



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

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



class 	__lowerCAmelCase       (		lowerCAmelCase__				):




	def __init__(  self		,			__UpperCAmelCase		,			__UpperCAmelCase = None		,			__UpperCAmelCase = None		,			__UpperCAmelCase = None		,			__UpperCAmelCase = False		,			__UpperCAmelCase = False		,			__UpperCAmelCase = None		,			**__UpperCAmelCase		,			):



				'''simple docstring'''
				super().__init__(
				    __UpperCAmelCase		,			split=__UpperCAmelCase		,			features=__UpperCAmelCase		,			cache_dir=__UpperCAmelCase		,			keep_in_memory=__UpperCAmelCase		,			streaming=__UpperCAmelCase		,			num_proc=__UpperCAmelCase		,			**__UpperCAmelCase		,			)
				__lowerCamelCase  							=      path_or_paths if isinstance(__UpperCAmelCase		,			__UpperCAmelCase			) else {self.split: path_or_paths}
				__lowerCamelCase  							=      Csv(
				    cache_dir=__UpperCAmelCase		,			data_files=__UpperCAmelCase		,			features=__UpperCAmelCase		,			**__UpperCAmelCase		,			)




	def     lowerCamelCase (  self			):



				'''simple docstring'''
				# Build iterable dataset
				if self.streaming:
							__lowerCamelCase  							=      self.builder.as_streaming_dataset(split=self.split			)
				# Build regular (map-style) dataset
				else:
							__lowerCamelCase  							=      None
							__lowerCamelCase  							=      None
							__lowerCamelCase  							=      None
							__lowerCamelCase  							=      None

							self.builder.download_and_prepare(
							    download_config=__UpperCAmelCase		,			download_mode=__UpperCAmelCase		,			verification_mode=__UpperCAmelCase		,			base_path=__UpperCAmelCase		,			num_proc=self.num_proc		,			)
							__lowerCamelCase  							=      self.builder.as_dataset(
							    split=self.split		,			verification_mode=__UpperCAmelCase		,			in_memory=self.keep_in_memory			)
				return dataset



class 	__lowerCAmelCase       :




	def __init__(  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase = None		,			__UpperCAmelCase = None		,			**__UpperCAmelCase		,			):



				'''simple docstring'''
				if num_proc is not None and num_proc <= 0:
							raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""			)

				__lowerCamelCase  							=      dataset
				__lowerCamelCase  							=      path_or_buf
				__lowerCamelCase  							=      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
				__lowerCamelCase  							=      num_proc
				__lowerCamelCase  							=      '''utf-8'''
				__lowerCamelCase  							=      to_csv_kwargs




	def     lowerCamelCase (  self			):



				'''simple docstring'''
				__lowerCamelCase  							=      self.to_csv_kwargs.pop('''path_or_buf'''		,			__UpperCAmelCase			)
				__lowerCamelCase  							=      self.to_csv_kwargs.pop('''header'''		,			__UpperCAmelCase			)
				__lowerCamelCase  							=      self.to_csv_kwargs.pop('''index'''		,			__UpperCAmelCase			)

				if isinstance(self.path_or_buf		,			(str, bytes, os.PathLike)			):
							with open(self.path_or_buf		,			'''wb+'''			) as buffer:
										__lowerCamelCase  							=      self._write(file_obj=__UpperCAmelCase		,			header=__UpperCAmelCase		,			index=__UpperCAmelCase		,			**self.to_csv_kwargs			)
				else:
							__lowerCamelCase  							=      self._write(file_obj=self.path_or_buf		,			header=__UpperCAmelCase		,			index=__UpperCAmelCase		,			**self.to_csv_kwargs			)
				return written




	def     lowerCamelCase (  self		,			__UpperCAmelCase			):



				'''simple docstring'''
				__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase  							=      args

				__lowerCamelCase  							=      query_table(
				    table=self.dataset.data		,			key=slice(__UpperCAmelCase		,			offset + self.batch_size			)		,			indices=self.dataset._indices		,			)
				__lowerCamelCase  							=      batch.to_pandas().to_csv(
				    path_or_buf=__UpperCAmelCase		,			header=header if (offset == 0) else False		,			index=__UpperCAmelCase		,			**__UpperCAmelCase			)
				return csv_str.encode(self.encoding			)




	def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			**__UpperCAmelCase			):



