def      lowerCAmelCase_				(  __A,						__A							)  ->       list[list[int]]:







      '''simple docstring'''


      UpperCAmelCase__			=						[]
      UpperCAmelCase__			=						len(__A							)
      for i in range(__A							):
            UpperCAmelCase__			=						[]
            for j in range(__A							):
                  UpperCAmelCase__			=						0
                  for k in range(__A							):
                        UpperCAmelCase__			=						val + matrix_a[i][k] * matrix_b[k][j]
                  list_a.append(__A							)
            matrix_c.append(__A							)
      return matrix_c




def      lowerCAmelCase_				(  __A							)  ->       list[list[int]]:







      '''simple docstring'''


      return [[int(row == column							) for column in range(__A							)] for row in range(__A							)]




def      lowerCAmelCase_				(  __A							)  ->       int:







      '''simple docstring'''


      if n <= 1:
            return n
      UpperCAmelCase__			=						identity(2							)
      UpperCAmelCase__			=						[[1, 1], [1, 0]]
      UpperCAmelCase__			=						n - 1
      while n > 0:
            if n % 2 == 1:
                  UpperCAmelCase__			=						multiply(__A,						__A							)
            UpperCAmelCase__			=						multiply(__A,						__A							)
            UpperCAmelCase__			=						int(n / 2							)
      return res_matrix[0][0]




def      lowerCAmelCase_				(  __A							)  ->       int:







      '''simple docstring'''


      if n <= 1:
            return n
      UpperCAmelCase__			=						0
      UpperCAmelCase__			=						1
      for _ in range(2,						n + 1							):
            UpperCAmelCase__	,   UpperCAmelCase__			=						fiba, fiba + fiba
      return fiba




def      lowerCAmelCase_				(  )  ->       None:







      '''simple docstring'''


      for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
            UpperCAmelCase__			=						int("".join(c for c in ordinal if c in "0123456789"							)							)  # 1000th --> 1000
            print(
                f"""{ordinal} fibonacci number using matrix exponentiation is """
                f"""{nth_fibonacci_matrix(__A							)} and using bruteforce is """
                f"""{nth_fibonacci_bruteforce(__A							)}\n"""							)
      # from timeit import timeit
      # print(timeit("nth_fibonacci_matrix(1000000)",
      #              "from main import nth_fibonacci_matrix", number=5))
      # print(timeit("nth_fibonacci_bruteforce(1000000)",
      #              "from main import nth_fibonacci_bruteforce", number=5))
      # 2.3342058970001744
      # 57.256506615000035


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def      lowerCAmelCase_				(  *__A							)  ->       Tuple:







      '''simple docstring'''


      UpperCAmelCase__			=						torch.cuda.device_count()
      print(f"""Device count: {num_processes}"""							)
      with patch_environment(
          world_size=__A,						master_addr="127.0.01",						master_port="29500",						mixed_precision=args[1].mixed_precision							):
            UpperCAmelCase__			=						PrepareForLaunch(__A,						distributed_type="MULTI_GPU"							)
            torch.multiprocessing.start_processes(__A,						args=__A,						nprocs=__A,						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': 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'])



