

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




def      UpperCAmelCase__							(     _A :					List[Any]				):





  '''simple docstring'''







  def remove_articles(_A :					Optional[int]				):
    return re.sub(r'''\b(a|an|the)\b'''      , ''' '''      , _lowerCAmelCase				)

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

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

  def lower(_A :					Union[str, Any]				):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase				)				)				)				)




def      UpperCAmelCase__							(     _A :					Any      , _A :					List[Any]				):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					Dict      , _A :					Any				):





  '''simple docstring'''







  return normalize_answer(_lowerCAmelCase				) == normalize_answer(_lowerCAmelCase				)




def      UpperCAmelCase__							(     _A :					Optional[int]      , _A :					int      , _A :					Any				):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					Tuple      , _A :					int				):





  '''simple docstring'''







  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(_lowerCAmelCase      , file=sys.stderr				)
        continue

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

      a__    =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				)

  a__    =exact_match / total
  a__    =fa / total

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


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

       with open(args.data_file) as data_file:
              lowerCamelCase 	=   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,
                     )
              lowerCamelCase 	=   dataset_json['''data''']

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

       lowerCamelCase	,			lowerCamelCase 	=   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'''

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A_	   = {
    "configuration_convnextv2": [
        "CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConvNextV2Config",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_	   = [
						    "CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ConvNextV2ForImageClassification",
						    "ConvNextV2Model",
						    "ConvNextV2PreTrainedModel",
						    "ConvNextV2Backbone",
						]


if TYPE_CHECKING:
						from .configuration_convnextva import (
						    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    ConvNextVaConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_convnextva import (
												    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConvNextVaBackbone,
												    ConvNextVaForImageClassification,
												    ConvNextVaModel,
												    ConvNextVaPreTrainedModel,
												)

else:
						import sys

						A_	   = _LazyModule(__name__, globals()["__file__"], _import_structure)





