

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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__magic_name__            =  logging.get_logger(__name__)

__magic_name__            =  {'''vocab_file''': '''spiece.model'''}

__magic_name__            =  {
    '''vocab_file''': {
        '''bert_for_seq_generation''': (
            '''https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'''
        ),
    }
}

__magic_name__            =  {'''bert_for_seq_generation''': 512}




class 					_SCREAMING_SNAKE_CASE  (      snake_case__ ):
      _A						:		Optional[Any]    	=     VOCAB_FILES_NAMES
      _A						:		Any    	=     PRETRAINED_VOCAB_FILES_MAP
      _A						:		List[str]    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A						:		List[int]    	=     []
      _A						:		Union[str, Any]    	=     ['''input_ids''', '''attention_mask''']
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase="<s>"		,	lowerCamelCase="</s>"		,	lowerCamelCase="<unk>"		,	lowerCamelCase="<pad>"		,	lowerCamelCase="<::::>"		,	lowerCamelCase = None		,	**lowerCamelCase		,	):
          snake_case__						=				{} if sp_model_kwargs is None else sp_model_kwargs

          # Add extra_ids to the special token list
          super().__init__(
              bos_token=_A		,	eos_token=_A		,	unk_token=_A		,	pad_token=_A		,	sep_token=_A		,	sp_model_kwargs=self.sp_model_kwargs		,	**_A		,	)

          snake_case__						=				vocab_file

          snake_case__						=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(_A )
      @property
      def A_			(			self ):
          return self.sp_model.get_piece_size()
      def A_			(			self ):
          snake_case__						=				{self.convert_ids_to_tokens(_A ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab
      def __getstate__(			self ):
          snake_case__						=				self.__dict__.copy()
          snake_case__						=				None
          return state
      def __setstate__(			self		,	lowerCamelCase ):
          snake_case__						=				d

          # for backward compatibility
          if not hasattr(self		,	"sp_model_kwargs" ):
              snake_case__						=				{}

          snake_case__						=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(self.vocab_file )
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.encode(_A		,	out_type=_A )
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.piece_to_id(_A )
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				self.sp_model.IdToPiece(_A )
          return token
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				[]
          snake_case__						=				''
          for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                  out_string += self.sp_model.decode(_A ) + token
                  snake_case__						=				[]
              else:
                  current_sub_tokens.append(_A )
          out_string += self.sp_model.decode(_A )
          return out_string.strip()



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          if not os.path.isdir(_A ):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
              return
          snake_case__						=				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 ) and os.path.isfile(self.vocab_file ):
              copyfile(self.vocab_file		,	_A )
          elif not os.path.isfile(self.vocab_file ):
              with open(_A		,	"wb" ) as fi:
                  snake_case__						=				self.sp_model.serialized_model_proto()
                  fi.write(_A )

          return (out_vocab_file,)





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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):

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

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

            # degree to radiant
            snake_case__						=				theta / 180 * np.pi
            snake_case__						=				np.cos(_theta							)
            snake_case__						=				np.sin(_theta							)

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

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

            # fill kernel
            snake_case__						=				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
     __magic_name__            =  imread('''../image_data/lena.jpg''')
     # turn image in gray scale value
     __magic_name__            =  cvtColor(img, COLOR_BGR2GRAY)

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

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

     waitKey(0)



