




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 a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def __init__( self :     Any				,							lowerCamelCase__ :     TransformeraDModel				,							lowerCamelCase__ :     AutoencoderKL				,							lowerCamelCase__ :     KarrasDiffusionSchedulers				,							lowerCamelCase__ :     Optional[Dict[int, str]] = None				,							)    ->						Dict:
													"""simple docstring"""


													super().__init__()
													self.register_modules(transformer=lowerCamelCase__				,							vae=lowerCamelCase__				,							scheduler=lowerCamelCase__				)

													# create a imagenet -> id dictionary for easier use
													__lowercase      =    {}
													if idalabel is not None:
																				for key, value in idalabel.items():
																											for label in value.split(''','''				):
																																		__lowercase      =    int(lowerCamelCase__				)
																				__lowercase      =    dict(sorted(self.labels.items()				)				)






						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     Union[str, List[str]]				)    ->						List[int]:
													"""simple docstring"""


													if not isinstance(lowerCamelCase__				,							lowerCamelCase__				):
																				__lowercase      =    list(lowerCamelCase__				)

													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 :     str				,							lowerCamelCase__ :     List[int]				,							lowerCamelCase__ :     float = 4.0				,							lowerCamelCase__ :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							lowerCamelCase__ :     int = 50				,							lowerCamelCase__ :     Optional[str] = "pil"				,							lowerCamelCase__ :     bool = True				,							)    ->						Union[ImagePipelineOutput, Tuple]:
													"""simple docstring"""


													__lowercase      =    len(lowerCamelCase__				)
													__lowercase      =    self.transformer.config.sample_size
													__lowercase      =    self.transformer.config.in_channels

													__lowercase      =    randn_tensor(
													    shape=(batch_size, latent_channels, latent_size, latent_size)				,							generator=lowerCamelCase__				,							device=self.device				,							dtype=self.transformer.dtype				,							)
													__lowercase      =    torch.cat([latents] * 2				) if guidance_scale > 1 else latents

													__lowercase      =    torch.tensor(lowerCamelCase__				,							device=self.device				).reshape(-1				)
													__lowercase      =    torch.tensor([1_000] * batch_size				,							device=self.device				)
													__lowercase      =    torch.cat([class_labels, class_null]				,							0				) if guidance_scale > 1 else class_labels

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

													for t in self.progress_bar(self.scheduler.timesteps				):
																				if guidance_scale > 1:
																											__lowercase      =    latent_model_input[: len(lowerCamelCase__				) // 2]
																											__lowercase      =    torch.cat([half, half]				,							dim=0				)
																				__lowercase      =    self.scheduler.scale_model_input(lowerCamelCase__				,							lowerCamelCase__				)

																				__lowercase      =    t
																				if not torch.is_tensor(lowerCamelCase__				):
																											# 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+)
																											__lowercase      =    latent_model_input.device.type == '''mps'''
																											if isinstance(lowerCamelCase__				,							lowerCamelCase__				):
																																		__lowercase      =    torch.floataa if is_mps else torch.floataa
																											else:
																																		__lowercase      =    torch.intaa if is_mps else torch.intaa
																											__lowercase      =    torch.tensor([timesteps]				,							dtype=lowerCamelCase__				,							device=latent_model_input.device				)
																				elif len(timesteps.shape				) == 0:
																											__lowercase      =    timesteps[None].to(latent_model_input.device				)
																				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																				__lowercase      =    timesteps.expand(latent_model_input.shape[0]				)
																				# predict noise model_output
																				__lowercase      =    self.transformer(
																				    lowerCamelCase__				,							timestep=lowerCamelCase__				,							class_labels=lowerCamelCase__				).sample

																				# perform guidance
																				if guidance_scale > 1:
																											__lowercase     ,     __lowercase      =    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																											__lowercase     ,     __lowercase      =    torch.split(lowerCamelCase__				,							len(lowerCamelCase__				) // 2				,							dim=0				)

