

import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int       )					->				str:
	def wrapper(*a__    :   List[Any]			,**a__    :   str       ):
		__A :			List[Any]		    =			timeit.default_timer()
		__A :			Any		    =			func(*a__			,**a__       )
		__A :			Union[str, Any]		    =			timeit.default_timer() - starttime
		return delta

	__A :			Dict		    =			func.__name__

	return wrapper
def 							__SCREAMING_SNAKE_CASE     (   a__    :   dict			,a__    :   Dict=100			,a__    :   Optional[Any]=None       )					->				Dict:
	__A :			Optional[int]		    =			[]
	__A :			Dict		    =			seq_shapes or {}
	for i in range(a__       ):
		__A :			Optional[Any]		    =			{}
		for col_id, (k, v) in enumerate(features.items()       ):
			if isinstance(a__			,_ArrayXD       ):
				__A :			Any		    =			np.random.rand(*v.shape       ).astype(v.dtype       )
			elif isinstance(a__			,datasets.Value       ):
				if v.dtype == "string":
					__A :			List[str]		    =			"""The small grey turtle was surprisingly fast when challenged."""
				else:
					__A :			int		    =			np.random.randint(10			,size=1       ).astype(v.dtype       ).item()
			elif isinstance(a__			,datasets.Sequence       ):
				while isinstance(a__			,datasets.Sequence       ):
					__A :			str		    =			v.feature
				__A :			List[Any]		    =			seq_shapes[k]
				__A :			Dict		    =			np.random.rand(*a__       ).astype(v.dtype       )
			__A :			Any		    =			data

		dummy_data.append((i, example)       )

	return dummy_data







def 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple			,a__    :   Optional[int]			,a__    :   Any=100			,a__    :   Optional[Any]=None       )					->				str:
	__A :			int		    =			generate_examples(a__			,num_examples=a__			,seq_shapes=a__       )

	with ArrowWriter(features=a__			,path=a__       ) as writer:
		for key, record in dummy_data:
			__A :			List[str]		    =			features.encode_example(a__       )
			writer.write(a__       )

		__A    ,				__A :			int		    =			writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""       )

	__A :			Optional[Any]		    =			datasets.Dataset.from_file(filename=a__			,info=datasets.DatasetInfo(features=a__       )       )

	return dataset








import fire

from utils import calculate_rouge, save_json
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Any			,a__    :   Tuple			,a__    :   Any=None			,**a__    :   Dict       )					->				Optional[Any]:
	__A :			int		    =			[x.strip() for x in open(a__       ).readlines()]
	__A :			List[str]		    =			[x.strip() for x in open(a__       ).readlines()][: len(a__       )]
	__A :			List[Any]		    =			calculate_rouge(a__			,a__			,**a__       )
	if save_path is not None:
		save_json(a__			,a__			,indent=a__       )
	return metrics  # these print nicely


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





