"""Text generation and output parsing."""

from __future__ import annotations

from dataclasses import dataclass
from typing import List, Tuple

from torch.nn.functional import softmax

from src.config import settings


@dataclass
class GenerationResult:
    code: str
    explanation: str
    confidence: float
    important_tokens: List[str]


def _split_code_and_explanation(text: str) -> Tuple[str, str]:
    marker = "Explanation:"
    if marker in text:
        code, explanation = text.split(marker, 1)
        return code.strip(), explanation.strip()
    return text.strip(), "Model did not provide explicit explanation."


def generate_response(model_bundle, prompt: str) -> GenerationResult:
    """Generate model response with token-level confidence signals."""
    if getattr(model_bundle, "is_mock", False):
        # Keep API runnable even when model download/loading is unavailable.
        fallback_code = (
            "def solve_task(input_data):\n"
            "    \"\"\"Fallback implementation when model is unavailable.\"\"\"\n"
            "    return input_data\n"
        )
        fallback_explanation = (
            "Running in mock fallback mode because no pretrained model could be loaded. "
            "Set MODEL_NAME/FALLBACK_MODEL_NAME and ensure network/model access."
        )
        load_error = getattr(model_bundle, "load_error", "")
        if load_error:
            fallback_explanation = f"{fallback_explanation}\n\nLoader error: {load_error}"
        return GenerationResult(
            code=fallback_code,
            explanation=fallback_explanation,
            confidence=0.15,
            important_tokens=["<mock-fallback>"],
        )

    tokenizer = model_bundle.tokenizer
    model = model_bundle.model

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(
        **inputs,
        max_new_tokens=settings.max_new_tokens,
        temperature=settings.temperature,
        top_p=settings.top_p,
        do_sample=True,
        return_dict_in_generate=True,
        output_scores=True,
    )

    generated_ids = outputs.sequences[0][inputs["input_ids"].shape[1] :]
    generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True)

    token_probs = []
    important = []
    for step_scores, token_id in zip(outputs.scores, generated_ids):
        probs = softmax(step_scores[0], dim=-1)
        p = probs[token_id].item()
        token_probs.append(p)
        if p < 0.30:
            important.append(tokenizer.decode([token_id]))

    confidence = float(sum(token_probs) / max(len(token_probs), 1))
    code, explanation = _split_code_and_explanation(generated_text)

    return GenerationResult(
        code=code,
        explanation=explanation,
        confidence=confidence,
        important_tokens=important[:20],
    )