import math
import torch

from . import misc


def _natural_cubic_spline_coeffs_without_missing_values(times, path):
    # path should be a tensor of shape (..., length)
    # Will return the b, two_c, three_d coefficients of the derivative of the cubic spline interpolating the path.

    length = path.size(-1)

    if length < 2:
        # In practice this should always already be caught in __init__.
        raise ValueError("Must have a time dimension of size at least 2.")
    elif length == 2:
        a = path[..., :1]
        b = (path[..., 1:] - path[..., :1]) / (times[..., 1:] - times[..., :1])
        two_c = torch.zeros(*path.shape[:-1], 1, dtype=path.dtype, device=path.device)
        three_d = torch.zeros(*path.shape[:-1], 1, dtype=path.dtype, device=path.device)
    else:
        # Set up some intermediate values
        time_diffs = times[1:] - times[:-1]
        time_diffs_reciprocal = time_diffs.reciprocal()
        time_diffs_reciprocal_squared = time_diffs_reciprocal ** 2
        three_path_diffs = 3 * (path[..., 1:] - path[..., :-1])
        six_path_diffs = 2 * three_path_diffs
        path_diffs_scaled = three_path_diffs * time_diffs_reciprocal_squared

        # Solve a tridiagonal linear system to find the derivatives at the knots
        system_diagonal = torch.empty(length, dtype=path.dtype, device=path.device)
        system_diagonal[:-1] = time_diffs_reciprocal
        system_diagonal[-1] = 0
        system_diagonal[1:] += time_diffs_reciprocal
        system_diagonal *= 2
        system_rhs = torch.empty_like(path)
        system_rhs[..., :-1] = path_diffs_scaled
        system_rhs[..., -1] = 0
        system_rhs[..., 1:] += path_diffs_scaled
        knot_derivatives = misc.tridiagonal_solve(system_rhs, time_diffs_reciprocal, system_diagonal,
                                                  time_diffs_reciprocal)

        # Do some algebra to find the coefficients of the spline
        a = path[..., :-1]
        b = knot_derivatives[..., :-1]
        two_c = (six_path_diffs * time_diffs_reciprocal
                 - 4 * knot_derivatives[..., :-1]
                 - 2 * knot_derivatives[..., 1:]) * time_diffs_reciprocal
        three_d = (-six_path_diffs * time_diffs_reciprocal
                   + 3 * (knot_derivatives[..., :-1]
                          + knot_derivatives[..., 1:])) * time_diffs_reciprocal_squared

    return a, b, two_c, three_d


def _natural_cubic_spline_coeffs_with_missing_values(t, path):
    if len(path.shape) == 1:
        # We have to break everything down to individual scalar paths because of the possibility of missing values
        # being different in different channels
        return _natural_cubic_spline_coeffs_with_missing_values_scalar(t, path)
    else:
        a_pieces = []
        b_pieces = []
        two_c_pieces = []
        three_d_pieces = []
        for p in path.unbind(dim=0):  # TODO: parallelise over this
            a, b, two_c, three_d = _natural_cubic_spline_coeffs_with_missing_values(t, p)
            a_pieces.append(a)
            b_pieces.append(b)
            two_c_pieces.append(two_c)
            three_d_pieces.append(three_d)
        return (misc.cheap_stack(a_pieces, dim=0),
                misc.cheap_stack(b_pieces, dim=0),
                misc.cheap_stack(two_c_pieces, dim=0),
                misc.cheap_stack(three_d_pieces, dim=0))


def _natural_cubic_spline_coeffs_with_missing_values_scalar(times, path):
    # times and path both have shape (length,)

    # How to deal with missing values at the start or end of the time series? We're creating some splines, so one
    # option is just to extend the first piece backwards, and the final piece forwards. But polynomials tend to
    # behave badly when extended beyond the interval they were constructed on, so the results can easily end up
    # being awful.
    # Instead we impute an observation at the very start equal to the first actual observation made, and impute an
    # observation at the very end equal to the last actual observation made, and then procede with splines as
    # normal.

    not_nan = ~torch.isnan(path)
    path_no_nan = path.masked_select(not_nan)

    if path_no_nan.size(0) == 0:
        # Every entry is a NaN, so we take a constant path with derivative zero, so return zero coefficients.
        # Note that we may assume that path.size(0) >= 2 by the checks in __init__ so "path.size(0) - 1" is a valid
        # thing to do.
        return (torch.zeros(path.size(0) - 1, dtype=path.dtype, device=path.device),
                torch.zeros(path.size(0) - 1, dtype=path.dtype, device=path.device),
                torch.zeros(path.size(0) - 1, dtype=path.dtype, device=path.device),
                torch.zeros(path.size(0) - 1, dtype=path.dtype, device=path.device))
    # else we have at least one non-NaN entry, in which case we're going to impute at least one more entry (as
    # the path is of length at least 2 so the start and the end aren't the same), so we will then have at least two
    # non-Nan entries. In particular we can call _compute_coeffs safely later.

