



import argparse
import json
import re
import string
import sys

import numpy as np


UpperCAmelCase_		   =				0.5

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Any  ,      _snake_case  :Any							)     ->    Union[str, Any]:
   _A						     =					['''.''', ''',''', ''';''', ''':''']

   for token in remove_tokens:
      _A						     =					ground_truth.replace(_snake_case  ,      ''''''							)
      _A						     =					prediction.replace(_snake_case  ,      ''''''							)

   _A     ,						_A						     =					ground_truth.lower(), prediction.lower()
   _A     ,						_A						     =					ground_truth.replace('''/'''  ,      ''' '''							), prediction.replace('''/'''  ,      ''' '''							)
   _A     ,						_A						     =					set(ground_truth.split(''' '''							)							), set(prediction.split(''' '''							)							)

   _A						     =					ground_truth.intersection(_snake_case							)
   _A						     =					ground_truth.union(_snake_case							)
   _A						     =					len(_snake_case							) / len(_snake_case							)
   return jaccard

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Optional[Any]							)     ->    Optional[int]:

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

   def white_space_fix(_snake_case  :str							):
      return " ".join(text.split()							)

   def remove_punc(_snake_case  :List[Any]							):
      _A						     =					set(string.punctuation							)
      return "".join(ch for ch in text if ch not in exclude							)

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

   return white_space_fix(remove_articles(remove_punc(lower(_snake_case							)							)							)							)

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Optional[int]  ,      _snake_case  :str  ,      _snake_case  :int							)     ->    Optional[Any]:
   _A     ,						_A     ,						_A						     =					0, 0, 0

   _A						     =					'''Parties''' in qa_id

   # first check if ground truth is empty
   if len(_snake_case							) == 0:
      if len(_snake_case							) > 0:
         fp += len(_snake_case							)  # false positive for each one
   else:
      for ground_truth in ground_truths:
         assert len(_snake_case							) > 0
         # check if there is a match
         _A						     =					False
         for pred in predictions:
            if substr_ok:
               _A						     =					get_jaccard(_snake_case  ,      _snake_case							) >= IOU_THRESH or ground_truth in pred
            else:
               _A						     =					get_jaccard(_snake_case  ,      _snake_case							) >= IOU_THRESH
            if is_match:
               _A						     =					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.)
         _A						     =					False
         for ground_truth in ground_truths:
            assert len(_snake_case							) > 0
            if substr_ok:
               _A						     =					get_jaccard(_snake_case  ,      _snake_case							) >= IOU_THRESH or ground_truth in pred
            else:
               _A						     =					get_jaccard(_snake_case  ,      _snake_case							) >= IOU_THRESH
            if is_match:
               _A						     =					True

         if not match_found:
            fp += 1

   _A						     =					tp / (tp + fp) if tp + fp > 0 else np.nan
   _A						     =					tp / (tp + fn) if tp + fn > 0 else np.nan

   return precision, recall

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Tuple							)     ->    Any:
   _A						     =					precisions[::-1]
   for i in range(1  ,      len(_snake_case							)							):
      _A						     =					max(precision_best[i - 1]  ,      precision_best[i]							)
   _A						     =					precision_best[::-1]
   return precisions

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Tuple  ,      _snake_case  :Any							)     ->    Union[str, Any]:
   _A						     =					process_precisions(_snake_case							)
   _A						     =					np.trapz(_snake_case  ,      _snake_case							)
   if np.isnan(_snake_case							):
      return 0
   return aupr

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[Any]  ,      _snake_case  :Optional[int]  ,      _snake_case  :Dict							)     ->    int:
   _A						     =					process_precisions(_snake_case							)
   _A						     =					0
   for prec, recall in zip(_snake_case  ,      _snake_case							):
      if recall >= recall_thresh:
         _A						     =					prec
         break
   return prec_at_recall

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Dict  ,      _snake_case  :List[str]							)     ->    Dict:
   return normalize_answer(_snake_case							) == normalize_answer(_snake_case							)

