



"""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        __A       (	A_    ):







		'''simple docstring'''





		def __init__(	self						:  str			,_snake_case						:  VQModel			,_snake_case						:  UNetaDModel			,_snake_case						:  DDIMScheduler						)					->		str:






				"""simple docstring"""



				super().__init__()
				self.register_modules(vqvae=_snake_case			,unet=_snake_case			,scheduler=_snake_case						)







		@torch.no_grad()
		def __call__(	self						:  Optional[int]			,_snake_case						:  int = 1			,_snake_case						:  Optional[Union[torch.Generator, List[torch.Generator]]] = None			,_snake_case						:  float = 0.0			,_snake_case						:  int = 50			,_snake_case						:  Optional[str] = "pil"			,_snake_case						:  bool = True			,**_snake_case						:  Union[str, Any]			,)					->		Union[Tuple, ImagePipelineOutput]:






				"""simple docstring"""



				lowercase__   :    List[str]				=			randn_tensor(
				    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,generator=_snake_case			,)
				lowercase__   :    Union[str, Any]				=			latents.to(self.device						)

				# scale the initial noise by the standard deviation required by the scheduler
				lowercase__   :    Union[str, Any]				=			latents * self.scheduler.init_noise_sigma

				self.scheduler.set_timesteps(_snake_case						)

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

				lowercase__   :    str				=			{}
				if accepts_eta:
						lowercase__   :    Tuple				=			eta

				for t in self.progress_bar(self.scheduler.timesteps						):
						lowercase__   :    Optional[Any]				=			self.scheduler.scale_model_input(_snake_case			,_snake_case						)
						# predict the noise residual
						lowercase__   :    int				=			self.unet(_snake_case			,_snake_case						).sample
						# compute the previous noisy sample x_t -> x_t-1
						lowercase__   :    List[str]				=			self.scheduler.step(_snake_case			,_snake_case			,_snake_case			,**_snake_case						).prev_sample

				# decode the image latents with the VAE
				lowercase__   :    Dict				=			self.vqvae.decode(_snake_case						).sample

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

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=_snake_case						)





"""simple docstring"""







from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
							from .features import FeatureType
@dataclass
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					List[str]
		lowerCAmelCase			:					Optional[str]   	=				None
		# Automatically constructed
		lowerCAmelCase			:					ClassVar[str]   	=				"dict"
		lowerCAmelCase			:					ClassVar[Any]   	=				None
		lowerCAmelCase			:					str   	=				field(default="Translation"     ,init=A_     ,repr=A_    )







		def __call__(	self						:  List[str]						)					->		Any:






				"""simple docstring"""



				return pa.struct({lang: pa.string() for lang in sorted(self.languages						)}						)







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Union["FeatureType", Dict[str, "FeatureType"]]:






				"""simple docstring"""



				from .features import Value

				return {k: Value('''string'''						) for k in sorted(self.languages						)}





@dataclass
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					Optional[List]   	=				None
		lowerCAmelCase			:					Optional[int]   	=				None
		lowerCAmelCase			:					Optional[str]   	=				None
		# Automatically constructed
		lowerCAmelCase			:					ClassVar[str]   	=				"dict"
		lowerCAmelCase			:					ClassVar[Any]   	=				None
		lowerCAmelCase			:					str   	=				field(default="TranslationVariableLanguages"     ,init=A_     ,repr=A_    )







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			sorted(set(self.languages						)						) if self.languages else None
				lowercase__   :    Dict				=			len(self.languages						) if self.languages else None







		def __call__(	self						:  List[Any]						)					->		List[Any]:






				"""simple docstring"""



				return pa.struct({'''language''': pa.list_(pa.string()						), '''translation''': pa.list_(pa.string()						)}						)







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  Tuple						)					->		int:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			set(self.languages						)
				if self.languages and set(_snake_case						) - lang_set:
						raise ValueError(
						    f"""Some languages in example ({", ".join(sorted(set(_snake_case						) - lang_set						)						)}) are not in valid set ({", ".join(_snake_case						)})."""						)

				# Convert dictionary into tuples, splitting out cases where there are
				# multiple translations for a single language.
				lowercase__   :    str				=			[]
				for lang, text in translation_dict.items():
						if isinstance(_snake_case			,_snake_case						):
								translation_tuples.append((lang, text)						)
						else:
								translation_tuples.extend([(lang, el) for el in text]						)

        # Ensure translations are in ascending order by language code.
				lowercase__		,			lowercase__   :    Optional[Any]				=			zip(*sorted(_snake_case						)						)

				return {"language": languages, "translation": translations}







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Union["FeatureType", Dict[str, "FeatureType"]]:






				"""simple docstring"""



				from .features import Sequence, Value

				return {
				    "language": Sequence(Value('''string'''						)						),
				    "translation": Sequence(Value('''string'''						)						),
				}
