






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

import numpy as np
import torch

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


__snake_case			:			Optional[Any]			   =   logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 			__SCREAMING_SNAKE_CASE    (      __lowercase):
						_SCREAMING_SNAKE_CASE		:				torch.FloatTensor
						_SCREAMING_SNAKE_CASE		:				Optional[torch.FloatTensor]            =    None







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

						"""simple docstring"""



						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(UpperCamelCase_				:    Tuple						):
																		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            =    [e.name for e in KarrasDiffusionSchedulers]
						_SCREAMING_SNAKE_CASE		:				Any            =    1






						@register_to_config
						def __init__(		self   ,  _UpperCamelCase = 10_00   ,  _UpperCamelCase = 0.00_01   ,  _UpperCamelCase = 0.02   ,  _UpperCamelCase = "linear"   ,  _UpperCamelCase = None   ,  _UpperCamelCase = "epsilon"   ,  _UpperCamelCase = "linear"   ,  _UpperCamelCase = False   ,  _UpperCamelCase = "linspace"   ,  _UpperCamelCase = 0   ,  ):






												"""simple docstring"""
												if trained_betas is not None:
																		lowerCAmelCase__			  =  torch.tensor(_UpperCamelCase   ,  dtype=torch.floataa      )
												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      )
												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__			  =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
												lowerCAmelCase__			  =  np.concatenate([sigmas[::-1], [0.0]]      ).astype(np.floataa      )
												lowerCAmelCase__			  =  torch.from_numpy(_UpperCamelCase      )

												# setable values
												lowerCAmelCase__			  =  None
												lowerCAmelCase__			  =  np.linspace(0   ,  num_train_timesteps - 1   ,  _UpperCamelCase   ,  dtype=_UpperCamelCase      )[::-1].copy()
												lowerCAmelCase__			  =  torch.from_numpy(_UpperCamelCase      )
												lowerCAmelCase__			  =  False
												lowerCAmelCase__			  =  use_karras_sigmas






						@property
						def 					UpperCamelCase__					(		self      ):






												"""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   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												if isinstance(_UpperCamelCase   ,  torch.Tensor      ):
																		lowerCAmelCase__			  =  timestep.to(self.timesteps.device      )
												lowerCAmelCase__			  =  (self.timesteps == timestep).nonzero().item()
												lowerCAmelCase__			  =  self.sigmas[step_index]

												lowerCAmelCase__			  =  sample / ((sigma**2 + 1) ** 0.5)

												lowerCAmelCase__			  =  True
												return sample






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






												"""simple docstring"""
												lowerCAmelCase__			  =  num_inference_steps

												# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
												if self.config.timestep_spacing == "linspace":
																		lowerCAmelCase__			  =  np.linspace(0   ,  self.config.num_train_timesteps - 1   ,  _UpperCamelCase   ,  dtype=_UpperCamelCase      )[
																		    ::-1
																		].copy()
												elif self.config.timestep_spacing == "leading":
																		lowerCAmelCase__			  =  self.config.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
																		lowerCAmelCase__			  =  (np.arange(0   ,  _UpperCamelCase      ) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase      )
																		timesteps += self.config.steps_offset
												elif self.config.timestep_spacing == "trailing":
																		lowerCAmelCase__			  =  self.config.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
																		lowerCAmelCase__			  =  (np.arange(self.config.num_train_timesteps   ,  0   ,  -step_ratio      )).round().copy().astype(_UpperCamelCase      )
																		timesteps -= 1
												else:
																		raise ValueError(
																		    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."      )

												lowerCAmelCase__			  =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
												lowerCAmelCase__			  =  np.log(_UpperCamelCase      )

												if self.config.interpolation_type == "linear":
																		lowerCAmelCase__			  =  np.interp(_UpperCamelCase   ,  np.arange(0   ,  len(_UpperCamelCase      )      )   ,  _UpperCamelCase      )
												elif self.config.interpolation_type == "log_linear":
																		lowerCAmelCase__			  =  torch.linspace(np.log(sigmas[-1]      )   ,  np.log(sigmas[0]      )   ,  num_inference_steps + 1      ).exp()
												else:
																		raise ValueError(
																		    F"{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"
																		    ' \'linear\' or \'log_linear\''      )

												if self.use_karras_sigmas:
																		lowerCAmelCase__			  =  self._convert_to_karras(in_sigmas=_UpperCamelCase   ,  num_inference_steps=self.num_inference_steps      )
																		lowerCAmelCase__			  =  np.array([self._sigma_to_t(_UpperCamelCase   ,  _UpperCamelCase      ) for sigma in sigmas]      )

												lowerCAmelCase__			  =  np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
												lowerCAmelCase__			  =  torch.from_numpy(_UpperCamelCase      ).to(device=_UpperCamelCase      )
												if str(_UpperCamelCase      ).startswith('mps'      ):
																		# mps does not support float64
																		lowerCAmelCase__			  =  torch.from_numpy(_UpperCamelCase      ).to(_UpperCamelCase   ,  dtype=torch.floataa      )
												else:
																		lowerCAmelCase__			  =  torch.from_numpy(_UpperCamelCase      ).to(device=_UpperCamelCase      )






