main.py

from __future__ import annotations
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#------------------------------------------------------------------------------
# Standard Library Imports - 3.13 std libs **ONLY**
#------------------------------------------------------------------------------
import os
import io
import gc
import re
import sys
import ast
import dis
import mmap
import json
import uuid
import site
import time
import cmath
import errno
import shlex
import ctypes
import signal
import random
import pickle
import socket
import struct
import pstats
import shutil
import weakref
import tomllib
import decimal
import pathlib
import logging
import inspect
import asyncio
import hashlib
import argparse
import cProfile
import platform
import tempfile
import mimetypes
import functools
import linecache
import traceback
import threading
import importlib
import subprocess
import tracemalloc
import http.server
from math import sqrt
from io import StringIO
from array import array
from queue import Queue, Empty
from abc import ABC, abstractmethod
from enum import Enum, auto, StrEnum
from collections import namedtuple
from operator import mul
from typing import (
    Any, Dict, List, Optional, Union, Callable, TypeVar,
    Tuple, Generic, Set, Coroutine, Type, NamedTuple,
    ClassVar, Protocol, runtime_checkable, AsyncIterator
)
from types import (
    SimpleNamespace, ModuleType, MethodType,
    FunctionType, CodeType, TracebackType, FrameType
)
from dataclasses import dataclass, field
from functools import reduce, lru_cache, partial, wraps
from collections.abc import Iterable, Mapping
from datetime import datetime
from pathlib import Path, PureWindowsPath
from contextlib import contextmanager, asynccontextmanager
from concurrent.futures import ThreadPoolExecutor
from functools import reduce
from importlib.util import spec_from_file_location, module_from_spec
T = TypeVar('T')
IS_WINDOWS = os.name == 'nt'
IS_POSIX = os.name == 'posix'
if IS_WINDOWS:
    from ctypes import windll
    from ctypes import wintypes
    from ctypes.wintypes import HANDLE, DWORD, LPWSTR, LPVOID, BOOL
    from pathlib import PureWindowsPath
    def set_process_priority(priority: int):
        windll.kernel32.SetPriorityClass(wintypes.HANDLE(-1), priority)
    WINDOWS_SANDBOX_DEFAULT_DESKTOP = Path(PureWindowsPath(r'C:\Users\WDAGUtilityAccount\Desktop'))
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger('ObsidianSandbox')
# Platform-specific optimizations
if IS_WINDOWS:
    WINDOWS_SANDBOX_DEFAULT_DESKTOP = Path(PureWindowsPath(r'C:\Users\WDAGUtilityAccount\Desktop'))
    from ctypes import windll
    from ctypes import wintypes
    from ctypes.wintypes import HANDLE, DWORD, LPWSTR, LPVOID, BOOL
    def set_process_priority(priority: int):
        windll.kernel32.SetPriorityClass(wintypes.HANDLE(-1), priority)

@dataclass
class SandboxConfig:
    mappings: List['FolderMapping']
    networking: bool = True
    logon_command: str = ""
    virtual_gpu: bool = True

    def to_wsb_config(self) -> Dict:
        """Generate Windows Sandbox configuration"""
        config = {
            'MappedFolders': [mapping.to_wsb_config() for mapping in self.mappings],
            'LogonCommand': {'Command': self.logon_command} if self.logon_command else None,
            'Networking': self.networking,
            'vGPU': self.virtual_gpu
        }
        return config

class SandboxException(Exception):
    """Base exception for sandbox-related errors"""
    pass

class ServerNotResponding(SandboxException):
    """Raised when server is not responding"""
    pass

@dataclass
class FolderMapping:
    """Represents a folder mapping between host and sandbox"""
    host_path: Path
    read_only: bool = True
    
    def __post_init__(self):
        self.host_path = Path(self.host_path)
        if not self.host_path.exists():
            raise ValueError(f"Host path does not exist: {self.host_path}")
    
    @property
    def sandbox_path(self) -> Path:
        """Get the mapped path inside the sandbox"""
        return WINDOWS_SANDBOX_DEFAULT_DESKTOP / self.host_path.name
    
    def to_wsb_config(self) -> Dict:
        """Convert to Windows Sandbox config format"""
        return {
            'HostFolder': str(self.host_path),
            'ReadOnly': self.read_only
        }

class PythonUserSiteMapper:
    def read_only(self):
        return True
    """
    Maps the current Python installation's user site packages to the new sandbox.
    """

    def site(self):
        return pathlib.Path(site.getusersitepackages())

    """
    Maps the current Python installation to the new sandbox.
    """
    def path(self):
        return pathlib.Path(sys.prefix)

class OnlineSession:
    """Manages the network connection to the sandbox"""
    def __init__(self, sandbox: 'SandboxEnvironment'):
        self.sandbox = sandbox
        self.shared_directory = self._get_shared_directory()
        self.server_address_path = self.shared_directory / 'server_address'
        self.server_address_path_in_sandbox = self._get_sandbox_server_path()

    def _get_shared_directory(self) -> Path:
        """Create and return shared directory path"""
        shared_dir = Path(tempfile.gettempdir()) / 'obsidian_sandbox_shared'
        shared_dir.mkdir(exist_ok=True)
        return shared_dir

    def _get_sandbox_server_path(self) -> Path:
        """Get the server address path as it appears in the sandbox"""
        return WINDOWS_SANDBOX_DEFAULT_DESKTOP / self.shared_directory.name / 'server_address'

    def configure_sandbox(self):
        """Configure sandbox for network communication"""
        self.sandbox.config.mappings.append(
            FolderMapping(self.shared_directory, read_only=False)
        )
        self._setup_logon_script()

    def _setup_logon_script(self):
        """Generate logon script for sandbox initialization"""
        commands = []
        
        # Setup Python environment
        python_path = sys.executable
        sandbox_python_path = WINDOWS_SANDBOX_DEFAULT_DESKTOP / 'Python' / 'python.exe'
        commands.append(f'copy "{python_path}" "{sandbox_python_path}"')
        
        # Start server
        commands.append(f'{sandbox_python_path} -m http.server 8000')
        
        self.sandbox.config.logon_command = 'cmd.exe /c "{}"'.format(' && '.join(commands))

    def connect(self, timeout: int = 60) -> Tuple[str, int]:
        """Establish connection to sandbox"""
        if self._wait_for_file(timeout):
            address, port = self.server_address_path.read_text().strip().split(':')
            if self._verify_connection(address, int(port)):
                return address, int(port)
            raise ServerNotResponding("Server is not responding")
        raise SandboxException("Failed to establish connection")

    def _wait_for_file(self, timeout: int) -> bool:
        """Wait for server address file creation"""
        end_time = time.time() + timeout
        while time.time() < end_time:
            if self.server_address_path.exists():
                return True
            time.sleep(1)
        return False

    def _verify_connection(self, address: str, port: int) -> bool:
        """Verify network connection to sandbox"""
        try:
            with socket.create_connection((address, port), timeout=3):
                return True
        except (socket.error, socket.timeout):
            return False

class SandboxEnvironment:
    """Manages the Windows Sandbox environment"""
    def __init__(self, config: SandboxConfig):
        self.config = config
        self._session = OnlineSession(self)
        self._connection: Optional[Tuple[str, int]] = None
        
        if config.networking:
            self._session.configure_sandbox()
            self._connection = self._session.connect()

    def run_executable(self, executable_args: List[str], **kwargs) -> subprocess.Popen:
        """Run an executable in the sandbox"""
        kwargs.setdefault('stdout', subprocess.PIPE)
        kwargs.setdefault('stderr', subprocess.PIPE)
        return subprocess.Popen(executable_args, **kwargs)

    def shutdown(self):
        """Safely shutdown the sandbox"""
        try:
            self.run_executable(['shutdown.exe', '/s', '/t', '0'])
        except Exception as e:
            logger.error(f"Failed to shutdown sandbox: {e}")
            raise SandboxException("Shutdown failed")

class SandboxCommServer:
    """Manages communication with the sandbox environment"""
    def __init__(self, shared_dir: Path):
        self.shared_dir = shared_dir
        self.server: Optional[http.server.HTTPServer] = None
        self._port = self._find_free_port()
    
    @staticmethod
    def _find_free_port() -> int:
        """Find an available port for the server"""
        with socket.socket() as s:
            s.bind(('', 0))
            return s.getsockname()[1]
    
    async def start(self):
        """Start the communication server"""
        class Handler(http.server.SimpleHTTPRequestHandler):
            def do_POST(self):
                content_length = int(self.headers['Content-Length'])
                data = self.rfile.read(content_length)
                # Process incoming messages from sandbox
                logger.info(f"Received from sandbox: {data.decode()}")
                self.send_response(200)
                self.end_headers()
        
        self.server = http.server.HTTPServer(('localhost', self._port), Handler)
        
        # Write server info for sandbox
        server_info = {'host': 'localhost', 'port': self._port}
        server_info_path = self.shared_dir / 'server_info.json'
        server_info_path.write_text(json.dumps(server_info))
        
        # Run server in background
        await asyncio.get_event_loop().run_in_executor(
            None, self.server.serve_forever
        )
    
    def stop(self):
        """Stop the communication server"""
        if self.server:
            self.server.shutdown()
            self.server = None

class SandboxManager:
    """Manages Windows Sandbox lifecycle and communication"""
    def __init__(self, config: SandboxConfig):
        self.config = config
        self.shared_dir = Path(tempfile.gettempdir()) / 'sandbox_shared'
        self.shared_dir.mkdir(exist_ok=True)
        
        # Add shared directory to mappings
        self.config.mappings.append(
            FolderMapping(self.shared_dir, read_only=False)
        )
        
        self.comm_server = SandboxCommServer(self.shared_dir)
        self._process: Optional[subprocess.Popen] = None
    
    async def _setup_sandbox(self):
        """Generate WSB file and prepare sandbox environment"""
        wsb_config = self.config.to_wsb_config()
        wsb_path = self.shared_dir / 'config.wsb'
        wsb_path.write_text(json.dumps(wsb_config, indent=2))
        
        # Start communication server
        await self.comm_server.start()
        
        # Launch sandbox
        self._process = subprocess.Popen(
            ['WindowsSandbox.exe', str(wsb_path)],
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE
        )
    
    async def _cleanup(self):
        """Clean up sandbox resources"""
        self.comm_server.stop()
        if self._process:
            self._process.terminate()
            await asyncio.get_event_loop().run_in_executor(
                None, self._process.wait
            )

