

# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class    _lowercase				(					__lowerCamelCase			):

   def __init__(				self	:    str			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Optional[Any]       ) ->			Optional[int]:
         """simple docstring"""





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





   @torch.no_grad()
   def __call__(				self	:    Any			,						lowerCamelCase__	:    int = 1			,						lowerCamelCase__	:    Optional[torch.Generator] = None			,						lowerCamelCase__	:    int = 5_0			,						lowerCamelCase__	:    Optional[str] = "pil"			,						lowerCamelCase__	:    bool = True			,						**lowerCamelCase__	:    Optional[Any]			,						) ->			Union[ImagePipelineOutput, Tuple]:
         """simple docstring"""





         A_							       =		torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,						generator=lowerCamelCase__			,						)
         A_							       =		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
               A_							       =		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
               A_							       =		self.scheduler.step(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__       ).prev_sample

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








def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      return int((input_a, input_a).count(0   ) != 0   )







def 							_lowerCamelCase   (			):
      '''simple docstring'''







      assert nand_gate(0		,				0   ) == 1
      assert nand_gate(0		,				1   ) == 1
      assert nand_gate(1		,				0   ) == 1
      assert nand_gate(1		,				1   ) == 0


if __name__ == "__main__":
      print(nand_gate(0, 0))
      print(nand_gate(0, 1))
      print(nand_gate(1, 0))
      print(nand_gate(1, 1))