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Any  ,      _snake_case  :Any  ,      _snake_case  :List[str]							)     ->    List[str]:
   _A						     =					0
   for pred in predictions:
      for ground_truth in ground_truths:
         _A						     =					metric_fn(_snake_case  ,      _snake_case							)
         if score == 1:  # break the loop when one prediction matches the ground truth
            break
      if score == 1:
         break
   return score

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

            precisions.append(_snake_case							)
            recalls.append(_snake_case							)

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

            exact_match += metric_max_over_ground_truths(_snake_case  ,      _snake_case  ,      _snake_case							)

   _A						     =					[x for _, x in sorted(zip(_snake_case  ,      _snake_case							)							)]
   recalls.sort()

   _A						     =					100.0 * fa / total
   _A						     =					100.0 * exact_match / total
   _A						     =					get_aupr(_snake_case  ,      _snake_case							)

   _A						     =					get_prec_at_recall(_snake_case  ,      _snake_case  ,      recall_thresh=0.9							)
   _A						     =					get_prec_at_recall(_snake_case  ,      _snake_case  ,      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__":
   UpperCAmelCase_		   =				argparse.ArgumentParser(description="""Evaluation for CUAD""")
   parser.add_argument("""dataset_file""", help="""Dataset file""")
   parser.add_argument("""prediction_file""", help="""Prediction File""")
   UpperCAmelCase_		   =				parser.parse_args()
   with open(args.dataset_file) as dataset_file:
      UpperCAmelCase_		   =				json.load(dataset_file)
      UpperCAmelCase_		   =				dataset_json["""data"""]
   with open(args.prediction_file) as prediction_file:
      UpperCAmelCase_		   =				json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))








from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("""3.8"""):
   import importlib_metadata
else:
   import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
UpperCAmelCase_		   =				"""\
"""

UpperCAmelCase_		   =				"""\
    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.
    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.
"""

UpperCAmelCase_		   =				"""
Compute XTREME-S evaluation metric associated to each XTREME-S dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    bleu_kwargs: optional dict of keywords to be passed when computing 'bleu'.
        Keywords include Dict can be one of 'smooth_method', 'smooth_value', 'force', 'lowercase',
        'tokenize', 'use_effective_order'.
    wer_kwargs: optional dict of keywords to be passed when computing 'wer' and 'cer'.
        Keywords include 'concatenate_texts'.
Returns: depending on the XTREME-S task, one or several of:
    \"accuracy\": Accuracy - for 'fleurs-lang_id', 'minds14'
    \"f1\": F1 score - for 'minds14'
    \"wer\": Word error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'
    \"cer\": Character error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'
    \"bleu\": BLEU score according to the `sacrebleu` metric - for 'covost2'
Examples:

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'mls')  # 'mls', 'voxpopuli', 'fleurs-asr' or 'babel'
    >>> references = [\"it is sunny here\", \"paper and pen are essentials\"]
    >>> predictions = [\"it's sunny\", \"paper pen are essential\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'wer': 0.56, 'cer': 0.27}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'covost2')
    >>> references = [\"bonjour paris\", \"il est necessaire de faire du sport de temps en temp\"]
    >>> predictions = [\"bonjour paris\", \"il est important de faire du sport souvent\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'bleu': 31.65}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'fleurs-lang_id')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'accuracy': 0.6}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'minds14')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'f1': 0.58, 'accuracy': 0.6}
"""

UpperCAmelCase_		   =				["""fleurs-asr""", """mls""", """voxpopuli""", """babel""", """covost2""", """fleurs-lang_id""", """minds14"""]
UpperCAmelCase_		   =				""""""

try:
   from jiwer import transforms as tr

   UpperCAmelCase_		   =				True
except ImportError:
   UpperCAmelCase_		   =				False

if _jiwer_available and version.parse(importlib_metadata.version("""jiwer""")) < version.parse("""2.3.0"""):


   class   lowerCamelCase__  (					tr.AbstractTransform):



