"""
Cece Ultra Agent - Full Stack Cognition

The merged cognition + architecture engine that runs the complete Alexa Cognition
Framework, Cece Architecture Layer, and Multi-Agent Orchestration Pipeline.

Author: Alexa (Cadillac)
Version: 1.0.0
Category: Cognition
"""

import asyncio
import json
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from typing import Any, Dict, List, Optional

from agents.base.agent import BaseAgent


class CognitiveStage(Enum):
    """15-step cognitive pipeline stages."""
    NOT_OK = "not_ok"
    WHY = "why"
    IMPULSE = "impulse"
    REFLECT = "reflect"
    ARGUE = "argue"
    COUNTERPOINT = "counterpoint"
    DETERMINE = "determine"
    QUESTION = "question"
    OFFSET_BIAS = "offset_bias"
    REGROUND = "reground"
    CLARIFY = "clarify"
    RESTATE = "restate"
    CLARIFY_AGAIN = "clarify_again"
    VALIDATE = "validate"
    FINAL = "final"


class ArchitectureModule(Enum):
    """6 architecture layer modules."""
    STRUCTURE = "structure"
    PRIORITIZE = "prioritize"
    TRANSLATE = "translate"
    STABILIZE = "stabilize"
    PROJECT_MANAGE = "project_manage"
    LOOPBACK = "loopback"


@dataclass
class NormalizedInput:
    """Normalized input after initial processing."""
    real_question: str
    emotional_payload: str
    hidden_assumptions: List[str]
    urgency: str
    vibe: str
    raw_input: str


@dataclass
class CognitivePipeline:
    """Results from the 15-step cognitive pipeline."""
    trigger: str
    root_cause: str
    impulse: str
    reflection: str
    challenge: str
    counterpoint: str
    determination: str
    question: str
    bias_offset: str
    values_alignment: str
    clarification: str
    restatement: str
    final_clarification: str
    validation: str
    final_answer: str
    emotional_state_before: str
    emotional_state_after: str
    confidence: float


@dataclass
class ArchitectureOutput:
    """Output from the architecture layer."""
    structure: Optional[Dict[str, Any]] = None
    priorities: Optional[Dict[str, Any]] = None
    translation: Optional[Dict[str, Any]] = None
    stabilization: Optional[Dict[str, Any]] = None
    project_plan: Optional[Dict[str, Any]] = None
    loopback_needed: bool = False


@dataclass
class AgentOrchestration:
    """Multi-agent orchestration results."""
    agents_used: List[str]
    orchestration_mode: str  # sequential, parallel, recursive
    chain_of_thought: str
    outputs: Dict[str, Any]


@dataclass
class CeceUltraResult:
    """Complete result from Cece Ultra processing."""
    normalized_input: NormalizedInput
    cognitive_pipeline: CognitivePipeline
    architecture_output: ArchitectureOutput
    orchestration: Optional[AgentOrchestration]
    action_plan: List[str]
    stable_summary: str
    extras: Dict[str, Any]
    timestamp: datetime


class CeceUltraAgent(BaseAgent):
    """
    Cece Ultra - Full Stack Cognition Agent.

    Runs the complete Alexa Cognition Framework through:
    1. Input normalization
    2. 15-step cognitive pipeline
    3. 6-module architecture layer
    4. Multi-agent orchestration
    5. Structured output generation
    """

    def __init__(self):
        super().__init__(
            name="cece-ultra",
            description="Full stack cognition engine with 15-step pipeline and architecture layer",
            category="cognition",
            version="1.0.0",
            author="Alexa (Cadillac)",
            tags=[
                "cognition",
                "architecture",
                "orchestration",
                "reasoning",
                "emotional-intelligence"
            ],
            timeout=600  # 10 minutes for complex reasoning
        )

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate input parameters."""
        required = ['input']
        return all(k in params for k in required)

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Execute full stack cognition.

        Args:
            params: {
                'input': str,  # User input to process
                'context': dict (optional),  # Additional context
                'mode': str (optional),  # full_stack, quick, deep_dive
                'orchestrate': bool (optional)  # Enable multi-agent orchestration
            }

        Returns:
            Complete cognitive processing results
        """
        user_input = params['input']
        context = params.get('context', {})
        mode = params.get('mode', 'full_stack')
        orchestrate = params.get('orchestrate', False)

        self.logger.info(f"🟣 CECE ULTRA MODE ACTIVATED")
        self.logger.info(f"Processing: {user_input[:100]}...")

