'''simple docstring'''
import argparse
import json
import re
import string
import sys

import numpy as np


_A		:      List[str]						   =0.5







def 				__UpperCamelCase (					__UpperCAmelCase  :					Union[str, Any] ,   __UpperCAmelCase  :					List[str]			)							->	Dict:
  lowercase				:							int									=   ['''.''', ''',''', ''';''', ''':''']

  for token in remove_tokens:
    lowercase				:							List[Any]									=   ground_truth.replace(__UpperCAmelCase ,   ''''''			)
    lowercase				:							Optional[Any]									=   prediction.replace(__UpperCAmelCase ,   ''''''			)

  lowercase				,      lowercase				:							Dict									=   ground_truth.lower(), prediction.lower()
  lowercase				,      lowercase				:							int									=   ground_truth.replace('''/''' ,   ''' '''			), prediction.replace('''/''' ,   ''' '''			)
  lowercase				,      lowercase				:							str									=   set(ground_truth.split(''' '''			)			), set(prediction.split(''' '''			)			)

  lowercase				:							List[str]									=   ground_truth.intersection(__UpperCAmelCase			)
  lowercase				:							str									=   ground_truth.union(__UpperCAmelCase			)
  lowercase				:							Tuple									=   len(__UpperCAmelCase			) / len(__UpperCAmelCase			)
  return jaccard







def 				__UpperCamelCase (					__UpperCAmelCase  :					Dict			)							->	Tuple:

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

  def white_space_fix(__UpperCAmelCase  :					List[Any]			):
    return " ".join(text.split()			)

  def remove_punc(__UpperCAmelCase  :					Optional[int]			):
    lowercase				:							int									=   set(string.punctuation			)
    return "".join(ch for ch in text if ch not in exclude			)

  def lower(__UpperCAmelCase  :					Dict			):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(__UpperCAmelCase			)			)			)			)







def 				__UpperCamelCase (					__UpperCAmelCase  :					Optional[int] ,   __UpperCAmelCase  :					List[Any] ,   __UpperCAmelCase  :					Optional[Any]			)							->	Optional[int]:
  lowercase				,      lowercase				,      lowercase				:							Union[str, Any]									=   0, 0, 0

  lowercase				:							str									=   '''Parties''' in qa_id

  # first check if ground truth is empty
  if len(__UpperCAmelCase			) == 0:
    if len(__UpperCAmelCase			) > 0:
      fp += len(__UpperCAmelCase			)  # false positive for each one
  else:
    for ground_truth in ground_truths:
      assert len(__UpperCAmelCase			) > 0
      # check if there is a match
      lowercase				:							List[str]									=   False
      for pred in predictions:
        if substr_ok:
          lowercase				:							Dict									=   get_jaccard(__UpperCAmelCase ,   __UpperCAmelCase			) >= IOU_THRESH or ground_truth in pred
        else:
          lowercase				:							int									=   get_jaccard(__UpperCAmelCase ,   __UpperCAmelCase			) >= IOU_THRESH
        if is_match:
          lowercase				:							Optional[Any]									=   True

      if match_found:
        tp += 1
      else:
        fn += 1

        # now also get any fps by looping through preds
    for pred in predictions:
      # Check if there's a match. if so, don't count (don't want to double count based on the above)
      # but if there's no match, then this is a false positive.
      # (Note: we get the true positives in the above loop instead of this loop so that we don't double count
      # multiple predictions that are matched with the same answer.)
      lowercase				:							int									=   False
      for ground_truth in ground_truths:
        assert len(__UpperCAmelCase			) > 0
        if substr_ok:
          lowercase				:							str									=   get_jaccard(__UpperCAmelCase ,   __UpperCAmelCase			) >= IOU_THRESH or ground_truth in pred
        else:
          lowercase				:							Dict									=   get_jaccard(__UpperCAmelCase ,   __UpperCAmelCase			) >= IOU_THRESH
        if is_match:
          lowercase				:							Any									=   True

      if not match_found:
        fp += 1

  lowercase				:							Dict									=   tp / (tp + fp) if tp + fp > 0 else np.nan
  lowercase				:							List[Any]									=   tp / (tp + fn) if tp + fn > 0 else np.nan

  return precision, recall







def 				__UpperCamelCase (					__UpperCAmelCase  :					List[Any]			)							->	Optional[int]:
  lowercase				:							List[Any]									=   precisions[::-1]
  for i in range(1 ,   len(__UpperCAmelCase			)			):
    lowercase				:							int									=   max(precision_best[i - 1] ,   precision_best[i]			)
  lowercase				:							Any									=   precision_best[::-1]
  return precisions







