"""
Indicator Parameters Parser and Helper Functions

Supports two core functions:
1. External transfer of indicator parameters - parse the @param statement in the indicator code
2. Indicators call other indicators - provide call_indicator() function

Parameter declaration format:
# @param param_name type default_value description
# @param ma_fast int 5 short-term moving average period
# @param ma_slow int 20 long-term moving average period
# @param threshold float 0.5 threshold

Supported types: int, float, bool, str
"""

from __future__ import annotations

import re
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

from app.utils.db import get_db_connection
from app.utils.logger import get_logger

if TYPE_CHECKING:
    import pandas as pd

logger = get_logger(__name__)


class IndicatorParamsParser:
    """Parsing parameter declarations in indicator code"""

    # Parameter declaration rules: # @param name type default description
    PARAM_PATTERN = re.compile(r"#\s*@param\s+(\w+)\s+(int|float|bool|str|string)\s+(\S+)\s*(.*)", re.IGNORECASE)

    @classmethod
    def parse_params(cls, indicator_code: str) -> List[Dict[str, Any]]:
        """
        Parsing parameter declarations in indicator code

        Returns:
            List of param definitions:
            [
                {
                    "name": "ma_fast",
                    "type": "int",
                    "default": 5,
                    "description": "Short-term moving average cycle"
                },
                ...
            ]
        """
        params = []
        if not indicator_code:
            return params

        for line in indicator_code.split("\n"):
            line = line.strip()
            match = cls.PARAM_PATTERN.match(line)
            if match:
                name = match.group(1)
                param_type = match.group(2).lower()
                default_str = match.group(3)
                description = match.group(4).strip() if match.group(4) else ""

                # Convert default value type
                default = cls._convert_value(default_str, param_type)

                # Canonical type name
                if param_type == "string":
                    param_type = "str"

                params.append({"name": name, "type": param_type, "default": default, "description": description})

        return params

    @classmethod
    def _convert_value(cls, value_str: str, param_type: str) -> Any:
        """Convert string value to corresponding type"""
        try:
            param_type = param_type.lower()
            if param_type == "int":
                return int(value_str)
            elif param_type == "float":
                return float(value_str)
            elif param_type == "bool":
                return value_str.lower() in ("true", "1", "yes", "on")
            else:  # str/string
                return value_str
        except (ValueError, TypeError):
            return value_str

    @classmethod
    def merge_params(cls, declared_params: List[Dict], user_params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Merge declared parameters with user-supplied parameters

        Args:
            declared_params: parameter declarations parsed from code
            user_params: user-provided parameter values

        Returns:
            Merged parameter dictionary (using user values ​​or default values)
        """
        result = {}
        for param in declared_params:
            name = param["name"]
            param_type = param["type"]
            default = param["default"]

            if name in user_params:
                # User supplied value, converted to correct type
                result[name] = cls._convert_value(str(user_params[name]), param_type)
            else:
                # Use default value
                result[name] = default

        return result


class IndicatorCaller:
    """
    Indicator caller - allows one indicator to call another indicator

    Usage (in indicator code):
        # Call by ID
        rsi_df = call_indicator(5, df)

        # Call by name (own indicator)
        macd_df = call_indicator('My MACD', df)
    """

    # Maximum call depth to prevent circular dependencies
    MAX_CALL_DEPTH = 5

    def __init__(self, user_id: int, current_indicator_id: int = None):
        self.user_id = user_id
        self.current_indicator_id = current_indicator_id
        self._call_stack = []  # Call stack for detecting circular dependencies

    def call_indicator(
        self,
        indicator_ref: Any,  # int (ID) or str (name)
        df: pd.DataFrame,
        params: Dict[str, Any] = None,
        _depth: int = 0,
    ) -> Optional[pd.DataFrame]:
        """
        Call another indicator and return the result

        Args:
            indicator_ref: indicator ID or name
            df: input K-line data
            params: parameters passed to the called indicator
            _depth: used internally to track call depth

        Returns:
            DataFrame after execution, containing columns calculated by the called indicator
        """
        import numpy as np
        import pandas as pd

        # Check call depth
        if _depth >= self.MAX_CALL_DEPTH:
            logger.error(f"Indicator call depth exceeded {self.MAX_CALL_DEPTH}")
            return df.copy()

        # Get indicator code
        indicator_code, indicator_id = self._get_indicator_code(indicator_ref)
        if not indicator_code:
            logger.warning(f"Indicator not found: {indicator_ref}")
            return df.copy()

