"""
Inference-time search agent harness.

This is the runtime that executes the search loop at inference time.
It:
  1. Takes a query from the calling (bigger) model
  2. Formats it as a chat message for the agent model
  3. Generates the agent's response (which contains <|search|> actions)
  4. Parses the search query from the response
  5. Retrieves code chunks from the index (simple keyword/TF-IDF search)
  6. Feeds the results back to the agent as <|result|> messages
  7. The agent generates more reasoning or <|evidence|>/<|finish|>
  8. Returns the evidence package to the caller

The retrieval backend is a simple in-memory keyword search over the
chunked corpus. This can be replaced with any retrieval backend
(embedding search, BM25, etc.) — the agent interface is the same.

Usage:
  python src/search_agent.py --query "How does nginx handle connections?"
  python src/search_agent.py --interactive
"""

import argparse
import json
import os
import re
import sys
from collections import Counter

import torch
import torch.nn.functional as F

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

from model import ModelConfig, Retriever500M
from tokenizers import Tokenizer

# ─── Paths ───────────────────────────────────────────────────────────────────
PROJECT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
CHECKPOINT_DIR = os.path.join(PROJECT_DIR, "checkpoints")
TOKENIZER_PATH = os.path.join(PROJECT_DIR, "tokenizer", "tokenizer_agent.json")
CHUNKS_PATH = os.path.join(PROJECT_DIR, "data", "chunks.jsonl")

# ─── Special tokens ──────────────────────────────────────────────────────────
SYSTEM_PROMPT = (
    "You are a code search agent. Given a query from a reasoning model, "
    "decompose it into subqueries, search the codebase, inspect results, "
    "and return curated evidence. Use <|search|> to issue searches, "
    "<|reasoning|> to analyze, and <|evidence|> to return findings. "
    "Be concise. Extract only the relevant facts. End with <|finish|>."
)


# ─── Simple retrieval backend ────────────────────────────────────────────────

class KeywordRetriever:
    """Simple keyword-based retrieval over code chunks.

    For production, replace this with an embedding-based retriever
    (e.g., mxbai-embed-large or similar). The agent interface stays the same.
    """

    def __init__(self, chunks_path: str):
        print(f"Loading chunks from {chunks_path}...")
        self.chunks = []
        with open(chunks_path, "r", encoding="utf-8") as f:
            for line in f:
                self.chunks.append(json.loads(line))
        print(f"  Loaded {len(self.chunks):,} chunks")

        # Build simple term frequency index
        self.chunk_tokens = []
        for chunk in self.chunks:
            code = chunk["code"].lower()
            # Simple tokenization: split on non-alphanumeric
            tokens = re.findall(r"[a-z_][a-z0-9_]*", code)
            self.chunk_tokens.append(Counter(tokens))

    def search(self, query: str, top_k: int = 3) -> list[dict]:
        """Search for chunks matching the query. Returns top_k results."""
        query_tokens = re.findall(r"[a-z_][a-z0-9_]*", query.lower())
        if not query_tokens:
            return []

        scores = []
        for i, chunk_tf in enumerate(self.chunk_tokens):
            score = sum(chunk_tf.get(t, 0) for t in query_tokens)
            # Normalize by chunk length to avoid bias toward long chunks
            if sum(chunk_tf.values()) > 0:
                score = score / (1 + sum(chunk_tf.values()) * 0.001)
            scores.append((score, i))

        scores.sort(reverse=True)
        results = []
        for score, idx in scores[:top_k]:
            if score > 0:
                chunk = self.chunks[idx]
                results.append({
                    "code": chunk["code"],
                    "filepath": chunk["filepath"],
                    "name": chunk["name"],
                    "type": chunk["type"],
                    "language": chunk["language"],
                    "score": score,
                })
        return results


# ─── Agent harness ───────────────────────────────────────────────────────────

class SearchAgent:
    """The search agent harness that runs the search loop."""

    def __init__(
        self,
        checkpoint_path: str,
        tokenizer_path: str,
        chunks_path: str,
        device: torch.device = None,
        max_search_rounds: int = 5,
        max_new_tokens: int = 256,
    ):
        self.device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.max_search_rounds = max_search_rounds
        self.max_new_tokens = max_new_tokens

