"""Background worker for processing article-to-podcast conversions."""

# : dep boto3
# : dep botocore
# : dep openai
# : dep pydub
# : dep pytest
# : dep pytest-asyncio
# : dep pytest-mock
# : dep trafilatura
# : out podcastitlater-worker
# : run ffmpeg
import Biz.PodcastItLater.Core as Core
import boto3  # type: ignore[import-untyped]
import io
import json
import Omni.App as App
import Omni.Log as Log
import Omni.Test as Test
import openai
import os
import pathlib
import pytest
import sys
import time
import trafilatura
import typing
import unittest.mock
from botocore.exceptions import ClientError  # type: ignore[import-untyped]
from datetime import datetime
from datetime import timedelta
from datetime import timezone
from pydub import AudioSegment  # type: ignore[import-untyped]
from typing import Any

logger = Log.setup()

# Configuration from environment variables
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
S3_ENDPOINT = os.getenv("S3_ENDPOINT")
S3_BUCKET = os.getenv("S3_BUCKET")
S3_ACCESS_KEY = os.getenv("S3_ACCESS_KEY")
S3_SECRET_KEY = os.getenv("S3_SECRET_KEY")
area = App.from_env()
if area == App.Area.Test:
    DATABASE_PATH = os.getenv(
        "DATABASE_PATH",
        "_/var/podcastitlater/podcast.db",
    )
else:
    DATABASE_PATH = os.getenv("DATABASE_PATH", "/var/podcastitlater/podcast.db")

# Worker configuration
MAX_CONTENT_LENGTH = 5000  # characters for TTS
POLL_INTERVAL = 30  # seconds
MAX_RETRIES = 3
TTS_MODEL = "tts-1"
TTS_VOICE = "alloy"


class ArticleProcessor:
    """Handles the complete article-to-podcast conversion pipeline."""

    def __init__(self) -> None:
        """Initialize the processor with required services.

        Raises:
            ValueError: If OPENAI_API_KEY environment variable is not set.
        """
        if not OPENAI_API_KEY:
            msg = "OPENAI_API_KEY environment variable is required"
            raise ValueError(msg)

        self.openai_client: openai.OpenAI = openai.OpenAI(
            api_key=OPENAI_API_KEY,
        )

        # Initialize S3 client for Digital Ocean Spaces
        if all([S3_ENDPOINT, S3_BUCKET, S3_ACCESS_KEY, S3_SECRET_KEY]):
            self.s3_client: Any = boto3.client(
                "s3",
                endpoint_url=S3_ENDPOINT,
                aws_access_key_id=S3_ACCESS_KEY,
                aws_secret_access_key=S3_SECRET_KEY,
            )
        else:
            logger.warning("S3 configuration incomplete, uploads will fail")
            self.s3_client = None

    @staticmethod
    def extract_article_content(url: str) -> tuple[str, str]:
        """Extract title and content from article URL using trafilatura.

        Raises:
            ValueError: If content cannot be downloaded or extracted.
        """
        try:
            downloaded = trafilatura.fetch_url(url)
            if not downloaded:
                msg = f"Failed to download content from {url}"
                raise ValueError(msg)  # noqa: TRY301

            # Extract with metadata
            result = trafilatura.extract(
                downloaded,
                include_comments=False,
                include_tables=False,
                with_metadata=True,
                output_format="json",
            )

            if not result:
                msg = f"Failed to extract content from {url}"
                raise ValueError(msg)  # noqa: TRY301

            data = json.loads(result)

            title = data.get("title", "Untitled Article")
            content = data.get("text", "")

            if not content:
                msg = f"No content extracted from {url}"
                raise ValueError(msg)  # noqa: TRY301

            # Don't truncate - we'll handle length in text_to_speech
            logger.info("Extracted article: %s (%d chars)", title, len(content))
        except Exception:
            logger.exception("Failed to extract content from %s", url)
            raise
        else:
            return title, content

    def text_to_speech(self, text: str, title: str) -> bytes:
        """Convert text to speech using OpenAI TTS API.

        Uses LLM to prepare text, then handles chunking and concatenation.

