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


A   :     List[Any]       =				logging.get_logger(__name__)

A   :     List[str]       =				{
    "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"""







  lowerCamelCase__   =  '''levit'''







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







  lowerCamelCase__   =  version.parse('''1.11'''    )







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







  @property
  def       __A   (							self       :				Union[str, Any]      )     ->		float:
        return 1e-4



from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     k_size // 2
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      SCREAMING_SNAKE_CASE_      =     1 / (2 * pi * sigma) * exp(-(square(__UpperCamelCase ) + square(__UpperCamelCase )) / (2 * square(__UpperCamelCase )) )
      return g


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     image.shape[0], image.shape[1]
      # dst image height and width
      SCREAMING_SNAKE_CASE_      =     height - k_size + 1
      SCREAMING_SNAKE_CASE_      =     width - k_size + 1

      # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
      SCREAMING_SNAKE_CASE_      =     zeros((dst_height * dst_width, k_size * k_size) )
      SCREAMING_SNAKE_CASE_      =     0
      for i, j in product(range(__UpperCamelCase )   ,					range(__UpperCamelCase ) ):
            SCREAMING_SNAKE_CASE_      =     ravel(image[i : i + k_size, j : j + k_size] )
            SCREAMING_SNAKE_CASE_      =     window
            row += 1

      #  turn the kernel into shape(k*k, 1)
      SCREAMING_SNAKE_CASE_      =     gen_gaussian_kernel(__UpperCamelCase   ,					__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     ravel(__UpperCamelCase )

      # reshape and get the dst image
      SCREAMING_SNAKE_CASE_      =     dot(__UpperCamelCase   ,					__UpperCamelCase ).reshape(__UpperCamelCase   ,					__UpperCamelCase ).astype(__UpperCamelCase )

      return dst


if __name__ == "__main__":
      # read original image
      A   :     Tuple       =				imread(r"../image_data/lena.jpg")
      # turn image in gray scale value
      A   :     Optional[int]       =				cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      A   :     Tuple       =				gaussian_filter(gray, 3, sigma=1)
      A   :     Optional[int]       =				gaussian_filter(gray, 5, sigma=0.8)

      # show result images
      imshow("gaussian filter with 3x3 mask", gaussianaxa)
      imshow("gaussian filter with 5x5 mask", gaussianaxa)
      waitKey()


