





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def        __snake_case       (						lowerCAmelCase_  )							->	str:
    SCREAMING_SNAKE_CASE__       							= args.serialization_dir
    SCREAMING_SNAKE_CASE__       							= args.pruning_method
    SCREAMING_SNAKE_CASE__       							= args.threshold

    SCREAMING_SNAKE_CASE__       							= torch.load(os.path.join(lowerCAmelCase_   ,       '''pytorch_model.bin'''  )   ,       map_location='''cpu'''  )

    SCREAMING_SNAKE_CASE__       							= 0  # Number of remaining (not pruned) params in the encoder
    SCREAMING_SNAKE_CASE__       							= 0  # Number of params in the encoder

    print('''name'''.ljust(6_0   ,       ''' '''  )   ,       '''Remaining Weights %'''   ,       '''Remaining Weight'''  )
    for name, param in st.items():
        if "encoder" not in name:
            continue

        if "mask_scores" in name:
            if pruning_method == "topK":
                SCREAMING_SNAKE_CASE__       							= TopKBinarizer.apply(lowerCAmelCase_   ,       lowerCAmelCase_  ).sum().item()
            elif pruning_method == "sigmoied_threshold":
                SCREAMING_SNAKE_CASE__       							= ThresholdBinarizer.apply(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  ).sum().item()
            elif pruning_method == "l0":
                SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= -0.1, 1.1
                SCREAMING_SNAKE_CASE__       							= torch.sigmoid(lowerCAmelCase_  )
                SCREAMING_SNAKE_CASE__       							= s * (r - l) + l
                SCREAMING_SNAKE_CASE__       							= s_bar.clamp(min=0.0   ,       max=1.0  )
                SCREAMING_SNAKE_CASE__       							= (mask > 0.0).sum().item()
            else:
                raise ValueError('''Unknown pruning method'''  )
            remaining_count += mask_ones
            print(name.ljust(6_0   ,       ''' '''  )   ,       str(round(1_0_0 * mask_ones / param.numel()   ,       3  )  ).ljust(2_0   ,       ''' '''  )   ,       str(lowerCAmelCase_  )  )
        else:
            encoder_count += param.numel()
            if "bias" in name or "LayerNorm" in name:
                remaining_count += param.numel()

    print(''''''  )
    print('''Remaining Weights (global) %: '''   ,       1_0_0 * remaining_count / encoder_count  )


if __name__ == "__main__":
  _A				:							List[str]							= argparse.ArgumentParser()

  parser.add_argument(
      """--pruning_method""",
      choices=["""l0""", """topK""", """sigmoied_threshold"""],
      type=str,
      required=True,
      help=(
          """Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"""
          """ pruning)"""
      ),
  )
  parser.add_argument(
      """--threshold""",
      type=float,
      required=False,
      help=(
          """For `topK`, it is the level of remaining weights (in %) in the fine-pruned model."""
          """For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."""
          """Not needed for `l0`"""
      ),
  )
  parser.add_argument(
      """--serialization_dir""",
      type=str,
      required=True,
      help="""Folder containing the model that was previously fine-pruned""",
  )

  _A				:							List[str]							= parser.parse_args()

  main(args)







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)
