"""simple docstring"""







import numpy as np
from PIL import Image



def 			A__						(  lowercase_    :  Union[str, Any]	)     ->       np.array:
			__lowerCamelCase								=				rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
			return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b



def 			A__						(  lowercase_    :  Any	)     ->       np.array:
			return (gray > 127) & (gray <= 255)



def 			A__						(  lowercase_    :  Optional[Any]			,   lowercase_    :  List[Any]	)     ->       np.array:
			__lowerCamelCase								=				np.zeros_like(SCREAMING_SNAKE_CASE__	)
			__lowerCamelCase								=				np.zeros(
			    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

			# Copy image to padded image
			__lowerCamelCase								=				image

			# Iterate over image & apply kernel
			for x in range(image.shape[1]	):
						for y in range(image.shape[0]	):
									__lowerCamelCase								=				(
									    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
									).sum()
									__lowerCamelCase								=				int(summation == 5	)
			return output


# kernel to be applied
_SCREAMING_SNAKE_CASE  :		Tuple        =      np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
					# read original image
					_SCREAMING_SNAKE_CASE  :		Tuple        =      np.array(Image.open(R'''..\image_data\lena.jpg'''))
					# Apply erosion operation to a binary image
					_SCREAMING_SNAKE_CASE  :		str        =      erosion(grayabinary(rgbagray(image)), structuring_element)
					# Save the output image
					_SCREAMING_SNAKE_CASE  :		List[str]        =      Image.fromarray(output).convert('''RGB''')
					pil_img.save('''result_erosion.png''')







"""simple docstring"""







import sys

from transformers import AutoTokenizer


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

_SCREAMING_SNAKE_CASE  :		List[str]        =      0

_SCREAMING_SNAKE_CASE  :		Optional[int]        =      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  :		List[Any]        =      line.rstrip()

          if not line:
               print(line)
               _SCREAMING_SNAKE_CASE  :		Union[str, Any]        =      0
               continue

          _SCREAMING_SNAKE_CASE  :		Dict        =      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  :		List[Any]        =      current_subwords_len
               continue

          subword_len_counter += current_subwords_len

          print(line)






