






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase			:							List[Any]       =logging.get_logger(__name__)

lowerCamelCase			:							Optional[Any]       ={
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}





class     __a						(					A__ ):
 _lowerCAmelCase : int 				=				'''ernie'''







 def __init__(	self      :     Any							,     SCREAMING_SNAKE_CASE      :     Optional[Any]=3_05_22							,     SCREAMING_SNAKE_CASE      :     Dict=7_68							,     SCREAMING_SNAKE_CASE      :     int=12							,     SCREAMING_SNAKE_CASE      :     Tuple=12							,     SCREAMING_SNAKE_CASE      :     Optional[int]=30_72							,     SCREAMING_SNAKE_CASE      :     int="gelu"							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]=0.1							,     SCREAMING_SNAKE_CASE      :     List[Any]=0.1							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]=5_12							,     SCREAMING_SNAKE_CASE      :     Optional[Any]=2							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]=3							,     SCREAMING_SNAKE_CASE      :     List[Any]=False							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]=0.0_2							,     SCREAMING_SNAKE_CASE      :     int=1e-1_2							,     SCREAMING_SNAKE_CASE      :     List[Any]=0							,     SCREAMING_SNAKE_CASE      :     Any="absolute"							,     SCREAMING_SNAKE_CASE      :     List[Any]=True							,     SCREAMING_SNAKE_CASE      :     Dict=None							,     **SCREAMING_SNAKE_CASE      :     Any							,     ):


      '''simple docstring'''






      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE							,     **SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     Optional[Any]        =						vocab_size
      UpperCamelCase__    :     Optional[Any]        =						hidden_size
      UpperCamelCase__    :     List[Any]        =						num_hidden_layers
      UpperCamelCase__    :     Tuple        =						num_attention_heads
      UpperCamelCase__    :     List[str]        =						hidden_act
      UpperCamelCase__    :     Tuple        =						intermediate_size
      UpperCamelCase__    :     List[str]        =						hidden_dropout_prob
      UpperCamelCase__    :     Optional[Any]        =						attention_probs_dropout_prob
      UpperCamelCase__    :     List[str]        =						max_position_embeddings
      UpperCamelCase__    :     Union[str, Any]        =						type_vocab_size
      UpperCamelCase__    :     List[Any]        =						task_type_vocab_size
      UpperCamelCase__    :     Dict        =						use_task_id
      UpperCamelCase__    :     str        =						initializer_range
      UpperCamelCase__    :     Tuple        =						layer_norm_eps
      UpperCamelCase__    :     List[str]        =						position_embedding_type
      UpperCamelCase__    :     Optional[int]        =						use_cache
      UpperCamelCase__    :     List[Any]        =						classifier_dropout







class     __a						(					A__ ):







 @property
 def       __lowercase      (	self      :     Any		):


      '''simple docstring'''






      if self.task == "multiple-choice":
           UpperCamelCase__    :     int        =						{0: "batch", 1: "choice", 2: "sequence"}
      else:
           UpperCamelCase__    :     Optional[int]        =						{0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("token_type_ids", dynamic_axis),
              ("task_type_ids", dynamic_axis),
          ]		)







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)