





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment



def 						__A						(       *lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							List[str]						=	torch.cuda.device_count()
		print(f'''Device count: {num_processes}'''					)
		with patch_environment(
		    world_size=lowerCamelCase_	,	master_addr="""127.0.01"""	,	master_port="""29500"""	,	mixed_precision=args[1].mixed_precision					):
				SCREAMING_SNAKE_CASE				:							int						=	PrepareForLaunch(lowerCamelCase_	,	distributed_type="""MULTI_GPU"""					)
				torch.multiprocessing.start_processes(lowerCamelCase_	,	args=lowerCamelCase_	,	nprocs=lowerCamelCase_	,	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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase         =   logging.get_logger(__name__)

__UpperCAmelCase         =   {
    """uw-madison/yoso-4096""": """https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json""",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					'''yoso'''


					def __init__(		self							:	str     ,		lowerCamelCase_							:	Tuple=5_02_65     ,		lowerCamelCase_							:	int=7_68     ,		lowerCamelCase_							:	Optional[Any]=12     ,		lowerCamelCase_							:	int=12     ,		lowerCamelCase_							:	Optional[Any]=30_72     ,		lowerCamelCase_							:	str="gelu"     ,		lowerCamelCase_							:	Union[str, Any]=0.1     ,		lowerCamelCase_							:	int=0.1     ,		lowerCamelCase_							:	Optional[int]=40_96     ,		lowerCamelCase_							:	str=1     ,		lowerCamelCase_							:	str=0.02     ,		lowerCamelCase_							:	Dict=1e-12     ,		lowerCamelCase_							:	Tuple="absolute"     ,		lowerCamelCase_							:	Any=True     ,		lowerCamelCase_							:	Dict=9     ,		lowerCamelCase_							:	Any=64     ,		lowerCamelCase_							:	Union[str, Any]=None     ,		lowerCamelCase_							:	List[str]=True     ,		lowerCamelCase_							:	Dict=True     ,		lowerCamelCase_							:	int=1     ,		lowerCamelCase_							:	Tuple=0     ,		lowerCamelCase_							:	Dict=2     ,		**lowerCamelCase_							:	Tuple     ,		):

							'''simple docstring'''



							super().__init__(pad_token_id=lowerCamelCase_     ,		bos_token_id=lowerCamelCase_     ,		eos_token_id=lowerCamelCase_     ,		**lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							int						=	vocab_size
							SCREAMING_SNAKE_CASE				:							List[Any]						=	max_position_embeddings
							SCREAMING_SNAKE_CASE				:							Tuple						=	hidden_size
							SCREAMING_SNAKE_CASE				:							Dict						=	num_hidden_layers
							SCREAMING_SNAKE_CASE				:							Any						=	num_attention_heads
							SCREAMING_SNAKE_CASE				:							int						=	intermediate_size
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	hidden_act
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	hidden_dropout_prob
							SCREAMING_SNAKE_CASE				:							List[Any]						=	attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE				:							str						=	initializer_range
							SCREAMING_SNAKE_CASE				:							List[str]						=	type_vocab_size
							SCREAMING_SNAKE_CASE				:							List[str]						=	layer_norm_eps
							SCREAMING_SNAKE_CASE				:							Any						=	position_embedding_type
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	use_expectation
							SCREAMING_SNAKE_CASE				:							int						=	hash_code_len
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	num_hash
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	conv_window
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	use_fast_hash
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	lsh_backward



