"""
Phase 3 — Track C: dataset loading, indexing, VQA metric and agent tools for
OCR-focused question answering over TextVQA + a student Portuguese dataset.
"""
import json
import re
from pathlib import Path
from typing import Optional

import pandas as pd
from PIL import Image
from opensearchpy import helpers


# ---------------------------------------------------------------------------
# Datasets
# ---------------------------------------------------------------------------

def load_textvqa_sample(n=1000, split="validation", seed=42):
    """
    Load the TextVQA split and return a randomly sampled subset (one row per question).
    Returns a HuggingFace Dataset with embedded PIL images.
    """
    from datasets import load_dataset
    ds = load_dataset("lmms-lab/textvqa", split=split)
    if n and n < len(ds):
        ds = ds.shuffle(seed=seed).select(range(n))
    return ds


def textvqa_records(ds, dataset_name="textvqa", language="en"):
    """
    Yield normalised records from a TextVQA dataset:
    {image_id, question_id, question, answers, language, dataset, ocr_tokens_ref}.
    'ocr_tokens_ref' is the dataset's own Rosetta-OCR tokens, used only as a
    reference for CER/WER — not as our extracted OCR.
    """
    for item in ds:
        yield {
            "image_id": str(item["image_id"]),
            "question_id": str(item.get("question_id", "")),
            "question": item["question"],
            "answers": list(item.get("answers", [])),
            "language": language,
            "dataset": dataset_name,
            "image_url": item.get("flickr_300k_url") or item.get("flickr_original_url") or "",
            "ocr_tokens_ref": " ".join(item.get("ocr_tokens", []) or []),
        }


def build_textvqa_image_lookup(ds) -> dict:
    """Build {image_id: PIL.Image} from a TextVQA dataset."""
    lookup = {}
    for item in ds:
        lookup[str(item["image_id"])] = item["image"].convert("RGB")
    return lookup


def load_pt_dataset(root="data/pt_textvqa"):
    """
    Load the student-created Portuguese dataset.
    Expects root/annotations.json (list of {image_id, source, question, answer, language})
    and root/images/<image_id>.<ext>.
    Returns (records_list, {image_id: PIL.Image}).
    """
    root = Path(root)
    ann_path = root / "annotations.json"
    if not ann_path.exists():
        raise FileNotFoundError(f"No annotations at {ann_path} — populate the PT dataset first.")
    annotations = json.loads(ann_path.read_text(encoding="utf-8"))

    records, lookup = [], {}
    for a in annotations:
        iid = str(a["image_id"])
        matches = list((root / "images").glob(f"{iid}.*"))
        if not matches:
            print(f"  warning: no image file for {iid}")
            continue
        lookup[iid] = Image.open(matches[0]).convert("RGB")
        records.append({
            "image_id": iid,
            "question_id": iid,
            "question": a["question"],
            "answers": [a["answer"]] if isinstance(a.get("answer"), str) else list(a.get("answer", [])),
            "language": a.get("language", "pt"),
            "dataset": "pt",
            "ocr_tokens_ref": a.get("ocr_text", ""),
        })
    return records, lookup


# ---------------------------------------------------------------------------
# Indexing
# ---------------------------------------------------------------------------

def create_textvqa_index(client, index_name, image_vec_dim=768,
                         k1=1.2, b=0.75, force_recreate=False, num_shards=1):
    """Create the OCR-QA index: BM25 over ocr_text + SigLIP image vectors + metadata.

    num_shards defaults to 1: this index holds only ~1k docs, and the shared
    classroom cluster has a global shard cap.
    """
    knn_method = {
        "name": "hnsw",
        "space_type": "innerproduct",
        "engine": "faiss",
        "parameters": {"ef_construction": 256, "m": 48},
    }
    index_body = {
        "settings": {
            "index": {
                "number_of_replicas": 0,
                "number_of_shards": num_shards,
                "refresh_interval": "-1",
                "knn": "true",
                "similarity": {"custom_bm25": {"type": "BM25", "k1": k1, "b": b}},
            }
        },
        "mappings": {
            "dynamic": "strict",
            "properties": {
                "image_id": {"type": "keyword"},
                "question_id": {"type": "keyword"},
                "dataset": {"type": "keyword"},
                "language": {"type": "keyword"},
                "question": {"type": "text", "analyzer": "standard"},
                "answers": {"type": "keyword"},
                "image_url": {"type": "keyword"},
                "ocr_text": {"type": "text", "analyzer": "standard", "similarity": "custom_bm25"},
                "ocr_tokens": {"type": "keyword"},
                "image_vec": {"type": "knn_vector", "dimension": image_vec_dim, "method": knn_method},
            },
        },
    }
    if client.indices.exists(index=index_name):
        if not force_recreate:
            print(f"Index '{index_name}' already exists. Skipping (force_recreate=True to replace).")
            return
        client.indices.delete(index=index_name)
        print(f"Deleted existing index '{index_name}'.")
    client.indices.create(index=index_name, body=index_body)
    print(f"Index '{index_name}' created.")


