code: 
  '''simple docstring'''
  import numpy as np
  from PIL import Image
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       np.array:
  
  
  		'''simple docstring'''
  
  
  		snake_case ,snake_case ,snake_case     :					str     =	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  		return 0.2989 * r + 0.5870 * g + 0.1140 * b
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       np.array:
  
  
  		'''simple docstring'''
  
  
  		return (gray > 127) & (gray <= 255)
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       np.array:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					List[str]     =	np.zeros_like(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					str     =	np.zeros(
  		    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
  
  		# Copy image to padded image
  		snake_case     :					List[Any]     =	image
  
  		# Iterate over image & apply kernel
  		for x in range(image.shape[1] ):
  				for y in range(image.shape[0] ):
  						snake_case     :					Dict     =	(
  						    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
  						).sum()
  						snake_case     :					List[str]     =	int(summation == 5 )
  		return output
  
  
  # kernel to be applied
  lowercase__						=			np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
  
  if __name__ == "__main__":
  						# read original image
  						lowercase__						=			np.array(Image.open(R"..\image_data\lena.jpg"))
  						# Apply erosion operation to a binary image
  						lowercase__						=			erosion(grayabinary(rgbagray(image)), structuring_element)
  						# Save the output image
  						lowercase__						=			Image.fromarray(output).convert("RGB")
  						pil_img.save("result_erosion.png")
  
  
  
style_context: 
  '''simple docstring'''
  import torch
  
  from transformers import AutoTokenizer
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: Union[str, Any]     ,  UpperCamelCase__							: List[str]			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[Any]     =	AutoTokenizer.from_pretrained(UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: str			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and all(isinstance(_x     ,  UpperCamelCase__			) for _x in x			):
  						snake_case     :					Any     =	None
  						for l in x:
  								snake_case     :					str     =	self.tokenizer(
  								    UpperCamelCase__     ,  padding='''max_length'''     ,  max_length=384     ,  truncation=UpperCamelCase__     ,  return_tensors='''pt'''     ,  )
  								snake_case     :					Tuple     =	torch.tensor([len(UpperCamelCase__			)]			)
  								if d is not None:
  										for k in d.keys():
  												snake_case     :					int     =	torch.cat((d[k], t[k])     ,  0			)
  								else:
  										snake_case     :					List[str]     =	t
  
  						snake_case     :					Union[str, Any]     =	torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''			)			)
  						snake_case     :					int     =	torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''			)			)
  
  				elif isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and all(isinstance(_x     ,  UpperCamelCase__			) for _x in x			):
  						snake_case     :					Optional[int]     =	self.tokenizer(
  						    UpperCamelCase__     ,  padding='''max_length'''     ,  max_length=384     ,  truncation=UpperCamelCase__     ,  return_tensors='''pt'''     ,  )
  
  				else:
  						raise Exception(
  						    '''Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'''
  						    ''' strings` for supports are supported.'''			)
  
  				return d
  
  
  
  		def 			lowerCAmelCase							(			self							: int     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Any=0.70			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					str     =	[]
  				for idx in range(len(W_query['''input_ids''']			)			):
  						snake_case     :					Optional[int]     =	range(p_start.shape[1]			)
  
  						snake_case     :					str     =	[]
  						for start_id in start_indexes:
  								for end_id in end_indexes:
  										if start_id < end_id:
  												output.append(
  												    (
  												        start_id,
  												        end_id,
  												        p_start[idx][start_id].item(),
  												        p_end[idx][end_id].item(),
  												    )			)
  
  						output.sort(key=lambda UpperCamelCase__			: (tup[2] * tup[3])     ,  reverse=UpperCamelCase__			)
  						snake_case     :					int     =	[]
  						for k in range(len(UpperCamelCase__			)			):
  								if output[k][2] * output[k][3] >= thresh:
  										snake_case ,snake_case     :					Tuple     =	output[k][0], output[k][1]
  										snake_case     :					str     =	self.tokenizer.decode(W_query['''input_ids'''][idx][c_start_pos:c_end_pos]			)
  										temp.append((decoded, output[k][2] * output[k][3])			)
  
  						final_outputs.append(UpperCamelCase__			)
  
  				return final_outputs
  
  
  