



'''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,
    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							:Tuple       = logging.get_logger(__name__)







class      _lowerCAmelCase							(      __UpperCAmelCase			):
 __SCREAMING_SNAKE_CASE       :		Optional[Any]		      = ['pixel_values']


 def __init__(self	,				lowercase = True	,				lowercase = None	,				lowercase = None	,				lowercase = PILImageResampling.BILINEAR	,				lowercase = True	,				lowercase = 1 / 255	,				lowercase = True	,				lowercase = None	,				lowercase = None	,				**lowercase	,				):
    super().__init__(**lowercase							)
    A_     :						str     =					size if size is not None else {"""shortest_edge""": 384}
    A_     :						Optional[int]     =					get_size_dict(lowercase	,				default_to_square=lowercase							)

    A_     :						Any     =					do_resize
    A_     :						int     =					size
    # Default value set here for backwards compatibility where the value in config is None
    A_     :						Tuple     =					crop_pct if crop_pct is not None else 224 / 256
    A_     :						Optional[int]     =					resample
    A_     :						Any     =					do_rescale
    A_     :						Optional[Any]     =					rescale_factor
    A_     :						str     =					do_normalize
    A_     :						int     =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    A_     :						List[Any]     =					image_std if image_std is not None else IMAGENET_STANDARD_STD


 def    _a     (self	,				lowercase	,				lowercase	,				lowercase	,				lowercase = PILImageResampling.BICUBIC	,				lowercase = None	,				**lowercase	,				):
    A_     :						Tuple     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
    if "shortest_edge" not in size:
       raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'							)
    A_     :						Union[str, Any]     =					size["""shortest_edge"""]

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


 def    _a     (self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
    return rescale(lowercase	,				scale=lowercase	,				data_format=lowercase	,				**lowercase							)


 def    _a     (self	,				lowercase	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
    return normalize(lowercase	,				mean=lowercase	,				std=lowercase	,				data_format=lowercase	,				**lowercase							)


 def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = ChannelDimension.FIRST	,				**lowercase	,				):
    A_     :						Tuple     =					do_resize if do_resize is not None else self.do_resize
    A_     :						Optional[int]     =					crop_pct if crop_pct is not None else self.crop_pct
    A_     :						Optional[Any]     =					resample if resample is not None else self.resample
    A_     :						Any     =					do_rescale if do_rescale is not None else self.do_rescale
    A_     :						Union[str, Any]     =					rescale_factor if rescale_factor is not None else self.rescale_factor
    A_     :						Optional[Any]     =					do_normalize if do_normalize is not None else self.do_normalize
    A_     :						int     =					image_mean if image_mean is not None else self.image_mean
    A_     :						List[Any]     =					image_std if image_std is not None else self.image_std

    A_     :						List[Any]     =					size if size is not None else self.size
    A_     :						Any     =					get_size_dict(lowercase	,				default_to_square=lowercase							)

    A_     :						str     =					make_list_of_images(lowercase							)

    if not valid_images(lowercase							):
       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"] < 384 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.
    A_     :						Tuple     =					[to_numpy_array(lowercase							) for image in images]

    if do_resize:
       A_     :						str     =					[self.resize(image=lowercase	,				size=lowercase	,				crop_pct=lowercase	,				resample=lowercase							) for image in images]

    if do_rescale:
       A_     :						Any     =					[self.rescale(image=lowercase	,				scale=lowercase							) for image in images]

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

    A_     :						Optional[int]     =					[to_channel_dimension_format(lowercase	,				lowercase							) for image in images]

    A_     :						int     =					{"""pixel_values""": images}
    return BatchFeature(data=lowercase	,				tensor_type=lowercase							)




'''simple docstring'''




def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			if density <= 0:
						raise ValueError("""Impossible fluid density"""   )
			if bulk_modulus <= 0:
						raise ValueError("""Impossible bulk modulus"""   )

			return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()