"""
  Qwen3-Reranker-0.6B backend using vLLM.
  
  Reference: https://huggingface.co/Qwen/Qwen3-Reranker-0.6B
  Requires: vllm>=0.8.5, transformers; GPU recommended.
  """
  
  from __future__ import annotations
  
  import logging
  import math

  import os

  import threading

  import time

  from typing import Any, Dict, List, Tuple
  

  logger = logging.getLogger("reranker.backends.qwen3_vllm")
  

  import torch
  from transformers import AutoTokenizer
  from vllm import LLM, SamplingParams
  from vllm.inputs.data import TokensPrompt

  def deduplicate_with_positions(texts: List[str]) -> Tuple[List[str], List[int]]:
      """
      Deduplicate texts globally while preserving first-seen order.
  
      Returns:
          unique_texts: deduplicated texts in first-seen order
          position_to_unique: mapping from each original position to unique index
      """
      unique_texts: List[str] = []
      position_to_unique: List[int] = []
      seen: Dict[str, int] = {}
  
      for text in texts:
          idx = seen.get(text)
          if idx is None:
              idx = len(unique_texts)
              seen[text] = idx
              unique_texts.append(text)
          position_to_unique.append(idx)
  
      return unique_texts, position_to_unique
  
  

  def _format_instruction(instruction: str, query: str, doc: str) -> List[Dict[str, str]]:
      """Build chat messages for one (query, doc) pair."""
      return [
          {
              "role": "system",
              "content": "Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".",
          },
          {
              "role": "user",
              "content": f"<Instruct>: {instruction}\n\n<Query>: {query}\n\n<Document>: {doc}",
          },
      ]
  
  
  class Qwen3VLLMRerankerBackend:
      """
      Qwen3-Reranker-0.6B with vLLM inference.
      Config from services.rerank.backends.qwen3_vllm.
      """
  
      def __init__(self, config: Dict[str, Any]) -> None:
          self._config = config or {}
          model_name = str(self._config.get("model_name") or "Qwen/Qwen3-Reranker-0.6B")

          max_model_len = int(self._config.get("max_model_len", 2048))

          tensor_parallel_size = int(self._config.get("tensor_parallel_size", 1))

          gpu_memory_utilization = float(self._config.get("gpu_memory_utilization", 0.4))
          enable_prefix_caching = bool(self._config.get("enable_prefix_caching", False))
          enforce_eager = bool(self._config.get("enforce_eager", True))
          dtype = str(self._config.get("dtype", "float16")).strip().lower()

          self._instruction = str(
              self._config.get("instruction")

              or "Given a query, score the product for relevance"

          )

          infer_batch_size = os.getenv("RERANK_VLLM_INFER_BATCH_SIZE") or self._config.get("infer_batch_size", 64)
          sort_by_doc_length = os.getenv("RERANK_VLLM_SORT_BY_DOC_LENGTH")
          if sort_by_doc_length is None:
              sort_by_doc_length = self._config.get("sort_by_doc_length", True)

  
          self._infer_batch_size = int(infer_batch_size)
          self._sort_by_doc_length = str(sort_by_doc_length).strip().lower() in {"1", "true", "yes", "y", "on"}

          if not torch.cuda.is_available():
              raise RuntimeError("qwen3_vllm backend requires CUDA GPU, but torch.cuda.is_available() is False")
          if dtype not in {"float16", "half", "auto"}:
              raise ValueError(f"Unsupported dtype for qwen3_vllm: {dtype!r}. Use float16/half/auto.")

          if self._infer_batch_size <= 0:

              raise ValueError(
                  f"infer_batch_size must be > 0, got {self._infer_batch_size}"
              )

  
          logger.info(

              "[Qwen3_VLLM] Loading model %s (max_model_len=%s, tp=%s, gpu_mem=%.2f, dtype=%s, prefix_caching=%s)",

              model_name,
              max_model_len,
              tensor_parallel_size,

              gpu_memory_utilization,
              dtype,

              enable_prefix_caching,
          )
  
          self._llm = LLM(
              model=model_name,
              tensor_parallel_size=tensor_parallel_size,
              max_model_len=max_model_len,
              gpu_memory_utilization=gpu_memory_utilization,
              enable_prefix_caching=enable_prefix_caching,

              enforce_eager=enforce_eager,
              dtype=dtype,

          )
          self._tokenizer = AutoTokenizer.from_pretrained(model_name)
          self._tokenizer.padding_side = "left"
          self._tokenizer.pad_token = self._tokenizer.eos_token
  
          # Suffix for generation prompt (assistant answer)
          self._suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
          self._suffix_tokens = self._tokenizer.encode(
              self._suffix, add_special_tokens=False
          )
          self._max_prompt_len = max_model_len - len(self._suffix_tokens)
  
          self._true_token = self._tokenizer("yes", add_special_tokens=False).input_ids[0]
          self._false_token = self._tokenizer("no", add_special_tokens=False).input_ids[0]
          self._sampling_params = SamplingParams(
              temperature=0,
              max_tokens=1,
              logprobs=20,
              allowed_token_ids=[self._true_token, self._false_token],
          )

          # vLLM generate path is unstable under concurrent calls in this process model.
          # Serialize infer calls to avoid engine-core protocol corruption.
          self._infer_lock = threading.Lock()

  
          self._model_name = model_name
          logger.info("[Qwen3_VLLM] Model ready | model=%s", model_name)
  
      def _process_inputs(
          self,
          pairs: List[Tuple[str, str]],
      ) -> List[TokensPrompt]:

