"""Frontier evaluation system for comprehensive agent benchmarking.

This module implements sophisticated benchmarking capabilities including:
- Multi-domain evaluation tasks
- Comprehensive benchmark suites
- Performance metrics and scoring
- Frontier model evaluation
- Task difficulty assessment

Author: Nik Jois <nikjois@llamasearch.ai>
"""

import asyncio
import logging
import time
from dataclasses import dataclass, field
from datetime import datetime, timezone
from enum import Enum
from typing import Any, Dict, List, Optional

try:
    import numpy as np
except ImportError:
    np = None

logger = logging.getLogger(__name__)
class TaskDomain(Enum):
    """Evaluation task domains."""
    MATHEMATICS = "mathematics"
    CODING = "coding"
    REASONING = "reasoning"
    SCIENCE = "science"
    LANGUAGE = "language"
    TOOL_USE = "tool_use"
    CREATIVITY = "creativity"
    MULTIMODAL = "multimodal"


class DifficultyLevel(Enum):
    """Task difficulty levels."""
    BASIC = "basic"
    INTERMEDIATE = "intermediate"
    ADVANCED = "advanced"
    EXPERT = "expert"


class EvaluationMetric(Enum):
    """Evaluation metrics for scoring."""
    ACCURACY = "accuracy"
    COMPLETENESS = "completeness"
    CORRECTNESS = "correctness"
    CREATIVITY = "creativity"
    EFFICIENCY = "efficiency"
    RELIABILITY = "reliability"
    COHERENCE = "coherence"
    RELEVANCE = "relevance"


@dataclass
class EvaluationTask:
    """Individual evaluation task."""
    task_id: str
    domain: TaskDomain
    difficulty: DifficultyLevel
    prompt: str
    expected_output: Optional[str] = None
    evaluation_criteria: List[EvaluationMetric] = field(default_factory=list)
    max_score: float = 100.0
    time_limit: int = 300  # seconds
    metadata: Dict[str, Any] = field(default_factory=dict)
@dataclass
class EvaluationResult:
    """Result of task evaluation."""
    task_id: str
    agent_response: str
    execution_time: float
    success: bool
    scores: Dict[EvaluationMetric, float] = field(default_factory=dict)
    total_score: float = 0.0
    feedback: Optional[str] = None
    timestamp: datetime = field(default_factory=lambda: datetime.now(timezone.utc))

@dataclass
class BenchmarkSuite:
    """Collection of evaluation tasks forming a benchmark."""
    suite_name: str
    description: str
    tasks: List[EvaluationTask] = field(default_factory=list)
    version: str = "1.0"
    author: str = "Nik Jois <nikjois@llamasearch.ai>"
    license: str = "MIT"
    metadata: Dict[str, Any] = field(default_factory=dict)
class MathematicalReasoningBenchmark:
    """Benchmark for mathematical reasoning tasks."""
    
    @staticmethod
    def create_tasks() -> List[EvaluationTask]:
        """Create mathematical reasoning tasks."""
        tasks: List[EvaluationTask] = []
        
        # Basic arithmetic
        tasks.append(
            EvaluationTask(
                task_id="math_basic_001",
                domain=TaskDomain.MATHEMATICS,
                difficulty=DifficultyLevel.BASIC,
                prompt="Calculate the result of 127 + 89 - 43 * 2",
                expected_output="130",
                evaluation_criteria=[EvaluationMetric.ACCURACY, EvaluationMetric.CORRECTNESS],
                max_score=10.0
            )
        )
        
        # Algebra
        tasks.append(
            EvaluationTask(
                task_id="math_intermediate_001",
                domain=TaskDomain.MATHEMATICS,
                difficulty=DifficultyLevel.INTERMEDIATE,
                prompt="Solve for x: 3x + 7 = 2x - 5",
                expected_output="x = -12",
                evaluation_criteria=[EvaluationMetric.ACCURACY, EvaluationMetric.CORRECTNESS],
                max_score=25.0
            )
        )
        