def 				__UpperCamelCase (					__UpperCAmelCase  :					Optional[int] ,   __UpperCAmelCase  :					Dict			)							->	Optional[Any]:
  lowercase				:							Union[str, Any]									=   process_precisions(__UpperCAmelCase			)
  lowercase				:							Dict									=   np.trapz(__UpperCAmelCase ,   __UpperCAmelCase			)
  if np.isnan(__UpperCAmelCase			):
    return 0
  return aupr







def 				__UpperCamelCase (					__UpperCAmelCase  :					Union[str, Any] ,   __UpperCAmelCase  :					Tuple ,   __UpperCAmelCase  :					Tuple			)							->	List[Any]:
  lowercase				:							Tuple									=   process_precisions(__UpperCAmelCase			)
  lowercase				:							Optional[Any]									=   0
  for prec, recall in zip(__UpperCAmelCase ,   __UpperCAmelCase			):
    if recall >= recall_thresh:
      lowercase				:							Tuple									=   prec
      break
  return prec_at_recall







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					Tuple			)							->	Any:
  return normalize_answer(__UpperCAmelCase			) == normalize_answer(__UpperCAmelCase			)







def 				__UpperCamelCase (					__UpperCAmelCase  :					Dict ,   __UpperCAmelCase  :					Dict ,   __UpperCAmelCase  :					Union[str, Any]			)							->	Any:
  lowercase				:							Dict									=   0
  for pred in predictions:
    for ground_truth in ground_truths:
      lowercase				:							Any									=   metric_fn(__UpperCAmelCase ,   __UpperCAmelCase			)
      if score == 1:  # break the loop when one prediction matches the ground truth
        break
    if score == 1:
      break
  return score







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					Optional[int]			)							->	Optional[int]:
  lowercase				:							str									=   0
  lowercase				:							Optional[Any]									=   []
  lowercase				:							str									=   []
  for article in dataset:
    for paragraph in article["paragraphs"]:
      for qa in paragraph["qas"]:
        total += 1
        if qa["id"] not in predictions:
          lowercase				:							Any									=   '''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
          print(__UpperCAmelCase ,   file=sys.stderr			)
          continue
        lowercase				:							Tuple									=   [x['''text'''] for x in qa['''answers''']]
        lowercase				:							int									=   predictions[qa['''id''']]
        lowercase				,      lowercase				:							List[Any]									=   compute_precision_recall(__UpperCAmelCase ,   __UpperCAmelCase ,   qa['''id''']			)

        precisions.append(__UpperCAmelCase			)
        recalls.append(__UpperCAmelCase			)

        if precision == 0 and recall == 0:
          fa += 0
        else:
          fa += 2 * (precision * recall) / (precision + recall)

        exact_match += metric_max_over_ground_truths(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

  lowercase				:							int									=   [x for _, x in sorted(zip(__UpperCAmelCase ,   __UpperCAmelCase			)			)]
  recalls.sort()

  lowercase				:							Optional[Any]									=   100.0 * fa / total
  lowercase				:							Optional[int]									=   100.0 * exact_match / total
  lowercase				:							List[str]									=   get_aupr(__UpperCAmelCase ,   __UpperCAmelCase			)

  lowercase				:							Union[str, Any]									=   get_prec_at_recall(__UpperCAmelCase ,   __UpperCAmelCase ,   recall_thresh=0.9			)
  lowercase				:							Union[str, Any]									=   get_prec_at_recall(__UpperCAmelCase ,   __UpperCAmelCase ,   recall_thresh=0.8			)

  return {
      "exact_match": exact_match,
      "f1": fa,
      "aupr": aupr,
      "prec_at_80_recall": prec_at_aa_recall,
      "prec_at_90_recall": prec_at_aa_recall,
  }


if __name__ == "__main__":
 _A		:      List[Any]						   =argparse.ArgumentParser(description='''Evaluation for CUAD''')
 parser.add_argument('''dataset_file''', help='''Dataset file''')
 parser.add_argument('''prediction_file''', help='''Prediction File''')
 _A		:      Optional[Any]						   =parser.parse_args()
 with open(args.dataset_file) as dataset_file:
  _A		:      List[Any]						   =json.load(dataset_file)
  _A		:      Optional[int]						   =dataset_json['''data''']
 with open(args.prediction_file) as prediction_file:
  _A		:      Any						   =json.load(prediction_file)
 print(json.dumps(evaluate(dataset, predictions)))
'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_A		:      Optional[Any]						   ={
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:      Union[str, Any]						   =[
     '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MobileViTV2ForImageClassification''',
     '''MobileViTV2ForSemanticSegmentation''',
     '''MobileViTV2Model''',
     '''MobileViTV2PreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

 _A		:      Tuple						   =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)