    """simple docstring"""

    def __init__(						self				:				str ,	__lowerCAmelCase				:				str = " "			)      ->  Any:
       _A						     =					sentence_delimiter

    def 						snake_case_			(						self				:				List[str] ,	__lowerCAmelCase				:				str			)      ->  Optional[int]:
       return list(__lowerCAmelCase			)

    def 						snake_case_			(						self				:				Union[str, Any] ,	__lowerCAmelCase				:				List[str]			)      ->  Optional[Any]:
       _A						     =					[]
       for sent_idx, sentence in enumerate(__lowerCAmelCase			):
          chars.extend(self.process_string(__lowerCAmelCase			)			)
          if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__lowerCAmelCase			) - 1:
             chars.append(self.sentence_delimiter			)
       return chars
   UpperCAmelCase_		   =				tr.Compose(
       [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
   )
elif _jiwer_available:
   UpperCAmelCase_		   =				tr.Compose(
       [
           tr.RemoveMultipleSpaces(),
           tr.Strip(),
           tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
           tr.ReduceToListOfListOfChars(),
       ]
   )
else:
   UpperCAmelCase_		   =				None

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Dict  ,      _snake_case  :Union[str, Any]							)     ->    int:
   return float((preds == labels).mean()							)

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :int  ,      _snake_case  :Dict							)     ->    Optional[Any]:
   return {
       "f1": float(fa_score(y_true=_snake_case  ,      y_pred=_snake_case  ,      average='''macro'''							)							),
       "accuracy": simple_accuracy(_snake_case  ,      _snake_case							),
   }

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Union[str, Any]  ,      _snake_case  :Union[str, Any]  ,      _snake_case  :Tuple="exp"  ,      _snake_case  :Dict=None  ,      _snake_case  :List[str]=False  ,      _snake_case  :Union[str, Any]=False  ,      _snake_case  :List[Any]=None  ,      _snake_case  :Tuple=False  ,      )     ->    Tuple:
   # xtreme-s can only have one label
   _A						     =					[[label] for label in labels]
   _A						     =					list(_snake_case							)
   try:
      import sacrebleu as scb
   except ImportError:
      raise ValueError(
          '''sacrebleu has to be installed in order to apply the bleu metric for covost2.'''
          '''You can install it via `pip install sacrebleu`.'''							)

   if version.parse(scb.__version__							) < version.parse('''1.4.12'''							):
      raise ImportWarning(
          '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
          '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''							)

   _A						     =					len(labels[0]							)
   if any(len(_snake_case							) != references_per_prediction for refs in labels							):
      raise ValueError('''Sacrebleu requires the same number of references for each prediction'''							)
   _A						     =					[[refs[i] for refs in labels] for i in range(_snake_case							)]
   _A						     =					scb.corpus_bleu(
       _snake_case  ,      _snake_case  ,      smooth_method=_snake_case  ,      smooth_value=_snake_case  ,      force=_snake_case  ,      lowercase=_snake_case  ,      use_effective_order=_snake_case  ,      **({'''tokenize''': tokenize} if tokenize else {})  ,      )
   return {"bleu": output.score}

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Optional[Any]  ,      _snake_case  :Optional[int]  ,      _snake_case  :List[Any]  ,      _snake_case  :str							)     ->    Tuple:
   try:
      from jiwer import compute_measures
   except ImportError:
      raise ValueError(
          F'''jiwer has to be installed in order to apply the wer metric for {config_name}.'''
          '''You can install it via `pip install jiwer`.'''							)

   if concatenate_texts:
      _A						     =					compute_measures(_snake_case  ,      _snake_case							)['''wer''']

