



import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__A   			=logging.get_logger(__name__)

def        a						(      _UpperCAmelCase							:					Optional[Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	torch.load(_UpperCAmelCase ,   map_location='''cpu'''						)
		if "model" in sd.keys():
				__UpperCAmelCase    :     Dict													=	torch.load(_UpperCAmelCase ,   map_location='''cpu'''						)['''model''']

		# pop unnecessary weights
		__UpperCAmelCase    :     int													=	[
		    '''decoder.version''',
		    '''decoder.output_projection.weight''',
		]
		for key in keys_to_delete:
				if key in sd:
						sd.pop(_UpperCAmelCase						)

		__UpperCAmelCase    :     List[str]													=	{
		    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
		    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
		    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
		    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
		}
		for old_key, new_key in keys_to_rename.items():
				if old_key in sd:
						__UpperCAmelCase    :     Tuple													=	sd.pop(_UpperCAmelCase						)

		__UpperCAmelCase    :     Tuple													=	list(sd.keys()						)
		for key in keys:
				if ".qkv_proj." in key:
						__UpperCAmelCase    :     Tuple													=	sd[key]
						# We split QKV in separate Q,K,V

						__UpperCAmelCase    :     List[str]													=	key.replace('''.qkv_proj.''' ,   '''.q_proj.'''						)
						__UpperCAmelCase    :     List[Any]													=	key.replace('''.qkv_proj.''' ,   '''.k_proj.'''						)
						__UpperCAmelCase    :     List[str]													=	key.replace('''.qkv_proj.''' ,   '''.v_proj.'''						)

						__UpperCAmelCase    :     Optional[Any]													=	value.shape[0]
						assert depth % 3 == 0
						# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
						# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
						__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[Any]													=	torch.split(_UpperCAmelCase ,   depth // 3 ,   dim=0						)

						__UpperCAmelCase    :     Dict													=	q
						__UpperCAmelCase    :     Any													=	k
						__UpperCAmelCase    :     Any													=	v
						del sd[key]

		return sd

@torch.no_grad()
def        a						(      _UpperCAmelCase							:					Union[str, Any] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					Union[str, Any]=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     int													=	load_checkpoint(_UpperCAmelCase						)

		if config is not None:
				__UpperCAmelCase    :     Optional[Any]													=	OPTConfig.from_pretrained(_UpperCAmelCase						)
		else:
				__UpperCAmelCase    :     str													=	OPTConfig()

		__UpperCAmelCase    :     Any													=	OPTModel(_UpperCAmelCase						).half().eval()
		model.load_state_dict(_UpperCAmelCase						)

		# Check results
		Path(_UpperCAmelCase						).mkdir(exist_ok=_UpperCAmelCase						)
		model.save_pretrained(_UpperCAmelCase						)


if __name__ == "__main__":
				__A   			=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--fairseq_path",
				    type=str,
				    help=(
				        "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
				        " https://huggingface.co/models?other=opt_metasq"
				    ),
				)
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
				__A   			=parser.parse_args()
				convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)










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)





