





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case				= {
    """configuration_nystromformer""": ["""NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NystromformerConfig"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """NystromformerForMaskedLM""",
      """NystromformerForMultipleChoice""",
      """NystromformerForQuestionAnswering""",
      """NystromformerForSequenceClassification""",
      """NystromformerForTokenClassification""",
      """NystromformerLayer""",
      """NystromformerModel""",
      """NystromformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_nystromformer import (
        NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        NystromformerForMaskedLM,
        NystromformerForMultipleChoice,
        NystromformerForQuestionAnswering,
        NystromformerForSequenceClassification,
        NystromformerForTokenClassification,
        NystromformerLayer,
        NystromformerModel,
        NystromformerPreTrainedModel,
    )


else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













from typing import List, Optional, Tuple, Union

import torch

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





class 					lowerCAmelCase							(   lowercase_					):
  __lowerCamelCase		=			42
  __lowerCamelCase		=			42







  def __init__(			self  :List[Any]    ,							_lowercase  :UNetaDModel    ,							_lowercase  :PNDMScheduler					):






     '''simple docstring'''





     super().__init__()

     lowercase__    =  PNDMScheduler.from_config(scheduler.config					)

     self.register_modules(unet=_lowercase    ,							scheduler=_lowercase					)







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






     '''simple docstring'''





     lowercase__    =  randn_tensor(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,							generator=_lowercase    ,							device=self.device    ,							)

     self.scheduler.set_timesteps(_lowercase					)
     for t in self.progress_bar(self.scheduler.timesteps					):
        lowercase__    =  self.unet(_lowercase    ,							_lowercase					).sample

        lowercase__    =  self.scheduler.step(_lowercase    ,							_lowercase    ,							_lowercase					).prev_sample

     lowercase__    =  (image / 2 + 0.5).clamp(0    ,							1					)
     lowercase__    =  image.cpu().permute(0    ,							2    ,							3    ,							1					).numpy()
     if output_type == "pil":
        lowercase__    =  self.numpy_to_pil(_lowercase					)

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowercase					)






