
'''simple docstring'''



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


_lowercase    =		logging.get_logger(__name__)


if is_vision_available():
						import PIL




class   UpperCAmelCase_					(		SCREAMING_SNAKE_CASE__			):






						'''simple docstring'''






						_lowercase		:				List[str]									=       ['''pixel_values''']




						def __init__( self   ,       _lowercase = True   ,       _lowercase = None   ,       _lowercase = PILImageResampling.BICUBIC   ,       _lowercase = True   ,       _lowercase = None   ,       _lowercase = True   ,       _lowercase = 1 / 255   ,       _lowercase = True   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = True   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(**_lowercase				)
												_lowerCAmelCase  						=			size if size is not None else {'shortest_edge': 224}
												_lowerCAmelCase  						=			get_size_dict(_lowercase   ,       default_to_square=_lowercase				)
												_lowerCAmelCase  						=			crop_size if crop_size is not None else {'height': 224, 'width': 224}
												_lowerCAmelCase  						=			get_size_dict(_lowercase   ,       default_to_square=_lowercase   ,       param_name="""crop_size"""				)

												_lowerCAmelCase  						=			do_resize
												_lowerCAmelCase  						=			size
												_lowerCAmelCase  						=			resample
												_lowerCAmelCase  						=			do_center_crop
												_lowerCAmelCase  						=			crop_size
												_lowerCAmelCase  						=			do_rescale
												_lowerCAmelCase  						=			rescale_factor
												_lowerCAmelCase  						=			do_normalize
												_lowerCAmelCase  						=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
												_lowerCAmelCase  						=			image_std if image_std is not None else OPENAI_CLIP_STD
												_lowerCAmelCase  						=			do_convert_rgb




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase = PILImageResampling.BICUBIC   ,       _lowercase = None   ,       **_lowercase   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			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()}'				)
												_lowerCAmelCase  						=			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  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase = None   ,       **_lowercase   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			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  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase = None   ,       **_lowercase   ,       ):

												"""simple docstring"""



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




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase = None   ,       **_lowercase   ,       ):

												"""simple docstring"""



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




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = ChannelDimension.FIRST   ,       **_lowercase   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			do_resize if do_resize is not None else self.do_resize
												_lowerCAmelCase  						=			size if size is not None else self.size
												_lowerCAmelCase  						=			get_size_dict(_lowercase   ,       param_name="""size"""   ,       default_to_square=_lowercase				)
												_lowerCAmelCase  						=			resample if resample is not None else self.resample
												_lowerCAmelCase  						=			do_center_crop if do_center_crop is not None else self.do_center_crop
												_lowerCAmelCase  						=			crop_size if crop_size is not None else self.crop_size
												_lowerCAmelCase  						=			get_size_dict(_lowercase   ,       param_name="""crop_size"""   ,       default_to_square=_lowercase				)
												_lowerCAmelCase  						=			do_rescale if do_rescale is not None else self.do_rescale
												_lowerCAmelCase  						=			rescale_factor if rescale_factor is not None else self.rescale_factor
												_lowerCAmelCase  						=			do_normalize if do_normalize is not None else self.do_normalize
												_lowerCAmelCase  						=			image_mean if image_mean is not None else self.image_mean
												_lowerCAmelCase  						=			image_std if image_std is not None else self.image_std
												_lowerCAmelCase  						=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

												_lowerCAmelCase  						=			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:
																		_lowerCAmelCase  						=			[convert_to_rgb(_lowercase				) for image in images]

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

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

												if do_center_crop:
																		_lowerCAmelCase  						=			[self.center_crop(image=_lowercase   ,       size=_lowercase				) for image in images]

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

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

												_lowerCAmelCase  						=			[to_channel_dimension_format(_lowercase   ,       _lowercase				) for image in images]

												_lowerCAmelCase  						=			{'pixel_values': images}
												return BatchFeature(data=_lowercase   ,       tensor_type=_lowercase				)


'''simple docstring'''



from __future__ import annotations




class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			order

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

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




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



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

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

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

												_lowerCAmelCase  						=			a_coeffs
												_lowerCAmelCase  						=			b_coeffs




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			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]
																		)

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

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

												_lowerCAmelCase  						=			sample
												_lowerCAmelCase  						=			result

												return result
