


"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__lowercase    					=      logging.get_logger(__name__)


if is_vision_available():
 import PIL






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							Dict	=       ["""pixel_values"""]






    def __init__(					self				:    List[str]				, __UpperCAmelCase				:    bool = True				, __UpperCAmelCase				:    Dict[str, int] = None				, __UpperCAmelCase				:    PILImageResampling = PILImageResampling.BICUBIC				, __UpperCAmelCase				:    bool = True				, __UpperCAmelCase				:    Dict[str, int] = None				, __UpperCAmelCase				:    bool = True				, __UpperCAmelCase				:    Union[int, float] = 1 / 255				, __UpperCAmelCase				:    bool = True				, __UpperCAmelCase				:    Optional[Union[float, List[float]]] = None				, __UpperCAmelCase				:    Optional[Union[float, List[float]]] = None				, __UpperCAmelCase				:    bool = True				, **__UpperCAmelCase				:    Optional[Any]				, ):
      super().__init__(**__UpperCAmelCase)
      a						:		Dict      							=			size if size is not None else {"shortest_edge": 224}
      a						:		Dict      							=			get_size_dict(__UpperCAmelCase				, default_to_square=__UpperCAmelCase)
      a						:		Union[str, Any]      							=			crop_size if crop_size is not None else {"height": 224, "width": 224}
      a						:		Optional[int]      							=			get_size_dict(__UpperCAmelCase				, default_to_square=__UpperCAmelCase				, param_name="crop_size")

      a						:		int      							=			do_resize
      a						:		List[Any]      							=			size
      a						:		Dict      							=			resample
      a						:		List[str]      							=			do_center_crop
      a						:		Any      							=			crop_size
      a						:		Dict      							=			do_rescale
      a						:		Optional[int]      							=			rescale_factor
      a						:		Tuple      							=			do_normalize
      a						:		int      							=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      a						:		Dict      							=			image_std if image_std is not None else OPENAI_CLIP_STD
      a						:		Union[str, Any]      							=			do_convert_rgb






    def 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    np.ndarray				, __UpperCAmelCase				:    Dict[str, int]				, __UpperCAmelCase				:    PILImageResampling = PILImageResampling.BICUBIC				, __UpperCAmelCase				:    Optional[Union[str, ChannelDimension]] = None				, **__UpperCAmelCase				:    List[Any]				, ):
      a						:		Optional[Any]      							=			get_size_dict(__UpperCAmelCase				, default_to_square=__UpperCAmelCase)
      if "shortest_edge" not in size:
        raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''')
      a						:		Any      							=			get_resize_output_image_size(__UpperCAmelCase				, size=size["shortest_edge"]				, default_to_square=__UpperCAmelCase)
      return resize(__UpperCAmelCase				, size=__UpperCAmelCase				, resample=__UpperCAmelCase				, data_format=__UpperCAmelCase				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Tuple				, __UpperCAmelCase				:    np.ndarray				, __UpperCAmelCase				:    Dict[str, int]				, __UpperCAmelCase				:    Optional[Union[str, ChannelDimension]] = None				, **__UpperCAmelCase				:    Union[str, Any]				, ):
      a						:		Tuple      							=			get_size_dict(__UpperCAmelCase)
      if "height" not in size or "width" not in size:
        raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''')
      return center_crop(__UpperCAmelCase				, size=(size["height"], size["width"])				, data_format=__UpperCAmelCase				, **__UpperCAmelCase)






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






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






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    ImageInput				, __UpperCAmelCase				:    bool = None				, __UpperCAmelCase				:    Dict[str, int] = None				, __UpperCAmelCase				:    PILImageResampling = None				, __UpperCAmelCase				:    bool = None				, __UpperCAmelCase				:    int = None				, __UpperCAmelCase				:    bool = None				, __UpperCAmelCase				:    float = None				, __UpperCAmelCase				:    bool = None				, __UpperCAmelCase				:    Optional[Union[float, List[float]]] = None				, __UpperCAmelCase				:    Optional[Union[float, List[float]]] = None				, __UpperCAmelCase				:    bool = None				, __UpperCAmelCase				:    Optional[Union[str, TensorType]] = None				, __UpperCAmelCase				:    Optional[ChannelDimension] = ChannelDimension.FIRST				, **__UpperCAmelCase				:    Union[str, Any]				, ):
      a						:		Optional[int]      							=			do_resize if do_resize is not None else self.do_resize
      a						:		Union[str, Any]      							=			size if size is not None else self.size
      a						:		Any      							=			get_size_dict(__UpperCAmelCase				, param_name="size"				, default_to_square=__UpperCAmelCase)
      a						:		Dict      							=			resample if resample is not None else self.resample
      a						:		Optional[Any]      							=			do_center_crop if do_center_crop is not None else self.do_center_crop
      a						:		Any      							=			crop_size if crop_size is not None else self.crop_size
      a						:		int      							=			get_size_dict(__UpperCAmelCase				, param_name="crop_size"				, default_to_square=__UpperCAmelCase)
      a						:		Optional[int]      							=			do_rescale if do_rescale is not None else self.do_rescale
      a						:		Optional[Any]      							=			rescale_factor if rescale_factor is not None else self.rescale_factor
      a						:		Optional[int]      							=			do_normalize if do_normalize is not None else self.do_normalize
      a						:		Optional[Any]      							=			image_mean if image_mean is not None else self.image_mean
      a						:		Union[str, Any]      							=			image_std if image_std is not None else self.image_std
      a						:		Tuple      							=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      a						:		Any      							=			make_list_of_images(__UpperCAmelCase)

      if not valid_images(__UpperCAmelCase):
        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_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.")

      # PIL RGBA images are converted to RGB
      if do_convert_rgb:
        a						:		Union[str, Any]      							=			[convert_to_rgb(__UpperCAmelCase) for image in images]

      # All transformations expect numpy arrays.
      a						:		List[str]      							=			[to_numpy_array(__UpperCAmelCase) for image in images]

      if do_resize:
        a						:		int      							=			[self.resize(image=__UpperCAmelCase				, size=__UpperCAmelCase				, resample=__UpperCAmelCase) for image in images]

      if do_center_crop:
        a						:		List[str]      							=			[self.center_crop(image=__UpperCAmelCase				, size=__UpperCAmelCase) for image in images]

      if do_rescale:
        a						:		List[str]      							=			[self.rescale(image=__UpperCAmelCase				, scale=__UpperCAmelCase) for image in images]

      if do_normalize:
        a						:		List[str]      							=			[self.normalize(image=__UpperCAmelCase				, mean=__UpperCAmelCase				, std=__UpperCAmelCase) for image in images]

      a						:		int      							=			[to_channel_dimension_format(__UpperCAmelCase				, __UpperCAmelCase) for image in images]

      a						:		List[Any]      							=			{"pixel_values": images}
      return BatchFeature(data=__UpperCAmelCase				, tensor_type=__UpperCAmelCase)










"""simple docstring"""







def      lowercase			(				A_  ,   A_       )->      float:
  '''simple docstring'''







  if mass < 0:
    raise ValueError("The mass of a body cannot be negative"       )
  return 0.5 * mass * abs(A_       ) * abs(A_       )


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)






