"""Tests for dynamic plan adjustment."""

import pytest
from datetime import datetime

from src.engines.plan_adjustment import (
    adjust_plan,
    identify_anomalies,
    _fallback_plan_adjustment
)
from src.models.analysis_plan import AnalysisPlan, AnalysisTask
from src.models.analysis_result import AnalysisResult
from src.models.requirement_spec import AnalysisObjective


# Feature: true-ai-agent, Property 8: 计划动态调整
def test_plan_adjustment_with_anomaly():
    """
    Property 8: For any analysis plan and intermediate results, if results
    contain anomaly findings, the plan adjustment function should be able to
    generate new deep-dive tasks or adjust existing task priorities.
    
    Validates: 场景4验收.2, 场景4验收.3, FR-3.3
    """
    # Create plan
    plan = AnalysisPlan(
        objectives=[
            AnalysisObjective(
                name="数据分析",
                description="分析数据",
                metrics=[],
                priority=3
            )
        ],
        tasks=[
            AnalysisTask(
                id="task_1",
                name="Task 1",
                description="First task",
                priority=3,
                status='completed'
            ),
            AnalysisTask(
                id="task_2",
                name="Task 2",
                description="Second task",
                priority=3,
                status='pending'
            )
        ],
        created_at=datetime.now(),
        updated_at=datetime.now()
    )
    
    # Create results with anomaly
    results = [
        AnalysisResult(
            task_id="task_1",
            task_name="Task 1",
            success=True,
            insights=["发现异常：某类别占比90%，远超正常范围"],
            execution_time=1.0
        )
    ]
    
    # Adjust plan (using fallback)
    adjusted_plan = _fallback_plan_adjustment(plan, results)
    
    # Verify: Plan should be updated
    assert adjusted_plan.updated_at >= plan.created_at
    
    # Verify: Pending task priority should be increased
    task_2 = next(t for t in adjusted_plan.tasks if t.id == "task_2")
    assert task_2.priority >= 3


def test_identify_anomalies():
    """Test anomaly identification from results."""
    results = [
        AnalysisResult(
            task_id="task_1",
            task_name="Task 1",
            success=True,
            insights=["发现异常数据", "正常分布"],
            execution_time=1.0
        ),
        AnalysisResult(
            task_id="task_2",
            task_name="Task 2",
            success=True,
            insights=["一切正常"],
            execution_time=1.0
        )
    ]
    
    anomalies = identify_anomalies(results)
    
    # Should identify one anomaly
    assert len(anomalies) >= 1
    assert anomalies[0]['task_id'] == "task_1"


def test_plan_adjustment_no_anomaly():
    """Test plan adjustment when no anomalies found."""
    plan = AnalysisPlan(
        objectives=[],
        tasks=[
            AnalysisTask(
                id="task_1",
                name="Task 1",
                description="First task",
                priority=3,
                status='completed'
            )
        ],
        created_at=datetime.now(),
        updated_at=datetime.now()
    )
    
    results = [
        AnalysisResult(
            task_id="task_1",
            task_name="Task 1",
            success=True,
            insights=["一切正常"],
            execution_time=1.0
        )
    ]
    
    adjusted_plan = _fallback_plan_adjustment(plan, results)
    
    # Should still update timestamp
    assert adjusted_plan.updated_at >= plan.created_at


def test_identify_anomalies_empty_results():
    """Test anomaly identification with empty results."""
    anomalies = identify_anomalies([])
    
    assert anomalies == []


def test_identify_anomalies_failed_results():
    """Test that failed results are skipped."""
    results = [
        AnalysisResult(
            task_id="task_1",
            task_name="Task 1",
            success=False,
            error="Failed",
            insights=["发现异常"],
            execution_time=1.0
        )
    ]
    
    anomalies = identify_anomalies(results)
    
    # Failed results should be skipped
    assert len(anomalies) == 0