'''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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


snake_case__	:     Optional[int]      =    logging.get_logger(__name__)


if is_vision_available():
 import PIL






class 	__SCREAMING_SNAKE_CASE		(			_a			):







  '''simple docstring'''






  lowerCamelCase_						:int		       =						["""pixel_values"""]


  def __init__(					self ,						snake_case_ = True ,						snake_case_ = None ,						snake_case_ = PILImageResampling.BILINEAR ,						snake_case_ = True ,						snake_case_ = False ,						snake_case_ = True ,						snake_case_ = True ,						snake_case_ = 1 / 2_5_5 ,						snake_case_ = True ,						snake_case_ = None ,						snake_case_ = None ,						**snake_case_ ,						):




       '''simple docstring'''

       super().__init__(**__lowerCamelCase		)

       UpperCAmelCase_				:  List[str]  = size if size is not None else {"""height""": 2_5_6_0, """width""": 1_9_2_0}
       if isinstance(__lowerCamelCase ,						(tuple, list)		):
            # The previous feature extractor size parameter was in (width, height) format
            UpperCAmelCase_				:  Tuple  = size[::-1]
       UpperCAmelCase_				:  Any  = get_size_dict(__lowerCamelCase		)

       UpperCAmelCase_				:  Union[str, Any]  = do_resize
       UpperCAmelCase_				:  Optional[Any]  = size
       UpperCAmelCase_				:  List[str]  = resample
       UpperCAmelCase_				:  Optional[Any]  = do_thumbnail
       UpperCAmelCase_				:  List[str]  = do_align_long_axis
       UpperCAmelCase_				:  Tuple  = do_pad
       UpperCAmelCase_				:  Union[str, Any]  = do_rescale
       UpperCAmelCase_				:  Optional[Any]  = rescale_factor
       UpperCAmelCase_				:  Union[str, Any]  = do_normalize
       UpperCAmelCase_				:  Optional[int]  = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCAmelCase_				:  List[str]  = image_std if image_std is not None else IMAGENET_STANDARD_STD


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ = None		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = get_image_size(__lowerCamelCase		)
       UpperCAmelCase_				:  Union[str, Any]  = size["""height"""], size["""width"""]

       if (output_width < output_height and input_width > input_height) or (
           output_width > output_height and input_width < input_height
       ):
            UpperCAmelCase_				:  Any  = np.rotaa(__lowerCamelCase ,						3		)

       if data_format is not None:
            UpperCAmelCase_				:  Union[str, Any]  = to_channel_dimension_format(__lowerCamelCase ,						__lowerCamelCase		)

       return image


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ = False ,						snake_case_ = None ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = size["""height"""], size["""width"""]
       UpperCAmelCase_				:  Optional[int]  = get_image_size(__lowerCamelCase		)

       UpperCAmelCase_				:  int  = output_width - input_width
       UpperCAmelCase_				:  Any  = output_height - input_height

       if random_padding:
            UpperCAmelCase_				:  Any  = np.random.randint(low=0 ,						high=delta_height + 1		)
            UpperCAmelCase_				:  int  = np.random.randint(low=0 ,						high=delta_width + 1		)
       else:
            UpperCAmelCase_				:  Union[str, Any]  = delta_height // 2
            UpperCAmelCase_				:  Optional[int]  = delta_width // 2

       UpperCAmelCase_				:  List[str]  = delta_height - pad_top
       UpperCAmelCase_				:  Union[str, Any]  = delta_width - pad_left

       UpperCAmelCase_				:  Any  = ((pad_top, pad_bottom), (pad_left, pad_right))
       return pad(__lowerCamelCase ,						__lowerCamelCase ,						data_format=__lowerCamelCase		)


  def _UpperCamelCase	(					self ,						*snake_case_ ,						**snake_case_		):




       '''simple docstring'''

       logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'		)
       return self.pad_image(*__lowerCamelCase ,						**__lowerCamelCase		)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ = PILImageResampling.BICUBIC ,						snake_case_ = None ,						**snake_case_ ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = get_image_size(__lowerCamelCase		)
       UpperCAmelCase_				:  str  = size["""height"""], size["""width"""]