        Raises:
            ValueError: If no chunks are generated from text.
        """
        try:
            # Use LLM to prepare and chunk the text
            chunks = prepare_text_for_tts(text, title)

            if not chunks:
                msg = "No chunks generated from text"
                raise ValueError(msg)  # noqa: TRY301

            logger.info("Processing %d chunks for TTS", len(chunks))

            # Generate audio for each chunk
            audio_segments = []
            for i, chunk in enumerate(chunks):
                logger.info(
                    "Generating TTS for chunk %d/%d (%d chars)",
                    i + 1,
                    len(chunks),
                    len(chunk),
                )

                response = self.openai_client.audio.speech.create(
                    model=TTS_MODEL,
                    voice=TTS_VOICE,
                    input=chunk,
                    response_format="mp3",
                )

                # Convert bytes to AudioSegment
                audio_segment = AudioSegment.from_mp3(
                    io.BytesIO(response.content),
                )
                audio_segments.append(audio_segment)

                # Small delay between API calls to be respectful
                if i < len(chunks) - 1:
                    time.sleep(0.5)

            # Concatenate all audio segments
            combined_audio = audio_segments[0]
            for segment in audio_segments[1:]:
                # Add a small silence between chunks for natural pacing
                silence = AudioSegment.silent(duration=300)
                combined_audio = combined_audio + silence + segment

            # Export combined audio to bytes
            output_buffer = io.BytesIO()
            combined_audio.export(output_buffer, format="mp3", bitrate="128k")
            audio_data = output_buffer.getvalue()

            logger.info(
                "Generated combined TTS audio: %d bytes",
                len(audio_data),
            )
        except Exception:
            logger.exception("TTS generation failed")
            raise
        else:
            return audio_data

    def upload_to_s3(self, audio_data: bytes, filename: str) -> str:
        """Upload audio file to S3-compatible storage and return public URL.

        Raises:
            ValueError: If S3 client is not configured.
            ClientError: If S3 upload fails.
        """
        if not self.s3_client:
            msg = "S3 client not configured"
            raise ValueError(msg)

        try:
            # Upload file
            self.s3_client.put_object(
                Bucket=S3_BUCKET,
                Key=filename,
                Body=audio_data,
                ContentType="audio/mpeg",
                ACL="public-read",
            )

            # Construct public URL
            audio_url = f"{S3_ENDPOINT}/{S3_BUCKET}/{filename}"
            logger.info("Uploaded audio to: %s", audio_url)
        except ClientError:
            logger.exception("S3 upload failed")
            raise
        else:
            return audio_url

    @staticmethod
    def estimate_duration(audio_data: bytes) -> int:
        """Estimate audio duration in seconds based on file size and bitrate."""
        # Rough estimation: MP3 at 128kbps = ~16KB per second
        estimated_seconds = len(audio_data) // 16000
        return max(1, estimated_seconds)  # Minimum 1 second

    @staticmethod
    def generate_filename(job_id: int, title: str) -> str:
        """Generate unique filename for audio file."""
        timestamp = int(datetime.now(tz=timezone.utc).timestamp())
        # Create safe filename from title
        safe_title = "".join(
            c for c in title if c.isalnum() or c in {" ", "-", "_"}
        ).rstrip()
        safe_title = safe_title.replace(" ", "_")[:50]  # Limit length
        return f"episode_{timestamp}_{job_id}_{safe_title}.mp3"

    def process_job(self, job: dict[str, Any]) -> None:
        """Process a single job through the complete pipeline."""
        job_id = job["id"]
        url = job["url"]

        try:
            logger.info("Processing job %d: %s", job_id, url)

            # Update status to processing
            Core.Database.update_job_status(
                job_id,
                "processing",
                db_path=DATABASE_PATH,
            )

            # Step 1: Extract article content
            title, content = ArticleProcessor.extract_article_content(url)

            # Step 2: Generate audio
            audio_data = self.text_to_speech(content, title)

            # Step 3: Upload to S3
            filename = ArticleProcessor.generate_filename(job_id, title)
            audio_url = self.upload_to_s3(audio_data, filename)

            # Step 4: Calculate duration
            duration = ArticleProcessor.estimate_duration(audio_data)

            # Step 5: Create episode record
            episode_id = Core.Database.create_episode(
                title=title,
                audio_url=audio_url,
                duration=duration,
                content_length=len(content),
                user_id=job.get("user_id"),
                db_path=DATABASE_PATH,
            )

            # Step 6: Mark job as complete
            Core.Database.update_job_status(
                job_id,
                "completed",
                db_path=DATABASE_PATH,
            )

            logger.info(
                "Successfully processed job %d -> episode %d",
                job_id,
                episode_id,
            )

        except Exception as e:
            error_msg = str(e)
            logger.exception("Job %d failed: %s", job_id, error_msg)
            Core.Database.update_job_status(
                job_id,
                "error",
                error_msg,
                DATABASE_PATH,
            )
            raise


def prepare_text_for_tts(text: str, title: str) -> list[str]:
    """Use LLM to prepare text for TTS, returning chunks ready for speech.