    @asynccontextmanager
    async def session(self) -> AsyncIterator['SandboxManager']:
        """Context manager for sandbox session"""
        try:
            await self._setup_sandbox()
            yield self
        finally:
            await self._cleanup()


class MemoryTraceLevel(Enum):
    """Granularity levels for memory tracing."""
    BASIC = auto()      # Basic memory usage
    DETAILED = auto()   # Include stack traces
    FULL = auto()       # Include object references

@dataclass
class MemoryStats:
    """Container for memory statistics with analysis capabilities."""
    size: int
    count: int
    traceback: str
    timestamp: float
    peak_memory: int
    
    def to_dict(self) -> Dict:
        return {
            'size': self.size,
            'count': self.count,
            'traceback': self.traceback,
            'timestamp': self.timestamp,
            'peak_memory': self.peak_memory
        }

class CustomFormatter(logging.Formatter):
    """Custom formatter for color-coded log levels."""
    COLORS = {
        logging.DEBUG: "\x1b[38;20m",
        logging.INFO: "\x1b[32;20m",
        logging.WARNING: "\x1b[33;20m",
        logging.ERROR: "\x1b[31;20m",
        logging.CRITICAL: "\x1b[31;1m"
    }
    RESET = "\x1b[0m"
    
    def format(self, record: logging.LogRecord) -> str:
        color = self.COLORS.get(record.levelno, self.COLORS[logging.DEBUG])
        record.msg = f"{color}{record.msg}{self.RESET}"
        return super().format(record)

class MemoryTracker:
    """Singleton memory tracking manager with enhanced logging."""
    _instance = None
    _lock = threading.Lock()
    _trace_filter = {"<frozen importlib._bootstrap>", "<frozen importlib._bootstrap_external>"}
    
    def __new__(cls):
        with cls._lock:
            if cls._instance is None:
                cls._instance = super().__new__(cls)
                cls._instance._initialize()
            return cls._instance
    
    def _initialize(self):
        """Initialize the memory tracker with logging and storage."""
        self._setup_logging()
        self._snapshots: Dict[str, List[MemoryStats]] = {}
        self._tracked_objects = weakref.WeakSet()
        self._trace_level = MemoryTraceLevel.DETAILED
        
        # Start tracemalloc if not already running
        if not tracemalloc.is_tracing():
            tracemalloc.start()
    
    def _setup_logging(self):
        """Configure logging with custom formatter."""
        self.logger = logging.getLogger("MemoryTracker")
        self.logger.setLevel(logging.DEBUG)
        
        # Console handler with color formatting
        console_handler = logging.StreamHandler()
        console_handler.setFormatter(CustomFormatter(
            "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
        ))
        self.logger.addHandler(console_handler)
        
        # File handler for persistent logging
        try:
            file_handler = logging.FileHandler("memory_tracker.log")
            file_handler.setFormatter(logging.Formatter(
                "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
            ))
            self.logger.addHandler(file_handler)
        except (PermissionError, IOError) as e:
            self.logger.warning(f"Could not create log file: {e}")

def trace_memory(level: MemoryTraceLevel = MemoryTraceLevel.DETAILED):
    """Enhanced decorator for memory tracking with configurable detail level."""
    def decorator(method: Callable) -> Callable:
        @wraps(method)
        def wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
            tracker = MemoryTracker()
            
            # Force garbage collection for accurate measurement
            gc.collect()
            
            # Take initial snapshot
            snapshot_before = tracemalloc.take_snapshot()
            
            try:
                result = method(self, *args, **kwargs)
                
                # Take final snapshot and compute statistics
                snapshot_after = tracemalloc.take_snapshot()
                stats = snapshot_after.compare_to(snapshot_before, 'lineno')
                
                # Filter and process statistics
                filtered_stats = [
                    stat for stat in stats 
                    if not any(f in str(stat.traceback) for f in tracker._trace_filter)
                ]
                
                # Log based on trace level
                if level in (MemoryTraceLevel.DETAILED, MemoryTraceLevel.FULL):
                    for stat in filtered_stats[:5]:
                        tracker.logger.info(
                            f"Memory change in {method.__name__}: "
                            f"+{stat.size_diff/1024:.1f} KB at:\n{stat.traceback}"
                        )
                
                return result
                
            finally:
                # Cleanup
                del snapshot_before
                gc.collect()
                
        return wrapper
    return decorator

class MemoryTrackedABC(ABC):
    """Abstract base class for memory-tracked classes with enhanced features."""
    
    def __init__(self):
        self._tracker = MemoryTracker()
        self._tracker._tracked_objects.add(self)
    
    def __init_subclass__(cls):
        super().__init_subclass__()
        
        # Store original methods for introspection
        cls._original_methods = {}
        
        # Automatically decorate public methods
        for attr_name, attr_value in cls.__dict__.items():
            if (callable(attr_value) and 
                not attr_name.startswith('_') and 
                not getattr(attr_value, '_skip_trace', False)):
                cls._original_methods[attr_name] = attr_value
                setattr(cls, attr_name, trace_memory()(attr_value))
    
    @staticmethod
    def skip_trace(method: Callable) -> Callable:
        """Decorator to exclude a method from memory tracking."""
        method._skip_trace = True
        return method
    
    @classmethod
    @contextmanager
    def trace_section(cls, section_name: str, level: MemoryTraceLevel = MemoryTraceLevel.DETAILED):
        """Context manager for tracking memory usage in specific code sections."""
        tracker = MemoryTracker()
        
        gc.collect()
        snapshot_before = tracemalloc.take_snapshot()
        
        try:
            yield
        finally:
            snapshot_after = tracemalloc.take_snapshot()
            stats = snapshot_after.compare_to(snapshot_before, 'lineno')
            
            filtered_stats = [
                stat for stat in stats 
                if not any(f in str(stat.traceback) for f in tracker._trace_filter)
            ]
            
            if level != MemoryTraceLevel.BASIC:
                tracker.logger.info(f"\nMemory usage for section '{section_name}':")
                for stat in filtered_stats[:5]:
                    tracker.logger.info(f"{stat}")
            
            del snapshot_before
            gc.collect()

def display_top(snapshot, key_type='lineno', limit=3):
    snapshot = snapshot.filter_traces((
        tracemalloc.Filter(True, "<module>"),
    ))
    top_stats = snapshot.statistics(key_type)
    print("Top %s lines" % limit)
    for index, stat in enumerate(top_stats[:limit], 1):
        frame = stat.traceback[0]
        print("#%s: %s:%s: %.1f KiB"
              % (index, frame.filename, frame.lineno, stat.size / 1024))
        line = linecache.getline(frame.filename, frame.lineno).strip()
        if line:
            print('    %s' % line)
    other = top_stats[limit:]
    if other:
        size = sum(stat.size for stat in other)
        print("%s other: %.1f KiB" % (len(other), size / 1024))
    total = sum(stat.size for stat in top_stats)
    print("Total allocated size: %.1f KiB" % (total / 1024))






class DebuggerMixin:
    """Mixin for debugging memory-tracked classes."""

    def __init__(self):
        self._tracker = MemoryTracker()
        self._tracker._tracked_objects.add(self)

    def __init_subclass__(cls):
        super().__init_subclass__()

        # Store original methods for introspection
        cls._original_methods = {}

        # Automatically decorate public methods
        for attr_name, attr_value in cls.__dict__.items():
            if (callable(attr_value) and
                not attr_name.startswith('_') and
                not getattr(attr_value, '_skip_trace', False)):
                cls._original_methods[attr_name] = attr_value
                setattr(cls, attr_name, trace_memory()(attr_value))

    @staticmethod
    def skip_trace(method: Callable) -> Callable:
        """Decorator to exclude a method from memory tracking."""
        method._skip_trace = True
        return method

    @classmethod
    @contextmanager
    def trace_section(cls, section_name: str, level: MemoryTraceLevel = MemoryTraceLevel.DETAILED):
        """Context manager for tracking memory usage in specific code sections."""
        tracker = MemoryTracker()

def main():
    class MyTrackedClass(MemoryTrackedABC):
        def tracked_method(self):
            """This method will be automatically tracked with detailed memory info."""
            large_list = [i for i in range(1000000)]
            return sum(large_list)
        
        @MemoryTrackedABC.skip_trace
        def untracked_method(self):
            """This method will not be tracked."""
            return "Not tracked"
        
        def tracked_with_section(self):
            """Example of using trace_section with different detail levels."""
            with self.trace_section("initialization", MemoryTraceLevel.BASIC):
                result = []
                
            with self.trace_section("processing", MemoryTraceLevel.DETAILED):
                result.extend(i * 2 for i in range(500000))
                
            with self.trace_section("cleanup", MemoryTraceLevel.FULL):
                result.clear()
                
            return len(result)
    
        @classmethod
        def introspect_methods(cls):
            """Introspect and display tracked methods with their original implementations."""
            for method_name, original_method in cls._original_methods.items():
                print(f"Method: {method_name}")
                print(f"Original implementation: {original_method}")
                print("---")

            return MyTrackedClass()
    return MyTrackedClass()


