


'''simple docstring'''
from pathlib import Path

import fire
from tqdm import tqdm


def 			a      (			__a="ro" ,					__a="en" ,					__a="wmt16" ,					__a=None   )  ->	None:



					'''simple docstring'''






					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('''run pip install datasets'''   )
					UpperCamelCase__   :int												= f'''{src_lang}-{tgt_lang}'''
					print(f'''Converting {dataset}-{pair}'''   )
					UpperCamelCase__   :Tuple												= datasets.load_dataset(__a ,					__a   )
					if save_dir is None:
										UpperCamelCase__   :Any												= f'''{dataset}-{pair}'''
					UpperCamelCase__   :Dict												= Path(__a   )
					save_dir.mkdir(exist_ok=__a   )

					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
										UpperCamelCase__   :Dict												= '''val''' if split == '''validation''' else split
										UpperCamelCase__   :List[Any]												= save_dir.joinpath(f'''{fn}.source'''   )
										UpperCamelCase__   :int												= save_dir.joinpath(f'''{fn}.target'''   )
										UpperCamelCase__   :Union[str, Any]												= src_path.open('''w+'''   )
										UpperCamelCase__   :Tuple												= 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]   ):
															UpperCamelCase__   :Union[str, Any]												= 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)



'''simple docstring'''
import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				ComputeEnvironment.AMAZON_SAGEMAKER
		_a     =				True
		_a     =				'ml.p3.2xlarge'
		_a     =				'accelerate_sagemaker_execution_role'
		_a     =				'hf-sm'
		_a     =				'us-east-1'
		_a     =				1
		_a     =				'accelerate-sagemaker-1'
		_a     =				'1.6'
		_a     =				'4.4'
		_a     =				'train.py'
		_a     =				[
		    '--model_name_or_path',
		    'bert',
		    '--do_train',
		    'False',
		    '--epochs',
		    '3',
		    '--learning_rate',
		    '5e-5',
		    '--max_steps',
		    '50.5',
		]
		_a     =				[
		    '--model_name_or_path',
		    'bert',
		    '--do_train',
		    '--do_test',
		    'False',
		    '--do_predict',
		    '--epochs',
		    '3',
		    '--learning_rate',
		    '5e-5',
		    '--max_steps',
		    '50.5',
		]



class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args							)
							assert isinstance(converted_args['''model_name_or_path''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''do_train''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''epochs''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''learning_rate''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''max_steps''']    ,   UpperCamelCase_							)

							with pytest.raises(UpperCamelCase_							):
												_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args							)