"""simple docstring"""



import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def    _A(		*lowerCAmelCase       ):
       A__					:    Union[str, Any]														= torch.cuda.device_count()
       print(F'''Device count: {num_processes}'''       )
       with patch_environment(
           world_size=lowerCAmelCase ,     master_addr="""127.0.01""" ,     master_port="""29500""" ,     mixed_precision=args[1].mixed_precision       ):
              A__					:    Union[str, Any]														= PrepareForLaunch(lowerCAmelCase ,     distributed_type="""MULTI_GPU"""       )
              torch.multiprocessing.start_processes(lowerCAmelCase ,     args=lowerCAmelCase ,     nprocs=lowerCAmelCase ,     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
    _UpperCamelCase         =		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
    _UpperCamelCase         =		[
        "pip:./",
        "transformers",
        "datasets",
        "evaluate",
        "tqdm",
        "scipy",
        "scikit-learn",
        "tensorboard",
        "torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117",
    ]
    _UpperCamelCase         =		rh.function(fn=launch_train, system=gpu, reqs=reqs, name="train_bert_glue")

    # Define train args/config, run train function
    _UpperCamelCase         =		argparse.Namespace(cpu=False, mixed_precision="fp16")
    _UpperCamelCase         =		{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    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"""



def    _A(		lowerCAmelCase       ):
							A__					:    str														= len(lowerCAmelCase       )
							A__					:    List[Any]														= [0] * length
							A__					:    Union[str, Any]														= [[False for i in range(lowerCAmelCase       )] for j in range(lowerCAmelCase       )]
							for i, c in enumerate(lowerCAmelCase       ):
														A__					:    Dict														= i
														for j in range(i + 1       ):
																					if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
																												A__					:    List[str]														= True
																												A__					:    Optional[Any]														= min(lowerCAmelCase ,     0 if j == 0 else (cut[j - 1] + 1)       )
														A__					:    Optional[int]														= mincut
							return cut[length - 1]


if __name__ == "__main__":
				_UpperCamelCase         =		input("Enter the string: ").strip()
				_UpperCamelCase         =		find_minimum_partitions(s)
				print(F'Minimum number of partitions required for the \'{s}\' is {ans}')





