


"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				str   ="llama"
       a  :				List[str]   =["past_key_values"]

       def __init__(						self		,     snake_case__=32_000		,     snake_case__=4_096		,     snake_case__=11_008		,     snake_case__=32		,     snake_case__=32		,     snake_case__=None		,     snake_case__="silu"		,     snake_case__=2_048		,     snake_case__=0.02		,     snake_case__=1e-6		,     snake_case__=True		,     snake_case__=0		,     snake_case__=1		,     snake_case__=2		,     snake_case__=1		,     snake_case__=False		,     snake_case__=None		,     **snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							vocab_size
         lowerCAmelCase     :     str								=							max_position_embeddings
         lowerCAmelCase     :     str								=							hidden_size
         lowerCAmelCase     :     Optional[int]								=							intermediate_size
         lowerCAmelCase     :     Any								=							num_hidden_layers
         lowerCAmelCase     :     List[str]								=							num_attention_heads

         # for backward compatibility
         if num_key_value_heads is None:
           lowerCAmelCase     :     Tuple								=							num_attention_heads

         lowerCAmelCase     :     Dict								=							num_key_value_heads
         lowerCAmelCase     :     Optional[Any]								=							hidden_act
         lowerCAmelCase     :     Optional[Any]								=							initializer_range
         lowerCAmelCase     :     Any								=							rms_norm_eps
         lowerCAmelCase     :     List[Any]								=							pretraining_tp
         lowerCAmelCase     :     int								=							use_cache
         lowerCAmelCase     :     List[str]								=							rope_scaling
         self._rope_scaling_validation()

         super().__init__(
             pad_token_id=snake_case__		,     bos_token_id=snake_case__		,     eos_token_id=snake_case__		,     tie_word_embeddings=snake_case__		,     **snake_case__		,     )







       def        lowercase__							(						self						):






         """simple docstring"""





         if self.rope_scaling is None:
           return

         if not isinstance(self.rope_scaling		,     snake_case__						) or len(self.rope_scaling						) != 2:
           raise ValueError(
               "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
               f"""got {self.rope_scaling}"""						)
         lowerCAmelCase     :     Optional[Any]								=							self.rope_scaling.get("type"		,     snake_case__						)
         lowerCAmelCase     :     int								=							self.rope_scaling.get("factor"		,     snake_case__						)
         if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
           raise ValueError(
               f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""						)
         if rope_scaling_factor is None or not isinstance(snake_case__		,     snake_case__						) or rope_scaling_factor <= 1.0:
           raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""						)






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
)
UpperCamelCase__											=					logging.getLogger(__name__)




def    lowerCAmelCase_ (     )     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  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."							)
      UpperCAmelCase__								=  parser.parse_args()

      logger.info(f"""Loading Tokenizer ({args.tokenizer_name})"""							)
      if args.tokenizer_type == "bert":
            UpperCAmelCase__								=  BertTokenizer.from_pretrained(args.tokenizer_name							)
            UpperCAmelCase__								=  tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
            UpperCAmelCase__								=  tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            UpperCAmelCase__								=  RobertaTokenizer.from_pretrained(args.tokenizer_name							)
            UpperCAmelCase__								=  tokenizer.special_tokens_map["cls_token"]  # `<s>`
            UpperCAmelCase__								=  tokenizer.special_tokens_map["sep_token"]  # `</s>`
      elif args.tokenizer_type == "gpt2":
            UpperCAmelCase__								=  GPTaTokenizer.from_pretrained(args.tokenizer_name							)
            UpperCAmelCase__								=  tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
            UpperCAmelCase__								=  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:
            UpperCAmelCase__								=  fp.readlines()

      logger.info("Start encoding"							)
      logger.info(f"""{len(__A							)} examples to process."""							)

      UpperCAmelCase__								=  []
      UpperCAmelCase__								=  0
      UpperCAmelCase__								=  10_000
      UpperCAmelCase__								=  time.time()
      for text in data:
            UpperCAmelCase__								=  f"""{bos} {text.strip()} {sep}"""
            UpperCAmelCase__								=  tokenizer.encode(__A,  add_special_tokens=__A							)
            rslt.append(__A							)

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

      UpperCAmelCase__								=  f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
      UpperCAmelCase__								=  tokenizer.vocab_size
      if vocab_size < (1 << 16):
            UpperCAmelCase__								=  [np.uintaa(__A							) for d in rslt]
      else:
            UpperCAmelCase__								=  [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()



