"""
Feature chain parser for handling feature name chaining across feature groups.
"""

from __future__ import annotations

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

from mloda.core.abstract_plugins.components.feature import Feature
from mloda.core.abstract_plugins.components.feature_name import FeatureName
from mloda.core.abstract_plugins.components.options import Options
from mloda_plugins.feature_group.experimental.default_options_key import DefaultOptionKeys

# Separator constants for feature name parsing
CHAIN_SEPARATOR = "__"  # Separates chained transformations (source→suffix)
COLUMN_SEPARATOR = "~"  # Separates multi-column output index
INPUT_SEPARATOR = "&"  # Separates multiple input features


class FeatureChainParser:
    """
    Mixin class for parsing feature names with chaining support.

    Feature chaining allows feature groups to be composed, where the output of one
    feature group becomes the input to another. This is reflected in the feature name
    using separators defined as module constants:

    Separators:
        - CHAIN_SEPARATOR ("__"): Separates chained transformations (source→suffix)
          Example: price__mean_imputed__sum_7_day_window__max_aggr
          (L→R: price is source, each suffix transforms the previous result)
        - COLUMN_SEPARATOR ("~"): Separates multi-column output index
          Example: feature__pca~0, feature__pca~1
        - INPUT_SEPARATOR ("&"): Separates multiple input features
          Example: point1&point2__haversine_distance

    Each feature group in the chain extracts its relevant portion and passes the
    rest to the next feature group in the chain.
    """

    @classmethod
    def is_chained_feature(cls, feature_name: str) -> bool:
        """Check if feature name contains the chain separator.

        Args:
            feature_name: The feature name to check

        Returns:
            True if the feature name contains CHAIN_SEPARATOR, False otherwise
        """
        return CHAIN_SEPARATOR in feature_name

    @classmethod
    def parse_feature_name(
        cls,
        feature_name: FeatureName | str,
        prefix_patterns: List[str],
        pattern: str = CHAIN_SEPARATOR,
    ) -> Tuple[str | None, str | None]:
        """Internal method for parsing feature names - used by match_configuration_feature_chain_parser."""
        _feature_name: str = feature_name.name if isinstance(feature_name, FeatureName) else feature_name

        parts = _feature_name.rsplit(pattern, 1)
        source_feature = parts[0] if len(parts) > 1 else ""
        operation_part = parts[1] if len(parts) > 1 else parts[0]

        for suffix_pattern in prefix_patterns:
            if re.match(suffix_pattern, _feature_name) is None:
                continue

            if len(parts) == 1 or not source_feature:
                raise ValueError(f"Matches the pattern {pattern}, but has no source feature: {_feature_name}")

            match = re.match(suffix_pattern, _feature_name)
            if match and match.groups():
                operation_config = match.group(1)
            else:
                operation_config = operation_part.split("_")[0]

            return operation_config, source_feature

        return None, None

    @classmethod
    def _match_pattern_based_feature(
        cls,
        feature_name: str | FeatureName,
        prefix_patterns: List[str],
        pattern: str = CHAIN_SEPARATOR,
    ) -> bool:
        """Internal method for matching pattern-based features - used by match_configuration_feature_chain_parser."""
        _feature_name: FeatureName = FeatureName(feature_name) if isinstance(feature_name, str) else feature_name

        has_prefix_configuration, source_feature = cls.parse_feature_name(_feature_name, prefix_patterns, pattern)
        if has_prefix_configuration is None or source_feature is None:
            return False
        return True

    @classmethod
    def _has_default_value(cls, property_value: Any) -> bool:
        """Check if property has a default value defined."""
        return isinstance(property_value, dict) and DefaultOptionKeys.default in property_value

    @classmethod
    def _is_context_parameter(cls, property_value: Any) -> bool:
        """Check if property is marked as context parameter in mapping."""
        return isinstance(property_value, dict) and property_value.get(DefaultOptionKeys.context, False)

    @classmethod
    def _is_strict_validation(cls, property_value: Any) -> bool:
        """Check if property requires strict validation (values must be in mapping)."""
        return isinstance(property_value, dict) and property_value.get(DefaultOptionKeys.strict_validation, False)

    @classmethod
    def _get_validation_function(cls, property_value: Any) -> Any:
        """Get validation function from property mapping if present."""
        if isinstance(property_value, dict):
            return property_value.get(DefaultOptionKeys.validation_function, None)
        return None

    @classmethod
    def _validate_property_value(
        cls, found_property_val: Any, property_value: Any, property_name: str, original_property_config: Any
    ) -> None:
        """
        Unified validation function: if strict validation -> apply validation function OR check membership.

        Raises ValueError if validation fails, otherwise returns None.
        """
        if not cls._is_strict_validation(original_property_config):
            return  # No validation needed

        validation_function = cls._get_validation_function(original_property_config)

        if validation_function is not None:
            # Use validation function if available
            if not validation_function(found_property_val):
                raise ValueError(f"Property value '{found_property_val}' failed validation for '{property_name}'")
        else:
            # Fallback to membership check
            if found_property_val not in property_value:
                raise ValueError(f"Property value '{found_property_val}' not found in mapping for '{property_name}'")

    @classmethod
    def _determine_parameter_category(cls, property_name: str, property_value: Any, options: Options) -> str:
        """
        Determine whether a parameter should be in group or context category.

