






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      (      lowerCAmelCase       ):
     '''simple docstring'''


     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							:						Optional[Any]       ,			UpperCAmelCase_							:						int = 1       ,			UpperCAmelCase_							:						Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,			UpperCAmelCase_							:						float = 0.0       ,			UpperCAmelCase_							:						int = 50       ,			UpperCAmelCase_							:						Optional[str] = "pil"       ,			UpperCAmelCase_							:						bool = True       ,			**UpperCAmelCase_							:						Tuple       ,			):
       SCREAMING_SNAKE_CASE							:   str						    =     randn_tensor(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,			generator=UpperCAmelCase_       ,			)
       SCREAMING_SNAKE_CASE							:   int						    =     latents.to(self.device   )

       # scale the initial noise by the standard deviation required by the scheduler
       SCREAMING_SNAKE_CASE							:   Tuple						    =     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
       SCREAMING_SNAKE_CASE							:   Dict						    =     "eta" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     {}
       if accepts_eta:
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     eta

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

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

       SCREAMING_SNAKE_CASE							:   int						    =     (image / 2 + 0.5).clamp(0       ,			1   )
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     image.cpu().permute(0       ,			2       ,			3       ,			1   ).numpy()
       if output_type == "pil":
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.numpy_to_pil(UpperCAmelCase_   )

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=UpperCAmelCase_   )









'''simple docstring'''



from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     import tensorflow as tf

     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

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

_UpperCamelCase						:  Optional[int]							       =				logging.get_logger(__name__)



@add_end_docstrings(a_			)
class 				_snake_case					(   a_			):

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      super().__init__(*_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)
      requires_backends(self      , 'vision'	)
      self.check_model_type(
          TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
          if self.framework == 'tf'
          else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE=None	):





      '''simple docstring'''

      lowerCAmelCase 						=							{}
      if top_k is not None:
          lowerCAmelCase 						=							top_k
      return {}, {}, postprocess_params

  def __call__(				self      , _SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      return super().__call__(_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							load_image(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.image_processor(images=_SCREAMING_SNAKE_CASE      , return_tensors=self.framework	)
      return model_inputs

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model(**_SCREAMING_SNAKE_CASE	)
      return model_outputs


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=5	):





      '''simple docstring'''

      if top_k > self.model.config.num_labels:
          lowerCAmelCase 						=							self.model.config.num_labels

      if self.framework == "pt":
          lowerCAmelCase 						=							model_outputs.logits.softmax(-1	)[0]
          lowerCAmelCase						,   lowerCAmelCase 						=							probs.topk(_SCREAMING_SNAKE_CASE	)
      elif self.framework == "tf":
          lowerCAmelCase 						=							stable_softmax(model_outputs.logits      , axis=-1	)[0]
          lowerCAmelCase 						=							tf.math.top_k(_SCREAMING_SNAKE_CASE      , k=_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase						,   lowerCAmelCase 						=							topk.values.numpy(), topk.indices.numpy()
      else:
          raise ValueError(F'Unsupported framework: {self.framework}'	)

      lowerCAmelCase 						=							scores.tolist()
      lowerCAmelCase 						=							ids.tolist()
      return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)]





