






"""simple docstring"""





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE					:				int					    =						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:				int					    =						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						{
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

_SCREAMING_SNAKE_CASE					:				List[Any]					    =						{
    '''gpt2''': 1024,
    '''gpt2-medium''': 1024,
    '''gpt2-large''': 1024,
    '''gpt2-xl''': 1024,
    '''distilgpt2''': 1024,
}






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE						:     int						= PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE						:     Optional[int]						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE						:     int						= ["""input_ids""", """attention_mask"""]
    SCREAMING_SNAKE_CASE						:     Union[str, Any]						= GPTaTokenizer






    def __init__(       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							int=None       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=None       ,						__SCREAMING_SNAKE_CASE     :							Any=None       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]="<|endoftext|>"       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]="<|endoftext|>"       ,						__SCREAMING_SNAKE_CASE     :							Any="<|endoftext|>"       ,						__SCREAMING_SNAKE_CASE     :							Dict=False       ,						**__SCREAMING_SNAKE_CASE     :							str       ,						)     ->		Any:
      super().__init__(
          __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						tokenizer_file=__SCREAMING_SNAKE_CASE       ,						unk_token=__SCREAMING_SNAKE_CASE       ,						bos_token=__SCREAMING_SNAKE_CASE       ,						eos_token=__SCREAMING_SNAKE_CASE       ,						add_prefix_space=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)

      lowerCamelCase_         =						kwargs.pop('add_bos_token'       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
      if pre_tok_state.get('add_prefix_space'       ,						__SCREAMING_SNAKE_CASE					) != add_prefix_space:
        lowerCamelCase_         =						getattr(__SCREAMING_SNAKE_CASE       ,						pre_tok_state.pop('type'					)					)
        lowerCamelCase_         =						add_prefix_space
        lowerCamelCase_         =						pre_tok_class(**__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						add_prefix_space






    def    UpperCamelCase       (       self     :							int       ,						*__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						**__SCREAMING_SNAKE_CASE     :							str					)     ->		BatchEncoding:
      lowerCamelCase_         =						kwargs.get('is_split_into_words'       ,						__SCREAMING_SNAKE_CASE					)
      assert self.add_prefix_space or not is_split_into_words, (
          F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Optional[int]       ,						*__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						**__SCREAMING_SNAKE_CASE     :							List[Any]					)     ->		BatchEncoding:
      lowerCamelCase_         =						kwargs.get('is_split_into_words'       ,						__SCREAMING_SNAKE_CASE					)

      assert self.add_prefix_space or not is_split_into_words, (
          F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = None					)     ->		Tuple[str]:
      lowerCamelCase_         =						self._tokenizer.model.save(__SCREAMING_SNAKE_CASE       ,						name=__SCREAMING_SNAKE_CASE					)
      return tuple(__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							"Conversation"					)     ->		List[int]:
      lowerCamelCase_         =						[]
      for is_user, text in conversation.iter_texts():
        input_ids.extend(self.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					) + [self.eos_token_id]					)

      if len(__SCREAMING_SNAKE_CASE					) > self.model_max_length:
        lowerCamelCase_         =						input_ids[-self.model_max_length :]
      return input_ids












"""simple docstring"""





from cva import destroyAllWindows, imread, imshow, waitKey







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple )      ->				Dict:
  # getting number of pixels in the image
  lowerCamelCase_     ,	lowerCamelCase_         =						img.shape[0], img.shape[1]

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

  return img


if __name__ == "__main__":
  # read original image
  _SCREAMING_SNAKE_CASE					:				List[Any]					    =						imread('''image_data/lena.jpg''', 1)

  # convert to its negative
  _SCREAMING_SNAKE_CASE					:				List[Any]					    =						convert_to_negative(img)

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




