# Copyright (c) MetaVoice Labs Inc., Meta Platforms, Inc. and affiliates.
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#
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# provided that the following conditions are met:
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# conditions and the following disclaimer.
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# 2. Redistributions in binary form must reproduce the above copyright notice, this
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# may be used to endorse or promote products derived from this software without
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import itertools
import time
from pathlib import Path
from typing import Optional, Tuple

import torch
import torch._dynamo.config
import torch._inductor.config
import tqdm


def device_sync(device):
    if "cuda" in device:
        torch.cuda.synchronize()
    elif "cpu" in device:
        pass
    else:
        print(f"device={device} is not yet suppported")


torch._inductor.config.coordinate_descent_tuning = True
torch._inductor.config.triton.unique_kernel_names = True
torch._inductor.config.fx_graph_cache = (
    True  # Experimental feature to reduce compilation times, will be on by default in future
)

# imports need to happen after setting above flags
from fam.llm.fast_model import Transformer
from fam.quantiser.audio.speaker_encoder.model import SpeakerEncoder
from fam.quantiser.text.tokenise import TrainedBPETokeniser


def multinomial_sample_one_no_sync(
    probs_sort,
):  # Does multinomial sampling without a cuda synchronization
    q = torch.empty_like(probs_sort).exponential_(1)
    return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)


def top_p_sample(logits: torch.Tensor, top_p: torch.Tensor):
    # ref: huggingface/transformers

    sorted_logits, sorted_indices = torch.sort(logits, descending=False)
    cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)

    # Remove tokens with cumulative top_p above the threshold (token with 0 are kept)
    sorted_indices_to_remove = cumulative_probs <= (1 - top_p)
    # Keep at least min_tokens_to_keep
    sorted_indices_to_remove[-1:] = 0

    # scatter sorted tensors to original indexing
    indices_to_remove = sorted_indices_to_remove.scatter(0, sorted_indices, sorted_indices_to_remove)
    scores = logits.masked_fill(indices_to_remove, -float("Inf"))
    return scores


def logits_to_probs(
    logits,
    *,
    temperature: torch.Tensor,
    top_p: Optional[torch.Tensor] = None,
    top_k: Optional[torch.Tensor] = None,
):
    logits = logits / torch.max(temperature, 1e-5 * torch.ones_like(temperature))

    if top_k is not None:
        v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
        pivot = v.select(-1, -1).unsqueeze(-1)
        logits = torch.where(logits < pivot, -float("Inf"), logits)

    if top_p is not None:
        logits = top_p_sample(logits, top_p)

    probs = torch.nn.functional.softmax(logits, dim=-1)

    return probs


def sample(
    logits,
    guidance_scale: torch.Tensor,
    temperature: torch.Tensor,
    top_p: Optional[torch.Tensor] = None,
    top_k: Optional[torch.Tensor] = None,
):
    # (b, t, vocab_size)
    logits = logits[:, -1]
    logits_cond, logits_uncond_spkemb = logits.split(logits.size(0) // 2, dim=0)
    logits = guidance_scale * logits_cond + (1 - guidance_scale) * logits_uncond_spkemb
    probs = logits_to_probs(logits[0], temperature=temperature, top_p=top_p, top_k=top_k)
    idx_next = multinomial_sample_one_no_sync(probs)
    return idx_next, probs


def prefill(
    model: Transformer,
    x: torch.Tensor,
    spk_emb: torch.Tensor,
    input_pos: torch.Tensor,
    **sampling_kwargs,
) -> torch.Tensor:
    # input_pos: [B, S]
    logits = model(x, spk_emb, input_pos)
    return sample(logits, **sampling_kwargs)[0]


def decode_one_token(
    model: Transformer,
    x: torch.Tensor,
    spk_emb: torch.Tensor,
    input_pos: torch.Tensor,
    **sampling_kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
    # input_pos: [B, 1]
    assert input_pos.shape[-1] == 1
    logits = model(x, spk_emb, input_pos)
    return sample(logits, **sampling_kwargs)


def decode_n_tokens(
    model: Transformer,
    cur_token: torch.Tensor,
    spk_emb: torch.Tensor,
    input_pos: torch.Tensor,
    num_new_tokens: int,
    callback=lambda _: _,
    return_probs: bool = False,
    end_of_audio_token: int = 2048,
    **sampling_kwargs,
):
    new_tokens, new_probs = [], []
    for i in tqdm.tqdm(range(num_new_tokens)):
        if (cur_token == end_of_audio_token).any():
            break
        with torch.backends.cuda.sdp_kernel(
            enable_flash=False, enable_mem_efficient=False, enable_math=True
        ):  # Actually better for Inductor to codegen attention here
            next_token, next_prob = decode_one_token(model, cur_token, spk_emb, input_pos, **sampling_kwargs)
            input_pos += 1
            new_tokens.append(next_token.clone())
            callback(new_tokens[-1])
            if return_probs:
                new_probs.append(next_prob.clone())
            cur_token = next_token.view(1, -1).repeat(2, 1)

    return new_tokens, new_probs


def model_forward(model, x, spk_emb, input_pos):
    return model(x, spk_emb, input_pos)