    need_new_not_nan = False
    if torch.isnan(path[0]):
        if not need_new_not_nan:
            path = path.clone()
            need_new_not_nan = True
        path[0] = path_no_nan[0]
    if torch.isnan(path[-1]):
        if not need_new_not_nan:
            path = path.clone()
            need_new_not_nan = True
        path[-1] = path_no_nan[-1]
    if need_new_not_nan:
        not_nan = ~torch.isnan(path)
        path_no_nan = path.masked_select(not_nan)
    times_no_nan = times.masked_select(not_nan)

    # Find the coefficients on the pieces we do understand
    # These all have shape (len - 1,)
    (a_pieces_no_nan,
     b_pieces_no_nan,
     two_c_pieces_no_nan,
     three_d_pieces_no_nan) = _natural_cubic_spline_coeffs_without_missing_values(times_no_nan, path_no_nan)

    # Now we're going to normalise them to give coefficients on every interval
    a_pieces = []
    b_pieces = []
    two_c_pieces = []
    three_d_pieces = []

    iter_times_no_nan = iter(times_no_nan)
    iter_coeffs_no_nan = iter(zip(a_pieces_no_nan, b_pieces_no_nan, two_c_pieces_no_nan, three_d_pieces_no_nan))
    next_time_no_nan = next(iter_times_no_nan)
    for time in times[:-1]:
        # will always trigger on the first iteration because of how we've imputed missing values at the start and
        # end of the time series.
        if time >= next_time_no_nan:
            prev_time_no_nan = next_time_no_nan
            next_time_no_nan = next(iter_times_no_nan)
            next_a_no_nan, next_b_no_nan, next_two_c_no_nan, next_three_d_no_nan = next(iter_coeffs_no_nan)
        offset = prev_time_no_nan - time
        a_inner = (0.5 * next_two_c_no_nan - next_three_d_no_nan * offset / 3) * offset
        a_pieces.append(next_a_no_nan + (a_inner - next_b_no_nan) * offset)
        b_pieces.append(next_b_no_nan + (next_three_d_no_nan * offset - next_two_c_no_nan) * offset)
        two_c_pieces.append(next_two_c_no_nan - 2 * next_three_d_no_nan * offset)
        three_d_pieces.append(next_three_d_no_nan)

    return (misc.cheap_stack(a_pieces, dim=0),
            misc.cheap_stack(b_pieces, dim=0),
            misc.cheap_stack(two_c_pieces, dim=0),
            misc.cheap_stack(three_d_pieces, dim=0))


# The mathematics of this are adapted from  http://mathworld.wolfram.com/CubicSpline.html, although they only treat the
# case of each piece being parameterised by [0, 1]. (We instead take the length of each piece to be the difference in
# time stamps.)
def natural_cubic_spline_coeffs(t, X):
    """Calculates the coefficients of the natural cubic spline approximation to the batch of controls given.

    Arguments:
        t: One dimensional tensor of times. Must be monotonically increasing.
        X: tensor of values, of shape (..., L, C), where ... is some number of batch dimensions, L is some length
            that must be the same as the length of t, and C is some number of channels. This is interpreted as a
            (batch of) paths taking values in a C-dimensional real vector space, with L observations. Missing values
            are supported, and should be represented as NaNs.

    In particular, the support for missing values allows for batching together elements that are observed at
    different times; just set them to have missing values at each other's observation times.

    Warning:
        Calling this function can be pretty slow. Make sure to cache the result, and don't reinstantiate it on every
        forward pass, if at all possible.

    Returns:
        Four tensors, which should in turn be passed to `controldiffeq.NaturalCubicSpline`.

        Why do we do it like this? Because typically you want to use PyTorch tensors at various interfaces, for example
        when loading a batch from a DataLoader. If we wrapped all of this up into just the
        `controldiffeq.NaturalCubicSpline` class then that sort of thing wouldn't be possible.

        As such the suggested use is to:
        (a) Load your data.
        (b) Preprocess it with this function.
        (c) Save the result.
        (d) Treat the result as your dataset as far as PyTorch's `torch.utils.data.Dataset` and
            `torch.utils.data.DataLoader` classes are concerned.
        (e) Call NaturalCubicSpline as the first part of your model.

