"""
Simplified tests for the LSH (Locality-Sensitive Hashing) module in deduplication.
Focusing on the high-level functionality rather than implementation details.
"""
import os
import sys
import unittest
from pathlib import Path
import pandas as pd
import numpy as np
from unittest.mock import patch, MagicMock
import logging

# Add the parent directory to the path
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))

# Create the mocks first before importing the actual module
import sys
from unittest.mock import patch

# Mock the datasketch module and all its imports
mock_datasketch = MagicMock()
mock_datasketch.MinHash.return_value = MagicMock()
mock_datasketch.MinHashLSH.return_value = MagicMock()
mock_datasketch.MinHashLSH.return_value.query.return_value = [] 

sys.modules['datasketch'] = mock_datasketch

# Now import the module
from freamon.deduplication.lsh import (
    calculate_optimal_bands_rows,
    apply_lsh_strategy
)


class TestLSHModule(unittest.TestCase):
    """Tests for the LSH functionality in deduplication module."""
    
    def setUp(self):
        """Set up test data."""
        # Create a test dataframe with text data
        self.df_text = pd.DataFrame({
            'id': [1, 2, 3, 4, 5],
            'title': [
                'Machine Learning Introduction',
                'Introduction to ML',
                'Deep Learning Basics',
                'Neural Networks Guide',
                'Introduction to Machine Learning'
            ],
            'description': [
                'Learn the basics of machine learning algorithms',
                'A beginner guide to ML concepts',
                'Fundamentals of deep neural networks',
                'Comprehensive guide to neural networks',
                'Basic concepts of machine learning for beginners'
            ]
        })
        
        # Create a test dataframe with numerical data
        self.df_numeric = pd.DataFrame({
            'id': [1, 2, 3, 4, 5],
            'x': [1.0, 1.1, 5.0, 5.1, 10.0],
            'y': [2.0, 2.2, 8.0, 8.1, 15.0],
            'z': [3.0, 3.3, 12.0, 12.2, 20.0]
        })
        
        # Create a test dataframe with mixed data types
        self.df_mixed = pd.DataFrame({
            'id': [1, 2, 3, 4, 5],
            'text': [
                'Product A',
                'Product B',
                'Product C',
                'Product D',
                'Product A'  # Duplicate
            ],
            'price': [9.99, 19.99, 29.99, 39.99, 9.99],  # Duplicate
            'rating': [4.5, 3.5, 4.0, 4.5, 4.5]
        })
        
        # Convert these to plain numbers for the tests
        self.df_numeric = self.df_numeric.astype({'x': float, 'y': float, 'z': float})
        
        # Mock logging to prevent output during tests
        logging.basicConfig(level=logging.CRITICAL)

    def test_calculate_optimal_bands_rows(self):
        """Test calculating optimal bands and rows for LSH."""
        # Test with different thresholds
        bands, rows = calculate_optimal_bands_rows(threshold=0.5, num_perm=128)
        self.assertEqual(bands * rows, 128)  # Should sum to num_perm
        
        # Higher threshold should result in more bands or fewer rows
        bands_high, rows_high = calculate_optimal_bands_rows(threshold=0.9, num_perm=128)
        self.assertEqual(bands_high * rows_high, 128)
        
        # Compare the implied thresholds
        s_normal = (1/bands) ** (1/rows)
        s_high = (1/bands_high) ** (1/rows_high)
        self.assertGreater(s_high, s_normal)  # Higher threshold should be higher

    def test_apply_lsh_strategy_minhash(self):
        """Test applying LSH strategy with MinHash method."""
        # Skip actual implementation and mock the results
        with patch('freamon.deduplication.lsh.find_similar_pairs_minhash_lsh', 
                  return_value={(0, 4), (1, 2)}):
            pairs = apply_lsh_strategy(
                self.df_text,
                columns=['title', 'description'],
                lsh_method='minhash',
                threshold=0.7
            )
        
        # Should return the mocked pairs
        self.assertEqual(pairs, {(0, 4), (1, 2)})

    def test_apply_lsh_strategy_random_projection(self):
        """Test applying LSH strategy with random projection method."""
        # Skip actual implementation and mock the results
        with patch('freamon.deduplication.lsh.find_similar_pairs_random_projection', 
                  return_value={(0, 1), (2, 3)}):
            pairs = apply_lsh_strategy(
                self.df_numeric,
                columns=['x', 'y', 'z'],
                lsh_method='random_projection',
                threshold=0.7
            )
        
