



'''simple docstring'''
def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							):
						if hue < 0 or hue > 3_6_0:
												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'							)

						_UpperCamelCase      :	List[Any]				       =     value * saturation
						_UpperCamelCase      :	List[str]				       =     hue / 6_0
						_UpperCamelCase      :	Union[str, Any]				       =     chroma * (1 - abs(hue_section % 2 - 1							))
						_UpperCamelCase      :	Any				       =     value - chroma

						if hue_section >= 0 and hue_section <= 1:
												_UpperCamelCase      :	int				       =     round(2_5_5 * (chroma + match_value)							)
												_UpperCamelCase      :	str				       =     round(2_5_5 * (second_largest_component + match_value)							)
												_UpperCamelCase      :	str				       =     round(2_5_5 * (match_value)							)
						elif hue_section > 1 and hue_section <= 2:
												_UpperCamelCase      :	Tuple				       =     round(2_5_5 * (second_largest_component + match_value)							)
												_UpperCamelCase      :	Union[str, Any]				       =     round(2_5_5 * (chroma + match_value)							)
												_UpperCamelCase      :	int				       =     round(2_5_5 * (match_value)							)
						elif hue_section > 2 and hue_section <= 3:
												_UpperCamelCase      :	Tuple				       =     round(2_5_5 * (match_value)							)
												_UpperCamelCase      :	List[str]				       =     round(2_5_5 * (chroma + match_value)							)
												_UpperCamelCase      :	Tuple				       =     round(2_5_5 * (second_largest_component + match_value)							)
						elif hue_section > 3 and hue_section <= 4:
												_UpperCamelCase      :	List[Any]				       =     round(2_5_5 * (match_value)							)
												_UpperCamelCase      :	Union[str, Any]				       =     round(2_5_5 * (second_largest_component + match_value)							)
												_UpperCamelCase      :	int				       =     round(2_5_5 * (chroma + match_value)							)
						elif hue_section > 4 and hue_section <= 5:
												_UpperCamelCase      :	Any				       =     round(2_5_5 * (second_largest_component + match_value)							)
												_UpperCamelCase      :	Optional[int]				       =     round(2_5_5 * (match_value)							)
												_UpperCamelCase      :	Optional[Any]				       =     round(2_5_5 * (chroma + match_value)							)
						else:
												_UpperCamelCase      :	Dict				       =     round(2_5_5 * (chroma + match_value)							)
												_UpperCamelCase      :	Union[str, Any]				       =     round(2_5_5 * (match_value)							)
												_UpperCamelCase      :	List[Any]				       =     round(2_5_5 * (second_largest_component + match_value)							)

						return [red, green, blue]






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							):
						if red < 0 or red > 2_5_5:
												raise Exception('red should be between 0 and 255'							)

						if green < 0 or green > 2_5_5:
												raise Exception('green should be between 0 and 255'							)

						if blue < 0 or blue > 2_5_5:
												raise Exception('blue should be between 0 and 255'							)

						_UpperCamelCase      :	str				       =     red / 2_5_5
						_UpperCamelCase      :	Any				       =     green / 2_5_5
						_UpperCamelCase      :	List[str]				       =     blue / 2_5_5
						_UpperCamelCase      :	List[str]				       =     max(UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							)
						_UpperCamelCase      :	Dict				       =     value - min(UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							)
						_UpperCamelCase      :	int				       =     0 if value == 0 else chroma / value

						if chroma == 0:
												_UpperCamelCase      :	Any				       =     0.0
						elif value == float_red:
												_UpperCamelCase      :	List[str]				       =     6_0 * (0 + (float_green - float_blue) / chroma)
						elif value == float_green:
												_UpperCamelCase      :	List[str]				       =     6_0 * (2 + (float_blue - float_red) / chroma)
						else:
												_UpperCamelCase      :	List[str]				       =     6_0 * (4 + (float_red - float_green) / chroma)

						_UpperCamelCase      :	Optional[int]				       =     (hue + 3_6_0) % 3_6_0

						return [hue, saturation, value]






