"""
OCR Data Validation Module

Provides multi-layer validation for OCR extraction results to prevent
incorrect data from entering the system.

Validation Layers:
1. Absolute sanity checks (value ranges)
2. Cross-field validation (correlation between fields)
3. Inter-OCR consistency (compare multiple OCR results)
4. Auto-correction (fix obvious errors)

Usage:
    engine = OCRValidationEngine()
    validated_result = engine.validate_extraction(
        merged_result,
        light_ocr_result,
        medium_ocr_result
    )
"""

from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import Any, Optional


@dataclass
class ValidationResult:
    """Result of a single validation rule execution.

    Attributes:
        is_valid: Whether the validation passed
        confidence_penalty: Penalty to apply to confidence score (0.0-1.0)
                          0.0 = no penalty, 1.0 = complete rejection
        message: Human-readable description of validation result
        severity: "info" | "warning" | "error"
    """
    is_valid: bool
    confidence_penalty: float = 0.0
    message: str = ""
    severity: str = "info"  # "info" | "warning" | "error"

    def __post_init__(self):
        """Validate penalty is in valid range."""
        if not 0.0 <= self.confidence_penalty <= 1.0:
            raise ValueError(f"Confidence penalty must be 0.0-1.0, got {self.confidence_penalty}")


class ValidationRule(ABC):
    """Abstract base class for OCR validation rules.

    Each rule implements a specific validation check and returns
    a ValidationResult indicating success/failure with optional
    confidence penalty.
    """

    @abstractmethod
    def validate(self, data: dict[str, Any]) -> ValidationResult:
        """Execute validation rule on extraction data.

        Args:
            data: Dictionary containing extraction fields to validate
                  Example: {"amount": 85.99, "tva": 14.92, ...}

        Returns:
            ValidationResult with is_valid flag and optional penalty
        """
        pass

    @property
    @abstractmethod
    def rule_name(self) -> str:
        """Human-readable name of this validation rule."""
        pass


# ============================================================================
# VALIDATION RULES
# ============================================================================


class AmountRangeRule(ValidationRule):
    """Validate amount is within reasonable bounds for Romanian receipts.

    Romanian receipts rarely exceed 100,000 RON. This catches obvious
    OCR errors like digit concatenation (85.99 → 859,762.16).

    Example:
        rule = AmountRangeRule(min_amount=0.01, max_amount=100_000.0)
        result = rule.validate({"amount": 859762.16})
        # result.is_valid = False, penalty = 0.5
    """

    def __init__(self, min_amount: float = 0.01, max_amount: float = 100_000.0):
        self.min_amount = min_amount
        self.max_amount = max_amount

    @property
    def rule_name(self) -> str:
        return "Amount Range Check"

    def validate(self, data: dict[str, Any]) -> ValidationResult:
        amount = data.get("amount")

        if amount is None:
            return ValidationResult(
                is_valid=True,
                message="No amount to validate"
            )

        if amount < self.min_amount:
            return ValidationResult(
                is_valid=False,
                confidence_penalty=0.5,
                message=f"Amount {amount:.2f} RON below minimum {self.min_amount:.2f} RON",
                severity="error"
            )

        if amount > self.max_amount:
            return ValidationResult(
                is_valid=False,
                confidence_penalty=0.5,
                message=f"Amount {amount:.2f} RON exceeds maximum {self.max_amount:.2f} RON (likely OCR error)",
                severity="error"
            )

        return ValidationResult(
            is_valid=True,
            message=f"Amount {amount:.2f} RON within valid range"
        )


class TVARatioRule(ValidationRule):
    """Validate TVA is reasonable percentage of TOTAL amount.

    Romanian TVA rates: 5%, 9%, 19%, 21% (most common: 19-21%)
    This catches errors where TVA > TOTAL (impossible).

    Example:
        rule = TVARatioRule(min_ratio=0.05, max_ratio=0.24)
        result = rule.validate({"amount": 85.99, "tva": 149.21})
        # result.is_valid = False (149.21 > 85.99!)
    """

    def __init__(self, min_ratio: float = 0.05, max_ratio: float = 0.24):
        self.min_ratio = min_ratio
        self.max_ratio = max_ratio

    @property
    def rule_name(self) -> str:
        return "TVA Ratio Check"

    def validate(self, data: dict[str, Any]) -> ValidationResult:
        amount = data.get("amount")
        tva = data.get("tva")

        if not amount or not tva:
            return ValidationResult(
                is_valid=True,
                message="Insufficient data for TVA correlation"
            )

        # Type safety: ensure numeric types before division
        if not isinstance(amount, (int, float)) or not isinstance(tva, (int, float)):
            return ValidationResult(
                is_valid=True,
                message="Non-numeric values, skipping TVA correlation"
            )

        # Avoid division by zero
        if amount <= 0:
            return ValidationResult(
                is_valid=True,
                message="Amount is zero or negative, skipping TVA ratio"
            )

        tva_ratio = tva / amount

        if tva_ratio < self.min_ratio or tva_ratio > self.max_ratio:
            return ValidationResult(
                is_valid=False,
                confidence_penalty=0.3,
                message=f"TVA ratio {tva_ratio:.1%} outside valid range ({self.min_ratio:.0%}-{self.max_ratio:.0%})",
                severity="warning"
            )

        return ValidationResult(
            is_valid=True,
            message=f"TVA ratio {tva_ratio:.1%} valid"
        )


class PaymentSumRule(ValidationRule):
    """Validate CARD + NUMERAR = TOTAL BON (within tolerance).

    This is a CRITICAL validation that catches cases where OCR extracts
    wrong TOTAL but correct payment methods.

