# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""The arguments of the server."""

import argparse
import dataclasses
import logging
import random
import tempfile
from typing import List, Optional

from sglang.srt.utils import (
    get_amdgpu_memory_capacity,
    get_nvgpu_memory_capacity,
    is_flashinfer_available,
    is_hip,
    is_ipv6,
    is_port_available,
)

logger = logging.getLogger(__name__)


@dataclasses.dataclass
class ServerArgs:
    # Model and tokenizer
    model_path: str
    tokenizer_path: Optional[str] = None
    tokenizer_mode: str = "auto"
    skip_tokenizer_init: bool = False
    load_format: str = "auto"
    trust_remote_code: bool = True
    dtype: str = "auto"
    kv_cache_dtype: str = "auto"
    quantization: Optional[str] = None
    context_length: Optional[int] = None
    device: str = "cuda"
    served_model_name: Optional[str] = None
    chat_template: Optional[str] = None
    is_embedding: bool = False

    # Port
    host: str = "127.0.0.1"
    port: int = 30000

    # Memory and scheduling
    mem_fraction_static: Optional[float] = None
    max_running_requests: Optional[int] = None
    max_total_tokens: Optional[int] = None
    chunked_prefill_size: int = 8192
    max_prefill_tokens: int = 16384
    schedule_policy: str = "lpm"
    schedule_conservativeness: float = 1.0
    cpu_offload_gb: int = 0

    # Other runtime options
    tp_size: int = 1
    stream_interval: int = 1
    random_seed: Optional[int] = None
    constrained_json_whitespace_pattern: Optional[str] = None
    watchdog_timeout: float = 300
    download_dir: Optional[str] = None
    base_gpu_id: int = 0

    # Logging
    log_level: str = "info"
    log_level_http: Optional[str] = None
    log_requests: bool = False
    show_time_cost: bool = False
    enable_metrics: bool = False
    decode_log_interval: int = 40

    # API related
    api_key: Optional[str] = None
    file_storage_pth: str = "SGLang_storage"
    enable_cache_report: bool = False

    # Data parallelism
    dp_size: int = 1
    load_balance_method: str = "round_robin"

    # Multi-node distributed serving
    dist_init_addr: Optional[str] = None
    nnodes: int = 1
    node_rank: int = 0

    # Model override args in JSON
    json_model_override_args: str = "{}"

    # Double Sparsity
    enable_double_sparsity: bool = False
    ds_channel_config_path: str = None
    ds_heavy_channel_num: int = 32
    ds_heavy_token_num: int = 256
    ds_heavy_channel_type: str = "qk"
    ds_sparse_decode_threshold: int = 4096

    # LoRA
    lora_paths: Optional[List[str]] = None
    max_loras_per_batch: int = 8

    # Kernel backend
    attention_backend: Optional[str] = None
    sampling_backend: Optional[str] = None
    grammar_backend: Optional[str] = "outlines"

    # Optimization/debug options
    disable_radix_cache: bool = False
    disable_jump_forward: bool = False
    disable_cuda_graph: bool = False
    disable_cuda_graph_padding: bool = False
    disable_disk_cache: bool = False
    disable_custom_all_reduce: bool = False
    disable_mla: bool = False
    disable_overlap_schedule: bool = False
    enable_mixed_chunk: bool = False
    enable_dp_attention: bool = False
    enable_torch_compile: bool = False
    torch_compile_max_bs: int = 32
    cuda_graph_max_bs: int = 160
    torchao_config: str = ""
    enable_nan_detection: bool = False
    enable_p2p_check: bool = False
    triton_attention_reduce_in_fp32: bool = False
    num_continuous_decode_steps: int = 1
    delete_ckpt_after_loading: bool = False

    def __post_init__(self):
        # Set missing default values
        if self.tokenizer_path is None:
            self.tokenizer_path = self.model_path

        if self.served_model_name is None:
            self.served_model_name = self.model_path

        if self.chunked_prefill_size is not None and self.chunked_prefill_size <= 0:
            # Disable chunked prefill
            self.chunked_prefill_size = None

        if self.random_seed is None:
            self.random_seed = random.randint(0, 1 << 30)

        # Mem fraction depends on the tensor parallelism size
        if self.mem_fraction_static is None:
            if self.tp_size >= 16:
                self.mem_fraction_static = 0.79
            elif self.tp_size >= 8:
                self.mem_fraction_static = 0.82
            elif self.tp_size >= 4:
                self.mem_fraction_static = 0.85
            elif self.tp_size >= 2:
                self.mem_fraction_static = 0.87
            else:
                self.mem_fraction_static = 0.88

        # Adjust for GPUs with small memory capacities
        if is_hip():
            gpu_mem = get_amdgpu_memory_capacity()
        else:
            gpu_mem = get_nvgpu_memory_capacity()
        if gpu_mem < 25000:
            self.chunked_prefill_size //= 4  # make it 2048
            self.cuda_graph_max_bs = 4
            logger.info("Automatically adjust --chunked-prefill-size for small GPUs.")

