"""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, 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__  :  List[str]											=     logging.get_logger(__name__)
class     snake_case	(			lowerCAmelCase__    ):




     """simple docstring"""

     snake_case__       =			["pixel_values"]







     def __init__(    self       :     List[Any]   ,lowerCamelCase__       :     bool = True   ,lowerCamelCase__       :     Dict[str, int] = None   ,lowerCamelCase__       :     PILImageResampling = PIL.Image.NEAREST   ,lowerCamelCase__       :     bool = False   ,lowerCamelCase__       :     Dict[str, int] = None   ,lowerCamelCase__       :     Union[int, float] = 1 / 255   ,lowerCamelCase__       :     bool = False   ,lowerCamelCase__       :     bool = True   ,lowerCamelCase__       :     bool = True   ,lowerCamelCase__       :     Optional[Union[float, List[float]]] = None   ,lowerCamelCase__       :     Optional[Union[float, List[float]]] = None   ,lowerCamelCase__       :     bool = True   ,**lowerCamelCase__       :     Tuple   ,):
         super().__init__(**_A      )
         UpperCAmelCase__					=							size if size is not None else {'''height''': 346, '''width''': 346}
         UpperCAmelCase__					=							get_size_dict(_A      )
         UpperCAmelCase__					=							crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
         UpperCAmelCase__					=							get_size_dict(_A   ,param_name='crop_size'      )

         UpperCAmelCase__					=							do_resize
         UpperCAmelCase__					=							size
         UpperCAmelCase__					=							resample
         UpperCAmelCase__					=							do_center_crop
         UpperCAmelCase__					=							crop_size
         UpperCAmelCase__					=							do_rescale
         UpperCAmelCase__					=							rescale_factor
         UpperCAmelCase__					=							rescale_offset
         UpperCAmelCase__					=							do_normalize
         UpperCAmelCase__					=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase__					=							image_std if image_std is not None else IMAGENET_STANDARD_STD
         UpperCAmelCase__					=							include_top







     def 		__lowerCAmelCase						(    self       :     Optional[Any]   ,lowerCamelCase__       :     np.ndarray   ,lowerCamelCase__       :     Dict[str, int]   ,lowerCamelCase__       :     PILImageResampling = PIL.Image.NEAREST   ,lowerCamelCase__       :     Optional[Union[str, ChannelDimension]] = None   ,**lowerCamelCase__       :     List[Any]   ,):
         UpperCAmelCase__					=							get_size_dict(_A      )
         if "height" not in size or "width" not in size:
             raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''      )
         return resize(
             _A   ,size=(size['height'], size['width'])   ,resample=_A   ,data_format=_A   ,**_A      )







     def 		__lowerCAmelCase						(    self       :     List[str]   ,lowerCamelCase__       :     np.ndarray   ,lowerCamelCase__       :     Dict[str, int]   ,lowerCamelCase__       :     Optional[Union[str, ChannelDimension]] = None   ,**lowerCamelCase__       :     str   ,):
         UpperCAmelCase__					=							get_size_dict(_A      )
         if "height" not in size or "width" not in size:
             raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''      )
         return center_crop(_A   ,size=(size['height'], size['width'])   ,data_format=_A   ,**_A      )







     def 		__lowerCAmelCase						(    self       :     List[Any]   ,lowerCamelCase__       :     np.ndarray   ,lowerCamelCase__       :     Union[int, float]   ,lowerCamelCase__       :     bool = True   ,lowerCamelCase__       :     Optional[Union[str, ChannelDimension]] = None   ,**lowerCamelCase__       :     Tuple   ,):




         if offset:
             UpperCAmelCase__					=							(image - 127.5) * scale
             if data_format is not None:
                 UpperCAmelCase__					=							to_channel_dimension_format(_A   ,_A      )
             UpperCAmelCase__					=							rescaled_image.astype(np.floataa      )
         else:
             UpperCAmelCase__					=							rescale(_A   ,scale=_A   ,data_format=_A   ,**_A      )
         return rescaled_image







