



"""simple docstring"""


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""
						__UpperCamelCase						=							(DDPMScheduler,)






						def 	UpperCAmelCase__     (					self :int   ,  **lowercase_ :Optional[Any]					)				->			Tuple:
												UpperCAmelCase             = {
												    'num_train_timesteps': 10_00,
												    'beta_start': 0.0001,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												    'variance_type': 'fixed_small',
												    'clip_sample': True,
												}

												config.update(**lowercase_					)
												return config






						def 	UpperCAmelCase__     (					self :int					)				->			Union[str, Any]:
												for timesteps in [1, 5, 1_00, 10_00]:
																		self.check_over_configs(num_train_timesteps=lowercase_					)






						def 	UpperCAmelCase__     (					self :int					)				->			Optional[Any]:
												for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,  [0.002, 0.02, 0.2, 2]					):
																		self.check_over_configs(beta_start=lowercase_   ,  beta_end=lowercase_					)






						def 	UpperCAmelCase__     (					self :int					)				->			int:
												for schedule in ["linear", "squaredcos_cap_v2"]:
																		self.check_over_configs(beta_schedule=lowercase_					)






						def 	UpperCAmelCase__     (					self :List[Any]					)				->			List[Any]:
												for variance in ["fixed_small", "fixed_large", "other"]:
																		self.check_over_configs(variance_type=lowercase_					)






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Tuple:
												for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=lowercase_					)






						def 	UpperCAmelCase__     (					self :List[str]					)				->			Dict:
												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 :List[str]					)				->			Dict:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																		self.check_over_configs(prediction_type=lowercase_					)






						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			Optional[Any]:
												for t in [0, 5_00, 9_99]:
																		self.check_over_forward(time_step=lowercase_					)






						def 	UpperCAmelCase__     (					self :Any					)				->			Tuple:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config()
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_87					) - 0.0_0979					)					) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_99					) - 0.02					)					) < 1E-5






						def 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			Dict:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config()
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												UpperCAmelCase             = len(lowercase_					)

												UpperCAmelCase             = self.dummy_model()
												UpperCAmelCase             = self.dummy_sample_deter
												UpperCAmelCase             = torch.manual_seed(0					)

												for t in reversed(range(lowercase_					)					):
																		# 1. predict noise residual
																		UpperCAmelCase             = model(lowercase_   ,  lowercase_					)

																		# 2. predict previous mean of sample x_t-1
																		UpperCAmelCase             = scheduler.step(lowercase_   ,  lowercase_   ,  lowercase_   ,  generator=lowercase_					).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		UpperCAmelCase             = pred_prev_sample

												UpperCAmelCase             = torch.sum(torch.abs(lowercase_					)					)
												UpperCAmelCase             = torch.mean(torch.abs(lowercase_					)					)

												assert abs(result_sum.item() - 258.9606					) < 1E-2
												assert abs(result_mean.item() - 0.3372					) < 1E-3






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Optional[Any]:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config(prediction_type='v_prediction'					)
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												UpperCAmelCase             = len(lowercase_					)

												UpperCAmelCase             = self.dummy_model()
												UpperCAmelCase             = self.dummy_sample_deter
												UpperCAmelCase             = torch.manual_seed(0					)

												for t in reversed(range(lowercase_					)					):
																		# 1. predict noise residual
																		UpperCAmelCase             = model(lowercase_   ,  lowercase_					)

																		# 2. predict previous mean of sample x_t-1
																		UpperCAmelCase             = scheduler.step(lowercase_   ,  lowercase_   ,  lowercase_   ,  generator=lowercase_					).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		UpperCAmelCase             = pred_prev_sample

												UpperCAmelCase             = torch.sum(torch.abs(lowercase_					)					)
												UpperCAmelCase             = torch.mean(torch.abs(lowercase_					)					)

												assert abs(result_sum.item() - 202.0296					) < 1E-2
												assert abs(result_mean.item() - 0.2631					) < 1E-3






						def 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			List[Any]:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config()
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												UpperCAmelCase             = [1_00, 87, 50, 1, 0]

												scheduler.set_timesteps(timesteps=lowercase_					)

