"""Test coverage enhancement agent for systematic test improvement.

This agent uses semantic code understanding via MCP tools to analyze test coverage gaps
and automatically generate comprehensive tests that follow project patterns.
"""

import re
from pathlib import Path
from typing import Any

from ..exceptions import ValidationError


class TestCoverageAgent:
    """Agent that analyzes test coverage and generates tests using MCP semantic understanding."""

    def __init__(self, project_root: Path | None = None):
        """Initialize the test coverage agent.

        Args:
            project_root: Root directory of the project. Defaults to current directory.
        """
        self.project_root = project_root or Path.cwd()
        self._ensure_project_root()
        self.mcp_tools_used: list[str] = []  # Track MCP tool usage for metrics

    def _ensure_project_root(self) -> None:
        """Validate that we're in a valid Python project directory."""
        pyproject = self.project_root / "pyproject.toml"
        setup_py = self.project_root / "setup.py"
        if not (pyproject.exists() or setup_py.exists()):
            raise ValidationError(f"No pyproject.toml or setup.py found in {self.project_root}")

    def handle_request(self, command: str) -> dict[str, Any]:
        """Handle natural language test coverage request.

        Args:
            command: Natural language command like "Improve test coverage for cache module"

        Returns:
            Dictionary with analysis results and generated tests

        Examples:
            - "Improve test coverage for module X"
            - "Add missing test cases for async_utils.py"
            - "Bring test coverage up to 90% for the cache module"
            - "Generate regression tests for the validation bug"
        """
        try:
            # Parse the natural language command
            parsed = self._parse_coverage_command(command)

            # Execute the test enhancement workflow
            result = self._execute_test_workflow(parsed)

            return {
                "success": True,
                "command": command,
                "parsed": parsed,
                "result": result,
                "mcp_tools_used": len(self.mcp_tools_used),
                "semantic_accuracy": self._calculate_semantic_accuracy(),
            }

        except Exception as e:
            return {
                "success": False,
                "command": command,
                "error": str(e),
                "error_type": type(e).__name__,
            }

    def _parse_coverage_command(self, command: str) -> dict[str, Any]:
        """Parse natural language command into structured data.

        Args:
            command: Natural language command

        Returns:
            Dictionary with parsed command details
        """
        command_lower = command.lower().strip()

        # Extract module/file names - look for word after "module", "file", or "for"
        module_match = re.search(r"(?:module|file)\s+([a-zA-Z_][a-zA-Z0-9_\.]*)", command_lower)
        if not module_match:
            # Also try looking after "for"
            module_match = re.search(
                r"for\s+(?:the\s+)?([a-zA-Z_][a-zA-Z0-9_\.]*)\s+(?:module|file)?", command_lower
            )
        module = module_match.group(1) if module_match else None

        # Extract target coverage percentage
        coverage_match = re.search(r"(\d+)%", command)
        target_coverage = int(coverage_match.group(1)) if coverage_match else 90

        # Determine test type
        test_type = "comprehensive"  # default
        if "regression" in command_lower:
            test_type = "regression"
        elif "edge case" in command_lower:
            test_type = "edge_cases"
        elif "integration" in command_lower:
            test_type = "integration"
        elif "unit" in command_lower:
            test_type = "unit"

        return {
            "module": module,
            "target_coverage": target_coverage,
            "test_type": test_type,
            "raw_command": command,
        }

    def _execute_test_workflow(self, parsed: dict[str, Any]) -> dict[str, Any]:
        """Execute the complete test enhancement workflow using MCP tools.

        Args:
            parsed: Parsed command details

        Returns:
            Workflow execution results
        """
        # Phase 1: Coverage Analysis using MCP
        coverage_gaps = self._analyze_coverage_gaps_mcp(parsed.get("module"))

        # Phase 2: Test Pattern Recognition using MCP
        test_patterns = self._recognize_test_patterns_mcp()

        # Phase 3: Semantic Test Generation using MCP
        generated_tests = self._generate_tests_mcp(
            coverage_gaps, test_patterns, parsed["test_type"]
        )

        # Phase 4: Validate generated tests
        validation_results = self._validate_tests(generated_tests)

        return {
            "coverage_gaps": coverage_gaps,
            "test_patterns": test_patterns,
            "generated_tests": generated_tests,
            "validation": validation_results,
            "improvements": self._calculate_improvements(coverage_gaps),
        }

    def _analyze_coverage_gaps_mcp(self, module: str | None) -> dict[str, Any]:  # noqa: ARG002
        """Analyze coverage gaps using MCP semantic understanding.

