




'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE							:   List[Any]      		= {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							:   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

		_SCREAMING_SNAKE_CASE							:   int      		= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





'''simple docstring'''

import re

from filelock import FileLock


try:
 import nltk

 _A			:      int					      =				True
except (ImportError, ModuleNotFoundError):
 _A			:      Optional[Any]					      =				False

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







def      UpperCamelCase_						(					snake_case_       :       str      )					->     str:






 '''simple docstring'''

 re.sub("""<n>"""						,		""""""						,		snake_case_      )  # 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(snake_case_      )      )






