from __future__ import annotations

import importlib
import importlib.util
import json
import logging
import pkgutil
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Callable


REQUIRED_INFO_FIELDS = {"name", "version", "category", "description"}
SUPPORTED_SETTING_TYPES = {
    "int",
    "float",
    "bool",
    "choice",
    "text",
    "multiline_text",
    "path",
    "folder",
    "slider",
}


class ModelPluginError(RuntimeError):
    pass


@dataclass(frozen=True, slots=True)
class ModelPlugin:
    id: str
    info: dict[str, Any]
    training_settings: dict[str, dict[str, Any]] = field(default_factory=dict)
    generation_settings: dict[str, dict[str, Any]] = field(default_factory=dict)
    training_tool: dict[str, Any] = field(default_factory=dict)
    generation_tool: dict[str, Any] = field(default_factory=dict)
    module_name: str = ""
    plugin_path: Path | None = None

    @property
    def name(self) -> str:
        return str(self.info.get("name", self.id))

    @property
    def trainer_id(self) -> str:
        return str(self.training_tool.get("id") or f"{self.id}_trainer")

    @property
    def generator_id(self) -> str:
        return str(self.generation_tool.get("id") or f"{self.id}_generator")


class ModelPluginRegistry:
    """Discovers model plugins and validates their setting schemas."""

    def __init__(self, root: Path, logger: logging.Logger | None = None) -> None:
        self.root = root.resolve()
        self.logger = logger or logging.getLogger(__name__)
        self.plugins: dict[str, ModelPlugin] = {}
        self.errors: list[str] = []
        self.discover()

    def discover(self) -> None:
        self.plugins = {}
        self.errors = []
        for module_name in self._candidate_modules():
            try:
                plugin = self._load_module_plugin(module_name)
            except Exception as exc:
                message = f"{module_name}: {exc}"
                self.errors.append(message)
                self.logger.warning("Model plugin failed to load: %s", message)
                continue
            if plugin.id in self.plugins:
                self.errors.append(f"{module_name}: duplicate model plugin id {plugin.id}")
                continue
            self.plugins[plugin.id] = plugin

    def _candidate_modules(self) -> list[str | Path]:
        modules: list[str | Path] = []
        try:
            package = importlib.import_module("adam.model_plugins_builtin")
            for item in pkgutil.iter_modules(package.__path__, package.__name__ + "."):
                if not item.ispkg:
                    continue
                modules.append(item.name + ".manifest")
        except Exception as exc:
            self.errors.append(f"adam.model_plugins_builtin: {exc}")

        models_dir = self.root / "models"
        if models_dir.is_dir():
            for folder in sorted(models_dir.iterdir()):
                manifest = folder / "manifest.py"
                if not folder.is_dir() or not manifest.is_file():
                    continue
                modules.append(manifest)
        return modules

    def _load_module_plugin(self, module_name: str | Path) -> ModelPlugin:
        if isinstance(module_name, Path):
            fallback_id = module_name.parent.name
            unique_name = f"adam_user_model_{fallback_id}_{abs(hash(str(module_name.resolve())))}"
            spec = importlib.util.spec_from_file_location(unique_name, module_name)
            if spec is None or spec.loader is None:
                raise ModelPluginError(f"Could not load manifest file: {module_name}")
            module = importlib.util.module_from_spec(spec)
            spec.loader.exec_module(module)
            module_label = str(module_name)
        else:
            module = importlib.import_module(module_name)
            fallback_id = module_name.split(".")[-2]
            module_label = module_name
        plugin_id = str(getattr(module, "PLUGIN_ID", "") or fallback_id)
        info = dict(getattr(module, "MODEL_INFO", {}))
        missing = REQUIRED_INFO_FIELDS - set(info)
        if missing:
            raise ModelPluginError(
                "MODEL_INFO is missing " + ", ".join(sorted(missing))
            )
        training_settings = self._validate_schema(
            dict(getattr(module, "TRAINING_SETTINGS", {})),
            f"{plugin_id} training",
        )
        generation_settings = self._validate_schema(
            dict(getattr(module, "GENERATION_SETTINGS", {})),
            f"{plugin_id} generation",
        )
        plugin_path = Path(getattr(module, "__file__", "")).resolve().parent
        return ModelPlugin(
            id=plugin_id,
            info=info,
            training_settings=training_settings,
            generation_settings=generation_settings,
            training_tool=dict(getattr(module, "TRAINING_TOOL", {})),
            generation_tool=dict(getattr(module, "GENERATION_TOOL", {})),
            module_name=module_label,
            plugin_path=plugin_path,
        )

