from pathlib import Path
import os
import json
import unicodedata
import numpy as np
import sentencepiece as spm
import torch
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

ROOT = Path(__file__).resolve().parent
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
MODEL = torch.jit.load(str(ROOT / "splinet_embedder.ts"), map_location=DEVICE).eval()
SP = spm.SentencePieceProcessor(model_file=str(ROOT / "spiece.model"))

PAD_ID = 3
CLS_ID = 4
SEP_ID = 5
MAX_LENGTH = 512
DEFAULT_BATCH = int(os.environ.get("SPLINET_BATCH_SIZE", "256" if DEVICE.type == "cuda" else "8"))

def normalize_text(text):
    text = unicodedata.normalize("NFKC", text or "").lower()
    return " ".join(text.split())

def tokenize(texts):
    ids = torch.full((len(texts), MAX_LENGTH), PAD_ID, dtype=torch.long)
    mask = torch.zeros((len(texts), MAX_LENGTH), dtype=torch.long)
    for i, text in enumerate(texts):
        pieces = list(SP.encode(normalize_text(text), out_type=int))[:MAX_LENGTH - 2]
        seq = [CLS_ID] + pieces + [SEP_ID]
        n = len(seq)
        ids[i, :n] = torch.tensor(seq, dtype=torch.long)
        mask[i, :n] = 1
    return ids, mask

def encode(texts, batch_size=None):
    if isinstance(texts, str):
        texts = [texts]
    if not texts:
        return np.empty((0, 768), dtype=np.float32)
    bs = max(1, int(batch_size or DEFAULT_BATCH))
    output = []
    start = 0
    while start < len(texts):
        current = min(bs, len(texts) - start)
        try:
            ids, mask = tokenize(texts[start:start + current])
            ids = ids.to(DEVICE)
            mask = mask.to(DEVICE)
            with torch.inference_mode():
                if DEVICE.type == "cuda":
                    with torch.autocast(device_type="cuda", dtype=torch.float16):
                        z = MODEL(ids, mask)
                else:
                    z = MODEL(ids, mask)
            output.append(z.float().cpu().numpy())
            start += current
        except torch.OutOfMemoryError:
            if DEVICE.type != "cuda" or current == 1:
                raise
            torch.cuda.empty_cache()
            bs = max(1, current // 2)
    return np.concatenate(output, axis=0).astype(np.float32)

def similarity(text_a, text_b):
    z = encode([text_a, text_b])
    return float(np.dot(z[0], z[1]))

class EmbedRequest(BaseModel):
    texts: list[str]
    batch_size: int | None = None

class SimilarityRequest(BaseModel):
    text_a: str
    text_b: str

app = FastAPI(title="SpliNet Embedder API", version="1.0.0")

@app.get("/health")
def health():
    return {"status": "ok", "device": str(DEVICE)}

@app.get("/model-info")
def model_info():
    with open(ROOT / "model_info.json", "r", encoding="utf-8") as f:
        return json.load(f)

@app.post("/embed")
def embed_endpoint(req: EmbedRequest):
    try:
        z = encode(req.texts, req.batch_size)
        return {"count": len(req.texts), "dimension": 768, "embeddings": z.tolist()}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/similarity")
def similarity_endpoint(req: SimilarityRequest):
    try:
        return {"cosine_similarity": similarity(req.text_a, req.text_b)}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", "8000")))