






import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


lowerCamelCase : Tuple						   =       datasets.utils.logging.get_logger(__name__)

@dataclass
class 		lowerCAmelCase     ( datasets.BuilderConfig			):

					'''simple docstring'''

					_A						:			int									=      10000
					_A						:			Optional[List[str]]									=      None
					_A						:			Optional[datasets.Features]									=      None







class 		lowerCAmelCase     ( datasets.ArrowBasedBuilder			):

					'''simple docstring'''

					_A						:			Dict									=      ParquetConfig



					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  str:


												"""simple docstring"""

												return datasets.DatasetInfo(features=self.config.features							)



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	Union[str, Any]							)			->  str:


												"""simple docstring"""

												if not self.config.data_files:
																			raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"							)
												__lowercase      :							Optional[Any]				=     dl_manager.download_and_extract(self.config.data_files							)
												if isinstance(__a		,     (str, list, tuple)							):
																			__lowercase      :							Any				=     data_files
																			if isinstance(__a		,     __a							):
																										__lowercase      :							Optional[Any]				=     [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			__lowercase      :							Any				=     [dl_manager.iter_files(__a							) for file in files]
																			return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,     gen_kwargs={"""files""": files}							)]
												__lowercase      :							Any				=     []
												for split_name, files in data_files.items():
																			if isinstance(__a		,     __a							):
																										__lowercase      :							str				=     [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			__lowercase      :							str				=     [dl_manager.iter_files(__a							) for file in files]
																			# Infer features is they are stoed in the arrow schema
																			if self.info.features is None:
																										for file in itertools.chain.from_iterable(__a							):
																																	with open(__a		,     """rb"""							) as f:
																																								__lowercase      :							Optional[int]				=     datasets.Features.from_arrow_schema(pq.read_schema(__a							)							)
																																	break
																			splits.append(datasets.SplitGenerator(name=__a		,     gen_kwargs={"""files""": files}							)							)
												return splits



					def       lowerCAmelCase				(  self				:	Dict		,     __a				:	pa.Table							)			->  pa.Table:


												"""simple docstring"""

												if self.info.features is not None:
																			# more expensive cast to support nested features with keys in a different order
																			# allows str <-> int/float or str to Audio for example
																			__lowercase      :							Dict				=     table_cast(__a		,     self.info.features.arrow_schema							)
												return pa_table



					def       lowerCAmelCase				(  self				:	int		,     __a				:	Any							)			->  int:


												"""simple docstring"""

												__lowercase      :							Tuple				=     self.info.features.arrow_schema if self.info.features is not None else None
												if self.info.features is not None and self.config.columns is not None:
																			if sorted(field.name for field in schema							) != sorted(self.config.columns							):
																										raise ValueError(
																										    F"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"							)
												for file_idx, file in enumerate(itertools.chain.from_iterable(__a							)							):
																			with open(__a		,     """rb"""							) as f:
																										__lowercase      :							str				=     pq.ParquetFile(__a							)
																										try:
																																	for batch_idx, record_batch in enumerate(
																																	    parquet_file.iter_batches(batch_size=self.config.batch_size		,     columns=self.config.columns							)							):
																																								__lowercase      :							Union[str, Any]				=     pa.Table.from_batches([record_batch]							)
																																								# Uncomment for debugging (will print the Arrow table size and elements)
																																								# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																								# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																								yield F"{file_idx}_{batch_idx}", self._cast_table(__a							)
																										except ValueError as e:
																																	logger.error(F"Failed to read file '{file}' with error {type(__a							)}: {e}"							)
																																	raise







import fire

from utils import calculate_rouge, save_json
def       snake_case_		(     lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						str=None       ,       **lowerCAmelCase_       :						str      ):
							__lowercase      :							Tuple				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()]
							__lowercase      :							Dict				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()][: len(lowerCAmelCase_      )]
							__lowercase      :							Tuple				=     calculate_rouge(lowerCAmelCase_       ,       lowerCAmelCase_       ,       **lowerCAmelCase_      )
							if save_path is not None:
														save_json(lowerCAmelCase_       ,       lowerCAmelCase_       ,       indent=lowerCAmelCase_      )
							return metrics  # these print nicely


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