




"""simple docstring"""





import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


_lowercase		 =      2 << 20  # 2mb
_lowercase		 =      get_logger(__name__)

class  __lowercase    (     snake_case_		):


				'''simple docstring'''


				def  _lowerCamelCase   (				self				:       str ):
										snake_case__    :   int							     =   self._options.get_all_options().get('''runner''' )
										return runner in [None, "DirectRunner", "PortableRunner"]







def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase=False						):
						'''simple docstring'''


						snake_case__    :   Any							     =   FileSystems
						if fs.exists(lowerCamelCase						):
												if force_upload:
																		logger.info(f'''Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''						)
												else:
																		logger.info(f'''Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'''						)
																		return
						with fs.create(lowerCamelCase						) as remote_file:
												with open(lowerCamelCase ,     '''rb'''						) as local_file:
																		snake_case__    :   Union[str, Any]							     =   local_file.read(lowerCamelCase						)
																		while chunk:
																								remote_file.write(lowerCamelCase						)
																								snake_case__    :   str							     =   local_file.read(lowerCamelCase						)

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase=False						):
						'''simple docstring'''


						snake_case__    :   Dict							     =   FileSystems
						if os.path.exists(lowerCamelCase						):
												if force_download:
																		logger.info(f'''Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''						)
												else:
																		logger.info(f'''Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'''						)
																		return
						with fs.open(lowerCamelCase						) as remote_file:
												with open(lowerCamelCase ,     '''wb'''						) as local_file:
																		snake_case__    :   str							     =   remote_file.read(lowerCamelCase						)
																		while chunk:
																								local_file.write(lowerCamelCase						)
																								snake_case__    :   Optional[Any]							     =   remote_file.read(lowerCamelCase						)








"""simple docstring"""





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

def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						def remove_articles(lowerCamelCase						):
												return re.sub(R'''\b(a|an|the)\b''' ,     ''' ''' ,     lowerCamelCase						)

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

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

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

						return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase						)						)						)						)

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Dict							     =   normalize_answer(lowerCamelCase						).split()
						snake_case__    :   Dict							     =   normalize_answer(lowerCamelCase						).split()
						snake_case__    :   List[Any]							     =   Counter(lowerCamelCase						) & Counter(lowerCamelCase						)
						snake_case__    :   List[str]							     =   sum(common.values()						)
						if num_same == 0:
												return 0
						snake_case__    :   int							     =   1.0 * num_same / len(lowerCamelCase						)
						snake_case__    :   str							     =   1.0 * num_same / len(lowerCamelCase						)
						snake_case__    :   Union[str, Any]							     =   (2 * precision * recall) / (precision + recall)
						return fa

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						return normalize_answer(lowerCamelCase						) == normalize_answer(lowerCamelCase						)

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   int							     =   []
						for ground_truth in ground_truths:
												snake_case__    :   Optional[Any]							     =   metric_fn(lowerCamelCase ,     lowerCamelCase						)
												scores_for_ground_truths.append(lowerCamelCase						)
						return max(lowerCamelCase						)

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Union[str, Any]							     =   0
						snake_case__    :   Any							     =   []
						for passage in dataset:
												for qa in passage["qas"]:
																		total += 1
																		if qa["id"] not in predictions:
																								snake_case__    :   int							     =   f'''Unanswered question {qa['id']} will receive score 0.'''
																								print(lowerCamelCase ,     file=sys.stderr						)
																								continue

																		snake_case__    :   Any							     =   [x['''text'''] for x in qa['''answers''']]
																		snake_case__    :   Dict							     =   predictions[qa['''id''']]

																		snake_case__    :   Union[str, Any]							     =   metric_max_over_ground_truths(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						)
																		if int(_exact_match						) == 1:
																								correct_ids.append(qa['''id''']						)
																		exact_match += _exact_match

																		fa += metric_max_over_ground_truths(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						)

						snake_case__    :   str							     =   exact_match / total
						snake_case__    :   Optional[int]							     =   fa / total

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


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

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

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

							_lowercase ,_lowercase		 =      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)


