





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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						42
							snake_case_							 =						None







def       UpperCAmelCase							(     _lowerCamelCase      :  List[Any]			,   _lowerCamelCase      :  str=0.9_9_9			,   _lowerCamelCase      :  List[str]="cosine"			,   ):





						'''simple docstring'''


						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_lowerCamelCase      :  Optional[Any]     ):
																		return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2     ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(_lowerCamelCase      :  Optional[Any]     ):
																		return math.exp(t * -1_2.0     )

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

						SCREAMING_SNAKE_CASE__     :				int   =					[]
						for i in range(_lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				List[Any]   =					i / num_diffusion_timesteps
												SCREAMING_SNAKE_CASE__     :				Dict   =					(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_lowerCamelCase     ) / alpha_bar_fn(_lowerCamelCase     )			,   _lowerCamelCase     )     )
						return torch.tensor(_lowerCamelCase			,   dtype=torch.floataa     )



class    _a     (				lowercase__					,							lowercase__							):






							"""simple docstring"""

							@register_to_config
							def __init__(      self				:     int							,	a				:     int = 10_00							,	a				:     str = "fixed_small_log"							,	a				:     bool = True							,	a				:     Optional[float] = 1.0							,	a				:     str = "epsilon"							,	a				:     str = "squaredcos_cap_v2"							,	)	->List[str]:
													if beta_schedule != "squaredcos_cap_v2":
																			raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"    )

													SCREAMING_SNAKE_CASE__     :				List[str]   =					betas_for_alpha_bar(a    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					1.0 - self.betas
													SCREAMING_SNAKE_CASE__     :				Dict   =					torch.cumprod(self.alphas							,	dim=0    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					torch.tensor(1.0    )

													# standard deviation of the initial noise distribution
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					1.0

													# setable values
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					None
													SCREAMING_SNAKE_CASE__     :				Any   =					torch.from_numpy(np.arange(0							,	a    )[::-1].copy()    )

													SCREAMING_SNAKE_CASE__     :				Tuple   =					variance_type

							def      A_  (      self				:     str							,	a				:     torch.FloatTensor							,	a				:     Optional[int] = None    )	->torch.FloatTensor:
													return sample

							def      A_  (      self				:     Optional[Any]							,	a				:     int							,	a				:     Union[str, torch.device] = None    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					num_inference_steps
													SCREAMING_SNAKE_CASE__     :				int   =					(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					(np.arange(0							,	a    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					torch.from_numpy(a    ).to(a    )

							def      A_  (      self				:     List[str]							,	a				:     str							,	a				:     Optional[Any]=None							,	a				:     Any=None							,	a				:     Optional[Any]=None    )	->str:
													if prev_timestep is None:
																			SCREAMING_SNAKE_CASE__     :				int   =					t - 1

													SCREAMING_SNAKE_CASE__     :				int   =					self.alphas_cumprod[t]
													SCREAMING_SNAKE_CASE__     :				Any   =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					1 - alpha_prod_t
													SCREAMING_SNAKE_CASE__     :				int   =					1 - alpha_prod_t_prev

													if prev_timestep == t - 1:
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.betas[t]
													else:
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					1 - alpha_prod_t / 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
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					beta_prod_t_prev / beta_prod_t * beta

													if variance_type is None:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.config.variance_type

													# hacks - were probably added for training stability
													if variance_type == "fixed_small_log":
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					torch.log(torch.clamp(a							,	min=1E-20    )    )
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					torch.exp(0.5 * variance    )
													elif variance_type == "learned_range":
																			# NOTE difference with DDPM scheduler
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					variance.log()
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					beta.log()

																			SCREAMING_SNAKE_CASE__     :				Dict   =					(predicted_variance + 1) / 2
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					frac * max_log + (1 - frac) * min_log

													return variance

							def      A_  (      self				:     Optional[int]							,	a				:     torch.FloatTensor							,	a				:     int							,	a				:     torch.FloatTensor							,	a				:     Optional[int] = None							,	a				:     Union[str, Any]=None							,	a				:     bool = True							,	)	->Union[UnCLIPSchedulerOutput, Tuple]:
													SCREAMING_SNAKE_CASE__     :				Dict   =					timestep

													if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				str   =					torch.split(a							,	sample.shape[1]							,	dim=1    )
													else:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					None

													# 1. compute alphas, betas
													if prev_timestep is None:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					t - 1

