


"""simple docstring"""






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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def 	lowerCamelCase			(	_UpperCamelCase						: Tuple			,     _UpperCamelCase						: Any=0.999			,     _UpperCamelCase						: Any="cosine"			,     )						->			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						: str				):
																					return math.exp(t * -12.0				)

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

							__UpperCAmelCase  :		List[Any] 				=   []
							for i in range(UpperCAmelCase_				):
														__UpperCAmelCase  :		List[Any] 				=   i / num_diffusion_timesteps
														__UpperCAmelCase  :		Dict 				=   (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 	lowerCamelCase__  (	A	,       A							):







					"""simple docstring"""


					__a      			=				[e.name for e in KarrasDiffusionSchedulers]
					__a      			=				2






					@register_to_config
					def __init__(			self       :					List[str]					,			UpperCamelCase       :					Optional[Any] = 1_000					,			UpperCamelCase       :					Dict = 0.00085					,			UpperCamelCase       :					List[Any] = 0.012					,			UpperCamelCase       :					Any = "linear"					,			UpperCamelCase       :					Union[str, Any] = None					,			UpperCamelCase       :					Dict = "epsilon"					,			UpperCamelCase       :					Optional[Any] = False					,			UpperCamelCase       :					Union[str, Any] = False					,			UpperCamelCase       :					Optional[Any] = 1.0					,			UpperCamelCase       :					Any = "linspace"					,			UpperCamelCase       :					Dict = 0					,			):
												'''simple docstring'''






												if trained_betas is not None:
																			__UpperCAmelCase  :		List[str] 				=   torch.tensor(UpperCamelCase_					,			dtype=torch.floataa    )
												elif beta_schedule == "linear":
																			__UpperCAmelCase  :		Any 				=   torch.linspace(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_					,			dtype=torch.floataa    )
												elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			__UpperCAmelCase  :		Union[str, Any] 				=   (
																			    torch.linspace(beta_start**0.5					,			beta_end**0.5					,			UpperCamelCase_					,			dtype=torch.floataa    ) ** 2
																			)
												elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			__UpperCAmelCase  :		Optional[Any] 				=   betas_for_alpha_bar(UpperCamelCase_					,			alpha_transform_type="""cosine"""    )
												elif beta_schedule == "exp":
																			__UpperCAmelCase  :		Any 				=   betas_for_alpha_bar(UpperCamelCase_					,			alpha_transform_type="""exp"""    )
												else:
																			raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''    )

												__UpperCAmelCase  :		Dict 				=   1.0 - self.betas
												__UpperCAmelCase  :		Dict 				=   torch.cumprod(self.alphas					,			dim=0    )

												#  set all values
												self.set_timesteps(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_    )
												__UpperCAmelCase  :		str 				=   use_karras_sigmas






					def lowerCamelCase__				(			self       :					str					,			UpperCamelCase       :					Dict					,			UpperCamelCase       :					int=None    ):
												'''simple docstring'''






												if schedule_timesteps is None:
																			__UpperCAmelCase  :		Dict 				=   self.timesteps

												__UpperCAmelCase  :		Tuple 				=   (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:
																			__UpperCAmelCase  :		Optional[Any] 				=   1 if len(UpperCamelCase_    ) > 1 else 0
												else:
																			__UpperCAmelCase  :		Any 				=   timestep.cpu().item() if torch.is_tensor(UpperCamelCase_    ) else timestep
																			__UpperCAmelCase  :		str 				=   self._index_counter[timestep_int]

												return indices[pos].item()






					@property
					def lowerCamelCase__				(			self       :					List[str]    ):
												'''simple docstring'''






												if self.config.timestep_spacing in ["linspace", "trailing"]:
																			return self.sigmas.max()

												return (self.sigmas.max() ** 2 + 1) ** 0.5






					def lowerCamelCase__				(			self       :					Union[str, Any]					,			UpperCamelCase       :					Tuple					,			UpperCamelCase       :					List[Any]					,			):
												'''simple docstring'''






