






"""simple docstring"""



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


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Optional[Any]							)->						List[str]:






     '''simple docstring'''



     def remove_articles(snake_case    :      List[str]							):
          return re.sub(r"\b(a|an|the)\b"       , " "       , snake_case							)

     def white_space_fix(snake_case    :      Union[str, Any]							):
          return " ".join(text.split()							)

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

     def lower(snake_case    :      Optional[int]							):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(snake_case							)							)							)							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      List[str]       , snake_case    :      List[str]							)->						Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  Tuple				=				normalize_answer(snake_case							).split()
     UpperCAmelCase__		:  Any				=				normalize_answer(snake_case							).split()
     UpperCAmelCase__		:  Optional[int]				=				Counter(snake_case							) & Counter(snake_case							)
     UpperCAmelCase__		:  Any				=				sum(common.values()							)
     if num_same == 0:
          return 0
     UpperCAmelCase__		:  List[str]				=				1.0 * num_same / len(snake_case							)
     UpperCAmelCase__		:  Dict				=				1.0 * num_same / len(snake_case							)
     UpperCAmelCase__		:  Union[str, Any]				=				(2 * precision * recall) / (precision + recall)
     return fa


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str       , snake_case    :      int							)->						List[Any]:






     '''simple docstring'''



     return normalize_answer(snake_case							) == normalize_answer(snake_case							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      List[str]       , snake_case    :      Tuple       , snake_case    :      Dict							)->						Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  str				=				[]
     for ground_truth in ground_truths:
          UpperCAmelCase__		:  Union[str, Any]				=				metric_fn(snake_case       , snake_case							)
          scores_for_ground_truths.append(snake_case							)
     return max(snake_case							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str       , snake_case    :      Optional[int]							)->						Union[str, Any]:






     '''simple docstring'''



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

               UpperCAmelCase__		:  Any				=				[x["text"] for x in qa["answers"]]
               UpperCAmelCase__		:  List[str]				=				predictions[qa["id"]]

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

               fa += metric_max_over_ground_truths(snake_case       , snake_case       , snake_case							)

     UpperCAmelCase__		:  Optional[Any]				=				exact_match / total
     UpperCAmelCase__		:  Optional[int]				=				fa / total

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


if __name__ == "__main__":
 _lowerCAmelCase     :     Tuple            =	"""1.0"""
 _lowerCAmelCase     :     List[Any]            =	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.""")
 _lowerCAmelCase     :     Optional[int]            =	parser.parse_args()

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

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

 _lowerCAmelCase       ,   _lowerCAmelCase     :     Union[str, Any]            =	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)










"""simple docstring"""



import shutil

import requests


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str       , snake_case    :      bool = False       , snake_case    :      str = "."							)->						dict:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				"https://api.nasa.gov/planetary/apod"
     return requests.get(snake_case       , params={"api_key": api_key}							).json()


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str       , snake_case    :      str = "."							)->						dict:






     '''simple docstring'''



     UpperCAmelCase__		:  List[Any]				=				get_apod_data(snake_case							)
     UpperCAmelCase__		:  str				=				apod_data["url"]
     UpperCAmelCase__		:  Tuple				=				img_url.split("/"							)[-1]
     UpperCAmelCase__		:  Dict				=				requests.get(snake_case       , stream=snake_case							)

     with open(f'{path}/{img_name}'       , "wb+"							) as img_file:
          shutil.copyfileobj(response.raw       , snake_case							)
     del response
     return apod_data


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str							)->						dict:






     '''simple docstring'''



     UpperCAmelCase__		:  List[str]				=				"https://images-api.nasa.gov/search"
     return requests.get(snake_case       , params={"q": query}							).json()


if __name__ == "__main__":
 print(save_apod("""YOUR API KEY"""))
 _lowerCAmelCase     :     List[Any]            =	get_archive_data("""apollo 2011""")["""collection"""]["""items"""]
 print(apollo_aaaa_items[0]["""data"""][0]["""description"""])


