



"""simple docstring"""




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 	a__		(    SCREAMING_SNAKE_CASE__  ):


   def __init__(				self   :				Any,  lowerCAmelCase   :				List[Any],  lowerCAmelCase   :				Optional[Any]      )     ->							Optional[Any]:
         super().__init__()
         self.register_modules(unet=lowerCAmelCase,  scheduler=lowerCAmelCase      )






   @torch.no_grad()
   def __call__(				self   :				List[str],  lowerCAmelCase   :				int = 1,  lowerCAmelCase   :				Optional[torch.Generator] = None,  lowerCAmelCase   :				int = 50,  lowerCAmelCase   :				Optional[str] = "pil",  lowerCAmelCase   :				bool = True,  **lowerCAmelCase   :				Tuple,  )     ->							Union[ImagePipelineOutput, Tuple]:
         lowercase	:   str											= torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),  generator=lowerCAmelCase,  )
         lowercase	:   Optional[Any]											= image.to(self.device      )

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

         for t in self.progress_bar(self.scheduler.timesteps      ):
               # 1. predict noise model_output
               lowercase	:   str											= self.unet(lowerCAmelCase,  lowerCAmelCase      ).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	:   Union[str, Any]											= self.scheduler.step(lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase      ).prev_sample

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

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

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





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase:					Tuple     				=	logging.get_logger(__name__)

_UpperCamelCase:					List[str]     				=	{
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}


class 	a__		(    SCREAMING_SNAKE_CASE__  ):
   _lowerCamelCase       				=					'regnet'
   _lowerCamelCase       				=					['x', 'y']


   def __init__(				self   :				Union[str, Any],  lowerCAmelCase   :				Union[str, Any]=3,  lowerCAmelCase   :				Tuple=32,  lowerCAmelCase   :				Optional[int]=[128, 192, 512, 1088],  lowerCAmelCase   :				Optional[int]=[2, 6, 12, 2],  lowerCAmelCase   :				str=64,  lowerCAmelCase   :				List[str]="y",  lowerCAmelCase   :				Optional[int]="relu",  **lowerCAmelCase   :				List[Any],  )     ->							Union[str, Any]:
         super().__init__(**lowerCAmelCase      )
         if layer_type not in self.layer_types:
               raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types      )}'''      )
         lowercase	:   str											= num_channels
         lowercase	:   str											= embedding_size
         lowercase	:   int											= hidden_sizes
         lowercase	:   Tuple											= depths
         lowercase	:   str											= groups_width
         lowercase	:   List[str]											= layer_type
         lowercase	:   Optional[int]											= hidden_act
         # always downsample in the first stage
         lowercase	:   Optional[Any]											= True