												__UpperCAmelCase  :		str 				=   self.index_for_timestep(UpperCamelCase_    )

												__UpperCAmelCase  :		str 				=   self.sigmas[step_index]
												__UpperCAmelCase  :		Optional[Any] 				=   sample / ((sigma**2 + 1) ** 0.5)
												return sample






					def lowerCamelCase__				(			self       :					List[Any]					,			UpperCamelCase       :					Optional[int]					,			UpperCamelCase       :					Union[str, Any] = None					,			UpperCamelCase       :					Optional[Any] = None					,			):
												'''simple docstring'''






												__UpperCAmelCase  :		List[str] 				=   num_inference_steps

												__UpperCAmelCase  :		Optional[Any] 				=   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":
																			__UpperCAmelCase  :		Any 				=   np.linspace(0					,			num_train_timesteps - 1					,			UpperCamelCase_					,			dtype=UpperCamelCase_    )[::-1].copy()
												elif self.config.timestep_spacing == "leading":
																			__UpperCAmelCase  :		str 				=   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
																			__UpperCAmelCase  :		int 				=   (np.arange(0					,			UpperCamelCase_    ) * step_ratio).round()[::-1].copy().astype(UpperCamelCase_    )
																			timesteps += self.config.steps_offset
												elif self.config.timestep_spacing == "trailing":
																			__UpperCAmelCase  :		Union[str, Any] 				=   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
																			__UpperCAmelCase  :		List[Any] 				=   (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\'.'''    )

												__UpperCAmelCase  :		Union[str, Any] 				=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
												__UpperCAmelCase  :		str 				=   np.log(UpperCamelCase_    )
												__UpperCAmelCase  :		Dict 				=   np.interp(UpperCamelCase_					,			np.arange(0					,			len(UpperCamelCase_    )    )					,			UpperCamelCase_    )

												if self.config.use_karras_sigmas:
																			__UpperCAmelCase  :		Optional[int] 				=   self._convert_to_karras(in_sigmas=UpperCamelCase_					,			num_inference_steps=self.num_inference_steps    )
																			__UpperCAmelCase  :		Union[str, Any] 				=   np.array([self._sigma_to_t(UpperCamelCase_					,			UpperCamelCase_    ) for sigma in sigmas]    )

												__UpperCAmelCase  :		List[Any] 				=   np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
												__UpperCAmelCase  :		Optional[int] 				=   torch.from_numpy(UpperCamelCase_    ).to(device=UpperCamelCase_    )
												__UpperCAmelCase  :		Tuple 				=   torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2    ), sigmas[-1:]]    )

												__UpperCAmelCase  :		Optional[Any] 				=   torch.from_numpy(UpperCamelCase_    )
												__UpperCAmelCase  :		Optional[Any] 				=   torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2    )]    )

												if str(UpperCamelCase_    ).startswith("""mps"""    ):
																			# mps does not support float64
																			__UpperCAmelCase  :		str 				=   timesteps.to(UpperCamelCase_					,			dtype=torch.floataa    )
												else:
																			__UpperCAmelCase  :		int 				=   timesteps.to(device=UpperCamelCase_    )

												# empty dt and derivative
												__UpperCAmelCase  :		Tuple 				=   None
												__UpperCAmelCase  :		Optional[Any] 				=   None

												# for exp beta schedules, such as the one for `pipeline_shap_e.py`
												# we need an index counter
												__UpperCAmelCase  :		str 				=   defaultdict(UpperCamelCase_    )






					def lowerCamelCase__				(			self       :					List[Any]					,			UpperCamelCase       :					Any					,			UpperCamelCase       :					Any    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Dict 				=   np.log(UpperCamelCase_    )

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

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

												__UpperCAmelCase  :		List[Any] 				=   log_sigmas[low_idx]
												__UpperCAmelCase  :		Union[str, Any] 				=   log_sigmas[high_idx]

												# interpolate sigmas
												__UpperCAmelCase  :		Any 				=   (low - log_sigma) / (low - high)
												__UpperCAmelCase  :		Tuple 				=   np.clip(UpperCamelCase_					,			0					,			1    )

