





"""simple docstring"""
import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
lowerCamelCase_								=		object()

# For specifying empty leaf dict `{}`
lowerCamelCase_								=		object()



def      __lowerCamelCase   (							a_     :		str ,   a_     :		Optional[Any]							)			->      Tuple:
      __SCREAMING_SNAKE_CASE   :Any						=  tuple((re.compile(x + '''$'''							) for x in qs)							)
      for i in range(len(a_							) - len(a_							) + 1							):
            __SCREAMING_SNAKE_CASE   :List[str]						=  [x.match(a_							) for x, y in zip(a_ ,   ks[i:]							)]
            if matches and all(a_							):
                  return True
      return False



def      __lowerCamelCase   (							a_     :		Dict							)			->      Optional[int]:
      def replace(a_     :		Optional[Any] ,   a_     :		List[str]							):
            for rule, replacement in rules:
                  if _match(a_ ,   a_							):
                        return replacement
            return val

      return replace



def      __lowerCamelCase   (							)			->      List[Any]:
      return [
          # embeddings
          (("transformer", "wpe", "embedding"), P('''mp''' ,   a_							)),
          (("transformer", "wte", "embedding"), P('''mp''' ,   a_							)),
          # atention
          (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(a_ ,   '''mp'''							)),
          (("attention", "out_proj", "kernel"), P('''mp''' ,   a_							)),
          (("attention", "out_proj", "bias"), None),
          # mlp
          (("mlp", "c_fc", "kernel"), P(a_ ,   '''mp'''							)),
          (("mlp", "c_fc", "bias"), P('''mp'''							)),
          (("mlp", "c_proj", "kernel"), P('''mp''' ,   a_							)),
          (("mlp", "c_proj", "bias"), None),
          # layer norms
          ((r"ln_\d+", "bias"), None),
          ((r"\d+", r"ln_\d+", "scale"), None),
          (("ln_f", "bias"), None),
          (("ln_f", "scale"), None),
      ]







def      __lowerCamelCase   (							a_     :		str							)			->      Union[str, Any]:
      __SCREAMING_SNAKE_CASE   :Any						=  _get_partition_rules()
      __SCREAMING_SNAKE_CASE   :Any						=  _replacement_rules(a_							)
      __SCREAMING_SNAKE_CASE   :str						=  {k: _unmatched for k in flatten_dict(a_							)}
      __SCREAMING_SNAKE_CASE   :List[str]						=  {k: replace(a_ ,   a_							) for k, v in initd.items()}
      assert _unmatched not in result.values(), "Incomplete partition spec."
      return freeze(unflatten_dict(a_							)							)






"""simple docstring"""
import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCamelCase_								=		logging.getLogger(__name__)



def      __lowerCamelCase   (							)			->      int:
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  argparse.ArgumentParser(
          description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''							)
      parser.add_argument('''--file_path''' ,   type=a_ ,   default='''data/dump.txt''' ,   help='''The path to the data.'''							)
      parser.add_argument('''--tokenizer_type''' ,   type=a_ ,   default='''bert''' ,   choices=['''bert''', '''roberta''', '''gpt2''']							)
      parser.add_argument('''--tokenizer_name''' ,   type=a_ ,   default='''bert-base-uncased''' ,   help='''The tokenizer to use.'''							)
      parser.add_argument('''--dump_file''' ,   type=a_ ,   default='''data/dump''' ,   help='''The dump file prefix.'''							)
      __SCREAMING_SNAKE_CASE   :Any						=  parser.parse_args()

      logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''							)
      if args.tokenizer_type == "bert":
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  BertTokenizer.from_pretrained(args.tokenizer_name							)
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            __SCREAMING_SNAKE_CASE   :str						=  RobertaTokenizer.from_pretrained(args.tokenizer_name							)
            __SCREAMING_SNAKE_CASE   :str						=  tokenizer.special_tokens_map['''cls_token''']  # `<s>`
            __SCREAMING_SNAKE_CASE   :str						=  tokenizer.special_tokens_map['''sep_token''']  # `</s>`
      elif args.tokenizer_type == "gpt2":
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  GPTaTokenizer.from_pretrained(args.tokenizer_name							)
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
            __SCREAMING_SNAKE_CASE   :str						=  tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

      logger.info(f'''Loading text from {args.file_path}'''							)
      with open(args.file_path ,   '''r''' ,   encoding='''utf8'''							) as fp:
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  fp.readlines()

      logger.info('''Start encoding'''							)
      logger.info(f'''{len(a_							)} examples to process.'''							)

      __SCREAMING_SNAKE_CASE   :Optional[int]						=  []
      __SCREAMING_SNAKE_CASE   :List[str]						=  0
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  1_00_00
      __SCREAMING_SNAKE_CASE   :List[Any]						=  time.time()
      for text in data:
            __SCREAMING_SNAKE_CASE   :Any						=  f'''{bos} {text.strip()} {sep}'''
            __SCREAMING_SNAKE_CASE   :int						=  tokenizer.encode(a_ ,   add_special_tokens=a_							)
            rslt.append(a_							)

            iter += 1
            if iter % interval == 0:
                  __SCREAMING_SNAKE_CASE   :Any						=  time.time()
                  logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''							)
                  __SCREAMING_SNAKE_CASE   :Any						=  time.time()
      logger.info('''Finished binarization'''							)
      logger.info(f'''{len(a_							)} examples processed.'''							)

      __SCREAMING_SNAKE_CASE   :Optional[int]						=  f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
      __SCREAMING_SNAKE_CASE   :str						=  tokenizer.vocab_size
      if vocab_size < (1 << 16):
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  [np.uintaa(a_							) for d in rslt]
      else:
            __SCREAMING_SNAKE_CASE   :List[Any]						=  [np.intaa(a_							) for d in rslt]
      random.shuffle(rslt_							)
      logger.info(f'''Dump to {dp_file}'''							)
      with open(a_ ,   '''wb'''							) as handle:
            pickle.dump(rslt_ ,   a_ ,   protocol=pickle.HIGHEST_PROTOCOL							)


if __name__ == "__main__":
  main()