





import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


snake_case__      :							Tuple									=  logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

snake_case__      :							Optional[int]									=  {
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
snake_case__      :							Union[str, Any]									=  {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
snake_case__      :							Union[str, Any]									=  sorted(arg_to_scheduler.keys())
snake_case__      :							int									=  '{' + ', '.join(arg_to_scheduler_choices) + '}'





class A_      (    pl.LightningModule     ):




   def __init__(self			:Union[str, Any]					,     _UpperCamelCase			:argparse.Namespace					,     _UpperCamelCase			:List[str]=None					,     _UpperCamelCase			:List[str]="base"					,     _UpperCamelCase			:Any=None					,     _UpperCamelCase			:Tuple=None					,     _UpperCamelCase			:Optional[int]=None					,     **_UpperCamelCase			:List[str]					,     )->      List[str]:
         super().__init__()
         # TODO: move to self.save_hyperparameters()
         # self.save_hyperparameters()
         # can also expand arguments into trainer signature for easier reading

         self.save_hyperparameters(_UpperCamelCase				)
         __A      =       0
         __A      =       Path(self.hparams.output_dir				)
         __A      =       self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
               __A      =       AutoConfig.from_pretrained(
                   self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path					,     **({'''num_labels''': num_labels} if num_labels is not None else {})					,     cache_dir=_UpperCamelCase					,     **_UpperCamelCase					,     )
         else:
               __A      =       config

         __A      =       ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
         for p in extra_model_params:
               if getattr(self.hparams					,     _UpperCamelCase					,     _UpperCamelCase				):
                     assert hasattr(self.config					,     _UpperCamelCase				), f"""model config doesn't have a `{p}` attribute"""
                     setattr(self.config					,     _UpperCamelCase					,     getattr(self.hparams					,     _UpperCamelCase				)				)

         if tokenizer is None:
               __A      =       AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,     cache_dir=_UpperCamelCase					,     )
         else:
               __A      =       tokenizer
         __A      =       MODEL_MODES[mode]
         if model is None:
               __A      =       self.model_type.from_pretrained(
                   self.hparams.model_name_or_path					,     from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path				)					,     config=self.config					,     cache_dir=_UpperCamelCase					,     )
         else:
               __A      =       model




   def _lowerCAmelCase   (self			:str					,     *_UpperCamelCase			:Dict					,     **_UpperCamelCase			:Optional[int]				)->      Tuple:
         __A      =       self.model_type.from_pretrained(*_UpperCamelCase					,     **_UpperCamelCase				)




   def _lowerCAmelCase   (self			:Tuple				)->      List[str]:
         __A      =       arg_to_scheduler[self.hparams.lr_scheduler]
         __A      =       get_schedule_func(
             self.opt					,     num_warmup_steps=self.hparams.warmup_steps					,     num_training_steps=self.total_steps()				)
         __A      =       {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
         return scheduler




   def _lowerCAmelCase   (self			:Optional[Any]				)->      Optional[int]:
         __A      =       self.model
         __A      =       ['''bias''', '''LayerNorm.weight''']
         __A      =       [
             {
                 '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay				)],
                 '''weight_decay''': self.hparams.weight_decay,
             },
             {
                 '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay				)],
                 '''weight_decay''': 0.0,
             },
         ]
         if self.hparams.adafactor:
               __A      =       Adafactor(
                   _UpperCamelCase					,     lr=self.hparams.learning_rate					,     scale_parameter=_UpperCamelCase					,     relative_step=_UpperCamelCase				)

         else:
               __A      =       AdamW(
                   _UpperCamelCase					,     lr=self.hparams.learning_rate					,     eps=self.hparams.adam_epsilon				)
         __A      =       optimizer

