






'''simple docstring'''




import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


__lowerCamelCase   :					Union[str, Any]   							=					datasets.utils.logging.get_logger(__name__)







@dataclass
class    A_	(datasets.BuilderConfig							):
							"""simple docstring"""




							a__   		=	None
							a__   		=	"utf-8"
							a__   		=	None
							a__   		=	None
							a__   		=	True  # deprecated
							a__   		=	None  # deprecated
							a__   		=	10 << 20  # 10MB
							a__   		=	None







class    A_	(datasets.ArrowBasedBuilder							):
							"""simple docstring"""




							a__   		=	JsonConfig

							def 							_A	(		self			:Optional[int]				)				->  str:


										'''simple docstring'''




										if self.config.block_size is not None:
													logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"				)
													snake_case_				:					Any   =       self.config.block_size
										if self.config.use_threads is not True:
													logger.warning(
													    "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."				)
										if self.config.newlines_in_values is not None:
													raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported"				)
										return datasets.DatasetInfo(features=self.config.features				)

							def 							_A	(		self			:List[Any]       ,							lowerCAmelCase__			:List[Any]				)				->  int:


										'''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}'''				)
										snake_case_				:					Dict   =       dl_manager.download_and_extract(self.config.data_files				)
										if isinstance(lowerCAmelCase__       ,							(str, list, tuple)				):
													snake_case_				:					List[str]   =       data_files
													if isinstance(lowerCAmelCase__       ,							lowerCAmelCase__				):
																snake_case_				:					Any   =       [files]
													snake_case_				:					Optional[int]   =       [dl_manager.iter_files(lowerCAmelCase__				) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,							gen_kwargs={"files": files}				)]
										snake_case_				:					Optional[Any]   =       []
										for split_name, files in data_files.items():
													if isinstance(lowerCAmelCase__       ,							lowerCAmelCase__				):
																snake_case_				:					Union[str, Any]   =       [files]
													snake_case_				:					Dict   =       [dl_manager.iter_files(lowerCAmelCase__				) for file in files]
													splits.append(datasets.SplitGenerator(name=lowerCAmelCase__       ,							gen_kwargs={"files": files}				)				)
										return splits

							def 							_A	(		self			:List[Any]       ,							lowerCAmelCase__			:pa.Table				)				->  pa.Table:


										'''simple docstring'''




										if self.config.features is not None:
													# adding missing columns
													for column_name in set(self.config.features				) - set(pa_table.column_names				):
																snake_case_				:					Optional[int]   =       self.config.features.arrow_schema.field(lowerCAmelCase__				).type
																snake_case_				:					Dict   =       pa_table.append_column(lowerCAmelCase__       ,							pa.array([None] * len(lowerCAmelCase__				)       ,							type=lowerCAmelCase__				)				)
													# more expensive cast to support nested structures with keys in a different order
													# allows str <-> int/float or str to Audio for example
													snake_case_				:					List[str]   =       table_cast(lowerCAmelCase__       ,							self.config.features.arrow_schema				)
										return pa_table






							def 							_A	(		self			:str       ,							lowerCAmelCase__			:Optional[int]				)				->  int:


										'''simple docstring'''




										for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__				)				):
													# If the file is one json object and if we need to look at the list of items in one specific field
													if self.config.field is not None:
																with open(lowerCAmelCase__       ,							encoding=self.config.encoding       ,							errors=self.config.encoding_errors				) as f:
																			snake_case_				:					Optional[Any]   =       json.load(lowerCAmelCase__				)

																# We keep only the field we are interested in
																snake_case_				:					Dict   =       dataset[self.config.field]

																# We accept two format: a list of dicts or a dict of lists
																if isinstance(lowerCAmelCase__       ,							(list, tuple)				):
																			snake_case_				:					Optional[Any]   =       set().union(*[row.keys() for row in dataset]				)
																			snake_case_				:					Any   =       {col: [row.get(lowerCAmelCase__				) for row in dataset] for col in keys}
																else:
																			snake_case_				:					str   =       dataset
																snake_case_				:					List[str]   =       pa.Table.from_pydict(lowerCAmelCase__				)
																yield file_idx, self._cast_table(lowerCAmelCase__				)

