"""simple docstring"""







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


def 	a__   (		UpperCAmelCase__							:      Optional[int]      )->						Any:

						def remove_articles(UpperCAmelCase__							:      str      ):
												return re.sub(R"""\b(a|an|the)\b"""		,      """ """		,      UpperCAmelCase__      )

						def white_space_fix(UpperCAmelCase__							:      Optional[int]      ):
												return " ".join(text.split()      )

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

						def lower(UpperCAmelCase__							:      List[Any]      ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase__      )      )      )      )


def 	a__   (		UpperCAmelCase__							:      Any		,      UpperCAmelCase__							:      List[Any]      )->						Optional[Any]:
						UpperCAmelCase__       						=		normalize_answer(UpperCAmelCase__      ).split()
						UpperCAmelCase__       						=		normalize_answer(UpperCAmelCase__      ).split()
						UpperCAmelCase__       						=		Counter(UpperCAmelCase__      ) & Counter(UpperCAmelCase__      )
						UpperCAmelCase__       						=		sum(common.values()      )
						if num_same == 0:
												return 0
						UpperCAmelCase__       						=		1.0 * num_same / len(UpperCAmelCase__      )
						UpperCAmelCase__       						=		1.0 * num_same / len(UpperCAmelCase__      )
						UpperCAmelCase__       						=		(2 * precision * recall) / (precision + recall)
						return fa


def 	a__   (		UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      str      )->						Tuple:
						return normalize_answer(UpperCAmelCase__      ) == normalize_answer(UpperCAmelCase__      )


def 	a__   (		UpperCAmelCase__							:      Union[str, Any]		,      UpperCAmelCase__							:      Dict		,      UpperCAmelCase__							:      Optional[Any]      )->						List[Any]:
						UpperCAmelCase__       						=		[]
						for ground_truth in ground_truths:
												UpperCAmelCase__       						=		metric_fn(UpperCAmelCase__		,      UpperCAmelCase__      )
												scores_for_ground_truths.append(UpperCAmelCase__      )
						return max(UpperCAmelCase__      )


def 	a__   (		UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      Optional[Any]      )->						Optional[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(UpperCAmelCase__		,      file=sys.stderr      )
																														continue
																								UpperCAmelCase__       						=		[x["""text"""] for x in qa["""answers"""]]
																								UpperCAmelCase__       						=		predictions[qa["""id"""]]
																								exact_match += metric_max_over_ground_truths(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )
																								fa += metric_max_over_ground_truths(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )

						UpperCAmelCase__       						=		1_00.0 * exact_match / total
						UpperCAmelCase__       						=		1_00.0 * fa / total

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


if __name__ == "__main__":
						UpperCamelCase_							:							Tuple				 =		"""1.1"""
						UpperCamelCase_							:							Union[str, Any]				 =		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_							:							int				 =		parser.parse_args()
						with open(args.dataset_file) as dataset_file:
												UpperCamelCase_							:							Any				 =		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_							:							Optional[Any]				 =		dataset_json["""data"""]
						with open(args.prediction_file) as prediction_file:
												UpperCamelCase_							:							str				 =		json.load(prediction_file)
						print(json.dumps(evaluate(dataset, predictions)))


"""simple docstring"""







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

