
'''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				( _SCREAMING_SNAKE_CASE			):
 def __init__(   self			:    Tuple				,   lowerCamelCase			:    VQModel				,   lowerCamelCase			:    UNetaDModel				,   lowerCamelCase			:    DDIMScheduler       ):



   """simple docstring"""





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






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



   """simple docstring"""





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

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

   self.scheduler.set_timesteps(lowerCamelCase       )

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

   __A		:     Any												=		{}
   if accepts_eta:
     __A		:     Dict												=		eta

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

   # decode the image latents with the VAE
   __A		:     int												=		self.vqvae.decode(lowerCamelCase       ).sample

   __A		:     Optional[int]												=		(image / 2 + 0.5).clamp(0				,   1       )
   __A		:     List[Any]												=		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       )





'''simple docstring'''
from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

A__   :		Any 		=logging.get_logger(__name__)







@add_end_docstrings(_SCREAMING_SNAKE_CASE			)
class  __A				( _SCREAMING_SNAKE_CASE			):
 def __init__(   self			:    Union[str, Any]				,   *lowerCamelCase			:    int				,   **lowerCamelCase			:    Optional[int]       ):



   """simple docstring"""





   super().__init__(*lowerCamelCase				,   **lowerCamelCase       )
   self.check_model_type(lowerCamelCase       )
 def 		lowercase_(   self			:    Any				,   lowerCamelCase			:    Dict=None				,   lowerCamelCase			:    Union[str, Any]=None				,   lowerCamelCase			:    int=None				,   **lowerCamelCase			:    int       ):



   """simple docstring"""





   __A							,					__A		:     Tuple												=		{}, {}
   if padding is not None:
     __A		:     Any												=		padding
   if truncation is not None:
     __A		:     Optional[Any]												=		truncation
   if top_k is not None:
     __A		:     List[str]												=		top_k
   return preprocess_params, {}, postprocess_params
 def __call__(   self			:    Any				,   lowerCamelCase			:    Union["Image.Image", str]				,   lowerCamelCase			:    str = None				,   **lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   if isinstance(lowerCamelCase				,   (Image.Image, str)       ) and isinstance(lowerCamelCase				,   lowerCamelCase       ):
     __A		:     Tuple												=		{"""image""": image, """question""": question}
   else:
     __A		:     List[Any]												=		image
   __A		:     Any												=		super().__call__(lowerCamelCase				,   **lowerCamelCase       )
   return results
 def 		lowercase_(   self			:    int				,   lowerCamelCase			:    Optional[int]				,   lowerCamelCase			:    Dict=False				,   lowerCamelCase			:    str=False       ):



   """simple docstring"""





   __A		:     List[str]												=		load_image(inputs["""image"""]       )
   __A		:     Optional[Any]												=		self.tokenizer(
       inputs["""question"""]				,   return_tensors=self.framework				,   padding=lowerCamelCase				,   truncation=lowerCamelCase       )
   __A		:     Union[str, Any]												=		self.image_processor(images=lowerCamelCase				,   return_tensors=self.framework       )
   model_inputs.update(lowerCamelCase       )
   return model_inputs
 def 		lowercase_(   self			:    List[str]				,   lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   __A		:     List[str]												=		self.model(**lowerCamelCase       )
   return model_outputs






 def 		lowercase_(   self			:    str				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    List[Any]=5       ):



   """simple docstring"""





   if top_k > self.model.config.num_labels:
     __A		:     str												=		self.model.config.num_labels

   if self.framework == "pt":
     __A		:     Optional[Any]												=		model_outputs.logits.sigmoid()[0]
     __A							,					__A		:     int												=		probs.topk(lowerCamelCase       )
   else:
     raise ValueError(f"Unsupported framework: {self.framework}"       )

   __A		:     Optional[Any]												=		scores.tolist()
   __A		:     Dict												=		ids.tolist()
   return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase				,   lowerCamelCase       )]



