"""
Data Flow Analysis Engine for Advanced Code Analysis.

[20251221_FEATURE] v3.0.0+ - Data flow tracking and analysis

This module provides comprehensive data flow analysis capabilities,
enabling detection of data dependencies, taint flow, and use-def chains.

Key features:
- Build use-definition chains
- Track variable lifetimes and scopes
- Analyze data dependencies between statements
- Support taint analysis for security
- Compute data flow facts (reaching definitions, etc.)

Example:
    >>> from code_scalpel.ast_tools.data_flow import DataFlowAnalyzer
    >>> analyzer = DataFlowAnalyzer()
    >>> dfa = analyzer.analyze("src/module.py")
    >>> defs = dfa.get_reaching_definitions("output", line=42)
    >>> for def_site in defs:
    ...     print(f"Definition at line {def_site.line}")
"""

import ast
import logging
from collections import defaultdict
from dataclasses import dataclass, field
from enum import Enum

logger = logging.getLogger(__name__)


class DataFlowFact(Enum):
    """Types of data flow facts."""

    REACHING_DEFINITIONS = "reaching_definitions"
    LIVE_VARIABLES = "live_variables"
    AVAILABLE_EXPRESSIONS = "available_expressions"
    ANTICIPATED_EXPRESSIONS = "anticipated_expressions"


@dataclass
class Definition:
    """A definition of a variable."""

    variable: str
    line: int
    block_id: str
    value: ast.expr | None = None
    is_parameter: bool = False
    is_global: bool = False
    is_mutation: bool = False  # e.g., x[0] = 5


@dataclass
class Usage:
    """A usage of a variable."""

    variable: str
    line: int
    block_id: str
    context: ast.expr | None = None


@dataclass
class DataFlow:
    """Data flow information for a program."""

    definitions: dict[str, list[Definition]] = field(default_factory=dict)
    usages: dict[str, list[Usage]] = field(default_factory=dict)
    def_use_chains: dict[Definition, list[Usage]] = field(default_factory=dict)
    use_def_chains: dict[Usage, list[Definition]] = field(default_factory=dict)


class DataFlowAnalyzer:
    """
    Advanced data flow analyzer.









    """

    def __init__(self):
        self.data_flow: DataFlow | None = None

    def analyze(self, file_path: str) -> DataFlow:
        """
        Perform data flow analysis on a file.

        Args:
            file_path: Path to Python file

        Returns:
            DataFlow object with analysis results


        """
        return DataFlow()

    def analyze_function(self, node: ast.FunctionDef) -> DataFlow:
        """
        Perform data flow analysis on a function.


        """
        data_flow = DataFlow()
        return data_flow

    def get_reaching_definitions(self, variable: str, line: int) -> set[Definition]:
        """
        Get definitions that reach a specific line.


        """
        return set()

    def get_live_variables(self, line: int) -> set[str]:
        """
        Get variables that are live at a specific line.

        Variables are live if they are used on some path after this line.


        """
        return set()

    def get_def_use_chains(self) -> dict[Definition, list[Usage]]:
        """
        Get def-use chains (all uses for each definition).

        """
        if self.data_flow is None:
            return {}
        return self.data_flow.def_use_chains

    def get_use_def_chains(self) -> dict[Usage, list[Definition]]:
        """
        Get use-def chains (all definitions for each use).

        """
        if self.data_flow is None:
            return {}
        return self.data_flow.use_def_chains

    def find_dead_code(self) -> set[tuple[int, int]]:
        """
        Find dead code (statements with unused variables).

        Returns:
            Set of (start_line, end_line) tuples for dead code blocks


        """
        return set()

    def find_unused_variables(self) -> set[str]:
        """
        Find variables that are defined but never used.

        """
        return set()

    def find_uninitialized_usage(self) -> set[tuple[str, int]]:
        """
        Find usages of variables before initialization.

        Returns:
            Set of (variable, line) tuples for uninitialized usage

        """
        return set()

    def taint_analysis(self, source: str, sink: str) -> list[list[Definition]]:
        """
        Perform taint analysis from source to sink.

        Returns:
            List of def chains from source to sink



        """
        return []

    def compute_data_flow_facts(self, fact_type: DataFlowFact) -> dict[int, set[str]]:
        """
        Compute specific data flow facts.

        Args:
            fact_type: Type of fact to compute

        Returns:
            Dict mapping line number to set of facts


        """
        return {}

    def _extract_definitions(self, node: ast.FunctionDef) -> dict[str, list[Definition]]:
        """Extract all variable definitions."""
        # Implementation placeholder
        return defaultdict(list)

    def _extract_usages(self, node: ast.FunctionDef) -> dict[str, list[Usage]]:
        """Extract all variable usages."""
        # Implementation placeholder
        return defaultdict(list)

    def _build_chains(self, definitions: dict, usages: dict) -> None:
        """Build def-use and use-def chains."""
        # Implementation placeholder
        pass
