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


_SCREAMING_SNAKE_CASE:    List[str]     						=							logging.get_logger(__name__)

class 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		A_    			=   ['''pixel_values''']



		def __init__(      self     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int] = None	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = PIL.Image.NEAREST	,       __SCREAMING_SNAKE_CASE     :					bool = False	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int] = None	,       __SCREAMING_SNAKE_CASE     :					Union[int, float] = 1 / 255	,       __SCREAMING_SNAKE_CASE     :					bool = False	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       **__SCREAMING_SNAKE_CASE     :					Optional[int]	,       ):






					"""simple docstring"""
					super().__init__(**__SCREAMING_SNAKE_CASE )
					_snake_case														=   size if size is not None else {"height": 346, "width": 346}
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					_snake_case														=   crop_size if crop_size is not None else {"height": 289, "width": 289}
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE	,       param_name="crop_size" )

					_snake_case														=   do_resize
					_snake_case														=   size
					_snake_case														=   resample
					_snake_case														=   do_center_crop
					_snake_case														=   crop_size
					_snake_case														=   do_rescale
					_snake_case														=   rescale_factor
					_snake_case														=   rescale_offset
					_snake_case														=   do_normalize
					_snake_case														=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					_snake_case														=   image_std if image_std is not None else IMAGENET_STANDARD_STD
					_snake_case														=   include_top



		def 					_a					(      self     :					Dict	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int]	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = PIL.Image.NEAREST	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Any	,       ):






					"""simple docstring"""
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					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(
					    __SCREAMING_SNAKE_CASE	,       size=(size["height"], size["width"])	,       resample=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int]	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					int	,       ):






					"""simple docstring"""
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					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(__SCREAMING_SNAKE_CASE	,       size=(size["height"], size["width"])	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Dict	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Union[int, float]	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Tuple	,       ):






					"""simple docstring"""
					if offset:
								_snake_case														=   (image - 1_2_7.5) * scale
								if data_format is not None:
											_snake_case														=   to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
								_snake_case														=   rescaled_image.astype(np.floataa )
					else:
								_snake_case														=   rescale(__SCREAMING_SNAKE_CASE	,       scale=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )
					return rescaled_image



		def 					_a					(      self     :					Dict	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Union[float, List[float]]	,       __SCREAMING_SNAKE_CASE     :					Union[float, List[float]]	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Union[str, Any]	,       ):






					"""simple docstring"""
					return normalize(__SCREAMING_SNAKE_CASE	,       mean=__SCREAMING_SNAKE_CASE	,       std=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )







		def 					_a					(      self     :					List[Any]	,       __SCREAMING_SNAKE_CASE     :					ImageInput	,       __SCREAMING_SNAKE_CASE     :					bool = None	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int] = None	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=None	,       __SCREAMING_SNAKE_CASE     :					bool = None	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int] = None	,       __SCREAMING_SNAKE_CASE     :					bool = None	,       __SCREAMING_SNAKE_CASE     :					float = None	,       __SCREAMING_SNAKE_CASE     :					bool = None	,       __SCREAMING_SNAKE_CASE     :					bool = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					bool = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, TensorType]] = None	,       __SCREAMING_SNAKE_CASE     :					ChannelDimension = ChannelDimension.FIRST	,       **__SCREAMING_SNAKE_CASE     :					int	,       ):






					"""simple docstring"""
					_snake_case														=   do_resize if do_resize is not None else self.do_resize
					_snake_case														=   resample if resample is not None else self.resample
					_snake_case														=   do_center_crop if do_center_crop is not None else self.do_center_crop
					_snake_case														=   do_rescale if do_rescale is not None else self.do_rescale
					_snake_case														=   rescale_factor if rescale_factor is not None else self.rescale_factor
					_snake_case														=   rescale_offset if rescale_offset is not None else self.rescale_offset
					_snake_case														=   do_normalize if do_normalize is not None else self.do_normalize
					_snake_case														=   image_mean if image_mean is not None else self.image_mean
					_snake_case														=   image_std if image_std is not None else self.image_std
					_snake_case														=   include_top if include_top is not None else self.include_top

					_snake_case														=   size if size is not None else self.size
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					_snake_case														=   crop_size if crop_size is not None else self.crop_size
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE	,       param_name="crop_size" )

					_snake_case														=   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 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.
					_snake_case														=   [to_numpy_array(__SCREAMING_SNAKE_CASE ) for image in images]

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

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

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

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

					if include_top:
								_snake_case														=   [self.normalize(image=__SCREAMING_SNAKE_CASE	,       mean=[0, 0, 0]	,       std=__SCREAMING_SNAKE_CASE ) for image in images]

					_snake_case														=   [to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) for image in images]

					_snake_case														=   {"pixel_values": images}
					return BatchFeature(data=__SCREAMING_SNAKE_CASE	,       tensor_type=__SCREAMING_SNAKE_CASE )


from __future__ import annotations

# Define the Gravitational Constant G and the function
_SCREAMING_SNAKE_CASE:    int     						=							6.6_743e-11  # unit of G : m^3 * kg^-1 * s^-2






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   mass_a * mass_a

			if (force, mass_a, mass_a, distance).count(0	) != 1:
						raise ValueError("One and only one argument must be 0"	)
			if force < 0:
						raise ValueError("Gravitational force can not be negative"	)
			if distance < 0:
						raise ValueError("Distance can not be negative"	)
			if mass_a < 0 or mass_a < 0:
						raise ValueError("Mass can not be negative"	)
			if force == 0:
						_snake_case														=   GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
						return {"force": force}
			elif mass_a == 0:
						_snake_case														=   (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
						return {"mass_1": mass_a}
			elif mass_a == 0:
						_snake_case														=   (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
						return {"mass_2": mass_a}
			elif distance == 0:
						_snake_case														=   (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
						return {"distance": distance}
			raise ValueError("One and only one argument must be 0"	)


# Run doctest
if __name__ == "__main__":
			import doctest

			doctest.testmod()

