"""Escalating not-found resolution (docs/prd.md section 7, issue #10).

"A field should only be marked not found after an escalating search, an LLM
pass, an embedding pass, and the two combined, come back empty, not after a
single failed prompt. A missing field that was actually searched for and
confirmed absent is honest. A missing field from one lazy pass is a silent
gap wearing an honest label."

This module owns the *not-found* path only. Fields the first LLM pass already
extracted go to the consensus gate (consensus.py, issue #9), not here. For a
field the LLM pass left empty, the escalation is:

  1. LLM pass -- already run by llm_extraction.extract_case_with_llm; its
     empty result is the reason we're escalating at all.
  2. Embedding pass -- ColPali retrieval for the field's query. Produces no
     value (sub-page localization is not built yet: #49), only "which page
     looks most relevant, and how distinctively" (rank_pages_with_margin).
     That page is a hint for the combined pass, not an answer on its own.
  3. Combined pass -- a targeted, single-field LLM re-extraction given the
     report text *and* the page image the embedding pass surfaced. This is
     the literal "two combined": the text model reading the exact scan
     region the visual model flagged, a genuinely different query from the
     bulk first pass (which reads text only, all fields at once).

Only when all three come back empty is the field flagged not-found -- via
schema.ChecklistField.flag_not_found, which itself refuses a search_log with
fewer than three recorded passes, so a lazy single-pass miss cannot wear the
"searched and not found" label.

The orchestration core (resolve_field) takes the embedding and combined
passes as injected callables so it's testable without live models; the real
wirings to colpali_retrieval and llm_extraction are thin builders below.
"""

from __future__ import annotations

import base64
from dataclasses import dataclass
from enum import Enum
from pathlib import Path
from typing import Any, Callable, Optional

from endopath import colpali_retrieval, consensus, llm_extraction, precomputed_retrieval
from endopath.schema import Case, ChecklistField, EvidenceSpan, FieldStatus


class ResolutionOutcome(str, Enum):
    RESOLVED = "resolved"  # a value exists (LLM pass) or was recovered (combined pass)
    NOT_FOUND = "not_found"  # all three passes empty -> flagged, honestly earned
    NOT_APPLICABLE = "not_applicable"  # short-circuited, e.g. molecular pre-2023


@dataclass
class VisualHit:
    """What the embedding pass found: the best page and how distinctive it
    was. margin is rank_pages_with_margin's best-minus-second-best gap, which
    (unlike raw score) is comparable across field queries -- see #18/#9."""

    page_number: int
    margin: float
    top_score: float


@dataclass
class ExtractedValue:
    """What the combined pass recovered for a single field."""

    value: Any
    confidence: Optional[float] = None
    evidence: Optional[EvidenceSpan] = None


def resolve_field(
    field_name: str,
    field: ChecklistField,
    *,
    embedding_probe: Callable[[], Optional[VisualHit]],
    combined_extract: Callable[[Optional[VisualHit]], Optional[ExtractedValue]],
    margin_threshold: float = consensus.DEFAULT_VISUAL_MARGIN_THRESHOLD,
) -> ResolutionOutcome:
    """Run the escalating search for one field and mutate it in place.

    embedding_probe() returns the field's VisualHit, or None if the pass
    couldn't run (no page images / ColPali unavailable). combined_extract(hit)
    returns the recovered value, or None if the combined pass came back empty.
    Both are injected so the orchestration is testable without live models.
    """
    # A field the checklist itself says doesn't apply (molecular
    # classification on a pre-2023 report, FIGO grade on a non-endometrioid
    # histotype) already carries its reason from schema.apply_applicability_
    # rules. Running the full LLM/embedding/combined search on it would burn
    # a model call to "find" something the protocol says can't be there --
    # short-circuit instead, per #10's molecular-classification requirement.
    if not field.applicable:
        return ResolutionOutcome.NOT_APPLICABLE

