
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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A     :	str    = logging.get_logger(__name__)







class 			__A( a			):
				snake_case_						   =    ['''pixel_values''']




				def __init__(     self		,     _snake_case = True		,     _snake_case = None		,     _snake_case = PILImageResampling.BILINEAR		,     _snake_case = True		,     _snake_case = None		,     _snake_case = True		,     _snake_case = 1 / 255		,     _snake_case = True		,     _snake_case = None		,     _snake_case = None		,     **_snake_case		,     )    ->      None:


								'''simple docstring'''




								super().__init__(**_snake_case				)
								__a										=    size if size is not None else {'''shortest_edge''': 256}
								__a										=    get_size_dict(_snake_case		,     default_to_square=_snake_case				)
								__a										=    crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								__a										=    get_size_dict(_snake_case				)
								__a										=    do_resize
								__a										=    size
								__a										=    resample
								__a										=    do_center_crop
								__a										=    crop_size
								__a										=    do_rescale
								__a										=    rescale_factor
								__a										=    do_normalize
								__a										=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__a										=    image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = PILImageResampling.BICUBIC		,     _snake_case = None		,     **_snake_case		,     )    ->      np.ndarray:


								'''simple docstring'''




								__a										=    get_size_dict(_snake_case		,     default_to_square=_snake_case				)
								if "shortest_edge" not in size:
												raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
								__a										=    get_resize_output_image_size(_snake_case		,     size=size['''shortest_edge''']		,     default_to_square=_snake_case				)
								return resize(_snake_case		,     size=_snake_case		,     resample=_snake_case		,     data_format=_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = None		,     **_snake_case		,     )    ->      np.ndarray:


								'''simple docstring'''




								__a										=    get_size_dict(_snake_case				)
								return center_crop(_snake_case		,     size=(size['''height'''], size['''width'''])		,     data_format=_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = None		,     **_snake_case				)    ->      np.ndarray:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case = None		,     **_snake_case		,     )    ->      np.ndarray:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = ChannelDimension.FIRST		,     **_snake_case		,     )    ->      Optional[int]:


								'''simple docstring'''




								__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		,     default_to_square=_snake_case				)
								__a										=    resample if resample is not None else self.resample
								__a										=    do_center_crop if do_center_crop is not None else self.do_center_crop
								__a										=    crop_size if crop_size is not None else self.crop_size
								__a										=    get_size_dict(_snake_case				)
								__a										=    do_rescale if do_rescale is not None else self.do_rescale
								__a										=    rescale_factor if rescale_factor is not None else self.rescale_factor
								__a										=    do_normalize if do_normalize is not None else self.do_normalize
								__a										=    image_mean if image_mean is not None else self.image_mean
								__a										=    image_std if image_std is not None else self.image_std

								__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:
												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.'''				)

								# 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_center_crop:
												__a										=    [self.center_crop(image=_snake_case		,     size=_snake_case				) for image in images]

								if do_rescale:
												__a										=    [self.rescale(image=_snake_case		,     scale=_snake_case				) for image in images]

								if do_normalize:
												__a										=    [self.normalize(image=_snake_case		,     mean=_snake_case		,     std=_snake_case				) for image in images]

								__a										=    [to_channel_dimension_format(_snake_case		,     _snake_case				) for image in images]

								__a										=    {'''pixel_values''': images}
								return BatchFeature(data=_snake_case		,     tensor_type=_snake_case				)

from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    (1 - _cos) / 2
				__a										=    1 - _cos

				__a										=    1 + alpha
				__a										=    -2 * _cos
				__a										=    1 - alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    (1 + _cos) / 2
				__a										=    -1 - _cos

				__a										=    1 + alpha
				__a										=    -2 * _cos
				__a										=    1 - alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    _sin / 2
				__a										=    0
				__a										=    -ba

				__a										=    1 + alpha
				__a										=    -2 * _cos
				__a										=    1 - alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    1 - alpha
				__a										=    -2 * _cos
				__a										=    1 + alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([ba, ba, ba]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__ = 1 / sqrt(2  )						,    )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)
				__a										=    10 ** (gain_db / 40)

				__a										=    1 + alpha * big_a
				__a										=    -2 * _cos
				__a										=    1 - alpha * big_a
				__a										=    1 + alpha / big_a
				__a										=    -2 * _cos
				__a										=    1 - alpha / big_a

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__ = 1 / sqrt(2  )						,    )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)
				__a										=    10 ** (gain_db / 40)
				__a										=    (big_a + 1) - (big_a - 1) * _cos
				__a										=    (big_a + 1) + (big_a - 1) * _cos
				__a										=    (big_a - 1) - (big_a + 1) * _cos
				__a										=    (big_a - 1) + (big_a + 1) * _cos
				__a										=    2 * sqrt(a__  ) * alpha

				__a										=    big_a * (pmc + aaa)
				__a										=    2 * big_a * mpc
				__a										=    big_a * (pmc - aaa)
				__a										=    ppmc + aaa
				__a										=    -2 * pmpc
				__a										=    ppmc - aaa

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt






def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__ = 1 / sqrt(2  )						,    )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)
				__a										=    10 ** (gain_db / 40)
				__a										=    (big_a + 1) - (big_a - 1) * _cos
				__a										=    (big_a + 1) + (big_a - 1) * _cos
				__a										=    (big_a - 1) - (big_a + 1) * _cos
				__a										=    (big_a - 1) + (big_a + 1) * _cos
				__a										=    2 * sqrt(a__  ) * alpha

				__a										=    big_a * (ppmc + aaa)
				__a										=    -2 * big_a * pmpc
				__a										=    big_a * (ppmc - aaa)
				__a										=    pmc + aaa
				__a										=    2 * mpc
				__a										=    pmc - aaa

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt