






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 			__SCREAMING_SNAKE_CASE    (      __lowercase):
						_SCREAMING_SNAKE_CASE		:				torch.FloatTensor
						_SCREAMING_SNAKE_CASE		:				torch.FloatTensor







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    List[Any]=0.999			,				UpperCamelCase_				:    str="cosine"			,				)     ->      int:

						"""simple docstring"""



						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(UpperCamelCase_				:    List[str]						):
																		return math.exp(t * -12.0						)

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

						lowerCAmelCase__			  =  []
						for i in range(UpperCamelCase_						):
												lowerCAmelCase__			  =  i / num_diffusion_timesteps
												lowerCAmelCase__			  =  (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 			__SCREAMING_SNAKE_CASE    (      __lowercase						,    __lowercase):
						_SCREAMING_SNAKE_CASE		:				Any            =    1






						@register_to_config
						def __init__(		self   ,  _UpperCamelCase = 10_00   ,  _UpperCamelCase = 0.00_01   ,  _UpperCamelCase = 0.02   ,  _UpperCamelCase = "linear"   ,  _UpperCamelCase = 0.0   ,  _UpperCamelCase = None   ,  _UpperCamelCase = True   ,  ):






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

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

												lowerCAmelCase__			  =  torch.tensor(1.0      )

												# standard deviation of the initial noise distribution
												lowerCAmelCase__			  =  1.0

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

												lowerCAmelCase__			  =  eta






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = None      ):






												"""simple docstring"""
												return sample






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = 10   ,  _UpperCamelCase = 10   ,  _UpperCamelCase = None   ,  ):






												"""simple docstring"""
												lowerCAmelCase__			  =  min(self.config.num_train_timesteps   ,  _UpperCamelCase      )
												lowerCAmelCase__			  =  num_inference_steps

												lowerCAmelCase__			  =  []

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

												lowerCAmelCase__			  =  num_inference_steps
												while t >= 1:
																		lowerCAmelCase__			  =  t - 1
																		timesteps.append(_UpperCamelCase      )

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

												lowerCAmelCase__			  =  np.array(_UpperCamelCase      ) * (self.config.num_train_timesteps // self.num_inference_steps)
												lowerCAmelCase__			  =  torch.from_numpy(_UpperCamelCase      ).to(_UpperCamelCase      )






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  t - self.config.num_train_timesteps // self.num_inference_steps

												lowerCAmelCase__			  =  self.alphas_cumprod[t]
												lowerCAmelCase__			  =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
												lowerCAmelCase__			  =  1 - alpha_prod_t
												lowerCAmelCase__			  =  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]
												lowerCAmelCase__			  =  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

												return variance






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase = None   ,  _UpperCamelCase = True   ,  ):






												"""simple docstring"""
												lowerCAmelCase__			  =  timestep
												lowerCAmelCase__			  =  timestep - self.config.num_train_timesteps // self.num_inference_steps

												# 1. compute alphas, betas
												lowerCAmelCase__			  =  self.alphas_cumprod[t]
												lowerCAmelCase__			  =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
												lowerCAmelCase__			  =  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
												lowerCAmelCase__			  =  (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

												# 3. Clip "predicted x_0"
												if self.config.clip_sample:
																		lowerCAmelCase__			  =  torch.clamp(_UpperCamelCase   ,  -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
												lowerCAmelCase__			  =  model_output.device
												lowerCAmelCase__			  =  randn_tensor(model_output.shape   ,  generator=_UpperCamelCase   ,  device=_UpperCamelCase   ,  dtype=model_output.dtype      )
												lowerCAmelCase__			  =  self.eta * self._get_variance(_UpperCamelCase      ) ** 0.5

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

												# 6. compute "direction pointing to x_t" of formula (12)
												# from https://arxiv.org/pdf/2010.02502.pdf
												lowerCAmelCase__			  =  (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
												lowerCAmelCase__			  =  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
												lowerCAmelCase__			  =  (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
												lowerCAmelCase__			  =  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=_UpperCamelCase   ,  pred_original_sample=_UpperCamelCase      )






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase=None      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self.config.num_train_timesteps // self.num_inference_steps

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

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

												return sample






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  ):






												"""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













def 		_UpperCamelCase      (			UpperCamelCase_				:    float			,				UpperCamelCase_				:    float			,				UpperCamelCase_				:    float						)     ->      list[int]:

						"""simple docstring"""



						if hue < 0 or hue > 360:
												raise Exception('hue should be between 0 and 360'						)

						if saturation < 0 or saturation > 1:
												raise Exception('saturation should be between 0 and 1'						)

						if value < 0 or value > 1:
												raise Exception('value should be between 0 and 1'						)

						lowerCAmelCase__			  =  value * saturation
						lowerCAmelCase__			  =  hue / 60
						lowerCAmelCase__			  =  chroma * (1 - abs(hue_section % 2 - 1						))
						lowerCAmelCase__			  =  value - chroma

						if hue_section >= 0 and hue_section <= 1:
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
						elif hue_section > 1 and hue_section <= 2:
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
						elif hue_section > 2 and hue_section <= 3:
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
						elif hue_section > 3 and hue_section <= 4:
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
						elif hue_section > 4 and hue_section <= 5:
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
						else:
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)

						return [red, green, blue]







def 		_UpperCamelCase      (			UpperCamelCase_				:    int			,				UpperCamelCase_				:    int			,				UpperCamelCase_				:    int						)     ->      list[float]:

						"""simple docstring"""



						if red < 0 or red > 255:
												raise Exception('red should be between 0 and 255'						)

						if green < 0 or green > 255:
												raise Exception('green should be between 0 and 255'						)

						if blue < 0 or blue > 255:
												raise Exception('blue should be between 0 and 255'						)

						lowerCAmelCase__			  =  red / 255
						lowerCAmelCase__			  =  green / 255
						lowerCAmelCase__			  =  blue / 255
						lowerCAmelCase__			  =  max(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  value - min(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  0 if value == 0 else chroma / value

						if chroma == 0:
												lowerCAmelCase__			  =  0.0
						elif value == float_red:
												lowerCAmelCase__			  =  60 * (0 + (float_green - float_blue) / chroma)
						elif value == float_green:
												lowerCAmelCase__			  =  60 * (2 + (float_blue - float_red) / chroma)
						else:
												lowerCAmelCase__			  =  60 * (4 + (float_red - float_green) / chroma)

						lowerCAmelCase__			  =  (hue + 360) % 360

						return [hue, saturation, value]







def 		_UpperCamelCase      (			UpperCamelCase_				:    list[float]			,				UpperCamelCase_				:    list[float]						)     ->      bool:

						"""simple docstring"""



						lowerCAmelCase__			  =  abs(hsv_a[0] - hsv_a[0]						) < 0.2
						lowerCAmelCase__			  =  abs(hsv_a[1] - hsv_a[1]						) < 0.002
						lowerCAmelCase__			  =  abs(hsv_a[2] - hsv_a[2]						) < 0.002

						return check_hue and check_saturation and check_value





