


'''simple docstring'''

import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

lowerCamelCase							:   Optional[int]  						=	'Usage of script: script_name <size_of_canvas:int>'

lowerCamelCase							:   Any  						=	[0] * 100 + [1] * 10
random.shuffle(choice)

def _SCREAMING_SNAKE_CASE      (A       )       ->       Any:






       """simple docstring"""







       lowercase__      			= [[False for i in range(snake_case__       )] for j in range(snake_case__       )]
       return canvas

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







       for i, row in enumerate(snake_case__       ):
              for j, _ in enumerate(snake_case__       ):
                     lowercase__      			= bool(random.getrandbits(1       )       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







       lowercase__      			= np.array(snake_case__       )
       lowercase__      			= np.array(create_canvas(current_canvas.shape[0]       )       )
       for r, row in enumerate(snake_case__       ):
              for c, pt in enumerate(snake_case__       ):
                     lowercase__      			= __judge_point(
                         snake_case__  , current_canvas[r - 1 : r + 2, c - 1 : c + 2]       )

       lowercase__      			= next_gen_canvas
       del next_gen_canvas  # cleaning memory as we move on.
       lowercase__      			= current_canvas.tolist()
       return return_canvas

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       int:






       """simple docstring"""







       lowercase__      			= 0
       lowercase__      			= 0
       # finding dead or alive neighbours count.
       for i in neighbours:
              for status in i:
                     if status:
                            alive += 1
                     else:
                            dead += 1

    # handling duplicate entry for focus pt.
       if pt:
              alive -= 1
       else:
              dead -= 1

       # running the rules of game here.
       lowercase__      			= pt
       if pt:
              if alive < 2:
                     lowercase__      			= False
              elif alive == 2 or alive == 3:
                     lowercase__      			= True
              elif alive > 3:
                     lowercase__      			= False
       else:
              if alive == 3:
                     lowercase__      			= True

       return state


if __name__ == "__main__":
 if len(sys.argv) != 2:
  raise Exception(usage_doc)

 lowerCamelCase							:   Optional[int]  						=	int(sys.argv[1])
 # main working structure of this module.
 lowerCamelCase							:   Optional[int]  						=	create_canvas(canvas_size)
 seed(c)
 lowerCamelCase							:   str  						=	plt.subplots()
 fig.show()
 lowerCamelCase							:   Any  						=	ListedColormap(['w', 'k'])
 try:
  while True:
   lowerCamelCase							:   List[str]  						=	run(c)
   ax.matshow(c, cmap=cmap)
   fig.canvas.draw()
   ax.cla()
 except KeyboardInterrupt:
  # do nothing.
  pass






'''simple docstring'''




import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
      import jax

      from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
      from transformers.models.bert.modeling_flax_bert import FlaxBertModel
      from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel





@require_flax
class  A				(							unittest.TestCase						):





     @slow
     def 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






         for model_name in ["bert-base-cased", "bert-large-uncased"]:
             with self.subTest(SCREAMING_SNAKE_CASE	):
                 A       :   int					=						AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(SCREAMING_SNAKE_CASE	)
                 self.assertIsInstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

                 A       :   List[str]					=						FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(SCREAMING_SNAKE_CASE	)
                 self.assertIsInstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     @slow
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         for model_name in ["roberta-base", "roberta-large"]:
             with self.subTest(SCREAMING_SNAKE_CASE	):
                 A       :   Any					=						AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(SCREAMING_SNAKE_CASE	)
                 self.assertIsInstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

                 A       :   Any					=						FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(SCREAMING_SNAKE_CASE	)
                 self.assertIsInstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         for model_name in ["bert-base-cased", "bert-large-uncased"]:
             A       :   Optional[int]					=						AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)
             A       :   List[str]					=						FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE	)
             A       :   Optional[Any]					=						tokenizer('''Do you support jax jitted function?'''						,			return_tensors=TensorType.JAX	)

             @jax.jit
             def eval(**SCREAMING_SNAKE_CASE	):
                 return model(**SCREAMING_SNAKE_CASE	)

             eval(**SCREAMING_SNAKE_CASE	).block_until_ready()





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






         """simple docstring"""






         for model_name in ["roberta-base", "roberta-large"]:
             A       :   List[str]					=						AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)
             A       :   Union[str, Any]					=						FlaxRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE	)
             A       :   int					=						tokenizer('''Do you support jax jitted function?'''						,			return_tensors=TensorType.JAX	)

             @jax.jit
             def eval(**SCREAMING_SNAKE_CASE	):
                 return model(**SCREAMING_SNAKE_CASE	)

             eval(**SCREAMING_SNAKE_CASE	).block_until_ready()





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         with self.assertRaisesRegex(
             SCREAMING_SNAKE_CASE						,			'''bert-base is not a local folder and is not a valid model identifier'''	):
             A       :   List[Any]					=						FlaxAutoModel.from_pretrained('''bert-base'''	)





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         with self.assertRaisesRegex(
             SCREAMING_SNAKE_CASE						,			R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''	):
             A       :   Optional[int]					=						FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE						,			revision='''aaaaaa'''	)





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         with self.assertRaisesRegex(
             SCREAMING_SNAKE_CASE						,			'''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'''						,			):
             A       :   List[str]					=						FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''	)





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






         """simple docstring"""






         with self.assertRaisesRegex(SCREAMING_SNAKE_CASE						,			'''Use `from_pt=True` to load this model'''	):
             A       :   Any					=						FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''	)




