
"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Any							=					["image_processor", "tokenizer"]
				SCREAMING_SNAKE_CASE_:							Optional[Any]							=					"AutoImageProcessor"
				SCREAMING_SNAKE_CASE_:							Union[str, Any]							=					"AutoTokenizer"


				def __init__(					self	:			int				,     UpperCAmelCase_	:			Optional[Any]=None				,     UpperCAmelCase_	:			Any=None				,     **UpperCAmelCase_	:			str  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.'				,     UpperCAmelCase_				,     )
																		_lowerCAmelCase  	=			kwargs.pop('feature_extractor'  )

											_lowerCAmelCase  	=			image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError('You need to specify an `image_processor`.'  )
											if tokenizer is None:
																		raise ValueError('You need to specify a `tokenizer`.'  )

											super().__init__(UpperCAmelCase_				,     UpperCAmelCase_  )
											_lowerCAmelCase  	=			self.image_processor
											_lowerCAmelCase  	=			False


				def __call__(					self	:			Dict				,     *UpperCAmelCase_	:			str				,     **UpperCAmelCase_	:			Any  )		->  Optional[int]:







											"""simple docstring"""




											if self._in_target_context_manager:
																		return self.current_processor(*UpperCAmelCase_				,     **UpperCAmelCase_  )

											_lowerCAmelCase  	=			kwargs.pop('images'				,     UpperCAmelCase_  )
											_lowerCAmelCase  	=			kwargs.pop('text'				,     UpperCAmelCase_  )
											if len(UpperCAmelCase_  ) > 0:
																		_lowerCAmelCase  	=			args[0]
																		_lowerCAmelCase  	=			args[1:]

											if images is None and text is None:
																		raise ValueError('You need to specify either an `images` or `text` input to process.'  )

											if images is not None:
																		_lowerCAmelCase  	=			self.image_processor(UpperCAmelCase_				,     *UpperCAmelCase_				,     **UpperCAmelCase_  )
											if text is not None:
																		_lowerCAmelCase  	=			self.tokenizer(UpperCAmelCase_				,     **UpperCAmelCase_  )

											if text is None:
																		return inputs
											elif images is None:
																		return encodings
											else:
																		_lowerCAmelCase  	=			encodings['input_ids']
																		return inputs


				def 				__lowerCamelCase	(					self	:			Union[str, Any]				,     *UpperCAmelCase_	:			Tuple				,     **UpperCAmelCase_	:			Dict  )		->  List[str]:







											"""simple docstring"""




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


				def 				__lowerCamelCase	(					self	:			Any				,     *UpperCAmelCase_	:			Any				,     **UpperCAmelCase_	:			Optional[int]  )		->  int:







											"""simple docstring"""




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


				@contextmanager
				def 				__lowerCamelCase	(					self	:			int  )		->  List[Any]:







											"""simple docstring"""




											warnings.warn(
											    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
											    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
											    'your images inputs, or in a separate call.'  )
											_lowerCAmelCase  	=			True
											_lowerCAmelCase  	=			self.tokenizer
											yield
											_lowerCAmelCase  	=			self.image_processor
											_lowerCAmelCase  	=			False


				def 				__lowerCamelCase	(					self	:			Optional[int]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			List[str]=False				,     UpperCAmelCase_	:			List[str]=None  )		->  str:







											"""simple docstring"""




											if added_vocab is None:
																		_lowerCAmelCase  	=			self.tokenizer.get_added_vocab()

											_lowerCAmelCase  	=			{}

											while tokens:
																		_lowerCAmelCase  	=			re.search(R'<s_(.*?)>'				,     UpperCAmelCase_				,     re.IGNORECASE  )
																		if start_token is None:
																									break
																		_lowerCAmelCase  	=			start_token.group(1  )
																		_lowerCAmelCase  	=			re.search(RF"""</s_{key}>"""				,     UpperCAmelCase_				,     re.IGNORECASE  )
																		_lowerCAmelCase  	=			start_token.group()
																		if end_token is None:
																									_lowerCAmelCase  	=			tokens.replace(UpperCAmelCase_				,     ''  )
																		else:
																									_lowerCAmelCase  	=			end_token.group()
																									_lowerCAmelCase  	=			re.escape(UpperCAmelCase_  )
																									_lowerCAmelCase  	=			re.escape(UpperCAmelCase_  )
																									_lowerCAmelCase  	=			re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""				,     UpperCAmelCase_				,     re.IGNORECASE  )
																									if content is not None:
																																_lowerCAmelCase  	=			content.group(1  ).strip()
																																if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																							_lowerCAmelCase  	=			self.tokenajson(UpperCAmelCase_				,     is_inner_value=UpperCAmelCase_				,     added_vocab=UpperCAmelCase_  )
																																							if value:
																																														if len(UpperCAmelCase_  ) == 1:
																																																					_lowerCAmelCase  	=			value[0]
																																														_lowerCAmelCase  	=			value
																																else:  # leaf nodes
																																							_lowerCAmelCase  	=			[]
																																							for leaf in content.split(R'<sep/>'  ):
																																														_lowerCAmelCase  	=			leaf.strip()
																																														if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																																					_lowerCAmelCase  	=			leaf[1:-2]  # for categorical special tokens
																																														output[key].append(UpperCAmelCase_  )
																																							if len(output[key]  ) == 1:
																																														_lowerCAmelCase  	=			output[key][0]

																									_lowerCAmelCase  	=			tokens[tokens.find(UpperCAmelCase_  ) + len(UpperCAmelCase_  ) :].strip()
																									if tokens[:6] == r"<sep/>":  # non-leaf nodes
																																return [output] + self.tokenajson(tokens[6:]				,     is_inner_value=UpperCAmelCase_				,     added_vocab=UpperCAmelCase_  )

											if len(UpperCAmelCase_  ):
																		return [output] if is_inner_value else output
											else:
																		return [] if is_inner_value else {"text_sequence": tokens}


				@property
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Any:







											"""simple docstring"""




											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,     UpperCAmelCase_				,     )
											return self.image_processor_class







				@property
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  int:







											"""simple docstring"""




											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,     UpperCAmelCase_				,     )
											return self.image_processor








"""simple docstring"""



from pathlib import Path

import numpy as np
from PIL import Image






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							return (gray > 127) & (gray <= 255)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray     ,  SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_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 > 0						)
							return output


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






