
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__		:Tuple			  =   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     lowercase		( SCREAMING_SNAKE_CASE__		):




														def __init__(    self       ,A__ = 1_0_1):
																			lowercase						     =						length




														def __len__(    self):
																			return self.length





														def __getitem__(    self       ,A__):
																			return i







							class     lowercase		:




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







							class     lowercase		( nn.Module		):




														def __init__(    self):
																			super().__init__()
																			# Add some (unused) params otherwise DDP will complain.
																			lowercase						     =						nn.Linear(1_2_0       ,8_0)





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







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							@require_torch_neuroncore
							def 			A__				(    self):
												lowercase						     =						f'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
												lowercase						     =						self.get_auto_remove_tmp_dir()
												lowercase						     =						f'--output_dir {output_dir}'.split()
												lowercase						     =						['''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     lowercase		( SCREAMING_SNAKE_CASE__		):




							@require_torch_multi_gpu
							def 			A__				(    self):
												lowercase						     =						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()
												lowercase						     =						self.get_auto_remove_tmp_dir()
												lowercase						     =						f'--output_dir {output_dir}'.split()
												lowercase						     =						['''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__		:List[str]			  =   HfArgumentParser((TrainingArguments,))
							lowercase__		: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 [101, 40, 7]:
														lowercase__		:Tuple			  =   DummyDataset(dataset_length)



														def 	UpperCamelCase   (							lowerCAmelCase__		):
																			'''simple docstring'''
																			lowercase						     =						list(range(len(lowerCAmelCase__		)		)		)
																			lowercase						     =						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__		:List[str]			  =   Trainer(
														    model=DummyModel(),
														    args=training_args,
														    data_collator=DummyDataCollator(),
														    eval_dataset=dataset,
														    compute_metrics=compute_metrics,
														)
														lowercase__		:Tuple			  =   trainer.evaluate()
														logger.info(metrics)
														if metrics["eval_success"] is not True:
																					logger.error(metrics)
																					exit(1)

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

														lowercase__		:Union[str, Any]			  =   2

														lowercase__		:Union[str, Any]			  =   trainer.evaluate()
														logger.info(metrics)
														if metrics["eval_success"] is not True:
																					logger.error(metrics)
																					exit(1)

														lowercase__		:Optional[int]			  =   trainer.predict(dataset)
														logger.info(p.metrics)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics)
																					exit(1)
														lowercase__		:List[str]			  =   None






from pathlib import Path

import fire
from tqdm import tqdm



def 	UpperCamelCase   (							lowerCAmelCase__="ro"			,      lowerCAmelCase__="en"			,      lowerCAmelCase__="wmt16"			,      lowerCAmelCase__=None		):
					'''simple docstring'''
					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('''run pip install datasets'''		)
					lowercase						     =						f'{src_lang}-{tgt_lang}'
					print(f'Converting {dataset}-{pair}'		)
					lowercase						     =						datasets.load_dataset(lowerCAmelCase__			,      lowerCAmelCase__		)
					if save_dir is None:
										lowercase						     =						f'{dataset}-{pair}'
					lowercase						     =						Path(lowerCAmelCase__		)
					save_dir.mkdir(exist_ok=lowerCAmelCase__		)

					for split in ds.keys():
										print(f'Splitting {split} with {ds[split].num_rows} records'		)

										# to save to val.source, val.target like summary datasets
										lowercase						     =						'''val''' if split == '''validation''' else split
										lowercase						     =						save_dir.joinpath(f'{fn}.source'		)
										lowercase						     =						save_dir.joinpath(f'{fn}.target'		)
										lowercase						     =						src_path.open('''w+'''		)
										lowercase						     =						tgt_path.open('''w+'''		)

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]		):
															lowercase						     =						x['''translation''']
															src_fp.write(ex[src_lang] + '''\n'''		)
															tgt_fp.write(ex[tgt_lang] + '''\n'''		)

					print(f'Saved {dataset} dataset to {save_dir}'		)


if __name__ == "__main__":
							fire.Fire(download_wmt_dataset)