    @staticmethod
    def _validate_schema(
        schema: dict[str, Any],
        label: str,
    ) -> dict[str, dict[str, Any]]:
        clean: dict[str, dict[str, Any]] = {}
        for key, raw in schema.items():
            if not isinstance(raw, dict):
                raise ModelPluginError(f"{label} setting {key} must be an object")
            spec = dict(raw)
            setting_type = str(spec.get("type", "text"))
            if setting_type not in SUPPORTED_SETTING_TYPES:
                raise ModelPluginError(
                    f"{label} setting {key} has unsupported type {setting_type}"
                )
            spec["type"] = setting_type
            spec.setdefault("label", key.replace("_", " ").title())
            spec.setdefault("group", "Basic")
            if setting_type == "choice":
                options = spec.get("options", [])
                if not isinstance(options, (list, tuple)) or not options:
                    raise ModelPluginError(f"{label} setting {key} needs options")
                spec["options"] = list(options)
                spec.setdefault("default", spec["options"][0])
            clean[str(key)] = spec
        return clean

    def get(self, plugin_id: str) -> ModelPlugin:
        return self.plugins[plugin_id]

    def all(self) -> list[ModelPlugin]:
        return list(self.plugins.values())

    def by_trainer(self, trainer: str) -> ModelPlugin | None:
        return next((plugin for plugin in self.plugins.values() if plugin.id == trainer), None)

    def training_schema(self, trainer: str) -> dict[str, dict[str, Any]]:
        plugin = self.by_trainer(trainer)
        return plugin.training_settings if plugin else {}

    def generation_schema_for_tool(self, tool_id: str) -> dict[str, dict[str, Any]]:
        for plugin in self.plugins.values():
            if plugin.generator_id == tool_id:
                return plugin.generation_settings
        return {}

    def training_tool_specs(self) -> list[dict[str, Any]]:
        return [
            self._tool_spec(plugin, mode="training")
            for plugin in self.plugins.values()
            if plugin.training_tool
        ]

    def generation_tool_specs(self) -> list[dict[str, Any]]:
        return [
            self._tool_spec(plugin, mode="generation")
            for plugin in self.plugins.values()
            if plugin.generation_tool
        ]

    @staticmethod
    def _tool_spec(plugin: ModelPlugin, *, mode: str) -> dict[str, Any]:
        tool = dict(plugin.training_tool if mode == "training" else plugin.generation_tool)
        schema = plugin.training_settings if mode == "training" else plugin.generation_settings
        core_arguments = (
            ["dataset_dir", "model_name", "epochs", "output_dir", "resume_from"]
            if mode == "training"
            else [
                "model_name", "model_path", "prompt", "image_count", "steps",
                "seed", "sampler", "aspect_ratio",
            ]
        )
        core_required = (
            ["dataset_dir", "model_name", "epochs", "output_dir"]
            if mode == "training"
            else ["model_name", "model_path", "image_count", "steps", "seed"]
        )
        defaults = {
            "id": plugin.trainer_id if mode == "training" else plugin.generator_id,
            "name": f"{plugin.name} {'Trainer' if mode == 'training' else 'Generator'}",
            "description": plugin.info.get("description", ""),
            "category": "Training" if mode == "training" else "Output",
            "entry_function": "train" if mode == "training" else "generate",
            "arguments": [*core_arguments, *list(schema)],
            "required_arguments": [
                *core_required,
                *[key for key, spec in schema.items() if bool(spec.get("required"))],
            ],
            "capabilities": (
                ["fresh_training", "progress", "pause", "cancel"]
                if mode == "training"
                else ["image_generation", "progress", "cancel"]
            ),
            "requires_confirmation": mode == "training",
            "enabled": True,
            "demo": False,
        }
        defaults.update(tool)
        defaults["arguments"] = list(defaults.get("arguments") or [*core_arguments, *list(schema)])
        defaults["required_arguments"] = list(defaults.get("required_arguments") or [])
        return defaults

    def validate_settings(
        self,
        trainer: str,
        values: dict[str, Any],
        *,
        mode: str = "training",
    ) -> list[str]:
        plugin = self.by_trainer(trainer)
        if not plugin:
            return [f"Unknown model plugin: {trainer}"]
        schema = plugin.training_settings if mode == "training" else plugin.generation_settings
        return validate_settings(schema, values)


