






from typing import TYPE_CHECKING

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


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

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

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __A					=	[
       "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

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





'''simple docstring'''

from __future__ import annotations







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if (direction == 1 and array[indexa] > array[indexa]) or (
       direction == 0 and array[indexa] < array[indexa]
   ):
      UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	array[indexa], array[indexa]







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if length > 1:
      UpperCAmelCase__				:					List[Any] 					=	int(length / 2     )
      for i in range(_A	,      low + middle     ):
         comp_and_swap(_A	,      _A	,      i + middle	,      _A     )
      bitonic_merge(_A	,      _A	,      _A	,      _A     )
      bitonic_merge(_A	,      low + middle	,      _A	,      _A     )







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if length > 1:
      UpperCAmelCase__				:					Optional[int] 					=	int(length / 2     )
      bitonic_sort(_A	,      _A	,      _A	,      1     )
      bitonic_sort(_A	,      low + middle	,      _A	,      0     )
      bitonic_merge(_A	,      _A	,      _A	,      _A     )


if __name__ == "__main__":
  UpperCamelCase__  :						Dict					=      input('Enter numbers separated by a comma:\n').strip()
  UpperCamelCase__  :						Tuple					=      [int(item.strip()) for item in user_input.split(',')]

  bitonic_sort(unsorted, 0, len(unsorted), 1)
  print('\nSorted array in ascending order is: ', end='')
  print(*unsorted, sep=', ')

  bitonic_merge(unsorted, 0, len(unsorted), 0)
  print('Sorted array in descending order is: ', end='')
  print(*unsorted, sep=', ')