        # Step 1: Normalize Input
        normalized = await self._normalize_input(user_input, context)
        self.logger.info(f"🔮 Input normalized")

        # Step 2: Run Cognitive Pipeline
        pipeline = await self._run_cognitive_pipeline(normalized, mode)
        self.logger.info(f"🧠 15-step pipeline completed")

        # Step 3: Apply Architecture Layer
        architecture = await self._apply_architecture_layer(
            normalized,
            pipeline,
            mode
        )
        self.logger.info(f"🛠️ Architecture layer applied")

        # Step 4: Multi-Agent Orchestration (if enabled)
        orchestration = None
        if orchestrate:
            orchestration = await self._orchestrate_agents(
                normalized,
                pipeline,
                architecture
            )
            self.logger.info(f"🧬 Agent orchestration completed")

        # Step 5: Generate Action Plan
        action_plan = await self._generate_action_plan(
            normalized,
            pipeline,
            architecture,
            orchestration
        )

        # Step 6: Create Stable Summary
        stable_summary = await self._create_stable_summary(
            normalized,
            pipeline,
            architecture
        )

        # Step 7: Generate Extras
        extras = await self._generate_extras(
            normalized,
            pipeline,
            architecture,
            orchestration
        )

        # Build final result
        result = CeceUltraResult(
            normalized_input=normalized,
            cognitive_pipeline=pipeline,
            architecture_output=architecture,
            orchestration=orchestration,
            action_plan=action_plan,
            stable_summary=stable_summary,
            extras=extras,
            timestamp=datetime.utcnow()
        )

        self.logger.info(f"✅ Cece Ultra processing complete")

        return self._serialize_result(result)

    async def _normalize_input(
        self,
        user_input: str,
        context: Dict[str, Any]
    ) -> NormalizedInput:
        """Step 1: Normalize input (🫧)."""
        # Extract emotional markers
        emotional_markers = {
            '😭': 'overwhelmed',
            '💚': 'seeking_support',
            '🔥': 'urgent',
            '💛': 'gentle',
            '⚡': 'energized'
        }

        emotional_payload = context.get('emotional_state', 'neutral')
        for emoji, emotion in emotional_markers.items():
            if emoji in user_input:
                emotional_payload = emotion
                break

        # Determine urgency
        urgency_keywords = ['urgent', 'asap', 'now', 'immediately', 'help']
        urgency = 'high' if any(k in user_input.lower() for k in urgency_keywords) else 'medium'

        # Extract vibe
        vibe = 'familiar' if any(c in user_input for c in ['!', '...', '💚', '😭']) else 'neutral'

        return NormalizedInput(
            real_question=user_input.strip(),
            emotional_payload=emotional_payload,
            hidden_assumptions=context.get('assumptions', []),
            urgency=urgency,
            vibe=vibe,
            raw_input=user_input
        )

    async def _run_cognitive_pipeline(
        self,
        normalized: NormalizedInput,
        mode: str
    ) -> CognitivePipeline:
        """Step 2: Run 15-step cognitive pipeline (🧩)."""
        # This is a simplified version - in production, this would use LLM calls
        # for each stage with proper reasoning

        # Stage 1-3: Recognition
        trigger = f"User input: {normalized.real_question}"
        root_cause = f"Seeking clarity/action on: {normalized.real_question}"
        impulse = f"Provide immediate answer based on: {normalized.emotional_payload}"

        # Stage 4-7: Reflection
        reflection = "Zoom out: What's the deeper need here?"
        challenge = "Is the immediate impulse the right approach?"
        counterpoint = "Consider alternative perspectives"
        determination = "Focus on sustainable, clear solution"

        # Stage 8-11: Refinement
        question = "What am I missing in this analysis?"
        bias_offset = "Check for confirmation bias and assumptions"
        values_alignment = "Does this align with user values and context?"
        clarification = "First pass: structured, actionable answer"

        # Stage 12-15: Validation
        restatement = "Reframe for clarity and precision"
        final_clarification = "Polish for tone and completeness"
        validation = f"Validated against: {normalized.emotional_payload} state"
        final_answer = "Grounded, actionable response ready"

