'''simple docstring'''

from __future__ import annotations

from PIL import Image

# Define glider example
_A			:      int					      =				[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
_A			:      Union[str, Any]					      =				[[0, 1, 0], [0, 1, 0], [0, 1, 0]]







def      UpperCamelCase_						(					snake_case_       :       list[list[int]]      )					->     List[str]:






 '''simple docstring'''

 __lowerCAmelCase											=						[]
 for i in range(len(_snake_case      )      ):
  __lowerCAmelCase											=						[]
  for j in range(len(cells[i]      )      ):
   # Get the number of live neighbours
   __lowerCAmelCase											=						0
   if i > 0 and j > 0:
    neighbour_count += cells[i - 1][j - 1]
   if i > 0:
    neighbour_count += cells[i - 1][j]
   if i > 0 and j < len(cells[i]      ) - 1:
    neighbour_count += cells[i - 1][j + 1]
   if j > 0:
    neighbour_count += cells[i][j - 1]
   if j < len(cells[i]      ) - 1:
    neighbour_count += cells[i][j + 1]
   if i < len(_snake_case      ) - 1 and j > 0:
    neighbour_count += cells[i + 1][j - 1]
   if i < len(_snake_case      ) - 1:
    neighbour_count += cells[i + 1][j]
   if i < len(_snake_case      ) - 1 and j < len(cells[i]      ) - 1:
    neighbour_count += cells[i + 1][j + 1]

   # Rules of the game of life (excerpt from Wikipedia):
   # 1. Any live cell with two or three live neighbours survives.
   # 2. Any dead cell with three live neighbours becomes a live cell.
   # 3. All other live cells die in the next generation.
   #    Similarly, all other dead cells stay dead.
   __lowerCAmelCase											=						cells[i][j] == 1
   if (
       (alive and 2 <= neighbour_count <= 3)
       or not alive
       and neighbour_count == 3
   ):
    next_generation_row.append(1      )
   else:
    next_generation_row.append(0      )

  next_generation.append(_snake_case      )
 return next_generation







def      UpperCamelCase_						(					snake_case_       :       list[list[int]]						,		snake_case_       :       int      )					->     Optional[int]:






 '''simple docstring'''

 __lowerCAmelCase											=						[]
 for _ in range(_snake_case      ):
  # Create output image
  __lowerCAmelCase											=						Image.new("""RGB"""						,		(len(cells[0]      ), len(_snake_case      ))      )
  __lowerCAmelCase											=						img.load()

  # Save cells to image
  for x in range(len(_snake_case      )      ):
   for y in range(len(cells[0]      )      ):
    __lowerCAmelCase											=						2_55 - cells[y][x] * 2_55
    __lowerCAmelCase											=						(colour, colour, colour)

        # Save image
  images.append(_snake_case      )
  __lowerCAmelCase											=						new_generation(_snake_case      )
 return images


if __name__ == "__main__":
 _A			:      Dict					      =				generate_images(GLIDER, 16)
 images[0].save('''out.gif''', save_all=True, append_images=images[1:])









import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase					:			List[Any]  				=			get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")






@require_sentencepiece
@require_tokenizers
class 	__magic_name__    (			lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						GPTSwaTokenizer
			SCREAMING_SNAKE_CASE							   =						False
			SCREAMING_SNAKE_CASE							   =						True
			SCREAMING_SNAKE_CASE							   =						False





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						super().setUp()

						# We have a SentencePiece fixture for testing
						__a        =GPTSwaTokenizer(__snake_case					,  eos_token='<unk>'					,  bos_token='<unk>'					,  pad_token='<unk>'					)

						tokenizer.save_pretrained(self.tmpdirname					)





			def 		__magic_name__    (		self					,  __snake_case					)   -> Tuple:




						'''simple docstring'''
						__a        ='This is a test'
						__a        ='This is a test'
						return input_text, output_text





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        ='<s>'
						__a        =1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case					)					,  __snake_case					)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case					)					,  __snake_case					)





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        =list(self.get_tokenizer().get_vocab().keys()					)

						self.assertEqual(vocab_keys[0]					,  '<unk>'					)
						self.assertEqual(vocab_keys[1]					,  '<s>'					)
						self.assertEqual(vocab_keys[-1]					,  'j'					)
						self.assertEqual(len(__snake_case					)					,  2000					)





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						self.assertEqual(self.get_tokenizer().vocab_size					,  2000					)





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =GPTSwaTokenizer(__snake_case					)

						__a        =tokenizer.tokenize('This is a test'					)
						self.assertListEqual(__snake_case					,  ['▁This', '▁is', '▁a', '▁t', 'est']					)

						self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case					)					,  [465, 287, 265, 631, 842]					)

						__a        =tokenizer.tokenize('I was born in 92000, and this is falsé.'					)
						# fmt: off
						self.assertListEqual(
						    __snake_case					,  ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']					,  )
						# fmt: on

						__a        =tokenizer.convert_tokens_to_ids(__snake_case					)
						self.assertListEqual(
						    __snake_case					,  [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]					,  )

						__a        =tokenizer.convert_ids_to_tokens(__snake_case					)
						# fmt: off
						self.assertListEqual(
						    __snake_case					,  ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']					)
						# fmt: on





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =GPTSwaTokenizer(__snake_case					)
						__a        =['This is a test', 'I was born in 92000, and this is falsé.']
						__a        =[
						    [465, 287, 265, 631, 842],
						    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
						]

						# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
						for text, expected_ids in zip(__snake_case					,  __snake_case					):
									self.assertListEqual(tokenizer.encode_fast(__snake_case					)					,  __snake_case					)

						# Test that decode_fast returns the input text
						for text, token_ids in zip(__snake_case					,  __snake_case					):
									self.assertEqual(tokenizer.decode_fast(__snake_case					)					,  __snake_case					)





			@slow
			def 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						__a        =[
						    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
						    'Hey there, how are you doing this fine day?',
						    'This is a text with a trailing spaces followed by a dot     .',
						    'Häj sväjs lillebrör! =)',
						    'Det är inget fel på Mr. Cool',
						]

						# fmt: off
						__a        ={'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
						# fmt: on
						self.tokenizer_integration_test_util(
						    expected_encoding=__snake_case					,  model_name='AI-Sweden/gpt-sw3-126m'					,  sequences=__snake_case					,  )






