


'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				['vqvae']




		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__()
							self.register_modules(unet=UpperCamelCase_    ,   scheduler=UpperCamelCase_    ,   mel=UpperCamelCase_    ,   vqvae=UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return 50 if isinstance(self.scheduler    ,   UpperCamelCase_							) else 1000




		@torch.no_grad()
		def __call__(		self    ,   UpperCamelCase_ = 1    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = 0    ,   UpperCamelCase_ = 0    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = 0    ,   UpperCamelCase_ = 0    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = 0    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_=True    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= steps or self.get_default_steps()
							self.scheduler.set_timesteps(UpperCamelCase_							)
							UpperCamelCase__   :Optional[int]												= step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size							) == int:
												UpperCamelCase__   :str												= (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
												UpperCamelCase__   :List[Any]												= randn_tensor(
												    (
												        batch_size,
												        self.unet.config.in_channels,
												        self.unet.config.sample_size[0],
												        self.unet.config.sample_size[1],
												    )    ,   generator=UpperCamelCase_    ,   device=self.device    ,   )
							UpperCamelCase__   :str												= noise
							UpperCamelCase__   :Dict												= None

							if audio_file is not None or raw_audio is not None:
												self.mel.load_audio(UpperCamelCase_    ,   UpperCamelCase_							)
												UpperCamelCase__   :List[str]												= self.mel.audio_slice_to_image(UpperCamelCase_							)
												UpperCamelCase__   :List[Any]												= np.frombuffer(input_image.tobytes()    ,   dtype='''uint8'''							).reshape(
												    (input_image.height, input_image.width)							)
												UpperCamelCase__   :List[str]												= (input_image / 255) * 2 - 1
												UpperCamelCase__   :Any												= torch.tensor(input_image[np.newaxis, :, :]    ,   dtype=torch.float							).to(self.device							)

												if self.vqvae is not None:
																	UpperCamelCase__   :int												= self.vqvae.encode(torch.unsqueeze(UpperCamelCase_    ,   0							)							).latent_dist.sample(
																	    generator=UpperCamelCase_							)[0]
																	UpperCamelCase__   :Dict												= self.vqvae.config.scaling_factor * input_images

												if start_step > 0:
																	UpperCamelCase__   :Any												= self.scheduler.add_noise(UpperCamelCase_    ,   UpperCamelCase_    ,   self.scheduler.timesteps[start_step - 1]							)

												UpperCamelCase__   :List[str]												= (
												    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
												)
												UpperCamelCase__   :Any												= int(mask_start_secs * pixels_per_second							)
												UpperCamelCase__   :str												= int(mask_end_secs * pixels_per_second							)
												UpperCamelCase__   :str												= self.scheduler.add_noise(UpperCamelCase_    ,   UpperCamelCase_    ,   torch.tensor(self.scheduler.timesteps[start_step:]							)							)

							for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]							)							):
												if isinstance(self.unet    ,   UpperCamelCase_							):
																	UpperCamelCase__   :List[str]												= self.unet(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)['''sample''']
												else:
																	UpperCamelCase__   :List[str]												= self.unet(UpperCamelCase_    ,   UpperCamelCase_							)['''sample''']

												if isinstance(self.scheduler    ,   UpperCamelCase_							):
																	UpperCamelCase__   :List[str]												= self.scheduler.step(
																	    model_output=UpperCamelCase_    ,   timestep=UpperCamelCase_    ,   sample=UpperCamelCase_    ,   eta=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   )['''prev_sample''']
												else:
																	UpperCamelCase__   :Union[str, Any]												= self.scheduler.step(
																	    model_output=UpperCamelCase_    ,   timestep=UpperCamelCase_    ,   sample=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   )['''prev_sample''']

												if mask is not None:
																	if mask_start > 0:
																						UpperCamelCase__   :Union[str, Any]												= mask[:, step, :, :mask_start]
																	if mask_end > 0:
																						UpperCamelCase__   :List[str]												= mask[:, step, :, -mask_end:]

							if self.vqvae is not None:
												# 0.18215 was scaling factor used in training to ensure unit variance
												UpperCamelCase__   :List[str]												= 1 / self.vqvae.config.scaling_factor * images
												UpperCamelCase__   :Optional[int]												= self.vqvae.decode(UpperCamelCase_							)['''sample''']

							UpperCamelCase__   :Union[str, Any]												= (images / 2 + 0.5).clamp(0    ,   1							)
							UpperCamelCase__   :List[Any]												= images.cpu().permute(0    ,   2    ,   3    ,   1							).numpy()
							UpperCamelCase__   :Optional[Any]												= (images * 255).round().astype('''uint8'''							)
							UpperCamelCase__   :Optional[int]												= list(
							    (Image.fromarray(_[:, :, 0]							) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(UpperCamelCase_    ,   mode='''RGB'''							).convert('''L'''							) for _ in images)							)

							UpperCamelCase__   :Optional[int]												= [self.mel.image_to_audio(UpperCamelCase_							) for _ in images]
							if not return_dict:
												return images, (self.mel.get_sample_rate(), audios)

							return BaseOutput(**AudioPipelineOutput(np.array(UpperCamelCase_							)[:, np.newaxis, :]							)    ,   **ImagePipelineOutput(UpperCamelCase_							)							)




		@torch.no_grad()
		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = 50							):

							'''simple docstring'''


							assert isinstance(self.scheduler    ,   UpperCamelCase_							)
							self.scheduler.set_timesteps(UpperCamelCase_							)
							UpperCamelCase__   :str												= np.array(
							    [np.frombuffer(image.tobytes()    ,   dtype='''uint8'''							).reshape((1, image.height, image.width)							) for image in images]							)
							UpperCamelCase__   :List[str]												= (sample / 255) * 2 - 1
							UpperCamelCase__   :Dict												= torch.Tensor(UpperCamelCase_							).to(self.device							)

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps    ,   (0,)							)							):
												UpperCamelCase__   :List[str]												= t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
												UpperCamelCase__   :List[Any]												= self.scheduler.alphas_cumprod[t]
												UpperCamelCase__   :Optional[int]												= (
												    self.scheduler.alphas_cumprod[prev_timestep]
												    if prev_timestep >= 0
												    else self.scheduler.final_alpha_cumprod
												)
												UpperCamelCase__   :int												= 1 - alpha_prod_t
												UpperCamelCase__   :Optional[Any]												= self.unet(UpperCamelCase_    ,   UpperCamelCase_							)['''sample''']
												UpperCamelCase__   :Optional[int]												= (1 - alpha_prod_t_prev) ** 0.5 * model_output
												UpperCamelCase__   :Dict												= (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
												UpperCamelCase__   :List[str]												= sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample




		@staticmethod
		def     lowerCAmelCase__				(		UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= acos(torch.dot(torch.flatten(UpperCamelCase_							)    ,   torch.flatten(UpperCamelCase_							)							) / torch.norm(UpperCamelCase_							) / torch.norm(UpperCamelCase_							)							)
							return sin((1 - alpha) * theta							) * xa / sin(UpperCamelCase_							) + sin(alpha * theta							) * xa / sin(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
		    )
		)