





import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


_A				:							str							= get_logger(__name__)


if TYPE_CHECKING:
  from .builder import DatasetBuilder


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_ = None  )							->	Tuple:
    SCREAMING_SNAKE_CASE__       							= importlib.import_module(lowerCAmelCase_  )

    # TODO(QL): always update the module to add subsequent new authentication
    if hasattr(lowerCAmelCase_   ,       '''_patched_for_streaming'''  ) and module._patched_for_streaming:
        return

    def wrap_auth(lowerCAmelCase_  ):
        @wraps(lowerCAmelCase_  )
        def wrapper(*lowerCAmelCase_   ,       **lowerCAmelCase_  ):
            return function(*lowerCAmelCase_   ,       use_auth_token=lowerCAmelCase_   ,       **lowerCAmelCase_  )

        SCREAMING_SNAKE_CASE__       							= '''wrap_auth'''
        return wrapper

    # open files in a streaming fashion
    patch_submodule(lowerCAmelCase_   ,       '''open'''   ,       wrap_auth(lowerCAmelCase_  )  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''os.listdir'''   ,       wrap_auth(lowerCAmelCase_  )  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''os.walk'''   ,       wrap_auth(lowerCAmelCase_  )  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''glob.glob'''   ,       wrap_auth(lowerCAmelCase_  )  ).start()
    # allow to navigate in remote zip files
    patch_submodule(lowerCAmelCase_   ,       '''os.path.join'''   ,       lowerCAmelCase_  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''os.path.dirname'''   ,       lowerCAmelCase_  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''os.path.basename'''   ,       lowerCAmelCase_  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''os.path.relpath'''   ,       lowerCAmelCase_  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''os.path.split'''   ,       lowerCAmelCase_  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''os.path.splitext'''   ,       lowerCAmelCase_  ).start()
    # allow checks on paths
    patch_submodule(lowerCAmelCase_   ,       '''os.path.exists'''   ,       wrap_auth(lowerCAmelCase_  )  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''os.path.isdir'''   ,       wrap_auth(lowerCAmelCase_  )  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''os.path.isfile'''   ,       wrap_auth(lowerCAmelCase_  )  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''os.path.getsize'''   ,       wrap_auth(lowerCAmelCase_  )  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''pathlib.Path'''   ,       lowerCAmelCase_  ).start()
    # file readers
    patch_submodule(lowerCAmelCase_   ,       '''gzip.open'''   ,       wrap_auth(lowerCAmelCase_  )  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''numpy.load'''   ,       wrap_auth(lowerCAmelCase_  )  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''pandas.read_csv'''   ,       wrap_auth(lowerCAmelCase_  )   ,       attrs=['''__version__''']  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''pandas.read_excel'''   ,       wrap_auth(lowerCAmelCase_  )   ,       attrs=['''__version__''']  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''scipy.io.loadmat'''   ,       wrap_auth(lowerCAmelCase_  )   ,       attrs=['''__version__''']  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''xml.etree.ElementTree.parse'''   ,       wrap_auth(lowerCAmelCase_  )  ).start()
    patch_submodule(lowerCAmelCase_   ,       '''xml.dom.minidom.parse'''   ,       wrap_auth(lowerCAmelCase_  )  ).start()
    SCREAMING_SNAKE_CASE__       							= True



def        __snake_case       (						lowerCAmelCase_  )							->	Optional[int]:
    extend_module_for_streaming(builder.__module__   ,       use_auth_token=builder.use_auth_token  )
    # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
    if not builder.__module__.startswith('''datasets.'''  ):  # check that it's not a packaged builder like csv
        for imports in get_imports(inspect.getfile(builder.__class__  )  ):
            if imports[0] == "internal":
                SCREAMING_SNAKE_CASE__       							= imports[1]
                SCREAMING_SNAKE_CASE__       							= '''.'''.join(builder.__module__.split('''.'''  )[:-1] + [internal_import_name]  )
                extend_module_for_streaming(lowerCAmelCase_   ,       use_auth_token=builder.use_auth_token  )

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
    from .builder import DatasetBuilder

    SCREAMING_SNAKE_CASE__       							= [
        cls.__module__
        for cls in type(lowerCAmelCase_  ).__mro__[1:]  # make sure it's not the same module we've already patched
        if issubclass(cls   ,       lowerCAmelCase_  ) and cls.__module__ != DatasetBuilder.__module__
    ]  # check it's not a standard builder from datasets.builder
    for module in parent_builder_modules:
        extend_module_for_streaming(lowerCAmelCase_   ,       use_auth_token=builder.use_auth_token  )







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

  _A				:							Tuple							= load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
  _A				:							Optional[Any]							= 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				:							int							= tr_dataset.shuffle().select(indices)
  _A				:							Optional[int]							= 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[Any]							= tr_dataset.shuffle().select(range(8_00_00))
    _A				:							Tuple							= val_dataset.shuffle().select(range(80_00))

  print(tr_dataset)
  print(val_dataset)

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

  _A				:							int							= {
      """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,					_A				:							Union[str, Any]							= build_tx(**tx_args)

  _A				:							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				:							Tuple							= None
  _A				:							Dict							= 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				:							Tuple							= jax_utils.unreplicate(state.params)
  model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)