@torch.no_grad()
def generate(
    model: Transformer,
    prompt: torch.Tensor,
    spk_emb: torch.Tensor,
    *,
    max_new_tokens: Optional[int] = None,
    callback=lambda x: x,
    end_of_audio_token: int = 2048,
    **sampling_kwargs,
) -> torch.Tensor:
    """
    Takes a conditioning sequence (prompt) as input and continues to generate as many tokens as requested.
    """
    # create an empty tensor of the expected final shape and fill in the current tokens
    T = prompt.size(0)
    if max_new_tokens is None:
        max_seq_length = model.config.block_size
    else:
        max_seq_length = T + max_new_tokens
        max_seq_length = min(max_seq_length, model.config.block_size)
    max_new_tokens = max_seq_length - T
    if max_new_tokens <= 0:
        raise ValueError("Prompt is too long to generate more tokens")

    device, dtype = prompt.device, prompt.dtype

    seq = torch.clone(prompt)
    input_pos = torch.arange(0, T, device=device)

    next_token = prefill(model, prompt.view(1, -1).repeat(2, 1), spk_emb, input_pos, **sampling_kwargs)
    seq = torch.cat([seq, next_token.view(1)])

    input_pos = torch.tensor([T], device=device, dtype=torch.int)

    generated_tokens, _ = decode_n_tokens(
        model,
        next_token.view(1, -1).repeat(2, 1),
        spk_emb,
        input_pos,
        max_new_tokens - 1,
        callback=callback,
        end_of_audio_token=end_of_audio_token,
        **sampling_kwargs,
    )
    seq = torch.cat([seq, torch.cat(generated_tokens)])

    return seq


def encode_tokens(tokenizer, string, device="cuda"):
    tokens = tokenizer.encode(string)
    return torch.tensor(tokens, dtype=torch.int, device=device)


def _load_model(checkpoint_path, spk_emb_ckpt_path, device, precision):
    ##### MODEL
    with torch.device("meta"):
        model = Transformer.from_name("metavoice-1B")

    # TODO(quantization): enable
    # if "int8" in str(checkpoint_path):
    #     print("Using int8 weight-only quantization!")
    #     from quantize import WeightOnlyInt8QuantHandler
    #     simple_quantizer = WeightOnlyInt8QuantHandler(model)
    #     model = simple_quantizer.convert_for_runtime()
    # from quantize import WeightOnlyInt8QuantHandler

    # if "int4" in str(checkpoint_path):
    #     print("Using int4 quantization!")
    #     path_comps = checkpoint_path.name.split(".")
    #     assert path_comps[-2].startswith("g")
    #     groupsize = int(path_comps[-2][1:])
    #     from quantize import WeightOnlyInt4QuantHandler
    #     simple_quantizer = WeightOnlyInt4QuantHandler(model, groupsize)
    #     model = simple_quantizer.convert_for_runtime()

    checkpoint = torch.load(str(checkpoint_path), mmap=True, weights_only=False)
    state_dict = checkpoint["model"]
    # convert MetaVoice-1B model weights naming to gptfast naming
    unwanted_prefix = "_orig_mod."
    for k, v in list(state_dict.items()):
        if k.startswith(unwanted_prefix):
            state_dict[k[len(unwanted_prefix) :]] = state_dict.pop(k)
    state_dict["tok_embeddings.weight"] = state_dict.pop("transformer.wtes.0.weight")
    state_dict["pos_embeddings.weight"] = state_dict.pop("transformer.wpe.weight")
    state_dict["output.weight"] = state_dict.pop("lm_heads.0.weight")
    state_dict["norm.weight"] = state_dict.pop("transformer.ln_f.weight")
    for k, v in list(state_dict.items()):
        if k.startswith("transformer.h."):
            state_dict[k.replace("transformer.h.", "layers.")] = state_dict.pop(k)
            k = k.replace("transformer.h.", "layers.")
        if ".attn.c_attn." in k:
            state_dict[k.replace(".attn.c_attn.", ".attention.wqkv.")] = state_dict.pop(k)
            k = k.replace(".attn.c_attn.", ".attention.wqkv.")
        if ".attn.c_proj." in k:
            state_dict[k.replace(".attn.c_proj.", ".attention.wo.")] = state_dict.pop(k)
            k = k.replace(".attn.c_proj.", ".attention.wo.")
        if ".mlp.swiglu.w1." in k:
            state_dict[k.replace(".mlp.swiglu.w1.", ".feed_forward.swiglu.w1.")] = state_dict.pop(k)
            k = k.replace(".mlp.swiglu.w1.", ".feed_forward.swiglu.w1.")
        if ".mlp.swiglu.w3." in k:
            state_dict[k.replace(".mlp.swiglu.w3.", ".feed_forward.swiglu.w3.")] = state_dict.pop(k)
            k = k.replace(".mlp.swiglu.w3.", ".feed_forward.swiglu.w3.")
        if ".ln_1." in k:
            state_dict[k.replace(".ln_1.", ".attention_norm.")] = state_dict.pop(k)
            k = k.replace(".ln_1.", ".attention_norm.")
        if ".ln_2." in k:
            state_dict[k.replace(".ln_2.", ".ffn_norm.")] = state_dict.pop(k)
            k = k.replace(".ln_2.", ".ffn_norm.")
        if ".mlp.c_proj." in k:
            state_dict[k.replace(".mlp.c_proj.", ".feed_forward.w2.")] = state_dict.pop(k)
            k = k.replace(".mlp.c_proj.", ".feed_forward.w2.")

