




def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			bool:
       # must be
       lowerCamelCase       :				int      			= [list(_SCREAMING_SNAKE_CASE   ) for row in matrix]
       if matrix and isinstance(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   ):
              if isinstance(matrix[0]			,_SCREAMING_SNAKE_CASE   ):
                     lowerCamelCase       :				Tuple      			= 0
                     for row in matrix:
                            if prev_len == 0:
                                   lowerCamelCase       :				Dict      			= len(_SCREAMING_SNAKE_CASE   )
                                   lowerCamelCase       :				Dict      			= True
                            else:
                                   lowerCamelCase       :				Optional[Any]      			= prev_len == len(_SCREAMING_SNAKE_CASE   )
              else:
                     lowerCamelCase       :				Dict      			= True
       else:
              lowerCamelCase       :				Tuple      			= False

       return result

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			None:
       if check_matrix(_SCREAMING_SNAKE_CASE   ) and len(_SCREAMING_SNAKE_CASE   ) > 0:
              lowerCamelCase       :				Dict      			= [list(_SCREAMING_SNAKE_CASE   ) for row in a]
              lowerCamelCase       :				str      			= len(_SCREAMING_SNAKE_CASE   )
              if isinstance(a[0]			,_SCREAMING_SNAKE_CASE   ):
                     lowerCamelCase       :				List[str]      			= len(a[0]   )
              else:
                     for dat in a:
                            print(_SCREAMING_SNAKE_CASE   )
                     return

              # horizotal printing increasing
              for i in range(0			,_SCREAMING_SNAKE_CASE   ):
                     print(a[0][i]   )
              # vertical printing down
              for i in range(1			,_SCREAMING_SNAKE_CASE   ):
                     print(a[i][mat_col - 1]   )
              # horizotal printing decreasing
              if mat_row > 1:
                     for i in range(mat_col - 2			,-1			,-1   ):
                            print(a[mat_row - 1][i]   )
        # vertical printing up
              for i in range(mat_row - 2			,0			,-1   ):
                     print(a[i][0]   )
              lowerCamelCase       :				Union[str, Any]      			= [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
              if len(_SCREAMING_SNAKE_CASE   ) > 0:
                     spiral_print_clockwise(_SCREAMING_SNAKE_CASE   )
              else:
                     return
       else:
              print("Not a valid matrix"   )
              return

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			list[int]:
       if matrix:
              return list(matrix.pop(0   )   ) + spiral_traversal(list(zip(*_SCREAMING_SNAKE_CASE   )   )[::-1]   )
       else:
              return []


# driver code
if __name__ == "__main__":
       import doctest

       doctest.testmod()

       SCREAMING_SNAKE_CASE__	:				Tuple  =       [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
       spiral_print_clockwise(a)












import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def 					A				(				*_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				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   ):
              lowerCamelCase       :				Union[str, Any]      			= 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
       SCREAMING_SNAKE_CASE__	:				Tuple  =       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
       SCREAMING_SNAKE_CASE__	:				int  =       [
           'pip:./',
           'transformers',
           'datasets',
           'evaluate',
           'tqdm',
           'scipy',
           'scikit-learn',
           'tensorboard',
           'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
       ]
       SCREAMING_SNAKE_CASE__	:				Dict  =       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

       # Define train args/config, run train function
       SCREAMING_SNAKE_CASE__	:				int  =       argparse.Namespace(cpu=False, mixed_precision='fp16')
       SCREAMING_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'])






