   				=      ['''input_features''', '''attention_mask''']
					def __init__(				self       :str      ,__snake_case       :int=80      ,__snake_case       :Optional[Any]=1_60_00      ,__snake_case       :str=0.0      ,__snake_case       :str=10      ,__snake_case       :str=25      ,__snake_case       :Dict="hamming_window"      ,__snake_case       :List[Any]=3_27_68.0      ,__snake_case       :Union[str, Any]=0.97      ,__snake_case       :Optional[Any]=1.0      ,__snake_case       :Optional[Any]=True      ,__snake_case       :Optional[Any]=True      ,__snake_case       :Optional[int]=False      ,**__snake_case       :Optional[int]      ,) ->   Any:
												super().__init__(feature_size=__snake_case      ,sampling_rate=__snake_case      ,padding_value=__snake_case      ,**__snake_case				)

												a__					=					feature_size
												a__					=					sampling_rate
												a__					=					padding_value
												a__					=					hop_length
												a__					=					win_length
												a__					=					frame_signal_scale
												a__					=					preemphasis_coeff
												a__					=					mel_floor
												a__					=					normalize_means
												a__					=					normalize_vars
												a__					=					win_function
												a__					=					return_attention_mask

												a__					=					win_length * sampling_rate // 10_00
												a__					=					hop_length * sampling_rate // 10_00

												a__					=					optimal_fft_length(self.sample_size				)
												a__					=					(self.n_fft // 2) + 1
					def 			lowerCamelCase__(				self       :str      ,__snake_case       :np.array				) ->   np.ndarray:
												if self.win_function == "hamming_window":
																			a__					=					window_function(window_length=self.sample_size      ,name=self.win_function      ,periodic=__snake_case				)
												else:
																			a__					=					window_function(window_length=self.sample_size      ,name=self.win_function				)

												a__					=					mel_filter_bank(
												    num_frequency_bins=self.n_freqs      ,num_mel_filters=self.feature_size      ,min_frequency=0.0      ,max_frequency=self.sampling_rate / 2.0      ,sampling_rate=self.sampling_rate      ,)

												a__					=					spectrogram(
												    one_waveform * self.frame_signal_scale      ,window=__snake_case      ,frame_length=self.sample_size      ,hop_length=self.sample_stride      ,fft_length=self.n_fft      ,center=__snake_case      ,preemphasis=self.preemphasis_coeff      ,mel_filters=__snake_case      ,mel_floor=self.mel_floor      ,log_mel='log'      ,)
												return msfc_features.T
					def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :Dict      ,__snake_case       :str      ,__snake_case       :str				) ->   List[str]:
												# make sure we normalize float32 arrays
												if self.normalize_means:
																			a__					=					x[:input_length].mean(axis=0				)
																			a__					=					np.subtract(__snake_case      ,__snake_case				)
												if self.normalize_vars:
																			a__					=					x[:input_length].std(axis=0				)
																			a__					=					np.divide(__snake_case      ,__snake_case				)

												if input_length < x.shape[0]:
																			a__					=					padding_value

												# make sure array is in float32
												a__					=					x.astype(np.floataa				)

												return x
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :List[np.ndarray]      ,__snake_case       :Optional[np.ndarray] = None				) ->   List[np.ndarray]:
												a__					=					attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
												return [self._normalize_one(__snake_case      ,__snake_case      ,self.padding_value				) for x, n in zip(__snake_case      ,__snake_case				)]



					def __call__(				self       :int      ,__snake_case       :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,__snake_case       :Union[bool, str, PaddingStrategy] = False      ,__snake_case       :Optional[int] = None      ,__snake_case       :bool = False      ,__snake_case       :Optional[int] = None      ,__snake_case       :Optional[bool] = None      ,__snake_case       :Optional[Union[str, TensorType]] = None      ,__snake_case       :Optional[int] = None      ,**__snake_case       :Union[str, Any]      ,) ->   BatchFeature:

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

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

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

												# always return batch
												if not is_batched:
																			a__					=					[raw_speech]

												# extract fbank features
												a__					=					[self._extract_mfsc_features(__snake_case				) for one_waveform in raw_speech]

												# convert into correct format for padding
												a__					=					BatchFeature({'input_features': features}				)

												a__					=					self.pad(
												    __snake_case      ,padding=__snake_case      ,max_length=__snake_case      ,truncation=__snake_case      ,pad_to_multiple_of=__snake_case      ,return_attention_mask=__snake_case      ,**__snake_case      ,)
												# make sure list is in array format
												a__					=					padded_inputs.get('input_features'				)
												if isinstance(input_features[0]      ,__snake_case				):
																			a__					=					[np.asarray(__snake_case      ,dtype=np.floataa				) for feature in input_features]

												a__					=					padded_inputs.get('attention_mask'				)
												if attention_mask is not None:
																			a__					=					[np.asarray(__snake_case      ,dtype=np.intaa				) for array in attention_mask]

												if self.normalize_means or self.normalize_vars:
																			a__					=					(
																			    np.array(__snake_case      ,dtype=np.intaa				)
																			    if self._get_padding_strategies(__snake_case      ,max_length=__snake_case				) is not PaddingStrategy.DO_NOT_PAD
																			    and padding
																			    else None
																			)
																			a__					=					self.normalize(
																			    padded_inputs['input_features']      ,attention_mask=__snake_case				)

