# What is this?
## Unit tests for ProxyConfig class


import sys, os
import traceback
from dotenv import load_dotenv

load_dotenv()
import os, io

sys.path.insert(
    0, os.path.abspath("../..")
)  # Adds the parent directory to the, system path
import pytest, litellm
from pydantic import BaseModel
from litellm.proxy.proxy_server import ProxyConfig
from litellm.proxy.utils import encrypt_value
from litellm.types.router import Deployment, LiteLLM_Params, ModelInfo


class DBModel(BaseModel):
    model_id: str
    model_name: str
    model_info: dict
    litellm_params: dict


@pytest.mark.asyncio
async def test_delete_deployment():
    """
    - Ensure the global llm router is not being reset
    - Ensure invalid model is deleted
    - Check if model id != model_info["id"], the model_info["id"] is picked
    """
    import base64

    litellm_params = LiteLLM_Params(
        model="azure/chatgpt-v-2",
        api_key=os.getenv("AZURE_API_KEY"),
        api_base=os.getenv("AZURE_API_BASE"),
        api_version=os.getenv("AZURE_API_VERSION"),
    )
    encrypted_litellm_params = litellm_params.dict(exclude_none=True)

    master_key = "sk-1234"

    setattr(litellm.proxy.proxy_server, "master_key", master_key)

    for k, v in encrypted_litellm_params.items():
        if isinstance(v, str):
            encrypted_value = encrypt_value(v, master_key)
            encrypted_litellm_params[k] = base64.b64encode(encrypted_value).decode(
                "utf-8"
            )

    deployment = Deployment(model_name="gpt-3.5-turbo", litellm_params=litellm_params)
    deployment_2 = Deployment(
        model_name="gpt-3.5-turbo-2", litellm_params=litellm_params
    )

    llm_router = litellm.Router(
        model_list=[
            deployment.to_json(exclude_none=True),
            deployment_2.to_json(exclude_none=True),
        ]
    )
    setattr(litellm.proxy.proxy_server, "llm_router", llm_router)
    print(f"llm_router: {llm_router}")

    pc = ProxyConfig()

    db_model = DBModel(
        model_id=deployment.model_info.id,
        model_name="gpt-3.5-turbo",
        litellm_params=encrypted_litellm_params,
        model_info={"id": deployment.model_info.id},
    )

    db_models = [db_model]
    deleted_deployments = await pc._delete_deployment(db_models=db_models)

    assert deleted_deployments == 1
    assert len(llm_router.model_list) == 1

    """
    Scenario 2 - if model id != model_info["id"]
    """

    llm_router = litellm.Router(
        model_list=[
            deployment.to_json(exclude_none=True),
            deployment_2.to_json(exclude_none=True),
        ]
    )
    print(f"llm_router: {llm_router}")
    setattr(litellm.proxy.proxy_server, "llm_router", llm_router)
    pc = ProxyConfig()

    db_model = DBModel(
        model_id="12340523",
        model_name="gpt-3.5-turbo",
        litellm_params=encrypted_litellm_params,
        model_info={"id": deployment.model_info.id},
    )

    db_models = [db_model]
    deleted_deployments = await pc._delete_deployment(db_models=db_models)

    assert deleted_deployments == 1
    assert len(llm_router.model_list) == 1


@pytest.mark.asyncio
async def test_add_existing_deployment():
    """
    - Only add new models
    - don't re-add existing models
    """
    import base64

    litellm_params = LiteLLM_Params(
        model="gpt-3.5-turbo",
        api_key=os.getenv("AZURE_API_KEY"),
        api_base=os.getenv("AZURE_API_BASE"),
        api_version=os.getenv("AZURE_API_VERSION"),
    )
    deployment = Deployment(model_name="gpt-3.5-turbo", litellm_params=litellm_params)
    deployment_2 = Deployment(
        model_name="gpt-3.5-turbo-2", litellm_params=litellm_params
    )

    llm_router = litellm.Router(
        model_list=[
            deployment.to_json(exclude_none=True),
            deployment_2.to_json(exclude_none=True),
        ]
    )
    print(f"llm_router: {llm_router}")
    master_key = "sk-1234"
    setattr(litellm.proxy.proxy_server, "llm_router", llm_router)
    setattr(litellm.proxy.proxy_server, "master_key", master_key)
    pc = ProxyConfig()

    encrypted_litellm_params = litellm_params.dict(exclude_none=True)

    for k, v in encrypted_litellm_params.items():
        if isinstance(v, str):
            encrypted_value = encrypt_value(v, master_key)
            encrypted_litellm_params[k] = base64.b64encode(encrypted_value).decode(
                "utf-8"
            )
    db_model = DBModel(
        model_id=deployment.model_info.id,
        model_name="gpt-3.5-turbo",
        litellm_params=encrypted_litellm_params,
        model_info={"id": deployment.model_info.id},
    )

    db_models = [db_model]
    num_added = pc._add_deployment(db_models=db_models)

    assert num_added == 0


@pytest.mark.asyncio
async def test_add_and_delete_deployments():
    pass