import asyncio
import re
import hashlib
from datetime import datetime
from typing import Dict, Tuple, Any
from aiva.db_connector import memory  # Assuming this exists

# Validation Gates (v2.0)
# Enhanced CNS of Genesis Patent Validation

class ValidationGate:
    def __init__(self, name: str, memory_stores):
        self.name = name
        self.memory = memory_stores

    async def validate(self, output: str, task_description: str, worker_id: str, metadata: Dict, context: Dict) -> Tuple[float, Dict]:
        """
        Abstract validation method to be implemented by subclasses.
        :param output: The output to validate.
        :param task_description: Description of the task.
        :param worker_id: ID of the worker.
        :param metadata: Metadata associated with the task.
        :param context: Contextual data for validation.
        :return: A tuple containing the validation score and a dictionary of checks.
        """
        raise NotImplementedError

    def log_decision(self, worker_id: str, check_name: str, passed: bool, reason: str):
        """Logs the validation decision with reasoning."""
        log_entry = {
            "gate": self.name,
            "timestamp": datetime.utcnow().isoformat(),
            "worker_id": worker_id,
            "check": check_name,
            "passed": passed,
            "reason": reason
        }
        try:
            self.memory.log_audit(f"{self.name}_validation", worker_id, log_entry)
        except Exception as e:
            print(f"[WARNING] Audit logging failed: {e}")

class GateAlpha(ValidationGate):
    """Gate Alpha: Input Validity - Verify source data quality."""
    def __init__(self, memory_stores):
        super().__init__("GateAlpha", memory_stores)

    async def validate(self, output: str, task_description: str, worker_id: str, metadata: Dict, context: Dict) -> Tuple[float, Dict]:
        checks = {}

        # Check 1: Data Source Reliability (Example: URL accessibility)
        source_url = metadata.get("source_url", None)
        data_valid = True  # Placeholder.  In reality, check URL availability and content.
        if source_url:
            try:
                # Simulate a check.  Real implementation would involve network calls.
                await asyncio.sleep(0.01)  # Simulate async operation
                data_valid = True # Replace with actual URL check logic
                reason = f"Source URL {source_url} is accessible." if data_valid else f"Source URL {source_url} is inaccessible."
            except Exception as e:
                data_valid = False
                reason = f"Error checking Source URL {source_url}: {e}"
        else:
            data_valid = False
            reason = "No source URL provided in metadata."

        checks["data_source_reliable"] = data_valid
        self.log_decision(worker_id, "data_source_reliable", data_valid, reason)

        # Check 2: Data Completeness (Example: Required fields present)
        required_fields = metadata.get("required_fields", [])
        fields_present = all(field in context for field in required_fields)
        checks["data_completeness"] = fields_present
        reason = "All required fields are present in the context." if fields_present else "Not all required fields are present in the context."
        self.log_decision(worker_id, "data_completeness", fields_present, reason)

        score = sum(checks.values()) / len(checks)
        return score, checks

class GateBeta(ValidationGate):
    """Gate Beta: Output Quality - Check accuracy and completeness."""
    def __init__(self, memory_stores):
        super().__init__("GateBeta", memory_stores)

    async def validate(self, output: str, task_description: str, worker_id: str, metadata: Dict, context: Dict) -> Tuple[float, Dict]:
        checks = {}

        # Check 1: Accuracy (Example: Keyword presence)
        required_keywords = metadata.get("required_keywords", [])
        keywords_present = all(keyword.lower() in output.lower() for keyword in required_keywords)
        checks["accuracy"] = keywords_present
        reason = "All required keywords are present in the output." if keywords_present else "Not all required keywords are present in the output."
        self.log_decision(worker_id, "accuracy", keywords_present, reason)

        # Check 2: Completeness (Example: Length constraints)
        min_length = metadata.get("min_length", 0)
        max_length = metadata.get("max_length", float('inf'))
        length_valid = min_length <= len(output) <= max_length
        checks["completeness"] = length_valid
        reason = f"Output length is within the allowed range ({min_length}-{max_length})." if length_valid else f"Output length is outside the allowed range ({min_length}-{max_length})."
        self.log_decision(worker_id, "completeness", length_valid, reason)

        score = sum(checks.values()) / len(checks)
        return score, checks

class GateGamma(ValidationGate):
    """Gate Gamma: Insight Purity - Detect hallucinations."""
    def __init__(self, memory_stores):
        super().__init__("GateGamma", memory_stores)

    async def validate(self, output: str, task_description: str, worker_id: str, metadata: Dict, context: Dict) -> Tuple[float, Dict]:
        checks = {}

