





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

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

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

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

   print(tr_dataset)
   print(val_dataset)

   lowerCamelCase_	:		Dict         =   FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   lowerCamelCase_	:		str         =   BigBirdTokenizerFast.from_pretrained(args.model_id)
   lowerCamelCase_	:		Any         =   DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

   lowerCamelCase_	:		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,
   }
   lowerCamelCase_   ,   lowerCamelCase_	:		List[Any]         =   build_tx(**tx_args)

   lowerCamelCase_	:		List[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_	:		Optional[Any]         =   None
   lowerCamelCase_	:		int         =   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_	:		Optional[Any]         =   jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_	:		Union[str, Any]         =   {
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Any         =   ['CamembertTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		int         =   ['CamembertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Optional[Any]         =   [
       'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'CamembertForCausalLM',
       'CamembertForMaskedLM',
       'CamembertForMultipleChoice',
       'CamembertForQuestionAnswering',
       'CamembertForSequenceClassification',
       'CamembertForTokenClassification',
       'CamembertModel',
       'CamembertPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		int         =   [
       'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFCamembertForCausalLM',
       'TFCamembertForMaskedLM',
       'TFCamembertForMultipleChoice',
       'TFCamembertForQuestionAnswering',
       'TFCamembertForSequenceClassification',
       'TFCamembertForTokenClassification',
       'TFCamembertModel',
       'TFCamembertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert import CamembertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert_fast import CamembertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_camembert import (
          CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CamembertForCausalLM,
          CamembertForMaskedLM,
          CamembertForMultipleChoice,
          CamembertForQuestionAnswering,
          CamembertForSequenceClassification,
          CamembertForTokenClassification,
          CamembertModel,
          CamembertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_camembert import (
          TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCamembertForCausalLM,
          TFCamembertForMaskedLM,
          TFCamembertForMultipleChoice,
          TFCamembertForQuestionAnswering,
          TFCamembertForSequenceClassification,
          TFCamembertForTokenClassification,
          TFCamembertModel,
          TFCamembertPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_	:		int         =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)