




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


UpperCamelCase__					    =    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 = 1_0_1    ):




																"""simple docstring"""



																__lowerCAmelCase            = 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.
																__lowerCAmelCase            = nn.Linear(1_2_0	,    8_0    )






											def        __SCREAMING_SNAKE_CASE(     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        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = f"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
											__lowerCAmelCase            = self.get_auto_remove_tmp_dir()
											__lowerCAmelCase            = f"""--output_dir {output_dir}""".split()
											__lowerCAmelCase            = ["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        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = f"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
											__lowerCAmelCase            = self.get_auto_remove_tmp_dir()
											__lowerCAmelCase            = f"""--output_dir {output_dir}""".split()
											__lowerCAmelCase            = ["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

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







										def 			_a   (		SCREAMING_SNAKE_CASE_							:       EvalPrediction  ):
															__lowerCAmelCase            = list(range(len(SCREAMING_SNAKE_CASE_  )  )  )
															__lowerCAmelCase            = 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}

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

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

										UpperCamelCase__					    =    2

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

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


										UpperCamelCase__					    =    None










def 			_a   (		SCREAMING_SNAKE_CASE_							:       int  ):
					if divisor % 5 == 0 or divisor % 2 == 0:
										return 0
					__lowerCAmelCase            = 1
					__lowerCAmelCase            = 1
					while repunit:
										__lowerCAmelCase            = (10 * repunit + 1) % divisor
										repunit_index += 1
					return repunit_index







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int = 1_00_00_00  ):
					__lowerCAmelCase            = limit - 1
					if divisor % 2 == 0:
										divisor += 1
					while least_divisible_repunit(SCREAMING_SNAKE_CASE_  ) <= limit:
										divisor += 2
					return divisor


if __name__ == "__main__":
					print(f'''{solution() = }''')