         __A      =       self.get_lr_scheduler()

         return [optimizer], [scheduler]




   def _lowerCAmelCase   (self			:Optional[Any]					,     _UpperCamelCase			:Any					,     _UpperCamelCase			:Tuple				)->      Dict:
         return self.validation_step(_UpperCamelCase					,     _UpperCamelCase				)




   def _lowerCAmelCase   (self			:int					,     _UpperCamelCase			:Dict				)->      List[Any]:
         return self.validation_end(_UpperCamelCase				)




   def _lowerCAmelCase   (self			:int				)->      int:
         __A      =       max(1					,     self.hparams.gpus				)  # TODO: consider num_tpu_cores
         __A      =       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




   def _lowerCAmelCase   (self			:Optional[int]					,     _UpperCamelCase			:Optional[int]				)->      Dict:
         if stage == "test":
               __A      =       len(self.test_dataloader().dataset				)
         else:
               __A      =       self.get_dataloader('''train'''					,     self.hparams.train_batch_size					,     shuffle=_UpperCamelCase				)
               __A      =       len(self.train_dataloader().dataset				)




   def _lowerCAmelCase   (self			:Dict					,     _UpperCamelCase			:str					,     _UpperCamelCase			:int					,     _UpperCamelCase			:bool = False				)->      Optional[Any]:
         raise NotImplementedError('''You must implement this for your task'''				)




   def _lowerCAmelCase   (self			:int				)->      Union[str, Any]:
         return self.train_loader




   def _lowerCAmelCase   (self			:List[str]				)->      Union[str, Any]:
         return self.get_dataloader('''dev'''					,     self.hparams.eval_batch_size					,     shuffle=_UpperCamelCase				)




   def _lowerCAmelCase   (self			:List[str]				)->      List[Any]:
         return self.get_dataloader('''test'''					,     self.hparams.eval_batch_size					,     shuffle=_UpperCamelCase				)




   def _lowerCAmelCase   (self			:Union[str, Any]					,     _UpperCamelCase			:Tuple				)->      str:
         return os.path.join(
             self.hparams.data_dir					,     '''cached_{}_{}_{}'''.format(
                 _UpperCamelCase					,     list(filter(_UpperCamelCase					,     self.hparams.model_name_or_path.split('''/'''				)				)				).pop()					,     str(self.hparams.max_seq_length				)					,     )					,     )




   @pl.utilities.rank_zero_only
   def _lowerCAmelCase   (self			:Union[str, Any]					,     _UpperCamelCase			:Dict[str, Any]				)->      None:
         __A      =       self.output_dir.joinpath('''best_tfmr'''				)
         __A      =       self.step_count
         self.model.save_pretrained(_UpperCamelCase				)
         self.tokenizer.save_pretrained(_UpperCamelCase				)






   @staticmethod
   def _lowerCAmelCase   (_UpperCamelCase			:str					,     _UpperCamelCase			:Dict				)->      Dict:
         parser.add_argument(
             '''--model_name_or_path'''					,     default=_UpperCamelCase					,     type=_UpperCamelCase					,     required=_UpperCamelCase					,     help='''Path to pretrained model or model identifier from huggingface.co/models'''					,     )
         parser.add_argument(
             '''--config_name'''					,     default=''''''					,     type=_UpperCamelCase					,     help='''Pretrained config name or path if not the same as model_name'''				)
         parser.add_argument(
             '''--tokenizer_name'''					,     default=_UpperCamelCase					,     type=_UpperCamelCase					,     help='''Pretrained tokenizer name or path if not the same as model_name'''					,     )
         parser.add_argument(
             '''--cache_dir'''					,     default=''''''					,     type=_UpperCamelCase					,     help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''					,     )
         parser.add_argument(
             '''--encoder_layerdrop'''					,     type=_UpperCamelCase					,     help='''Encoder layer dropout probability (Optional). Goes into model.config'''					,     )
         parser.add_argument(
             '''--decoder_layerdrop'''					,     type=_UpperCamelCase					,     help='''Decoder layer dropout probability (Optional). Goes into model.config'''					,     )
         parser.add_argument(
             '''--dropout'''					,     type=_UpperCamelCase					,     help='''Dropout probability (Optional). Goes into model.config'''					,     )
         parser.add_argument(
             '''--attention_dropout'''					,     type=_UpperCamelCase					,     help='''Attention dropout probability (Optional). Goes into model.config'''					,     )
         parser.add_argument('''--learning_rate'''					,     default=5e-5					,     type=_UpperCamelCase					,     help='''The initial learning rate for Adam.'''				)
         parser.add_argument(
             '''--lr_scheduler'''					,     default='''linear'''					,     choices=_UpperCamelCase					,     metavar=_UpperCamelCase					,     type=_UpperCamelCase					,     help='''Learning rate scheduler'''					,     )
         parser.add_argument('''--weight_decay'''					,     default=0.0					,     type=_UpperCamelCase					,     help='''Weight decay if we apply some.'''				)
         parser.add_argument('''--adam_epsilon'''					,     default=1e-8					,     type=_UpperCamelCase					,     help='''Epsilon for Adam optimizer.'''				)
         parser.add_argument('''--warmup_steps'''					,     default=0					,     type=_UpperCamelCase					,     help='''Linear warmup over warmup_steps.'''				)
         parser.add_argument('''--num_workers'''					,     default=4					,     type=_UpperCamelCase					,     help='''kwarg passed to DataLoader'''				)
         parser.add_argument('''--num_train_epochs'''					,     dest='''max_epochs'''					,     default=3					,     type=_UpperCamelCase				)
         parser.add_argument('''--train_batch_size'''					,     default=32					,     type=_UpperCamelCase				)
         parser.add_argument('''--eval_batch_size'''					,     default=32					,     type=_UpperCamelCase				)
         parser.add_argument('''--adafactor'''					,     action='''store_true'''				)





