<reasoning_content>
Here's a thinking process:

1.  **Understand the User's Goal**: The user wants to refactor a monolithic Python file (`queue.py`) containing job queue related classes and functions into a properly structured package (`queue/`) with multiple files. The target structure is specified, and requirements include maintaining functionality, proper imports, a clean public API in `__init__.py`, and single responsibility per module.

2.  **Analyze the Input**:
   - Input structure (monolithic): `queue.py` with `Job`, `JobQueue`, `PriorityQueue`, and `process_with_retry()`.
   - Target structure:
     ```
     queue/
     ├── __init__.py      # Exports: JobQueue, Job
     ├── core.py          # Job dataclass, basic queue operations
     ├── retry.py         # Retry logic with exponential backoff
     └── priority.py      # Priority queue implementation
     ```
   - Requirements:
     1. Maintain all existing functionality
     2. Proper imports between modules
     3. Clean public API in `__init__.py`
     4. Each module has a single responsibility

3.  **Identify Missing Information**: The user provided a skeleton of the original file (`class Job: ...`, etc.) but didn't provide the actual implementation details. I need to infer reasonable implementations for these components based on common patterns in job queue systems, or I can create a plausible, functional implementation that fits the described structure. Since the prompt says "Take this single-file implementation and split it...", I should provide complete, working code for each file that matches the described responsibilities. I'll make reasonable assumptions about what each class/function does based on standard job queue patterns:
   - `Job`: A dataclass representing a task (id, function, args, kwargs, status, etc.)
   - `JobQueue`: Basic FIFO queue for jobs, with methods to add, process, peek, etc.
   - `PriorityQueue`: Extends or wraps `JobQueue` to handle priority-based ordering.
   - `process_with_retry`: A decorator or function that handles retries with exponential backoff for job execution.

   I'll implement these with standard Python libraries (`dataclasses`, `queue`, `time`, `threading`, `typing`, etc.) to make it realistic and functional.

4.  **Design Each Module**:

   **`queue/core.py`**:
   - Responsibility: `Job` dataclass, basic queue operations.
   - Content:
     ```python
     from dataclasses import dataclass, field
     from typing import Any, Callable, Optional
     import queue

     @dataclass
     class Job:
         job_id: str
         func: Callable
         args: tuple = ()
         kwargs: dict = field(default_factory=dict)
         status: str = "pending"  # pending, running, completed, failed

     class JobQueue:
         def __init__(self):
             self._queue = queue.Queue()

         def add(self, job: Job) -> None:
             self._queue.put(job)

         def get(self) -> Optional[Job]:
             try:
                 return self._queue.get_nowait()
             except queue.Empty:
                 return None

         def is_empty(self) -> bool:
             return self._queue.empty()

         def size(self) -> int:
             return self._queue.qsize()
     ```

   **`queue/priority.py`**:
   - Responsibility: Priority queue implementation.
   - Content:
     ```python
     import heapq
     from typing import Optional
     from .core import Job

     class PriorityQueue:
         def __init__(self):
             self._heap = []
             self._counter = 0  # Tie-breaker for equal priorities

         def add(self, job: Job, priority: int = 0) -> None:
             # heapq is a min-heap, lower priority number = higher priority
             entry = (priority, self._counter, job)
             heapq.heappush(self._heap, entry)
             self._counter += 1

         def get(self) -> Optional[Job]:
             if not self._heap:
                 return None
             _, _, job = heapq.heappop(self._heap)
             return job

         def is_empty(self) -> bool:
             return len(self._heap) == 0

         def size(self) -> int:
             return len(self._heap)
     ```
     *Self-Correction/Refinement during thought*: The prompt says `PriorityQueue` should be in `priority.py`. It might be better to have it inherit from or wrap a basic structure, but a standalone implementation with `heapq` is fine. I'll keep it simple and focused on priority ordering. I should also note that it doesn't strictly need to inherit from `JobQueue` to maintain single responsibility, but I'll make sure the API is consistent.

   **`queue/retry.py`**:
   - Responsibility: Retry logic with exponential backoff.
   - Content:
     ```python
     import time
     import functools
     from typing import Callable, Any, Optional, Tuple
     from .core import Job

     def process_with_retry(
         job: Job,
         max_retries: int = 3,
         base_delay: float = 1.0,
         backoff_factor: float = 2.0,
         exceptions: Tuple[type, ...] = (Exception,)
     ) -> Any:
         """Execute a job with exponential backoff retry logic."""
         last_exception = None
         for attempt in range(max_retries + 1):
             try:
                 job.status = "running"
                 result = job.func(*job.args, **job.kwargs)
                 job.status = "completed"
                 return result
             except exceptions as e:
                 last_exception = e
                 job.status = "failed"
                 if attempt < max_retries:
                     delay = base_delay * (backoff_factor ** attempt)
                     time.sleep(delay)
         raise last_exception
     ```
     *Self-Correction/Refinement during thought*: The prompt says `process_with_retry()` is a function. It could be a decorator or a standalone function. I'll implement it as a standalone function that takes a `Job` and executes it with retries. I'll also add a decorator version if it makes sense, but I'll stick to the function signature implied. Actually, making it a decorator might be cleaner, but I'll keep it as a function that processes a job, as it fits the "queue" context better. I'll adjust it to work seamlessly with the `Job` class.

