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


def  A			(   lowercase					)  ->    Tuple:
		'''simple docstring'''

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

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

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

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

		return white_space_fix(remove_articles(remove_punc(lower(lowercase					)					)					)					)


def  A			(   lowercase       ,			lowercase					)  ->    int:
		'''simple docstring'''

		UpperCamelCase            =	normalize_answer(lowercase					).split()
		UpperCamelCase            =	normalize_answer(lowercase					).split()
		UpperCamelCase            =	Counter(lowercase					) & Counter(lowercase					)
		UpperCamelCase            =	sum(common.values()					)
		if num_same == 0:
				return 0
		UpperCamelCase            =	1.0 * num_same / len(lowercase					)
		UpperCamelCase            =	1.0 * num_same / len(lowercase					)
		UpperCamelCase            =	(2 * precision * recall) / (precision + recall)
		return fa


def  A			(   lowercase       ,			lowercase					)  ->    Union[str, Any]:
		'''simple docstring'''

		return normalize_answer(lowercase					) == normalize_answer(lowercase					)


def  A			(   lowercase       ,			lowercase       ,			lowercase					)  ->    Optional[int]:
		'''simple docstring'''

		UpperCamelCase            =	[]
		for ground_truth in ground_truths:
				UpperCamelCase            =	metric_fn(lowercase       ,			lowercase					)
				scores_for_ground_truths.append(lowercase					)
		return max(lowercase					)


def  A			(   lowercase       ,			lowercase					)  ->    str:
		'''simple docstring'''

		UpperCamelCase            =	UpperCamelCase            =	UpperCamelCase            =	0
		for article in dataset:
				for paragraph in article["paragraphs"]:
						for qa in paragraph["qas"]:
								total += 1
								if qa["id"] not in predictions:
										UpperCamelCase            =	'Unanswered question ' + qa['id'] + ' will receive score 0.'
										print(lowercase       ,			file=sys.stderr					)
										continue
								UpperCamelCase            =	[x['text'] for x in qa['answers']]
								UpperCamelCase            =	predictions[qa['id']]
								exact_match += metric_max_over_ground_truths(lowercase       ,			lowercase       ,			lowercase					)
								fa += metric_max_over_ground_truths(lowercase       ,			lowercase       ,			lowercase					)

		UpperCamelCase            =	1_0_0.0 * exact_match / total
		UpperCamelCase            =	1_0_0.0 * fa / total

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


if __name__ == "__main__":
					_UpperCAmelCase   :					List[str]    =			"1.1"
					_UpperCAmelCase   :					List[str]    =			argparse.ArgumentParser(description="Evaluation for SQuAD " + expected_version)
					parser.add_argument("dataset_file", help="Dataset file")
					parser.add_argument("prediction_file", help="Prediction File")
					_UpperCAmelCase   :					Union[str, Any]    =			parser.parse_args()
					with open(args.dataset_file) as dataset_file:
										_UpperCAmelCase   :					Tuple    =			json.load(dataset_file)
										if dataset_json["version"] != expected_version:
															print(
															    "Evaluation expects v-" + expected_version + ", but got dataset with v-" + dataset_json["version"],
															    file=sys.stderr,
															)
										_UpperCAmelCase   :					int    =			dataset_json["data"]
					with open(args.prediction_file) as prediction_file:
										_UpperCAmelCase   :					List[Any]    =			json.load(prediction_file)
					print(json.dumps(evaluate(dataset, predictions)))


from __future__ import annotations


def  A			(   lowercase       ,			lowercase					)  ->    int:
		'''simple docstring'''

		UpperCamelCase      ,    UpperCamelCase            =	0, len(lowercase					) - 1
		while start_ind <= end_ind:
				if array[start_ind] == search_item:
						return start_ind
				elif array[end_ind] == search_item:
						return end_ind
				else:
						start_ind += 1
						end_ind -= 1
    # returns -1 if search_item is not found in array
		return -1


if __name__ == "__main__":
					print(double_linear_search(list(range(100)), 40))