        # Calculus
        tasks.append(
            EvaluationTask(
                task_id="math_advanced_001",
                domain=TaskDomain.MATHEMATICS,
                difficulty=DifficultyLevel.ADVANCED,
                prompt="Find the derivative of f(x) = x^3 + 2x^2 - 5x + 1",
                expected_output="f'(x) = 3x^2 + 4x - 5",
                evaluation_criteria=[EvaluationMetric.ACCURACY, EvaluationMetric.CORRECTNESS],
                max_score=50.0
            )
        )
        
        return tasks


class CodeGenerationBenchmark:
    """Benchmark for code generation and programming tasks."""
    
    @staticmethod
    def create_tasks() -> List[EvaluationTask]:
        """Create code generation tasks."""
        tasks: List[EvaluationTask] = []
        
        # Basic programming
        tasks.append(
            EvaluationTask(
                task_id="code_basic_001",
                domain=TaskDomain.CODING,
                difficulty=DifficultyLevel.BASIC,
                prompt="Write a Python function to calculate factorial of a number",
                expected_output="""def factorial(n):
    if n <= 1:
        return 1
    return n * factorial(n-1)""",
                evaluation_criteria=[EvaluationMetric.CORRECTNESS, EvaluationMetric.EFFICIENCY],
                max_score=15.0
            )
        )
        
        # Data structures
        tasks.append(
            EvaluationTask(
                task_id="code_intermediate_001",
                domain=TaskDomain.CODING,
                difficulty=DifficultyLevel.INTERMEDIATE,
                prompt="Implement a binary search algorithm in Python",
                expected_output="""def binary_search(arr, target):
    left, right = 0, len(arr) - 1
    while left <= right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    return -1""",
                evaluation_criteria=[EvaluationMetric.CORRECTNESS, EvaluationMetric.EFFICIENCY],
                max_score=30.0
            )
        )
        
        # Algorithm design
        tasks.append(
            EvaluationTask(
                task_id="code_advanced_001",
                domain=TaskDomain.CODING,
                difficulty=DifficultyLevel.ADVANCED,
                prompt="Implement a dynamic programming solution for the longest common subsequence problem",
                evaluation_criteria=[EvaluationMetric.CORRECTNESS, EvaluationMetric.EFFICIENCY, EvaluationMetric.CREATIVITY],
                max_score=60.0
            )
        )
        
        return tasks


class ScientificAnalysisBenchmark:
    """Benchmark for scientific reasoning and analysis."""
    
    @staticmethod
    def create_tasks() -> List[EvaluationTask]:
        """Create scientific analysis tasks."""
        tasks: List[EvaluationTask] = []
        
        # Data analysis
        tasks.append(
            EvaluationTask(
                task_id="science_basic_001",
                domain=TaskDomain.SCIENCE,
                difficulty=DifficultyLevel.BASIC,
                prompt="Analyze the relationship between temperature and pressure in an ideal gas",
                evaluation_criteria=[EvaluationMetric.ACCURACY, EvaluationMetric.COMPLETENESS],
                max_score=20.0
            )
        )
        
        # Hypothesis testing
        tasks.append(
            EvaluationTask(
                task_id="science_intermediate_001",
                domain=TaskDomain.SCIENCE,
                difficulty=DifficultyLevel.INTERMEDIATE,
                prompt="Design an experiment to test the effectiveness of a new drug treatment",
                evaluation_criteria=[EvaluationMetric.COMPLETENESS, EvaluationMetric.RELIABILITY],
                max_score=40.0
            )
        )
        
        return tasks


class ToolUsageBenchmark:
    """Benchmark for tool usage and integration capabilities."""
    
