

from collections import defaultdict


class        _SCREAMING_SNAKE_CASE	:







						def __init__(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE				)->				int:
													lowerCamelCase_				     =total  # total no of tasks (N)

													# DP table will have a dimension of (2^M)*N
													# initially all values are set to -1
													lowerCamelCase_				     =[
													    [-1 for i in range(total + 1				)] for j in range(2 ** len(lowerCAmelCase__				)				)
													]

													lowerCamelCase_				     =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
													lowerCamelCase_				     =(1 << len(lowerCAmelCase__				)) - 1







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE				)->				Dict:
													# 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
													lowerCamelCase_				     =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.
													lowerCamelCase_				     =total_ways_util

													return self.dp[mask][task_no]







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE				)->				Optional[Any]:
													# 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     :					List[Any]     = 5  # total no of tasks (the value of N)

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







"""simple docstring"""




import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Union[str, Any]					) ->str:






	'''simple docstring'''






	a,       a      :		str	=				image.size
	a,       a      :		Tuple	=				(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
	a      :		Union[str, Any]	=				image.resize((w, h)    ,       resample=PIL_INTERPOLATION["lanczos"]					)
	a      :		int	=				np.array(_lowercase					).astype(np.floataa					) / 255.0
	a      :		List[str]	=				image[None].transpose(0    ,       3    ,       1    ,       2					)
	a      :		Dict	=				torch.from_numpy(_lowercase					)
	return 2.0 * image - 1.0







class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> str:
								super().__init__()
								self.register_modules(vqvae=lowerCAmelCase__      ,					unet=lowerCAmelCase__      ,					scheduler=lowerCAmelCase__      )






							@torch.no_grad()
							def __call__(   self      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = 1      ,					lowerCAmelCase__ = 100      ,					lowerCAmelCase__ = 0.0      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = "pil"      ,					lowerCAmelCase__ = True      ,					)	-> Union[Tuple, ImagePipelineOutput]:
								if isinstance(lowerCAmelCase__      ,					PIL.Image.Image      ):
									a      :		int	=				1
								elif isinstance(lowerCAmelCase__      ,					torch.Tensor      ):
									a      :		str	=				image.shape[0]
								else:
									raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCAmelCase__      )}"""      )

								if isinstance(lowerCAmelCase__      ,					PIL.Image.Image      ):
									a      :		Tuple	=				preprocess(lowerCAmelCase__      )

								a,       a      :		Optional[Any]	=				image.shape[-2:]

								# in_channels should be 6: 3 for latents, 3 for low resolution image
								a      :		Tuple	=				(batch_size, self.unet.config.in_channels // 2, height, width)
								a      :		List[str]	=				next(self.unet.parameters()      ).dtype

								a      :		Any	=				randn_tensor(lowerCAmelCase__      ,					generator=lowerCAmelCase__      ,					device=self.device      ,					dtype=lowerCAmelCase__      )

								a      :		Union[str, Any]	=				image.to(device=self.device      ,					dtype=lowerCAmelCase__      )

								# set timesteps and move to the correct device
								self.scheduler.set_timesteps(lowerCAmelCase__      ,					device=self.device      )
								a      :		int	=				self.scheduler.timesteps

								# scale the initial noise by the standard deviation required by the scheduler
								a      :		str	=				latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								a      :		str	=				"eta" in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
								a      :		List[Any]	=				{}
								if accepts_eta:
									a      :		Any	=				eta

								for t in self.progress_bar(lowerCAmelCase__      ):
									# concat latents and low resolution image in the channel dimension.
									a      :		str	=				torch.cat([latents, image]      ,					dim=1      )
									a      :		int	=				self.scheduler.scale_model_input(lowerCAmelCase__      ,					lowerCAmelCase__      )
									# predict the noise residual
									a      :		Union[str, Any]	=				self.unet(lowerCAmelCase__      ,					lowerCAmelCase__      ).sample
									# compute the previous noisy sample x_t -> x_t-1
									a      :		Tuple	=				self.scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      ).prev_sample

								# decode the image latents with the VQVAE
								a      :		List[Any]	=				self.vqvae.decode(lowerCAmelCase__      ).sample
								a      :		int	=				torch.clamp(lowerCAmelCase__      ,					-1.0      ,					1.0      )
								a      :		Optional[int]	=				image / 2 + 0.5
								a      :		Optional[int]	=				image.cpu().permute(0      ,					2      ,					3      ,					1      ).numpy()

								if output_type == "pil":
									a      :		Union[str, Any]	=				self.numpy_to_pil(lowerCAmelCase__      )

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase__      )


