



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


lowercase_	       =			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 (     _lowercase				):



									def __init__(						self							:    str				,	snake_case_							:    int = 1_01)->							Tuple:
														__lowerCAmelCase	   =length



									def __len__(						self							:    int)->							Any:
														return self.length



									def __getitem__(						self							:    Optional[int]				,	snake_case_							:    List[Any])->							int:
														return i






				class 						__a :



									def __call__(						self							:    str				,	snake_case_							:    List[Any])->							Any:
														return {"input_ids": torch.tensor(A_), "labels": torch.tensor(A_)}






				class 						__a (     nn.Module				):



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



									def 		UpperCamelCase							(						self							:    Tuple				,	snake_case_							:    Tuple				,	snake_case_							:    Tuple=None)->							Optional[Any]:
														if labels is not None:
																			return torch.tensor(0.0				,	device=input_ids.device), input_ids
														else:
																			return input_ids






class 						__a (     _lowercase				):



					@require_torch_neuroncore
					def 		UpperCamelCase							(						self							:    Tuple)->							Optional[Any]:
										__lowerCAmelCase	   =F"""--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        """.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 (     _lowercase				):



					@require_torch_multi_gpu
					def 		UpperCamelCase							(						self							:    Any)->							Any:
										__lowerCAmelCase	   =F"""--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        """.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

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






								def 					__lowerCAmelCase		(  __lowerCamelCase	:    EvalPrediction	)  ->						List[str]:
													__lowerCAmelCase	   =list(range(len(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}

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

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

								lowercase_	       =			2

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

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


								lowercase_	       =			None









def 					__lowerCAmelCase		(  __lowerCamelCase	:    list	)  ->						list:
					if len(__lowerCamelCase	) <= 1:
										return lst

					__lowerCAmelCase	   =1

					while i < len(__lowerCamelCase	):
										if lst[i - 1] <= lst[i]:
															i += 1
										else:
															__lowerCAmelCase   ,	__lowerCAmelCase	   =lst[i], lst[i - 1]
															i -= 1
															if i == 0:
																				__lowerCAmelCase	   =1

					return lst


if __name__ == "__main__":
				lowercase_	       =			input('''Enter numbers separated by a comma:\n''').strip()
				lowercase_	       =			[int(item) for item in user_input.split(''',''')]
				print(gnome_sort(unsorted))




