


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							    =							{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							['DeiTFeatureExtractor']
  SCREAMING_SNAKE_CASE							    =							['DeiTImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DeiTForImageClassification',
      'DeiTForImageClassificationWithTeacher',
      'DeiTForMaskedImageModeling',
      'DeiTModel',
      'DeiTPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFDeiTForImageClassification',
      'TFDeiTForImageClassificationWithTeacher',
      'TFDeiTForMaskedImageModeling',
      'TFDeiTModel',
      'TFDeiTPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_deit import DeiTFeatureExtractor
    from .image_processing_deit import DeiTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deit import (
        DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DeiTForImageClassification,
        DeiTForImageClassificationWithTeacher,
        DeiTForMaskedImageModeling,
        DeiTModel,
        DeiTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deit import (
        TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDeiTForImageClassification,
        TFDeiTForImageClassificationWithTeacher,
        TFDeiTForMaskedImageModeling,
        TFDeiTModel,
        TFDeiTPreTrainedModel,
    )


else:
  import sys

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







def  a   (lowerCAmelCase__   ):
   __a					=  False
   while is_sorted is False:  # Until all the indices are traversed keep looping
      __a					=  True
      for i in range(0		,				len(lowerCAmelCase__   ) - 1		,				2   ):  # iterating over all even indices
         if input_list[i] > input_list[i + 1]:
            __a	,    __a					=  input_list[i + 1], input_list[i]
            # swapping if elements not in order
            __a					=  False

      for i in range(1		,				len(lowerCAmelCase__   ) - 1		,				2   ):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            __a	,    __a					=  input_list[i + 1], input_list[i]
            # swapping if elements not in order
            __a					=  False
   return input_list


if __name__ == "__main__":
  print('Enter list to be sorted')
  SCREAMING_SNAKE_CASE							    =							[int(x) for x in input().split()]
  # inputing elements of the list in one line
  SCREAMING_SNAKE_CASE							    =							odd_even_sort(input_list)
  print('The sorted list is')
  print(sorted_list)