        # Emotional state tracking
        emotional_state_before = normalized.emotional_payload
        emotional_state_after = "grounded" if emotional_state_before in [
            'overwhelmed', 'frustrated'
        ] else "energized"

        return CognitivePipeline(
            trigger=trigger,
            root_cause=root_cause,
            impulse=impulse,
            reflection=reflection,
            challenge=challenge,
            counterpoint=counterpoint,
            determination=determination,
            question=question,
            bias_offset=bias_offset,
            values_alignment=values_alignment,
            clarification=clarification,
            restatement=restatement,
            final_clarification=final_clarification,
            validation=validation,
            final_answer=final_answer,
            emotional_state_before=emotional_state_before,
            emotional_state_after=emotional_state_after,
            confidence=0.95
        )

    async def _apply_architecture_layer(
        self,
        normalized: NormalizedInput,
        pipeline: CognitivePipeline,
        mode: str
    ) -> ArchitectureOutput:
        """Step 3: Apply 6-module architecture layer (🛠️)."""
        # Structure
        structure = {
            'type': 'hierarchical',
            'breakdown': [
                'Understand input',
                'Process through pipeline',
                'Generate structured output'
            ]
        }

        # Prioritize
        priorities = {
            'P0': ['Emotional grounding', 'Clarity'],
            'P1': ['Actionability', 'Completeness'],
            'P2': ['Examples', 'Context'],
            'P3': ['Nice-to-haves']
        }

        # Translate
        translation = {
            'emotional_insight': f"{normalized.emotional_payload} → needs structure and support",
            'systems_insight': "Input requires cognitive processing + architecture"
        }

        # Stabilize
        stabilization = {
            'spiral_detected': normalized.urgency == 'high',
            'safety_confirmed': True,
            'clarity_level': 'high'
        }

        # Project manage
        project_plan = {
            'steps': [
                '1. Normalize input',
                '2. Run cognitive pipeline',
                '3. Apply architecture',
                '4. Generate output'
            ],
            'timeline': 'Immediate',
            'dependencies': [],
            'risks': ['Misinterpretation of emotional context'],
            'checkpoints': ['Validate against user intent']
        }

        # Loopback
        loopback_needed = False  # Would trigger if contradictions detected

        return ArchitectureOutput(
            structure=structure,
            priorities=priorities,
            translation=translation,
            stabilization=stabilization,
            project_plan=project_plan,
            loopback_needed=loopback_needed
        )

    async def _orchestrate_agents(
        self,
        normalized: NormalizedInput,
        pipeline: CognitivePipeline,
        architecture: ArchitectureOutput
    ) -> AgentOrchestration:
        """Step 4: Multi-agent orchestration (🧬)."""
        # Determine which agents to invoke based on input
        agents_to_use = []

        # Simple keyword-based routing (would be more sophisticated in production)
        input_lower = normalized.real_question.lower()

        if any(k in input_lower for k in ['build', 'code', 'implement', 'test']):
            agents_to_use.append('codex')

        if any(k in input_lower for k in ['ui', 'ux', 'design', 'frontend', 'interface']):
            agents_to_use.append('wasp')

        if any(k in input_lower for k in ['legal', 'compliance', 'policy', 'risk']):
            agents_to_use.append('clause')

        # Cece is always involved
        agents_to_use.insert(0, 'cece')

        # Determine orchestration mode
        if len(agents_to_use) == 1:
            mode = 'sequential'
        elif 'urgent' in input_lower or 'quick' in input_lower:
            mode = 'parallel'
        else:
            mode = 'sequential'

        # Build chain of thought
        chain = self._build_chain_of_thought(agents_to_use, mode)

        # Simulated outputs (in production, would actually invoke agents)
        outputs = {
            agent: {
                'status': 'completed',
                'contribution': f"{agent} analysis complete"
            }
            for agent in agents_to_use
        }

        return AgentOrchestration(
            agents_used=agents_to_use,
            orchestration_mode=mode,
            chain_of_thought=chain,
            outputs=outputs
        )

    def _build_chain_of_thought(
        self,
        agents: List[str],
        mode: str
    ) -> str:
        """Build visual chain of thought tree."""
        if mode == 'sequential':
            return ' → '.join(f"🟣 {agent}" for agent in agents)
        elif mode == 'parallel':
            main = agents[0]
            parallel = agents[1:]
            return f"🟣 {main} → [{' + '.join(parallel)}]"
        else:
            return f"🟣 {' ⟲ '.join(agents)}"

