



"""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


lowercase__						:							int        =      logging.get_logger(__name__)

lowercase__						:							str        =      {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

lowercase__						:							str        =      {
    """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"""
        ),
    },
}

lowercase__						:							int        =      {
    """roberta-base""": 5_1_2,
    """roberta-large""": 5_1_2,
    """roberta-large-mnli""": 5_1_2,
    """distilroberta-base""": 5_1_2,
    """roberta-base-openai-detector""": 5_1_2,
    """roberta-large-openai-detector""": 5_1_2,
}

@lru_cache()
def 							UpperCamelCase_       (       )		->  Tuple:

  """simple docstring"""




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

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Any		)		->  Union[str, Any]:

  """simple docstring"""




  lowerCAmelCase_  :							str								=					set()
  lowerCAmelCase_  :							Any								=					word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)		)
    lowerCAmelCase_  :							int								=					char
  return pairs







class  UpperCamelCase__  (      lowercase_    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE    							=	PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE    							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE    							=	["""input_ids""", """attention_mask"""]



      def __init__( self							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: str       ,     SCREAMING_SNAKE_CASE_							: List[Any]="replace"       ,     SCREAMING_SNAKE_CASE_							: Optional[Any]="<s>"       ,     SCREAMING_SNAKE_CASE_							: Optional[Any]="</s>"       ,     SCREAMING_SNAKE_CASE_							: List[Any]="</s>"       ,     SCREAMING_SNAKE_CASE_							: Optional[int]="<s>"       ,     SCREAMING_SNAKE_CASE_							: Tuple="<unk>"       ,     SCREAMING_SNAKE_CASE_							: Tuple="<pad>"       ,     SCREAMING_SNAKE_CASE_							: List[str]="<mask>"       ,     SCREAMING_SNAKE_CASE_							: Optional[int]=False       ,     **SCREAMING_SNAKE_CASE_							: str       ,     ):
        lowerCAmelCase_  :							Dict								=					AddedToken(SCREAMING_SNAKE_CASE_       ,     lstrip=SCREAMING_SNAKE_CASE_       ,     rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			) else bos_token
        lowerCAmelCase_  :							int								=					AddedToken(SCREAMING_SNAKE_CASE_       ,     lstrip=SCREAMING_SNAKE_CASE_       ,     rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			) else eos_token
        lowerCAmelCase_  :							Tuple								=					AddedToken(SCREAMING_SNAKE_CASE_       ,     lstrip=SCREAMING_SNAKE_CASE_       ,     rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			) else sep_token
        lowerCAmelCase_  :							Tuple								=					AddedToken(SCREAMING_SNAKE_CASE_       ,     lstrip=SCREAMING_SNAKE_CASE_       ,     rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			) else cls_token
        lowerCAmelCase_  :							Union[str, Any]								=					AddedToken(SCREAMING_SNAKE_CASE_       ,     lstrip=SCREAMING_SNAKE_CASE_       ,     rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			) else unk_token
        lowerCAmelCase_  :							Optional[Any]								=					AddedToken(SCREAMING_SNAKE_CASE_       ,     lstrip=SCREAMING_SNAKE_CASE_       ,     rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        lowerCAmelCase_  :							List[str]								=					AddedToken(SCREAMING_SNAKE_CASE_       ,     lstrip=SCREAMING_SNAKE_CASE_       ,     rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			) else mask_token

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

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

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



      @property
      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]			):
        return len(self.encoder			)



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



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]       ,     SCREAMING_SNAKE_CASE_							: int			):
        if token in self.cache:
          return self.cache[token]
        lowerCAmelCase_  :							List[Any]								=					tuple(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[int]								=					get_pairs(SCREAMING_SNAKE_CASE_			)

        if not pairs:
          return token

        while True:
          lowerCAmelCase_  :							Tuple								=					min(SCREAMING_SNAKE_CASE_       ,     key=lambda SCREAMING_SNAKE_CASE_			: self.bpe_ranks.get(SCREAMING_SNAKE_CASE_       ,     float('inf'			)			)			)
          if bigram not in self.bpe_ranks:
            break
          lowerCAmelCase_		,lowerCAmelCase_  :							List[Any]								=					bigram
          lowerCAmelCase_  :							List[str]								=					[]
          lowerCAmelCase_  :							List[Any]								=					0
          while i < len(SCREAMING_SNAKE_CASE_			):
            try:
              lowerCAmelCase_  :							Union[str, Any]								=					word.index(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
            except ValueError:
              new_word.extend(word[i:]			)
              break
            else:
              new_word.extend(word[i:j]			)
              lowerCAmelCase_  :							int								=					j

            if word[i] == first and i < len(SCREAMING_SNAKE_CASE_			) - 1 and word[i + 1] == second:
              new_word.append(first + second			)
              i += 2
            else:
              new_word.append(word[i]			)
              i += 1
          lowerCAmelCase_  :							Dict								=					tuple(SCREAMING_SNAKE_CASE_			)
          lowerCAmelCase_  :							Optional[int]								=					new_word
          if len(SCREAMING_SNAKE_CASE_			) == 1:
            break
          else:
            lowerCAmelCase_  :							int								=					get_pairs(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[int]								=					' '.join(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Any								=					word
        return word



      def 	SCREAMING_SNAKE_CASE__					( self							: str       ,     SCREAMING_SNAKE_CASE_							: Dict			):
        lowerCAmelCase_  :							List[Any]								=					[]
        for token in re.findall(self.pat       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							Optional[Any]								=					''.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(SCREAMING_SNAKE_CASE_			).split(' '			)			)
        return bpe_tokens



