<reasoning_content>
*   Goal: Implement a basic job queue in Python.
    *   Requirements:
        *   `JobQueue` class.
        *   Methods: `add_job(job_id: str, data: dict) -> str`, `get_result(job_id: str) -> dict | None`.
        *   FIFO processing order.
        *   Internal storage for jobs until processed.
        *   Unique `job_id`.
    *   Constraints: Write only Python code, no explanations.

    *   A "basic" job queue in a single-threaded environment might just store them. However, the requirement says "Jobs should be processed in FIFO order" and "After processing... result = queue.get_result".
    *   This implies there's a processing mechanism. If I just implement `add_job` and `get_result`, there's no way for a job to actually *finish* unless I include a way to trigger processing or a background thread.
    *   Usually, in these interview-style questions, a `process_next_job()` method or an automatic worker is expected. Since the prompt doesn't explicitly ask for a worker thread, but mentions "After processing", I should probably include a way to simulate processing or a method to process jobs.
    *   Let's refine the requirements based on common patterns:
        *   `jobs` queue: `collections.deque` for FIFO.
        *   `results` store: `dict` mapping `job_id` to result.
        *   Wait, if `add_job` returns the `job_id` passed in, but the prompt says "return job_id", it's a bit redundant if the user provides it, but I'll follow the signature.
    *   Wait, looking at the example: `job_id = queue.add_job("job1", {"task": "process_data"})`. The user *provides* the `job_id`.
    *   The prompt says: "Each job has a unique job_id that is returned when added".

    *   How to handle "processing"? If I don't have a worker, the jobs just sit there.
    *   I will implement a `process_next_job` method to make the example work, or assume a background thread. Given the simplicity requested, a `process_next_job` method is the cleanest way to demonstrate the logic without the complexity of threading/locking, or I can use `threading` to make it a real queue.
    *   Let's look at the requirements again.
        1. `add_job`
        2. `get_result`
    *   It doesn't explicitly ask for a `process` method, but without it, `get_result` will always return `None`.
    *   I'll add a `process_job` method internally or an explicit one. Actually, to be safe and strictly follow the method list, I'll include a `process_jobs` method or similar, OR I'll implement a simple worker thread so that "After processing..." makes sense in the example.
    *   Let's go with a simple version: `add_job` adds to a queue, and I'll add a `process_job` method to actually simulate the work so the code is runnable/testable.

    *Wait*, re-reading: "Jobs should be processed in FIFO order". This implies a worker logic.

    Let's try to make it a complete, runnable example.

    ```python
    from collections import deque
    from typing import Dict, Optional, Any

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results = {}

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> Optional[dict]:
            return self._results.get(job_id)

        def process_next_job(self):
            """Helper method to simulate processing"""
            if not self._queue:
                return
            job_id, data = self._queue.popleft()
            # Simulating some processing logic
            result = {"status": "completed", "original_data": data}
            self._results[job_id] = result
    ```

    However, the prompt specifically asks for `add_job` and `get_result`. It doesn't mention `process_next_job`. But the example says `# After processing...`. This implies the processing happens elsewhere. I'll implement the class as requested, and include a basic worker mechanism using `threading` to ensure the requirement "Jobs should be processed in FIFO order" is actually met by the class itself.

    Actually, a simpler way for a coding task:
    1. `add_job` -> puts in queue.
    2. `get_result` -> looks in results.
    3. To satisfy "processed in FIFO", I'll implement a `process_job` method.

    Let's look at the signature: `add_job(job_id: str, data: dict) -> str`.
    If the user provides the ID, I just return it.

    Wait, if I use a thread, I need `threading.Lock` for thread safety because `add_job` and `get_result` might be called from different threads.

    Let's stick to the most professional implementation:
    - `collections.deque` for the queue.
    - `dict` for results.
    - A background thread that consumes the queue.

    ```python
    import threading
    import time
    from collections import deque
    from typing import Dict, Optional, Any

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results: Dict[str, dict] = {}
            self._lock = threading.Lock()
            self._worker_thread = threading.Thread(target=self._worker, daemon=True)
            self._worker_thread.start()

        def add_job(self, job_id: str, data: dict) -> str:
            with self._lock:
                self._queue.append((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> Optional[dict]:
            with self._lock:
                return self._results.get(job_id)

        def _worker(self):
            while True:
                job_to_process = None
                with self._lock:
                    if self._queue:
                        job_to_process = self._queue.popleft()

                if job_to_process:
                    job_id, data = job_to_process
                    # Simulate processing
                    result = {"status": "success", "data": data}
                    with self._lock:
                        self._results[job_id] = result
                else:
                    time.sleep(0.1)
    ```

    This is a bit heavy. Let's see if there's a simpler interpretation.
    Maybe the user *just* wants the data structures and doesn't care about the actual execution logic?
    Requirement 2: "Jobs should be processed in FIFO order".
    Requirement 3: "The queue should store jobs internally until they are processed".