        # Choose kernel backends
        if not is_flashinfer_available():
            self.attention_backend = "triton"
            self.sampling_backend = "pytorch"

        if self.attention_backend is None:
            self.attention_backend = "flashinfer"
        if self.sampling_backend is None:
            self.sampling_backend = "flashinfer"

        # Others
        if self.enable_dp_attention:
            self.dp_size = self.tp_size
            self.chunked_prefill_size = self.chunked_prefill_size // 2
            self.cuda_graph_max_bs = min(self.cuda_graph_max_bs, 96)
            self.schedule_conservativeness = self.schedule_conservativeness * 0.3
            self.disable_overlap_schedule = True
            logger.info(
                f"DP attention is enabled. The chunked prefill size is adjusted to {self.chunked_prefill_size} to avoid MoE kernel issues. "
                f"The CUDA graph max batch size is adjusted to {self.cuda_graph_max_bs}. "
                f"The schedule conservativeness is adjusted to {self.schedule_conservativeness}. "
                "Data parallel size is adjusted to be the same as tensor parallel size. "
                "Overlap schedule is disabled."
            )

    @staticmethod
    def add_cli_args(parser: argparse.ArgumentParser):
        # Model and port args
        parser.add_argument(
            "--model-path",
            type=str,
            help="The path of the model weights. This can be a local folder or a Hugging Face repo ID.",
            required=True,
        )
        parser.add_argument(
            "--tokenizer-path",
            type=str,
            default=ServerArgs.tokenizer_path,
            help="The path of the tokenizer.",
        )
        parser.add_argument(
            "--host", type=str, default=ServerArgs.host, help="The host of the server."
        )
        parser.add_argument(
            "--port", type=int, default=ServerArgs.port, help="The port of the server."
        )
        parser.add_argument(
            "--tokenizer-mode",
            type=str,
            default=ServerArgs.tokenizer_mode,
            choices=["auto", "slow"],
            help="Tokenizer mode. 'auto' will use the fast "
            "tokenizer if available, and 'slow' will "
            "always use the slow tokenizer.",
        )
        parser.add_argument(
            "--skip-tokenizer-init",
            action="store_true",
            help="If set, skip init tokenizer and pass input_ids in generate request",
        )
        parser.add_argument(
            "--load-format",
            type=str,
            default=ServerArgs.load_format,
            choices=["auto", "pt", "safetensors", "npcache", "dummy"],
            help="The format of the model weights to load. "
            '"auto" will try to load the weights in the safetensors format '
            "and fall back to the pytorch bin format if safetensors format "
            "is not available. "
            '"pt" will load the weights in the pytorch bin format. '
            '"safetensors" will load the weights in the safetensors format. '
            '"npcache" will load the weights in pytorch format and store '
            "a numpy cache to speed up the loading. "
            '"dummy" will initialize the weights with random values, '
            "which is mainly for profiling.",
        )
        parser.add_argument(
            "--trust-remote-code",
            action="store_true",
            help="Whether or not to allow for custom models defined on the Hub in their own modeling files.",
        )
        parser.add_argument(
            "--dtype",
            type=str,
            default=ServerArgs.dtype,
            choices=["auto", "half", "float16", "bfloat16", "float", "float32"],
            help="Data type for model weights and activations.\n\n"
            '* "auto" will use FP16 precision for FP32 and FP16 models, and '
            "BF16 precision for BF16 models.\n"
            '* "half" for FP16. Recommended for AWQ quantization.\n'
            '* "float16" is the same as "half".\n'
            '* "bfloat16" for a balance between precision and range.\n'
            '* "float" is shorthand for FP32 precision.\n'
            '* "float32" for FP32 precision.',
        )
        parser.add_argument(
            "--kv-cache-dtype",
            type=str,
            default=ServerArgs.kv_cache_dtype,
            choices=["auto", "fp8_e5m2"],
            help='Data type for kv cache storage. "auto" will use model data type. "fp8_e5m2" is supported for CUDA 11.8+.',
        )
        parser.add_argument(
            "--quantization",
            type=str,
            default=ServerArgs.quantization,
            choices=[
                "awq",
                "fp8",
                "gptq",
                "marlin",
                "gptq_marlin",
                "awq_marlin",
                "bitsandbytes",
            ],
            help="The quantization method.",
        )
        parser.add_argument(
            "--context-length",
            type=int,
            default=ServerArgs.context_length,
            help="The model's maximum context length. Defaults to None (will use the value from the model's config.json instead).",
        )
        parser.add_argument(
            "--device",
            type=str,
            default="cuda",
            choices=["cuda", "xpu", "hpu"],
            help="The device type.",
        )
        parser.add_argument(
            "--served-model-name",
            type=str,
            default=ServerArgs.served_model_name,
            help="Override the model name returned by the v1/models endpoint in OpenAI API server.",
        )
        parser.add_argument(
            "--chat-template",
            type=str,
            default=ServerArgs.chat_template,
            help="The buliltin chat template name or the path of the chat template file. This is only used for OpenAI-compatible API server.",
        )
        parser.add_argument(
            "--is-embedding",
            action="store_true",
            help="Whether to use a CausalLM as an embedding model.",
        )

