"""Unility functions for Transformer."""

import multiprocessing 
import math
from typing import Tuple, List

import torch
from torch.nn.utils.rnn import pad_sequence

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.]])

    """
    n_batch = len(xs)
    max_len = max([x.size(0) for x in xs])
    pad = torch.zeros(n_batch, max_len, dtype=xs[0].dtype, device=xs[0].device)
    pad = pad.fill_(pad_value)
    for i in range(n_batch):
        pad[i, :xs[i].size(0)] = xs[i]

    return pad

class add_sos_eos_class:
    def __init__(self):
        self.sos = None
        self.eos = None
        self.lock = multiprocessing.Lock()
    def _init_sos(self, ys_pad: torch.Tensor, sos: int, eos: int):
        if self.sos is None:
            self.lock.acquire()
            if self.sos is None:
                self.sos = torch.tensor([sos],
                        dtype=torch.long,
                        requires_grad=False,
                        device=ys_pad.device)
                self.eos = torch.tensor([eos],
                        dtype=torch.long,
                        requires_grad=False,
                        device=ys_pad.device)
            self.lock.release()

    def __call__(self, ys_pad: torch.Tensor, sos: int, eos: int,
                ignore_id: int):
        # solve race condition in multiprocessing by lock
        self._init_sos(ys_pad, sos, eos)
        ys = [y[y != ignore_id] for y in ys_pad]  # parse padded ys
        ys_in = [torch.cat([self.sos, y], dim=0) for y in ys]
        ys_out = [torch.cat([y, self.eos], dim=0) for y in ys]
        return pad_list(ys_in, eos), pad_list(ys_out, ignore_id)
        
def add_sos_eos(ys_pad: torch.Tensor, sos: int, eos: int,
                ignore_id: int) -> Tuple[torch.Tensor, torch.Tensor]:
    """Add <sos> and <eos> labels.

    Args:
        ys_pad (torch.Tensor): batch of padded target sequences (B, Lmax)
        sos (int): index of <sos>
        eos (int): index of <eeos>
        ignore_id (int): index of padding

    Returns:
        ys_in (torch.Tensor) : (B, Lmax + 1)
        ys_out (torch.Tensor) : (B, Lmax + 1)

    Examples:
        >>> sos_id = 10
        >>> eos_id = 11
        >>> ignore_id = -1
        >>> ys_pad
        tensor([[ 1,  2,  3,  4,  5],
                [ 4,  5,  6, -1, -1],
                [ 7,  8,  9, -1, -1]], dtype=torch.int32)
        >>> ys_in,ys_out=add_sos_eos(ys_pad, sos_id , eos_id, ignore_id)
        >>> ys_in
        tensor([[10,  1,  2,  3,  4,  5],
                [10,  4,  5,  6, 11, 11],
                [10,  7,  8,  9, 11, 11]])
        >>> ys_out
        tensor([[ 1,  2,  3,  4,  5, 11],
                [ 4,  5,  6, 11, -1, -1],
                [ 7,  8,  9, 11, -1, -1]])
    """
    _sos = torch.tensor([sos],
                        dtype=torch.long,
                        requires_grad=False,
                        device=ys_pad.device)
    _eos = torch.tensor([eos],
                        dtype=torch.long,
                        requires_grad=False,
                        device=ys_pad.device)
    ys = [y[y != ignore_id] for y in ys_pad]  # parse padded ys
    ys_in = [torch.cat([_sos, y], dim=0) for y in ys]
    ys_out = [torch.cat([y, _eos], dim=0) for y in ys]
    return pad_list(ys_in, eos), pad_list(ys_out, ignore_id)


def reverse_pad_list(ys_pad: torch.Tensor,
                     ys_lens: torch.Tensor,
                     pad_value: float = -1.0) -> torch.Tensor:
    """Reverse padding for the list of tensors.

    Args:
        ys_pad (tensor): The padded tensor (B, Tokenmax).
        ys_lens (tensor): The lens of token seqs (B)
        pad_value (int): Value for padding.

    Returns:
        Tensor: Padded tensor (B, Tokenmax).

    Examples:
        >>> x
        tensor([[1, 2, 3, 4], [5, 6, 7, 0], [8, 9, 0, 0]])
        >>> pad_list(x, 0)
        tensor([[4, 3, 2, 1],
                [7, 6, 5, 0],
                [9, 8, 0, 0]])

    """
    r_ys_pad = pad_sequence([(torch.flip(y.int()[:i], [0]))
                             for y, i in zip(ys_pad, ys_lens)], True,
                            pad_value)
    return r_ys_pad


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

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

    Returns:
        float: 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 float(numerator) / float(denominator)


def get_activation(act):
    """Return activation function."""
    # Lazy load to avoid unused import
    from wenet.transformer.swish import Swish

    activation_funcs = {
        "hardtanh": torch.nn.Hardtanh,
        "tanh": torch.nn.Tanh,
        "relu": torch.nn.ReLU,
        "selu": torch.nn.SELU,
        "swish": Swish,
        "gelu": torch.nn.GELU
    }

    return activation_funcs[act]()


def get_subsample(config):
    input_layer = config["encoder_conf"]["input_layer"]
    assert input_layer in ["conv2d", "conv2d6", "conv2d8"]
    if input_layer == "conv2d":
        return 4
    elif input_layer == "conv2d6":
        return 6
    elif input_layer == "conv2d8":
        return 8


def remove_duplicates_and_blank(hyp: List[int]) -> List[int]:
    new_hyp: List[int] = []
    cur = 0
    while cur < len(hyp):
        if hyp[cur] != 0:
            new_hyp.append(hyp[cur])
        prev = cur
        while cur < len(hyp) and hyp[cur] == hyp[prev]:
            cur += 1
    return new_hyp


def log_add(args: List[int]) -> float:
    """
    Stable log add
    """
    if all(a == -float('inf') for a in args):
        return -float('inf')
    a_max = max(args)
    lsp = math.log(sum(math.exp(a - a_max) for a in args))
    return a_max + lsp
