






'''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 a_    (    UpperCamelCase_							):







         def __init__(self					,   __a = 1_0_1)			->      Dict:
            """simple docstring"""






            __snake_case				:      Union[str, Any]     							= length







         def __len__(self)			->      List[str]:
            """simple docstring"""






            return self.length







         def __getitem__(self					,   __a)			->      int:
            """simple docstring"""






            return i




      class a_    :







         def __call__(self					,   __a)			->      List[str]:
            """simple docstring"""






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




      class a_    (    nn.Module							):







         def __init__(self)			->      Union[str, Any]:
            """simple docstring"""






            super().__init__()
            # Add some (unused) params otherwise DDP will complain.
            __snake_case				:      List[str]     							= nn.Linear(1_2_0					,   8_0)







         def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a=None)			->      str:
            """simple docstring"""






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




class a_    (    UpperCamelCase_							):







   @require_torch_neuroncore
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
      __snake_case				:      List[Any]     							= self.get_auto_remove_tmp_dir()
      __snake_case				:      Any     							= F"""--output_dir {output_dir}""".split()
      __snake_case				:      Union[str, 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 a_    (    UpperCamelCase_							):







   @require_torch_multi_gpu
   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      __snake_case				:      Dict     							= F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
      __snake_case				:      Dict     							= self.get_auto_remove_tmp_dir()
      __snake_case				:      Optional[Any]     							= F"""--output_dir {output_dir}""".split()
      __snake_case				:      str     							= ['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_0_1, 4_0, 7]:
            __A    	=   DummyDataset(dataset_length)




            def        _SCREAMING_SNAKE_CASE  (				A  :       EvalPrediction  )    -> Dict:







               """simple docstring"""
               __snake_case				:      str     							= list(range(len(A  )  )  )
               __snake_case				:      Dict     							= 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 TYPE_CHECKING

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


__A    	=   {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BioGptForCausalLM''',
          '''BioGptForTokenClassification''',
          '''BioGptForSequenceClassification''',
          '''BioGptModel''',
          '''BioGptPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

      __A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)