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

						# Define train args/config, run train function
						_snake_case					: Dict     			=					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'])

"""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						)