												if return_tensors is not None:
																			a__					=					padded_inputs.convert_to_tensors(__snake_case				)

												return padded_inputs








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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


snake_case							:	str					      =				logging.get_logger(__name__)






def 							__lowercase       (			__lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Optional[Any]       ):
							a__					=					b.T
							a__					=					np.sum(np.square(__lowerCAmelCase       ) ,   axis=1       )
							a__					=					np.sum(np.square(__lowerCAmelCase       ) ,   axis=0       )
							a__					=					np.matmul(__lowerCAmelCase ,   __lowerCAmelCase       )
							a__					=					aa[:, None] - 2 * ab + ba[None, :]
							return d






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    str       ):
							a__					=					x.reshape(-1 ,   3       )
							a__					=					squared_euclidean_distance(__lowerCAmelCase ,   __lowerCAmelCase       )
							return np.argmin(__lowerCAmelCase ,   axis=1       )







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Optional[Any]   				=      ['''pixel_values''']
					def __init__(				self       :Dict      ,__snake_case       :Optional[Union[List[List[int]], np.ndarray]] = None      ,__snake_case       :bool = True      ,__snake_case       :Dict[str, int] = None      ,__snake_case       :PILImageResampling = PILImageResampling.BILINEAR      ,__snake_case       :bool = True      ,__snake_case       :bool = True      ,**__snake_case       :Optional[int]      ,) ->   None:
												super().__init__(**__snake_case				)
												a__					=					size if size is not None else {'height': 2_56, 'width': 2_56}
												a__					=					get_size_dict(__snake_case				)
												a__					=					np.array(__snake_case				) if clusters is not None else None
												a__					=					do_resize
												a__					=					size
												a__					=					resample
												a__					=					do_normalize
												a__					=					do_color_quantize
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :np.ndarray      ,__snake_case       :Dict[str, int]      ,__snake_case       :PILImageResampling = PILImageResampling.BILINEAR      ,__snake_case       :Optional[Union[str, ChannelDimension]] = None      ,**__snake_case       :Any      ,) ->   np.ndarray:
												a__					=					get_size_dict(__snake_case				)
												if "height" not in size or "width" not in size:
																			raise ValueError(F'Size dictionary must contain both height and width keys. Got {size.keys()}'				)
												return resize(
												    __snake_case      ,size=(size['height'], size['width'])      ,resample=__snake_case      ,data_format=__snake_case      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :np.ndarray      ,__snake_case       :Optional[Union[str, ChannelDimension]] = None      ,) ->   np.ndarray:
												a__					=					rescale(image=__snake_case      ,scale=1 / 1_27.5      ,data_format=__snake_case				)
												a__					=					image - 1
												return image



					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :ImageInput      ,__snake_case       :bool = None      ,__snake_case       :Dict[str, int] = None      ,__snake_case       :PILImageResampling = None      ,__snake_case       :bool = None      ,__snake_case       :Optional[bool] = None      ,__snake_case       :Optional[Union[List[List[int]], np.ndarray]] = None      ,__snake_case       :Optional[Union[str, TensorType]] = None      ,__snake_case       :Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST      ,**__snake_case       :List[str]      ,) ->   PIL.Image.Image:
												a__					=					do_resize if do_resize is not None else self.do_resize
												a__					=					size if size is not None else self.size
												a__					=					get_size_dict(__snake_case				)
												a__					=					resample if resample is not None else self.resample
												a__					=					do_normalize if do_normalize is not None else self.do_normalize
												a__					=					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
												a__					=					clusters if clusters is not None else self.clusters
												a__					=					np.array(__snake_case				)

												a__					=					make_list_of_images(__snake_case				)

												if not valid_images(__snake_case				):
																			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_color_quantize and clusters is None:
																			raise ValueError('Clusters must be specified if do_color_quantize is True.'				)

												# All transformations expect numpy arrays.
												a__					=					[to_numpy_array(__snake_case				) for image in images]

												if do_resize:
																			a__					=					[self.resize(image=__snake_case      ,size=__snake_case      ,resample=__snake_case				) for image in images]

												if do_normalize:
																			a__					=					[self.normalize(image=__snake_case				) for image in images]

												if do_color_quantize:
																			a__					=					[to_channel_dimension_format(__snake_case      ,ChannelDimension.LAST				) for image in images]
																			# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																			a__					=					np.array(__snake_case				)
																			a__					=					color_quantize(__snake_case      ,__snake_case				).reshape(images.shape[:-1]				)

																			# flatten to (batch_size, height*width)
																			a__					=					images.shape[0]
																			a__					=					images.reshape(__snake_case      ,-1				)

																			# We need to convert back to a list of images to keep consistent behaviour across processors.
																			a__					=					list(__snake_case				)
												else:
																			a__					=					[to_channel_dimension_format(__snake_case      ,__snake_case				) for image in images]

												a__					=					{'input_ids': images}
												return BatchFeature(data=__snake_case      ,tensor_type=__snake_case				)




