





import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

snake_case__   :				Dict						=	180



def _a							(      lowerCamelCase:  Optional[Any]   ,							lowerCamelCase:  Union[str, Any]					)      ->		Tuple:







      '''simple docstring'''






      __A			  =							k_size // 2
      __A  ,		__A			  =							np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      __A			  =							(
          1
          / (2 * np.pi * sigma)
          * np.exp(-(np.square(lowerCamelCase_					) + np.square(lowerCamelCase_					)) / (2 * np.square(lowerCamelCase_					))					)
      )
      return g



def _a							(      lowerCamelCase:  int   ,							lowerCamelCase:  Dict   ,							lowerCamelCase:  Tuple					)      ->		List[Any]:







      '''simple docstring'''






      __A			  =							np.zeros(lowerCamelCase_					)

      for row in range(1   ,							image_shape[0] - 1					):
            for col in range(1   ,							image_shape[1] - 1					):
                  __A			  =							gradient_direction[row, col]

                  if (
                      0 <= direction < PI / 8
                      or 15 * PI / 8 <= direction <= 2 * PI
                      or 7 * PI / 8 <= direction <= 9 * PI / 8
                  ):
                        __A			  =							sobel_grad[row, col - 1]
                        __A			  =							sobel_grad[row, col + 1]
                        if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                              __A			  =							sobel_grad[row, col]

                  elif (
                      PI / 8 <= direction < 3 * PI / 8
                      or 9 * PI / 8 <= direction < 11 * PI / 8
                  ):
                        __A			  =							sobel_grad[row + 1, col - 1]
                        __A			  =							sobel_grad[row - 1, col + 1]
                        if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                              __A			  =							sobel_grad[row, col]

                  elif (
                      3 * PI / 8 <= direction < 5 * PI / 8
                      or 11 * PI / 8 <= direction < 13 * PI / 8
                  ):
                        __A			  =							sobel_grad[row - 1, col]
                        __A			  =							sobel_grad[row + 1, col]
                        if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                              __A			  =							sobel_grad[row, col]

                  elif (
                      5 * PI / 8 <= direction < 7 * PI / 8
                      or 13 * PI / 8 <= direction < 15 * PI / 8
                  ):
                        __A			  =							sobel_grad[row - 1, col - 1]
                        __A			  =							sobel_grad[row + 1, col + 1]
                        if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                              __A			  =							sobel_grad[row, col]

      return destination



def _a							(      lowerCamelCase:  str   ,							lowerCamelCase:  List[Any]   ,							lowerCamelCase:  Optional[Any]   ,							lowerCamelCase:  Tuple   ,							lowerCamelCase:  Optional[int]   ,							lowerCamelCase:  Union[str, Any]					)      ->		Tuple:







      '''simple docstring'''






      for row in range(1   ,							image_shape[0] - 1					):
            for col in range(1   ,							image_shape[1] - 1					):
                  if destination[row, col] >= threshold_high:
                        __A			  =							strong
                  elif destination[row, col] <= threshold_low:
                        __A			  =							0
                  else:
                        __A			  =							weak



def _a							(      lowerCamelCase:  List[str]   ,							lowerCamelCase:  str   ,							lowerCamelCase:  Tuple   ,							lowerCamelCase:  str					)      ->		Tuple:







      '''simple docstring'''






      for row in range(1   ,							image_shape[0]					):
            for col in range(1   ,							image_shape[1]					):
                  if destination[row, col] == weak:
                        if 2_55 in (
                            destination[row, col + 1],
                            destination[row, col - 1],
                            destination[row - 1, col],
                            destination[row + 1, col],
                            destination[row - 1, col - 1],
                            destination[row + 1, col - 1],
                            destination[row - 1, col + 1],
                            destination[row + 1, col + 1],
                        ):
                              __A			  =							strong
                        else:
                              __A			  =							0



def _a							(      lowerCamelCase:  Optional[int]   ,							lowerCamelCase:  List[str]=15   ,							lowerCamelCase:  Union[str, Any]=30   ,							lowerCamelCase:  int=1_28   ,							lowerCamelCase:  str=2_55					)      ->		List[Any]:







      '''simple docstring'''






      __A			  =							img_convolve(lowerCamelCase_   ,							gen_gaussian_kernel(9   ,							sigma=1.4					)					)
      # get the gradient and degree by sobel_filter
      __A  ,		__A			  =							sobel_filter(lowerCamelCase_					)
      __A			  =							PI + np.radadeg(lowerCamelCase_					)

      __A			  =							suppress_non_maximum(image.shape   ,							lowerCamelCase_   ,							lowerCamelCase_					)

      detect_high_low_threshold(
          image.shape   ,							lowerCamelCase_   ,							lowerCamelCase_   ,							lowerCamelCase_   ,							lowerCamelCase_   ,							lowerCamelCase_					)

      track_edge(image.shape   ,							lowerCamelCase_   ,							lowerCamelCase_   ,							lowerCamelCase_					)

      return destination


if __name__ == "__main__":
       # read original image in gray mode
       snake_case__   :				Dict						=	cva.imread(r'../image_data/lena.jpg', 0)
       # canny edge detection
       snake_case__   :				List[Any]						=	canny(lena)
       cva.imshow('canny', canny_destination)
       cva.waitKey(0)






import sys

from transformers import AutoTokenizer


_SCREAMING_SNAKE_CASE:    Optional[int]     						=							sys.argv[1]
_SCREAMING_SNAKE_CASE:    int     						=							sys.argv[2]
_SCREAMING_SNAKE_CASE:    str     						=							int(sys.argv[3])

_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							0

_SCREAMING_SNAKE_CASE:    Any     						=							AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
			for line in f_p:
						_SCREAMING_SNAKE_CASE:    Any     						=							line.rstrip()

						if not line:
									print(line)
									_SCREAMING_SNAKE_CASE:    str     						=							0
									continue

						_SCREAMING_SNAKE_CASE:    Optional[int]     						=							line.split()[0]

						_SCREAMING_SNAKE_CASE:    Tuple     						=							len(tokenizer.tokenize(token))

						# Token contains strange control characters like \x96 or \x95
						# Just filter out the complete line
						if current_subwords_len == 0:
									continue

						if (subword_len_counter + current_subwords_len) > max_len:
									print('''''')
									print(line)
									_SCREAMING_SNAKE_CASE:    Any     						=							current_subwords_len
									continue

						subword_len_counter += current_subwords_len

						print(line)

