


from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case			 =	logging.get_logger(__name__)

_snake_case			 =	{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}
class       UpperCAmelCase_   (   UpperCamelCase       ):

   '''simple docstring'''




   __A					: Any							=						"efficientformer"






   def __init__(						self		,							__A = [3, 2, 6, 4]		,							__A = [48, 96, 224, 448]		,							__A = [True, True, True, True]		,							__A = 448		,							__A = 32		,							__A = 4		,							__A = 7		,							__A = 5		,							__A = 8		,							__A = 4		,							__A = 0.0		,							__A = 16		,							__A = 3		,							__A = 3		,							__A = 3		,							__A = 2		,							__A = 1		,							__A = 0.0		,							__A = 1		,							__A = True		,							__A = True		,							__A = 1e-5		,							__A = "gelu"		,							__A = 0.02		,							__A = 1e-12		,							__A = 224		,							__A = 1e-05		,							**__A		,							):






     """simple docstring"""





     super().__init__(**lowerCamelCase_					)

     lowerCamelCase					:   Tuple       =							hidden_act
     lowerCamelCase					:   Union[str, Any]       =							hidden_dropout_prob
     lowerCamelCase					:   Union[str, Any]       =							hidden_sizes
     lowerCamelCase					:   int       =							num_hidden_layers
     lowerCamelCase					:   Any       =							num_attention_heads
     lowerCamelCase					:   List[Any]       =							initializer_range
     lowerCamelCase					:   Tuple       =							layer_norm_eps
     lowerCamelCase					:   Union[str, Any]       =							patch_size
     lowerCamelCase					:   Union[str, Any]       =							num_channels
     lowerCamelCase					:   Union[str, Any]       =							depths
     lowerCamelCase					:   str       =							mlp_expansion_ratio
     lowerCamelCase					:   str       =							downsamples
     lowerCamelCase					:   Dict       =							dim
     lowerCamelCase					:   List[Any]       =							key_dim
     lowerCamelCase					:   Tuple       =							attention_ratio
     lowerCamelCase					:   Optional[Any]       =							resolution
     lowerCamelCase					:   int       =							pool_size
     lowerCamelCase					:   Tuple       =							downsample_patch_size
     lowerCamelCase					:   Tuple       =							downsample_stride
     lowerCamelCase					:   Tuple       =							downsample_pad
     lowerCamelCase					:   int       =							drop_path_rate
     lowerCamelCase					:   str       =							num_metaad_blocks
     lowerCamelCase					:   Union[str, Any]       =							distillation
     lowerCamelCase					:   str       =							use_layer_scale
     lowerCamelCase					:   str       =							layer_scale_init_value
     lowerCamelCase					:   int       =							image_size
     lowerCamelCase					:   Tuple       =							batch_norm_eps






"""simple docstring"""


from __future__ import annotations



def   _A					(	_a	:						list[float]     ,   _a	:						list[float]				):



      """simple docstring"""






      A				  =		sorted(numsa + numsa				)
      A  ,			A				  =		divmod(len(_a				)     ,   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)}""")




