
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    lowerCAmelCase__     (	__lowercase			):
		a__       :						List[Any]        =		["""vqvae"""]






		def __init__(		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							AutoencoderKL							,			SCREAMING_SNAKE_CASE__		:							UNetaDConditionModel							,			SCREAMING_SNAKE_CASE__		:							Mel							,			SCREAMING_SNAKE_CASE__		:							Union[DDIMScheduler, DDPMScheduler]							,			)     ->							List[str]:
						super().__init__()
						self.register_modules(unet=SCREAMING_SNAKE_CASE__							,			scheduler=SCREAMING_SNAKE_CASE__							,			mel=SCREAMING_SNAKE_CASE__							,			vqvae=SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Optional[Any]  )     ->							int:
						return 50 if isinstance(self.scheduler							,			SCREAMING_SNAKE_CASE__  ) else 10_00






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

						if audio_file is not None or raw_audio is not None:
										self.mel.load_audio(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
										__lowerCamelCase    					=	self.mel.audio_slice_to_image(SCREAMING_SNAKE_CASE__  )
										__lowerCamelCase    					=	np.frombuffer(input_image.tobytes()							,			dtype='''uint8'''  ).reshape(
										    (input_image.height, input_image.width)  )
										__lowerCamelCase    					=	(input_image / 2_55) * 2 - 1
										__lowerCamelCase    					=	torch.tensor(input_image[np.newaxis, :, :]							,			dtype=torch.float  ).to(self.device  )

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

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

										__lowerCamelCase    					=	(
										    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
										)
										__lowerCamelCase    					=	int(mask_start_secs * pixels_per_second  )
										__lowerCamelCase    					=	int(mask_end_secs * pixels_per_second  )
										__lowerCamelCase    					=	self.scheduler.add_noise(SCREAMING_SNAKE_CASE__							,			SCREAMING_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							,			SCREAMING_SNAKE_CASE__  ):
														__lowerCamelCase    					=	self.unet(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )['''sample''']
										else:
														__lowerCamelCase    					=	self.unet(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )['''sample''']

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

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

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

						__lowerCamelCase    					=	(images / 2 + 0.5).clamp(0							,			1  )
						__lowerCamelCase    					=	images.cpu().permute(0							,			2							,			3							,			1  ).numpy()
						__lowerCamelCase    					=	(images * 2_55).round().astype('''uint8'''  )
						__lowerCamelCase    					=	list(
						    (Image.fromarray(_[:, :, 0]  ) for _ in images)
						    if images.shape[3] == 1
						    else (Image.fromarray(SCREAMING_SNAKE_CASE__							,			mode='''RGB'''  ).convert('''L'''  ) for _ in images)  )

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

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






		@torch.no_grad()
		def  __A       (		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							List[Image.Image]							,			SCREAMING_SNAKE_CASE__		:							int = 50  )     ->							np.ndarray:
						assert isinstance(self.scheduler							,			SCREAMING_SNAKE_CASE__  )
						self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	np.array(
						    [np.frombuffer(image.tobytes()							,			dtype='''uint8'''  ).reshape((1, image.height, image.width)  ) for image in images]  )
						__lowerCamelCase    					=	(sample / 2_55) * 2 - 1
						__lowerCamelCase    					=	torch.Tensor(SCREAMING_SNAKE_CASE__  ).to(self.device  )

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

						return sample







		@staticmethod
		def  __A       (		SCREAMING_SNAKE_CASE__		:							torch.Tensor							,			SCREAMING_SNAKE_CASE__		:							torch.Tensor							,			SCREAMING_SNAKE_CASE__		:							float  )     ->							torch.Tensor:
						__lowerCamelCase    					=	acos(torch.dot(torch.flatten(SCREAMING_SNAKE_CASE__  )							,			torch.flatten(SCREAMING_SNAKE_CASE__  )  ) / torch.norm(SCREAMING_SNAKE_CASE__  ) / torch.norm(SCREAMING_SNAKE_CASE__  )  )
						return sin((1 - alpha) * theta  ) * xa / sin(SCREAMING_SNAKE_CASE__  ) + sin(alpha * theta  ) * xa / sin(SCREAMING_SNAKE_CASE__  )





import os




def  __magic_name__ (   )			->   str:
				__lowerCamelCase    					=	os.path.join(os.path.dirname(__lowerCAmelCase							)		,  '''num.txt'''							)
				with open(__lowerCAmelCase							) as file_hand:
								return str(sum(int(__lowerCAmelCase							) for line in file_hand							)							)[:10]


if __name__ == "__main__":
					print(solution())