        See also the accompanying example.py.
    """
    if not t.is_floating_point():
        raise ValueError("t and X must both be floating point/")
    if not X.is_floating_point():
        raise ValueError("t and X must both be floating point/")
    if len(t.shape) != 1:
        raise ValueError("t must be one dimensional.")
    prev_t_i = -math.inf
    for t_i in t:
        if t_i <= prev_t_i:
            raise ValueError("t must be monotonically increasing.")

    if len(X.shape) < 2:
        raise ValueError("X must have at least two dimensions, corresponding to time and channels.")

    if X.size(-2) != t.size(0):
        raise ValueError("The time dimension of X must equal the length of t.")

    if t.size(0) < 2:
        raise ValueError("Must have a time dimension of size at least 2.")

    if torch.isnan(X).any():
        # Transpose because channels are a batch dimension for the purpose of finding interpolating polynomials.
        # b, two_c, three_d have shape (..., channels, length - 1)
        a, b, two_c, three_d = _natural_cubic_spline_coeffs_with_missing_values(t, X.transpose(-1, -2))
    else:
        # Can do things more quickly in this case.
        a, b, two_c, three_d = _natural_cubic_spline_coeffs_without_missing_values(t, X.transpose(-1, -2))

    # These all have shape (..., length - 1, channels)
    a = a.transpose(-1, -2)
    b = b.transpose(-1, -2)
    two_c = two_c.transpose(-1, -2)
    three_d = three_d.transpose(-1, -2)
    return a, b, two_c, three_d


class NaturalCubicSpline:
    """Calculates the natural cubic spline approximation to the batch of controls given. Also calculates its derivative.

    Example:
        times = torch.linspace(0, 1, 7)
        # (2, 1) are batch dimensions. 7 is the time dimension (of the same length as t). 3 is the channel dimension.
        X = torch.rand(2, 1, 7, 3)
        coeffs = natural_cubic_spline_coeffs(times, X)
        # ...at this point you can save the coeffs, put them through PyTorch's Datasets and DataLoaders, etc...
        spline = NaturalCubicSpline(times, coeffs)
        t = torch.tensor(0.4)
        # will be a tensor of shape (2, 1, 3), corresponding to batch and channel dimensions
        out = spline.derivative(t)
    """

    def __init__(self, times, coeffs, **kwargs):
        """
        Arguments:
            times: As was passed as an argument to natural_cubic_spline_coeffs.
            coeffs: As returned by natural_cubic_spline_coeffs.
        """
        super(NaturalCubicSpline, self).__init__(**kwargs)

        a, b, two_c, three_d = coeffs

        self._times = times
        self._a = a
        self._b = b
        # as we're typically computing derivatives, we store the multiples of these coefficients that are more useful
        self._two_c = two_c
        self._three_d = three_d

    def _interpret_t(self, t):
        maxlen = self._b.size(-2) - 1
        index = (t > self._times).sum() - 1
        index = index.clamp(0, maxlen)  # clamp because t may go outside of [t[0], t[-1]]; this is fine
        # will never access the last element of self._times; this is correct behaviour
        fractional_part = t - self._times[index]
        return fractional_part, index

    def evaluate(self, t):
        """Evaluates the natural cubic spline interpolation at a point t, which should be a scalar tensor."""
        fractional_part, index = self._interpret_t(t)
        inner = 0.5 * self._two_c[..., index, :] + self._three_d[..., index, :] * fractional_part / 3
        inner = self._b[..., index, :] + inner * fractional_part
        return self._a[..., index, :] + inner * fractional_part

    def derivative(self, t):
        """Evaluates the derivative of the natural cubic spline at a point t, which should be a scalar tensor."""
        fractional_part, index = self._interpret_t(t)
        inner = self._two_c[..., index, :] + self._three_d[..., index, :] * fractional_part
        deriv = self._b[..., index, :] + inner * fractional_part
        return deriv



class NaturalCubicSpline2:
    def __init__(self, times, coeffs_h, coeffs_l):
        """
        Arguments:
            times: 时间点，与传递给 natural_cubic_spline_coeffs 的相同。
            coeffs_h: 第一个数据集的系数。
            coeffs_l: 第二个数据集的系数。
        """
        self.spline_h = NaturalCubicSpline(times, coeffs_h)
        self.spline_l = NaturalCubicSpline(times, coeffs_l)

    def _interpret_t(self, t):
        """
        由于两个样条曲线共享相同的时间点，我们只需要调用其中一个的 _interpret_t 方法。
        """
        return self.spline_h._interpret_t(t)

    def evaluate(self, t):
        """
        在给定的时间点 t 计算两个样条曲线的插值，并在特征维度上连接结果。
        """
        eval_h = self.spline_h.evaluate(t)
        eval_l = self.spline_l.evaluate(t)
        # print(torch.cat((eval_h, eval_l), dim=-1).shape)
        # exit()
        return torch.cat((eval_h, eval_l), dim=-1)

    def derivative(self, t):
        """
        在给定的时间点 t 计算两个样条曲线的导数，并在特征维度上连接结果。
        """
        deriv_h = self.spline_h.derivative(t)
        deriv_l = self.spline_l.derivative(t)
        return torch.cat((deriv_h, deriv_l), dim=-1)