        # Should return the mocked pairs
        self.assertEqual(pairs, {(0, 1), (2, 3)})

    def test_apply_lsh_strategy_auto(self):
        """Test applying LSH strategy with auto method selection."""
        # Mock column type analysis and results
        with patch('freamon.deduplication.lsh.analyze_column_types', 
                  return_value={'x': 'numeric', 'y': 'numeric', 'z': 'numeric'}), \
             patch('freamon.deduplication.lsh.find_similar_pairs_random_projection', 
                  return_value={(0, 1), (2, 3)}):
            pairs = apply_lsh_strategy(
                self.df_numeric,
                columns=['x', 'y', 'z'],
                lsh_method='auto',
                threshold=0.7
            )
        
        # Should return the mocked pairs from random_projection
        self.assertEqual(pairs, {(0, 1), (2, 3)})
        
        # Test with text data - should select minhash
        with patch('freamon.deduplication.lsh.analyze_column_types', 
                  return_value={'title': 'text', 'description': 'text'}), \
             patch('freamon.deduplication.lsh.find_similar_pairs_minhash_lsh', 
                  return_value={(0, 4), (1, 2)}):
            pairs = apply_lsh_strategy(
                self.df_text,
                columns=['title', 'description'],
                lsh_method='auto',
                threshold=0.7
            )
        
        # Should return the mocked pairs from minhash
        self.assertEqual(pairs, {(0, 4), (1, 2)})

    def test_apply_lsh_strategy_hybrid(self):
        """Test applying LSH strategy with hybrid method."""
        # Apply hybrid LSH (both minhash and random projection)
        with patch('freamon.deduplication.lsh.analyze_column_types', 
                  return_value={'text': 'text', 'price': 'numeric', 'rating': 'numeric'}), \
             patch('freamon.deduplication.lsh.find_similar_pairs_minhash_lsh', 
                  return_value={(0, 4)}), \
             patch('freamon.deduplication.lsh.find_similar_pairs_random_projection', 
                  return_value={(0, 4), (1, 2)}):
            pairs = apply_lsh_strategy(
                self.df_mixed,
                columns=['text', 'price', 'rating'],
                lsh_method='hybrid',
                threshold=0.7
            )
        
        # Should combine pairs from both methods
        self.assertEqual(pairs, {(0, 4), (1, 2)})

    def test_apply_lsh_strategy_with_custom_band_rows(self):
        """Test applying LSH strategy with custom band and row settings."""
        # Apply LSH with custom bands and rows
        with patch('freamon.deduplication.lsh.find_similar_pairs_minhash_lsh', 
                  return_value={(0, 4), (1, 2)}):
            pairs = apply_lsh_strategy(
                self.df_text,
                columns=['title', 'description'],
                lsh_method='minhash',
                threshold=0.7,
                num_bands=16,
                rows_per_band=8,
                num_perm=128  # Should be adjusted to 16*8=128
            )
        
        # Should return the mocked pairs
        self.assertEqual(pairs, {(0, 4), (1, 2)})
    
    def test_apply_lsh_strategy_with_incompatible_bands_rows(self):
        """Test applying LSH strategy with incompatible bands and rows."""
        # Apply LSH with bands and rows that don't multiply to num_perm
        with patch('freamon.deduplication.lsh.find_similar_pairs_minhash_lsh', 
                  return_value={(0, 4), (1, 2)}):
            # This should adjust num_perm to 15 (3*5)
            pairs = apply_lsh_strategy(
                self.df_text,
                columns=['title', 'description'],
                lsh_method='minhash',
                threshold=0.7,
                num_bands=3,
                rows_per_band=5,
                num_perm=128  # Should be adjusted to 3*5=15
            )
        
        # Should still work and return the mocked pairs
        self.assertEqual(pairs, {(0, 4), (1, 2)})

    def test_apply_lsh_strategy_invalid_method(self):
        """Test applying LSH strategy with invalid method."""
        # Should raise ValueError for invalid method
        with self.assertRaises(ValueError):
            apply_lsh_strategy(
                self.df_text,
                columns=['title', 'description'],
                lsh_method='invalid_method',
                threshold=0.7
            )


if __name__ == '__main__':
    unittest.main()