



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


__UpperCAmelCase    	=  logging.get_logger(__name__)

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

__UpperCAmelCase    	=  {
    "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"
        ),
    },
}

__UpperCAmelCase    	=  {
    "microsoft/deberta-base": 5_12,
    "microsoft/deberta-large": 5_12,
    "microsoft/deberta-xlarge": 5_12,
    "microsoft/deberta-base-mnli": 5_12,
    "microsoft/deberta-large-mnli": 5_12,
    "microsoft/deberta-xlarge-mnli": 5_12,
}

__UpperCAmelCase    	=  {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}


class 						lowerCamelCase__			(		_a ):
						_lowerCAmelCase             =			VOCAB_FILES_NAMES
						_lowerCAmelCase             =			PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase             =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase             =			["""input_ids""", """attention_mask""", """token_type_ids"""]
						_lowerCAmelCase             =			DebertaTokenizer


						def __init__(			self    :	str   ,							_a    :	str=None   ,							_a    :	int=None   ,							_a    :	Any=None   ,							_a    :	Any="replace"   ,							_a    :	Union[str, Any]="[CLS]"   ,							_a    :	Union[str, Any]="[SEP]"   ,							_a    :	Optional[int]="[SEP]"   ,							_a    :	Any="[CLS]"   ,							_a    :	Tuple="[UNK]"   ,							_a    :	List[Any]="[PAD]"   ,							_a    :	Union[str, Any]="[MASK]"   ,							_a    :	Optional[Any]=False   ,							**_a    :	Optional[int]   ,							):
													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   ,							)
													a__:							Optional[int]									=kwargs.pop("add_bos_token"   ,							_A      )

													a__:							Dict									=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
													if pre_tok_state.get("add_prefix_space"   ,							_A      ) != add_prefix_space:
																				a__:							Tuple									=getattr(_A   ,							pre_tok_state.pop("type"      )      )
																				a__:							Dict									=add_prefix_space
																				a__:							Dict									=pre_tok_class(**_A      )

													a__:							List[Any]									=add_prefix_space


						@property
						def 	_lowerCamelCase      (			self    :	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    :	int   ,							_a    :	List[Any]      ):
													a__:							int									=AddedToken(_A   ,							lstrip=_A   ,							rstrip=_A      ) if isinstance(_A   ,							_A      ) else value
													a__:							Any									=value


						def 	_lowerCamelCase      (			self    :	Tuple   ,							_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]
													a__:							List[Any]									=[self.cls_token_id]
													a__:							Optional[Any]									=[self.sep_token_id]
													return cls + token_ids_a + sep + token_ids_a + sep


						def 	_lowerCamelCase      (			self    :	Optional[Any]   ,							_a    :	List[int]   ,							_a    :	Optional[List[int]] = None      ):
													a__:							List[Any]									=[self.sep_token_id]
													a__:							int									=[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    :	int   ,							*_a    :	Tuple   ,							**_a    :	List[Any]      ):
													a__:							Union[str, Any]									=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    :	Tuple   ,							*_a    :	str   ,							**_a    :	int      ):
													a__:							Optional[Any]									=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[Any]   ,							_a    :	str   ,							_a    :	Optional[str] = None      ):
													a__:							Tuple									=self._tokenizer.model.save(_A   ,							name=_A      )
													return tuple(_A      )







						def 	_lowerCamelCase      (			self    :	Tuple   ,							_a    :	"Conversation"      ):
													a__:							List[str]									=[]
													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:
																				a__:							Union[str, Any]									=input_ids[-self.model_max_length :]
													return input_ids






"""simple docstring"""







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

_a	:	int=					180
def     __UpperCAmelCase							(       UpperCAmelCase_       :							str     ,				UpperCAmelCase_       :							Tuple    )				->     Union[str, Any]:

	'''simple docstring'''





	__snake_case     :				Optional[Any] 						=					k_size // 2
	__snake_case						,							__snake_case     :				Union[str, Any] 						=					np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
	__snake_case     :				List[Any] 						=					(
	    1
	    / (2 * np.pi * sigma)
	    * np.exp(-(np.square(UpperCAmelCase_    ) + np.square(UpperCAmelCase_    )) / (2 * np.square(UpperCAmelCase_    ))    )
	)
	return g
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Tuple     ,				UpperCAmelCase_       :							int     ,				UpperCAmelCase_       :							Optional[int]    )				->     List[str]:

	'''simple docstring'''





