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


_SCREAMING_SNAKE_CASE										=						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		,				lowerCAmelCase_ = 1_01							)  ->					str:
            _A        =  length



     def __len__(					self							)  ->					Optional[Any]:
            return self.length



     def __getitem__(					self		,				lowerCAmelCase_							)  ->					int:
            return i



    class 				a							:







     """simple docstring"""






     def __call__(					self		,				lowerCAmelCase_							)  ->					int:
            return {"input_ids": torch.tensor(lowerCAmelCase_							), "labels": torch.tensor(lowerCAmelCase_							)}



    class 				a							(			nn.Module       ):







     """simple docstring"""






     def __init__(					self							)  ->					List[str]:
            super().__init__()
            # Add some (unused) params otherwise DDP will complain.
            _A        =  nn.Linear(1_20		,				80							)



     def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=None							)  ->					Any:
            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      UpperCAmelCase							(					self							)  ->					str:
        _A        =  F'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
        _A        =  self.get_auto_remove_tmp_dir()
        _A        =  F'''--output_dir {output_dir}'''.split()
        _A        =  ["""torchrun"""] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase_		,				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      UpperCAmelCase							(					self							)  ->					int:
        _A        =  F'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
        _A        =  self.get_auto_remove_tmp_dir()
        _A        =  F'''--output_dir {output_dir}'''.split()
        _A        =  ["""torchrun"""] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase_		,				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

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






        def  snake_case  (       snake_case__			:EvalPrediction)						->   Dict:
               _A        =  list(range(len(snake_case__)))
               _A        =  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}

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

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

        _SCREAMING_SNAKE_CASE										=						2

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

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







        _SCREAMING_SNAKE_CASE										=						None





import math
import sys






def  snake_case  (       snake_case__			:int)						->   int:
       if number != int(snake_case__):
              raise ValueError("""the value of input must be a natural number""")
       if number < 0:
              raise ValueError("""the value of input must not be a negative number""")
       if number == 0:
              return 1
       _A        =  [-1] * (number + 1)
       _A        =  0
       for i in range(1   ,	number + 1):
              _A        =  sys.maxsize
              _A        =  int(math.sqrt(snake_case__))
              for j in range(1   ,	root + 1):
                     _A        =  1 + answers[i - (j**2)]
                     _A        =  min(snake_case__   ,	snake_case__)
              _A        =  answer
       return answers[number]


if __name__ == "__main__":
    import doctest

    doctest.testmod()




