





import re

from filelock import FileLock


try:
       import nltk

       snake_case__					:							Any													=     True
except (ImportError, ModuleNotFoundError):
       snake_case__					:							int													=     False

if NLTK_AVAILABLE:
       with FileLock('.lock') as lock:
              nltk.download('punkt', quiet=True)



def _a				(	lowerCamelCase:   str )	->						str:







      '''simple docstring'''






      re.sub('''<n>'''					,				''''''					,				lowerCamelCase )  # remove pegasus newline char
      assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
      return "\n".join(nltk.sent_tokenize(lowerCamelCase ) )












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__					:							Optional[Any]													=     {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__					:							List[str]													=     [
           'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'IBertForMaskedLM',
           'IBertForMultipleChoice',
           'IBertForQuestionAnswering',
           'IBertForSequenceClassification',
           'IBertForTokenClassification',
           'IBertModel',
           'IBertPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_ibert import (
                  IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  IBertForMaskedLM,
                  IBertForMultipleChoice,
                  IBertForQuestionAnswering,
                  IBertForSequenceClassification,
                  IBertForTokenClassification,
                  IBertModel,
                  IBertPreTrainedModel,
              )

else:
       import sys

       snake_case__					:							Dict													=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





