


import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
   import transformer_engine.pytorch as te

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   Optional[int]=True     ,	SCREAMING_SNAKE_CASE__     :   List[Any]=True     ,	SCREAMING_SNAKE_CASE__     :   List[str]=True							):






     if not is_fpa_available():
          raise ImportError('Using `convert_model` requires transformer_engine to be installed.'							)
     for name, module in model.named_children():
          if isinstance(lowerCAmelCase_     ,	nn.Linear							) and to_transformer_engine and _convert_linear:
               # Return early if the linear layer weights are not multiples of 16
               if any(p % 16 != 0 for p in module.weight.shape							):
                    return
               __UpperCamelCase       				=module.bias is not None
               __UpperCamelCase       				=te.Linear(
                   module.in_features     ,	module.out_features     ,	bias=lowerCAmelCase_     ,	params_dtype=module.weight.dtype							)
               __UpperCamelCase       				=module.weight.data.clone()
               if has_bias:
                    __UpperCamelCase       				=module.bias.data.clone()

               setattr(lowerCAmelCase_     ,	lowerCAmelCase_     ,	lowerCAmelCase_							)
          elif isinstance(lowerCAmelCase_     ,	nn.LayerNorm							) and to_transformer_engine and _convert_ln:
               __UpperCamelCase       				=te.LayerNorm(module.normalized_shape[0]     ,	eps=module.eps     ,	params_dtype=module.weight.dtype							)
               __UpperCamelCase       				=module.weight.data.clone()
               __UpperCamelCase       				=module.bias.data.clone()

               setattr(lowerCAmelCase_     ,	lowerCAmelCase_     ,	lowerCAmelCase_							)
          elif isinstance(lowerCAmelCase_     ,	te.Linear							) and not to_transformer_engine and _convert_linear:
               __UpperCamelCase       				=module.bias is not None
               __UpperCamelCase       				=nn.Linear(
                   module.in_features     ,	module.out_features     ,	bias=lowerCAmelCase_     ,	params_dtype=module.weight.dtype							)
               __UpperCamelCase       				=module.weight.data.clone()
               if has_bias:
                    __UpperCamelCase       				=module.bias.data.clone()

               setattr(lowerCAmelCase_     ,	lowerCAmelCase_     ,	lowerCAmelCase_							)
          elif isinstance(lowerCAmelCase_     ,	te.LayerNorm							) and not to_transformer_engine and _convert_ln:
               __UpperCamelCase       				=nn.LayerNorm(module.normalized_shape[0]     ,	eps=module.eps     ,	params_dtype=module.weight.dtype							)
               __UpperCamelCase       				=module.weight.data.clone()
               __UpperCamelCase       				=module.bias.data.clone()

               setattr(lowerCAmelCase_     ,	lowerCAmelCase_     ,	lowerCAmelCase_							)
          else:
               convert_model(
                   lowerCAmelCase_     ,	to_transformer_engine=lowerCAmelCase_     ,	_convert_linear=_convert_linear     ,	_convert_ln=_convert_ln     ,	)





def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int							):






     if not is_fpa_available():
          raise ImportError('Using `has_transformer_engine_layers` requires transformer_engine to be installed.'							)
     for m in model.modules():
          if isinstance(lowerCAmelCase_     ,	(te.LayerNorm, te.Linear)							):
               return True
     return False






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(80_000))
            UpperCAmelCase_    		=				val_dataset.shuffle().select(range(8_000))

      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=4_096)

      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)