    Example:
        rule = PaymentSumRule(tolerance=0.02)
        result = rule.validate({
            "amount": 859762.16,  # Wrong from OCR
            "card_amount": 85.99,  # Correct
            "cash_amount": 0.0
        })
        # result.is_valid = False, suggests auto-correction
    """

    def __init__(self, tolerance: float = 0.02):
        self.tolerance = tolerance

    @property
    def rule_name(self) -> str:
        return "Payment Sum Check"

    def validate(self, data: dict[str, Any]) -> ValidationResult:
        total = data.get("amount")
        card = data.get("card_amount", 0.0) or 0.0
        cash = data.get("cash_amount", 0.0) or 0.0

        if not total:
            return ValidationResult(
                is_valid=True,
                message="No total amount to validate"
            )

        payment_sum = card + cash

        if payment_sum == 0:
            return ValidationResult(
                is_valid=True,
                message="No payment methods extracted"
            )

        diff = abs(total - payment_sum)

        if diff > self.tolerance:
            return ValidationResult(
                is_valid=False,
                confidence_penalty=0.4,
                message=f"Payment sum {payment_sum:.2f} RON ≠ Total {total:.2f} RON (diff: {diff:.2f} RON). Consider auto-correction.",
                severity="error"
            )

        return ValidationResult(
            is_valid=True,
            message=f"Payment sum matches total (diff: {diff:.2f} RON)"
        )


class TVAEntriesSumRule(ValidationRule):
    """Validate Σ(TVA entries) = TVA TOTAL (within tolerance).

    TVA breakdown (A, B, C, D rates) should sum to total TVA.

    Example:
        rule = TVAEntriesSumRule(tolerance=0.02)
        result = rule.validate({
            "tva": 14.92,
            "tva_entries": {"A": 14.92, "B": 0.0}
        })
        # result.is_valid = True
    """

    def __init__(self, tolerance: float = 0.02):
        self.tolerance = tolerance

    @property
    def rule_name(self) -> str:
        return "TVA Entries Sum Check"

    def validate(self, data: dict[str, Any]) -> ValidationResult:
        tva_total = data.get("tva")
        tva_entries = data.get("tva_entries", {})

        if not tva_total:
            return ValidationResult(
                is_valid=True,
                message="No TVA total to validate"
            )

        if not tva_entries:
            return ValidationResult(
                is_valid=True,
                message="No TVA entries extracted"
            )

        entries_sum = sum(tva_entries.values())

        if entries_sum == 0:
            return ValidationResult(
                is_valid=True,
                message="TVA entries sum is zero"
            )

        diff = abs(tva_total - entries_sum)

        if diff > self.tolerance:
            return ValidationResult(
                is_valid=False,
                confidence_penalty=0.2,
                message=f"TVA entries sum {entries_sum:.2f} RON ≠ TVA total {tva_total:.2f} RON (diff: {diff:.2f} RON)",
                severity="warning"
            )

        return ValidationResult(
            is_valid=True,
            message=f"TVA entries sum matches total (diff: {diff:.2f} RON)"
        )


class CUIFormatRule(ValidationRule):
    """Validate CUI format: RO + 6-10 digits.

    Romanian CUI (Cod Unic de Identificare) format:
    - Optional "RO" prefix (or "R0" from OCR errors)
    - 6-10 numeric digits

    Example:
        rule = CUIFormatRule()
        result = rule.validate({"cui": "RO10562600"})
        # result.is_valid = True
    """

    @property
    def rule_name(self) -> str:
        return "CUI Format Check"

    def validate(self, data: dict[str, Any]) -> ValidationResult:
        cui = data.get("cui")

        if not cui:
            return ValidationResult(
                is_valid=True,
                message="No CUI to validate"
            )

        # Normalize: remove RO/R0 prefix
        cui_clean = cui.strip().upper()
        if cui_clean.startswith("RO"):
            cui_clean = cui_clean[2:]
        elif cui_clean.startswith("R0"):
            cui_clean = cui_clean[2:]

        # Check if numeric
        if not cui_clean.isdigit():
            return ValidationResult(
                is_valid=False,
                confidence_penalty=0.3,
                message=f"CUI '{cui}' contains non-numeric characters",
                severity="warning"
            )

        # Check length
        if len(cui_clean) < 6 or len(cui_clean) > 10:
            return ValidationResult(
                is_valid=False,
                confidence_penalty=0.3,
                message=f"CUI '{cui}' length {len(cui_clean)} outside valid range (6-10 digits)",
                severity="warning"
            )

        return ValidationResult(
            is_valid=True,
            message=f"CUI '{cui}' format valid"
        )


class CUIChecksumRule(ValidationRule):
    """Validate Romanian CIF/CUI using Mod 11 checksum algorithm.

    Algorithm:
    1. Remove RO prefix if present
    2. Extract last digit as declared checksum
    3. Apply multipliers [7,5,3,2,1,7,5,3,2] to first N-1 digits
    4. Calculate: (sum * 10) mod 11
    5. If result = 10, expected checksum = 0
    6. Else, expected checksum = result
    7. Compare with declared checksum

    Example:
        rule = CUIChecksumRule()
        result = rule.validate({"cui": "RO10562600"})
        # result.is_valid = True (checksum correct)

        result = rule.validate({"cui": "R01879855"})
        # result.is_valid = False (checksum mismatch)

    Static methods available for direct use:
        CUIChecksumRule.calculate_checksum("1056260") -> 0
        CUIChecksumRule.validate_checksum("10562600") -> True
        CUIChecksumRule.has_ro_prefix("RO10562600") -> True
    """

    # Fixed multipliers for 9 positions (Romanian Mod 11)
    MULTIPLIERS = [7, 5, 3, 2, 1, 7, 5, 3, 2]

    @staticmethod
    def calculate_checksum(cui_base: str) -> int:
        """Calculate expected CUI checksum using Romanian Mod 11 algorithm.