        Priority:
        1. User explicit override (if property exists in specific category)
        2. Property mapping default (mloda_context flag)
        3. Fallback to group

        Args:
            property_name: Name of the property
            property_value: Property configuration from mapping
            options: Options object containing user's parameter placement

        Returns:
            "group" or "context" indicating target category

        Raises:
            ValueError: If parameter exists in both group and context
        """

        if property_name in options.group and property_name in options.context:
            raise ValueError(
                f"Parameter '{property_name}' exists in both group and context. "
                "This is not allowed. Please choose one category."
            )

        if property_name in options.group:
            return DefaultOptionKeys.group.value
        elif property_name in options.context:
            return DefaultOptionKeys.context.value
        elif cls._is_context_parameter(property_value):
            return DefaultOptionKeys.context.value
        else:
            return DefaultOptionKeys.group.value

    @classmethod
    def _extract_property_values(cls, property_value: Any) -> Any:
        """Extract property values, removing metadata keys."""
        if isinstance(property_value, dict):
            # Remove metadata keys, keep only the actual valid values
            metadata_keys = {
                DefaultOptionKeys.default,
                DefaultOptionKeys.context,
                DefaultOptionKeys.group,
                DefaultOptionKeys.strict_validation,
                DefaultOptionKeys.validation_function,
            }
            return {k: v for k, v in property_value.items() if k not in metadata_keys}
        return property_value

    @classmethod
    def _process_found_property_value(
        cls, found_property_value: Any, property_value: Any, property_name: str, original_property_config: Any
    ) -> Set[str]:
        if not isinstance(found_property_value, frozenset):
            found_property_value = frozenset([found_property_value])

        collected_property_value = set()
        for found_property_val in found_property_value:
            if isinstance(found_property_val, Feature):
                found_property_val = found_property_val.get_name()

            if isinstance(found_property_val, tuple):
                # Convert tuple to string representation for hashability
                found_property_val = str(found_property_val)

            # Use unified validation function
            cls._validate_property_value(found_property_val, property_value, property_name, original_property_config)

            collected_property_value.add(found_property_val)

        return collected_property_value

    @classmethod
    def _validate_final_properties(
        cls, property_tracker: Dict[str, None | Set[str]], property_mapping: Dict[str, Any]
    ) -> bool:
        """Validate that all required properties have values."""
        for key, value in property_tracker.items():
            property_config = property_mapping[key]
            has_default = cls._has_default_value(property_config)

            if not value and not has_default:
                return False
        return True

    @classmethod
    def _validate_options_against_property_mapping(cls, options: Options, property_mapping: Dict[str, Any]) -> bool:
        """
        Shared validation logic for both string-based and configuration-based approaches.

        Args:
            options: Options object containing the parameters to validate
            property_mapping: Property mapping with validation rules

        Returns:
            True if validation passes, False otherwise
        """
        property_tracker: Dict[str, None | Set[str]] = {}
        for key in property_mapping:
            property_tracker[key] = None

        # Process each property in the mapping
        for property_name, property_value in property_mapping.items():
            found_property_value = options.get(property_name)
            has_default = cls._has_default_value(property_value)
            property_value = cls._extract_property_values(property_value)

            # Handle missing properties
            if found_property_value is None:
                if has_default:
                    # Property with default not present - mark as processed but empty
                    property_tracker[property_name] = set()
                    continue
                else:
                    # Required property not present - skip (will fail validation later)
                    continue

            collected_property_value = cls._process_found_property_value(
                found_property_value, property_value, property_name, property_mapping[property_name]
            )

            if property_tracker[property_name] is not None:
                raise ValueError(f"Feature name has duplicate values for property '{property_name}'.")

            property_tracker[property_name] = collected_property_value
        return cls._validate_final_properties(property_tracker, property_mapping)

    @classmethod
    def match_configuration_feature_chain_parser(
        cls,
        feature_name: str | FeatureName,
        options: Options,
        property_mapping: Optional[Dict[str, Any]] = None,
        prefix_patterns: Optional[List[str]] = None,
        pattern: str = CHAIN_SEPARATOR,
    ) -> bool:
        """
        Unified method for matching features using either configuration-based or pattern-based parsing.

        Args:
            feature_name: The feature name to match
            options: Options object containing configuration
            property_mapping: Optional property mapping for configuration-based parsing
            prefix_patterns: Optional prefix patterns for pattern-based parsing
            pattern: Pattern string for pattern-based parsing (defaults to CHAIN_SEPARATOR)

        Returns:
            True if the feature matches either pattern-based or configuration-based parsing, False otherwise
        """

        # string based matching
        if prefix_patterns is not None:
            if cls._match_pattern_based_feature(feature_name, prefix_patterns, pattern):
                return True

        # configuration-based
        if property_mapping is not None:
            return cls._validate_options_against_property_mapping(options, property_mapping)

        # If neither pattern-based nor configuration-based matching succeeded, return False
        return False

    @classmethod
    def extract_in_feature(cls, feature_name: str, suffix_pattern: str) -> str:
        """
        Extract the in_feature from a feature name based on the suffix pattern.

        Args:
            feature_name: The feature name to parse
            suffix_pattern: Regex pattern for the suffix (e.g., r"^.+__([w]+)$")

        Returns:
            The in_feature part of the name

        Raises:
            ValueError: If the feature name doesn't match the expected pattern
        """
        match = re.match(suffix_pattern, feature_name)
        if not match:
            raise ValueError(f"Invalid feature name format: {feature_name}")

        # For L→R: source is everything BEFORE the last CHAIN_SEPARATOR
        suffix_start = feature_name.rfind(CHAIN_SEPARATOR)
        if suffix_start == -1:
            raise ValueError(
                f"Invalid feature name format: {feature_name}. Missing chain separator '{CHAIN_SEPARATOR}'."
            )

        # Return everything BEFORE the last double underscore (the source)
        return feature_name[:suffix_start]