												UpperCAmelCase             = scheduler.timesteps

												for i, timestep in enumerate(lowercase_					):
																		if i == len(lowercase_					) - 1:
																								UpperCAmelCase             = -1
																		else:
																								UpperCAmelCase             = timesteps[i + 1]

																		UpperCAmelCase             = scheduler.previous_timestep(lowercase_					)
																		UpperCAmelCase             = prev_t.item()

																		self.assertEqual(lowercase_   ,  lowercase_					)






						def 	UpperCAmelCase__     (					self :Any					)				->			Union[str, Any]:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config()
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												UpperCAmelCase             = [1_00, 87, 50, 51, 0]

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






						def 	UpperCAmelCase__     (					self :Any					)				->			Optional[Any]:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config()
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												UpperCAmelCase             = [1_00, 87, 50, 1, 0]
												UpperCAmelCase             = 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 :Optional[Any]					)				->			Union[str, Any]:
												UpperCAmelCase             = self.scheduler_classes[0]
												UpperCAmelCase             = self.get_scheduler_config()
												UpperCAmelCase             = scheduler_class(**lowercase_					)

												UpperCAmelCase             = [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_					)












from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


__lowerCamelCase       :     str   =     logging.get_logger(__name__)  # pylint: disable=invalid-name



class 					A__   (       __snake_case			):





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




       super().__init__()
       self.register_modules(unet=A_       ,							scheduler=A_   )





      @torch.no_grad()
      def __call__( self       ,							A_ = 1       ,							A_ = 100       ,							A_ = None       ,							A_ = None       ,							A_ = True       ,							):
       '''simple docstring'''




       if audio_length_in_s is None:
        UpperCamelCase	:					str       	=   self.unet.config.sample_size / self.unet.config.sample_rate

       UpperCamelCase	:					Optional[Any]       	=   audio_length_in_s * self.unet.config.sample_rate

       UpperCamelCase	:					Any       	=   2 ** len(self.unet.up_blocks   )
       if sample_size < 3 * down_scale_factor:
        raise ValueError(
            F"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
            F""" {3 * down_scale_factor / self.unet.config.sample_rate}."""   )

       UpperCamelCase	:					Union[str, Any]       	=   int(A_   )
       if sample_size % down_scale_factor != 0:
        UpperCamelCase	:					List[str]       	=   (
            (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
        ) * down_scale_factor
        logger.info(
            F"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
            F""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
            " process."   )
       UpperCamelCase	:					Any       	=   int(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   next(iter(self.unet.parameters()   )   ).dtype
       UpperCamelCase	:					Optional[int]       	=   (batch_size, self.unet.config.in_channels, sample_size)
       if isinstance(A_       ,							A_   ) and len(A_   ) != batch_size:
        raise ValueError(
            F"""You have passed a list of generators of length {len(A_   )}, but requested an effective batch"""
            F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""   )

       UpperCamelCase	:					Optional[Any]       	=   randn_tensor(A_       ,							generator=A_       ,							device=self.device       ,							dtype=A_   )

       # set step values
       self.scheduler.set_timesteps(A_       ,							device=audio.device   )
       UpperCamelCase	:					Optional[int]       	=   self.scheduler.timesteps.to(A_   )

       for t in self.progress_bar(self.scheduler.timesteps   ):
        # 1. predict noise model_output
        UpperCamelCase	:					Dict       	=   self.unet(A_       ,							A_   ).sample

        # 2. compute previous image: x_t -> t_t-1
        UpperCamelCase	:					int       	=   self.scheduler.step(A_       ,							A_       ,							A_   ).prev_sample

       UpperCamelCase	:					Optional[Any]       	=   audio.clamp(-1       ,							1   ).float().cpu().numpy()

       UpperCamelCase	:					Dict       	=   audio[:, :, :original_sample_size]

       if not return_dict:
        return (audio,)

       return AudioPipelineOutput(audios=A_   )