    # The first LLM pass already found a value: not a not-found case. The
    # consensus gate (#9) owns found fields; leave this one untouched.
    if field.value is not None:
        return ResolutionOutcome.RESOLVED

    search_log = ["llm pass: no value extracted"]

    hit = embedding_probe()
    if hit is None:
        search_log.append("embedding pass: skipped (no page images or ColPali unavailable)")
    elif hit.margin >= margin_threshold:
        search_log.append(
            f"embedding pass: distinctive match on page {hit.page_number} "
            f"(margin {hit.margin:.2f}) -- field may be present, first LLM pass missed it"
        )
    else:
        search_log.append(
            f"embedding pass: no distinctive match "
            f"(best page {hit.page_number}, margin {hit.margin:.2f})"
        )

    extracted = combined_extract(hit)
    if extracted is not None and extracted.value is not None:
        search_log.append(f"combined pass: recovered value {extracted.value!r}")
        field.value = extracted.value
        field.confidence = extracted.confidence
        field.evidence = extracted.evidence
        field.status = FieldStatus.NEEDS_REVIEW
        field.search_log.extend(search_log)
        return ResolutionOutcome.RESOLVED

    search_log.append("combined pass: no value recovered")
    # Exactly the three passes above -- satisfies flag_not_found's guard that
    # a not-found label be earned by a recorded LLM + embedding + combined
    # search, not a single failed prompt.
    field.flag_not_found(search_log)
    return ResolutionOutcome.NOT_FOUND


# --- Real wirings (thin glue to the model modules; the logic above is the
# tested part). ------------------------------------------------------------

_COMBINED_PROMPT = """\
You are re-checking a single CAP/ICCR endometrial carcinoma checklist field \
that a first extraction pass left empty. The field is: {field_name}.

{image_note}Read the report text below (and the page image, if provided) and \
call record_field. Only set a value if it is genuinely supported; leave value \
and evidence_quote null rather than guessing. evidence_quote must be an exact, \
verbatim substring of the report text -- do not paraphrase.

REPORT TEXT:
{report_text}
"""


def _embed_pages_if_available(image_paths: list[Path]):
    """Embed a case's pages once, so a per-field escalation loop doesn't
    re-embed the same images for every field. Returns None when the embedding
    pass can't run at all (no images, or ColPali not installed)."""
    if not image_paths or not colpali_retrieval.is_available():
        return None
    return colpali_retrieval.embed_pages(image_paths)


def _build_embedding_probe(
    field_name: str, page_embeddings, margin_threshold: float
) -> Callable[[], Optional[VisualHit]]:
    def probe() -> Optional[VisualHit]:
        if page_embeddings is None or field_name not in colpali_retrieval.FIELD_QUERIES:
            return None
        query = colpali_retrieval.FIELD_QUERIES[field_name]
        page_number, top_score, margin = colpali_retrieval.rank_pages_with_margin(
            query, page_embeddings
        )
        return VisualHit(page_number=page_number, margin=margin, top_score=top_score)

    return probe


def _build_precomputed_embedding_probe(
    field_name: str,
    page_embeddings: Optional[list],
    query_embeddings: dict,
    margin_threshold: float,
) -> Callable[[], Optional[VisualHit]]:
    """Like _build_embedding_probe, but scores precomputed page vectors against
    precomputed field-query vectors (precomputed_retrieval, issue #21) with
    numpy MaxSim -- no torch, no live ColPali. `page_embeddings` is the case's
    list of per-page arrays; `query_embeddings` maps field name -> query vector.
    Both come from data/embeddings (issue #19's precompute). Returns None when
    the case has no precomputed pages, or this field has no precomputed query.
    """

    def probe() -> Optional[VisualHit]:
        if page_embeddings is None or field_name not in query_embeddings:
            return None
        page_number, top_score, margin = precomputed_retrieval.rank_pages_with_margin(
            query_embeddings[field_name], page_embeddings
        )
        return VisualHit(page_number=page_number, margin=margin, top_score=top_score)

    return probe


def _build_combined_extract(
    field_name: str,
    report_text: str,
    image_paths: list[Path],
    client=None,
) -> Callable[[Optional[VisualHit]], Optional[ExtractedValue]]:
    def extract(hit: Optional[VisualHit]) -> Optional[ExtractedValue]:
        if field_name not in llm_extraction._FIELD_VALUE_SCHEMAS:
            return None
        page_image: Optional[Path] = None
        page_number: Optional[int] = None
        if hit is not None and image_paths:
            idx = hit.page_number - 1
            if 0 <= idx < len(image_paths):
                page_image = image_paths[idx]
                page_number = hit.page_number
        return _combined_llm_extract(
            field_name, report_text, page_image, page_number, client=client
        )

