
"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_snake_case          =						{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

_snake_case          =						{
    "roberta-base": 5_1_2,
    "roberta-large": 5_1_2,
    "roberta-large-mnli": 5_1_2,
    "distilroberta-base": 5_1_2,
    "roberta-base-openai-detector": 5_1_2,
    "roberta-large-openai-detector": 5_1_2,
}
class        _SCREAMING_SNAKE_CASE  (  _UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str							=					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_:							Dict							=					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_:							int							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_:							Tuple							=					['''input_ids''', '''attention_mask''']
				SCREAMING_SNAKE_CASE_:							Optional[Any]							=					RobertaTokenizer


				def __init__(					self	:			str				,     UpperCAmelCase_	:			Optional[Any]=None				,     UpperCAmelCase_	:			Optional[int]=None				,     UpperCAmelCase_	:			List[Any]=None				,     UpperCAmelCase_	:			Optional[Any]="replace"				,     UpperCAmelCase_	:			Optional[int]="<s>"				,     UpperCAmelCase_	:			int="</s>"				,     UpperCAmelCase_	:			List[Any]="</s>"				,     UpperCAmelCase_	:			str="<s>"				,     UpperCAmelCase_	:			Tuple="<unk>"				,     UpperCAmelCase_	:			List[str]="<pad>"				,     UpperCAmelCase_	:			Optional[int]="<mask>"				,     UpperCAmelCase_	:			str=False				,     UpperCAmelCase_	:			Union[str, Any]=True				,     **UpperCAmelCase_	:			Dict				,     )		->  Dict:







											"""simple docstring"""




											super().__init__(
											    A_				,     A_				,     tokenizer_file=A_				,     errors=A_				,     bos_token=A_				,     eos_token=A_				,     sep_token=A_				,     cls_token=A_				,     unk_token=A_				,     pad_token=A_				,     mask_token=A_				,     add_prefix_space=A_				,     trim_offsets=A_				,     **A_				,     )

											_lowerCAmelCase  	=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
											if pre_tok_state.get('add_prefix_space'				,     A_  ) != add_prefix_space:
																		_lowerCAmelCase  	=			getattr(A_				,     pre_tok_state.pop('type'  )  )
																		_lowerCAmelCase  	=			add_prefix_space
																		_lowerCAmelCase  	=			pre_tok_class(**A_  )

											_lowerCAmelCase  	=			add_prefix_space

											_lowerCAmelCase  	=			'post_processor'
											_lowerCAmelCase  	=			getattr(self.backend_tokenizer				,     A_				,     A_  )
											if tokenizer_component_instance:
																		_lowerCAmelCase  	=			json.loads(tokenizer_component_instance.__getstate__()  )

																		# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																		if "sep" in state:
																									_lowerCAmelCase  	=			tuple(state['sep']  )
																		if "cls" in state:
																									_lowerCAmelCase  	=			tuple(state['cls']  )

																		_lowerCAmelCase  	=			False

																		if state.get('add_prefix_space'				,     A_  ) != add_prefix_space:
																									_lowerCAmelCase  	=			add_prefix_space
																									_lowerCAmelCase  	=			True

																		if state.get('trim_offsets'				,     A_  ) != trim_offsets:
																									_lowerCAmelCase  	=			trim_offsets
																									_lowerCAmelCase  	=			True

																		if changes_to_apply:
																									_lowerCAmelCase  	=			getattr(A_				,     state.pop('type'  )  )
																									_lowerCAmelCase  	=			component_class(**A_  )
																									setattr(self.backend_tokenizer				,     A_				,     A_  )


				@property
				def 				__lowerCamelCase	(					self	:			List[str]  )		->  str:







											"""simple docstring"""




											if self._mask_token is None:
																		if self.verbose:
																									logger.error('Using mask_token, but it is not set yet.'  )
																		return None
											return str(self._mask_token  )


				@mask_token.setter
				def 				__lowerCamelCase	(					self	:			Union[str, Any]				,     UpperCAmelCase_	:			List[str]  )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			AddedToken(A_				,     lstrip=A_				,     rstrip=A_  ) if isinstance(A_				,     A_  ) else value
											_lowerCAmelCase  	=			value


				def 				__lowerCamelCase	(					self	:			Dict				,     *UpperCAmelCase_	:			Optional[int]				,     **UpperCAmelCase_	:			Tuple  )		->  BatchEncoding:







											"""simple docstring"""




											_lowerCAmelCase  	=			kwargs.get('is_split_into_words'				,     A_  )
											assert self.add_prefix_space or not is_split_into_words, (
											    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
											    "to use it with pretokenized inputs."
											)

											return super()._batch_encode_plus(*A_				,     **A_  )


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







											"""simple docstring"""




											_lowerCAmelCase  	=			kwargs.get('is_split_into_words'				,     A_  )

											assert self.add_prefix_space or not is_split_into_words, (
											    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
											    "to use it with pretokenized inputs."
											)

											return super()._encode_plus(*A_				,     **A_  )


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







											"""simple docstring"""




											_lowerCAmelCase  	=			self._tokenizer.model.save(A_				,     name=A_  )
											return tuple(A_  )


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







											"""simple docstring"""




											_lowerCAmelCase  	=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
											if token_ids_a is None:
																		return output

											return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







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







											"""simple docstring"""




											_lowerCAmelCase  	=			[self.sep_token_id]
											_lowerCAmelCase  	=			[self.cls_token_id]

											if token_ids_a is None:
																		return len(cls + token_ids_a + sep  ) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]








"""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''')






