



'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   ="""levit"""




  def __init__(					self :      int     ,      _a :      Optional[int]=224     ,      _a :      int=3     ,      _a :      Any=3     ,      _a :      Any=2     ,      _a :      int=1     ,      _a :      int=16     ,      _a :      str=[128, 256, 384]     ,      _a :      Tuple=[4, 8, 12]     ,      _a :      List[str]=[4, 4, 4]     ,      _a :      Optional[Any]=[16, 16, 16]     ,      _a :      Optional[int]=0     ,      _a :      Tuple=[2, 2, 2]     ,      _a :      Dict=[2, 2, 2]     ,      _a :      Union[str, Any]=0.02     ,      **_a :      Optional[Any]     ,      )   ->   Dict:
    super().__init__(**_a  )
    __lowerCamelCase			:			Optional[int]       			=					image_size
    __lowerCamelCase			:			Union[str, Any]       			=					num_channels
    __lowerCamelCase			:			str       			=					kernel_size
    __lowerCamelCase			:			Optional[Any]       			=					stride
    __lowerCamelCase			:			Dict       			=					padding
    __lowerCamelCase			:			Tuple       			=					hidden_sizes
    __lowerCamelCase			:			Union[str, Any]       			=					num_attention_heads
    __lowerCamelCase			:			Tuple       			=					depths
    __lowerCamelCase			:			Any       			=					key_dim
    __lowerCamelCase			:			List[Any]       			=					drop_path_rate
    __lowerCamelCase			:			List[Any]       			=					patch_size
    __lowerCamelCase			:			Tuple       			=					attention_ratio
    __lowerCamelCase			:			List[Any]       			=					mlp_ratio
    __lowerCamelCase			:			str       			=					initializer_range
    __lowerCamelCase			:			str       			=					[
        ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
        ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
    ]






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =version.parse("""1.11"""	)




  @property
  def 		_lowercase	(					self :      str  )   ->   Mapping[str, Mapping[int, str]]:
    return OrderedDict(
        [
            ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
        ]  )




  @property
  def 		_lowercase	(					self :      Optional[int]  )   ->   float:
    return 1e-4











'''simple docstring'''





# Algorithm for the pigeonhole sorting



def 			a_	(				_lowerCAmelCase			) -> List[str]:
  __lowerCamelCase			:			int       			=					min(_lowerCAmelCase			)  # min() finds the minimum value
  __lowerCamelCase			:			List[Any]       			=					max(_lowerCAmelCase			)  # max() finds the maximum value

  __lowerCamelCase			:			List[Any]       			=					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
  __lowerCamelCase			:			List[Any]       			=					[0] * size

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

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

def 			a_	(				) -> str:
  __lowerCamelCase			:			Optional[Any]       			=					[8, 3, 2, 7, 4, 6, 8]
  pigeonhole_sort(_lowerCAmelCase			)
  print('Sorted order is:'			,' '.join(_lowerCAmelCase			)			)


if __name__ == "__main__":
   main()






