





'''simple docstring'''

from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase__						:      List[Any]	=    logging.get_logger(__name__)
class     __lowerCAmelCase					(   _a			):






       """simple docstring"""
       _snake_case     :					Any						    =   ["""pixel_values"""]





       def __init__(							self       :		List[str]	,			lowerCAmelCase__       :		Optional[int] = True	,			lowerCAmelCase__       :		int = None	,			lowerCAmelCase__       :		Any = PILImageResampling.BICUBIC	,			lowerCAmelCase__       :		int = True	,			lowerCAmelCase__       :		List[Any] = None	,			lowerCAmelCase__       :		Optional[Any] = True	,			lowerCAmelCase__       :		Optional[int] = 1 / 255	,			lowerCAmelCase__       :		List[Any] = True	,			lowerCAmelCase__       :		str = IMAGENET_DEFAULT_MEAN	,			lowerCAmelCase__       :		Union[str, Any] = IMAGENET_DEFAULT_STD	,			**lowerCAmelCase__       :		str	,			)   ->					None:

            '''simple docstring'''




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

            _UpperCamelCase       =	do_resize
            _UpperCamelCase       =	size
            _UpperCamelCase       =	resample
            _UpperCamelCase       =	do_center_crop
            _UpperCamelCase       =	crop_size
            _UpperCamelCase       =	do_rescale
            _UpperCamelCase       =	rescale_factor
            _UpperCamelCase       =	do_normalize
            _UpperCamelCase       =	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
            _UpperCamelCase       =	image_std if image_std is not None else IMAGENET_DEFAULT_STD





       def snake_case__						(							self       :		Optional[Any]	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		Union[str, Any] = PILImageResampling.BICUBIC	,			lowerCAmelCase__       :		int = None	,			**lowerCAmelCase__       :		Any	,			)   ->					np.ndarray:

            '''simple docstring'''




            _UpperCamelCase       =	get_size_dict(lowerCAmelCase__	,			default_to_square=lowerCAmelCase__					)
            # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
            if "shortest_edge" in size:
                 _UpperCamelCase       =	int((256 / 224) * size['''shortest_edge''']					)
                 _UpperCamelCase       =	get_resize_output_image_size(lowerCAmelCase__	,			size=lowerCAmelCase__	,			default_to_square=lowerCAmelCase__					)
                 _UpperCamelCase       =	{'height': output_size[0], 'width': output_size[1]}
            if "height" not in size_dict or "width" not in size_dict:
                 raise ValueError(
                     f"""Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}"""					)
            return resize(
                lowerCAmelCase__	,			size=(size_dict['''height'''], size_dict['''width'''])	,			resample=lowerCAmelCase__	,			data_format=lowerCAmelCase__	,			**lowerCAmelCase__					)





       def snake_case__						(							self       :		Dict	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		Tuple = None	,			**lowerCAmelCase__       :		Union[str, Any]	,			)   ->					np.ndarray:

            '''simple docstring'''




            _UpperCamelCase       =	get_size_dict(lowerCAmelCase__					)
            if "height" not in size or "width" not in size:
                 raise ValueError(f"""Size dict must have keys \'height\' and \'width\'. Got {size.keys()}"""					)
            return center_crop(lowerCAmelCase__	,			size=(size['''height'''], size['''width'''])	,			data_format=lowerCAmelCase__	,			**lowerCAmelCase__					)





       def snake_case__						(							self       :		List[Any]	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		Union[str, Any] = None	,			**lowerCAmelCase__       :		List[Any]	,			)   ->					np.ndarray:

            '''simple docstring'''




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





       def snake_case__						(							self       :		List[Any]	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		Any = None	,			**lowerCAmelCase__       :		Optional[int]	,			)   ->					np.ndarray:

            '''simple docstring'''




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







       def snake_case__						(							self       :		List[str]	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Tuple = None	,			lowerCAmelCase__       :		Dict = None	,			lowerCAmelCase__       :		Tuple = None	,			lowerCAmelCase__       :		Optional[int] = None	,			lowerCAmelCase__       :		Optional[Any] = None	,			lowerCAmelCase__       :		Tuple = None	,			lowerCAmelCase__       :		Optional[Any] = None	,			lowerCAmelCase__       :		int = None	,			lowerCAmelCase__       :		Optional[Any] = None	,			lowerCAmelCase__       :		int = None	,			lowerCAmelCase__       :		int = None	,			lowerCAmelCase__       :		List[Any] = ChannelDimension.FIRST	,			**lowerCAmelCase__       :		List[str]	,			)   ->					BatchFeature:

            '''simple docstring'''




            _UpperCamelCase       =	do_resize if do_resize is not None else self.do_resize
            _UpperCamelCase       =	resample if resample is not None else self.resample
            _UpperCamelCase       =	do_center_crop if do_center_crop is not None else self.do_center_crop
            _UpperCamelCase       =	do_rescale if do_rescale is not None else self.do_rescale
            _UpperCamelCase       =	rescale_factor if rescale_factor is not None else self.rescale_factor
            _UpperCamelCase       =	do_normalize if do_normalize is not None else self.do_normalize
            _UpperCamelCase       =	image_mean if image_mean is not None else self.image_mean
            _UpperCamelCase       =	image_std if image_std is not None else self.image_std

            _UpperCamelCase       =	size if size is not None else self.size
            _UpperCamelCase       =	get_size_dict(lowerCAmelCase__	,			default_to_square=lowerCAmelCase__					)
            _UpperCamelCase       =	crop_size if crop_size is not None else self.crop_size
            _UpperCamelCase       =	get_size_dict(lowerCAmelCase__	,			param_name='''crop_size'''					)

            _UpperCamelCase       =	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_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.
            _UpperCamelCase       =	[to_numpy_array(lowerCAmelCase__					) for image in images]

            if do_resize:
                 _UpperCamelCase       =	[self.resize(lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					) for image in images]

            if do_center_crop:
                 _UpperCamelCase       =	[self.center_crop(lowerCAmelCase__	,			lowerCAmelCase__					) for image in images]

            if do_rescale:
                 _UpperCamelCase       =	[self.rescale(lowerCAmelCase__	,			lowerCAmelCase__					) for image in images]

            if do_normalize:
                 _UpperCamelCase       =	[self.normalize(lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					) for image in images]

            _UpperCamelCase       =	[to_channel_dimension_format(lowerCAmelCase__	,			lowerCAmelCase__					) for image in images]

            _UpperCamelCase       =	{'pixel_values': images}
            return BatchFeature(data=lowerCAmelCase__	,			tensor_type=lowerCAmelCase__					)










'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey







def  a__    (    lowercase :					str				)		->		Optional[int]:





     """simple docstring"""

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

     # converting each pixel's color to its negative
     for i in range(lowercase				):
          for j in range(lowercase				):
               _UpperCamelCase       =	[255, 255, 255] - img[i][j]

     return img


if __name__ == "__main__":
   # read original image
   lowercase__						:      Optional[int]	=    imread('image_data/lena.jpg', 1)

   # convert to its negative
   lowercase__						:      Union[str, Any]	=    convert_to_negative(img)

   # show result image
   imshow('negative of original image', img)
   waitKey(0)
   destroyAllWindows()



