



"""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'''







							_UpperCAmelCase      				=					['''vqvae''']






							def __init__(  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       )			->							List[Any]:
									super().__init__()
									self.register_modules(unet=snake_case	,       scheduler=snake_case	,       mel=snake_case	,       vqvae=snake_case						)






							def        lowerCamelCase_   (  self						)			->							int:
									return 50 if isinstance(self.scheduler	,       snake_case						) else 1000






							@torch.no_grad()
							def __call__(  self	,       snake_case = 1	,       snake_case = None	,       snake_case = None	,       snake_case = 0	,       snake_case = 0	,       snake_case = None	,       snake_case = None	,       snake_case = 0	,       snake_case = 0	,       snake_case = None	,       snake_case = 0	,       snake_case = None	,       snake_case = None	,       snake_case=True	,       )			->							Union[
							    Union[AudioPipelineOutput, ImagePipelineOutput],
							    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
							]:
									_UpperCAmelCase							 =		steps or self.get_default_steps()
									self.scheduler.set_timesteps(snake_case						)
									_UpperCAmelCase							 =		step_generator or generator
									# For backwards compatibility
									if type(self.unet.config.sample_size						) == int:
											_UpperCAmelCase							 =		(self.unet.config.sample_size, self.unet.config.sample_size)
									if noise is None:
											_UpperCAmelCase							 =		randn_tensor(
											    (
											        batch_size,
											        self.unet.config.in_channels,
											        self.unet.config.sample_size[0],
											        self.unet.config.sample_size[1],
											    )	,       generator=snake_case	,       device=self.device	,       )
									_UpperCAmelCase							 =		noise
									_UpperCAmelCase							 =		None

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

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

											if start_step > 0:
													_UpperCAmelCase							 =		self.scheduler.add_noise(snake_case	,       snake_case	,       self.scheduler.timesteps[start_step - 1]						)

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

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

											if isinstance(self.scheduler	,       snake_case						):
													_UpperCAmelCase							 =		self.scheduler.step(
													    model_output=snake_case	,       timestep=snake_case	,       sample=snake_case	,       eta=snake_case	,       generator=snake_case	,       )['prev_sample']
											else:
													_UpperCAmelCase							 =		self.scheduler.step(
													    model_output=snake_case	,       timestep=snake_case	,       sample=snake_case	,       generator=snake_case	,       )['prev_sample']

											if mask is not None:
													if mask_start > 0:
															_UpperCAmelCase							 =		mask[:, step, :, :mask_start]
													if mask_end > 0:
															_UpperCAmelCase							 =		mask[:, step, :, -mask_end:]

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

									_UpperCAmelCase							 =		(images / 2 + 0.5).clamp(0	,       1						)
									_UpperCAmelCase							 =		images.cpu().permute(0	,       2	,       3	,       1						).numpy()
									_UpperCAmelCase							 =		(images * 255).round().astype('uint8'						)
									_UpperCAmelCase							 =		list(
									    (Image.fromarray(_[:, :, 0]						) for _ in images)
									    if images.shape[3] == 1
									    else (Image.fromarray(snake_case	,       mode='RGB'						).convert('L'						) for _ in images)						)

									_UpperCAmelCase							 =		[self.mel.image_to_audio(snake_case						) for _ in images]
									if not return_dict:
											return images, (self.mel.get_sample_rate(), audios)

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






							@torch.no_grad()
							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case = 50						)			->							np.ndarray:
									assert isinstance(self.scheduler	,       snake_case						)
									self.scheduler.set_timesteps(snake_case						)
									_UpperCAmelCase							 =		np.array(
									    [np.frombuffer(image.tobytes()	,       dtype='uint8'						).reshape((1, image.height, image.width)						) for image in images]						)
									_UpperCAmelCase							 =		(sample / 255) * 2 - 1
									_UpperCAmelCase							 =		torch.Tensor(snake_case						).to(self.device						)

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

									return sample






							@staticmethod
							def        lowerCamelCase_   (  snake_case	,       snake_case	,       snake_case						)			->							torch.Tensor:
									_UpperCAmelCase							 =		acos(torch.dot(torch.flatten(snake_case						)	,       torch.flatten(snake_case						)						) / torch.norm(snake_case						) / torch.norm(snake_case						)						)
									return sin((1 - alpha) * theta						) * xa / sin(snake_case						) + sin(alpha * theta						) * xa / sin(snake_case						)







"""simple docstring"""





from __future__ import annotations




def 	UpperCAmelCase     (		A	:						int     ,				A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		[]
		create_all_state(1     ,				A     ,				A     ,				[]     ,				A       )
		return result




def 	UpperCAmelCase     (		A	:						int     ,				A	:						int     ,				A	:						int     ,				A	:						list[int]     ,				A	:						list[list[int]]     ,				):

		'''simple docstring'''


		if level == 0:
				total_list.append(current_list[:]       )
				return

		for i in range(A     ,				total_number - level + 2       ):
				current_list.append(A       )
				create_all_state(i + 1     ,				A     ,				level - 1     ,				A     ,				A       )
				current_list.pop()




def 	UpperCAmelCase     (		A	:						list[list[int]]       ):

		'''simple docstring'''


		for i in total_list:
				print(*A       )


if __name__ == "__main__":
					lowercase  							=					4
					lowercase  							=					2
					lowercase  							=					generate_all_combinations(n, k)
					print_all_state(total_list)


