
import os



def 	UpperCamelCase   (							lowerCAmelCase__ = "input.txt"		):
					'''simple docstring'''

					with open(os.path.join(os.path.dirname(lowerCAmelCase__		)			,      lowerCAmelCase__		)		) as input_file:
										lowercase						     =						[
										    [int(lowerCAmelCase__		) for element in line.split(''','''		)]
										    for line in input_file.readlines()
										]

					lowercase						     =						len(lowerCAmelCase__		)
					lowercase						     =						len(matrix[0]		)

					lowercase						     =						[[-1 for _ in range(lowerCAmelCase__		)] for _ in range(lowerCAmelCase__		)]
					for i in range(lowerCAmelCase__		):
										lowercase						     =						matrix[i][0]

					for j in range(1			,      lowerCAmelCase__		):
										for i in range(lowerCAmelCase__		):
															lowercase						     =						minimal_path_sums[i][j - 1] + matrix[i][j]

										for i in range(1			,      lowerCAmelCase__		):
															lowercase						     =						min(
															    minimal_path_sums[i][j]			,      minimal_path_sums[i - 1][j] + matrix[i][j]		)

										for i in range(rows - 2			,      -1			,      -1		):
															lowercase						     =						min(
															    minimal_path_sums[i][j]			,      minimal_path_sums[i + 1][j] + matrix[i][j]		)

					return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums		)


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






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)




