
from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve
def        __lowercase(							__snake_case				:		np.ndarray    ,__snake_case				:		np.ndarray    ,__snake_case				:		np.ndarray					)       ->   np.ndarray:
       __snake_case						       =   np.stack((horizontal_flow, vertical_flow)    ,2					)

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

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

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

       # Set pixels at invalid locations to 0
       __snake_case						       =   0

       return warped
def        __lowercase(							__snake_case				:		np.ndarray    ,__snake_case				:		np.ndarray    ,__snake_case				:		SupportsIndex    ,__snake_case				:		float | None = None    ,)       ->   tuple[np.ndarray, np.ndarray]:
       if alpha is None:
              __snake_case						       =   0.1

       # Initialize flow
       __snake_case						       =   np.zeros_like(__snake_case					)
       __snake_case						       =   np.zeros_like(__snake_case					)

       # Prepare kernels for the calculation of the derivatives and the average velocity
       __snake_case						       =   np.array([[-1, 1], [-1, 1]]					) * 0.25
       __snake_case						       =   np.array([[-1, -1], [1, 1]]					) * 0.25
       __snake_case						       =   np.array([[1, 1], [1, 1]]					) * 0.25
       __snake_case						       =   np.array(
           [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]					)

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

              __snake_case						       =   convolve(__snake_case    ,__snake_case					)
              __snake_case						       =   convolve(__snake_case    ,__snake_case					)

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

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

       return horizontal_flow, vertical_flow


if __name__ == "__main__":
 import doctest

 doctest.testmod()


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  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			["""pixel_values"""]

     def __init__(  self							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = PIL.Image.NEAREST							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 1 / 255							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = True							,       **SCREAMING_SNAKE_CASE_							,       ):
            super().__init__(**SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   size if size is not None else {'height': 346, 'width': 346}
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   crop_size if crop_size is not None else {'height': 289, 'width': 289}
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_							,       param_name='crop_size'    )

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

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = PIL.Image.NEAREST							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_    )
            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(
                SCREAMING_SNAKE_CASE_							,       size=(size['height'], size['width'])							,       resample=SCREAMING_SNAKE_CASE_							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_    )
            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(SCREAMING_SNAKE_CASE_							,       size=(size['height'], size['width'])							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            if offset:
                   __snake_case						       =   (image - 1_2_7.5) * scale
                   if data_format is not None:
                          __snake_case						       =   to_channel_dimension_format(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   rescaled_image.astype(np.floataa    )
            else:
                   __snake_case						       =   rescale(SCREAMING_SNAKE_CASE_							,       scale=SCREAMING_SNAKE_CASE_							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )
            return rescaled_image

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            return normalize(SCREAMING_SNAKE_CASE_							,       mean=SCREAMING_SNAKE_CASE_							,       std=SCREAMING_SNAKE_CASE_							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   do_resize if do_resize is not None else self.do_resize
            __snake_case						       =   resample if resample is not None else self.resample
            __snake_case						       =   do_center_crop if do_center_crop is not None else self.do_center_crop
            __snake_case						       =   do_rescale if do_rescale is not None else self.do_rescale
            __snake_case						       =   rescale_factor if rescale_factor is not None else self.rescale_factor
            __snake_case						       =   rescale_offset if rescale_offset is not None else self.rescale_offset
            __snake_case						       =   do_normalize if do_normalize is not None else self.do_normalize
            __snake_case						       =   image_mean if image_mean is not None else self.image_mean
            __snake_case						       =   image_std if image_std is not None else self.image_std
            __snake_case						       =   include_top if include_top is not None else self.include_top

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

            __snake_case						       =   make_list_of_images(SCREAMING_SNAKE_CASE_    )

            if not valid_images(SCREAMING_SNAKE_CASE_    ):
                   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.
            __snake_case						       =   [to_numpy_array(SCREAMING_SNAKE_CASE_    ) for image in images]

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

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

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

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

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

            __snake_case						       =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ) for image in images]

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