



import argparse
import json
import re
import string
import sys

import numpy as np


UpperCamelCase_									=   0.5







def       lowerCamelCase_  (    _a       :						str   ,		_a       :						str    ):



    '''simple docstring'''




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

    for token in remove_tokens:
        UpperCAmelCase_     :       Dict  		=					ground_truth.replace(_a   ,		""""""    )
        UpperCAmelCase_     :       List[Any]  		=					prediction.replace(_a   ,		""""""    )

    UpperCAmelCase_		, UpperCAmelCase_     :       Any  		=					ground_truth.lower(), prediction.lower()
    UpperCAmelCase_		, UpperCAmelCase_     :       Dict  		=					ground_truth.replace("""/"""   ,		""" """    ), prediction.replace("""/"""   ,		""" """    )
    UpperCAmelCase_		, UpperCAmelCase_     :       Tuple  		=					set(ground_truth.split(""" """    )    ), set(prediction.split(""" """    )    )

    UpperCAmelCase_     :       Tuple  		=					ground_truth.intersection(_a    )
    UpperCAmelCase_     :       Optional[Any]  		=					ground_truth.union(_a    )
    UpperCAmelCase_     :       Tuple  		=					len(_a    ) / len(_a    )
    return jaccard







def       lowerCamelCase_  (    _a       :						Optional[int]    ):



    '''simple docstring'''




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

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

    def remove_punc(_a       :						List[str]    ):
        UpperCAmelCase_     :       Tuple  		=					set(string.punctuation    )
        return "".join(ch for ch in text if ch not in exclude    )

    def lower(_a       :						int    ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(_a    )    )    )    )







def       lowerCamelCase_  (    _a       :						Union[str, Any]   ,		_a       :						Optional[int]   ,		_a       :						int    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					0, 0, 0

    UpperCAmelCase_     :       int  		=					"""Parties""" in qa_id

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

            if not match_found:
                fp += 1

    UpperCAmelCase_     :       Tuple  		=					tp / (tp + fp) if tp + fp > 0 else np.nan
    UpperCAmelCase_     :       Union[str, Any]  		=					tp / (tp + fn) if tp + fn > 0 else np.nan

    return precision, recall







def       lowerCamelCase_  (    _a       :						Optional[int]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       str  		=					precisions[::-1]
    for i in range(1   ,		len(_a    )    ):
        UpperCAmelCase_     :       Dict  		=					max(precision_best[i - 1]   ,		precision_best[i]    )
    UpperCAmelCase_     :       str  		=					precision_best[::-1]
    return precisions







def       lowerCamelCase_  (    _a       :						List[str]   ,		_a       :						str    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Optional[int]  		=					process_precisions(_a    )
    UpperCAmelCase_     :       Tuple  		=					np.trapz(_a   ,		_a    )
    if np.isnan(_a    ):
        return 0
    return aupr







def       lowerCamelCase_  (    _a       :						Union[str, Any]   ,		_a       :						Optional[Any]   ,		_a       :						Union[str, Any]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Optional[Any]  		=					process_precisions(_a    )
    UpperCAmelCase_     :       List[Any]  		=					0
    for prec, recall in zip(_a   ,		_a    ):
        if recall >= recall_thresh:
            UpperCAmelCase_     :       Union[str, Any]  		=					prec
            break
    return prec_at_recall







def       lowerCamelCase_  (    _a       :						List[Any]   ,		_a       :						Tuple    ):



    '''simple docstring'''




    return normalize_answer(_a    ) == normalize_answer(_a    )







def       lowerCamelCase_  (    _a       :						Optional[int]   ,		_a       :						Optional[Any]   ,		_a       :						Optional[int]    ):



    '''simple docstring'''




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







def       lowerCamelCase_  (    _a       :						Optional[int]   ,		_a       :						List[Any]    ):



    '''simple docstring'''




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

                precisions.append(_a    )
                recalls.append(_a    )

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

                exact_match += metric_max_over_ground_truths(_a   ,		_a   ,		_a    )

    UpperCAmelCase_     :       Any  		=					[x for _, x in sorted(zip(_a   ,		_a    )    )]
    recalls.sort()

    UpperCAmelCase_     :       List[str]  		=					1_0_0.0 * fa / total
    UpperCAmelCase_     :       Tuple  		=					1_0_0.0 * exact_match / total
    UpperCAmelCase_     :       Union[str, Any]  		=					get_aupr(_a   ,		_a    )

    UpperCAmelCase_     :       Optional[Any]  		=					get_prec_at_recall(_a   ,		_a   ,		recall_thresh=0.9    )
    UpperCAmelCase_     :       List[Any]  		=					get_prec_at_recall(_a   ,		_a   ,		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)))

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   ArgumentParser(
           description=(
               'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
           )      )

       # Optional arguments for the launch helper
       parser.add_argument('--num_cores'      ,    type=lowerCAmelCase__      ,    default=1      ,    help='Number of TPU cores to use (1 or 8).'      )

       # positional
       parser.add_argument(
           'training_script'      ,    type=lowerCAmelCase__      ,    help=(
               'The full path to the single TPU training '
               'program/script to be launched in parallel, '
               'followed by all the arguments for the '
               'training script'
           )      ,    )

       # rest from the training program
       parser.add_argument('training_script_args'      ,    nargs=lowerCAmelCase__      )

       return parser.parse_args()






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   parse_args()

       # Import training_script as a module.
       lowerCAmelCase__	      =   Path(args.training_script      )
       sys.path.append(str(script_fpath.parent.resolve()      )      )
       lowerCAmelCase__	      =   script_fpath.stem
       lowerCAmelCase__	      =   importlib.import_module(lowerCAmelCase__      )

       # Patch sys.argv
       lowerCAmelCase__	      =   [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores      )]

       xmp.spawn(mod._mp_fn      ,    args=()      ,    nprocs=args.num_cores      )


if __name__ == "__main__":
    main()