        # Memory and scheduling
        parser.add_argument(
            "--mem-fraction-static",
            type=float,
            default=ServerArgs.mem_fraction_static,
            help="The fraction of the memory used for static allocation (model weights and KV cache memory pool). Use a smaller value if you see out-of-memory errors.",
        )
        parser.add_argument(
            "--max-running-requests",
            type=int,
            default=ServerArgs.max_running_requests,
            help="The maximum number of running requests.",
        )
        parser.add_argument(
            "--max-total-tokens",
            type=int,
            default=ServerArgs.max_total_tokens,
            help="The maximum number of tokens in the memory pool. If not specified, it will be automatically calculated based on the memory usage fraction. "
            "This option is typically used for development and debugging purposes.",
        )
        parser.add_argument(
            "--chunked-prefill-size",
            type=int,
            default=ServerArgs.chunked_prefill_size,
            help="The maximum number of tokens in a chunk for the chunked prefill. Setting this to -1 means disabling chunked prefill",
        )
        parser.add_argument(
            "--max-prefill-tokens",
            type=int,
            default=ServerArgs.max_prefill_tokens,
            help="The maximum number of tokens in a prefill batch. The real bound will be the maximum of this value and the model's maximum context length.",
        )
        parser.add_argument(
            "--schedule-policy",
            type=str,
            default=ServerArgs.schedule_policy,
            choices=["lpm", "random", "fcfs", "dfs-weight"],
            help="The scheduling policy of the requests.",
        )
        parser.add_argument(
            "--schedule-conservativeness",
            type=float,
            default=ServerArgs.schedule_conservativeness,
            help="How conservative the schedule policy is. A larger value means more conservative scheduling. Use a larger value if you see requests being retracted frequently.",
        )

        parser.add_argument(
            "--cpu-offload-gb",
            type=int,
            default=ServerArgs.cpu_offload_gb,
            help="How many GBs of RAM to reserve for CPU offloading",
        )

        # Other runtime options
        parser.add_argument(
            "--tensor-parallel-size",
            "--tp-size",
            type=int,
            default=ServerArgs.tp_size,
            help="The tensor parallelism size.",
        )
        parser.add_argument(
            "--stream-interval",
            type=int,
            default=ServerArgs.stream_interval,
            help="The interval (or buffer size) for streaming in terms of the token length. A smaller value makes streaming smoother, while a larger value makes the throughput higher",
        )
        parser.add_argument(
            "--random-seed",
            type=int,
            default=ServerArgs.random_seed,
            help="The random seed.",
        )
        parser.add_argument(
            "--constrained-json-whitespace-pattern",
            type=str,
            default=ServerArgs.constrained_json_whitespace_pattern,
            help=r"Regex pattern for syntactic whitespaces allowed in JSON constrained output. For example, to allow the model generate consecutive whitespaces, set the pattern to [\n\t ]*",
        )
        parser.add_argument(
            "--watchdog-timeout",
            type=float,
            default=ServerArgs.watchdog_timeout,
            help="Set watchdog timeout in seconds. If a forward batch takes longer than this, the server will crash to prevent hanging.",
        )
        parser.add_argument(
            "--download-dir",
            type=str,
            default=ServerArgs.download_dir,
            help="Model download directory.",
        )
        parser.add_argument(
            "--base-gpu-id",
            type=int,
            default=ServerArgs.base_gpu_id,
            help="The base GPU ID to start allocating GPUs from. Useful when running multiple instances on the same machine.",
        )

