"""
Tests for AI-enhanced DataFlow operations.

Verifies:
- Natural language to SQL query generation
- Query optimization suggestions
- Intelligent data transformation
- Data quality assessment
- Automated data cleaning
- Semantic search generation
- Field mapping intelligence
- Bulk operation optimization

These tests use TDD methodology - written FIRST before implementation.
"""

from unittest.mock import MagicMock

import pytest


class TestNLToSQLAgent:
    """Test suite for Natural Language to SQL conversion agent."""

    @pytest.fixture
    def mock_dataflow(self):
        """Create a mock DataFlow instance with tables."""
        mock_db = MagicMock()
        mock_db.list_models.return_value = ["User", "Product", "Order"]
        mock_db.get_table_schema = MagicMock(
            side_effect=lambda t: {
                "User": {
                    "columns": {
                        "id": {"type": "int", "nullable": False},
                        "name": {"type": "str", "nullable": False},
                        "email": {"type": "str", "nullable": False},
                        "age": {"type": "int", "nullable": True},
                        "created_at": {"type": "datetime", "nullable": False},
                    }
                },
                "Product": {
                    "columns": {
                        "id": {"type": "int", "nullable": False},
                        "name": {"type": "str", "nullable": False},
                        "inventory": {"type": "int", "nullable": False},
                        "demand_score": {"type": "float", "nullable": True},
                        "category": {"type": "str", "nullable": True},
                    }
                },
            }.get(t, {})
        )
        return mock_db

    @pytest.fixture
    def agent_config(self):
        """Create NLToSQL agent configuration."""
        from kaizen.core.config import BaseAgentConfig

        return BaseAgentConfig(llm_provider="mock", model="gpt-4", temperature=0.2)

    @pytest.fixture
    def nl_to_sql_agent_class(self):
        """Get NLToSQLAgent class."""
        try:
            from kaizen.integrations.dataflow.ai_enhanced_ops import NLToSQLAgent

            return NLToSQLAgent
        except ImportError:
            pytest.skip("AI enhanced operations not implemented yet")

    def test_nl_to_sql_query_generation(
        self, nl_to_sql_agent_class, agent_config, mock_dataflow
    ):
        """
        Test natural language query conversion to DataFlow filter.

        Given: Natural language query "Show me all users who signed up last month"
        When: Agent converts to DataFlow query
        Then: Returns MongoDB-style filter with date range
        """
        agent = nl_to_sql_agent_class(config=agent_config, db=mock_dataflow)

        result = agent.query("Show me all users who signed up last month")

        # Should return query results
        assert result is not None
        assert "results" in result
        assert "explanation" in result

        # Should have query explanation
        assert isinstance(result["explanation"], str)
        assert len(result["explanation"]) > 0

    def test_nl_query_with_complex_conditions(
        self, nl_to_sql_agent_class, agent_config, mock_dataflow
    ):
        """
        Test complex query with multiple conditions.

        Given: Query "Find products with less than 10 items and high demand"
        When: Agent converts to DataFlow query
        Then: Returns filter with multiple conditions
        """
        agent = nl_to_sql_agent_class(config=agent_config, db=mock_dataflow)

        result = agent.query(
            "Find products with less than 10 items in stock and high demand"
        )

        # Should parse complex conditions
        assert result is not None
        assert "results" in result
        assert "explanation" in result

    def test_nl_query_table_selection(
        self, nl_to_sql_agent_class, agent_config, mock_dataflow
    ):
        """
        Test agent correctly selects target table.

        Given: Query mentioning specific entity ("users", "products")
        When: Agent converts query
        Then: Identifies correct target table
        """
        agent = nl_to_sql_agent_class(config=agent_config, db=mock_dataflow)

        result = agent.query("Show me all users")

        # Should identify table from natural language
        assert result is not None

    def test_nl_query_field_projection(
        self, nl_to_sql_agent_class, agent_config, mock_dataflow
    ):
        """
        Test agent identifies fields to return.

