




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		(__snake_case							):




      """simple docstring"""





      _snake_case												=						['vqvae']

      def __init__(				self    ,				A    ,				A    ,				A    ,				A    ,				)		->    Union[str, Any]:
         super().__init__()
         self.register_modules(unet=A    ,				scheduler=A    ,				mel=A    ,				vqvae=A     )

      def      UpperCAmelCase				(				self     )		->    int:
         return 5_0 if isinstance(self.scheduler    ,				A     ) else 1_0_0_0

      @torch.no_grad()
      def __call__(				self    ,				A = 1    ,				A = None    ,				A = None    ,				A = 0    ,				A = 0    ,				A = None    ,				A = None    ,				A = 0    ,				A = 0    ,				A = None    ,				A = 0    ,				A = None    ,				A = None    ,				A=True    ,				)		->    Union[
          Union[AudioPipelineOutput, ImagePipelineOutput],
          Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
      ]:
         snake_case				:    int           =	steps or self.get_default_steps()
         self.scheduler.set_timesteps(A     )
         snake_case				:    Optional[Any]           =	step_generator or generator
         # For backwards compatibility
         if type(self.unet.config.sample_size     ) == int:
            snake_case				:    Optional[int]           =	(self.unet.config.sample_size, self.unet.config.sample_size)
         if noise is None:
            snake_case				:    List[str]           =	randn_tensor(
                (
                    batch_size,
                    self.unet.config.in_channels,
                    self.unet.config.sample_size[0],
                    self.unet.config.sample_size[1],
                )    ,				generator=A    ,				device=self.device    ,				)
         snake_case				:    Tuple           =	noise
         snake_case				:    int           =	None

         if audio_file is not None or raw_audio is not None:
            self.mel.load_audio(A    ,				A     )
            snake_case				:    str           =	self.mel.audio_slice_to_image(A     )
            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				:    str           =	torch.tensor(input_image[np.newaxis, :, :]    ,				dtype=torch.float     ).to(self.device     )

            if self.vqvae is not None:
               snake_case				:    Optional[int]           =	self.vqvae.encode(torch.unsqueeze(A    ,				0     )     ).latent_dist.sample(
                   generator=A     )[0]
               snake_case				:    List[str]           =	self.vqvae.config.scaling_factor * input_images

            if start_step > 0:
               snake_case				:    str           =	self.scheduler.add_noise(A    ,				A    ,				self.scheduler.timesteps[start_step - 1]     )

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

         for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]     )     ):
            if isinstance(self.unet    ,				A     ):
               snake_case				:    Any           =	self.unet(A    ,				A    ,				A     )["""sample"""]
            else:
               snake_case				:    Union[str, Any]           =	self.unet(A    ,				A     )["""sample"""]

            if isinstance(self.scheduler    ,				A     ):
               snake_case				:    Any           =	self.scheduler.step(
                   model_output=A    ,				timestep=A    ,				sample=A    ,				eta=A    ,				generator=A    ,				)["""prev_sample"""]
            else:
               snake_case				:    str           =	self.scheduler.step(
                   model_output=A    ,				timestep=A    ,				sample=A    ,				generator=A    ,				)["""prev_sample"""]

            if mask is not None:
               if mask_start > 0:
                  snake_case				:    Union[str, Any]           =	mask[:, step, :, :mask_start]
               if mask_end > 0:
                  snake_case				:    Any           =	mask[:, step, :, -mask_end:]

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

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

         snake_case				:    Optional[Any]           =	[self.mel.image_to_audio(A     ) for _ in images]
         if not return_dict:
            return images, (self.mel.get_sample_rate(), audios)

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

      @torch.no_grad()
      def      UpperCAmelCase				(				self    ,				A    ,				A = 5_0     )		->    np.ndarray:
         assert isinstance(self.scheduler    ,				A     )
         self.scheduler.set_timesteps(A     )
         snake_case				:    List[str]           =	np.array(
             [np.frombuffer(image.tobytes()    ,				dtype="""uint8"""     ).reshape((1, image.height, image.width)     ) for image in images]     )
         snake_case				:    str           =	(sample / 2_5_5) * 2 - 1
         snake_case				:    str           =	torch.Tensor(A     ).to(self.device     )

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

         return sample

      @staticmethod
      def      UpperCAmelCase				(				A    ,				A    ,				A     )		->    torch.Tensor:
         snake_case				:    str           =	acos(torch.dot(torch.flatten(A     )    ,				torch.flatten(A     )     ) / torch.norm(A     ) / torch.norm(A     )     )
         return sin((1 - alpha) * theta     ) * xa / sin(A     ) + sin(alpha * theta     ) * xa / sin(A     )











import warnings
from functools import wraps
from typing import Callable




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Callable:

   @wraps(lowercase					)
   def _inner_fn(*lowercase						,**lowercase					):
      warnings.warn(
          (f"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")						,lowercase						,)
      return fn(*lowercase						,**lowercase					)

   return _inner_fn





