




'''simple docstring'''





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment


def   UpperCamelCase_(			*snake_case							: Tuple  ):

							'''simple docstring'''






							snake_case_			      =   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  ):
														snake_case_			      =   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
						_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				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							:     Optional[Any]   	=				rh.function(fn=launch_train, system=gpu, reqs=reqs, name="train_bert_glue")

						# Define train args/config, run train function
						_SCREAMING_SNAKE_CASE							:     str   	=				argparse.Namespace(cpu=False, mixed_precision="fp16")
						_SCREAMING_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'])










'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 				_snake_case	(			unittest.TestCase							):




			@slow
			def      lowerCAmelCase__       (      self							)       ->       List[Any]:

										'''simple docstring'''




										snake_case_			      =   CamembertModel.from_pretrained("camembert-base"							)
										model.to(a__							)

										snake_case_			      =   torch.tensor(
										    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]       ,							device=a__       ,							dtype=torch.long       ,							)  # J'aime le camembert !
										with torch.no_grad():
																	snake_case_			      =   model(a__							)["last_hidden_state"]
										snake_case_			      =   torch.Size((1, 10, 768)							)
										self.assertEqual(output.shape       ,							a__							)
										# compare the actual values for a slice.
										snake_case_			      =   torch.tensor(
										    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]       ,							device=a__       ,							dtype=torch.float       ,							)
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(torch.allclose(output[:, :3, :3]       ,							a__       ,							atol=1e-4							)							)