def validate_settings(schema: dict[str, dict[str, Any]], values: dict[str, Any]) -> list[str]:
    errors: list[str] = []
    for key, spec in schema.items():
        value = values.get(key, spec.get("default"))
        label = str(spec.get("label", key))
        if spec.get("required") and (value is None or str(value).strip() == ""):
            errors.append(f"{label} is required.")
            continue
        if value in (None, "") and not spec.get("required"):
            continue
        setting_type = str(spec.get("type", "text"))
        try:
            if setting_type in {"int", "slider"}:
                if isinstance(value, bool):
                    raise ValueError
                numeric = int(value)
            elif setting_type == "float":
                if isinstance(value, bool):
                    raise ValueError
                numeric = float(value)
            else:
                numeric = None
        except (TypeError, ValueError):
            errors.append(f"{label} must be a number.")
            continue
        if numeric is not None:
            if "min" in spec and numeric < float(spec["min"]):
                errors.append(f"{label} must be at least {spec['min']}.")
            if "max" in spec and numeric > float(spec["max"]):
                errors.append(f"{label} must be at most {spec['max']}.")
        if setting_type == "choice" and "options" in spec and value not in spec["options"]:
            errors.append(f"{label} must be one of: {', '.join(map(str, spec['options']))}.")
        if setting_type == "path" and spec.get("must_exist") and not Path(str(value)).expanduser().is_file():
            errors.append(f"{label} must point to an existing file.")
        if setting_type == "folder" and spec.get("must_exist") and not Path(str(value)).expanduser().is_dir():
            errors.append(f"{label} must point to an existing folder.")
    return errors


def load_presets(root: Path, plugin_id: str, mode: str) -> dict[str, dict[str, Any]]:
    path = root.resolve() / "config" / "model_presets.json"
    try:
        payload = json.loads(path.read_text(encoding="utf-8"))
    except (OSError, json.JSONDecodeError):
        return {}
    presets = payload.get(plugin_id, {}).get(mode, {})
    return dict(presets) if isinstance(presets, dict) else {}


def save_preset(
    root: Path,
    plugin_id: str,
    mode: str,
    name: str,
    settings: dict[str, Any],
) -> None:
    path = root.resolve() / "config" / "model_presets.json"
    try:
        payload = json.loads(path.read_text(encoding="utf-8"))
    except (OSError, json.JSONDecodeError):
        payload = {}
    payload.setdefault(plugin_id, {}).setdefault(mode, {})[name] = settings
    temporary = path.with_suffix(".tmp")
    temporary.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8")
    temporary.replace(path)


def plugin_function(plugin: ModelPlugin, function_name: str) -> Callable[..., Any] | None:
    if plugin.module_name.endswith("manifest.py"):
        spec = importlib.util.spec_from_file_location(
            f"adam_user_model_{plugin.id}_{abs(hash(plugin.module_name))}",
            plugin.module_name,
        )
        if spec is None or spec.loader is None:
            return None
        module = importlib.util.module_from_spec(spec)
        spec.loader.exec_module(module)
    else:
        module = importlib.import_module(plugin.module_name)
    function = getattr(module, function_name, None)
    return function if callable(function) else None


def safe_plugin_id(name: str) -> str:
    cleaned = "".join(
        character.lower() if character.isalnum() else "_"
        for character in name.strip()
    )
    cleaned = "_".join(part for part in cleaned.split("_") if part)
    return cleaned[:48] or "my_model"


def scaffold_model_plugin(
    root: Path,
    *,
    plugin_id: str,
    name: str,
    architecture: str = "custom",
    output_type: str = "image",
    include_training: bool = True,
    include_generation: bool = True,
) -> Path:
    """Create a simple user-editable model plugin folder."""
    plugin_id = safe_plugin_id(plugin_id)
    if plugin_id in {"ddpm", "flow", "lora", "model_template"}:
        raise ModelPluginError("Choose a plugin id that does not conflict with a built-in model.")
    folder = root.resolve() / "models" / plugin_id
    if folder.exists():
        raise ModelPluginError(f"A model plugin folder already exists: {folder}")
    folder.mkdir(parents=True)
    (folder / "__init__.py").write_text(
        f'"""ADAM model plugin: {name}."""\n',
        encoding="utf-8",
    )
    (folder / "manifest.py").write_text(
        _manifest_template(
            plugin_id=plugin_id,
            name=name,
            architecture=architecture,
            output_type=output_type,
            include_training=include_training,
            include_generation=include_generation,
        ),
        encoding="utf-8",
    )
    (folder / "model.py").write_text(_model_template(), encoding="utf-8")
    if include_training:
        (folder / "trainer.py").write_text(_trainer_template(), encoding="utf-8")
    if include_generation:
        (folder / "generator.py").write_text(_generator_template(), encoding="utf-8")
    return folder


