





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> np.ndarray:




    """simple docstring"""




    if (ksize % 2) == 0:
        _A     =							ksize + 1
    _A     =							np.zeros((ksize, ksize)							,     dtype=np.floataa				)

    # each value
    for y in range(_SCREAMING_SNAKE_CASE				):
        for x in range(_SCREAMING_SNAKE_CASE				):
            # distance from center
            _A     =							x - ksize // 2
            _A     =							y - ksize // 2

            # degree to radiant
            _A     =							theta / 180 * np.pi
            _A     =							np.cos(_theta				)
            _A     =							np.sin(_theta				)

            # get kernel x
            _A     =							cos_theta * px + sin_theta * py

            # get kernel y
            _A     =							-sin_theta * px + cos_theta * py

            # fill kernel
            _A     =							np.exp(
                -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)				) * np.cos(2 * np.pi * _x / lambd + psi				)

    return gabor


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # read original image
 __A							:			Optional[int]         =			imread("../image_data/lena.jpg")
 # turn image in gray scale value
 __A							:			int         =			cvtColor(img, COLOR_BGR2GRAY)

 # Apply multiple Kernel to detect edges
 __A							:			Any         =			np.zeros(gray.shape[:2])
 for theta in [0, 30, 60, 90, 120, 150]:
  __A							:			int         =			gabor_filter_kernel(10, 8, theta, 10, 0, 0)
  out += filteraD(gray, CV_8UC3, kernel_aa)
 __A							:			str         =			out / out.max() * 255
 __A							:			Any         =			out.astype(np.uinta)

 imshow("Original", gray)
 imshow("Gabor filter with 20x20 mask and 6 directions", out)

 waitKey(0)












from typing import TYPE_CHECKING

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


__A							:			Dict         =			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			["BlenderbotTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			[
     "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "BlenderbotForCausalLM",
     "BlenderbotForConditionalGeneration",
     "BlenderbotModel",
     "BlenderbotPreTrainedModel",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Union[str, Any]         =			[
     "TFBlenderbotForConditionalGeneration",
     "TFBlenderbotModel",
     "TFBlenderbotPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Optional[Any]         =			[
     "FlaxBlenderbotForConditionalGeneration",
     "FlaxBlenderbotModel",
     "FlaxBlenderbotPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_blenderbot import (
     BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlenderbotConfig,
     BlenderbotOnnxConfig,
 )
 from .tokenization_blenderbot import BlenderbotTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blenderbot import (
      BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlenderbotForCausalLM,
      BlenderbotForConditionalGeneration,
      BlenderbotModel,
      BlenderbotPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_blenderbot import (
      TFBlenderbotForConditionalGeneration,
      TFBlenderbotModel,
      TFBlenderbotPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_blenderbot import (
      FlaxBlenderbotForConditionalGeneration,
      FlaxBlenderbotModel,
      FlaxBlenderbotPreTrainedModel,
  )

else:
 import sys

 __A							:			str         =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





