"""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,
    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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_A   					=    logging.get_logger(__name__)

class lowerCamelCase		(__lowerCAmelCase      ):






					'''simple docstring'''


					a   	=		['''pixel_values''']



					def __init__(						self			:				List[Any]   , _snake_case			:				List[str] = True   , _snake_case			:				List[str] = None   , _snake_case			:				Dict = PILImageResampling.BICUBIC   , _snake_case			:				Tuple = True   , _snake_case			:				Union[str, Any] = True   , _snake_case			:				int = 1 / 255   , _snake_case			:				Dict = None   , _snake_case			:				Union[str, Any] = True   , _snake_case			:				str = None   , _snake_case			:				Dict = None   , **_snake_case			:				Any   , )							->       None:
									super().__init__(**lowerCAmelCase_		)
									SCREAMING_SNAKE_CASE__		  =				size if size is not None else {"height": 224, "width": 224}
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(lowerCAmelCase_		)
									SCREAMING_SNAKE_CASE__		  =				crop_size if crop_size is not None else {"height": 224, "width": 224}
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(lowerCAmelCase_   , default_to_square=lowerCAmelCase_   , param_name="crop_size"		)

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



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				str   , _snake_case			:				Union[str, Any]   , _snake_case			:				str = PILImageResampling.BILINEAR   , _snake_case			:				Union[str, Any] = None   , **_snake_case			:				List[str]   , )							->       np.ndarray:
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(lowerCAmelCase_		)

									if "shortest_edge" in size:
													SCREAMING_SNAKE_CASE__		  =				get_resize_output_image_size(lowerCAmelCase_   , size=size["shortest_edge"]   , default_to_square=lowerCAmelCase_		)
													# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
									elif "height" in size and "width" in size:
													SCREAMING_SNAKE_CASE__		  =				(size["height"], size["width"])
									else:
													raise ValueError(F"""Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}"""		)
									return resize(lowerCAmelCase_   , size=lowerCAmelCase_   , resample=lowerCAmelCase_   , data_format=lowerCAmelCase_   , **lowerCAmelCase_		)



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Tuple   , _snake_case			:				Optional[int] = None   , **_snake_case			:				str   , )							->       np.ndarray:
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(lowerCAmelCase_		)
									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(lowerCAmelCase_   , size=(size["height"], size["width"])   , data_format=lowerCAmelCase_   , **lowerCAmelCase_		)



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				Dict   , _snake_case			:				Tuple   , _snake_case			:				Optional[Any] = None   , **_snake_case			:				Dict		)							->       np.ndarray:
									return rescale(lowerCAmelCase_   , scale=lowerCAmelCase_   , data_format=lowerCAmelCase_   , **lowerCAmelCase_		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				int   , _snake_case			:				List[Any]   , _snake_case			:				int   , _snake_case			:				str = None   , **_snake_case			:				Dict   , )							->       np.ndarray:
									return normalize(lowerCAmelCase_   , mean=lowerCAmelCase_   , std=lowerCAmelCase_   , data_format=lowerCAmelCase_   , **lowerCAmelCase_		)



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				Union[str, Any]   , _snake_case			:				Union[str, Any] = None   , _snake_case			:				Optional[Any] = None   , _snake_case			:				str = None   , _snake_case			:				Any = None   , _snake_case			:				str = None   , _snake_case			:				int = None   , _snake_case			:				List[str] = None   , _snake_case			:				Optional[Any] = None   , _snake_case			:				Union[str, Any] = None   , _snake_case			:				List[str] = None   , _snake_case			:				Any = None   , _snake_case			:				List[Any] = ChannelDimension.FIRST   , **_snake_case			:				List[str]   , )							->       BatchFeature:
									SCREAMING_SNAKE_CASE__		  =				do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE__		  =				do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE__		  =				do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE__		  =				do_center_crop if do_center_crop is not None else self.do_center_crop
									SCREAMING_SNAKE_CASE__		  =				crop_size if crop_size is not None else self.crop_size
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(lowerCAmelCase_   , param_name="crop_size"   , default_to_square=lowerCAmelCase_		)
									SCREAMING_SNAKE_CASE__		  =				resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE__		  =				rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE__		  =				image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE__		  =				image_std if image_std is not None else self.image_std

									SCREAMING_SNAKE_CASE__		  =				size if size is not None else self.size
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(lowerCAmelCase_		)

									if not is_batched(lowerCAmelCase_		):
													SCREAMING_SNAKE_CASE__		  =				[images]

									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."		)

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

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

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

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

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

									SCREAMING_SNAKE_CASE__		  =				[to_channel_dimension_format(lowerCAmelCase_   , lowerCAmelCase_		) for image in images]

									SCREAMING_SNAKE_CASE__		  =				{"pixel_values": images}
									return BatchFeature(data=lowerCAmelCase_   , tensor_type=lowerCAmelCase_		)





"""simple docstring"""






import math




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   float:

				if initial_intensity < 0:
								raise ValueError("The value of intensity cannot be negative"       )
								# handling of negative values of initial intensity
				if angle < 0 or angle > 360:
								raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"       )
								# handling of values out of allowed range
				return initial_intensity * (math.cos(math.radians(__UpperCAmelCase       )       ) ** 2)


if __name__ == "__main__":
						import doctest

						doctest.testmod(name='malus_law')