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







     def 		__lowerCAmelCase						(    self       :     str   ,lowerCamelCase__       :     ImageInput   ,lowerCamelCase__       :     bool = None   ,lowerCamelCase__       :     Dict[str, int] = None   ,lowerCamelCase__       :     Union[str, Any]=None   ,lowerCamelCase__       :     bool = None   ,lowerCamelCase__       :     Dict[str, int] = None   ,lowerCamelCase__       :     bool = None   ,lowerCamelCase__       :     float = None   ,lowerCamelCase__       :     bool = None   ,lowerCamelCase__       :     bool = None   ,lowerCamelCase__       :     Optional[Union[float, List[float]]] = None   ,lowerCamelCase__       :     Optional[Union[float, List[float]]] = None   ,lowerCamelCase__       :     bool = None   ,lowerCamelCase__       :     Optional[Union[str, TensorType]] = None   ,lowerCamelCase__       :     ChannelDimension = ChannelDimension.FIRST   ,**lowerCamelCase__       :     Any   ,):
         UpperCAmelCase__					=							do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase__					=							resample if resample is not None else self.resample
         UpperCAmelCase__					=							do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase__					=							do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase__					=							rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase__					=							rescale_offset if rescale_offset is not None else self.rescale_offset
         UpperCAmelCase__					=							do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase__					=							image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase__					=							image_std if image_std is not None else self.image_std
         UpperCAmelCase__					=							include_top if include_top is not None else self.include_top

         UpperCAmelCase__					=							size if size is not None else self.size
         UpperCAmelCase__					=							get_size_dict(_A      )
         UpperCAmelCase__					=							crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase__					=							get_size_dict(_A   ,param_name='crop_size'      )

         UpperCAmelCase__					=							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_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.
         UpperCAmelCase__					=							[to_numpy_array(_A      ) for image in images]

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

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

         if do_rescale:
             UpperCAmelCase__					=							[self.rescale(image=_A   ,scale=_A   ,offset=_A      ) for image in images]

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

         if include_top:
             UpperCAmelCase__					=							[self.normalize(image=_A   ,mean=[0, 0, 0]   ,std=_A      ) for image in images]

         UpperCAmelCase__					=							[to_channel_dimension_format(_A   ,_A      ) for image in images]

         UpperCAmelCase__					=							{'''pixel_values''': images}
         return BatchFeature(data=_A   ,tensor_type=_A      )




"""simple docstring"""






from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							np.stack((horizontal_flow, vertical_flow) ,      2	)

				# Create a grid of all pixel coordinates and subtract the flow to get the
				# target pixels coordinates
				UpperCAmelCase__					=							np.stack(
				    np.meshgrid(np.arange(0 ,      image.shape[1]	) ,      np.arange(0 ,      image.shape[0]	)	) ,      2	)
				UpperCAmelCase__					=							np.round(grid - flow	).astype(np.intaa	)

				# Find the locations outside of the original image
				UpperCAmelCase__					=							(grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]	))
				UpperCAmelCase__					=							0

				UpperCAmelCase__					=							image[grid[:, :, 1], grid[:, :, 0]]

				# Set pixels at invalid locations to 0
				UpperCAmelCase__					=							0

				return warped



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase = None ,      ):
				if alpha is None:
								UpperCAmelCase__					=							0.1

				# Initialize flow
				UpperCAmelCase__					=							np.zeros_like(lowerCamelCase	)
				UpperCAmelCase__					=							np.zeros_like(lowerCamelCase	)

				# Prepare kernels for the calculation of the derivatives and the average velocity
				UpperCAmelCase__					=							np.array([[-1, 1], [-1, 1]]	) * 0.25
				UpperCAmelCase__					=							np.array([[-1, -1], [1, 1]]	) * 0.25
				UpperCAmelCase__					=							np.array([[1, 1], [1, 1]]	) * 0.25
				UpperCAmelCase__					=							np.array(
				    [[1 / 1_2, 1 / 6, 1 / 1_2], [1 / 6, 0, 1 / 6], [1 / 1_2, 1 / 6, 1 / 1_2]]	)

				# Iteratively refine the flow
				for _ in range(lowerCamelCase	):
								UpperCAmelCase__					=							warp(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	)
								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	) + convolve(lowerCamelCase ,      lowerCamelCase	)
								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	) + convolve(lowerCamelCase ,      lowerCamelCase	)
								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	) + convolve(lowerCamelCase ,      -kernel_t	)

								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	)
								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	)

								# This updates the flow as proposed in the paper (Step 12)
								UpperCAmelCase__					=							(
								    derivative_x * avg_horizontal_velocity
								    + derivative_y * avg_vertical_velocity
								    + derivative_t
								)
								UpperCAmelCase__					=							update / (alpha**2 + derivative_x**2 + derivative_y**2)

								UpperCAmelCase__					=							avg_horizontal_velocity - derivative_x * update
								UpperCAmelCase__					=							avg_vertical_velocity - derivative_y * update

				return horizontal_flow, vertical_flow


if __name__ == "__main__":
						import doctest

						doctest.testmod()



