






from collections import defaultdict




class 							_lowerCAmelCase:





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   List[str]        =							total  # total no of tasks (N)

         # DP table will have a dimension of (2^M)*N
         # initially all values are set to -1
         UpperCamelCase_:   Union[str, Any]        =							[
             [-1 for i in range(total + 1				)] for j in range(2 ** len(_lowerCamelCase				)				)
         ]

         UpperCamelCase_:   Optional[int]        =							defaultdict(_lowerCamelCase				)  # stores the list of persons for each task

         # final_mask is used to check if all persons are included by setting all bits
         # to 1
         UpperCamelCase_:   Optional[Any]        =							(1 << len(_lowerCamelCase				)) - 1





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         # if mask == self.finalmask all persons are distributed tasks, return 1
         if mask == self.final_mask:
           return 1

         # if not everyone gets the task and no more tasks are available, return 0
         if task_no > self.total_tasks:
           return 0

         # if case already considered
         if self.dp[mask][task_no] != -1:
           return self.dp[mask][task_no]

         # Number of ways when we don't this task in the arrangement
         UpperCamelCase_:   Any        =							self.count_ways_until(_lowerCamelCase    ,     task_no + 1				)

         # now assign the tasks one by one to all possible persons and recursively
         # assign for the remaining tasks.
         if task_no in self.task:
           for p in self.task[task_no]:
             # if p is already given a task
             if mask & (1 << p):
               continue

             # assign this task to p and change the mask value. And recursively
             # assign tasks with the new mask value.
             total_ways_util += self.count_ways_until(mask | (1 << p)    ,     task_no + 1				)

        # save the value.
         UpperCamelCase_:   Tuple        =							total_ways_util

         return self.dp[mask][task_no]





       def      _a       (   self    ,     _lowerCamelCase				):
         # Store the list of persons for each task
         for i in range(len(_lowerCamelCase				)				):
           for j in task_performed[i]:
             self.task[j].append(_lowerCamelCase				)

        # call the function to fill the DP table, final answer is stored in dp[0][1]
         return self.count_ways_until(0    ,     1				)


if __name__ == "__main__":
   A_						:						int						      =					5  # total no of tasks (the value of N)

   # the list of tasks that can be done by M persons.
   A_						:						Union[str, Any]						      =					[[1, 3, 4], [1, 2, 5], [3, 4]]
   print(
       AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
           task_performed
       )
   )



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 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		UNetaDModel
	_lowerCAmelCase     :		ScoreSdeVeScheduler


	def __init__(    self				,     lowercase__				,     lowercase__):
							super().__init__()
							self.register_modules(unet=lowercase__				,     scheduler=lowercase__)


	@torch.no_grad()
	def __call__(    self				,     lowercase__ = 1				,     lowercase__ = 2_0_0_0				,     lowercase__ = None				,     lowercase__ = "pil"				,     lowercase__ = True				,     **lowercase__				,     ):
							__UpperCAmelCase			:							int	    =	self.unet.config.sample_size
							__UpperCAmelCase			:							Optional[int]	    =	(batch_size, 3, img_size, img_size)

							__UpperCAmelCase			:							int	    =	self.unet

							__UpperCAmelCase			:							Tuple	    =	randn_tensor(lowercase__				,     generator=lowercase__) * self.scheduler.init_noise_sigma
							__UpperCAmelCase			:							List[str]	    =	sample.to(self.device)

							self.scheduler.set_timesteps(lowercase__)
							self.scheduler.set_sigmas(lowercase__)

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

													# correction step
													for _ in range(self.scheduler.config.correct_steps):
																			__UpperCAmelCase			:							int	    =	self.unet(lowercase__				,     lowercase__).sample
																			__UpperCAmelCase			:							str	    =	self.scheduler.step_correct(lowercase__				,     lowercase__				,     generator=lowercase__).prev_sample

													# prediction step
													__UpperCAmelCase			:							Tuple	    =	model(lowercase__				,     lowercase__).sample
													__UpperCAmelCase			:							Dict	    =	self.scheduler.step_pred(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__)

													__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	output.prev_sample, output.prev_sample_mean

							__UpperCAmelCase			:							List[str]	    =	sample_mean.clamp(0				,     1)
							__UpperCAmelCase			:							Union[str, Any]	    =	sample.cpu().permute(0				,     2				,     3				,     1).numpy()
							if output_type == "pil":
													__UpperCAmelCase			:							List[Any]	    =	self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (sample,)

							return ImagePipelineOutput(images=lowercase__)

