






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  (    A__       ):







 def __init__(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   VQModel							,   SCREAMING_SNAKE_CASE					:   UNetaDModel							,   SCREAMING_SNAKE_CASE					:   DDIMScheduler						):


      '''simple docstring'''






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







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


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,   generator=SCREAMING_SNAKE_CASE							,   )
      UpperCamelCase__			:   Optional[int]       				=  latents.to(self.device						)

      # scale the initial noise by the standard deviation required by the scheduler
      UpperCamelCase__			:   Optional[Any]       				=  latents * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)

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

      UpperCamelCase__			:   int       				=  {}
      if accepts_eta:
           UpperCamelCase__			:   str       				=  eta

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

      # decode the image latents with the VAE
      UpperCamelCase__			:   Optional[int]       				=  self.vqvae.decode(SCREAMING_SNAKE_CASE						).sample

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

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE						)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:							Tuple				={
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 lowerCamelCase	:							Dict				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)