


"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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 logging


if is_vision_available():
     import PIL

lowerCAmelCase__				    =       logging.get_logger(__name__)
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  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 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Any       =["pixel_values"]

       def __init__(						self		,					snake_case__ = True		,					snake_case__ = None		,					snake_case__ = PILImageResampling.BILINEAR		,					snake_case__ = True		,					snake_case__ = None		,					snake_case__ = True		,					snake_case__ = 1 / 255		,					snake_case__ = True		,					snake_case__ = True		,					snake_case__ = None		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         super().__init__(**snake_case__						)
         lowerCAmelCase					:					Optional[Any]								=							size if size is not None else {"shortest_edge": 256}
         lowerCAmelCase					:					Tuple								=							get_size_dict(snake_case__		,					default_to_square=snake_case__						)
         lowerCAmelCase					:					Any								=							crop_size if crop_size is not None else {"height": 224, "width": 224}
         lowerCAmelCase					:					Optional[Any]								=							get_size_dict(snake_case__		,					param_name="crop_size"						)

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

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__ = PILImageResampling.BILINEAR		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							get_size_dict(snake_case__		,					default_to_square=snake_case__						)
         if "shortest_edge" in size:
           lowerCAmelCase					:					Optional[int]								=							get_resize_output_image_size(snake_case__		,					size["shortest_edge"]		,					default_to_square=snake_case__						)
         elif "height" in size and "width" in size:
           lowerCAmelCase					:					int								=							(size["height"], size["width"])
         else:
           raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""						)
         return resize(snake_case__		,					size=snake_case__		,					resample=snake_case__		,					data_format=snake_case__		,					**snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							get_size_dict(snake_case__						)
         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(snake_case__		,					size=(size["height"], size["width"])		,					data_format=snake_case__		,					**snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__ = True		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							image.astype(np.floataa						)
         if offset:
           lowerCAmelCase					:					Tuple								=							image - (scale / 2)
         return rescale(snake_case__		,					scale=snake_case__		,					data_format=snake_case__		,					**snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





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

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = ChannelDimension.FIRST		,					):






         """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."						)

         if offset and not do_rescale:
           raise ValueError("For offset, do_rescale must also be set to True."						)

         # All transformations expect numpy arrays.
         lowerCAmelCase					:					Tuple								=							to_numpy_array(snake_case__						)

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

         if do_center_crop:
           lowerCAmelCase					:					Union[str, Any]								=							self.center_crop(snake_case__		,					size=snake_case__						)

         if do_rescale:
           lowerCAmelCase					:					List[Any]								=							self.rescale(image=snake_case__		,					scale=snake_case__		,					offset=snake_case__						)

         if do_normalize:
           lowerCAmelCase					:					Tuple								=							self.normalize(image=snake_case__		,					mean=snake_case__		,					std=snake_case__						)

         lowerCAmelCase					:					Any								=							to_channel_dimension_format(snake_case__		,					snake_case__						)
         return image







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					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"""





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

         lowerCAmelCase					:					Optional[int]								=							size if size is not None else self.size
         lowerCAmelCase					:					Optional[Any]								=							get_size_dict(snake_case__		,					default_to_square=snake_case__						)
         lowerCAmelCase					:					Dict								=							crop_size if crop_size is not None else self.crop_size
         lowerCAmelCase					:					List[str]								=							get_size_dict(snake_case__		,					param_name="crop_size"						)

         if not valid_images(snake_case__						):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."						)

         lowerCAmelCase					:					List[str]								=							make_batched(snake_case__						)

         lowerCAmelCase					:					Optional[Any]								=							[
             [
                 self._preprocess_image(
                     image=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__		,					offset=snake_case__		,					do_normalize=snake_case__		,					image_mean=snake_case__		,					image_std=snake_case__		,					data_format=snake_case__		,					)
                 for img in video
             ]
             for video in videos
         ]

         lowerCAmelCase					:					Optional[Any]								=							{"pixel_values": videos}
         return BatchFeature(data=snake_case__		,					tensor_type=snake_case__						)









"""simple docstring"""







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
lowerCAmelCase__				    =       1.0_5_4_5_7_1_8_1_7e-3_4  # unit of ℏ : J * s

lowerCAmelCase__				    =       3e8  # unit of c : m * s^-1
def  a__      (    SCREAMING_SNAKE_CASE   :   float    ,				SCREAMING_SNAKE_CASE   :   float    ,				SCREAMING_SNAKE_CASE   :   float    ):


  '''simple docstring'''







  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:
    lowerCAmelCase					:					Any								=							(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
        2_4_0 * (distance) ** 4
    )
    return {"force": force}
  elif area == 0:
    lowerCAmelCase					:					Any								=							(2_4_0 * force * (distance) ** 4) / (
        REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
    )
    return {"area": area}
  elif distance == 0:
    lowerCAmelCase					:					Dict								=							(
        (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_4_0 * 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()





