"""
language.py — robust loader + adapter for language.bin

This loader attempts multiple safe options to load a local language model file `language.bin`
and adapt it into a small, predictable translation API:
  - translate(text, src, tgt)
  - translate_to_en(text, src)
  - translate_from_en(text, tgt)
  - detect(text) / detect_language(text) (if provided by model)
  - model_info() for debugging

Loading strategy (in order):
  1. If a language.py module is present (importable) we prefer it (the app already tries this).
  2. If language.bin exists:
     - Try to detect if it's a safetensors file and (if safetensors is installed) attempt to load.
     - Try torch.load with weights_only=True (safe for "weights-only" files).
     - If that fails and you explicitly allow insecure loading, try torch.load(..., weights_only=False).
       To allow this, set the environment variable LANGUAGE_LOAD_ALLOW_INSECURE=1.
       NOTE: loading with weights_only=False may execute arbitrary code from the file. Only do this
       when you trust the source of language.bin.
     - Try pickle.load as a last attempt (may fail for many binary formats).
  3. Fallback: no model loaded (the app will fall back to heuristics).

Security note:
  - Re-running torch.load with weights_only=False can run arbitrary code embedded in the file.
    Only enable LANGUAGE_LOAD_ALLOW_INSECURE if you trust the file origin.
"""

from pathlib import Path
import logging
import importlib
import io
import sys

logger = logging.getLogger("local_language")
logger.setLevel(logging.INFO)

_model = None
_load_errors = []

def _try_import_language_module():
    # If a language.py exists, prefer importing it (app already tries this but we expose here)
    try:
        mod = importlib.import_module("language")
        logger.info("Found importable language.py module; using it.")
        return mod
    except Exception as e:
        _load_errors.append(("import_language_py", repr(e)))
        return None

def _is_likely_safetensors(path: Path) -> bool:
    # Heuristic: safetensors files are usually small header-less binary; if file ends with .safetensors we try it.
    return path.suffix == ".safetensors" or path.name.endswith(".safetensors")

def _try_safetensors_load(path: Path):
    try:
        from safetensors.torch import load_file as st_load  # type: ignore
    except Exception as e:
        _load_errors.append(("safetensors_not_installed", repr(e)))
        return None
    try:
        tensors = st_load(str(path))
        logger.info("Loaded safetensors file into tensor dict (language.bin treated as safetensors).")
        # Return the dict; user wrapper may adapt it.
        return tensors
    except Exception as e:
        _load_errors.append(("safetensors_load_failed", repr(e)))
        return None

def _try_torch_load(path: Path, weights_only: bool):
    try:
        import torch
    except Exception as e:
        _load_errors.append(("torch_not_installed", repr(e)))
        return None
    try:
        # In PyTorch 2.6+, torch.load defaults weights_only=True. Passing explicitly for clarity.
        obj = torch.load(str(path), map_location="cpu", weights_only=weights_only)
        logger.info(f"torch.load succeeded (weights_only={weights_only}).")
        return obj
    except TypeError as e:
        # Older torch versions don't accept weights_only kwarg; try without it (older API)
        try:
            obj = torch.load(str(path), map_location="cpu")
            logger.info("torch.load succeeded (no weights_only argument supported by local torch).")
            return obj
        except Exception as e2:
            _load_errors.append(("torch_load_typeerror_then_failed", repr(e2)))
            return None
    except Exception as e:
        _load_errors.append((f"torch_load_failed_weights_only={weights_only}", repr(e)))
        return None

def _try_pickle_load(path: Path):
    try:
        import pickle
        with open(path, "rb") as f:
            obj = pickle.load(f)
        logger.info("Loaded language.bin via pickle.")
        return obj
    except Exception as e:
        _load_errors.append(("pickle_load_failed", repr(e)))
        return None

def _attempt_load(path: Path):
    # 1) Safetensors heuristics
    if _is_likely_safetensors(path):
        logger.info("language.bin looks like safetensors (by filename). Attempting safetensors load.")
        obj = _try_safetensors_load(path)
        if obj is not None:
            return obj

    # 2) Try torch.load in safe (weights-only) mode first (PyTorch 2.6+ default is weights_only=True)
    obj = _try_torch_load(path, weights_only=True)
    if obj is not None:
        return obj

    # 3) If env var allows insecure loading, try weights_only=False (dangerous)
    allow_insecure = str(os.environ.get("LANGUAGE_LOAD_ALLOW_INSECURE", "")).lower() in ("1", "true", "yes")
    if allow_insecure:
        logger.warning("LANGUAGE_LOAD_ALLOW_INSECURE is set -> attempting torch.load with weights_only=False (INSECURE).")
        obj = _try_torch_load(path, weights_only=False)
        if obj is not None:
            return obj
        else:
            logger.warning("torch.load(weights_only=False) failed or returned None.")