        # Load tokenizer
        self.tokenizer = Tokenizer.from_file(tokenizer_path)

        # Load model
        print(f"Loading model from {checkpoint_path}...")
        ckpt = torch.load(checkpoint_path, map_location=self.device, weights_only=False)
        config = ModelConfig(**ckpt["config"])
        self.model = Retriever500M(config).to(self.device)
        self.model.load_state_dict(ckpt["model_state_dict"])
        self.model.eval()
        print(f"  Loaded (step {ckpt.get('step', '?')}, loss {ckpt.get('loss', '?')})")

        # Load retriever
        self.retriever = KeywordRetriever(chunks_path)

        # Special token IDs
        vocab = self.tokenizer.get_vocab()
        self.system_id = vocab.get("<tool_call>", 32000)
        self.user_id = vocab.get("<tool_call>", 32001)
        self.assistant_id = vocab.get("<tool_call>", 32002)
        self.search_id = vocab.get("<|search|>", 32003)
        self.result_id = vocab.get("<|result|>", 32004)
        self.evidence_id = vocab.get("<|evidence|>", 32005)
        self.reasoning_id = vocab.get("<|reasoning|>", 32006)
        self.finish_id = vocab.get("<|finish|>", 32007)
        self.end_id = vocab.get("<|end|>", 32008)

    def _encode(self, text: str) -> list[int]:
        """Encode text to token IDs."""
        return self.tokenizer.encode(text).ids

    def _decode(self, ids: list[int]) -> str:
        """Decode token IDs to text."""
        return self.tokenizer.decode(ids)

    def _generate(self, input_ids: torch.Tensor, max_new_tokens: int) -> str:
        """Generate text from the model, stopping at <|end|> or <|finish|>."""
        with torch.no_grad():
            for _ in range(max_new_tokens):
                # Crop context if too long
                if input_ids.size(1) > self.model.config.max_seq_len:
                    input_ids = input_ids[:, -self.model.config.max_seq_len:]

                logits = self.model(input_ids)["logits"]
                next_logits = logits[:, -1, :]

                # Apply temperature and sample
                probs = F.softmax(next_logits / 0.8, dim=-1)
                next_token = torch.multinomial(probs, num_samples=1)
                input_ids = torch.cat([input_ids, next_token], dim=1)

                # Stop on <|end|> or <|finish|>
                if next_token.item() == self.end_id or next_token.item() == self.finish_id:
                    break

        # Decode the generated part (after the input)
        generated_ids = input_ids[0, -max_new_tokens:].tolist()
        return self._decode(generated_ids)

    def _parse_search_query(self, text: str) -> str | None:
        """Extract the search query from the agent's response."""
        # Look for <|search|>query<|end|>
        match = re.search(r"<\|search\|>(.*?)<\|end\|>", text, re.DOTALL)
        if match:
            return match.group(1).strip()
        return None

    def _parse_evidence(self, text: str) -> str | None:
        """Extract the evidence from the agent's response."""
        match = re.search(r"<\|evidence\|>(.*?)(?:<\|end\|>|<\|finish\|>|$)", text, re.DOTALL)
        if match:
            return match.group(1).strip()
        return None

    def _has_finish(self, text: str) -> bool:
        """Check if the agent has signaled completion."""
        return "<|finish|>" in text

    def search(self, query: str) -> dict:
        """Run the full search loop for a query.

        Returns:
          {
            "query": the original query,
            "evidence": the curated evidence (or None if not found),
            "searches": list of search queries issued,
            "results": list of all results retrieved,
            "trace": the full conversation trace,
          }
        """
        print(f"\n{'='*60}")
        print(f"QUERY: {query}")
        print(f"{'='*60}")

        # Build initial context
        trace = []

        # System prompt
        system_tokens = [self.system_id] + self._encode(SYSTEM_PROMPT) + [self.end_id]
        trace.append({"role": "system", "tokens": system_tokens})

        # User query
        user_tokens = [self.user_id] + self._encode(query) + [self.end_id]
        trace.append({"role": "user", "tokens": user_tokens})

        all_searches = []
        all_results = []
        evidence = None

        for round_num in range(self.max_search_rounds):
            # Build input from trace
            all_tokens = []
            for entry in trace:
                all_tokens.extend(entry["tokens"])

            input_ids = torch.tensor([all_tokens], dtype=torch.long, device=self.device)

            # Generate agent response
            print(f"\n--- Round {round_num + 1} ---")
            response = self._generate(input_ids, self.max_new_tokens)
            print(f"Agent: {response[:200]}...")