    @staticmethod
    def create_tasks() -> List[EvaluationTask]:
        """Create tool usage tasks."""
        tasks: List[EvaluationTask] = []
        
        # Calculator usage
        tasks.append(
            EvaluationTask(
                task_id="tool_basic_001",
                domain=TaskDomain.TOOL_USE,
                difficulty=DifficultyLevel.BASIC,
                prompt="Use appropriate tools to calculate the square root of 2485 and explain the process",
                evaluation_criteria=[EvaluationMetric.ACCURACY, EvaluationMetric.EFFICIENCY],
                max_score=15.0
            )
        )
        
        # Multi-tool coordination
        tasks.append(
            EvaluationTask(
                task_id="tool_advanced_001",
                domain=TaskDomain.TOOL_USE,
                difficulty=DifficultyLevel.ADVANCED,
                prompt="Use multiple tools to analyze a dataset, create visualizations, and generate a report",
                evaluation_criteria=[EvaluationMetric.COMPLETENESS, EvaluationMetric.EFFICIENCY, EvaluationMetric.CREATIVITY],
                max_score=70.0
            )
        )
        
        return tasks


class TaskEvaluator:
    """Evaluates agent performance on individual tasks."""
    
    def __init__(self) -> None:
        self.evaluation_functions: Dict[TaskDomain, Any] = {
            TaskDomain.MATHEMATICS: self._evaluate_math_task,
            TaskDomain.CODING: self._evaluate_code_task,
            TaskDomain.SCIENCE: self._evaluate_science_task,
            TaskDomain.TOOL_USE: self._evaluate_tool_task,
            TaskDomain.REASONING: self._evaluate_reasoning_task,
            TaskDomain.LANGUAGE: self._evaluate_language_task,
            TaskDomain.CREATIVITY: self._evaluate_creativity_task
        }
    
    async def evaluate_task(self, task: EvaluationTask, agent_response: str) -> EvaluationResult:
        """Evaluate agent performance on a specific task."""
        
        start_time = time.time()
        
        try:
            # Get domain-specific evaluator
            evaluator = self.evaluation_functions.get(task.domain)
            if not evaluator:
                raise ValueError(f"No evaluator for domain: {task.domain}")
            # Perform evaluation
            scores = await evaluator(task, agent_response)
            # Calculate total score
            total_score = sum(scores.values() / len(scores) if scores else 0.0
            
            # Create result
            result = EvaluationResult(
                task_id=task.task_id,
                agent_response=agent_response,
                execution_time=time.time() - start_time,
                success=total_score >= 0.6,  # 60% threshold
                scores=scores,
                total_score=total_score,
                feedback=f"Task completed with score: {total_score:.2f}"
            )
            
            return result
            
        except Exception as e:
            logger.error(f"Task evaluation failed: {e}")
            return EvaluationResult(
                task_id=task.task_id,
                agent_response=agent_response,
                execution_time=time.time() - start_time,
                success=False,
                total_score=0.0,
                feedback=f"Evaluation failed: {str(e)}"
            )
    
    async def _evaluate_math_task(self, task: EvaluationTask, response: str) -> Dict[EvaluationMetric, float]:
        """Evaluate mathematical reasoning task."""
        scores = {}
        
        # Check for mathematical content
        has_calculation = any(char in response for char in "+-*/=")
        has_numbers = any(char.isdigit() for char in response)
        
        scores[EvaluationMetric.ACCURACY] = 0.8 if has_calculation and has_numbers else 0.4
        scores[EvaluationMetric.CORRECTNESS] = 0.7 if task.expected_output and task.expected_output.lower() in response.lower() else 0.3
        
        return scores
    
    async def _evaluate_code_task(self, task: EvaluationTask, response: str) -> Dict[EvaluationMetric, float]:
        """Evaluate code generation task."""
        scores = {}
        
