






"""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_ (       a_  ):
  _A       :					List[str]				=	['vqvae']



  def __init__(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		)			->			List[str]:



         """simple docstring"""





         super().__init__()
         self.register_modules(unet=snake_case__ ,		scheduler=snake_case__ ,		mel=snake_case__ ,		vqvae=snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         return 50 if isinstance(self.scheduler ,		snake_case__				) else 10_00



  @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]]],
  ]:



         """simple docstring"""





         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 / 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(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 * 2_55).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 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = 50				)			->			np.ndarray:



         """simple docstring"""





         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 / 2_55) * 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 		UpperCamelCase_				(      snake_case__ ,		snake_case__ ,		snake_case__				)			->			torch.Tensor:



         """simple docstring"""





         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"""







import random
from typing import Any






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       for _ in range(len(lowerCAmelCase		)		):
              UpperCAmelCase	    =			random.randint(0	,      len(lowerCAmelCase		) - 1		)
              UpperCAmelCase	    =			random.randint(0	,      len(lowerCAmelCase		) - 1		)
              UpperCAmelCase    ,       UpperCAmelCase	    =			data[b], data[a]
       return data


if __name__ == "__main__":
 lowerCAmelCase_	:    Tuple            =      [0, 1, 2, 3, 4, 5, 6, 7]
 lowerCAmelCase_	:    List[str]            =      ['''python''', '''says''', '''hello''', '''!''']
 print('''Fisher-Yates Shuffle:''')
 print('''List''', integers, strings)
 print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