				'''simple docstring'''
				__lowerCamelCase  							=      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 CSV from Arrow format'''		,			):
										__lowerCamelCase  							=      self._batch_csv((offset, header, index, to_csv_kwargs)			)
										written += file_obj.write(__UpperCAmelCase			)

				else:
							__lowerCamelCase							,__lowerCamelCase  							=      len(self.dataset			), self.batch_size
							with multiprocessing.Pool(self.num_proc			) as pool:
										for csv_str in logging.tqdm(
										    pool.imap(
										        self._batch_csv		,			[(offset, header, index, to_csv_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 CSV from Arrow format'''		,			):
													written += file_obj.write(__UpperCAmelCase			)

				return written











import argparse
import json
import re
import string
import sys
from collections import Counter





def 						a__     (						_UpperCamelCase     :	Tuple							):

   def remove_articles(_UpperCamelCase     :	int							):
      return re.sub(R'''\b(a|an|the)\b'''	,''' '''	,_UpperCamelCase							)

   def white_space_fix(_UpperCamelCase     :	int							):
      return " ".join(text.split()							)

   def remove_punc(_UpperCamelCase     :	int							):
      __lowerCamelCase  							=      set(string.punctuation							)
      return "".join(ch for ch in text if ch not in exclude							)

   def lower(_UpperCamelCase     :	str							):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase							)							)							)							)





def 						a__     (						_UpperCamelCase     :	Union[str, Any]	,_UpperCamelCase     :	List[str]							):
   __lowerCamelCase  							=      normalize_answer(_UpperCamelCase							).split()
   __lowerCamelCase  							=      normalize_answer(_UpperCamelCase							).split()
   __lowerCamelCase  							=      Counter(_UpperCamelCase							) & Counter(_UpperCamelCase							)
   __lowerCamelCase  							=      sum(common.values()							)
   if num_same == 0:
      return 0
   __lowerCamelCase  							=      1.0 * num_same / len(_UpperCamelCase							)
   __lowerCamelCase  							=      1.0 * num_same / len(_UpperCamelCase							)
   __lowerCamelCase  							=      (2 * precision * recall) / (precision + recall)
   return fa





def 						a__     (						_UpperCamelCase     :	Optional[Any]	,_UpperCamelCase     :	str							):
   return normalize_answer(_UpperCamelCase							) == normalize_answer(_UpperCamelCase							)





def 						a__     (						_UpperCamelCase     :	int	,_UpperCamelCase     :	Dict	,_UpperCamelCase     :	Dict							):
   __lowerCamelCase  							=      []
   for ground_truth in ground_truths:
      __lowerCamelCase  							=      metric_fn(_UpperCamelCase	,_UpperCamelCase							)
      scores_for_ground_truths.append(_UpperCamelCase							)
   return max(_UpperCamelCase							)





def 						a__     (						_UpperCamelCase     :	Optional[int]	,_UpperCamelCase     :	Optional[Any]							):
   __lowerCamelCase  							=      __lowerCamelCase  							=      __lowerCamelCase  							=      0
   __lowerCamelCase  							=      []
   for passage in dataset:
      for qa in passage["qas"]:
         total += 1
         if qa["id"] not in predictions:
            __lowerCamelCase  							=      F"""Unanswered question {qa["id"]} will receive score 0."""
            print(_UpperCamelCase	,file=sys.stderr							)
            continue

         __lowerCamelCase  							=      [x['''text'''] for x in qa['''answers''']]
         __lowerCamelCase  							=      predictions[qa['''id''']]

         __lowerCamelCase  							=      metric_max_over_ground_truths(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase							)
         if int(_exact_match							) == 1:
            correct_ids.append(qa['''id''']							)
         exact_match += _exact_match

         fa += metric_max_over_ground_truths(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase							)

   __lowerCamelCase  							=      exact_match / total
   __lowerCamelCase  							=      fa / total

   return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
  a_								=   """1.0"""
  a_								=   argparse.ArgumentParser("""Official evaluation script for ReCoRD v1.0.""")
  parser.add_argument("""data_file""", help="""The dataset file in JSON format.""")
  parser.add_argument("""pred_file""", help="""The model prediction file in JSON format.""")
  parser.add_argument("""--output_correct_ids""", action="""store_true""", help="""Output the correctly answered query IDs.""")
  a_								=   parser.parse_args()

  with open(args.data_file) as data_file:
    a_								=   json.load(data_file)
    if dataset_json["version"] != expected_version:
      print(
          f"Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}",
          file=sys.stderr,
      )
    a_								=   dataset_json["""data"""]

  with open(args.pred_file) as pred_file:
    a_								=   json.load(pred_file)

  a_						,  a_								=   evaluate(dataset, predictions)

  if args.output_correct_ids:
    print(f"Output {len(correct_ids)} correctly answered question IDs.")
    with open("""correct_ids.json""", """w""") as f:
      json.dump(correct_ids, f)