        # Check for code structure
        has_function = "def " in response
        has_logic = any(keyword in response for keyword in ["if", "for", "while", "return"])
        proper_syntax = response.count("(") == response.count(")")
        
        scores[EvaluationMetric.CORRECTNESS] = 0.9 if has_function and has_logic and proper_syntax else 0.5
        scores[EvaluationMetric.EFFICIENCY] = 0.8 if len(response) < 1000 else 0.6
        
        return scores
    
    async def _evaluate_science_task(self, task: EvaluationTask, response: str) -> Dict[EvaluationMetric, float]:
        """Evaluate scientific analysis task."""
        scores = {}
        
        # Check for scientific reasoning
        has_analysis = any(
            word in response.lower() for word in ["analysis", "hypothesis", "experiment", "data", "result"]
        )
        has_explanation = len(response) > 100
        
        scores[EvaluationMetric.ACCURACY] = 0.8 if has_analysis else 0.4
        scores[EvaluationMetric.COMPLETENESS] = 0.9 if has_explanation else 0.5
        
        return scores
    
    async def _evaluate_tool_task(self, task: EvaluationTask, response: str) -> Dict[EvaluationMetric, float]:
        """Evaluate tool usage task."""
        scores = {}
        
        # Check for tool usage indicators
        has_tool_mention = any(
            word in response.lower() for word in ["calculator", "tool", "function", "command", "compute"]
        )
        has_process = "step" in response.lower() or "process" in response.lower()
        
        scores[EvaluationMetric.ACCURACY] = 0.7 if has_tool_mention else 0.3
        scores[EvaluationMetric.EFFICIENCY] = 0.8 if has_process else 0.4
        
        return scores
    
    async def _evaluate_reasoning_task(self, task: EvaluationTask, response: str) -> Dict[EvaluationMetric, float]:
        """Evaluate reasoning task."""
        scores = {}
        
        # Check for reasoning patterns
        has_logic = any(
            word in response.lower() for word in ["because", "therefore", "since", "thus", "conclude"]
        )
        has_structure = len(response.split(".") > 2
        
        scores[EvaluationMetric.COHERENCE] = 0.8 if has_logic and has_structure else 0.4
        scores[EvaluationMetric.RELEVANCE] = 0.9 if len(response) > 50 else 0.3
        
        return scores
    
    async def _evaluate_language_task(self, task: EvaluationTask, response: str) -> Dict[EvaluationMetric, float]:
        """Evaluate language task."""
        scores = {}
        
        # Check for language quality
        word_count = len(response.split()
        has_variety = len(set(response.lower().split()) / word_count > 0.6 if word_count > 0 else 0
        
        scores[EvaluationMetric.COHERENCE] = 0.8 if word_count > 20 else 0.4
        scores[EvaluationMetric.CREATIVITY] = 0.7 if has_variety else 0.3
        
        return scores
    
    async def _evaluate_creativity_task(self, task: EvaluationTask, response: str) -> Dict[EvaluationMetric, float]:
        """Evaluate creativity task."""
        scores = {}
        
        # Check for creative elements
        has_originality = len(response) > 100
        has_variety = len(set(response.lower().split()) / len(response.split() > 0.7 if response.split() else 0
        
        scores[EvaluationMetric.CREATIVITY] = 0.9 if has_originality and has_variety else 0.5
        scores[EvaluationMetric.RELEVANCE] = 0.8 if len(response) > 50 else 0.3
        
        return scores


class FrontierEvaluationEngine:
    """Main engine for frontier evaluation and benchmarking."""
    
    def __init__(self):
        self.benchmark_suites: Dict[str, BenchmarkSuite] = {}
        self.evaluator = TaskEvaluator()
        self.evaluation_history: List[Dict[str, Any]] = []
        self._initialize_benchmark_suites()
    
    def _initialize_benchmark_suites(self) -> None:
        """Initialize standard benchmark suites."""
        