    First splits text mechanically, then has LLM edit each chunk.
    """
    # First, split the text into manageable chunks
    raw_chunks = split_text_into_chunks(text, max_chars=3000)

    logger.info("Split article into %d raw chunks", len(raw_chunks))

    # Prepare the first chunk with intro
    edited_chunks = []

    for i, chunk in enumerate(raw_chunks):
        is_first = i == 0
        is_last = i == len(raw_chunks) - 1

        try:
            edited_chunk = edit_chunk_for_speech(
                chunk,
                title=title if is_first else None,
                is_first=is_first,
                is_last=is_last,
            )
            edited_chunks.append(edited_chunk)
        except Exception:
            logger.exception("Failed to edit chunk %d", i + 1)
            # Fall back to raw chunk if LLM fails
            if is_first:
                edited_chunks.append(
                    f"This is an audio version of {title}. {chunk}",
                )
            elif is_last:
                edited_chunks.append(f"{chunk} This concludes the article.")
            else:
                edited_chunks.append(chunk)

    return edited_chunks


def split_text_into_chunks(text: str, max_chars: int = 3000) -> list[str]:
    """Split text into chunks at sentence boundaries."""
    chunks = []
    current_chunk = ""

    # Split into paragraphs first
    paragraphs = text.split("\n\n")

    for para in paragraphs:
        para_stripped = para.strip()
        if not para_stripped:
            continue

        # If paragraph itself is too long, split by sentences
        if len(para_stripped) > max_chars:
            sentences = para_stripped.split(". ")
            for sentence in sentences:
                if len(current_chunk) + len(sentence) + 2 < max_chars:
                    current_chunk += sentence + ". "
                else:
                    if current_chunk:
                        chunks.append(current_chunk.strip())
                    current_chunk = sentence + ". "
        # If adding this paragraph would exceed limit, start new chunk
        elif len(current_chunk) + len(para_stripped) + 2 > max_chars:
            if current_chunk:
                chunks.append(current_chunk.strip())
            current_chunk = para_stripped + " "
        else:
            current_chunk += para_stripped + " "

    # Don't forget the last chunk
    if current_chunk:
        chunks.append(current_chunk.strip())

    return chunks


def edit_chunk_for_speech(
    chunk: str,
    title: str | None = None,
    *,
    is_first: bool = False,
    is_last: bool = False,
) -> str:
    """Use LLM to lightly edit a single chunk for speech.

    Raises:
        ValueError: If no content is returned from LLM.
    """
    system_prompt = (
        "You are a podcast script editor. Your job is to lightly edit text "
        "to make it sound natural when spoken aloud.\n\n"
        "Guidelines:\n"
    )
    system_prompt += """
- Remove URLs and email addresses, replacing with descriptive phrases
- Convert bullet points and lists into flowing sentences
- Fix any awkward phrasing for speech
- Remove references like "click here" or "see below"
- Keep edits minimal - preserve the original content and style
- Do NOT add commentary or explanations
- Return ONLY the edited text, no JSON or formatting
"""

    user_prompt = chunk

    # Add intro/outro if needed
    if is_first and title:
        user_prompt = (
            f"Add a brief intro mentioning this is an audio version of "
            f"'{title}', then edit this text:\n\n{chunk}"
        )
    elif is_last:
        user_prompt = f"Edit this text and add a brief closing:\n\n{chunk}"

    try:
        client: openai.OpenAI = openai.OpenAI(api_key=OPENAI_API_KEY)
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt},
            ],
            temperature=0.3,  # Lower temperature for more consistent edits
            max_tokens=4000,
        )

        content = response.choices[0].message.content
        if not content:
            msg = "No content returned from LLM"
            raise ValueError(msg)  # noqa: TRY301

