





"""simple docstring"""







from typing import TYPE_CHECKING

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


_UpperCamelCase					      =  {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""FNetTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""FNetTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """FNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FNetForMaskedLM""",
      """FNetForMultipleChoice""",
      """FNetForNextSentencePrediction""",
      """FNetForPreTraining""",
      """FNetForQuestionAnswering""",
      """FNetForSequenceClassification""",
      """FNetForTokenClassification""",
      """FNetLayer""",
      """FNetModel""",
      """FNetPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet import FNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet_fast import FNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_fnet import (
        FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FNetForMaskedLM,
        FNetForMultipleChoice,
        FNetForNextSentencePrediction,
        FNetForPreTraining,
        FNetForQuestionAnswering,
        FNetForSequenceClassification,
        FNetForTokenClassification,
        FNetLayer,
        FNetModel,
        FNetPreTrainedModel,
    )


else:
  import sys

  _UpperCamelCase					      =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











"""simple docstring"""







from __future__ import annotations

def _a			(		_snake_case				,	_snake_case = None				,	_snake_case = None							):







      """simple docstring"""






      if start is None:
            UpperCAmelCase          =	0

      if end is None:
            UpperCAmelCase          =	len(_snake_case							) - 1

      if start >= end:
            return

      UpperCAmelCase          =	(start + end) // 2

      slowsort(_snake_case				,	_snake_case				,	_snake_case							)
      slowsort(_snake_case				,	mid + 1				,	_snake_case							)

      if sequence[end] < sequence[mid]:
            UpperCAmelCase      , UpperCAmelCase          =	sequence[mid], sequence[end]

      slowsort(_snake_case				,	_snake_case				,	end - 1							)


if __name__ == "__main__":
  from doctest import testmod

  testmod()



