

from pathlib import Path

import fire
from tqdm import tqdm
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[Any]="ro"			,a__    :   List[str]="en"			,a__    :   Dict="wmt16"			,a__    :   List[Any]=None       )					->				None:


	try:
		import datasets
	except (ModuleNotFoundError, ImportError):
		raise ImportError("""run pip install datasets"""       )
	__A :			Dict		    =			f"""{src_lang}-{tgt_lang}"""
	print(f"""Converting {dataset}-{pair}"""       )
	__A :			Tuple		    =			datasets.load_dataset(_lowerCamelCase			,_lowerCamelCase       )
	if save_dir is None:
		__A :			Optional[Any]		    =			f"""{dataset}-{pair}"""
	__A :			List[Any]		    =			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
		__A :			List[str]		    =			"""val""" if split == """validation""" else split
		__A :			List[Any]		    =			save_dir.joinpath(f"""{fn}.source"""       )
		__A :			Union[str, Any]		    =			save_dir.joinpath(f"""{fn}.target"""       )
		__A :			Union[str, Any]		    =			src_path.open("""w+"""       )
		__A :			Dict		    =			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]       ):
			__A :			Optional[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'''






from __future__ import annotations

import math







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				int:



			"""simple docstring"""


			if depth < 0:
						raise ValueError("""Depth cannot be less than 0""" )

			if not scores:
						raise ValueError("""Scores cannot be empty""" )

			if depth == height:
						return scores[node_index]

			return (
			    max(
			        minimax(depth + 1    ,	node_index * 2    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )    ,	minimax(depth + 1    ,	node_index * 2 + 1    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )    ,	)
			    if is_max
			    else min(
			        minimax(depth + 1    ,	node_index * 2    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )    ,	minimax(depth + 1    ,	node_index * 2 + 1    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )    ,	)
			)







def 					_a						(       ) ->				None:



			"""simple docstring"""


			__snake_case					:      Union[str, Any]							      =     [90, 23, 6, 33, 21, 65, 123, 3_4423]
			__snake_case					:      Optional[int]							      =     math.log(len(_lowerCamelCase )    ,	2 )
			print(F'''Optimal value : {minimax(0    ,	0    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )}''' )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()