        # Ensure the chunk isn't too long
        max_chunk_length = 4000
        if len(content) > max_chunk_length:
            # Truncate at sentence boundary
            sentences = content.split(". ")
            truncated = ""
            for sentence in sentences:
                if len(truncated) + len(sentence) + 2 < max_chunk_length:
                    truncated += sentence + ". "
                else:
                    break
            content = truncated.strip()

    except Exception:
        logger.exception("LLM chunk editing failed")
        raise
    else:
        return content


def parse_datetime_with_timezone(created_at: str | datetime) -> datetime:
    """Parse datetime string and ensure it has timezone info."""
    if isinstance(created_at, str):
        # Handle timezone-aware datetime strings
        if created_at.endswith("Z"):
            created_at = created_at[:-1] + "+00:00"
        last_attempt = datetime.fromisoformat(created_at)
        if last_attempt.tzinfo is None:
            last_attempt = last_attempt.replace(tzinfo=timezone.utc)
    else:
        last_attempt = created_at
        if last_attempt.tzinfo is None:
            last_attempt = last_attempt.replace(tzinfo=timezone.utc)
    return last_attempt


def should_retry_job(job: dict[str, Any], max_retries: int) -> bool:
    """Check if a job should be retried based on retry count and backoff time.

    Uses exponential backoff to determine if enough time has passed.
    """
    retry_count = job["retry_count"]
    if retry_count >= max_retries:
        return False

    # Exponential backoff: 30s, 60s, 120s
    backoff_time = 30 * (2**retry_count)
    last_attempt = parse_datetime_with_timezone(job["created_at"])
    time_since_attempt = datetime.now(tz=timezone.utc) - last_attempt

    return time_since_attempt > timedelta(seconds=backoff_time)


def process_pending_jobs(processor: ArticleProcessor) -> None:
    """Process all pending jobs."""
    pending_jobs = Core.Database.get_pending_jobs(
        limit=5,
        db_path=DATABASE_PATH,
    )

    for job in pending_jobs:
        current_job = job["id"]
        try:
            processor.process_job(job)
        except Exception as e:
            # Ensure job is marked as error even if process_job didn't handle it
            logger.exception("Failed to process job: %d", current_job)
            # Check if job is still in processing state
            current_status = Core.Database.get_job_by_id(
                current_job,
                DATABASE_PATH,
            )
            if current_status and current_status.get("status") == "processing":
                Core.Database.update_job_status(
                    current_job,
                    "error",
                    str(e),
                    DATABASE_PATH,
                )
            continue


def process_retryable_jobs() -> None:
    """Check and retry failed jobs with exponential backoff."""
    retryable_jobs = Core.Database.get_retryable_jobs(
        MAX_RETRIES,
        DATABASE_PATH,
    )

    for job in retryable_jobs:
        if should_retry_job(job, MAX_RETRIES):
            logger.info(
                "Retrying job %d (attempt %d)",
                job["id"],
                job["retry_count"] + 1,
            )
            Core.Database.update_job_status(
                job["id"],
                "pending",
                db_path=DATABASE_PATH,
            )


def main_loop() -> None:
    """Poll for jobs and process them in a continuous loop."""
    processor = ArticleProcessor()
    logger.info("Worker started, polling for jobs...")

    while True:
        try:
            # Process pending jobs
            process_pending_jobs(processor)
            process_retryable_jobs()

            # Check if there's any work
            pending_jobs = Core.Database.get_pending_jobs(
                limit=1,
                db_path=DATABASE_PATH,
            )
            retryable_jobs = Core.Database.get_retryable_jobs(
                MAX_RETRIES,
                DATABASE_PATH,
            )

            if not pending_jobs and not retryable_jobs:
                logger.debug("No jobs to process, sleeping...")

        except Exception:
            logger.exception("Error in main loop")

        time.sleep(POLL_INTERVAL)


def move() -> None:
    """Make the worker move."""
    try:
        # Initialize database
        Core.Database.init_db(DATABASE_PATH)

        # Start main processing loop
        main_loop()

    except KeyboardInterrupt:
        logger.info("Worker stopped by user")
    except Exception:
        logger.exception("Worker crashed")
        raise


class TestArticleExtraction(Test.TestCase):
    """Test article extraction functionality."""

    def test_extract_valid_article(self) -> None:
        """Extract from well-formed HTML."""
        # Mock trafilatura.fetch_url and extract
        mock_html = (
            "<html><body><h1>Test Article</h1><p>Content here</p></body></html>"
        )
        mock_result = json.dumps({
            "title": "Test Article",
            "text": "Content here",
        })

        with (
            unittest.mock.patch(
                "trafilatura.fetch_url",
                return_value=mock_html,
            ),
            unittest.mock.patch(
                "trafilatura.extract",
                return_value=mock_result,
            ),
        ):
            title, content = ArticleProcessor.extract_article_content(
                "https://example.com",
            )

        self.assertEqual(title, "Test Article")
        self.assertEqual(content, "Content here")

