



from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve


def     UpperCamelCase(						__UpperCamelCase							:	np.ndarray  ,__UpperCamelCase							:	np.ndarray  ,__UpperCamelCase							:	np.ndarray				):
		lowerCAmelCase_    :  Any             =				np.stack((horizontal_flow, vertical_flow)  ,2				)

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

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

		lowerCAmelCase_    :  List[Any]             =				image[grid[:, :, 1], grid[:, :, 0]]

		# Set pixels at invalid locations to 0
		lowerCAmelCase_    :  str             =				0

		return warped


def     UpperCamelCase(						__UpperCamelCase							:	np.ndarray  ,__UpperCamelCase							:	np.ndarray  ,__UpperCamelCase							:	SupportsIndex  ,__UpperCamelCase							:	float | None = None  ,):
		if alpha is None:
				lowerCAmelCase_    :  Tuple             =				0.1

		# Initialize flow
		lowerCAmelCase_    :  str             =				np.zeros_like(__UpperCamelCase				)
		lowerCAmelCase_    :  List[str]             =				np.zeros_like(__UpperCamelCase				)

		# Prepare kernels for the calculation of the derivatives and the average velocity
		lowerCAmelCase_    :  List[Any]             =				np.array([[-1, 1], [-1, 1]]				) * 0.2_5
		lowerCAmelCase_    :  str             =				np.array([[-1, -1], [1, 1]]				) * 0.2_5
		lowerCAmelCase_    :  Optional[int]             =				np.array([[1, 1], [1, 1]]				) * 0.2_5
		lowerCAmelCase_    :  int             =				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(__UpperCamelCase				):
				lowerCAmelCase_    :  Optional[Any]             =				warp(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase				)
				lowerCAmelCase_    :  Optional[int]             =				convolve(__UpperCamelCase  ,__UpperCamelCase				) + convolve(__UpperCamelCase  ,__UpperCamelCase				)
				lowerCAmelCase_    :  Tuple             =				convolve(__UpperCamelCase  ,__UpperCamelCase				) + convolve(__UpperCamelCase  ,__UpperCamelCase				)
				lowerCAmelCase_    :  Union[str, Any]             =				convolve(__UpperCamelCase  ,__UpperCamelCase				) + convolve(__UpperCamelCase  ,-kernel_t				)

				lowerCAmelCase_    :  Dict             =				convolve(__UpperCamelCase  ,__UpperCamelCase				)
				lowerCAmelCase_    :  str             =				convolve(__UpperCamelCase  ,__UpperCamelCase				)

				# This updates the flow as proposed in the paper (Step 12)
				lowerCAmelCase_    :  List[Any]             =				(
				    derivative_x * avg_horizontal_velocity
				    + derivative_y * avg_vertical_velocity
				    + derivative_t
				)
				lowerCAmelCase_    :  Any             =				update / (alpha**2 + derivative_x**2 + derivative_y**2)

				lowerCAmelCase_    :  Dict             =				avg_horizontal_velocity - derivative_x * update
				lowerCAmelCase_    :  Tuple             =				avg_vertical_velocity - derivative_y * update

		return horizontal_flow, vertical_flow


if __name__ == "__main__":
						import doctest

						doctest.testmod()










import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


A__						:						str          =      logging.get_logger(__name__)



class        __snake_case		(  UpperCamelCase_		):




							def __init__( self  : Union[str, Any]       ,       *A_  : int       ,       **A_  : List[str]):
									warnings.warn(
									    '''The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
									    ''' Please use DetrImageProcessor instead.'''       ,       A_       ,       )
									super().__init__(*A_       ,       **A_)





