




'''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


UpperCamelCase_						:      Optional[Any]       =						datasets.utils.logging.get_logger(__name__)

@dataclass
class        _a    (						datasets.BuilderConfig							):
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						None
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						"""utf-8"""
     SCREAMING_SNAKE_CASE_							:   int       =						None
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						None
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						True  # deprecated
     SCREAMING_SNAKE_CASE_							:   Any       =						None  # deprecated
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						10 << 20  # 10MB
     SCREAMING_SNAKE_CASE_							:   int       =						None







class        _a    (						datasets.ArrowBasedBuilder							):
     SCREAMING_SNAKE_CASE_							:   Tuple       =						JsonConfig






     def     _lowercase     (			self   )							->      Optional[int]:
          if self.config.block_size is not None:
               logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"   )
               _snake_case							=				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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Optional[Any]:
          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							=				dl_manager.download_and_extract(self.config.data_files   )
          if isinstance(lowercase_ ,(str, list, tuple)   ):
               _snake_case							=				data_files
               if isinstance(lowercase_ ,lowercase_   ):
                    _snake_case							=				[files]
               _snake_case							=				[dl_manager.iter_files(lowercase_   ) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={"files": files}   )]
          _snake_case							=				[]
          for split_name, files in data_files.items():
               if isinstance(lowercase_ ,lowercase_   ):
                    _snake_case							=				[files]
               _snake_case							=				[dl_manager.iter_files(lowercase_   ) for file in files]
               splits.append(datasets.SplitGenerator(name=lowercase_ ,gen_kwargs={"files": files}   )   )
          return splits






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[str]:
          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							=				self.config.features.arrow_schema.field(lowercase_   ).type
                    _snake_case							=				pa_table.append_column(lowercase_ ,pa.array([None] * len(lowercase_   ) ,type=lowercase_   )   )
               # 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							=				table_cast(lowercase_ ,self.config.features.arrow_schema   )
          return pa_table






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Union[str, Any]:
          for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase_   )   ):
               # 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(lowercase_ ,encoding=self.config.encoding ,errors=self.config.encoding_errors   ) as f:
                         _snake_case							=				json.load(lowercase_   )

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

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

               # If the file has one json object per line
               else:
                    with open(lowercase_ ,"rb"   ) as f:
                         _snake_case							=				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							=				max(self.config.chunksize // 32 ,16 << 10   )
                         _snake_case							=				(
                             self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
                         )
                         while True:
                              _snake_case							=				f.read(self.config.chunksize   )
                              if not batch:
                                   break
                              # Finish current line
                              try:
                                   batch += f.readline()
                              except (AttributeError, io.UnsupportedOperation):
                                   batch += readline(lowercase_   )
                              # PyArrow only accepts utf-8 encoded bytes
                              if self.config.encoding != "utf-8":
                                   _snake_case							=				batch.decode(self.config.encoding ,errors=lowercase_   ).encode("utf-8"   )
                              try:
                                   while True:
                                        try:
                                             _snake_case							=				paj.read_json(
                                                 io.BytesIO(lowercase_   ) ,read_options=paj.ReadOptions(block_size=lowercase_   )   )
                                             break
                                        except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                             if (
                                                 isinstance(lowercase_ ,pa.ArrowInvalid   )
                                                 and "straddling" not in str(lowercase_   )
                                                 or block_size > len(lowercase_   )
                                             ):
                                                  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(lowercase_   )} 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(
                                            lowercase_ ,encoding=self.config.encoding ,errors=self.config.encoding_errors   ) as f:
                                             _snake_case							=				json.load(lowercase_   )
                                   except json.JSONDecodeError:
                                        logger.error(f"""Failed to read file \'{file}\' with error {type(lowercase_   )}: {e}"""   )
                                        raise e
                                   # If possible, parse the file as a list of json objects and exit the loop
                                   if isinstance(lowercase_ ,lowercase_   ):  # list is the only sequence type supported in JSON
                                        try:
                                             _snake_case							=				set().union(*[row.keys() for row in dataset]   )
                                             _snake_case							=				{col: [row.get(lowercase_   ) for row in dataset] for col in keys}
                                             _snake_case							=				pa.Table.from_pydict(lowercase_   )
                                        except (pa.ArrowInvalid, AttributeError) as e:
                                             logger.error(f"""Failed to read file \'{file}\' with error {type(lowercase_   )}: {e}"""   )
                                             raise ValueError(f"""Not able to read records in the JSON file at {file}."""   ) from None
                                        yield file_idx, self._cast_table(lowercase_   )
                                        break
                                   else:
                                        logger.error(f"""Failed to read file \'{file}\' with error {type(lowercase_   )}: {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(lowercase_   )
                              batch_idx += 1








'''simple docstring'''





import fire

from utils import calculate_rouge, save_json





def      __a     (       _UpperCamelCase:   Tuple ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Optional[int]=None ,						**_UpperCamelCase:   Any   )						->			Optional[Any]:

     """simple docstring"""




     _snake_case							=				[x.strip() for x in open(_UpperCamelCase   ).readlines()]
     _snake_case							=				[x.strip() for x in open(_UpperCamelCase   ).readlines()][: len(_UpperCamelCase   )]
     _snake_case							=				calculate_rouge(_UpperCamelCase ,						_UpperCamelCase ,						**_UpperCamelCase   )
     if save_path is not None:
          save_json(_UpperCamelCase ,						_UpperCamelCase ,						indent=_UpperCamelCase   )
     return metrics  # these print nicely


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


