


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class  A_ (			a_  ):







 """simple docstring"""





 def __init__(      self       :			Tuple							,     _a       :			Tuple							,     _a       :			List[str]					)     ->      Optional[int]:
      super().__init__()
      self.register_modules(unet=_a							,     scheduler=_a					)

 @torch.no_grad()
 def __call__(      self       :			Union[str, Any]							,     _a       :			int = 1							,     _a       :			Optional[torch.Generator] = None							,     _a       :			int = 5_0							,     _a       :			Optional[str] = "pil"							,     _a       :			bool = True							,     **_a       :			Any							,     )     ->      Union[ImagePipelineOutput, Tuple]:
      UpperCAmelCase    :					Dict			=      torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,     generator=_a							,     )
      UpperCAmelCase    :					List[str]			=      image.to(self.device					)

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

      for t in self.progress_bar(self.scheduler.timesteps					):
           # 1. predict noise model_output
           UpperCAmelCase    :					str			=      self.unet(_a							,     _a					).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
           UpperCAmelCase    :					Optional[int]			=      self.scheduler.step(_a							,     _a							,     _a					).prev_sample

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

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

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







from __future__ import annotations







def 	A (		A__		,		A__		,		A__		,		A__			):

     # Base Case
     if counter == 0 or capacity == 0:
          return 0

     # If weight of the nth item is more than Knapsack of capacity,
     #   then this item cannot be included in the optimal solution,
     # else return the maximum of two cases:
     #   (1) nth item included
     #   (2) not included
     if weights[counter - 1] > capacity:
          return knapsack(A__		,		A__		,		A__		,		counter - 1			)
     else:
          UpperCAmelCase    :					List[str]			=      capacity - weights[counter - 1]
          UpperCAmelCase    :					Tuple			=      values[counter - 1] + knapsack(
              A__		,		A__		,		A__		,		counter - 1			)
          UpperCAmelCase    :					Optional[int]			=      knapsack(A__		,		A__		,		A__		,		counter - 1			)
          return max(A__		,		A__			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



