

"""simple docstring"""






from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 	__UpperCamelCase    (    a__						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A = None    ,):
      '''simple docstring'''

      super().__init__()
      self.register_modules(transformer=_A    ,vae=_A    ,scheduler=_A      )

      # create a imagenet -> id dictionary for easier use
      _lowerCAmelCase       :   Union[str, Any]  =    {}
      if idalabel is not None:
          for key, value in idalabel.items():
              for label in value.split(','      ):
                  _lowerCAmelCase       :   Tuple  =    int(_A      )
          _lowerCAmelCase       :   Dict  =    dict(sorted(self.labels.items()      )      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if not isinstance(_A    ,_A      ):
          _lowerCAmelCase       :   Tuple  =    list(_A      )

      for l in label:
          if l not in self.labels:
              raise ValueError(
                  F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""      )

      return [self.labels[l] for l in label]







  @torch.no_grad()
  def __call__(		self    ,_A    ,_A = 4.0    ,_A = None    ,_A = 50    ,_A = "pil"    ,_A = True    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    len(_A      )
      _lowerCAmelCase       :   List[Any]  =    self.transformer.config.sample_size
      _lowerCAmelCase       :   Dict  =    self.transformer.config.in_channels

      _lowerCAmelCase       :   str  =    randn_tensor(
          shape=(batch_size, latent_channels, latent_size, latent_size)    ,generator=_A    ,device=self.device    ,dtype=self.transformer.dtype    ,)
      _lowerCAmelCase       :   str  =    torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

      _lowerCAmelCase       :   Optional[Any]  =    torch.tensor(_A    ,device=self.device      ).reshape(-1      )
      _lowerCAmelCase       :   str  =    torch.tensor([1000] * batch_size    ,device=self.device      )
      _lowerCAmelCase       :   Dict  =    torch.cat([class_labels, class_null]    ,0      ) if guidance_scale > 1 else class_labels

      # set step values
      self.scheduler.set_timesteps(_A      )

      for t in self.progress_bar(self.scheduler.timesteps      ):
          if guidance_scale > 1:
              _lowerCAmelCase       :   int  =    latent_model_input[: len(_A      ) // 2]
              _lowerCAmelCase       :   int  =    torch.cat([half, half]    ,dim=0      )
          _lowerCAmelCase       :   Union[str, Any]  =    self.scheduler.scale_model_input(_A    ,_A      )

          _lowerCAmelCase       :   Union[str, Any]  =    t
          if not torch.is_tensor(_A      ):
              # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
              # This would be a good case for the `match` statement (Python 3.10+)
              _lowerCAmelCase       :   Union[str, Any]  =    latent_model_input.device.type == 'mps'
              if isinstance(_A    ,_A      ):
                  _lowerCAmelCase       :   List[str]  =    torch.floataa if is_mps else torch.floataa
              else:
                  _lowerCAmelCase       :   Union[str, Any]  =    torch.intaa if is_mps else torch.intaa
              _lowerCAmelCase       :   Optional[Any]  =    torch.tensor([timesteps]    ,dtype=_A    ,device=latent_model_input.device      )
          elif len(timesteps.shape      ) == 0:
              _lowerCAmelCase       :   Any  =    timesteps[None].to(latent_model_input.device      )
          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          _lowerCAmelCase       :   Dict  =    timesteps.expand(latent_model_input.shape[0]      )
          # predict noise model_output
          _lowerCAmelCase       :   Optional[int]  =    self.transformer(
              _A    ,timestep=_A    ,class_labels=_A      ).sample

          # perform guidance
          if guidance_scale > 1:
              _lowerCAmelCase,					_lowerCAmelCase       :   Any  =    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
              _lowerCAmelCase,					_lowerCAmelCase       :   Optional[Any]  =    torch.split(_A    ,len(_A      ) // 2    ,dim=0      )

              _lowerCAmelCase       :   str  =    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
              _lowerCAmelCase       :   int  =    torch.cat([half_eps, half_eps]    ,dim=0      )

              _lowerCAmelCase       :   List[Any]  =    torch.cat([eps, rest]    ,dim=1      )