    def test_extract_missing_title(self) -> None:
        """Handle articles without titles."""
        mock_html = "<html><body><p>Content without title</p></body></html>"
        mock_result = json.dumps({"text": "Content without title"})

        with (
            unittest.mock.patch(
                "trafilatura.fetch_url",
                return_value=mock_html,
            ),
            unittest.mock.patch(
                "trafilatura.extract",
                return_value=mock_result,
            ),
        ):
            title, content = ArticleProcessor.extract_article_content(
                "https://example.com",
            )

        self.assertEqual(title, "Untitled Article")
        self.assertEqual(content, "Content without title")

    def test_extract_empty_content(self) -> None:
        """Handle empty articles."""
        mock_html = "<html><body></body></html>"
        mock_result = json.dumps({"title": "Empty Article", "text": ""})

        with (
            unittest.mock.patch(
                "trafilatura.fetch_url",
                return_value=mock_html,
            ),
            unittest.mock.patch(
                "trafilatura.extract",
                return_value=mock_result,
            ),
            pytest.raises(ValueError, match="No content extracted") as cm,
        ):
            ArticleProcessor.extract_article_content(
                "https://example.com",
            )

        self.assertIn("No content extracted", str(cm.value))

    def test_extract_network_error(self) -> None:
        """Handle connection failures."""
        with (
            unittest.mock.patch("trafilatura.fetch_url", return_value=None),
            pytest.raises(ValueError, match="Failed to download") as cm,
        ):
            ArticleProcessor.extract_article_content("https://example.com")

        self.assertIn("Failed to download", str(cm.value))

    @staticmethod
    def test_extract_timeout() -> None:
        """Handle slow responses."""
        with (
            unittest.mock.patch(
                "trafilatura.fetch_url",
                side_effect=TimeoutError("Timeout"),
            ),
            pytest.raises(TimeoutError),
        ):
            ArticleProcessor.extract_article_content("https://example.com")

    def test_content_sanitization(self) -> None:
        """Remove unwanted elements."""
        mock_html = """
        <html><body>
            <h1>Article</h1>
            <p>Good content</p>
            <script>alert('bad')</script>
            <table><tr><td>data</td></tr></table>
        </body></html>
        """
        mock_result = json.dumps({
            "title": "Article",
            "text": "Good content",  # Tables and scripts removed
        })

        with (
            unittest.mock.patch(
                "trafilatura.fetch_url",
                return_value=mock_html,
            ),
            unittest.mock.patch(
                "trafilatura.extract",
                return_value=mock_result,
            ),
        ):
            _title, content = ArticleProcessor.extract_article_content(
                "https://example.com",
            )

        self.assertEqual(content, "Good content")
        self.assertNotIn("script", content)
        self.assertNotIn("table", content)


class TestTextToSpeech(Test.TestCase):
    """Test text-to-speech functionality."""

    def setUp(self) -> None:
        """Set up mocks."""
        # Mock OpenAI API key
        self.env_patcher = unittest.mock.patch.dict(
            os.environ,
            {"OPENAI_API_KEY": "test-key"},
        )
        self.env_patcher.start()

        # Mock OpenAI response
        self.mock_audio_response: unittest.mock.MagicMock = (
            unittest.mock.MagicMock()
        )
        self.mock_audio_response.content = b"fake-audio-data"

        # Mock AudioSegment to avoid ffmpeg issues in tests
        self.mock_audio_segment: unittest.mock.MagicMock = (
            unittest.mock.MagicMock()
        )
        self.mock_audio_segment.export.return_value = None
        self.audio_segment_patcher = unittest.mock.patch(
            "pydub.AudioSegment.from_mp3",
            return_value=self.mock_audio_segment,
        )
        self.audio_segment_patcher.start()

        # Mock the concatenation operations
        self.mock_audio_segment.__add__.return_value = self.mock_audio_segment

    def tearDown(self) -> None:
        """Clean up mocks."""
        self.env_patcher.stop()
        self.audio_segment_patcher.stop()

    def test_tts_generation(self) -> None:
        """Generate audio from text."""

