from typing import List, Optional, Tuple, Union

import torch

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


lowerCamelCase_    :		Union[str, Any]							=	logging.get_logger(__name__)  # pylint: disable=invalid-name


class 						a__							(       __snake_case		):



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Optional[Any]:
								super().__init__()
								self.register_modules(unet=UpperCAmelCase				,						scheduler=UpperCAmelCase   )







			@torch.no_grad()
			def __call__(						self				,						UpperCAmelCase = 1				,						UpperCAmelCase = 1_0_0				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = True				,						)			->   Union[AudioPipelineOutput, Tuple]:

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

								__a		 =       audio_length_in_s * self.unet.config.sample_rate

								__a		 =       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}.'''   )

								__a		 =       int(UpperCAmelCase   )
								if sample_size % down_scale_factor != 0:
													__a		 =       (
													    (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.'   )
								__a		 =       int(UpperCAmelCase   )

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

								__a		 =       randn_tensor(UpperCAmelCase				,						generator=UpperCAmelCase				,						device=self.device				,						dtype=UpperCAmelCase   )

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

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

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

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

								__a		 =       audio[:, :, :original_sample_size]

								if not return_dict:
													return (audio,)

								return AudioPipelineOutput(audios=UpperCAmelCase   )





import warnings

from ..trainer import Trainer
from ..utils import logging


lowerCamelCase_    :		Union[str, Any]							=	logging.get_logger(__name__)


class 						a__							(       __snake_case		):



			def __init__(						self				,						UpperCAmelCase=None				,						**UpperCAmelCase   )			->   Any:
								warnings.warn(
								    '`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '
								    'instead.'				,						UpperCAmelCase				,						)
								super().__init__(args=UpperCAmelCase				,						**UpperCAmelCase   )




