





"""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       (*SCREAMING_SNAKE_CASE    ):
						UpperCamelCase							:		Tuple							      = 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							:		Tuple							      = 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
		__magic_name__					:				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
		__magic_name__					:				str	= [
		    """pip:./""",
		    """transformers""",
		    """datasets""",
		    """evaluate""",
		    """tqdm""",
		    """scipy""",
		    """scikit-learn""",
		    """tensorboard""",
		    """torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117""",
		]
		__magic_name__					:				Dict	= rh.function(fn=launch_train, system=gpu, reqs=reqs, name="""train_bert_glue""")

		# Define train args/config, run train function
		__magic_name__					:				Any	= argparse.Namespace(cpu=False, mixed_precision="""fp16""")
		__magic_name__					:				Optional[int]	= {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
		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


__magic_name__					:				Any	= logging.get_logger(__name__)

__magic_name__					:				Dict	= {
    """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        lowercase__ (      __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""
			__lowerCAmelCase    :							Optional[int]								=     """yoso"""







			def __init__(			self	,      _A=5_0_2_6_5	,      _A=7_6_8	,      _A=1_2	,      _A=1_2	,      _A=3_0_7_2	,      _A="gelu"	,      _A=0.1	,      _A=0.1	,      _A=4_0_9_6	,      _A=1	,      _A=0.02	,      _A=1e-1_2	,      _A="absolute"	,      _A=True	,      _A=9	,      _A=6_4	,      _A=None	,      _A=True	,      _A=True	,      _A=1	,      _A=0	,      _A=2	,      **_A	,      ):






									'''simple docstring'''







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

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



