"""
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.
"""

import pytest
from unittest.mock import MagicMock, patch
from typing import Dict, List, Any


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},  # Violates age rule
            {'age': 25}
        ]

        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'])
