

'''simple docstring'''






import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment


def  __lowerCamelCase      (				*SCREAMING_SNAKE_CASE_					:     Dict   )							->       int:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	torch.cuda.device_count()
      print(F"Device count: {num_processes}"   )
      with patch_environment(
          world_size=SCREAMING_SNAKE_CASE_  ,  master_addr="127.0.01"  ,  master_port="29500"  ,  mixed_precision=args[1].mixed_precision   ):
            SCREAMING_SNAKE_CASE_				:      int					=	PrepareForLaunch(SCREAMING_SNAKE_CASE_  ,  distributed_type="MULTI_GPU"   )
            torch.multiprocessing.start_processes(SCREAMING_SNAKE_CASE_  ,  args=SCREAMING_SNAKE_CASE_  ,  nprocs=SCREAMING_SNAKE_CASE_  ,  start_method="spawn"   )


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
      # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

      # on-demand GPU
      # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
      snake_case_					  =	rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
      gpu.up_if_not()

      # on-prem GPU
      # gpu = rh.cluster(
      #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
      #       )

      # Set up remote function
      snake_case_					  =	[
          'pip:./',
          'transformers',
          'datasets',
          'evaluate',
          'tqdm',
          'scipy',
          'scikit-learn',
          'tensorboard',
          'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
      ]
      snake_case_					  =	rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

      # Define train args/config, run train function
      snake_case_					  =	argparse.Namespace(cpu=False, mixed_precision='fp16')
      snake_case_					  =	{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
      launch_train_gpu(config, train_args, stream_logs=True)

      # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
      # gpu.install_packages(reqs)
      # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])




'''simple docstring'''






from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
snake_case_					  =	[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     int   )							->       list[int]:
      """simple docstring"""

      return [int(SCREAMING_SNAKE_CASE_   ) for c in F"{ruleset:08}"[:8]]


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     list[list[int]]  ,  SCREAMING_SNAKE_CASE_					:     list[int]  ,  SCREAMING_SNAKE_CASE_					:     int   )							->       list[int]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	len(cells[0]   )  # 31
      SCREAMING_SNAKE_CASE_				:      Dict					=	[]
      for i in range(SCREAMING_SNAKE_CASE_   ):
            # Get the neighbors of each cell
            # Handle neighbours outside bounds by using 0 as their value
            SCREAMING_SNAKE_CASE_				:      Any					=	0 if i == 0 else cells[time][i - 1]
            SCREAMING_SNAKE_CASE_				:      Dict					=	0 if i == population - 1 else cells[time][i + 1]
            # Define a new cell and add it to the new generation
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	7 - int(F"{left_neighbor}{cells[time][i]}{right_neighbor}"  ,  2   )
            next_generation.append(rule[situation]   )
      return next_generation


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     list[list[int]]   )							->       Image.Image:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Tuple					=	Image.new("RGB"  ,  (len(cells[0]   ), len(SCREAMING_SNAKE_CASE_   ))   )
      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	img.load()
      # Generates image
      for w in range(img.width   ):
            for h in range(img.height   ):
                  SCREAMING_SNAKE_CASE_				:      List[Any]					=	2_5_5 - int(2_5_5 * cells[h][w]   )
                  SCREAMING_SNAKE_CASE_				:      Optional[int]					=	(color, color, color)
      return img


if __name__ == "__main__":
      snake_case_					  =	bin(int(input('Rule:\n').strip()))[2:]
      snake_case_					  =	format_ruleset(int(rule_num))
      for time in range(1_6):
            CELLS.append(new_generation(CELLS, rule, time))
      snake_case_					  =	generate_image(CELLS)
      # Uncomment to save the image
      # img.save(f"rule_{rule_num}.png")
      img.show()

