


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCamelCase__				       =			{'configuration_regnet': ['REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RegNetConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase__				       =			[
       'REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'RegNetForImageClassification',
       'RegNetModel',
       'RegNetPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase__				       =			[
       'TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFRegNetForImageClassification',
       'TFRegNetModel',
       'TFRegNetPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase__				       =			[
       'FlaxRegNetForImageClassification',
       'FlaxRegNetModel',
       'FlaxRegNetPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_regnet import (
          REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          RegNetForImageClassification,
          RegNetModel,
          RegNetPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_regnet import (
          TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRegNetForImageClassification,
          TFRegNetModel,
          TFRegNetPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_regnet import (
          FlaxRegNetForImageClassification,
          FlaxRegNetModel,
          FlaxRegNetPreTrainedModel,
      )


else:
   import sys

   UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure)



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''




     def __init__( self       :       List[str]	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       int		):





            """simple docstring"""



            super().__init__()
            self.register_modules(unet=UpperCamelCase	,      scheduler=UpperCamelCase		)




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





            """simple docstring"""



            _lowercase					:				Optional[int]           =			torch.randn(
                (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,      generator=UpperCamelCase	,      )
            _lowercase					:				Dict           =			image.to(self.device		)

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

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

                   # 2. predict previous mean of image x_t-1 and add variance depending on eta
                   # eta corresponds to η in paper and should be between [0, 1]
                   # do x_t -> x_t-1
                   _lowercase					:				Tuple           =			self.scheduler.step(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		).prev_sample

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

            if not return_dict:
                   return (image,), "This is a local test"

            return ImagePipelineOutput(images=UpperCamelCase		), "This is a local test"