
'''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 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


__SCREAMING_SNAKE_CASE		:  Optional[int]        =      logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__       (  snake_case_   ):
	lowercase__	    =							["pixel_values"]
	def __init__(				self  ,    __UpperCamelCase = True  ,    __UpperCamelCase = None  ,    __UpperCamelCase = PILImageResampling.BILINEAR  ,    __UpperCamelCase = True  ,    __UpperCamelCase = 1 / 255  ,    __UpperCamelCase = True  ,    __UpperCamelCase = None  ,    __UpperCamelCase = None  ,    **__UpperCamelCase  ,    ):



					'''simple docstring'''






					super().__init__(**__lowerCamelCase			)
					__a       :     Optional[Any]								=	size if size is not None else {"""height""": 224, """width""": 224}
					__a       :     str								=	get_size_dict(__lowerCamelCase			)
					__a       :     List[Any]								=	do_resize
					__a       :     List[Any]								=	do_rescale
					__a       :     Optional[int]								=	do_normalize
					__a       :     int								=	size
					__a       :     Optional[Any]								=	resample
					__a       :     List[Any]								=	rescale_factor
					__a       :     Any								=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					__a       :     List[Any]								=	image_std if image_std is not None else IMAGENET_STANDARD_STD
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase = PILImageResampling.BILINEAR  ,    __UpperCamelCase = None  ,    **__UpperCamelCase  ,    ):



					'''simple docstring'''






					__a       :     int								=	get_size_dict(__lowerCamelCase			)
					if "height" not in size or "width" not in size:
									raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""			)
					return resize(
					    __lowerCamelCase  ,    size=(size["""height"""], size["""width"""])  ,    resample=__lowerCamelCase  ,    data_format=__lowerCamelCase  ,    **__lowerCamelCase			)
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase = None  ,    **__UpperCamelCase			):



					'''simple docstring'''






					return rescale(__lowerCamelCase  ,    scale=__lowerCamelCase  ,    data_format=__lowerCamelCase  ,    **__lowerCamelCase			)
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase = None  ,    **__UpperCamelCase  ,    ):



					'''simple docstring'''






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

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



					'''simple docstring'''






					__a       :     Union[str, Any]								=	do_resize if do_resize is not None else self.do_resize
					__a       :     Union[str, Any]								=	do_rescale if do_rescale is not None else self.do_rescale
					__a       :     Optional[int]								=	do_normalize if do_normalize is not None else self.do_normalize
					__a       :     int								=	resample if resample is not None else self.resample
					__a       :     Dict								=	rescale_factor if rescale_factor is not None else self.rescale_factor
					__a       :     Any								=	image_mean if image_mean is not None else self.image_mean
					__a       :     int								=	image_std if image_std is not None else self.image_std

					__a       :     Tuple								=	size if size is not None else self.size
					__a       :     Any								=	get_size_dict(__lowerCamelCase			)

					__a       :     List[Any]								=	make_list_of_images(__lowerCamelCase			)

					if not valid_images(__lowerCamelCase			):
									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_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""			)

					# All transformations expect numpy arrays.
					__a       :     List[Any]								=	[to_numpy_array(__lowerCamelCase			) for image in images]

					if do_resize:
									__a       :     List[Any]								=	[self.resize(image=__lowerCamelCase  ,    size=__lowerCamelCase  ,    resample=__lowerCamelCase			) for image in images]

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

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

					__a       :     Optional[int]								=	[to_channel_dimension_format(__lowerCamelCase  ,    __lowerCamelCase			) for image in images]

					__a       :     Optional[int]								=	{"""pixel_values""": images}
					return BatchFeature(data=__lowerCamelCase  ,    tensor_type=__lowerCamelCase			)







"""simple docstring"""

from cva import destroyAllWindows, imread, imshow, waitKey






def _lowerCAmelCase						(	__lowerCamelCase:Tuple			,    __lowerCamelCase:int ):

						'''simple docstring'''






						__magic_name__   ,     __magic_name__      =  img.shape[0], img.shape[1]

						def to_grayscale(__lowerCamelCase:List[str]			,    __lowerCamelCase:Optional[Any]			,    __lowerCamelCase:int ):
												return 0.2_1_2_6 * red + 0.5_8_7 * green + 0.1_1_4 * blue

						def normalize(__lowerCamelCase:Union[str, Any] ):
												return min(__lowerCamelCase			,    2_5_5 )

						for i in range(__lowerCamelCase ):
												for j in range(__lowerCamelCase ):
																		__magic_name__      =  int(to_grayscale(*img[i][j] ) )
																		__magic_name__      =  [
																		    normalize(__lowerCamelCase ),
																		    normalize(greyscale + factor ),
																		    normalize(greyscale + 2 * factor ),
																		]

						return img


if __name__ == "__main__":
			# read original image
			lowercase           =							{
			    percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
			}

			for percentage, img in images.items():
						make_sepia(img, percentage)

			for percentage, img in images.items():
						imshow(f'''Original image with sepia (factor: {percentage})''', img)

			waitKey(0)
			destroyAllWindows()