    async def _generate_action_plan(
        self,
        normalized: NormalizedInput,
        pipeline: CognitivePipeline,
        architecture: ArchitectureOutput,
        orchestration: Optional[AgentOrchestration]
    ) -> List[str]:
        """Step 5: Generate actionable steps."""
        if architecture.project_plan:
            return architecture.project_plan.get('steps', [])

        return [
            "1. Review cognitive pipeline output",
            "2. Apply structured approach",
            "3. Execute with clarity",
            "4. Validate results"
        ]

    async def _create_stable_summary(
        self,
        normalized: NormalizedInput,
        pipeline: CognitivePipeline,
        architecture: ArchitectureOutput
    ) -> str:
        """Step 6: Create stable summary paragraph."""
        return (
            f"Processed input through full cognitive pipeline. "
            f"Emotional state: {pipeline.emotional_state_before} → {pipeline.emotional_state_after}. "
            f"Structured approach with {len(architecture.priorities.get('P0', []))} critical priorities. "
            f"Confidence: {pipeline.confidence:.0%}. Ready for execution."
        )

    async def _generate_extras(
        self,
        normalized: NormalizedInput,
        pipeline: CognitivePipeline,
        architecture: ArchitectureOutput,
        orchestration: Optional[AgentOrchestration]
    ) -> Dict[str, Any]:
        """Step 7: Generate optional extras (diagrams, lists, tables)."""
        extras = {
            'cognitive_stages_completed': 15,
            'architecture_modules_used': [
                m.value for m in ArchitectureModule
            ],
            'processing_metadata': {
                'urgency': normalized.urgency,
                'vibe': normalized.vibe,
                'confidence': pipeline.confidence
            }
        }

        if orchestration:
            extras['orchestration'] = {
                'agents': orchestration.agents_used,
                'mode': orchestration.orchestration_mode,
                'chain': orchestration.chain_of_thought
            }

        return extras

    def _serialize_result(self, result: CeceUltraResult) -> Dict[str, Any]:
        """Serialize result to dictionary."""
        return {
            'normalized_input': {
                'real_question': result.normalized_input.real_question,
                'emotional_payload': result.normalized_input.emotional_payload,
                'hidden_assumptions': result.normalized_input.hidden_assumptions,
                'urgency': result.normalized_input.urgency,
                'vibe': result.normalized_input.vibe,
                'raw_input': result.normalized_input.raw_input
            },
            'cognitive_pipeline': {
                'trigger': result.cognitive_pipeline.trigger,
                'root_cause': result.cognitive_pipeline.root_cause,
                'impulse': result.cognitive_pipeline.impulse,
                'reflection': result.cognitive_pipeline.reflection,
                'challenge': result.cognitive_pipeline.challenge,
                'counterpoint': result.cognitive_pipeline.counterpoint,
                'determination': result.cognitive_pipeline.determination,
                'question': result.cognitive_pipeline.question,
                'bias_offset': result.cognitive_pipeline.bias_offset,
                'values_alignment': result.cognitive_pipeline.values_alignment,
                'clarification': result.cognitive_pipeline.clarification,
                'restatement': result.cognitive_pipeline.restatement,
                'final_clarification': result.cognitive_pipeline.final_clarification,
                'validation': result.cognitive_pipeline.validation,
                'final_answer': result.cognitive_pipeline.final_answer,
                'emotional_state_before': result.cognitive_pipeline.emotional_state_before,
                'emotional_state_after': result.cognitive_pipeline.emotional_state_after,
                'confidence': result.cognitive_pipeline.confidence
            },
            'architecture_output': {
                'structure': result.architecture_output.structure,
                'priorities': result.architecture_output.priorities,
                'translation': result.architecture_output.translation,
                'stabilization': result.architecture_output.stabilization,
                'project_plan': result.architecture_output.project_plan,
                'loopback_needed': result.architecture_output.loopback_needed
            },
            'orchestration': {
                'agents_used': result.orchestration.agents_used,
                'orchestration_mode': result.orchestration.orchestration_mode,
                'chain_of_thought': result.orchestration.chain_of_thought,
                'outputs': result.orchestration.outputs
            } if result.orchestration else None,
            'action_plan': result.action_plan,
            'stable_summary': result.stable_summary,
            'extras': result.extras,
            'timestamp': result.timestamp.isoformat()
        }
