
'''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")




'''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