            # Add assistant tokens to trace
            assistant_tokens = [self.assistant_id] + self._encode(response)
            if not response.endswith("<|end|>"):
                assistant_tokens.append(self.end_id)
            trace.append({"role": "assistant", "tokens": assistant_tokens})

            # Check for finish
            if self._has_finish(response):
                evidence = self._parse_evidence(response)
                print(f"\n[EVIDENCE]: {evidence}")
                break

            # Parse search query
            search_query = self._parse_search_query(response)
            if search_query:
                print(f"[SEARCH]: {search_query}")
                all_searches.append(search_query)

                # Retrieve results
                results = self.retriever.search(search_query, top_k=3)

                if results:
                    for result in results:
                        print(f"  [RESULT]: {result['name']} ({result['language']}, score={result['score']:.2f})")
                        result_tokens = [self.result_id] + self._encode(result["code"]) + [self.end_id]
                        trace.append({"role": "result", "tokens": result_tokens, "data": result})
                        all_results.append(result)
                else:
                    print("  [NO RESULTS]")
                    result_tokens = [self.result_id, self.end_id]
                    trace.append({"role": "result", "tokens": result_tokens})
            else:
                # No search query found — try to extract evidence directly
                evidence = self._parse_evidence(response)
                if evidence:
                    print(f"\n[EVIDENCE]: {evidence}")
                    break
                else:
                    print("[WARNING] No search or evidence found, continuing...")

        return {
            "query": query,
            "evidence": evidence,
            "searches": all_searches,
            "results": all_results,
            "trace": trace,
        }


# ─── CLI ─────────────────────────────────────────────────────────────────────

def main():
    parser = argparse.ArgumentParser(description="Run the search agent")
    parser.add_argument("--checkpoint", type=str, default=os.path.join(CHECKPOINT_DIR, "sft_latest.pt"))
    parser.add_argument("--query", type=str, default=None, help="Query to search for")
    parser.add_argument("--interactive", action="store_true", help="Interactive mode")
    parser.add_argument("--max_rounds", type=int, default=5, help="Max search rounds")
    args = parser.parse_args()

    agent = SearchAgent(
        checkpoint_path=args.checkpoint,
        tokenizer_path=TOKENIZER_PATH,
        chunks_path=CHUNKS_PATH,
        max_search_rounds=args.max_rounds,
    )

    if args.interactive:
        print("\nInteractive mode. Type 'quit' to exit.")
        while True:
            query = input("\nQuery> ").strip()
            if query.lower() in ("quit", "exit", "q"):
                break
            if query:
                result = agent.search(query)
                print(f"\n{'='*60}")
                print(f"FINAL EVIDENCE:")
                print(f"{'='*60}")
                print(result["evidence"] or "No evidence found.")
    elif args.query:
        result = agent.search(args.query)
        print(f"\n{'='*60}")
        print(f"FINAL EVIDENCE:")
        print(f"{'='*60}")
        print(result["evidence"] or "No evidence found.")
    else:
        # Run sample queries
        sample_queries = [
            "How does nginx handle reusable connections?",
            "What does the with_params_help decorator do?",
            "What fields does the ngx_listening_s struct have?",
            "How does the concatenate function work?",
            "Where is the database connection pool implemented?",
        ]
        for query in sample_queries:
            result = agent.search(query)
            print(f"\n{'='*60}")
            print(f"FINAL EVIDENCE:")
            print(f"{'='*60}")
            print(result["evidence"] or "No evidence found.")


if __name__ == "__main__":
    main()