


'''simple docstring'''
from typing import TYPE_CHECKING

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


UpperCamelCase__    					=      {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''ReformerTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''ReformerTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''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

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




'''simple docstring'''
import re

from filelock import FileLock


try:
  import nltk

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

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





def 					a__       (					lowerCAmelCase__						)    ->	str:
    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__						)						)