          """Build tokenized prompts for vLLM from (query, doc) pairs. Batch apply_chat_template."""
          messages_batch = [
              _format_instruction(self._instruction, q, d) for q, d in pairs
          ]
          tokenized = self._tokenizer.apply_chat_template(
              messages_batch,
              tokenize=True,
              add_generation_prompt=False,
              enable_thinking=False,
          )
          # Single conv returns flat list; batch returns list of lists
          if tokenized and not isinstance(tokenized[0], list):
              tokenized = [tokenized]
          prompts = [
              TokensPrompt(
                  prompt_token_ids=ids[: self._max_prompt_len] + self._suffix_tokens

              )

              for ids in tokenized
          ]

          return prompts
  
      def _compute_scores(
          self,
          prompts: List[TokensPrompt],
      ) -> List[float]:
          """Run vLLM generate and compute yes/no probability per prompt."""
          if not prompts:
              return []
          outputs = self._llm.generate(prompts, self._sampling_params, use_tqdm=False)
          scores = []
          for i in range(len(outputs)):
              out = outputs[i]
              if not out.outputs:
                  scores.append(0.0)
                  continue

              final_logits = out.outputs[0].logprobs
              if not final_logits:

                  scores.append(0.0)
                  continue

              last = final_logits[-1]
              # Match official: missing token -> logprob = -10
              if self._true_token not in last:
                  true_logit = -10
              else:
                  true_logit = last[self._true_token].logprob
              if self._false_token not in last:
                  false_logit = -10
              else:
                  false_logit = last[self._false_token].logprob
              true_score = math.exp(true_logit)
              false_score = math.exp(false_logit)
              score = true_score / (true_score + false_score)

              scores.append(float(score))
          return scores
  

      def _estimate_doc_lengths(self, docs: List[str]) -> List[int]:
          """
          Estimate token lengths for sorting documents into similar-length batches.
          Falls back to character length when tokenizer length output is unavailable.
          """
          if not docs:
              return []

          # Use simple character length to approximate document length.

          return [len(text) for text in docs]
  

      def score_with_meta(
          self,
          query: str,
          docs: List[str],
          normalize: bool = True,
      ) -> Tuple[List[float], Dict[str, Any]]:
          start_ts = time.time()
          total_docs = len(docs) if docs else 0
          output_scores: List[float] = [0.0] * total_docs
  
          query = "" if query is None else str(query).strip()
          indexed: List[Tuple[int, str]] = []
          for i, doc in enumerate(docs or []):
              if doc is None:
                  continue
              text = str(doc).strip()
              if not text:
                  continue
              indexed.append((i, text))
  
          if not query or not indexed:
              elapsed_ms = (time.time() - start_ts) * 1000.0
              return output_scores, {
                  "input_docs": total_docs,
                  "usable_docs": len(indexed),
                  "unique_docs": 0,
                  "dedup_ratio": 0.0,
                  "elapsed_ms": round(elapsed_ms, 3),
                  "model": self._model_name,
                  "backend": "qwen3_vllm",
                  "normalize": normalize,

                  "infer_batch_size": self._infer_batch_size,
                  "inference_batches": 0,
                  "sort_by_doc_length": self._sort_by_doc_length,

              }
  

          # Deduplicate globally by text, keep mapping to original indices.
          indexed_texts = [text for _, text in indexed]
          unique_texts, position_to_unique = deduplicate_with_positions(indexed_texts)
  
          lengths = self._estimate_doc_lengths(unique_texts)
          order = list(range(len(unique_texts)))
          if self._sort_by_doc_length and len(unique_texts) > 1:

              order = sorted(order, key=lambda i: lengths[i])

  
          unique_scores: List[float] = [0.0] * len(unique_texts)
          inference_batches = 0

          for start in range(0, len(order), self._infer_batch_size):
              batch_indices = order[start : start + self._infer_batch_size]

              inference_batches += 1
              pairs = [(query, unique_texts[i]) for i in batch_indices]

              prompts = self._process_inputs(pairs)

              with self._infer_lock:
                  batch_scores = self._compute_scores(prompts)
              if len(batch_scores) != len(batch_indices):
                  raise RuntimeError(
                      f"Reranker score size mismatch: expected {len(batch_indices)}, got {len(batch_scores)}"
                  )
              for idx, score in zip(batch_indices, batch_scores):
                  unique_scores[idx] = float(score)

  
          for (orig_idx, _), unique_idx in zip(indexed, position_to_unique):
              # Score is already P(yes) in [0,1] from yes/(yes+no)
              output_scores[orig_idx] = float(unique_scores[unique_idx])
  
          elapsed_ms = (time.time() - start_ts) * 1000.0
          dedup_ratio = 0.0
          if indexed:
              dedup_ratio = 1.0 - (len(unique_texts) / float(len(indexed)))
  
          meta = {
              "input_docs": total_docs,
              "usable_docs": len(indexed),
              "unique_docs": len(unique_texts),
              "dedup_ratio": round(dedup_ratio, 4),
              "elapsed_ms": round(elapsed_ms, 3),
              "model": self._model_name,
              "backend": "qwen3_vllm",
              "normalize": normalize,

              "infer_batch_size": self._infer_batch_size,
              "inference_batches": inference_batches,

              "sort_by_doc_length": self._sort_by_doc_length

          }
          return output_scores, meta