

"""simple docstring"""






import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_lowerCAmelCase						=		os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCAmelCase						=		"    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"







class 	__UpperCamelCase    (    unittest.TestCase						):

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

      _lowerCAmelCase       :   Optional[Any]  =    tempfile.mkdtemp()
      os.makedirs(os.path.join(self.diffusers_dir    ,'schedulers/'      )      )
      _lowerCAmelCase       :   Optional[Any]  =    self.diffusers_dir
      shutil.copy(
          os.path.join(A__    ,'src/diffusers/schedulers/scheduling_ddpm.py'      )    ,os.path.join(self.diffusers_dir    ,'schedulers/scheduling_ddpm.py'      )    ,)

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

      _lowerCAmelCase       :   List[Any]  =    'src/diffusers'
      shutil.rmtree(self.diffusers_dir      )

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

      _lowerCAmelCase       :   Optional[int]  =    comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
      if overwrite_result is not None:
          _lowerCAmelCase       :   Union[str, Any]  =    comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
      _lowerCAmelCase       :   Optional[int]  =    black.Mode(target_versions={black.TargetVersion.PYaa}    ,line_length=119      )
      _lowerCAmelCase       :   List[str]  =    black.format_str(A__    ,mode=A__      )
      _lowerCAmelCase       :   int  =    os.path.join(self.diffusers_dir    ,'new_code.py'      )
      with open(A__    ,'w'    ,newline='\n'      ) as f:
          f.write(A__      )
      if overwrite_result is None:
          self.assertTrue(len(check_copies.is_copy_consistent(A__      )      ) == 0      )
      else:
          check_copies.is_copy_consistent(f.name    ,overwrite=A__      )
          with open(A__    ,'r'      ) as f:
              self.assertTrue(f.read()    ,A__      )

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

      _lowerCAmelCase       :   Any  =    check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'      )
      self.assertEqual(A__    ,A__      )







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

      self.check_copy_consistency(
          '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'    ,'DDPMSchedulerOutput'    ,REFERENCE_CODE + '\n'    ,)

      # With no empty line at the end
      self.check_copy_consistency(
          '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'    ,'DDPMSchedulerOutput'    ,A__    ,)

      # Copy consistency with rename
      self.check_copy_consistency(
          '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'    ,'TestSchedulerOutput'    ,re.sub('DDPM'    ,'Test'    ,A__      )    ,)

      # Copy consistency with a really long name
      _lowerCAmelCase       :   str  =    'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
      self.check_copy_consistency(
          F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""    ,F"""{long_class_name}SchedulerOutput"""    ,re.sub('Bert'    ,A__    ,A__      )    ,)

      # Copy consistency with overwrite
      self.check_copy_consistency(
          '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'    ,'TestSchedulerOutput'    ,A__    ,overwrite_result=re.sub('DDPM'    ,'Test'    ,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      )





