

from typing import TYPE_CHECKING

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


a_     :    int     		=   {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a_     :    List[Any]     		=   ["ReformerTokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a_     :    Optional[int]     		=   ["ReformerTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a_     :    List[str]     		=   [
          "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "ReformerAttention",
          "ReformerForMaskedLM",
          "ReformerForQuestionAnswering",
          "ReformerForSequenceClassification",
          "ReformerLayer",
          "ReformerModel",
          "ReformerModelWithLMHead",
          "ReformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer import ReformerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer_fast import ReformerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_reformer import (
                REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                ReformerAttention,
                ReformerForMaskedLM,
                ReformerForQuestionAnswering,
                ReformerForSequenceClassification,
                ReformerLayer,
                ReformerModel,
                ReformerModelWithLMHead,
                ReformerPreTrainedModel,
            )

else:
      import sys

      a_     :    Any     		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




import re

from filelock import FileLock


try:
      import nltk

      a_     :    Optional[Any]     		=   True
except (ImportError, ModuleNotFoundError):
      a_     :    Union[str, Any]     		=   False

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


def 							__lowerCAmelCase     (							_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						)						)

