


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

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

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

	if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
		lowerCamelCase							:       Tuple  						=     tr_dataset.shuffle().select(range(80_000))
		lowerCamelCase							:       List[Any]  						=     val_dataset.shuffle().select(range(8_000))

	print(tr_dataset)
	print(val_dataset)

	lowerCamelCase							:       List[str]  						=     FlaxBigBirdForNaturalQuestions.from_pretrained(
	    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
	)
	lowerCamelCase							:       Any  						=     BigBirdTokenizerFast.from_pretrained(args.model_id)
	lowerCamelCase							:       int  						=     DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

	lowerCamelCase							:       Optional[Any]  						=     {
	    '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							:       Dict  						=     build_tx(**tx_args)

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








'''simple docstring'''

import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm

def _SCREAMING_SNAKE_CASE				(A  ,			A = False	)	->	Tuple:






       """simple docstring"""







       if device_specific:
              seed += AcceleratorState().process_index
       random.seed(A	)
       np.random.seed(A	)
       torch.manual_seed(A	)
       if is_xpu_available():
              torch.xpu.manual_seed_all(A	)
       elif is_npu_available():
              torch.npu.manual_seed_all(A	)
       else:
              torch.cuda.manual_seed_all(A	)
       # ^^ safe to call this function even if cuda is not available
       if is_tpu_available():
              xm.set_rng_state(A	)

def _SCREAMING_SNAKE_CASE				(A = None  ,			A = None	)	->	Tuple:






       """simple docstring"""







       if rng_type == RNGType.TORCH:
              lowercase__				      =			torch.get_rng_state()
       elif rng_type == RNGType.CUDA:
              lowercase__				      =			torch.cuda.get_rng_state()
       elif rng_type == RNGType.XLA:
              assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
              lowercase__				      =			torch.tensor(xm.get_rng_state()	)
       elif rng_type == RNGType.NPU:
              assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
              lowercase__				      =			torch.npu.get_rng_state()
       elif rng_type == RNGType.XPU:
              assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
              lowercase__				      =			torch.xpu.get_rng_state()
       elif rng_type == RNGType.GENERATOR:
              assert generator is not None, "Need a generator to synchronize its seed."
              lowercase__				      =			generator.get_state()

       # Broadcast the rng state from device 0 to other devices
       lowercase__				      =			AcceleratorState()
       if state.distributed_type == DistributedType.TPU:
              lowercase__				      =			rng_state.to(xm.xla_device()	)
              xm.collective_broadcast([rng_state]	)
              xm.mark_step()
              lowercase__				      =			rng_state.cpu()
       elif (
           state.distributed_type in CUDA_DISTRIBUTED_TYPES
           or state.distributed_type == DistributedType.MULTI_NPU
           or state.distributed_type == DistributedType.MULTI_XPU
       ):
              lowercase__				      =			rng_state.to(state.device	)
              torch.distributed.broadcast(A  ,			0	)
              lowercase__				      =			rng_state.cpu()
       elif state.distributed_type == DistributedType.MULTI_CPU:
              torch.distributed.broadcast(A  ,			0	)

       # Set the broadcast rng state
       if rng_type == RNGType.TORCH:
              torch.set_rng_state(A	)
       elif rng_type == RNGType.CUDA:
              torch.cuda.set_rng_state(A	)
       elif rng_type == RNGType.NPU:
              torch.npu.set_rng_state(A	)
       elif rng_type == RNGType.XPU:
              torch.xpu.set_rng_state(A	)
       elif rng_type == RNGType.XLA:
              xm.set_rng_state(rng_state.item()	)
       elif rng_type == RNGType.GENERATOR:
              generator.set_state(A	)

def _SCREAMING_SNAKE_CASE				(A  ,			A = None	)	->	int:






       """simple docstring"""







       for rng_type in rng_types:
              synchronize_rng_state(RNGType(A	)  ,			generator=A	)