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												# get log sigma
												lowerCAmelCase__			  =  np.log(_UpperCamelCase      )

												# get distribution
												lowerCAmelCase__			  =  log_sigma - log_sigmas[:, np.newaxis]

												# get sigmas range
												lowerCAmelCase__			  =  np.cumsum((dists >= 0)   ,  axis=0      ).argmax(axis=0      ).clip(max=log_sigmas.shape[0] - 2      )
												lowerCAmelCase__			  =  low_idx + 1

												lowerCAmelCase__			  =  log_sigmas[low_idx]
												lowerCAmelCase__			  =  log_sigmas[high_idx]

												# interpolate sigmas
												lowerCAmelCase__			  =  (low - log_sigma) / (low - high)
												lowerCAmelCase__			  =  np.clip(_UpperCamelCase   ,  0   ,  1      )

												# transform interpolation to time range
												lowerCAmelCase__			  =  (1 - w) * low_idx + w * high_idx
												lowerCAmelCase__			  =  t.reshape(sigma.shape      )
												return t






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  in_sigmas[-1].item()
												lowerCAmelCase__			  =  in_sigmas[0].item()

												lowerCAmelCase__			  =  7.0  # 7.0 is the value used in the paper
												lowerCAmelCase__			  =  np.linspace(0   ,  1   ,  _UpperCamelCase      )
												lowerCAmelCase__			  =  sigma_min ** (1 / rho)
												lowerCAmelCase__			  =  sigma_max ** (1 / rho)
												lowerCAmelCase__			  =  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
												return sigmas






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase = 0.0   ,  _UpperCamelCase = 0.0   ,  _UpperCamelCase = float('inf'      )   ,  _UpperCamelCase = 1.0   ,  _UpperCamelCase = None   ,  _UpperCamelCase = True   ,  ):






												"""simple docstring"""

												if (
												    isinstance(_UpperCamelCase   ,  _UpperCamelCase      )
												    or isinstance(_UpperCamelCase   ,  torch.IntTensor      )
												    or isinstance(_UpperCamelCase   ,  torch.LongTensor      )
												):
																		raise ValueError(
																		    (
																		        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
																		        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
																		        ' one of the `scheduler.timesteps` as a timestep.'
																		    )   ,  )

												if not self.is_scale_input_called:
																		logger.warning(
																		    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
																		    'See `StableDiffusionPipeline` for a usage example.'      )

												if isinstance(_UpperCamelCase   ,  torch.Tensor      ):
																		lowerCAmelCase__			  =  timestep.to(self.timesteps.device      )

												lowerCAmelCase__			  =  (self.timesteps == timestep).nonzero().item()
												lowerCAmelCase__			  =  self.sigmas[step_index]

												lowerCAmelCase__			  =  min(s_churn / (len(self.sigmas      ) - 1)   ,  2**0.5 - 1      ) if s_tmin <= sigma <= s_tmax else 0.0

												lowerCAmelCase__			  =  randn_tensor(
												    model_output.shape   ,  dtype=model_output.dtype   ,  device=model_output.device   ,  generator=_UpperCamelCase      )

												lowerCAmelCase__			  =  noise * s_noise
												lowerCAmelCase__			  =  sigma * (gamma + 1)

												if gamma > 0:
																		lowerCAmelCase__			  =  sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

												# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
												# NOTE: "original_sample" should not be an expected prediction_type but is left in for
												# backwards compatibility
												if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
																		lowerCAmelCase__			  =  model_output
												elif self.config.prediction_type == "epsilon":
																		lowerCAmelCase__			  =  sample - sigma_hat * model_output
												elif self.config.prediction_type == "v_prediction":
																		# * c_out + input * c_skip
																		lowerCAmelCase__			  =  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
												else:
																		raise ValueError(
																		    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"      )

												# 2. Convert to an ODE derivative
												lowerCAmelCase__			  =  (sample - pred_original_sample) / sigma_hat

												lowerCAmelCase__			  =  self.sigmas[step_index + 1] - sigma_hat

												lowerCAmelCase__			  =  sample + derivative * dt

												if not return_dict:
																		return (prev_sample,)

												return EulerDiscreteSchedulerOutput(prev_sample=_UpperCamelCase   ,  pred_original_sample=_UpperCamelCase      )






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






												"""simple docstring"""
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												lowerCAmelCase__			  =  self.sigmas.to(device=original_samples.device   ,  dtype=original_samples.dtype      )
												if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase      ):
																		# mps does not support float64
																		lowerCAmelCase__			  =  self.timesteps.to(original_samples.device   ,  dtype=torch.floataa      )
																		lowerCAmelCase__			  =  timesteps.to(original_samples.device   ,  dtype=torch.floataa      )
												else:
																		lowerCAmelCase__			  =  self.timesteps.to(original_samples.device      )
																		lowerCAmelCase__			  =  timesteps.to(original_samples.device      )

												lowerCAmelCase__			  =  [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

												lowerCAmelCase__			  =  original_samples + noise * sigma
												return noisy_samples







						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