        # Mock the export to write test audio data
        def mock_export(buffer: io.BytesIO, **_kwargs: typing.Any) -> None:
            buffer.write(b"test-audio-output")
            buffer.seek(0)

        self.mock_audio_segment.export.side_effect = mock_export

        # Mock OpenAI client
        mock_client = unittest.mock.MagicMock()
        mock_audio = unittest.mock.MagicMock()
        mock_speech = unittest.mock.MagicMock()
        mock_speech.create.return_value = self.mock_audio_response
        mock_audio.speech = mock_speech
        mock_client.audio = mock_audio

        with (
            unittest.mock.patch("openai.OpenAI", return_value=mock_client),
            unittest.mock.patch(
                "Biz.PodcastItLater.Worker.prepare_text_for_tts",
                return_value=["Test content"],
            ),
        ):
            processor = ArticleProcessor()
            audio_data = processor.text_to_speech(
                "Test content",
                "Test Title",
            )

        self.assertIsInstance(audio_data, bytes)
        self.assertEqual(audio_data, b"test-audio-output")

    def test_tts_chunking(self) -> None:
        """Handle long articles with chunking."""
        long_text = "Long content " * 1000
        chunks = ["Chunk 1", "Chunk 2", "Chunk 3"]

        def mock_export(buffer: io.BytesIO, **_kwargs: typing.Any) -> None:
            buffer.write(b"test-audio-output")
            buffer.seek(0)

        self.mock_audio_segment.export.side_effect = mock_export

        # Mock AudioSegment.silent
        # Mock OpenAI client
        mock_client = unittest.mock.MagicMock()
        mock_audio = unittest.mock.MagicMock()
        mock_speech = unittest.mock.MagicMock()
        mock_speech.create.return_value = self.mock_audio_response
        mock_audio.speech = mock_speech
        mock_client.audio = mock_audio

        with (
            unittest.mock.patch("openai.OpenAI", return_value=mock_client),
            unittest.mock.patch(
                "Biz.PodcastItLater.Worker.prepare_text_for_tts",
                return_value=chunks,
            ),
            unittest.mock.patch(
                "pydub.AudioSegment.silent",
                return_value=self.mock_audio_segment,
            ),
        ):
            processor = ArticleProcessor()
            audio_data = processor.text_to_speech(
                long_text,
                "Long Article",
            )

        # Should have called TTS for each chunk
        self.assertIsInstance(audio_data, bytes)
        self.assertEqual(audio_data, b"test-audio-output")

    def test_tts_empty_text(self) -> None:
        """Handle empty input."""
        with unittest.mock.patch(
            "Biz.PodcastItLater.Worker.prepare_text_for_tts",
            return_value=[],
        ):
            processor = ArticleProcessor()
            with pytest.raises(ValueError, match="No chunks generated") as cm:
                processor.text_to_speech("", "Empty")

        self.assertIn("No chunks generated", str(cm.value))

    def test_tts_special_characters(self) -> None:
        """Handle unicode and special chars."""
        special_text = 'Unicode: 你好世界 Émojis: 🎙️📰 Special: <>&"'

        def mock_export(buffer: io.BytesIO, **_kwargs: typing.Any) -> None:
            buffer.write(b"test-audio-output")
            buffer.seek(0)

        self.mock_audio_segment.export.side_effect = mock_export

        # Mock OpenAI client
        mock_client = unittest.mock.MagicMock()
        mock_audio = unittest.mock.MagicMock()
        mock_speech = unittest.mock.MagicMock()
        mock_speech.create.return_value = self.mock_audio_response
        mock_audio.speech = mock_speech
        mock_client.audio = mock_audio

        with (
            unittest.mock.patch("openai.OpenAI", return_value=mock_client),
            unittest.mock.patch(
                "Biz.PodcastItLater.Worker.prepare_text_for_tts",
                return_value=[special_text],
            ),
        ):
            processor = ArticleProcessor()
            audio_data = processor.text_to_speech(
                special_text,
                "Special",
            )

        self.assertIsInstance(audio_data, bytes)
        self.assertEqual(audio_data, b"test-audio-output")

    def test_llm_text_preparation(self) -> None:
        """Verify LLM editing."""
        # Test the actual text preparation functions
        chunks = split_text_into_chunks("Short text", max_chars=100)
        self.assertEqual(len(chunks), 1)
        self.assertEqual(chunks[0], "Short text")

        # Test long text splitting
        long_text = "Sentence one. " * 100
        chunks = split_text_into_chunks(long_text, max_chars=100)
        self.assertGreater(len(chunks), 1)
        for chunk in chunks:
            self.assertLessEqual(len(chunk), 100)

    @staticmethod
    def test_llm_failure_fallback() -> None:
        """Handle LLM API failures."""
        # Mock LLM failure
        with unittest.mock.patch("openai.OpenAI") as mock_openai:
            mock_client = mock_openai.return_value
            mock_client.chat.completions.create.side_effect = Exception(
                "API Error",
            )

