






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


snake_case_   :	Optional[Any]							      =logging.get_logger(__name__)

snake_case_   :	str							      ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_   :	List[Any]							      ={
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''allenai/longformer-base-4096''': (
            '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case_   :	Any							      ={
    '''allenai/longformer-base-4096''': 4096,
    '''allenai/longformer-large-4096''': 4096,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       List[Any] 					= VOCAB_FILES_NAMES
		UpperCAmelCase_							:       Union[str, Any] 					= PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase_							:       Tuple 					= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase_							:       Optional[int] 					= ['input_ids', 'attention_mask']
		UpperCAmelCase_							:       Optional[Any] 					= LongformerTokenizer






		def __init__(      self							, lowercase__=None							, lowercase__=None							, lowercase__=None							, lowercase__="replace"							, lowercase__="<s>"							, lowercase__="</s>"							, lowercase__="</s>"							, lowercase__="<s>"							, lowercase__="<unk>"							, lowercase__="<pad>"							, lowercase__="<mask>"							, lowercase__=False							, lowercase__=True							, **lowercase__							, )				->				Union[str, Any]:
				super().__init__(
				    lowercase__							, lowercase__							, tokenizer_file=lowercase__							, errors=lowercase__							, bos_token=lowercase__							, eos_token=lowercase__							, sep_token=lowercase__							, cls_token=lowercase__							, unk_token=lowercase__							, pad_token=lowercase__							, mask_token=lowercase__							, add_prefix_space=lowercase__							, trim_offsets=lowercase__							, **lowercase__							, )

				__A    =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
				if pre_tok_state.get("add_prefix_space"							, lowercase__						) != add_prefix_space:
						__A    =							getattr(lowercase__							, pre_tok_state.pop("type"						)						)
						__A    =							add_prefix_space
						__A    =							pre_tok_class(**lowercase__						)

				__A    =							add_prefix_space

				__A    =							"post_processor"
				__A    =							getattr(self.backend_tokenizer							, lowercase__							, lowercase__						)
				if tokenizer_component_instance:
						__A    =							json.loads(tokenizer_component_instance.__getstate__()						)

						# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
						if "sep" in state:
								__A    =							tuple(state["sep"]						)
						if "cls" in state:
								__A    =							tuple(state["cls"]						)

						__A    =							False

						if state.get("add_prefix_space"							, lowercase__						) != add_prefix_space:
								__A    =							add_prefix_space
								__A    =							True

						if state.get("trim_offsets"							, lowercase__						) != trim_offsets:
								__A    =							trim_offsets
								__A    =							True

						if changes_to_apply:
								__A    =							getattr(lowercase__							, state.pop("type"						)						)
								__A    =							component_class(**lowercase__						)
								setattr(self.backend_tokenizer							, lowercase__							, lowercase__						)






		@property
		def 					_lowerCamelCase					(      self						)				->				str:
				if self._mask_token is None:
						if self.verbose:
								logger.error("Using mask_token, but it is not set yet."						)
						return None
				return str(self._mask_token						)






		@mask_token.setter
		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Optional[Any]:
				__A    =							AddedToken(lowercase__							, lstrip=lowercase__							, rstrip=lowercase__						) if isinstance(lowercase__							, lowercase__						) else value
				__A    =							value






		def 					_lowerCamelCase					(      self							, *lowercase__							, **lowercase__						)				->				BatchEncoding:
				__A    =							kwargs.get("is_split_into_words"							, lowercase__						)
				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(*lowercase__							, **lowercase__						)






		def 					_lowerCamelCase					(      self							, *lowercase__							, **lowercase__						)				->				BatchEncoding:
				__A    =							kwargs.get("is_split_into_words"							, lowercase__						)

				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(*lowercase__							, **lowercase__						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				Tuple[str]:
				__A    =							self._tokenizer.model.save(lowercase__							, name=lowercase__						)
				return tuple(lowercase__						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__=None						)				->				int:
				__A    =							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
				if token_ids_a is None:
						return output

				return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				List[int]:
				__A    =							[self.sep_token_id]
				__A    =							[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]









from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
a 			=							[[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    a__    (	A       ):
   return [int(A       ) for c in F'''{ruleset:08}'''[:8]]
def    a__    (	A		,					A		,					A       ):
   lowercase__    :						List[Any]       =			len(cells[0]       )  # 31
   lowercase__    :						int       =			[]
   for i in range(A       ):
      # Get the neighbors of each cell
      # Handle neighbours outside bounds by using 0 as their value
      lowercase__    :						Optional[Any]       =			0 if i == 0 else cells[time][i - 1]
      lowercase__    :						Dict       =			0 if i == population - 1 else cells[time][i + 1]
      # Define a new cell and add it to the new generation
      lowercase__    :						str       =			7 - int(F'''{left_neighbor}{cells[time][i]}{right_neighbor}'''		,					2       )
      next_generation.append(rule[situation]       )
   return next_generation
def    a__    (	A       ):
   lowercase__    :						Tuple       =			Image.new("""RGB"""		,					(len(cells[0]       ), len(A       ))       )
   lowercase__    :						Optional[Any]       =			img.load()
   # Generates image
   for w in range(img.width       ):
      for h in range(img.height       ):
         lowercase__    :						Union[str, Any]       =			255 - int(255 * cells[h][w]       )
         lowercase__    :						int       =			(color, color, color)
   return img


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


