





"""simple docstring"""




import random
import sys

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

lowercase				:	List[str]      						=  'Usage of script: script_name <size_of_canvas:int>'

lowercase				:	str      						=  [0] * 1_0_0 + [1] * 1_0
random.shuffle(choice)



def A_     (		A__    )  ->		list[list[bool]]:
       a__						:     Optional[Any]			=    [[False for i in range(_UpperCAmelCase    )] for j in range(_UpperCAmelCase    )]
       return canvas



def A_     (		A__    )  ->		None:


       for i, row in enumerate(_UpperCAmelCase    ):
              for j, _ in enumerate(_UpperCAmelCase    ):
                     a__						:     Any			=    bool(random.getrandbits(1    )    )



def A_     (		A__    )  ->		list[list[bool]]:
       a__						:     Optional[Any]			=    np.array(_UpperCAmelCase    )
       a__						:     List[str]			=    np.array(create_canvas(current_canvas.shape[0]    )    )
       for r, row in enumerate(_UpperCAmelCase    ):
              for c, pt in enumerate(_UpperCAmelCase    ):
                     a__						:     Any			=    __judge_point(
                         _UpperCAmelCase		,				current_canvas[r - 1 : r + 2, c - 1 : c + 2]    )

       a__						:     Union[str, Any]			=    next_gen_canvas
       del next_gen_canvas  # cleaning memory as we move on.
       a__						:     list[list[bool]]			=    current_canvas.tolist()
       return return_canvas



def A_     (		A__		,				A__    )  ->		bool:
       a__						:     Dict			=    0
       a__						:     int			=    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__						:     List[str]			=    False
              elif alive == 2 or alive == 3:
                     a__						:     Dict			=    True
              elif alive > 3:
                     a__						:     List[str]			=    False
       else:
              if alive == 3:
                     a__						:     Tuple			=    True

       return state


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

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









import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
    from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm



def A_     (		A__    )  ->		Optional[int]:
       if is_torch_version('<'		,				'2.0.0'    ) or not hasattr(A__		,				'_dynamo'    ):
              return False
       return isinstance(A__		,				torch._dynamo.eval_frame.OptimizedModule    )



def A_     (		A__		,				A__ = True    )  ->		int:
       a__						:     Optional[Any]			=    (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

       a__						:     Union[str, Any]			=    is_compiled_module(A__    )
       if is_compiled:
              a__						:     List[str]			=    model
              a__						:     Dict			=    model._orig_mod

       if is_deepspeed_available():
              options += (DeepSpeedEngine,)

       while isinstance(A__		,				A__    ):
              a__						:     str			=    model.module

       if not keep_fpaa_wrapper:
              a__						:     Union[str, Any]			=    getattr(A__		,				'forward'    )
              a__						:     List[Any]			=    model.__dict__.pop('_original_forward'		,				A__    )
              if original_forward is not None:
                     while hasattr(A__		,				'__wrapped__'    ):
                            a__						:     int			=    forward.__wrapped__
                            if forward == original_forward:
                                   break
                     a__						:     List[Any]			=    forward
              if getattr(A__		,				'_converted_to_transformer_engine'		,				A__    ):
                     convert_model(A__		,				to_transformer_engine=A__    )

       if is_compiled:
              a__						:     List[str]			=    model
              a__						:     Any			=    compiled_model

       return model



def A_     (		)  ->		int:
       PartialState().wait_for_everyone()



def A_     (		A__		,				A__    )  ->		Dict:
       if PartialState().distributed_type == DistributedType.TPU:
              xm.save(A__		,				A__    )
       elif PartialState().local_process_index == 0:
              torch.save(A__		,				A__    )



@contextmanager
def A_     (		**A__    )  ->		Any:
       for key, value in kwargs.items():
              a__						:     Optional[int]			=    str(A__    )

       yield

       for key in kwargs:
              if key.upper() in os.environ:
                     del os.environ[key.upper()]



def A_     (		A__    )  ->		List[str]:
       if not hasattr(A__		,				'__qualname__'    ) and not hasattr(A__		,				'__name__'    ):
              a__						:     Dict			=    getattr(A__		,				'__class__'		,				A__    )
       if hasattr(A__		,				'__qualname__'    ):
              return obj.__qualname__
       if hasattr(A__		,				'__name__'    ):
              return obj.__name__
       return str(A__    )



def A_     (		A__		,				A__    )  ->		Dict:
       for key, value in source.items():
              if isinstance(A__		,				A__    ):
                     a__						:     Optional[Any]			=    destination.setdefault(A__		,				{}    )
                     merge_dicts(A__		,				A__    )
              else:
                     a__						:     Optional[int]			=    value

       return destination



def A_     (		A__ = None    )  ->		bool:
       if port is None:
              a__						:     List[Any]			=    2_9500
       with socket.socket(socket.AF_INET		,				socket.SOCK_STREAM    ) as s:
              return s.connect_ex(('localhost', port)    ) == 0


