


"""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 _lowerCAmelCase				:
   __UpperCAmelCase :       Tuple							  =  42
   __UpperCAmelCase :       Any							  =  None
   # Automatically constructed
   __UpperCAmelCase :       Any							  =  '''dict'''
   __UpperCAmelCase :       List[Any]							  =  None
   __UpperCAmelCase :       Union[str, Any]							  =  field(default='''Translation'''    ,							init=a__    ,							repr=a__	)







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

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




       from .features import Value

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







@dataclass
class _lowerCAmelCase				:
   __UpperCAmelCase :       int							  =  None
   __UpperCAmelCase :       Dict							  =  None
   __UpperCAmelCase :       Union[str, Any]							  =  None
   # Automatically constructed
   __UpperCAmelCase :       Optional[Any]							  =  '''dict'''
   __UpperCAmelCase :       Union[str, Any]							  =  None
   __UpperCAmelCase :       List[Any]							  =  field(default='''TranslationVariableLanguages'''    ,							init=a__    ,							repr=a__	)







   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    sorted(set(self.languages						)						) if self.languages else None
       snake_case    :      int      					=    len(self.languages						) if self.languages else None







   def __call__(	self						)	->		Tuple:

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




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

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

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

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







   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




       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 torch

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



class 						snake_case_(				a__	):






     def __init__(		self     :    int    , UpperCamelCase_     :    VQModel    , UpperCamelCase_     :    UNetaDModel    , UpperCamelCase_     :    DDIMScheduler ):
           super().__init__()
           self.register_modules(vqvae=UpperCamelCase_    , unet=UpperCamelCase_    , scheduler=UpperCamelCase_ )






     @torch.no_grad()
     def __call__(		self     :    Union[str, Any]    , UpperCamelCase_     :    int = 1    , UpperCamelCase_     :    Optional[Union[torch.Generator, List[torch.Generator]]] = None    , UpperCamelCase_     :    float = 0.0    , UpperCamelCase_     :    int = 5_0    , UpperCamelCase_     :    Optional[str] = "pil"    , UpperCamelCase_     :    bool = True    , **UpperCamelCase_     :    Optional[int]    , ):
           lowerCAmelCase    :		Dict				=      randn_tensor(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    , generator=UpperCamelCase_    , )
           lowerCAmelCase    :		Optional[int]				=      latents.to(self.device )

           # scale the initial noise by the standard deviation required by the scheduler
           lowerCAmelCase    :		List[str]				=      latents * self.scheduler.init_noise_sigma

           self.scheduler.set_timesteps(UpperCamelCase_ )

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

           lowerCAmelCase    :		List[str]				=      {}
           if accepts_eta:
                 lowerCAmelCase    :		List[Any]				=      eta

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

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

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

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=UpperCamelCase_ )


