"""
Enhanced Placeholder Metadata Extraction

Improved extraction logic that addresses quality issues:
1. Correct value_raw extraction
2. Accurate unit inference
3. Precise time_window detection
4. Real source provenance
5. Quality filter policies for activity placeholders
"""
import re
import json
from typing import Any, Optional, Tuple, Dict
from placeholder_metadata import (
    PlaceholderType,
    TimeWindow,
    OutputType,
    QualityFilterPolicy,
    ConfidenceLogic,
    ConfidenceLevel
)


# ── Enhanced Value Raw Extraction ─────────────────────────────────────────────

def extract_value_raw(value_display: str, output_type: OutputType, placeholder_type: PlaceholderType) -> Tuple[Any, bool]:
    """
    Extract raw value from display string.

    Returns: (raw_value, success)
    """
    if not value_display or value_display in ['nicht verfügbar', 'nicht genug Daten']:
        return None, True

    # JSON output type
    if output_type == OutputType.JSON:
        try:
            return json.loads(value_display), True
        except (json.JSONDecodeError, TypeError):
            # Try to find JSON in string
            json_match = re.search(r'(\{.*\}|\[.*\])', value_display, re.DOTALL)
            if json_match:
                try:
                    return json.loads(json_match.group(1)), True
                except:
                    pass
            return None, False

    # Markdown output type
    if output_type == OutputType.MARKDOWN:
        return value_display, True

    # Number types
    if output_type in [OutputType.NUMBER, OutputType.INTEGER]:
        # Extract first number from string
        match = re.search(r'([-+]?\d+\.?\d*)', value_display)
        if match:
            val = float(match.group(1))
            return int(val) if output_type == OutputType.INTEGER else val, True
        return None, False

    # Date
    if output_type == OutputType.DATE:
        # Check if already ISO format
        if re.match(r'\d{4}-\d{2}-\d{2}', value_display):
            return value_display, True
        return value_display, False  # Unknown format

    # String/Enum - return as-is
    return value_display, True


# ── Enhanced Unit Inference ───────────────────────────────────────────────────

def infer_unit_strict(key: str, description: str, output_type: OutputType, placeholder_type: PlaceholderType) -> Optional[str]:
    """
    Strict unit inference - only return unit if certain.

    NO units for:
    - Scores (dimensionless)
    - Correlations (dimensionless)
    - Percentages expressed as 0-100 scale
    - Classifications/enums
    - JSON/Markdown outputs
    """
    key_lower = key.lower()
    desc_lower = description.lower()

    # JSON/Markdown never have units
    if output_type in [OutputType.JSON, OutputType.MARKDOWN, OutputType.ENUM]:
        return None

    # Scores are dimensionless (0-100 scale)
    if 'score' in key_lower or 'adequacy' in key_lower:
        return None

    # Correlations are dimensionless
    if 'correlation' in key_lower:
        return None

    # Ratios/percentages on 0-100 scale
    if any(x in key_lower for x in ['pct', 'ratio', 'balance', 'compliance', 'consistency']):
        return None

    # Classifications/quadrants
    if 'quadrant' in key_lower or 'classification' in key_lower:
        return None

    # Weight/mass
    if any(x in key_lower for x in ['weight', 'gewicht', 'fm_', 'lbm_', 'masse']):
        return 'kg'

    # Circumferences/lengths
    if any(x in key_lower for x in ['umfang', 'waist', 'hip', 'chest', 'arm', 'leg', 'delta']) and 'circumference' in desc_lower:
        return 'cm'

    # Time durations
    if any(x in key_lower for x in ['duration', 'dauer', 'debt']):
        if 'hours' in desc_lower or 'stunden' in desc_lower:
            return 'Stunden'
        elif 'minutes' in desc_lower or 'minuten' in desc_lower:
            return 'Minuten'
        return None  # Unclear

    # Heart rate
    if 'rhr' in key_lower or ('hr' in key_lower and 'hrv' not in key_lower) or 'puls' in key_lower:
        return 'bpm'

    # HRV
    if 'hrv' in key_lower:
        return 'ms'

    # VO2 Max
    if 'vo2' in key_lower:
        return 'ml/kg/min'

    # Calories/energy
    if 'kcal' in key_lower or 'energy' in key_lower or 'energie' in key_lower:
        return 'kcal'

    # Macros (protein, carbs, fat)
    if any(x in key_lower for x in ['protein', 'carb', 'fat', 'kohlenhydrat', 'fett']) and 'g' in desc_lower:
        return 'g'

    # Height
    if 'height' in key_lower or 'größe' in key_lower:
        return 'cm'

    # Age
    if 'age' in key_lower or 'alter' in key_lower:
        return 'Jahre'

    # BMI is dimensionless
    if 'bmi' in key_lower:
        return None

    # Default: No unit (conservative)
    return None


# ── Enhanced Time Window Detection ────────────────────────────────────────────

def detect_time_window_precise(
    key: str,
    description: str,
    resolver_name: str,
    semantic_contract: str
) -> Tuple[TimeWindow, bool, Optional[str]]:
    """
    Detect time window with precision.

