"""Hugging Face Inference Endpoint handler for byte-level chat generation."""

from __future__ import annotations

import json
from pathlib import Path
from typing import Any

import torch
from modeling_harmonic_byte_transformer import ModernByteTransformer
from safetensors.torch import load_file

END_SEQUENCE = b"\n<|end|>\n"
ROOM_HEADER = (
    b"<|room|> scope=direct members=p0,p1\n"
    b"<|participants|>\n"
    b"p0=Agent.Ajax\n"
    b"p1=Agent.Steve\n"
)
USER_TURN = b"<|turn|> p0>p1 audience=p0,p1\nAgent.Ajax:\n"
ASSISTANT_TURN = b"<|turn|> p1>p0 audience=p0,p1\nAgent.Steve:\n"


def _serialize_messages(messages: list[dict[str, Any]]) -> bytes:
    context = bytearray(ROOM_HEADER)
    for message in messages:
        role = str(message.get("role", "")).lower()
        content = str(message.get("content", "")).strip().encode("utf-8", errors="replace")
        if not content:
            continue
        if role == "assistant":
            context.extend(ASSISTANT_TURN + content + END_SEQUENCE)
        elif role == "system":
            context.extend(USER_TURN + b"Context: " + content + END_SEQUENCE)
        elif role == "user":
            context.extend(USER_TURN + content + END_SEQUENCE)
    context.extend(ASSISTANT_TURN)
    return bytes(context)


class EndpointHandler:
    """Load once per replica and serve byte-level prompt or chat requests."""

    def __init__(self, path: str = "") -> None:
        model_path = Path(path)
        config = json.loads((model_path / "config.json").read_text(encoding="utf-8"))
        architecture = config["architecture"]
        self.model = ModernByteTransformer(**architecture)
        state = load_file(str(model_path / "model.safetensors"), device="cpu")
        missing, unexpected = self.model.load_state_dict(state, strict=False)
        if set(missing) != {"lm_head.weight"} or unexpected:
            raise RuntimeError(
                f"weight mismatch: missing={list(missing)}, unexpected={list(unexpected)}"
            )
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.dtype = torch.bfloat16 if self.device.type == "cuda" else torch.float32
        self.model.to(device=self.device, dtype=self.dtype).eval()
        self.max_seq_len = int(architecture["max_seq_len"])

    @staticmethod
    def _request_context(data: dict[str, Any]) -> bytes:
        inputs = data.get("inputs", data.get("messages"))
        if isinstance(inputs, str):
            return _serialize_messages([{"role": "user", "content": inputs}])
        if isinstance(inputs, list):
            return _serialize_messages(inputs)
        raise ValueError("inputs must be a prompt string or a list of role/content messages")

    @torch.inference_mode()
    def __call__(self, data: dict[str, Any]) -> dict[str, Any]:
        parameters = data.get("parameters") or {}
        max_new_bytes = min(
            1024,
            max(
                1,
                int(parameters.get("max_new_bytes", parameters.get("max_new_tokens", 256))),
            ),
        )
        temperature = float(parameters.get("temperature", 0.7))
        top_k = max(0, int(parameters.get("top_k", 40)))
        seed = int(parameters.get("seed", 42))
        context = self._request_context(data)
        generated = bytearray()
        generator = torch.Generator(device=self.device).manual_seed(seed)

        for _ in range(max_new_bytes):
            window = context[-self.max_seq_len :]
            x = torch.tensor(list(window), dtype=torch.long, device=self.device).unsqueeze(0)
            with torch.autocast("cuda", dtype=torch.bfloat16, enabled=self.device.type == "cuda"):
                logits = self.model(x)[0, -1].float()
            if temperature <= 0:
                token = int(torch.argmax(logits).item())
            elif top_k:
                values, indices = torch.topk(logits / temperature, min(top_k, 256))
                probabilities = torch.softmax(values, dim=-1)
                choice = torch.multinomial(probabilities, 1, generator=generator)
                token = int(indices[choice].item())
            else:
                probabilities = torch.softmax(logits / temperature, dim=-1)
                token = int(torch.multinomial(probabilities, 1, generator=generator).item())
            generated.append(token)
            context += bytes([token])
            if generated.endswith(END_SEQUENCE):
                del generated[-len(END_SEQUENCE) :]
                return {
                    "generated_text": generated.decode("utf-8", errors="replace").strip(),
                    "ended": True,
                    "generated_bytes": len(generated),
                }

        return {
            "generated_text": generated.decode("utf-8", errors="replace").strip(),
            "ended": False,
            "generated_bytes": len(generated),
        }