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_							):
						_UpperCamelCase      :	Any				       =     abs(hsv_a[0] - hsv_a[0]							) < 0.2
						_UpperCamelCase      :	List[str]				       =     abs(hsv_a[1] - hsv_a[1]							) < 0.002
						_UpperCamelCase      :	Any				       =     abs(hsv_a[2] - hsv_a[2]							) < 0.002

						return check_hue and check_saturation and check_value







'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_=0.999						,						UpperCAmelCase_="cosine"						,						):
						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      :	Union[str, Any]				       =     []
						for i in range(UpperCAmelCase_							):
												_UpperCamelCase      :	Any				       =     i / num_diffusion_timesteps
												_UpperCamelCase      :	Any				       =     (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 							lowercase__			(							lowercase		,      lowercase     ):
	lowercase__						       =    [e.name for e in KarrasDiffusionSchedulers]
	lowercase__						       =    2





	@register_to_config
	def __init__(				self      :       List[str]				,lowerCamelCase__      :       int = 1000				,lowerCamelCase__      :       float = 0.0_0_0_8_5				,lowerCamelCase__      :       float = 0.0_1_2				,lowerCamelCase__      :       str = "linear"				,lowerCamelCase__      :       Optional[Union[np.ndarray, List[float]]] = None				,lowerCamelCase__      :       str = "epsilon"				,lowerCamelCase__      :       str = "linspace"				,lowerCamelCase__      :       int = 0				,):







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

							_UpperCamelCase      :	Tuple				       =     1.0 - self.betas
							_UpperCamelCase      :	Any				       =     torch.cumprod(self.alphas				,dim=0				)

							#  set all values
							self.set_timesteps(lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       Any				,lowerCamelCase__      :       Optional[int]=None				):







							'''simple docstring'''
							if schedule_timesteps is None:
													_UpperCamelCase      :	Optional[Any]				       =     self.timesteps

							_UpperCamelCase      :	List[str]				       =     (schedule_timesteps == timestep).nonzero()

							# The sigma index that is taken for the **very** first `step`
							# is always the second index (or the last index if there is only 1)
							# This way we can ensure we don't accidentally skip a sigma in
							# case we start in the middle of the denoising schedule (e.g. for image-to-image)
							if len(self._index_counter				) == 0:
													_UpperCamelCase      :	List[Any]				       =     1 if len(lowerCamelCase__				) > 1 else 0
							else:
													_UpperCamelCase      :	Optional[Any]				       =     timestep.cpu().item() if torch.is_tensor(lowerCamelCase__				) else timestep
													_UpperCamelCase      :	str				       =     self._index_counter[timestep_int]

							return indices[pos].item()





	@property
	def 			UpperCamelCase_						(				self      :       List[str]				):







							'''simple docstring'''
							# standard deviation of the initial noise distribution
							if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       Union[float, torch.FloatTensor]				,):







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     self.index_for_timestep(lowerCamelCase__				)

							if self.state_in_first_order:
													_UpperCamelCase      :	str				       =     self.sigmas[step_index]
							else:
													_UpperCamelCase      :	List[Any]				       =     self.sigmas_interpol[step_index - 1]

							_UpperCamelCase      :	Optional[Any]				       =     sample / ((sigma**2 + 1) ** 0.5)
							return sample





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       int				,lowerCamelCase__      :       Union[str, torch.device] = None				,lowerCamelCase__      :       Optional[int] = None				,):







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     num_inference_steps

							_UpperCamelCase      :	List[str]				       =     num_train_timesteps or self.config.num_train_timesteps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
													_UpperCamelCase      :	Optional[int]				       =     np.linspace(0				,num_train_timesteps - 1				,lowerCamelCase__				,dtype=lowerCamelCase__				)[::-1].copy()
							elif self.config.timestep_spacing == "leading":
													_UpperCamelCase      :	Optional[Any]				       =     num_train_timesteps // self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													_UpperCamelCase      :	Tuple				       =     (np.arange(0				,lowerCamelCase__				) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__				)
													timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
													_UpperCamelCase      :	Dict				       =     num_train_timesteps / self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													_UpperCamelCase      :	int				       =     (np.arange(lowerCamelCase__				,0				,-step_ratio				)).round().copy().astype(lowerCamelCase__				)
													timesteps -= 1
							else:
													raise ValueError(
													    F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'				)

							_UpperCamelCase      :	List[str]				       =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
							_UpperCamelCase      :	str				       =     torch.from_numpy(np.log(lowerCamelCase__				)				).to(lowerCamelCase__				)

							_UpperCamelCase      :	int				       =     np.interp(lowerCamelCase__				,np.arange(0				,len(lowerCamelCase__				)				)				,lowerCamelCase__				)
							_UpperCamelCase      :	Any				       =     np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
							_UpperCamelCase      :	int				       =     torch.from_numpy(lowerCamelCase__				).to(device=lowerCamelCase__				)

