





"""simple docstring"""






import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
lowerCAmelCase_      =				{
    '''translation''': ['''bleu'''],
    '''summarization''': ROUGE_KEYS,
}

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							Optional[Any]:






       """simple docstring"""




       _UpperCAmelCase			     =  search.split()
       _UpperCAmelCase			     =  dict((g.split('='				) for g in groups)				)
       _UpperCAmelCase			     =  list(entries.keys()				)
       _UpperCAmelCase			     =  [[F"""--{k} {v}""" for v in vs.split(':'				)] for k, vs in entries.items()]
       _UpperCAmelCase			     =  [list(SCREAMING_SNAKE_CASE				) for x in itertools.product(*SCREAMING_SNAKE_CASE				)]
       return matrix, entry_names

def    __lowerCamelCase     (							)		->							List[str]:






       """simple docstring"""




       _UpperCAmelCase			     =  sys.argv[0]

       _UpperCAmelCase			     =  argparse.ArgumentParser(
           usage=(
               '\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore'
               ' refer to `run_eval.py -h` for the complete list.'
           )				)
       parser.add_argument(
           '--search',type=SCREAMING_SNAKE_CASE,required=SCREAMING_SNAKE_CASE,help='param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"',)
       parser.add_argument(
           '--bs',type=SCREAMING_SNAKE_CASE,default=8,required=SCREAMING_SNAKE_CASE,help='initial batch size (may get reduced if it\'s too big)'				)
       parser.add_argument('--task',type=SCREAMING_SNAKE_CASE,help='used for task_specific_params + metrics'				)
       parser.add_argument(
           '--info',nargs='?',type=SCREAMING_SNAKE_CASE,const=datetime_now(),help=(
               'add custom notes to be printed before the results table. If no value is passed, the current datetime'
               ' string will be used.'
           ),)
       _UpperCAmelCase     ,				_UpperCAmelCase			     =  parser.parse_known_args()
       # we share some of the args
       args_main.extend(['--task', args.task]				)
       _UpperCAmelCase			     =  [prog] + args_main

       # to support variations like translation_en_to_de"
       _UpperCAmelCase			     =  'translation' if 'translation' in args.task else 'summarization'

       _UpperCAmelCase     ,				_UpperCAmelCase			     =  parse_search_arg(args.search				)
       _UpperCAmelCase			     =  task_score_names[task]  # score cols first
       _UpperCAmelCase			     =  {col: len(str(SCREAMING_SNAKE_CASE				)				) for col in col_names}
       _UpperCAmelCase			     =  []
       for r in matrix:
              _UpperCAmelCase			     =  dict((x.replace('--',''				).split() for x in r)				)
              _UpperCAmelCase			     =  ' '.join(SCREAMING_SNAKE_CASE				).split()
              args_exp.extend(['--bs', str(args.bs				)]				)  # in case we need to reduce its size due to CUDA OOM
              _UpperCAmelCase			     =  args_normal + args_exp

              # XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

              _UpperCAmelCase			     =  run_generate(verbose=SCREAMING_SNAKE_CASE				)
              # make sure scores are first in the table
              _UpperCAmelCase			     =  OrderedDict()
              for score in task_score_names[task]:
                     _UpperCAmelCase			     =  scores[score]
              result.update(SCREAMING_SNAKE_CASE				)
              results.append(SCREAMING_SNAKE_CASE				)

              # find widest entries
              for k, v in result.items():
                     _UpperCAmelCase			     =  len(str(SCREAMING_SNAKE_CASE				)				)
                     if l > col_widths[k]:
                            _UpperCAmelCase			     =  l

       _UpperCAmelCase			     =  sorted(SCREAMING_SNAKE_CASE,key=operator.itemgetter(*task_score_names[task]				),reverse=SCREAMING_SNAKE_CASE				)
       print(' | '.join([F"""{col:{col_widths[col]}}""" for col in col_names]				)				)
       print(' | '.join([F"""{"-"*col_widths[col]}""" for col in col_names]				)				)
       for row in results_sorted:
              print(' | '.join([F"""{row[col]:{col_widths[col]}}""" for col in col_names]				)				)

       _UpperCAmelCase			     =  results_sorted[0]
       for score in task_score_names[task]:
              del best[score]
       _UpperCAmelCase			     =  [F"""--{k} {v}""" for k, v in best.items()]
       _UpperCAmelCase			     =  ['--bs', str(args.bs				)]
       if args.info:
              print(F"""\nInfo: {args.info}"""				)
       print('\nBest score args:'				)
       print(' '.join(args_main + best_args + dyn_args				)				)

       return results_sorted


if __name__ == "__main__":
    # Usage:
    # [normal-run_eval_search.py cmd plus] \
    # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
    #
    # Example:
    # PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
    # $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
    # --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
    # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
    run_search()













"""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
    lowerCAmelCase_      =				Args()
    lowerCAmelCase_      =				wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
    lowerCAmelCase_      =				dict(logger.config)
    del wandb_args["batch_size"]
    lowerCAmelCase_      =				replace(args, **wandb_args)
    lowerCAmelCase_      =				args.base_dir + '''-''' + wandb.run.id
    lowerCAmelCase_      =				replace(args, base_dir=base_dir)
    print(args)

    lowerCAmelCase_      =				load_dataset('''json''', data_files=args.tr_data_path)['''train''']
    lowerCAmelCase_      =				load_dataset('''json''', data_files=args.val_data_path)['''train''']

    # drop extra batch for now
    lowerCAmelCase_      =				range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
    lowerCAmelCase_      =				tr_dataset.shuffle().select(indices)
    lowerCAmelCase_      =				range(len(val_dataset) - len(val_dataset) % args.batch_size)
    lowerCAmelCase_      =				val_dataset.shuffle().select(indices)

    if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
        lowerCAmelCase_      =				tr_dataset.shuffle().select(range(80_000))
        lowerCAmelCase_      =				val_dataset.shuffle().select(range(8_000))

    print(tr_dataset)
    print(val_dataset)

    lowerCAmelCase_      =				FlaxBigBirdForNaturalQuestions.from_pretrained(
        args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
    )
    lowerCAmelCase_      =				BigBirdTokenizerFast.from_pretrained(args.model_id)
    lowerCAmelCase_      =				DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

    lowerCAmelCase_      =				{
        '''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,
    }
    lowerCAmelCase_      ,					lowerCAmelCase_      =				build_tx(**tx_args)

    lowerCAmelCase_      =				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,
    )

    lowerCAmelCase_      =				None
    lowerCAmelCase_      =				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`''')
    lowerCAmelCase_      =				jax_utils.unreplicate(state.params)
    model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)