            # Should fall back to raw text
            with pytest.raises(Exception, match="API Error"):
                edit_chunk_for_speech("Test chunk", "Title", is_first=True)

    def test_chunk_concatenation(self) -> None:
        """Verify audio joining."""
        # Mock multiple audio segments
        chunks = ["Chunk 1", "Chunk 2"]

        def mock_export(buffer: io.BytesIO, **_kwargs: typing.Any) -> None:
            buffer.write(b"test-audio-output")
            buffer.seek(0)

        self.mock_audio_segment.export.side_effect = mock_export

        # Mock OpenAI client
        mock_client = unittest.mock.MagicMock()
        mock_audio = unittest.mock.MagicMock()
        mock_speech = unittest.mock.MagicMock()
        mock_speech.create.return_value = self.mock_audio_response
        mock_audio.speech = mock_speech
        mock_client.audio = mock_audio

        with (
            unittest.mock.patch("openai.OpenAI", return_value=mock_client),
            unittest.mock.patch(
                "Biz.PodcastItLater.Worker.prepare_text_for_tts",
                return_value=chunks,
            ),
            unittest.mock.patch(
                "pydub.AudioSegment.silent",
                return_value=self.mock_audio_segment,
            ),
        ):
            processor = ArticleProcessor()
            audio_data = processor.text_to_speech("Test", "Title")

        # Should produce combined audio
        self.assertIsInstance(audio_data, bytes)
        self.assertEqual(audio_data, b"test-audio-output")


class TestJobProcessing(Test.TestCase):
    """Test job processing functionality."""

    def setUp(self) -> None:
        """Set up test environment."""
        self.test_db = "_/var/podcastitlater/test_podcast_worker.db"

        # Ensure test directory exists
        test_db_dir = pathlib.Path(self.test_db).parent
        test_db_dir.mkdir(parents=True, exist_ok=True)

        # Clean up any existing test database
        test_db_path = pathlib.Path(self.test_db)
        if test_db_path.exists():
            test_db_path.unlink()
        Core.Database.init_db(self.test_db)

        # Create test user and job
        self.user_id, _ = Core.Database.create_user(
            "test@example.com",
            self.test_db,
        )
        self.job_id = Core.Database.add_to_queue(
            "https://example.com/article",
            "test@example.com",
            self.user_id,
            self.test_db,
        )

        # Mock environment
        self.env_patcher = unittest.mock.patch.dict(
            os.environ,
            {
                "OPENAI_API_KEY": "test-key",
                "S3_ENDPOINT": "https://s3.example.com",
                "S3_BUCKET": "test-bucket",
                "S3_ACCESS_KEY": "test-access",
                "S3_SECRET_KEY": "test-secret",
            },
        )
        self.env_patcher.start()

    def tearDown(self) -> None:
        """Clean up."""
        self.env_patcher.stop()
        test_db_path = pathlib.Path(self.test_db)
        if test_db_path.exists():
            test_db_path.unlink()

    def test_process_job_success(self) -> None:
        """Complete pipeline execution."""
        processor = ArticleProcessor()
        job = Core.Database.get_job_by_id(self.job_id, self.test_db)
        if job is None:
            msg = "no job found for %s"
            raise Test.TestError(msg, self.job_id)

        # Mock all external calls
        with (
            unittest.mock.patch.object(
                ArticleProcessor,
                "extract_article_content",
                return_value=("Test Title", "Test content"),
            ),
            unittest.mock.patch.object(
                ArticleProcessor,
                "text_to_speech",
                return_value=b"audio-data",
            ),
            unittest.mock.patch.object(
                processor,
                "upload_to_s3",
                return_value="https://s3.example.com/audio.mp3",
            ),
            unittest.mock.patch(
                "Biz.PodcastItLater.Core.Database.update_job_status",
            ) as mock_update,
            unittest.mock.patch(
                "Biz.PodcastItLater.Core.Database.create_episode",
            ) as mock_create,
        ):
            mock_create.return_value = 1
            processor.process_job(job)

