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# SPDX-License-Identifier: Apache-2.0
#
# 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.

import argparse
from pathlib import Path


def parse_args():
    parser = argparse.ArgumentParser(
        description="Run an example of the TensorRT-LLM pipeline."
    )

    example_dir = Path(__file__).parent.absolute().parent.absolute()

    default_operator_repository = example_dir.joinpath("operators")

    default_log_dir = ""

    parser.add_argument(
        "--log-dir",
        type=str,
        default=str(default_log_dir),
        help="log dir folder",
    )

    parser.add_argument(
        "--initialize-request-plane",
        default=False,
        action="store_true",
        help="Initialize the request plane, should only be done once per deployment",
    )

    parser.add_argument(
        "--log-level", type=int, default=1, help="log level applied to all workers"
    )

    parser.add_argument(
        "--request-plane-uri",
        type=str,
        default="nats://localhost:4222",
        help="URI of request plane",
    )

    parser.add_argument(
        "--nats-port",
        type=int,
        default=4222,
        help="Port for NATS server",
    )

    parser.add_argument(
        "--metrics-port",
        type=int,
        default=50000,
        help="Metrics port",
    )

    parser.add_argument(
        "--worker-type",
        type=str,
        default="aggregate",
        help="Type of worker",
        choices=["aggregate", "context", "generate", "disaggregated-serving"],
    )

    parser.add_argument("--gpu-device-id", type=int, default=0, help="gpu id")

    parser.add_argument(
        "--context-worker-count", type=int, default=0, help="Number of context workers"
    )

    parser.add_argument(
        "--generate-worker-count",
        type=int,
        default=0,
        help="Number of generate workers",
    )

    parser.add_argument(
        "--aggregate-worker-count",
        type=int,
        required=False,
        default=0,
        help="Number of baseline workers",
    )

    parser.add_argument(
        "--operator-repository",
        type=str,
        default=str(default_operator_repository),
        help="Operator repository",
    )

    parser.add_argument(
        "--worker-name",
        type=str,
        required=False,
        default="llama",
        help="Name of the worker",
    )

    parser.add_argument(
        "--model",
        type=str,
        required=False,
        default="llama-3.1-8b-instruct",
        choices=[
            "mock",
            "llama-3.1-70b-instruct",
            "llama-3.1-8b-instruct",
            "llama-3-8b-instruct-generate",
            "llama-3-8b-instruct-context",
            "llama-3-8b-instruct",
            "llama-3-8b-instruct-default",
            "llama-3-70b-instruct-context",
            "llama-3-70b-instruct-generate",
            "llama-3-70b-instruct",
        ],
        help="model to serve",
    )

    parser.add_argument(
        "--ignore-eos",
        action=argparse.BooleanOptionalAction,
        required=False,
        default=False,
        help="Ignore EOS token when generating",
    )

    parser.add_argument(
        "--dry-run",
        action=argparse.BooleanOptionalAction,
        required=False,
        default=False,
        help="Dry run the command",
    )

    parser.add_argument(
        "--disaggregated-serving",
        action=argparse.BooleanOptionalAction,
        required=False,
        default=False,
        help="Enable disaggregated serving",
    )

    return parser.parse_args()