




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 		lowercase_   (			a__       ):


   """simple docstring"""

   lowerCamelCase_      =						['vqvae']



   def __init__(   self :     Optional[Any]  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     List[Any]  ,				):

      """simple docstring"""
      super().__init__()
      self.register_modules(unet=_lowerCamelCase  ,				scheduler=_lowerCamelCase  ,				mel=_lowerCamelCase  ,				vqvae=_lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      return 5_0 if isinstance(self.scheduler  ,				_lowerCamelCase   ) else 1_0_0_0



   @torch.no_grad()
   def __call__(   self :     List[str]  ,				__lowerCamelCase :     Union[str, Any] = 1  ,				__lowerCamelCase :     Optional[int] = None  ,				__lowerCamelCase :     Any = None  ,				__lowerCamelCase :     str = 0  ,				__lowerCamelCase :     Tuple = 0  ,				__lowerCamelCase :     List[str] = None  ,				__lowerCamelCase :     Union[str, Any] = None  ,				__lowerCamelCase :     Optional[Any] = 0  ,				__lowerCamelCase :     int = 0  ,				__lowerCamelCase :     List[Any] = None  ,				__lowerCamelCase :     List[str] = 0  ,				__lowerCamelCase :     Dict = None  ,				__lowerCamelCase :     Any = None  ,				__lowerCamelCase :     Dict=True  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				steps or self.get_default_steps()
      self.scheduler.set_timesteps(_lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				step_generator or generator
      # For backwards compatibility
      if type(self.unet.config.sample_size   ) == int:
         _SCREAMING_SNAKE_CASE	=				(self.unet.config.sample_size, self.unet.config.sample_size)
      if noise is None:
         _SCREAMING_SNAKE_CASE	=				randn_tensor(
             (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size[0],
                 self.unet.config.sample_size[1],
             )  ,				generator=_lowerCamelCase  ,				device=self.device  ,				)
      _SCREAMING_SNAKE_CASE	=				noise
      _SCREAMING_SNAKE_CASE	=				None

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

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

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

         _SCREAMING_SNAKE_CASE	=				(
             self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
         )
         _SCREAMING_SNAKE_CASE	=				int(mask_start_secs * pixels_per_second   )
         _SCREAMING_SNAKE_CASE	=				int(mask_end_secs * pixels_per_second   )
         _SCREAMING_SNAKE_CASE	=				self.scheduler.add_noise(_lowerCamelCase  ,				_lowerCamelCase  ,				torch.tensor(self.scheduler.timesteps[start_step:]   )   )

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

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

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

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

      _SCREAMING_SNAKE_CASE	=				(images / 2 + 0.5).clamp(0  ,				1   )
      _SCREAMING_SNAKE_CASE	=				images.cpu().permute(0  ,				2  ,				3  ,				1   ).numpy()
      _SCREAMING_SNAKE_CASE	=				(images * 2_5_5).round().astype("uint8"   )
      _SCREAMING_SNAKE_CASE	=				list(
          (Image.fromarray(_[:, :, 0]   ) for _ in images)
          if images.shape[3] == 1
          else (Image.fromarray(_lowerCamelCase  ,				mode="RGB"   ).convert("L"   ) for _ in images)   )

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

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



   @torch.no_grad()
   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Tuple = 5_0   ):

      """simple docstring"""
      assert isinstance(self.scheduler  ,				_lowerCamelCase   )
      self.scheduler.set_timesteps(_lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				np.array(
          [np.frombuffer(image.tobytes()  ,				dtype="uint8"   ).reshape((1, image.height, image.width)   ) for image in images]   )
      _SCREAMING_SNAKE_CASE	=				(sample / 2_5_5) * 2 - 1
      _SCREAMING_SNAKE_CASE	=				torch.Tensor(_lowerCamelCase   ).to(self.device   )

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

      return sample



   @staticmethod
   def 							lowerCAmelCase_      (   __lowerCamelCase :     Dict  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				acos(torch.dot(torch.flatten(_lowerCamelCase   )  ,				torch.flatten(_lowerCamelCase   )   ) / torch.norm(_lowerCamelCase   ) / torch.norm(_lowerCamelCase   )   )
      return sin((1 - alpha) * theta   ) * xa / sin(_lowerCamelCase   ) + sin(alpha * theta   ) * xa / sin(_lowerCamelCase   )







'''simple docstring'''



import random
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int	,					__A    :			float	,					__A    :			bool = False  )			->			dict:
   _SCREAMING_SNAKE_CASE	=				{i: [] for i in range(__A  )}

   # if probability is greater or equal than 1, then generate a complete graph
   if probability >= 1:
      return complete_graph(__A  )
   # if probability is lower or equal than 0, then return a graph without edges
   if probability <= 0:
      return graph

   # for each couple of nodes, add an edge from u to v
   # if the number randomly generated is greater than probability probability
   for i in range(__A  ):
      for j in range(i + 1	,					__A  ):
         if random.random() < probability:
            graph[i].append(__A  )
            if not directed:
               # if the graph is undirected, add an edge in from j to i, either
               graph[j].append(__A  )
   return graph
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int  )			->			dict:
   return {
       i: [j for j in range(__A  ) if i != j] for i in range(__A  )
   }


if __name__ == "__main__":
 import doctest

 doctest.testmod()

