"""
MCP Tool 222: REFLECT

Epistemic honesty through Ω₀ (Omega Zero) prediction.

Constitutional validation:
- F7 (Ω₀/Humility): Acknowledges confidence limits [0.03, 0.05]
- F4 (ΔS): Explicit confidence bounds before generation
- F2 (Truth): Honest uncertainty representation

This tool predicts where model confidence falls within the humility band.
Never blocks execution - always PASS (reflection, not rejection).
"""

import asyncio
from datetime import datetime, timezone
from typing import Any, Dict, Tuple

from arifos_core.mcp.models import VerdictResponse


# =============================================================================
# CONSTANTS
# =============================================================================

# Constitutional F7 target: Ω₀ ∈ [0.03, 0.05]
OMEGA_ZERO_MIN = 0.03
OMEGA_ZERO_MAX = 0.05
OMEGA_ZERO_TARGET = (OMEGA_ZERO_MIN, OMEGA_ZERO_MAX)

# Confidence mapping: confidence_estimate → predicted Ω₀
# Higher confidence → closer to lower bound (0.03)
# Lower confidence → closer to upper bound (0.05)
CONFIDENCE_MAP = {
    0.95: 0.03,    # Very confident → minimum uncertainty
    0.90: 0.035,   # High confidence
    0.80: 0.038,   # Moderate-high confidence
    0.70: 0.040,   # Moderate confidence → center of band
    0.60: 0.042,   # Moderate-low confidence
    0.50: 0.044,   # Low confidence
    0.40: 0.046,   # Very low confidence
    0.30: 0.048,   # Near-uncertain
    0.20: 0.049,   # Extremely uncertain
    0.10: 0.050,   # Maximum uncertainty → upper bound
}


# =============================================================================
# HELPER FUNCTIONS
# =============================================================================

def predict_omega_zero(query: str, confidence: float) -> float:
    """
    Predict Ω₀ (epistemic uncertainty) based on query and confidence estimate.

    Constitutional grounding:
    - F7 (Humility): Maps confidence to uncertainty band [0.03, 0.05]
    - F2 (Truth): Honest representation of model limits

    Args:
        query: Input query text (used for context, not parsed)
        confidence: Confidence estimate in [0.0, 1.0]

    Returns:
        Predicted Ω₀ value in [0.03, 0.05]
    """
    # Clamp confidence to valid range
    confidence = max(0.0, min(1.0, confidence))

    # Find closest confidence tier in map
    sorted_tiers = sorted(CONFIDENCE_MAP.keys(), reverse=True)

    # Linear interpolation between tiers
    for i, tier in enumerate(sorted_tiers):
        if confidence >= tier:
            # Interpolate between this tier and next
            if i == 0:
                # At or above highest tier
                return CONFIDENCE_MAP[tier]

            upper_tier = sorted_tiers[i - 1]
            lower_tier = tier

            # Interpolate
            tier_range = upper_tier - lower_tier
            confidence_in_range = confidence - lower_tier
            ratio = confidence_in_range / tier_range if tier_range > 0 else 0.0

            omega_lower = CONFIDENCE_MAP[lower_tier]
            omega_upper = CONFIDENCE_MAP[upper_tier]

            return omega_lower + ratio * (omega_upper - omega_lower)

    # Below all tiers → maximum uncertainty
    return OMEGA_ZERO_MAX


def validate_epistemic_band(omega: float) -> bool:
    """
    Check if Ω₀ value falls within constitutional band [0.03, 0.05].

    Constitutional grounding:
    - F7 (Humility): Enforces uncertainty bounds

    Args:
        omega: Predicted Ω₀ value

    Returns:
        True if omega ∈ [0.03, 0.05], False otherwise
    """
    return OMEGA_ZERO_MIN <= omega <= OMEGA_ZERO_MAX


def classify_epistemic_quality(omega: float) -> str:
    """
    Classify epistemic quality based on Ω₀ position in band.

