




'''simple docstring'''

from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


a       :						Any								=		logging.get_logger(__name__)



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			["""pixel_values"""]





			def __init__(      self						:    str      ,		a_						:    bool = True      ,		a_						:    Union[int, float] = 1 / 255      ,		a_						:    bool = True      ,		a_						:    int = 8      ,		**a_						:    Any      ,		):



						"""simple docstring"""




						super().__init__(**a_							)

						__snake_case			=		do_rescale
						__snake_case			=		rescale_factor
						__snake_case			=		do_pad
						__snake_case			=		pad_size





			def 							A (      self						:    Optional[Any]      ,		a_						:    np.ndarray      ,		a_						:    float      ,		a_						:    Optional[Union[str, ChannelDimension]] = None      ,		**a_						:    Tuple							):



						"""simple docstring"""




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





			def 							A (      self						:    Optional[int]      ,		a_						:    np.ndarray      ,		a_						:    int      ,		a_						:    Optional[Union[str, ChannelDimension]] = None							):



						"""simple docstring"""




						__snake_case		,     __snake_case			=		get_image_size(a_							)
						__snake_case			=		(old_height // size + 1) * size - old_height
						__snake_case			=		(old_width // size + 1) * size - old_width

						return pad(a_      ,		((0, pad_height), (0, pad_width))      ,		mode="symmetric"      ,		data_format=a_							)






			def 							A (      self						:    int      ,		a_						:    ImageInput      ,		a_						:    Optional[bool] = None      ,		a_						:    Optional[float] = None      ,		a_						:    Optional[bool] = None      ,		a_						:    Optional[int] = None      ,		a_						:    Optional[Union[str, TensorType]] = None      ,		a_						:    Union[str, ChannelDimension] = ChannelDimension.FIRST      ,		**a_						:    str      ,		):



						"""simple docstring"""




						__snake_case			=		do_rescale if do_rescale is not None else self.do_rescale
						__snake_case			=		rescale_factor if rescale_factor is not None else self.rescale_factor
						__snake_case			=		do_pad if do_pad is not None else self.do_pad
						__snake_case			=		pad_size if pad_size is not None else self.pad_size

						__snake_case			=		make_list_of_images(a_							)

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

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

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

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

						if do_pad:
									__snake_case			=		[self.pad(a_      ,		size=a_							) for image in images]

						__snake_case			=		[to_channel_dimension_format(a_      ,		a_							) for image in images]

						__snake_case			=		{"pixel_values": images}
						return BatchFeature(data=a_      ,		tensor_type=a_							)













from __future__ import annotations






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		List[str]					,   __UpperCAmelCase :		int    )       ->None:




       """simple docstring"""


       a						    = order

       # a_{0} ... a_{k}
       a						    = [1.0] + [0.0] * order
       # b_{0} ... b_{k}
       a						    = [1.0] + [0.0] * order

       # x[n-1] ... x[n-k]
       a						    = [0.0] * self.order
       # y[n-1] ... y[n-k]
       a						    = [0.0] * self.order







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		list[float]					,   __UpperCAmelCase :		list[float]    )       ->None:




       """simple docstring"""


       if len(__UpperCAmelCase    ) < self.order:
        a						    = [1.0, *a_coeffs]

       if len(__UpperCAmelCase    ) != self.order + 1:
        a						    = (
            F"""Expected a_coeffs to have {self.order + 1} elements """
            F"""for {self.order}-order filter, got {len(__UpperCAmelCase    )}"""
        )
        raise ValueError(__UpperCAmelCase    )

       if len(__UpperCAmelCase    ) != self.order + 1:
        a						    = (
            F"""Expected b_coeffs to have {self.order + 1} elements """
            F"""for {self.order}-order filter, got {len(__UpperCAmelCase    )}"""
        )
        raise ValueError(__UpperCAmelCase    )

       a						    = a_coeffs
       a						    = b_coeffs







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		float    )       ->float:




       """simple docstring"""


       a						    = 0.0

       # Start at index 1 and do index 0 at the end.
       for i in range(1					,   self.order + 1    ):
        result += (
            self.b_coeffs[i] * self.input_history[i - 1]
            - self.a_coeffs[i] * self.output_history[i - 1]
        )

       a						    = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

       a						    = self.input_history[:-1]
       a						    = self.output_history[:-1]

       a						    = sample
       a						    = result

       return result


