from __future__ import annotations

from collections.abc import Callable




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A = 100,						)  ->       float:







      '''simple docstring'''


      UpperCAmelCase__			=						x_start
      UpperCAmelCase__			=						fnc(__A							)
      UpperCAmelCase__			=						0.0

      for _ in range(__A							):
            # Approximates small segments of curve as linear and solve
            # for trapezoidal area
            UpperCAmelCase__			=						(x_end - x_start) / steps + xa
            UpperCAmelCase__			=						fnc(__A							)
            area += abs(fxa + fxa							) * (xa - xa) / 2

            # Increment step
            UpperCAmelCase__			=						xa
            UpperCAmelCase__			=						fxa
      return area


if __name__ == "__main__":




      def      lowerCAmelCase_				(  __A							)  ->       Dict:







            '''simple docstring'''


            return x**3

      print('f(x) = x^3')
      print('The area between the curve, x = -10, x = 10 and the x axis is:')
      UpperCamelCase__       							=      1_0
      while i <= 1_0_0_0_0_0:
            UpperCamelCase__       							=      trapezoidal_area(f, -5, 5, i)
            print(f'''with {i} steps: {area}''')
            i *= 1_0




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'])



