"""real_batch.py — build a real, tokenized 3-channel batch from a HF tokenizer.

Used by Spike 006's smoke to generate inputs for `compose_loss` from a real
chat-template-formatted conversation, NOT random ints.
"""
from __future__ import annotations

from typing import Any

import torch


def build_batch(
    tokenizer: Any,
    *,
    device: torch.device | str = "cpu",
    seed: int = 42,
) -> dict[str, torch.Tensor]:
    """Construct a full 3-channel input batch from a real tokenizer.

    Returns a dict with all keys `compose_loss` may consume:
        input_ids, response_mask
        ctx_teacher_input_ids, sdpo_loss_mask
        dpo_chosen_input_ids, dpo_chosen_response_mask
        dpo_rejected_input_ids, dpo_rejected_response_mask
        dpo_chosen_ref_logprobs, dpo_rejected_ref_logprobs

    The DPO ref logprobs are dummy tensors (not from a real reference policy
    forward); the smoke is verifying the loss composition wires together,
    not the reference-policy precompute pipeline.
    """
    torch.manual_seed(seed)

    # ------------------------------------------------------------------
    # Conversation 1: student rollout
    # ------------------------------------------------------------------
    student_msgs = [
        {"role": "system", "content": "You are a careful coding assistant."},
        {"role": "user", "content": "Write a Python function to compute the factorial of n."},
        {"role": "assistant", "content": "def factorial(n):\n    if n <= 1: return 1\n    return n * factorial(n - 1)"},
    ]
    student_text = tokenizer.apply_chat_template(student_msgs, tokenize=False, add_generation_prompt=False)
    student_enc = tokenizer(student_text, return_tensors="pt", add_special_tokens=False)
    input_ids = student_enc["input_ids"].to(device)

    # response_mask: rough heuristic — last 30% of tokens are "the response"
    # (good enough for a smoke; production uses chat-template offsets)
    T = input_ids.shape[1]
    response_mask = torch.zeros_like(input_ids)
    response_mask[:, int(T * 0.7):] = 1

    # ------------------------------------------------------------------
    # Conversation 2: hint-conditioned teacher context (SDPO)
    # ------------------------------------------------------------------
    teacher_msgs = [
        {"role": "system", "content": "You are a careful coding assistant."},
        {"role": "user", "content": "Write a Python function to compute the factorial of n."},
        {"role": "user", "content": "[HINT] Recursion overflows for n>1000. Use an iterative loop."},
        {"role": "assistant", "content": "def factorial(n):\n    result = 1\n    for i in range(2, n + 1):\n        result *= i\n    return result"},
    ]
    teacher_text = tokenizer.apply_chat_template(teacher_msgs, tokenize=False, add_generation_prompt=False)
    teacher_enc = tokenizer(teacher_text, return_tensors="pt", add_special_tokens=False)
    ctx_teacher_input_ids = teacher_enc["input_ids"].to(device)

    # SDPO loss mask: 1 on the post-hint assistant tokens (the "error site")
    T_t = ctx_teacher_input_ids.shape[1]
    sdpo_loss_mask = torch.zeros_like(ctx_teacher_input_ids)
    sdpo_loss_mask[:, int(T_t * 0.7):] = 1

    # ------------------------------------------------------------------
    # Conversation 3 + 4: DPO chosen / rejected pairs
    # ------------------------------------------------------------------
    dpo_chosen_msgs = [
        {"role": "system", "content": "You are a careful coding assistant."},
        {"role": "user", "content": "What's the time complexity of binary search?"},
        {"role": "assistant", "content": "Binary search is O(log n) because each comparison halves the search space."},
    ]
    dpo_rejected_msgs = [
        {"role": "system", "content": "You are a careful coding assistant."},
        {"role": "user", "content": "What's the time complexity of binary search?"},
        {"role": "assistant", "content": "It's O(n) I think, you have to look at every element."},
    ]
    chosen_text = tokenizer.apply_chat_template(dpo_chosen_msgs, tokenize=False, add_generation_prompt=False)
    rejected_text = tokenizer.apply_chat_template(dpo_rejected_msgs, tokenize=False, add_generation_prompt=False)

    # Pad both sequences to the same length so we can stack them
    chosen_enc = tokenizer(chosen_text, return_tensors="pt", add_special_tokens=False, padding=False)
    rejected_enc = tokenizer(rejected_text, return_tensors="pt", add_special_tokens=False, padding=False)

    pad_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else tokenizer.eos_token_id

    chosen_ids = chosen_enc["input_ids"]
    rejected_ids = rejected_enc["input_ids"]
    L = max(chosen_ids.shape[1], rejected_ids.shape[1])

    def _pad(ids: torch.Tensor, length: int) -> torch.Tensor:
        cur = ids.shape[1]
        if cur >= length:
            return ids[:, :length]
        return torch.cat([ids, torch.full((1, length - cur), pad_id, dtype=ids.dtype)], dim=1)

    dpo_chosen_input_ids = _pad(chosen_ids, L).to(device)
    dpo_rejected_input_ids = _pad(rejected_ids, L).to(device)

    chosen_resp_mask = torch.zeros_like(dpo_chosen_input_ids)
    chosen_resp_mask[:, int(L * 0.6):chosen_ids.shape[1]] = 1
    rejected_resp_mask = torch.zeros_like(dpo_rejected_input_ids)
    rejected_resp_mask[:, int(L * 0.6):rejected_ids.shape[1]] = 1

    # Dummy reference-policy logprobs (in production: precomputed by data collator)
    dpo_chosen_ref_logprobs = torch.tensor([-30.0], device=device)
    dpo_rejected_ref_logprobs = torch.tensor([-35.0], device=device)

    return {
        "input_ids": input_ids,
        "response_mask": response_mask,
        "ctx_teacher_input_ids": ctx_teacher_input_ids,
        "sdpo_loss_mask": sdpo_loss_mask,
        "dpo_chosen_input_ids": dpo_chosen_input_ids,
        "dpo_chosen_response_mask": chosen_resp_mask,
        "dpo_rejected_input_ids": dpo_rejected_input_ids,
        "dpo_rejected_response_mask": rejected_resp_mask,
        "dpo_chosen_ref_logprobs": dpo_chosen_ref_logprobs,
        "dpo_rejected_ref_logprobs": dpo_rejected_ref_logprobs,
    }


__all__ = ["build_batch"]