


import argparse
import json
import re
import string
import sys
from collections import Counter
def     UpperCamelCase_(			_A				:Dict					)->						Any:

			def remove_articles(_A				:Union[str, Any]					):
						return re.sub(r"\b(a|an|the)\b"			,       " "			,       _lowerCAmelCase					)

			def white_space_fix(_A				:List[Any]					):
						return " ".join(text.split()					)

			def remove_punc(_A				:Union[str, Any]					):
						UpperCamelCase__   =    set(string.punctuation					)
						return "".join(ch for ch in text if ch not in exclude					)

			def lower(_A				:int					):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase					)					)					)					)
def     UpperCamelCase_(			_A				:Union[str, Any]			,       _A				:List[Any]					)->						Tuple:
			UpperCamelCase__   =    normalize_answer(_lowerCAmelCase					).split()
			UpperCamelCase__   =    normalize_answer(_lowerCAmelCase					).split()
			UpperCamelCase__   =    Counter(_lowerCAmelCase					) & Counter(_lowerCAmelCase					)
			UpperCamelCase__   =    sum(common.values()					)
			if num_same == 0:
						return 0
			UpperCamelCase__   =    1.0 * num_same / len(_lowerCAmelCase					)
			UpperCamelCase__   =    1.0 * num_same / len(_lowerCAmelCase					)
			UpperCamelCase__   =    (2 * precision * recall) / (precision + recall)
			return fa
def     UpperCamelCase_(			_A				:Any			,       _A				:Optional[Any]					)->						Optional[int]:
			return normalize_answer(_lowerCAmelCase					) == normalize_answer(_lowerCAmelCase					)
def     UpperCamelCase_(			_A				:List[str]			,       _A				:Any			,       _A				:List[Any]					)->						Dict:
			UpperCamelCase__   =    []
			for ground_truth in ground_truths:
						UpperCamelCase__   =    metric_fn(_lowerCAmelCase			,       _lowerCAmelCase					)
						scores_for_ground_truths.append(_lowerCAmelCase					)
			return max(_lowerCAmelCase					)
def     UpperCamelCase_(			_A				:Any			,       _A				:List[Any]					)->						List[Any]:
			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(_lowerCAmelCase			,       file=sys.stderr					)
															continue
												UpperCamelCase__   =    [x["text"] for x in qa["answers"]]
												UpperCamelCase__   =    predictions[qa["id"]]
												exact_match += metric_max_over_ground_truths(_lowerCAmelCase			,       _lowerCAmelCase			,       _lowerCAmelCase					)
												fa += metric_max_over_ground_truths(_lowerCAmelCase			,       _lowerCAmelCase			,       _lowerCAmelCase					)

			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      						=    '1.1'
						__UpperCamelCase      						=    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      						=    parser.parse_args()
						with open(args.dataset_file) as dataset_file:
												__UpperCamelCase      						=    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      						=    dataset_json['data']
						with open(args.prediction_file) as prediction_file:
												__UpperCamelCase      						=    json.load(prediction_file)
						print(json.dumps(evaluate(dataset, predictions)))






from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


__UpperCamelCase      						=    [
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

__UpperCamelCase      						=    get_logger(__name__)


__UpperCamelCase      						=    {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}
def     UpperCamelCase_(			_A				:dict					)->						Optional[TaskTemplate]:
			UpperCamelCase__   =    task_template_dict.get("task"					)
			if task_name is None:
						logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(_A					)}'''					)
						return None
			UpperCamelCase__   =    NAMEaTEMPLATE.get(_A					)
			return template.from_dict(_A					)


