import os
import json
import time
from concurrent.futures import ThreadPoolExecutor

import requests
import numpy as np
import torch

try:
    import spaces
except Exception:
    class _SpacesShim:
        @staticmethod
        def GPU(fn=None, **kwargs):
            if fn is not None:
                return fn
            def decorator(f):
                return f
            return decorator
    spaces = _SpacesShim()

from src.config import (
    ENTAILMENT_MODEL_NAME,
    ENTAILMENT_BATCH_SIZE,
    ENTAILMENT_USE_API,
    ENTAILMENT_API_MODEL,
    ENTAILMENT_API_WORKERS,
)

_deberta_tokenizer = None
_deberta_model = None


# ---------------------------------------------------------------------------
# Label mapping (model-agnostic): map a label STRING to our convention
#   0 = contradiction, 1 = neutral, 2 = entailment
# This works regardless of a model's internal label ordering.
# ---------------------------------------------------------------------------

def _label_to_id(label):
    l = str(label).lower()
    if "contradict" in l:
        return 0
    if "neutral" in l:
        return 1
    if "entail" in l:
        return 2
    # Fallback for generic "LABEL_0/1/2" (standard MNLI: 0=contra,1=neutral,2=entail)
    if l.endswith("0"):
        return 0
    if l.endswith("1"):
        return 1
    if l.endswith("2"):
        return 2
    return 1  # safest default: neutral


def _parse_classification(data):
    """Extract the argmax label id from a text-classification API response.
    Accepts [{'label','score'}, ...] or nested [[...]]."""
    if isinstance(data, (bytes, bytearray, str)):
        data = json.loads(data)
    if isinstance(data, list) and data and isinstance(data[0], list):
        data = data[0]
    if not isinstance(data, list) or not data:
        return 1
    best = max(data, key=lambda d: d.get("score", 0.0))
    return _label_to_id(best.get("label", ""))


_API_URL = "https://api-inference.huggingface.co/models/" + ENTAILMENT_API_MODEL
_warned_once = {"done": False}


def _api_classify_pair(token, premise, hypothesis):
    """One serverless text-classification call for a (premise, hypothesis) pair.
    Uses the raw Inference API with the sentence-pair payload."""
    headers = {"Authorization": f"Bearer {token}"} if token else {}
    payload = {
        "inputs": {"text": premise, "text_pair": hypothesis},
        "options": {"wait_for_model": True},
    }
    for attempt in range(5):
        try:
            r = requests.post(_API_URL, headers=headers, json=payload, timeout=60)
            if r.status_code in (429, 503):
                time.sleep(min(2 ** attempt * 2, 20))
                continue
            if r.status_code >= 400:
                if not _warned_once["done"]:
                    print(f"[entailment-api] HTTP {r.status_code}: {r.text[:300]}", flush=True)
                    _warned_once["done"] = True
                return 1
            return _parse_classification(r.json())
        except Exception as e:
            if not _warned_once["done"]:
                print(f"[entailment-api] request error: {e}", flush=True)
                _warned_once["done"] = True
            time.sleep(min(2 ** attempt, 8))
    return 1


def api_check_implication(pairs, client=None):
    """
    Entailment via HF serverless Inference API (parallel requests).
    Returns list[int] in {0,1,2}, aligned with `pairs`.
    """
    if not pairs:
        return []
    token = getattr(client, "token", None) or os.environ.get("HF_TOKEN")
    total = len(pairs)
    print(f"[entailment-api] Classifying {total} pairs via Inference API "
          f"(model={ENTAILMENT_API_MODEL}, workers={ENTAILMENT_API_WORKERS})...", flush=True)

    def work(pair):
        return _api_classify_pair(token, pair[0], pair[1])

    with ThreadPoolExecutor(max_workers=ENTAILMENT_API_WORKERS) as ex:
        results = list(ex.map(work, pairs))

    print(f"[entailment-api] Done ({total} pairs).", flush=True)
    return results


@spaces.GPU
def batch_check_implication(pairs):
    """
    pairs: list of (premise, hypothesis) str tuples
    Returns list of int (0=contradiction, 1=neutral, 2=entailment).
    DeBERTa is loaded lazily inside this GPU-bound function.
    """
    global _deberta_tokenizer, _deberta_model

    if not pairs:
        return []

    if _deberta_model is None:
        from transformers import AutoModelForSequenceClassification, AutoTokenizer
        device = "cuda" if torch.cuda.is_available() else "cpu"
        print(f"[entailment] Loading DeBERTa onto {device} (first GPU call)...", flush=True)
        _deberta_tokenizer = AutoTokenizer.from_pretrained(ENTAILMENT_MODEL_NAME)
        _deberta_model = AutoModelForSequenceClassification.from_pretrained(
            ENTAILMENT_MODEL_NAME
        ).to(device)
        _deberta_model.eval()
        print("[entailment] DeBERTa loaded.", flush=True)

    device = next(_deberta_model.parameters()).device
    results = []

    total = len(pairs)
    n_batches = (total + ENTAILMENT_BATCH_SIZE - 1) // ENTAILMENT_BATCH_SIZE
    print(f"[entailment] Running entailment on {total} pairs in {n_batches} batches "
          f"of {ENTAILMENT_BATCH_SIZE} (device={device})...", flush=True)

    for bi, i in enumerate(range(0, total, ENTAILMENT_BATCH_SIZE), 1):
        batch = pairs[i : i + ENTAILMENT_BATCH_SIZE]
        premises = [p for p, _ in batch]
        hypotheses = [h for _, h in batch]
        inputs = _deberta_tokenizer(
            premises, hypotheses, padding=True, truncation=True, return_tensors="pt"
        ).to(device)
        with torch.no_grad():
            logits = _deberta_model(**inputs).logits
        preds = torch.argmax(logits, dim=1).tolist()
        results.extend(preds)
        print(f"[entailment]   batch {bi}/{n_batches} done "
              f"({min(i + ENTAILMENT_BATCH_SIZE, total)}/{total} pairs)", flush=True)

    print(f"[entailment] Entailment complete ({total} pairs).", flush=True)
    return results


def get_semantic_ids(strings_list, strict_entailment=False):
    """
    3-phase:
    1. Gather all ordered pairs (both directions).
    2. One batched GPU call to batch_check_implication.
    3. Pure-Python greedy clustering via lookup dict.
    """
    n = len(strings_list)
    if n == 0:
        return []
    if n == 1:
        return [0]

