





import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class      A_	(a_      ):
	UpperCAmelCase__ =      4_2
	UpperCAmelCase__ =      4_2





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__=0.999      ,      UpperCamelCase__="cosine"      ,      )  ->    List[str]:







			'''simple docstring'''

			if alpha_transform_type == "cosine":

						def alpha_bar_fn(UpperCamelCase__			):
									return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(UpperCamelCase__			):
									return math.exp(t * -12.0			)

			else:
						raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""			)

			UpperCAmelCase       		=    []
			for i in range(UpperCamelCase__			):
						UpperCAmelCase       		=    i / num_diffusion_timesteps
						UpperCAmelCase       		=    (i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(UpperCamelCase__			) / alpha_bar_fn(UpperCamelCase__			)      ,      UpperCamelCase__			)			)
			return torch.tensor(UpperCamelCase__      ,      dtype=torch.floataa			)






class      A_	(a_					,							a_      ):
	UpperCAmelCase__ =      1
	@register_to_config
	def __init__(   self							, _A = 1_0_0_0							, _A = 0.00_01							, _A = 0.02							, _A = "linear"							, _A = 0.0							, _A = None							, _A = True							, ):






				'''simple docstring'''







				if trained_betas is not None:
							UpperCAmelCase       		=    torch.from_numpy(_A		)
				elif beta_schedule == "linear":
							UpperCAmelCase       		=    torch.linspace(_A							, _A							, _A							, dtype=torch.floataa		)
				elif beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							UpperCAmelCase       		=    (
							    torch.linspace(beta_start**0.5							, beta_end**0.5							, _A							, dtype=torch.floataa		) ** 2
							)
				elif beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							UpperCAmelCase       		=    betas_for_alpha_bar(_A		)
				elif beta_schedule == "sigmoid":
							# GeoDiff sigmoid schedule
							UpperCAmelCase       		=    torch.linspace(-6							, 6							, _A		)
							UpperCAmelCase       		=    torch.sigmoid(_A		) * (beta_end - beta_start) + beta_start
				else:
							raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""		)

				UpperCAmelCase       		=    1.0 - self.betas
				UpperCAmelCase       		=    torch.cumprod(self.alphas							, dim=0		)
				UpperCAmelCase       		=    torch.tensor(1.0		)

				UpperCAmelCase       		=    torch.tensor(1.0		)

				# standard deviation of the initial noise distribution
				UpperCAmelCase       		=    1.0

				# setable values
				UpperCAmelCase       		=    None
				UpperCAmelCase       		=    torch.from_numpy(np.arange(0							, _A		)[::-1].copy()		)

				UpperCAmelCase       		=    eta
	def 					_lowercase    (   self							, _A							, _A = None		):






				'''simple docstring'''







				return sample
	def 					_lowercase    (   self							, _A							, _A = 1_0							, _A = 1_0							, _A = None							, ):






				'''simple docstring'''







				UpperCAmelCase       		=    min(self.config.num_train_timesteps							, _A		)
				UpperCAmelCase       		=    num_inference_steps

				UpperCAmelCase       		=    []

				UpperCAmelCase       		=    {}
				for j in range(0							, num_inference_steps - jump_length							, _A		):
							UpperCAmelCase       		=    jump_n_sample - 1

				UpperCAmelCase       		=    num_inference_steps
				while t >= 1:
							UpperCAmelCase       		=    t - 1
							timesteps.append(_A		)

							if jumps.get(_A							, 0		) > 0:
										UpperCAmelCase       		=    jumps[t] - 1
										for _ in range(_A		):
													UpperCAmelCase       		=    t + 1
													timesteps.append(_A		)

				UpperCAmelCase       		=    np.array(_A		) * (self.config.num_train_timesteps // self.num_inference_steps)
				UpperCAmelCase       		=    torch.from_numpy(_A		).to(_A		)
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    t - self.config.num_train_timesteps // self.num_inference_steps

				UpperCAmelCase       		=    self.alphas_cumprod[t]
				UpperCAmelCase       		=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
				UpperCAmelCase       		=    1 - alpha_prod_t
				UpperCAmelCase       		=    1 - alpha_prod_t_prev

				# For t > 0, compute predicted variance βt (see formula (6) and (7) from
				# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
				# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
				# variance to pred_sample
				# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
				# without eta.
				# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
				UpperCAmelCase       		=    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

				return variance
	def 					_lowercase    (   self							, _A							, _A							, _A							, _A							, _A							, _A = None							, _A = True							, ):






				'''simple docstring'''







				UpperCAmelCase       		=    timestep
				UpperCAmelCase       		=    timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 1. compute alphas, betas
				UpperCAmelCase       		=    self.alphas_cumprod[t]
				UpperCAmelCase       		=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
				UpperCAmelCase       		=    1 - alpha_prod_t

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				UpperCAmelCase       		=    (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

				# 3. Clip "predicted x_0"
				if self.config.clip_sample:
							UpperCAmelCase       		=    torch.clamp(_A							, -1							, 1		)

				# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
				# substitute formula (7) in the algorithm coming from DDPM paper
				# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
				# DDIM schedule gives the same results as DDPM with eta = 1.0
				# Noise is being reused in 7. and 8., but no impact on quality has
				# been observed.

				# 5. Add noise
				UpperCAmelCase       		=    model_output.device
				UpperCAmelCase       		=    randn_tensor(model_output.shape							, generator=_A							, device=_A							, dtype=model_output.dtype		)
				UpperCAmelCase       		=    self.eta * self._get_variance(_A		) ** 0.5

				UpperCAmelCase       		=    0
				if t > 0 and self.eta > 0:
							UpperCAmelCase       		=    std_dev_t * noise

				# 6. compute "direction pointing to x_t" of formula (12)
				# from https://arxiv.org/pdf/2010.02502.pdf
				UpperCAmelCase       		=    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

				# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				UpperCAmelCase       		=    alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

				# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
				UpperCAmelCase       		=    (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

				# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
				UpperCAmelCase       		=    mask * prev_known_part + (1.0 - mask) * prev_unknown_part

				if not return_dict:
							return (
							    pred_prev_sample,
							    pred_original_sample,
							)

				return RePaintSchedulerOutput(prev_sample=_A							, pred_original_sample=_A		)
	def 					_lowercase    (   self							, _A							, _A							, _A=None		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.config.num_train_timesteps // self.num_inference_steps

				for i in range(_A		):
							UpperCAmelCase       		=    self.betas[timestep + i]
							if sample.device.type == "mps":
										# randn does not work reproducibly on mps
										UpperCAmelCase       		=    randn_tensor(sample.shape							, dtype=sample.dtype							, generator=_A		)
										UpperCAmelCase       		=    noise.to(sample.device		)
							else:
										UpperCAmelCase       		=    randn_tensor(sample.shape							, generator=_A							, device=sample.device							, dtype=sample.dtype		)

							# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
							UpperCAmelCase       		=    (1 - beta) ** 0.5 * sample + beta**0.5 * noise

				return sample
	def 					_lowercase    (   self							, _A							, _A							, _A							, ):






				'''simple docstring'''







				raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''		)







	def __len__(   self		):






				'''simple docstring'''







				return self.config.num_train_timesteps










from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    np.ndarray:







			'''simple docstring'''

			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 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__ = None      ,      )  ->    tuple[np.ndarray, np.ndarray]:







			'''simple docstring'''

			if alpha is None:
						UpperCAmelCase       		=    0.1

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

			# 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(UpperCamelCase__			):
						UpperCAmelCase       		=    warp(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      -kernel_t			)

						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			)

						# 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()



