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     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		UNetaDModel
 _lowerCamelCase  :		PNDMScheduler

 def __init__(	self				:						Union[str, Any]				,	lowerCamelCase				:						UNetaDModel				,	lowerCamelCase				:						PNDMScheduler     )       ->							Any:
        super().__init__()

        _UpperCamelCase   :				Any       =     PNDMScheduler.from_config(scheduler.config     )

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

 @torch.no_grad()
 def __call__(	self				:						Optional[int]				,	lowerCamelCase				:						int = 1				,	lowerCamelCase				:						int = 5_0				,	lowerCamelCase				:						Optional[Union[torch.Generator, List[torch.Generator]]] = None				,	lowerCamelCase				:						Optional[str] = "pil"				,	lowerCamelCase				:						bool = True				,	**lowerCamelCase				:						Optional[int]				,	)       ->							Union[ImagePipelineOutput, Tuple]:
        _UpperCamelCase   :				str       =     randn_tensor(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,	generator=lowerCamelCase				,	device=self.device				,	)

        self.scheduler.set_timesteps(lowerCamelCase     )
        for t in self.progress_bar(self.scheduler.timesteps     ):
               _UpperCamelCase   :				Tuple       =     self.unet(lowerCamelCase				,	lowerCamelCase     ).sample

               _UpperCamelCase   :				int       =     self.scheduler.step(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase     ).prev_sample

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

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=lowerCamelCase     )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase							= {'''configuration_nat''': ['''NAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NatConfig''']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase							= [
     '''NAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''NatForImageClassification''',
     '''NatModel''',
     '''NatPreTrainedModel''',
     '''NatBackbone''',
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase							= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



