



from __future__ import annotations




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Any:



    """simple docstring"""






    if len(_lowerCAmelCase		) <= 1 or n <= 1:
        return

    insert_next(_lowerCAmelCase				,					n - 1		)
    rec_insertion_sort(_lowerCAmelCase				,					n - 1		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Union[str, Any]:



    """simple docstring"""






    if index >= len(_lowerCAmelCase		) or collection[index - 1] <= collection[index]:
        return

    # Swaps adjacent elements since they are not in ascending order
    A					,							A			:	int										=       (
        collection[index],
        collection[index - 1],
    )

    insert_next(_lowerCAmelCase				,					index + 1		)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Union[str, Any]					=						input("""Enter integers separated by spaces: """)
     SCREAMING_SNAKE_CASE_:list[int]					=						[int(num) for num in numbers.split()]
     rec_insertion_sort(number_list, len(number_list))
     print(number_list)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_:Any					=						{
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:List[Any]					=						["""MobileNetV2FeatureExtractor"""]
     SCREAMING_SNAKE_CASE_:Tuple					=						["""MobileNetV2ImageProcessor"""]


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Any					=						[
         """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MobileNetV2ForImageClassification""",
         """MobileNetV2ForSemanticSegmentation""",
         """MobileNetV2Model""",
         """MobileNetV2PreTrainedModel""",
         """load_tf_weights_in_mobilenet_v2""",
     ]


if TYPE_CHECKING:
     from .configuration_mobilenet_va import (
         MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileNetVaConfig,
         MobileNetVaOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
          from .image_processing_mobilenet_va import MobileNetVaImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilenet_va import (
              MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileNetVaForImageClassification,
              MobileNetVaForSemanticSegmentation,
              MobileNetVaModel,
              MobileNetVaPreTrainedModel,
              load_tf_weights_in_mobilenet_va,
          )


else:
     import sys

     SCREAMING_SNAKE_CASE_:Optional[Any]					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


