






import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__UpperCAmelCase     							=						logging.get_logger(__name__)




def 					lowercase__				(					__snake_case   :							Optional[Any]=None					,	__snake_case   :							str=None  ):


					'''simple docstring'''


					return field(default_factory=lambda: default					,	metadata=__snake_case  )






@dataclass
class   lowerCamelCase				:







		'''simple docstring'''



		_snake_case   :   List[str]							=  list_field(
		    default=[]			,  metadata={
		        '''help''': (
		            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
		            ''' of all available models'''
		        )
		    }			,  )

		_snake_case   :   List[int]							=  list_field(
		    default=[8]			,  metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}      )

		_snake_case   :   List[int]							=  list_field(
		    default=[8, 3_2, 1_2_8, 5_1_2]			,  metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}			,  )

		_snake_case   :   bool							=  field(
		    default=_snake_case			,  metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}			,  )
		_snake_case   :   bool							=  field(
		    default=_snake_case			,  metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}			,  )
		_snake_case   :   bool							=  field(
		    default=_snake_case			,  metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}      )
		_snake_case   :   bool							=  field(default=_snake_case			,  metadata={'''help''': '''Use FP16 to accelerate inference.'''}      )
		_snake_case   :   bool							=  field(default=_snake_case			,  metadata={'''help''': '''Benchmark training of model'''}      )
		_snake_case   :   bool							=  field(default=_snake_case			,  metadata={'''help''': '''Verbose memory tracing'''}      )
		_snake_case   :   bool							=  field(
		    default=_snake_case			,  metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}			,  )
		_snake_case   :   bool							=  field(
		    default=_snake_case			,  metadata={
		        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
		    }			,  )
		_snake_case   :   bool							=  field(default=_snake_case			,  metadata={'''help''': '''Trace memory line by line'''}      )
		_snake_case   :   bool							=  field(default=_snake_case			,  metadata={'''help''': '''Save result to a CSV file'''}      )
		_snake_case   :   bool							=  field(default=_snake_case			,  metadata={'''help''': '''Save all print statements in a log file'''}      )
		_snake_case   :   bool							=  field(default=_snake_case			,  metadata={'''help''': '''Whether to print environment information'''}      )
		_snake_case   :   bool							=  field(
		    default=_snake_case			,  metadata={
		        '''help''': (
		            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
		            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
		            ''' for debugging / testing and on TPU.'''
		        )
		    }			,  )
		_snake_case   :   str							=  field(
		    default=f'inference_time_{round(time()      )}.csv'			,  metadata={'''help''': '''CSV filename used if saving time results to csv.'''}			,  )
		_snake_case   :   str							=  field(
		    default=f'inference_memory_{round(time()      )}.csv'			,  metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}			,  )
		_snake_case   :   str							=  field(
		    default=f'train_time_{round(time()      )}.csv'			,  metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}			,  )
		_snake_case   :   str							=  field(
		    default=f'train_memory_{round(time()      )}.csv'			,  metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}			,  )
		_snake_case   :   str							=  field(
		    default=f'env_info_{round(time()      )}.csv'			,  metadata={'''help''': '''CSV filename used if saving environment information.'''}			,  )
		_snake_case   :   str							=  field(
		    default=f'log_{round(time()      )}.csv'			,  metadata={'''help''': '''Log filename used if print statements are saved in log.'''}			,  )
		_snake_case   :   int							=  field(default=3			,  metadata={'''help''': '''Times an experiment will be run.'''}      )
		_snake_case   :   bool							=  field(
		    default=_snake_case			,  metadata={
		        '''help''': (
		            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
		            ''' model weights.'''
		        )
		    }			,  )



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							warnings.warn(
							    f"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
							    ' are deprecated in general and it is advised to use external Benchmarking libraries '
							    ' to benchmark Transformer models.'    ,		_UpperCamelCase    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							return json.dumps(dataclasses.asdict(self       )    ,		indent=2       )



		@property
		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							if len(self.models       ) <= 0:
												raise ValueError(
												    'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
												    ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'       )
							return self.models



		@property
		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							if not self.multi_process:
												return False
							elif self.is_tpu:
												logger.info('Multiprocessing is currently not possible on TPU.'       )
												return False
							else:
												return True














import random
import sys

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

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

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




def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Any						=						[[False for i in range(__snake_case  )] for j in range(__snake_case  )]
					return canvas




def 					lowercase__				(					__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


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




def 					lowercase__				(					__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


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

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




def 					lowercase__				(					__snake_case   :							bool					,	__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[int]						=						0
					UpperCAmelCase_   :				List[Any]						=						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_   :				List[Any]						=						pt
					if pt:
										if alive < 2:
															UpperCAmelCase_   :				str						=						False
										elif alive == 2 or alive == 3:
															UpperCAmelCase_   :				int						=						True
										elif alive > 3:
															UpperCAmelCase_   :				List[Any]						=						False
					else:
										if alive == 3:
															UpperCAmelCase_   :				int						=						True

					return state


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

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






