# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
#               2024 Alibaba Inc (authors: Xiang Lyu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Modified from ESPnet(https://github.com/espnet/espnet)
"""Unility functions for Transformer."""

import random
import threading
from typing import List

import numpy as np
import torch

IGNORE_ID = -1


def pad_list(xs: List[torch.Tensor], pad_value: int):
    """Perform padding for the list of tensors.

    Args:
        xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
        pad_value (float): Value for padding.

    Returns:
        Tensor: Padded tensor (B, Tmax, `*`).

    Examples:
        >>> x = [torch.ones(4), torch.ones(2), torch.ones(1)]
        >>> x
        [tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])]
        >>> pad_list(x, 0)
        tensor([[1., 1., 1., 1.],
                [1., 1., 0., 0.],
                [1., 0., 0., 0.]])

    """
    max_len = max([len(item) for item in xs])
    batchs = len(xs)
    ndim = xs[0].ndim
    if ndim == 1:
        pad_res = torch.zeros(batchs, max_len, dtype=xs[0].dtype, device=xs[0].device)
    elif ndim == 2:
        pad_res = torch.zeros(
            batchs, max_len, xs[0].shape[1], dtype=xs[0].dtype, device=xs[0].device
        )
    elif ndim == 3:
        pad_res = torch.zeros(
            batchs,
            max_len,
            xs[0].shape[1],
            xs[0].shape[2],
            dtype=xs[0].dtype,
            device=xs[0].device,
        )
    else:
        raise ValueError(f"Unsupported ndim: {ndim}")
    pad_res.fill_(pad_value)
    for i in range(batchs):
        pad_res[i, : len(xs[i])] = xs[i]
    return pad_res


def th_accuracy(
    pad_outputs: torch.Tensor, pad_targets: torch.Tensor, ignore_label: int
) -> torch.Tensor:
    """Calculate accuracy.

    Args:
        pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
        pad_targets (LongTensor): Target label tensors (B, Lmax).
        ignore_label (int): Ignore label id.

    Returns:
        torch.Tensor: Accuracy value (0.0 - 1.0).

    """
    pad_pred = pad_outputs.view(
        pad_targets.size(0), pad_targets.size(1), pad_outputs.size(1)
    ).argmax(2)
    mask = pad_targets != ignore_label
    numerator = torch.sum(pad_pred.masked_select(mask) == pad_targets.masked_select(mask))
    denominator = torch.sum(mask)
    return (numerator / denominator).detach()


def get_padding(kernel_size, dilation=1):
    return int((kernel_size * dilation - dilation) / 2)


def init_weights(m, mean=0.0, std=0.01):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        m.weight.data.normal_(mean, std)


# Repetition Aware Sampling in VALL-E 2
def ras_sampling(
    weighted_scores,
    decoded_tokens,
    sampling,
    top_p=0.8,
    top_k=25,
    win_size=10,
    tau_r=0.1,
):
    top_ids = nucleus_sampling(weighted_scores, top_p=top_p, top_k=top_k)
    rep_num = (
        (torch.tensor(decoded_tokens[-win_size:]).to(weighted_scores.device) == top_ids)
        .sum()
        .item()
    )
    if rep_num >= win_size * tau_r:
        top_ids = random_sampling(weighted_scores, decoded_tokens, sampling)
    return top_ids


def nucleus_sampling(weighted_scores, top_p=0.8, top_k=25):
    prob, indices = [], []
    cum_prob = 0.0
    sorted_value, sorted_idx = weighted_scores.softmax(dim=0).sort(descending=True, stable=True)
    for i in range(len(sorted_idx)):
        # sampling both top-p and numbers.
        if cum_prob < top_p and len(prob) < top_k:
            cum_prob += sorted_value[i]
            prob.append(sorted_value[i])
            indices.append(sorted_idx[i])
        else:
            break
    prob = torch.tensor(prob).to(weighted_scores)
    indices = torch.tensor(indices, dtype=torch.long).to(weighted_scores.device)
    top_ids = indices[prob.multinomial(1, replacement=True)]
    return top_ids


def random_sampling(weighted_scores, decoded_tokens, sampling):
    top_ids = weighted_scores.softmax(dim=0).multinomial(1, replacement=True)
    return top_ids


def fade_in_out(fade_in_mel, fade_out_mel, window):
    device = fade_in_mel.device
    fade_in_mel, fade_out_mel = fade_in_mel.cpu(), fade_out_mel.cpu()
    mel_overlap_len = int(window.shape[0] / 2)
    fade_in_mel[..., :mel_overlap_len] = (
        fade_in_mel[..., :mel_overlap_len] * window[:mel_overlap_len]
        + fade_out_mel[..., -mel_overlap_len:] * window[mel_overlap_len:]
    )
    return fade_in_mel.to(device)


def set_all_random_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)


class ThreadSafeDict:
    def __init__(self):
        self._dict = {}
        # 使用 RLock 可重入锁，避免死锁
        self._lock = threading.RLock()

    def get(self, key, default=None):
        with self._lock:
            return self._dict.get(key, default)

    def set(self, key, value):
        with self._lock:
            self._dict[key] = value

    def pop(self, key):
        with self._lock:
            return self._dict.pop(key, None)