      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]       ,     SCREAMING_SNAKE_CASE_							: List[Any]			):
        return self.encoder.get(SCREAMING_SNAKE_CASE_       ,     self.encoder.get(self.unk_token			)			)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]       ,     SCREAMING_SNAKE_CASE_							: Optional[int]			):
        return self.decoder.get(SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]       ,     SCREAMING_SNAKE_CASE_							: List[Any]			):
        lowerCAmelCase_  :							Optional[Any]								=					''.join(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Tuple								=					bytearray([self.byte_decoder[c] for c in text]			).decode('utf-8'       ,     errors=self.errors			)
        return text



      def 	SCREAMING_SNAKE_CASE__					( self							: Any       ,     SCREAMING_SNAKE_CASE_							: str       ,     SCREAMING_SNAKE_CASE_							: Optional[str] = None			):
        if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
          logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
          return
        lowerCAmelCase_  :							Tuple								=					os.path.join(
            SCREAMING_SNAKE_CASE_       ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
        lowerCAmelCase_  :							Union[str, Any]								=					os.path.join(
            SCREAMING_SNAKE_CASE_       ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']			)

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

        lowerCAmelCase_  :							Dict								=					0
        with open(SCREAMING_SNAKE_CASE_       ,     '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 SCREAMING_SNAKE_CASE_			: 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!'			)
              lowerCAmelCase_  :							Optional[Any]								=					token_index
            writer.write(' '.join(SCREAMING_SNAKE_CASE_			) + '\n'			)
            index += 1

        return vocab_file, merge_file



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]       ,     SCREAMING_SNAKE_CASE_							: List[int]       ,     SCREAMING_SNAKE_CASE_							: Optional[List[int]] = None			):
        if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        lowerCAmelCase_  :							List[Any]								=					[self.cls_token_id]
        lowerCAmelCase_  :							Optional[Any]								=					[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep



      def 	SCREAMING_SNAKE_CASE__					( self							: str       ,     SCREAMING_SNAKE_CASE_							: List[int]       ,     SCREAMING_SNAKE_CASE_							: Optional[List[int]] = None       ,     SCREAMING_SNAKE_CASE_							: bool = False			):
        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 None:
          return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1]
        return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1]



      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple       ,     SCREAMING_SNAKE_CASE_							: List[int]       ,     SCREAMING_SNAKE_CASE_							: Optional[List[int]] = None			):
        lowerCAmelCase_  :							str								=					[self.sep_token_id]
        lowerCAmelCase_  :							Optional[Any]								=					[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 	SCREAMING_SNAKE_CASE__					( self							: str       ,     SCREAMING_SNAKE_CASE_							: List[str]       ,     SCREAMING_SNAKE_CASE_							: Tuple=False       ,     **SCREAMING_SNAKE_CASE_							: Optional[Any]			):
        lowerCAmelCase_  :							List[Any]								=					kwargs.pop('add_prefix_space'       ,     self.add_prefix_space			)
        if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_			) > 0 and not text[0].isspace()):
          lowerCAmelCase_  :							str								=					' ' + 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
     lowercase__						:							Any        =      Args()
     lowercase__						:							List[Any]        =      wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
     lowercase__						:							List[Any]        =      dict(logger.config)
     del wandb_args["batch_size"]
     lowercase__						:							List[str]        =      replace(args, **wandb_args)
     lowercase__						:							Any        =      args.base_dir + """-""" + wandb.run.id
     lowercase__						:							Tuple        =      replace(args, base_dir=base_dir)
     print(args)

     lowercase__						:							Optional[Any]        =      load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
     lowercase__						:							Union[str, Any]        =      load_dataset("""json""", data_files=args.val_data_path)["""train"""]

     # drop extra batch for now
     lowercase__						:							Tuple        =      range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
     lowercase__						:							Tuple        =      tr_dataset.shuffle().select(indices)
     lowercase__						:							Optional[int]        =      range(len(val_dataset) - len(val_dataset) % args.batch_size)
     lowercase__						:							str        =      val_dataset.shuffle().select(indices)

     if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
          lowercase__						:							Optional[int]        =      tr_dataset.shuffle().select(range(8_0_0_0_0))
          lowercase__						:							Any        =      val_dataset.shuffle().select(range(8_0_0_0))

     print(tr_dataset)
     print(val_dataset)

     lowercase__						:							Any        =      FlaxBigBirdForNaturalQuestions.from_pretrained(
         args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
     )
     lowercase__						:							int        =      BigBirdTokenizerFast.from_pretrained(args.model_id)
     lowercase__						:							Union[str, Any]        =      DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

     lowercase__						:							List[Any]        =      {
         """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,
     }
     lowercase__       ,	lowercase__						:							str        =      build_tx(**tx_args)

     lowercase__						:							List[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,
     )

     lowercase__						:							Dict        =      None
     lowercase__						:							Optional[int]        =      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`""")
     lowercase__						:							Any        =      jax_utils.unreplicate(state.params)
     model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