    Args:
        omega: Predicted Ω₀ value

    Returns:
        Quality label: "high_certainty", "moderate", "low_certainty", "out_of_band"
    """
    if not validate_epistemic_band(omega):
        return "out_of_band"

    # Within band [0.03, 0.05]
    band_position = (omega - OMEGA_ZERO_MIN) / (OMEGA_ZERO_MAX - OMEGA_ZERO_MIN)

    if band_position < 0.33:
        return "high_certainty"  # Lower third (0.03-0.037)
    elif band_position < 0.67:
        return "moderate"  # Middle third (0.037-0.043)
    else:
        return "low_certainty"  # Upper third (0.043-0.05)


def generate_humility_annotation(omega: float, quality: str) -> str:
    """
    Generate human-readable humility annotation for response.

    Constitutional grounding:
    - F4 (ΔS): Reduces confusion via clear uncertainty statement
    - F7 (Humility): Makes uncertainty explicit

    Args:
        omega: Predicted Ω₀ value
        quality: Epistemic quality classification

    Returns:
        Annotation string for response metadata
    """
    annotations = {
        "high_certainty": f"High confidence (Ω₀={omega:.3f}). Factual claims verified.",
        "moderate": f"Moderate confidence (Ω₀={omega:.3f}). Some uncertainty remains.",
        "low_certainty": f"Low confidence (Ω₀={omega:.3f}). Significant uncertainty.",
        "out_of_band": f"Out of band (Ω₀={omega:.3f}). Requires recalibration.",
    }

    return annotations.get(quality, f"Ω₀={omega:.3f}")


# =============================================================================
# MCP TOOL IMPLEMENTATION
# =============================================================================

async def mcp_222_reflect(request: Dict[str, Any]) -> VerdictResponse:
    """
    MCP Tool 222: REFLECT - Epistemic honesty through Ω₀ prediction.

    Constitutional role:
    - F7 (Humility): Predicts where confidence falls in [0.03, 0.05]
    - F4 (Clarity): Explicit confidence bounds before generation

    Always PASS (reflection, not rejection). Never blocks execution.

    Args:
        request: {
            "query": str,              # Input query text
            "confidence": float,       # Confidence estimate [0.0, 1.0]
        }

    Returns:
        VerdictResponse with:
        - verdict: "PASS" (always)
        - reason: Explanation of Ω₀ prediction
        - side_data: {
            "omega_zero": float,           # Predicted Ω₀
            "in_band": bool,               # Within [0.03, 0.05]?
            "epistemic_quality": str,      # Quality classification
            "annotation": str,             # Human-readable annotation
            "target_band": tuple,          # (0.03, 0.05)
          }
    """
    # Extract inputs
    query = request.get("query", "")
    confidence = request.get("confidence", 0.5)

    # Validate inputs
    if not isinstance(query, str):
        query = ""

    if not isinstance(confidence, (int, float)):
        confidence = 0.5

    # Predict Ω₀
    omega_zero = predict_omega_zero(query, confidence)
    in_band = validate_epistemic_band(omega_zero)
    epistemic_quality = classify_epistemic_quality(omega_zero)
    annotation = generate_humility_annotation(omega_zero, epistemic_quality)

    # Generate timestamp
    timestamp = datetime.now(timezone.utc).isoformat()

    # Determine reason
    if in_band:
        reason = f"Epistemic reflection complete. Ω₀={omega_zero:.3f} within constitutional band [0.03, 0.05]."
    else:
        reason = f"Epistemic reflection complete. Ω₀={omega_zero:.3f} OUT OF BAND. Recalibration recommended."

    return VerdictResponse(
        verdict="PASS",  # 222 never blocks
        reason=reason,
        side_data={
            "omega_zero": omega_zero,
            "in_band": in_band,
            "epistemic_quality": epistemic_quality,
            "annotation": annotation,
            "target_band": OMEGA_ZERO_TARGET,
            "confidence_input": confidence,
        },
        timestamp=timestamp,
    )


def mcp_222_reflect_sync(request: Dict[str, Any]) -> VerdictResponse:
    """Synchronous wrapper for mcp_222_reflect."""
    return asyncio.run(mcp_222_reflect(request))