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






					def lowerCamelCase__				(			self       :					Optional[int]					,			UpperCamelCase       :					Optional[int]					,			UpperCamelCase       :					Optional[Any]    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Tuple 				=   in_sigmas[-1].item()
												__UpperCAmelCase  :		List[Any] 				=   in_sigmas[0].item()

												__UpperCAmelCase  :		str 				=   7.0  # 7.0 is the value used in the paper
												__UpperCAmelCase  :		Tuple 				=   np.linspace(0					,			1					,			UpperCamelCase_    )
												__UpperCAmelCase  :		Optional[Any] 				=   sigma_min ** (1 / rho)
												__UpperCAmelCase  :		Optional[Any] 				=   sigma_max ** (1 / rho)
												__UpperCAmelCase  :		Tuple 				=   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
												return sigmas






					@property
					def lowerCamelCase__				(			self       :					List[Any]    ):
												'''simple docstring'''






												return self.dt is None






					def lowerCamelCase__				(			self       :					int					,			UpperCamelCase       :					Union[str, Any]					,			UpperCamelCase       :					List[str]					,			UpperCamelCase       :					Optional[int]					,			UpperCamelCase       :					Optional[Any] = True					,			):
												'''simple docstring'''






												__UpperCAmelCase  :		List[str] 				=   self.index_for_timestep(UpperCamelCase_    )

												# advance index counter by 1
												__UpperCAmelCase  :		int 				=   timestep.cpu().item() if torch.is_tensor(UpperCamelCase_    ) else timestep
												self._index_counter[timestep_int] += 1

												if self.state_in_first_order:
																			__UpperCAmelCase  :		Union[str, Any] 				=   self.sigmas[step_index]
																			__UpperCAmelCase  :		List[str] 				=   self.sigmas[step_index + 1]
												else:
																			# 2nd order / Heun's method
																			__UpperCAmelCase  :		List[Any] 				=   self.sigmas[step_index - 1]
																			__UpperCAmelCase  :		List[str] 				=   self.sigmas[step_index]

												# 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
												__UpperCAmelCase  :		Tuple 				=   0
												__UpperCAmelCase  :		Tuple 				=   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

												# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
												if self.config.prediction_type == "epsilon":
																			__UpperCAmelCase  :		Optional[int] 				=   sigma_hat if self.state_in_first_order else sigma_next
																			__UpperCAmelCase  :		Any 				=   sample - sigma_input * model_output
												elif self.config.prediction_type == "v_prediction":
																			__UpperCAmelCase  :		Tuple 				=   sigma_hat if self.state_in_first_order else sigma_next
																			__UpperCAmelCase  :		int 				=   model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																			    sample / (sigma_input**2 + 1)
																			)
												elif self.config.prediction_type == "sample":
																			__UpperCAmelCase  :		Union[str, Any] 				=   model_output
												else:
																			raise ValueError(
																			    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''    )

												if self.config.clip_sample:
																			__UpperCAmelCase  :		Dict 				=   pred_original_sample.clamp(
																			    -self.config.clip_sample_range					,			self.config.clip_sample_range    )

												if self.state_in_first_order:
																			# 2. Convert to an ODE derivative for 1st order
																			__UpperCAmelCase  :		Union[str, Any] 				=   (sample - pred_original_sample) / sigma_hat
																			# 3. delta timestep
																			__UpperCAmelCase  :		int 				=   sigma_next - sigma_hat

																			# store for 2nd order step
																			__UpperCAmelCase  :		Tuple 				=   derivative
																			__UpperCAmelCase  :		Optional[Any] 				=   dt
																			__UpperCAmelCase  :		Optional[Any] 				=   sample
												else:
																			# 2. 2nd order / Heun's method
																			__UpperCAmelCase  :		int 				=   (sample - pred_original_sample) / sigma_next
																			__UpperCAmelCase  :		Any 				=   (self.prev_derivative + derivative) / 2

																			# 3. take prev timestep & sample
																			__UpperCAmelCase  :		List[Any] 				=   self.dt
																			__UpperCAmelCase  :		Optional[int] 				=   self.sample

