
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[Any]           =   logging.getLogger(__name__)

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       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."""					)
  __magic_name__				     =       parser.parse_args()

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

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

  __magic_name__				     =       []
  __magic_name__				     =       0
  __magic_name__				     =       10000
  __magic_name__				     =       time.time()
  for text in data:
    __magic_name__				     =       f'''{bos} {text.strip()} {sep}'''
    __magic_name__				     =       tokenizer.encode(A_,     add_special_tokens=A_					)
    rslt.append(A_					)

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

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



from typing import TYPE_CHECKING

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


__lowerCAmelCase			:     str           =   {
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Any           =   ['FunnelTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Optional[int]           =   [
        'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'FunnelBaseModel',
        'FunnelForMaskedLM',
        'FunnelForMultipleChoice',
        'FunnelForPreTraining',
        'FunnelForQuestionAnswering',
        'FunnelForSequenceClassification',
        'FunnelForTokenClassification',
        'FunnelModel',
        'FunnelPreTrainedModel',
        'load_tf_weights_in_funnel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Tuple           =   [
        'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFFunnelBaseModel',
        'TFFunnelForMaskedLM',
        'TFFunnelForMultipleChoice',
        'TFFunnelForPreTraining',
        'TFFunnelForQuestionAnswering',
        'TFFunnelForSequenceClassification',
        'TFFunnelForTokenClassification',
        'TFFunnelModel',
        'TFFunnelPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
    from .tokenization_funnel import FunnelTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_funnel_fast import FunnelTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_funnel import (
            FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
            FunnelBaseModel,
            FunnelForMaskedLM,
            FunnelForMultipleChoice,
            FunnelForPreTraining,
            FunnelForQuestionAnswering,
            FunnelForSequenceClassification,
            FunnelForTokenClassification,
            FunnelModel,
            FunnelPreTrainedModel,
            load_tf_weights_in_funnel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_funnel import (
            TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFFunnelBaseModel,
            TFFunnelForMaskedLM,
            TFFunnelForMultipleChoice,
            TFFunnelForPreTraining,
            TFFunnelForQuestionAnswering,
            TFFunnelForSequenceClassification,
            TFFunnelForTokenClassification,
            TFFunnelModel,
            TFFunnelPreTrainedModel,
        )

else:
    import sys

    __lowerCAmelCase			:     List[Any]           =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

