This refactoring separates concerns by placing data structures, algorithmic implementations, and business logic into dedicated modules.

Here is the content for each file in the `queue/` package structure.

***

## File: `queue/core.py`

This module handles the fundamental data structure (`Job`) and the main queue interface (`JobQueue`).

```python
# queue/core.py

from dataclasses import dataclass
from typing import Any, Optional
import time

@dataclass(order=True)
class Job:
    """
    Represents a single task to be executed.
    The 'priority' field is used for ordering in the PriorityQueue.
    """
    priority: int = 0  # Lower number means higher priority
    job_id: str = None
    payload: Any = None
    retries: int = 0

    def __post_init__(self):
        if self.job_id is None:
            self.job_id = f"job-{hash(self)}"

    def __str__(self):
        return f"Job(ID={self.job_id}, Priority={self.priority}, Retries={self.retries})"

class JobQueue:
    """
    The main interface for the job queue.
    It relies on an internal PriorityQueue implementation.
    """
    def __init__(self, priority_queue):
        # Dependency Injection: The queue uses a specific priority queue implementation
        self._queue = priority_queue

    def enqueue(self, job: Job):
        """Adds a job to the queue."""
        self._queue.put(job)
        print(f"[CORE] Enqueued job: {job.job_id} (P={job.priority})")

    def dequeue(self) -> Optional[Job]:
        """Removes and returns the highest priority job."""
        return self._queue.get()

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

    def size(self) -> int:
        """Returns the number of items in the queue."""
        return self._queue.qsize()

```

## File: `queue/priority.py`

This module implements the specific data structure needed for ordering jobs (a min-heap based Priority Queue).

```python
# queue/priority.py

import heapq
from typing import List, Any
from .core import Job

class PriorityQueue:
    """
    A min-heap implementation of a Priority Queue.
    Jobs are ordered based on their 'priority' attribute (lower is better).
    """
    def __init__(self):
        # The heap stores (priority, job_object) tuples
        self._heap: List[Job] = []

    def put(self, job: Job):
        """Adds a job to the heap."""
        # heapq uses the first element of the tuple for comparison
        heapq.heappush(self._heap, job)

    def get(self) -> Job:
        """Removes and returns the highest priority job."""
        if self.empty():
            raise IndexError("get from empty priority queue")
        return heapq.heappop(self._heap)

    def empty(self) -> bool:
        """Checks if the queue is empty."""
        return not self._heap

    def qsize(self) -> int:
        """Returns the number of items in the queue."""
        return len(self._heap)

```

## File: `queue/retry.py`

This module contains the business logic for processing jobs, including the retry mechanism with exponential backoff.

```python
# queue/retry.py

import time
import random
from typing import Callable
from .core import Job

# --- Configuration ---
MAX_RETRIES = 5
BASE_DELAY_SECONDS = 1

def process_job(job: Job, processing_function: Callable) -> bool:
    """
    Attempts to execute the job using the provided function.

    Returns True if successful, False if failed.
    """
    try:
        print(f"\n[PROCESS] Starting job: {job.job_id}")
        # Assume processing_function returns True on success, False on failure
        success = processing_function(job.payload)
        if success:
            print(f"[PROCESS] Successfully completed job: {job.job_id}")
        else:
            print(f"[PROCESS] Job failed execution logic: {job.job_id}")
        return success
    except Exception as e:
        print(f"[PROCESS] Job failed with exception: {job.job_id}. Error: {e}")
        return False

def process_with_retry(job: Job, processing_function: Callable):
    """
    Executes a job, applying exponential backoff and retries on failure.
    """
    while job.retries <= MAX_RETRIES:
        # 1. Attempt to process the job
        success = process_job(job, processing_function)

        if success:
            return # Job succeeded, exit the retry loop

        # 2. Handle failure and increment retry count
        job.retries += 1
        if job.retries > MAX_RETRIES:
            print(f"\n[RETRY] FATAL: Job {job.job_id} failed after {MAX_RETRIES} attempts. Giving up.")
            # In a real system, this would move to a Dead Letter Queue (DLQ)
            return

        # 3. Calculate exponential backoff delay
        # Delay = BaseDelay * (2^retries) + jitter
        delay = BASE_DELAY_SECONDS * (2 ** (job.retries - 1))
        # Add jitter (randomness) to prevent thundering herd problem
        jitter = random.uniform(0, 1)
        wait_time = delay + jitter

        print(f"[RETRY] Job {job.job_id} failed. Retrying in {wait_time:.2f} seconds (Attempt {job.retries}/{MAX_RETRIES}).")
        time.sleep(wait_time)

```

## File: `queue/__init__.py`

This file defines the public API, allowing users to import the necessary components cleanly.

```python
# queue/__init__.py

# Import core components
from .core import Job, JobQueue

# Import business logic components
from .retry import process_with_retry

# Define what is exposed when 'from queue import *' is used
__all__ = [
    "Job",
    "JobQueue",
    "process_with_retry",
]
```

***

### Usage Example (Demonstration)

To show how this structure is used, here is a simple driver script:

```python
# main.py

from queue import Job, JobQueue, process_with_retry
from queue.priority import PriorityQueue # Need to import the specific PQ for instantiation

# --- Mock Processing Function ---
# This function simulates a task that sometimes fails
FAILURE_COUNT = 0
def mock_task(payload):
    global FAILURE_COUNT
    if FAILURE_COUNT < 3:
        FAILURE_COUNT += 1
        print(f"--- Task execution failed (Attempt {FAILURE_COUNT}) ---")
        return False # Signal failure
    else:
        print("--- Task execution succeeded! ---")
        return True # Signal success

# --- Setup ---
# 1. Instantiate the underlying priority queue
pq = PriorityQueue()
# 2. Instantiate the queue interface, injecting the PQ
job_queue = JobQueue(pq)

# --- Enqueue Jobs ---
# Job 1: Low priority (P=10)
job_queue.enqueue(Job(priority=10, payload="Low Priority Task"))

# Job 2: High priority (P=1) - This should be processed first
job_queue.enqueue(Job(priority=1, payload="CRITICAL Task"))

# Job 3: Medium priority (P=5)
job_queue.enqueue(Job(priority=5, payload="Medium Priority Task"))

# --- Processing Loop ---
print("\n" + "="*50)
print("STARTING JOB PROCESSING LOOP")
print("="*50)

while not job_queue.is_empty():
    # Dequeue the highest priority job
    job = job_queue.dequeue()

    # Process the job using the retry logic
    process_with_retry(job, mock_task)

print("\n" + "="*50)
print("JOB PROCESSING COMPLETE")
print("="*50)
```