        Args:
            cui_base: CUI digits WITHOUT the checksum digit (last digit)

        Returns:
            Expected checksum digit (0-9), or -1 if invalid input
        """
        if not cui_base or not cui_base.isdigit():
            return -1

        # Pad base to 9 digits from LEFT
        base_padded = cui_base.zfill(9)
        base_digits = [int(d) for d in base_padded]

        # Calculate weighted sum
        weighted_sum = sum(d * m for d, m in zip(base_digits, CUIChecksumRule.MULTIPLIERS))

        # Calculate checksum
        checksum = (weighted_sum * 10) % 11
        if checksum == 10:
            checksum = 0

        return checksum

    @staticmethod
    def validate_checksum(cui_digits: str) -> bool:
        """Check if CUI checksum is valid.

        Args:
            cui_digits: Full CUI digits (including checksum as last digit)

        Returns:
            True if checksum is valid, False otherwise
        """
        if not cui_digits or len(cui_digits) < 6 or not cui_digits.isdigit():
            return False

        base = cui_digits[:-1]
        declared = int(cui_digits[-1])
        expected = CUIChecksumRule.calculate_checksum(base)

        return expected == declared

    @staticmethod
    def has_ro_prefix(cui: str) -> bool:
        """Check if CUI has RO prefix (proper format for VAT payers)."""
        if not cui:
            return False
        return cui.upper().strip().startswith('RO')

    @staticmethod
    def extract_digits(cui: str) -> str:
        """Extract digits from CUI, removing RO/R0 prefix."""
        if not cui:
            return ""
        cui = cui.strip().upper()
        if cui.startswith("RO"):
            cui = cui[2:]
        elif cui.startswith("R0"):  # Fix OCR error R0 → RO
            cui = cui[2:]
        return ''.join(c for c in cui if c.isdigit())

    @property
    def rule_name(self) -> str:
        return "CUI Checksum Check (Mod 11)"

    def validate(self, data: dict[str, Any]) -> ValidationResult:
        cui = data.get("cui")

        if not cui:
            return ValidationResult(
                is_valid=True,
                message="No CUI to validate"
            )

        # Use static method to extract digits
        cui_clean = CUIChecksumRule.extract_digits(cui)

        # Check format first
        if not cui_clean:
            return ValidationResult(
                is_valid=True,  # Don't fail checksum if format invalid (handled by CUIFormatRule)
                message="CUI format invalid, skipping checksum"
            )

        if len(cui_clean) < 6 or len(cui_clean) > 10:
            return ValidationResult(
                is_valid=True,
                message="CUI length invalid, skipping checksum"
            )

        # Use static method to validate checksum
        if not CUIChecksumRule.validate_checksum(cui_clean):
            # Calculate expected for error message
            expected = CUIChecksumRule.calculate_checksum(cui_clean[:-1])
            declared = int(cui_clean[-1])
            return ValidationResult(
                is_valid=False,
                confidence_penalty=0.3,
                message=f"CUI '{cui}' checksum mismatch: expected {expected}, got {declared}",
                severity="warning"
            )

        return ValidationResult(
            is_valid=True,
            message=f"CUI '{cui}' checksum valid"
        )


class TVABasedTotalRule(ValidationRule):
    """Validate TOTAL using reverse calculation from TVA amount.

    This is a CRITICAL validation that catches cases where OCR extracts
    wrong TOTAL but correct TVA. Since TVA = BASE * rate and TOTAL = BASE + TVA,
    we can calculate expected TOTAL from TVA alone.

    Formula:
        Expected TOTAL = TVA / rate * (1 + rate)
        Or equivalently: Expected TOTAL = TVA * (1 + rate) / rate

    For TVA rate 21%:
        Expected TOTAL = TVA / 0.21 * 1.21 = TVA * 5.7619

    Example (benzina 27 oct):
        TVA = 49.58, rate = 21%
        Expected TOTAL = 49.58 / 0.21 * 1.21 = 285.68
        Extracted TOTAL = 205.66 (WRONG!)
        Rule detects mismatch and flags for escalation

    Usage in multi-tier processing (e.g., doctr_plus):
        If this rule fails, the engine should proceed to next tier
        instead of returning early with potentially wrong data.
    """

    def __init__(self, tolerance_percent: float = 0.02):
        """
        Args:
            tolerance_percent: Allowed difference as percentage (0.02 = 2%)
        """
        self.tolerance_percent = tolerance_percent

    @property
    def rule_name(self) -> str:
        return "TVA-Based Total Check"

    def validate(self, data: dict[str, Any]) -> ValidationResult:
        total = data.get("amount")
        tva = data.get("tva")
        tva_entries = data.get("tva_entries", [])

        if not total or not tva:
            return ValidationResult(
                is_valid=True,
                message="Insufficient data for TVA-based total validation"
            )

        # Type safety
        try:
            total = float(total)
            tva = float(tva)
        except (TypeError, ValueError):
            return ValidationResult(
                is_valid=True,
                message="Non-numeric values, skipping TVA-based total validation"
            )

        if tva <= 0 or total <= 0:
            return ValidationResult(
                is_valid=True,
                message="Zero or negative values, skipping TVA-based total validation"
            )

        # Try to determine TVA rate from entries
        tva_rate = None

        # Check tva_entries for rate information
        if tva_entries:
            for entry in tva_entries:
                if isinstance(entry, dict):
                    percent = entry.get('percent')
                    if percent:
                        try:
                            tva_rate = float(percent) / 100.0
                            break
                        except (TypeError, ValueError):
                            pass