    model.load_state_dict(state_dict, assign=True)
    # simple_quantizer = WeightOnlyInt8QuantHandler(model)
    # quantized_state_dict = simple_quantizer.create_quantized_state_dict()
    # model = simple_quantizer.convert_for_runtime()
    # model.load_state_dict(quantized_state_dict, assign=True)
    model = model.to(device=device, dtype=precision)

    ###### TOKENIZER
    tokenizer_info = checkpoint.get("meta", {}).get("tokenizer", {})
    tokenizer = TrainedBPETokeniser(**tokenizer_info)

    ###### SPEAKER EMBEDDER
    # TODO: fix!
    smodel = SpeakerEncoder(
        weights_fpath=spk_emb_ckpt_path,
        device=device,
        eval=True,
        verbose=False,
    )
    return model.eval(), tokenizer, smodel


def build_model(
    *,
    precision: torch.dtype,
    checkpoint_path: Path = Path(""),
    spk_emb_ckpt_path: Path = Path(""),
    compile_prefill: bool = False,
    compile: bool = True,
    device: str = "cuda",
):
    assert checkpoint_path.is_file(), checkpoint_path

    print(f"Using device={device}")

    print("Loading model ...")
    t0 = time.time()
    model, tokenizer, smodel = _load_model(checkpoint_path, spk_emb_ckpt_path, device, precision)

    device_sync(device=device)  # MKG
    print(f"Time to load model: {time.time() - t0:.02f} seconds")

    torch.manual_seed(1234)
    model_size = sum([p.numel() * p.dtype.itemsize for p in itertools.chain(model.parameters(), model.buffers())])

    with torch.device(device):
        model.setup_spk_cond_mask()
        model.setup_caches(max_batch_size=2, max_seq_length=model.config.block_size)

    if compile:
        print("Compiling...Can take up to 2 mins.")
        global decode_one_token, prefill
        decode_one_token = torch.compile(
            decode_one_token,
            mode="max-autotune",
            fullgraph=True,
        )

        if compile_prefill:
            prefill = torch.compile(
                prefill,
                fullgraph=True,
                dynamic=True,
            )

    encoded = encode_tokens(tokenizer, "Hello, what's up?", device=device)
    spk_emb = torch.randn((1, 256), device=device, dtype=precision)

    device_sync(device=device)  # MKG
    t0 = time.perf_counter()
    y = generate(
        model,
        encoded,
        spk_emb,
        max_new_tokens=200,
        callback=lambda x: x,
        temperature=torch.tensor(1.0, device=device, dtype=precision),
        top_k=None,
        top_p=torch.tensor(0.95, device=device, dtype=precision),
        guidance_scale=torch.tensor(3.0, device=device, dtype=precision),
        end_of_audio_token=9999,  # don't end early for compilation stage.
    )

    device_sync(device=device)  # MKG

    print(f"Compilation time: {time.perf_counter() - t0:.2f} seconds")

    return model, tokenizer, smodel, model_size


def main(
    *,
    model,
    tokenizer,
    model_size,
    prompt: str,
    guidance_scale: torch.Tensor,
    temperature: torch.Tensor,
    spk_emb: torch.Tensor,
    top_k: Optional[torch.Tensor] = None,
    top_p: Optional[torch.Tensor] = None,
    device: str = "cuda",
) -> list:
    """Generates text samples based on a pre-trained Transformer model and tokenizer."""

    encoded = encode_tokens(tokenizer, prompt, device=device)
    prompt_length = encoded.size(0)

    aggregate_metrics: dict = {
        "tokens_per_sec": [],
    }

    device_sync(device=device)  # MKG

    if True:
        callback = lambda x: x
    t0 = time.perf_counter()

    y = generate(
        model,
        encoded,
        spk_emb,
        callback=callback,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
        guidance_scale=guidance_scale,
    )

    device_sync(device=device)  # MKG
    t = time.perf_counter() - t0

    tokens_generated = y.size(0) - prompt_length
    tokens_sec = tokens_generated / t
    aggregate_metrics["tokens_per_sec"].append(tokens_sec)
    print(f"Time for 1st stage LLM inference: {t:.02f} sec total, {tokens_sec:.02f} tokens/sec")
    print(f"Bandwidth achieved: {model_size * tokens_sec / 1e9:.02f} GB/s")
    # print(f"Average tokens/sec: {torch.mean(torch.tensor(aggregate_metrics['tokens_per_sec'])).item():.2f}")
    print(f"Memory used: {torch.cuda.max_memory_reserved() / 1e9:.02f} GB\n")

    return y.tolist()