





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       :     Dict   =     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__   (       __snake_case			):





          def __init__( self       ,							A_ = 101   ):
           '''simple docstring'''




           UpperCamelCase	:					Dict       	=   length





          def __len__( self   ):
           '''simple docstring'''




           return self.length





          def __getitem__( self       ,							A_   ):
           '''simple docstring'''




           return i



    class 					A__   :





          def __call__( self       ,							A_   ):
           '''simple docstring'''




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



    class 					A__   (       nn.Module			):





          def __init__( self   ):
           '''simple docstring'''




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





          def        __UpperCamelCase( self       ,							A_       ,							A_=None   ):
           '''simple docstring'''




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



class 					A__   (       __snake_case			):





      @require_torch_neuroncore
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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





      @require_torch_multi_gpu
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
       UpperCamelCase	:					Optional[Any]       	=   self.get_auto_remove_tmp_dir()
       UpperCamelCase	:					List[str]       	=   F"""--output_dir {output_dir}""".split()
       UpperCamelCase	:					Optional[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


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       :     List[str]   =     HfArgumentParser((TrainingArguments,))
    __lowerCamelCase       :     List[str]   =     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 [101, 40, 7]:
        __lowerCamelCase       :     str   =     DummyDataset(dataset_length)



        def 		A_				(     _lowerCAmelCase     )						-> Dict:
         UpperCamelCase	:					Optional[Any]       	=   list(range(len(_lowerCAmelCase     )     )     )
         UpperCamelCase	:					List[Any]       	=   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       :     str   =     Trainer(
            model=DummyModel(),
            args=training_args,
            data_collator=DummyDataCollator(),
            eval_dataset=dataset,
            compute_metrics=compute_metrics,
        )
        __lowerCamelCase       :     Any   =     trainer.evaluate()
        logger.info(metrics)
        if metrics["eval_success"] is not True:
            logger.error(metrics)
            exit(1)

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

        __lowerCamelCase       :     Optional[int]   =     2

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

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


        __lowerCamelCase       :     Dict   =     None






from typing import TYPE_CHECKING

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


lowerCamelCase__ :   str					=							{
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   int					=							['Mask2FormerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   Dict					=							[
       'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Mask2FormerForUniversalSegmentation',
       'Mask2FormerModel',
       'Mask2FormerPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_maskaformer import MaskaFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskaformer import (
          MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskaFormerForUniversalSegmentation,
          MaskaFormerModel,
          MaskaFormerPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__ :   Tuple					=							_LazyModule(__name__, globals()['__file__'], _import_structure)