
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


__magic_name__           =					logging.get_logger(__name__)

__magic_name__           =					{'''vocab_file''': '''vocab.txt'''}

__magic_name__           =					{
    '''vocab_file''': {
        '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt''',
        '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt''',
        '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt''',
        '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt''',
    }
}

__magic_name__           =					{
    '''tau/splinter-base''': 512,
    '''tau/splinter-base-qass''': 512,
    '''tau/splinter-large''': 512,
    '''tau/splinter-large-qass''': 512,
}

__magic_name__           =					{
    '''tau/splinter-base''': {'''do_lower_case''': False},
    '''tau/splinter-base-qass''': {'''do_lower_case''': False},
    '''tau/splinter-large''': {'''do_lower_case''': False},
    '''tau/splinter-large-qass''': {'''do_lower_case''': False},
}




class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						str      		=       VOCAB_FILES_NAMES
      _A  :						List[Any]      		=       PRETRAINED_VOCAB_FILES_MAP
      _A  :						List[Any]      		=       PRETRAINED_INIT_CONFIGURATION
      _A  :						Union[str, Any]      		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A  :						Union[str, Any]      		=       SplinterTokenizer
      def __init__(     self						,		lowerCamelCase=None						,		lowerCamelCase=None						,		lowerCamelCase=True						,		lowerCamelCase="[UNK]"						,		lowerCamelCase="[SEP]"						,		lowerCamelCase="[PAD]"						,		lowerCamelCase="[CLS]"						,		lowerCamelCase="[MASK]"						,		lowerCamelCase="[QUESTION]"						,		lowerCamelCase=True						,		lowerCamelCase=None						,		**lowerCamelCase						,		):
          super().__init__(
              lowerCamelCase						,		tokenizer_file=lowerCamelCase						,		do_lower_case=lowerCamelCase						,		unk_token=lowerCamelCase						,		sep_token=lowerCamelCase						,		pad_token=lowerCamelCase						,		cls_token=lowerCamelCase						,		mask_token=lowerCamelCase						,		tokenize_chinese_chars=lowerCamelCase						,		strip_accents=lowerCamelCase						,		additional_special_tokens=(question_token,)						,		**lowerCamelCase						,		)

          snake_case__  =			json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
          if (
              pre_tok_state.get("lowercase"						,		lowerCamelCase	) != do_lower_case
              or pre_tok_state.get("strip_accents"						,		lowerCamelCase	) != strip_accents
          ):
              snake_case__  =			getattr(lowerCamelCase						,		pre_tok_state.pop("type"	)	)
              snake_case__  =			do_lower_case
              snake_case__  =			strip_accents
              snake_case__  =			pre_tok_class(**lowerCamelCase	)

          snake_case__  =			do_lower_case
      @property
      def A_     (     self	):
          return self.convert_tokens_to_ids(self.question_token	)
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase = None	):
          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          snake_case__  =			[self.cls_token_id]
          snake_case__  =			[self.sep_token_id]
          snake_case__  =			[self.question_token_id] + [self.convert_tokens_to_ids("."	)]
          if self.padding_side == "right":
              # Input is question-then-context
              return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
          else:
              # Input is context-then-question
              return cls + token_ids_a + sep + token_ids_a + question_suffix + sep
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase = None	):
          snake_case__  =			[self.sep_token_id]
          snake_case__  =			[self.cls_token_id]
          snake_case__  =			[self.question_token_id] + [self.convert_tokens_to_ids("."	)]
          if token_ids_a is None:
              return len(cls + token_ids_a + sep	) * [0]

          if self.padding_side == "right":
              # Input is question-then-context
              return len(cls + token_ids_a + question_suffix + sep	) * [0] + len(token_ids_a + sep	) * [1]
          else:
              # Input is context-then-question
              return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + question_suffix + sep	) * [1]



      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase = None	):
          snake_case__  =			self._tokenizer.model.save(lowerCamelCase						,		name=lowerCamelCase	)
          return tuple(lowerCamelCase	)





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
__magic_name__           =					[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    return [int(__lowerCAmelCase							) for c in F"""{ruleset:08}"""[:8]]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			len(cells[0]							)  # 31
    snake_case__  =			[]
    for i in range(__lowerCAmelCase							):
        # Get the neighbors of each cell
        # Handle neighbours outside bounds by using 0 as their value
        snake_case__  =			0 if i == 0 else cells[time][i - 1]
        snake_case__  =			0 if i == population - 1 else cells[time][i + 1]
        # Define a new cell and add it to the new generation
        snake_case__  =			7 - int(F"""{left_neighbor}{cells[time][i]}{right_neighbor}"""      ,						2							)
        next_generation.append(rule[situation]							)
    return next_generation





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    snake_case__  =			Image.new("RGB"      ,						(len(cells[0]							), len(__lowerCAmelCase							))							)
    snake_case__  =			img.load()
    # Generates image
    for w in range(img.width							):
        for h in range(img.height							):
            snake_case__  =			255 - int(255 * cells[h][w]							)
            snake_case__  =			(color, color, color)
    return img


if __name__ == "__main__":
     __magic_name__           =					bin(int(input('''Rule:\n''').strip()))[2:]
     __magic_name__           =					format_ruleset(int(rule_num))
     for time in range(16):
          CELLS.append(new_generation(CELLS, rule, time))
     __magic_name__           =					generate_image(CELLS)
     # Uncomment to save the image
     # img.save(f"rule_{rule_num}.png")
     img.show()



