



from typing import List, Optional, Tuple, Union

import torch

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


UpperCAmelCase   :							Optional[Any]							 =logging.get_logger(__name__)  # pylint: disable=invalid-name


class       _lowercase   (a_      ):
				'''simple docstring'''







				def __init__( self     , snake_case__     , snake_case__    ):

									'''simple docstring'''


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

				@torch.no_grad()
				def __call__( self     , snake_case__ = 1     , snake_case__ = 100     , snake_case__ = None     , snake_case__ = None     , snake_case__ = True     , ):

									'''simple docstring'''


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

									UpperCamelCase_		  =   audio_length_in_s * self.unet.config.sample_rate

									UpperCamelCase_		  =   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_		  =   int(snake_case__    )
									if sample_size % down_scale_factor != 0:
														UpperCamelCase_		  =   (
														    (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_		  =   int(snake_case__    )

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

									UpperCamelCase_		  =   randn_tensor(snake_case__     , generator=snake_case__     , device=self.device     , dtype=snake_case__    )

									# set step values
									self.scheduler.set_timesteps(snake_case__     , device=audio.device    )
									UpperCamelCase_		  =   self.scheduler.timesteps.to(snake_case__    )

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

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

									UpperCamelCase_		  =   audio.clamp(-1     , 1    ).float().cpu().numpy()

									UpperCamelCase_		  =   audio[:, :, :original_sample_size]

									if not return_dict:
														return (audio,)

									return AudioPipelineOutput(audios=snake_case__    )





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class       _lowercase   (a_      ):
				'''simple docstring'''







				lowercase__   						=						(CMStochasticIterativeScheduler,)
				lowercase__   						=						10

				def       _lowerCamelCase					( self     , **snake_case__    ):

									'''simple docstring'''


									UpperCamelCase_		  =   {
									    "num_train_timesteps": 201,
									    "sigma_min": 0.002,
									    "sigma_max": 80.0,
									}

									config.update(**snake_case__    )
									return config

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   10

									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   self.scheduler_classes[0](**snake_case__    )

									scheduler.set_timesteps(snake_case__    )

									UpperCamelCase_		  =   scheduler.timesteps[0]
									UpperCamelCase_		  =   scheduler.timesteps[1]

									UpperCamelCase_		  =   self.dummy_sample
									UpperCamelCase_		  =   0.1 * sample

									UpperCamelCase_		  =   scheduler.step(snake_case__     , snake_case__     , snake_case__    ).prev_sample
									UpperCamelCase_		  =   scheduler.step(snake_case__     , snake_case__     , snake_case__    ).prev_sample

									self.assertEqual(output_a.shape     , sample.shape    )
									self.assertEqual(output_a.shape     , output_a.shape    )

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=snake_case__    )

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									for clip_denoised in [True, False]:
														self.check_over_configs(clip_denoised=snake_case__    )

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.scheduler_classes[0]
									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   scheduler_class(**snake_case__    )

									UpperCamelCase_		  =   1
									scheduler.set_timesteps(snake_case__    )
									UpperCamelCase_		  =   scheduler.timesteps

									UpperCamelCase_		  =   torch.manual_seed(0    )

									UpperCamelCase_		  =   self.dummy_model()
									UpperCamelCase_		  =   self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(snake_case__    ):
														# 1. scale model input
														UpperCamelCase_		  =   scheduler.scale_model_input(snake_case__     , snake_case__    )

														# 2. predict noise residual
														UpperCamelCase_		  =   model(snake_case__     , snake_case__    )

														# 3. predict previous sample x_t-1
														UpperCamelCase_		  =   scheduler.step(snake_case__     , snake_case__     , snake_case__     , generator=snake_case__    ).prev_sample

														UpperCamelCase_		  =   pred_prev_sample

									UpperCamelCase_		  =   torch.sum(torch.abs(snake_case__    )    )
									UpperCamelCase_		  =   torch.mean(torch.abs(snake_case__    )    )

									assert abs(result_sum.item() - 192.7_614    ) < 1e-2
									assert abs(result_mean.item() - 0.2_510    ) < 1e-3

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.scheduler_classes[0]
									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   scheduler_class(**snake_case__    )

									UpperCamelCase_		  =   [106, 0]
									scheduler.set_timesteps(timesteps=snake_case__    )
									UpperCamelCase_		  =   scheduler.timesteps

									UpperCamelCase_		  =   torch.manual_seed(0    )

									UpperCamelCase_		  =   self.dummy_model()
									UpperCamelCase_		  =   self.dummy_sample_deter * scheduler.init_noise_sigma

									for t in timesteps:
														# 1. scale model input
														UpperCamelCase_		  =   scheduler.scale_model_input(snake_case__     , snake_case__    )

														# 2. predict noise residual
														UpperCamelCase_		  =   model(snake_case__     , snake_case__    )

														# 3. predict previous sample x_t-1
														UpperCamelCase_		  =   scheduler.step(snake_case__     , snake_case__     , snake_case__     , generator=snake_case__    ).prev_sample

														UpperCamelCase_		  =   pred_prev_sample

									UpperCamelCase_		  =   torch.sum(torch.abs(snake_case__    )    )
									UpperCamelCase_		  =   torch.mean(torch.abs(snake_case__    )    )

									assert abs(result_sum.item() - 347.6_357    ) < 1e-2
									assert abs(result_mean.item() - 0.4_527    ) < 1e-3

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.scheduler_classes[0]
									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   scheduler_class(**snake_case__    )

									UpperCamelCase_		  =   [39, 30, 12, 15, 0]

									with self.assertRaises(snake_case__     , msg="`timesteps` must be in descending order."    ):
														scheduler.set_timesteps(timesteps=snake_case__    )

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.scheduler_classes[0]
									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   scheduler_class(**snake_case__    )

									UpperCamelCase_		  =   [39, 30, 12, 1, 0]
									UpperCamelCase_		  =   len(snake_case__    )

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

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.scheduler_classes[0]
									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   scheduler_class(**snake_case__    )

									UpperCamelCase_		  =   [scheduler.config.num_train_timesteps]

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