



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 						snake_case_ (	__A ):
   __A			:	UNetaDModel
   __A			:	PNDMScheduler






   def __init__(   self       :  Optional[int]			,		lowercase_       :  UNetaDModel			,		lowercase_       :  PNDMScheduler						)      ->		int:
       super().__init__()

       lowercase__				:     Union[str, Any]					      =       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_       :  int			,		)      ->		Union[ImagePipelineOutput, Tuple]:
       lowercase__				:     List[Any]					      =       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__				:     Union[str, Any]					      =       self.unet(lowercase_			,		lowercase_						).sample

           lowercase__				:     List[Any]					      =       self.scheduler.step(lowercase_			,		lowercase_			,		lowercase_						).prev_sample

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

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=lowercase_						)




from typing import TYPE_CHECKING

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


UpperCamelCase													=		{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		['''MobileNetV1FeatureExtractor''']
      UpperCamelCase													=		['''MobileNetV1ImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		[
          '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''MobileNetV1ForImageClassification''',
          '''MobileNetV1Model''',
          '''MobileNetV1PreTrainedModel''',
          '''load_tf_weights_in_mobilenet_v1''',
      ]


if TYPE_CHECKING:
      from .configuration_mobilenet_va import (
          MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileNetVaConfig,
          MobileNetVaOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
            from .image_processing_mobilenet_va import MobileNetVaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilenet_va import (
                MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileNetVaForImageClassification,
                MobileNetVaModel,
                MobileNetVaPreTrainedModel,
                load_tf_weights_in_mobilenet_va,
            )


else:
      import sys

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



