import sys

if sys.version_info < (3, 9):
    pass
else:
    pass
import pipmaster as pm  # Pipmaster for dynamic library install

# install specific modules
if not pm.is_installed("lmdeploy"):
    pm.install("lmdeploy")

from openai import (
    APIConnectionError,
    RateLimitError,
    APITimeoutError,
)
from tenacity import (
    retry,
    stop_after_attempt,
    wait_exponential,
    retry_if_exception_type,
)


import numpy as np
import aiohttp
import base64
import struct


@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=60),
    retry=retry_if_exception_type(
        (RateLimitError, APIConnectionError, APITimeoutError)
    ),
)
async def siliconcloud_embedding(
    texts: list[str],
    model: str = "netease-youdao/bce-embedding-base_v1",
    base_url: str = "https://api.siliconflow.cn/v1/embeddings",
    max_token_size: int = 512,
    api_key: str = None,
) -> np.ndarray:
    if api_key and not api_key.startswith("Bearer "):
        api_key = "Bearer " + api_key

    headers = {"Authorization": api_key, "Content-Type": "application/json"}

    truncate_texts = [text[0:max_token_size] for text in texts]

    payload = {"model": model, "input": truncate_texts, "encoding_format": "base64"}

    base64_strings = []
    async with aiohttp.ClientSession() as session:
        async with session.post(base_url, headers=headers, json=payload) as response:
            content = await response.json()
            if "code" in content:
                raise ValueError(content)
            base64_strings = [item["embedding"] for item in content["data"]]

    embeddings = []
    for string in base64_strings:
        decode_bytes = base64.b64decode(string)
        n = len(decode_bytes) // 4
        float_array = struct.unpack("<" + "f" * n, decode_bytes)
        embeddings.append(float_array)
    return np.array(embeddings)