    # 4) Try pickle as last resort
    obj = _try_pickle_load(path)
    if obj is not None:
        return obj

    return None

def _load_language_bin_if_present():
    global _model
    p = Path("language.bin")
    if not p.exists():
        return None
    logger.info("language.bin found; attempting to load with safe fallbacks...")
    # Try multiple strategies
    obj = _attempt_load(p)
    if obj is None:
        logger.warning("All attempts to load language.bin failed. See _load_errors for details.")
    else:
        _model = obj
    return obj

def load():
    """
    Public loader. Returns the loaded model/object or None.
    """
    global _model
    # Prefer an explicit language.py module if present on sys.path.
    mod = _try_import_language_module()
    if mod is not None:
        _model = mod
        return _model
    # Attempt to load language.bin if present
    obj = _load_language_bin_if_present()
    return obj

# Run load on import (app calls load_local_language_module separately too)
try:
    load()
except Exception as e:
    logger.warning(f"language.py loader encountered error during import: {e}")

# --- Adapter / API functions the app expects --- #

def model_info() -> dict:
    """
    Return a small summary about the loaded model/object to help debugging.
    """
    info = {"loaded": False, "type": None, "repr": None, "load_errors": list(_load_errors)[:20], "has_translate": False, "has_detect": False, "callable": False}
    if _model is None:
        return info
    info["loaded"] = True
    try:
        info["type"] = type(_model).__name__
    except Exception:
        info["type"] = "<unknown>"
    try:
        info["repr"] = repr(_model)[:1000]
    except Exception:
        info["repr"] = "<unreprable>"
    try:
        info["has_translate"] = hasattr(_model, "translate")
        info["has_translate_to_en"] = hasattr(_model, "translate_to_en")
        info["has_translate_from_en"] = hasattr(_model, "translate_from_en")
        info["has_detect"] = hasattr(_model, "detect") or hasattr(_model, "detect_language")
        info["callable"] = callable(_model)
        if hasattr(_model, "__dir__"):
            try:
                info["dir"] = [n for n in dir(_model) if not n.startswith("_")]
            except Exception:
                info["dir"] = []
    except Exception:
        pass
    return info

def _safe_call_translate(text: str, src: str, tgt: str) -> str:
    """
    Try multiple call patterns to invoke translation functions on the loaded object.
    Fall back to returning original text if nothing works.
    """
    if _model is None:
        return text
    # 1) Preferred explicit API
    try:
        if hasattr(_model, "translate"):
            try:
                return _model.translate(text, src, tgt)
            except TypeError:
                try:
                    # some translate implementations take (text, "src->tgt")
                    return _model.translate(text, f"{src}->{tgt}")
                except Exception:
                    pass
    except Exception as e:
        logger.debug(f"_model.translate attempt failed: {e}")

    # 2) Dedicated helpers
    try:
        if tgt.lower() in ("en", "eng") and hasattr(_model, "translate_to_en"):
            return _model.translate_to_en(text, src)
    except Exception as e:
        logger.debug(f"_model.translate_to_en attempt failed: {e}")
    try:
        if src.lower() in ("en", "eng") and hasattr(_model, "translate_from_en"):
            return _model.translate_from_en(text, tgt)
    except Exception as e:
        logger.debug(f"_model.translate_from_en attempt failed: {e}")

    # 3) Callable model (call signature may vary)
    try:
        if callable(_model):
            try:
                return _model(text, src, tgt)
            except TypeError:
                try:
                    return _model(text, src)  # maybe (text, src)
                except TypeError:
                    try:
                        return _model(text)  # maybe (text)
                    except Exception:
                        pass
    except Exception as e:
        logger.debug(f"_model callable attempts failed: {e}")

    # 4) HF-style model object with attached tokenizer (best-effort)
    try:
        # model could be a dict of tensors (weights-only) - not directly usable for translation
        tokenizer = getattr(_model, "tokenizer", None)
        generate = getattr(_model, "generate", None)
        if tokenizer and generate:
            inputs = tokenizer([text], return_tensors="pt", truncation=True)
            outputs = _model.generate(**inputs, max_length=1024)
            decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
            return decoded
    except Exception as e:
        logger.debug(f"_model HF-style generate attempt failed: {e}")

    # 5) dict-like mapping (('src','tgt') -> fn or str)
    try:
        if isinstance(_model, dict):
            key = (src, tgt)
            if key in _model:
                val = _model[key]
                if callable(val):
                    return val(text)
                if isinstance(val, str):
                    return val
            key2 = f"{src}->{tgt}"
            if key2 in _model:
                val = _model[key2]
                if callable(val):
                    return val(text)
                if isinstance(val, str):
                    return val
    except Exception as e:
        logger.debug(f"_model dict-like attempt failed: {e}")

    # Nothing worked: return input (no hallucination)
    return text

def translate(text: str, src: str, tgt: str) -> str:
    if not text:
        return text
    return _safe_call_translate(text, src or "und", tgt or "und")

def translate_to_en(text: str, src: str) -> str:
    if not text:
        return text
    # prefer dedicated helper if present
    try:
        if _model is not None and hasattr(_model, "translate_to_en"):
            return _model.translate_to_en(text, src)
    except Exception:
        pass
    return translate(text, src, "en")

def translate_from_en(text: str, tgt: str) -> str:
    if not text:
        return text
    try:
        if _model is not None and hasattr(_model, "translate_from_en"):
            return _model.translate_from_en(text, tgt)
    except Exception:
        pass
    return translate(text, "en", tgt)

def detect(text: str) -> str:
    """
    Call detection if the model exposes it. Returns None if not available.
    """
    if not text:
        return None
    if _model is None:
        return None
    try:
        if hasattr(_model, "detect_language"):
            return _model.detect_language(text)
        if hasattr(_model, "detect"):
            return _model.detect(text)
    except Exception as e:
        logger.debug(f"model detect attempt failed: {e}")
    return None

# Small helper for CLI testing
if __name__ == "__main__":
    import sys
    print("model_info:", model_info())
    if len(sys.argv) >= 4:
        src = sys.argv[1]
        tgt = sys.argv[2]
        txt = " ".join(sys.argv[3:])
        print("translate:", translate(txt, src, tgt))
    else:
        print("Usage: python language.py <src> <tgt> <text...>")
        print("Example: python language.py es en 'hola mundo'")