



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__snake_case						:List[str]      		=     {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case						:Dict      		=     [
     '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''XLMForMultipleChoice''',
     '''XLMForQuestionAnswering''',
     '''XLMForQuestionAnsweringSimple''',
     '''XLMForSequenceClassification''',
     '''XLMForTokenClassification''',
     '''XLMModel''',
     '''XLMPreTrainedModel''',
     '''XLMWithLMHeadModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case						:Union[str, Any]      		=     [
     '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFXLMForMultipleChoice''',
     '''TFXLMForQuestionAnsweringSimple''',
     '''TFXLMForSequenceClassification''',
     '''TFXLMForTokenClassification''',
     '''TFXLMMainLayer''',
     '''TFXLMModel''',
     '''TFXLMPreTrainedModel''',
     '''TFXLMWithLMHeadModel''',
 ]


if TYPE_CHECKING:
 from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
 from .tokenization_xlm import XLMTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm import (
      XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMForMultipleChoice,
      XLMForQuestionAnswering,
      XLMForQuestionAnsweringSimple,
      XLMForSequenceClassification,
      XLMForTokenClassification,
      XLMModel,
      XLMPreTrainedModel,
      XLMWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xlm import (
      TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLMForMultipleChoice,
      TFXLMForQuestionAnsweringSimple,
      TFXLMForSequenceClassification,
      TFXLMForTokenClassification,
      TFXLMMainLayer,
      TFXLMModel,
      TFXLMPreTrainedModel,
      TFXLMWithLMHeadModel,
  )

else:
 import sys

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









'''simple docstring'''
import re

from filelock import FileLock


try:
		import nltk

		__snake_case							      =     True
except (ImportError, ModuleNotFoundError):
		__snake_case							      =     False

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


def 			a      (			__a   )  ->	str:



					'''simple docstring'''






					re.sub('''<n>''' ,					'''''' ,					__a   )  # 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(__a   )   )