




from __future__ import annotations

from random import choice





def 						__A						(      __lowerCamelCase       ) ->			List[Any]:
	return choice(__lowerCamelCase       )





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			int:
	a									=				random_pivot(__lowerCamelCase       )

	# partition based on pivot
	# linear time
	a									=				[e for e in lst if e < pivot]
	a									=				[e for e in lst if e > pivot]

	# if we get lucky, pivot might be the element we want.
	# we can easily see this:
	# small (elements smaller than k)
	# + pivot (kth element)
	# + big (elements larger than k)
	if len(__lowerCamelCase       ) == k - 1:
		return pivot
	# pivot is in elements bigger than k
	elif len(__lowerCamelCase       ) < k - 1:
		return kth_number(__lowerCamelCase				,     k - len(__lowerCamelCase       ) - 1       )
	# pivot is in elements smaller than k
	else:
		return kth_number(__lowerCamelCase				,     __lowerCamelCase       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()







import operator as op


__UpperCamelCase   :			Optional[Any]          = "scaler.pt"
__UpperCamelCase   :			Optional[Any]          = "pytorch_model"
__UpperCamelCase   :			str          = "random_states"
__UpperCamelCase   :			Optional[int]          = "optimizer"
__UpperCamelCase   :			Optional[int]          = "scheduler"
__UpperCamelCase   :			str          = "pytorch_model.bin"
__UpperCamelCase   :			List[str]          = "pytorch_model.bin.index.json"
__UpperCamelCase   :			List[str]          = "model.safetensors"
__UpperCamelCase   :			Optional[int]          = "model.safetensors.index.json"
__UpperCamelCase   :			List[str]          = "1.10.2"
__UpperCamelCase   :			Dict          = "py38"
__UpperCamelCase   :			List[str]          = "4.17.0"
__UpperCamelCase   :			Any          = ["ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4dn.24xlarge"]
__UpperCamelCase   :			Any          = ["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD", "HYBRID_SHARD", "HYBRID_SHARD_ZERO2"]
__UpperCamelCase   :			int          = ["TRANSFORMER_BASED_WRAP", "SIZE_BASED_WRAP", "NO_WRAP"]
__UpperCamelCase   :			Dict          = ["BACKWARD_PRE", "BACKWARD_POST", "NO_PREFETCH"]
__UpperCamelCase   :			str          = ["FULL_STATE_DICT", "LOCAL_STATE_DICT", "SHARDED_STATE_DICT"]
__UpperCamelCase   :			List[Any]          = "2.0.1"
__UpperCamelCase   :			int          = ["pdsh", "standard", "openmpi", "mvapich"]
__UpperCamelCase   :			List[str]          = ["default", "reduce-overhead", "max-autotune"]

__UpperCamelCase   :			List[Any]          = {">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
__UpperCamelCase   :			List[Any]          = [
    "nnodes",
    "nproc_per_node",
    "rdzv_backend",
    "rdzv_endpoint",
    "rdzv_id",
    "rdzv_conf",
    "standalone",
    "max_restarts",
    "monitor_interval",
    "start_method",
    "role",
    "module",
    "m",
    "no_python",
    "run_path",
    "log_dir",
    "r",
    "redirects",
    "t",
    "tee",
    "node_rank",
    "master_addr",
    "master_port",
]

__UpperCamelCase   :			List[str]          = ["DEEPSPEED", "MULTI_GPU", "FSDP", "MEGATRON_LM"]
__UpperCamelCase   :			int          = ["DEEPSPEED", "MULTI_XPU", "FSDP"]

