



"""simple docstring"""
from typing import List, Optional, Tuple, Union

import torch

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


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


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__   ):
									super().__init__()
									self.register_modules(unet=lowerCamelCase__				,       scheduler=lowerCamelCase__   )





					@torch.no_grad()
					def __call__(    self				,       lowerCamelCase__ = 1				,       lowerCamelCase__ = 1_0_0				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       lowerCamelCase__ = True				,       ):

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

									_lowerCamelCase        =				audio_length_in_s * self.unet.config.sample_rate

									_lowerCamelCase        =				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}."""   )

									_lowerCamelCase        =				int(lowerCamelCase__   )
									if sample_size % down_scale_factor != 0:
													_lowerCamelCase        =				(
													    (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.'''   )
									_lowerCamelCase        =				int(lowerCamelCase__   )

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

									_lowerCamelCase        =				randn_tensor(lowerCamelCase__				,       generator=lowerCamelCase__				,       device=self.device				,       dtype=lowerCamelCase__   )

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

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

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

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

									_lowerCamelCase        =				audio[:, :, :original_sample_size]

									if not return_dict:
													return (audio,)

									return AudioPipelineOutput(audios=lowerCamelCase__   )





"""simple docstring"""
import warnings

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


__SCREAMING_SNAKE_CASE  :						Dict    							=      logging.get_logger(__name__)


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



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