




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     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: Dict  ):


						'''simple docstring'''



						lowerCamelCase_	  =					k_size // 2
						lowerCamelCase_	  =					mgrid[0 - center : k_size - center, 0 - center : k_size - center]
						lowerCamelCase_	  =					1 / (2 * pi * sigma) * exp(-(square(__lowerCAmelCase  ) + square(__lowerCAmelCase  )) / (2 * square(__lowerCAmelCase  ))  )
						return g

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: str			,							lowercase			: Any  ):


						'''simple docstring'''



						lowerCamelCase_	  =					image.shape[0], image.shape[1]
						# dst image height and width
						lowerCamelCase_	  =					height - k_size + 1
						lowerCamelCase_	  =					width - k_size + 1

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

						#  turn the kernel into shape(k*k, 1)
						lowerCamelCase_	  =					gen_gaussian_kernel(__lowerCAmelCase			,							__lowerCAmelCase  )
						lowerCamelCase_	  =					ravel(__lowerCAmelCase  )

						# reshape and get the dst image
						lowerCamelCase_	  =					dot(__lowerCAmelCase			,							__lowerCAmelCase  ).reshape(__lowerCAmelCase			,							__lowerCAmelCase  ).astype(__lowerCAmelCase  )

						return dst


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

	# get values with two different mask size
	lowerCamelCase       :	Tuple									=     gaussian_filter(gray, 3, sigma=1)
	lowerCamelCase       :	Dict									=     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()









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase       :	Dict									=     {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Optional[int]									=     ["ViTFeatureExtractor"]
 lowerCamelCase       :	Dict									=     ["ViTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	int									=     [
     "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTForImageClassification",
     "ViTForMaskedImageModeling",
     "ViTModel",
     "ViTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Tuple									=     [
     "TFViTForImageClassification",
     "TFViTModel",
     "TFViTPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Dict									=     [
     "FlaxViTForImageClassification",
     "FlaxViTModel",
     "FlaxViTPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_vit import ViTFeatureExtractor
  from .image_processing_vit import ViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit import (
      VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTForImageClassification,
      ViTForMaskedImageModeling,
      ViTModel,
      ViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
 import sys

 lowerCamelCase       :	Any									=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



