'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__a:			Tuple	       =       {
    """configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig""", """ConvNextOnnxConfig"""]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:			int	       =       ["""ConvNextFeatureExtractor"""]
  __a:			str	       =       ["""ConvNextImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:			Tuple	       =       [
      """CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ConvNextForImageClassification""",
      """ConvNextModel""",
      """ConvNextPreTrainedModel""",
      """ConvNextBackbone""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:			List[str]	       =       [
      """TFConvNextForImageClassification""",
      """TFConvNextModel""",
      """TFConvNextPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_convnext import ConvNextFeatureExtractor
    from .image_processing_convnext import ConvNextImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_convnext import (
        CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConvNextBackbone,
        ConvNextForImageClassification,
        ConvNextModel,
        ConvNextPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
  import sys

  __a:			Optional[int]	       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure)


'''simple docstring'''

import re

from filelock import FileLock


try:
  import nltk

  __a:			Tuple	       =       True
except (ImportError, ModuleNotFoundError):
  __a:			List[Any]	       =       False

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

