

from ..utils import DummyObject, requires_backends







class    UpperCamelCase_			(  metaclass=A_			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: List[str]										=      ["""torch""", """librosa"""]



  def __init__(	self    :				Tuple     ,  *A_    :				int     ,  **A_    :				Optional[Any]	)					-> List[str]:
      requires_backends(self     ,  ["torch", "librosa"]	)



  @classmethod
  def      _UpperCamelCase    (	cls    :				List[Any]     ,  *A_    :				Optional[int]     ,  **A_    :				List[str]	)					-> List[Any]:
      requires_backends(cls     ,  ["torch", "librosa"]	)







  @classmethod
  def      _UpperCamelCase    (	cls    :				List[str]     ,  *A_    :				Union[str, Any]     ,  **A_    :				List[str]	)					-> Optional[Any]:
      requires_backends(cls     ,  ["torch", "librosa"]	)







class    UpperCamelCase_			(  metaclass=A_			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: List[Any]										=      ["""torch""", """librosa"""]



  def __init__(	self    :				Dict     ,  *A_    :				str     ,  **A_    :				int	)					-> Dict:
      requires_backends(self     ,  ["torch", "librosa"]	)



  @classmethod
  def      _UpperCamelCase    (	cls    :				Tuple     ,  *A_    :				List[str]     ,  **A_    :				str	)					-> str:
      requires_backends(cls     ,  ["torch", "librosa"]	)







  @classmethod
  def      _UpperCamelCase    (	cls    :				List[str]     ,  *A_    :				Optional[int]     ,  **A_    :				List[Any]	)					-> Optional[int]:
      requires_backends(cls     ,  ["torch", "librosa"]	)





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





def 							__a       (					__A	):

    def remove_articles(__A	):
        return re.sub(r"\b(a|an|the)\b" ,       " " ,       __A	)

    def white_space_fix(__A	):
        return " ".join(text.split()	)

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

    def lower(__A	):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(__A	)	)	)	)





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						normalize_answer(__A	).split()
    SCREAMING_SNAKE_CASE__					     =						normalize_answer(__A	).split()
    SCREAMING_SNAKE_CASE__					     =						Counter(__A	) & Counter(__A	)
    SCREAMING_SNAKE_CASE__					     =						sum(common.values()	)
    if num_same == 0:
        return 0
    SCREAMING_SNAKE_CASE__					     =						1.0 * num_same / len(__A	)
    SCREAMING_SNAKE_CASE__					     =						1.0 * num_same / len(__A	)
    SCREAMING_SNAKE_CASE__					     =						(2 * precision * recall) / (precision + recall)
    return fa





def 							__a       (					__A ,       __A	):
    return normalize_answer(__A	) == normalize_answer(__A	)





def 							__a       (					__A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						[]
    for ground_truth in ground_truths:
        SCREAMING_SNAKE_CASE__					     =						metric_fn(__A ,       __A	)
        scores_for_ground_truths.append(__A	)
    return max(__A	)





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

            SCREAMING_SNAKE_CASE__					     =						[x["text"] for x in qa["answers"]]
            SCREAMING_SNAKE_CASE__					     =						predictions[qa["id"]]

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

            fa += metric_max_over_ground_truths(__A ,       __A ,       __A	)

    SCREAMING_SNAKE_CASE__					     =						exact_match / total
    SCREAMING_SNAKE_CASE__					     =						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)


