



from typing import TYPE_CHECKING

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


_UpperCAmelCase     =			{
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			["""MaskFormerFeatureExtractor"""]
   _UpperCAmelCase     =			["""MaskFormerImageProcessor"""]


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """MaskFormerForInstanceSegmentation""",
       """MaskFormerModel""",
       """MaskFormerPreTrainedModel""",
   ]
   _UpperCAmelCase     =			[
       """MaskFormerSwinBackbone""",
       """MaskFormerSwinModel""",
       """MaskFormerSwinPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
   from .configuration_maskformer_swin import MaskFormerSwinConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_maskformer import MaskFormerFeatureExtractor
      from .image_processing_maskformer import MaskFormerImageProcessor
   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskformer import (
          MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskFormerForInstanceSegmentation,
          MaskFormerModel,
          MaskFormerPreTrainedModel,
      )
      from .modeling_maskformer_swin import (
          MaskFormerSwinBackbone,
          MaskFormerSwinModel,
          MaskFormerSwinPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)








"""simple docstring"""
from __future__ import annotations





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=sorted(numsa + numsa )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=divmod(len(lowercase )							,			2 )
   if mod == 1:
      return all_numbers[div]
   else:
      return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _UpperCAmelCase     =			[float(x) for x in input("""Enter the elements of first array: """).split()]
   _UpperCAmelCase     =			[float(x) for x in input("""Enter the elements of second array: """).split()]
   print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")



