




from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class        lowerCamelCase__(   __lowerCamelCase):
      def __init__(							self:					List[str]       ,			UpperCamelCase_:					int       ,			UpperCamelCase_:					List[Any]  ):
             super().__init__()
             self.register_modules(unet=UpperCamelCase_       ,			scheduler=UpperCamelCase_  )




      @torch.no_grad()
      def __call__(							self:					Tuple       ,			UpperCamelCase_:					int = 1       ,			UpperCamelCase_:					Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,			UpperCamelCase_:					int = 10_00       ,			UpperCamelCase_:					Optional[str] = "pil"       ,			UpperCamelCase_:					bool = True       ,			):
             # Sample gaussian noise to begin loop
             if isinstance(self.unet.config.sample_size       ,			UpperCamelCase_  ):
                    __lowerCamelCase  =		(
                        batch_size,
                        self.unet.config.in_channels,
                        self.unet.config.sample_size,
                        self.unet.config.sample_size,
                    )
             else:
                    __lowerCamelCase  =		(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

             if self.device.type == "mps":
                    # randn does not work reproducibly on mps
                    __lowerCamelCase  =		randn_tensor(UpperCamelCase_       ,			generator=UpperCamelCase_  )
                    __lowerCamelCase  =		image.to(self.device  )
             else:
                    __lowerCamelCase  =		randn_tensor(UpperCamelCase_       ,			generator=UpperCamelCase_       ,			device=self.device  )

             # set step values
             self.scheduler.set_timesteps(UpperCamelCase_  )

             for t in self.progress_bar(self.scheduler.timesteps  ):
                    # 1. predict noise model_output
                    __lowerCamelCase  =		self.unet(UpperCamelCase_       ,			UpperCamelCase_  ).sample

                    # 2. compute previous image: x_t -> x_t-1
                    __lowerCamelCase  =		self.scheduler.step(UpperCamelCase_       ,			UpperCamelCase_       ,			UpperCamelCase_       ,			generator=UpperCamelCase_  ).prev_sample

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

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=UpperCamelCase_  )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_   							=      {'configuration_dinat': ['DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DinatConfig']}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_   							=      [
           'DINAT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'DinatForImageClassification',
           'DinatModel',
           'DinatPreTrainedModel',
           'DinatBackbone',
       ]

if TYPE_CHECKING:
       from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_dinat import (
                  DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DinatBackbone,
                  DinatForImageClassification,
                  DinatModel,
                  DinatPreTrainedModel,
              )

else:
       import sys

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

