


"""simple docstring"""

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


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: int				,   UpperCamelCase__	: int				,   UpperCamelCase__	: int				,   UpperCamelCase__	: int				,   UpperCamelCase__	: int ):






 """simple docstring"""







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

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

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

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

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

   # fill kernel
   __lowercase									=	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
       UpperCAmelCase__  							=imread("../image_data/lena.jpg")
       # turn image in gray scale value
       UpperCAmelCase__  							=cvtColor(img, COLOR_BGR2GRAY)

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

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

       waitKey(0)




"""simple docstring"""

import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
UpperCAmelCase__  							=logging.getLogger(__name__)

if __name__ == "__main__":
       UpperCAmelCase__  							=argparse.ArgumentParser(
           description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
       )
       parser.add_argument(
           "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
       )
       parser.add_argument(
           "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
       )
       parser.add_argument("--vocab_size", default=3_0522, type=int)
       UpperCAmelCase__  							=parser.parse_args()

       logger.info(f"""Loading data from {args.data_file}""")
       with open(args.data_file, "rb") as fp:
              UpperCAmelCase__  							=pickle.load(fp)

       logger.info("Counting occurrences for MLM.")
       UpperCAmelCase__  							=Counter()
       for tk_ids in data:
              counter.update(tk_ids)
       UpperCAmelCase__  							=[0] * args.vocab_size
       for k, v in counter.items():
              UpperCAmelCase__  							=v

       logger.info(f"""Dump to {args.token_counts_dump}""")
       with open(args.token_counts_dump, "wb") as handle:
              pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
