"""File persistence, extraction, and parser injection for resume imports."""

from __future__ import annotations

import hashlib
from collections import OrderedDict
from pathlib import Path
from typing import Protocol
from uuid import uuid4

from .document_extractors import extract_text, normalize_upload_name, validate_upload
from .import_parser_fast import slim_parser
from .resume_import_models import ParsedResumeDraft
from .resume_import_rules import parse_resume_text

MAX_IMPORT_BYTES = 10 * 1024 * 1024
_PARSE_CACHE_SIZE = 64


class ResumeImportParser(Protocol):
    def parse(self, *, text: str, source_name: str) -> ParsedResumeDraft: ...


class RuleBasedResumeImportParser:
    """Local structured fallback when the model parser is unavailable."""

    def parse(self, *, text: str, source_name: str) -> ParsedResumeDraft:
        return parse_resume_text(text=text, source_name=source_name)

class ResumeImportService:
    def __init__(self, *, storage_root: str | Path, parser: ResumeImportParser | None = None) -> None:
        self.storage_root = Path(storage_root)
        # OpenAI parsers are wrapped in the slim-schema variant (no model-emitted
        # evidence quotes; roughly half the output tokens and latency).
        self.parser = slim_parser(parser) if parser is not None else RuleBasedResumeImportParser()
        # Re-uploading an unchanged file must not re-run the LLM parse; keyed by
        # content hash so the cache works across sessions. Process-local by design.
        self._parse_cache: OrderedDict[str, ParsedResumeDraft] = OrderedDict()

    def prepare(self, *, file_name: str, declared_mime: str | None, content: bytes) -> dict:
        if len(content) > MAX_IMPORT_BYTES:
            raise ValueError("import_file_too_large")
        safe_name, extension = normalize_upload_name(file_name)
        mime_type = validate_upload(extension=extension, declared_mime=declared_mime, content=content)
        sha256 = hashlib.sha256(content).hexdigest()
        draft = self._parse_cache.get(sha256)
        if draft is None:
            text = extract_text(extension=extension, content=content)
            draft = self.parser.parse(text=text, source_name=safe_name)
            self._validate_document(draft.document)
            self._parse_cache[sha256] = draft
            self._parse_cache.move_to_end(sha256)
            while len(self._parse_cache) > _PARSE_CACHE_SIZE:
                self._parse_cache.popitem(last=False)
        else:
            self._parse_cache.move_to_end(sha256)
            draft = draft.model_copy(deep=True)
        object_key = f"{sha256[:2]}/{uuid4().hex}{extension}"
        target = self.storage_root / object_key
        target.parent.mkdir(parents=True, exist_ok=True)
        target.write_bytes(content)
        return {
            "file_name": safe_name,
            "mime_type": mime_type,
            "size_bytes": len(content),
            "sha256": sha256,
            "object_key": object_key,
            "document": draft.document,
            "field_reviews": [item.model_dump(mode="json") for item in draft.field_reviews],
        }

    def remove(self, object_key: str | None) -> None:
        if not object_key:
            return
        path = (self.storage_root / object_key).resolve()
        root = self.storage_root.resolve()
        if root not in path.parents:
            return
        path.unlink(missing_ok=True)

    @staticmethod
    def _validate_document(document: dict) -> None:
        required = {"schema_version", "basics", "target", "sections", "skill_groups"}
        if document.get("schema_version") != 3 or not required.issubset(document):
            raise ValueError("invalid_import_document")
        if not isinstance(document["sections"], list) or not isinstance(document["skill_groups"], list):
            raise ValueError("invalid_import_document")