


"""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 		snake_case_(       a__			):
     __UpperCamelCase  =					['''vqvae''']






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






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






     @torch.no_grad()
     def __call__(	self  :					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_  :					Tuple=True					,				):
           lowerCAmelCase					:	Any       =   steps or self.get_default_steps()
           self.scheduler.set_timesteps(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   step_generator or generator
           # For backwards compatibility
           if type(self.unet.config.sample_size				) == int:
                 lowerCAmelCase					:	Optional[int]       =   (self.unet.config.sample_size, self.unet.config.sample_size)
           if noise is None:
                 lowerCAmelCase					:	Optional[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					,				)
           lowerCAmelCase					:	Tuple       =   noise
           lowerCAmelCase					:	Tuple       =   None

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

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

                 if start_step > 0:
                       lowerCAmelCase					:	Optional[int]       =   self.scheduler.add_noise(UpperCamelCase_					,				UpperCamelCase_					,				self.scheduler.timesteps[start_step - 1]				)

                 lowerCAmelCase					:	str       =   (
                     self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                 )
                 lowerCAmelCase					:	List[str]       =   int(mask_start_secs * pixels_per_second				)
                 lowerCAmelCase					:	Dict       =   int(mask_end_secs * pixels_per_second				)
                 lowerCAmelCase					:	List[Any]       =   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_				):
                       lowerCAmelCase					:	Dict       =   self.unet(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)['''sample''']
                 else:
                       lowerCAmelCase					:	Optional[Any]       =   self.unet(UpperCamelCase_					,				UpperCamelCase_				)['''sample''']

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

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

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

           lowerCAmelCase					:	Tuple       =   (images / 2 + 0.5).clamp(0					,				1				)
           lowerCAmelCase					:	List[Any]       =   images.cpu().permute(0					,				2					,				3					,				1				).numpy()
           lowerCAmelCase					:	Optional[int]       =   (images * 2_5_5).round().astype('''uint8'''				)
           lowerCAmelCase					:	List[str]       =   list(
               (Image.fromarray(_[:, :, 0]				) for _ in images)
               if images.shape[3] == 1
               else (Image.fromarray(UpperCamelCase_					,				mode='''RGB'''				).convert('''L'''				) for _ in images)				)

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

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

           return sample






     @staticmethod
     def      lowerCamelCase__     (	UpperCamelCase_  :					torch.Tensor					,				UpperCamelCase_  :					torch.Tensor					,				UpperCamelCase_  :					float				):
           lowerCAmelCase					:	Optional[int]       =   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

from PIL import Image

# Define glider example
snake_case__		:     int    				=				[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
snake_case__		:     Any    				=				[[0, 1, 0], [0, 1, 0], [0, 1, 0]]


def      _snake_case						(						_snake_case				:			list[list[int]]			):
      lowerCAmelCase					:	Union[str, Any]       =   []
      for i in range(len(_snake_case			)			):
            lowerCAmelCase					:	Any       =   []
            for j in range(len(cells[i]			)			):
                  # Get the number of live neighbours
                  lowerCAmelCase					:	Optional[int]       =   0
                  if i > 0 and j > 0:
                        neighbour_count += cells[i - 1][j - 1]
                  if i > 0:
                        neighbour_count += cells[i - 1][j]
                  if i > 0 and j < len(cells[i]			) - 1:
                        neighbour_count += cells[i - 1][j + 1]
                  if j > 0:
                        neighbour_count += cells[i][j - 1]
                  if j < len(cells[i]			) - 1:
                        neighbour_count += cells[i][j + 1]
                  if i < len(_snake_case			) - 1 and j > 0:
                        neighbour_count += cells[i + 1][j - 1]
                  if i < len(_snake_case			) - 1:
                        neighbour_count += cells[i + 1][j]
                  if i < len(_snake_case			) - 1 and j < len(cells[i]			) - 1:
                        neighbour_count += cells[i + 1][j + 1]

                  # Rules of the game of life (excerpt from Wikipedia):
                  # 1. Any live cell with two or three live neighbours survives.
                  # 2. Any dead cell with three live neighbours becomes a live cell.
                  # 3. All other live cells die in the next generation.
                  #    Similarly, all other dead cells stay dead.
                  lowerCAmelCase					:	str       =   cells[i][j] == 1
                  if (
                      (alive and 2 <= neighbour_count <= 3)
                      or not alive
                      and neighbour_count == 3
                  ):
                        next_generation_row.append(1			)
                  else:
                        next_generation_row.append(0			)

            next_generation.append(_snake_case			)
      return next_generation


def      _snake_case						(						_snake_case				:			list[list[int]] ,   _snake_case				:			int			):
      lowerCAmelCase					:	int       =   []
      for _ in range(_snake_case			):
            # Create output image
            lowerCAmelCase					:	Union[str, Any]       =   Image.new('''RGB''' ,   (len(cells[0]			), len(_snake_case			))			)
            lowerCAmelCase					:	Union[str, Any]       =   img.load()

            # Save cells to image
            for x in range(len(_snake_case			)			):
                  for y in range(len(cells[0]			)			):
                        lowerCAmelCase					:	Optional[int]       =   255 - cells[y][x] * 255
                        lowerCAmelCase					:	List[Any]       =   (colour, colour, colour)

        # Save image
            images.append(_snake_case			)
            lowerCAmelCase					:	Union[str, Any]       =   new_generation(_snake_case			)
      return images


if __name__ == "__main__":
 snake_case__		:     Union[str, Any]    				=				generate_images(GLIDER, 16)
 images[0].save('''out.gif''', save_all=True, append_images=images[1:])


