





"""simple docstring"""






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_      =				logging.get_logger(__name__)

lowerCAmelCase_      =				{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

lowerCAmelCase_      =				{
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
}

lowerCAmelCase_      =				{
    '''roberta-base''': 512,
    '''roberta-large''': 512,
    '''roberta-large-mnli''': 512,
    '''distilroberta-base''': 512,
    '''roberta-base-openai-detector''': 512,
    '''roberta-large-openai-detector''': 512,
}

@lru_cache()
def    __lowerCamelCase     (							)		->							Union[str, Any]:






       """simple docstring"""




       _UpperCAmelCase			     =  (
           list(range(ord('!'				),ord('~'				) + 1				)				) + list(range(ord('¡'				),ord('¬'				) + 1				)				) + list(range(ord('®'				),ord('ÿ'				) + 1				)				)
       )
       _UpperCAmelCase			     =  bs[:]
       _UpperCAmelCase			     =  0
       for b in range(2**8				):
              if b not in bs:
                     bs.append(SCREAMING_SNAKE_CASE				)
                     cs.append(2**8 + n				)
                     n += 1
       _UpperCAmelCase			     =  [chr(SCREAMING_SNAKE_CASE				) for n in cs]
       return dict(zip(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)				)

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							Tuple:






       """simple docstring"""




       _UpperCAmelCase			     =  set()
       _UpperCAmelCase			     =  word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)				)
              _UpperCAmelCase			     =  char
       return pairs



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	VOCAB_FILES_NAMES
       lowerCAmelCase__    			=	PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__    			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__    			=	["""input_ids""", """attention_mask"""]






       def __init__( self     ,	a__     ,	a__     ,	a__="replace"     ,	a__="<s>"     ,	a__="</s>"     ,	a__="</s>"     ,	a__="<s>"     ,	a__="<unk>"     ,	a__="<pad>"     ,	a__="<mask>"     ,	a__=False     ,	**a__     ,	):
              _UpperCAmelCase			     =  AddedToken(a__     ,	lstrip=a__     ,	rstrip=a__  ) if isinstance(a__     ,	a__  ) else bos_token
              _UpperCAmelCase			     =  AddedToken(a__     ,	lstrip=a__     ,	rstrip=a__  ) if isinstance(a__     ,	a__  ) else eos_token
              _UpperCAmelCase			     =  AddedToken(a__     ,	lstrip=a__     ,	rstrip=a__  ) if isinstance(a__     ,	a__  ) else sep_token
              _UpperCAmelCase			     =  AddedToken(a__     ,	lstrip=a__     ,	rstrip=a__  ) if isinstance(a__     ,	a__  ) else cls_token
              _UpperCAmelCase			     =  AddedToken(a__     ,	lstrip=a__     ,	rstrip=a__  ) if isinstance(a__     ,	a__  ) else unk_token
              _UpperCAmelCase			     =  AddedToken(a__     ,	lstrip=a__     ,	rstrip=a__  ) if isinstance(a__     ,	a__  ) else pad_token

              # Mask token behave like a normal word, i.e. include the space before it
              _UpperCAmelCase			     =  AddedToken(a__     ,	lstrip=a__     ,	rstrip=a__  ) if isinstance(a__     ,	a__  ) else mask_token

              super().__init__(
                  errors=a__     ,	bos_token=a__     ,	eos_token=a__     ,	unk_token=a__     ,	sep_token=a__     ,	cls_token=a__     ,	pad_token=a__     ,	mask_token=a__     ,	add_prefix_space=a__     ,	**a__     ,	)

              with open(a__     ,	encoding='utf-8'  ) as vocab_handle:
                     _UpperCAmelCase			     =  json.load(a__  )
              _UpperCAmelCase			     =  {v: k for k, v in self.encoder.items()}
              _UpperCAmelCase			     =  errors  # how to handle errors in decoding
              _UpperCAmelCase			     =  bytes_to_unicode()
              _UpperCAmelCase			     =  {v: k for k, v in self.byte_encoder.items()}
              with open(a__     ,	encoding='utf-8'  ) as merges_handle:
                     _UpperCAmelCase			     =  merges_handle.read().split('\n'  )[1:-1]
              _UpperCAmelCase			     =  [tuple(merge.split()  ) for merge in bpe_merges]
              _UpperCAmelCase			     =  dict(zip(a__     ,	range(len(a__  )  )  )  )
              _UpperCAmelCase			     =  {}
              _UpperCAmelCase			     =  add_prefix_space

              # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
              _UpperCAmelCase			     =  re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'  )






       @property
       def 				__A			( self  ):
              return len(self.encoder  )






       def 				__A			( self  ):
              return dict(self.encoder     ,	**self.added_tokens_encoder  )






       def 				__A			( self     ,	a__  ):
              if token in self.cache:
                     return self.cache[token]
              _UpperCAmelCase			     =  tuple(a__  )
              _UpperCAmelCase			     =  get_pairs(a__  )

              if not pairs:
                     return token

              while True:
                     _UpperCAmelCase			     =  min(a__     ,	key=lambda a__  : self.bpe_ranks.get(a__     ,	float('inf'  )  )  )
                     if bigram not in self.bpe_ranks:
                            break
                     _UpperCAmelCase     ,				_UpperCAmelCase			     =  bigram
                     _UpperCAmelCase			     =  []
                     _UpperCAmelCase			     =  0
                     while i < len(a__  ):
                            try:
                                   _UpperCAmelCase			     =  word.index(a__     ,	a__  )
                            except ValueError:
                                   new_word.extend(word[i:]  )
                                   break
                            else:
                                   new_word.extend(word[i:j]  )
                                   _UpperCAmelCase			     =  j

