






import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					**__lowerCAmelCase    )				->			Union[str, Any]:
     return RunBeamCommand(
         args.dataset      ,					args.name      ,					args.cache_dir      ,					args.beam_pipeline_options      ,					args.data_dir      ,					args.all_configs      ,					args.save_info or args.save_infos      ,					args.ignore_verifications      ,					args.force_redownload      ,					**__lowerCAmelCase      ,					)





class     __a						(					A__ ):







 @staticmethod
 def       __lowercase      (	SCREAMING_SNAKE_CASE      :     ArgumentParser		):


      '''simple docstring'''






      UpperCamelCase__    :     str        =						parser.add_parser("run_beam"							,     help="Run a Beam dataset processing pipeline"		)
      run_beam_parser.add_argument("dataset"							,     type=SCREAMING_SNAKE_CASE							,     help="Name of the dataset to download"		)
      run_beam_parser.add_argument("--name"							,     type=SCREAMING_SNAKE_CASE							,     default=SCREAMING_SNAKE_CASE							,     help="Dataset config name"		)
      run_beam_parser.add_argument(
          "--cache_dir"							,     type=SCREAMING_SNAKE_CASE							,     default=SCREAMING_SNAKE_CASE							,     help="Cache directory where the datasets are stored"							,     )
      run_beam_parser.add_argument(
          "--beam_pipeline_options"							,     type=SCREAMING_SNAKE_CASE							,     default=""							,     help="Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"							,     )
      run_beam_parser.add_argument(
          "--data_dir"							,     type=SCREAMING_SNAKE_CASE							,     default=SCREAMING_SNAKE_CASE							,     help="Can be used to specify a manual directory to get the files from"							,     )
      run_beam_parser.add_argument("--all_configs"							,     action="store_true"							,     help="Test all dataset configurations"		)
      run_beam_parser.add_argument("--save_info"							,     action="store_true"							,     help="Save the dataset infos file"		)
      run_beam_parser.add_argument(
          "--ignore_verifications"							,     action="store_true"							,     help="Run the test without checksums and splits checks"		)
      run_beam_parser.add_argument("--force_redownload"							,     action="store_true"							,     help="Force dataset redownload"		)
      # aliases
      run_beam_parser.add_argument("--save_infos"							,     action="store_true"							,     help="alias for save_info"		)
      run_beam_parser.set_defaults(func=SCREAMING_SNAKE_CASE		)







 def __init__(	self      :     Optional[Any]							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     bool							,     SCREAMING_SNAKE_CASE      :     bool							,     SCREAMING_SNAKE_CASE      :     bool							,     SCREAMING_SNAKE_CASE      :     bool							,     **SCREAMING_SNAKE_CASE      :     Union[str, Any]							,     ):


      '''simple docstring'''






      UpperCamelCase__    :     int        =						dataset
      UpperCamelCase__    :     Dict        =						name
      UpperCamelCase__    :     Optional[Any]        =						cache_dir
      UpperCamelCase__    :     int        =						beam_pipeline_options
      UpperCamelCase__    :     Any        =						data_dir
      UpperCamelCase__    :     Union[str, Any]        =						all_configs
      UpperCamelCase__    :     List[Any]        =						save_infos
      UpperCamelCase__    :     Optional[Any]        =						ignore_verifications
      UpperCamelCase__    :     List[str]        =						force_redownload
      UpperCamelCase__    :     Optional[Any]        =						config_kwargs







 def       __lowercase      (	self      :     int		):


