'''simple docstring'''




import argparse
import json
import re
import string
import sys

import numpy as np


snake_case  =			0.5





def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							List[Any]    ):
      _lowerCAmelCase		   =   ['.', ',', ';', ':']

      for token in remove_tokens:
            _lowerCAmelCase		   =   ground_truth.replace(_lowerCamelCase  ,  ''    )
            _lowerCAmelCase		   =   prediction.replace(_lowerCamelCase  ,  ''    )

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

      _lowerCAmelCase		   =   ground_truth.intersection(_lowerCamelCase    )
      _lowerCAmelCase		   =   ground_truth.union(_lowerCamelCase    )
      _lowerCAmelCase		   =   len(_lowerCamelCase    ) / len(_lowerCamelCase    )
      return jaccard





def 			A_   ( _lowerCamelCase			:							List[Any]    ):

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

      def white_space_fix(_lowerCamelCase			:							Any    ):
            return " ".join(text.split()    )

      def remove_punc(_lowerCamelCase			:							int    ):
            _lowerCAmelCase		   =   set(string.punctuation    )
            return "".join(ch for ch in text if ch not in exclude    )

      def lower(_lowerCamelCase			:							List[str]    ):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase    )    )    )    )





def 			A_   ( _lowerCamelCase			:							List[str]  ,  _lowerCamelCase			:							str  ,  _lowerCamelCase			:							int    ):
      _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   0, 0, 0

      _lowerCAmelCase		   =   'Parties' in qa_id

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

                  if not match_found:
                        fp += 1

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

      return precision, recall





def 			A_   ( _lowerCamelCase			:							Union[str, Any]    ):
      _lowerCAmelCase		   =   precisions[::-1]
      for i in range(1  ,  len(_lowerCamelCase    )    ):
            _lowerCAmelCase		   =   max(precision_best[i - 1]  ,  precision_best[i]    )
      _lowerCAmelCase		   =   precision_best[::-1]
      return precisions





def 			A_   ( _lowerCamelCase			:							List[str]  ,  _lowerCamelCase			:							str    ):
      _lowerCAmelCase		   =   process_precisions(_lowerCamelCase    )
      _lowerCAmelCase		   =   np.trapz(_lowerCamelCase  ,  _lowerCamelCase    )
      if np.isnan(_lowerCamelCase    ):
            return 0
      return aupr





def 			A_   ( _lowerCamelCase			:							Optional[Any]  ,  _lowerCamelCase			:							Union[str, Any]  ,  _lowerCamelCase			:							Tuple    ):
      _lowerCAmelCase		   =   process_precisions(_lowerCamelCase    )
      _lowerCAmelCase		   =   0
      for prec, recall in zip(_lowerCamelCase  ,  _lowerCamelCase    ):
            if recall >= recall_thresh:
                  _lowerCAmelCase		   =   prec
                  break
      return prec_at_recall





def 			A_   ( _lowerCamelCase			:							Optional[int]  ,  _lowerCamelCase			:							Union[str, Any]    ):
      return normalize_answer(_lowerCamelCase    ) == normalize_answer(_lowerCamelCase    )





def 			A_   ( _lowerCamelCase			:							Dict  ,  _lowerCamelCase			:							Tuple  ,  _lowerCamelCase			:							List[Any]    ):
      _lowerCAmelCase		   =   0
      for pred in predictions:
            for ground_truth in ground_truths:
                  _lowerCAmelCase		   =   metric_fn(_lowerCamelCase  ,  _lowerCamelCase    )
                  if score == 1:  # break the loop when one prediction matches the ground truth
                        break
            if score == 1:
                  break
      return score





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

                        precisions.append(_lowerCamelCase    )
                        recalls.append(_lowerCamelCase    )

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

                        exact_match += metric_max_over_ground_truths(_lowerCamelCase  ,  _lowerCamelCase  ,  _lowerCamelCase    )

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

      _lowerCAmelCase		   =   100.0 * fa / total
      _lowerCAmelCase		   =   100.0 * exact_match / total
      _lowerCAmelCase		   =   get_aupr(_lowerCamelCase  ,  _lowerCamelCase    )

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






'''simple docstring'''




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment







def 			A_   ( *_lowerCamelCase			:							Union[str, Any]    ):
      _lowerCAmelCase		   =   torch.cuda.device_count()
      print(F"Device count: {num_processes}"    )
      with patch_environment(
          world_size=_lowerCamelCase  ,  master_addr='127.0.01'  ,  master_port='29500'  ,  mixed_precision=args[1].mixed_precision    ):
            _lowerCAmelCase		   =   PrepareForLaunch(_lowerCamelCase  ,  distributed_type='MULTI_GPU'    )
            torch.multiprocessing.start_processes(_lowerCamelCase  ,  args=_lowerCamelCase  ,  nprocs=_lowerCamelCase  ,  start_method='spawn'    )


if __name__ == "__main__":
 # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
 # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

 # on-demand GPU
 # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
 snake_case  =			rh.cluster(name='''rh-cluster''', instance_type='''V100:4''', provider='''cheapest''', use_spot=False)  # multi GPU
 gpu.up_if_not()

 # on-prem GPU
 # gpu = rh.cluster(
 #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
 #       )

 # Set up remote function
 snake_case  =			[
     '''pip:./''',
     '''transformers''',
     '''datasets''',
     '''evaluate''',
     '''tqdm''',
     '''scipy''',
     '''scikit-learn''',
     '''tensorboard''',
     '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
 ]
 snake_case  =			rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

 # Define train args/config, run train function
 snake_case  =			argparse.Namespace(cpu=False, mixed_precision='''fp16''')
 snake_case  =			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 launch_train_gpu(config, train_args, stream_logs=True)

 # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
 # gpu.install_packages(reqs)
 # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])