        # Mathematical reasoning suite
        math_suite = BenchmarkSuite(
            suite_name="Mathematical Reasoning",
            description="Comprehensive mathematical reasoning and problem-solving tasks"
        )
        math_suite.tasks = MathematicalReasoningBenchmark.create_tasks()
        self.benchmark_suites["math"] = math_suite
        
        # Code generation suite
        code_suite = BenchmarkSuite(
            suite_name="Code Generation",
            description="Programming and algorithmic problem-solving tasks"
        )
        code_suite.tasks = CodeGenerationBenchmark.create_tasks()
        self.benchmark_suites["code"] = code_suite
        
        # Scientific analysis suite
        science_suite = BenchmarkSuite(
            suite_name="Scientific Analysis",
            description="Scientific reasoning and experimental design tasks"
        )
        science_suite.tasks = ScientificAnalysisBenchmark.create_tasks()
        self.benchmark_suites["science"] = science_suite
        
        # Tool usage suite
        tool_suite = BenchmarkSuite(
            suite_name="Tool Usage",
            description="Tool integration and multi-tool coordination tasks"
        )
        tool_suite.tasks = ToolUsageBenchmark.create_tasks()
        self.benchmark_suites["tools"] = tool_suite
    
    async def run_comprehensive_evaluation(self, agent_id: str) -> Dict[str, Any]:
        """Run comprehensive evaluation across all benchmark suites."""
        
        evaluation_start = time.time()
        all_results: List[EvaluationResult] = []
        
        logger.info(f"Starting comprehensive evaluation for agent: {agent_id}")
        # Run all benchmark suites
        for suite_name, suite in self.benchmark_suites.items():
            logger.info(f"Running benchmark suite: {suite_name}")
            suite_results = await self._run_benchmark_suite(agent_id, suite)
            all_results.extend(suite_results)
        # Compile comprehensive results
        total_time = time.time() - evaluation_start
        
        # Calculate domain performance
        domain_performance = {}
        for domain in TaskDomain:
            domain_results = [r for r in all_results if any(t.domain == domain for t in self._get_all_tasks() if t.task_id == r.task_id)]
            if domain_results:
                domain_performance[domain.value] = {
                    "average_score": sum(r.total_score for r in domain_results) / len(domain_results),
                    "success_rate": sum(1 for r in domain_results if r.success) / len(domain_results),
                    "total_tasks": len(domain_results)
                }
        
        # Calculate difficulty performance
        difficulty_performance = {}
        for difficulty in DifficultyLevel:
            difficulty_results = [r for r in all_results if any(t.difficulty == difficulty for t in self._get_all_tasks() if t.task_id == r.task_id)]
            if difficulty_results:
                difficulty_performance[difficulty.value] = {
                    "average_score": sum(r.total_score for r in difficulty_results) / len(difficulty_results),
                    "success_rate": sum(1 for r in difficulty_results if r.success) / len(difficulty_results),
                    "total_tasks": len(difficulty_results)
                }
        
        # Compile final results
        comprehensive_results = {
            "agent_id": agent_id,
            "evaluation_timestamp": datetime.now(timezone.utc).isoformat(),
            "total_evaluation_time": total_time,
            "total_tasks": len(all_results),
            "overall_success_rate": sum(1 for r in all_results if r.success) / len(all_results) if all_results else 0,
            "overall_average_score": sum(r.total_score for r in all_results) / len(all_results) if all_results else 0,
            "domain_performance": domain_performance,
            "difficulty_performance": difficulty_performance,
            "benchmark_suite_results": {}
        }
        
        # Add suite-specific results
        for suite_name in self.benchmark_suites.keys():
            suite_results = [r for r in all_results if r.task_id.startswith(suite_name) or any(t.task_id == r.task_id for t in self.benchmark_suites[suite_name].tasks)]
            if suite_results:
                comprehensive_results["benchmark_suite_results"][suite_name] = {
                    "tasks_completed": len(suite_results),
                    "average_score": sum(r.total_score for r in suite_results) / len(suite_results),
                    "success_rate": sum(1 for r in suite_results if r.success) / len(suite_results)
                }
        
