


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





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







       @torch.no_grad()
       def __call__(      self				,       _SCREAMING_SNAKE_CASE = 1				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = 50				,       _SCREAMING_SNAKE_CASE = "pil"				,       _SCREAMING_SNAKE_CASE = True				,       **_SCREAMING_SNAKE_CASE				,       )->Union[ImagePipelineOutput, Tuple]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,       generator=_SCREAMING_SNAKE_CASE				,       )
         A_      :				Optional[Any]		     =					image.to(self.device		)

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

         for t in self.progress_bar(self.scheduler.timesteps		):
           # 1. predict noise model_output
           A_      :				Optional[Any]		     =					self.unet(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		).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
           A_      :				Any		     =					self.scheduler.step(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		).prev_sample

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

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

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








from typing import Dict

from .base import GenericTensor, Pipeline






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       **_SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         if tokenize_kwargs is None:
           A_      :				Optional[int]		     =					{}

         if truncation is not None:
           if "truncation" in tokenize_kwargs:
             raise ValueError(
                 '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''		)
           A_      :				List[str]		     =					truncation

         A_      :				str		     =					tokenize_kwargs

         A_      :				Optional[Any]		     =					{}
         if return_tensors is not None:
           A_      :				Union[str, Any]		     =					return_tensors

         return preprocess_params, {}, postprocess_params



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Dict[str, GenericTensor]:




         '''simple docstring'''





         A_      :				str		     =					self.framework
         A_      :				Any		     =					self.tokenizer(_SCREAMING_SNAKE_CASE				,       return_tensors=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)
         return model_inputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					self.model(**_SCREAMING_SNAKE_CASE		)
         return model_outputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->List[Any]:




         '''simple docstring'''





         if return_tensors:
           return model_outputs[0]
         if self.framework == "pt":
           return model_outputs[0].tolist()
         elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







       def __call__(      self				,       *_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         return super().__call__(*_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)