def generate_textvqa_docs(records, index_name, ocr_df=None, image_vec_df=None):
    """
    Yield index-ready docs. records: list of normalised dicts (see *_records helpers).
    ocr_df: DataFrame [image_id, ocr_text, ocr_tokens]. image_vec_df: [image_id, embedding].
    """
    ocr_lookup = {}
    if ocr_df is not None:
        ocr_lookup = {
            r.image_id: (r.ocr_text, list(r.ocr_tokens))
            for r in ocr_df.itertuples()
        }
    vec_lookup = {}
    if image_vec_df is not None:
        vec_lookup = dict(zip(image_vec_df["image_id"], image_vec_df["embedding"]))

    for rec in records:
        iid = rec["image_id"]
        doc = {
            "_index": index_name,
            "image_id": iid,
            "question_id": rec["question_id"],
            "dataset": rec["dataset"],
            "language": rec["language"],
            "question": rec["question"],
            "answers": rec["answers"],
            "image_url": rec.get("image_url", ""),
        }
        if iid in ocr_lookup:
            text, tokens = ocr_lookup[iid]
            doc["ocr_text"] = text
            doc["ocr_tokens"] = tokens
        vec = vec_lookup.get(iid)
        if vec is not None:
            doc["image_vec"] = vec if isinstance(vec, list) else vec.tolist()
        yield doc


def bulk_index(client, docs, chunk_size=500):
    ok_count, failed = 0, 0
    for ok, _ in helpers.streaming_bulk(client, docs, chunk_size=chunk_size, raise_on_error=False):
        ok_count += ok
        failed += (not ok)
    print(f"Indexed {ok_count} docs, {failed} failures.")


def get_ocr_text(client, index_name, image_id) -> str:
    """Fetch the cached OCR text for an image_id from the index."""
    resp = client.search(
        index=index_name,
        body={"size": 1, "query": {"term": {"image_id": image_id}}},
    )
    hits = resp["hits"]["hits"]
    return hits[0]["_source"].get("ocr_text", "") if hits else ""


def retrieve_by_ocr_text(client, query, index_name, top_k=5, dataset=None):
    """BM25 retrieval over the ocr_text field (for Recall@K by textual content)."""
    must = [{"match": {"ocr_text": query}}]
    body = {"size": top_k, "query": {"bool": {"must": must}}}
    if dataset:
        body["query"]["bool"]["filter"] = [{"term": {"dataset": dataset}}]
    resp = client.search(index=index_name, body=body)
    return [h["_source"] for h in resp["hits"]["hits"]]


# ---------------------------------------------------------------------------
# QA tools (OCR-based vs vision-based)
# ---------------------------------------------------------------------------

OCR_SYSTEM_PROMPT = (
    "You answer questions using ONLY text that an OCR engine extracted from an image. "
    "You cannot see the image itself — work solely from the provided text. Give the "
    "shortest exact answer (a word or short phrase), with no explanation. If the text "
    "does not clearly contain the answer, give your best guess from it. Never reply that "
    "you cannot see an image or that no image was provided."
)
VISION_SYSTEM_PROMPT = (
    "You are a visual question answering assistant. Read any text visible in the image "
    "and answer the question. Reply with the shortest exact answer (a word or short "
    "phrase), with no explanation."
)


def answer_with_ocr(llm_client, ocr_text, question, model=None) -> str:
    """Answer using ONLY the pre-extracted OCR text as context (no image sent)."""
    messages = [
        {"role": "system", "content": OCR_SYSTEM_PROMPT},
        {"role": "user", "content": f"OCR text from the image:\n\"{ocr_text}\"\n\nQuestion: {question}"},
    ]
    resp = llm_client.chat.completions.create(model=model, messages=messages, max_tokens=64)
    return resp.choices[0].message.content.strip()


def answer_with_vision(llm_client, image: Image.Image, question, model=None) -> str:
    """Answer by sending the image directly to the LVLM (it reads the text itself)."""
    from vlps.data import image_to_base64
    image_url = image_to_base64(image)
    messages = [
        {"role": "system", "content": VISION_SYSTEM_PROMPT},
        {"role": "user", "content": [
            {"type": "image_url", "image_url": {"url": image_url}},
            {"type": "text", "text": question},
        ]},
    ]
    resp = llm_client.chat.completions.create(model=model, messages=messages, max_tokens=64)
    return resp.choices[0].message.content.strip()


# ---------------------------------------------------------------------------
# VQA accuracy (standard TextVQA / VQAv2 metric)
# ---------------------------------------------------------------------------

_PUNCT = re.compile(r"[^\w\s]")
_ARTICLES = {"a", "an", "the"}


def normalise_answer(ans: str) -> str:
    ans = str(ans).lower().strip()
    ans = _PUNCT.sub("", ans)
    ans = " ".join(w for w in ans.split() if w not in _ARTICLES)
    return ans


def vqa_accuracy(prediction: str, ground_truths: list) -> float:
    """
    Standard VQA accuracy: min(#humans-that-said-it / 3, 1), averaged over the
    10-choose-9 subsets — here approximated by the common closed form.
    """
    pred = normalise_answer(prediction)
    gts = [normalise_answer(g) for g in ground_truths]
    if not gts:
        return 0.0
    matches = sum(1 for g in gts if g == pred)
    return min(matches / 3.0, 1.0)