





'''simple docstring'''







import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment





def 							lowerCamelCase_   (	*SCREAMING_SNAKE_CASE__   :      Optional[int] )			->						Any:
						UpperCAmelCase_      :   List[str]      =	torch.cuda.device_count()
						print(F"""Device count: {num_processes}""" )
						with patch_environment(
						    world_size=SCREAMING_SNAKE_CASE__,      master_addr='''127.0.01''',      master_port='''29500''',      mixed_precision=args[1].mixed_precision ):
												UpperCAmelCase_      :   str      =	PrepareForLaunch(SCREAMING_SNAKE_CASE__,      distributed_type='''MULTI_GPU''' )
												torch.multiprocessing.start_processes(SCREAMING_SNAKE_CASE__,      args=SCREAMING_SNAKE_CASE__,      nprocs=SCREAMING_SNAKE_CASE__,      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_  :							Dict            =	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_  :							int            =	[
					    "pip:./",
					    "transformers",
					    "datasets",
					    "evaluate",
					    "tqdm",
					    "scipy",
					    "scikit-learn",
					    "tensorboard",
					    "torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117",
					]
					snake_case_  :							int            =	rh.function(fn=launch_train, system=gpu, reqs=reqs, name="train_bert_glue")

					# Define train args/config, run train function
					snake_case_  :							List[Any]            =	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'''







import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
					snake_case_  :							Union[str, Any]            =	[]

					snake_case_  :							List[Any]            =	os.path.join("tests", "models", "**", "test_modeling_*.py")
					snake_case_  :							int            =	glob.glob(pattern)
					# TODO: deal with TF/Flax too
					snake_case_  :							Optional[int]            =	[
					    x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_"))
					]

					for test_file in test_files:
										snake_case_  :							Any            =	get_tester_classes(test_file)
										for tester_class in tester_classes:
															# A few tester classes don't have `parent` parameter in `__init__`.
															# TODO: deal this better
															try:
																				snake_case_  :							Tuple            =	tester_class(parent=None)
															except Exception:
																				continue
															if hasattr(tester, "get_config"):
																				snake_case_  :							Any            =	tester.get_config()
																				for k, v in config.to_dict().items():
																									if isinstance(v, int):
																														snake_case_  :							int            =	None
																														if k in ["vocab_size"]:
																																			snake_case_  :							Optional[Any]            =	1_00
																														elif k in ["max_position_embeddings"]:
																																			snake_case_  :							Any            =	1_28
																														elif k in ["hidden_size", "d_model"]:
																																			snake_case_  :							str            =	40
																														elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																			snake_case_  :							str            =	5
																														if target is not None and v > target:
																																			failures.append(
																																			    f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
																																			    f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
																																			    f''' than {target}.'''
																																			)

					if len(failures) > 0:
										raise Exception(f'''There were {len(failures)} failures:\n''' + "\n".join(failures))