        # Check 1: Factuality (Example: Cross-reference with source data)
        source_data = context.get("source_data", "")
        factuality_valid = True  # Placeholder. Implement fact-checking against source data.
        if source_data:
            # Simulate fact-checking.  Real implementation would involve NLP.
            await asyncio.sleep(0.01)
            factuality_valid = True #Replace with actual factuality check
            reason = "Output is consistent with source data." if factuality_valid else "Output contradicts source data."
        else:
            factuality_valid = False
            reason = "No source data provided for fact-checking."

        checks["factuality"] = factuality_valid
        self.log_decision(worker_id, "factuality", factuality_valid, reason)

        # Check 2: Consistency (Example: Internal logical consistency)
        consistency_valid = True  # Placeholder. Implement logic to check for internal contradictions.
        await asyncio.sleep(0.01) #simulate async operation
        checks["consistency"] = consistency_valid
        reason = "Output is internally consistent." if consistency_valid else "Output contains internal contradictions."
        self.log_decision(worker_id, "consistency", consistency_valid, reason)

        score = sum(checks.values()) / len(checks)
        return score, checks

class GateDelta(ValidationGate):
    """Gate Delta: Memory Integration - Validate storage operations."""
    def __init__(self, memory_stores):
        super().__init__("GateDelta", memory_stores)

    async def validate(self, output: str, task_description: str, worker_id: str, metadata: Dict, context: Dict) -> Tuple[float, Dict]:
        checks = {}

        # Check 1: Successful Storage (Example: Data stored correctly)
        storage_key = metadata.get("storage_key", None)
        storage_valid = True  # Placeholder. Implement a check that the data was stored correctly and can be retrieved.
        if storage_key:
            # Simulate storage verification. Real implementation would involve database checks.
            await asyncio.sleep(0.01)
            storage_valid = True # replace with actual storage validation
            reason = f"Data stored successfully under key {storage_key}." if storage_valid else f"Failed to store data under key {storage_key}."
        else:
            storage_valid = False
            reason = "No storage key provided."

        checks["successful_storage"] = storage_valid
        self.log_decision(worker_id, "successful_storage", storage_valid, reason)

        # Check 2: Data Integrity (Example: Hash comparison after retrieval)
        original_hash = metadata.get("original_hash", None)
        retrieved_hash = "" # Placeholder. Calculate hash of retrieved data.

        integrity_valid = True # Placeholder. Implement integrity check.
        if original_hash:
            # Simulate retrieval and hash comparison.
            await asyncio.sleep(0.01)
            retrieved_hash = hashlib.sha256(output.encode()).hexdigest()
            integrity_valid = original_hash == retrieved_hash
            reason = "Data integrity verified." if integrity_valid else "Data integrity compromised."
        else:
            integrity_valid = False
            reason = "No original hash provided for integrity check."

        checks["data_integrity"] = integrity_valid
        self.log_decision(worker_id, "data_integrity", integrity_valid, reason)

        score = sum(checks.values()) / len(checks)
        return score, checks

class GateEpsilon(ValidationGate):
    """Gate Epsilon: Strategy Alignment - Ensure revenue pathway fit."""
    def __init__(self, memory_stores):
        super().__init__("GateEpsilon", memory_stores)

    async def validate(self, output: str, task_description: str, worker_id: str, metadata: Dict, context: Dict) -> Tuple[float, Dict]:
        checks = {}

        # Check 1: Strategic Relevance (Example: Aligns with company objectives)
        strategic_alignment = metadata.get("strategic_alignment", True)  # Placeholder. Determine if output aligns with strategic goals.
        await asyncio.sleep(0.01)
        checks["strategic_relevance"] = strategic_alignment
        reason = "Output aligns with strategic objectives." if strategic_alignment else "Output does not align with strategic objectives."
        self.log_decision(worker_id, "strategic_relevance", strategic_alignment, reason)

        # Check 2: Revenue Potential (Example: Identifies potential revenue streams)
        revenue_potential = metadata.get("revenue_potential", True)  # Placeholder. Determine if output identifies potential revenue streams.
        await asyncio.sleep(0.01)
        checks["revenue_potential"] = revenue_potential
        reason = "Output identifies potential revenue streams." if revenue_potential else "Output does not identify potential revenue streams."
        self.log_decision(worker_id, "revenue_potential", revenue_potential, reason)

        score = sum(checks.values()) / len(checks)
        return score, checks

class GateZeta(ValidationGate):
    """Gate Zeta: Budget Compliance - Resource monitoring."""
    def __init__(self, memory_stores):
        super().__init__("GateZeta", memory_stores)

    async def validate(self, output: str, task_description: str, worker_id: str, metadata: Dict, context: Dict) -> Tuple[float, Dict]:
        checks = {}