        Given: Query "Get user names and emails"
        When: Agent converts query
        Then: Returns projection fields
        """
        agent = nl_to_sql_agent_class(config=agent_config, db=mock_dataflow)

        result = agent.query("Get user names and emails")

        # Should identify fields to project
        assert result is not None

    def test_nl_query_aggregations(
        self, nl_to_sql_agent_class, agent_config, mock_dataflow
    ):
        """
        Test agent handles aggregation queries.

        Given: Query "Count active users"
        When: Agent converts query
        Then: Generates appropriate aggregation
        """
        agent = nl_to_sql_agent_class(config=agent_config, db=mock_dataflow)

        result = agent.query("Count active users")

        # Should handle aggregations
        assert result is not None

    def test_nl_query_error_handling(
        self, nl_to_sql_agent_class, agent_config, mock_dataflow
    ):
        """
        Test agent handles invalid queries gracefully.

        Given: Ambiguous or invalid query
        When: Agent attempts conversion
        Then: Returns helpful error message
        """
        agent = nl_to_sql_agent_class(config=agent_config, db=mock_dataflow)

        # Should handle gracefully
        result = agent.query("xyz invalid query abc")

        # Should not crash
        assert result is not None


class TestQueryOptimizationAgent:
    """Test suite for query optimization suggestions."""

    @pytest.fixture
    def agent_config(self):
        """Create query optimization agent configuration."""
        from kaizen.core.config import BaseAgentConfig

        return BaseAgentConfig(llm_provider="mock", model="gpt-4", temperature=0.1)

    @pytest.fixture
    def query_optimizer_class(self):
        """Get QueryOptimizer class."""
        try:
            from kaizen.integrations.dataflow.query_optimizer import QueryOptimizer

            return QueryOptimizer
        except ImportError:
            pytest.skip("Query optimizer not implemented yet")

    def test_query_optimization_suggestions(self, query_optimizer_class, agent_config):
        """
        Test AI suggests query improvements.

        Given: Slow or inefficient query
        When: Optimizer analyzes query
        Then: Returns optimization suggestions
        """
        optimizer = query_optimizer_class(config=agent_config)

        query = {"table": "users", "filter": {"age": {"$gte": 18}}, "limit": 1000}

        suggestions = optimizer.analyze_query(query)

        # Should provide suggestions
        assert suggestions is not None
        assert isinstance(suggestions, dict)
        assert "optimizations" in suggestions or "recommendations" in suggestions

    def test_index_suggestions(self, query_optimizer_class, agent_config):
        """
        Test optimizer suggests indexes.

        Given: Query with frequent filters
        When: Optimizer analyzes
        Then: Suggests appropriate indexes
        """
        optimizer = query_optimizer_class(config=agent_config)

        query = {"table": "users", "filter": {"email": "test@example.com"}}

        suggestions = optimizer.analyze_query(query)

        # Should suggest indexes
        assert suggestions is not None


class TestDataTransformAgent:
    """Test suite for intelligent data transformation."""

    @pytest.fixture
    def mock_dataflow(self):
        """Create a mock DataFlow instance."""
        mock_db = MagicMock()
        mock_db.get_table_schema = MagicMock(
            return_value={
                "columns": {
                    "id": {"type": "int", "nullable": False},
                    "full_name": {"type": "str", "nullable": False},
                    "email_address": {"type": "str", "nullable": False},
                }
            }
        )
        return mock_db

    @pytest.fixture
    def agent_config(self):
        """Create data transform agent configuration."""
        from kaizen.core.config import BaseAgentConfig

        return BaseAgentConfig(llm_provider="mock", model="gpt-4", temperature=0.3)

    @pytest.fixture
    def transform_agent_class(self):
        """Get DataTransformAgent class."""
        try:
            from kaizen.integrations.dataflow.ai_enhanced_ops import DataTransformAgent

            return DataTransformAgent
        except ImportError:
            pytest.skip("Data transform agent not implemented yet")

    def test_intelligent_data_transformation(
        self, transform_agent_class, agent_config, mock_dataflow
    ):
        """
        Test AI-driven schema mapping.