        # Logging
        parser.add_argument(
            "--log-level",
            type=str,
            default=ServerArgs.log_level,
            help="The logging level of all loggers.",
        )
        parser.add_argument(
            "--log-level-http",
            type=str,
            default=ServerArgs.log_level_http,
            help="The logging level of HTTP server. If not set, reuse --log-level by default.",
        )
        parser.add_argument(
            "--log-requests",
            action="store_true",
            help="Log the inputs and outputs of all requests.",
        )
        parser.add_argument(
            "--show-time-cost",
            action="store_true",
            help="Show time cost of custom marks.",
        )
        parser.add_argument(
            "--enable-metrics",
            action="store_true",
            help="Enable log prometheus metrics.",
        )
        parser.add_argument(
            "--decode-log-interval",
            type=int,
            default=ServerArgs.decode_log_interval,
            help="The log interval of decode batch",
        )

        # API related
        parser.add_argument(
            "--api-key",
            type=str,
            default=ServerArgs.api_key,
            help="Set API key of the server. It is also used in the OpenAI API compatible server.",
        )
        parser.add_argument(
            "--file-storage-pth",
            type=str,
            default=ServerArgs.file_storage_pth,
            help="The path of the file storage in backend.",
        )
        parser.add_argument(
            "--enable-cache-report",
            action="store_true",
            help="Return number of cached tokens in usage.prompt_tokens_details for each openai request.",
        )

        # Data parallelism
        parser.add_argument(
            "--data-parallel-size",
            "--dp-size",
            type=int,
            default=ServerArgs.dp_size,
            help="The data parallelism size.",
        )
        parser.add_argument(
            "--load-balance-method",
            type=str,
            default=ServerArgs.load_balance_method,
            help="The load balancing strategy for data parallelism.",
            choices=[
                "round_robin",
                "shortest_queue",
            ],
        )

        # Multi-node distributed serving
        parser.add_argument(
            "--dist-init-addr",
            "--nccl-init-addr",  # For backward compatbility. This will be removed in the future.
            type=str,
            help="The host address for initializing distributed backend (e.g., `192.168.0.2:25000`).",
        )
        parser.add_argument(
            "--nnodes", type=int, default=ServerArgs.nnodes, help="The number of nodes."
        )
        parser.add_argument(
            "--node-rank", type=int, default=ServerArgs.node_rank, help="The node rank."
        )

        # Model override args
        parser.add_argument(
            "--json-model-override-args",
            type=str,
            help="A dictionary in JSON string format used to override default model configurations.",
            default=ServerArgs.json_model_override_args,
        )

        # Double Sparsity
        parser.add_argument(
            "--enable-double-sparsity",
            action="store_true",
            help="Enable double sparsity attention",
        )
        parser.add_argument(
            "--ds-channel-config-path",
            type=str,
            default=ServerArgs.ds_channel_config_path,
            help="The path of the double sparsity channel config",
        )
        parser.add_argument(
            "--ds-heavy-channel-num",
            type=int,
            default=ServerArgs.ds_heavy_channel_num,
            help="The number of heavy channels in double sparsity attention",
        )
        parser.add_argument(
            "--ds-heavy-token-num",
            type=int,
            default=ServerArgs.ds_heavy_token_num,
            help="The number of heavy tokens in double sparsity attention",
        )
        parser.add_argument(
            "--ds-heavy-channel-type",
            type=str,
            default=ServerArgs.ds_heavy_channel_type,
            help="The type of heavy channels in double sparsity attention",
        )
        parser.add_argument(
            "--ds-sparse-decode-threshold",
            type=int,
            default=ServerArgs.ds_sparse_decode_threshold,
            help="The type of heavy channels in double sparsity attention",
        )

        # LoRA
        parser.add_argument(
            "--lora-paths",
            type=str,
            nargs="*",
            default=None,
            action=LoRAPathAction,
            help="The list of LoRA adapters. You can provide a list of either path in str or renamed path in the format {name}={path}",
        )
        parser.add_argument(
            "--max-loras-per-batch",
            type=int,
            default=8,
            help="Maximum number of adapters for a running batch, include base-only request",
        )

