



"""simple docstring"""


from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_		):
						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 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_ = None						,      ):
						if alpha is None:
												UpperCAmelCase			 =					0.1

						# Initialize flow
						UpperCAmelCase			 =					np.zeros_like(lowercase_		)
						UpperCAmelCase			 =					np.zeros_like(lowercase_		)

						# Prepare kernels for the calculation of the derivatives and the average velocity
						UpperCAmelCase			 =					np.array([[-1, 1], [-1, 1]]		) * 0.2_5
						UpperCAmelCase			 =					np.array([[-1, -1], [1, 1]]		) * 0.2_5
						UpperCAmelCase			 =					np.array([[1, 1], [1, 1]]		) * 0.2_5
						UpperCAmelCase			 =					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(lowercase_		):
												UpperCAmelCase			 =					warp(lowercase_						,      lowercase_						,      lowercase_		)
												UpperCAmelCase			 =					convolve(lowercase_						,      lowercase_		) + convolve(lowercase_						,      lowercase_		)
												UpperCAmelCase			 =					convolve(lowercase_						,      lowercase_		) + convolve(lowercase_						,      lowercase_		)
												UpperCAmelCase			 =					convolve(lowercase_						,      lowercase_		) + convolve(lowercase_						,      -kernel_t		)

												UpperCAmelCase			 =					convolve(lowercase_						,      lowercase_		)
												UpperCAmelCase			 =					convolve(lowercase_						,      lowercase_		)

												# 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()










"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:List[str]  ,    lowercase_					:List[str]  ,    lowercase_					:List[Any]   )							->     List[str]:
												super().__init__()
												self.register_modules(unet=lowercase_  ,    scheduler=lowercase_   )






						@torch.no_grad()
						def __call__(   self					:int  ,    lowercase_					:int = 1  ,    lowercase_					:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,    lowercase_					:int = 10_00  ,    lowercase_					:Optional[str] = "pil"  ,    lowercase_					:bool = True  ,    )							->     Union[ImagePipelineOutput, Tuple]:
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size  ,    lowercase_   ):
																		UpperCAmelCase			 =					(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		UpperCAmelCase			 =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if self.device.type == "mps":
																		# randn does not work reproducibly on mps
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_   )
																		UpperCAmelCase			 =					image.to(self.device   )
												else:
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_  ,    device=self.device   )

												# set step values
												self.scheduler.set_timesteps(lowercase_   )

												for t in self.progress_bar(self.scheduler.timesteps   ):
																		# 1. predict noise model_output
																		UpperCAmelCase			 =					self.unet(lowercase_  ,    lowercase_   ).sample

																		# 2. compute previous image: x_t -> x_t-1
																		UpperCAmelCase			 =					self.scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_  ,    generator=lowercase_   ).prev_sample

												UpperCAmelCase			 =					(image / 2 + 0.5).clamp(0  ,    1   )
												UpperCAmelCase			 =					image.cpu().permute(0  ,    2  ,    3  ,    1   ).numpy()
												if output_type == "pil":
																		UpperCAmelCase			 =					self.numpy_to_pil(lowercase_   )

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowercase_   )





