






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

						"""simple docstring"""



						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(UpperCamelCase_				:    Optional[int]						):
																		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		:				Optional[Any]            =    [e.name for e in KarrasDiffusionSchedulers]
						_SCREAMING_SNAKE_CASE		:				Union[str, Any]            =    2






						@register_to_config
						def __init__(		self   ,  _UpperCamelCase = 10_00   ,  _UpperCamelCase = 0.0_00_85   ,  _UpperCamelCase = 0.0_12   ,  _UpperCamelCase = "linear"   ,  _UpperCamelCase = None   ,  _UpperCamelCase = "epsilon"   ,  _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      )

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






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






												"""simple docstring"""
												if schedule_timesteps is None:
																		lowerCAmelCase__			  =  self.timesteps

												lowerCAmelCase__			  =  (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:
																		lowerCAmelCase__			  =  1 if len(_UpperCamelCase      ) > 1 else 0
												else:
																		lowerCAmelCase__			  =  timestep.cpu().item() if torch.is_tensor(_UpperCamelCase      ) else timestep
																		lowerCAmelCase__			  =  self._index_counter[timestep_int]

												return indices[pos].item()






						@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"""
												lowerCAmelCase__			  =  self.index_for_timestep(_UpperCamelCase      )

												if self.state_in_first_order:
																		lowerCAmelCase__			  =  self.sigmas[step_index]
												else:
																		lowerCAmelCase__			  =  self.sigmas_interpol[step_index - 1]

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






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






												"""simple docstring"""
												lowerCAmelCase__			  =  num_inference_steps

												lowerCAmelCase__			  =  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":
																		lowerCAmelCase__			  =  np.linspace(0   ,  num_train_timesteps - 1   ,  _UpperCamelCase   ,  dtype=_UpperCamelCase      )[::-1].copy()
												elif self.config.timestep_spacing == "leading":
																		lowerCAmelCase__			  =  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__			  =  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(_UpperCamelCase   ,  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__			  =  torch.from_numpy(np.log(_UpperCamelCase      )      ).to(_UpperCamelCase      )

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

												# compute up and down sigmas
												lowerCAmelCase__			  =  sigmas.roll(-1      )
												lowerCAmelCase__			  =  0.0
												lowerCAmelCase__			  =  (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
												lowerCAmelCase__			  =  (sigmas_next**2 - sigmas_up**2) ** 0.5
												lowerCAmelCase__			  =  0.0

												# compute interpolated sigmas
												lowerCAmelCase__			  =  sigmas.log().lerp(sigmas_down.log()   ,  0.5      ).exp()
												lowerCAmelCase__			  =  0.0

												# set sigmas
												lowerCAmelCase__			  =  torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
												lowerCAmelCase__			  =  torch.cat(
												    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )
												lowerCAmelCase__			  =  torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2      ), sigmas_up[-1:]]      )
												lowerCAmelCase__			  =  torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2      ), sigmas_down[-1:]]      )

												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(_UpperCamelCase      )

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

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

												lowerCAmelCase__			  =  None

												# for exp beta schedules, such as the one for `pipeline_shap_e.py`
												# we need an index counter
												lowerCAmelCase__			  =  defaultdict(_UpperCamelCase      )






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






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

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

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

												lowerCAmelCase__			  =  self.log_sigmas[low_idx]
												lowerCAmelCase__			  =  self.log_sigmas[high_idx]

												# interpolate sigmas
												lowerCAmelCase__			  =  (low - log_sigma) / (low - high)
												lowerCAmelCase__			  =  w.clamp(0   ,  1      )

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






						@property
						def 					UpperCamelCase__					(		self      ):






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






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






												"""simple docstring"""
												lowerCAmelCase__			  =  self.index_for_timestep(_UpperCamelCase      )

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

												if self.state_in_first_order:
																		lowerCAmelCase__			  =  self.sigmas[step_index]
																		lowerCAmelCase__			  =  self.sigmas_interpol[step_index]
																		lowerCAmelCase__			  =  self.sigmas_up[step_index]
																		lowerCAmelCase__			  =  self.sigmas_down[step_index - 1]
												else:
																		# 2nd order / KPDM2's method
																		lowerCAmelCase__			  =  self.sigmas[step_index - 1]
																		lowerCAmelCase__			  =  self.sigmas_interpol[step_index - 1]
																		lowerCAmelCase__			  =  self.sigmas_up[step_index - 1]
																		lowerCAmelCase__			  =  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
												lowerCAmelCase__			  =  0
												lowerCAmelCase__			  =  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

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

												# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
												if self.config.prediction_type == "epsilon":
																		lowerCAmelCase__			  =  sigma_hat if self.state_in_first_order else sigma_interpol
																		lowerCAmelCase__			  =  sample - sigma_input * model_output
												elif self.config.prediction_type == "v_prediction":
																		lowerCAmelCase__			  =  sigma_hat if self.state_in_first_order else sigma_interpol
																		lowerCAmelCase__			  =  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
																		lowerCAmelCase__			  =  (sample - pred_original_sample) / sigma_hat
																		# 3. delta timestep
																		lowerCAmelCase__			  =  sigma_interpol - sigma_hat

																		# store for 2nd order step
																		lowerCAmelCase__			  =  sample
																		lowerCAmelCase__			  =  dt
																		lowerCAmelCase__			  =  sample + derivative * dt
												else:
																		# DPM-Solver-2
																		# 2. Convert to an ODE derivative for 2nd order
																		lowerCAmelCase__			  =  (sample - pred_original_sample) / sigma_interpol
																		# 3. delta timestep
																		lowerCAmelCase__			  =  sigma_down - sigma_hat

																		lowerCAmelCase__			  =  self.sample
																		lowerCAmelCase__			  =  None

																		lowerCAmelCase__			  =  sample + derivative * dt
																		lowerCAmelCase__			  =  prev_sample + noise * sigma_up

												if not return_dict:
																		return (prev_sample,)

												return SchedulerOutput(prev_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__			  =  [self.index_for_timestep(_UpperCamelCase   ,  _UpperCamelCase      ) 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