        # Kernel backend
        parser.add_argument(
            "--attention-backend",
            type=str,
            choices=["flashinfer", "triton"],
            default=ServerArgs.attention_backend,
            help="Choose the kernels for attention layers.",
        )
        parser.add_argument(
            "--sampling-backend",
            type=str,
            choices=["flashinfer", "pytorch"],
            default=ServerArgs.sampling_backend,
            help="Choose the kernels for sampling layers.",
        )
        parser.add_argument(
            "--grammar-backend",
            type=str,
            choices=["xgrammar", "outlines"],
            default=ServerArgs.grammar_backend,
            help="Choose the backend for grammar-guided decoding.",
        )

        # Optimization/debug options
        parser.add_argument(
            "--disable-radix-cache",
            action="store_true",
            help="Disable RadixAttention for prefix caching.",
        )
        parser.add_argument(
            "--disable-jump-forward",
            action="store_true",
            help="Disable jump-forward for grammar-guided decoding.",
        )
        parser.add_argument(
            "--disable-cuda-graph",
            action="store_true",
            help="Disable cuda graph.",
        )
        parser.add_argument(
            "--disable-cuda-graph-padding",
            action="store_true",
            help="Disable cuda graph when padding is needed. Still uses cuda graph when padding is not needed.",
        )
        parser.add_argument(
            "--disable-disk-cache",
            action="store_true",
            help="Disable disk cache to avoid possible crashes related to file system or high concurrency.",
        )
        parser.add_argument(
            "--disable-custom-all-reduce",
            action="store_true",
            help="Disable the custom all-reduce kernel and fall back to NCCL.",
        )
        parser.add_argument(
            "--disable-mla",
            action="store_true",
            help="Disable Multi-head Latent Attention (MLA) for DeepSeek-V2.",
        )
        parser.add_argument(
            "--disable-nan-detection",
            action="store_true",
            help="Disable the NaN detection for better performance.",
        )
        parser.add_argument(
            "--disable-overlap-schedule",
            action="store_true",
            help="Disable the overlap scheduler, which overlaps the CPU scheduler with GPU model worker.",
        )
        parser.add_argument(
            "--enable-mixed-chunk",
            action="store_true",
            help="Enabling mixing prefill and decode in a batch when using chunked prefill.",
        )
        parser.add_argument(
            "--enable-dp-attention",
            action="store_true",
            help="Enabling data parallelism for attention and tensor parallelism for FFN. The dp size should be equal to the tp size. Currently only DeepSeek-V2 is supported.",
        )
        parser.add_argument(
            "--enable-torch-compile",
            action="store_true",
            help="Optimize the model with torch.compile. Experimental feature.",
        )
        parser.add_argument(
            "--torch-compile-max-bs",
            type=int,
            default=ServerArgs.torch_compile_max_bs,
            help="Set the maximum batch size when using torch compile.",
        )
        parser.add_argument(
            "--cuda-graph-max-bs",
            type=int,
            default=ServerArgs.cuda_graph_max_bs,
            help="Set the maximum batch size for cuda graph.",
        )
        parser.add_argument(
            "--torchao-config",
            type=str,
            default=ServerArgs.torchao_config,
            help="Optimize the model with torchao. Experimental feature. Current choices are: int8dq, int8wo, int4wo-<group_size>, fp8wo, fp8dq-per_tensor, fp8dq-per_row",
        )
        parser.add_argument(
            "--enable-nan-detection",
            action="store_true",
            help="Enable the NaN detection for debugging purposes.",
        )
        parser.add_argument(
            "--enable-p2p-check",
            action="store_true",
            help="Enable P2P check for GPU access, otherwise the p2p access is allowed by default.",
        )
        parser.add_argument(
            "--triton-attention-reduce-in-fp32",
            action="store_true",
            help="Cast the intermidiate attention results to fp32 to avoid possible crashes related to fp16."
            "This only affects Triton attention kernels.",
        )
        parser.add_argument(
            "--num-continuous-decode-steps",
            type=int,
            default=ServerArgs.num_continuous_decode_steps,
            help="Run multiple continuous decoding steps to reduce scheduling overhead. "
            "This can potentially increase throughput but may also increase time-to-first-token latency. "
            "The default value is 1, meaning only run one decoding step at a time.",
        )
        parser.add_argument(
            "--delete-ckpt-after-loading",
            action="store_true",
            help="Delete the model checkpoint after loading the model.",
        )

