


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 :  Optional[int]										=  logging.getLogger(__name__)



def 	A_       (  )		->				Optional[int]:
 UpperCamelCase			:      List[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=_lowercase							,     default="data/dump.txt"							,     help="The path to the data."  )
 parser.add_argument("--tokenizer_type"							,     type=_lowercase							,     default="bert"							,     choices=["bert", "roberta", "gpt2"]  )
 parser.add_argument("--tokenizer_name"							,     type=_lowercase							,     default="bert-base-uncased"							,     help="The tokenizer to use."  )
 parser.add_argument("--dump_file"							,     type=_lowercase							,     default="data/dump"							,     help="The dump file prefix."  )
 UpperCamelCase			:      List[str] 			=     parser.parse_args()

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

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

 UpperCamelCase			:      Optional[int] 			=     []
 UpperCamelCase			:      Tuple 			=     0
 UpperCamelCase			:      Union[str, Any] 			=     1_0000
 UpperCamelCase			:      Tuple 			=     time.time()
 for text in data:
  UpperCamelCase			:      int 			=     F"""{bos} {text.strip()} {sep}"""
  UpperCamelCase			:      Union[str, Any] 			=     tokenizer.encode(_lowercase							,     add_special_tokens=_lowercase  )
  rslt.append(_lowercase  )

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

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


if __name__ == "__main__":
    main()







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase :  int										=  {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  Dict										=  ["""ConvBertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  int										=  [
        """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ConvBertForMaskedLM""",
        """ConvBertForMultipleChoice""",
        """ConvBertForQuestionAnswering""",
        """ConvBertForSequenceClassification""",
        """ConvBertForTokenClassification""",
        """ConvBertLayer""",
        """ConvBertModel""",
        """ConvBertPreTrainedModel""",
        """load_tf_weights_in_convbert""",
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  str										=  [
        """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFConvBertForMaskedLM""",
        """TFConvBertForMultipleChoice""",
        """TFConvBertForQuestionAnswering""",
        """TFConvBertForSequenceClassification""",
        """TFConvBertForTokenClassification""",
        """TFConvBertLayer""",
        """TFConvBertModel""",
        """TFConvBertPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
    from .tokenization_convbert import ConvBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_convbert_fast import ConvBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convbert import (
            CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvBertForMaskedLM,
            ConvBertForMultipleChoice,
            ConvBertForQuestionAnswering,
            ConvBertForSequenceClassification,
            ConvBertForTokenClassification,
            ConvBertLayer,
            ConvBertModel,
            ConvBertPreTrainedModel,
            load_tf_weights_in_convbert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_convbert import (
            TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFConvBertForMaskedLM,
            TFConvBertForMultipleChoice,
            TFConvBertForQuestionAnswering,
            TFConvBertForSequenceClassification,
            TFConvBertForTokenClassification,
            TFConvBertLayer,
            TFConvBertModel,
            TFConvBertPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase :  List[str]										=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
