






'''simple docstring'''







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

import torch

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





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):






			def __init__( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			VQModel  ,						__SCREAMING_SNAKE_CASE      :			UNetaDModel  ,						__SCREAMING_SNAKE_CASE      :			DDIMScheduler					)			->			Tuple:
							super().__init__()
							self.register_modules(vqvae=__SCREAMING_SNAKE_CASE  ,						unet=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)







			@torch.no_grad()
			def __call__( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			int = 1  ,						__SCREAMING_SNAKE_CASE      :			Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,						__SCREAMING_SNAKE_CASE      :			float = 0.0  ,						__SCREAMING_SNAKE_CASE      :			int = 50  ,						__SCREAMING_SNAKE_CASE      :			Optional[str] = "pil"  ,						__SCREAMING_SNAKE_CASE      :			bool = True  ,						**__SCREAMING_SNAKE_CASE      :			List[Any]  ,						)			->			Union[Tuple, ImagePipelineOutput]:
							a_		:  List[str]											=      randn_tensor(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)  ,						generator=__SCREAMING_SNAKE_CASE  ,						)
							a_		:  Tuple											=      latents.to(self.device					)

							# scale the initial noise by the standard deviation required by the scheduler
							a_		:  Dict											=      latents * self.scheduler.init_noise_sigma

							self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE					)

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							a_		:  Tuple											=      '''eta''' in set(inspect.signature(self.scheduler.step					).parameters.keys()					)

							a_		:  Tuple											=      {}
							if accepts_eta:
											a_		:  int											=      eta

							for t in self.progress_bar(self.scheduler.timesteps					):
											a_		:  Union[str, Any]											=      self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
											# predict the noise residual
											a_		:  Tuple											=      self.unet(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					).sample
											# compute the previous noisy sample x_t -> x_t-1
											a_		:  str											=      self.scheduler.step(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					).prev_sample

							# decode the image latents with the VAE
							a_		:  Tuple											=      self.vqvae.decode(__SCREAMING_SNAKE_CASE					).sample

							a_		:  Dict											=      (image / 2 + 0.5).clamp(0  ,						1					)
							a_		:  Tuple											=      image.cpu().permute(0  ,						2  ,						3  ,						1					).numpy()
							if output_type == "pil":
											a_		:  List[str]											=      self.numpy_to_pil(__SCREAMING_SNAKE_CASE					)

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE					)












'''simple docstring'''







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate





@dataclass(frozen=SCREAMING_SNAKE_CASE_       )
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				field(default="image-classification"	,   metadata={"include_in_asdict_even_if_is_default": True}       )
			snake_case__  =				Features({"image": Image()}       )
			snake_case__  =				Features({"labels": ClassLabel}       )
			snake_case__  =				"image"
			snake_case__  =				"labels"






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Any:
							if self.label_column not in features:
											raise ValueError(f'Column {self.label_column} is not present in features.'					)
							if not isinstance(features[self.label_column]  ,						__SCREAMING_SNAKE_CASE					):
											raise ValueError(f'Column {self.label_column} is not a ClassLabel.'					)
							a_		:  Optional[int]											=      copy.deepcopy(self					)
							a_		:  int											=      self.label_schema.copy()
							a_		:  Tuple											=      features[self.label_column]
							a_		:  str											=      label_schema
							return task_template







			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Dict[str, str]:
							return {
							    self.image_column: "image",
							    self.label_column: "labels",
							}




