




import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 	A	(						_UpperCAmelCase	,  unittest.TestCase						):
     """simple docstring"""

     _snake_case:		Any   =PreTrainedTokenizerFast
     _snake_case:		Union[str, Any]   =False
     _snake_case:		Dict   =True
     _snake_case:		Union[str, Any]   ="tokenizer_file"




     def        __snake_case      (			self     :      Optional[int]	) ->				int:
           lowerCamelCase__					     = False  # because we don't have pretrained_vocab_files_map
           super().setUp()
           lowerCamelCase__					     = True

           lowerCamelCase__					     = ["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
           lowerCamelCase__					     = "SaulLu/dummy-tokenizer-bytelevel-bpe"

           # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
           lowerCamelCase__					     = [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

           lowerCamelCase__					     = PreTrainedTokenizerFast.from_pretrained(model_paths[0]	)
           tokenizer.save_pretrained(self.tmpdirname	)




     def        __snake_case      (			self     :      Dict	) ->				int:
           pass




     def        __snake_case      (			self     :      Union[str, Any]	) ->				Tuple:
           pass




     def        __snake_case      (			self     :      List[Any]	) ->				List[Any]:
           pass




     def        __snake_case      (			self     :      Optional[int]	) ->				Tuple:
           pass




     def        __snake_case      (			self     :      List[str]	) ->				int:
           lowerCamelCase__					     = self.tmpdirname
           # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
                       try:
                             lowerCamelCase__					     = tempfile.mkdtemp()
                             lowerCamelCase__					     = self.rust_tokenizer_class.from_pretrained(lowercase__		,     **lowercase__	)

                             tokenizer.save_pretrained(self.tmpdirname	)
                             super().test_training_new_tokenizer()
                       finally:
                             # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                             # is restored
                             shutil.rmtree(self.tmpdirname	)
                             lowerCamelCase__					     = tmpdirname_orig




     def        __snake_case      (			self     :      Optional[int]	) ->				List[Any]:
           lowerCamelCase__					     = self.tmpdirname
           # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
                       try:
                             lowerCamelCase__					     = tempfile.mkdtemp()
                             lowerCamelCase__					     = self.rust_tokenizer_class.from_pretrained(lowercase__		,     **lowercase__	)

                             tokenizer.save_pretrained(self.tmpdirname	)
                             super().test_training_new_tokenizer_with_special_tokens_change()
                       finally:
                             # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                             # is restored
                             shutil.rmtree(self.tmpdirname	)
                             lowerCamelCase__					     = tmpdirname_orig




     def        __snake_case      (			self     :      Dict	) ->				Union[str, Any]:
           lowerCamelCase__					     = self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name	)

           lowerCamelCase__					     = ("a" for _ in range(1_0_0_0	))
           lowerCamelCase__					     = tokenizer.train_new_from_iterator(text_iterator=lowercase__		,     length=1_0_0_0		,     vocab_size=5_0	)

           lowerCamelCase__					     = new_tokenizer.encode('a🤗'	)
           self.assertEqual(lowercase__		,     [6_4, 1_7_2, 2_5_3, 9_7, 2_4_5]	)






@require_tokenizers
class 	A	(						unittest.TestCase						):
     """simple docstring"""

     def        __snake_case      (			self     :      int	) ->				List[Any]:
           lowerCamelCase__					     = AutoTokenizer.from_pretrained('bert-base-cased'	)
           lowerCamelCase__					     = json.loads(tokenizer._tokenizer.to_str()	)
           lowerCamelCase__					     = len(lowercase__	)

           with tempfile.TemporaryDirectory() as tmp_dir:
                 # Hack to save this in the tokenizer_config.json
                 lowerCamelCase__					     = ["tokenizer.4.0.0.json"]
                 tokenizer.save_pretrained(lowercase__	)
                 json.dump(lowercase__		,     open(os.path.join(lowercase__		,     'tokenizer.4.0.0.json'	)		,     'w'	)	)

                 # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
                 lowerCamelCase__					     = AutoTokenizer.from_pretrained(lowercase__	)
                 self.assertEqual(len(lowercase__	)		,     len(lowercase__	) + 1	)
                 lowerCamelCase__					     = json.loads(new_tokenizer._tokenizer.to_str()	)
                 self.assertIn('huggingface'		,     json_tokenizer['model']['vocab']	)