    # Phase 1
    pairs = []
    pair_indices = []
    for i in range(n):
        for j in range(n):
            if i != j:
                pairs.append((strings_list[i], strings_list[j]))
                pair_indices.append((i, j))

    # Phase 2
    raw = batch_check_implication(pairs)

    # Phase 3
    impl = {(i, j): r for (i, j), r in zip(pair_indices, raw)}

    def are_equivalent(i, j):
        i1 = impl.get((i, j), 1)
        i2 = impl.get((j, i), 1)
        if strict_entailment:
            return i1 == 2 and i2 == 2
        return 0 not in [i1, i2] and [i1, i2] != [1, 1]

    semantic_ids = [-1] * n
    next_id = 0
    for i in range(n):
        if semantic_ids[i] == -1:
            semantic_ids[i] = next_id
            for j in range(i + 1, n):
                if semantic_ids[j] == -1 and are_equivalent(i, j):
                    semantic_ids[j] = next_id
            next_id += 1

    return semantic_ids


def _cluster_from_impl(n, impl, strict_entailment):
    """Pure-Python greedy clustering given an implication lookup dict."""
    def are_equivalent(i, j):
        i1 = impl.get((i, j), 1)
        i2 = impl.get((j, i), 1)
        if strict_entailment:
            return i1 == 2 and i2 == 2
        return 0 not in [i1, i2] and [i1, i2] != [1, 1]

    semantic_ids = [-1] * n
    next_id = 0
    for i in range(n):
        if semantic_ids[i] == -1:
            semantic_ids[i] = next_id
            for j in range(i + 1, n):
                if semantic_ids[j] == -1 and are_equivalent(i, j):
                    semantic_ids[j] = next_id
            next_id += 1
    return semantic_ids


def get_semantic_ids_batched(groups, client=None, strict_entailment=False, use_api=None):
    """
    Cluster MANY groups of strings using a SINGLE pass of entailment checks.

    groups: list of list[str]
    client: HF InferenceClient (required when using the API path)
    use_api: True → Inference API, False → local GPU/CPU, None → config default
    Returns: list of list[int] — semantic ids per group, aligned with `groups`.

    Pattern 3: gather pairs (both directions) from ALL groups → one entailment
    pass (serverless API OR one batched GPU call) → pure-Python greedy
    clustering per group via lookup.
    """
    if use_api is None:
        use_api = ENTAILMENT_USE_API
    all_pairs = []
    group_meta = []  # (offset, n_uniq, local_indices, orig_to_uniq)

    for strings in groups:
        # Deduplicate identical answers (normalized) — these are trivially
        # equivalent, so we only run entailment on the UNIQUE strings and
        # expand cluster ids back afterwards. Big reduction in entailment calls.
        uniq = []
        norm_to_uniq = {}
        orig_to_uniq = []
        for s in strings:
            key = s.strip().lower()
            if key not in norm_to_uniq:
                norm_to_uniq[key] = len(uniq)
                uniq.append(s)
            orig_to_uniq.append(norm_to_uniq[key])

        m = len(uniq)
        offset = len(all_pairs)
        local_indices = []
        for i in range(m):
            for j in range(m):
                if i != j:
                    all_pairs.append((uniq[i], uniq[j]))
                    local_indices.append((i, j))
        group_meta.append((offset, m, local_indices, orig_to_uniq))

    print(f"[entailment] {len(all_pairs)} pairs after dedup "
          f"(across {len(groups)} groups)", flush=True)

    # One entailment pass for the entire pipeline
    if use_api and client is not None:
        raw = api_check_implication(all_pairs, client)
    else:
        # Retry transient GPU faults (e.g. ECC errors on a bad ZeroGPU node) —
        # each call re-acquires a fresh worker/GPU.
        raw = None
        last_err = None
        for attempt in range(3):
            try:
                raw = batch_check_implication(all_pairs)
                break
            except Exception as e:
                last_err = e
                print(f"[entailment] GPU attempt {attempt+1}/3 failed: {e}", flush=True)
                time.sleep(3)
        if raw is None:
            raise RuntimeError(f"Entailment GPU call failed after retries: {last_err}")

    results = []
    for offset, m, local_indices, orig_to_uniq in group_meta:
        n_orig = len(orig_to_uniq)
        if m == 0:
            results.append([])
            continue
        if m == 1:
            # all originals identical → one cluster
            results.append([0] * n_orig)
            continue
        impl = {
            (i, j): raw[offset + k] for k, (i, j) in enumerate(local_indices)
        }
        uniq_ids = _cluster_from_impl(m, impl, strict_entailment)
        # expand unique-cluster ids back to every original string
        results.append([uniq_ids[orig_to_uniq[t]] for t in range(n_orig)])

    return results


def cluster_assignment_entropy(semantic_ids):
    if not semantic_ids:
        return 0.0
    counts = np.bincount(semantic_ids)
    probs = counts / counts.sum()
    # guard log(0)
    return float(-np.sum(probs * np.log(np.where(probs > 0, probs, 1.0))))