#------------------------------------------------------------------------------
# Type Definitions
#------------------------------------------------------------------------------
"""Type Definitions for Morphological Source Code.
These type definitions establish the foundational elements of the MSC framework, 
enabling the representation of various constructs as first-class citizens.
- T: Represents Type structures (static).
- V: Represents Value spaces (dynamic).
- C: Represents Computation spaces (transformative).
The relationships between these types are crucial for maintaining the 
nominative invariance across transformations.
1. **Identity Preservation (T)**: The type structure remains consistent across
transformations.
2. **Content Preservation (V)**: The value space is dynamically maintained,
allowing for fluid data manipulation.
3. **Behavioral Preservation (C)**: The computation space is transformative,
enabling the execution of operations that modify the state of the system.
    Homoiconism dictates that, upon runtime validation, all objects are code and data. To facilitate this;
    we utilize first class functions and a static typing system.
This maps perfectly to the three aspects of nominative invariance:
    Identity preservation, T: Type structure (static)
    Content preservation, V: Value space (dynamic)
    Behavioral preservation, C: Computation space (transformative)
    [[T (Type) ←→ V (Value) ←→ C (Callable)]] == 'quantum infodynamics, a triparte element; our Particle()(s)'
    Meta-Language (High Level)
      ↓ [First Collapse - Compilation]
    Intermediate Form (Like a quantum superposition)
      ↓ [Second Collapse - Runtime]
    Executed State (Measured Reality)
What's conserved across these transformations:
    Nominative relationships
    Information content
    Causal structure
    Computational potential"""
# Particle()(s) are a wrapper that can represent any Python object, including values, methods, functions, and classes
"""The type system forms the "boundary" theory
The runtime forms the "bulk" theory
The homoiconic property ensures they encode the same information
The holoiconic property enables:
    States as quantum superpositions
    Computations as measurements
    Types as boundary conditions
    Runtime as bulk geometry"""
T = TypeVar('T', bound=any) # T for TypeVar, V for ValueVar. Homoicons are T+V, 'Particle()(s)' are all-three
V = TypeVar('V', bound=Union[int, float, str, bool, list, dict, tuple, set, object, Callable, type])
C = TypeVar('C', bound=Callable[..., Any])  # callable 'T'/'V' (including all objects) + 'FFI'
# Callable, 'C' TypeVar(s) include Foreign Function Interface, git and (power)shell, principally
DataType = StrEnum('DataType', 'INTEGER FLOAT STRING BOOLEAN NONE LIST TUPLE') # 'T' vars (stdlib)
PyType = StrEnum('ModuleType', 'FUNCTION CLASS MODULE OBJECT') 
# PyType: first class functions applies to objects, classes and even modules, 'C' vars which are not FFI(s)
AccessLevel = StrEnum('AccessLevel', 'READ WRITE EXECUTE ADMIN USER')
QuantumState = StrEnum('QuantumState', ['SUPERPOSITION', 'ENTANGLED', 'COLLAPSED', 'DECOHERENT'])
class MemoryState(StrEnum):
    ALLOCATED = auto()
    INITIALIZED = auto()
    PAGED = auto()
    SHARED = auto()
    DEALLOCATED = auto()
class __QuantumState__(StrEnum):
    SUPERPOSITION = "SUPERPOSITION"
    COLLAPSED = "COLLAPSED"
    ENTANGLED = "ENTANGLED"
    DECOHERENT = "DECOHERENT"
    DEGENERATE = "DEGENERATE"
    COHERENT = "COHERENT"
@dataclass
class StateVector:
    amplitude: complex
    state: __QuantumState__
    coherence_length: float
    entropy: float
@dataclass
class MemoryVector:
    address_space: complex
    coherence: float
    entanglement: float
    state: MemoryState
    size: int
class Symmetry(Protocol, Generic[T, V, C]):
    def preserve_identity(self, type_structure: T) -> T: ...
    def preserve_content(self, value_space: V) -> V: ...
    def preserve_behavior(self, computation: C) -> C: ...
class QuantumNumbers(NamedTuple):
    n: int  # Principal quantum number
    l: int  # Azimuthal quantum number
    m: int  # Magnetic quantum number
    s: float   # Spin quantum number
class QuantumNumber:
    def __init__(self, hilbert_space: HilbertSpace):
        self.hilbert_space = hilbert_space
        self.amplitudes = [complex(0, 0)] * hilbert_space.dimension
        self._quantum_numbers = None
    @property
    def quantum_numbers(self):
        return self._quantum_numbers
    @quantum_numbers.setter
    def quantum_numbers(self, numbers: QuantumNumbers):
        n, l, m, s = numbers
        if self.hilbert_space.is_fermionic():
            # Fermionic quantum number constraints
            if not (n > 0 and 0 <= l < n and -l <= m <= l and s in (-0.5, 0.5)):
                raise ValueError("Invalid fermionic quantum numbers")
        elif self.hilbert_space.is_bosonic():
            # Bosonic quantum number constraints
            if not (n >= 0 and l >= 0 and m >= 0 and s == 0):
                raise ValueError("Invalid bosonic quantum numbers")
        self._quantum_numbers = numbers
class QuantumParticle(Protocol):
    """Base protocol for mathematical operations with quantum properties.
    This is a quantum 'protocol' rather than a quantum 'class' like what appear
    before and after this, because this is more like an ABC but which applies to
    meta-resolving intepreted python object, but not necessarilly the only, or
    indeed, the active one.
    Enables AP lazy C meta-pythonic runtime (mutlti-instantiation) resolution."""
    id: str
    quantum_numbers: QuantumNumbers
    quantum_state: '_QuantumState'
    def __init__(self, *args, **kwargs):
        pass
    def __add__(self, other: 'MathProtocol') -> 'MathProtocol':
        """Add/Commute two mathematical objects together"""
        raise NotImplementedError
    def __sub__(self, other: 'MathProtocol') -> 'MathProtocol':
        """Subtract two mathematical objects"""
        raise NotImplementedError
    _decimal_places = decimal.getcontext()
"""py objects are implemented as C structures.
typedef struct _object {
    Py_ssize_t ob_refcnt;
    PyTypeObject *ob_type;
} PyObject; """
# Everything in Python is an object, and every object has a type. The type of an object is a class. Even the
# type class itself is an instance of type. Functions defined within a class become method objects when
# accessed through an instance of the class
"""Functions are instances of the function class
Methods are instances of the method class (which wraps functions)
Both function and method are subclasses of object
homoiconism dictates the need for a way to represent all Python constructs as first class citizen(fcc):
    (functions, classes, control structures, operations, primitive values)
nominative 'true OOP'(SmallTalk) and my specification demands code as data and value as logic, structure.
The Particle(), our polymorph of object and fcc-apparent at runtime, always represents the literal source code
    which makes up their logic and possess the ability to be stateful source code data structure. """
# HOMOICONISTIC morphological source code displays 'modified quine' behavior
# within a validated runtime, if and only if the valid python interpreter
# has r/w/x permissions to the source code file and some method of writing
# state to the source code file is available. Any interruption of the
# '__exit__` method or misuse of '__enter__' will result in a runtime error
# AP (Availability + Partition Tolerance): A system that prioritizes availability and partition
# tolerance may use a distributed architecture with eventual consistency (e.g., Cassandra or Riak).
# This ensures that the system is always available (availability), even in the presence of network
# partitions (partition tolerance). However, the system may sacrifice consistency, as nodes may have
# different views of the data (no consistency). A homoiconic piece of source code is eventually
# consistent, assuming it is able to re-instantiated.
#------------------------------------------------------------------------------
# Particle Class and Decorator
#------------------------------------------------------------------------------
@runtime_checkable
class Particle(Protocol):
    """
    Protocol defining the minimal interface for Particles in the Morphological 
    Source Code framework.
    Particles represent the fundamental building blocks of the system, encapsulating 
    both data and behavior. Each Particle must have a unique identifier.
    """
    id: str
class FundamentalParticle(Particle, Protocol):
    """
    A base class for fundamental particles, incorporating quantum numbers.
    """
    quantum_numbers: QuantumNumbers
    @property
    @abstractmethod
    def statistics(self) -> str:
        """
        Should return 'fermi-dirac' for fermions or 'bose-einstein' for bosons.
        """
        pass
class QuantumParticle(Protocol):
    id: str
    quantum_numbers: QuantumNumbers
    quantum_state: '_QuantumState'
    particle_type: ParticleType
class Fermion(FundamentalParticle, Protocol):
    """
    Fermions follow the Pauli exclusion principle.
    """
    @property
    def statistics(self) -> str:
        return 'fermi-dirac'
class Boson(FundamentalParticle, Protocol):
    """
    Bosons follow the Bose-Einstein statistics.
    """
    @property
    def statistics(self) -> str:
        return 'bose-einstein'
class Electron(Fermion):
    def __init__(self, quantum_numbers: QuantumNumbers):
        self.quantum_numbers = quantum_numbers
class Photon(Boson):
    def __init__(self, quantum_numbers: QuantumNumbers):
        self.quantum_numbers = quantum_numbers
def __particle__(cls: Type[{T, V, C}]) -> Type[{T, V, C}]:
    """
    Decorator to create a homoiconic Particle.
    This decorator enhances a class to ensure it adheres to the Particle protocol, 
    providing it with a unique identifier upon initialization. This allows 
    the class to be treated as a first-class citizen in the MSC framework.
    Parameters:
    - cls: The class to be transformed into a homoiconic Particle.
    Returns:
    - The modified class with homoiconic properties.
    """
    original_init = cls.__init__
    def new_init(self, *args, **kwargs):
        original_init(self, *args, **kwargs)
        if not hasattr(self, 'id'):
            self.id = hashlib.sha256(self.__class__.__name__.encode('utf-8')).hexdigest()
    cls.__init__ = new_init
    return cls
@dataclass
class DegreeOfFreedom:
    operator: QuantumOperator
    state_space: HilbertSpace
    constraints: List[Symmetry]
    def evolve(self, state: StateVector) -> StateVector:
        # Apply constraints
        for symmetry in self.constraints:
            state = symmetry.preserve_behavior(state)
        # Apply operator
        return self.operator.apply(state)
class _QuantumState:
    def __init__(self, hilbert_space: HilbertSpace):
        self.hilbert_space = hilbert_space
        self.amplitudes = [complex(0, 0)] * hilbert_space.dimension
        self.is_normalized = False
    def normalize(self):
        norm = sqrt(sum(abs(x)**2 for x in self.amplitudes))
        if norm != 0:
            self.amplitudes = [x / norm for x in self.amplitudes]  
            self.is_normalized = True
        elif norm == 0:
            raise ValueError("State vector norm cannot be zero.")
        self.state_vector = [x / norm for x in self.state_vector]

