


"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					UNetaDModel
     snake_case__	:					ScoreSdeVeScheduler

     def __init__(	self:						Any     ,		__a:						UNetaDModel     ,		__a:						ScoreSdeVeScheduler	)->    Dict:
           super().__init__()
           self.register_modules(unet=__a     ,		scheduler=__a	)







     @torch.no_grad()
     def __call__(	self:						List[str]     ,		__a:						int = 1     ,		__a:						int = 2_000     ,		__a:						Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		__a:						Optional[str] = "pil"     ,		__a:						bool = True     ,		**__a:						Any     ,		)->    Union[ImagePipelineOutput, Tuple]:
           lowerCamelCase					:					str        =	self.unet.config.sample_size
           lowerCamelCase					:					List[Any]        =	(batch_size, 3, img_size, img_size)

           lowerCamelCase					:					str        =	self.unet

           lowerCamelCase					:					Any        =	randn_tensor(__a     ,		generator=__a	) * self.scheduler.init_noise_sigma
           lowerCamelCase					:					Any        =	sample.to(self.device	)

           self.scheduler.set_timesteps(__a	)
           self.scheduler.set_sigmas(__a	)

           for i, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
                 lowerCamelCase					:					int        =	self.scheduler.sigmas[i] * torch.ones(shape[0]     ,		device=self.device	)

                 # correction step
                 for _ in range(self.scheduler.config.correct_steps	):
                       lowerCamelCase					:					Optional[Any]        =	self.unet(__a     ,		__a	).sample
                       lowerCamelCase					:					Any        =	self.scheduler.step_correct(__a     ,		__a     ,		generator=__a	).prev_sample

                 # prediction step
                 lowerCamelCase					:					Any        =	model(__a     ,		__a	).sample
                 lowerCamelCase					:					int        =	self.scheduler.step_pred(__a     ,		__a     ,		__a     ,		generator=__a	)

                 lowerCamelCase     ,			lowerCamelCase					:					List[str]        =	output.prev_sample, output.prev_sample_mean

           lowerCamelCase					:					Tuple        =	sample_mean.clamp(0     ,		1	)
           lowerCamelCase					:					Dict        =	sample.cpu().permute(0     ,		2     ,		3     ,		1	).numpy()
           if output_type == "pil":
                 lowerCamelCase					:					List[Any]        =	self.numpy_to_pil(__a	)

           if not return_dict:
                 return (sample,)

           return ImagePipelineOutput(images=__a	)








"""simple docstring"""


from __future__ import annotations

import math




def   snake_case		(     UpperCamelCase__       :						float			,				UpperCamelCase__       :						int    )   ->      float:
      lowerCamelCase					:					Dict        =	u
      for i in range(1			,				UpperCamelCase__    ):
            lowerCamelCase					:					List[str]        =	temp * (u - i)
      return temp




def   snake_case		(     )   ->      None:
      lowerCamelCase					:					List[Any]        =	int(input("""enter the numbers of values: """    )    )
      lowerCamelCase					:					list[list[float]]        =	[]
      for _ in range(UpperCamelCase__    ):
            y.append([]    )
      for i in range(UpperCamelCase__    ):
            for j in range(UpperCamelCase__    ):
                  y[i].append(UpperCamelCase__    )
                  lowerCamelCase					:					Union[str, Any]        =	0

      print("""enter the values of parameters in a list: """    )
      lowerCamelCase					:					Any        =	list(map(UpperCamelCase__			,				input().split()    )    )

      print("""enter the values of corresponding parameters: """    )
      for i in range(UpperCamelCase__    ):
            lowerCamelCase					:					int        =	float(input()    )

      lowerCamelCase					:					Dict        =	int(input("""enter the value to interpolate: """    )    )
      lowerCamelCase					:					List[Any]        =	(value - x[0]) / (x[1] - x[0])

      # for calculating forward difference table

      for i in range(1			,				UpperCamelCase__    ):
            for j in range(n - i    ):
                  lowerCamelCase					:					str        =	y[j + 1][i - 1] - y[j][i - 1]

      lowerCamelCase					:					Any        =	y[0][0]
      for i in range(1			,				UpperCamelCase__    ):
            summ += (ucal(UpperCamelCase__			,				UpperCamelCase__    ) * y[0][i]) / math.factorial(UpperCamelCase__    )

      print(F'the value at {value} is {summ}'    )


if __name__ == "__main__":
    main()




