'''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 (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Union[str, Any]							=						["vqvae"]






    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							)							-> Dict:
         super().__init__()
         self.register_modules(unet=lowercase__    ,							scheduler=lowercase__    ,							mel=lowercase__    ,							vqvae=lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         return 50 if isinstance(self.scheduler    ,							lowercase__ ) else 1_000






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

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

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

              if start_step > 0:
                   SCREAMING_SNAKE_CASE					:			Dict    	=       self.scheduler.add_noise(lowercase__    ,							lowercase__    ,							self.scheduler.timesteps[start_step - 1] )

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

         for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
              if isinstance(self.unet    ,							lowercase__ ):
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.unet(lowercase__    ,							lowercase__    ,							lowercase__ )['sample']
              else:
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.unet(lowercase__    ,							lowercase__ )['sample']

              if isinstance(self.scheduler    ,							lowercase__ ):
                   SCREAMING_SNAKE_CASE					:			int    	=       self.scheduler.step(
                       model_output=lowercase__    ,							timestep=lowercase__    ,							sample=lowercase__    ,							eta=lowercase__    ,							generator=lowercase__    ,							)['prev_sample']
              else:
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler.step(
                       model_output=lowercase__    ,							timestep=lowercase__    ,							sample=lowercase__    ,							generator=lowercase__    ,							)['prev_sample']

              if mask is not None:
                   if mask_start > 0:
                        SCREAMING_SNAKE_CASE					:			Tuple    	=       mask[:, step, :, :mask_start]
                   if mask_end > 0:
                        SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       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					:			List[Any]    	=       1 / self.vqvae.config.scaling_factor * images
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.vqvae.decode(lowercase__ )['sample']

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

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

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






    @torch.no_grad()
    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = 50 )							-> np.ndarray:
         assert isinstance(self.scheduler    ,							lowercase__ )
         self.scheduler.set_timesteps(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       np.array(
             [np.frombuffer(image.tobytes()    ,							dtype='uint8' ).reshape((1, image.height, image.width) ) for image in images] )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       (sample / 255) * 2 - 1
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.Tensor(lowercase__ ).to(self.device )

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

         return sample






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




'''simple docstring'''





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						(DDPMParallelScheduler,)






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       {
             'num_train_timesteps': 1_000,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
             'variance_type': 'fixed_small',
             'clip_sample': True,
         }

         config.update(**lowercase__ )
         return config






    def       _UpperCamelCase    (       self )							-> int:
         for timesteps in [1, 5, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,							[0.0_0_2, 0.0_2, 0.2, 2] ):
              self.check_over_configs(beta_start=lowercase__    ,							beta_end=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase__ )






    def       _UpperCamelCase    (       self )							-> str:
         for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[str]:
         self.check_over_configs(thresholding=lowercase__ )
         for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=lowercase__    ,							prediction_type=lowercase__    ,							sample_max_value=lowercase__    ,							)






    def       _UpperCamelCase    (       self )							-> Dict:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for t in [0, 500, 999]:
              self.check_over_forward(time_step=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1E-5






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			str    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.dummy_sample_deter + 0.1
         SCREAMING_SNAKE_CASE					:			int    	=       self.dummy_sample_deter - 0.1

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       samplea.shape[0]
         SCREAMING_SNAKE_CASE					:			str    	=       torch.stack([samplea, samplea, samplea]    ,							dim=0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.arange(lowercase__ )[0:3, None].repeat(1    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(samples.flatten(0    ,							1 )    ,							timesteps.flatten(0    ,							1 ) )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler.batch_step_no_noise(lowercase__    ,							timesteps.flatten(0    ,							1 )    ,							samples.flatten(0    ,							1 ) )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1E-2
         assert abs(result_mean.item() - 0.5_0_0_5 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.manual_seed(0 )

         for t in reversed(range(lowercase__ ) ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE					:			int    	=       model(lowercase__    ,							lowercase__ )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE					:			Dict    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

              SCREAMING_SNAKE_CASE					:			List[str]    	=       pred_prev_sample

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			Dict    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1E-2
         assert abs(result_mean.item() - 0.3_3_7_2 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_scheduler_config(prediction_type='v_prediction' )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			int    	=       torch.manual_seed(0 )

         for t in reversed(range(lowercase__ ) ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE					:			Tuple    	=       model(lowercase__    ,							lowercase__ )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

              SCREAMING_SNAKE_CASE					:			List[Any]    	=       pred_prev_sample

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			str    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1E-2
         assert abs(result_mean.item() - 0.2_6_3_1 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       scheduler.timesteps

         for i, timestep in enumerate(lowercase__ ):
              if i == len(lowercase__ ) - 1:
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       -1
              else:
                   SCREAMING_SNAKE_CASE					:			Any    	=       timesteps[i + 1]

              SCREAMING_SNAKE_CASE					:			str    	=       scheduler.previous_timestep(lowercase__ )
              SCREAMING_SNAKE_CASE					:			str    	=       prev_t.item()

              self.assertEqual(lowercase__    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       [100, 87, 50, 51, 0]

         with self.assertRaises(lowercase__    ,							msg='`custom_timesteps` must be in descending order.' ):
              scheduler.set_timesteps(timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       len(lowercase__ )

         with self.assertRaises(lowercase__    ,							msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
              scheduler.set_timesteps(num_inference_steps=lowercase__    ,							timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       [scheduler.config.num_train_timesteps]

         with self.assertRaises(
             lowercase__    ,							msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'    ,							):
              scheduler.set_timesteps(timesteps=lowercase__ )