def _manifest_template(
    *,
    plugin_id: str,
    name: str,
    architecture: str,
    output_type: str,
    include_training: bool,
    include_generation: bool,
) -> str:
    plugin_id_json = json.dumps(plugin_id)
    name_json = json.dumps(name)
    architecture_json = json.dumps(architecture)
    output_type_json = json.dumps(output_type)
    training_tool = (
        "{\n"
        f'    "id": "{plugin_id}_trainer",\n'
        f'    "name": {json.dumps(name + " Trainer")},\n'
        f'    "backend": {{"type": "python", "module": "models.{plugin_id}.trainer", "function": "train"}},\n'
        "}"
        if include_training else "{}"
    )
    generation_tool = (
        "{\n"
        f'    "id": "{plugin_id}_generator",\n'
        f'    "name": {json.dumps(name + " Generator")},\n'
        f'    "model_trainers": ["{plugin_id}"],\n'
        f'    "backend": {{"type": "python", "module": "models.{plugin_id}.generator", "function": "generate"}},\n'
        "}"
        if include_generation else "{}"
    )
    return f'''PLUGIN_ID = {plugin_id_json}

MODEL_INFO = {{
    "name": {name_json},
    "version": "0.1",
    "category": "Image Generation",
    "description": {json.dumps("Describe what " + name + " trains or generates.")},
    "architecture": {architecture_json},
    "status": "experimental",
    "output_type": {output_type_json},
}}

TRAINING_SETTINGS = {{
    "resolution": {{"label": "Resolution", "type": "choice", "options": [64, 128, 256, 384, 512], "default": 256, "group": "Basic"}},
    "batch_size": {{"label": "Batch size", "type": "int", "default": 1, "min": 1, "max": 64, "group": "Basic"}},
    "learning_rate": {{"label": "Learning rate", "type": "float", "default": 0.0001, "min": 0.0000001, "max": 0.1, "decimals": 7, "group": "Optimization"}},
    "mixed_precision": {{"label": "Precision", "type": "choice", "options": ["fp16", "no"], "default": "fp16", "group": "Optimization"}},
    "preview_enabled": {{"label": "Generate previews while training", "type": "bool", "default": True, "group": "Preview"}},
    "preview_every": {{"label": "Preview interval", "type": "int", "default": 5, "min": 1, "max": 100000, "group": "Preview"}},
    "preview_prompt": {{"label": "Preview prompt", "type": "text", "default": "", "group": "Preview"}},
    "preview_seed": {{"label": "Preview seed", "type": "int", "default": 123456789, "min": 0, "max": 2147483647, "group": "Preview"}},
}}

GENERATION_SETTINGS = {{
    "prompt": {{"label": "Prompt", "type": "multiline_text", "default": "", "group": "Prompt"}},
    "image_count": {{"label": "Images", "type": "int", "default": 1, "min": 1, "max": 48, "group": "Generation"}},
    "steps": {{"label": "Steps", "type": "int", "default": 30, "min": 1, "max": 500, "group": "Generation"}},
    "seed": {{"label": "Seed", "type": "int", "default": 0, "min": 0, "max": 2147483647, "group": "Generation"}},
}}

TRAINING_TOOL = {training_tool}

GENERATION_TOOL = {generation_tool}
'''


def _model_template() -> str:
    return '''from __future__ import annotations

from typing import Any


def load_model(model_path: str, settings: dict[str, Any] | None = None) -> Any:
    """Load your model or inference pipeline here."""
    raise NotImplementedError("Add your model loading code.")
'''


def _trainer_template() -> str:
    return '''from __future__ import annotations

from typing import Any


def train(context, **settings: Any) -> dict[str, Any]:
    """Train the model and report progress back to ADAM."""
    context.log("Replace this with real training code.")
    context.progress(100, "Training placeholder complete")
    return {}
'''


def _generator_template() -> str:
    return '''from __future__ import annotations

from typing import Any


def generate(context, **settings: Any) -> dict[str, Any]:
    """Generate outputs and report progress back to ADAM."""
    context.log("Replace this with real generation code.")
    context.progress(100, "Generation placeholder complete")
    return {}
'''