          # learned sigma
          if self.transformer.config.out_channels // 2 == latent_channels:
              _lowerCAmelCase,					_lowerCAmelCase       :   Any  =    torch.split(_A    ,_A    ,dim=1      )
          else:
              _lowerCAmelCase       :   Optional[int]  =    noise_pred

          # compute previous image: x_t -> x_t-1
          _lowerCAmelCase       :   Tuple  =    self.scheduler.step(_A    ,_A    ,_A      ).prev_sample

      if guidance_scale > 1:
          _lowerCAmelCase,					_lowerCAmelCase       :   Union[str, Any]  =    latent_model_input.chunk(2    ,dim=0      )
      else:
          _lowerCAmelCase       :   int  =    latent_model_input

      _lowerCAmelCase       :   List[str]  =    1 / self.vae.config.scaling_factor * latents
      _lowerCAmelCase       :   Dict  =    self.vae.decode(_A      ).sample

      _lowerCAmelCase       :   int  =    (samples / 2 + 0.5).clamp(0    ,1      )

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      _lowerCAmelCase       :   List[str]  =    samples.cpu().permute(0    ,2    ,3    ,1      ).float().numpy()

      if output_type == "pil":
          _lowerCAmelCase       :   List[str]  =    self.numpy_to_pil(_A      )

      if not return_dict:
          return (samples,)

      return ImagePipelineOutput(images=_A      )








"""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__						):
  _UpperCAmelCase          =		["vqvae"]

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return 50 if isinstance(self.scheduler    ,_A      ) else 1000

  @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    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    steps or self.get_default_steps()
      self.scheduler.set_timesteps(_A      )
      _lowerCAmelCase       :   Optional[Any]  =    step_generator or generator
      # For backwards compatibility
      if type(self.unet.config.sample_size      ) == int:
          _lowerCAmelCase       :   Tuple  =    (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=_A    ,device=self.device    ,)
      _lowerCAmelCase       :   Dict  =    noise
      _lowerCAmelCase       :   Optional[Any]  =    None

      if audio_file is not None or raw_audio is not None:
          self.mel.load_audio(_A    ,_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    self.mel.audio_slice_to_image(_A      )
          _lowerCAmelCase       :   int  =    np.frombuffer(input_image.tobytes()    ,dtype='uint8'      ).reshape(
              (input_image.height, input_image.width)      )
          _lowerCAmelCase       :   int  =    (input_image / 255) * 2 - 1
          _lowerCAmelCase       :   str  =    torch.tensor(input_image[np.newaxis, :, :]    ,dtype=torch.float      ).to(self.device      )

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

          if start_step > 0:
              _lowerCAmelCase       :   List[Any]  =    self.scheduler.add_noise(_A    ,_A    ,self.scheduler.timesteps[start_step - 1]      )

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

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

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

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

      _lowerCAmelCase       :   Any  =    (images / 2 + 0.5).clamp(0    ,1      )
      _lowerCAmelCase       :   Tuple  =    images.cpu().permute(0    ,2    ,3    ,1      ).numpy()
      _lowerCAmelCase       :   Any  =    (images * 255).round().astype('uint8'      )
      _lowerCAmelCase       :   Any  =    list(
          (Image.fromarray(_[:, :, 0]      ) for _ in images)
          if images.shape[3] == 1
          else (Image.fromarray(_A    ,mode='RGB'      ).convert('L'      ) for _ in images)      )

      _lowerCAmelCase       :   Dict  =    [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 __lowerCamelCase		(		self    ,_A    ,_A = 50      ):
      '''simple docstring'''

      assert isinstance(self.scheduler    ,_A      )
      self.scheduler.set_timesteps(_A      )
      _lowerCAmelCase       :   Dict  =    np.array(
          [np.frombuffer(image.tobytes()    ,dtype='uint8'      ).reshape((1, image.height, image.width)      ) for image in images]      )
      _lowerCAmelCase       :   Dict  =    (sample / 255) * 2 - 1
      _lowerCAmelCase       :   List[str]  =    torch.Tensor(_A      ).to(self.device      )

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

      _lowerCAmelCase       :   int  =    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      )





