






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

def 			__lowerCamelCase     (						snake_case__						)				->    Optional[int]:






						"""simple docstring"""





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

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

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

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

						return white_space_fix(remove_articles(remove_punc(lower(snake_case__						)						)						)						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    int:






						"""simple docstring"""





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

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Union[str, Any]:






						"""simple docstring"""





						return normalize_answer(snake_case__						) == normalize_answer(snake_case__						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__			,snake_case__						)				->    Optional[Any]:






						"""simple docstring"""





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

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Optional[int]:






						"""simple docstring"""





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

						_SCREAMING_SNAKE_CASE       =    100.0 * exact_match / total
						_SCREAMING_SNAKE_CASE       =    100.0 * 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 ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
		if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.27.0''')):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import (
		    AudioLDMPipeline,
		)
else:
		from .pipeline_audioldm import AudioLDMPipeline

