
'''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			:  Tuple   							=	logging.get_logger(__name__)


if is_vision_available():
							import PIL






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					['''pixel_values''']






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = 1 / 2_55    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = True    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											super().__init__(**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[int]													=	size if size is not None else {'''shortest_edge''': 2_24}
											lowercase_     :					Any													=	get_size_dict(__SCREAMING_SNAKE_CASE    ,							default_to_square=__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Union[str, Any]													=	crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
											lowercase_     :					Union[str, Any]													=	get_size_dict(__SCREAMING_SNAKE_CASE    ,							default_to_square=__SCREAMING_SNAKE_CASE    ,							param_name='''crop_size'''		)

											lowercase_     :					Optional[Any]													=	do_resize
											lowercase_     :					int													=	size
											lowercase_     :					Tuple													=	resample
											lowercase_     :					str													=	do_center_crop
											lowercase_     :					Optional[Any]													=	crop_size
											lowercase_     :					Dict													=	do_rescale
											lowercase_     :					Union[str, Any]													=	rescale_factor
											lowercase_     :					Optional[int]													=	do_normalize
											lowercase_     :					Tuple													=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
											lowercase_     :					List[str]													=	image_std if image_std is not None else OPENAI_CLIP_STD
											lowercase_     :					List[str]													=	do_convert_rgb






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


											"""simple docstring"""





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






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	get_size_dict(__SCREAMING_SNAKE_CASE		)
											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(__SCREAMING_SNAKE_CASE    ,							size=(size['''height'''], size['''width'''])    ,							data_format=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





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






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





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






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


											"""simple docstring"""





											lowercase_     :					int													=	do_resize if do_resize is not None else self.do_resize
											lowercase_     :					Optional[Any]													=	size if size is not None else self.size
											lowercase_     :					int													=	get_size_dict(__SCREAMING_SNAKE_CASE    ,							param_name='''size'''    ,							default_to_square=__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Union[str, Any]													=	resample if resample is not None else self.resample
											lowercase_     :					str													=	do_center_crop if do_center_crop is not None else self.do_center_crop
											lowercase_     :					str													=	crop_size if crop_size is not None else self.crop_size
											lowercase_     :					Any													=	get_size_dict(__SCREAMING_SNAKE_CASE    ,							param_name='''crop_size'''    ,							default_to_square=__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Dict													=	do_rescale if do_rescale is not None else self.do_rescale
											lowercase_     :					Dict													=	rescale_factor if rescale_factor is not None else self.rescale_factor
											lowercase_     :					Dict													=	do_normalize if do_normalize is not None else self.do_normalize
											lowercase_     :					str													=	image_mean if image_mean is not None else self.image_mean
											lowercase_     :					Optional[Any]													=	image_std if image_std is not None else self.image_std
											lowercase_     :					Optional[int]													=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											lowercase_     :					Any													=	make_list_of_images(__SCREAMING_SNAKE_CASE		)

											if not valid_images(__SCREAMING_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.'''		)

											# PIL RGBA images are converted to RGB
											if do_convert_rgb:
																	lowercase_     :					Optional[Any]													=	[convert_to_rgb(__SCREAMING_SNAKE_CASE		) for image in images]

											# All transformations expect numpy arrays.
											lowercase_     :					Optional[int]													=	[to_numpy_array(__SCREAMING_SNAKE_CASE		) for image in images]

											if do_resize:
																	lowercase_     :					Optional[int]													=	[self.resize(image=__SCREAMING_SNAKE_CASE    ,							size=__SCREAMING_SNAKE_CASE    ,							resample=__SCREAMING_SNAKE_CASE		) for image in images]

											if do_center_crop:
																	lowercase_     :					List[Any]													=	[self.center_crop(image=__SCREAMING_SNAKE_CASE    ,							size=__SCREAMING_SNAKE_CASE		) for image in images]

											if do_rescale:
																	lowercase_     :					str													=	[self.rescale(image=__SCREAMING_SNAKE_CASE    ,							scale=__SCREAMING_SNAKE_CASE		) for image in images]

											if do_normalize:
																	lowercase_     :					Optional[Any]													=	[self.normalize(image=__SCREAMING_SNAKE_CASE    ,							mean=__SCREAMING_SNAKE_CASE    ,							std=__SCREAMING_SNAKE_CASE		) for image in images]

											lowercase_     :					List[Any]													=	[to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) for image in images]

											lowercase_     :					int													=	{'''pixel_values''': images}
											return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,							tensor_type=__SCREAMING_SNAKE_CASE		)





'''simple docstring'''







from PIL import Image
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Image				,				__SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	(259 * (level + 255)) / (255 * (259 - level))

						def contrast(__SCREAMING_SNAKE_CASE :			int							) -> int:
												return int(128 + factor * (c - 128)							)

						return img.point(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							# Load image
							with Image.open("image_data/lena.jpg") as img:
														# Change contrast to 170
														_lowercase			:  Union[str, Any]   							=	change_contrast(img, 1_7_0)
														cont_img.save("image_data/lena_high_contrast.png", format="png")