                 # Will need to be adjusted if we reach v42 and this test is still here.
                 # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
                 shutil.move(os.path.join(lowercase__		,     'tokenizer.4.0.0.json'	)		,     os.path.join(lowercase__		,     'tokenizer.42.0.0.json'	)	)
                 lowerCamelCase__					     = ["tokenizer.42.0.0.json"]
                 tokenizer.save_pretrained(lowercase__	)
                 lowerCamelCase__					     = AutoTokenizer.from_pretrained(lowercase__	)
                 self.assertEqual(len(lowercase__	)		,     len(lowercase__	)	)
                 lowerCamelCase__					     = json.loads(new_tokenizer._tokenizer.to_str()	)
                 self.assertNotIn('huggingface'		,     json_tokenizer['model']['vocab']	)




     def        __snake_case      (			self     :      Optional[int]	) ->				Tuple:
           lowerCamelCase__					     = "hf-internal-testing/test-two-tokenizers"

           # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
           lowerCamelCase__					     = AutoTokenizer.from_pretrained(lowercase__	)
           self.assertEqual(len(lowercase__	)		,     2_8_9_9_7	)
           lowerCamelCase__					     = json.loads(tokenizer._tokenizer.to_str()	)
           self.assertIn('huggingface'		,     json_tokenizer['model']['vocab']	)

           # Testing an older version by monkey-patching the version in the module it's used.
           import transformers as old_transformers

           lowerCamelCase__					     = "3.0.0"
           lowerCamelCase__					     = old_transformers.models.auto.AutoTokenizer.from_pretrained(lowercase__	)
           self.assertEqual(len(lowercase__	)		,     2_8_9_9_6	)
           lowerCamelCase__					     = json.loads(old_tokenizer._tokenizer.to_str()	)
           self.assertNotIn('huggingface'		,     json_tokenizer['model']['vocab']	)






@require_tokenizers
class 	A	(						unittest.TestCase						):
     """simple docstring"""

     def        __snake_case      (			self     :      Any	) ->				List[Any]:
           lowerCamelCase__					     = PreTrainedTokenizerFast.from_pretrained('robot-test/dummy-tokenizer-wordlevel'	)
           lowerCamelCase__					     = "The Matrix is a 1999 science fiction action film."

           with concurrent.futures.ThreadPoolExecutor() as executor:
                 lowerCamelCase__					     = [executor.submit(self.fetch		,     lowercase__		,     lowercase__	) for i in range(1_0	)]
                 lowerCamelCase__					     = [future.result() for future in futures]
                 self.assertEqual(lowercase__		,     [[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0	)]	)




     def        __snake_case      (			self     :      Tuple		,     A__     :      Tuple		,     A__     :      Any	) ->				List[Any]:
           return tokenizer.encode(lowercase__		,     truncation='longest_first'		,     padding='longest'	)


'''simple docstring'''






from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
snake_case_       =       [[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_     :	int    )							->   list[int]:
      """simple docstring"""

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


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	list[list[int]]  ,  SCREAMING_SNAKE_CASE_     :	list[int]  ,  SCREAMING_SNAKE_CASE_     :	int    )							->   list[int]:
      """simple docstring"""

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


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	list[list[int]]    )							->   Image.Image:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			List[Any]     =       Image.new("RGB"  ,  (len(cells[0]    ), len(SCREAMING_SNAKE_CASE_    ))    )
      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       img.load()
      # Generates image
      for w in range(img.width    ):
            for h in range(img.height    ):
                  SCREAMING_SNAKE_CASE_      :			List[str]     =       2_5_5 - int(2_5_5 * cells[h][w]    )
                  SCREAMING_SNAKE_CASE_      :			str     =       (color, color, color)
      return img


if __name__ == "__main__":
      snake_case_       =       bin(int(input('Rule:\n').strip()))[2:]
      snake_case_       =       format_ruleset(int(rule_num))
      for time in range(1_6):
            CELLS.append(new_generation(CELLS, rule, time))
      snake_case_       =       generate_image(CELLS)
      # Uncomment to save the image
      # img.save(f"rule_{rule_num}.png")
      img.show()

