"""Document parsing service using markitdown and LLM."""

import logging
import re
import tempfile
from pathlib import Path
from typing import Any

from markitdown import MarkItDown

from app.llm import complete_json, get_llm_config, get_model_name, get_safe_max_tokens
from app.prompts import PARSE_RESUME_PROMPT
from app.prompts.templates import RESUME_SCHEMA_EXAMPLE
from app.schemas import ResumeData

logger = logging.getLogger(__name__)

# Matches date ranges like "Jan 2020 - Dec 2023", "May 2021 - Present",
# "January 2020 - Current", and single dates like "Jun 2023".
_MD_DATE_RE = re.compile(
    r"(?:(?:Jan(?:uary)?|Feb(?:ruary)?|Mar(?:ch)?|Apr(?:il)?|May|Jun(?:e)?"
    r"|Jul(?:y)?|Aug(?:ust)?|Sep(?:tember)?|Oct(?:ober)?|Nov(?:ember)?"
    r"|Dec(?:ember)?)"
    r"\.?\s+\d{4})"
    r"(?:\s*[-–—]\s*"
    r"(?:(?:Jan(?:uary)?|Feb(?:ruary)?|Mar(?:ch)?|Apr(?:il)?|May|Jun(?:e)?"
    r"|Jul(?:y)?|Aug(?:ust)?|Sep(?:tember)?|Oct(?:ober)?|Nov(?:ember)?"
    r"|Dec(?:ember)?)"
    r"\.?\s+\d{4}"
    r"|Present|Current|Now|Ongoing))?",
    re.IGNORECASE,
)


def _extract_markdown_dates(markdown: str) -> list[str]:
    """Extract all month-inclusive date ranges from markdown text."""
    return _MD_DATE_RE.findall(markdown)


def restore_dates_from_markdown(
    parsed_data: dict[str, Any],
    markdown: str,
) -> dict[str, Any]:
    """Patch year-only dates in parsed data with month-inclusive dates from markdown.

    The LLM sometimes drops months during parsing (e.g. "Jun 2020 - Aug 2021"
    becomes "2020 - 2021"). This function extracts all month-inclusive dates
    from the raw markdown and replaces year-only entries where a match exists.
    """
    md_dates = _extract_markdown_dates(markdown)
    if not md_dates:
        return parsed_data

    # Build a lookup: "2020 - 2021" → "Jun 2020 - Aug 2021"
    year_to_full: dict[str, str] = {}
    year_only_re = re.compile(r"\d{4}")
    for md_date in md_dates:
        years_in_date = year_only_re.findall(md_date)
        if years_in_date:
            # Create year-only key like "2020 - 2021" or "2023"
            year_key = " - ".join(years_in_date)
            # Keep the first (most specific) match
            if year_key not in year_to_full:
                # Normalize separators
                normalized = re.sub(r"\s*[-–—]\s*", " - ", md_date.strip())
                year_to_full[year_key] = normalized

    if not year_to_full:
        return parsed_data

    patched = 0
    for section_key in ("workExperience", "education", "personalProjects"):
        for entry in parsed_data.get(section_key, []):
            if not isinstance(entry, dict):
                continue
            years = entry.get("years", "")
            if not isinstance(years, str) or not years:
                continue
            # Skip if already has months
            if re.search(
                r"(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)",
                years,
                re.IGNORECASE,
            ):
                continue
            # Try to find a matching month-inclusive date
            if years in year_to_full:
                entry["years"] = year_to_full[years]
                patched += 1

    # Custom sections
    custom = parsed_data.get("customSections", {})
    if isinstance(custom, dict):
        for section in custom.values():
            if not isinstance(section, dict) or section.get("sectionType") != "itemList":
                continue
            for item in section.get("items", []):
                if not isinstance(item, dict):
                    continue
                years = item.get("years", "")
                if not isinstance(years, str) or not years:
                    continue
                if re.search(
                    r"(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)",
                    years,
                    re.IGNORECASE,
                ):
                    continue
                if years in year_to_full:
                    item["years"] = year_to_full[years]
                    patched += 1

    if patched:
        logger.info("Restored months in %d date fields from raw markdown", patched)

    return parsed_data


async def parse_document(content: bytes, filename: str) -> str:
    """Convert PDF/DOCX to Markdown using markitdown.

    Args:
        content: Raw file bytes
        filename: Original filename for extension detection

    Returns:
        Markdown text content
    """
    suffix = Path(filename).suffix.lower()

    # Write to temp file for markitdown
    with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp:
        tmp.write(content)
        tmp_path = Path(tmp.name)

    try:
        md = MarkItDown()
        result = md.convert(str(tmp_path))
        return result.text_content
    finally:
        tmp_path.unlink(missing_ok=True)


async def parse_resume_to_json(markdown_text: str) -> dict[str, Any]:
    """Parse resume markdown to structured JSON using LLM.

    After LLM parsing, patches any year-only dates with month-inclusive
    dates extracted from the raw markdown. This ensures months are never
    lost regardless of LLM behavior.

    Args:
        markdown_text: Resume content in markdown format

    Returns:
        Structured resume data matching ResumeData schema
    """
    prompt = PARSE_RESUME_PROMPT.format(
        schema=RESUME_SCHEMA_EXAMPLE,
        resume_text=markdown_text,
    )

    config = get_llm_config()
    model_name = get_model_name(config)
    result = await complete_json(
        prompt=prompt,
        system_prompt="You are a JSON extraction engine. Output only valid JSON, no explanations.",
        max_tokens=get_safe_max_tokens(model_name),
        retries=3,
    )

    # Patch dates: restore months the LLM may have dropped
    result = restore_dates_from_markdown(result, markdown_text)

    # Validate against schema
    validated = ResumeData.model_validate(result)
    return validated.model_dump()