      '''simple docstring'''






      import apache_beam as beam

      if self._name is not None and self._all_configs:
           print("Both parameters `name` and `all_configs` can't be used at once."		)
           exit(1		)
      UpperCamelCase__    :     Optional[int]        =						self._dataset, self._name
      UpperCamelCase__    :     Dict        =						dataset_module_factory(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     List[str]        =						import_main_class(dataset_module.module_path		)
      UpperCamelCase__    :     List[DatasetBuilder]        =						[]
      if self._beam_pipeline_options:
           UpperCamelCase__    :     Optional[int]        =						beam.options.pipeline_options.PipelineOptions(
               flags=[F'--{opt.strip()}' for opt in self._beam_pipeline_options.split(","		) if opt]		)
      else:
           UpperCamelCase__    :     str        =						None
      if self._all_configs and len(builder_cls.BUILDER_CONFIGS		) > 0:
           for builder_config in builder_cls.BUILDER_CONFIGS:
                builders.append(
                    builder_cls(
                        config_name=builder_config.name							,     data_dir=self._data_dir							,     hash=dataset_module.hash							,     beam_options=SCREAMING_SNAKE_CASE							,     cache_dir=self._cache_dir							,     base_path=dataset_module.builder_kwargs.get("base_path"		)							,     )		)
      else:
           builders.append(
               builder_cls(
                   config_name=SCREAMING_SNAKE_CASE							,     data_dir=self._data_dir							,     beam_options=SCREAMING_SNAKE_CASE							,     cache_dir=self._cache_dir							,     base_path=dataset_module.builder_kwargs.get("base_path"		)							,     **self._config_kwargs							,     )		)

      for builder in builders:
           builder.download_and_prepare(
               download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
               if not self._force_redownload
               else DownloadMode.FORCE_REDOWNLOAD							,     download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH		)							,     verification_mode=VerificationMode.NO_CHECKS
               if self._ignore_verifications
               else VerificationMode.ALL_CHECKS							,     try_from_hf_gcs=SCREAMING_SNAKE_CASE							,     )
           if self._save_infos:
                builder._save_infos()

      print("Apache beam run successful."		)

      # If save_infos=True, the dataset infos file is created next to the loaded module file.
      # Let's move it to the original directory of the dataset script, to allow the user to
      # upload them on S3 at the same time afterwards.
      if self._save_infos:
           UpperCamelCase__    :     str        =						os.path.join(builder_cls.get_imported_module_dir()							,     config.DATASETDICT_INFOS_FILENAME		)

           UpperCamelCase__    :     int        =						Path(SCREAMING_SNAKE_CASE		).name + ".py"

           UpperCamelCase__    :     int        =						os.path.join(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
           if os.path.isfile(SCREAMING_SNAKE_CASE		):
                UpperCamelCase__    :     List[str]        =						os.path.dirname(SCREAMING_SNAKE_CASE		)
           elif os.path.isfile(SCREAMING_SNAKE_CASE		):
                UpperCamelCase__    :     Optional[int]        =						path
           else:  # in case of a remote dataset
                print(F'Dataset Infos file saved at {dataset_infos_path}'		)
                exit(1		)

           # Move datasetinfo back to the user
           UpperCamelCase__    :     int        =						os.path.join(SCREAMING_SNAKE_CASE							,     config.DATASETDICT_INFOS_FILENAME		)
           copyfile(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
           print(F'Dataset Infos file saved at {user_dataset_infos_path}'		)







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

 lowerCamelCase			:							List[Any]       =load_dataset('''json''', data_files=args.tr_data_path)['''train''']
 lowerCamelCase			:							str       =load_dataset('''json''', data_files=args.val_data_path)['''train''']

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

 if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
  lowerCamelCase			:							Optional[int]       =tr_dataset.shuffle().select(range(80000))
  lowerCamelCase			:							List[Any]       =val_dataset.shuffle().select(range(8000))

 print(tr_dataset)
 print(val_dataset)

 lowerCamelCase			:							int       =FlaxBigBirdForNaturalQuestions.from_pretrained(
     args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
 )
 lowerCamelCase			:							Union[str, Any]       =BigBirdTokenizerFast.from_pretrained(args.model_id)
 lowerCamelCase			:							Dict       =DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

 lowerCamelCase			:							List[str]       ={
     '''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			:							Tuple       =build_tx(**tx_args)

 lowerCamelCase			:							Optional[Any]       =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			:							Tuple       =None
 lowerCamelCase			:							str       =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			:							str       =jax_utils.unreplicate(state.params)
 model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)