													# If the file has one json object per line
													else:
																with open(lowerCAmelCase__       ,							"rb"				) as f:
																			snake_case_				:					Union[str, Any]   =       0
																			# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																			# Set a default minimum value of 16kB if the chunk size is really small
																			snake_case_				:					List[str]   =       max(self.config.chunksize // 32       ,							16 << 10				)
																			snake_case_				:					Any   =       (
																			    self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
																			)
																			while True:
																						snake_case_				:					Dict   =       f.read(self.config.chunksize				)
																						if not batch:
																									break
																						# Finish current line
																						try:
																									batch += f.readline()
																						except (AttributeError, io.UnsupportedOperation):
																									batch += readline(lowerCAmelCase__				)
																						# PyArrow only accepts utf-8 encoded bytes
																						if self.config.encoding != "utf-8":
																									snake_case_				:					Union[str, Any]   =       batch.decode(self.config.encoding       ,							errors=lowerCAmelCase__				).encode("utf-8"				)
																						try:
																									while True:
																												try:
																															snake_case_				:					Dict   =       paj.read_json(
																															    io.BytesIO(lowerCAmelCase__				)       ,							read_options=paj.ReadOptions(block_size=lowerCAmelCase__				)				)
																															break
																												except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																															if (
																															    isinstance(lowerCAmelCase__       ,							pa.ArrowInvalid				)
																															    and "straddling" not in str(lowerCAmelCase__				)
																															    or block_size > len(lowerCAmelCase__				)
																															):
																																		raise
																															else:
																																		# Increase the block size in case it was too small.
																																		# The block size will be reset for the next file.
																																		logger.debug(
																																		    F'''Batch of {len(lowerCAmelCase__				)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''				)
																																		block_size *= 2
																						except pa.ArrowInvalid as e:
																									try:
																												with open(
																												    lowerCAmelCase__       ,							encoding=self.config.encoding       ,							errors=self.config.encoding_errors				) as f:
																															snake_case_				:					int   =       json.load(lowerCAmelCase__				)
																									except json.JSONDecodeError:
																												logger.error(F'''Failed to read file \'{file}\' with error {type(lowerCAmelCase__				)}: {e}'''				)
																												raise e
																									# If possible, parse the file as a list of json objects and exit the loop
																									if isinstance(lowerCAmelCase__       ,							lowerCAmelCase__				):  # list is the only sequence type supported in JSON
																												try:
																															snake_case_				:					Union[str, Any]   =       set().union(*[row.keys() for row in dataset]				)
																															snake_case_				:					str   =       {col: [row.get(lowerCAmelCase__				) for row in dataset] for col in keys}
																															snake_case_				:					Optional[int]   =       pa.Table.from_pydict(lowerCAmelCase__				)
																												except (pa.ArrowInvalid, AttributeError) as e:
																															logger.error(F'''Failed to read file \'{file}\' with error {type(lowerCAmelCase__				)}: {e}'''				)
																															raise ValueError(F'''Not able to read records in the JSON file at {file}.'''				) from None
																												yield file_idx, self._cast_table(lowerCAmelCase__				)
																												break
																									else:
																												logger.error(F'''Failed to read file \'{file}\' with error {type(lowerCAmelCase__				)}: {e}'''				)
																												raise ValueError(
																												    F'''Not able to read records in the JSON file at {file}. '''
																												    F'''You should probably indicate the field of the JSON file containing your records. '''
																												    F'''This JSON file contain the following fields: {str(list(dataset.keys()				)				)}. '''
																												    F'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''				) from None
                        # 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 (file_idx, batch_idx), self._cast_table(lowerCAmelCase__				)
																						batch_idx += 1












'''simple docstring'''




import fire

from utils import calculate_rouge, save_json

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__=None     ,**__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					int   =       [x.strip() for x in open(__magic_name__						).readlines()]
			snake_case_				:					Optional[int]   =       [x.strip() for x in open(__magic_name__						).readlines()][: len(__magic_name__						)]
			snake_case_				:					List[Any]   =       calculate_rouge(__magic_name__     ,__magic_name__     ,**__magic_name__						)
			if save_path is not None:
						save_json(__magic_name__     ,__magic_name__     ,indent=__magic_name__						)
			return metrics  # these print nicely


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




