


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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


lowerCAmelCase__			:      Union[str, Any]								=logging.get_logger(__name__)







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""pixel_values"""]






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





										"""simple docstring"""




										super().__init__(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					size if size is not None else {'shortest_edge': 3_8_4}
										SCREAMING_SNAKE_CASE_		:     List[str]					=					get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					do_resize
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					size
										# Default value set here for backwards compatibility where the value in config is None
										SCREAMING_SNAKE_CASE_		:     str					=					crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
										SCREAMING_SNAKE_CASE_		:     Dict					=					resample
										SCREAMING_SNAKE_CASE_		:     int					=					do_rescale
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					rescale_factor
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					do_normalize
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					image_std if image_std is not None else IMAGENET_STANDARD_STD






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = PILImageResampling.BICUBIC			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__       )
										if "shortest_edge" not in size:
													raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					size['shortest_edge']

										if shortest_edge < 3_8_4:
													# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
													SCREAMING_SNAKE_CASE_		:     Tuple					=					int(shortest_edge / crop_pct       )
													SCREAMING_SNAKE_CASE_		:     List[str]					=					get_resize_output_image_size(lowerCAmelCase__			,						size=lowerCAmelCase__			,						default_to_square=lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					resize(image=lowerCAmelCase__			,						size=lowerCAmelCase__			,						resample=lowerCAmelCase__			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__       )
													# then crop to (shortest_edge, shortest_edge)
													return center_crop(image=lowerCAmelCase__			,						size=(shortest_edge, shortest_edge)			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__       )
										else:
													# warping (no cropping) when evaluated at 384 or larger
													return resize(
													    lowerCAmelCase__			,						size=(shortest_edge, shortest_edge)			,						resample=lowerCAmelCase__			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = ChannelDimension.FIRST			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




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

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					size if size is not None else self.size
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					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 or resample is None:
													raise ValueError('Size and resample must be specified if do_resize is True.'       )

										if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
													raise ValueError('crop_pct must be specified if size < 384.'       )

										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.
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[to_numpy_array(lowerCAmelCase__       ) for image in images]

										if do_resize:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					[self.resize(image=lowerCAmelCase__			,						size=lowerCAmelCase__			,						crop_pct=lowerCAmelCase__			,						resample=lowerCAmelCase__       ) for image in images]

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

										if do_normalize:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[self.normalize(image=lowerCAmelCase__			,						mean=lowerCAmelCase__			,						std=lowerCAmelCase__       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     str					=					[to_channel_dimension_format(lowerCAmelCase__			,						lowerCAmelCase__       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     int					=					{'pixel_values': images}
										return BatchFeature(data=lowerCAmelCase__			,						tensor_type=lowerCAmelCase__       )








def 	__a     (      A__			:  float			,  A__			:  float       ):
    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)