





"""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, logging


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      ['pixel_values']





      def __init__(       self      ,						lowercase = True      ,						lowercase = None      ,						lowercase = PILImageResampling.BILINEAR      ,						lowercase = True      ,						lowercase = None      ,						lowercase = True      ,						lowercase = 1 / 255      ,						lowercase = True      ,						lowercase = None      ,						lowercase = None      ,						**lowercase      ,						)		->     None:
            super().__init__(**A_ )
            lowerCAmelCase  						=       size if size is not None else {'''shortest_edge''': 256}
            lowerCAmelCase  						=       get_size_dict(A_      ,						default_to_square=A_ )
            lowerCAmelCase  						=       crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
            lowerCAmelCase  						=       get_size_dict(A_ )
            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 if image_mean is not None else IMAGENET_STANDARD_MEAN
            lowerCAmelCase  						=       image_std if image_std is not None else IMAGENET_STANDARD_STD





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = PILImageResampling.BICUBIC      ,						lowercase = None      ,						**lowercase      ,						)		->     np.ndarray:
            lowerCAmelCase  						=       get_size_dict(A_      ,						default_to_square=A_ )
            if "shortest_edge" not in size:
                  raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
            lowerCAmelCase  						=       get_resize_output_image_size(A_      ,						size=size["""shortest_edge"""]      ,						default_to_square=A_ )
            return resize(A_      ,						size=A_      ,						resample=A_      ,						data_format=A_      ,						**A_ )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = None      ,						**lowercase      ,						)		->     np.ndarray:
            lowerCAmelCase  						=       get_size_dict(A_ )
            return center_crop(A_      ,						size=(size["""height"""], size["""width"""])      ,						data_format=A_      ,						**A_ )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = None      ,						**lowercase )		->     np.ndarray:
            return rescale(A_      ,						scale=A_      ,						data_format=A_      ,						**A_ )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase = None      ,						**lowercase      ,						)		->     np.ndarray:
            return normalize(A_      ,						mean=A_      ,						std=A_      ,						data_format=A_      ,						**A_ )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = ChannelDimension.FIRST      ,						**lowercase      ,						)		->     Tuple:
            lowerCAmelCase  						=       do_resize if do_resize is not None else self.do_resize
            lowerCAmelCase  						=       size if size is not None else self.size
            lowerCAmelCase  						=       get_size_dict(A_      ,						default_to_square=A_ )
            lowerCAmelCase  						=       resample if resample is not None else self.resample
            lowerCAmelCase  						=       do_center_crop if do_center_crop is not None else self.do_center_crop
            lowerCAmelCase  						=       crop_size if crop_size is not None else self.crop_size
            lowerCAmelCase  						=       get_size_dict(A_ )
            lowerCAmelCase  						=       do_rescale if do_rescale is not None else self.do_rescale
            lowerCAmelCase  						=       rescale_factor if rescale_factor is not None else self.rescale_factor
            lowerCAmelCase  						=       do_normalize if do_normalize is not None else self.do_normalize
            lowerCAmelCase  						=       image_mean if image_mean is not None else self.image_mean
            lowerCAmelCase  						=       image_std if image_std is not None else self.image_std

            lowerCAmelCase  						=       make_list_of_images(A_ )

            if not valid_images(A_ ):
                  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.""" )

            # All transformations expect numpy arrays.
            lowerCAmelCase  						=       [to_numpy_array(A_ ) for image in images]

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

            if do_center_crop:
                  lowerCAmelCase  						=       [self.center_crop(image=A_      ,						size=A_ ) for image in images]

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

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

            lowerCAmelCase  						=       [to_channel_dimension_format(A_      ,						A_ ) for image in images]

            lowerCAmelCase  						=       {'''pixel_values''': images}
            return BatchFeature(data=A_      ,						tensor_type=A_ )









def 		_lowercase (     lowercase__   ,	lowercase__						):

      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()