        Given: Source data with different schema
        When: Agent transforms to target schema
        Then: Returns transformed data matching target
        """
        agent = transform_agent_class(config=agent_config, db=mock_dataflow)

        source_data = [
            {"name": "Alice Smith", "email": "alice@example.com"},
            {"name": "Bob Jones", "email": "bob@example.com"},
        ]

        result = agent.transform_data(source_data=source_data, target_table="users")

        # Should transform data
        assert result is not None
        assert "inserted_count" in result or "transformed_data" in result

    def test_field_mapping_intelligence(
        self, transform_agent_class, agent_config, mock_dataflow
    ):
        """
        Test agent maps fields between schemas intelligently.

        Given: Source fields don't match target exactly
        When: Agent analyzes schemas
        Then: Generates intelligent field mappings
        """
        agent = transform_agent_class(config=agent_config, db=mock_dataflow)

        source_data = [
            {
                "firstName": "Alice",
                "lastName": "Smith",
                "emailAddr": "alice@example.com",
            }
        ]

        result = agent.transform_data(source_data=source_data, target_table="users")

        # Should map fields intelligently
        assert result is not None

    def test_data_quality_issues_detection(
        self, transform_agent_class, agent_config, mock_dataflow
    ):
        """
        Test agent identifies data quality issues during transformation.

        Given: Source data with quality issues
        When: Agent transforms data
        Then: Reports quality issues found
        """
        agent = transform_agent_class(config=agent_config, db=mock_dataflow)

        source_data = [
            {"name": "", "email": "invalid-email"},  # Quality issues
            {"name": "Valid User", "email": "valid@example.com"},
        ]

        result = agent.transform_data(source_data=source_data, target_table="users")

        # Should identify quality issues
        assert result is not None
        # Note: Mock LLM may return various formats, just verify result exists
        assert "quality_issues" in result or "inserted_count" in result

    def test_transformation_confidence_score(
        self, transform_agent_class, agent_config, mock_dataflow
    ):
        """
        Test agent provides confidence score for transformations.

        Given: Data transformation task
        When: Agent transforms data
        Then: Returns confidence score (0-1)
        """
        agent = transform_agent_class(config=agent_config, db=mock_dataflow)

        source_data = [{"name": "Alice", "email": "alice@example.com"}]

        result = agent.transform_data(source_data=source_data, target_table="users")

        # Should provide confidence score
        assert result is not None
        if "confidence" in result:
            assert 0.0 <= result["confidence"] <= 1.0


class TestDataQualityAgent:
    """Test suite for data quality assessment."""

    @pytest.fixture
    def agent_config(self):
        """Create data quality agent configuration."""
        from kaizen.core.config import BaseAgentConfig

        return BaseAgentConfig(llm_provider="mock", model="gpt-4", temperature=0.2)

    @pytest.fixture
    def quality_agent_class(self):
        """Get DataQualityAgent class."""
        try:
            from kaizen.integrations.dataflow.ai_enhanced_ops import DataQualityAgent

            return DataQualityAgent
        except ImportError:
            pytest.skip("Data quality agent not implemented yet")

    def test_data_quality_assessment(self, quality_agent_class, agent_config):
        """
        Test AI validates data quality.

        Given: Sample data for quality check
        When: Agent assesses quality
        Then: Returns quality score and issues
        """
        agent = quality_agent_class(config=agent_config)

        data_sample = [
            {"name": "Alice", "email": "alice@example.com", "age": 25},
            {"name": "", "email": "invalid", "age": -5},  # Quality issues
        ]

        schema = {
            "columns": {
                "name": {"type": "str", "nullable": False},
                "email": {"type": "str", "nullable": False},
                "age": {"type": "int", "nullable": False},
            }
        }

        result = agent.assess_quality(data_sample=data_sample, schema=schema)

        # Should assess quality
        assert result is not None
        assert "quality_score" in result or "issues_found" in result

    def test_automated_data_cleaning(self, quality_agent_class, agent_config):
        """
        Test AI cleans and normalizes data.

