



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A   			=logging.get_logger(__name__)

__A   			={
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       """gpt_neox"""




			def __init__(   self							: List[Any]       ,       a_							: int=5_04_32       ,       a_							: Optional[Any]=61_44       ,       a_							: Tuple=44       ,       a_							: List[Any]=64       ,       a_							: int=2_45_76       ,       a_							: Optional[int]="gelu"       ,       a_							: str=0.2_5       ,       a_							: str=1_00_00       ,       a_							: List[str]=0.0       ,       a_							: List[Any]=0.0       ,       a_							: Optional[Any]=0.1       ,       a_							: List[Any]=20_48       ,       a_							: Tuple=0.0_2       ,       a_							: Dict=1e-5       ,       a_							: Union[str, Any]=True       ,       a_							: List[Any]=0       ,       a_							: Union[str, Any]=2       ,       a_							: int=False       ,       a_							: Optional[int]=True       ,       a_							: str=None       ,       **a_							: List[Any]       ,       ):
					'''simple docstring'''





					super().__init__(bos_token_id=a_       ,       eos_token_id=a_       ,       **a_ )
					__UpperCAmelCase    :     Dict													=	vocab_size
					__UpperCAmelCase    :     Dict													=	max_position_embeddings
					__UpperCAmelCase    :     int													=	hidden_size
					__UpperCAmelCase    :     Tuple													=	num_hidden_layers
					__UpperCAmelCase    :     Union[str, Any]													=	num_attention_heads
					__UpperCAmelCase    :     List[str]													=	intermediate_size
					__UpperCAmelCase    :     Optional[int]													=	hidden_act
					__UpperCAmelCase    :     Optional[int]													=	rotary_pct
					__UpperCAmelCase    :     Any													=	rotary_emb_base
					__UpperCAmelCase    :     Dict													=	attention_dropout
					__UpperCAmelCase    :     Optional[Any]													=	hidden_dropout
					__UpperCAmelCase    :     int													=	classifier_dropout
					__UpperCAmelCase    :     List[Any]													=	initializer_range
					__UpperCAmelCase    :     List[Any]													=	layer_norm_eps
					__UpperCAmelCase    :     List[str]													=	use_cache
					__UpperCAmelCase    :     Dict													=	tie_word_embeddings
					__UpperCAmelCase    :     Optional[Any]													=	use_parallel_residual
					__UpperCAmelCase    :     List[str]													=	rope_scaling
					self._rope_scaling_validation()

					if self.hidden_size % self.num_attention_heads != 0:
							raise ValueError(
							    '''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )




			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					if self.rope_scaling is None:
							return

					if not isinstance(self.rope_scaling       ,       a_ ) or len(self.rope_scaling ) != 2:
							raise ValueError(
							    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
							    F'got {self.rope_scaling}' )
					__UpperCAmelCase    :     int													=	self.rope_scaling.get('''type'''       ,       a_ )
					__UpperCAmelCase    :     str													=	self.rope_scaling.get('''factor'''       ,       a_ )
					if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
							raise ValueError(
							    F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}' )
					if rope_scaling_factor is None or not isinstance(a_       ,       a_ ) or rope_scaling_factor <= 1.0:
							raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )










import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
				# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
				# setup instructions, if using on-demand hardware

				# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
				# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
				# Throw an error if user passes both BYO and on-demand cluster args
				# Otherwise, use default values
				__A   			=argparse.ArgumentParser()
				parser.add_argument("--user", type=str, default="ubuntu")
				parser.add_argument("--host", type=str, default="localhost")
				parser.add_argument("--key_path", type=str, default=None)
				parser.add_argument("--instance", type=str, default="V100:1")
				parser.add_argument("--provider", type=str, default="cheapest")
				parser.add_argument("--use_spot", type=bool, default=False)
				parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
				__A			,			__A   			=parser.parse_known_args()
				if args.host != "localhost":
								if args.instance != "V100:1" or args.provider != "cheapest":
												raise ValueError("Cannot specify both BYO and on-demand cluster args")
								__A   			=rh.cluster(
								    name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
								)
				else:
								__A   			=rh.cluster(
								    name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
								)
				__A   			=args.example.rsplit("/", 1)[0]

				# Set up remote environment
				cluster.install_packages(["pip:./"])  # Installs transformers from local source
				# Note transformers is copied into the home directory on the remote machine, so we can install from there
				cluster.run([f'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
				cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])

				# Run example. You can bypass the CLI wrapper and paste your own code here.
				cluster.run([f'''python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'''])

				# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
				# from my_script... import train
				# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
				# launch_train_gpu = rh.function(fn=train,
				#                                system=gpu,
				#                                reqs=reqs,
				#                                name='train_bert_glue')
				#
				# We can pass in arguments just like we would to a function:
				# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
				#                  stream_logs=True)





