






"""simple docstring"""





import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_reformer import ReformerTokenizer
else:
  A__       :			str=						None


A__       :			List[str]=						logging.get_logger(__name__)


A__       :			Optional[int]=						"""▁"""

A__       :			Optional[int]=						{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

A__       :			List[Any]=						{
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    },
    """tokenizer_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"""
        )
    },
}

A__       :			List[Any]=						{
    """google/reformer-crime-and-punishment""": 52_42_88,
}
class  __lowerCamelCase     (	_a	):
   a   :					Tuple			    =VOCAB_FILES_NAMES
   a   :					Union[str, Any]			    =PRETRAINED_VOCAB_FILES_MAP
   a   :					Union[str, Any]			    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a   :					Any			    =["""input_ids""", """attention_mask"""]
   a   :					str			    =ReformerTokenizer

   def __init__(       self			,					snake_case_=None			,					snake_case_=None			,					snake_case_="</s>"			,					snake_case_="<unk>"			,					snake_case_=[]			,					**snake_case_			,					)		->							Tuple:
       super().__init__(
           snake_case_			,					tokenizer_file=snake_case_			,					eos_token=snake_case_			,					unk_token=snake_case_			,					additional_special_tokens=snake_case_			,					**snake_case_			,					)

       UpperCamelCase__								=     vocab_file
       UpperCamelCase__								=     False if not self.vocab_file else True







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_ = None   )		->							Tuple[str]:
       if not self.can_save_slow_tokenizer:
           raise ValueError(
               'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
               'tokenizer.'   )

       if not os.path.isdir(snake_case_   ):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
           return
       UpperCamelCase__								=     os.path.join(
           snake_case_			,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

       if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case_   ):
           copyfile(self.vocab_file			,					snake_case_   )

       return (out_vocab_file,)












"""simple docstring"""





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
A__       :			str=						[[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 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						list[int]:
    """simple docstring"""

    return [int(SCREAMING_SNAKE_CASE       ) for c in F'{ruleset:08}'[:8]]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						list[int]:
    """simple docstring"""

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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						Image.Image:
    """simple docstring"""

    UpperCamelCase__								=     Image.new('RGB'			,						(len(cells[0]       ), len(SCREAMING_SNAKE_CASE       ))       )
    UpperCamelCase__								=     img.load()
    # Generates image
    for w in range(img.width       ):
        for h in range(img.height       ):
            UpperCamelCase__								=     2_55 - int(2_55 * cells[h][w]       )
            UpperCamelCase__								=     (color, color, color)
    return img


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




