




"""simple docstring"""


from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


A_      =    logging.get_logger(__name__)


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset

 from transformers import Trainer






 class  lowercase(					__a			):







  '''simple docstring'''





  def __init__(    self:		Optional[int],				a_:		int = 101		):




    '''simple docstring'''



    _snake_case							:		List[Any]								=  length


  def __len__(    self:		List[str]		):




    '''simple docstring'''



    return self.length


  def __getitem__(    self:		List[str],				a_:		Optional[Any]		):




    '''simple docstring'''



    return i







 class  lowercase:







  '''simple docstring'''





  def __call__(    self:		Union[str, Any],				a_:		Optional[int]		):




    '''simple docstring'''



    return {"input_ids": torch.tensor(a_		), "labels": torch.tensor(a_		)}







 class  lowercase(					nn.Module			):







  '''simple docstring'''





  def __init__(    self:		Optional[int]		):




    '''simple docstring'''



    super().__init__()
    # Add some (unused) params otherwise DDP will complain.
    _snake_case							:		Any								=  nn.Linear(120,				80		)


  def 			UpperCamelCase_       (    self:		Dict,				a_:		Optional[Any],				a_:		Union[str, Any]=None		):




    '''simple docstring'''



    if labels is not None:
      return torch.tensor(0.0,				device=input_ids.device		), input_ids
    else:
      return input_ids






class  lowercase(					__a			):







 '''simple docstring'''





 @require_torch_neuroncore
 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  f"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
   _snake_case							:		List[str]								=  self.get_auto_remove_tmp_dir()
   _snake_case							:		int								=  f"--output_dir {output_dir}".split()
   _snake_case							:		Any								=  ["""torchrun"""] + distributed_args + args
   execute_subprocess_async(a_,				env=self.get_env()		)
   # successful return here == success - any errors would have caused an error in the sub-call







class  lowercase(					__a			):







 '''simple docstring'''





 @require_torch_multi_gpu
 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   _snake_case							:		Dict								=  f"--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
   _snake_case							:		str								=  self.get_auto_remove_tmp_dir()
   _snake_case							:		Optional[Any]								=  f"--output_dir {output_dir}".split()
   _snake_case							:		Dict								=  ["""torchrun"""] + distributed_args + args
   execute_subprocess_async(a_,				env=self.get_env()		)
   # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
 # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
 #
 # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

 A_      =    HfArgumentParser((TrainingArguments,))
 A_      =    parser.parse_args_into_dataclasses()[0]

 logger.warning(
     F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
     F'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
 )

 # Essentially, what we want to verify in the distributed case is that we get all samples back,
 # in the right order. (this is crucial for prediction for instance)
 for dataset_length in [1_01, 40, 7]:
  A_      =    DummyDataset(dataset_length)


  def  UpperCAmelCase__	(snake_case__							: EvalPrediction   ):
    """simple docstring"""

    _snake_case							:		Any								=  list(range(len(snake_case__   )   )   )
    _snake_case							:		List[str]								=  p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
    if not success and training_args.local_rank == 0:
      logger.warning(
          """Predictions and/or labels do not match expected results:\n  - predictions: """
          F"{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"   )
    return {"success": success}

  A_      =    Trainer(
      model=DummyModel(),
      args=training_args,
      data_collator=DummyDataCollator(),
      eval_dataset=dataset,
      compute_metrics=compute_metrics,
  )
  A_      =    trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  A_      =    trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)

  A_      =    2

  A_      =    trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  A_      =    trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)






  A_      =    None








"""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:		Optional[int],				a_:		str,				a_:		Optional[Any]		):




   '''simple docstring'''



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


 @torch.no_grad()
 def __call__(    self:		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							:		Optional[int]								=  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							:		Union[str, Any]								=  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							:		str								=  int(a_		)

   _snake_case							:		int								=  next(iter(self.unet.parameters()		)		).dtype
   _snake_case							:		Optional[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							:		Optional[Any]								=  randn_tensor(a_,				generator=a_,				device=self.device,				dtype=a_		)

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

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

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

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

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

   if not return_dict:
     return (audio,)

   return AudioPipelineOutput(audios=a_		)


