




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    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=4_2
			UpperCamelCase__								=None







def 		lowercase__   (		__A:	int							,__A:	Optional[Any]=0.999							,__A:	int="cosine"							,):





					'''simple docstring'''






					if alpha_transform_type == "cosine":

										def alpha_bar_fn(__A:	Tuple				):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(__A:	List[str]				):
															return math.exp(t * -12.0				)

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

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


class    lowerCamelCase    (		_lowerCamelCase				,_lowerCamelCase						):






			'''simple docstring'''





			@register_to_config
			def __init__( self			:   Union[str, Any]       ,						lowerCamelCase_			:   int = 1000       ,						lowerCamelCase_			:   str = "fixed_small_log"       ,						lowerCamelCase_			:   bool = True       ,						lowerCamelCase_			:   Optional[float] = 1.0       ,						lowerCamelCase_			:   str = "epsilon"       ,						lowerCamelCase_			:   str = "squaredcos_cap_v2"       ,						)						->   List[Any]:
								if beta_schedule != "squaredcos_cap_v2":
													raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''  )

								__magic_name__							:	Optional[int]    							=							betas_for_alpha_bar(lowerCamelCase_  )

								__magic_name__							:	Union[str, Any]    							=							1.0 - self.betas
								__magic_name__							:	Optional[int]    							=							torch.cumprod(self.alphas       ,						dim=0  )
								__magic_name__							:	str    							=							torch.tensor(1.0  )

								# standard deviation of the initial noise distribution
								__magic_name__							:	Tuple    							=							1.0

								# setable values
								__magic_name__							:	List[str]    							=							None
								__magic_name__							:	List[str]    							=							torch.from_numpy(np.arange(0       ,						lowerCamelCase_  )[::-1].copy()  )

								__magic_name__							:	str    							=							variance_type

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   torch.FloatTensor       ,						lowerCamelCase_			:   Optional[int] = None  )						->   torch.FloatTensor:
								return sample

			def 							UpperCAmelCase__       ( self			:   Any       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Union[str, torch.device] = None  )						->   List[str]:
								__magic_name__							:	int    							=							num_inference_steps
								__magic_name__							:	Any    							=							(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
								__magic_name__							:	int    							=							(np.arange(0       ,						lowerCamelCase_  ) * step_ratio).round()[::-1].copy().astype(np.intaa  )
								__magic_name__							:	Tuple    							=							torch.from_numpy(lowerCamelCase_  ).to(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   List[str]=None       ,						lowerCamelCase_			:   Any=None       ,						lowerCamelCase_			:   str=None  )						->   Union[str, Any]:
								if prev_timestep is None:
													__magic_name__							:	Dict    							=							t - 1

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

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

								if variance_type is None:
													__magic_name__							:	str    							=							self.config.variance_type

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

													__magic_name__							:	Any    							=							(predicted_variance + 1) / 2
													__magic_name__							:	str    							=							frac * max_log + (1 - frac) * min_log

								return variance

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   torch.FloatTensor       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   torch.FloatTensor       ,						lowerCamelCase_			:   Optional[int] = None       ,						lowerCamelCase_			:   Optional[int]=None       ,						lowerCamelCase_			:   bool = True       ,						)						->   Union[UnCLIPSchedulerOutput, Tuple]:
								__magic_name__							:	List[str]    							=							timestep

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

								# 1. compute alphas, betas
								if prev_timestep is None:
													__magic_name__							:	Optional[Any]    							=							t - 1

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

								if prev_timestep == t - 1:
													__magic_name__							:	List[Any]    							=							self.betas[t]
													__magic_name__							:	Union[str, Any]    							=							self.alphas[t]
								else:
													__magic_name__							:	List[str]    							=							1 - alpha_prod_t / alpha_prod_t_prev
													__magic_name__							:	List[Any]    							=							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":
													__magic_name__							:	List[str]    							=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
								elif self.config.prediction_type == "sample":
													__magic_name__							:	Union[str, Any]    							=							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:
													__magic_name__							:	Any    							=							torch.clamp(
													    lowerCamelCase_       ,						-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
								__magic_name__							:	Optional[Any]    							=							(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
								__magic_name__							:	Dict    							=							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
								__magic_name__							:	Optional[int]    							=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

								# 6. Add noise
								__magic_name__							:	List[Any]    							=							0
								if t > 0:
													__magic_name__							:	Tuple    							=							randn_tensor(
													    model_output.shape       ,						dtype=model_output.dtype       ,						generator=lowerCamelCase_       ,						device=model_output.device  )

													__magic_name__							:	Optional[Any]    							=							self._get_variance(
													    lowerCamelCase_       ,						predicted_variance=lowerCamelCase_       ,						prev_timestep=lowerCamelCase_       ,						)

													if self.variance_type == "fixed_small_log":
																		__magic_name__							:	List[Any]    							=							variance
													elif self.variance_type == "learned_range":
																		__magic_name__							:	Any    							=							(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.'''  )

													__magic_name__							:	int    							=							variance * variance_noise

								__magic_name__							:	Tuple    							=							pred_prev_sample + variance

								if not return_dict:
													return (pred_prev_sample,)

								return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase_       ,						pred_original_sample=lowerCamelCase_  )


			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   torch.FloatTensor       ,						lowerCamelCase_			:   torch.FloatTensor       ,						lowerCamelCase_			:   torch.IntTensor       ,						)						->   torch.FloatTensor:
								# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
								__magic_name__							:	Optional[Any]    							=							self.alphas_cumprod.to(device=original_samples.device       ,						dtype=original_samples.dtype  )
								__magic_name__							:	Union[str, Any]    							=							timesteps.to(original_samples.device  )

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

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

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









# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    '''stable diffusion controlnet''',
    '''0.22.0''',
    '''Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.''',
    standard_warn=False,
    stacklevel=3,
)



