





"""simple docstring"""





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
_lowerCamelCase       =	[[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_	(  lowercase_   :							List[Any]		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							Dict ,      lowercase_   :							Optional[Any] ,      lowercase_   :							Tuple		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			str												=       Image.new('''RGB''' ,      (len(cells[0]		), len(__UpperCamelCase		))		)
 __SCREAMING_SNAKE_CASE :			str												=       img.load()
 # Generates image
 for w in range(img.width		):
  for h in range(img.height		):
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       255 - int(255 * cells[h][w]		)
   __SCREAMING_SNAKE_CASE :			Dict												=       (color, color, color)
 return img


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



"""simple docstring"""


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

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


if is_sentencepiece_available():
			from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
			__A       						=       None


__A       						=       logging.get_logger(__name__)

__A       						=       {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__A       						=       {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

__A       						=       {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''
			lowerCamelCase  					=		VOCAB_FILES_NAMES
			lowerCamelCase  					=		PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase  					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase  					=		['''input_ids''', '''attention_mask''']
			lowerCamelCase  					=		XLMRobertaTokenizer



			def __init__(							self     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase="<s>"     ,     __UpperCAmelCase="</s>"     ,     __UpperCAmelCase="</s>"     ,     __UpperCAmelCase="<s>"     ,     __UpperCAmelCase="<unk>"     ,     __UpperCAmelCase="<pad>"     ,     __UpperCAmelCase="<mask>"     ,     **__UpperCAmelCase     ,     ) ->     str:
				# Mask token behave like a normal word, i.e. include the space before it
				_lowerCAmelCase								=AddedToken(__UpperCAmelCase     ,     lstrip=__UpperCAmelCase     ,     rstrip=__UpperCAmelCase  ) if isinstance(__UpperCAmelCase     ,     __UpperCAmelCase  ) else mask_token

				super().__init__(
				    __UpperCAmelCase     ,     tokenizer_file=__UpperCAmelCase     ,     bos_token=__UpperCAmelCase     ,     eos_token=__UpperCAmelCase     ,     sep_token=__UpperCAmelCase     ,     cls_token=__UpperCAmelCase     ,     unk_token=__UpperCAmelCase     ,     pad_token=__UpperCAmelCase     ,     mask_token=__UpperCAmelCase     ,     **__UpperCAmelCase     ,     )

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



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     List[int]:

				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				_lowerCAmelCase								=[self.cls_token_id]
				_lowerCAmelCase								=[self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     List[int]:
				_lowerCAmelCase								=[self.sep_token_id]
				_lowerCAmelCase								=[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]



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = 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(__UpperCAmelCase  ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''  )
					return
				_lowerCAmelCase								=os.path.join(
				    __UpperCAmelCase     ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

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

				return (out_vocab_file,)



