





"""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    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = False				)		->							Tuple:






       """simple docstring"""




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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE = None,SCREAMING_SNAKE_CASE = None				)		->							Optional[Any]:






       """simple docstring"""




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

       # Broadcast the rng state from device 0 to other devices
       _UpperCAmelCase			     =  AcceleratorState()
       if state.distributed_type == DistributedType.TPU:
              _UpperCAmelCase			     =  rng_state.to(xm.xla_device()				)
              xm.collective_broadcast([rng_state]				)
              xm.mark_step()
              _UpperCAmelCase			     =  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
       ):
              _UpperCAmelCase			     =  rng_state.to(state.device				)
              torch.distributed.broadcast(SCREAMING_SNAKE_CASE,0				)
              _UpperCAmelCase			     =  rng_state.cpu()
       elif state.distributed_type == DistributedType.MULTI_CPU:
              torch.distributed.broadcast(SCREAMING_SNAKE_CASE,0				)

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


def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = None				)		->							Dict:






       """simple docstring"""




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













"""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)






