

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







						def __call__(       self				)->				Optional[Any]:
													return pa.struct({lang: pa.string() for lang in sorted(self.languages				)}				)







						def     _snake_case							(       self				)->				Union["FeatureType", Dict[str, "FeatureType"]]:
													from .features import Value

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


@dataclass
class        _SCREAMING_SNAKE_CASE	:
						_UpperCamelCase:Optional[List]     =						None
						_UpperCamelCase:Optional[int]     =						None
						_UpperCamelCase:Optional[str]     =						None
						# Automatically constructed
						_UpperCamelCase:ClassVar[str]     =						"dict"
						_UpperCamelCase:ClassVar[Any]     =						None
						_UpperCamelCase:str     =						field(default="TranslationVariableLanguages"      ,  init=a__      ,  repr=a__)







						def     _snake_case							(       self				)->				Optional[Any]:
													lowerCamelCase_				     =sorted(set(self.languages				)				) if self.languages else None
													lowerCamelCase_				     =len(self.languages				) if self.languages else None







						def __call__(       self				)->				Any:
													return pa.struct({"""language""": pa.list_(pa.string()				), """translation""": pa.list_(pa.string()				)}				)







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE				)->				Union[str, Any]:
													lowerCamelCase_				     =set(self.languages				)
													if self.languages and set(lowerCAmelCase__				) - lang_set:
																				raise ValueError(
																				    f'Some languages in example ({", ".join(sorted(set(lowerCAmelCase__				) - lang_set				)				)}) are not in valid set ({", ".join(lowerCAmelCase__				)}).'				)

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

        # Ensure translations are in ascending order by language code.
													lowerCamelCase_				     =zip(*sorted(lowerCAmelCase__				)				)

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







						def     _snake_case							(       self				)->				Union["FeatureType", Dict[str, "FeatureType"]]:
													from .features import Sequence, Value

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







"""simple docstring"""




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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Union[str, Any]					) ->str:






	'''simple docstring'''






	a,       a      :		str	=				image.size
	a,       a      :		Tuple	=				(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
	a      :		Union[str, Any]	=				image.resize((w, h)    ,       resample=PIL_INTERPOLATION["lanczos"]					)
	a      :		int	=				np.array(_lowercase					).astype(np.floataa					) / 255.0
	a      :		List[str]	=				image[None].transpose(0    ,       3    ,       1    ,       2					)
	a      :		Dict	=				torch.from_numpy(_lowercase					)
	return 2.0 * image - 1.0







class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> str:
								super().__init__()
								self.register_modules(vqvae=lowerCAmelCase__      ,					unet=lowerCAmelCase__      ,					scheduler=lowerCAmelCase__      )






							@torch.no_grad()
							def __call__(   self      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = 1      ,					lowerCAmelCase__ = 100      ,					lowerCAmelCase__ = 0.0      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = "pil"      ,					lowerCAmelCase__ = True      ,					)	-> Union[Tuple, ImagePipelineOutput]:
								if isinstance(lowerCAmelCase__      ,					PIL.Image.Image      ):
									a      :		int	=				1
								elif isinstance(lowerCAmelCase__      ,					torch.Tensor      ):
									a      :		str	=				image.shape[0]
								else:
									raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCAmelCase__      )}"""      )

								if isinstance(lowerCAmelCase__      ,					PIL.Image.Image      ):
									a      :		Tuple	=				preprocess(lowerCAmelCase__      )

								a,       a      :		Optional[Any]	=				image.shape[-2:]

								# in_channels should be 6: 3 for latents, 3 for low resolution image
								a      :		Tuple	=				(batch_size, self.unet.config.in_channels // 2, height, width)
								a      :		List[str]	=				next(self.unet.parameters()      ).dtype

								a      :		Any	=				randn_tensor(lowerCAmelCase__      ,					generator=lowerCAmelCase__      ,					device=self.device      ,					dtype=lowerCAmelCase__      )

								a      :		Union[str, Any]	=				image.to(device=self.device      ,					dtype=lowerCAmelCase__      )

								# set timesteps and move to the correct device
								self.scheduler.set_timesteps(lowerCAmelCase__      ,					device=self.device      )
								a      :		int	=				self.scheduler.timesteps

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

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								a      :		str	=				"eta" in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
								a      :		List[Any]	=				{}
								if accepts_eta:
									a      :		Any	=				eta

								for t in self.progress_bar(lowerCAmelCase__      ):
									# concat latents and low resolution image in the channel dimension.
									a      :		str	=				torch.cat([latents, image]      ,					dim=1      )
									a      :		int	=				self.scheduler.scale_model_input(lowerCAmelCase__      ,					lowerCAmelCase__      )
									# predict the noise residual
									a      :		Union[str, Any]	=				self.unet(lowerCAmelCase__      ,					lowerCAmelCase__      ).sample
									# compute the previous noisy sample x_t -> x_t-1
									a      :		Tuple	=				self.scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      ).prev_sample

								# decode the image latents with the VQVAE
								a      :		List[Any]	=				self.vqvae.decode(lowerCAmelCase__      ).sample
								a      :		int	=				torch.clamp(lowerCAmelCase__      ,					-1.0      ,					1.0      )
								a      :		Optional[int]	=				image / 2 + 0.5
								a      :		Optional[int]	=				image.cpu().permute(0      ,					2      ,					3      ,					1      ).numpy()

								if output_type == "pil":
									a      :		Union[str, Any]	=				self.numpy_to_pil(lowerCAmelCase__      )

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase__      )