class A_      (    pl.Callback     ):




   def _lowerCAmelCase   (self			:Optional[Any]					,     _UpperCamelCase			:Tuple					,     _UpperCamelCase			:Optional[Any]				)->      Tuple:
         if (
             trainer.is_global_zero and trainer.global_rank == 0
         ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
               pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.





class A_      (    pl.Callback     ):




   def _lowerCAmelCase   (self			:str					,     _UpperCamelCase			:Union[str, Any]					,     _UpperCamelCase			:List[str]				)->      Tuple:
         __A      =       trainer.lr_schedulers[0]['''scheduler''']
         __A      =       {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
         pl_module.logger.log_metrics(_UpperCamelCase				)




   def _lowerCAmelCase   (self			:Any					,     _UpperCamelCase			:pl.Trainer					,     _UpperCamelCase			:pl.LightningModule				)->      Optional[int]:
         rank_zero_info('''***** Validation results *****'''				)
         __A      =       trainer.callback_metrics
         # Log results
         for key in sorted(_UpperCamelCase				):
               if key not in ["log", "progress_bar"]:
                     rank_zero_info('''{} = {}\n'''.format(_UpperCamelCase					,     str(metrics[key]				)				)				)






   def _lowerCAmelCase   (self			:Any					,     _UpperCamelCase			:pl.Trainer					,     _UpperCamelCase			:pl.LightningModule				)->      Optional[Any]:
         rank_zero_info('''***** Test results *****'''				)
         __A      =       trainer.callback_metrics
         # Log and save results to file
         __A      =       os.path.join(pl_module.hparams.output_dir					,     '''test_results.txt'''				)
         with open(_UpperCamelCase					,     '''w'''				) as writer:
               for key in sorted(_UpperCamelCase				):
                     if key not in ["log", "progress_bar"]:
                           rank_zero_info('''{} = {}\n'''.format(_UpperCamelCase					,     str(metrics[key]				)				)				)
                           writer.write('''{} = {}\n'''.format(_UpperCamelCase					,     str(metrics[key]				)				)				)





def _a       (			lowerCamelCase:     int      ,       lowerCamelCase:     Tuple				)			->		None:







      '''simple docstring'''






      parser.add_argument(
          '''--output_dir'''      ,       default=lowerCamelCase      ,       type=lowerCamelCase      ,       required=lowerCamelCase      ,       help='''The output directory where the model predictions and checkpoints will be written.'''      ,       )
      parser.add_argument(
          '''--fp16'''      ,       action='''store_true'''      ,       help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''      ,       )

      parser.add_argument(
          '''--fp16_opt_level'''      ,       type=lowerCamelCase      ,       default='''O2'''      ,       help=(
              '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
              '''See details at https://nvidia.github.io/apex/amp.html'''
          )      ,       )
      parser.add_argument('''--n_tpu_cores'''      ,       dest='''tpu_cores'''      ,       type=lowerCamelCase				)
      parser.add_argument('''--max_grad_norm'''      ,       dest='''gradient_clip_val'''      ,       default=1.0      ,       type=lowerCamelCase      ,       help='''Max gradient norm'''				)
      parser.add_argument('''--do_train'''      ,       action='''store_true'''      ,       help='''Whether to run training.'''				)
      parser.add_argument('''--do_predict'''      ,       action='''store_true'''      ,       help='''Whether to run predictions on the test set.'''				)
      parser.add_argument(
          '''--gradient_accumulation_steps'''      ,       dest='''accumulate_grad_batches'''      ,       type=lowerCamelCase      ,       default=1      ,       help='''Number of updates steps to accumulate before performing a backward/update pass.'''      ,       )
      parser.add_argument('''--seed'''      ,       type=lowerCamelCase      ,       default=42      ,       help='''random seed for initialization'''				)
      parser.add_argument(
          '''--data_dir'''      ,       default=lowerCamelCase      ,       type=lowerCamelCase      ,       required=lowerCamelCase      ,       help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''      ,       )







def _a       (			lowerCamelCase:     BaseTransformer      ,       lowerCamelCase:     argparse.Namespace      ,       lowerCamelCase:     str=None      ,       lowerCamelCase:     Dict=True      ,       lowerCamelCase:     List[Any]=None      ,       lowerCamelCase:     int=[]      ,       lowerCamelCase:     List[Any]=None      ,       lowerCamelCase:     List[Any]=None      ,       **lowerCamelCase:     List[str]      ,       )			->		Dict:







      '''simple docstring'''






      pl.seed_everything(args.seed				)

      # init model
      __A      =       Path(model.hparams.output_dir				)
      odir.mkdir(exist_ok=lowerCamelCase				)

      # add custom checkpoints
      if checkpoint_callback is None:
            __A      =       pl.callbacks.ModelCheckpoint(
                filepath=args.output_dir      ,       prefix='''checkpoint'''      ,       monitor='''val_loss'''      ,       mode='''min'''      ,       save_top_k=1				)
      if early_stopping_callback:
            extra_callbacks.append(lowerCamelCase				)
      if logging_callback is None:
            __A      =       LoggingCallback()

      __A      =       {}

      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
            __A      =       16
            # train_params["amp_level"] = args.fp16_opt_level

      if args.gpus > 1:
            __A      =       '''auto'''  # "ddp"
            __A      =       '''ddp'''

      __A      =       args.accumulate_grad_batches
      __A      =       None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
      __A      =       '''auto'''

      __A      =       pl.Trainer.from_argparse_args(
          lowerCamelCase      ,       weights_summary=lowerCamelCase      ,       callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]      ,       logger=lowerCamelCase      ,       **lowerCamelCase      ,       )

      if args.do_train:
            trainer.fit(lowerCamelCase				)

      return trainer












import argparse
import json
import re
import string
import sys

import numpy as np


snake_case__      :							Dict									=  0.5



def _a       (			lowerCamelCase:     List[str]      ,       lowerCamelCase:     List[str]				)			->		List[Any]:







      '''simple docstring'''






      __A      =       ['''.''', ''',''', ''';''', ''':''']

      for token in remove_tokens:
            __A      =       ground_truth.replace(lowerCamelCase      ,       ''''''				)
            __A      =       prediction.replace(lowerCamelCase      ,       ''''''				)

      __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(lowerCamelCase				)
      __A      =       ground_truth.union(lowerCamelCase				)
      __A      =       len(lowerCamelCase				) / len(lowerCamelCase				)
      return jaccard



def _a       (			lowerCamelCase:     List[str]				)			->		Dict:







      '''simple docstring'''






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

      def white_space_fix(lowerCamelCase:     Tuple				):
            return " ".join(text.split()				)

