



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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


a__    =	logging.get_logger(__name__)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     List[List[ImageInput]]:
	if isinstance(SCREAMING_SNAKE_CASE__				,       (list, tuple)				) and isinstance(videos[0]				,       (list, tuple)				) and is_valid_image(videos[0][0]				):
		return videos

	elif isinstance(SCREAMING_SNAKE_CASE__				,       (list, tuple)				) and is_valid_image(videos[0]				):
		return [videos]

	elif is_valid_image(SCREAMING_SNAKE_CASE__				):
		return [[videos]]

	raise ValueError(F'''Could not make batched video from {videos}'''				)







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



			'''simple docstring'''






			snake_case_					: Optional[int]          =		["""pixel_values"""]

			def __init__(					self							:			Union[str, Any]				,		lowerCAmelCase							:			bool = True				,		lowerCAmelCase							:			Dict[str, int] = None				,		lowerCAmelCase							:			PILImageResampling = PILImageResampling.BILINEAR				,		lowerCAmelCase							:			bool = True				,		lowerCAmelCase							:			Dict[str, int] = None				,		lowerCAmelCase							:			bool = True				,		lowerCAmelCase							:			Union[int, float] = 1 / 255				,		lowerCAmelCase							:			bool = True				,		lowerCAmelCase							:			Optional[Union[float, List[float]]] = None				,		lowerCAmelCase							:			Optional[Union[float, List[float]]] = None				,		**lowerCAmelCase							:			str				,		)				->				None:

				"""simple docstring"""







				super().__init__(**lowerCAmelCase)
				_snake_case    :  Tuple					=					size if size is not None else {"""shortest_edge""": 224}
				_snake_case    :  Optional[int]					=					get_size_dict(lowerCAmelCase				,		default_to_square=lowerCAmelCase)
				_snake_case    :  Optional[int]					=					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
				_snake_case    :  Optional[int]					=					get_size_dict(lowerCAmelCase				,		param_name="""crop_size""")

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

			def      UpperCamelCase_  (					self							:			Union[str, Any]				,		lowerCAmelCase							:			np.ndarray				,		lowerCAmelCase							:			Dict[str, int]				,		lowerCAmelCase							:			PILImageResampling = PILImageResampling.BILINEAR				,		lowerCAmelCase							:			Optional[Union[str, ChannelDimension]] = None				,		**lowerCAmelCase							:			Optional[Any]				,		)				->				np.ndarray:

				"""simple docstring"""







				_snake_case    :  Any					=					get_size_dict(lowerCAmelCase				,		default_to_square=lowerCAmelCase)
				if "shortest_edge" in size:
					_snake_case    :  List[str]					=					get_resize_output_image_size(lowerCAmelCase				,		size["""shortest_edge"""]				,		default_to_square=lowerCAmelCase)
				elif "height" in size and "width" in size:
					_snake_case    :  Dict					=					(size["""height"""], size["""width"""])
				else:
					raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''')
				return resize(lowerCAmelCase				,		size=lowerCAmelCase				,		resample=lowerCAmelCase				,		data_format=lowerCAmelCase				,		**lowerCAmelCase)

			def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			np.ndarray				,		lowerCAmelCase							:			Dict[str, int]				,		lowerCAmelCase							:			Optional[Union[str, ChannelDimension]] = None				,		**lowerCAmelCase							:			Optional[int]				,		)				->				np.ndarray:

				"""simple docstring"""







				_snake_case    :  int					=					get_size_dict(lowerCAmelCase)
				if "height" not in size or "width" not in size:
					raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''')
				return center_crop(lowerCAmelCase				,		size=(size["""height"""], size["""width"""])				,		data_format=lowerCAmelCase				,		**lowerCAmelCase)

			def      UpperCamelCase_  (					self							:			Union[str, Any]				,		lowerCAmelCase							:			np.ndarray				,		lowerCAmelCase							:			Union[int, float]				,		lowerCAmelCase							:			Optional[Union[str, ChannelDimension]] = None				,		**lowerCAmelCase							:			int				,		)				->				Dict:

				"""simple docstring"""







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

			def      UpperCamelCase_  (					self							:			Any				,		lowerCAmelCase							:			np.ndarray				,		lowerCAmelCase							:			Union[float, List[float]]				,		lowerCAmelCase							:			Union[float, List[float]]				,		lowerCAmelCase							:			Optional[Union[str, ChannelDimension]] = None				,		**lowerCAmelCase							:			Union[str, Any]				,		)				->				np.ndarray:

				"""simple docstring"""







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

			def      UpperCamelCase_  (					self							:			Dict				,		lowerCAmelCase							:			ImageInput				,		lowerCAmelCase							:			bool = None				,		lowerCAmelCase							:			Dict[str, int] = None				,		lowerCAmelCase							:			PILImageResampling = None				,		lowerCAmelCase							:			bool = None				,		lowerCAmelCase							:			Dict[str, int] = None				,		lowerCAmelCase							:			bool = None				,		lowerCAmelCase							:			float = None				,		lowerCAmelCase							:			bool = None				,		lowerCAmelCase							:			Optional[Union[float, List[float]]] = None				,		lowerCAmelCase							:			Optional[Union[float, List[float]]] = None				,		lowerCAmelCase							:			Optional[ChannelDimension] = ChannelDimension.FIRST				,		)				->				np.ndarray:

				"""simple docstring"""







				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    :  Tuple					=					to_numpy_array(lowerCAmelCase)

				if do_resize:
					_snake_case    :  Any					=					self.resize(image=lowerCAmelCase				,		size=lowerCAmelCase				,		resample=lowerCAmelCase)

				if do_center_crop:
					_snake_case    :  List[str]					=					self.center_crop(lowerCAmelCase				,		size=lowerCAmelCase)

				if do_rescale:
					_snake_case    :  str					=					self.rescale(image=lowerCAmelCase				,		scale=lowerCAmelCase)

				if do_normalize:
					_snake_case    :  List[str]					=					self.normalize(image=lowerCAmelCase				,		mean=lowerCAmelCase				,		std=lowerCAmelCase)

				_snake_case    :  Tuple					=					to_channel_dimension_format(lowerCAmelCase				,		lowerCAmelCase)
				return image


			def      UpperCamelCase_  (					self							:			int				,		lowerCAmelCase							:			ImageInput				,		lowerCAmelCase							:			bool = None				,		lowerCAmelCase							:			Dict[str, int] = None				,		lowerCAmelCase							:			PILImageResampling = None				,		lowerCAmelCase							:			bool = None				,		lowerCAmelCase							:			Dict[str, int] = None				,		lowerCAmelCase							:			bool = None				,		lowerCAmelCase							:			float = None				,		lowerCAmelCase							:			bool = None				,		lowerCAmelCase							:			Optional[Union[float, List[float]]] = None				,		lowerCAmelCase							:			Optional[Union[float, List[float]]] = None				,		lowerCAmelCase							:			Optional[Union[str, TensorType]] = None				,		lowerCAmelCase							:			ChannelDimension = ChannelDimension.FIRST				,		**lowerCAmelCase							:			List[Any]				,		)				->				PIL.Image.Image:

				"""simple docstring"""







				_snake_case    :  Any					=					do_resize if do_resize is not None else self.do_resize
				_snake_case    :  Any					=					resample if resample is not None else self.resample
				_snake_case    :  Optional[int]					=					do_center_crop if do_center_crop is not None else self.do_center_crop
				_snake_case    :  Dict					=					do_rescale if do_rescale is not None else self.do_rescale
				_snake_case    :  Tuple					=					rescale_factor if rescale_factor is not None else self.rescale_factor
				_snake_case    :  List[str]					=					do_normalize if do_normalize is not None else self.do_normalize
				_snake_case    :  List[Any]					=					image_mean if image_mean is not None else self.image_mean
				_snake_case    :  Dict					=					image_std if image_std is not None else self.image_std

				_snake_case    :  Any					=					size if size is not None else self.size
				_snake_case    :  Tuple					=					get_size_dict(lowerCAmelCase				,		default_to_square=lowerCAmelCase)
				_snake_case    :  str					=					crop_size if crop_size is not None else self.crop_size
				_snake_case    :  Union[str, Any]					=					get_size_dict(lowerCAmelCase				,		param_name="""crop_size""")

				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.""")

				_snake_case    :  Optional[Any]					=					make_batched(lowerCAmelCase)

				_snake_case    :  Dict					=					[
				    [
				        self._preprocess_image(
				            image=lowerCAmelCase				,		do_resize=lowerCAmelCase				,		size=lowerCAmelCase				,		resample=lowerCAmelCase				,		do_center_crop=lowerCAmelCase				,		crop_size=lowerCAmelCase				,		do_rescale=lowerCAmelCase				,		rescale_factor=lowerCAmelCase				,		do_normalize=lowerCAmelCase				,		image_mean=lowerCAmelCase				,		image_std=lowerCAmelCase				,		data_format=lowerCAmelCase				,		)
				        for img in video
				    ]
				    for video in videos
				]

				_snake_case    :  int					=					{"""pixel_values""": videos}
				return BatchFeature(data=lowerCAmelCase				,		tensor_type=lowerCAmelCase)





from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
a__    =	1.0_54_57_18_17E-34  # unit of ℏ : J * s

a__    =	3E8  # unit of c : m * s^-1



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	float				,       SCREAMING_SNAKE_CASE__       :	float				,       SCREAMING_SNAKE_CASE__       :	float				)  ->     dict[str, float]:

 if (force, area, distance).count(0				) != 1:
  raise ValueError("""One and only one argument must be 0"""				)
 if force < 0:
  raise ValueError("""Magnitude of force can not be negative"""				)
 if distance < 0:
  raise ValueError("""Distance can not be negative"""				)
 if area < 0:
  raise ValueError("""Area can not be negative"""				)
 if force == 0:
  _snake_case    :  Union[str, Any]					=					(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
      240 * (distance) ** 4
  )
  return {"force": force}
 elif area == 0:
  _snake_case    :  Optional[Any]					=					(240 * force * (distance) ** 4) / (
      REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
  )
  return {"area": area}
 elif distance == 0:
  _snake_case    :  str					=					(
      (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
  ) ** (1 / 4)
  return {"distance": distance}
 raise ValueError("""One and only one argument must be 0"""				)


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

  doctest.testmod()