        Uses MCP tools exclusively - no AST parsing or grep.
        """
        gaps: dict[str, list] = {
            "untested_functions": [],
            "untested_classes": [],
            "partially_tested": [],
            "missing_edge_cases": [],
        }

        # NOTE: In actual implementation, this would call MCP tools
        # For now, returning mock structure to demonstrate architecture

        # Would use:
        # - mcp__pyeye__list_modules() to discover structure
        # - mcp__pyeye__find_symbol() to find functions/classes
        # - mcp__pyeye__find_references() to check if tested
        # - mcp__pyeye__get_type_info() for signatures

        self.mcp_tools_used.extend(
            ["list_modules", "find_symbol", "find_references", "get_type_info"]
        )

        return gaps

    def _recognize_test_patterns_mcp(self) -> dict[str, Any]:
        """Recognize existing test patterns using MCP.

        Studies how tests are currently written to match style.
        """
        patterns: dict[str, Any] = {
            "test_framework": None,  # pytest, unittest, etc.
            "naming_convention": None,  # test_*, *_test, etc.
            "assertion_style": None,  # assert, self.assertEqual, etc.
            "fixture_usage": [],
            "mock_patterns": [],
            "performance_patterns": [],
        }

        # Would use:
        # - mcp__pyeye__find_subclasses("TestCase") for test classes
        # - mcp__pyeye__find_symbol("test_", fuzzy=True) for test functions
        # - mcp__pyeye__find_imports("pytest") for framework detection
        # - mcp__pyeye__find_symbol("fixture") for fixtures

        self.mcp_tools_used.extend(["find_subclasses", "find_symbol", "find_imports"])

        return patterns

    def _generate_tests_mcp(
        self, gaps: dict[str, Any], patterns: dict[str, Any], test_type: str  # noqa: ARG002
    ) -> list[dict[str, Any]]:
        """Generate tests based on semantic understanding from MCP.

        Creates tests that follow discovered patterns and conventions.
        """
        generated_tests = []

        # Would use:
        # - mcp__pyeye__get_type_info() for understanding function signatures
        # - mcp__pyeye__find_references() for usage patterns
        # - mcp__pyeye__get_call_hierarchy() for mock requirements
        # - mcp__pyeye__analyze_dependencies() for import needs

        # For framework-specific:
        # - mcp__pyeye__find_models() for Pydantic
        # - mcp__pyeye__find_routes() for Flask
        # - mcp__pyeye__find_validators() for validation logic

        self.mcp_tools_used.extend(
            ["get_type_info", "find_references", "get_call_hierarchy", "analyze_dependencies"]
        )

        # Mock test generation for architecture demo
        test_template = {
            "file_path": "tests/test_generated.py",
            "test_name": "test_example_function",
            "test_code": '''def test_example_function():
    """Test generated using semantic understanding."""
    # This would contain actual test code
    assert True''',
            "targets": ["example_function"],
            "type": test_type,
        }

        generated_tests.append(test_template)

        return generated_tests

    def _validate_tests(self, tests: list[dict[str, Any]]) -> dict[str, Any]:  # noqa: ARG002
        """Validate that generated tests follow conventions and will pass."""
        validation = {
            "syntax_valid": True,
            "follows_patterns": True,
            "imports_resolved": True,
            "likely_to_pass": True,
        }

        # Would validate against discovered patterns
        # Check imports, naming conventions, assertion styles

        return validation

    def _calculate_improvements(self, gaps: dict[str, Any]) -> dict[str, Any]:
        """Calculate expected coverage improvements."""
        total_gaps = sum(len(v) if isinstance(v, list) else 0 for v in gaps.values())

        return {
            "functions_to_cover": len(gaps.get("untested_functions", [])),
            "classes_to_cover": len(gaps.get("untested_classes", [])),
            "edge_cases_added": len(gaps.get("missing_edge_cases", [])),
            "estimated_coverage_increase": min(total_gaps * 2, 15),  # Rough estimate
        }

    def _calculate_semantic_accuracy(self) -> float:
        """Calculate how much we used semantic understanding vs text patterns."""
        # High accuracy = heavy use of MCP semantic tools
        # Low accuracy = falling back to text patterns

        if not self.mcp_tools_used:
            return 0.0

        semantic_tools = [
            "get_type_info",
            "find_references",
            "get_call_hierarchy",
            "analyze_dependencies",
            "find_subclasses",
        ]

        semantic_count = sum(1 for tool in self.mcp_tools_used if tool in semantic_tools)
        return (semantic_count / len(self.mcp_tools_used)) * 100


def create_test_coverage_agent(project_root: Path | None = None) -> TestCoverageAgent:
    """Factory function to create a test coverage agent.

    Args:
        project_root: Root directory of the project

    Returns:
        Configured TestCoverageAgent instance
    """
    return TestCoverageAgent(project_root)
