



"""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       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""
						__UpperCamelCase						=							["""vqvae"""]






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






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






						@torch.no_grad()
						def __call__(					self :Optional[Any]   ,  lowercase_ :int = 1   ,  lowercase_ :str = None   ,  lowercase_ :np.ndarray = None   ,  lowercase_ :int = 0   ,  lowercase_ :int = 0   ,  lowercase_ :int = None   ,  lowercase_ :torch.Generator = None   ,  lowercase_ :float = 0   ,  lowercase_ :float = 0   ,  lowercase_ :torch.Generator = None   ,  lowercase_ :float = 0   ,  lowercase_ :torch.Tensor = None   ,  lowercase_ :torch.Tensor = None   ,  lowercase_ :str=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(lowercase_					)
												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=lowercase_   ,  device=self.device   ,  )
												UpperCAmelCase             = noise
												UpperCAmelCase             = None

												if audio_file is not None or raw_audio is not None:
																		self.mel.load_audio(lowercase_   ,  lowercase_					)
																		UpperCAmelCase             = self.mel.audio_slice_to_image(lowercase_					)
																		UpperCAmelCase             = np.frombuffer(input_image.tobytes()   ,  dtype='uint8'					).reshape(
																		    (input_image.height, input_image.width)					)
																		UpperCAmelCase             = (input_image / 2_55) * 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(lowercase_   ,  0					)					).latent_dist.sample(
																								    generator=lowercase_					)[0]
																								UpperCAmelCase             = self.vqvae.config.scaling_factor * input_images

																		if start_step > 0:
																								UpperCAmelCase             = self.scheduler.add_noise(lowercase_   ,  lowercase_   ,  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(lowercase_   ,  lowercase_   ,  torch.tensor(self.scheduler.timesteps[start_step:]					)					)

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

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

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

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

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






						@torch.no_grad()
						def 	UpperCAmelCase__     (					self :str   ,  lowercase_ :List[Image.Image]   ,  lowercase_ :int = 50					)				->			np.ndarray:
												assert isinstance(self.scheduler   ,  lowercase_					)
												self.scheduler.set_timesteps(lowercase_					)
												UpperCAmelCase             = np.array(
												    [np.frombuffer(image.tobytes()   ,  dtype='uint8'					).reshape((1, image.height, image.width)					) for image in images]					)
												UpperCAmelCase             = (sample / 2_55) * 2 - 1
												UpperCAmelCase             = torch.Tensor(lowercase_					).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(lowercase_   ,  lowercase_					)['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 	UpperCAmelCase__     (					lowercase_ :torch.Tensor   ,  lowercase_ :torch.Tensor   ,  lowercase_ :float					)				->			torch.Tensor:
												UpperCAmelCase             = acos(torch.dot(torch.flatten(lowercase_					)   ,  torch.flatten(lowercase_					)					) / torch.norm(lowercase_					) / torch.norm(lowercase_					)					)
												return sin((1 - alpha) * theta					) * xa / sin(lowercase_					) + sin(alpha * theta					) * xa / sin(lowercase_					)













"""simple docstring"""
import os

def 				snake_case      (   ):
			with open(os.path.dirname(A__						) + "/grid.txt"						) as f:
						UpperCAmelCase_     : Any			     =					[]  # noqa: E741
						for _ in range(20						):
									l.append([int(A__						) for x in f.readline().split()]						)

						UpperCAmelCase_     : Any			     =					0

						# right
						for i in range(20						):
									for j in range(17						):
												UpperCAmelCase_     : Union[str, Any]			     =					l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
												if temp > maximum:
															UpperCAmelCase_     : Any			     =					temp

        # down
						for i in range(17						):
									for j in range(20						):
												UpperCAmelCase_     : List[Any]			     =					l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
												if temp > maximum:
															UpperCAmelCase_     : Tuple			     =					temp

        # diagonal 1
						for i in range(17						):
									for j in range(17						):
												UpperCAmelCase_     : str			     =					l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
												if temp > maximum:
															UpperCAmelCase_     : List[str]			     =					temp

        # diagonal 2
						for i in range(17						):
									for j in range(3	,20						):
												UpperCAmelCase_     : List[Any]			     =					l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
												if temp > maximum:
															UpperCAmelCase_     : List[str]			     =					temp
						return maximum


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



