"""simple docstring"""





import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class  _snake_case     (					a__		):
						snake_case__			  =			42
						snake_case__			  =			42

class  _snake_case     (					a__				, a__		):
						snake_case__			  =			1






						@register_to_config
						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			int = 2000							,					UpperCAmelCase      :			float = 0.1_5							,					UpperCAmelCase      :			float = 0.0_1							,					UpperCAmelCase      :			float = 1348.0							,					UpperCAmelCase      :			float = 1E-5							,					UpperCAmelCase      :			int = 1							,					):
													# standard deviation of the initial noise distribution
													__lowerCamelCase					:		List[str]		   =     sigma_max

													# setable values
													__lowerCamelCase					:		Optional[int]		   =     None

													self.set_sigmas(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			torch.FloatTensor							,					UpperCAmelCase      :			Optional[int] = None    ):
													return sample






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			float = None							,					UpperCAmelCase      :			Union[str, torch.device] = None    ):
													__lowerCamelCase					:		Optional[int]		   =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

													__lowerCamelCase					:		List[str]		   =     torch.linspace(1							,					UpperCAmelCase							,					UpperCAmelCase							,					device=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			float = None							,					UpperCAmelCase      :			float = None							,					UpperCAmelCase      :			float = None    ):
													__lowerCamelCase					:		Dict		   =     sigma_min if sigma_min is not None else self.config.sigma_min
													__lowerCamelCase					:		Any		   =     sigma_max if sigma_max is not None else self.config.sigma_max
													__lowerCamelCase					:		Dict		   =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
													if self.timesteps is None:
																				self.set_timesteps(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
													__lowerCamelCase					:		Tuple		   =     torch.exp(torch.linspace(math.log(UpperCAmelCase    )							,					math.log(UpperCAmelCase    )							,					UpperCAmelCase    )    )
													__lowerCamelCase					:		Optional[Any]		   =     torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Optional[int]    ):
													return torch.where(
													    timesteps == 0							,					torch.zeros_like(t.to(timesteps.device    )    )							,					self.discrete_sigmas[timesteps - 1].to(timesteps.device    )							,					)






						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			torch.FloatTensor							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			torch.FloatTensor							,					UpperCAmelCase      :			Optional[torch.Generator] = None							,					UpperCAmelCase      :			bool = True							,					):
													if self.timesteps is None:
																				raise ValueError(
																				    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

													__lowerCamelCase					:		str		   =     timestep * torch.ones(
													    sample.shape[0]							,					device=sample.device    )  # torch.repeat_interleave(timestep, sample.shape[0])
													__lowerCamelCase					:		Union[str, Any]		   =     (timestep * (len(self.timesteps    ) - 1)).long()

													# mps requires indices to be in the same device, so we use cpu as is the default with cuda
													__lowerCamelCase					:		List[Any]		   =     timesteps.to(self.discrete_sigmas.device    )

													__lowerCamelCase					:		Dict		   =     self.discrete_sigmas[timesteps].to(sample.device    )
													__lowerCamelCase					:		Tuple		   =     self.get_adjacent_sigma(UpperCAmelCase							,					UpperCAmelCase    ).to(sample.device    )
													__lowerCamelCase					:		Optional[int]		   =     torch.zeros_like(UpperCAmelCase    )
													__lowerCamelCase					:		Union[str, Any]		   =     (sigma**2 - adjacent_sigma**2) ** 0.5

													# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
													# also equation 47 shows the analog from SDE models to ancestral sampling methods
													__lowerCamelCase					:		List[str]		   =     diffusion.flatten()
													while len(diffusion.shape    ) < len(sample.shape    ):
																				__lowerCamelCase					:		Union[str, Any]		   =     diffusion.unsqueeze(-1    )
													__lowerCamelCase					:		int		   =     drift - diffusion**2 * model_output

													#  equation 6: sample noise for the diffusion term of
													__lowerCamelCase					:		str		   =     randn_tensor(
													    sample.shape							,					layout=sample.layout							,					generator=UpperCAmelCase							,					device=sample.device							,					dtype=sample.dtype    )
													__lowerCamelCase					:		Tuple		   =     sample - drift  # subtract because `dt` is a small negative timestep
													# TODO is the variable diffusion the correct scaling term for the noise?
													__lowerCamelCase					:		Tuple		   =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

													if not return_dict:
																				return (prev_sample, prev_sample_mean)

													return SdeVeOutput(prev_sample=UpperCAmelCase							,					prev_sample_mean=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			torch.FloatTensor							,					UpperCAmelCase      :			torch.FloatTensor							,					UpperCAmelCase      :			Optional[torch.Generator] = None							,					UpperCAmelCase      :			bool = True							,					):
													if self.timesteps is None:
																				raise ValueError(
																				    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

													# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
													# sample noise for correction
													__lowerCamelCase					:		List[Any]		   =     randn_tensor(sample.shape							,					layout=sample.layout							,					generator=UpperCAmelCase    ).to(sample.device    )

													# compute step size from the model_output, the noise, and the snr
													__lowerCamelCase					:		str		   =     torch.norm(model_output.reshape(model_output.shape[0]							,					-1    )							,					dim=-1    ).mean()
													__lowerCamelCase					:		int		   =     torch.norm(noise.reshape(noise.shape[0]							,					-1    )							,					dim=-1    ).mean()
													__lowerCamelCase					:		Tuple		   =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
													__lowerCamelCase					:		List[str]		   =     step_size * torch.ones(sample.shape[0]    ).to(sample.device    )
													# self.repeat_scalar(step_size, sample.shape[0])

													# compute corrected sample: model_output term and noise term
													__lowerCamelCase					:		List[str]		   =     step_size.flatten()
													while len(step_size.shape    ) < len(sample.shape    ):
																				__lowerCamelCase					:		str		   =     step_size.unsqueeze(-1    )
													__lowerCamelCase					:		List[str]		   =     sample + step_size * model_output
													__lowerCamelCase					:		int		   =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

													if not return_dict:
																				return (prev_sample,)

													return SchedulerOutput(prev_sample=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			torch.FloatTensor							,					UpperCAmelCase      :			torch.FloatTensor							,					UpperCAmelCase      :			torch.FloatTensor							,					):
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													__lowerCamelCase					:		Any		   =     timesteps.to(original_samples.device    )
													__lowerCamelCase					:		Optional[int]		   =     self.discrete_sigmas.to(original_samples.device    )[timesteps]
													__lowerCamelCase					:		Any		   =     (
													    noise * sigmas[:, None, None, None]
													    if noise is not None
													    else torch.randn_like(UpperCAmelCase    ) * sigmas[:, None, None, None]
													)
													__lowerCamelCase					:		Tuple		   =     noise + original_samples
													return noisy_samples







						def __len__(       self      :			List[str]    ):
													return self.config.num_train_timesteps






"""simple docstring"""





from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class  _snake_case     (					a__		):






						def __init__(       self      :			str							,					UpperCAmelCase      :			TransformeraDModel							,					UpperCAmelCase      :			AutoencoderKL							,					UpperCAmelCase      :			KarrasDiffusionSchedulers							,					UpperCAmelCase      :			Optional[Dict[int, str]] = None							,					):
													super().__init__()
													self.register_modules(transformer=UpperCAmelCase							,					vae=UpperCAmelCase							,					scheduler=UpperCAmelCase    )

													# create a imagenet -> id dictionary for easier use
													__lowerCamelCase					:		Optional[Any]		   =     {}
													if idalabel is not None:
																				for key, value in idalabel.items():
																											for label in value.split(","    ):
																																		__lowerCamelCase					:		List[str]		   =     int(UpperCAmelCase    )
																				__lowerCamelCase					:		Union[str, Any]		   =     dict(sorted(self.labels.items()    )    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Union[str, List[str]]    ):

													if not isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																				__lowerCamelCase					:		List[str]		   =     list(UpperCAmelCase    )

													for l in label:
																				if l not in self.labels:
																											raise ValueError(
																											    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""    )

													return [self.labels[l] for l in label]







						@torch.no_grad()
						def __call__(       self      :			int							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			float = 4.0							,					UpperCAmelCase      :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					UpperCAmelCase      :			int = 50							,					UpperCAmelCase      :			Optional[str] = "pil"							,					UpperCAmelCase      :			bool = True							,					):
													__lowerCamelCase					:		int		   =     len(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     self.transformer.config.sample_size
													__lowerCamelCase					:		Dict		   =     self.transformer.config.in_channels

