






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__      :			Optional[int]							=		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 						lowerCamelCase_				(      lowerCamelCase ):







         def __init__(						self       ,					__lowerCAmelCase = 1_0_1     ):
             """simple docstring"""
             __magic_name__		:Tuple        =      length







         def __len__(						self     ):
             """simple docstring"""
             return self.length







         def __getitem__(						self       ,					__lowerCAmelCase     ):
             """simple docstring"""
             return i







     class 						lowerCamelCase_				:







         def __call__(						self       ,					__lowerCAmelCase     ):
             """simple docstring"""
             return {"input_ids": torch.tensor(__lowerCAmelCase     ), "labels": torch.tensor(__lowerCAmelCase     )}







     class 						lowerCamelCase_				(      nn.Module ):







         def __init__(						self     ):
             """simple docstring"""
             super().__init__()
             # Add some (unused) params otherwise DDP will complain.
             __magic_name__		:Optional[int]        =      nn.Linear(1_2_0       ,					8_0     )







         def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=None     ):
             """simple docstring"""
             if labels is not None:
                 return torch.tensor(0.0       ,					device=input_ids.device     ), input_ids
             else:
                 return input_ids
class 						lowerCamelCase_				(      lowerCamelCase ):







    @require_torch_neuroncore
    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Optional[Any]        =      F'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
        __magic_name__		:Union[str, Any]        =      self.get_auto_remove_tmp_dir()
        __magic_name__		:Dict        =      F'''--output_dir {output_dir}'''.split()
        __magic_name__		:Any        =      ['''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 						lowerCamelCase_				(      lowerCamelCase ):







    @require_torch_multi_gpu
    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		: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()
        __magic_name__		:Optional[int]        =      self.get_auto_remove_tmp_dir()
        __magic_name__		:str        =      F'''--output_dir {output_dir}'''.split()
        __magic_name__		:Tuple        =      ['''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__      :			List[Any]							=		HfArgumentParser((TrainingArguments,))
     SCREAMING_SNAKE_CASE__      :			List[Any]							=		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]:
          SCREAMING_SNAKE_CASE__      :			Any							=		DummyDataset(dataset_length)

          def      __lowercase					( snake_case ):







              """simple docstring"""






              __magic_name__		:int        =      list(range(len(snake_case ) ) )
              __magic_name__		:Union[str, 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}

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

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

          SCREAMING_SNAKE_CASE__      :			List[Any]							=		2

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

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

          SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		None














from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url

@pytest.mark.parametrize('''repo_id''',  ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''',  ['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.mark.parametrize('''revision''',  [None, '''v2'''] )
def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      hf_hub_url(repo_id=snake_case,  path=snake_case,  revision=snake_case )
				assert url == f'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(snake_case )}'''






