
"""simple docstring"""






import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def 		_snake_case			(      *lowercase__						:    Dict						)    ->							Optional[Any]:


			'''simple docstring'''





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






from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
				import torch

__UpperCAmelCase 					=    logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
				from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






				def 		_snake_case			(      cls						:    Tuple						)    ->							List[Any]:


							'''simple docstring'''





							return cls






def 		_snake_case			(      lowercase__						:    Union[Tuple, List] ,	lowercase__						:    Optional[Union[List["torch.Generator"], "torch.Generator"]] = None ,	lowercase__						:    Optional["torch.device"] = None ,	lowercase__						:    Optional["torch.dtype"] = None ,	lowercase__						:    Optional["torch.layout"] = None ,	)    ->							Optional[Any]:


			'''simple docstring'''





			lowerCAmelCase_       :Any     	=    device
			lowerCAmelCase_       :Any     	=    shape[0]

			lowerCAmelCase_       :Dict     	=    layout or torch.strided
			lowerCAmelCase_       :List[str]     	=    device or torch.device("""cpu"""						)

			if generator is not None:
						lowerCAmelCase_       :Optional[Any]     	=    generator.device.type if not isinstance(lowercase__ ,	lowercase__						) else generator[0].device.type
						if gen_device_type != device.type and gen_device_type == "cpu":
									lowerCAmelCase_       :Optional[int]     	=    """cpu"""
									if device != "mps":
												logger.info(
												    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
												    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
												    f""" slighly speed up this function by passing a generator that was created on the {device} device."""						)
						elif gen_device_type != device.type and gen_device_type == "cuda":
									raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""						)

			if isinstance(lowercase__ ,	lowercase__						):
						lowerCAmelCase_       :List[str]     	=    (1,) + shape[1:]
						lowerCAmelCase_       :Optional[Any]     	=    [
						    torch.randn(lowercase__ ,	generator=generator[i] ,	device=lowercase__ ,	dtype=lowercase__ ,	layout=lowercase__						)
						    for i in range(lowercase__						)
						]
						lowerCAmelCase_       :str     	=    torch.cat(lowercase__ ,	dim=0						).to(lowercase__						)
			else:
						lowerCAmelCase_       :Dict     	=    torch.randn(lowercase__ ,	generator=lowercase__ ,	device=lowercase__ ,	dtype=lowercase__ ,	layout=lowercase__						).to(lowercase__						)

			return latents






def 		_snake_case			(      lowercase__						:    Union[str, Any]						)    ->							int:


			'''simple docstring'''





			if is_torch_version("""<""" ,	"""2.0.0"""						) or not hasattr(lowercase__ ,	"""_dynamo"""						):
						return False
			return isinstance(lowercase__ ,	torch._dynamo.eval_frame.OptimizedModule						)






