import argparse
import json
import re
import string
import sys
from collections import Counter

def      lowercase_							(     _lowerCamelCase				:			int):


    def remove_articles(_lowerCamelCase				:			List[Any]):
        return re.sub(R"\b(a|an|the)\b"						,      " "						,      _lowerCamelCase)

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

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

    def lower(_lowerCamelCase				:			Any):
        return text.lower()

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

def      lowercase_							(     _lowerCamelCase				:			int						,      _lowerCamelCase				:			Dict):
    lowercase__					:  str          =							normalize_answer(_lowerCamelCase).split()
    lowercase__					:  Dict          =							normalize_answer(_lowerCamelCase).split()
    lowercase__					:  str          =							Counter(_lowerCamelCase) & Counter(_lowerCamelCase)
    lowercase__					:  List[Any]          =							sum(common.values())
    if num_same == 0:
        return 0
    lowercase__					:  Tuple          =							1.0 * num_same / len(_lowerCamelCase)
    lowercase__					:  List[Any]          =							1.0 * num_same / len(_lowerCamelCase)
    lowercase__					:  int          =							(2 * precision * recall) / (precision + recall)
    return fa

def      lowercase_							(     _lowerCamelCase				:			Any						,      _lowerCamelCase				:			str):
    return normalize_answer(_lowerCamelCase) == normalize_answer(_lowerCamelCase)

def      lowercase_							(     _lowerCamelCase				:			Optional[Any]						,      _lowerCamelCase				:			Optional[int]						,      _lowerCamelCase				:			Optional[int]):
    lowercase__					:  Optional[int]          =							[]
    for ground_truth in ground_truths:
        lowercase__					:  Union[str, Any]          =							metric_fn(_lowerCamelCase						,      _lowerCamelCase)
        scores_for_ground_truths.append(_lowerCamelCase)
    return max(_lowerCamelCase)

def      lowercase_							(     _lowerCamelCase				:			int						,      _lowerCamelCase				:			str):
    lowercase__					:  Union[str, Any]          =							0
    lowercase__					:  List[Any]          =							[]
    for passage in dataset:
        for qa in passage["qas"]:
            total += 1
            if qa["id"] not in predictions:
                lowercase__					:  str          =							f'''Unanswered question {qa["id"]} will receive score 0.'''
                print(_lowerCamelCase						,      file=sys.stderr)
                continue

            lowercase__					:  Optional[int]          =							[x['''text'''] for x in qa['''answers''']]
            lowercase__					:  Union[str, Any]          =							predictions[qa['''id''']]

            lowercase__					:  Optional[Any]          =							metric_max_over_ground_truths(_lowerCamelCase						,      _lowerCamelCase						,      _lowerCamelCase)
            if int(_exact_match) == 1:
                correct_ids.append(qa["id"])
            exact_match += _exact_match

            fa += metric_max_over_ground_truths(_lowerCamelCase						,      _lowerCamelCase						,      _lowerCamelCase)

    lowercase__					:  Dict          =							exact_match / total
    lowercase__					:  Dict          =							fa / total

    return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
      UpperCamelCase    				=		'''1.0'''
      UpperCamelCase    				=		argparse.ArgumentParser('''Official evaluation script for ReCoRD v1.0.''')
      parser.add_argument('''data_file''', help='''The dataset file in JSON format.''')
      parser.add_argument('''pred_file''', help='''The model prediction file in JSON format.''')
      parser.add_argument('''--output_correct_ids''', action='''store_true''', help='''Output the correctly answered query IDs.''')
      UpperCamelCase    				=		parser.parse_args()

      with open(args.data_file) as data_file:
            UpperCamelCase    				=		json.load(data_file)
            if dataset_json["version"] != expected_version:
                  print(
                      f"Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}",
                      file=sys.stderr,
                  )
            UpperCamelCase    				=		dataset_json['''data''']

      with open(args.pred_file) as pred_file:
            UpperCamelCase    				=		json.load(pred_file)

      UpperCamelCase    				=		evaluate(dataset, predictions)

      if args.output_correct_ids:
            print(f"Output {len(correct_ids)} correctly answered question IDs.")
            with open('''correct_ids.json''', '''w''') as f:
                  json.dump(correct_ids, f)










'''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)
