"""
Unit tests for embedding business logic

Tests the core embedding functionality without external dependencies,
focusing on data processing, validation, and business rules.
"""

import pytest
import numpy as np
from unittest.mock import Mock, patch


class TestEmbeddingBusinessLogic:
    """Test embedding business logic and data processing"""

    def test_embedding_vector_validation(self):
        """Test validation of embedding vectors"""
        # Arrange
        valid_vectors = [
            [0.1, 0.2, 0.3],
            [-0.5, 0.0, 0.8],
            [],  # Empty vector
            [1.0] * 1536  # Large vector
        ]
        
        invalid_vectors = [
            None,
            "not a vector",
            [1, 2, "string"],
            [[1, 2], [3, 4]]  # Nested
        ]
        
        # Act & Assert
        def is_valid_vector(vec):
            if not isinstance(vec, list):
                return False
            return all(isinstance(x, (int, float)) for x in vec)
        
        for vec in valid_vectors:
            assert is_valid_vector(vec), f"Should be valid: {vec}"
        
        for vec in invalid_vectors:
            assert not is_valid_vector(vec), f"Should be invalid: {vec}"

    def test_dimension_consistency_check(self):
        """Test dimension consistency validation"""
        # Arrange
        same_dimension_vectors = [
            [0.1, 0.2, 0.3, 0.4, 0.5],
            [0.6, 0.7, 0.8, 0.9, 1.0],
            [-0.1, -0.2, -0.3, -0.4, -0.5]
        ]
        
        mixed_dimension_vectors = [
            [0.1, 0.2, 0.3],
            [0.4, 0.5, 0.6, 0.7],
            [0.8, 0.9]
        ]
        
        # Act
        def check_dimension_consistency(vectors):
            if not vectors:
                return True
            expected_dim = len(vectors[0])
            return all(len(vec) == expected_dim for vec in vectors)
        
        # Assert
        assert check_dimension_consistency(same_dimension_vectors)
        assert not check_dimension_consistency(mixed_dimension_vectors)

    def test_text_preprocessing_logic(self):
        """Test text preprocessing for embeddings"""
        # Arrange
        test_cases = [
            ("Simple text", "Simple text"),
            ("", ""),
            ("Text with\nnewlines", "Text with\nnewlines"),
            ("Unicode: 世界 🌍", "Unicode: 世界 🌍"),
            ("  Whitespace  ", "  Whitespace  ")
        ]
        
        # Act & Assert
        for input_text, expected in test_cases:
            # Simple preprocessing (identity in this case)
            processed = str(input_text) if input_text is not None else ""
            assert processed == expected

    def test_batch_processing_logic(self):
        """Test batch processing logic for multiple texts"""
        # Arrange
        texts = ["Text 1", "Text 2", "Text 3"]
        
        def mock_embed_single(text):
            # Simulate embedding generation based on text length
            return [len(text) / 10.0] * 5
        
        # Act
        results = []
        for text in texts:
            embedding = mock_embed_single(text)
            results.append((text, embedding))
        
        # Assert
        assert len(results) == len(texts)
        for i, (original_text, embedding) in enumerate(results):
            assert original_text == texts[i]
            assert len(embedding) == 5
            expected_value = len(texts[i]) / 10.0
            assert all(abs(val - expected_value) < 0.001 for val in embedding)

    def test_numpy_array_conversion_logic(self):
        """Test numpy array to list conversion"""
        # Arrange
        test_arrays = [
            np.array([1, 2, 3], dtype=np.int32),
            np.array([1.0, 2.0, 3.0], dtype=np.float64),
            np.array([0.1, 0.2, 0.3], dtype=np.float32)
        ]
        
        # Act
        converted = []
        for arr in test_arrays:
            result = arr.tolist()
            converted.append(result)
        
        # Assert
        assert converted[0] == [1, 2, 3]
        assert converted[1] == [1.0, 2.0, 3.0]
        # Float32 might have precision differences, so check approximately
        assert len(converted[2]) == 3
        assert all(isinstance(x, float) for x in converted[2])

    def test_error_response_generation(self):
        """Test error response generation logic"""
        # Arrange
        error_scenarios = [
            ("model_not_found", "Model 'xyz' not found"),
            ("connection_error", "Failed to connect to service"),
            ("rate_limit", "Rate limit exceeded"),
            ("invalid_input", "Invalid input format")
        ]
        
