



'''simple docstring'''
import numpy as np






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							):
						_UpperCamelCase      :	str				       =     n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						_UpperCamelCase      :	Optional[int]				       =     np.floor(UpperCAmelCase_							)
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						_UpperCamelCase      :	Optional[int]				       =     int(n_draws - floored.sum()							)
						if need_to_add > 0:
												_UpperCamelCase      :	Dict				       =     continuous - floored
												_UpperCamelCase      :	Tuple				       =     np.sort(np.unique(UpperCAmelCase_							)							)[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((_UpperCamelCase)       ,    )      :	Union[str, Any]				       =     np.where(remainder == value							)
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		_UpperCamelCase      :	List[Any]				       =     min(len(UpperCAmelCase_							)						,						UpperCAmelCase_							)
																		_UpperCamelCase      :	Tuple				       =     rng.choice(UpperCAmelCase_						,						size=UpperCAmelCase_						,						replace=UpperCAmelCase_							)
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa							)






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_=1_0							):
						_UpperCamelCase       ,    _UpperCamelCase      :	List[Any]				       =     np.unique(UpperCAmelCase_						,						return_inverse=UpperCAmelCase_							)
						_UpperCamelCase      :	List[str]				       =     classes.shape[0]
						_UpperCamelCase      :	Union[str, Any]				       =     np.bincount(UpperCAmelCase_							)
						if np.min(UpperCAmelCase_							) < 2:
												raise ValueError('Minimum class count error'							)
						if n_train < n_classes:
												raise ValueError(
												    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)							)
						if n_test < n_classes:
												raise ValueError(
												    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)							)
						_UpperCamelCase      :	Optional[int]				       =     np.split(np.argsort(UpperCAmelCase_						,						kind='mergesort'							)						,						np.cumsum(UpperCAmelCase_							)[:-1]							)
						for _ in range(UpperCAmelCase_							):
												_UpperCamelCase      :	Optional[Any]				       =     approximate_mode(UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							)
												_UpperCamelCase      :	Dict				       =     class_counts - n_i
												_UpperCamelCase      :	Tuple				       =     approximate_mode(UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							)

												_UpperCamelCase      :	Tuple				       =     []
												_UpperCamelCase      :	str				       =     []

												for i in range(UpperCAmelCase_							):
																		_UpperCamelCase      :	List[Any]				       =     rng.permutation(class_counts[i]							)
																		_UpperCamelCase      :	Optional[int]				       =     class_indices[i].take(UpperCAmelCase_						,						mode='clip'							)
																		train.extend(perm_indices_class_i[: n_i[i]]							)
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]							)
												_UpperCamelCase      :	str				       =     rng.permutation(UpperCAmelCase_							)
												_UpperCamelCase      :	Union[str, Any]				       =     rng.permutation(UpperCAmelCase_							)

												yield train, test







'''simple docstring'''
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def A__					(  *UpperCAmelCase_							):
						_UpperCamelCase      :	str				       =     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      :	Optional[int]				       =     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[Any]						 =						[
					    'pip:./',
					    'transformers',
					    'datasets',
					    'evaluate',
					    'tqdm',
					    'scipy',
					    'scikit-learn',
					    'tensorboard',
					    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
					]
					snake_case_				:			Union[str, Any]						 =						rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

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