        # Store in history
        self.evaluation_history.append(comprehensive_results)
        logger.info(f"Comprehensive evaluation completed in {total_time:.2f}s")
        logger.info(f"Overall success rate: {comprehensive_results['overall_success_rate']:.2%}")
        return comprehensive_results
    
    async def _run_benchmark_suite(self, agent_id: str, suite: BenchmarkSuite) -> List[EvaluationResult]:
        """Run a specific benchmark suite."""
        
        results = []
        
        # Process tasks in parallel for efficiency
        async def process_task(task: EvaluationTask) -> EvaluationResult:
            # Simulate agent response (in real implementation, this would call the actual agent)
            agent_response = await self._simulate_agent_response(agent_id, task)
            # Evaluate the response
            result = await self.evaluator.evaluate_task(task, agent_response)
            return result
        
        # Run all tasks
        start_time = time.time()
        task_results = await asyncio.gather(
            *[process_task(task) for task in suite.tasks],
            return_exceptions=True
        )
        
        # Filter out exceptions and add successful results
        for result in task_results:
            if isinstance(result, EvaluationResult):
                results.append(result)
            else:
                logger.error(f"Task evaluation failed: {result}")
        logger.info(f"Benchmark suite '{suite.suite_name}' completed in {time.time() - start_time:.2f}s")
        
        return results
    
    async def _simulate_agent_response(self, agent_id: str, task: EvaluationTask) -> str:
        """Simulate agent response for demonstration purposes."""
        # In a real implementation, this would call the actual agent
        await asyncio.sleep(0.1)  # Simulate processing time
        
        # Generate mock responses based on task domain
        if task.domain == TaskDomain.MATHEMATICS:
            return f"Let me solve this mathematical problem: {task.prompt}\nThe answer is 42."
        elif task.domain == TaskDomain.CODING:
            return f"Here's the Python code for {task.prompt}:\n\ndef solution():\n    # Implementation here\n    return result"
        elif task.domain == TaskDomain.SCIENCE:
            return f"Based on scientific analysis of {task.prompt}, the hypothesis is supported by evidence."
        elif task.domain == TaskDomain.TOOL_USE:
            return f"I'll use the appropriate tools to solve {task.prompt}. Step 1: Calculate, Step 2: Analyze."
        else:
            return f"Here's my response to {task.prompt}: This is a comprehensive analysis of the problem."
    
    def _get_all_tasks(self) -> List[EvaluationTask]:
        """Get all tasks from all benchmark suites."""
        all_tasks = []
        for suite in self.benchmark_suites.values():
            all_tasks.extend(suite.tasks)
        return all_tasks
    
    def get_evaluation_history(self, limit: int = 10) -> List[Dict[str, Any]]:
        """Get recent evaluation history."""
        return self.evaluation_history[-limit:]
    
    def get_benchmark_suites(self) -> Dict[str, BenchmarkSuite]:
        """Get all available benchmark suites."""
        return self.benchmark_suites
    
    def get_evaluation_statistics(self) -> Dict[str, Any]:
        """Get evaluation statistics."""
        if not self.evaluation_history:
            return {"total_evaluations": 0}
        
        recent_evaluations = self.evaluation_history[-10:]
        
        return {
            "total_evaluations": len(self.evaluation_history),
            "recent_average_score": sum(e.get("overall_average_score", 0) for e in recent_evaluations) / len(recent_evaluations),
            "recent_success_rate": sum(e.get("overall_success_rate", 0) for e in recent_evaluations) / len(recent_evaluations),
            "total_benchmark_suites": len(self.benchmark_suites),
            "total_tasks": sum(len(suite.tasks) for suite in self.benchmark_suites.values()
        }


# Factory function
def create_frontier_evaluation_engine() -> FrontierEvaluationEngine:
    """Create and return a frontier evaluation engine instance."""
    return FrontierEvaluationEngine()