        # Verify job was marked complete
        mock_update.assert_called_with(self.job_id, "completed")
        mock_create.assert_called_once()

    def test_process_job_extraction_failure(self) -> None:
        """Handle bad URLs."""
        processor = ArticleProcessor()
        job = Core.Database.get_job_by_id(self.job_id, self.test_db)
        if job is None:
            msg = "no job found for %s"
            raise Test.TestError(msg, self.job_id)

        with (
            unittest.mock.patch.object(
                ArticleProcessor,
                "extract_article_content",
                side_effect=ValueError("Bad URL"),
            ),
            unittest.mock.patch(
                "Biz.PodcastItLater.Core.Database.update_job_status",
            ) as mock_update,
            pytest.raises(ValueError, match="Bad URL"),
        ):
            processor.process_job(job)

        # Job should be marked as error
        mock_update.assert_called_with(self.job_id, "error", "Bad URL")

    def test_process_job_tts_failure(self) -> None:
        """Handle TTS errors."""
        processor = ArticleProcessor()
        job = Core.Database.get_job_by_id(self.job_id, self.test_db)
        if job is None:
            msg = "no job found for %s"
            raise Test.TestError(msg, self.job_id)

        with (
            unittest.mock.patch.object(
                ArticleProcessor,
                "extract_article_content",
                return_value=("Title", "Content"),
            ),
            unittest.mock.patch.object(
                ArticleProcessor,
                "text_to_speech",
                side_effect=Exception("TTS Error"),
            ),
            unittest.mock.patch(
                "Biz.PodcastItLater.Core.Database.update_job_status",
            ) as mock_update,
            pytest.raises(Exception, match="TTS Error"),
        ):
            processor.process_job(job)

        mock_update.assert_called_with(self.job_id, "error", "TTS Error")

    def test_process_job_s3_failure(self) -> None:
        """Handle upload errors."""
        processor = ArticleProcessor()
        job = Core.Database.get_job_by_id(self.job_id, self.test_db)
        if job is None:
            msg = "no job found for %s"
            raise Test.TestError(msg, self.job_id)

        with (
            unittest.mock.patch.object(
                ArticleProcessor,
                "extract_article_content",
                return_value=("Title", "Content"),
            ),
            unittest.mock.patch.object(
                ArticleProcessor,
                "text_to_speech",
                return_value=b"audio",
            ),
            unittest.mock.patch.object(
                processor,
                "upload_to_s3",
                side_effect=ClientError({}, "PutObject"),
            ),
            unittest.mock.patch(
                "Biz.PodcastItLater.Core.Database.update_job_status",
            ),
            pytest.raises(ClientError),
        ):
            processor.process_job(job)

    def test_job_retry_logic(self) -> None:
        """Verify exponential backoff."""
        # Set job to error with retry count
        Core.Database.update_job_status(
            self.job_id,
            "error",
            "First failure",
            self.test_db,
        )
        Core.Database.update_job_status(
            self.job_id,
            "error",
            "Second failure",
            self.test_db,
        )

        job = Core.Database.get_job_by_id(self.job_id, self.test_db)
        if job is None:
            msg = "no job found for %s"
            raise Test.TestError(msg, self.job_id)

        self.assertEqual(job["retry_count"], 2)

        # Should be retryable
        retryable = Core.Database.get_retryable_jobs(
            max_retries=3,
            db_path=self.test_db,
        )
        self.assertEqual(len(retryable), 1)

    def test_max_retries(self) -> None:
        """Stop after max attempts."""
        # Exceed retry limit
        for i in range(4):
            Core.Database.update_job_status(
                self.job_id,
                "error",
                f"Failure {i}",
                self.test_db,
            )

        # Should not be retryable
        retryable = Core.Database.get_retryable_jobs(
            max_retries=3,
            db_path=self.test_db,
        )
        self.assertEqual(len(retryable), 0)

    def test_concurrent_processing(self) -> None:
        """Handle multiple jobs."""
        # Create multiple jobs
        job2 = Core.Database.add_to_queue(
            "https://example.com/2",
            "test@example.com",
            self.user_id,
            self.test_db,
        )
        job3 = Core.Database.add_to_queue(
            "https://example.com/3",
            "test@example.com",
            self.user_id,
            self.test_db,
        )

        # Get pending jobs
        jobs = Core.Database.get_pending_jobs(limit=5, db_path=self.test_db)

        self.assertEqual(len(jobs), 3)
        self.assertEqual({j["id"] for j in jobs}, {self.job_id, job2, job3})


def test() -> None:
    """Run the tests."""
    Test.run(
        App.Area.Test,
        [
            TestArticleExtraction,
            TestTextToSpeech,
            TestJobProcessing,
        ],
    )


def main() -> None:
    """Entry point for the worker."""
    if "test" in sys.argv:
        test()
    else:
        move()