


from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_snake_case  :	Any			      =       logging.get_logger(__name__)

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							str  						=						["pixel_values"]

							def __init__(							self :						Any					,			lowerCamelCase :						bool = True					,			lowerCamelCase :						Union[int, float] = 1 / 255					,			lowerCamelCase :						bool = True					,			lowerCamelCase :						int = 8					,			**lowerCamelCase :						List[str]					,			)			->					None:
									super().__init__(**lowerCamelCase       )

									__snake_case	:    str											=     do_rescale
									__snake_case	:    Union[str, Any]											=     rescale_factor
									__snake_case	:    Optional[Any]											=     do_pad
									__snake_case	:    Optional[int]											=     pad_size

							def 					__snake_case      (							self :						List[Any]					,			lowerCamelCase :						np.ndarray					,			lowerCamelCase :						float					,			lowerCamelCase :						Optional[Union[str, ChannelDimension]] = None					,			**lowerCamelCase :						Union[str, Any]       )			->					np.ndarray:
									return rescale(lowerCamelCase					,			scale=lowerCamelCase					,			data_format=lowerCamelCase					,			**lowerCamelCase       )

							def 					__snake_case      (							self :						List[Any]					,			lowerCamelCase :						np.ndarray					,			lowerCamelCase :						int					,			lowerCamelCase :						Optional[Union[str, ChannelDimension]] = None       )			->					Optional[Any]:
									__snake_case	:    int											=     get_image_size(lowerCamelCase       )
									__snake_case	:    str											=     (old_height // size + 1) * size - old_height
									__snake_case	:    Optional[Any]											=     (old_width // size + 1) * size - old_width

									return pad(lowerCamelCase					,			((0, pad_height), (0, pad_width))					,			mode="symmetric"					,			data_format=lowerCamelCase       )






							def 					__snake_case      (							self :						Union[str, Any]					,			lowerCamelCase :						ImageInput					,			lowerCamelCase :						Optional[bool] = None					,			lowerCamelCase :						Optional[float] = None					,			lowerCamelCase :						Optional[bool] = None					,			lowerCamelCase :						Optional[int] = None					,			lowerCamelCase :						Optional[Union[str, TensorType]] = None					,			lowerCamelCase :						Union[str, ChannelDimension] = ChannelDimension.FIRST					,			**lowerCamelCase :						Optional[int]					,			)			->					List[Any]:
									__snake_case	:    str											=     do_rescale if do_rescale is not None else self.do_rescale
									__snake_case	:    List[str]											=     rescale_factor if rescale_factor is not None else self.rescale_factor
									__snake_case	:    Union[str, Any]											=     do_pad if do_pad is not None else self.do_pad
									__snake_case	:    str											=     pad_size if pad_size is not None else self.pad_size

									__snake_case	:    str											=     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_rescale and rescale_factor is None:
											raise ValueError("Rescale factor must be specified if do_rescale is True."       )

									# All transformations expect numpy arrays.
									__snake_case	:    Union[str, Any]											=     [to_numpy_array(lowerCamelCase       ) for image in images]

									if do_rescale:
											__snake_case	:    Any											=     [self.rescale(image=lowerCamelCase					,			scale=lowerCamelCase       ) for image in images]

									if do_pad:
											__snake_case	:    List[Any]											=     [self.pad(lowerCamelCase					,			size=lowerCamelCase       ) for image in images]

									__snake_case	:    List[str]											=     [to_channel_dimension_format(lowerCamelCase					,			lowerCamelCase       ) for image in images]

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





import math







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):

							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 > 3_6_0:
														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(__lowerCamelCase			)			) ** 2)


if __name__ == "__main__":
			import doctest

			doctest.testmod(name="malus_law")