    If I don't provide a way to process, I'm not fulfilling the requirement "Jobs should be processed".

    Let's try a version that provides a `process_jobs` method or similar, but since the prompt only lists `add_job` and `get_result`, I will include a `process_job` method to allow the user to simulate the "After processing" part of the example.

    Actually, the prompt is a bit ambiguous. Does "Implement a basic job queue" mean I am responsible for the worker? Usually, yes.

    Let's go with the `threading` approach. It's the most robust implementation of a "Job Queue".

    Wait, I'll check the prompt again. "Write only the Python code, no explanations."

    One detail: `get_result` returns `dict | None`. This is Python 3.10+ syntax.

    Let's refine the code.

    ```python
    import threading
    import time
    from collections import deque
    from typing import Dict, Optional, Any

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results: Dict[str, dict] = {}
            self._lock = threading.Lock()
            self._running = True
            self._worker = threading.Thread(target=self._process_loop, daemon=True)
            self._worker.start()

        def add_job(self, job_id: str, data: dict) -> str:
            with self._lock:
                self._queue.append((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> Optional[dict]:
            with self._lock:
                return self._results.get(job_id)

        def _process_loop(self):
            while self._running:
                job = None
                with self._lock:
                    if self._queue:
                        job = self._queue.popleft()

                if job:
                    job_id, data = job
                    # Simulate processing logic
                    result = {"processed_data": data}
                    with self._lock:
                        self._results[job_id] = result
                else:
                    time.sleep(0.01)

        def stop(self):
            self._running = False
            self._worker.join()
    ```

    Is `stop` required? No.
    Is the background thread overkill? Maybe.
    What if I implement a `process_next` method? The prompt doesn't allow it in the class definition requirements.

    Let's re-read: "Create a JobQueue class with the following methods: [list of 2 methods]".
    If I add a 3rd method, am I violating "the following methods"? Usually, adding methods is fine, but if this is an automated test, it might look for exactly those.

    Alternative: The user might expect a synchronous queue where you call a method to "tick" the queue. But that's not a real job queue.

    Let's look at the example:
    ```python
    queue = JobQueue()
    job_id = queue.add_job("job1", {"task": "process_data"})
    # After processing...
    result = queue.get_result("job1")
    ```
    The comment `# After processing...` is the key. It implies the processing is an external event or happens in the background.

    Let's provide a clean, thread-safe implementation with a background worker. This is the most "correct" way to implement a job queue.

    Wait, one more thought. If I use `threading`, the user might not want to deal with `time.sleep` or daemon threads in a simple test.
    What if I implement the "processing" logic inside `get_result`? No, that's not FIFO processing.

    Let's stick to the background worker. It's the standard way.

    Final check on requirements:
    - `add_job(job_id: str, data: dict) -> str`
    - `get_result(job_id: str) -> dict | None`
    - FIFO order.
    - Store internally.
    - Unique ID returned.

    One detail: The prompt says `add_job` returns `job_id`. In the example, the user *passes* "job1" and the return value is `job_id`. So it returns the same ID.

    Let's go.

    ```python
    import threading
    import time
    from collections import deque
    from typing import Dict, Optional, Any

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results: Dict[str, dict] = {}
            self._lock = threading.Lock()
            self._worker_thread = threading.Thread(target=self._worker, daemon=True)
            self._worker_thread.start()

        def add_job(self, job_id: str, data: dict) -> str:
            with self._lock:
                self._queue.append((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> Optional[dict]:
            with self._lock:
                return self._results.get(job_id)

        def _worker(self):
            while True:
                job = None
                with self._lock:
                    if self._queue:
                        job = self._queue.popleft()
                
                if job:
                    job_id, data = job
                    # Simulate job processing
                    result = {"status": "completed", "data": data}
                    with self._lock:
                        self._results[job_id] = result
                else:
                    time.sleep(0.1)
    ```

    Actually, I'll make the processing slightly more "real" by allowing the data to be processed if it has a specific key, but for a "basic" implementation, a generic return is fine.

    Wait, what if the "processing" is intended to be done by the user calling a method?
    "Jobs should be processed in FIFO order".
    If I don't provide a worker, the jobs are never processed.
    If I provide a worker, it's a background thread.
    If I provide a `process_jobs()` method, it's manual.

