



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 	__a					(   lowerCAmelCase_       : int    )				->			Dict:






						'''simple docstring'''



						UpperCAmelCase_=       [[False for i in range(UpperCamelCase__    )] for j in range(UpperCamelCase__    )]
						return canvas



def 	__a					(   lowerCAmelCase_       : list[list[bool]]    )				->			Dict:






						'''simple docstring'''



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



def 	__a					(   lowerCAmelCase_       : list[list[bool]]    )				->			str:






						'''simple docstring'''



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

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



def 	__a					(   lowerCAmelCase_       : bool      ,lowerCAmelCase_       : list[list[bool]]    )				->			List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       0
						UpperCAmelCase_=       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.
						UpperCAmelCase_=       pt
						if pt:
												if alive < 2:
																		UpperCAmelCase_=       False
												elif alive == 2 or alive == 3:
																		UpperCAmelCase_=       True
												elif alive > 3:
																		UpperCAmelCase_=       False
						else:
												if alive == 3:
																		UpperCAmelCase_=       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









import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm
	import torch_xla.debug.metrics as met






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						Any						,  *__UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						List[str]=None						,  __UpperCAmelCase     :						Optional[int]=None						,  **__UpperCAmelCase     :						Dict					)      ->			Optional[int]:
							super().__init__(*__UpperCAmelCase						,  **__UpperCAmelCase					)
							UpperCAmelCase_=       eval_examples
							UpperCAmelCase_=       post_process_function




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						Optional[Dataset] = None						,  __UpperCAmelCase     :						Optional[Any]=None						,  __UpperCAmelCase     :						Optional[List[str]] = None						,  __UpperCAmelCase     :						str = "eval"						,  **__UpperCAmelCase     :						Any						,  )      ->			Dict[str, float]:
							UpperCAmelCase_=       gen_kwargs.copy()
							UpperCAmelCase_=       (
							    gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""					) is not None else self.args.generation_max_length
							)
							UpperCAmelCase_=       (
							    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""					) is not None else self.args.generation_num_beams
							)
							UpperCAmelCase_=       gen_kwargs

							UpperCAmelCase_=       self.eval_dataset if eval_dataset is None else eval_dataset
							UpperCAmelCase_=       self.get_eval_dataloader(__UpperCAmelCase					)
							UpperCAmelCase_=       self.eval_examples if eval_examples is None else eval_examples

							# Temporarily disable metric computation, we will do it in the loop here.
							UpperCAmelCase_=       self.compute_metrics
							UpperCAmelCase_=       None
							UpperCAmelCase_=       time.time()
							UpperCAmelCase_=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
													UpperCAmelCase_=       eval_loop(
													    __UpperCAmelCase						,  description="""Evaluation"""						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=__UpperCAmelCase						,  metric_key_prefix=__UpperCAmelCase						,  )
							finally:
													UpperCAmelCase_=       compute_metrics
							UpperCAmelCase_=       self.args.eval_batch_size * self.args.world_size
							if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
													start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
							output.metrics.update(
							    speed_metrics(
							        __UpperCAmelCase						,  __UpperCAmelCase						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size					)						,  )					)

							if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
													# Only the main node write the results by default
													UpperCAmelCase_=       self.post_process_function(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)
													UpperCAmelCase_=       self.compute_metrics(__UpperCAmelCase					)

													# Prefix all keys with metric_key_prefix + '_'
													for key in list(metrics.keys()					):
																			if not key.startswith(F"""{metric_key_prefix}_"""					):
																									UpperCAmelCase_=       metrics.pop(__UpperCAmelCase					)

													metrics.update(output.metrics					)
							else:
													UpperCAmelCase_=       output.metrics

							if self.args.should_log:
													# Only the main node log the results by default
													self.log(__UpperCAmelCase					)

							if self.args.tpu_metrics_debug or self.args.debug:
													# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
													xm.master_print(met.metrics_report()					)

							UpperCAmelCase_=       self.callback_handler.on_evaluate(self.args						,  self.state						,  self.control						,  __UpperCAmelCase					)
							return metrics




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						Dict						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						Optional[int]=None						,  __UpperCAmelCase     :						str = "test"						,  **__UpperCAmelCase     :						List[str]					)      ->			Tuple:
							UpperCAmelCase_=       gen_kwargs.copy()

							UpperCAmelCase_=       self.get_test_dataloader(__UpperCAmelCase					)

							# Temporarily disable metric computation, we will do it in the loop here.
							UpperCAmelCase_=       self.compute_metrics
							UpperCAmelCase_=       None
							UpperCAmelCase_=       time.time()
							UpperCAmelCase_=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
													UpperCAmelCase_=       eval_loop(
													    __UpperCAmelCase						,  description="""Prediction"""						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=__UpperCAmelCase						,  metric_key_prefix=__UpperCAmelCase						,  )
							finally:
													UpperCAmelCase_=       compute_metrics

							UpperCAmelCase_=       self.args.eval_batch_size * self.args.world_size
							if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
													start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
							output.metrics.update(
							    speed_metrics(
							        __UpperCAmelCase						,  __UpperCAmelCase						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size					)						,  )					)
							if self.post_process_function is None or self.compute_metrics is None:
													return output

							UpperCAmelCase_=       self.post_process_function(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  """predict"""					)
							UpperCAmelCase_=       self.compute_metrics(__UpperCAmelCase					)

							# Prefix all keys with metric_key_prefix + '_'
							for key in list(metrics.keys()					):
													if not key.startswith(F"""{metric_key_prefix}_"""					):
																			UpperCAmelCase_=       metrics.pop(__UpperCAmelCase					)
							metrics.update(output.metrics					)
							return PredictionOutput(predictions=predictions.predictions						,  label_ids=predictions.label_ids						,  metrics=__UpperCAmelCase					)




