






import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
 from .tokenization_deberta_va import DebertaVaTokenizer
else:
 lowerCamelCase			:							Optional[int]       =None

lowerCamelCase			:							Any       =logging.get_logger(__name__)

lowerCamelCase			:							List[str]       ={'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase			:							str       ={
    '''vocab_file''': {
        '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'''
        ),
        '''microsoft/deberta-v2-xxlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'''
        ),
    }
}

lowerCamelCase			:							List[str]       ={
    '''microsoft/deberta-v2-xlarge''': 512,
    '''microsoft/deberta-v2-xxlarge''': 512,
    '''microsoft/deberta-v2-xlarge-mnli''': 512,
    '''microsoft/deberta-v2-xxlarge-mnli''': 512,
}

lowerCamelCase			:							Optional[int]       ={
    '''microsoft/deberta-v2-xlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xlarge-mnli''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge-mnli''': {'''do_lower_case''': False},
}





class     __a						(					A__ ):
 _lowerCAmelCase : str 				=				VOCAB_FILES_NAMES
 _lowerCAmelCase : Optional[Any] 				=				PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase : List[Any] 				=				PRETRAINED_INIT_CONFIGURATION
 _lowerCAmelCase : Optional[Any] 				=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase : Tuple 				=				DebertaVaTokenizer







 def __init__(	self      :     List[str]							,     SCREAMING_SNAKE_CASE      :     List[str]=None							,     SCREAMING_SNAKE_CASE      :     Any=None							,     SCREAMING_SNAKE_CASE      :     Tuple=False							,     SCREAMING_SNAKE_CASE      :     Any=False							,     SCREAMING_SNAKE_CASE      :     List[str]="[CLS]"							,     SCREAMING_SNAKE_CASE      :     List[str]="[SEP]"							,     SCREAMING_SNAKE_CASE      :     Tuple="[UNK]"							,     SCREAMING_SNAKE_CASE      :     int="[SEP]"							,     SCREAMING_SNAKE_CASE      :     Tuple="[PAD]"							,     SCREAMING_SNAKE_CASE      :     Optional[Any]="[CLS]"							,     SCREAMING_SNAKE_CASE      :     Optional[Any]="[MASK]"							,     **SCREAMING_SNAKE_CASE      :     Tuple							,     ):


      '''simple docstring'''






      super().__init__(
          SCREAMING_SNAKE_CASE							,     tokenizer_file=SCREAMING_SNAKE_CASE							,     do_lower_case=SCREAMING_SNAKE_CASE							,     bos_token=SCREAMING_SNAKE_CASE							,     eos_token=SCREAMING_SNAKE_CASE							,     unk_token=SCREAMING_SNAKE_CASE							,     sep_token=SCREAMING_SNAKE_CASE							,     pad_token=SCREAMING_SNAKE_CASE							,     cls_token=SCREAMING_SNAKE_CASE							,     mask_token=SCREAMING_SNAKE_CASE							,     split_by_punct=SCREAMING_SNAKE_CASE							,     **SCREAMING_SNAKE_CASE							,     )

      UpperCamelCase__    :     Union[str, Any]        =						do_lower_case
      UpperCamelCase__    :     Optional[int]        =						split_by_punct
      UpperCamelCase__    :     Optional[Any]        =						vocab_file
      UpperCamelCase__    :     List[Any]        =						False if not self.vocab_file else True







 def       __lowercase      (	self      :     List[str]							,     SCREAMING_SNAKE_CASE      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     Tuple=None		):


      '''simple docstring'''






      if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase__    :     Tuple        =						[self.cls_token_id]
      UpperCamelCase__    :     Any        =						[self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep







 def       __lowercase      (	self      :     Optional[Any]							,     SCREAMING_SNAKE_CASE      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     str=None							,     SCREAMING_SNAKE_CASE      :     Tuple=False		):


      '''simple docstring'''






      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=SCREAMING_SNAKE_CASE							,     token_ids_a=SCREAMING_SNAKE_CASE							,     already_has_special_tokens=SCREAMING_SNAKE_CASE		)

      if token_ids_a is not None:
           return [1] + ([0] * len(SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE		)) + [1]
      return [1] + ([0] * len(SCREAMING_SNAKE_CASE		)) + [1]







 def       __lowercase      (	self      :     int							,     SCREAMING_SNAKE_CASE      :     int							,     SCREAMING_SNAKE_CASE      :     Dict=None		):


      '''simple docstring'''






      UpperCamelCase__    :     Tuple        =						[self.sep_token_id]
      UpperCamelCase__    :     Optional[int]        =						[self.cls_token_id]
      if token_ids_a is None:
           return len(cls + token_ids_a + sep		) * [0]
      return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







 def       __lowercase      (	self      :     Optional[Any]							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     Optional[str] = None		):


      '''simple docstring'''






      if not self.can_save_slow_tokenizer:
           raise ValueError(
               "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
               "tokenizer."		)

      if not os.path.isdir(SCREAMING_SNAKE_CASE		):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
           return
      UpperCamelCase__    :     Union[str, Any]        =						os.path.join(
          SCREAMING_SNAKE_CASE							,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

      if os.path.abspath(self.vocab_file		) != os.path.abspath(SCREAMING_SNAKE_CASE		):
           copyfile(self.vocab_file							,     SCREAMING_SNAKE_CASE		)

      return (out_vocab_file,)







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)