

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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


_UpperCamelCase 			=		logging.get_logger(__name__)







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  ["""pixel_values"""]
 def __init__(							self      ,							A_ = True      ,							A_ = None      ,							A_ = 0.9      ,							A_ = PILImageResampling.BICUBIC      ,							A_ = True      ,							A_ = None      ,							A_ = 1 / 255      ,							A_ = True      ,							A_ = True      ,							A_ = None      ,							A_ = None      ,							**A_      ,							)					->None:



       '''simple docstring'''





       super().__init__(**A_					)
       __lowerCAmelCase					:						Optional[int]					=					size if size is not None else {'''shortest_edge''': 224}
       __lowerCAmelCase					:						List[str]					=					get_size_dict(A_      ,							default_to_square=A_					)
       __lowerCAmelCase					:						str					=					crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       __lowerCAmelCase					:						List[Any]					=					get_size_dict(A_      ,							param_name='''crop_size'''					)

       __lowerCAmelCase					:						int					=					do_resize
       __lowerCAmelCase					:						Union[str, Any]					=					size
       __lowerCAmelCase					:						List[Any]					=					crop_pct
       __lowerCAmelCase					:						Optional[int]					=					resample
       __lowerCAmelCase					:						str					=					do_center_crop
       __lowerCAmelCase					:						Dict					=					crop_size
       __lowerCAmelCase					:						List[str]					=					do_rescale
       __lowerCAmelCase					:						Union[str, Any]					=					rescale_factor
       __lowerCAmelCase					:						Dict					=					do_normalize
       __lowerCAmelCase					:						List[str]					=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       __lowerCAmelCase					:						Tuple					=					image_std if image_std is not None else IMAGENET_DEFAULT_STD
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = None      ,							A_ = PILImageResampling.BICUBIC      ,							A_ = None      ,							**A_      ,							)					->np.ndarray:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					get_size_dict(A_      ,							default_to_square=A_					)
       if "shortest_edge" not in size and ("height" not in size or "width" not in size):
             raise ValueError(f"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""					)
       if crop_pct is not None:
             if "shortest_edge" in size:
                   __lowerCAmelCase					:						Optional[Any]					=					int(size['''shortest_edge'''] / crop_pct					)
             elif "height" in size and "width" in size:
                   if size["height"] == size["width"]:
                         __lowerCAmelCase					:						str					=					int(size['''height'''] / crop_pct					)
                   else:
                         __lowerCAmelCase					:						List[Any]					=					(int(size['''height'''] / crop_pct					), int(size['''width'''] / crop_pct					))
             else:
                   raise ValueError('''Invalid size for resize: {}'''.format(A_					)					)

             __lowerCAmelCase					:						Tuple					=					get_resize_output_image_size(A_      ,							size=A_      ,							default_to_square=A_					)
       else:
             if "shortest_edge" in size:
                   __lowerCAmelCase					:						Optional[Any]					=					get_resize_output_image_size(A_      ,							size=size['''shortest_edge''']      ,							default_to_square=A_					)
             elif "height" in size and "width" in size:
                   __lowerCAmelCase					:						str					=					(size['''height'''], size['''width'''])
             else:
                   raise ValueError('''Invalid size for resize: {}'''.format(A_					)					)

       return resize(A_      ,							size=A_      ,							resample=A_      ,							data_format=A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = None      ,							**A_      ,							)					->np.ndarray:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					get_size_dict(A_					)
       if "height" not in size or "width" not in size:
             raise ValueError(f"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""					)
       return center_crop(A_      ,							size=(size['''height'''], size['''width'''])      ,							data_format=A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = None      ,							**A_      ,							)					->Union[str, Any]:



       '''simple docstring'''





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



       '''simple docstring'''





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





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = ChannelDimension.FIRST      ,							**A_      ,							)					->PIL.Image.Image:



       '''simple docstring'''





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

       __lowerCAmelCase					:						Tuple					=					size if size is not None else self.size
       __lowerCAmelCase					:						Any					=					get_size_dict(A_      ,							default_to_square=A_					)
       __lowerCAmelCase					:						int					=					crop_size if crop_size is not None else self.crop_size
       __lowerCAmelCase					:						List[Any]					=					get_size_dict(A_      ,							param_name='''crop_size'''					)

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

       if do_center_crop and crop_pct is None:
             raise ValueError('''Crop_pct 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					:						int					=					[to_numpy_array(A_					) for image in images]

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

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

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

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

       __lowerCAmelCase					:						str					=					[to_channel_dimension_format(A_      ,							A_					) for image in images]

       __lowerCAmelCase					:						Optional[Any]					=					{'''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()