													__lowerCamelCase					:		Any		   =     randn_tensor(
													    shape=(batch_size, latent_channels, latent_size, latent_size)							,					generator=UpperCAmelCase							,					device=self.device							,					dtype=self.transformer.dtype							,					)
													__lowerCamelCase					:		Optional[Any]		   =     torch.cat([latents] * 2    ) if guidance_scale > 1 else latents

													__lowerCamelCase					:		Optional[int]		   =     torch.tensor(UpperCAmelCase							,					device=self.device    ).reshape(-1    )
													__lowerCamelCase					:		Optional[int]		   =     torch.tensor([1000] * batch_size							,					device=self.device    )
													__lowerCamelCase					:		Any		   =     torch.cat([class_labels, class_null]							,					0    ) if guidance_scale > 1 else class_labels

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

													for t in self.progress_bar(self.scheduler.timesteps    ):
																				if guidance_scale > 1:
																											__lowerCamelCase					:		str		   =     latent_model_input[: len(UpperCAmelCase    ) // 2]
																											__lowerCamelCase					:		Optional[Any]		   =     torch.cat([half, half]							,					dim=0    )
																				__lowerCamelCase					:		Dict		   =     self.scheduler.scale_model_input(UpperCAmelCase							,					UpperCAmelCase    )

																				__lowerCamelCase					:		Dict		   =     t
																				if not torch.is_tensor(UpperCAmelCase    ):
																											# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																											# This would be a good case for the `match` statement (Python 3.10+)
																											__lowerCamelCase					:		List[str]		   =     latent_model_input.device.type == "mps"
																											if isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																																		__lowerCamelCase					:		List[Any]		   =     torch.floataa if is_mps else torch.floataa
																											else:
																																		__lowerCamelCase					:		Optional[Any]		   =     torch.intaa if is_mps else torch.intaa
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.tensor([timesteps]							,					dtype=UpperCAmelCase							,					device=latent_model_input.device    )
																				elif len(timesteps.shape    ) == 0:
																											__lowerCamelCase					:		Union[str, Any]		   =     timesteps[None].to(latent_model_input.device    )
																				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																				__lowerCamelCase					:		List[Any]		   =     timesteps.expand(latent_model_input.shape[0]    )
																				# predict noise model_output
																				__lowerCamelCase					:		Union[str, Any]		   =     self.transformer(
																				    UpperCAmelCase							,					timestep=UpperCAmelCase							,					class_labels=UpperCAmelCase    ).sample

																				# perform guidance
																				if guidance_scale > 1:
																											__lowerCamelCase   ,  __lowerCamelCase					:		str		   =     noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																											__lowerCamelCase   ,  __lowerCamelCase					:		Union[str, Any]		   =     torch.split(UpperCAmelCase							,					len(UpperCAmelCase    ) // 2							,					dim=0    )

																											__lowerCamelCase					:		List[str]		   =     uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.cat([half_eps, half_eps]							,					dim=0    )

																											__lowerCamelCase					:		Optional[Any]		   =     torch.cat([eps, rest]							,					dim=1    )

																				# learned sigma
																				if self.transformer.config.out_channels // 2 == latent_channels:
																											__lowerCamelCase   ,  __lowerCamelCase					:		int		   =     torch.split(UpperCAmelCase							,					UpperCAmelCase							,					dim=1    )
																				else:
																											__lowerCamelCase					:		int		   =     noise_pred

																				# compute previous image: x_t -> x_t-1
																				__lowerCamelCase					:		Optional[Any]		   =     self.scheduler.step(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    ).prev_sample

													if guidance_scale > 1:
																				__lowerCamelCase   ,  __lowerCamelCase					:		List[str]		   =     latent_model_input.chunk(2							,					dim=0    )
													else:
																				__lowerCamelCase					:		Optional[Any]		   =     latent_model_input

													__lowerCamelCase					:		Tuple		   =     1 / self.vae.config.scaling_factor * latents
													__lowerCamelCase					:		Any		   =     self.vae.decode(UpperCAmelCase    ).sample

													__lowerCamelCase					:		Tuple		   =     (samples / 2 + 0.5).clamp(0							,					1    )

													# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
													__lowerCamelCase					:		Optional[int]		   =     samples.cpu().permute(0							,					2							,					3							,					1    ).float().numpy()

													if output_type == "pil":
																				__lowerCamelCase					:		Dict		   =     self.numpy_to_pil(UpperCAmelCase    )

													if not return_dict:
																				return (samples,)

													return ImagePipelineOutput(images=UpperCAmelCase    )