	__snake_case     :				List[Any] 						=					np.zeros(UpperCAmelCase_    )

	for row in range(1     ,				image_shape[0] - 1    ):
		for col in range(1     ,				image_shape[1] - 1    ):
			__snake_case     :				Any 						=					gradient_direction[row, col]

			if (
			    0 <= direction < PI / 8
			    or 15 * PI / 8 <= direction <= 2 * PI
			    or 7 * PI / 8 <= direction <= 9 * PI / 8
			):
				__snake_case     :				str 						=					sobel_grad[row, col - 1]
				__snake_case     :				str 						=					sobel_grad[row, col + 1]
				if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
					__snake_case     :				Union[str, Any] 						=					sobel_grad[row, col]

			elif (
			    PI / 8 <= direction < 3 * PI / 8
			    or 9 * PI / 8 <= direction < 11 * PI / 8
			):
				__snake_case     :				Optional[int] 						=					sobel_grad[row + 1, col - 1]
				__snake_case     :				Optional[Any] 						=					sobel_grad[row - 1, col + 1]
				if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
					__snake_case     :				Union[str, Any] 						=					sobel_grad[row, col]

			elif (
			    3 * PI / 8 <= direction < 5 * PI / 8
			    or 11 * PI / 8 <= direction < 13 * PI / 8
			):
				__snake_case     :				Union[str, Any] 						=					sobel_grad[row - 1, col]
				__snake_case     :				Optional[int] 						=					sobel_grad[row + 1, col]
				if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
					__snake_case     :				int 						=					sobel_grad[row, col]

			elif (
			    5 * PI / 8 <= direction < 7 * PI / 8
			    or 13 * PI / 8 <= direction < 15 * PI / 8
			):
				__snake_case     :				Optional[int] 						=					sobel_grad[row - 1, col - 1]
				__snake_case     :				Union[str, Any] 						=					sobel_grad[row + 1, col + 1]
				if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
					__snake_case     :				List[str] 						=					sobel_grad[row, col]

	return destination
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Tuple     ,				UpperCAmelCase_       :							List[str]     ,				UpperCAmelCase_       :							Optional[Any]     ,				UpperCAmelCase_       :							Optional[Any]     ,				UpperCAmelCase_       :							List[str]     ,				UpperCAmelCase_       :							List[Any]    )				->     Any:

	'''simple docstring'''





	for row in range(1     ,				image_shape[0] - 1    ):
		for col in range(1     ,				image_shape[1] - 1    ):
			if destination[row, col] >= threshold_high:
				__snake_case     :				str 						=					strong
			elif destination[row, col] <= threshold_low:
				__snake_case     :				int 						=					0
			else:
				__snake_case     :				Tuple 						=					weak
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Union[str, Any]     ,				UpperCAmelCase_       :							Tuple     ,				UpperCAmelCase_       :							List[Any]     ,				UpperCAmelCase_       :							int    )				->     List[Any]:

	'''simple docstring'''





	for row in range(1     ,				image_shape[0]    ):
		for col in range(1     ,				image_shape[1]    ):
			if destination[row, col] == weak:
				if 2_55 in (
				    destination[row, col + 1],
				    destination[row, col - 1],
				    destination[row - 1, col],
				    destination[row + 1, col],
				    destination[row - 1, col - 1],
				    destination[row + 1, col - 1],
				    destination[row - 1, col + 1],
				    destination[row + 1, col + 1],
				):
					__snake_case     :				Tuple 						=					strong
				else:
					__snake_case     :				Tuple 						=					0
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Union[str, Any]     ,				UpperCAmelCase_       :							Any=15     ,				UpperCAmelCase_       :							int=30     ,				UpperCAmelCase_       :							Optional[Any]=1_28     ,				UpperCAmelCase_       :							Dict=2_55    )				->     Optional[Any]:

	'''simple docstring'''





	__snake_case     :				Tuple 						=					img_convolve(UpperCAmelCase_     ,				gen_gaussian_kernel(9     ,				sigma=1.4    )    )
	# get the gradient and degree by sobel_filter
	__snake_case						,							__snake_case     :				str 						=					sobel_filter(UpperCAmelCase_    )
	__snake_case     :				str 						=					PI + np.radadeg(UpperCAmelCase_    )

	__snake_case     :				int 						=					suppress_non_maximum(image.shape     ,				UpperCAmelCase_     ,				UpperCAmelCase_    )

	detect_high_low_threshold(
	    image.shape     ,				UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_    )

	track_edge(image.shape     ,				UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_    )

	return destination


if __name__ == "__main__":
	# read original image in gray mode
	_a	:	List[Any]=					cva.imread(R"../image_data/lena.jpg", 0)
	# canny edge detection
	_a	:	Optional[int]=					canny(lena)
	cva.imshow("canny", canny_destination)
	cva.waitKey(0)





