import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		(LMSDiscreteScheduler,)
							_lowerCamelCase						=		10



							def        UpperCamelCase       (						self      ,				**lowercase_				):
													_snake_case						:     Optional[int]				=    {
													    "num_train_timesteps": 1_100,
													    "beta_start": 0.0_001,
													    "beta_end": 0.02,
													    "beta_schedule": "linear",
													}

													config.update(**lowercase_				)
													return config



							def        UpperCamelCase       (						self				):
													for timesteps in [10, 50, 100, 1_000]:
																			self.check_over_configs(num_train_timesteps=lowercase_				)



							def        UpperCamelCase       (						self				):
													for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]      ,				[0.0_002, 0.002, 0.02]				):
																			self.check_over_configs(beta_start=lowercase_      ,				beta_end=lowercase_				)



							def        UpperCamelCase       (						self				):
													for schedule in ["linear", "scaled_linear"]:
																			self.check_over_configs(beta_schedule=lowercase_				)



							def        UpperCamelCase       (						self				):
													for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=lowercase_				)



							def        UpperCamelCase       (						self				):
													for t in [0, 500, 800]:
																			self.check_over_forward(time_step=lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[Any]				=    self.scheduler_classes[0]
													_snake_case						:     str				=    self.get_scheduler_config()
													_snake_case						:     str				=    scheduler_class(**lowercase_				)

													scheduler.set_timesteps(self.num_inference_steps				)

													_snake_case						:     Union[str, Any]				=    self.dummy_model()
													_snake_case						:     List[Any]				=    self.dummy_sample_deter * scheduler.init_noise_sigma

													for i, t in enumerate(scheduler.timesteps				):
																			_snake_case						:     List[Any]				=    scheduler.scale_model_input(lowercase_      ,				lowercase_				)

																			_snake_case						:     str				=    model(lowercase_      ,				lowercase_				)

																			_snake_case						:     Tuple				=    scheduler.step(lowercase_      ,				lowercase_      ,				lowercase_				)
																			_snake_case						:     Tuple				=    output.prev_sample

													_snake_case						:     str				=    torch.sum(torch.abs(lowercase_				)				)
													_snake_case						:     Tuple				=    torch.mean(torch.abs(lowercase_				)				)

													assert abs(result_sum.item() - 1_006.388				) < 1e-2
													assert abs(result_mean.item() - 1.31				) < 1e-3



							def        UpperCamelCase       (						self				):
													_snake_case						:     Any				=    self.scheduler_classes[0]
													_snake_case						:     str				=    self.get_scheduler_config(prediction_type="v_prediction"				)
													_snake_case						:     Tuple				=    scheduler_class(**lowercase_				)

													scheduler.set_timesteps(self.num_inference_steps				)

													_snake_case						:     Optional[int]				=    self.dummy_model()
													_snake_case						:     List[str]				=    self.dummy_sample_deter * scheduler.init_noise_sigma

													for i, t in enumerate(scheduler.timesteps				):
																			_snake_case						:     Tuple				=    scheduler.scale_model_input(lowercase_      ,				lowercase_				)

																			_snake_case						:     Tuple				=    model(lowercase_      ,				lowercase_				)

																			_snake_case						:     int				=    scheduler.step(lowercase_      ,				lowercase_      ,				lowercase_				)
																			_snake_case						:     Any				=    output.prev_sample

													_snake_case						:     List[Any]				=    torch.sum(torch.abs(lowercase_				)				)
													_snake_case						:     Tuple				=    torch.mean(torch.abs(lowercase_				)				)

													assert abs(result_sum.item() - 0.0_017				) < 1e-2
													assert abs(result_mean.item() - 2.26_76e-06				) < 1e-3



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[Any]				=    self.scheduler_classes[0]
													_snake_case						:     Any				=    self.get_scheduler_config()
													_snake_case						:     int				=    scheduler_class(**lowercase_				)

													scheduler.set_timesteps(self.num_inference_steps      ,				device=lowercase_				)

													_snake_case						:     int				=    self.dummy_model()
													_snake_case						:     str				=    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													_snake_case						:     int				=    sample.to(lowercase_				)

													for i, t in enumerate(scheduler.timesteps				):
																			_snake_case						:     str				=    scheduler.scale_model_input(lowercase_      ,				lowercase_				)

																			_snake_case						:     List[Any]				=    model(lowercase_      ,				lowercase_				)

																			_snake_case						:     Optional[Any]				=    scheduler.step(lowercase_      ,				lowercase_      ,				lowercase_				)
																			_snake_case						:     List[Any]				=    output.prev_sample

													_snake_case						:     Optional[Any]				=    torch.sum(torch.abs(lowercase_				)				)
													_snake_case						:     Union[str, Any]				=    torch.mean(torch.abs(lowercase_				)				)

													assert abs(result_sum.item() - 1_006.388				) < 1e-2
													assert abs(result_mean.item() - 1.31				) < 1e-3



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[Any]				=    self.scheduler_classes[0]
													_snake_case						:     Tuple				=    self.get_scheduler_config()
													_snake_case						:     List[str]				=    scheduler_class(**lowercase_      ,				use_karras_sigmas=lowercase_				)

													scheduler.set_timesteps(self.num_inference_steps      ,				device=lowercase_				)

													_snake_case						:     str				=    self.dummy_model()
													_snake_case						:     Dict				=    self.dummy_sample_deter.to(lowercase_				) * scheduler.init_noise_sigma
													_snake_case						:     Any				=    sample.to(lowercase_				)

													for t in scheduler.timesteps:
																			_snake_case						:     int				=    scheduler.scale_model_input(lowercase_      ,				lowercase_				)

																			_snake_case						:     Any				=    model(lowercase_      ,				lowercase_				)

																			_snake_case						:     Dict				=    scheduler.step(lowercase_      ,				lowercase_      ,				lowercase_				)
																			_snake_case						:     Any				=    output.prev_sample

													_snake_case						:     Any				=    torch.sum(torch.abs(lowercase_				)				)
													_snake_case						:     Any				=    torch.mean(torch.abs(lowercase_				)				)

													assert abs(result_sum.item() - 3_812.9_927				) < 2e-2
													assert abs(result_mean.item() - 4.9_648				) < 1e-3

'''simple docstring'''
from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve







def 						A_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						np.ndarray):
							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_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						SupportsIndex						,			A		:						float | None = None						,			):
							if alpha is None:
														UpperCamelCase					       =			0.1

							# Initialize flow
							UpperCamelCase					       =			np.zeros_like(A)
							UpperCamelCase					       =			np.zeros_like(A)

							# 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 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]])

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

														UpperCamelCase					       =			convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A)

														# 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()




