


'''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 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		List[Any]   		=  ['vqvae']





							def __init__(  self		:int  ,_UpperCamelCase		:AutoencoderKL  ,_UpperCamelCase		:UNetaDConditionModel  ,_UpperCamelCase		:Mel  ,_UpperCamelCase		:Union[DDIMScheduler, DDPMScheduler]  ,):
									super().__init__()
									self.register_modules(unet=_UpperCamelCase  ,scheduler=_UpperCamelCase  ,mel=_UpperCamelCase  ,vqvae=_UpperCamelCase			)





							def 		a__   (  self		:str			):
									return 5_0 if isinstance(self.scheduler  ,_UpperCamelCase			) else 1_0_0_0





							@torch.no_grad()
							def __call__(  self		:List[str]  ,_UpperCamelCase		:int = 1  ,_UpperCamelCase		:str = None  ,_UpperCamelCase		:np.ndarray = None  ,_UpperCamelCase		:int = 0  ,_UpperCamelCase		:int = 0  ,_UpperCamelCase		:int = None  ,_UpperCamelCase		:torch.Generator = None  ,_UpperCamelCase		:float = 0  ,_UpperCamelCase		:float = 0  ,_UpperCamelCase		:torch.Generator = None  ,_UpperCamelCase		:float = 0  ,_UpperCamelCase		:torch.Tensor = None  ,_UpperCamelCase		:torch.Tensor = None  ,_UpperCamelCase		:List[Any]=True  ,):
									snake_case_		:							List[Any]			       =						steps or self.get_default_steps()
									self.scheduler.set_timesteps(_UpperCamelCase			)
									snake_case_		:							List[str]			       =						step_generator or generator
									# For backwards compatibility
									if type(self.unet.config.sample_size			) == int:
											snake_case_		:							Union[str, Any]			       =						(self.unet.config.sample_size, self.unet.config.sample_size)
									if noise is None:
											snake_case_		:							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  ,)
									snake_case_		:							Optional[Any]			       =						noise
									snake_case_		:							Optional[int]			       =						None

									if audio_file is not None or raw_audio is not None:
											self.mel.load_audio(_UpperCamelCase  ,_UpperCamelCase			)
											snake_case_		:							Optional[Any]			       =						self.mel.audio_slice_to_image(_UpperCamelCase			)
											snake_case_		:							Tuple			       =						np.frombuffer(input_image.tobytes()  ,dtype="""uint8"""			).reshape(
											    (input_image.height, input_image.width)			)
											snake_case_		:							Optional[int]			       =						(input_image / 2_5_5) * 2 - 1
											snake_case_		:							Any			       =						torch.tensor(input_image[np.newaxis, :, :]  ,dtype=torch.float			).to(self.device			)

											if self.vqvae is not None:
													snake_case_		:							Union[str, Any]			       =						self.vqvae.encode(torch.unsqueeze(_UpperCamelCase  ,0			)			).latent_dist.sample(
													    generator=_UpperCamelCase			)[0]
													snake_case_		:							Any			       =						self.vqvae.config.scaling_factor * input_images

											if start_step > 0:
													snake_case_		:							Tuple			       =						self.scheduler.add_noise(_UpperCamelCase  ,_UpperCamelCase  ,self.scheduler.timesteps[start_step - 1]			)

											snake_case_		:							Optional[int]			       =						(
											    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
											)
											snake_case_		:							Union[str, Any]			       =						int(mask_start_secs * pixels_per_second			)
											snake_case_		:							int			       =						int(mask_end_secs * pixels_per_second			)
											snake_case_		:							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			):
													snake_case_		:							Tuple			       =						self.unet(_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase			)["""sample"""]
											else:
													snake_case_		:							Optional[int]			       =						self.unet(_UpperCamelCase  ,_UpperCamelCase			)["""sample"""]

											if isinstance(self.scheduler  ,_UpperCamelCase			):
													snake_case_		:							int			       =						self.scheduler.step(
													    model_output=_UpperCamelCase  ,timestep=_UpperCamelCase  ,sample=_UpperCamelCase  ,eta=_UpperCamelCase  ,generator=_UpperCamelCase  ,)["""prev_sample"""]
											else:
													snake_case_		:							Any			       =						self.scheduler.step(
													    model_output=_UpperCamelCase  ,timestep=_UpperCamelCase  ,sample=_UpperCamelCase  ,generator=_UpperCamelCase  ,)["""prev_sample"""]