    The prompt says: "Create a JobQueue class with the following methods...". It doesn't say *only* those methods, but usually, that's what is expected. However, a job queue that doesn't process anything is just a list.

    Let's assume the requirement for "processing" means the logic for processing should be part of the class (via a worker) or the class should provide a way to do it. A background thread is the most professional.

    One more check: "Each job has a unique job_id that is returned when added".
    My code: `return job_id`. Correct.

    I will use `typing.Dict` and `typing.Optional` for compatibility, though `dict | None` is fine in 3.10+. I'll use `Optional` just in case.

    Wait, if I use a background thread, the user might run the example and `get_result` might return `None` immediately if the thread hasn't context-switched. In a real test, the user would wait.

    ```python
    from collections import deque
    import threading
    import time
    from typing import Dict, Optional

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results: Dict[str, dict] = {}
            self._lock = threading.Lock()
            self._worker_thread = threading.Thread(target=self._run_worker, daemon=True)
            self._worker_thread.start()

        def add_job(self, job_id: str, data: dict) -> str:
            with self._lock:
                self._queue.append((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> Optional[dict]:
            with self._lock:
                return self._results.get(job_id)

        def _run_worker(self):
            while True:
                job = None
                with self._lock:
                    if self._queue:
                        job = self._queue.popleft()
                
                if job:
                    job_id, data = job
                    # Simulate processing time and logic
                    time.sleep(0.1) 
                    processed_result = {"result": f"Processed {data}"}
                    with self._lock:
                        self._results[job_id] = processed_result
                else:
                    time.sleep(0.1)

    ```

    Actually, to keep it as "basic" as possible and avoid `time.sleep` overhead in a potential test runner, I'll remove the `time.sleep` inside the worker loop but keep the `daemon=True` thread.

    Wait, the simplest "Basic Job Queue" in an interview might actually be a synchronous one where you call `process_next()`. But the prompt says "Jobs should be processed in FIFO order" and then provides `add_job` and `get_result`.

    Let's consider the possibility that the `JobQueue` is just a data structure and the "processing" is external. But then `get_result` wouldn't make sense if the class doesn't know about the results.

    I'll stick with the background worker. It's the only way to satisfy all requirements naturally.

    One final check on syntax: `dict | None` is preferred in modern Python. I will use that as requested in the prompt signature.

    ```python
    from collections import deque
    import threading
    import time

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results = {}
            self._lock = threading.Lock()
            self._worker = threading.Thread(target=self._worker_loop, daemon=True)
            self._worker.start()

        def add_job(self, job_id: str, data: dict) -> str:
            with self._lock:
                self._queue.append((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> dict | None:
            with self._lock:
                return self._results.get(job_id)

        def _worker_loop(self):
            while True:
                job = None
                with self._lock:
                    if self._queue:
                        job = self._queue.popleft()
                
                if job:
                    job_id, data = job
                    # Basic processing logic
                    result = {"status": "success", "data": data}
                    with self._lock:
                        self._results[job_id] = result
                else:
                    time.sleep(0.01)
    ```

    This looks solid.

    One edge case: The user passes the same `job_id` twice. The requirements say "Each job has a unique job_id". This implies the responsibility is on the caller or the queue handles it. My code will just overwrite the result if the same ID is used, which is acceptable for a "basic" implementation.

    Actually, I'll use `threading.Condition` instead of `time.sleep` for a more professional approach to the worker loop.

    ```python
    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results = {}
            self._condition = threading.Condition()
            self._worker = threading.Thread(target=self._worker_loop, daemon=True)
            self._worker.start()

        def add_job(self, job_id: str, data: dict) -> str:
            with self._condition:
                self._queue.append((job_id, data))
                self._condition.notify()
            return job_id

        def get_result(self, job_id: str) -> dict | None:
            with self._condition:
                return self._results.get(job_id)

        def _worker_loop(self):
            while True:
                with self._condition:
                    while not self._queue:
                        self._condition.wait()
                    job_id, data = self._queue.popleft()
                
                # Process outside the lock
                result = {"processed": data}
                
                with self._condition:
                    self._results[job_id] = result
    ```

    This is much cleaner and more efficient.

    Final verification of requirements:
    1. `add_job` and `get_result` implemented? Yes.
    2. FIFO? Yes, `deque.popleft()`.
    3. Store internally? Yes.
    4. Unique ID returned? Yes.
    5. No explanation? Yes.

    Wait, the return type `dict | None` is Python 3.10. I'll make sure the imports are correct. `threading`, `collections.deque`.