    def apply_operator(self, operator: List[List[complex]]):
        if len(operator) != self.dimension:
            raise ValueError("Operator dimensions do not match state dimensions.")
        self.state_vector = [
            sum(operator[i][j] * self.state_vector[j] for j in range(self.dimension))
            for i in range(self.dimension)
        ]
        self.normalize()
    def apply_quantum_symmetry(self):
        if self.hilbert_space.is_fermionic():
            # Apply antisymmetric projection or handling of fermions
            self.apply_fermionic_antisymmetrization()
        elif self.hilbert_space.is_bosonic():
            # Apply symmetric projection or handling of bosons
            self.apply_bosonic_symmetrization()
    def apply_fermionic_antisymmetrization(self):
        # Logic to handle fermionic antisymmetrization
        pass
    def apply_bosonic_symmetrization(self):
        # Logic to handle bosonic symmetrization
        pass
class QuantumOperator:
    def __init__(self, dimension: int):
        self.hilbert_space = HilbertSpace(dimension)
        self.matrix: List[List[complex]] = [[complex(0,0)] * dimension] * dimension
    def apply(self, state_vector: StateVector) -> StateVector:
        # Combine both mathematical and runtime transformations
        quantum_state = QuantumState(
            [state_vector.amplitude], 
            self.hilbert_space.dimension
        )
        # Apply operator
        result = self.matrix_multiply(quantum_state)
        return StateVector(
            amplitude=result.state_vector[0],
            state=state_vector.state,
            coherence_length=state_vector.coherence_length * 0.9,  # Decoherence
            entropy=state_vector.entropy + 0.1  # Information gain
        )
    def apply_to(self, state: '_QuantumState'):
        if state.hilbert_space.dimension != self.hilbert_space.dimension:
            raise ValueError("Hilbert space dimensions don't match")
        # Implement fermionic / bosonic specific operator logic here
        result = [sum(self.matrix[i][j] * state.amplitudes[j] 
                 for j in range(self.hilbert_space.dimension))
                 for i in range(self.hilbert_space.dimension)]
        state.amplitudes = result
        state.normalize()
"""
In thermodynamics, extensive properties depend on the amount of matter (like energy or entropy), while intensive properties (like temperature or pressure) are independent of the amount. Zero-copy or the C std-lib buffer pointer derefrencing method may be interacting with Landauer's Principle in not-classical ways, potentially maintaining 'intensive character' (despite correlated d/x raise in heat/cost of computation, underlying the computer abstraction itself, and inspite of 'reversibility'; this could be the 'singularity' of entailment, quantum informatics, and the computationally irreducible membrane where intensive character manifests or fascilitates the emergence of extensive behavior and possibility). Applying this analogy to software architecture, you might think of:
    Extensive optimizations as focusing on reducing the amount of “work” (like data copying, memory allocation, or modification). This is the kind of efficiency captured by zero-copy techniques and immutability: they reduce “heat” by avoiding unnecessary entropy-increasing operations.
    Intensive optimizations would be about maximizing the “intensity” or informational density of operations—essentially squeezing more meaning, functionality, or insight out of each “unit” of computation or data structure.
If we take information as the fundamental “material” of computation, we might ask how we can concentrate and use it more efficiently. In the same way that a materials scientist looks at atomic structures, we might look at data structures not just in terms of speed or memory but as densely packed packets of potential computation.
The future might lie in quantum-inspired computation or probabilistic computation that treats data structures and algorithms as intensively optimized, differentiated structures. What does this mean?
    Differentiation in Computation: Imagine that a data structure could be “differentiable,” i.e., it could smoothly respond to changes in the computation “field” around it. This is close to what we see in machine learning (e.g., gradient-based optimization), but it could be applied more generally to all computation.
    Dense Information Storage and Use: Instead of treating data as isolated, we might treat it as part of a dense web of informational potential—where each data structure holds not just values, but metadata about the potential operations it could undergo without losing its state.
If data structures were treated like atoms with specific “energy levels” (quantum number of Fermions/Bosons) we could think of them as having intensive properties related to how they transform, share, and conserve information. For instance:
    Higher Energy States (Mutable Structures): Mutable structures would represent “higher energy” forms that can be modified but come with the thermodynamic cost of state transitions.
    Lower Energy States (Immutable Structures): Immutable structures would be lower energy and more stable, useful for storage and retrieval without transformation.
Such an approach would modulate data structures like we do materials, seeking stable configurations for long-term storage and flexible configurations for computation.
Maybe what we’re looking for is a computational thermodynamics, a new layer of software design that considers the energetic cost of computation at every level of the system:
    Data Structures as Quanta: Rather than thinking of memory as passive, this approach would treat each structure as a dynamic, interactive quantum of information that has both extensive (space, memory) and intensive (potential operations, entropy) properties.
    Algorithms as Energy Management: Each algorithm would be not just a function but a thermodynamic process that operates within constraints, aiming to minimize entropy production and energy consumption.
    Utilize Information to its Fullest Extent: For example, by reusing results across parallel processes in ways we don’t currently prioritize.
    Operate in a Field-like Environment: Computation could occur in “fields” where each computation affects and is affected by its informational neighbors, maximizing the density of computation per unit of data and memory.
In essence, we’re looking at the possibility of a thermodynamically optimized computing environment, where each memory pointer and buffer act as elements in a network of information flow, optimized to respect the principles of both Landauer’s and Shannon’s theories.
"""
class HoloiconicTransform(Generic[T, V, C]):
    @staticmethod
    def flip(value: V) -> C:
        return lambda: value
    @staticmethod
    def flop(computation: C) -> V:
        return computation()
    @staticmethod
    def entangle(a: V, b: V) -> Tuple[C, C]:
        shared_state = [a, b]
        return (lambda: shared_state[0], lambda: shared_state[1])
class SymmetryBreaker(Generic[T, V, C]):
    def __init__(self):
        self._state = StateVector(
            amplitude=complex(1, 0),
            state=__QuantumState__.SUPERPOSITION,
            coherence_length=1.0,
            entropy=0.0
        )
    def break_symmetry(self, original: Symmetry[T, V, C], breaking_factor: float) -> tuple[Symmetry[T, V, C], StateVector]:
        new_entropy = self._state.entropy + breaking_factor
        new_coherence = self._state.coherence_length * (1 - breaking_factor)
        new_state = __QuantumState__.SUPERPOSITION if new_coherence > 0.5 else __QuantumState__.COLLAPSED
        new_state_vector = StateVector(
            amplitude=self._state.amplitude * complex(1 - breaking_factor, breaking_factor),
            state=new_state,
            coherence_length=new_coherence,
            entropy=new_entropy
        )
        return original, new_state_vector
class HoloiconicQuantumParticle(Generic[T, V, C]):
    def __init__(self, quantum_numbers: QuantumNumbers):
        self.hilbert_space = HilbertSpace(n_qubits=quantum_numbers.n)
        self.quantum_state = _QuantumState(self.hilbert_space)
        self.quantum_state.quantum_numbers = quantum_numbers
    def superposition(self, other: 'HoloiconicQuantumParticle'):
        """Creates quantum superposition of two particles"""
        if self.hilbert_space.dimension != other.hilbert_space.dimension:
            raise ValueError("Incompatible Hilbert spaces")
        result = HoloiconicQuantumParticle(self.quantum_state.quantum_numbers)
        for i in range(self.hilbert_space.dimension):
            result.quantum_state.amplitudes[i] = (
                self.quantum_state.amplitudes[i] + 
                other.quantum_state.amplitudes[i]
            ) / sqrt(2)
        return result
    def collapse(self) -> V:
        """Collapses quantum state to classical value"""
        # Simplified collapse mechanism
        max_amplitude_idx = max(range(len(self.quantum_state.amplitudes)), 
                              key=lambda i: abs(self.quantum_state.amplitudes[i]))
        return max_amplitude_idx
@dataclass
class HilbertSpace:
    dimension: int
    states: List[QuantumState] = field(default_factory=list)
    def __init__(self, n_qubits: int, particle_type: ParticleType):
        if particle_type not in (ParticleType.FERMION, ParticleType.BOSON):
            raise ValueError("Unsupported particle type")
        self.n_qubits = n_qubits
        self.particle_type = particle_type
        if self.is_fermionic():
            self.dimension = 2 ** n_qubits  # Fermi-Dirac: 2^n dimensional
        elif self.is_bosonic():
            self.dimension = n_qubits + 1   # Bose-Einstein: Allow occupation numbers
    def is_fermionic(self) -> bool:
        return self.particle_type == ParticleType.FERMION
    def is_bosonic(self) -> bool:
        return self.particle_type == ParticleType.BOSON
    def add_state(self, state: QuantumState):
        if state.dimension != self.dimension:
            raise ValueError("State dimension does not match Hilbert space dimension.")
        self.states.append(state)
def quantum_transform(particle: HoloiconicQuantumParticle[T, V, C]) -> HoloiconicQuantumParticle[T, V, C]:
    """Quantum transformation preserving holoiconic properties"""
    if particle.hilbert_space.is_fermionic():
        # Apply transformations particular to fermions
        pass
    elif particle.hilbert_space.is_bosonic():
        # Apply transformations particular to bosons
        pass
    hadamard = QuantumOperator(particle.hilbert_space)
    hadamard.apply_to(particle.quantum_state)
    return particle
class QuantumField:
    """
    Represents a quantum field capable of interacting with other fields.
    Attributes:
    - field_type: Can be 'fermion' or 'boson'.
    - dimension: The dimensionality of the field.
    - normal_vector: A unit vector in complex space representing the dipole direction.
    """
    def __init__(self, field_type: str, dimension: int):
        self.field_type = field_type
        self.dimension = dimension
        self.normal_vector = self._generate_normal_vector()
    def _generate_normal_vector(self) -> complex:
        """
        Generate a random unit vector in complex space.
        This vector represents the dipole direction in the field.
        """
        angle = random.uniform(0, 2 * cmath.pi)
        return cmath.rect(1, angle)  # Unit complex number (magnitude 1)
    def interact(self, other_field: 'QuantumField') -> Optional['QuantumField']:
        """
        Define interaction between two fields.
        Returns a new QuantumField or None if fields annihilate.
        """
        if self.field_type == 'fermion' and other_field.field_type == 'fermion':
            # Fermion-Fermion annihilation (quantum collapse)
            return self._annihilate(other_field)
        elif self.field_type == 'fermion' and other_field.field_type == 'boson':
            # Fermion-Boson interaction: message passing (data transfer)
            return self._pass_message(other_field)
        # Implement any further interactions if needed, such as boson-boson.
        return None
    def _annihilate(self, other_field: 'QuantumField') -> Optional['QuantumField']:
        """
        Fermion-Fermion annihilation: fields cancel each other out.
        """
        print(f"Fermion-Fermion annihilation: Field {self.normal_vector} annihilates {other_field.normal_vector}")
        return None  # Fields annihilate, leaving no field
    def _pass_message(self, other_field: 'QuantumField') -> 'QuantumField':
        """
        Fermion-Boson interaction: message passing (data transmission).
        Returns a new QuantumField in a bosonic state.
        """
        print(f"Fermion-Boson message passing: Field {self.normal_vector} communicates with {other_field.normal_vector}")
        # In this case, the fermion 'sends' a message to the boson endpoint.
        return QuantumField('boson', self.dimension)  # Transform into a bosonic state after interaction
class LaplaceDomain(Generic[T]):
    def __init__(self, operator: QuantumOperator):
        self.operator = operator
        
