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_			(						SCREAMING_SNAKE_CASE						):






	'''simple docstring'''






	UpperCAmelCase_     			= 42
	UpperCAmelCase_     			= None






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__=0.9_9_9     ,  lowerCAmelCase__="cosine"     ,  ):
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(lowerCAmelCase__     ):
																					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__     ):
																					return math.exp(t * -1_2.0     )

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

							lowerCAmelCase__					     = []
							for i in range(lowerCAmelCase__     ):
														lowerCAmelCase__					     = i / num_diffusion_timesteps
														lowerCAmelCase__					     = (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_			(						SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE						):






	'''simple docstring'''






	@register_to_config
	def __init__(      self :  Dict      ,   lowercase__ :  int = 1_000      ,   lowercase__ :  str = "fixed_small_log"      ,   lowercase__ :  bool = True      ,   lowercase__ :  Optional[float] = 1.0      ,   lowercase__ :  str = "epsilon"      ,   lowercase__ :  str = "squaredcos_cap_v2"      ,   ):







								'''simple docstring'''



								if beta_schedule != "squaredcos_cap_v2":
															raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'')

								lowerCAmelCase__					     = betas_for_alpha_bar(lowercase__)

								lowerCAmelCase__					     = 1.0 - self.betas
								lowerCAmelCase__					     = torch.cumprod(self.alphas      ,   dim=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      ,   lowercase__)[::-1].copy())

								lowerCAmelCase__					     = variance_type




	def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  torch.FloatTensor      ,   lowercase__ :  Optional[int] = None):







								'''simple docstring'''



								return sample




	def 		__snake_case						(      self :  str      ,   lowercase__ :  int      ,   lowercase__ :  Union[str, torch.device] = None):







								'''simple docstring'''



								lowerCAmelCase__					     = num_inference_steps
								lowerCAmelCase__					     = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
								lowerCAmelCase__					     = (np.arange(0      ,   lowercase__) * step_ratio).round()[::-1].copy().astype(np.intaa)
								lowerCAmelCase__					     = torch.from_numpy(lowercase__).to(lowercase__)




	def 		__snake_case						(      self :  str      ,   lowercase__ :  Dict      ,   lowercase__ :  Tuple=None      ,   lowercase__ :  Any=None      ,   lowercase__ :  Any=None):







								'''simple docstring'''



								if prev_timestep is None:
															lowerCAmelCase__					     = t - 1

								lowerCAmelCase__					     = self.alphas_cumprod[t]
								lowerCAmelCase__					     = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
								lowerCAmelCase__					     = 1 - alpha_prod_t
								lowerCAmelCase__					     = 1 - alpha_prod_t_prev

								if prev_timestep == t - 1:
															lowerCAmelCase__					     = self.betas[t]
								else:
															lowerCAmelCase__					     = 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
								lowerCAmelCase__					     = beta_prod_t_prev / beta_prod_t * beta

								if variance_type is None:
															lowerCAmelCase__					     = self.config.variance_type

								# hacks - were probably added for training stability
								if variance_type == "fixed_small_log":
															lowerCAmelCase__					     = torch.log(torch.clamp(lowercase__      ,   min=1e-20))
															lowerCAmelCase__					     = torch.exp(0.5 * variance)
								elif variance_type == "learned_range":
															# NOTE difference with DDPM scheduler
															lowerCAmelCase__					     = variance.log()
															lowerCAmelCase__					     = beta.log()

															lowerCAmelCase__					     = (predicted_variance + 1) / 2
															lowerCAmelCase__					     = frac * max_log + (1 - frac) * min_log

								return variance




	def 		__snake_case						(      self :  Tuple      ,   lowercase__ :  torch.FloatTensor      ,   lowercase__ :  int      ,   lowercase__ :  torch.FloatTensor      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  Dict=None      ,   lowercase__ :  bool = True      ,   ):







								'''simple docstring'''



								lowerCAmelCase__					     = timestep

								if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
															lowerCAmelCase__     , lowerCAmelCase__					     = torch.split(lowercase__      ,   sample.shape[1]      ,   dim=1)
								else:
															lowerCAmelCase__					     = None

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

								lowerCAmelCase__					     = self.alphas_cumprod[t]
								lowerCAmelCase__					     = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
								lowerCAmelCase__					     = 1 - alpha_prod_t
								lowerCAmelCase__					     = 1 - alpha_prod_t_prev

								if prev_timestep == t - 1:
															lowerCAmelCase__					     = self.betas[t]
															lowerCAmelCase__					     = self.alphas[t]
								else:
															lowerCAmelCase__					     = 1 - alpha_prod_t / alpha_prod_t_prev
															lowerCAmelCase__					     = 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":
															lowerCAmelCase__					     = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
								elif self.config.prediction_type == "sample":
															lowerCAmelCase__					     = 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:
															lowerCAmelCase__					     = torch.clamp(
															    lowercase__      ,   -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
								lowerCAmelCase__					     = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
								lowerCAmelCase__					     = 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
								lowerCAmelCase__					     = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

								# 6. Add noise
								lowerCAmelCase__					     = 0
								if t > 0:
															lowerCAmelCase__					     = randn_tensor(
															    model_output.shape      ,   dtype=model_output.dtype      ,   generator=lowercase__      ,   device=model_output.device)

															lowerCAmelCase__					     = self._get_variance(
															    lowercase__      ,   predicted_variance=lowercase__      ,   prev_timestep=lowercase__      ,   )

															if self.variance_type == "fixed_small_log":
																						lowerCAmelCase__					     = variance
															elif self.variance_type == "learned_range":
																						lowerCAmelCase__					     = (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.')

															lowerCAmelCase__					     = variance * variance_noise

								lowerCAmelCase__					     = pred_prev_sample + variance

								if not return_dict:
															return (pred_prev_sample,)

								return UnCLIPSchedulerOutput(prev_sample=lowercase__      ,   pred_original_sample=lowercase__)




	def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  torch.FloatTensor      ,   lowercase__ :  torch.FloatTensor      ,   lowercase__ :  torch.IntTensor      ,   ):







								'''simple docstring'''



								lowerCAmelCase__					     = self.alphas_cumprod.to(device=original_samples.device      ,   dtype=original_samples.dtype)
								lowerCAmelCase__					     = timesteps.to(original_samples.device)

								lowerCAmelCase__					     = alphas_cumprod[timesteps] ** 0.5
								lowerCAmelCase__					     = sqrt_alpha_prod.flatten()
								while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
															lowerCAmelCase__					     = sqrt_alpha_prod.unsqueeze(-1)

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

								lowerCAmelCase__					     = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
								return noisy_samples

import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= True
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= 1
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= None




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        return self.__class__(**{k: copy.deepcopy(lowercase__) for k, v in self.__dict__.items()})
