


'''simple docstring'''







import argparse
import json
import re
import string
import sys

import numpy as np


lowerCAmelCase			:   int 		=  0.5






def 	lowercase (_A      ,  _A    ):







       """simple docstring"""







       _lowerCAmelCase			:							List[Any]      	=				[".", ",", ";", ":"]

       for token in remove_tokens:
              _lowerCAmelCase			:							Dict      	=				ground_truth.replace(lowercase_      ,  ''    )
              _lowerCAmelCase			:							List[Any]      	=				prediction.replace(lowercase_      ,  ''    )

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

       _lowerCAmelCase			:							int      	=				ground_truth.intersection(lowercase_    )
       _lowerCAmelCase			:							Dict      	=				ground_truth.union(lowercase_    )
       _lowerCAmelCase			:							str      	=				len(lowercase_    ) / len(lowercase_    )
       return jaccard






def 	lowercase (_A    ):







       """simple docstring"""







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

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

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

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

       return white_space_fix(remove_articles(remove_punc(lower(lowercase_    )    )    )    )






def 	lowercase (_A      ,  _A      ,  _A    ):







       """simple docstring"""







       _lowerCAmelCase			:							Tuple      	=				0, 0, 0

       _lowerCAmelCase			:							Optional[int]      	=				"Parties" in qa_id

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

                     if not match_found:
                            fp += 1

       _lowerCAmelCase			:							Optional[int]      	=				tp / (tp + fp) if tp + fp > 0 else np.nan
       _lowerCAmelCase			:							Tuple      	=				tp / (tp + fn) if tp + fn > 0 else np.nan

       return precision, recall






def 	lowercase (_A    ):







       """simple docstring"""







       _lowerCAmelCase			:							List[str]      	=				precisions[::-1]
       for i in range(1      ,  len(lowercase_    )    ):
              _lowerCAmelCase			:							Any      	=				max(precision_best[i - 1]      ,  precision_best[i]    )
       _lowerCAmelCase			:							List[Any]      	=				precision_best[::-1]
       return precisions






def 	lowercase (_A      ,  _A    ):







       """simple docstring"""







       _lowerCAmelCase			:							Tuple      	=				process_precisions(lowercase_    )
       _lowerCAmelCase			:							Union[str, Any]      	=				np.trapz(lowercase_      ,  lowercase_    )
       if np.isnan(lowercase_    ):
              return 0
       return aupr






def 	lowercase (_A      ,  _A      ,  _A    ):







       """simple docstring"""







       _lowerCAmelCase			:							Dict      	=				process_precisions(lowercase_    )
       _lowerCAmelCase			:							Dict      	=				0
       for prec, recall in zip(lowercase_      ,  lowercase_    ):
              if recall >= recall_thresh:
                     _lowerCAmelCase			:							Union[str, Any]      	=				prec
                     break
       return prec_at_recall






def 	lowercase (_A      ,  _A    ):







       """simple docstring"""







       return normalize_answer(lowercase_    ) == normalize_answer(lowercase_    )






def 	lowercase (_A      ,  _A      ,  _A    ):







       """simple docstring"""







       _lowerCAmelCase			:							str      	=				0
       for pred in predictions:
              for ground_truth in ground_truths:
                     _lowerCAmelCase			:							Tuple      	=				metric_fn(lowercase_      ,  lowercase_    )
                     if score == 1:  # break the loop when one prediction matches the ground truth
                            break
              if score == 1:
                     break
       return score






def 	lowercase (_A      ,  _A    ):







       """simple docstring"""







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

                            precisions.append(lowercase_    )
                            recalls.append(lowercase_    )

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

                            exact_match += metric_max_over_ground_truths(lowercase_      ,  lowercase_      ,  lowercase_    )

       _lowerCAmelCase			:							Tuple      	=				[x for _, x in sorted(zip(lowercase_      ,  lowercase_    )    )]
       recalls.sort()

       _lowerCAmelCase			:							Tuple      	=				100.0 * fa / total
       _lowerCAmelCase			:							List[str]      	=				100.0 * exact_match / total
       _lowerCAmelCase			:							Union[str, Any]      	=				get_aupr(lowercase_      ,  lowercase_    )

       _lowerCAmelCase			:							Any      	=				get_prec_at_recall(lowercase_      ,  lowercase_      ,  recall_thresh=0.9    )
       _lowerCAmelCase			:							List[Any]      	=				get_prec_at_recall(lowercase_      ,  lowercase_      ,  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__":
       lowerCAmelCase			:   List[Any] 		=  argparse.ArgumentParser(description="""Evaluation for CUAD""")
       parser.add_argument("""dataset_file""", help="""Dataset file""")
       parser.add_argument("""prediction_file""", help="""Prediction File""")
       lowerCAmelCase			:   Dict 		=  parser.parse_args()
       with open(args.dataset_file) as dataset_file:
              lowerCAmelCase			:   Tuple 		=  json.load(dataset_file)
              lowerCAmelCase			:   Optional[int] 		=  dataset_json["""data"""]
       with open(args.prediction_file) as prediction_file:
              lowerCAmelCase			:   Optional[Any] 		=  json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))








"""simple docstring"""


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_reformer import ReformerTokenizer
else:
  lowerCamelCase__      =      None


lowerCamelCase__      =      logging.get_logger(__name__)


lowerCamelCase__      =      "▁"

lowerCamelCase__      =      {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase__      =      {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

lowerCamelCase__      =      {
    "google/reformer-crime-and-punishment": 52_4288,
}



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]		   =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__							:Any		   =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    ["input_ids", "attention_mask"]
  SCREAMING_SNAKE_CASE__							:str		   =    ReformerTokenizer

  def __init__(						self      :				List[str]    ,      __a      :				str=None    ,      __a      :				List[Any]=None    ,      __a      :				Optional[int]="</s>"    ,      __a      :				str="<unk>"    ,      __a      :				Dict=[]    ,      **__a      :				Tuple    ,      )						-> Optional[int]:
        super().__init__(
            __a    ,      tokenizer_file=__a    ,      eos_token=__a    ,      unk_token=__a    ,      additional_special_tokens=__a    ,      **__a    ,      )

        _UpperCamelCase					:		Dict  =				vocab_file
        _UpperCamelCase					:		Optional[Any]  =				False if not self.vocab_file else True


  def       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				str    ,      __a      :				Optional[str] = None		)						-> Tuple[str]:
        if not self.can_save_slow_tokenizer:
              raise ValueError(
                  "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                  "tokenizer."		)

        if not os.path.isdir(__a		):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
              return
        _UpperCamelCase					:		Optional[Any]  =				os.path.join(
            __a    ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(__a		):
              copyfile(self.vocab_file    ,      __a		)

        return (out_vocab_file,)
