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

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

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

  if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
    UpperCAmelCase				 =   tr_dataset.shuffle().select(range(80000))
    UpperCAmelCase				 =   val_dataset.shuffle().select(range(8000))

  print(tr_dataset)
  print(val_dataset)

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

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

  UpperCAmelCase				 =   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,
  )

  UpperCAmelCase				 =   None
  UpperCAmelCase				 =   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`""")
  UpperCAmelCase				 =   jax_utils.unreplicate(state.params)
  model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase				 =   logging.get_logger(__name__)

UpperCAmelCase				 =   {
    """facebook/opt-125m""": """https://huggingface.co/facebook/opt-125m/blob/main/config.json""",
    """facebook/opt-350m""": """https://huggingface.co/facebook/opt-350m/blob/main/config.json""",
    """facebook/opt-1.3b""": """https://huggingface.co/facebook/opt-1.3b/blob/main/config.json""",
    """facebook/opt-2.7b""": """https://huggingface.co/facebook/opt-2.7b/blob/main/config.json""",
    """facebook/opt-6.7b""": """https://huggingface.co/facebook/opt-6.7b/blob/main/config.json""",
    """facebook/opt-13b""": """https://huggingface.co/facebook/opt-13b/blob/main/config.json""",
}






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    List[str]			      = 'opt'
   UpperCamelCase_					:    List[str]			      = ['past_key_values']
   def __init__(     self				,  lowerCAmelCase_=5_02_72				,  lowerCAmelCase_=7_68				,  lowerCAmelCase_=12				,  lowerCAmelCase_=30_72				,  lowerCAmelCase_=20_48				,  lowerCAmelCase_=True				,  lowerCAmelCase_=False				,  lowerCAmelCase_=None				,  lowerCAmelCase_=0.1				,  lowerCAmelCase_=0.0				,  lowerCAmelCase_=12				,  lowerCAmelCase_="relu"				,  lowerCAmelCase_=0.0				,  lowerCAmelCase_=0.02				,  lowerCAmelCase_=True				,  lowerCAmelCase_=1				,  lowerCAmelCase_=2				,  lowerCAmelCase_=2				,  lowerCAmelCase_=True				,  lowerCAmelCase_=True				,  **lowerCAmelCase_				,  )						->Any:
        super().__init__(
            pad_token_id=lowerCAmelCase_				,  bos_token_id=lowerCAmelCase_				,  eos_token_id=lowerCAmelCase_				,  **lowerCAmelCase_				,  )
        a	:     Dict						      =    vocab_size
        a	:     int						      =    max_position_embeddings
        a	:     List[Any]						      =    num_attention_heads
        a	:     Union[str, Any]						      =    word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        a	:     Any						      =    ffn_dim
        a	:     Optional[int]						      =    hidden_size
        a	:     Optional[Any]						      =    num_hidden_layers
        a	:     Optional[int]						      =    dropout
        a	:     Optional[Any]						      =    attention_dropout
        a	:     Union[str, Any]						      =    activation_function
        a	:     Any						      =    init_std
        a	:     List[Any]						      =    layerdrop
        a	:     str						      =    use_cache
        a	:     Any						      =    do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        a	:     List[Any]						      =    enable_bias
        a	:     List[str]						      =    layer_norm_elementwise_affine

        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        a	:     Dict						      =    _remove_final_layer_norm