






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


a						:			List[str]     	=					logging.get_logger(__name__)

a						:			Dict     	=					{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

a						:			Any     	=					{
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

a						:			List[str]     	=					{
    "yjernite/retribert-base-uncased": 512,
}


a						:			int     	=					{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}
class        a       (					lowercase__  ):



      """simple docstring"""


      a    :					int							=      VOCAB_FILES_NAMES
      a    :					List[str]							=      PRETRAINED_VOCAB_FILES_MAP
      a    :					Optional[Any]							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a    :					List[Any]							=      PRETRAINED_INIT_CONFIGURATION
      a    :					Dict							=      RetriBertTokenizer
      a    :					Any							=      ['input_ids', 'attention_mask']
      def __init__( self   :     Dict						, __lowercase   :     Optional[Any]=None						, __lowercase   :     Any=None						, __lowercase   :     Optional[int]=True						, __lowercase   :     Tuple="[UNK]"						, __lowercase   :     List[str]="[SEP]"						, __lowercase   :     Any="[PAD]"						, __lowercase   :     str="[CLS]"						, __lowercase   :     List[Any]="[MASK]"						, __lowercase   :     Tuple=True						, __lowercase   :     Optional[Any]=None						, **__lowercase   :     List[str]						, )				-> Optional[int]:
        super().__init__(
            __lowercase						, tokenizer_file=__lowercase						, do_lower_case=__lowercase						, unk_token=__lowercase						, sep_token=__lowercase						, pad_token=__lowercase						, cls_token=__lowercase						, mask_token=__lowercase						, tokenize_chinese_chars=__lowercase						, strip_accents=__lowercase						, **__lowercase						, )

        __UpperCAmelCase				:      List[str]	 =    json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
        if (
            normalizer_state.get("""lowercase"""						, __lowercase   ) != do_lower_case
            or normalizer_state.get("""strip_accents"""						, __lowercase   ) != strip_accents
            or normalizer_state.get("""handle_chinese_chars"""						, __lowercase   ) != tokenize_chinese_chars
        ):
          __UpperCAmelCase				:      List[str]	 =    getattr(__lowercase						, normalizer_state.pop("""type"""   )   )
          __UpperCAmelCase				:      str	 =    do_lower_case
          __UpperCAmelCase				:      Optional[int]	 =    strip_accents
          __UpperCAmelCase				:      Optional[int]	 =    tokenize_chinese_chars
          __UpperCAmelCase				:      List[str]	 =    normalizer_class(**__lowercase   )

        __UpperCAmelCase				:      str	 =    do_lower_case
      def       UpperCAmelCase ( self   :     Union[str, Any]						, __lowercase   :     Any						, __lowercase   :     Optional[int]=None   )				-> Union[str, Any]:
        __UpperCAmelCase				:      str	 =    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
          output += token_ids_a + [self.sep_token_id]

        return output
      def       UpperCAmelCase ( self   :     int						, __lowercase   :     List[int]						, __lowercase   :     Optional[List[int]] = None   )				-> List[int]:
        __UpperCAmelCase				:      str	 =    [self.sep_token_id]
        __UpperCAmelCase				:      int	 =    [self.cls_token_id]
        if token_ids_a is None:
          return len(cls + token_ids_a + sep   ) * [0]
        return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]




      def       UpperCAmelCase ( self   :     List[Any]						, __lowercase   :     str						, __lowercase   :     Optional[str] = None   )				-> Tuple[str]:
        __UpperCAmelCase				:      Any	 =    self._tokenizer.model.save(__lowercase						, name=__lowercase   )
        return tuple(__lowercase   )



"""simple docstring"""




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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




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

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

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

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

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

						# fill kernel
						lowerCAmelCase__   = 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     :				Tuple    				=    imread("../image_data/lena.jpg")
			# turn image in gray scale value
			__lowerCAmelCase     :				List[str]    				=    cvtColor(img, COLOR_BGR2GRAY)

			# Apply multiple Kernel to detect edges
			__lowerCAmelCase     :				Union[str, Any]    				=    np.zeros(gray.shape[:2])
			for theta in [0, 30, 60, 90, 1_20, 1_50]:
						__lowerCAmelCase     :				Union[str, Any]    				=    gabor_filter_kernel(10, 8, theta, 10, 0, 0)
						out += filteraD(gray, CV_8UC3, kernel_aa)
			__lowerCAmelCase     :				Optional[int]    				=    out / out.max() * 2_55
			__lowerCAmelCase     :				Tuple    				=    out.astype(np.uinta)

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

			waitKey(0)