       # We always resize to the smallest of either the input or output size.
       UpperCAmelCase_				:  Dict  = min(__lowerCamelCase ,						__lowerCamelCase		)
       UpperCAmelCase_				:  Optional[int]  = min(__lowerCamelCase ,						__lowerCamelCase		)

       if height == input_height and width == input_width:
            return image

       if input_height > input_width:
            UpperCAmelCase_				:  Dict  = int(input_width * height / input_height		)
       elif input_width > input_height:
            UpperCAmelCase_				:  Dict  = int(input_height * width / input_width		)

       return resize(
           __lowerCamelCase ,						size=(height, width) ,						resample=__lowerCamelCase ,						reducing_gap=2.0 ,						data_format=__lowerCamelCase ,						**__lowerCamelCase		)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ = PILImageResampling.BICUBIC ,						snake_case_ = None ,						**snake_case_ ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = get_size_dict(__lowerCamelCase		)
       UpperCAmelCase_				:  Dict  = min(size['height'] ,						size['width']		)
       UpperCAmelCase_				:  Optional[Any]  = get_resize_output_image_size(__lowerCamelCase ,						size=__lowerCamelCase ,						default_to_square=__lowerCamelCase		)
       UpperCAmelCase_				:  Dict  = resize(__lowerCamelCase ,						size=__lowerCamelCase ,						resample=__lowerCamelCase ,						data_format=__lowerCamelCase ,						**__lowerCamelCase		)
       return resized_image


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ = None ,						**snake_case_ ,						):




       '''simple docstring'''

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


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ = None ,						**snake_case_ ,						):




       '''simple docstring'''

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






  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = False ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = ChannelDimension.FIRST ,						**snake_case_ ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = do_resize if do_resize is not None else self.do_resize
       UpperCAmelCase_				:  List[Any]  = size if size is not None else self.size
       if isinstance(__lowerCamelCase ,						(tuple, list)		):
            # Previous feature extractor had size in (width, height) format
            UpperCAmelCase_				:  Union[str, Any]  = size[::-1]
       UpperCAmelCase_				:  List[str]  = get_size_dict(__lowerCamelCase		)
       UpperCAmelCase_				:  Optional[int]  = resample if resample is not None else self.resample
       UpperCAmelCase_				:  Dict  = do_thumbnail if do_thumbnail is not None else self.do_thumbnail
       UpperCAmelCase_				:  Tuple  = do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
       UpperCAmelCase_				:  int  = do_pad if do_pad is not None else self.do_pad
       UpperCAmelCase_				:  Any  = do_rescale if do_rescale is not None else self.do_rescale
       UpperCAmelCase_				:  Dict  = rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCAmelCase_				:  Dict  = do_normalize if do_normalize is not None else self.do_normalize
       UpperCAmelCase_				:  List[Any]  = image_mean if image_mean is not None else self.image_mean
       UpperCAmelCase_				:  List[Any]  = image_std if image_std is not None else self.image_std

       UpperCAmelCase_				:  List[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_resize and size is None:
            raise ValueError('Size must be specified if do_resize is True.'		)

       if do_rescale and rescale_factor is None:
            raise ValueError('Rescale factor must be specified if do_rescale is True.'		)

       if do_pad and size is None:
            raise ValueError('Size must be specified if do_pad 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.
       UpperCAmelCase_				:  Optional[Any]  = [to_numpy_array(__lowerCamelCase		) for image in images]

       if do_align_long_axis:
            UpperCAmelCase_				:  Union[str, Any]  = [self.align_long_axis(__lowerCamelCase ,						size=__lowerCamelCase		) for image in images]

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

       if do_thumbnail:
            UpperCAmelCase_				:  Dict  = [self.thumbnail(image=__lowerCamelCase ,						size=__lowerCamelCase		) for image in images]

       if do_pad:
            UpperCAmelCase_				:  Any  = [self.pad_image(image=__lowerCamelCase ,						size=__lowerCamelCase ,						random_padding=__lowerCamelCase		) for image in images]

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

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

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

       UpperCAmelCase_				:  Optional[Any]  = {"""pixel_values""": images}
       return BatchFeature(data=__lowerCamelCase ,						tensor_type=__lowerCamelCase		)














from __future__ import annotations

# Define the Gravitational Constant G and the function
UpperCAmelCase_					:		Union[str, Any]           = 6.6_743E-11  # unit of G : m^3 * kg^-1 * s^-2

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    float      ,     __magic_name__   :    float      ,     __magic_name__   :    float      ,     __magic_name__   :    float   )						->     dict[str, float]:






	"""simple docstring"""



	UpperCamelCase	:List[str]		=      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:
		UpperCamelCase	:Optional[int]		=      GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
		return {"force": force}
	elif mass_a == 0:
		UpperCamelCase	:Any		=      (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
		return {"mass_1": mass_a}
	elif mass_a == 0:
		UpperCamelCase	:Optional[int]		=      (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
		return {"mass_2": mass_a}
	elif distance == 0:
		UpperCamelCase	:str		=      (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()
