






from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							np.stack((horizontal_flow, vertical_flow)					,     2	)

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

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

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

		# Set pixels at invalid locations to 0
		__A    =							0

		return warped



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = None					,     ):


		'''simple docstring'''

		if alpha is None:
				__A    =							0.1

		# Initialize flow
		__A    =							np.zeros_like(lowerCAmelCase__	)
		__A    =							np.zeros_like(lowerCAmelCase__	)

		# Prepare kernels for the calculation of the derivatives and the average velocity
		__A    =							np.array([[-1, 1], [-1, 1]]	) * 0.25
		__A    =							np.array([[-1, -1], [1, 1]]	) * 0.25
		__A    =							np.array([[1, 1], [1, 1]]	) * 0.25
		__A    =							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(lowerCAmelCase__	):
				__A    =							warp(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)
				__A    =							convolve(lowerCAmelCase__					,     lowerCAmelCase__	) + convolve(lowerCAmelCase__					,     lowerCAmelCase__	)
				__A    =							convolve(lowerCAmelCase__					,     lowerCAmelCase__	) + convolve(lowerCAmelCase__					,     lowerCAmelCase__	)
				__A    =							convolve(lowerCAmelCase__					,     lowerCAmelCase__	) + convolve(lowerCAmelCase__					,     -kernel_t	)

				__A    =							convolve(lowerCAmelCase__					,     lowerCAmelCase__	)
				__A    =							convolve(lowerCAmelCase__					,     lowerCAmelCase__	)

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

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

		return horizontal_flow, vertical_flow


if __name__ == "__main__":
					import doctest

					doctest.testmod()









import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize





class        a__      :






		def __init__(      self							, lowercase__							, lowercase__=sys.maxsize						)				->				str:
				__A    =							"bilinear"
				__A    =							max_size
				__A    =							short_edge_length






		def __call__(      self							, lowercase__						)				->				Dict:
				__A    =							[]
				for img in imgs:
						__A ,   __A    =							img.shape[:2]
						# later: provide list and randomly choose index for resize
						__A    =							np.random.randint(self.short_edge_length[0]							, self.short_edge_length[1] + 1						)
						if size == 0:
								return img
						__A    =							size * 1.0 / min(lowercase__							, lowercase__						)
						if h < w:
								__A ,   __A    =							size, scale * w
						else:
								__A ,   __A    =							scale * h, size
						if max(lowercase__							, lowercase__						) > self.max_size:
								__A    =							self.max_size * 1.0 / max(lowercase__							, lowercase__						)
								__A    =							newh * scale
								__A    =							neww * scale
						__A    =							int(neww + 0.5						)
						__A    =							int(newh + 0.5						)

						if img.dtype == np.uinta:
								__A    =							Image.fromarray(lowercase__						)
								__A    =							pil_image.resize((neww, newh)							, PILImageResampling.BILINEAR						)
								__A    =							np.asarray(lowercase__						)
						else:
								__A    =							img.permute(2							, 0							, 1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
								__A    =							nn.functional.interpolate(
								    lowercase__							, (newh, neww)							, mode=self.interp_method							, align_corners=lowercase__						).squeeze(0						)
						img_augs.append(lowercase__						)

				return img_augs





class        a__      :






		def __init__(      self							, lowercase__						)				->				Union[str, Any]:
				__A    =							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]							, cfg.INPUT.MAX_SIZE_TEST						)
				__A    =							cfg.INPUT.FORMAT
				__A    =							cfg.SIZE_DIVISIBILITY
				__A    =							cfg.PAD_VALUE
				__A    =							cfg.INPUT.MAX_SIZE_TEST
				__A    =							cfg.MODEL.DEVICE
				__A    =							torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)							, 1							, 1						)
				__A    =							torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)							, 1							, 1						)
				__A    =							lambda lowercase__						: (x - self.pixel_mean) / self.pixel_std






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Optional[int]:
				__A    =							tuple(max(lowercase__						) for s in zip(*[img.shape for img in images]						)						)
				__A    =							[im.shape[-2:] for im in images]
				__A    =							[
				    nn.functional.pad(
				        lowercase__							, [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]							, value=self.pad_value							, )
				    for size, im in zip(lowercase__							, lowercase__						)
				]

				return torch.stack(lowercase__						), torch.tensor(lowercase__						)






		def __call__(      self							, lowercase__							, lowercase__=False						)				->				int:
				with torch.no_grad():
						if not isinstance(lowercase__							, lowercase__						):
								__A    =							[images]
						if single_image:
								assert len(lowercase__						) == 1
						for i in range(len(lowercase__						)						):
								if isinstance(images[i]							, torch.Tensor						):
										images.insert(lowercase__							, images.pop(lowercase__						).to(self.device						).float()						)
								elif not isinstance(images[i]							, torch.Tensor						):
										images.insert(
										    lowercase__							, torch.as_tensor(img_tensorize(images.pop(lowercase__						)							, input_format=self.input_format						)						)
										    .to(self.device						)
										    .float()							, )
            # resize smallest edge
						__A    =							torch.tensor([im.shape[:2] for im in images]						)
						__A    =							self.aug(lowercase__						)
						# transpose images and convert to torch tensors
						# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
						# now normalize before pad to avoid useless arithmetic
						__A    =							[self.normalizer(lowercase__						) for x in images]
						# now pad them to do the following operations
						__A ,   __A    =							self.pad(lowercase__						)
						# Normalize

						if self.size_divisibility > 0:
								raise NotImplementedError()
						# pad
						__A    =							torch.true_divide(lowercase__							, lowercase__						)
						if single_image:
								return images[0], sizes[0], scales_yx[0]
						else:
								return images, sizes, scales_yx



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes





def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		assert torch.isfinite(lowerCAmelCase__	).all(), "Box tensor contains infinite or NaN!"
		__A ,   __A    =							box_size
		tensor[:, 0].clamp_(min=0					,     max=lowerCAmelCase__	)
		tensor[:, 1].clamp_(min=0					,     max=lowerCAmelCase__	)
		tensor[:, 2].clamp_(min=0					,     max=lowerCAmelCase__	)
		tensor[:, 3].clamp_(min=0					,     max=lowerCAmelCase__	)

