





from cva import destroyAllWindows, imread, imshow, waitKey






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		str:






				"""simple docstring"""


				snake_case_     ,				snake_case_		:       Tuple           =    img.shape[0], img.shape[1]

				# converting each pixel's color to its negative
				for i in range(_UpperCamelCase					):
								for j in range(_UpperCamelCase					):
												snake_case_		:       List[str]           =    [255, 255, 255] - img[i][j]

				return img


if __name__ == "__main__":
	# read original image
	lowerCAmelCase_        =							imread('''image_data/lena.jpg''', 1)

	# convert to its negative
	lowerCAmelCase_        =							convert_to_negative(img)

	# show result image
	imshow('''negative of original image''', img)
	waitKey(0)
	destroyAllWindows()



'''simple docstring'''




import os
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


_a					:			Tuple							=    logging.get_logger(__name__)

_a					:			Optional[int]							=    '▁'

_a					:			str							=    {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

_a					:			Optional[Any]							=    {
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

_a					:			Union[str, Any]							=    {'vinai/bartpho-syllable': 1_024}



class 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			Dict     	= VOCAB_FILES_NAMES
  __lowerCAmelCase       :			str     	= PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase       :			Any     	= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase       :			int     	= ["input_ids", "attention_mask"]
  def __init__(  self ,			a_ ,			a_ ,			a_="<s>" ,			a_="</s>" ,			a_="</s>" ,			a_="<s>" ,			a_="<unk>" ,			a_="<pad>" ,			a_="<mask>" ,			a_ = None ,			**a_ ,			)				->					None:


    """simple docstring"""
    # Mask token behave like a normal word, i.e. include the space before it
    UpperCAmelCase    				=						AddedToken(a_ ,			lstrip=a_ ,			rstrip=a_  ) if isinstance(a_ ,			a_  ) else mask_token

    UpperCAmelCase    				=						{} if sp_model_kwargs is None else sp_model_kwargs

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

    UpperCAmelCase    				=						vocab_file
    UpperCAmelCase    				=						monolingual_vocab_file
    UpperCAmelCase    				=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
    self.sp_model.Load(str(a_  )  )

    # Load the reduced vocab

    # Keep order of special tokens for backward compatibility
    UpperCAmelCase    				=						{}
    UpperCAmelCase    				=						0
    for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
      if str(a_  ) not in self.fairseq_tokens_to_ids:
        UpperCAmelCase    				=						cnt
        cnt += 1
    with open(a_ ,			'r' ,			encoding='utf-8'  ) as f:
      for line in f.readlines():
        UpperCAmelCase    				=						line.strip().split()[0]
        UpperCAmelCase    				=						len(self.fairseq_tokens_to_ids  )
    if str(a_  ) not in self.fairseq_tokens_to_ids:
      UpperCAmelCase    				=						len(self.fairseq_tokens_to_ids  )

    UpperCAmelCase    				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
  def __getstate__(  self  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						self.__dict__.copy()
    UpperCAmelCase    				=						None
    UpperCAmelCase    				=						self.sp_model.serialized_model_proto()
    return state
  def __setstate__(  self ,			a_  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						d

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

    UpperCAmelCase    				=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )
  def     snake_case_      (  self ,			a_ ,			a_ = None  )				->					List[int]:


    """simple docstring"""

    if token_ids_a is None:
      return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    UpperCAmelCase    				=						[self.cls_token_id]
    UpperCAmelCase    				=						[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep
  def     snake_case_      (  self ,			a_ ,			a_ = None ,			a_ = False  )				->					List[int]:


    """simple docstring"""

    if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=a_ ,			token_ids_a=a_ ,			already_has_special_tokens=a_  )

    if token_ids_a is None:
      return [1] + ([0] * len(a_  )) + [1]
    return [1] + ([0] * len(a_  )) + [1, 1] + ([0] * len(a_  )) + [1]
  def     snake_case_      (  self ,			a_ ,			a_ = None  )				->					List[int]:


    """simple docstring"""
    UpperCAmelCase    				=						[self.sep_token_id]
    UpperCAmelCase    				=						[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]
  @property
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    return len(self.fairseq_ids_to_tokens  )
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						{self.convert_ids_to_tokens(a_  ): i for i in range(self.vocab_size  )}
    vocab.update(self.added_tokens_encoder  )
    return vocab
  def     snake_case_      (  self ,			a_  )				->					List[str]:


    """simple docstring"""
    return self.sp_model.encode(a_ ,			out_type=a_  )
  def     snake_case_      (  self ,			a_  )				->					Any:


    """simple docstring"""
    if token in self.fairseq_tokens_to_ids:
      return self.fairseq_tokens_to_ids[token]
    else:
      return self.unk_token_id
  def     snake_case_      (  self ,			a_  )				->					Dict:


    """simple docstring"""
    return self.fairseq_ids_to_tokens[index]
  def     snake_case_      (  self ,			a_  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						''.join(a_  ).replace(a_ ,			' '  ).strip()
    return out_string





  def     snake_case_      (  self ,			a_ ,			a_ = None  )				->					Tuple[str]:


    """simple docstring"""
    if not os.path.isdir(a_  ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
      return
    UpperCAmelCase    				=						os.path.join(
        a_ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )
    UpperCAmelCase    				=						os.path.join(
        a_ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_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:
        UpperCAmelCase    				=						self.sp_model.serialized_model_proto()
        fi.write(a_  )

    if os.path.abspath(self.monolingual_vocab_file  ) != os.path.abspath(
        a_  ) and os.path.isfile(self.monolingual_vocab_file  ):
      copyfile(self.monolingual_vocab_file ,			a_  )
    elif not os.path.isfile(self.monolingual_vocab_file  ):
      with open(a_ ,			'w' ,			encoding='utf-8'  ) as fp:
        for token in self.fairseq_tokens_to_ids:
          if token not in self.all_special_tokens:
            fp.write(F'''{str(a_  )} \n'''  )

    return out_vocab_file, out_monolingual_vocab_file


