
'''simple docstring'''







from pathlib import Path

import numpy as np
from PIL import Image







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			np.ndarray:
	_snake_case		,				_snake_case		,				_snake_case     		=	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        SCREAMING_SNAKE_CASE__			(     __A			) ->			np.ndarray:
	return (gray > 127) & (gray <= 255)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			np.ndarray:
	_snake_case     		=	np.zeros_like(__A			)
	_snake_case     		=	np.zeros(
	    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)			)

	# Copy image to padded image
	_snake_case     		=	image

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


if __name__ == "__main__":
					# read original image
					lowercase					:							List[Any]   =      Path(__file__).resolve().parent / "image_data" / "lena.jpg"
					lowercase					:							List[Any]   =      np.array(Image.open(lena_path))
					# kernel to be applied
					lowercase					:							str   =      np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
					lowercase					:							Optional[Any]   =      dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
					# Save the output image
					lowercase					:							Optional[int]   =      Image.fromarray(output).convert("RGB")
					pil_img.save("result_dilation.png")



'''simple docstring'''







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A=5			) ->			Dict:
	# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
	assert masked_input.count('<mask>'			) == 1
	_snake_case     		=	torch.tensor(tokenizer.encode(__A    ,       add_special_tokens=__A			)			).unsqueeze(0			)  # Batch size 1
	_snake_case     		=	model(__A			)[0]  # The last hidden-state is the first element of the output tuple
	_snake_case     		=	(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
	_snake_case     		=	logits[0, masked_index, :]
	_snake_case     		=	logits.softmax(dim=0			)
	_snake_case		,				_snake_case     		=	prob.topk(k=__A    ,       dim=0			)
	_snake_case     		=	' '.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()			) for i in range(len(__A			)			)]			)
	_snake_case     		=	tokenizer.mask_token
	_snake_case     		=	[]
	for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' '			)			):
		_snake_case     		=	predicted_token_bpe.replace('\u2581'    ,       ' '			)
		if " {0}".format(__A			) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(' {0}'.format(__A			)    ,       __A			),
			        values[index].item(),
			        predicted_token,
			    )			)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(__A    ,       __A			),
			        values[index].item(),
			        predicted_token,
			    )			)
	return topk_filled_outputs


lowercase					:							str   =      CamembertTokenizer.from_pretrained("camembert-base")
lowercase					:							str   =      CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowercase					:							Tuple   =      "Le camembert est <mask> :)"





print(fill_mask(masked_input, model, tokenizer, topk=3))

