


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

				__lowercase	:   Optional[int]   		=					load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
				__lowercase	:   int   		=					load_dataset("""json""", data_files=args.val_data_path)["""train"""]

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

				if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
								__lowercase	:   Dict   		=					tr_dataset.shuffle().select(range(8_00_00))
								__lowercase	:   Any   		=					val_dataset.shuffle().select(range(80_00))

				print(tr_dataset)
				print(val_dataset)

				__lowercase	:   List[str]   		=					FlaxBigBirdForNaturalQuestions.from_pretrained(
				    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
				)
				__lowercase	:   Union[str, Any]   		=					BigBirdTokenizerFast.from_pretrained(args.model_id)
				__lowercase	:   Optional[int]   		=					DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

				__lowercase	:   Union[str, 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,
				}
				__lowercase		,		__lowercase	:   Any   		=					build_tx(**tx_args)

				__lowercase	:   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,
				)

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











"""simple docstring"""





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment

class    __lowercase							(      unittest.TestCase						):


				'''simple docstring'''


				def    _lowerCamelCase       ( self :  Any   ):
										snake_case__							:       Dict				      =       inspect.getfile(accelerate.test_utils   )
										snake_case__							:       Tuple				      =       os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_sync.py''']   )

				@require_cpu
				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										debug_launcher(test_sync.main   ,num_processes=1   )

				@require_cpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										debug_launcher(test_sync.main   )

				@require_single_gpu
				def    _lowerCamelCase       ( self :  Union[str, Any]   ):
										test_sync.main()





				@require_multi_gpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										print(F'''Found {torch.cuda.device_count()} devices.'''   )
										snake_case__							:       Dict				      =       ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
										with patch_environment(omp_num_threads=1   ):
																execute_subprocess_async(SCREAMING_SNAKE_CASE_   ,env=os.environ.copy()   )


