

"""simple docstring"""





from typing import TYPE_CHECKING

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


_a							=							{
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _a							=							[
          'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
          'AdaptiveEmbedding',
          'TransfoXLForSequenceClassification',
          'TransfoXLLMHeadModel',
          'TransfoXLModel',
          'TransfoXLPreTrainedModel',
          'load_tf_weights_in_transfo_xl',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _a							=							[
          'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFAdaptiveEmbedding',
          'TFTransfoXLForSequenceClassification',
          'TFTransfoXLLMHeadModel',
          'TFTransfoXLMainLayer',
          'TFTransfoXLModel',
          'TFTransfoXLPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
      from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_transfo_xl import (
                TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
                AdaptiveEmbedding,
                TransfoXLForSequenceClassification,
                TransfoXLLMHeadModel,
                TransfoXLModel,
                TransfoXLPreTrainedModel,
                load_tf_weights_in_transfo_xl,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_transfo_xl import (
                TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFAdaptiveEmbedding,
                TFTransfoXLForSequenceClassification,
                TFTransfoXLLMHeadModel,
                TFTransfoXLMainLayer,
                TFTransfoXLModel,
                TFTransfoXLPreTrainedModel,
            )

else:
      import sys

      _a							=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







"""simple docstring"""





import re

from filelock import FileLock


try:
   import nltk

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

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




def   _A  (  UpperCamelCase_					:	str)     ->						str:
   '''simple docstring'''


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