																			# free dt and derivative
																			# Note, this puts the scheduler in "first order mode"
																			__UpperCAmelCase  :		Dict 				=   None
																			__UpperCAmelCase  :		int 				=   None
																			__UpperCAmelCase  :		Tuple 				=   None

												__UpperCAmelCase  :		Optional[int] 				=   sample + derivative * dt

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=UpperCamelCase_    )






					def lowerCamelCase__				(			self       :					Tuple					,			UpperCamelCase       :					Any					,			UpperCamelCase       :					Optional[int]					,			UpperCamelCase       :					int					,			):
												'''simple docstring'''






												__UpperCAmelCase  :		Tuple 				=   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
																			__UpperCAmelCase  :		Union[str, Any] 				=   self.timesteps.to(original_samples.device					,			dtype=torch.floataa    )
																			__UpperCAmelCase  :		Optional[Any] 				=   timesteps.to(original_samples.device					,			dtype=torch.floataa    )
												else:
																			__UpperCAmelCase  :		Any 				=   self.timesteps.to(original_samples.device    )
																			__UpperCAmelCase  :		Dict 				=   timesteps.to(original_samples.device    )

												__UpperCAmelCase  :		Dict 				=   [self.index_for_timestep(UpperCamelCase_					,			UpperCamelCase_    ) for t in timesteps]

												__UpperCAmelCase  :		List[str] 				=   sigmas[step_indices].flatten()
												while len(sigma.shape    ) < len(original_samples.shape    ):
																			__UpperCAmelCase  :		Optional[int] 				=   sigma.unsqueeze(-1    )

												__UpperCAmelCase  :		Union[str, Any] 				=   original_samples + noise * sigma
												return noisy_samples






					def __len__(			self       :					str    ):
												'''simple docstring'''






												return self.config.num_train_timesteps







'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		int:
		super().__init__()

		# make sure scheduler can always be converted to DDIM
		snake_case__	       =   DDIMScheduler.from_config(scheduler.config		)

		self.register_modules(unet=UpperCamelCase_      ,       scheduler=UpperCamelCase_		)





	@torch.no_grad()
	def __call__(		self      ,       UpperCamelCase_ = 1      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = 0.0      ,       UpperCamelCase_ = 50      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = "pil"      ,       UpperCamelCase_ = True      ,       )      ->		Union[ImagePipelineOutput, Tuple]:

		# Sample gaussian noise to begin loop
		if isinstance(self.unet.config.sample_size      ,       UpperCamelCase_		):
			snake_case__	       =   (
			    batch_size,
			    self.unet.config.in_channels,
			    self.unet.config.sample_size,
			    self.unet.config.sample_size,
			)
		else:
			snake_case__	       =   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

		if isinstance(UpperCamelCase_      ,       UpperCamelCase_		) and len(UpperCamelCase_		) != batch_size:
			raise ValueError(
			    F'''You have passed a list of generators of length {len(UpperCamelCase_		)}, but requested an effective batch'''
			    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

		snake_case__	       =   randn_tensor(UpperCamelCase_      ,       generator=UpperCamelCase_      ,       device=self.device      ,       dtype=self.unet.dtype		)

		# set step values
		self.scheduler.set_timesteps(UpperCamelCase_		)

		for t in self.progress_bar(self.scheduler.timesteps		):
			# 1. predict noise model_output
			snake_case__	       =   self.unet(UpperCamelCase_      ,       UpperCamelCase_		).sample

			# 2. predict previous mean of image x_t-1 and add variance depending on eta
			# eta corresponds to η in paper and should be between [0, 1]
			# do x_t -> x_t-1
			snake_case__	       =   self.scheduler.step(
			    UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       eta=UpperCamelCase_      ,       use_clipped_model_output=UpperCamelCase_      ,       generator=UpperCamelCase_		).prev_sample

		snake_case__	       =   (image / 2 + 0.5).clamp(0      ,       1		)
		snake_case__	       =   image.cpu().permute(0      ,       2      ,       3      ,       1		).numpy()
		if output_type == "pil":
			snake_case__	       =   self.numpy_to_pil(UpperCamelCase_		)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=UpperCamelCase_		)



