


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_					(					lowercase__    ):

   '''simple docstring'''




   __A :      Union[str, Any]     		=		['''vqvae''']






   def __init__(		self						,				__A						,				__A						,				__A						,				__A						,				):






     """simple docstring"""





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






   def _snake_case    (		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 :					Union[str, Any]    =				steps or self.get_default_steps()
     self.scheduler.set_timesteps(_a )
     lowerCamelCase :					Optional[int]    =				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 :					Optional[int]    =				noise
     lowerCamelCase :					str    =				None

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

       if self.vqvae is not None:
         lowerCamelCase :					Dict    =				self.vqvae.encode(torch.unsqueeze(_a						,				0 ) ).latent_dist.sample(
             generator=_a )[0]
         lowerCamelCase :					Optional[Any]    =				self.vqvae.config.scaling_factor * input_images

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

       lowerCamelCase :					int    =				(
           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 :					Any    =				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 :					Union[str, Any]    =				self.unet(_a						,				_a						,				_a )['sample']
       else:
         lowerCamelCase :					List[Any]    =				self.unet(_a						,				_a )['sample']

       if isinstance(self.scheduler						,				_a ):
         lowerCamelCase :					Optional[Any]    =				self.scheduler.step(
             model_output=_a						,				timestep=_a						,				sample=_a						,				eta=_a						,				generator=_a						,				)['prev_sample']
       else:
         lowerCamelCase :					str    =				self.scheduler.step(
             model_output=_a						,				timestep=_a						,				sample=_a						,				generator=_a						,				)['prev_sample']

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

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

     lowerCamelCase :					List[Any]    =				(images / 2 + 0.5).clamp(0						,				1 )
     lowerCamelCase :					Union[str, Any]    =				images.cpu().permute(0						,				2						,				3						,				1 ).numpy()
     lowerCamelCase :					Union[str, Any]    =				(images * 255).round().astype("uint8" )
     lowerCamelCase :					Optional[int]    =				list(
         (Image.fromarray(_[:, :, 0] ) for _ in images)
         if images.shape[3] == 1
         else (Image.fromarray(_a						,				mode="RGB" ).convert("L" ) for _ in images) )

     lowerCamelCase :					int    =				[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 _snake_case    (		self						,				__A						,				__A = 50 ):






     """simple docstring"""





     assert isinstance(self.scheduler						,				_a )
     self.scheduler.set_timesteps(_a )
     lowerCamelCase :					int    =				np.array(
         [np.frombuffer(image.tobytes()						,				dtype="uint8" ).reshape((1, image.height, image.width) ) for image in images] )
     lowerCamelCase :					Union[str, Any]    =				(sample / 255) * 2 - 1
     lowerCamelCase :					str    =				torch.Tensor(_a ).to(self.device )

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

     return sample






   @staticmethod
   def _snake_case    (		__A						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				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 )





'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	int			=   UNetaDModel(
         block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,)
     return model


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	str			=   self.dummy_uncond_unet
     _a				:	int			=   PNDMScheduler()

     _a				:	str			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Any			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	List[Any]			=   image[0, -3:, -3:, -1]
     _a				:	Any			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	List[Any]			=   np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2







@slow
@require_torch
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[str]			=   'google/ddpm-cifar10-32'

     _a				:	str			=   UNetaDModel.from_pretrained(_a       )
     _a				:	Union[str, Any]			=   PNDMScheduler()

     _a				:	Tuple			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )
     _a				:	str			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,output_type='numpy'       ).images

     _a				:	Dict			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	Tuple			=   np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2



