

"""simple docstring"""





import math



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							float    ,						SCREAMING_SNAKE_CASE       :							float   ):


							'''simple docstring'''







							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(SCREAMING_SNAKE_CASE   )   ) ** 2)


if __name__ == "__main__":
							import doctest

							doctest.testmod(name='malus_law')











"""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, 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, is_vision_available, logging


if is_vision_available():
							import PIL


_UpperCamelCase							=      logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       List[str]	 =		["""pixel_values"""]




				def __init__(     self  :List[str]						,    __lowercase  :bool = True						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :PILImageResampling = PIL.Image.BICUBIC						,    __lowercase  :bool = True						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :Union[int, float] = 1 / 255						,    __lowercase  :bool = True						,    __lowercase  :bool = True						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    **__lowercase  :Union[str, Any]						,    ):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      Dict         =size if size is not None else {'''height''': 256, '''width''': 256}
											__lowerCamelCase      :      Union[str, Any]         =get_size_dict(__lowercase			)
											__lowerCamelCase      :      Tuple         =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
											__lowerCamelCase      :      int         =get_size_dict(__lowercase						,    param_name='''crop_size'''			)

											__lowerCamelCase      :      Tuple         =do_resize
											__lowerCamelCase      :      int         =size
											__lowerCamelCase      :      Union[str, Any]         =resample
											__lowerCamelCase      :      Dict         =do_center_crop
											__lowerCamelCase      :      Any         =crop_size
											__lowerCamelCase      :      Dict         =do_rescale
											__lowerCamelCase      :      List[Any]         =rescale_factor
											__lowerCamelCase      :      Any         =do_normalize
											__lowerCamelCase      :      int         =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase      :      List[str]         =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :np.ndarray						,    __lowercase  :Dict[str, int]						,    __lowercase  :PILImageResampling = PIL.Image.BICUBIC						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :Optional[int]						,    ):
											__lowerCamelCase      :      Optional[int]         =get_size_dict(__lowercase			)
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
											return resize(
											    __lowercase						,    size=(size['''height'''], size['''width'''])						,    resample=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :np.ndarray						,    __lowercase  :Dict[str, int]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :Optional[Any]						,    ):
											__lowerCamelCase      :      Dict         =get_size_dict(__lowercase			)
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
											return center_crop(__lowercase						,    size=(size['''height'''], size['''width'''])						,    data_format=__lowercase						,    **__lowercase			)




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




				def 					__lowercase  (     self  :Dict						,    __lowercase  :np.ndarray						,    __lowercase  :Union[float, List[float]]						,    __lowercase  :Union[float, List[float]]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :int						,    ):
											return normalize(__lowercase						,    mean=__lowercase						,    std=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :ImageInput						,    __lowercase  :bool = None						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :Optional[Any]=None						,    __lowercase  :bool = None						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :bool = None						,    __lowercase  :float = None						,    __lowercase  :bool = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[str, TensorType]] = None						,    __lowercase  :ChannelDimension = ChannelDimension.FIRST						,    **__lowercase  :Any						,    ):
											__lowerCamelCase      :      Optional[Any]         =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase      :      List[Any]         =resample if resample is not None else self.resample
											__lowerCamelCase      :      Optional[int]         =do_center_crop if do_center_crop is not None else self.do_center_crop
											__lowerCamelCase      :      List[str]         =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase      :      Optional[Any]         =rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCamelCase      :      Optional[int]         =do_normalize if do_normalize is not None else self.do_normalize
											__lowerCamelCase      :      Dict         =image_mean if image_mean is not None else self.image_mean
											__lowerCamelCase      :      Union[str, Any]         =image_std if image_std is not None else self.image_std

											__lowerCamelCase      :      Dict         =size if size is not None else self.size
											__lowerCamelCase      :      int         =get_size_dict(__lowercase			)
											__lowerCamelCase      :      List[Any]         =crop_size if crop_size is not None else self.crop_size
											__lowerCamelCase      :      Optional[int]         =get_size_dict(__lowercase						,    param_name='''crop_size'''			)

											__lowerCamelCase      :      int         =make_list_of_images(__lowercase			)

											if not valid_images(__lowercase			):
																		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 or resample is None:
																		raise ValueError('''Size and resample 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.
											__lowerCamelCase      :      str         =[to_numpy_array(__lowercase			) for image in images]

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

											if do_center_crop:
																		__lowerCamelCase      :      Optional[Any]         =[self.center_crop(image=__lowercase						,    size=__lowercase			) for image in images]

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

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

											__lowerCamelCase      :      Tuple         =[to_channel_dimension_format(__lowercase						,    __lowercase			) for image in images]

											__lowerCamelCase      :      Any         ={'''pixel_values''': images}
											return BatchFeature(data=__lowercase						,    tensor_type=__lowercase			)