													SCREAMING_SNAKE_CASE__     :				int   =					self.alphas_cumprod[t]
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
													SCREAMING_SNAKE_CASE__     :				int   =					1 - alpha_prod_t
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					1 - alpha_prod_t_prev

													if prev_timestep == t - 1:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					self.betas[t]
																			SCREAMING_SNAKE_CASE__     :				int   =					self.alphas[t]
													else:
																			SCREAMING_SNAKE_CASE__     :				Any   =					1 - alpha_prod_t / alpha_prod_t_prev
																			SCREAMING_SNAKE_CASE__     :				Dict   =					1 - beta

													# 2. compute predicted original sample from predicted noise also called
													# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
													if self.config.prediction_type == "epsilon":
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
													elif self.config.prediction_type == "sample":
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					model_output
													else:
																			raise ValueError(
																			    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
																			    " for the UnCLIPScheduler."    )

													# 3. Clip "predicted x_0"
													if self.config.clip_sample:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					torch.clamp(
																			    a							,	-self.config.clip_sample_range							,	self.config.clip_sample_range    )

													# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
													# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
													SCREAMING_SNAKE_CASE__     :				Tuple   =					(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
													SCREAMING_SNAKE_CASE__     :				Tuple   =					alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

													# 5. Compute predicted previous sample µ_t
													# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

													# 6. Add noise
													SCREAMING_SNAKE_CASE__     :				List[str]   =					0
													if t > 0:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					randn_tensor(
																			    model_output.shape							,	dtype=model_output.dtype							,	generator=a							,	device=model_output.device    )

																			SCREAMING_SNAKE_CASE__     :				int   =					self._get_variance(
																			    a							,	predicted_variance=a							,	prev_timestep=a							,	)

																			if self.variance_type == "fixed_small_log":
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					variance
																			elif self.variance_type == "learned_range":
																									SCREAMING_SNAKE_CASE__     :				List[str]   =					(0.5 * variance).exp()
																			else:
																									raise ValueError(
																									    f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																									    " for the UnCLIPScheduler."    )

																			SCREAMING_SNAKE_CASE__     :				Tuple   =					variance * variance_noise

													SCREAMING_SNAKE_CASE__     :				List[str]   =					pred_prev_sample + variance

													if not return_dict:
																			return (pred_prev_sample,)

													return UnCLIPSchedulerOutput(prev_sample=a							,	pred_original_sample=a    )






							def      A_  (      self				:     List[Any]							,	a				:     torch.FloatTensor							,	a				:     torch.FloatTensor							,	a				:     torch.IntTensor							,	)	->torch.FloatTensor:
													# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.alphas_cumprod.to(device=original_samples.device							,	dtype=original_samples.dtype    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					timesteps.to(original_samples.device    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					alphas_cumprod[timesteps] ** 0.5
													SCREAMING_SNAKE_CASE__     :				Any   =					sqrt_alpha_prod.flatten()
													while len(sqrt_alpha_prod.shape    ) < len(original_samples.shape    ):
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					sqrt_alpha_prod.unsqueeze(-1    )

													SCREAMING_SNAKE_CASE__     :				Any   =					(1 - alphas_cumprod[timesteps]) ** 0.5
													SCREAMING_SNAKE_CASE__     :				Any   =					sqrt_one_minus_alpha_prod.flatten()
													while len(sqrt_one_minus_alpha_prod.shape    ) < len(original_samples.shape    ):
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					sqrt_one_minus_alpha_prod.unsqueeze(-1    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
													return noisy_samples






from __future__ import annotations







def       UpperCAmelCase							(     _lowerCamelCase      :  list			,   _lowerCamelCase      :  int | None = None			,   _lowerCamelCase      :  int | None = None     ):





						'''simple docstring'''


						if start is None:
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					0

						if end is None:
												SCREAMING_SNAKE_CASE__     :				Any   =					len(_lowerCamelCase     ) - 1

						if start >= end:
												return

						SCREAMING_SNAKE_CASE__     :				List[str]   =					(start + end) // 2

						slowsort(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )
						slowsort(_lowerCamelCase			,   mid + 1			,   _lowerCamelCase     )

						if sequence[end] < sequence[mid]:
												SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					sequence[mid], sequence[end]

						slowsort(_lowerCamelCase			,   _lowerCamelCase			,   end - 1     )


if __name__ == "__main__":
				from doctest import testmod

				testmod()