        # Fallback: try to calculate rate from TVA/TOTAL ratio
        if not tva_rate:
            # TVA = BASE * rate, TOTAL = BASE + TVA = BASE * (1 + rate)
            # TVA/TOTAL = rate / (1 + rate)
            # So rate = TVA / (TOTAL - TVA) = TVA / BASE
            base = total - tva
            if base > 0:
                calculated_rate = tva / base
                # Validate it's a reasonable Romanian TVA rate (5%, 9%, 19%, 21%)
                if 0.04 <= calculated_rate <= 0.25:
                    tva_rate = calculated_rate

        if not tva_rate:
            # Assume most common rate: 21%
            tva_rate = 0.21

        # Calculate expected TOTAL from TVA
        # TVA = BASE * rate → BASE = TVA / rate
        # TOTAL = BASE + TVA = (TVA / rate) + TVA = TVA * (1 + 1/rate) = TVA * (1 + rate) / rate
        expected_total = tva * (1 + tva_rate) / tva_rate

        # Calculate difference
        diff = abs(total - expected_total)
        diff_percent = diff / expected_total if expected_total > 0 else 1.0

        if diff_percent > self.tolerance_percent:
            # Significant mismatch - OCR likely extracted TOTAL wrong
            return ValidationResult(
                is_valid=False,
                confidence_penalty=0.5,  # High penalty - this is a critical error
                message=(
                    f"TOTAL mismatch: Extracted {total:.2f} RON vs "
                    f"TVA-calculated {expected_total:.2f} RON "
                    f"(TVA={tva:.2f}, rate={tva_rate:.0%}, diff={diff_percent:.1%}). "
                    f"Likely OCR error on TOTAL."
                ),
                severity="error"
            )

        return ValidationResult(
            is_valid=True,
            message=f"TOTAL {total:.2f} matches TVA-calculated {expected_total:.2f} (diff: {diff_percent:.1%})"
        )


class InterOCRConsistencyRule(ValidationRule):
    """Validate consistency between multiple OCR results.

    If Light OCR and Medium OCR produce values that differ by >10x,
    one is clearly wrong (likely digit concatenation error).

    Example:
        rule = InterOCRConsistencyRule(max_ratio=10.0)
        result = rule.validate({
            "light_amount": 85.99,
            "medium_amount": 859762.16
        })
        # result.is_valid = False (ratio = 10,000x!)
    """

    def __init__(self, max_ratio: float = 10.0):
        self.max_ratio = max_ratio

    @property
    def rule_name(self) -> str:
        return "Inter-OCR Consistency Check"

    def validate(self, data: dict[str, Any]) -> ValidationResult:
        light_value = data.get("light_value")
        medium_value = data.get("medium_value")
        field_name = data.get("field_name", "value")

        if not light_value or not medium_value:
            return ValidationResult(
                is_valid=True,
                message="Insufficient OCR results for consistency check"
            )

        # Avoid division by zero
        if light_value == 0 or medium_value == 0:
            return ValidationResult(
                is_valid=True,
                message="One value is zero, skipping consistency check"
            )

        ratio = max(light_value, medium_value) / min(light_value, medium_value)

        if ratio > self.max_ratio:
            return ValidationResult(
                is_valid=False,
                confidence_penalty=0.2,
                message=f"{field_name}: OCR results differ by {ratio:.1f}x (Light: {light_value}, Medium: {medium_value})",
                severity="warning"
            )

        return ValidationResult(
            is_valid=True,
            message=f"{field_name}: OCR results consistent (ratio: {ratio:.2f}x)"
        )


# ============================================================================
# VALIDATION ENGINE
# ============================================================================


@dataclass
class EnhancedExtractionResult:
    """Enhanced extraction result with validation metadata.

    This wraps the original extraction data and adds validation results.
    """
    # Original data
    data: dict[str, Any]

    # Validation results
    needs_manual_review: bool = False
    validation_warnings: list[str] = field(default_factory=list)
    validation_errors: list[str] = field(default_factory=list)
    confidence_adjustments: dict[str, float] = field(default_factory=dict)

    # Inter-OCR metadata
    inter_ocr_ratios: dict[str, float] = field(default_factory=dict)


class OCRValidationEngine:
    """Orchestrate all validation rules for OCR extraction results.

    This engine applies validation rules in order:
    1. Sanity checks (amount range, format checks)
    2. Cross-field correlation (TVA ratio, payment sum)
    3. Inter-OCR consistency checks

    Example:
        engine = OCRValidationEngine()
        result = engine.validate_extraction(
            extraction_result=merged_data,
            light_result=light_ocr_data,
            medium_result=medium_ocr_data
        )
    """

    def __init__(self):
        """Initialize validation engine with default rules."""
        # Sanity check rules (absolute value validation)
        self.sanity_rules = [
            AmountRangeRule(min_amount=0.01, max_amount=100_000.0),
            CUIFormatRule(),
            CUIChecksumRule(),
        ]

        # Cross-field validation rules (correlation between fields)
        self.cross_field_rules = [
            TVARatioRule(min_ratio=0.05, max_ratio=0.24),
            PaymentSumRule(tolerance=0.02),
            TVAEntriesSumRule(tolerance=0.02),
            TVABasedTotalRule(tolerance_percent=0.02),  # Critical: detect TOTAL errors via TVA
        ]

        # Inter-OCR consistency rules
        self.inter_ocr_rules = [
            InterOCRConsistencyRule(max_ratio=10.0),
        ]

    def validate_extraction(
        self,
        extraction_result: dict[str, Any],
        light_result: Optional[dict[str, Any]] = None,
        medium_result: Optional[dict[str, Any]] = None
    ) -> EnhancedExtractionResult:
        """Run all validation rules and return enhanced result.