    def transform(self, time_domain: StateVector) -> StateVector:
        # Convert to frequency domain
        s_domain = self.to_laplace(time_domain)
        # Apply operator in frequency domain
        result = self.operator.apply(s_domain)
        # Convert back to time domain
        return self.inverse_laplace(result)
class QuantumPage:
    def __init__(self, size: int):
        self.vector = MemoryVector(
            address_space=complex(1, 0),
            coherence=1.0,
            entanglement=0.0,
            state=MemoryState.ALLOCATED,
            size=size
        )
        self.references: Dict[int, weakref.ref] = {}
        
    def entangle(self, other: 'QuantumPage') -> float:
        entanglement_strength = min(1.0, (self.vector.coherence + other.vector.coherence) / 2)
        self.vector.entanglement = entanglement_strength
        other.vector.entanglement = entanglement_strength
        return entanglement_strength

class QuantumMemoryManager(Generic[T, V, C]):
    def __init__(self, total_memory: int):
        self.total_memory = total_memory
        self.allocated_memory = 0
        self.pages: Dict[int, QuantumPage] = {}
        self.page_size = 4096
        
    def allocate(self, size: int) -> Optional[QuantumPage]:
        if self.allocated_memory + size > self.total_memory:
            return None
        pages_needed = (size + self.page_size - 1) // self.page_size
        total_size = pages_needed * self.page_size
        page = QuantumPage(total_size)
        page_id = id(page)
        self.pages[page_id] = page
        self.allocated_memory += total_size
        return page
        
    def share_memory(self, source_runtime_id: int, target_runtime_id: int, page: QuantumPage) -> bool:
        if page.vector.state == MemoryState.DEALLOCATED:
            return False
        page.references[source_runtime_id] = weakref.ref(source_runtime_id)
        page.references[target_runtime_id] = weakref.ref(target_runtime_id)
        page.vector.state = MemoryState.SHARED
        page.vector.coherence *= 0.9
        return True
        
    def measure_memory_state(self, page: QuantumPage) -> MemoryVector:
        page.vector.coherence *= 0.8
        if page.vector.coherence < 0.3 and page.vector.state != MemoryState.PAGED:
            page.vector.state = MemoryState.PAGED
        return page.vector
        
    def deallocate(self, page: QuantumPage):
        page_id = id(page)
        if page.vector.entanglement > 0:
            for ref in page.references.values():
                runtime_id = ref()
                if runtime_id is not None:
                    runtime_page = self.pages.get(runtime_id)
                    if runtime_page:
                        runtime_page.vector.coherence *= (1 - page.vector.entanglement)
        page.vector.state = MemoryState.DEALLOCATED
        self.allocated_memory -= page.vector.size
        del self.pages[page_id]

class QuineRuntime(Generic[T, V, C]):
    def __init__(self):
        self.symmetry_breaker = SymmetryBreaker()
        self.state_history: List[StateVector] = []
    
    def __enter__(self):
        self.state_history.append(StateVector(
            amplitude=complex(1, 0),
            state=QuantumState.SUPERPOSITION,
            coherence_length=1.0,
            entropy=0.0
        ))
        return self
    
    def __exit__(self, exc_type, exc_val, exc_tb):
        final_state = self.state_history[-1]
        if final_state.state != QuantumState.COLLAPSED:
            self.measure()
    
    def measure(self) -> StateVector:
        current_state = self.state_history[-1]
        if current_state.state == QuantumState.COLLAPSED:
            return current_state
        collapsed_state = StateVector(
            amplitude=abs(current_state.amplitude),
            state=QuantumState.COLLAPSED,
            coherence_length=0.0,
            entropy=current_state.entropy + 1.0
        )
        self.state_history.append(collapsed_state)
        return collapsed_state

    def replicate(self) -> 'QuineRuntime[T, V, C]':
        new_runtime = QuineRuntime()
        current_state = self.state_history[-1]
        entangled_state = StateVector(
            amplitude=current_state.amplitude,
            state=QuantumState.ENTANGLED,
            coherence_length=current_state.coherence_length,
            entropy=current_state.entropy
        )
        new_runtime.state_history.append(entangled_state)
        return new_runtime

class QuantumRuntimeMemory(Generic[T, V, C]):
    def __init__(self, memory_size: int):
        self.memory_manager = QuantumMemoryManager(memory_size)
        self.runtime_id = id(self)
        self.allocated_pages: Dict[int, QuantumPage] = {}
        
    def allocate_memory(self, size: int) -> Optional[QuantumPage]:
        page = self.memory_manager.allocate(size)
        if page:
            self.allocated_pages[id(page)] = page
        return page
        
    def share_with_runtime(self, other_runtime: 'QuantumRuntimeMemory[T, V, C]', page: QuantumPage) -> bool:
        return self.memory_manager.share_memory(self.runtime_id, other_runtime.runtime_id, page)
        
    def __enter__(self):
        return self
        
    def __exit__(self, exc_type, exc_val, exc_tb):
        for page in list(self.allocated_pages.values()):
            self.memory_manager.deallocate(page)
        self.allocated_pages.clear()


#------------------------------------------------------------------------------
# BaseModel (no-copy immutable dataclasses for data models)
#------------------------------------------------------------------------------
class BaseModel:
    __slots__ = ('__dict__', '__weakref__')
    def __init__(self, **data):
        for name, value in data.items():
            setattr(self, name, value)
    def __setattr__(self, name, value):
        if name in self.__annotations__:
            expected_type = self.__annotations__[name]
            if not isinstance(value, expected_type):
                raise TypeError(f"Expected {expected_type} for {name}, got {type(value)}")
            validator = getattr(self.__class__, f'validate_{name}', None)
            if validator:
                validator(self, value)
        super().__setattr__(name, value)
    @classmethod
    def create(cls, **kwargs):
        return cls(**kwargs)
    def dict(self):
        return {name: getattr(self, name) for name in self.__annotations__}
    def __repr__(self):
        attrs = ', '.join(f"{name}={getattr(self, name)!r}" for name in self.__annotations__)
        return f"{self.__class__.__name__}({attrs})"
    def __str__(self):
        attrs = ', '.join(f"{name}={getattr(self, name)}" for name in self.__annotations__)
        return f"{self.__class__.__name__}({attrs})"
    def clone(self):
        return self.__class__(**self.dict())
def frozen(cls): # decorator
    original_setattr = cls.__setattr__
    def __setattr__(self, name, value):
        if hasattr(self, name):
            raise AttributeError(f"Cannot modify frozen attribute '{name}'")
        original_setattr(self, name, value)
    cls.__setattr__ = __setattr__
    return cls
def validate(validator: Callable[[Any], None]):
    def decorator(func):
        @wraps(func)
        def wrapper(self, value):
            return validator(value)
        return wrapper
    return decorator
@frozen
class FileModel(BaseModel):
    file_name: str
    file_content: str
    def save(self, directory: pathlib.Path):
        with (directory / self.file_name).open('w') as file:
            file.write(self.file_content)
@frozen
class Module(BaseModel):
    file_path: pathlib.Path
    module_name: str
    @validate(lambda x: x.endswith('.py'))
    def validate_file_path(self, value):
        return value
    @validate(lambda x: x.isidentifier())
    def validate_module_name(self, value):
        return value
    @frozen
    def __init__(self, file_path: pathlib.Path, module_name: str):
        super().__init__(file_path=file_path, module_name=module_name)
        self.file_path = file_path
        self.module_name = module_name
def create_model_from_file(file_path: pathlib.Path):
    try:
        with file_path.open('r', encoding='utf-8', errors='ignore') as file:
            content = file.read()
        model_name = file_path.stem.capitalize() + 'Model'
        model_class = type(model_name, (FileModel,), {})
        instance = model_class.create(file_name=file_path.name, file_content=content)
        logging.info(f"Created {model_name} from {file_path}")
        return model_name, instance
    except Exception as e:
        logging.error(f"Failed to create model from {file_path}: {e}")
        return None, None
def load_files_as_models(root_dir: pathlib.Path, file_extensions: List[str]) -> Dict[str, BaseModel]:
    models = {}
    for file_path in root_dir.rglob('*'):
        if file_path.is_file() and file_path.suffix in file_extensions:
            model_name, instance = create_model_from_file(file_path)
            if model_name and instance:
                models[model_name] = instance
                sys.modules[model_name] = instance
    return models
#------------------------------------------------------------------------------
# Logging Configuration
#------------------------------------------------------------------------------
class CustomFormatter(logging.Formatter):
    """Custom formatter for colored console output."""
    COLORS = {
        'grey': "\x1b[38;20m",
        'yellow': "\x1b[33;20m",
        'red': "\x1b[31;20m",
        'bold_red': "\x1b[31;1m",
        'green': "\x1b[32;20m",
        'reset': "\x1b[0m"
    }
    FORMAT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s (%(filename)s:%(lineno)d)"
    FORMATS = {
        logging.DEBUG: COLORS['grey'] + FORMAT + COLORS['reset'],
        logging.INFO: COLORS['green'] + FORMAT + COLORS['reset'],
        logging.WARNING: COLORS['yellow'] + FORMAT + COLORS['reset'],
        logging.ERROR: COLORS['red'] + FORMAT + COLORS['reset'],
        logging.CRITICAL: COLORS['bold_red'] + FORMAT + COLORS['reset']
    }
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.log_queue = Queue()
        self.log_thread = threading.Thread(target=self._log_thread_func, daemon=True)
        self.log_thread.start()
    def format(self, record):
        log_fmt = self.FORMATS.get(record.levelno, self.FORMAT)
        formatter = logging.Formatter(log_fmt)
        return formatter.format(record)
    def _log_thread_func(self):
        while True:
            try:
                record = self.log_queue.get()
                if record is None:
                    break
                super().handle(record)
            except Exception:
                import traceback
                print("Error in log thread:", file=sys.stderr)
                traceback.print_exc()
    def emit(self, record):
        self.log_queue.put(record)
    def close(self):
        self.log_queue.put(None)
        self.log_thread.join()
class AdminLogger(logging.LoggerAdapter):
    """Logger adapter for administrative logging."""
    def __init__(self, logger, extra=None):
        super().__init__(logger, extra or {})
    def process(self, msg, kwargs):
        return f"{self.extra.get('name', 'Admin')}: {msg}", kwargs
logger = AdminLogger(logging.getLogger(__name__))
#------------------------------------------------------------------------------
# Security
#------------------------------------------------------------------------------
AccessLevel = Enum('AccessLevel', 'READ WRITE EXECUTE ADMIN USER')
@dataclass
class AccessPolicy:
    """Defines access control policies for runtime operations."""
    level: AccessLevel
    namespace_patterns: list[str] = field(default_factory=list)
    allowed_operations: list[str] = field(default_factory=list)
    def can_access(self, namespace: str, operation: str) -> bool:
        return any(pattern in namespace for pattern in self.namespace_patterns) and \
               operation in self.allowed_operations
class SecurityContext:
    """Manages security context and audit logging for runtime operations."""
    def __init__(self, user_id: str, access_policy: AccessPolicy):
        self.user_id = user_id
        self.access_policy = access_policy
        self._audit_log = []
    def log_access(self, namespace: str, operation: str, success: bool):
        self._audit_log.append({
            "user_id": self.user_id,
            "namespace": namespace,
            "operation": operation,
            "success": success,
            "timestamp": datetime.now().timestamp()
        })
class SecurityValidator(ast.NodeVisitor):
    """Validates AST nodes against security policies."""
    def __init__(self, security_context: SecurityContext):
        self.security_context = security_context
    def visit_Name(self, node):
        if not self.security_context.access_policy.can_access(node.id, "read"):
            raise PermissionError(f"Access denied to name: {node.id}")
        self.generic_visit(node)
    def visit_Call(self, node):
        if isinstance(node.func, ast.Name):
            if not self.security_context.access_policy.can_access(node.func.id, "execute"):
                raise PermissionError(f"Access denied to function: {node.func.id}")
        self.generic_visit(node)
#------------------------------------------------------------------------------
# Runtime State Management
#------------------------------------------------------------------------------
def register_models(models: Dict[str, BaseModel]):
    for model_name, instance in models.items():
        globals()[model_name] = instance
        logging.info(f"Registered {model_name} in the global namespace")
def runtime(root_dir: pathlib.Path):
    file_models = load_files_as_models(root_dir, ['.md', '.txt'])
    register_models(file_models)
@dataclass
class RuntimeState:
    """Manages runtime state and filesystem operations."""
    pdm_installed: bool = False
    virtualenv_created: bool = False
    dependencies_installed: bool = False
    lint_passed: bool = False
    code_formatted: bool = False
    tests_passed: bool = False
    benchmarks_run: bool = False
    pre_commit_installed: bool = False
    variables: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)
    allowed_root: str = field(init=False)
    def __post_init__(self):
        try:
            self.allowed_root = os.path.dirname(os.path.realpath(__file__))
            if not any(os.listdir(self.allowed_root)):
                raise FileNotFoundError(f"Allowed root directory empty: {self.allowed_root}")
            logging.info(f"Allowed root directory found: {self.allowed_root}")
        except Exception as e:
            logging.error(f"Error initializing RuntimeState: {e}")
            raise
    @classmethod
    def platform(cls):
        """Initialize platform-specific state."""
        if IS_POSIX:
            from ctypes import cdll
        elif IS_WINDOWS:
            from ctypes import windll
            from ctypes.wintypes import DWORD, HANDLE
        try:
            state = cls()
            tracemalloc.start()
            return state
        except Exception as e:
            logging.warning(f"Failed to initialize runtime state: {e}")
            return None
    async def run_command_async(self, command: str, shell: bool = False, timeout: int = 120):
        """Run a system command asynchronously with timeout."""
        logging.info(f"Running command: {command}")
        split_command = shlex.split(command, posix=IS_POSIX)
        try:
            process = await asyncio.create_subprocess_exec(
                *split_command,
                stdout=asyncio.subprocess.PIPE,
                stderr=asyncio.subprocess.PIPE,
                shell=shell
            )
            stdout, stderr = await asyncio.wait_for(process.communicate(), timeout=timeout)
            return {
                "return_code": process.returncode,
                "output": stdout.decode() if stdout else "",
                "error": stderr.decode() if stderr else "",
            }
        except asyncio.TimeoutError:
            logging.error(f"Command '{command}' timed out.")
            return {"return_code": -1, "output": "", "error": "Command timed out"}
        except Exception as e:
            logging.error(f"Error running command '{command}': {str(e)}")
            return {"return_code": -1, "output": "", "error": str(e)}
#------------------------------------------------------------------------------
# Runtime Namespace Management
#------------------------------------------------------------------------------
class RuntimeNamespace:
    """Manages hierarchical runtime namespaces with security controls."""
    def __init__(self, name: str = "root", parent: Optional['RuntimeNamespace'] = None):
        self._name = name
        self._parent = parent
        self._children: Dict[str, 'RuntimeNamespace'] = {}
        self._content = SimpleNamespace()
        self._security_context: Optional[SecurityContext] = None
        self.available_modules: Dict[str, Any] = {}
    @property
    def full_path(self) -> str:
        if self._parent:
            return f"{self._parent.full_path}.{self._name}"
        return self._name
    def add_child(self, name: str) -> 'RuntimeNamespace':
        child = RuntimeNamespace(name, self)
        self._children[name] = child
        return child
    def get_child(self, path: str) -> Optional['RuntimeNamespace']:
        parts = path.split(".", 1)
        if len(parts) == 1:
            return self._children.get(parts[0])
        child = self._children.get(parts[0])
        return child.get_child(parts[1]) if child and len(parts) > 1 else None

