





'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


a__							:							Optional[int]			=logging.get_logger(__name__)

a__							:							Optional[int]			={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a__							:							Tuple			={
    '''vocab_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'''
        ),
    },
}

a__							:							int			={
    '''microsoft/deberta-base''': 512,
    '''microsoft/deberta-large''': 512,
    '''microsoft/deberta-xlarge''': 512,
    '''microsoft/deberta-base-mnli''': 512,
    '''microsoft/deberta-large-mnli''': 512,
    '''microsoft/deberta-xlarge-mnli''': 512,
}

a__							:							Dict			={
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}

class 					snake_case					(   __lowerCamelCase	):




	"""simple docstring"""



	SCREAMING_SNAKE_CASE_     : Optional[int]      		=VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_     : Union[str, Any]      		=PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_     : Any      		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_     : Dict      		=["input_ids", "attention_mask", "token_type_ids"]
	SCREAMING_SNAKE_CASE_     : Optional[Any]      		=DebertaTokenizer






	def __init__(	self	:							List[Any]							,     __A	:							Any=None							,     __A	:							List[Any]=None							,     __A	:							int=None							,     __A	:							Optional[int]="replace"							,     __A	:							Tuple="[CLS]"							,     __A	:							Tuple="[SEP]"							,     __A	:							List[Any]="[SEP]"							,     __A	:							str="[CLS]"							,     __A	:							Tuple="[UNK]"							,     __A	:							int="[PAD]"							,     __A	:							int="[MASK]"							,     __A	:							Tuple=False							,     **__A	:							Any							,     ):
								super().__init__(
								    __A							,     __A							,     tokenizer_file=__A							,     errors=__A							,     bos_token=__A							,     eos_token=__A							,     unk_token=__A							,     sep_token=__A							,     cls_token=__A							,     pad_token=__A							,     mask_token=__A							,     add_prefix_space=__A							,     **__A							,     )
								__UpperCamelCase	=  kwargs.pop('add_bos_token'							,     __A     )

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

								__UpperCamelCase	=  add_prefix_space






	@property
	def 	_lowerCamelCase (	self	:							List[str]     ):
								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	:							Optional[int]							,     __A	:							Dict     ):
								__UpperCamelCase	=  AddedToken(__A							,     lstrip=__A							,     rstrip=__A     ) if isinstance(__A							,     __A     ) else value
								__UpperCamelCase	=  value






	def 	_lowerCamelCase (	self	:							Optional[Any]							,     __A	:							List[int]							,     __A	:							Optional[List[int]] = None     ):
								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__UpperCamelCase	=  [self.cls_token_id]
								__UpperCamelCase	=  [self.sep_token_id]
								return cls + token_ids_a + sep + token_ids_a + sep






	def 	_lowerCamelCase (	self	:							int							,     __A	:							List[int]							,     __A	:							Optional[List[int]] = None     ):
								__UpperCamelCase	=  [self.sep_token_id]
								__UpperCamelCase	=  [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     ) * [0] + len(token_ids_a + sep     ) * [1]






	def 	_lowerCamelCase (	self	:							List[str]							,     *__A	:							Tuple							,     **__A	:							List[str]     ):
								__UpperCamelCase	=  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	:							Dict							,     *__A	:							Any							,     **__A	:							Any     ):
								__UpperCamelCase	=  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	:							Any							,     __A	:							str							,     __A	:							Optional[str] = None     ):
								__UpperCamelCase	=  self._tokenizer.model.save(__A							,     name=__A     )
								return tuple(__A     )






	def 	_lowerCamelCase (	self	:							str							,     __A	:							"Conversation"     ):
								__UpperCamelCase	=  []
								for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(__A							,     add_special_tokens=__A     ) + [self.eos_token_id]     )

								if len(__A     ) > self.model_max_length:
															__UpperCamelCase	=  input_ids[-self.model_max_length :]
								return input_ids







'''simple docstring'''
import numpy as np
from PIL import Image
def     lowercase__       (       __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       __UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  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     lowercase__       (       __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       return (gray > 127) & (gray <= 255)
def     lowercase__       (       __lowercase						:  np.array    ,  __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       __UpperCamelCase	=  np.zeros_like(__lowercase					)
       __UpperCamelCase	=  np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

       # Copy image to padded image
       __UpperCamelCase	=  image

       # Iterate over image & apply kernel
       for x in range(image.shape[1]					):
              for y in range(image.shape[0]					):
                     __UpperCamelCase	=  (
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     __UpperCamelCase	=  int(summation == 5					)
       return output


# kernel to be applied
a__							:							Dict			=np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
    # read original image
    a__							:							Any			=np.array(Image.open(r'''..\image_data\lena.jpg'''))
    # Apply erosion operation to a binary image
    a__							:							Union[str, Any]			=erosion(grayabinary(rgbagray(image)), structuring_element)
    # Save the output image
    a__							:							Tuple			=Image.fromarray(output).convert('''RGB''')
    pil_img.save('''result_erosion.png''')