        Args:
            extraction_result: Merged OCR extraction data (required)
            light_result: Light OCR preprocessing results (optional)
            medium_result: Medium OCR preprocessing results (optional)

        Returns:
            EnhancedExtractionResult with validation warnings and metadata
        """
        warnings = []
        errors = []
        confidence_adjustments = {}
        inter_ocr_ratios = {}

        # Step 1: Sanity checks
        print("\n[Validation] Step 1: Sanity checks...", flush=True)
        for rule in self.sanity_rules:
            result = rule.validate(extraction_result)

            if not result.is_valid:
                msg = f"[{rule.rule_name}] {result.message}"

                if result.severity == "error":
                    errors.append(msg)
                else:
                    warnings.append(msg)

                print(f"  ❌ {msg}", flush=True)

                # Track confidence penalty for the relevant field based on rule
                if result.confidence_penalty > 0:
                    rule_field_map = {
                        "Amount Range Check": ["amount"],
                        "CUI Format Check": ["cui"],
                        "CUI Checksum Check (Mod 11)": ["cui"],
                    }
                    fields = rule_field_map.get(rule.rule_name, ["amount", "tva", "cui"])
                    for f in fields:
                        if f in extraction_result:
                            confidence_adjustments[f] = result.confidence_penalty
            else:
                print(f"  ✅ {rule.rule_name}: {result.message}", flush=True)

        # Step 2: Cross-field validation
        print("\n[Validation] Step 2: Cross-field validation...", flush=True)
        for rule in self.cross_field_rules:
            result = rule.validate(extraction_result)

            if not result.is_valid:
                msg = f"[{rule.rule_name}] {result.message}"

                if result.severity == "error":
                    errors.append(msg)
                else:
                    warnings.append(msg)

                print(f"  ❌ {msg}", flush=True)

                # Track confidence penalty for the relevant field based on rule
                if result.confidence_penalty > 0:
                    rule_field_map = {
                        "TVA Ratio Check": ["tva"],
                        "Payment Sum Check": ["amount"],
                        "TVA Entries Sum Check": ["tva"],
                    }
                    fields = rule_field_map.get(rule.rule_name, ["amount", "tva"])
                    for f in fields:
                        if f in extraction_result:
                            confidence_adjustments[f] = result.confidence_penalty
            else:
                print(f"  ✅ {rule.rule_name}: {result.message}", flush=True)

        # Step 3: Inter-OCR consistency checks
        if light_result and medium_result:
            print("\n[Validation] Step 3: Inter-OCR consistency...", flush=True)

            # Check amount consistency
            if "amount" in light_result and "amount" in medium_result:
                consistency_data = {
                    "light_value": light_result["amount"],
                    "medium_value": medium_result["amount"],
                    "field_name": "amount"
                }

                result = self.inter_ocr_rules[0].validate(consistency_data)

                if not result.is_valid:
                    msg = f"[Inter-OCR] {result.message}"
                    warnings.append(msg)
                    print(f"  ❌ {msg}", flush=True)

                    # Store ratio for metadata
                    ratio = max(
                        light_result["amount"],
                        medium_result["amount"]
                    ) / min(light_result["amount"], medium_result["amount"])
                    inter_ocr_ratios["amount"] = ratio
                else:
                    print(f"  ✅ {result.message}", flush=True)

        # Determine if manual review is needed
        # Only flag for review if there are errors OR high-severity warnings
        high_severity_warnings = [w for w in warnings if "[Amount Range" in w or "[Payment Sum" in w or "[Inter-OCR]" in w]
        needs_manual_review = (
            len(errors) > 0 or
            len(high_severity_warnings) > 0 or
            any(ratio > 10.0 for ratio in inter_ocr_ratios.values())
        )

        print(f"\n[Validation] Summary:", flush=True)
        print(f"  Errors: {len(errors)}", flush=True)
        print(f"  Warnings: {len(warnings)}", flush=True)
        print(f"  Manual review needed: {needs_manual_review}", flush=True)

        return EnhancedExtractionResult(
            data=extraction_result,
            needs_manual_review=needs_manual_review,
            validation_warnings=warnings,
            validation_errors=errors,
            confidence_adjustments=confidence_adjustments,
            inter_ocr_ratios=inter_ocr_ratios
        )

    def quick_validate_for_hybrid(self, extraction_result: dict[str, Any]) -> tuple[bool, float, list[str]]:
        """Quick validation for early-exit decisions (e.g., doctr_plus Tier 1).

        Runs critical cross-validation rules to detect obvious OCR errors.
        Used to decide whether to proceed to next processing tier or exit early.

        Args:
            extraction_result: Extraction data dict with fields:
                - amount: Extracted TOTAL
                - tva: Extracted TVA total
                - tva_entries: List of TVA entries with rates

        Returns:
            Tuple of (passes_validation, confidence_penalty, error_messages)
            - passes_validation: True if no critical errors detected
            - confidence_penalty: Cumulative penalty (0.0-1.0)
            - error_messages: List of validation error messages

        Example usage:
            passes, penalty, errors = validation_engine.quick_validate_for_hybrid(extraction_data)
            if not passes:
                print(f"Validation failed: {errors}, proceeding to next tier")
                # Continue to next processing tier instead of early exit
        """
        errors = []
        total_penalty = 0.0

        # Critical rules for early-exit decision-making
        # These determine if we can trust the extraction or need to proceed to next tier
        critical_rules = [
            # Cross-field validations (most important for detecting OCR errors)
            TVABasedTotalRule(tolerance_percent=0.02),  # Critical: detect TOTAL errors via TVA calculation
            PaymentSumRule(tolerance=0.05),  # Cross-validate TOTAL vs CARD+CASH payments
            TVARatioRule(min_ratio=0.05, max_ratio=0.24),  # TVA should be 5-24% of TOTAL
            TVAEntriesSumRule(tolerance=0.05),  # Sum of TVA entries should match TVA total

            # Format & checksum validations
            CUIChecksumRule(),  # Validate CUI/CIF with Romanian Mod11 checksum algorithm
            CUIFormatRule(),  # CUI should be 6-10 digits

            # Sanity checks
            AmountRangeRule(min_amount=0.01, max_amount=100_000.0),  # Reasonable amount range
        ]

        for rule in critical_rules:
            result = rule.validate(extraction_result)
            if not result.is_valid:
                errors.append(result.message)
                total_penalty += result.confidence_penalty

        # Cap penalty at 1.0
        total_penalty = min(1.0, total_penalty)

        passes = len(errors) == 0
        return passes, total_penalty, errors

    # NOTE: _calculate_cui_checksum and _is_cui_checksum_valid removed
    # Use CUIChecksumRule.calculate_checksum() and CUIChecksumRule.validate_checksum() instead

    @staticmethod
    def _repair_cui_checksum(cui_digits: str) -> Optional[str]:
        """Try to repair CUI by attempting 1-digit corrections.

