"""
model_context.py

Query and cache model context window sizes from OpenAI-compatible APIs.
Provides token estimation for context usage tracking.
"""

import logging
from typing import Dict, List, Optional

from urllib.parse import urlparse

import httpx

logger = logging.getLogger(__name__)

_LOCAL_HOSTS = {"localhost", "127.0.0.1", "0.0.0.0", "::1"}
_PRIVATE_PREFIXES = ("10.", "172.16.", "172.17.", "172.18.", "172.19.",
                     "172.20.", "172.21.", "172.22.", "172.23.", "172.24.",
                     "172.25.", "172.26.", "172.27.", "172.28.", "172.29.",
                     "172.30.", "172.31.", "192.168.", "100.")


def _is_local_endpoint(url: str) -> bool:
    """Check if URL points to a local/private/tailscale address."""
    try:
        host = urlparse(url).hostname or ""
        return host in _LOCAL_HOSTS or host.startswith(_PRIVATE_PREFIXES)
    except Exception:
        return False

# ---------------------------------------------------------------------------
# Constants
# ---------------------------------------------------------------------------
DEFAULT_CONTEXT = 128000
REQUEST_TIMEOUT = 5

# Known context windows for major API models (used as fallback when /models
# endpoint doesn't report context_length).
# Substring matching — use the shortest unique prefix so variants get caught.
KNOWN_CONTEXT_WINDOWS = {
    # --- Anthropic ---
    'claude-sonnet-4-5': 200000,
    'claude-sonnet-4-6': 200000,
    'claude-sonnet-4': 200000,
    'claude-opus-4': 200000,
    'claude-haiku-4': 200000,
    'claude-haiku-3-5': 200000,
    'claude-3-5-sonnet': 200000,
    'claude-3-5-haiku': 200000,
    'claude-3-opus': 200000,
    'claude-3-sonnet': 200000,
    'claude-3-haiku': 200000,

    # --- OpenAI ---
    'gpt-5': 400000,
    'gpt-4.1': 1047576,
    'gpt-4.1-mini': 1047576,
    'gpt-4.1-nano': 1047576,
    'gpt-4o': 128000,
    'gpt-4o-mini': 128000,
    'gpt-4-turbo': 128000,
    'gpt-4': 8192,
    'gpt-3.5-turbo': 16385,
    'o1': 200000,
    'o1-mini': 128000,
    'o1-pro': 200000,
    'o3': 200000,
    'o3-mini': 200000,
    'o4-mini': 200000,

    # --- DeepSeek ---
    'deepseek-chat': 64000,
    'deepseek-coder': 64000,
    'deepseek-reasoner': 64000,
    'deepseek-r1': 64000,
    'deepseek-v3': 64000,
    'deepseek-v2': 64000,

    # --- Google ---
    'gemini-2.5-pro': 1048576,
    'gemini-2.5-flash': 1048576,
    'gemini-2.0-flash': 1048576,
    'gemini-1.5-pro': 1048576,
    'gemini-1.5-flash': 1048576,
    'gemma-3': 128000,
    'gemma-2': 8192,

    # --- Mistral ---
    'mistral-large': 128000,
    'mistral-medium': 32000,
    'mistral-small': 32000,
    'mistral-nemo': 128000,
    'mistral-7b': 32000,
    'mixtral': 32000,
    'codestral': 32000,
    'pixtral': 128000,

    # --- xAI ---
    'grok-4': 131072,
    'grok-3': 131072,
    'grok-2': 131072,

    # --- Meta / Llama ---
    'llama-4': 1048576,
    'llama-3.3': 131072,
    'llama-3.2': 131072,
    'llama-3.1': 131072,
    'llama-3': 131072,

    # --- Qwen ---
    'qwen3': 131072,
    'qwen2.5': 131072,
    'qwen2': 32768,
    'qwq': 32768,

    # --- Cohere ---
    'command-r-plus': 128000,
    'command-r': 128000,
    'command-a': 256000,

    # --- Perplexity ---
    'sonar-pro': 200000,
    'sonar': 128000,

    # --- MiniMax ---
    'minimax': 1000000,

    # --- Moonshot / Kimi ---
    'moonshot': 128000,
    'kimi': 128000,

    # --- Microsoft ---
    'phi-4': 16000,
    'phi-3': 128000,

    # --- Nvidia ---
    'nemotron': 131072,

    # --- Yi ---
    'yi-large': 32768,
    'yi-1.5': 16384,

    # --- 01.ai ---
    'yi-lightning': 16384,

    # --- Nous ---
    'hermes': 131072,
    'nous-hermes': 131072,

    # --- Open community ---
    'dolphin': 32768,
    'mythomax': 4096,
    'wizard': 32768,
    'openchat': 8192,
    'solar': 32768,
}

# ---------------------------------------------------------------------------
# Cache
# ---------------------------------------------------------------------------
_context_cache: Dict[str, int] = {}


def get_context_length(endpoint_url: str, model: str) -> int:
    """Get the context window size for a model.

