



"""simple docstring"""


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

def 				_lowerCAmelCase   (     lowercase_		):

						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 				_lowerCAmelCase   (     lowercase_	,			lowercase_		):
						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 				_lowerCAmelCase   (     lowercase_	,			lowercase_		):
						return normalize_answer(lowercase_		) == normalize_answer(lowercase_		)

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			lowercase_		):
						UpperCAmelCase    =				[]
						for ground_truth in ground_truths:
												UpperCAmelCase    =				metric_fn(lowercase_	,			lowercase_		)
												scores_for_ground_truths.append(lowercase_		)
						return max(lowercase_		)

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_		):
						UpperCAmelCase    =				UpperCAmelCase    =				UpperCAmelCase    =				0
						UpperCAmelCase    =				[]
						for passage in dataset:
												for qa in passage["qas"]:
																		total += 1
																		if qa["id"] not in predictions:
																								UpperCAmelCase    =				F"""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']]

																		UpperCAmelCase    =				metric_max_over_ground_truths(lowercase_	,			lowercase_	,			lowercase_		)
																		if int(_exact_match		) == 1:
																								correct_ids.append(qa['id']		)
																		exact_match += _exact_match

																		fa += metric_max_over_ground_truths(lowercase_	,			lowercase_	,			lowercase_		)

						UpperCAmelCase    =				exact_match / total
						UpperCAmelCase    =				fa / total

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


if __name__ == "__main__":
				snake_case_         = """1.0"""
				snake_case_         = 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.""")
				snake_case_         = parser.parse_args()

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

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

				snake_case_  ,     snake_case_         = 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 warnings

from .generation import FlaxGenerationMixin







class    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""
						# warning at import time
						warnings.warn(
						    """Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will """
						    """be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."""			,     SCREAMING_SNAKE_CASE_			,     )





