
"""simple docstring"""

def 							A		(       snake_case   :Optional[Any]	)   ->			Any:
 __UpperCamelCase       						= min(_lowercase	)  # min() finds the minimum value
 __UpperCamelCase       						= max(_lowercase	)  # max() finds the maximum value

 __UpperCamelCase       						= max_val - min_val + 1  # size is difference of max and min values plus one

 # list of pigeonholes of size equal to the variable size
 __UpperCamelCase       						= [0] * size

 # Populate the pigeonholes.
 for x in a:
  assert isinstance(_lowercase       ,      _lowercase	), "integers only please"
  holes[x - min_val] += 1

 # Putting the elements back into the array in an order.
 __UpperCamelCase       						= 0
 for count in range(_lowercase	):
  while holes[count] > 0:
   holes[count] -= 1
   __UpperCamelCase       						= count + min_val
   i += 1






def 							A		(       )   ->			Tuple:
 __UpperCamelCase       						= [8, 3, 2, 7, 4, 6, 8]
 pigeonhole_sort(_lowercase	)
 print('Sorted order is:'       ,      ' '.join(_lowercase	)	)


if __name__ == "__main__":
      main()





"""simple docstring"""

from typing import List

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


UpperCamelCase			:       Tuple									=				logging.get_logger(__name__)

UpperCamelCase			:       List[str]									=				{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"efficientformer"





     def __init__(			self			,  __UpperCAmelCase = [3, 2, 6, 4]			,  __UpperCAmelCase = [48, 96, 224, 448]			,  __UpperCAmelCase = [True, True, True, True]			,  __UpperCAmelCase = 448			,  __UpperCAmelCase = 32			,  __UpperCAmelCase = 4			,  __UpperCAmelCase = 7			,  __UpperCAmelCase = 5			,  __UpperCAmelCase = 8			,  __UpperCAmelCase = 4			,  __UpperCAmelCase = 0.0			,  __UpperCAmelCase = 16			,  __UpperCAmelCase = 3			,  __UpperCAmelCase = 3			,  __UpperCAmelCase = 3			,  __UpperCAmelCase = 2			,  __UpperCAmelCase = 1			,  __UpperCAmelCase = 0.0			,  __UpperCAmelCase = 1			,  __UpperCAmelCase = True			,  __UpperCAmelCase = True			,  __UpperCAmelCase = 1E-5			,  __UpperCAmelCase = "gelu"			,  __UpperCAmelCase = 0.0_2			,  __UpperCAmelCase = 1E-12			,  __UpperCAmelCase = 224			,  __UpperCAmelCase = 1E-05			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )

      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= hidden_dropout_prob
      __UpperCamelCase       						= hidden_sizes
      __UpperCamelCase       						= num_hidden_layers
      __UpperCamelCase       						= num_attention_heads
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= layer_norm_eps
      __UpperCamelCase       						= patch_size
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= depths
      __UpperCamelCase       						= mlp_expansion_ratio
      __UpperCamelCase       						= downsamples
      __UpperCamelCase       						= dim
      __UpperCamelCase       						= key_dim
      __UpperCamelCase       						= attention_ratio
      __UpperCamelCase       						= resolution
      __UpperCamelCase       						= pool_size
      __UpperCamelCase       						= downsample_patch_size
      __UpperCamelCase       						= downsample_stride
      __UpperCamelCase       						= downsample_pad
      __UpperCamelCase       						= drop_path_rate
      __UpperCamelCase       						= num_metaad_blocks
      __UpperCamelCase       						= distillation
      __UpperCamelCase       						= use_layer_scale
      __UpperCamelCase       						= layer_scale_init_value
      __UpperCamelCase       						= image_size
      __UpperCamelCase       						= batch_norm_eps



