






import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.bert.modeling_flax_bert import (
							    FlaxBertForMaskedLM,
							    FlaxBertForMultipleChoice,
							    FlaxBertForNextSentencePrediction,
							    FlaxBertForPreTraining,
							    FlaxBertForQuestionAnswering,
							    FlaxBertForSequenceClassification,
							    FlaxBertForTokenClassification,
							    FlaxBertModel,
							)

class 				lowerCAmelCase_	(		unittest.TestCase			):


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=1_3 ,						_lowerCAmelCase=7 ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=9_9 ,						_lowerCAmelCase=3_2 ,						_lowerCAmelCase=5 ,						_lowerCAmelCase=4 ,						_lowerCAmelCase=3_7 ,						_lowerCAmelCase="gelu" ,						_lowerCAmelCase=0.1 ,						_lowerCAmelCase=0.1 ,						_lowerCAmelCase=5_1_2 ,						_lowerCAmelCase=1_6 ,						_lowerCAmelCase=2 ,						_lowerCAmelCase=0.02 ,						_lowerCAmelCase=4 ,						):
											_lowercase							:   Optional[Any] 							=   parent
											_lowercase							:   Any 							=   batch_size
											_lowercase							:   str 							=   seq_length
											_lowercase							:   Union[str, Any] 							=   is_training
											_lowercase							:   Tuple 							=   use_attention_mask
											_lowercase							:   List[str] 							=   use_token_type_ids
											_lowercase							:   Tuple 							=   use_labels
											_lowercase							:   Tuple 							=   vocab_size
											_lowercase							:   List[Any] 							=   hidden_size
											_lowercase							:   List[Any] 							=   num_hidden_layers
											_lowercase							:   List[Any] 							=   num_attention_heads
											_lowercase							:   Any 							=   intermediate_size
											_lowercase							:   List[str] 							=   hidden_act
											_lowercase							:   Dict 							=   hidden_dropout_prob
											_lowercase							:   int 							=   attention_probs_dropout_prob
											_lowercase							:   str 							=   max_position_embeddings
											_lowercase							:   Tuple 							=   type_vocab_size
											_lowercase							:   Optional[int] 							=   type_sequence_label_size
											_lowercase							:   Optional[int] 							=   initializer_range
											_lowercase							:   List[Any] 							=   num_choices


				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size      )

											_lowercase							:   str 							=   None
											if self.use_attention_mask:
																		_lowercase							:   int 							=   random_attention_mask([self.batch_size, self.seq_length]      )

											_lowercase							:   Optional[Any] 							=   None
											if self.use_token_type_ids:
																		_lowercase							:   Dict 							=   ids_tensor([self.batch_size, self.seq_length] ,						self.type_vocab_size      )

											_lowercase							:   Any 							=   BertConfig(
											    vocab_size=self.vocab_size ,						hidden_size=self.hidden_size ,						num_hidden_layers=self.num_hidden_layers ,						num_attention_heads=self.num_attention_heads ,						intermediate_size=self.intermediate_size ,						hidden_act=self.hidden_act ,						hidden_dropout_prob=self.hidden_dropout_prob ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob ,						max_position_embeddings=self.max_position_embeddings ,						type_vocab_size=self.type_vocab_size ,						is_decoder=_lowerCAmelCase ,						initializer_range=self.initializer_range ,						)

											return config, input_ids, token_type_ids, attention_mask


				def 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   self.prepare_config_and_inputs()
											_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							:   Dict 							=   config_and_inputs
											_lowercase							:   Dict 							=   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
											return config, inputs_dict


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   self.prepare_config_and_inputs()
											_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							:   List[Any] 							=   config_and_inputs

											_lowercase							:   Any 							=   True
											_lowercase							:   str 							=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
											_lowercase							:   Tuple 							=   ids_tensor([self.batch_size, self.seq_length] ,						vocab_size=2      )

											return (
											    config,
											    input_ids,
											    attention_mask,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)




@require_flax
class 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       Optional[Any]							       = True

				_UpperCamelCase					:       int							       = (
				    (
				        FlaxBertModel,
				        FlaxBertForPreTraining,
				        FlaxBertForMaskedLM,
				        FlaxBertForMultipleChoice,
				        FlaxBertForQuestionAnswering,
				        FlaxBertForNextSentencePrediction,
				        FlaxBertForSequenceClassification,
				        FlaxBertForTokenClassification,
				        FlaxBertForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)


				def 						__a					(					self      ):
											_lowercase							:   Optional[Any] 							=   FlaxBertModelTester(self      )


				@slow
				def 						__a					(					self      ):
											# Only check this for base model, not necessary for all model classes.
											# This will also help speed-up tests.
											_lowercase							:   Union[str, Any] 							=   FlaxBertModel.from_pretrained('bert-base-cased'      )
											_lowercase							:   int 							=   model(np.ones((1, 1)      )      )
											self.assertIsNotNone(_lowerCAmelCase      )







import random
import sys

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

a__												=						'''Usage of script: script_name <size_of_canvas:int>'''

a__												=						[0] * 100 + [1] * 10
random.shuffle(choice)



def   __UpperCAmelCase					(      __a :     int      )     ->	list[list[bool]]:

					"""simple docstring"""

					_a      :       int   =						[[False for i in range(__a      )] for j in range(__a      )]
					return canvas



def   __UpperCAmelCase					(      __a :     list[list[bool]]      )     ->	None:

					"""simple docstring"""

					for i, row in enumerate(__a      ):
										for j, _ in enumerate(__a      ):
															_a      :       Optional[int]   =						bool(random.getrandbits(1      )      )



def   __UpperCAmelCase					(      __a :     list[list[bool]]      )     ->	list[list[bool]]:

					"""simple docstring"""

					_a      :       Any   =						np.array(__a      )
					_a      :       Optional[int]   =						np.array(create_canvas(current_canvas.shape[0]      )      )
					for r, row in enumerate(__a      ):
										for c, pt in enumerate(__a      ):
															_a      :       Tuple   =						__judge_point(
															    __a						,current_canvas[r - 1 : r + 2, c - 1 : c + 2]      )

					_a      :       List[str]   =						next_gen_canvas
					del next_gen_canvas  # cleaning memory as we move on.
					_a      :       list[list[bool]]   =						current_canvas.tolist()
					return return_canvas



def   __UpperCAmelCase					(      __a :     bool						,__a :     list[list[bool]]      )     ->	bool:

					"""simple docstring"""

					_a      :       Optional[Any]   =						0
					_a      :       str   =						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.
					_a      :       Optional[int]   =						pt
					if pt:
										if alive < 2:
															_a      :       Dict   =						False
										elif alive == 2 or alive == 3:
															_a      :       Optional[Any]   =						True
										elif alive > 3:
															_a      :       str   =						False
					else:
										if alive == 3:
															_a      :       int   =						True

					return state


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

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