        # Check 1: Resource Usage (Example: Compute time, API calls)
        resource_usage = metadata.get("resource_usage", 0) #Placeholder
        budget_limit = metadata.get("budget_limit", 100) #Placeholder.
        compliance = resource_usage <= budget_limit
        await asyncio.sleep(0.01)
        checks["resource_usage_compliance"] = compliance
        reason = "Resource usage is within the budget limit." if compliance else "Resource usage exceeds the budget limit."
        self.log_decision(worker_id, "resource_usage_compliance", compliance, reason)

        # Check 2: Cost Efficiency (Example: Output value vs. resource cost)
        cost_efficiency = metadata.get("cost_efficiency", True)  # Placeholder. Determine if output value justifies resource cost.
        await asyncio.sleep(0.01)
        checks["cost_efficiency"] = cost_efficiency
        reason = "Output value justifies resource cost." if cost_efficiency else "Output value does not justify resource cost."
        self.log_decision(worker_id, "cost_efficiency", cost_efficiency, reason)

        score = sum(checks.values()) / len(checks)
        return score, checks

# Triple-Gate Validator Orchestrator (v2.0)
# The CNS of Genesis Patent Validation

class SixGateValidator:
    def __init__(self, memory):
        self.memory = memory
        self.gate_alpha = GateAlpha(memory)
        self.gate_beta = GateBeta(memory)
        self.gate_gamma = GateGamma(memory)
        self.gate_delta = GateDelta(memory)
        self.gate_epsilon = GateEpsilon(memory)
        self.gate_zeta = GateZeta(memory)

    async def validate_worker_output(self, output: str, task_description: str, worker_id: str, metadata: Dict) -> Dict:
        """Run complete 6-patent validation suite"""

        context = metadata.copy()  # Start with metadata as the base context

        # Gate Alpha: Input Validity
        g_alpha_score, g_alpha_checks = await self.gate_alpha.validate(output, task_description, worker_id, metadata, context)
        context["gate_alpha_checks"] = g_alpha_checks # update context

        # Gate Beta: Output Quality
        g_beta_score, g_beta_checks = await self.gate_beta.validate(output, task_description, worker_id, metadata, context)
        context["gate_beta_checks"] = g_beta_checks # update context

        # Gate Gamma: Insight Purity
        g_gamma_score, g_gamma_checks = await self.gate_gamma.validate(output, task_description, worker_id, metadata, context)
        context["gate_gamma_checks"] = g_gamma_checks # update context

        # Gate Delta: Memory Integration
        g_delta_score, g_delta_checks = await self.gate_delta.validate(output, task_description, worker_id, metadata, context)
        context["gate_delta_checks"] = g_delta_checks # update context

        # Gate Epsilon: Strategy Alignment
        g_epsilon_score, g_epsilon_checks = await self.gate_epsilon.validate(output, task_description, worker_id, metadata, context)
        context["gate_epsilon_checks"] = g_epsilon_checks # update context

        # Gate Zeta: Budget Compliance
        g_zeta_score, g_zeta_checks = await self.gate_zeta.validate(output, task_description, worker_id, metadata, context)
        context["gate_zeta_checks"] = g_zeta_checks # update context


        # Calculate overall score
        overall_score = (g_alpha_score + g_beta_score + g_gamma_score + g_delta_score + g_epsilon_score + g_zeta_score) / 6
        passed = overall_score >= 0.8

        report = {
            "timestamp": datetime.utcnow().isoformat(),
            "worker_id": worker_id,
            "passed": passed,
            "overall_score": overall_score,
            "gates": {
                "alpha": {"score": g_alpha_score, "checks": g_alpha_checks},
                "beta": {"score": g_beta_score, "checks": g_beta_checks},
                "gamma": {"score": g_gamma_score, "checks": g_gamma_checks},
                "delta": {"score": g_delta_score, "checks": g_delta_checks},
                "epsilon": {"score": g_epsilon_score, "checks": g_epsilon_checks},
                "zeta": {"score": g_zeta_score, "checks": g_zeta_checks}
            }
        }

        # Log to immutable audit trail (Patent P4)
        try:
            self.memory.log_audit("six_gate_validation", worker_id, report)
        except Exception as e:
            print(f"[WARNING] Audit logging failed: {e}")

        # Update worker trust (Patent P9)
        try:
            self.memory.update_worker_trust(worker_id, passed)
        except Exception as e:
            print(f"[WARNING] Trust update failed: {e}")

        return report

# Global validator instance
validator = SixGateValidator(memory)