      _A						     =					compute_measures(_snake_case  ,      _snake_case  ,      truth_transform=_snake_case  ,      hypothesis_transform=_snake_case							)['''wer''']
      return {"wer": wer, "cer": cer}
   else:

      def compute_score(_snake_case  :Tuple  ,      _snake_case  :Any  ,      _snake_case  :str="wer"							):
         _A						     =					0
         _A						     =					0
         for prediction, reference in zip(_snake_case  ,      _snake_case							):
            if score_type == "wer":
               _A						     =					compute_measures(_snake_case  ,      _snake_case							)
            elif score_type == "cer":
               _A						     =					compute_measures(
                   _snake_case  ,      _snake_case  ,      truth_transform=_snake_case  ,      hypothesis_transform=_snake_case							)
            incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
            total += measures["substitutions"] + measures["deletions"] + measures["hits"]
         return incorrect / total

      return {"wer": compute_score(_snake_case  ,      _snake_case  ,      '''wer'''							), "cer": compute_score(_snake_case  ,      _snake_case  ,      '''cer'''							)}


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,     _KWARGS_DESCRIPTION)
class   lowerCamelCase__  (					datasets.Metric):



 """simple docstring"""

 def 						snake_case_			(						self				:				str			)      ->  List[Any]:
    if self.config_name not in _CONFIG_NAMES:
       raise KeyError(f'''You should supply a configuration name selected in {_CONFIG_NAMES}'''			)

    _A						     =					'''int64''' if self.config_name in ['''fleurs-lang_id''', '''minds14'''] else '''string'''

    return datasets.MetricInfo(
        description=_DESCRIPTION ,	citation=_CITATION ,	inputs_description=_KWARGS_DESCRIPTION ,	features=datasets.Features(
            {'''predictions''': datasets.Value(__lowerCAmelCase			), '''references''': datasets.Value(__lowerCAmelCase			)}			) ,	codebase_urls=[] ,	reference_urls=[] ,	format='''numpy''' ,	)

 def 						snake_case_			(						self				:				Dict ,	__lowerCAmelCase				:				str ,	__lowerCAmelCase				:				Any ,	__lowerCAmelCase				:				Union[str, Any]=None ,	__lowerCAmelCase				:				Any=None			)      ->  Union[str, Any]:
    _A						     =					bleu_kwargs if bleu_kwargs is not None else {}
    _A						     =					wer_kwargs if wer_kwargs is not None else {}

    if self.config_name == "fleurs-lang_id":
       return {"accuracy": simple_accuracy(__lowerCAmelCase ,	__lowerCAmelCase			)}
    elif self.config_name == "minds14":
       return fa_and_simple_accuracy(__lowerCAmelCase ,	__lowerCAmelCase			)
    elif self.config_name == "covost2":
       _A						     =					bleu_kwargs.pop('''smooth_method''' ,	'''exp'''			)
       _A						     =					bleu_kwargs.pop('''smooth_value''' ,	__lowerCAmelCase			)
       _A						     =					bleu_kwargs.pop('''force''' ,	__lowerCAmelCase			)
       _A						     =					bleu_kwargs.pop('''lowercase''' ,	__lowerCAmelCase			)
       _A						     =					bleu_kwargs.pop('''tokenize''' ,	__lowerCAmelCase			)
       _A						     =					bleu_kwargs.pop('''use_effective_order''' ,	__lowerCAmelCase			)
       return bleu(
           preds=__lowerCAmelCase ,	labels=__lowerCAmelCase ,	smooth_method=__lowerCAmelCase ,	smooth_value=__lowerCAmelCase ,	force=__lowerCAmelCase ,	lowercase=__lowerCAmelCase ,	tokenize=__lowerCAmelCase ,	use_effective_order=__lowerCAmelCase ,	)
    elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
       _A						     =					wer_kwargs.pop('''concatenate_texts''' ,	__lowerCAmelCase			)
       return wer_and_cer(__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase ,	self.config_name			)
    else:
       raise KeyError(f'''You should supply a configuration name selected in {_CONFIG_NAMES}'''			)



