


'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_big_bird import BigBirdTokenizer
else:
							lowerCAmelCase      :Dict    	=			None

lowerCAmelCase      :Dict    	=			logging.get_logger(__name__)
lowerCAmelCase      :Optional[Any]    	=			{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase      :str    	=			{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    },
    '''tokenizer_file''': {
        '''google/bigbird-roberta-base''': (
            '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCAmelCase      :Tuple    	=			{
    '''google/bigbird-roberta-base''': 4_0_9_6,
    '''google/bigbird-roberta-large''': 4_0_9_6,
    '''google/bigbird-base-trivia-itc''': 4_0_9_6,
}


lowerCAmelCase      :int    	=			'''▁'''
class _lowerCamelCase       (						__UpperCamelCase							):



		'''simple docstring'''







		A_	:			Union[str, Any]          =				VOCAB_FILES_NAMES
		A_	:			Optional[Any]          =				PRETRAINED_VOCAB_FILES_MAP
		A_	:			Union[str, Any]          =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_	:			List[Any]          =				BigBirdTokenizer
		A_	:			Any          =				["""input_ids""", """attention_mask"""]
		A_	:			str          =				[]







		def __init__(					self		:						str  ,     _A		:						Tuple=None  ,     _A		:						Optional[int]=None  ,     _A		:						List[str]="<unk>"  ,     _A		:						Any="<s>"  ,     _A		:						Dict="</s>"  ,     _A		:						int="<pad>"  ,     _A		:						int="[SEP]"  ,     _A		:						int="[MASK]"  ,     _A		:						Optional[int]="[CLS]"  ,     **_A		:						int  ,     )  -> str:
			__magic_name__       :    Optional[int]     	=     AddedToken(a_  ,     lstrip=a_  ,     rstrip=a_			) if isinstance(a_  ,     a_			) else bos_token
			__magic_name__       :    Optional[int]     	=     AddedToken(a_  ,     lstrip=a_  ,     rstrip=a_			) if isinstance(a_  ,     a_			) else eos_token
			__magic_name__       :    Optional[Any]     	=     AddedToken(a_  ,     lstrip=a_  ,     rstrip=a_			) if isinstance(a_  ,     a_			) else unk_token
			__magic_name__       :    Optional[Any]     	=     AddedToken(a_  ,     lstrip=a_  ,     rstrip=a_			) if isinstance(a_  ,     a_			) else pad_token
			__magic_name__       :    Any     	=     AddedToken(a_  ,     lstrip=a_  ,     rstrip=a_			) if isinstance(a_  ,     a_			) else cls_token
			__magic_name__       :    Union[str, Any]     	=     AddedToken(a_  ,     lstrip=a_  ,     rstrip=a_			) if isinstance(a_  ,     a_			) else sep_token

			# Mask token behave like a normal word, i.e. include the space before it
			__magic_name__       :    Optional[Any]     	=     AddedToken(a_  ,     lstrip=a_  ,     rstrip=a_			) if isinstance(a_  ,     a_			) else mask_token

			super().__init__(
			    a_  ,     tokenizer_file=a_  ,     bos_token=a_  ,     eos_token=a_  ,     unk_token=a_  ,     sep_token=a_  ,     pad_token=a_  ,     cls_token=a_  ,     mask_token=a_  ,     **a_  ,     )

			__magic_name__       :    Tuple     	=     vocab_file
			__magic_name__       :    List[Any]     	=     False if not self.vocab_file else True







		def    __lowerCAmelCase		(					self		:						Union[str, Any]  ,     _A		:						List[int]  ,     _A		:						Optional[List[int]] = None			)  -> List[Any]:
			__magic_name__       :    List[Any]     	=     [self.sep_token_id]
			__magic_name__       :    List[Any]     	=     [self.cls_token_id]
			if token_ids_a is None:
				return cls + token_ids_a + sep
			return cls + token_ids_a + sep + token_ids_a + sep







		def    __lowerCAmelCase		(					self		:						List[Any]  ,     _A		:						List[int]  ,     _A		:						Optional[List[int]] = None  ,     _A		:						bool = False			)  -> int:





			if already_has_special_tokens:
				if token_ids_a is not None:
					raise ValueError(
					    'You should not supply a second sequence if the provided sequence of '
					    'ids is already formatted with special tokens for the model.'			)
				return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

			if token_ids_a is None:
				return [1] + ([0] * len(a_			)) + [1]
			return [1] + ([0] * len(a_			)) + [1] + ([0] * len(a_			)) + [1]







		def    __lowerCAmelCase		(					self		:						Optional[Any]  ,     _A		:						List[int]  ,     _A		:						Optional[List[int]] = None			)  -> Union[str, Any]:
			__magic_name__       :    List[Any]     	=     [self.sep_token_id]
			__magic_name__       :    Optional[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		:						Any  ,     _A		:						str  ,     _A		:						Optional[str] = None			)  -> Dict:





			if not self.can_save_slow_tokenizer:
				raise ValueError(
				    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
				    'tokenizer.'			)

			if not os.path.isdir(a_			):
				logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
				return
			__magic_name__       :    Union[str, Any]     	=     os.path.join(
			    a_  ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

			if os.path.abspath(self.vocab_file			) != os.path.abspath(a_			):
				copyfile(self.vocab_file  ,     a_			)

			return (out_vocab_file,)




from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros

def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Optional[int]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	k_size // 2
		__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[Any]													=	mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		__UpperCAmelCase    :     Any													=	1 / (2 * pi * sigma) * exp(-(square(_UpperCAmelCase						) + square(_UpperCAmelCase						)) / (2 * square(_UpperCAmelCase						))						)
		return g

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	image.shape[0], image.shape[1]
		# dst image height and width
		__UpperCAmelCase    :     str													=	height - k_size + 1
		__UpperCAmelCase    :     Optional[int]													=	width - k_size + 1

		# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
		__UpperCAmelCase    :     str													=	zeros((dst_height * dst_width, k_size * k_size)						)
		__UpperCAmelCase    :     Optional[Any]													=	0
		for i, j in product(range(_UpperCAmelCase						) ,   range(_UpperCAmelCase						)						):
				__UpperCAmelCase    :     int													=	ravel(image[i : i + k_size, j : j + k_size]						)
				__UpperCAmelCase    :     Union[str, Any]													=	window
				row += 1

		#  turn the kernel into shape(k*k, 1)
		__UpperCAmelCase    :     Tuple													=	gen_gaussian_kernel(_UpperCAmelCase ,   _UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	ravel(_UpperCAmelCase						)

		# reshape and get the dst image
		__UpperCAmelCase    :     Optional[Any]													=	dot(_UpperCAmelCase ,   _UpperCAmelCase						).reshape(_UpperCAmelCase ,   _UpperCAmelCase						).astype(_UpperCAmelCase						)

		return dst


if __name__ == "__main__":
				# read original image
				__A   			=imread(R"../image_data/lena.jpg")
				# turn image in gray scale value
				__A   			=cvtColor(img, COLOR_BGR2GRAY)

				# get values with two different mask size
				__A   			=gaussian_filter(gray, 3, sigma=1)
				__A   			=gaussian_filter(gray, 5, sigma=0.8)

				# show result images
				imshow("gaussian filter with 3x3 mask", gaussianaxa)
				imshow("gaussian filter with 5x5 mask", gaussianaxa)
				waitKey()





