



'''simple docstring'''





import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = False  ,__UpperCAmelCase = False  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,**__UpperCAmelCase  ,)    ->		str:
      super().__init__(
          __UpperCAmelCase  ,split=__UpperCAmelCase  ,features=__UpperCAmelCase  ,cache_dir=__UpperCAmelCase  ,keep_in_memory=__UpperCAmelCase  ,streaming=__UpperCAmelCase  ,num_proc=__UpperCAmelCase  ,**__UpperCAmelCase  ,)
      lowerCAmelCase__ :  Optional[int]							      =					field
      lowerCAmelCase__ :  Tuple							      =					path_or_paths if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ) else {self.split: path_or_paths}
      lowerCAmelCase__ :  int							      =					Json(
          cache_dir=__UpperCAmelCase  ,data_files=__UpperCAmelCase  ,features=__UpperCAmelCase  ,field=__UpperCAmelCase  ,**__UpperCAmelCase  ,)






  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      # Build iterable dataset
      if self.streaming:
          lowerCAmelCase__ :  Union[str, Any]							      =					self.builder.as_streaming_dataset(split=self.split      )
      # Build regular (map-style) dataset
      else:
          lowerCAmelCase__ :  int							      =					None
          lowerCAmelCase__ :  int							      =					None
          lowerCAmelCase__ :  Dict							      =					None
          lowerCAmelCase__ :  Union[str, Any]							      =					None

          self.builder.download_and_prepare(
              download_config=__UpperCAmelCase  ,download_mode=__UpperCAmelCase  ,verification_mode=__UpperCAmelCase  ,base_path=__UpperCAmelCase  ,num_proc=self.num_proc  ,)
          lowerCAmelCase__ :  List[str]							      =					self.builder.as_dataset(
              split=self.split  ,verification_mode=__UpperCAmelCase  ,in_memory=self.keep_in_memory      )
      return dataset







class        lowerCAmelCase_:
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,**__UpperCAmelCase  ,)    ->		List[str]:
      if num_proc is not None and num_proc <= 0:
          raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""      )

      lowerCAmelCase__ :  Union[str, Any]							      =					dataset
      lowerCAmelCase__ :  int							      =					path_or_buf
      lowerCAmelCase__ :  Union[str, Any]							      =					batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
      lowerCAmelCase__ :  Tuple							      =					num_proc
      lowerCAmelCase__ :  Optional[int]							      =					"""utf-8"""
      lowerCAmelCase__ :  Tuple							      =					to_json_kwargs




  def 							UpperCAmelCase_	(    self      )    ->		int:
      lowerCAmelCase__ :  List[str]							      =					self.to_json_kwargs.pop("""path_or_buf"""  ,__UpperCAmelCase      )
      lowerCAmelCase__ :  Union[str, Any]							      =					self.to_json_kwargs.pop("""orient"""  ,"""records"""      )
      lowerCAmelCase__ :  List[str]							      =					self.to_json_kwargs.pop("""lines"""  ,True if orient == """records""" else False      )
      lowerCAmelCase__ :  List[Any]							      =					self.to_json_kwargs.pop("""index"""  ,False if orient in ["""split""", """table"""] else True      )
      lowerCAmelCase__ :  Union[str, Any]							      =					self.to_json_kwargs.pop("""compression"""  ,__UpperCAmelCase      )

      if compression not in [None, "infer", "gzip", "bz2", "xz"]:
          raise NotImplementedError(F"""`datasets` currently does not support {compression} compression"""      )

      if isinstance(self.path_or_buf  ,(str, bytes, os.PathLike)      ):
          with fsspec.open(self.path_or_buf  ,"""wb"""  ,compression=__UpperCAmelCase      ) as buffer:
              lowerCAmelCase__ :  Optional[Any]							      =					self._write(file_obj=__UpperCAmelCase  ,orient=__UpperCAmelCase  ,lines=__UpperCAmelCase  ,index=__UpperCAmelCase  ,**self.to_json_kwargs      )
      else:
          if compression:
              raise NotImplementedError(
                  F"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
                  """ was passed. Please provide a local path instead."""      )
          lowerCAmelCase__ :  Any							      =					self._write(
              file_obj=self.path_or_buf  ,orient=__UpperCAmelCase  ,lines=__UpperCAmelCase  ,index=__UpperCAmelCase  ,**self.to_json_kwargs      )
      return written