        OCR often misreads similar-looking digits:
        - 5 ↔ 8 (most common in receipts)
        - 6 ↔ 0
        - 1 ↔ 7
        - 3 ↔ 8

        Algorithm:
        1. Check middle positions first (2,3,4,5...) - OCR errors more common there
        2. Skip first digit (position 0) - usually reliable in CUI
        3. Check checksum digit (last position) last
        4. Prefer common OCR digit confusions (5↔8, 6↔0)

        Args:
            cui_digits: Original CUI digits (without RO prefix)

        Returns:
            Repaired CUI digits if 1-digit fix found, else None
        """
        if len(cui_digits) < 6 or not cui_digits.isdigit():
            return None

        # If already valid, return as-is
        if CUIChecksumRule.validate_checksum(cui_digits):
            return cui_digits

        # Common OCR digit confusions (try these first)
        confusion_pairs = {
            '5': ['8', '6'],      # 5 often misread as 8 or 6
            '8': ['5', '3', '0'], # 8 often misread as 5, 3, or 0
            '6': ['0', '8'],      # 6 often misread as 0 or 8
            '0': ['6', '8'],      # 0 often misread as 6 or 8
            '1': ['7', '4'],      # 1 often misread as 7 or 4
            '7': ['1'],           # 7 often misread as 1
            '3': ['8'],           # 3 often misread as 8
            '4': ['1'],           # 4 often misread as 1
            '2': ['7'],           # 2 sometimes misread as 7
            '9': ['0'],           # 9 sometimes misread as 0
        }

        n = len(cui_digits)
        last_pos = n - 1  # checksum position

        # Position check order: middle positions first, then position 1, then 0, then checksum
        # Skip position 0 (first digit) - it's usually reliable
        # Example for 8-digit CUI: [2,3,4,5,6, 1, 7(checksum)]
        middle_positions = list(range(2, last_pos))  # positions 2 to n-2
        position_order = middle_positions + [1, last_pos, 0]  # check pos 0 last (rarely wrong)

        for pos in position_order:
            if pos >= n:
                continue

            original_digit = cui_digits[pos]

            # Try common confusions first for this digit
            candidates = confusion_pairs.get(original_digit, [])
            # Then try all other digits
            all_digits = [d for d in '0123456789' if d != original_digit and d not in candidates]

            for replacement in candidates + all_digits:
                candidate = cui_digits[:pos] + replacement + cui_digits[pos+1:]
                if CUIChecksumRule.validate_checksum(candidate):
                    print(f"[CUI Repair] Fixed {cui_digits} -> {candidate} (position {pos}: {original_digit}->{replacement})", flush=True)
                    return candidate

        # No single-digit fix found
        return None

    @staticmethod
    def normalize_cui(cui: Optional[str]) -> Optional[str]:
        """Normalize CUI - fix OCR errors but preserve original format.

        Rules:
        - R0 → RO (fix OCR error where O is read as 0)
        - Keep RO prefix if original had it (platitor TVA)
        - Do NOT add RO if original didn't have it (neplatitor TVA)
        - Try to repair 1-digit checksum errors (OCR mistakes like 5↔8)

        Examples:
            45417955 → 45417955 (no prefix = neplatitor TVA, keep as-is)
            R010562600 → RO10562600 (fix R0 OCR error)
            RO10562600 → RO10562600 (unchanged)
            RO10862600 → RO10562600 (repaired: 8→5 at position 2)

        Args:
            cui: Raw CUI string from OCR

        Returns:
            Normalized CUI, or None if invalid
        """
        if not cui:
            return None

        cui = cui.strip().upper()

        # Check if original had RO/R0 prefix
        had_ro_prefix = cui.startswith("RO") or cui.startswith("R0")

        # Extract digits
        if cui.startswith("RO"):
            cui_digits = cui[2:]
        elif cui.startswith("R0"):  # Fix OCR error R0 → RO
            cui_digits = cui[2:]
        else:
            cui_digits = cui

        # Remove any non-digit characters
        cui_digits = ''.join(c for c in cui_digits if c.isdigit())

        # Validate length
        if len(cui_digits) < 6 or len(cui_digits) > 10:
            print(f"[CUI Normalize] Invalid length: {len(cui_digits)} digits (expected 6-10)", flush=True)
            return None

        # Try to repair checksum if invalid
        if not CUIChecksumRule.validate_checksum(cui_digits):
            repaired = OCRValidationEngine._repair_cui_checksum(cui_digits)
            if repaired:
                cui_digits = repaired

        # Return with RO prefix only if original had it
        if had_ro_prefix:
            return f"RO{cui_digits}"
        else:
            return cui_digits

    @staticmethod
    async def fuzzy_match_cui_from_db(
        cui: Optional[str],
        db_session
    ) -> Optional[tuple[str, str]]:
        """Fuzzy match CUI against database of known suppliers.