                            if word[i] == first and i < len(a__  ) - 1 and word[i + 1] == second:
                                   new_word.append(first + second  )
                                   i += 2
                            else:
                                   new_word.append(word[i]  )
                                   i += 1
                     _UpperCAmelCase			     =  tuple(a__  )
                     _UpperCAmelCase			     =  new_word
                     if len(a__  ) == 1:
                            break
                     else:
                            _UpperCAmelCase			     =  get_pairs(a__  )
              _UpperCAmelCase			     =  ' '.join(a__  )
              _UpperCAmelCase			     =  word
              return word






       def 				__A			( self     ,	a__  ):
              _UpperCAmelCase			     =  []
              for token in re.findall(self.pat     ,	a__  ):
                     _UpperCAmelCase			     =  ''.join(
                         self.byte_encoder[b] for b in token.encode('utf-8'  )  )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                     bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a__  ).split(' '  )  )
              return bpe_tokens






       def 				__A			( self     ,	a__  ):
              return self.encoder.get(a__     ,	self.encoder.get(self.unk_token  )  )






       def 				__A			( self     ,	a__  ):
              return self.decoder.get(a__  )






       def 				__A			( self     ,	a__  ):
              _UpperCAmelCase			     =  ''.join(a__  )
              _UpperCAmelCase			     =  bytearray([self.byte_decoder[c] for c in text]  ).decode('utf-8'     ,	errors=self.errors  )
              return text






       def 				__A			( self     ,	a__     ,	a__ = None  ):
              if not os.path.isdir(a__  ):
                     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
                     return
              _UpperCAmelCase			     =  os.path.join(
                  a__     ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )
              _UpperCAmelCase			     =  os.path.join(
                  a__     ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']  )

              with open(a__     ,	'w'     ,	encoding='utf-8'  ) as f:
                     f.write(json.dumps(self.encoder     ,	indent=2     ,	sort_keys=a__     ,	ensure_ascii=a__  ) + '\n'  )

              _UpperCAmelCase			     =  0
              with open(a__     ,	'w'     ,	encoding='utf-8'  ) as writer:
                     writer.write('#version: 0.2\n'  )
                     for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,	key=lambda a__  : kv[1]  ):
                            if index != token_index:
                                   logger.warning(
                                       f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                                       ' Please check that the tokenizer is not corrupted!'  )
                                   _UpperCAmelCase			     =  token_index
                            writer.write(' '.join(a__  ) + '\n'  )
                            index += 1

              return vocab_file, merge_file






       def 				__A			( self     ,	a__     ,	a__ = None  ):
              if token_ids_a is None:
                     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              _UpperCAmelCase			     =  [self.cls_token_id]
              _UpperCAmelCase			     =  [self.sep_token_id]
              return cls + token_ids_a + sep + sep + token_ids_a + sep






       def 				__A			( self     ,	a__     ,	a__ = None     ,	a__ = False  ):
              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=a__     ,	token_ids_a=a__     ,	already_has_special_tokens=a__  )

              if token_ids_a is None:
                     return [1] + ([0] * len(a__  )) + [1]
              return [1] + ([0] * len(a__  )) + [1, 1] + ([0] * len(a__  )) + [1]






       def 				__A			( self     ,	a__     ,	a__ = None  ):
              _UpperCAmelCase			     =  [self.sep_token_id]
              _UpperCAmelCase			     =  [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 + sep + token_ids_a + sep  ) * [0]






       def 				__A			( self     ,	a__     ,	a__=False     ,	**a__  ):
              _UpperCAmelCase			     =  kwargs.pop('add_prefix_space'     ,	self.add_prefix_space  )
              if (is_split_into_words or add_prefix_space) and (len(a__  ) > 0 and not text[0].isspace()):
                     _UpperCAmelCase			     =  ' ' + text
              return (text, kwargs)













"""simple docstring"""






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

    lowerCAmelCase_      =				load_dataset('''json''', data_files=args.tr_data_path)['''train''']
    lowerCAmelCase_      =				load_dataset('''json''', data_files=args.val_data_path)['''train''']

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

    if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
        lowerCAmelCase_      =				tr_dataset.shuffle().select(range(80_000))
        lowerCAmelCase_      =				val_dataset.shuffle().select(range(8_000))

    print(tr_dataset)
    print(val_dataset)

    lowerCAmelCase_      =				FlaxBigBirdForNaturalQuestions.from_pretrained(
        args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
    )
    lowerCAmelCase_      =				BigBirdTokenizerFast.from_pretrained(args.model_id)
    lowerCAmelCase_      =				DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

    lowerCAmelCase_      =				{
        '''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_      =				build_tx(**tx_args)

    lowerCAmelCase_      =				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_      =				None
    lowerCAmelCase_      =				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_      =				jax_utils.unreplicate(state.params)
    model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)






