


'''simple docstring'''
from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


__snake_case							      =     transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def 			a      (			__a   )  ->	Optional[Any]:



					'''simple docstring'''






					if isinstance(__a ,					torch.Tensor   ):
										return image
					elif isinstance(__a ,					PIL.Image.Image   ):
										UpperCamelCase__   :Any												= [image]

					UpperCamelCase__   :Any												= [trans(img.convert('''RGB'''   )   ) for img in image]
					UpperCamelCase__   :List[str]												= torch.stack(__a   )
					return image







class    lowercase	(							A__					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							super().__init__()

							# make sure scheduler can always be converted to DDIM
							UpperCamelCase__   :str												= DDIMScheduler.from_config(scheduler.config							)

							self.register_modules(unet=UpperCamelCase_    ,   scheduler=UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							if strength < 0 or strength > 1:
												raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= min(int(num_inference_steps * strength							)    ,   UpperCamelCase_							)

							UpperCamelCase__   :List[str]												= max(num_inference_steps - init_timestep    ,   0							)
							UpperCamelCase__   :Dict												= self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_=None							):

							'''simple docstring'''


							if not isinstance(UpperCamelCase_    ,   (torch.Tensor, PIL.Image.Image, list)							):
												raise ValueError(
												    F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCamelCase_							)}'''							)

							UpperCamelCase__   :List[str]												= image.to(device=UpperCamelCase_    ,   dtype=UpperCamelCase_							)

							if isinstance(UpperCamelCase_    ,   UpperCamelCase_							) and len(UpperCamelCase_							) != batch_size:
												raise ValueError(
												    F'''You have passed a list of generators of length {len(UpperCamelCase_							)}, but requested an effective batch'''
												    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''							)

							UpperCamelCase__   :List[Any]												= init_latents.shape
							UpperCamelCase__   :int												= randn_tensor(UpperCamelCase_    ,   generator=UpperCamelCase_    ,   device=UpperCamelCase_    ,   dtype=UpperCamelCase_							)

							# get latents
							print('''add noise to latents at timestep'''    ,   UpperCamelCase_							)
							UpperCamelCase__   :Union[str, Any]												= self.scheduler.add_noise(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)
							UpperCamelCase__   :Any												= init_latents

							return latents




		@torch.no_grad()
		def __call__(		self    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = 0.8    ,   UpperCamelCase_ = 1    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = 0.0    ,   UpperCamelCase_ = 50    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = "pil"    ,   UpperCamelCase_ = True    ,   ):

							'''simple docstring'''


							self.check_inputs(UpperCamelCase_							)

							# 2. Preprocess image
							UpperCamelCase__   :Tuple												= preprocess(UpperCamelCase_							)

							# 3. set timesteps
							self.scheduler.set_timesteps(UpperCamelCase_    ,   device=self.device							)
							UpperCamelCase__					,				UpperCamelCase__   :Tuple												= self.get_timesteps(UpperCamelCase_    ,   UpperCamelCase_    ,   self.device							)
							UpperCamelCase__   :Tuple												= timesteps[:1].repeat(UpperCamelCase_							)

							# 4. Prepare latent variables
							UpperCamelCase__   :int												= self.prepare_latents(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   self.unet.dtype    ,   self.device    ,   UpperCamelCase_							)
							UpperCamelCase__   :int												= latents

							# 5. Denoising loop
							for t in self.progress_bar(UpperCamelCase_							):
												# 1. predict noise model_output
												UpperCamelCase__   :Optional[Any]												= self.unet(UpperCamelCase_    ,   UpperCamelCase_							).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__   :Dict												= self.scheduler.step(
												    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   eta=UpperCamelCase_    ,   use_clipped_model_output=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   ).prev_sample

							UpperCamelCase__   :Dict												= (image / 2 + 0.5).clamp(0    ,   1							)
							UpperCamelCase__   :Any												= image.cpu().permute(0    ,   2    ,   3    ,   1							).numpy()
							if output_type == "pil":
												UpperCamelCase__   :Dict												= self.numpy_to_pil(UpperCamelCase_							)

							if not return_dict:
												return (image, latent_timestep.item())

							return ImagePipelineOutput(images=UpperCamelCase_							)



'''simple docstring'''
from collections import defaultdict
class    lowercase	:

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= 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(UpperCamelCase_							)							)
							]

							UpperCamelCase__   :str												= defaultdict(UpperCamelCase_							)  # 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[int]												= (1 << len(UpperCamelCase_							)) - 1




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							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__   :str												= self.count_ways_until(UpperCamelCase_    ,   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__   :Optional[int]												= total_ways_util

							return self.dp[mask][task_no]




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							for i in range(len(UpperCamelCase_							)							):
												for j in task_performed[i]:
																	self.task[j].append(UpperCamelCase_							)

        # 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__":
		__snake_case							      =     5  # total no of tasks (the value of N)

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