        This function:
        1. Validates CUI checksum
        2. If valid, looks up in database (exact match)
        3. If invalid, tries 1-digit corrections and looks up each candidate
        4. Returns the first match found in database

        Args:
            cui: Extracted CUI from OCR (may be invalid)
            db_session: SQLAlchemy async session for database lookups

        Returns:
            Tuple of (corrected_cui, supplier_name) if found, else None

        Usage in OCR extraction:
            from backend.modules.data_entry.services.ocr.validation import OCRValidationEngine
            match = await OCRValidationEngine.fuzzy_match_cui_from_db(extracted_cui, session)
            if match:
                corrected_cui, supplier_name = match
        """
        from sqlalchemy import select, or_
        from backend.modules.data_entry.db.models.nomenclature import SyncedSupplier, LocalSupplier

        if not cui:
            return None

        cui = cui.strip().upper()

        # Check if original had RO/R0 prefix
        had_ro_prefix = cui.startswith("RO") or cui.startswith("R0")

        # Extract digits
        if cui.startswith("RO"):
            cui_digits = cui[2:]
        elif cui.startswith("R0"):  # Fix OCR error R0 → RO
            cui_digits = cui[2:]
        else:
            cui_digits = cui

        # Remove any non-digit characters
        cui_digits = ''.join(c for c in cui_digits if c.isdigit())

        # Validate length
        if len(cui_digits) < 6 or len(cui_digits) > 10:
            return None

        # Helper to format CUI with optional RO prefix
        def format_cui(digits: str) -> str:
            if had_ro_prefix:
                return f"RO{digits}"
            return digits

        # Helper to search database for CUI
        async def lookup_cui_in_db(digits: str) -> Optional[tuple[str, str]]:
            """Search both synced and local suppliers for CUI."""
            # Search patterns: with and without RO prefix
            search_patterns = [digits, f"RO{digits}"]

            # Search synced_suppliers first (more data)
            stmt = select(SyncedSupplier.fiscal_code, SyncedSupplier.name).where(
                or_(
                    SyncedSupplier.fiscal_code == digits,
                    SyncedSupplier.fiscal_code == f"RO{digits}",
                    SyncedSupplier.fiscal_code == digits.lstrip('0'),  # Handle leading zeros
                )
            ).limit(1)
            result = await db_session.execute(stmt)
            row = result.first()
            if row:
                return (format_cui(digits), row.name)

            # Search local_suppliers
            stmt = select(LocalSupplier.fiscal_code, LocalSupplier.name).where(
                or_(
                    LocalSupplier.fiscal_code == digits,
                    LocalSupplier.fiscal_code == f"RO{digits}",
                    LocalSupplier.fiscal_code == digits.lstrip('0'),
                )
            ).limit(1)
            result = await db_session.execute(stmt)
            row = result.first()
            if row:
                return (format_cui(digits), row.name)

            return None

        # 1. If checksum is valid, check if it exists in database (exact match)
        if CUIChecksumRule.validate_checksum(cui_digits):
            match = await lookup_cui_in_db(cui_digits)
            if match:
                print(f"[Fuzzy CUI] Exact match found: {cui} -> {match[0]} ({match[1]})", flush=True)
                return match
            # Valid checksum but not in DB - return as-is (it might be a new supplier)
            return None

        # 2. Invalid checksum - try 1-digit corrections and verify against database
        print(f"[Fuzzy CUI] Invalid checksum for {cui}, trying corrections...", flush=True)

        # Common OCR digit confusions (try these first)
        confusion_pairs = {
            '5': ['8', '6'],      # 5 often misread as 8 or 6
            '8': ['5', '3', '0'], # 8 often misread as 5, 3, or 0
            '6': ['0', '8'],      # 6 often misread as 0 or 8
            '0': ['6', '8'],      # 0 often misread as 6 or 8
            '1': ['7', '4'],      # 1 often misread as 7 or 4
            '7': ['1'],           # 7 often misread as 1
            '3': ['8'],           # 3 often misread as 8
            '4': ['1'],           # 4 often misread as 1
            '2': ['7'],           # 2 sometimes misread as 7
            '9': ['0'],           # 9 sometimes misread as 0
        }

        n = len(cui_digits)
        last_pos = n - 1  # checksum position

        # Position check order: middle positions first, then ends
        middle_positions = list(range(2, last_pos))
        position_order = middle_positions + [1, last_pos, 0]

        for pos in position_order:
            if pos >= n:
                continue

            original_digit = cui_digits[pos]

            # Try common confusions first for this digit
            candidates = confusion_pairs.get(original_digit, [])
            # Then try all other digits
            all_digits = [d for d in '0123456789' if d != original_digit and d not in candidates]

            for replacement in candidates + all_digits:
                candidate = cui_digits[:pos] + replacement + cui_digits[pos+1:]

                # Only consider if checksum is valid
                if not CUIChecksumRule.validate_checksum(candidate):
                    continue

                # Check if this corrected CUI exists in database
                match = await lookup_cui_in_db(candidate)
                if match:
                    print(f"[Fuzzy CUI] DB match: {cui} -> {match[0]} ({match[1]}) [pos {pos}: {original_digit}->{replacement}]", flush=True)
                    return match

        # No match found in database
        print(f"[Fuzzy CUI] No database match found for {cui}", flush=True)
        return None

    @staticmethod
    async def fuzzy_match_by_name_and_cui(
        vendor_name: Optional[str],
        cui: Optional[str],
        db_session
    ) -> Optional[tuple[str, str]]:
        """Fuzzy match supplier by NAME, then narrow down by CUI.

