


"""simple docstring"""


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






def   a__			(					__lowercase )						->						Optional[int]:

				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 ):
								_A       	=      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 )						->						List[str]:
				_A       	=      normalize_answer(__lowercase ).split()
				_A       	=      normalize_answer(__lowercase ).split()
				_A       	=      Counter(__lowercase ) & Counter(__lowercase )
				_A       	=      sum(common.values() )
				if num_same == 0:
								return 0
				_A       	=      1.0 * num_same / len(__lowercase )
				_A       	=      1.0 * num_same / len(__lowercase )
				_A       	=      (2 * precision * recall) / (precision + recall)
				return fa






def   a__			(					__lowercase  ,      __lowercase )						->						List[str]:
				return normalize_answer(__lowercase ) == normalize_answer(__lowercase )






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						Tuple:
				_A       	=      []
				for ground_truth in ground_truths:
								_A       	=      metric_fn(__lowercase  ,      __lowercase )
								scores_for_ground_truths.append(__lowercase )
				return max(__lowercase )






def   a__			(					__lowercase  ,      __lowercase )						->						List[str]:
				_A       	=      _A       	=      _A       	=      0
				_A       	=      []
				for passage in dataset:
								for qa in passage["qas"]:
												total += 1
												if qa["id"] not in predictions:
																_A       	=      f"""Unanswered question {qa['id']} will receive score 0."""
																print(__lowercase  ,      file=sys.stderr )
																continue

												_A       	=      [x["text"] for x in qa["answers"]]
												_A       	=      predictions[qa["id"]]

												_A       	=      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 )

				_A       	=      exact_match / total
				_A       	=      fa / total

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


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

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

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

		a_				,  a_											=     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 unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils
class snake_case   (	unittest.TestCase):







		@patch("diffusers.utils.hub_utils.get_full_repo_name"		)
		def 			a_			(       self   :							Any     , a__   :							Mock		)							->    None:





						'''simple docstring'''







						_A       	=      "full_repo_name"
						with TemporaryDirectory() as tmpdir:
										# Dummy args values
										_A       	=      Mock()
										_A       	=      tmpdir
										_A       	=      0
										_A       	=      "hub_token"
										_A       	=      "dataset_name"
										_A       	=      0.0_1
										_A       	=      10_00_00
										_A       	=      1_00_00
										_A       	=      0.0_1
										_A       	=      0.0_2
										_A       	=      0.0_3
										_A       	=      0.0_0_0_5
										_A       	=      0.0_0_0_0_0_1
										_A       	=      1
										_A       	=      10
										_A       	=      0.0_0_1
										_A       	=      0.1
										_A       	=      0.2
										_A       	=      True

										# Model card mush be rendered and saved
										diffusers.utils.hub_utils.create_model_card(a__     , model_name="model_name"		)
										self.assertTrue((Path(a__		) / "README.md").is_file()		)