


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  UpperCAmelCase_						(				__UpperCamelCase):
       '''simple docstring'''
       __UpperCamelCase   :  UNetaDModel
       __UpperCamelCase   :  PNDMScheduler






       def __init__(  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              super().__init__()

              UpperCamelCase     :						List[Any] 						=		PNDMScheduler.from_config(scheduler.config    )

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






       @torch.no_grad()
       def __call__(  self			,       __SCREAMING_SNAKE_CASE = 1			,       __SCREAMING_SNAKE_CASE = 50			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = "pil"			,       __SCREAMING_SNAKE_CASE = True			,       **__SCREAMING_SNAKE_CASE			,       ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		randn_tensor(
                  (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,       generator=UpperCAmelCase__			,       device=self.device			,       )

              self.scheduler.set_timesteps(UpperCAmelCase__    )
              for t in self.progress_bar(self.scheduler.timesteps    ):
                     UpperCamelCase     :						List[Any] 						=		self.unet(UpperCAmelCase__			,       UpperCAmelCase__    ).sample

                     UpperCamelCase     :						Tuple 						=		self.scheduler.step(UpperCAmelCase__			,       UpperCAmelCase__			,       UpperCAmelCase__    ).prev_sample

              UpperCamelCase     :						Tuple 						=		(image / 2 + 0.5).clamp(0			,       1    )
              UpperCamelCase     :						Tuple 						=		image.cpu().permute(0			,       2			,       3			,       1    ).numpy()
              if output_type == "pil":
                     UpperCamelCase     :						List[Any] 						=		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,
    is_vision_available,
)


a 			=							{
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a 			=							[
     'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MobileViTV2ForImageClassification',
     'MobileViTV2ForSemanticSegmentation',
     'MobileViTV2Model',
     'MobileViTV2PreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

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


