


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

     a_  :    List[Any]      =			load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
     a_  :    str      =			load_dataset("""json""", data_files=args.val_data_path)["""train"""]

     # drop extra batch for now
     a_  :    Dict      =			range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
     a_  :    List[Any]      =			tr_dataset.shuffle().select(indices)
     a_  :    List[str]      =			range(len(val_dataset) - len(val_dataset) % args.batch_size)
     a_  :    Dict      =			val_dataset.shuffle().select(indices)

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

     print(tr_dataset)
     print(val_dataset)

     a_  :    List[str]      =			FlaxBigBirdForNaturalQuestions.from_pretrained(
         args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
     )
     a_  :    Tuple      =			BigBirdTokenizerFast.from_pretrained(args.model_id)
     a_  :    Dict      =			DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

     a_  :    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,
     }
     a_  :    Optional[Any]      =			build_tx(**tx_args)

     a_  :    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,
     )

     a_  :    List[Any]      =			None
     a_  :    List[Any]      =			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`""")
     a_  :    Any      =			jax_utils.unreplicate(state.params)
     model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)







'''simple docstring'''

def __snake_case (				UpperCAmelCase_						:						float		):
 return -num if num < 0 else num





def __snake_case (				UpperCAmelCase_						:						list[int]		):
 if len(UpperCAmelCase_		) == 0:
  raise ValueError("abs_min() arg is an empty sequence"		)
 lowerCamelCase_								=  x[0]
 for i in x:
  if abs_val(UpperCAmelCase_		) < abs_val(UpperCAmelCase_		):
   lowerCamelCase_								=  i
 return j





def __snake_case (				UpperCAmelCase_						:						list[int]		):
 if len(UpperCAmelCase_		) == 0:
  raise ValueError("abs_max() arg is an empty sequence"		)
 lowerCamelCase_								=  x[0]
 for i in x:
  if abs(UpperCAmelCase_		) > abs(UpperCAmelCase_		):
   lowerCamelCase_								=  i
 return j





def __snake_case (				UpperCAmelCase_						:						list[int]		):
 if len(UpperCAmelCase_		) == 0:
  raise ValueError("abs_max_sort() arg is an empty sequence"		)
 return sorted(UpperCAmelCase_ ,    key=UpperCAmelCase_		)[-1]





def __snake_case (				):
 assert abs_val(0		) == 0
 assert abs_val(34		) == 34
 assert abs_val(-100000000000		) == 100000000000

 lowerCamelCase_								=  [-3, -1, 2, -11]
 assert abs_max(UpperCAmelCase_		) == -11
 assert abs_max_sort(UpperCAmelCase_		) == -11
 assert abs_min(UpperCAmelCase_		) == -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     test_abs_val()
     print(abs_val(-34))  # --> 34



