





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 			__A(    __lowercase			):

     def __init__(   self,							A,							A   ):





            """simple docstring"""

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



     @torch.no_grad()
     def __call__(   self,							A = 1,							A = None,							A = 50,							A = "pil",							A = True,							**A,							):





            """simple docstring"""

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

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









def  snake_case_(_UpperCamelCase	)				->      int:



  """simple docstring"""






  _snake_case		=				len(_UpperCamelCase	)
  _snake_case		=				[0] * length
  _snake_case		=				[[False for i in range(_UpperCamelCase	)] for j in range(_UpperCamelCase	)]
  for i, c in enumerate(_UpperCamelCase	):
    _snake_case		=				i
    for j in range(i + 1	):
      if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
        _snake_case		=				True
        _snake_case		=				min(_UpperCamelCase					,			0 if j == 0 else (cut[j - 1] + 1)	)
    _snake_case		=				mincut
  return cut[length - 1]


if __name__ == "__main__":
 __A     						=      input('''Enter the string: ''').strip()
 __A     						=      find_minimum_partitions(s)
 print(f'''Minimum number of partitions required for the \'{s}\' is {ans}''')