        # Deprecated arguments
        parser.add_argument(
            "--enable-overlap-schedule",
            action=DeprecatedAction,
            help="'--enable-overlap-schedule' is deprecated. It is enabled by default now. Please drop this argument.",
        )
        parser.add_argument(
            "--disable-flashinfer",
            action=DeprecatedAction,
            help="'--disable-flashinfer' is deprecated. Please use '--attention-backend triton' instead.",
        )
        parser.add_argument(
            "--disable-flashinfer-sampling",
            action=DeprecatedAction,
            help="'--disable-flashinfer-sampling' is deprecated. Please use '--sampling-backend pytroch' instead.",
        )

    @classmethod
    def from_cli_args(cls, args: argparse.Namespace):
        args.tp_size = args.tensor_parallel_size
        args.dp_size = args.data_parallel_size
        attrs = [attr.name for attr in dataclasses.fields(cls)]
        return cls(**{attr: getattr(args, attr) for attr in attrs})

    def url(self):
        if is_ipv6(self.host):
            return f"http://[{self.host}]:{self.port}"
        else:
            return f"http://{self.host}:{self.port}"

    def check_server_args(self):
        assert (
            self.tp_size % self.nnodes == 0
        ), "tp_size must be divisible by number of nodes"
        assert not (
            self.dp_size > 1 and self.nnodes != 1
        ), "multi-node data parallel is not supported"
        assert (
            self.max_loras_per_batch > 0
            # FIXME
            and (self.lora_paths is None or self.disable_cuda_graph)
            and (self.lora_paths is None or self.disable_radix_cache)
        ), "compatibility of lora and cuda graph and radix attention is in progress"
        assert self.base_gpu_id >= 0, "base_gpu_id must be non-negative"

        if isinstance(self.lora_paths, list):
            lora_paths = self.lora_paths
            self.lora_paths = {}
            for lora_path in lora_paths:
                if "=" in lora_path:
                    name, path = lora_path.split("=", 1)
                    self.lora_paths[name] = path
                else:
                    self.lora_paths[lora_path] = lora_path


def prepare_server_args(argv: List[str]) -> ServerArgs:
    """
    Prepare the server arguments from the command line arguments.

    Args:
        args: The command line arguments. Typically, it should be `sys.argv[1:]`
            to ensure compatibility with `parse_args` when no arguments are passed.

    Returns:
        The server arguments.
    """
    parser = argparse.ArgumentParser()
    ServerArgs.add_cli_args(parser)
    raw_args = parser.parse_args(argv)
    server_args = ServerArgs.from_cli_args(raw_args)
    return server_args


@dataclasses.dataclass
class PortArgs:
    # The ipc filename for tokenizer to receive inputs from detokenizer (zmq)
    tokenizer_ipc_name: str
    # The ipc filename for scheduler (rank 0) to receive inputs from tokenizer (zmq)
    scheduler_input_ipc_name: str
    # The ipc filename for detokenizer to receive inputs from scheduler (zmq)
    detokenizer_ipc_name: str

    # The port for nccl initialization (torch.dist)
    nccl_port: int

    @staticmethod
    def init_new(server_args) -> "PortArgs":
        port = server_args.port + random.randint(100, 1000)
        while True:
            if is_port_available(port):
                break
            port += 42

        return PortArgs(
            tokenizer_ipc_name=tempfile.NamedTemporaryFile(delete=False).name,
            scheduler_input_ipc_name=tempfile.NamedTemporaryFile(delete=False).name,
            detokenizer_ipc_name=tempfile.NamedTemporaryFile(delete=False).name,
            nccl_port=port,
        )


class LoRAPathAction(argparse.Action):
    def __call__(self, parser, namespace, values, option_string=None):
        setattr(namespace, self.dest, {})
        for lora_path in values:
            if "=" in lora_path:
                name, path = lora_path.split("=", 1)
                getattr(namespace, self.dest)[name] = path
            else:
                getattr(namespace, self.dest)[lora_path] = lora_path


class DeprecatedAction(argparse.Action):
    def __init__(self, option_strings, dest, nargs=0, **kwargs):
        super(DeprecatedAction, self).__init__(
            option_strings, dest, nargs=nargs, **kwargs
        )

    def __call__(self, parser, namespace, values, option_string=None):
        raise ValueError(self.help)