

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


_lowerCamelCase							       =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 		a_       (							lowerCamelCase_     ):



       """simple docstring"""







       def __init__(      self     :							Tuple			,snake_case     :							int = 101				):
            SCREAMING_SNAKE_CASE      						=length
       def __len__(      self     :							List[Any]				):
            return self.length


       def __getitem__(      self     :							Tuple			,snake_case     :							Any				):
            return i







 class 		a_       :



       """simple docstring"""







       def __call__(      self     :							Dict			,snake_case     :							int				):
            return {"input_ids": torch.tensor(snake_case				), "labels": torch.tensor(snake_case				)}







 class 		a_       (							nn.Module     ):



       """simple docstring"""







       def __init__(      self     :							Optional[Any]				):
            super().__init__()
            # Add some (unused) params otherwise DDP will complain.
            SCREAMING_SNAKE_CASE      						=nn.Linear(120			,80				)


       def        _lowerCAmelCase     (      self     :							str			,snake_case     :							Optional[int]			,snake_case     :							Union[str, Any]=None				):



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



class 		a_       (							lowerCamelCase_     ):



      """simple docstring"""







      @require_torch_neuroncore
      def        _lowerCAmelCase     (      self     :							Union[str, Any]				):
           SCREAMING_SNAKE_CASE      						=f'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
           SCREAMING_SNAKE_CASE      						=self.get_auto_remove_tmp_dir()
           SCREAMING_SNAKE_CASE      						=f'--output_dir {output_dir}'.split()
           SCREAMING_SNAKE_CASE      						=['torchrun'] + distributed_args + args
           execute_subprocess_async(snake_case			,env=self.get_env()				)
           # successful return here == success - any errors would have caused an error in the sub-call







class 		a_       (							lowerCamelCase_     ):



      """simple docstring"""







      @require_torch_multi_gpu
      def        _lowerCAmelCase     (      self     :							str				):
           SCREAMING_SNAKE_CASE      						=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()
           SCREAMING_SNAKE_CASE      						=self.get_auto_remove_tmp_dir()
           SCREAMING_SNAKE_CASE      						=f'--output_dir {output_dir}'.split()
           SCREAMING_SNAKE_CASE      						=['torchrun'] + distributed_args + args
           execute_subprocess_async(snake_case			,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

 _lowerCamelCase							       =HfArgumentParser((TrainingArguments,))
 _lowerCamelCase							       =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]:
  _lowerCamelCase							       =DummyDataset(dataset_length)







  def 			snake_case__				(   lowerCAmelCase_			):





       """simple docstring"""







       SCREAMING_SNAKE_CASE      						=list(range(len(__A			)			)			)
       SCREAMING_SNAKE_CASE      						=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}

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

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

  _lowerCamelCase							       =2

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

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



  _lowerCamelCase							       =None






'''simple docstring'''





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_		       =  {"configuration_mmbt": ["MMBTConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_		       =  ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]


if TYPE_CHECKING:
  from .configuration_mmbt import MMBTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
  import sys

  lowercase_		       =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