        Given: Dirty data with formatting issues
        When: Agent cleans data
        Then: Returns cleaned data
        """
        agent = quality_agent_class(config=agent_config)

        data_sample = [
            {"name": "  Alice  ", "email": "ALICE@EXAMPLE.COM"},
            {"name": "Bob", "email": "bob@example.com  "},
        ]

        result = agent.clean_data(data_sample)

        # Should clean data
        assert result is not None
        if "cleaned_data" in result:
            assert isinstance(result["cleaned_data"], list)

    def test_quality_rules_validation(self, quality_agent_class, agent_config):
        """
        Test agent validates against custom quality rules.

        Given: Quality rules and data
        When: Agent validates
        Then: Returns violations found
        """
        agent = quality_agent_class(config=agent_config)

        data_sample = [{"age": 150}, {"age": 25}]  # Violates age rule

        quality_rules = {"age": {"min": 0, "max": 120}}

        result = agent.assess_quality(
            data_sample=data_sample,
            schema={"columns": {"age": {"type": "int"}}},
            quality_rules=quality_rules,
        )

        # Should find violations
        assert result is not None


class TestSemanticSearchAgent:
    """Test suite for semantic database search."""

    @pytest.fixture
    def mock_dataflow(self):
        """Create a mock DataFlow instance."""
        mock_db = MagicMock()
        mock_db.list_models.return_value = ["User", "Product", "Order"]
        return mock_db

    @pytest.fixture
    def agent_config(self):
        """Create semantic search agent configuration."""
        from kaizen.core.config import BaseAgentConfig

        return BaseAgentConfig(llm_provider="mock", model="gpt-4", temperature=0.3)

    @pytest.fixture
    def semantic_search_class(self):
        """Get SemanticSearchAgent class."""
        try:
            from kaizen.integrations.dataflow.ai_enhanced_ops import SemanticSearchAgent

            return SemanticSearchAgent
        except ImportError:
            pytest.skip("Semantic search agent not implemented yet")

    def test_semantic_search_generation(
        self, semantic_search_class, agent_config, mock_dataflow
    ):
        """
        Test AI generates semantic queries.

        Given: Semantic search query
        When: Agent analyzes query
        Then: Returns relevant tables and search strategy
        """
        agent = semantic_search_class(config=agent_config, db=mock_dataflow)

        result = agent.search(query="Find customers who bought electronics recently")

        # Should generate search strategy
        assert result is not None
        assert "relevant_tables" in result or "search_strategy" in result

    def test_cross_table_semantic_search(
        self, semantic_search_class, agent_config, mock_dataflow
    ):
        """
        Test semantic search across multiple tables.

        Given: Query requiring multiple tables
        When: Agent analyzes
        Then: Identifies all relevant tables
        """
        agent = semantic_search_class(config=agent_config, db=mock_dataflow)

        result = agent.search(query="Users who ordered products in the last week")

        # Should identify multiple tables
        assert result is not None


class TestBulkOperationOptimization:
    """Test suite for AI-optimized bulk operations."""

    @pytest.fixture
    def agent_config(self):
        """Create bulk optimization agent configuration."""
        from kaizen.core.config import BaseAgentConfig

        return BaseAgentConfig(llm_provider="mock", model="gpt-4", temperature=0.1)

    @pytest.fixture
    def bulk_optimizer_class(self):
        """Get bulk operation optimizer class."""
        try:
            from kaizen.integrations.dataflow.query_optimizer import (
                BulkOperationOptimizer,
            )

            return BulkOperationOptimizer
        except ImportError:
            pytest.skip("Bulk optimizer not implemented yet")

    def test_bulk_operation_optimization(self, bulk_optimizer_class, agent_config):
        """
        Test AI optimizes bulk operations.

        Given: Large dataset for bulk insert
        When: Agent analyzes operation
        Then: Suggests optimal batch size and strategy
        """
        optimizer = bulk_optimizer_class(config=agent_config)

        data = [{"id": i, "value": f"item_{i}"} for i in range(10000)]

        result = optimizer.optimize_bulk_insert(data)

        # Should provide optimization
        assert result is not None
        assert "batch_size" in result or "strategy" in result


if __name__ == "__main__":
    pytest.main([__file__, "-v"])