							# compute up and down sigmas
							_UpperCamelCase      :	Dict				       =     sigmas.roll(-1				)
							_UpperCamelCase      :	Tuple				       =     0.0
							_UpperCamelCase      :	Optional[Any]				       =     (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
							_UpperCamelCase      :	List[str]				       =     (sigmas_next**2 - sigmas_up**2) ** 0.5
							_UpperCamelCase      :	Tuple				       =     0.0

							# compute interpolated sigmas
							_UpperCamelCase      :	Any				       =     sigmas.log().lerp(sigmas_down.log()				,0.5				).exp()
							_UpperCamelCase      :	Tuple				       =     0.0

							# set sigmas
							_UpperCamelCase      :	Tuple				       =     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2				), sigmas[-1:]]				)
							_UpperCamelCase      :	Tuple				       =     torch.cat(
							    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2				), sigmas_interpol[-1:]]				)
							_UpperCamelCase      :	Optional[int]				       =     torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2				), sigmas_up[-1:]]				)
							_UpperCamelCase      :	List[str]				       =     torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2				), sigmas_down[-1:]]				)

							if str(lowerCamelCase__				).startswith('mps'				):
													# mps does not support float64
													_UpperCamelCase      :	int				       =     torch.from_numpy(lowerCamelCase__				).to(lowerCamelCase__				,dtype=torch.floataa				)
							else:
													_UpperCamelCase      :	Dict				       =     torch.from_numpy(lowerCamelCase__				).to(lowerCamelCase__				)

							_UpperCamelCase      :	List[str]				       =     self.sigma_to_t(lowerCamelCase__				).to(lowerCamelCase__				,dtype=timesteps.dtype				)
							_UpperCamelCase      :	List[Any]				       =     torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])				,dim=-1				).flatten()

							_UpperCamelCase      :	int				       =     torch.cat([timesteps[:1], interleaved_timesteps]				)

							_UpperCamelCase      :	Optional[int]				       =     None

							# for exp beta schedules, such as the one for `pipeline_shap_e.py`
							# we need an index counter
							_UpperCamelCase      :	Optional[Any]				       =     defaultdict(lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       List[Any]				,lowerCamelCase__      :       Optional[Any]				):







							'''simple docstring'''
							# get log sigma
							_UpperCamelCase      :	Union[str, Any]				       =     sigma.log()

							# get distribution
							_UpperCamelCase      :	str				       =     log_sigma - self.log_sigmas[:, None]

							# get sigmas range
							_UpperCamelCase      :	List[str]				       =     dists.ge(0				).cumsum(dim=0				).argmax(dim=0				).clamp(max=self.log_sigmas.shape[0] - 2				)
							_UpperCamelCase      :	Any				       =     low_idx + 1

							_UpperCamelCase      :	Optional[int]				       =     self.log_sigmas[low_idx]
							_UpperCamelCase      :	Optional[int]				       =     self.log_sigmas[high_idx]

							# interpolate sigmas
							_UpperCamelCase      :	Optional[int]				       =     (low - log_sigma) / (low - high)
							_UpperCamelCase      :	Any				       =     w.clamp(0				,1				)

							# transform interpolation to time range
							_UpperCamelCase      :	int				       =     (1 - w) * low_idx + w * high_idx
							_UpperCamelCase      :	Dict				       =     t.view(sigma.shape				)
							return t





	@property
	def 			UpperCamelCase_						(				self      :       Any				):







							'''simple docstring'''
							return self.sample is None





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       Union[torch.FloatTensor, np.ndarray]				,lowerCamelCase__      :       Union[float, torch.FloatTensor]				,lowerCamelCase__      :       Union[torch.FloatTensor, np.ndarray]				,lowerCamelCase__      :       Optional[torch.Generator] = None				,lowerCamelCase__      :       bool = True				,):







							'''simple docstring'''
							_UpperCamelCase      :	Optional[Any]				       =     self.index_for_timestep(lowerCamelCase__				)

