



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        lowerCAmelCase__					(  _lowerCamelCase       ):


				def __init__(							self      :  int						, __UpperCamelCase      :  VQModel						, __UpperCamelCase      :  UNetaDModel						, __UpperCamelCase      :  DDIMScheduler       )		->     Any:
											super().__init__()
											self.register_modules(vqvae=__UpperCamelCase						, unet=__UpperCamelCase						, scheduler=__UpperCamelCase       )


				@torch.no_grad()
				def __call__(							self      :  int						, __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						, )		->     Union[Tuple, ImagePipelineOutput]:
											A      						=    randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						, generator=__UpperCamelCase						, )
											A      						=    latents.to(self.device       )

											# scale the initial noise by the standard deviation required by the scheduler
											A      						=    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
											A      						=    'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

											A      						=    {}
											if accepts_eta:
																		A      						=    eta

											for t in self.progress_bar(self.scheduler.timesteps       ):
																		A      						=    self.scheduler.scale_model_input(__UpperCamelCase						, __UpperCamelCase       )
																		# predict the noise residual
																		A      						=    self.unet(__UpperCamelCase						, __UpperCamelCase       ).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		A      						=    self.scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, **__UpperCamelCase       ).prev_sample

											# decode the image latents with the VAE
											A      						=    self.vqvae.decode(__UpperCamelCase       ).sample

											A      						=    (image / 2 + 0.5).clamp(0						, 1       )
											A      						=    image.cpu().permute(0						, 2						, 3						, 1       ).numpy()
											if output_type == "pil":
																		A      						=    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	)]





