






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput
class  lowerCAmelCase_       (				__a					):




       def __init__(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   int	):
           super().__init__()
           self.register_modules(unet=snake_case__ ,							scheduler=snake_case__	)




       @torch.no_grad()
       def __call__(   self     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   int = 1 ,							SCREAMING_SNAKE_CASE_     :   Optional[torch.Generator] = None ,							SCREAMING_SNAKE_CASE_     :   int = 50 ,							SCREAMING_SNAKE_CASE_     :   Optional[str] = "pil" ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							**SCREAMING_SNAKE_CASE_     :   Tuple ,							):
           lowerCAmelCase__      			=						torch.randn(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,							generator=snake_case__ ,							)
           lowerCAmelCase__      			=						image.to(self.device	)

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

           for t in self.progress_bar(self.scheduler.timesteps	):
               # 1. predict noise model_output
               lowerCAmelCase__      			=						self.unet(snake_case__ ,							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
               lowerCAmelCase__      			=						self.scheduler.step(snake_case__ ,							snake_case__ ,							snake_case__	).prev_sample

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

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

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













from pathlib import Path

import fire





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       str     ,					lowercase__						:       int       )		->    Dict:


				'''simple docstring'''




				lowerCAmelCase__      			=						Path(lowercase__       )
				lowerCAmelCase__      			=						Path(lowercase__       )
				dest_dir.mkdir(exist_ok=lowercase__       )
				for path in src_dir.iterdir():
								lowerCAmelCase__      			=						[x.rstrip() for x in list(path.open().readlines()       )][:n]
								lowerCAmelCase__      			=						dest_dir.joinpath(path.name       )
								print(lowercase__       )
								dest_path.open('''w'''       ).write('''\n'''.join(lowercase__       )       )


if __name__ == "__main__":
			fire.Fire(minify)