def log(level=logging.INFO):
    def decorator(func):
        @wraps(func)
        async def async_wrapper(*args, **kwargs):
            Logger.log(level, f"Executing {func.__name__} with args: {args}, kwargs: {kwargs}")
            try:
                result = await func(*args, **kwargs)
                Logger.log(level, f"Completed {func.__name__} with result: {result}")
                return result
            except Exception as e:
                Logger.exception(f"Error in {func.__name__}: {str(e)}")
                raise

        @wraps(func)
        def sync_wrapper(*args, **kwargs):
            Logger.log(level, f"Executing {func.__name__} with args: {args}, kwargs: {kwargs}")
            try:
                result = func(*args, **kwargs)
                Logger.log(level, f"Completed {func.__name__} with result: {result}")
                return result
            except Exception as e:
                Logger.exception(f"Error in {func.__name__}: {str(e)}")
                raise

        return async_wrapper if asyncio.iscoroutinefunction(func) else sync_wrapper
    return decorator
def bench(func):
    @wraps(func)
    async def wrapper(*args, **kwargs):
        if not getattr(sys, 'bench', True):
            return await func(*args, **kwargs)
        start_time = time.perf_counter()
        try:
            result = await func(*args, **kwargs)
            end_time = time.perf_counter()
            Logger.info(f"{func.__name__} executed in {end_time - start_time:.4f} seconds")
            return result
        except Exception as e:
            Logger.exception(f"Error in {func.__name__}: {str(e)}")
            raise
    return wrapper
#------------------------------------------------------------------------------
# Type Definitions
#------------------------------------------------------------------------------
class FrameModel(ABC):
    """A frame model is a data structure that contains the data of a frame aka a chunk of text contained by dilimiters.
        Delimiters are defined as '---' and '\n' or its analogues (EOF) or <|in_end|> or "..." etc for the start and end of a frame respectively.)
        the frame model is a data structure that is independent of the source of the data.
        portability note: "dilimiters" are established by the type of encoding and the arbitrary writing-style of the source data. eg: ASCII
    """
    @abstractmethod
    def to_bytes(self) -> bytes:
        """Return the frame data as bytes."""
        pass


class AbstractDataModel(FrameModel, ABC):
    """A data model is a data structure that contains the data of a frame aka a chunk of text contained by dilimiters.
        It has abstract methods --> to str and --> to os.pipe() which are implemented by the concrete classes.
    """
    @abstractmethod
    def to_pipe(self, pipe) -> None:
        """Write the model to a named pipe."""
        pass

    @abstractmethod
    def to_str(self) -> str:
        """Return the frame data as a string representation."""
        pass


class SerialObject(AbstractDataModel, ABC):
    """SerialObject is an abstract class that defines the interface for serializable objects within the abstract data model.
        Inputs:
            AbstractDataModel: The base class for the SerialObject class

        Returns:
            SerialObject object
    
    """
    @abstractmethod
    def dict(self) -> dict:
        """Return a dictionary representation of the model."""
        pass

    @abstractmethod
    def json(self) -> str:
        """Return a JSON string representation of the model."""
        pass
@dataclass
class ConcreteSerialModel(SerialObject):
    """
    This concrete implementation of SerialObject ensures that instances can
    be used wherever a FrameModel, AbstractDataModel, or SerialObject is required,
    hence demonstrating polymorphism.
        Inputs:
            SerialObject: The base class for the ConcreteSerialModel class

        Returns:
            ConcreteSerialModel object        
    """

    name: str
    age: int
    timestamp: datetime = field(default_factory=datetime.now)

    def to_bytes(self) -> bytes:
        """Return the JSON representation as bytes."""
        return self.json().encode()

    def to_pipe(self, pipe) -> None:
        """
        Write the JSON representation of the model to a named pipe.
        TODO: actual implementation needed for communicating with the pipe.
        """
        pass

    def to_str(self) -> str:
        """Return the JSON representation as a string."""
        return self.json()

    def dict(self) -> dict:
        """Return a dictionary representation of the model."""
        return {
            "name": self.name,
            "age": self.age,
            "timestamp": self.timestamp.isoformat(),
        }

    def json(self) -> str:
        """Return a JSON representation of the model as a string."""
        return json.dumps(self.dict())
    
    def write_to_pipe(self, pipe) -> None:
        """Write the JSON representation of the model to a named pipe."""
        if pipe:
            pipe.write(self.to_bytes())
            pipe.close()
        else:
            print(f'PipeError')
        pass