																											__lowercase      =    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																											__lowercase      =    torch.cat([half_eps, half_eps]				,							dim=0				)

																											__lowercase      =    torch.cat([eps, rest]				,							dim=1				)

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

																				# compute previous image: x_t -> x_t-1
																				__lowercase      =    self.scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				).prev_sample

													if guidance_scale > 1:
																				__lowercase     ,     __lowercase      =    latent_model_input.chunk(2				,							dim=0				)
													else:
																				__lowercase      =    latent_model_input

													__lowercase      =    1 / self.vae.config.scaling_factor * latents
													__lowercase      =    self.vae.decode(lowerCamelCase__				).sample

													__lowercase      =    (samples / 2 + 0.5).clamp(0				,							1				)

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

													if output_type == "pil":
																				__lowercase      =    self.numpy_to_pil(lowerCamelCase__				)

													if not return_dict:
																				return (samples,)

													return ImagePipelineOutput(images=lowerCamelCase__				)












from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       UNetaDModel
						UpperCamelCase_	:       KarrasVeScheduler






						def __init__( self :     Tuple				,							lowerCamelCase__ :     UNetaDModel				,							lowerCamelCase__ :     KarrasVeScheduler				)    ->						Optional[Any]:
													"""simple docstring"""


													super().__init__()
													self.register_modules(unet=lowerCamelCase__				,							scheduler=lowerCamelCase__				)






						@torch.no_grad()
						def __call__( self :     str				,							lowerCamelCase__ :     int = 1				,							lowerCamelCase__ :     int = 50				,							lowerCamelCase__ :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							lowerCamelCase__ :     Optional[str] = "pil"				,							lowerCamelCase__ :     bool = True				,							**lowerCamelCase__ :     int				,							)    ->						Union[Tuple, ImagePipelineOutput]:
													"""simple docstring"""


													__lowercase      =    self.unet.config.sample_size
													__lowercase      =    (batch_size, 3, img_size, img_size)

													__lowercase      =    self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													__lowercase      =    randn_tensor(lowerCamelCase__				,							generator=lowerCamelCase__				,							device=self.device				) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(lowerCamelCase__				)

													for t in self.progress_bar(self.scheduler.timesteps				):
																				# here sigma_t == t_i from the paper
																				__lowercase      =    self.scheduler.schedule[t]
																				__lowercase      =    self.scheduler.schedule[t - 1] if t > 0 else 0

																				# 1. Select temporarily increased noise level sigma_hat
																				# 2. Add new noise to move from sample_i to sample_hat
																				__lowercase     ,     __lowercase      =    self.scheduler.add_noise_to_input(lowerCamelCase__				,							lowerCamelCase__				,							generator=lowerCamelCase__				)

																				# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																				# The model inputs and output are adjusted by following eq. (213) in [1].
																				__lowercase      =    (sigma_hat / 2) * model((sample_hat + 1) / 2				,							sigma_hat / 2				).sample

																				# 4. Evaluate dx/dt at sigma_hat
																				# 5. Take Euler step from sigma to sigma_prev
																				__lowercase      =    self.scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)

																				if sigma_prev != 0:
																											# 6. Apply 2nd order correction
																											# The model inputs and output are adjusted by following eq. (213) in [1].
																											__lowercase      =    (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2				,							sigma_prev / 2				).sample
																											__lowercase      =    self.scheduler.step_correct(
																											    lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							step_output.prev_sample				,							step_output['''derivative''']				,							)
																				__lowercase      =    step_output.prev_sample

													__lowercase      =    (sample / 2 + 0.5).clamp(0				,							1				)
													__lowercase      =    sample.cpu().permute(0				,							2				,							3				,							1				).numpy()
													if output_type == "pil":
																				__lowercase      =    self.numpy_to_pil(lowerCamelCase__				)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=lowerCamelCase__				)






