from __future__ import annotations

from collections.abc import Sequence
from typing import TYPE_CHECKING, Any, Optional, Union

import numpy as np
import onnxruntime
from numpy.typing import NDArray

from style_bert_vits2.constants import Languages
from style_bert_vits2.nlp import bert_models, onnx_bert_models
from style_bert_vits2.utils import get_onnx_device_options


if TYPE_CHECKING:
    import torch


def extract_bert_feature(
    text: str,
    word2ph: list[int],
    device: str,
    assist_text: Optional[str] = None,
    assist_text_weight: float = 0.7,
) -> torch.Tensor:
    """
    英語のテキストから BERT の特徴量を抽出する (PyTorch 推論)

    Args:
        text (str): 英語のテキスト
        word2ph (list[int]): 元のテキストの各文字に音素が何個割り当てられるかを表すリスト
        device (str): 推論に利用するデバイス
        assist_text (Optional[str], optional): 補助テキスト (デフォルト: None)
        assist_text_weight (float, optional): 補助テキストの重み (デフォルト: 0.7)

    Returns:
        torch.Tensor: BERT の特徴量
    """

    import torch

    if device == "cuda" and not torch.cuda.is_available():
        device = "cpu"
    model = bert_models.load_model(Languages.EN, device_map=device)
    bert_models.transfer_model(Languages.EN, device)

    style_res_mean = None
    with torch.no_grad():
        tokenizer = bert_models.load_tokenizer(Languages.EN)
        inputs = tokenizer(text, return_tensors="pt")
        for i in inputs:
            inputs[i] = inputs[i].to(device)  # type: ignore
        res = model(**inputs, output_hidden_states=True)
        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
        if assist_text:
            style_inputs = tokenizer(assist_text, return_tensors="pt")
            for i in style_inputs:
                style_inputs[i] = style_inputs[i].to(device)  # type: ignore
            style_res = model(**style_inputs, output_hidden_states=True)
            style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].cpu()
            style_res_mean = style_res.mean(0)

    assert len(word2ph) == res.shape[0], (text, res.shape[0], len(word2ph))
    word2phone = word2ph
    phone_level_feature = []
    for i in range(len(word2phone)):
        if assist_text:
            assert style_res_mean is not None
            repeat_feature = (
                res[i].repeat(word2phone[i], 1) * (1 - assist_text_weight)
                + style_res_mean.repeat(word2phone[i], 1) * assist_text_weight
            )
        else:
            repeat_feature = res[i].repeat(word2phone[i], 1)
        phone_level_feature.append(repeat_feature)

    phone_level_feature = torch.cat(phone_level_feature, dim=0)

    return phone_level_feature.T


def extract_bert_feature_onnx(
    text: str,
    word2ph: list[int],
    onnx_providers: Sequence[Union[str, tuple[str, dict[str, Any]]]],
    assist_text: Optional[str] = None,
    assist_text_weight: float = 0.7,
) -> NDArray[Any]:
    """
    英語のテキストから BERT の特徴量を抽出する (ONNX 推論)

    Args:
        text (str): 英語のテキスト
        word2ph (list[int]): 元のテキストの各文字に音素が何個割り当てられるかを表すリスト
        onnx_providers (list[str]): ONNX 推論で利用する ExecutionProvider (CPUExecutionProvider, CUDAExecutionProvider など)
        assist_text (Optional[str], optional): 補助テキスト (デフォルト: None)
        assist_text_weight (float, optional): 補助テキストの重み (デフォルト: 0.7)

    Returns:
        NDArray[Any]: BERT の特徴量
    """

    # トークナイザーとモデルの読み込み
    tokenizer = onnx_bert_models.load_tokenizer(Languages.EN)
    session = onnx_bert_models.load_model(
        language=Languages.EN,
        onnx_providers=onnx_providers,
    )
    input_names = [input.name for input in session.get_inputs()]
    output_name = session.get_outputs()[0].name

    # 入力テンソルの転送に使用するデバイス種別, デバイス ID, 実行オプションを取得
    device_type, device_id, run_options = get_onnx_device_options(session, onnx_providers)  # fmt: skip

    # 入力をテンソルに変換
    inputs = tokenizer(text, return_tensors="np")
    input_tensor = [
        inputs["input_ids"].astype(np.int64),  # type: ignore
        inputs["attention_mask"].astype(np.int64),  # type: ignore
    ]
    # 推論デバイスに入力テンソルを割り当て
    ## GPU 推論の場合、device_type + device_id に対応する GPU デバイスに入力テンソルが割り当てられる
    io_binding = session.io_binding()
    for name, value in zip(input_names, input_tensor):
        gpu_tensor = onnxruntime.OrtValue.ortvalue_from_numpy(
            value, device_type, device_id
        )
        io_binding.bind_ortvalue_input(name, gpu_tensor)
    # text から BERT 特徴量を抽出
    io_binding.bind_output(output_name, device_type)
    session.run_with_iobinding(io_binding, run_options=run_options)
    res = io_binding.get_outputs()[0].numpy()

    style_res_mean = None
    if assist_text:
        # 入力をテンソルに変換
        style_inputs = tokenizer(assist_text, return_tensors="np")
        style_input_tensor = [
            style_inputs["input_ids"].astype(np.int64),  # type: ignore
            style_inputs["attention_mask"].astype(np.int64),  # type: ignore
        ]
        # 推論デバイスに入力テンソルを割り当て
        ## GPU 推論の場合、device_type + device_id に対応する GPU デバイスに入力テンソルが割り当てられる
        io_binding = session.io_binding()  # IOBinding は作り直す必要がある
        for name, value in zip(input_names, style_input_tensor):
            gpu_tensor = onnxruntime.OrtValue.ortvalue_from_numpy(
                value, device_type, device_id
            )
            io_binding.bind_ortvalue_input(name, gpu_tensor)
        # assist_text から BERT 特徴量を抽出
        io_binding.bind_output(output_name, device_type)
        session.run_with_iobinding(io_binding, run_options=run_options)
        style_res = io_binding.get_outputs()[0].numpy()
        style_res_mean = np.mean(style_res, axis=0)

    assert len(word2ph) == res.shape[0], (text, res.shape[0], len(word2ph))
    word2phone = word2ph
    phone_level_feature = []
    for i in range(len(word2phone)):
        if assist_text:
            assert style_res_mean is not None
            repeat_feature = (
                np.tile(res[i], (word2phone[i], 1)) * (1 - assist_text_weight)
                + np.tile(style_res_mean, (word2phone[i], 1)) * assist_text_weight
            )
        else:
            repeat_feature = np.tile(res[i], (word2phone[i], 1))
        phone_level_feature.append(repeat_feature)

    phone_level_feature = np.concatenate(phone_level_feature, axis=0)

    return phone_level_feature.T