



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





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):





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

    # each value
    for y in range(_snake_case    ):
        for x in range(_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
 __snake_case						:Tuple				=					imread('''../image_data/lena.jpg''')
 # turn image in gray scale value
 __snake_case						:str				=					cvtColor(img, COLOR_BGR2GRAY)

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


__snake_case						:Tuple						=					{
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case						:List[Any]						=					['''BloomTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case						:Any						=					[
     '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''BloomForCausalLM''',
     '''BloomModel''',
     '''BloomPreTrainedModel''',
     '''BloomForSequenceClassification''',
     '''BloomForTokenClassification''',
     '''BloomForQuestionAnswering''',
 ]

if TYPE_CHECKING:
 from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_bloom_fast import BloomTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bloom import (
      BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
      BloomForCausalLM,
      BloomForQuestionAnswering,
      BloomForSequenceClassification,
      BloomForTokenClassification,
      BloomModel,
      BloomPreTrainedModel,
  )

else:
 import sys

 __snake_case						:Any						=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





