main.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from __future__ import annotations
#------------------------------------------------------------------------------
# Standard Library Imports - 3.13 std libs **ONLY**
#------------------------------------------------------------------------------
import os
import io
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 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
try:
    __all__ = []
    if not __all__:
        __all__ = []
    else:
        __all__ += __file__
except ImportError:
    __all__ = []
    __all__ += __file__
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'))
#------------------------------------------------------------------------------
# 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()
@dataclass
class ProjectConfig:
    """Project configuration container"""
    name: str
    version: str
    python_version: str
    dependencies: List[str]
    dev_dependencies: List[str] = field(default_factory=list)
    ruff_config: Dict[str, Any] = field(default_factory=dict)
    ffi_modules: List[str] = field(default_factory=list)
    src_path: Path = Path("src")
    tests_path: Path = Path("tests")
class ProjectManager:
    def __init__(self, root_dir: Union[str, Path]):
        self.root_dir = Path(root_dir)
        self.logger = self._setup_logging()
        self.config = self._load_or_create_config()
        self.project_config = self._load_project_config()
        self.ffi_modules = self.project_config["ffi_modules"]
        self._ensure_directory_structure()
    def _setup_logging(self) -> logging.Logger:
        logger = logging.getLogger(__name__)
        handler = logging.StreamHandler()
        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
        handler.setFormatter(formatter)
        logger.addHandler(handler)
        logger.setLevel(logging.INFO)
        return logger
    def _load_project_config(self) -> Dict[str, Any]:
        """Load project-specific configuration from demiurge.json"""
        config_path = self.root_dir / "demiurge.json"
        default_config = {
            "ffi_modules": [],
            "src_path": "src",
            "dev_dependencies": [],
            "profile_enabled": True,
            "platform_specific": {
                "priority": 32
            }
        }
        if not config_path.exists():
            with open(config_path, 'w') as f:
                json.dump(default_config, f, indent=4)
            return default_config
        with open(config_path) as f:
            return json.load(f)
    def _load_or_create_config(self) -> ProjectConfig:
        pyproject_path = self.root_dir / "pyproject.toml"
        if not pyproject_path.exists():
            self.logger.info("No pyproject.toml found. Creating default configuration.")
            config = ProjectConfig(
                name=self.root_dir.name,
                version="0.1.0",
                python_version=">=3.13",
                dependencies=["uvx>=0.1.0","uvx>=0.1.0"],
                dev_dependencies=[
                    "ruff>=0.3.0",
                    "pytest>=8.0.0",
                    "pytest-asyncio>=0.23.0"
                ],
                ruff_config={
                    "line-length": 88,
                    "target-version": "py313",
                    "select": ["E", "F", "I", "N", "W"],
                    "ignore": [],
                    "fixable": ["A", "B", "C", "D", "E", "F", "I"]
                },
                ffi_modules=[]
            )
            self._write_pyproject_toml(config)
            return config
        with open(pyproject_path, "rb") as f:
            data = tomllib.load(f)
        return ProjectConfig(
            name=data["project"]["name"],
            version=data["project"]["version"],
            python_version=data["project"]["requires-python"],
            dependencies=data["project"].get("dependencies", []),
            dev_dependencies=data["project"].get("dev-dependencies", []),
            ruff_config=data.get("tool", {}).get("ruff", {}),
            ffi_modules=data["project"].get("ffi-modules", []),
            src_path=Path(data["project"].get("src-path", "src")),
            tests_path=Path(data["project"].get("tests-path", "tests"))
        )
    def _write_pyproject_toml(self, config: ProjectConfig):
        data = {
            "project": {
                "name": config.name,
                "version": config.version,
                "requires-python": config.python_version,
                "dependencies": config.dependencies,
                "dev-dependencies": config.dev_dependencies,
                "ffi-modules": config.ffi_modules,
                "src-path": str(config.src_path),
                "tests-path": str(config.tests_path)
            },
            "tool": {
                "ruff": config.ruff_config
            }
        }
        with open(self.root_dir / "pyproject.toml", "w", encoding='utf-8') as f:
            toml_str = tomllib.dumps(data)
            f.write(toml_str)
    def _ensure_directory_structure(self):
        """Create necessary project directories if they don't exist"""
        dirs = [
            self.config.src_path,
            self.config.tests_path,
            self.config.src_path / "ffi"
        ]
        for dir_path in dirs:
            (self.root_dir / dir_path).mkdir(parents=True, exist_ok=True)
            init_file = self.root_dir / dir_path / "__init__.py"
            if not init_file.exists():
                init_file.touch()
    @contextmanager
    def _temp_requirements(self, requirements: List[str]):
        """Create a temporary requirements file"""
        with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False) as f:
            f.write('\n'.join(requirements))
            temp_path = f.name
        try:
            yield temp_path
        finally:
            os.unlink(temp_path)
    async def run_uv_command(self, cmd: List[str], timeout: Optional[float] = None) -> subprocess.CompletedProcess:
        """Run a UV command asynchronously with timeout support"""
        self.logger.debug(f"Running UV command: {' '.join(cmd)}")
        try:
            process = await asyncio.create_subprocess_exec(
                *cmd,
                stdout=subprocess.PIPE,
                stderr=subprocess.PIPE
            )
            try:
                stdout, stderr = await asyncio.wait_for(
                    process.communicate(),
                    timeout=timeout
                )
            except asyncio.TimeoutError:
                try:
                    process.terminate()
                    await process.wait()
                except ProcessLookupError:
                    pass
                raise TimeoutError(f"Command timed out after {timeout} seconds")
            if process.returncode != 0:
                error_msg = stderr.decode()
                self.logger.error(f"UV command failed: {error_msg}")
                raise RuntimeError(f"UV command failed: {error_msg}")
            return subprocess.CompletedProcess(
                cmd, process.returncode, stdout.decode(), stderr.decode()
            )
        except FileNotFoundError:
            self.logger.error(f"Command not found: {cmd[0]}")
            raise RuntimeError(f"Command not found: {cmd[0]}. Is UV installed?")
