


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

       __UpperCamelCase							:							Union[str, Any]    							=					load_dataset("json", data_files=args.tr_data_path)["""train"""]
       __UpperCamelCase							:							Optional[Any]    							=					load_dataset("json", data_files=args.val_data_path)["""train"""]

       # drop extra batch for now
       __UpperCamelCase							:							Dict    							=					range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
       __UpperCamelCase							:							Dict    							=					tr_dataset.shuffle().select(indices)
       __UpperCamelCase							:							Dict    							=					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							:							Optional[Any]    							=					tr_dataset.shuffle().select(range(8_0000))
              __UpperCamelCase							:							Optional[Any]    							=					val_dataset.shuffle().select(range(8000))

       print(tr_dataset)
       print(val_dataset)

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

       __UpperCamelCase							:							str    							=					{
           """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							:							List[Any]    							=					build_tx(**tx_args)

       __UpperCamelCase							:							Dict    							=					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							:							Optional[Any]    							=					None
       __UpperCamelCase							:							Tuple    							=					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							:							int    							=					jax_utils.unreplicate(state.params)
       model.save_pretrained(os.path.join(args.base_dir, "final-weights"), params=params)









import numpy as np






def 				_lowerCAmelCase			(	_a :       Optional[int]       , _a :       str      )						->      Union[str, Any]:
     lowerCAmelCase_       :							int			 =		np.array(_a      )
     lowerCAmelCase_       :							str			 =		np.array(_a      )

     lowerCAmelCase_       :							Optional[Any]			 =		abs(predict - actual      )
     lowerCAmelCase_       :							Dict			 =		difference.mean()

     return score






def 				_lowerCAmelCase			(	_a :       Union[str, Any]       , _a :       int      )						->      Tuple:
     lowerCAmelCase_       :							int			 =		np.array(_a      )
     lowerCAmelCase_       :							int			 =		np.array(_a      )

     lowerCAmelCase_       :							Union[str, Any]			 =		predict - actual
     lowerCAmelCase_       :							Tuple			 =		np.square(_a      )

     lowerCAmelCase_       :							Tuple			 =		square_diff.mean()
     return score






def 				_lowerCAmelCase			(	_a :       Optional[Any]       , _a :       Tuple      )						->      Optional[int]:
     lowerCAmelCase_       :							Optional[Any]			 =		np.array(_a      )
     lowerCAmelCase_       :							Optional[Any]			 =		np.array(_a      )

     lowerCAmelCase_       :							int			 =		predict - actual
     lowerCAmelCase_       :							Any			 =		np.square(_a      )
     lowerCAmelCase_       :							Union[str, Any]			 =		square_diff.mean()
     lowerCAmelCase_       :							Optional[Any]			 =		np.sqrt(_a      )
     return score






def 				_lowerCAmelCase			(	_a :       Optional[Any]       , _a :       Optional[Any]      )						->      List[Any]:
     lowerCAmelCase_       :							Dict			 =		np.array(_a      )
     lowerCAmelCase_       :							List[Any]			 =		np.array(_a      )

     lowerCAmelCase_       :							str			 =		np.log(predict + 1      )
     lowerCAmelCase_       :							Any			 =		np.log(actual + 1      )

     lowerCAmelCase_       :							List[str]			 =		log_predict - log_actual
     lowerCAmelCase_       :							Dict			 =		np.square(_a      )
     lowerCAmelCase_       :							Optional[Any]			 =		square_diff.mean()

     lowerCAmelCase_       :							int			 =		np.sqrt(_a      )

     return score






def 				_lowerCAmelCase			(	_a :       str       , _a :       List[Any]      )						->      int:
     lowerCAmelCase_       :							Any			 =		np.array(_a      )
     lowerCAmelCase_       :							Optional[int]			 =		np.array(_a      )

     lowerCAmelCase_       :							Optional[int]			 =		predict - actual
     lowerCAmelCase_       :							str			 =		np.sum(_a      ) / len(_a      )
     lowerCAmelCase_       :							List[str]			 =		np.sum(_a      ) / len(_a      )
     # print(numerator, denumerator)
     lowerCAmelCase_       :							int			 =		float(_a      ) / denumerator * 1_00

     return score







def 				_lowerCAmelCase			(	_a :       Any       , _a :       Union[str, Any]      )						->      str:
     return np.mean(np.array(_a      ) == np.array(_a      )      )
