





'''simple docstring'''



import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment



def  lowercase__(		*_UpperCamelCase  : List[Any]				)->   Optional[Any]:






					"""simple docstring"""

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

			# Define train args/config, run train function
			snake_case_   :  str								=      argparse.Namespace(cpu=False, mixed_precision='''fp16''')
			snake_case_   :  int								=      {'''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'''



from __future__ import annotations



def  lowercase__(		_UpperCamelCase  : list[list[int]] ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int | float				)->   None:






					"""simple docstring"""

					_UpperCamelCase				,	_UpperCamelCase											=   m - 1, 0
					while i >= 0 and j < n:
										if key == mat[i][j]:
															print(f"Key {key} found at row- {i + 1} column- {j + 1}"				)
															return
										if key < mat[i][j]:
															i -= 1
										else:
															j += 1
					print(f"Key {key} not found"				)



def  lowercase__(		)->   None:






					"""simple docstring"""

					_UpperCamelCase											=   [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
					_UpperCamelCase											=   int(input("Enter the element to be searched:"				)				)
					print(_UpperCamelCase				)
					search_in_a_sorted_matrix(_UpperCamelCase ,   len(_UpperCamelCase				) ,   len(mat[0]				) ,   _UpperCamelCase				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