        # Check for circular dependencies
        if indicator_id in self._call_stack:
            logger.error(f"Circular dependency detected: {self._call_stack} -> {indicator_id}")
            return df.copy()

        self._call_stack.append(indicator_id)

        try:
            # Parse and merge parameters
            declared_params = IndicatorParamsParser.parse_params(indicator_code)
            merged_params = IndicatorParamsParser.merge_params(declared_params, params or {})

            # Prepare execution environment
            df_copy = df.copy()
            local_vars = {
                "df": df_copy,
                "open": df_copy["open"].astype("float64") if "open" in df_copy.columns else pd.Series(dtype="float64"),
                "high": df_copy["high"].astype("float64") if "high" in df_copy.columns else pd.Series(dtype="float64"),
                "low": df_copy["low"].astype("float64") if "low" in df_copy.columns else pd.Series(dtype="float64"),
                "close": df_copy["close"].astype("float64")
                if "close" in df_copy.columns
                else pd.Series(dtype="float64"),
                "volume": df_copy["volume"].astype("float64")
                if "volume" in df_copy.columns
                else pd.Series(dtype="float64"),
                "signals": pd.Series(0, index=df_copy.index, dtype="float64"),
                "np": np,
                "pd": pd,
                "params": merged_params,
                # Recursive call support
                "call_indicator": lambda ref, d, p=None: self.call_indicator(ref, d, p, _depth + 1),
            }

            # Safe execution
            import builtins

            def safe_import(name, *args, **kwargs):
                allowed_modules = ["numpy", "pandas", "math", "json", "time"]
                if name in allowed_modules or name.split(".")[0] in allowed_modules:
                    return builtins.__import__(name, *args, **kwargs)
                raise ImportError(f"Module not allowed: {name}")

            safe_builtins = {
                k: getattr(builtins, k)
                for k in dir(builtins)
                if not k.startswith("_")
                and k
                not in [
                    "eval",
                    "exec",
                    "compile",
                    "open",
                    "input",
                    "help",
                    "exit",
                    "quit",
                    "__import__",
                    "copyright",
                    "credits",
                    "license",
                ]
            }
            safe_builtins["__import__"] = safe_import

            exec_env = local_vars.copy()
            exec_env["__builtins__"] = safe_builtins

            pre_import = "import numpy as np\nimport pandas as pd\n"
            exec(pre_import, exec_env)
            exec(indicator_code, exec_env)

            return exec_env.get("df", df_copy)

        except Exception as e:
            logger.error(f"Error calling indicator {indicator_ref}: {e}")
            return df.copy()
        finally:
            self._call_stack.pop()

    def _get_indicator_code(self, indicator_ref: Any) -> Tuple[Optional[str], Optional[int]]:
        """Get indicator code"""
        try:
            with get_db_connection() as db:
                cursor = db.cursor()

                if isinstance(indicator_ref, int):
                    # Query by ID
                    cursor.execute(
                        """
                        SELECT id, code FROM qd_indicator_codes
                        WHERE id = %s AND (user_id = %s OR publish_to_community = 1)
                    """,
                        (indicator_ref, self.user_id),
                    )
                else:
                    # Query by name (priority to own indicators)
                    cursor.execute(
                        """
                        SELECT id, code FROM qd_indicator_codes
                        WHERE name = %s AND user_id = %s
                        UNION
                        SELECT id, code FROM qd_indicator_codes
                        WHERE name = %s AND publish_to_community = 1
                        LIMIT 1
                    """,
                        (str(indicator_ref), self.user_id, str(indicator_ref)),
                    )

                row = cursor.fetchone()
                cursor.close()

                if row:
                    return row["code"], row["id"]
                return None, None

        except Exception as e:
            logger.error(f"Error fetching indicator code: {e}")
            return None, None


def get_indicator_params(indicator_id: int) -> List[Dict[str, Any]]:
    """
    Get the indicator parameter declarations for API usage

    Args:
        indicator_id: Indicator ID

    Returns:
        A list of parameter declarations
    """
    try:
        with get_db_connection() as db:
            cursor = db.cursor()
            cursor.execute("SELECT code FROM qd_indicator_codes WHERE id = %s", (indicator_id,))
            row = cursor.fetchone()
            cursor.close()

            if row and row["code"]:
                return IndicatorParamsParser.parse_params(row["code"])
            return []
    except Exception as e:
        logger.error(f"Error getting indicator params: {e}")
        return []