    Queries /v1/models on the endpoint and looks for context_length
    or context_window fields. Caches result per model ID.
    Falls back to DEFAULT_CONTEXT if unavailable.
    """
    if model in _context_cache:
        return _context_cache[model]

    ctx = _query_context_length(endpoint_url, model)
    # Only cache non-default values to allow retry on next request
    if ctx != DEFAULT_CONTEXT:
        _context_cache[model] = ctx
    logger.info(f"Context length for {model}: {ctx}")
    return ctx


def _lookup_known(model: str) -> Optional[int]:
    """Check known context windows by substring match."""
    name = model.lower()
    basename = name.split("/")[-1] if "/" in name else name
    basename = basename.split(":")[0]  # strip :free, :extended etc.
    for key, ctx in KNOWN_CONTEXT_WINDOWS.items():
        if key in basename or key in name:
            return ctx
    return None


def _query_context_length(endpoint_url: str, model: str) -> int:
    """Query the model API for context length."""
    known = _lookup_known(model)
    api_ctx = None

    # Try llama.cpp /slots endpoint first — reports actual serving context
    if _is_local_endpoint(endpoint_url):
        try:
            base = endpoint_url.split("/v1")[0] if "/v1" in endpoint_url else endpoint_url.rsplit("/", 1)[0]
            r = httpx.get(f"{base}/slots", timeout=REQUEST_TIMEOUT)
            if r.is_success:
                slots = r.json()
                if isinstance(slots, list) and slots:
                    n_ctx = slots[0].get("n_ctx")
                    if n_ctx and isinstance(n_ctx, int) and n_ctx > 0:
                        logger.info(f"llama.cpp /slots reports n_ctx={n_ctx} for {model}")
                        return n_ctx
        except Exception:
            pass

    models_url = endpoint_url.replace("/chat/completions", "/models")
    try:
        r = httpx.get(models_url, timeout=REQUEST_TIMEOUT)
        if r.is_success:
            data = r.json()
            models_list = data.get("data") or []

            for m in models_list:
                mid = m.get("id", "")
                if mid == model or mid.split("/")[-1] == model.split("/")[-1]:
                    for field in (
                        "context_length",
                        "context_window",
                        "max_model_len",
                        "max_context_length",
                        "max_seq_len",
                    ):
                        val = m.get(field)
                        if val and isinstance(val, (int, float)) and val > 0:
                            api_ctx = int(val)
                            break

                    if not api_ctx:
                        meta = m.get("meta") or m.get("model_extra") or {}
                        if isinstance(meta, dict):
                            # n_ctx is the actual serving context (set via -c flag in llama.cpp)
                            for field in ("n_ctx", "context_length", "context_window", "max_model_len"):
                                val = meta.get(field)
                                if val and isinstance(val, (int, float)) and val > 0:
                                    api_ctx = int(val)
                                    break
                    break
    except Exception as e:
        logger.debug(f"Failed to query context length for {model}: {e}")

    # For local/self-hosted endpoints, trust the API value (user set --max-model-len)
    # For cloud APIs, use the larger value (API can report low defaults)
    if api_ctx and known:
        _is_local = _is_local_endpoint(endpoint_url)
        if _is_local and api_ctx < known:
            logger.info(f"Local endpoint reports {api_ctx} for {model} (known max: {known}) — using API value")
            return api_ctx
        result = max(api_ctx, known)
        if api_ctx < known:
            logger.info(f"API reported {api_ctx} for {model}, using known {known} instead")
        return result
    if api_ctx:
        return api_ctx
    if known:
        logger.info(f"Using known context window for {model}: {known}")
        return known

    return DEFAULT_CONTEXT


def estimate_tokens(messages: List[Dict]) -> int:
    """Rough token estimate for a list of messages.

    Uses chars * 0.3 which is closer to real BPE tokenizer output
    than the commonly-cited chars/4 (which underestimates by ~20-30%).
    Also adds ~4 tokens per message for role/formatting overhead.
    """
    total = 0
    for msg in messages:
        total += 4  # per-message overhead (role, separators)
        content = msg.get("content", "")
        if isinstance(content, str):
            total += int(len(content) * 0.3)
        elif isinstance(content, list):
            for item in content:
                if isinstance(item, dict) and item.get("type") == "text":
                    total += int(len(item.get("text", "")) * 0.3)
    return total