





"""simple docstring"""


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




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						int    )   ->      np.ndarray:

      # prepare kernel
      # the kernel size have to be odd
      if (ksize % 2) == 0:
            lowerCamelCase					:					Tuple        =	ksize + 1
      lowerCamelCase					:					Optional[Any]        =	np.zeros((ksize, ksize)			,				dtype=np.floataa    )

      # each value
      for y in range(UpperCamelCase__    ):
            for x in range(UpperCamelCase__    ):
                  # distance from center
                  lowerCamelCase					:					int        =	x - ksize // 2
                  lowerCamelCase					:					Optional[Any]        =	y - ksize // 2

                  # degree to radiant
                  lowerCamelCase					:					Dict        =	theta / 180 * np.pi
                  lowerCamelCase					:					Dict        =	np.cos(_theta    )
                  lowerCamelCase					:					str        =	np.sin(_theta    )

                  # get kernel x
                  lowerCamelCase					:					List[str]        =	cos_theta * px + sin_theta * py

                  # get kernel y
                  lowerCamelCase					:					Tuple        =	-sin_theta * px + cos_theta * py

                  # fill kernel
                  lowerCamelCase					:					int        =	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
    __lowerCamelCase      :int	    =		imread('../image_data/lena.jpg')
    # turn image in gray scale value
    __lowerCamelCase      :int	    =		cvtColor(img, COLOR_BGR2GRAY)

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

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

    waitKey(0)








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase      :Optional[int]	    =		logging.get_logger(__name__)

__lowerCamelCase      :List[str]	    =		{
    'google/realm-cc-news-pretrained-embedder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-encoder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-scorer': (
        'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-openqa': (
        'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
    ),
    'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
    'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
    'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
    'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
    # See all REALM models at https://huggingface.co/models?filter=realm
}


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Optional[Any]													='''realm'''

     def __init__(	self:						Union[str, Any]     ,		__a:						List[Any]=30_522     ,		__a:						List[Any]=768     ,		__a:						List[Any]=128     ,		__a:						Union[str, Any]=12     ,		__a:						Union[str, Any]=12     ,		__a:						Optional[Any]=8     ,		__a:						Dict=3_072     ,		__a:						List[Any]="gelu_new"     ,		__a:						List[Any]=0.1     ,		__a:						Tuple=0.1     ,		__a:						Optional[Any]=512     ,		__a:						Optional[int]=2     ,		__a:						str=0.02     ,		__a:						int=1e-1_2     ,		__a:						Optional[Any]=256     ,		__a:						Any=10     ,		__a:						Dict=1e-3     ,		__a:						Optional[Any]=5     ,		__a:						Dict=320     ,		__a:						Tuple=13_353_718     ,		__a:						List[Any]=5_000     ,		__a:						Dict=1     ,		__a:						int=0     ,		__a:						Dict=2     ,		**__a:						List[str]     ,		)->    Any:
           super().__init__(pad_token_id=__a     ,		bos_token_id=__a     ,		eos_token_id=__a     ,		**__a	)

           # Common config
           lowerCamelCase					:					Optional[Any]        =	vocab_size
           lowerCamelCase					:					str        =	max_position_embeddings
           lowerCamelCase					:					Dict        =	hidden_size
           lowerCamelCase					:					Dict        =	retriever_proj_size
           lowerCamelCase					:					Optional[Any]        =	num_hidden_layers
           lowerCamelCase					:					List[str]        =	num_attention_heads
           lowerCamelCase					:					Tuple        =	num_candidates
           lowerCamelCase					:					int        =	intermediate_size
           lowerCamelCase					:					Dict        =	hidden_act
           lowerCamelCase					:					List[str]        =	hidden_dropout_prob
           lowerCamelCase					:					Dict        =	attention_probs_dropout_prob
           lowerCamelCase					:					Optional[int]        =	initializer_range
           lowerCamelCase					:					Dict        =	type_vocab_size
           lowerCamelCase					:					Optional[Any]        =	layer_norm_eps

           # Reader config
           lowerCamelCase					:					List[str]        =	span_hidden_size
           lowerCamelCase					:					Dict        =	max_span_width
           lowerCamelCase					:					Optional[Any]        =	reader_layer_norm_eps
           lowerCamelCase					:					Optional[int]        =	reader_beam_size
           lowerCamelCase					:					List[Any]        =	reader_seq_len

           # Retrieval config
           lowerCamelCase					:					int        =	num_block_records
           lowerCamelCase					:					Dict        =	searcher_beam_size