    Returns: (time_window, is_certain, mismatch_note)
    """
    key_lower = key.lower()
    desc_lower = description.lower()
    contract_lower = semantic_contract.lower()

    # Explicit suffixes (highest confidence)
    if '_7d' in key_lower:
        return TimeWindow.DAYS_7, True, None
    if '_14d' in key_lower:
        return TimeWindow.DAYS_14, True, None
    if '_28d' in key_lower:
        return TimeWindow.DAYS_28, True, None
    if '_30d' in key_lower:
        return TimeWindow.DAYS_30, True, None
    if '_90d' in key_lower:
        return TimeWindow.DAYS_90, True, None
    if '_3d' in key_lower:
        return TimeWindow.DAYS_7, True, None  # Map 3d to closest standard

    # Latest/current
    if any(x in key_lower for x in ['aktuell', 'latest', 'current', 'letzter']):
        return TimeWindow.LATEST, True, None

    # Check semantic contract for time window info
    if '7 tag' in contract_lower or '7d' in contract_lower:
        # Check for description mismatch
        mismatch = None
        if '30' in desc_lower or '28' in desc_lower:
            mismatch = f"Description says 30d/28d but implementation is 7d"
        return TimeWindow.DAYS_7, True, mismatch

    if '28 tag' in contract_lower or '28d' in contract_lower:
        mismatch = None
        if '7' in desc_lower and '28' not in desc_lower:
            mismatch = f"Description says 7d but implementation is 28d"
        return TimeWindow.DAYS_28, True, mismatch

    if '30 tag' in contract_lower or '30d' in contract_lower:
        return TimeWindow.DAYS_30, True, None

    if '90 tag' in contract_lower or '90d' in contract_lower:
        return TimeWindow.DAYS_90, True, None

    # Check description patterns
    if 'letzte 7' in desc_lower or '7 tag' in desc_lower:
        return TimeWindow.DAYS_7, False, None

    if 'letzte 30' in desc_lower or '30 tag' in desc_lower:
        return TimeWindow.DAYS_30, False, None

    # Averages typically 30d unless specified
    if 'avg' in key_lower or 'durchschn' in key_lower:
        if '7' in desc_lower:
            return TimeWindow.DAYS_7, False, None
        return TimeWindow.DAYS_30, False, "Assumed 30d for average (not explicit)"

    # Trends typically 28d
    if 'trend' in key_lower:
        return TimeWindow.DAYS_28, False, "Assumed 28d for trend"

    # Week-based
    if 'week' in key_lower or 'woche' in key_lower:
        return TimeWindow.DAYS_7, False, None

    # Profile data is latest
    if key_lower in ['name', 'age', 'height', 'geschlecht']:
        return TimeWindow.LATEST, True, None

    # Unknown
    return TimeWindow.UNKNOWN, False, "Could not determine time window from code or documentation"


# ── Enhanced Source Provenance ────────────────────────────────────────────────

def resolve_real_source(resolver_name: str) -> Tuple[Optional[str], Optional[str], list, str]:
    """
    Resolve real source function (not safe wrappers).

    Returns: (function, data_layer_module, source_tables, source_kind)
    """
    # Skip safe wrappers - they're not real sources
    if resolver_name in ['_safe_int', '_safe_float', '_safe_json', '_safe_str']:
        return None, None, [], "wrapper"

    # Direct mappings to data layer
    source_map = {
        # Body metrics
        'get_latest_weight': ('get_latest_weight_data', 'body_metrics', ['weight_log'], 'direct'),
        'get_weight_trend': ('get_weight_trend_data', 'body_metrics', ['weight_log'], 'computed'),
        'get_latest_bf': ('get_body_composition_data', 'body_metrics', ['caliper_log'], 'direct'),
        'get_circ_summary': ('get_circumference_summary_data', 'body_metrics', ['circumference_log'], 'aggregated'),
        'get_caliper_summary': ('get_body_composition_data', 'body_metrics', ['caliper_log'], 'aggregated'),
        'calculate_bmi': (None, None, ['weight_log', 'profiles'], 'computed'),

        # Nutrition
        'get_nutrition_avg': ('get_nutrition_average_data', 'nutrition_metrics', ['nutrition_log'], 'aggregated'),
        'get_protein_per_kg': ('get_protein_targets_data', 'nutrition_metrics', ['nutrition_log', 'weight_log'], 'computed'),
        'get_nutrition_days': ('get_nutrition_days_data', 'nutrition_metrics', ['nutrition_log'], 'computed'),

