


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


__lowerCamelCase   	=  get_logger(__name__)


if TYPE_CHECKING:
       from .builder import DatasetBuilder



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase = None			):
     a_     :  int				      =     importlib.import_module(__UpperCamelCase			)

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

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

          a_     :  str				      =     """wrap_auth"""
          return wrapper

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



def 			_a      (    __UpperCamelCase			):
     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":
                    a_     :  int				      =     imports[1]
                    a_     :  int				      =     """.""".join(builder.__module__.split("""."""			)[:-1] + [internal_import_name]			)
                    extend_module_for_streaming(__UpperCamelCase	,	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

     a_     :  Optional[int]				      =     [
         cls.__module__
         for cls in type(__UpperCamelCase			).__mro__[1:]  # make sure it's not the same module we've already patched
         if issubclass(cls	,	__UpperCamelCase			) 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(__UpperCamelCase	,	use_auth_token=builder.use_auth_token			)








"""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
				snake_case_								=			Args()
				snake_case_								=			wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
				snake_case_								=			dict(logger.config)
				del wandb_args["batch_size"]
				snake_case_								=			replace(args, **wandb_args)
				snake_case_								=			args.base_dir + """-""" + wandb.run.id
				snake_case_								=			replace(args, base_dir=base_dir)
				print(args)

				snake_case_								=			load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
				snake_case_								=			load_dataset("""json""", data_files=args.val_data_path)["""train"""]

				# drop extra batch for now
				snake_case_								=			range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
				snake_case_								=			tr_dataset.shuffle().select(indices)
				snake_case_								=			range(len(val_dataset) - len(val_dataset) % args.batch_size)
				snake_case_								=			val_dataset.shuffle().select(indices)

				if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
								snake_case_								=			tr_dataset.shuffle().select(range(8_0000))
								snake_case_								=			val_dataset.shuffle().select(range(8000))

				print(tr_dataset)
				print(val_dataset)

				snake_case_								=			FlaxBigBirdForNaturalQuestions.from_pretrained(
				    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
				)
				snake_case_								=			BigBirdTokenizerFast.from_pretrained(args.model_id)
				snake_case_								=			DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

				snake_case_								=			{
				    """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,
				}
				snake_case_	,  snake_case_								=			build_tx(**tx_args)

				snake_case_								=			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,
				)

				snake_case_								=			None
				snake_case_								=			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`""")
				snake_case_								=			jax_utils.unreplicate(state.params)
				model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





