




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	:						Dict			=     logging.getLogger(__name__)



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

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

  logger.info("Start encoding"   )
  logger.info(F"{len(lowerCamelCase   )} examples to process."   )

  lowercase    :Tuple      	=						[]
  lowercase    :List[Any]      	=						0
  lowercase    :List[str]      	=						10000
  lowercase    :str      	=						time.time()
  for text in data:
    lowercase    :List[Any]      	=						F"{bos} {text.strip()} {sep}"
    lowercase    :Optional[Any]      	=						tokenizer.encode(lowerCamelCase,			add_special_tokens=lowerCamelCase   )
    rslt.append(lowerCamelCase   )

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

  lowercase    :Any      	=						F"{args.dump_file}.{args.tokenizer_name}.pickle"
  lowercase    :Optional[int]      	=						tokenizer.vocab_size
  if vocab_size < (1 << 16):
    lowercase    :int      	=						[np.uintaa(lowerCamelCase   ) for d in rslt]
  else:
    lowercase    :Dict      	=						[np.intaa(lowerCamelCase   ) for d in rslt]
  random.shuffle(rslt_   )
  logger.info(F"Dump to {dp_file}"   )
  with open(lowerCamelCase,			"wb"   ) as handle:
    pickle.dump(rslt_,			lowerCamelCase,			protocol=pickle.HIGHEST_PROTOCOL   )


if __name__ == "__main__":
     main()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase	:						List[Any]			=     {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						Union[str, Any]			=     [
         "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "Swinv2ForImageClassification",
         "Swinv2ForMaskedImageModeling",
         "Swinv2Model",
         "Swinv2PreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swinva import (
              SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwinvaForImageClassification,
              SwinvaForMaskedImageModeling,
              SwinvaModel,
              SwinvaPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase	:						Tuple			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