@dataclass
class _Atom_(AbstractDataModel):
    """Monolithic Atom class that integrates with the middleware model."""
    name: str
    value: Any
    elements: List[AbstractDataModel] = field(default_factory=list)
    serializer: ConcreteSerialModel = None  # Optional serializer

    def to_bytes(self) -> bytes:
        """Return the Atom's data as bytes."""
        return self.json().encode()

    def to_pipe(self, pipe_name) -> None:
        """Write the Atom's data to a named pipe."""
        # write_to_pipe(pipe_name, self.json())
        pass

    def to_str(self) -> str:
        """Return the Atom's data as a string representation."""
        return self.json()

    def dict(self) -> dict:
        """Return a dictionary representation of the Atom."""
        return {
            "name": self.name,
            "value": self.value,
            "elements": [element.dict() for element in self.elements],
        }

    def json(self) -> str:
        """Return a JSON representation of the Atom."""
        return json.dumps(self.dict())

    def serialize(self):
        """Serialize the Atom using its serializer, if provided."""
        if self.serializer:
            return self.serializer.to_json()
        else:
            return self.json()
"""Homoiconism dictates that, upon runtime validation, all objects are code and data.
To facilitate; we utilize first class functions and a static typing system.
This maps perfectly to the three aspects of nominative invariance:
    Identity preservation, T: Type structure (static)
    Content preservation, V: Value space (dynamic)
    Behavioral preservation, C: Computation space (transformative)
    [[T (Type) ←→ V (Value) ←→ C (Callable)]] == 'quantum infodynamics, a triparte element; our Atom()(s)'
    Meta-Language (High Level)
      ↓ [First Collapse - Compilation]
    Intermediate Form (Like a quantum superposition)
      ↓ [Second Collapse - Runtime]
    Executed State (Measured Reality)
What's conserved across these transformations:
    Nominative relationships
    Information content
    Causal structure
    Computational potential"""
# Atom()(s) are a wrapper that can represent any Python object, including values, methods, functions, and classes.
T = TypeVar('T', bound=any) # T for TypeVar, V for ValueVar. Homoicons are T+V.
V = TypeVar('V', bound=Union[int, float, str, bool, list, dict, tuple, set, object, Callable, type])
C = TypeVar('C', bound=Callable[..., Any])  # callable 'T'/'V' first class function interface
"""py objects are implemented as C structures.
typedef struct _object {
    Py_ssize_t ob_refcnt;
    PyTypeObject *ob_type;
} PyObject; """
# Everything in Python is an object, and every object has a type. The type of an object is a class. Even the
# type class itself is an instance of type. Functions defined within a class become method objects when
# accessed through an instance of the class
"""(3.13 std lib)Functions are instances of the function class
Methods are instances of the method class (which wraps functions)
Both function and method are subclasses of object
homoiconism dictates the need for a way to represent all Python constructs as first class citizen(fcc):
    (functions, classes, control structures, operations, primitive values)
nominative 'true OOP'(SmallTalk) and my specification demands code as data and value as logic, structure.
The Atom(), our polymorph of object and fcc-apparent at runtime, always represents the literal source code
    which makes up their logic and possess the ability to be stateful source code data structure. """
# HOMOICONISTIC morphological source code displays 'modified quine' behavior
# within a validated runtime, if and only if the valid python interpreter
# has r/w/x permissions to the source code file and some method of writing
# state to the source code file is available. Any interruption of the
# '__exit__` method or misuse of '__enter__' will result in a runtime error
# AP (Availability + Partition Tolerance): A system that prioritizes availability and partition
# tolerance may use a distributed architecture with eventual consistency (e.g., Cassandra or Riak).
# This ensures that the system is always available (availability), even in the presence of network
# partitions (partition tolerance). However, the system may sacrifice consistency, as nodes may have
# different views of the data (no consistency). A homoiconic piece of source code is eventually
# consistent, assuming it is able to re-instantiated.
# Enums for type system
DataType = Enum('DataType', 'INTEGER FLOAT STRING BOOLEAN NONE LIST TUPLE')
AccessLevel = Enum('AccessLevel', 'READ WRITE EXECUTE ADMIN USER')
QuantumState = Enum('QuantumState', ['SUPERPOSITION', 'ENTANGLED', 'COLLAPSED', 'DECOHERENT'])
@runtime_checkable
class Atom(Protocol):
    """
    Structural typing protocol for Atoms.
    Defines the minimal interface that an Atom must implement.
    """
    id: str
def __atom__(cls: Type[{T, V, C}]) -> Type[{T, V, C}]: # homoicon decorator
    """Decorator to create a homoiconic atom."""
    original_init = cls.__init__
    def new_init(self, *args, **kwargs):
        original_init(self, *args, **kwargs)
        if not hasattr(self, 'id'):
            self.id = hashlib.sha256(self.__class__.__name__.encode('utf-8')).hexdigest()

    cls.__init__ = new_init
    return cls
AtomType = TypeVar('AtomType', bound=Atom)
"""The type system forms the "boundary" theory
The runtime forms the "bulk" theory
The homoiconic property ensures they encode the same information
The holoiconic property enables:
    States as quantum superpositions
    Computations as measurements
    Types as boundary conditions
    Runtime as bulk geometry"""
class HoloiconicTransform(Generic[T, V, C]):
    """A square matrix `A` is Hermitian if and only if it is unitarily diagonalizable with real eigenvalues. """
    @staticmethod
    def flip(value: V) -> C:
        """Transform value to computation (inside-out)"""
        return lambda: value
    @staticmethod
    def flop(computation: C) -> V:
        """Transform computation to value (outside-in)"""
        return computation()
"""
The Heisenberg Uncertainty Principle tells us that we can’t precisely measure both the position and momentum of a particle. In computation, we encounter similar trade-offs between precision and performance:
    For instance, with approximate computing or probabilistic algorithms, we trade off exact accuracy for faster or less resource-intensive computation.
    Quantum computing itself takes advantage of this principle, allowing certain computations to run probabilistically rather than deterministically.
The idea that data could be "uncertain" in some way until acted upon or observed might open new doors in software architecture. Just as quantum computing uses uncertainty productively, conventional computing might benefit from intentionally embracing imprecise states or probabilistic pathways in specific contexts, especially in AI, optimization, and real-time computation.
Zero-copy and immutable data structures are, in a way, a step toward this quantum principle. By reducing the “work” done on data, they minimize thermodynamic loss. We could imagine architectures that go further, preserving computational history or chaining operations in such a way that information isn't “erased” but transformed, making the process more like a conservation of informational “energy.”
If algorithms were seen as “wavefunctions” representing possible computational outcomes, then choosing a specific outcome (running the algorithm) would be like collapsing a quantum state. In this view:
    Each step of an algorithm could be seen as an evolution of the wavefunction, transforming the data structure through time.
    Non-deterministic algorithms could explore multiple “paths” through data, and the most efficient or relevant one could be selected probabilistically.
    Treating data and computation as probabilistic, field-like entities rather than fixed operations on fixed memory.
    Embracing superpositions, potential operations, and entanglement within software architecture, allowing for context-sensitive, energy-efficient, and exploratory computation.
    Leveraging thermodynamic principles more deeply, designing architectures that conserve “informational energy” by reducing unnecessary state changes and maximizing information flow efficiency.
I want to prove that, under the right conditions, a classical system optimized with the right software architecture and hardware platform can display behaviors indicative of quantum informatics. One's experimental setup would ideally confirm that even if the underlying hardware is classical, certain complex interactions within the software/hardware could bring about phenomena reminiscent of quantum mechanics.
Cognosis is rooted in the idea that classical architectures (like the von Neumann model and Turing machines) weren't able to exploit quantum properties due to their deterministic, state-by-state execution model. But modern neural networks and transformers, with their probabilistic computations, massive parallelism, and high-dimensional state spaces, could approach a threshold where quantum-like behaviors begin to appear—especially in terms of entangling information or decoherence These models’ emergent properties might align more closely with quantum processes, as they involve not just deterministic processing but complex probabilistic states that "collapse" during inference (analogous to quantum measurement). If one can exploit this probabilistic, distributed nature, it might actually push classical hardware into a quasi-quantum regime.
"""

"""Self-Adjoint Operators on a Hilbert Space: In quantum mechanics, the state space of a system is typically modeled as a Hilbert space—a complete vector space equipped with an inner product. States within this space can be represented as vectors (ket vectors, ∣ψ⟩∣ψ⟩), and observables (like position, momentum, or energy) are modeled by self-adjoint operators.

    Self-adjoint operators are crucial because they guarantee that the eigenvalues (which represent possible measurement outcomes in quantum mechanics) are real numbers, which is a necessary condition for observable quantities in a physical theory. In quantum mechanics, the evolution of a state ∣ψ⟩∣ψ⟩ under an observable A^A^ can be described as the action of the operator A^A^ on ∣ψ⟩∣ψ⟩, and these operators must be self-adjoint to maintain physical realism.
    
    In-other words, self-adjoint operators are equal to their Hermitian conjugates."""

@dataclass
class ErrorAtom(Atom):
    error_type: str
    message: str
    context: Dict[str, Any] = field(default_factory=dict)
    traceback: Optional[str] = None

    @classmethod
    def from_exception(cls, exception: Exception, context: Dict[str, Any] = None):
        return cls(
            error_type=type(exception).__name__,
            message=str(exception),
            context=context or {},
            traceback=traceback.format_exc()
        )

class EventBus(Atom):  # Pub/Sub homoiconic event bus
    def __init__(self, id: str = "event_bus"):
        super().__init__(id=id)
        self._subscribers: Dict[str, List[Callable[[str, Any], Coroutine[Any, Any, None]]]] = {}

    async def subscribe(self, event_type: str, handler: Callable[[str, Any], Coroutine[Any, Any, None]]) -> None:
        if event_type not in self._subscribers:
            self._subscribers[event_type] = []
        self._subscribers[event_type].append(handler)

    async def unsubscribe(self, event_type: str, handler: Callable[[str, Any], Coroutine[Any, Any, None]]) -> None:
        if event_type in self._subscribers:
            self._subscribers[event_type].remove(handler)

    async def publish(self, event_type: str, event_data: Any) -> None:
        if event_type in self._subscribers:
            for handler in self._subscribers[event_type]:
                asyncio.create_task(handler(event_type, event_data))

    def encode(self) -> bytes:
        raise NotImplementedError("EventBus cannot be directly encoded")

    @classmethod
    def decode(cls, data: bytes) -> None:
        raise NotImplementedError("EventBus cannot be directly decoded")

# Centralized Error Handling
class ErrorHandler:
    def __init__(self, event_bus: Optional[EventBus] = None):
        self.event_bus = event_bus or EventBus()

    async def handle(self, error_atom: ErrorAtom):
        """
        Central error handling method with flexible error management
        """
        try:
            # Publish to system error channel
            await self.event_bus.publish('system.error', error_atom)
            
