






import argparse
import json
import re
import string
import sys

import numpy as np


__UpperCAmelCase      =				0.5





def 						A_					(						lowercase_	,  lowercase_			)				->Tuple:







 """simple docstring"""

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

 for token in remove_tokens:
  SCREAMING_SNAKE_CASE				    =		ground_truth.replace(lowercase_	,  ''			)
  SCREAMING_SNAKE_CASE				    =		prediction.replace(lowercase_	,  ''			)

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

 SCREAMING_SNAKE_CASE				    =		ground_truth.intersection(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		ground_truth.union(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		len(lowercase_			) / len(lowercase_			)
 return jaccard





def 						A_					(						lowercase_			)				->List[Any]:







 """simple docstring"""

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

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

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

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

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





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		0, 0, 0

 SCREAMING_SNAKE_CASE				    =		'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
   SCREAMING_SNAKE_CASE				    =		False
   for pred in predictions:
    if substr_ok:
     SCREAMING_SNAKE_CASE				    =		get_jaccard(lowercase_	,  lowercase_			) >= IOU_THRESH or ground_truth in pred
    else:
     SCREAMING_SNAKE_CASE				    =		get_jaccard(lowercase_	,  lowercase_			) >= IOU_THRESH
    if is_match:
     SCREAMING_SNAKE_CASE				    =		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.)
   SCREAMING_SNAKE_CASE				    =		False
   for ground_truth in ground_truths:
    assert len(lowercase_			) > 0
    if substr_ok:
     SCREAMING_SNAKE_CASE				    =		get_jaccard(lowercase_	,  lowercase_			) >= IOU_THRESH or ground_truth in pred
    else:
     SCREAMING_SNAKE_CASE				    =		get_jaccard(lowercase_	,  lowercase_			) >= IOU_THRESH
    if is_match:
     SCREAMING_SNAKE_CASE				    =		True

   if not match_found:
    fp += 1

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

 return precision, recall





def 						A_					(						lowercase_			)				->Dict:







 """simple docstring"""

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





def 						A_					(						lowercase_	,  lowercase_			)				->int:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		process_precisions(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		np.trapz(lowercase_	,  lowercase_			)
 if np.isnan(lowercase_			):
  return 0
 return aupr





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->List[str]:







 """simple docstring"""

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





def 						A_					(						lowercase_	,  lowercase_			)				->Optional[int]:







 """simple docstring"""

 return normalize_answer(lowercase_			) == normalize_answer(lowercase_			)





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		0
 for pred in predictions:
  for ground_truth in ground_truths:
   SCREAMING_SNAKE_CASE				    =		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 						A_					(						lowercase_	,  lowercase_			)				->Dict:







 """simple docstring"""

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

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

 SCREAMING_SNAKE_CASE				    =		1_0_0.0 * fa / total
 SCREAMING_SNAKE_CASE				    =		1_0_0.0 * exact_match / total
 SCREAMING_SNAKE_CASE				    =		get_aupr(lowercase_	,  lowercase_			)

 SCREAMING_SNAKE_CASE				    =		get_prec_at_recall(lowercase_	,  lowercase_	,  recall_thresh=0.9			)
 SCREAMING_SNAKE_CASE				    =		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__":
      __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)))











'''simple docstring'''






import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
	print('''#################### AVAILABLE DEVICES ####################''')
	print(jax.devices())
	print('''###########################################################''')

	# setup for wandb sweep
	__UpperCamelCase      :   str								=     Args()
	__UpperCamelCase      :   Optional[int]								=     wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
	__UpperCamelCase      :   Dict								=     dict(logger.config)
	del wandb_args["batch_size"]
	__UpperCamelCase      :   int								=     replace(args, **wandb_args)
	__UpperCamelCase      :   str								=     args.base_dir + '''-''' + wandb.run.id
	__UpperCamelCase      :   List[Any]								=     replace(args, base_dir=base_dir)
	print(args)

	__UpperCamelCase      :   Any								=     load_dataset('''json''', data_files=args.tr_data_path)['''train''']
	__UpperCamelCase      :   Any								=     load_dataset('''json''', data_files=args.val_data_path)['''train''']

	# drop extra batch for now
	__UpperCamelCase      :   int								=     range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
	__UpperCamelCase      :   Optional[int]								=     tr_dataset.shuffle().select(indices)
	__UpperCamelCase      :   str								=     range(len(val_dataset) - len(val_dataset) % args.batch_size)
	__UpperCamelCase      :   int								=     val_dataset.shuffle().select(indices)

	if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
		__UpperCamelCase      :   str								=     tr_dataset.shuffle().select(range(80000))
		__UpperCamelCase      :   List[Any]								=     val_dataset.shuffle().select(range(8000))

	print(tr_dataset)
	print(val_dataset)

	__UpperCamelCase      :   Tuple								=     FlaxBigBirdForNaturalQuestions.from_pretrained(
	    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
	)
	__UpperCamelCase      :   Optional[Any]								=     BigBirdTokenizerFast.from_pretrained(args.model_id)
	__UpperCamelCase      :   int								=     DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

	__UpperCamelCase      :   Dict								=     {
	    '''lr''': args.lr,
	    '''init_lr''': args.init_lr,
	    '''warmup_steps''': args.warmup_steps,
	    '''num_train_steps''': args.max_epochs * (len(tr_dataset) // args.batch_size),
	    '''weight_decay''': args.weight_decay,
	}
	__UpperCamelCase							,  __UpperCamelCase      :   str								=     build_tx(**tx_args)

	__UpperCamelCase      :   List[str]								=     Trainer(
	    args=args,
	    data_collator=data_collator,
	    model_save_fn=model.save_pretrained,
	    train_step_fn=train_step,
	    val_step_fn=val_step,
	    logger=logger,
	    scheduler_fn=lr,
	)

	__UpperCamelCase      :   List[Any]								=     None
	__UpperCamelCase      :   str								=     trainer.create_state(model, tx, num_train_steps=tx_args['''num_train_steps'''], ckpt_dir=ckpt_dir)
	try:
		trainer.train(state, tr_dataset, val_dataset)
	except KeyboardInterrupt:
		print('''Oooops; TRAINING STOPPED UNFORTUNATELY''')

	print('''SAVING WEIGHTS IN `final-weights`''')
	__UpperCamelCase      :   List[Any]								=     jax_utils.unreplicate(state.params)
	model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)
