



from pathlib import Path

import numpy as np
from PIL import Image


def A						(       __UpperCamelCase					)						->      np.ndarray:
			A__     , A__     , A__       =    rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
			return 0.2989 * r + 0.5870 * g + 0.1140 * b


def A						(       __UpperCamelCase					)						->      np.ndarray:
			return (gray > 127) & (gray <= 255)


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      np.ndarray:
			A__       =    np.zeros_like(__UpperCamelCase					)
			A__       =    np.zeros(
			    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

			# Copy image to padded image
			A__       =    image

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


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









from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Union[str, Any]			       =    ["image_processor", "tokenizer"]
					A__				:		Optional[Any]			       =    "BridgeTowerImageProcessor"
					A__				:		List[Any]			       =    ("RobertaTokenizer", "RobertaTokenizerFast")



					def __init__(     self					:       List[Any]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								super().__init__(_snake_case				,					_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       int				,					_snake_case					:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,					_snake_case					:       bool = True				,					_snake_case					:       Union[bool, str, PaddingStrategy] = False				,					_snake_case					:       Union[bool, str, TruncationStrategy] = None				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       int = 0				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = True				,					_snake_case					:       Optional[Union[str, TensorType]] = None				,					**_snake_case					:       Optional[int]				,					):






								"""simple docstring"""


								A__       =    self.tokenizer(
								    text=_snake_case				,					add_special_tokens=_snake_case				,					padding=_snake_case				,					truncation=_snake_case				,					max_length=_snake_case				,					stride=_snake_case				,					pad_to_multiple_of=_snake_case				,					return_token_type_ids=_snake_case				,					return_attention_mask=_snake_case				,					return_overflowing_tokens=_snake_case				,					return_special_tokens_mask=_snake_case				,					return_offsets_mapping=_snake_case				,					return_length=_snake_case				,					verbose=_snake_case				,					return_tensors=_snake_case				,					**_snake_case				,					)
								# add pixel_values + pixel_mask
								A__       =    self.image_processor(
								    _snake_case				,					return_tensors=_snake_case				,					do_normalize=_snake_case				,					do_center_crop=_snake_case				,					**_snake_case					)
								encoding.update(_snake_case					)

								return encoding



					def     _a							(     self					:       Any				,					*_snake_case					:       Tuple				,					**_snake_case					:       List[Any]					):






								"""simple docstring"""


								return self.tokenizer.batch_decode(*_snake_case				,					**_snake_case					)



					def     _a							(     self					:       Dict				,					*_snake_case					:       Dict				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


								return self.tokenizer.decode(*_snake_case				,					**_snake_case					)



					@property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    self.tokenizer.model_input_names
								A__       =    self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)