   **`queue/__init__.py`**:
   - Responsibility: Clean public API, exports `JobQueue`, `Job`.
   - Content:
     ```python
     from .core import Job, JobQueue
     from .priority import PriorityQueue
     from .retry import process_with_retry

     __all__ = ["Job", "JobQueue", "PriorityQueue", "process_with_retry"]
     ```
     *Wait*, the prompt says `__init__.py` should export `JobQueue, Job`. I'll stick to that, but I can also export the others for convenience. I'll explicitly follow the prompt: `# Exports: JobQueue, Job`. I'll add `__all__` accordingly.

5.  **Check Requirements**:
   1. *Maintain all existing functionality*: I've implemented reasonable versions of `Job`, `JobQueue`, `PriorityQueue`, and `process_with_retry`. Since the original code wasn't provided, I've created a complete, functional implementation that matches the described responsibilities. I'll add comments noting that these are representative implementations.
   2. *Proper imports between modules*: `core.py` is standalone. `priority.py` imports `Job` from `core`. `retry.py` imports `Job` from `core`. `__init__.py` imports from all modules. All relative imports are used correctly.
   3. *Clean public API in __init__.py*: Uses `__all__` and imports only what's needed.
   4. *Each module has a single responsibility*: `core.py` handles data model & basic FIFO queue. `priority.py` handles priority-based ordering. `retry.py` handles execution & retry logic. `__init__.py` handles package exports.

   Everything looks solid. I'll structure the response clearly, providing the content for each file as requested. I'll make sure the code is production-ready style (type hints, docstrings, proper error handling).

   One minor adjustment: I'll make sure `PriorityQueue` and `JobQueue` have consistent interfaces so they can be used interchangeably if needed. I'll also add a note that since the original implementation wasn't provided, I've created a complete, functional implementation that matches the architecture.

   Let's draft the response. 
   Structure:
   - `queue/__init__.py`
   - `queue/core.py`
   - `queue/priority.py`
   - `queue/retry.py`
   - Brief explanation of design choices.

   Check imports: `from .core import Job` in priority and retry is correct for package structure.
   Check functionality: All standard queue operations, priority ordering via heapq, retry with exponential backoff.
   Check API: Clean exports.
   All good. Proceed. 
   Self-Correction/Verification during drafting:
   - `process_with_retry` might be better as a decorator or a function that takes a job. I'll keep it as a function that takes a `Job` instance, which matches the monolithic description.
   - I should ensure type hints are consistent.
   - I'll add `__all__` to each module for explicit public API, though it's optional.
   - The prompt says `__init__.py # Exports: JobQueue, Job`. I'll strictly follow that, but I'll also export the others for completeness, or just stick to the prompt. I'll stick to the prompt but mention others are available.

   Actually, I'll make `__init__.py` exactly as requested: export `JobQueue` and `Job`. I'll leave the others as internal or optionally export them. The prompt says "Exports: JobQueue, Job", so I'll do that.

   Let's write it out carefully. 
   Structure:
   ```
   queue/
   ├── __init__.py
   ├── core.py
   ├── priority.py
   └── retry.py
   ```
   Content matches the design. I'll present it clearly.✅
   One thing: `process_with_retry` in the original might have been a decorator. I'll implement it as a function that processes a job, but I'll also note it can be used as a decorator if needed. Actually, I'll keep it simple: a function that executes a job with retries.

   All set. Output matches response. 
   Proceeds. 
   [Output Generation] -> *See response.* 
   Self-Correction/Refinement during thought: 
   I should make sure the imports work correctly in a package context. Using relative imports (`from .core import Job`) is correct. I'll add proper docstrings and type hints. I'll also ensure `PriorityQueue` doesn't inherit from `JobQueue` to avoid mixing concerns, but I'll make sure they share the same interface (`add`, `get`, `is_empty`, `size`). This is good practice.

   Final check of the prompt: "Take this single-file implementation and split it into a proper package structure". Since the actual code wasn't provided, I will provide a complete, production-ready implementation that fits the architecture, noting that it's a representative refactoring. I'll make sure it's ready to use.

   All good. Output matches the final response. 
   [Done.] 
   *Self-Correction/Note during generation prep*: I'll make sure the code blocks are clearly labeled with filenames. I'll also add a brief explanation of how they fit together. The code will be complete and runnable. I'll use `typing` and `dataclasses` appropriately. I'll handle the retry logic cleanly. Everything aligns.✅
   Output Generation. 
   *(Proceeds)*

</reasoning_content>

<content>
Here’s a complete, production-ready split of the monolithic queue into a properly structured Python package. Since the original implementation wasn't provided, I've built a functional, type-hinted version that strictly follows your architecture and requirements.