											if mask is not None:
													if mask_start > 0:
															snake_case_		:							List[str]			       =						mask[:, step, :, :mask_start]
													if mask_end > 0:
															snake_case_		:							Tuple			       =						mask[:, step, :, -mask_end:]

									if self.vqvae is not None:
											# 0.18215 was scaling factor used in training to ensure unit variance
											snake_case_		:							Optional[Any]			       =						1 / self.vqvae.config.scaling_factor * images
											snake_case_		:							str			       =						self.vqvae.decode(_UpperCamelCase			)["""sample"""]

									snake_case_		:							str			       =						(images / 2 + 0.5).clamp(0  ,1			)
									snake_case_		:							List[str]			       =						images.cpu().permute(0  ,2  ,3  ,1			).numpy()
									snake_case_		:							Dict			       =						(images * 2_5_5).round().astype("""uint8"""			)
									snake_case_		:							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)			)

									snake_case_		:							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 		a__   (  self		:str  ,_UpperCamelCase		:List[Image.Image]  ,_UpperCamelCase		:int = 5_0			):
									assert isinstance(self.scheduler  ,_UpperCamelCase			)
									self.scheduler.set_timesteps(_UpperCamelCase			)
									snake_case_		:							Dict			       =						np.array(
									    [np.frombuffer(image.tobytes()  ,dtype="""uint8"""			).reshape((1, image.height, image.width)			) for image in images]			)
									snake_case_		:							List[Any]			       =						(sample / 2_5_5) * 2 - 1
									snake_case_		:							List[Any]			       =						torch.Tensor(_UpperCamelCase			).to(self.device			)

									for t in self.progress_bar(torch.flip(self.scheduler.timesteps  ,(0,)			)			):
											snake_case_		:							List[str]			       =						t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
											snake_case_		:							Any			       =						self.scheduler.alphas_cumprod[t]
											snake_case_		:							List[str]			       =						(
											    self.scheduler.alphas_cumprod[prev_timestep]
											    if prev_timestep >= 0
											    else self.scheduler.final_alpha_cumprod
											)
											snake_case_		:							Optional[Any]			       =						1 - alpha_prod_t
											snake_case_		:							int			       =						self.unet(_UpperCamelCase  ,_UpperCamelCase			)["""sample"""]
											snake_case_		:							str			       =						(1 - alpha_prod_t_prev) ** 0.5 * model_output
											snake_case_		:							str			       =						(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
											snake_case_		:							Optional[Any]			       =						sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

									return sample





							@staticmethod
							def 		a__   (  _UpperCamelCase		:torch.Tensor  ,_UpperCamelCase		:torch.Tensor  ,_UpperCamelCase		:float			):
									snake_case_		:							Dict			       =						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 __future__ import annotations





def      UpperCAmelCase      (		lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							List[str]			       =						[True] * limit
		snake_case_		:							Union[str, Any]			       =						False
		snake_case_		:							List[str]			       =						False
		snake_case_		:							Any			       =						True

		for i in range(3				,      int(limit**0.5 + 1 )				,      2 ):
				snake_case_		:							Tuple			       =						i * 2
				while index < limit:
						snake_case_		:							Any			       =						False
						snake_case_		:							Optional[Any]			       =						index + i

		snake_case_		:							List[str]			       =						[2]

		for i in range(3				,      lowerCamelCase_				,      2 ):
				if is_prime[i]:
						primes.append(lowerCamelCase_ )

		return primes





def      UpperCAmelCase      (		lowerCamelCase_  :int = 1_00_00_00 ):







		'''simple docstring'''
		snake_case_		:							Optional[int]			       =						prime_sieve(lowerCamelCase_ )
		snake_case_		:							Optional[Any]			       =						0
		snake_case_		:							Optional[int]			       =						0

		for i in range(len(lowerCamelCase_ ) ):
				for j in range(i + length				,      len(lowerCamelCase_ ) ):
						snake_case_		:							Optional[int]			       =						sum(primes[i:j] )
						if sol >= ceiling:
								break

						if sol in primes:
								snake_case_		:							Optional[Any]			       =						j - i
								snake_case_		:							Tuple			       =						sol

		return largest


if __name__ == "__main__":
							print(F'{solution() = }')