



import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil





@pytest.fixture
def 						a__     (						_UpperCamelCase     :	Union[str, Any]							):
   __lowerCamelCase  							=      tmp_path / '''text.txt'''
   __lowerCamelCase  							=      textwrap.dedent(
       '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        '''							)
   with open(_UpperCamelCase	,'''w'''	,encoding='''utf-8'''							) as f:
      f.write(_UpperCamelCase							)
   return str(_UpperCamelCase							)





@pytest.fixture
def 						a__     (						_UpperCamelCase     :	Tuple	,_UpperCamelCase     :	Optional[Any]							):
   __lowerCamelCase  							=      tmp_path / '''text_with_image.txt'''
   with open(_UpperCamelCase	,'''w'''	,encoding='''utf-8'''							) as f:
      f.write(_UpperCamelCase							)
   return str(_UpperCamelCase							)





@pytest.mark.parametrize('''keep_linebreaks'''	,[True, False]							)
def 						a__     (						_UpperCamelCase     :	Optional[Any]	,_UpperCamelCase     :	Optional[int]							):
   with open(_UpperCamelCase	,encoding='''utf-8'''							) as f:
      __lowerCamelCase  							=      f.read().splitlines(keepends=_UpperCamelCase							)
   __lowerCamelCase  							=      Text(keep_linebreaks=_UpperCamelCase	,encoding='''utf-8'''							)
   __lowerCamelCase  							=      text._generate_tables([[text_file]]							)
   __lowerCamelCase  							=      pa.concat_tables([table for _, table in generator]							).to_pydict()['''text''']
   assert generated_content == expected_content





@require_pil
def 						a__     (						_UpperCamelCase     :	Dict							):
   with open(_UpperCamelCase	,encoding='''utf-8'''							) as f:
      __lowerCamelCase  							=      f.read().splitlines()[0]
   __lowerCamelCase  							=      Text(encoding='''utf-8'''	,features=Features({'''image''': Image()}							)							)
   __lowerCamelCase  							=      text._generate_tables([[text_file_with_image]]							)
   __lowerCamelCase  							=      pa.concat_tables([table for _, table in generator]							)
   assert pa_table.schema.field('''image'''							).type == Image()()
   __lowerCamelCase  							=      pa_table.to_pydict()['''image''']
   assert generated_content == [{"path": image_file, "bytes": None}]





@pytest.mark.parametrize('''sample_by'''	,['''line''', '''paragraph''', '''document''']							)
def 						a__     (						_UpperCamelCase     :	Optional[int]	,_UpperCamelCase     :	Tuple							):
   with open(_UpperCamelCase	,encoding='''utf-8'''							) as f:
      __lowerCamelCase  							=      f.read()
   if sample_by == "line":
      __lowerCamelCase  							=      expected_content.splitlines()
   elif sample_by == "paragraph":
      __lowerCamelCase  							=      expected_content.split('''\n\n'''							)
   elif sample_by == "document":
      __lowerCamelCase  							=      [expected_content]
   __lowerCamelCase  							=      Text(sample_by=_UpperCamelCase	,encoding='''utf-8'''	,chunksize=1_00							)
   __lowerCamelCase  							=      text._generate_tables([[text_file]]							)
   __lowerCamelCase  							=      pa.concat_tables([table for _, table in generator]							).to_pydict()['''text''']
   assert generated_content == expected_content











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)






