"""Text-mode pipeline: 2-agent chain where Coder passes text to Reviewer."""

import time
from typing import Any, Dict, List

import torch

from benchmarks.shared.generation import generate_text, render_prompt, tokenize_prompt
from benchmarks.shared.metrics import gpu_memory_tracker
from .agents import AGENTS, build_text_prompt
from .evaluate import check_correct


def run_text_pipeline(
    model: Any,
    tokenizer: Any,
    device: str,
    prompt: str,
    test: str,
    entry_point: str,
    task_id: str,
    max_new_tokens: int = 512,
    temperature: float = 0.01,
    top_p: float = 0.95,
    verbose: bool = False,
) -> Dict:
    """Run the 2-agent text-mode pipeline on a single HumanEval problem.

    Coder generates code draft, which is included in Reviewer's prompt.
    """
    with gpu_memory_tracker(device) as mem:
        t0 = time.perf_counter()
        agent_traces: List[Dict] = []
        total_prompt_tokens = 0
        total_output_tokens = 0
        total_context_tokens = 0

        # --- Agent 1: Coder ---
        coder = AGENTS[0]
        reviewer = AGENTS[1]

        agent_t0 = time.perf_counter()
        messages = build_text_prompt(coder.role, prompt)
        prompt_text = render_prompt(tokenizer, messages)
        input_ids, attention_mask = tokenize_prompt(tokenizer, prompt_text, device)
        p_tokens = int(input_ids.shape[-1])
        total_prompt_tokens += p_tokens

        coder_text, _ = generate_text(
            model, tokenizer, input_ids, attention_mask, device,
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            top_p=top_p,
        )

        output_encoded = tokenizer(coder_text, add_special_tokens=False)
        output_tokens = len(output_encoded["input_ids"])
        total_output_tokens += output_tokens
        agent_time_ms = (time.perf_counter() - agent_t0) * 1000

        agent_traces.append({
            "name": coder.name,
            "role": coder.role,
            "prompt_tokens": p_tokens,
            "output_tokens": output_tokens,
            "context_tokens": 0,
            "agent_time_ms": agent_time_ms,
            "output": coder_text,
        })

        if verbose:
            print(f"  [{coder.name}] output ({len(coder_text)} chars): "
                  f"{coder_text[:200]}...")

        # --- Agent 2: Reviewer ---
        agent_t0 = time.perf_counter()

        context_encoded = tokenizer(coder_text, add_special_tokens=False)
        context_token_count = len(context_encoded["input_ids"])
        total_context_tokens += context_token_count

        messages = build_text_prompt(reviewer.role, prompt, coder_text)
        prompt_text = render_prompt(tokenizer, messages)
        input_ids, attention_mask = tokenize_prompt(tokenizer, prompt_text, device)
        p_tokens = int(input_ids.shape[-1])
        total_prompt_tokens += p_tokens

        reviewer_text, _ = generate_text(
            model, tokenizer, input_ids, attention_mask, device,
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            top_p=top_p,
        )

        output_encoded = tokenizer(reviewer_text, add_special_tokens=False)
        output_tokens = len(output_encoded["input_ids"])
        total_output_tokens += output_tokens
        agent_time_ms = (time.perf_counter() - agent_t0) * 1000

        agent_traces.append({
            "name": reviewer.name,
            "role": reviewer.role,
            "prompt_tokens": p_tokens,
            "output_tokens": output_tokens,
            "context_tokens": context_token_count,
            "agent_time_ms": agent_time_ms,
            "output": reviewer_text,
        })

        if verbose:
            print(f"  [{reviewer.name}] output ({len(reviewer_text)} chars): "
                  f"{reviewer_text[:200]}...")

        wall_time = time.perf_counter() - t0

    total_tokens = total_prompt_tokens + total_output_tokens
    tokens_per_sec = total_tokens / wall_time if wall_time > 0 else 0

    eval_result = check_correct(
        agent_traces[-1]["output"], prompt, test, entry_point,
    )
    correct = eval_result["passed"]

    return {
        "task_id": task_id,
        "prompt": prompt,
        "raw_output": agent_traces[-1]["output"],
        "extracted_code": eval_result["code"],
        "correct": correct,
        "error": eval_result["error"],
        "wall_time": wall_time,
        "total_prompt_tokens": total_prompt_tokens,
        "total_output_tokens": total_output_tokens,
        "total_tokens": total_tokens,
        "total_context_tokens": total_context_tokens,
        "tokens_per_sec": tokens_per_sec,
        "peak_memory_mb": mem["peak_memory_mb"],
        "agents": agent_traces,
        "mode": "text",
    }


def run_text_benchmark(
    model: Any,
    tokenizer: Any,
    device: str,
    dataset: List[Dict],
    max_new_tokens: int = 512,
    temperature: float = 0.01,
    top_p: float = 0.95,
    verbose: bool = False,
) -> List[Dict]:
    """Run text-mode pipeline on a list of HumanEval samples."""
    results = []
    for i, sample in enumerate(dataset):
        if verbose:
            print(f"\n[Text] Sample {i + 1}/{len(dataset)}: {sample['task_id']}")

        result = run_text_pipeline(
            model, tokenizer, device,
            prompt=sample["prompt"],
            test=sample["test"],
            entry_point=sample["entry_point"],
            task_id=sample["task_id"],
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            top_p=top_p,
            verbose=verbose,
        )
        results.append(result)

        if verbose:
            status = "PASS" if result["correct"] else "FAIL"
            print(f"  => {status} (time={result['wall_time']:.1f}s)")
        else:
            passed = sum(1 for r in results if r["correct"])
            print(f"  [Text] {i + 1}/{len(dataset)} "
                  f"({passed}/{i + 1} passed, {result['wall_time']:.1f}s)",
                  flush=True)

    return results
