



"""simple docstring"""







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

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Optional[Any]		)		->  List[Any]:

  """simple docstring"""




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

  def white_space_fix(lowerCAmelCase__ :							Optional[Any]		):
    return " ".join(text.split()		)

  def remove_punc(lowerCAmelCase__ :							str		):
    lowerCAmelCase_  :							str								=					set(string.punctuation		)
    return "".join(ch for ch in text if ch not in exclude		)

  def lower(lowerCAmelCase__ :							Optional[Any]		):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase__		)		)		)		)

def 							UpperCamelCase_       (       lowerCAmelCase__ :							int       ,  lowerCAmelCase__ :							int		)		->  List[Any]:

  """simple docstring"""




  lowerCAmelCase_  :							str								=					normalize_answer(lowerCAmelCase__		).split()
  lowerCAmelCase_  :							Optional[int]								=					normalize_answer(lowerCAmelCase__		).split()
  lowerCAmelCase_  :							int								=					Counter(lowerCAmelCase__		) & Counter(lowerCAmelCase__		)
  lowerCAmelCase_  :							List[str]								=					sum(common.values()		)
  if num_same == 0:
    return 0
  lowerCAmelCase_  :							Optional[Any]								=					1.0 * num_same / len(lowerCAmelCase__		)
  lowerCAmelCase_  :							Tuple								=					1.0 * num_same / len(lowerCAmelCase__		)
  lowerCAmelCase_  :							Optional[int]								=					(2 * precision * recall) / (precision + recall)
  return fa

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Optional[int]       ,  lowerCAmelCase__ :							Optional[Any]		)		->  int:

  """simple docstring"""




  return normalize_answer(lowerCAmelCase__		) == normalize_answer(lowerCAmelCase__		)

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Dict       ,  lowerCAmelCase__ :							int       ,  lowerCAmelCase__ :							int		)		->  List[Any]:

  """simple docstring"""




  lowerCAmelCase_  :							List[Any]								=					[]
  for ground_truth in ground_truths:
    lowerCAmelCase_  :							Union[str, Any]								=					metric_fn(lowerCAmelCase__       ,  lowerCAmelCase__		)
    scores_for_ground_truths.append(lowerCAmelCase__		)
  return max(lowerCAmelCase__		)

def 							UpperCamelCase_       (       lowerCAmelCase__ :							List[Any]       ,  lowerCAmelCase__ :							List[Any]		)		->  Dict:

  """simple docstring"""




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

      lowerCAmelCase_  :							Tuple								=					[x['text'] for x in qa['answers']]
      lowerCAmelCase_  :							Optional[Any]								=					predictions[qa['id']]

      lowerCAmelCase_  :							Tuple								=					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__		)

  lowerCAmelCase_  :							int								=					exact_match / total
  lowerCAmelCase_  :							Any								=					fa / total

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


if __name__ == "__main__":
     lowercase__						:							List[Any]        =      """1.0"""
     lowercase__						:							Union[str, Any]        =      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__						:							str        =      parser.parse_args()

     with open(args.data_file) as data_file:
          lowercase__						:							Optional[int]        =      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__						:							List[str]        =      dataset_json["""data"""]

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

     lowercase__       ,	lowercase__						:							Any        =      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 inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax







@require_flax
class  UpperCamelCase__  :




      """simple docstring"""




      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							Any								=					self.prepare_init_args_and_inputs_for_common()

        lowerCAmelCase_  :							List[str]								=					self.model_class(**SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Any								=					model.init(inputs_dict['prng_key']       ,     inputs_dict['sample']			)
        jax.lax.stop_gradient(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Tuple								=					model.apply(SCREAMING_SNAKE_CASE_       ,     inputs_dict['sample']			)

        if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							str								=					output.sample

        self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Any								=					inputs_dict['sample'].shape
        self.assertEqual(output.shape       ,     SCREAMING_SNAKE_CASE_       ,     'Input and output shapes do not match'			)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							Union[str, Any]								=					self.prepare_init_args_and_inputs_for_common()

        lowerCAmelCase_  :							Tuple								=					1_6
        lowerCAmelCase_  :							Dict								=					(1_6, 3_2)

        lowerCAmelCase_  :							Any								=					self.model_class(**SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[Any]								=					model.init(inputs_dict['prng_key']       ,     inputs_dict['sample']			)
        jax.lax.stop_gradient(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Optional[Any]								=					model.apply(SCREAMING_SNAKE_CASE_       ,     inputs_dict['sample']			)

        if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							Dict								=					output.sample

        self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[Any]								=					inputs_dict['sample'].shape
        self.assertEqual(output.shape       ,     SCREAMING_SNAKE_CASE_       ,     'Input and output shapes do not match'			)





      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        lowerCAmelCase_  :							List[str]								=					'kwargs' in inspect.signature(self.model_class.__init__			).parameters
        lowerCAmelCase_  :							Optional[Any]								=					len(self.model_class._deprecated_kwargs			) > 0

        if has_kwarg_in_model_class and not has_deprecated_kwarg:
          raise ValueError(
              F"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
              ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
              ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
              ' [<deprecated_argument>]`'			)

        if not has_kwarg_in_model_class and has_deprecated_kwarg:
          raise ValueError(
              F"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
              ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
              F" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
              ' from `_deprecated_kwargs = [<deprecated_argument>]`'			)