    async def setup_environment(self):
            """Set up the environment based on mode"""
            self.logger.info("Setting up environment...")
            # Create virtual environment
            await self.run_uv_command(["uv", "venv"])
            # Create requirements files
            requirements_path = self.root_dir / "requirements.txt"
            dev_requirements_path = self.root_dir / "requirements-dev.txt"
            # Write main requirements
            if self.config.dependencies:
                with open(requirements_path, 'w') as f:
                    f.write('\n'.join(self.config.dependencies) + '\n')
            # Write dev requirements
            if self.config.dev_dependencies:
                with open(dev_requirements_path, 'w') as f:
                    f.write('\n'.join(self.config.dev_dependencies) + '\n')
            # Generate lock file for main requirements
            if requirements_path.exists():
                self.logger.info("Compiling requirements...")
                await self.run_uv_command([
                    "uv", "pip", "compile", 
                    str(requirements_path), 
                    "--output-file", 
                    str(self.root_dir / "requirements.lock")
                ])
            # Generate lock file for dev requirements
            if dev_requirements_path.exists():
                self.logger.info("Compiling dev requirements...")
                await self.run_uv_command([
                    "uv", "pip", "compile", 
                    str(dev_requirements_path), 
                    "--output-file", 
                    str(self.root_dir / "requirements-dev.lock")
                ])
            # Install from lock files
            if (self.root_dir / "requirements.lock").exists():
                self.logger.info("Installing dependencies from lock file...")
                await self.run_uv_command([
                    "uv", "pip", "install", 
                    "-r", str(self.root_dir / "requirements.lock")
                ])
            if (self.root_dir / "requirements-dev.lock").exists():
                self.logger.info("Installing dev dependencies from lock file...")
                await self.run_uv_command([
                    "uv", "pip", "install", 
                    "-r", str(self.root_dir / "requirements-dev.lock")
                ])
            # Install the project in editable mode if setup.py exists
            if (self.root_dir / "setup.py").exists():
                self.logger.info("Installing project in editable mode...")
                await self.run_uv_command(["uv", "pip", "install", "-e", "."])
    async def run_app(self, module_path: str, *args, timeout: Optional[float] = None):
        """Run the application using Python directly"""
        module_path = str(Path(module_path))
        cmd = ["python", module_path, *map(str, args)]
        self.logger.info(f"Running: {' '.join(cmd)}")
        return await self.run_uv_command(cmd, timeout=timeout)
    async def run_tests(self):
        """Run tests using pytest"""
        await self.run_uv_command(["uvx", "run", "-m", "pytest", str(self.config.tests_path)])
    async def run_linter(self):
        """Run Ruff linter"""
        await self.run_uv_command(["uvx", "run", "-m", "ruff", "check", "."])
    async def format_code(self):
        """Format code using Ruff"""
        await self.run_uv_command(["uvx", "run", "-m", "ruff", "format", "."])
    async def run_dev_mode(self):
        """Setup and run operations specific to Developer Mode"""
        self.logger.info("Running Developer Mode tasks...")
        # Setup development environment, dependencies, local servers, etc.
        await self.setup_environment()
        await self.run_tests()
    async def run_admin_mode(self):
        """Setup and run operations specific to Admin Mode"""
        self.logger.info("Running Admin Mode tasks...")
        # Perform administrative tasks like configuration adjustments and monitoring
    async def run_user_mode(self):
        """Setup and run operations specific to User Mode"""
        self.logger.info("Running User Mode tasks...")
        # Deploy and run the main application
    async def teardown(self):
        """Teardown any setup done by modes or setup_environment"""
        self.logger.info("Tearing down environment...")
        # Clean-up tasks like stopping services
async def main():
    """Main entry point for the project manager"""
    parser = argparse.ArgumentParser(description="Mode-based Project Manager")
    parser.add_argument("--root", default=".", help="Project root directory")
    parser.add_argument("mode", choices=["DEV", "ADMIN", "USER", "TEARDOWN"], help="Mode to execute")
    parser.add_argument("--timeout", type=float, help="Timeout in seconds for commands")
    args = parser.parse_args()
    manager = ProjectManager(args.root)
    try:
        if args.mode == "DEV":
            await manager.run_dev_mode()
        elif args.mode == "ADMIN":
            await manager.run_admin_mode()
        elif args.mode == "USER":
            await manager.run_user_mode()
        elif args.mode == "TEARDOWN":
            await manager.teardown()
    except Exception as e:
        manager.logger.error(f"Error: {e}")
        return 1
    return 0
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)
    sys.exit(asyncio.run(main()))