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[Any]:
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Dict							      =					args

      lowerCAmelCase__ :  Optional[Any]							      =					query_table(
          table=self.dataset.data  ,key=slice(__UpperCAmelCase  ,offset + self.batch_size      )  ,indices=self.dataset._indices  ,)
      lowerCAmelCase__ :  Any							      =					batch.to_pandas().to_json(
          path_or_buf=__UpperCAmelCase  ,orient=__UpperCAmelCase  ,lines=__UpperCAmelCase  ,index=__UpperCAmelCase  ,**__UpperCAmelCase      )
      if not json_str.endswith("""\n"""      ):
          json_str += "\n"
      return json_str.encode(self.encoding      )






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,**__UpperCAmelCase  ,)    ->		int:
      lowerCAmelCase__ :  Union[str, Any]							      =					0

      if self.num_proc is None or self.num_proc == 1:
          for offset in logging.tqdm(
              range(0  ,len(self.dataset      )  ,self.batch_size      )  ,unit="""ba"""  ,disable=not logging.is_progress_bar_enabled()  ,desc="""Creating json from Arrow format"""  ,):
              lowerCAmelCase__ :  Tuple							      =					self._batch_json((offset, orient, lines, index, to_json_kwargs)      )
              written += file_obj.write(__UpperCAmelCase      )
      else:
          lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					len(self.dataset      ), self.batch_size
          with multiprocessing.Pool(self.num_proc      ) as pool:
              for json_str in logging.tqdm(
                  pool.imap(
                      self._batch_json  ,[(offset, orient, lines, index, to_json_kwargs) for offset in range(0  ,__UpperCAmelCase  ,__UpperCAmelCase      )]  ,)  ,total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size  ,unit="""ba"""  ,disable=not logging.is_progress_bar_enabled()  ,desc="""Creating json from Arrow format"""  ,):
                  written += file_obj.write(__UpperCAmelCase      )

      return written






'''simple docstring'''

import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


a_  :				str						= """\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

a_  :				int						= """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

a_  :				Tuple						= """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,						_KWARGS_DESCRIPTION			)
class      snake_case    (    datasets.Metric			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   return datasets.MetricInfo(
       description=_DESCRIPTION			,  citation=_CITATION			,  inputs_description=_KWARGS_DESCRIPTION			,  features=datasets.Features(
           {
               "predictions": datasets.Value("string"			,  id="sequence"     ),
               "references": datasets.Value("string"			,  id="sequence"     ),
           }     )			,  codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"]			,  reference_urls=[
           "https://en.wikipedia.org/wiki/ROUGE_(metric)",
           "https://github.com/google-research/google-research/tree/master/rouge",
       ]			,  )







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  UpperCamelCase=True			,  UpperCamelCase=False     ):







   """simple docstring"""
   if rouge_types is None:
    lowerCamelCase_      =  ["rouge1", "rouge2", "rougeL", "rougeLsum"]

   lowerCamelCase_      =  rouge_scorer.RougeScorer(rouge_types=UpperCamelCase			,  use_stemmer=UpperCamelCase     )
   if use_aggregator:
    lowerCamelCase_      =  scoring.BootstrapAggregator()
   else:
    lowerCamelCase_      =  []

   for ref, pred in zip(UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  scorer.score(UpperCamelCase			,  UpperCamelCase     )
    if use_aggregator:
     aggregator.add_scores(UpperCamelCase     )
    else:
     scores.append(UpperCamelCase     )

   if use_aggregator:
    lowerCamelCase_      =  aggregator.aggregate()
   else:
    lowerCamelCase_      =  {}
    for key in scores[0]:
     lowerCamelCase_      =  [score[key] for score in scores]

   return result