        Algorithm:
        1. Normalize vendor name (remove S.R.L., S.A., punctuation, etc.)
        2. Search suppliers by fuzzy name match (LIKE %name%)
        3. If multiple results, use fuzzy CUI matching to pick best one
        4. Return the best match

        Args:
            vendor_name: Extracted vendor name from OCR
            cui: Extracted CUI from OCR (may be invalid/incomplete)
            db_session: SQLAlchemy async session

        Returns:
            Tuple of (matched_cui, supplier_name) if found, else None
        """
        from sqlalchemy import select, or_, func
        from backend.modules.data_entry.db.models.nomenclature import SyncedSupplier, LocalSupplier
        import re

        if not vendor_name or len(vendor_name) < 3:
            return None

        # Normalize vendor name for search
        def normalize_name(name: str) -> str:
            """Normalize name for fuzzy matching."""
            name = name.upper()
            # Remove company type suffixes
            for suffix in ['S.R.L.', 'SRL', 'S.A.', 'SA', 'S.C.', 'SC', 'I.F.', 'IF', 'P.F.A.', 'PFA']:
                name = name.replace(suffix, '')
            # Remove punctuation and extra spaces
            name = re.sub(r'[.,\-_/\\()"\']', ' ', name)
            name = ' '.join(name.split())
            return name.strip()

        # Extract key words from vendor name (for fuzzy search)
        normalized_name = normalize_name(vendor_name)
        name_words = [w for w in normalized_name.split() if len(w) >= 3]

        if not name_words:
            return None

        print(f"[Fuzzy Name] Searching for vendor: '{vendor_name}' -> keywords: {name_words}", flush=True)

        # Build search pattern - use first significant word
        primary_word = name_words[0]
        search_pattern = f"%{primary_word}%"

        candidates = []

        # Search synced_suppliers
        stmt = select(SyncedSupplier.fiscal_code, SyncedSupplier.name).where(
            func.upper(SyncedSupplier.name).like(search_pattern)
        ).limit(20)
        result = await db_session.execute(stmt)
        for row in result:
            if row.fiscal_code:
                candidates.append((row.fiscal_code, row.name))

        # Search local_suppliers
        stmt = select(LocalSupplier.fiscal_code, LocalSupplier.name).where(
            func.upper(LocalSupplier.name).like(search_pattern)
        ).limit(20)
        result = await db_session.execute(stmt)
        for row in result:
            if row.fiscal_code:
                candidates.append((row.fiscal_code, row.name))

        if not candidates:
            print(f"[Fuzzy Name] No name matches found for '{primary_word}'", flush=True)
            return None

        print(f"[Fuzzy Name] Found {len(candidates)} name matches for '{primary_word}'", flush=True)

        # If only one candidate, return it
        if len(candidates) == 1:
            print(f"[Fuzzy Name] Single match: {candidates[0][0]} ({candidates[0][1]})", flush=True)
            return candidates[0]

        # Multiple candidates - try to narrow down by CUI
        if cui:
            cui_digits = ''.join(c for c in cui.upper().replace('RO', '').replace('R0', '') if c.isdigit())

            if len(cui_digits) >= 6:
                # Score each candidate by how similar their CUI is to the extracted one
                def cui_similarity(candidate_cui: str) -> int:
                    """Calculate how many digits match in the same position."""
                    cand_digits = ''.join(c for c in candidate_cui.upper().replace('RO', '') if c.isdigit())
                    if len(cand_digits) != len(cui_digits):
                        return 0
                    return sum(1 for a, b in zip(cand_digits, cui_digits) if a == b)

                # Sort candidates by CUI similarity (descending)
                scored = [(cui_similarity(c[0]), c) for c in candidates]
                scored.sort(key=lambda x: x[0], reverse=True)

                best_score, best_match = scored[0]
                # Require at least 70% digit match for CUI similarity
                min_matching = int(len(cui_digits) * 0.7)

                if best_score >= min_matching:
                    print(f"[Fuzzy Name] Best CUI match: {best_match[0]} ({best_match[1]}) - score {best_score}/{len(cui_digits)}", flush=True)
                    return best_match

                print(f"[Fuzzy Name] No strong CUI match (best score: {best_score}/{len(cui_digits)})", flush=True)

        # If still multiple and no CUI match, try name similarity
        def name_similarity(candidate_name: str) -> int:
            """Count how many keywords match."""
            norm_cand = normalize_name(candidate_name)
            return sum(1 for w in name_words if w in norm_cand)

        scored = [(name_similarity(c[1]), c) for c in candidates]
        scored.sort(key=lambda x: x[0], reverse=True)

        if scored[0][0] >= 2:  # At least 2 keywords match
            best_match = scored[0][1]
            print(f"[Fuzzy Name] Best name match: {best_match[0]} ({best_match[1]})", flush=True)
            return best_match

        # Return first candidate if nothing else works
        print(f"[Fuzzy Name] Returning first candidate: {candidates[0][0]} ({candidates[0][1]})", flush=True)
        return candidates[0]

    @staticmethod
    async def fuzzy_match_supplier(
        cui: Optional[str],
        vendor_name: Optional[str],
        db_session
    ) -> Optional[tuple[str, str]]:
        """Combined fuzzy matching: try CUI first, then fallback to NAME+CUI.

        Strategy:
        1. Try fuzzy CUI matching (1-digit corrections with checksum validation)
        2. If no CUI match, try fuzzy NAME matching, narrowed by CUI similarity

        Args:
            cui: Extracted CUI from OCR (may be invalid/incomplete)
            vendor_name: Extracted vendor name from OCR
            db_session: SQLAlchemy async session

        Returns:
            Tuple of (matched_cui, supplier_name) if found, else None
        """
        # Step 1: Try fuzzy CUI matching
        cui_match = await OCRValidationEngine.fuzzy_match_cui_from_db(cui, db_session)
        if cui_match:
            return cui_match

        # Step 2: Fallback to fuzzy NAME + CUI matching
        name_match = await OCRValidationEngine.fuzzy_match_by_name_and_cui(
            vendor_name, cui, db_session
        )
        if name_match:
            return name_match

        return None