


'''simple docstring'''





from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


UpperCAmelCase__  			=				logging.get_logger(__name__)

UpperCAmelCase__  			=				{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

UpperCAmelCase__  			=				{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

UpperCAmelCase__  			=				{
    '''facebook/blenderbot_small-90M''': 5_1_2,
}
class 						a__					(					_a   ):





			'''simple docstring'''

			A						:				Any					      =			VOCAB_FILES_NAMES
			A						:				Tuple					      =			PRETRAINED_VOCAB_FILES_MAP
			A						:				List[Any]					      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A						:				str					      =			BlenderbotSmallTokenizer
			def __init__(  self							:			Any			,    lowerCAmelCase_							:			int=None			,    lowerCAmelCase_							:			Optional[int]=None			,    lowerCAmelCase_							:			int="<|endoftext|>"			,    lowerCAmelCase_							:			int="<|endoftext|>"			,    lowerCAmelCase_							:			Union[str, Any]="<|endoftext|>"			,    lowerCAmelCase_							:			str=False			,    lowerCAmelCase_							:			List[Any]=True			,    **lowerCAmelCase_							:			Optional[Any]			,    )      ->		Optional[int]:
						super().__init__(
						    ByteLevelBPETokenizer(
						        vocab=__UpperCamelCase			,    merges=__UpperCamelCase			,    add_prefix_space=__UpperCamelCase			,    trim_offsets=__UpperCamelCase			,    )			,    bos_token=__UpperCamelCase			,    eos_token=__UpperCamelCase			,    unk_token=__UpperCamelCase			,    **__UpperCamelCase			,    )
						__A=	add_prefix_space
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			List[str]=None	)      ->		Tuple:
						__A=	[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							:			List[Any]			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Any = None	)      ->		List[Any]:
						__A=	[self.sep_token_id]
						__A=	[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]





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     np.ndarray:

						# prepare kernel
						# the kernel size have to be odd
						if (ksize % 2) == 0:
												_lowercase					       =					ksize + 1
						_lowercase					       =					np.zeros((ksize, ksize)		,   dtype=np.floataa       )

						# each value
						for y in range(SCREAMING_SNAKE_CASE_       ):
												for x in range(SCREAMING_SNAKE_CASE_       ):
																		# distance from center
																		_lowercase					       =					x - ksize // 2
																		_lowercase					       =					y - ksize // 2

																		# degree to radiant
																		_lowercase					       =					theta / 1_80 * np.pi
																		_lowercase					       =					np.cos(_theta       )
																		_lowercase					       =					np.sin(_theta       )

																		# get kernel x
																		_lowercase					       =					cos_theta * px + sin_theta * py

																		# get kernel y
																		_lowercase					       =					-sin_theta * px + cos_theta * py

																		# fill kernel
																		_lowercase					       =					np.exp(
																		    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)       ) * np.cos(2 * np.pi * _x / lambd + psi       )

						return gabor


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# read original image
		A		: Optional[Any]								=     imread('''../image_data/lena.jpg''')
		# turn image in gray scale value
		A		: Optional[int]								=     cvtColor(img, COLOR_BGR2GRAY)

		# Apply multiple Kernel to detect edges
		A		: Optional[int]								=     np.zeros(gray.shape[:2])
		for theta in [0, 30, 60, 90, 120, 150]:
				A		: Optional[int]								=     gabor_filter_kernel(10, 8, theta, 10, 0, 0)
				out += filteraD(gray, CV_8UC3, kernel_aa)
		A		: int								=     out / out.max() * 255
		A		: str								=     out.astype(np.uinta)

		imshow('''Original''', gray)
		imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

		waitKey(0)