

import argparse
import json
import re
import string
import sys
from collections import Counter
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Any )				-> str:





    '''simple docstring'''






    def remove_articles(SCREAMING_SNAKE_CASE__     :      Union[str, Any] ):
        return re.sub(R'\b(a|an|the)\b'       ,     ' '       ,     SCREAMING_SNAKE_CASE__ )

    def white_space_fix(SCREAMING_SNAKE_CASE__     :      List[str] ):
        return " ".join(text.split() )

    def remove_punc(SCREAMING_SNAKE_CASE__     :      List[Any] ):
        A__        =  set(string.punctuation )
        return "".join(ch for ch in text if ch not in exclude )

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

    return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__ ) ) ) )
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      int       ,     SCREAMING_SNAKE_CASE__     :      Dict )				-> Optional[Any]:





    '''simple docstring'''






    A__        =  normalize_answer(SCREAMING_SNAKE_CASE__ ).split()
    A__        =  normalize_answer(SCREAMING_SNAKE_CASE__ ).split()
    A__        =  Counter(SCREAMING_SNAKE_CASE__ ) & Counter(SCREAMING_SNAKE_CASE__ )
    A__        =  sum(common.values() )
    if num_same == 0:
        return 0
    A__        =  1.0 * num_same / len(SCREAMING_SNAKE_CASE__ )
    A__        =  1.0 * num_same / len(SCREAMING_SNAKE_CASE__ )
    A__        =  (2 * precision * recall) / (precision + recall)
    return fa
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[str]       ,     SCREAMING_SNAKE_CASE__     :      Optional[int] )				-> List[Any]:





    '''simple docstring'''






    return normalize_answer(SCREAMING_SNAKE_CASE__ ) == normalize_answer(SCREAMING_SNAKE_CASE__ )
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Union[str, Any]       ,     SCREAMING_SNAKE_CASE__     :      Optional[Any]       ,     SCREAMING_SNAKE_CASE__     :      Union[str, Any] )				-> List[str]:





    '''simple docstring'''






    A__        =  []
    for ground_truth in ground_truths:
        A__        =  metric_fn(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__ )
        scores_for_ground_truths.append(SCREAMING_SNAKE_CASE__ )
    return max(SCREAMING_SNAKE_CASE__ )
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Dict       ,     SCREAMING_SNAKE_CASE__     :      Tuple )				-> Any:





    '''simple docstring'''






    A__        =  A__        =  A__        =  0
    A__        =  []
    for passage in dataset:
        for qa in passage["qas"]:
            total += 1
            if qa["id"] not in predictions:
                A__        =  f'Unanswered question {qa["id"]} will receive score 0.'
                print(SCREAMING_SNAKE_CASE__       ,     file=sys.stderr )
                continue

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

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

            fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__ )

    A__        =  exact_match / total
    A__        =  fa / total

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


if __name__ == "__main__":
     lowercase_									=    "1.0"
     lowercase_									=    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.")
     lowercase_									=    parser.parse_args()

     with open(args.data_file) as data_file:
          lowercase_									=    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,
               )
          lowercase_									=    dataset_json["data"]

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

     lowercase_						,     lowercase_									=    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)




import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      str )				-> str:





    '''simple docstring'''






    A__        =  min(re.search('^ +'       ,     SCREAMING_SNAKE_CASE__ ).end() if t.startswith(' ' ) else 0 for t in string.splitlines() )
    return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(SCREAMING_SNAKE_CASE__ )] )


lowercase_									=    "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

lowercase_									=    "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


lowercase_									=    "\\n# Begin of markdown\n\nSome cool dataset card\n"


class 					A		(				unittest.TestCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			List[Any]						)->       str:





        '''simple docstring'''







        with tempfile.TemporaryDirectory() as tmp_dir:
            A__        =  Path(lowercase_						) / 'README.md'
            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertDictEqual(lowercase_,{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}						)

            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertDictEqual(lowercase_,{}						)

            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertEqual(lowercase_,{}						)


    def 		snake_case__	( self					:			Tuple						)->       List[Any]:





        '''simple docstring'''







        A__        =  _dedent(
            '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '						)
        assert DatasetMetadata.from_yaml_string(lowercase_						)

        A__        =  _dedent(
            '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '						)
        with self.assertRaises(lowercase_						):
            DatasetMetadata.from_yaml_string(lowercase_						)

        A__        =  _dedent(
            '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '						)
        assert DatasetMetadata.from_yaml_string(lowercase_						)