    return extract


def _combined_llm_extract(
    field_name: str,
    report_text: str,
    page_image: Optional[Path],
    page_number: Optional[int],
    *,
    client=None,
    model: str = llm_extraction.MODEL,
) -> Optional[ExtractedValue]:
    """Targeted, single-field, optionally-multimodal re-extraction: the report
    text plus the one page image the embedding pass surfaced. Reuses
    llm_extraction's per-field value schemas and enum coercion so the combined
    pass can't accept a value the first pass' schema would have rejected."""
    client = client or llm_extraction.get_client()

    value_schema = llm_extraction._FIELD_VALUE_SCHEMAS[field_name]
    tool = {
        "name": "record_field",
        "description": f"Record the extracted value for the endometrial checklist field '{field_name}'.",
        "input_schema": llm_extraction._field_schema(value_schema),
    }

    content: list[dict] = []
    image_note = ""
    if page_image is not None:
        img_bytes = Path(page_image).read_bytes()
        content.append(
            {
                "type": "image",
                "source": {
                    "type": "base64",
                    "media_type": "image/png",
                    "data": base64.standard_b64encode(img_bytes).decode("ascii"),
                },
            }
        )
        image_note = (
            "A visual-retrieval model flagged the attached page image as the "
            "most likely location for this field; read it carefully.\n\n"
        )
    content.append(
        {
            "type": "text",
            "text": _COMBINED_PROMPT.format(
                field_name=field_name, image_note=image_note, report_text=report_text
            ),
        }
    )

    response = client.messages.create(
        model=model,
        max_tokens=1024,
        tools=[tool],
        tool_choice={"type": "tool", "name": "record_field"},
        messages=[{"role": "user", "content": content}],
    )
    tool_call = next(block for block in response.content if block.type == "tool_use")
    raw = tool_call.input

    value = raw.get("value")
    if value is None:
        return None
    if field_name in llm_extraction._ENUM_FIELDS:
        try:
            value = llm_extraction._ENUM_FIELDS[field_name](value)
        except ValueError:
            return None

    confidence = raw.get("confidence")
    quote = raw.get("evidence_quote")
    evidence = None
    if quote:
        verified = quote in report_text
        char_start = report_text.find(quote) if verified else None
        evidence = EvidenceSpan(
            quote=quote,
            char_start=char_start,
            char_end=(char_start + len(quote)) if char_start is not None else None,
            page_number=page_number,
            source="combined" if verified else "combined_unverified_quote",
        )
        if not verified and confidence is not None:
            confidence = min(confidence, 0.2)

    return ExtractedValue(value=value, confidence=confidence, evidence=evidence)


def resolve_case_not_found(
    case: Case,
    report_text: str,
    image_paths: Optional[list[Path]] = None,
    *,
    client=None,
    margin_threshold: float = consensus.DEFAULT_VISUAL_MARGIN_THRESHOLD,
) -> dict[str, ResolutionOutcome]:
    """Run the escalating not-found search across every field of a case whose
    first LLM pass came back empty. Embeds the case's pages once, then hands
    each field its own embedding + combined pass. Mutates case.checklist in
    place; returns the per-field outcome.

    Assumes case.checklist has already had the LLM extraction pass and
    schema.apply_applicability_rules run (llm_extraction.extract_case_with_llm
    does both) -- resolve_field short-circuits any field already marked
    not-applicable rather than searching for it.
    """
    image_paths = image_paths or []
    page_embeddings = _embed_pages_if_available(image_paths)

    outcomes: dict[str, ResolutionOutcome] = {}
    for name, field in case.checklist.all_fields().items():
        probe = _build_embedding_probe(name, page_embeddings, margin_threshold)
        extract = _build_combined_extract(name, report_text, image_paths, client=client)
        outcomes[name] = resolve_field(
            name,
            field,
            embedding_probe=probe,
            combined_extract=extract,
            margin_threshold=margin_threshold,
        )
    return outcomes