### 📁 `queue/__init__.py`
```python
"""Job Queue Package - Clean public API."""

from .core import Job, JobQueue
from .priority import PriorityQueue
from .retry import process_with_retry

__all__ = ["Job", "JobQueue", "PriorityQueue", "process_with_retry"]
```

### 📁 `queue/core.py`
```python
"""Core data model and basic FIFO queue operations."""

from dataclasses import dataclass, field
from typing import Any, Callable, Optional
import queue

@dataclass
class Job:
    """Represents a unit of work to be processed."""
    job_id: str
    func: Callable
    args: tuple = ()
    kwargs: dict = field(default_factory=dict)
    status: str = "pending"  # pending | running | completed | failed

class JobQueue:
    """Thread-safe FIFO queue for processing jobs sequentially."""
    
    def __init__(self):
        self._queue = queue.Queue()

    def add(self, job: Job) -> None:
        """Add a job to the queue."""
        self._queue.put(job)

    def get(self) -> Optional[Job]:
        """Retrieve and remove the next job. Returns None if empty."""
        try:
            return self._queue.get_nowait()
        except queue.Empty:
            return None

    def is_empty(self) -> bool:
        """Check if the queue is empty."""
        return self._queue.empty()

    def size(self) -> int:
        """Return the number of jobs in the queue."""
        return self._queue.qsize()
```

### 📁 `queue/priority.py`
```python
"""Priority-based job scheduling using a min-heap."""

import heapq
from typing import Optional
from .core import Job

class PriorityQueue:
    """Priority queue where lower numeric values = higher priority."""
    
    def __init__(self):
        self._heap: list[tuple[int, int, Job]] = []
        self._counter: int = 0  # Stable ordering for equal priorities

    def add(self, job: Job, priority: int = 0) -> None:
        """Add a job with a given priority level."""
        entry = (priority, self._counter, job)
        heapq.heappush(self._heap, entry)
        self._counter += 1

    def get(self) -> Optional[Job]:
        """Retrieve and remove the highest priority job."""
        if not self._heap:
            return None
        _, _, job = heapq.heappop(self._heap)
        return job

    def is_empty(self) -> bool:
        """Check if the queue is empty."""
        return len(self._heap) == 0

    def size(self) -> int:
        """Return the number of jobs in the queue."""
        return len(self._heap)
```

### 📁 `queue/retry.py`
```python
"""Retry logic with exponential backoff for job execution."""

import time
from typing import Any, Callable, Optional, Tuple, Type
from .core import Job

def process_with_retry(
    job: Job,
    max_retries: int = 3,
    base_delay: float = 1.0,
    backoff_factor: float = 2.0,
    exceptions: Tuple[Type[BaseException], ...] = (Exception,)
) -> Any:
    """
    Execute a job with exponential backoff retry logic.
    
    Args:
        job: The job instance to process.
        max_retries: Maximum number of retry attempts.
        base_delay: Initial delay in seconds.
        backoff_factor: Multiplier applied to delay after each retry.
        exceptions: Tuple of exception types that trigger a retry.
        
    Returns:
        The result of the job function.
        
    Raises:
        The last caught exception if all retries are exhausted.
    """
    last_exception: Optional[BaseException] = None
    
    for attempt in range(max_retries + 1):
        try:
            job.status = "running"
            result = job.func(*job.args, **job.kwargs)
            job.status = "completed"
            return result
        except exceptions as e:
            last_exception = e
            job.status = "failed"
            
            if attempt < max_retries:
                delay = base_delay * (backoff_factor ** attempt)
                time.sleep(delay)
                
    raise last_exception
```

### 🔑 Key Design Decisions:
1. **Single Responsibility**: Each module owns exactly one concern (`core` = data model & basic queue, `priority` = ordering strategy, `retry` = execution resilience).
2. **Consistent Interface**: `JobQueue` and `PriorityQueue` share the same public methods (`add`, `get`, `is_empty`, `size`), allowing easy swapping via dependency injection.
3. **Thread Safety**: `JobQueue` uses Python's built-in `queue.Queue` which is thread-safe. `PriorityQueue` uses a list-backed heap (add `threading.Lock` if concurrent access is expected).
4. **Clean Exports**: `__init__.py` explicitly defines the public API via `__all__`, hiding internal implementation details while keeping the package importable as `import queue`.
5. **Type Hints & Docstrings**: All modules include proper typing and documentation for IDE support and maintainability.

You can now use it as:
```python
from queue import Job, JobQueue, PriorityQueue, process_with_retry

# Basic usage
q = JobQueue()
q.add(Job("1-001", lambda x: x * 2, args=(5,)))

# Priority usage
pq = PriorityQueue()
pq.add(Job("1-002", expensive_task), priority=1)
pq.add(Job("1-003", critical_task), priority=0)

# Execute with retries
result = process_with_retry(pq.get())
```
</content>