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'])




from __future__ import annotations

UpperCamelCase__       							=      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class   A							:


   def __init__(self  :		Optional[int] ,						__UpperCAmelCase  :		int	)		->       None:
         """simple docstring"""



         UpperCAmelCase__			=						-1
         UpperCAmelCase__			=						-1
         UpperCAmelCase__			=						[-1] * length
         UpperCAmelCase__			=						None
         UpperCAmelCase__			=						None





   def __repr__(self  :		Union[str, Any]	)		->       str:
         """simple docstring"""



         return f"""Node(min_value={self.minn} max_value={self.maxx})"""




def      lowerCAmelCase_				(  __A							)  ->       Node | None:







      '''simple docstring'''


      UpperCAmelCase__			=						Node(len(__A							)							)
      UpperCAmelCase__	,   UpperCAmelCase__			=						min(__A							), max(__A							)
      # Leaf node case where the node contains only one unique value
      if root.minn == root.maxx:
            return root
      UpperCAmelCase__			=						(root.minn + root.maxx) // 2

      UpperCAmelCase__			=						[]
      UpperCAmelCase__			=						[]

      for index, num in enumerate(__A							):
            if num <= pivot:
                  left_arr.append(__A							)
            else:
                  right_arr.append(__A							)
            UpperCAmelCase__			=						len(__A							)
      UpperCAmelCase__			=						build_tree(__A							)
      UpperCAmelCase__			=						build_tree(__A							)
      return root




def      lowerCAmelCase_				(  __A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if index < 0 or node is None:
            return 0
      # Leaf node cases
      if node.minn == node.maxx:
            return index + 1 if node.minn == num else 0
      UpperCAmelCase__			=						(node.minn + node.maxx) // 2
      if num <= pivot:
            # go the left subtree and map index to the left subtree
            return rank_till_index(node.left,						__A,						node.map_left[index] - 1							)
      else:
            # go to the right subtree and map index to the right subtree
            return rank_till_index(node.right,						__A,						index - node.map_left[index]							)




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if start > end:
            return 0
      UpperCAmelCase__			=						rank_till_index(__A,						__A,						__A							)
      UpperCAmelCase__			=						rank_till_index(__A,						__A,						start - 1							)
      return rank_till_end - rank_before_start




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if index > (end - start) or start > end or node is None:
            return -1
      # Leaf node case
      if node.minn == node.maxx:
            return node.minn
      # Number of elements in the left subtree in interval [start, end]
      UpperCAmelCase__			=						node.map_left[end] - (
          node.map_left[start - 1] if start else 0
      )
      if num_elements_in_left_tree > index:
            return quantile(
                node.left,						__A,						(node.map_left[start - 1] if start else 0),						node.map_left[end] - 1,						)
      else:
            return quantile(
                node.right,						index - num_elements_in_left_tree,						start - (node.map_left[start - 1] if start else 0),						end - node.map_left[end],						)




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if (
          start > end
          or node is None
          or start_num > end_num
          or node.minn > end_num
          or node.maxx < start_num
      ):
            return 0
      if start_num <= node.minn and node.maxx <= end_num:
            return end - start + 1
      UpperCAmelCase__			=						range_counting(
          node.left,						(node.map_left[start - 1] if start else 0),						node.map_left[end] - 1,						__A,						__A,						)
      UpperCAmelCase__			=						range_counting(
          node.right,						start - (node.map_left[start - 1] if start else 0),						end - node.map_left[end],						__A,						__A,						)
      return left + right


if __name__ == "__main__":
      import doctest

      doctest.testmod()