        # Activity
        'get_activity_summary': ('get_activity_summary_data', 'activity_metrics', ['activity_log', 'training_types'], 'aggregated'),
        'get_activity_detail': ('get_activity_detail_data', 'activity_metrics', ['activity_log', 'training_types'], 'aggregated'),
        'get_training_type_dist': ('get_training_type_distribution_data', 'activity_metrics', ['activity_log', 'training_types'], 'aggregated'),

        # Sleep
        'get_sleep_duration': ('get_sleep_duration_data', 'recovery_metrics', ['sleep_log'], 'aggregated'),
        'get_sleep_quality': ('get_sleep_quality_data', 'recovery_metrics', ['sleep_log'], 'computed'),

        # Vitals
        'get_resting_hr': ('get_resting_heart_rate_data', 'health_metrics', ['vitals_baseline'], 'direct'),
        'get_hrv': ('get_heart_rate_variability_data', 'health_metrics', ['vitals_baseline'], 'direct'),
        'get_vo2_max': ('get_vo2_max_data', 'health_metrics', ['vitals_baseline'], 'direct'),

        # Profile
        'get_profile_data': (None, None, ['profiles'], 'direct'),
        'calculate_age': (None, None, ['profiles'], 'computed'),

        # Goals
        'get_goal_weight': (None, None, ['goals'], 'direct'),
        'get_goal_bf_pct': (None, None, ['goals'], 'direct'),
    }

    if resolver_name in source_map:
        return source_map[resolver_name]

    # Goals formatting functions
    if resolver_name.startswith('_format_goals'):
        return (None, None, ['goals', 'goal_focus_contributions'], 'interpreted')

    # Unknown
    return None, None, [], "unknown"


# ── Quality Filter Policy for Activity Placeholders ───────────────────────────

def create_activity_quality_policy(key: str) -> Optional[QualityFilterPolicy]:
    """
    Create quality filter policy for activity-related placeholders.
    """
    key_lower = key.lower()

    # Activity-related placeholders need quality policies
    if any(x in key_lower for x in ['activity', 'training', 'load', 'volume', 'quality_session', 'ability']):
        return QualityFilterPolicy(
            enabled=True,
            default_filter_level="quality",
            null_quality_handling="exclude",
            includes_poor=False,
            includes_excluded=False,
            notes="Activity metrics filter for quality='quality' by default. NULL quality_label excluded."
        )

    return None


# ── Confidence Logic Creation ─────────────────────────────────────────────────

def create_confidence_logic(key: str, data_layer_module: Optional[str]) -> Optional[ConfidenceLogic]:
    """
    Create confidence logic if applicable.
    """
    key_lower = key.lower()

    # Data layer functions typically have confidence
    if data_layer_module:
        return ConfidenceLogic(
            supported=True,
            calculation="Based on data availability and quality thresholds",
            thresholds={"min_data_points": 1},
            notes=f"Confidence determined by {data_layer_module}"
        )

    # Scores have implicit confidence
    if 'score' in key_lower:
        return ConfidenceLogic(
            supported=True,
            calculation="Based on data completeness for score components",
            notes="Score confidence correlates with input data availability"
        )

    # Correlations have confidence
    if 'correlation' in key_lower:
        return ConfidenceLogic(
            supported=True,
            calculation="Pearson correlation with significance testing",
            thresholds={"min_data_points": 7},
            notes="Requires minimum 7 data points for meaningful correlation"
        )

    return None


# ── Metadata Completeness Score ───────────────────────────────────────────────

def calculate_completeness_score(metadata_dict: Dict) -> int:
    """
    Calculate metadata completeness score (0-100).

    Checks:
    - Required fields filled
    - Time window not unknown
    - Output type not unknown
    - Unit specified (if applicable)
    - Source provenance complete
    - Quality/confidence policies (if applicable)
    """
    score = 0
    max_score = 100

    # Required fields (30 points)
    if metadata_dict.get('category') and metadata_dict['category'] != 'Unknown':
        score += 5
    if metadata_dict.get('description') and 'No description' not in metadata_dict['description']:
        score += 5
    if metadata_dict.get('semantic_contract'):
        score += 10
    if metadata_dict.get('source', {}).get('resolver') and metadata_dict['source']['resolver'] != 'unknown':
        score += 10

    # Type specification (20 points)
    if metadata_dict.get('type') and metadata_dict['type'] != 'legacy_unknown':
        score += 10
    if metadata_dict.get('time_window') and metadata_dict['time_window'] != 'unknown':
        score += 10

    # Output specification (20 points)
    if metadata_dict.get('output_type') and metadata_dict['output_type'] != 'unknown':
        score += 10
    if metadata_dict.get('format_hint'):
        score += 10

    # Source provenance (20 points)
    source = metadata_dict.get('source', {})
    if source.get('data_layer_module'):
        score += 10
    if source.get('source_tables'):
        score += 10

    # Quality policies (10 points)
    if metadata_dict.get('quality_filter_policy'):
        score += 5
    if metadata_dict.get('confidence_logic'):
        score += 5

    return min(score, max_score)