


import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase	: str  				=logging.get_logger(__name__)

__lowerCAmelCase	: Dict  				={'vocab_file': 'spiece.model'}

__lowerCAmelCase	: int  				={
    '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'
        ),
    }
}

__lowerCAmelCase	: Any  				={
    '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,
}
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Dict						=		VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__				:			List[str]						=		PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__				:			Dict						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__				:			Dict						=		['''input_ids''', '''attention_mask''']
				SCREAMING_SNAKE_CASE__				:			List[int]						=		[]


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

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

										__SCREAMING_SNAKE_CASE						:     Dict					=		{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=lowerCAmelCase__			,			eos_token=lowerCAmelCase__			,			unk_token=lowerCAmelCase__			,			pad_token=lowerCAmelCase__			,			sep_token=lowerCAmelCase__			,			mask_token=lowerCAmelCase__			,			cls_token=lowerCAmelCase__			,			sp_model_kwargs=self.sp_model_kwargs			,			**lowerCAmelCase__			,			)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		vocab_file

										__SCREAMING_SNAKE_CASE						:     Any					=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
										self.sp_model.Load(lowerCAmelCase__	)


				@property
				def 		__magic_name__(       self			:Tuple	)		->			List[str]:
										return self.sp_model.get_piece_size()


				def 		__magic_name__(       self			:List[str]	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     str					=		{self.convert_ids_to_tokens(lowerCAmelCase__	): i for i in range(self.vocab_size	)}
										vocab.update(self.added_tokens_encoder	)
										return vocab


				def __getstate__(       self			:int	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Any					=		self.__dict__.copy()
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		None
										return state


				def __setstate__(       self			:Dict			,			lowerCAmelCase__			:Any	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		d

										# for backward compatibility
										if not hasattr(self			,			'''sp_model_kwargs'''	):
																__SCREAMING_SNAKE_CASE						:     List[Any]					=		{}

										__SCREAMING_SNAKE_CASE						:     int					=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
										self.sp_model.Load(self.vocab_file	)


				def 		__magic_name__(       self			:Dict			,			lowerCAmelCase__			:str	)		->			List[str]:
										return self.sp_model.encode(lowerCAmelCase__			,			out_type=lowerCAmelCase__	)


				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:Optional[Any]	)		->			List[Any]:
										return self.sp_model.piece_to_id(lowerCAmelCase__	)


				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:Optional[Any]	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		self.sp_model.IdToPiece(lowerCAmelCase__	)
										return token


				def 		__magic_name__(       self			:Any			,			lowerCAmelCase__			:List[str]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		[]
										__SCREAMING_SNAKE_CASE						:     str					=		''''''
										__SCREAMING_SNAKE_CASE						:     str					=		False
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						if not prev_is_special:
																												out_string += " "
																						out_string += self.sp_model.decode(lowerCAmelCase__	) + token
																						__SCREAMING_SNAKE_CASE						:     Any					=		True
																						__SCREAMING_SNAKE_CASE						:     List[str]					=		[]
																else:
																						current_sub_tokens.append(lowerCAmelCase__	)
																						__SCREAMING_SNAKE_CASE						:     Tuple					=		False
										out_string += self.sp_model.decode(lowerCAmelCase__	)
										return out_string.strip()


				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:List[int]			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:bool = None			,			lowerCAmelCase__			:bool = True			,			**lowerCAmelCase__			:List[Any]			,			)		->			str:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		kwargs.pop('''use_source_tokenizer'''			,			lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Any					=		self.convert_ids_to_tokens(lowerCAmelCase__			,			skip_special_tokens=lowerCAmelCase__	)

										# To avoid mixing byte-level and unicode for byte-level BPT
										# we need to build string separately for added tokens and byte-level tokens
										# cf. https://github.com/huggingface/transformers/issues/1133
										__SCREAMING_SNAKE_CASE						:     Dict					=		[]
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[]
										for token in filtered_tokens:
																if skip_special_tokens and token in self.all_special_ids:
																						continue
																if token in self.added_tokens_encoder:
																						if current_sub_text:
																												sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__	)	)
																												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		[]
																						sub_texts.append(lowerCAmelCase__	)
																else:
																						current_sub_text.append(lowerCAmelCase__	)
										if current_sub_text:
																sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__	)	)

										# Mimic the behavior of the Rust tokenizer:
										# No space before [MASK] and [SEP]
										if spaces_between_special_tokens:
																__SCREAMING_SNAKE_CASE						:     Any					=		re.sub(r''' (\[(MASK|SEP)\])'''			,			r'''\1'''			,			''' '''.join(lowerCAmelCase__	)	)
										else:
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		''''''.join(lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		(
										    clean_up_tokenization_spaces
										    if clean_up_tokenization_spaces is not None
										    else self.clean_up_tokenization_spaces
										)
										if clean_up_tokenization_spaces:
																__SCREAMING_SNAKE_CASE						:     Dict					=		self.clean_up_tokenization(lowerCAmelCase__	)
																return clean_text
										else:
																return text


				def 		__magic_name__(       self			:Tuple			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Optional[str] = None	)		->			Tuple[str]:
										if not os.path.isdir(lowerCAmelCase__	):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
																return
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		os.path.join(
										    lowerCAmelCase__			,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

										if os.path.abspath(self.vocab_file	) != os.path.abspath(lowerCAmelCase__	) and os.path.isfile(self.vocab_file	):
																copyfile(self.vocab_file			,			lowerCAmelCase__	)
										elif not os.path.isfile(self.vocab_file	):
																with open(lowerCAmelCase__			,			'''wb'''	) as fi:
																						__SCREAMING_SNAKE_CASE						:     Any					=		self.sp_model.serialized_model_proto()
																						fi.write(lowerCAmelCase__	)

										return (out_vocab_file,)


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


				def 		__magic_name__(       self			:List[str]			,			lowerCAmelCase__			:List[int]			,			lowerCAmelCase__			:Optional[List[int]] = None			,			lowerCAmelCase__			:bool = False	)		->			List[int]:
										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=lowerCAmelCase__			,			token_ids_a=lowerCAmelCase__			,			already_has_special_tokens=lowerCAmelCase__	)

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



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


"""simple docstring"""





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



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		np.ndarray:


					"""simple docstring"""





					if (ksize % 2) == 0:
										_UpperCamelCase							 =  ksize + 1
					_UpperCamelCase							 =  np.zeros((ksize, ksize),     dtype=np.floataa	)

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

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

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

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

															# fill kernel
															_UpperCamelCase							 =  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														=		imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a														=		cvtColor(img, COLOR_BGR2GRAY)

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

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

				waitKey(0)