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

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

      return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase				)				)				)				)



def _a       (			lowerCamelCase:     Dict      ,       lowerCamelCase:     int      ,       lowerCamelCase:     List[str]				)			->		str:







      '''simple docstring'''






      __A     ,		__A     ,		__A      =       0, 0, 0

      __A      =       '''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
                  __A      =       False
                  for pred in predictions:
                        if substr_ok:
                              __A      =       get_jaccard(lowerCamelCase      ,       lowerCamelCase				) >= IOU_THRESH or ground_truth in pred
                        else:
                              __A      =       get_jaccard(lowerCamelCase      ,       lowerCamelCase				) >= 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(lowerCamelCase				) > 0
                        if substr_ok:
                              __A      =       get_jaccard(lowerCamelCase      ,       lowerCamelCase				) >= IOU_THRESH or ground_truth in pred
                        else:
                              __A      =       get_jaccard(lowerCamelCase      ,       lowerCamelCase				) >= 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 _a       (			lowerCamelCase:     Dict				)			->		List[str]:







      '''simple docstring'''






      __A      =       precisions[::-1]
      for i in range(1      ,       len(lowerCamelCase				)				):
            __A      =       max(precision_best[i - 1]      ,       precision_best[i]				)
      __A      =       precision_best[::-1]
      return precisions



def _a       (			lowerCamelCase:     Optional[int]      ,       lowerCamelCase:     Dict				)			->		Tuple:







      '''simple docstring'''






      __A      =       process_precisions(lowerCamelCase				)
      __A      =       np.trapz(lowerCamelCase      ,       lowerCamelCase				)
      if np.isnan(lowerCamelCase				):
            return 0
      return aupr



def _a       (			lowerCamelCase:     Any      ,       lowerCamelCase:     List[str]      ,       lowerCamelCase:     Optional[int]				)			->		str:







      '''simple docstring'''






      __A      =       process_precisions(lowerCamelCase				)
      __A      =       0
      for prec, recall in zip(lowerCamelCase      ,       lowerCamelCase				):
            if recall >= recall_thresh:
                  __A      =       prec
                  break
      return prec_at_recall



def _a       (			lowerCamelCase:     Tuple      ,       lowerCamelCase:     Dict				)			->		Optional[Any]:







      '''simple docstring'''






      return normalize_answer(lowerCamelCase				) == normalize_answer(lowerCamelCase				)



def _a       (			lowerCamelCase:     Any      ,       lowerCamelCase:     List[Any]      ,       lowerCamelCase:     Optional[int]				)			->		List[str]:







      '''simple docstring'''






      __A      =       0
      for pred in predictions:
            for ground_truth in ground_truths:
                  __A      =       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:     Union[str, Any]      ,       lowerCamelCase:     int				)			->		int:







      '''simple docstring'''






      __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(lowerCamelCase      ,       file=sys.stderr				)
                              continue
                        __A      =       [x['''text'''] for x in qa['''answers''']]
                        __A      =       predictions[qa['''id''']]
                        __A     ,		__A      =       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				)

      __A      =       [x for _, x in sorted(zip(lowerCamelCase      ,       lowerCamelCase				)				)]
      recalls.sort()

      __A      =       100.0 * fa / total
      __A      =       100.0 * exact_match / total
      __A      =       get_aupr(lowerCamelCase      ,       lowerCamelCase				)

      __A      =       get_prec_at_recall(lowerCamelCase      ,       lowerCamelCase      ,       recall_thresh=0.9				)
      __A      =       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__      :							Dict									=  argparse.ArgumentParser(description='Evaluation for CUAD')
       parser.add_argument('dataset_file', help='Dataset file')
       parser.add_argument('prediction_file', help='Prediction File')
       snake_case__      :							List[str]									=  parser.parse_args()
       with open(args.dataset_file) as dataset_file:
              snake_case__      :							List[Any]									=  json.load(dataset_file)
              snake_case__      :							int									=  dataset_json['data']
       with open(args.prediction_file) as prediction_file:
              snake_case__      :							Union[str, Any]									=  json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))





