





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


_A				:							Optional[Any]							= logging.get_logger(__name__)

_A				:							List[str]							= {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

_A				:							Union[str, Any]							= {
    '''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'''
        ),
    },
}

_A				:							int							= {
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}






@lru_cache()
def        __snake_case       (						)							->	Union[str, Any]:
    SCREAMING_SNAKE_CASE__       							= (
        list(range(ord('''!'''  )   ,       ord('''~'''  ) + 1  )  ) + list(range(ord('''¡'''  )   ,       ord('''¬'''  ) + 1  )  ) + list(range(ord('''®'''  )   ,       ord('''ÿ'''  ) + 1  )  )
    )
    SCREAMING_SNAKE_CASE__       							= bs[:]
    SCREAMING_SNAKE_CASE__       							= 0
    for b in range(2**8  ):
        if b not in bs:
            bs.append(_UpperCAmelCase  )
            cs.append(2**8 + n  )
            n += 1
    SCREAMING_SNAKE_CASE__       							= [chr(_UpperCAmelCase  ) for n in cs]
    return dict(zip(_UpperCAmelCase   ,       _UpperCAmelCase  )  )






def        __snake_case       (						lowerCAmelCase_  )							->	Any:
    SCREAMING_SNAKE_CASE__       							= set()
    SCREAMING_SNAKE_CASE__       							= word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)  )
        SCREAMING_SNAKE_CASE__       							= char
    return pairs



class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    lowerCamelCase__						:		str							=  VOCAB_FILES_NAMES
    lowerCamelCase__						:		Optional[int]							=  PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase__						:		Union[str, Any]							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase__						:		Dict							=  ["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_      ,							):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= AddedToken(UpperCAmelCase_      ,							lstrip=UpperCAmelCase_      ,							rstrip=UpperCAmelCase_    ) if isinstance(UpperCAmelCase_      ,							UpperCAmelCase_    ) else bos_token
        SCREAMING_SNAKE_CASE__       							= AddedToken(UpperCAmelCase_      ,							lstrip=UpperCAmelCase_      ,							rstrip=UpperCAmelCase_    ) if isinstance(UpperCAmelCase_      ,							UpperCAmelCase_    ) else eos_token
        SCREAMING_SNAKE_CASE__       							= AddedToken(UpperCAmelCase_      ,							lstrip=UpperCAmelCase_      ,							rstrip=UpperCAmelCase_    ) if isinstance(UpperCAmelCase_      ,							UpperCAmelCase_    ) else sep_token
        SCREAMING_SNAKE_CASE__       							= AddedToken(UpperCAmelCase_      ,							lstrip=UpperCAmelCase_      ,							rstrip=UpperCAmelCase_    ) if isinstance(UpperCAmelCase_      ,							UpperCAmelCase_    ) else cls_token
        SCREAMING_SNAKE_CASE__       							= AddedToken(UpperCAmelCase_      ,							lstrip=UpperCAmelCase_      ,							rstrip=UpperCAmelCase_    ) if isinstance(UpperCAmelCase_      ,							UpperCAmelCase_    ) else unk_token
        SCREAMING_SNAKE_CASE__       							= AddedToken(UpperCAmelCase_      ,							lstrip=UpperCAmelCase_      ,							rstrip=UpperCAmelCase_    ) if isinstance(UpperCAmelCase_      ,							UpperCAmelCase_    ) else pad_token

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

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

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

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



    @property
    def    lowercase_				(					self    ):





        '''simple docstring'''

        return len(self.encoder    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        return dict(self.encoder      ,							**self.added_tokens_encoder    )



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        if token in self.cache:
            return self.cache[token]
        SCREAMING_SNAKE_CASE__       							= tuple(UpperCAmelCase_    )
        SCREAMING_SNAKE_CASE__       							= get_pairs(UpperCAmelCase_    )

        if not pairs:
            return token

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

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



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= []
        for token in re.findall(self.pat      ,							UpperCAmelCase_    ):
            SCREAMING_SNAKE_CASE__       							= ''.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(UpperCAmelCase_    ).split(''' '''    )    )
        return bpe_tokens



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        return self.encoder.get(UpperCAmelCase_      ,							self.encoder.get(self.unk_token    )    )



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        return self.decoder.get(UpperCAmelCase_    )



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= ''.join(UpperCAmelCase_    )
        SCREAMING_SNAKE_CASE__       							= bytearray([self.byte_decoder[c] for c in text]    ).decode('''utf-8'''      ,							errors=self.errors    )
        return text



    def    lowercase_				(					self      ,							A_      ,							A_ = None    ):





        '''simple docstring'''

        if not os.path.isdir(UpperCAmelCase_    ):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
            return
        SCREAMING_SNAKE_CASE__       							= os.path.join(
            UpperCAmelCase_      ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
        SCREAMING_SNAKE_CASE__       							= os.path.join(
            UpperCAmelCase_      ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']    )

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

        SCREAMING_SNAKE_CASE__       							= 0
        with open(UpperCAmelCase_      ,							'''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!'''    )
                    SCREAMING_SNAKE_CASE__       							= token_index
                writer.write(''' '''.join(UpperCAmelCase_    ) + '''\n'''    )
                index += 1

        return vocab_file, merge_file



    def    lowercase_				(					self      ,							A_      ,							A_ = None    ):





        '''simple docstring'''

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



    def    lowercase_				(					self      ,							A_      ,							A_ = None      ,							A_ = False    ):





        '''simple docstring'''

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

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



    def    lowercase_				(					self      ,							A_      ,							A_ = None    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= [self.sep_token_id]
        SCREAMING_SNAKE_CASE__       							= [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    lowercase_				(					self      ,							A_      ,							A_=False      ,							**A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= kwargs.pop('''add_prefix_space'''      ,							self.add_prefix_space    )
        if (is_split_into_words or add_prefix_space) and (len(UpperCAmelCase_    ) > 0 and not text[0].isspace()):
            SCREAMING_SNAKE_CASE__       							= ' ' + text
        return (text, kwargs)







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
  _A				:							Any							= Args()
  _A				:							Union[str, Any]							= wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
  _A				:							Optional[Any]							= dict(logger.config)
  del wandb_args["batch_size"]
  _A				:							Any							= replace(args, **wandb_args)
  _A				:							Dict							= args.base_dir + """-""" + wandb.run.id
  _A				:							Dict							= replace(args, base_dir=base_dir)
  print(args)

  _A				:							Tuple							= load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
  _A				:							Optional[Any]							= load_dataset("""json""", data_files=args.val_data_path)["""train"""]

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

  if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
    _A				:							Optional[Any]							= tr_dataset.shuffle().select(range(8_00_00))
    _A				:							Tuple							= val_dataset.shuffle().select(range(80_00))

  print(tr_dataset)
  print(val_dataset)

  _A				:							int							= FlaxBigBirdForNaturalQuestions.from_pretrained(
      args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
  )
  _A				:							Any							= BigBirdTokenizerFast.from_pretrained(args.model_id)
  _A				:							Optional[Any]							= DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

  _A				:							int							= {
      """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,
  }
  _A,					_A				:							Union[str, Any]							= build_tx(**tx_args)

  _A				:							str							= 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,
  )

  _A				:							Tuple							= None
  _A				:							Dict							= 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`""")
  _A				:							Tuple							= jax_utils.unreplicate(state.params)
  model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)