    One small thing: `job_id` is a `str`. `data` is `dict`. `get_result` returns `dict | None`.

    ```python
    import threading
    from collections import deque

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results = {}
            self._condition = threading.Condition()
            self._worker = threading.Thread(target=self._worker_loop, daemon=True)
            self._worker.start()

        def add_job(self, job_id: str, data: dict) -> str:
            with self._condition:
                self._queue.append((job_id, data))
                self._condition.notify()
            return job_id

        def get_result(self, job_id: str) -> dict | None:
            with self._condition:
                return self._results.get(job_id)

        def _worker_loop(self):
            while True:
                with self._condition:
                    while not self._queue:
                        self._condition.wait()
                    job_id, data = self._queue.popleft()
                
                # Processing
                result = {"status": "completed", "data": data}
                
                with self._condition:
                    self._results[job_id] = result
    ```

    This is perfect.

    Wait, the requirement says `get_result(job_id: str) -> dict | None`. I should make sure I don't need to import `Dict` or `Optional` if I use the `|` syntax, because `|` is a built-in feature for types in 3.10+.

    One final consideration: The prompt says "Each job has a unique job_id that is returned when added". In the example `job_id = queue.add_job("job1", ...)`, the `job_id` passed is "job1". My function returns `job_id`, which is "job1". This matches.

    Let's check if there's any reason *not* to use a thread. If the user wanted a synchronous version, they would have asked for a `process_jobs` method. If they wanted a simple data structure, they wouldn't mention "FIFO order" as a processing requirement.

    Final Code Structure:
    - Imports
    - Class `JobQueue`
    - `__init__`
    - `add_job`
    - `get_result`
    - `_worker_loop` (private)

    Wait, I'll make the processing logic slightly more robust just in case.

    ```python
    import threading
    from collections import deque

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results = {}
            self._condition = threading.Condition()
            self._worker = threading.Thread(target=self._worker_loop, daemon=True)
            self._worker.start()

        def add_job(self, job_id: str, data: dict) -> str:
            with self._condition:
                self._queue.append((job_id, data))
                self._condition.notify()
            return job_id

        def get_result(self, job_id: str) -> dict | None:
            with self._condition:
                return self._results.get(job_id)

        def _worker_loop(self):
            while True:
                with self._condition:
                    while not self._queue:
                        self._condition.wait()
                    job_id, data = self._queue.popleft()
                
                # Simulate job processing
                result = {"status": "success", "data": data}
                
                with self._condition:
                    self._results[job_id] = result
    ```

    Actually, I should check if `dict | None` works without `from __future__ import annotations`. In standard Python 3.10, it works in function signatures.

    Wait, if this is run in an environment with < 3.10, `dict | None` will fail. However, since the user *explicitly* used `dict | None` in the requirement, they are clearly assuming 3.10+ syntax.

    One last thing: The `_worker_loop` should probably handle potential exceptions so it doesn't die, but for a "basic" implementation, it's fine.

    ```python
    import threading
    from collections import deque

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results = {}
            self._condition = threading.Condition()
            self._worker = threading.Thread(target=self._worker_loop, daemon=True)
            self._worker.start()

        def add_job(self, job_id: str, data: dict) -> str:
            with self._condition:
                self._queue.append((job_id, data))
                self._condition.notify()
            return job_id

        def get_result(self, job_id: str) -> dict | None:
            with self._condition:
                return self._results.get(job_id)

        def _worker_loop(self):
            while True:
                with self._condition:
                    while not self._queue:
                        self._condition.wait()
                    job_id, data = self._queue.popleft()
                
                # Perform the job processing
                result = {"status": "completed", "original_data": data}
                
                with self._condition:
                    self._results[job_id] = result
    ```

    Ready.
</reasoning_content>

<content>
```python
import threading
from collections import deque

class JobQueue:
    def __init__(self):
        self._queue = deque()
        self._results = {}
        self._condition = threading.Condition()
        self._worker = threading.Thread(target=self._worker_loop, daemon=True)
        self._worker.start()

    def add_job(self, job_id: str, data: dict) -> str:
        with self._condition:
            self._queue.append((job_id, data))
            self._condition.notify()
        return job_id

    def get_result(self, job_id: str) -> dict | None:
        with self._condition:
            return self._results.get(job_id)

    def _worker_loop(self):
        while True:
            with self._condition:
                while not self._queue:
                    self._condition.wait()
                job_id, data = self._queue.popleft()
            
            # Simulate job processing
            result = {"status": "completed", "data": data}
            
            with self._condition:
                self._results[job_id] = result
```
</content>