        # Act & Assert
        for error_type, error_message in error_scenarios:
            error_response = {
                "error": {
                    "type": error_type,
                    "message": error_message
                },
                "vectors": None
            }
            
            assert error_response["error"]["type"] == error_type
            assert error_response["error"]["message"] == error_message
            assert error_response["vectors"] is None

    def test_success_response_generation(self):
        """Test success response generation logic"""
        # Arrange
        test_vectors = [0.1, 0.2, 0.3, 0.4, 0.5]
        
        # Act
        success_response = {
            "error": None,
            "vectors": test_vectors
        }
        
        # Assert
        assert success_response["error"] is None
        assert success_response["vectors"] == test_vectors
        assert len(success_response["vectors"]) == 5

    def test_model_parameter_handling(self):
        """Test model parameter validation and handling"""
        # Arrange
        valid_models = {
            "ollama": ["mxbai-embed-large", "nomic-embed-text"],
            "fastembed": ["sentence-transformers/all-MiniLM-L6-v2", "BAAI/bge-small-en-v1.5"]
        }
        
        # Act & Assert
        for provider, models in valid_models.items():
            for model in models:
                assert isinstance(model, str)
                assert len(model) > 0
                if provider == "fastembed":
                    assert "/" in model or "-" in model

    def test_concurrent_processing_simulation(self):
        """Test concurrent processing simulation"""
        # Arrange
        import asyncio
        
        async def mock_async_embed(text, delay=0.001):
            await asyncio.sleep(delay)
            return [ord(text[0]) / 255.0] if text else [0.0]
        
        # Act
        async def run_concurrent():
            texts = ["A", "B", "C", "D", "E"]
            tasks = [mock_async_embed(text) for text in texts]
            results = await asyncio.gather(*tasks)
            return list(zip(texts, results))
        
        # Run test
        results = asyncio.run(run_concurrent())
        
        # Assert
        assert len(results) == 5
        for i, (text, embedding) in enumerate(results):
            expected_char = chr(ord('A') + i)
            assert text == expected_char
            expected_value = ord(expected_char) / 255.0
            assert abs(embedding[0] - expected_value) < 0.001

    def test_empty_and_edge_cases(self):
        """Test empty inputs and edge cases"""
        # Arrange
        edge_cases = [
            ("", "empty string"),
            (" ", "single space"),
            ("a", "single character"),
            ("A" * 10000, "very long string"),
            ("\\n\\t\\r", "special characters"),
            ("混合English中文", "mixed languages")
        ]
        
        # Act & Assert
        for text, description in edge_cases:
            # Basic validation that text can be processed
            assert isinstance(text, str), f"Failed for {description}"
            assert len(text) >= 0, f"Failed for {description}"
            
            # Simulate embedding generation would work
            mock_embedding = [len(text) % 10] * 3
            assert len(mock_embedding) == 3, f"Failed for {description}"

    def test_vector_normalization_logic(self):
        """Test vector normalization calculations"""
        # Arrange
        test_vectors = [
            [3.0, 4.0],  # Should normalize to [0.6, 0.8]
            [1.0, 0.0],  # Should normalize to [1.0, 0.0]
            [0.0, 0.0],  # Zero vector edge case
        ]
        
        # Act & Assert
        for vector in test_vectors:
            magnitude = sum(x**2 for x in vector) ** 0.5
            
            if magnitude > 0:
                normalized = [x / magnitude for x in vector]
                # Check unit length (approximately)
                norm_magnitude = sum(x**2 for x in normalized) ** 0.5
                assert abs(norm_magnitude - 1.0) < 0.0001
            else:
                # Zero vector case
                assert all(x == 0 for x in vector)

    def test_cosine_similarity_calculation(self):
        """Test cosine similarity computation"""
        # Arrange
        vector_pairs = [
            ([1, 0], [0, 1], 0.0),  # Orthogonal
            ([1, 0], [1, 0], 1.0),  # Identical
            ([1, 1], [-1, -1], -1.0),  # Opposite
        ]
        
        # Act & Assert
        def cosine_similarity(v1, v2):
            dot = sum(a * b for a, b in zip(v1, v2))
            mag1 = sum(x**2 for x in v1) ** 0.5
            mag2 = sum(x**2 for x in v2) ** 0.5
            return dot / (mag1 * mag2) if mag1 * mag2 > 0 else 0
        
        for v1, v2, expected in vector_pairs:
            similarity = cosine_similarity(v1, v2)
            assert abs(similarity - expected) < 0.0001