            # Optional: Log to file or external system
            self._log_error(error_atom)
            
            # Optional: Perform additional error tracking or notification
            await self._notify_error(error_atom)
        
        except Exception as secondary_error:
            # Fallback error handling
            print(f"Critical error in error handling: {secondary_error}")
            print(f"Original error: {error_atom}")

    def _log_error(self, error_atom: ErrorAtom):
        """
        Optional method to log errors to file or external system
        """
        with open('system_errors.log', 'a') as log_file:
            log_file.write(f"{error_atom.error_type}: {error_atom.message}\n")
            if error_atom.traceback:
                log_file.write(f"Traceback: {error_atom.traceback}\n")

    async def _notify_error(self, error_atom: ErrorAtom):
        """
        Optional method for additional error notification
        """
        # Could integrate with external monitoring systems
        # Could send alerts via different channels
        pass

    def decorator(self, func):
        """
        Error handling decorator for both sync and async functions
        """
        @wraps(func)
        async def async_wrapper(*args, **kwargs):
            try:
                return await func(*args, **kwargs)
            except Exception as e:
                error_atom = ErrorAtom.from_exception(e, context={
                    'args': args,
                    'kwargs': kwargs,
                    'function': func.__name__
                })
                await self.handle(error_atom)
                raise
        
        @wraps(func)
        def sync_wrapper(*args, **kwargs):
            try:
                return func(*args, **kwargs)
            except Exception as e:
                error_atom = ErrorAtom.from_exception(e, context={
                    'args': args,
                    'kwargs': kwargs,
                    'function': func.__name__
                })
                asyncio.run(self.handle(error_atom))
                raise
        
        return async_wrapper if asyncio.iscoroutinefunction(func) else sync_wrapper

global_error_handler = ErrorHandler()

# Convenience decorator
def handle_errors(func):
    return global_error_handler.decorator(func)

EventBus = EventBus('EventBus')


@dataclass
class TaskAtom(Atom): # Tasks are atoms that represent asynchronous potential actions
    task_id: int
    atom: Atom
    args: tuple = field(default_factory=tuple)
    kwargs: Dict[str, Any] = field(default_factory=dict)
    result: Any = None
    id: str = field(default_factory=lambda: str(uuid.uuid4()))  # Base class field
    value: Any = None  # Base class field
    data_type: str = field(init=False)  # Base class field
    attributes: Dict[str, Any] = field(default_factory=dict)  # Base class field
    subscribers: Set['Atom'] = field(default_factory=set)  # Base class field

    async def run(self) -> Any:
        logging.info(f"Running task {self.task_id}")
        try:
            self.result = await self.atom.execute(*self.args, **self.kwargs)
            logging.info(f"Task {self.task_id} completed with result: {self.result}")
        except Exception as e:
            logging.error(f"Task {self.task_id} failed with error: {e}")
        return self.result

    def encode(self) -> bytes:
        return json.dumps(self.to_dict()).encode()

    @classmethod
    def decode(cls, data: bytes) -> 'TaskAtom':
        obj = json.loads(data.decode())
        return cls.from_dict(obj)

    def to_dict(self) -> Dict[str, Any]:
        return {
            'task_id': self.task_id,
            'atom': self.atom.to_dict(),
            'args': self.args,
            'kwargs': self.kwargs,
            'result': self.result
        }

    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'TaskAtom':
        return cls(
            task_id=data['task_id'],
            atom=Atom.from_dict(data['atom']),
            args=tuple(data['args']),
            kwargs=data['kwargs'],
            result=data['result']
        )

class ArenaAtom(Atom):  # Arenas are threaded virtual memory Atoms appropriately-scoped when invoked
    def __init__(self, name: str):
        super().__init__(id=name)
        self.name = name
        self.local_data: Dict[str, Any] = {}
        self.task_queue: asyncio.Queue = asyncio.Queue()
        self.executor = ThreadPoolExecutor()
        self.running = False
        self.lock = threading.Lock()
    
    async def allocate(self, key: str, value: Any) -> None:
        with self.lock:
            self.local_data[key] = value
            logging.info(f"Arena {self.name}: Allocated {key} = {value}")
    
    async def deallocate(self, key: str) -> None:
        with self.lock:
            value = self.local_data.pop(key, None)
            logging.info(f"Arena {self.name}: Deallocated {key}, value was {value}")
    
    def get(self, key: str) -> Any:
        return self.local_data.get(key)
    
    def encode(self) -> bytes:
        data = {
            'name': self.name,
            'local_data': {key: value.to_dict() if isinstance(value, Atom) else value 
                           for key, value in self.local_data.items()}
        }
        return json.dumps(data).encode()

    @classmethod
    def decode(cls, data: bytes) -> 'ArenaAtom':
        obj = json.loads(data.decode())
        instance = cls(obj['name'])
        instance.local_data = {key: Atom.from_dict(value) if isinstance(value, dict) else value 
                               for key, value in obj['local_data'].items()}
        return instance
    
    async def submit_task(self, atom: Atom, args=(), kwargs=None) -> int:
        task_id = uuid.uuid4().int
        task = TaskAtom(task_id, atom, args, kwargs or {})
        await self.task_queue.put(task)
        logging.info(f"Submitted task {task_id}")
        return task_id
    
    async def task_notification(self, task: TaskAtom) -> None:
        notification_atom = AtomNotification(f"Task {task.task_id} completed")
        await self.send_message(notification_atom)
    
    async def run(self) -> None:
        self.running = True
        asyncio.create_task(self._worker())
        logging.info(f"Arena {self.name} is running")

    async def stop(self) -> None:
        self.running = False
        self.executor.shutdown(wait=True)
        logging.info(f"Arena {self.name} has stopped")
    
    async def _worker(self) -> None:
        while self.running:
            try:
                task: TaskAtom = await asyncio.wait_for(self.task_queue.get(), timeout=1)
                logging.info(f"Worker in {self.name} picked up task {task.task_id}")
                await self.allocate(f"current_task_{task.task_id}", task)
                await task.run()
                await self.task_notification(task)
                await self.deallocate(f"current_task_{task.task_id}")
            except asyncio.TimeoutError:
                continue
            except Exception as e:
                logging.error(f"Error in worker: {e}")

@dataclass
class AtomNotification(Atom):  # nominative async message passing interface
    message: str

    def encode(self) -> bytes:
        return json.dumps({'message': self.message}).encode()

    @classmethod
    def decode(cls, data: bytes) -> 'AtomNotification':
        obj = json.loads(data.decode())
        return cls(message=obj['message'])

class EventBus(Atom):  # Pub/Sub homoiconic event bus
    def __init__(self):
        super().__init__(id="event_bus")
        self._subscribers: Dict[str, List[Callable[[Atom], Coroutine[Any, Any, None]]]] = {}

    async def subscribe(self, event_type: str, handler: Callable[[Atom], Coroutine[Any, Any, None]]) -> None:
        if event_type not in self._subscribers:
            self._subscribers[event_type] = []
        self._subscribers[event_type].append(handler)

    async def unsubscribe(self, event_type: str, handler: Callable[[Atom], Coroutine[Any, Any, None]]) -> None:
        if event_type in self._subscribers:
            self._subscribers[event_type].remove(handler)

    async def publish(self, event_type: str, event_data: Any) -> None:
        if event_type in self._subscribers:
            for handler in self._subscribers[event_type]:
                asyncio.create_task(handler(event_type, event_data))


    def encode(self) -> bytes:
        raise NotImplementedError("EventBus cannot be directly encoded")

    @classmethod
    def decode(cls, data: bytes) -> None:
        raise NotImplementedError("EventBus cannot be directly decoded")

@dataclass
class EventAtom(Atom):  # Events are network-friendly Atoms, associates with a type and an id (USER-scoped), think; datagram
    id: str
    type: str
    detail_type: Optional[str] = None
    message: Union[str, List[Dict[str, Any]]] = field(default_factory=list)
    source: Optional[str] = None
    target: Optional[str] = None
    content: Optional[str] = None
    metadata: Optional[Dict[str, Any]] = field(default_factory=dict)

    def encode(self) -> bytes:
        return json.dumps(self.to_dict()).encode()

    @classmethod
    def decode(cls, data: bytes) -> 'EventAtom':
        obj = json.loads(data.decode())
        return cls.from_dict(obj)

    def to_dict(self) -> Dict[str, Any]:
        return {
            "id": self.id,
            "type": self.type,
            "detail_type": self.detail_type,
            "message": self.message,
            "source": self.source,
            "target": self.target,
            "content": self.content,
            "metadata": self.metadata
        }

    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'EventAtom':
        return cls(
            id=data["id"],
            type=data["type"],
            detail_type=data.get("detail_type"),
            message=data.get("message"),
            source=data.get("source"),
            target=data.get("target"),
            content=data.get("content"),
            metadata=data.get("metadata", {})
        )

    def validate(self) -> bool:
        required_fields = ['id', 'type']
        for field in required_fields:
            if not getattr(self, field):
                raise ValueError(f"Missing required field: {field}")
        return True

@dataclass
class ActionRequestAtom(Atom):  # User-initiated action request
    action: str
    params: Dict[str, Any]
    self_info: Dict[str, Any]
    echo: Optional[str] = None

    def encode(self) -> bytes:
        return json.dumps(self.to_dict()).encode()

    @classmethod
    def decode(cls, data: bytes) -> 'ActionRequestAtom':
        obj = json.loads(data.decode())
        return cls.from_dict(obj)

    def to_dict(self) -> Dict[str, Any]:
        return {
            "action": self.action,
            "params": self.params,
            "self_info": self.self_info,
            "echo": self.echo
        }

    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'ActionRequestAtom':
        return cls(
            action=data["action"],
            params=data["params"],
            self_info=data["self_info"],
            echo=data.get("echo")
        )



if __name__ == "__main__":
    fermion1 = QuantumField('fermion', 1)
    fermion2 = QuantumField('fermion', 1)
    boson = QuantumField('boson', 1)
    # Fermion-Fermion interaction (annihilation)
    fermion1.interact(fermion2)
    # Fermion-Boson interaction (message passing)
    fermion1.interact(boson)




    tracker = MemoryTracker()
    tracker.logger.setLevel(logging.DEBUG)
    tracker.logger.addHandler(logging.StreamHandler())
    tracker.logger.addHandler(logging.FileHandler("memory_tracker.log"))
    my_instance = main()
    my_instance.__class__.introspect_methods()

    MyTrackedClass = main().__class__
    MyTrackedClass.introspect_methods()


    # Basic usage
    obj = MyTrackedClass()
    obj.tracked_method()  # Automatically tracked with detailed info

    # Custom section tracking with different detail levels
    obj.tracked_with_section()

    # Customize tracking level for specific methods
    @trace_memory(level=MemoryTraceLevel.FULL)
    def custom_tracked_method(self):
        pass