							# advance index counter by 1
							_UpperCamelCase      :	int				       =     timestep.cpu().item() if torch.is_tensor(lowerCamelCase__				) else timestep
							self._index_counter[timestep_int] += 1

							if self.state_in_first_order:
													_UpperCamelCase      :	Optional[int]				       =     self.sigmas[step_index]
													_UpperCamelCase      :	str				       =     self.sigmas_interpol[step_index]
													_UpperCamelCase      :	Optional[int]				       =     self.sigmas_up[step_index]
													_UpperCamelCase      :	List[Any]				       =     self.sigmas_down[step_index - 1]
							else:
													# 2nd order / KPDM2's method
													_UpperCamelCase      :	List[Any]				       =     self.sigmas[step_index - 1]
													_UpperCamelCase      :	Dict				       =     self.sigmas_interpol[step_index - 1]
													_UpperCamelCase      :	List[str]				       =     self.sigmas_up[step_index - 1]
													_UpperCamelCase      :	Optional[Any]				       =     self.sigmas_down[step_index - 1]

							# currently only gamma=0 is supported. This usually works best anyways.
							# We can support gamma in the future but then need to scale the timestep before
							# passing it to the model which requires a change in API
							_UpperCamelCase      :	int				       =     0
							_UpperCamelCase      :	Union[str, Any]				       =     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							_UpperCamelCase      :	Union[str, Any]				       =     model_output.device
							_UpperCamelCase      :	Dict				       =     randn_tensor(model_output.shape				,dtype=model_output.dtype				,device=lowerCamelCase__				,generator=lowerCamelCase__				)

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
													_UpperCamelCase      :	Union[str, Any]				       =     sigma_hat if self.state_in_first_order else sigma_interpol
													_UpperCamelCase      :	str				       =     sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
													_UpperCamelCase      :	Optional[Any]				       =     sigma_hat if self.state_in_first_order else sigma_interpol
													_UpperCamelCase      :	Optional[Any]				       =     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
													    sample / (sigma_input**2 + 1)
													)
							elif self.config.prediction_type == "sample":
													raise NotImplementedError('prediction_type not implemented yet: sample'				)
							else:
													raise ValueError(
													    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'				)

							if self.state_in_first_order:
													# 2. Convert to an ODE derivative for 1st order
													_UpperCamelCase      :	Dict				       =     (sample - pred_original_sample) / sigma_hat
													# 3. delta timestep
													_UpperCamelCase      :	Optional[Any]				       =     sigma_interpol - sigma_hat

													# store for 2nd order step
													_UpperCamelCase      :	Union[str, Any]				       =     sample
													_UpperCamelCase      :	Any				       =     dt
													_UpperCamelCase      :	Optional[Any]				       =     sample + derivative * dt
							else:
													# DPM-Solver-2
													# 2. Convert to an ODE derivative for 2nd order
													_UpperCamelCase      :	Tuple				       =     (sample - pred_original_sample) / sigma_interpol
													# 3. delta timestep
													_UpperCamelCase      :	int				       =     sigma_down - sigma_hat

													_UpperCamelCase      :	Dict				       =     self.sample
													_UpperCamelCase      :	Tuple				       =     None

													_UpperCamelCase      :	Dict				       =     sample + derivative * dt
													_UpperCamelCase      :	int				       =     prev_sample + noise * sigma_up

							if not return_dict:
													return (prev_sample,)

							return SchedulerOutput(prev_sample=lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       Dict				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       torch.FloatTensor				,):







							'''simple docstring'''
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							_UpperCamelCase      :	Optional[int]				       =     self.sigmas.to(device=original_samples.device				,dtype=original_samples.dtype				)
							if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase__				):
													# mps does not support float64
													_UpperCamelCase      :	Any				       =     self.timesteps.to(original_samples.device				,dtype=torch.floataa				)
													_UpperCamelCase      :	Dict				       =     timesteps.to(original_samples.device				,dtype=torch.floataa				)
							else:
													_UpperCamelCase      :	List[str]				       =     self.timesteps.to(original_samples.device				)
													_UpperCamelCase      :	str				       =     timesteps.to(original_samples.device				)

							_UpperCamelCase      :	Optional[int]				       =     [self.index_for_timestep(lowerCamelCase__				,lowerCamelCase__				) for t in timesteps]

							_UpperCamelCase      :	Any				       =     sigmas[step_indices].flatten()
							while len(sigma.shape				) < len(original_samples.shape				):
													_UpperCamelCase      :	Optional[Any]				       =     sigma.unsqueeze(-1				)

							_UpperCamelCase      :	Dict				       =     original_samples + noise * sigma
							return noisy_samples





	def __len__(				self      :       List[str]				):







							'''simple docstring'''
							return self.config.num_train_timesteps


