




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

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

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

      if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
            __magic_name__     :     Any  							=					tr_dataset.shuffle().select(range(8_00_00))
            __magic_name__     :     Optional[int]  							=					val_dataset.shuffle().select(range(80_00))

      print(tr_dataset)
      print(val_dataset)

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

      __magic_name__     :     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,
      }
      __magic_name__							,   __magic_name__     :     Optional[int]  							=					build_tx(**tx_args)

      __magic_name__     :     List[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,
      )

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







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


__magic_name__     :     List[Any]  							=					get_logger(__name__)


if TYPE_CHECKING:
      from .builder import DatasetBuilder
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase = None							):
  lowerCAmelCase__								=			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							)

    lowerCAmelCase__								=			'''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()
  lowerCAmelCase__								=			True




def 		a_       (   __lowerCAmelCase							):
  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":
        lowerCAmelCase__								=			imports[1]
        lowerCAmelCase__								=			'''.'''.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

  lowerCAmelCase__								=			[
      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							)

