




"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

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


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






class 					lowercase(     __a ):







 '''simple docstring'''





 def __init__(				self:		List[Any],      a_:		List[Any],      a_:		Tuple		):




   '''simple docstring'''



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


 @torch.no_grad()
 def __call__(				self:		Optional[Any],      a_:		int = 1,      a_:		int = 100,      a_:		Optional[Union[torch.Generator, List[torch.Generator]]] = None,      a_:		Optional[float] = None,      a_:		bool = True,      ):




   '''simple docstring'''



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

   _snake_case							:		Any								=  audio_length_in_s * self.unet.config.sample_rate

   _snake_case							:		int								=  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}."		)

   _snake_case							:		str								=  int(a_		)
   if sample_size % down_scale_factor != 0:
     _snake_case							:		Optional[Any]								=  (
         (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."""		)
   _snake_case							:		List[Any]								=  int(a_		)

   _snake_case							:		Dict								=  next(iter(self.unet.parameters()		)		).dtype
   _snake_case							:		Any								=  (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."		)

   _snake_case							:		Union[str, Any]								=  randn_tensor(a_,      generator=a_,      device=self.device,      dtype=a_		)

   # set step values
   self.scheduler.set_timesteps(a_,      device=audio.device		)
   _snake_case							:		Union[str, Any]								=  self.scheduler.timesteps.to(a_		)

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

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

   _snake_case							:		Any								=  audio.clamp(-1,      1		).float().cpu().numpy()

   _snake_case							:		str								=  audio[:, :, :original_sample_size]

   if not return_dict:
     return (audio,)

   return AudioPipelineOutput(audios=a_		)







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     List[str]									= ArgumentParser(
		    description=(
		        '''PyTorch TPU distributed training launch '''
		        '''helper utility that will spawn up '''
		        '''multiple distributed processes'''
		    ) )

		# Optional arguments for the launch helper
		parser.add_argument('''--num_cores'''     ,type=__UpperCamelCase     ,default=1     ,help='''Number of TPU cores to use (1 or 8).''' )

		# positional
		parser.add_argument(
		    '''training_script'''     ,type=__UpperCamelCase     ,help=(
		        '''The full path to the single TPU training '''
		        '''program/script to be launched in parallel, '''
		        '''followed by all the arguments for the '''
		        '''training script'''
		    )     ,)

		# rest from the training program
		parser.add_argument('''training_script_args'''     ,nargs=__UpperCamelCase )

		return parser.parse_args()


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     str									= parse_args()

		# Import training_script as a module.
		lowerCAmelCase_			:     str									= Path(args.training_script )
		sys.path.append(str(script_fpath.parent.resolve() ) )
		lowerCAmelCase_			:     Tuple									= script_fpath.stem
		lowerCAmelCase_			:     Union[str, Any]									= importlib.import_module(__UpperCamelCase )

		# Patch sys.argv
		lowerCAmelCase_			:     Optional[int]									= [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores )]
		xmp.spawn(mod._mp_fn     ,args=()     ,nprocs=args.num_cores )


if __name__ == "__main__":
						main()





