"""Performance tests for Visual Scheduler with large datasets."""

import pytest
from playwright.sync_api import Page, expect


@pytest.mark.skip("Test needs to be rewritten")
class TestVisualSchedulerPerformance:
    """
    Performance test suite for Visual Scheduler with large datasets.

    This test class focuses on validating the Visual Scheduler's performance
    characteristics when handling large tournament datasets. The Visual Scheduler
    is a critical component for managing match scheduling through drag-and-drop
    interfaces.

    Key performance concerns being tested:
    - Page load times with hundreds/thousands of matches
    - Drag-and-drop responsiveness with complex layouts
    - Memory usage and potential leaks during extended sessions
    - JavaScript execution performance with DOM manipulation

    All tests are currently skipped pending Visual Scheduler implementation
    and proper dataset fixture creation.
    """

    @pytest.fixture
    def large_dataset(self, db):
        """
        Create a large dataset for realistic performance testing.

        This fixture should generate a substantial tournament structure
        with realistic data volumes to stress-test the Visual Scheduler:

        Expected data scale:
        - Multiple competitions and seasons
        - 5-10 divisions per season
        - 8-16 teams per division
        - 50-100 matches per division
        - 6-12 playing fields
        - Multiple time slots per day

        This simulates a large regional tournament with hundreds of
        matches that need to be scheduled efficiently.

        Prerequisites:
        - Tournament control models (Competition, Season, Division, etc.)
        - Match and scheduling models available
        - Factory classes for efficient data generation

        Returns:
        - Dictionary containing created objects and statistics

        Currently not implemented - requires model structure finalization.
        """
        # This would need to be implemented with proper Django models
        # For now, we'll create a placeholder that can be extended
        pass

    def test_visual_scheduler_load_time(self, authenticated_page: Page, live_server):
        """
        Test Visual Scheduler page load performance with large datasets.

        Measures the time required to load and render the Visual Scheduler
        interface when populated with a realistic volume of tournament data.
        This test identifies performance bottlenecks in initial page rendering.

        Performance targets:
        - Initial page load: < 3 seconds for 500+ matches
        - DOM ready state: < 2 seconds for basic interactivity
        - Full render complete: < 5 seconds for complex layouts

        Prerequisites:
        - Visual Scheduler interface implemented and accessible
        - Large dataset fixture providing realistic data volumes
        - Tournament structure with matches and scheduling constraints

        Expected behavior:
        - Page loads without timeout errors
        - All matches render correctly in scheduler grid
        - Drag-and-drop functionality initializes properly
        - Performance metrics stay within acceptable limits

        Limitations:
        - Currently tests basic competition admin page as placeholder
        - Requires actual Visual Scheduler URL and implementation
        - Performance thresholds need calibration with real data

        Args:
            authenticated_page: Pre-authenticated Playwright page
            live_server: Django live server fixture
        """
        page = authenticated_page

        # Navigate to visual scheduler (this URL would need to be adjusted based on actual routing)
        # page.goto(f"{live_server.url}/admin/competition/season/1/visual-schedule/")

        # For now, we'll test navigation to the competitions section
        page.goto(f"{live_server.url}/admin/competition/")

        # Measure page load time
        with page.expect_response("**") as response_info:
            page.reload()

        response = response_info.value
        # Basic performance check - page should load in under 5 seconds
        # In a real implementation, we'd measure JavaScript execution time,
        # DOM rendering time, and drag-and-drop responsiveness

        assert response.status == 200

    def test_drag_drop_performance(self, authenticated_page: Page, live_server):
        """
        Test drag-and-drop operation responsiveness with large datasets.

        Validates that drag-and-drop scheduling operations remain responsive
        and accurate when the Visual Scheduler contains hundreds of matches
        and complex scheduling constraints.

        Performance targets:
        - Drag initiation response: < 100ms
        - Drop operation completion: < 500ms
        - UI feedback during drag: Smooth 60fps animation
        - Validation and conflict detection: < 200ms

        Test scenarios:
        - Drag match to different time slot
        - Drag match to different field
        - Drag match causing scheduling conflicts
        - Multiple rapid drag operations
        - Drag operations while scrolling through large dataset

        Prerequisites:
        - Visual Scheduler with full drag-and-drop implementation
        - Large dataset with realistic scheduling constraints
        - Conflict detection and validation logic active

        Expected behavior:
        - All drag operations complete within performance targets
        - Visual feedback remains smooth throughout operation
        - Scheduling conflicts properly detected and indicated
        - No UI freezing or unresponsiveness during operations

        Limitations:
        - Currently tests basic page navigation as placeholder
        - Requires actual Visual Scheduler implementation
        - Performance measurement needs Chrome DevTools integration

        Args:
            authenticated_page: Pre-authenticated Playwright page
            live_server: Django live server fixture
        """
        page = authenticated_page

        # This test would:
        # 1. Create a season with many matches, fields, and time slots
        # 2. Open the Visual Scheduler
        # 3. Measure the time it takes to perform drag and drop operations
        # 4. Assert that operations complete within acceptable time limits

        # Placeholder implementation
        page.goto(f"{live_server.url}/admin/competition/")
        expect(page.locator("h1")).to_contain_text("Competition administration")

    def test_scheduler_memory_usage(self, authenticated_page: Page, live_server):
        """
        Test memory usage patterns and leak detection for Visual Scheduler.

        Monitors browser memory consumption during extended Visual Scheduler
        sessions to identify potential memory leaks and excessive usage that
        could impact user experience during long scheduling sessions.

        Memory targets:
        - Initial load: < 50MB for typical dataset (500 matches)
        - After 30 operations: < 100MB total
        - Memory growth rate: < 1MB per 10 drag operations
        - No continuous memory leaks over extended usage

        Test scenarios:
        - Baseline memory usage after initial load
        - Memory usage after repeated drag-and-drop operations
        - Memory behavior during view switching (day/week/field views)
        - Memory cleanup after session completion

        Prerequisites:
        - Visual Scheduler implementation with complex data handling
        - Chrome DevTools Protocol integration for memory monitoring
        - Large dataset for realistic memory pressure testing

        Expected behavior:
        - Memory usage remains within acceptable limits
        - No continuous memory growth over extended sessions
        - Proper cleanup of event listeners and DOM references
        - Stable performance over multiple hours of usage

        Limitations:
        - Currently tests basic page navigation as placeholder
        - Requires Chrome DevTools Protocol implementation
        - Memory measurement needs specialized Playwright configuration
        - Baseline targets need calibration with actual implementation

        Args:
            authenticated_page: Pre-authenticated Playwright page
            live_server: Django live server fixture
        """
        page = authenticated_page

        # This test would:
        # 1. Monitor browser memory usage
        # 2. Load Visual Scheduler with large dataset
        # 3. Perform various operations
        # 4. Check for memory leaks or excessive usage

        # Placeholder implementation
        page.goto(f"{live_server.url}/admin/competition/")

        # In a real implementation, we'd use Chrome DevTools Protocol
        # to monitor memory usage and performance metrics

        expect(page.locator("h1")).to_contain_text("Competition administration")
