import torch
import torch.nn as nn
import numpy as np

import ms_pred.common as common


class IntFeaturizer(nn.Module):
    """
    Base class for mapping integers to a vector representation (primarily to be used as a "richer" embedding for NNs
    processing integers).

    Subclasses should define `self.int_to_feat_matrix`, a matrix where each row is the vector representation for that
    integer, i.e. to get a vector representation for `5`, one could call `self.int_to_feat_matrix[5]`.

    Note that this class takes care of creating a fixed number (`self.NUM_EXTRA_EMBEDDINGS` to be precise) of extra
    "learned" embeddings these will be concatenated after the integer embeddings in the forward pass,
    be learned, and be used for extra  non-integer tokens such as the "to be confirmed token" (i.e., pad) token.
    They are indexed starting from `self.MAX_COUNT_INT`.
    """

    MAX_COUNT_INT = 255  # the maximum number of integers that we are going to see as a "count", i.e. 0 to MAX_COUNT_INT-1
    NUM_EXTRA_EMBEDDINGS = 1  # Number of extra embeddings to learn -- one for the "to be confirmed" embedding.

    def __init__(self, embedding_dim):
        super().__init__()
        weights = torch.zeros(self.NUM_EXTRA_EMBEDDINGS, embedding_dim)
        self._extra_embeddings = nn.Parameter(weights, requires_grad=True)
        nn.init.normal_(self._extra_embeddings, 0.0, 1.0)
        self.embedding_dim = embedding_dim

    def forward(self, tensor):
        """
        Convert the integer `tensor` into its new representation -- note that it gets stacked along final dimension.
        """
        orig_shape = tensor.shape
        out_tensor = torch.empty(
            (*orig_shape, self.embedding_dim), device=tensor.device
        )
        extra_embed = tensor >= self.MAX_COUNT_INT

        tensor = tensor.long()
        norm_embeds = self.int_to_feat_matrix[tensor[~extra_embed]]
        extra_embeds = self._extra_embeddings[tensor[extra_embed] - self.MAX_COUNT_INT]

        out_tensor[~extra_embed] = norm_embeds
        out_tensor[extra_embed] = extra_embeds

        temp_out = out_tensor.reshape(*orig_shape[:-1], -1)
        return temp_out

    @property
    def num_dim(self):
        return self.int_to_feat_matrix.shape[1]

    @property
    def full_dim(self):
        return self.num_dim * common.NORM_VEC.shape[0]


class FourierFeaturizer(IntFeaturizer):
    """
    Inspired by:
    Tancik, M., Srinivasan, P.P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R.,
    Barron, J.T. and Ng, R. (2020) ‘Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional
     Domains’, arXiv [cs.CV]. Available at: http://arxiv.org/abs/2006.10739.

    Some notes:
    * we'll put the frequencies at powers of 1/2 rather than random Gaussian samples; this means it will match the
        Binarizer quite closely but be a bit smoother.
    """

    def __init__(self):

        num_freqs = int(np.ceil(np.log2(self.MAX_COUNT_INT))) + 2
        # ^ need at least this many to ensure that the whole input range can be represented on the half circle.

        freqs = 0.5 ** torch.arange(num_freqs, dtype=torch.float32)
        freqs_time_2pi = 2 * np.pi * freqs

        super().__init__(
            embedding_dim=2 * freqs_time_2pi.shape[0]
        )  # 2 for cosine and sine

        # we will define the features at this frequency up front (as we only will ever see a fixed number of counts):
        combo_of_sinusoid_args = (
            torch.arange(self.MAX_COUNT_INT, dtype=torch.float32)[:, None]
            * freqs_time_2pi[None, :]
        )
        all_features = torch.cat(
            [torch.cos(combo_of_sinusoid_args), torch.sin(combo_of_sinusoid_args)],
            dim=1,
        )

        # ^ shape:  MAX_COUNT_INT x 2 * num_freqs
        self.int_to_feat_matrix = nn.Parameter(all_features.float())
        self.int_to_feat_matrix.requires_grad = False


class FourierFeaturizerSines(IntFeaturizer):
    """
    Like other fourier feats but sines only

    Inspired by:
    Tancik, M., Srinivasan, P.P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R.,
    Barron, J.T. and Ng, R. (2020) ‘Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional
     Domains’, arXiv [cs.CV]. Available at: http://arxiv.org/abs/2006.10739.

    Some notes:
    * we'll put the frequencies at powers of 1/2 rather than random Gaussian samples; this means it will match the
        Binarizer quite closely but be a bit smoother.
    """

    def __init__(self):

        num_freqs = int(np.ceil(np.log2(self.MAX_COUNT_INT))) + 2
        # ^ need at least this many to ensure that the whole input range can be represented on the half circle.

        freqs = (0.5 ** torch.arange(num_freqs, dtype=torch.float32))[2:]
        freqs_time_2pi = 2 * np.pi * freqs

        super().__init__(embedding_dim=freqs_time_2pi.shape[0])

        # we will define the features at this frequency up front (as we only will ever see a fixed number of counts):
        combo_of_sinusoid_args = (
            torch.arange(self.MAX_COUNT_INT, dtype=torch.float32)[:, None]
            * freqs_time_2pi[None, :]
        )
        # ^ shape:  MAX_COUNT_INT x 2 * num_freqs
        self.int_to_feat_matrix = nn.Parameter(
            torch.sin(combo_of_sinusoid_args).float()
        )
        self.int_to_feat_matrix.requires_grad = False


class FourierFeaturizerAbsoluteSines(IntFeaturizer):
    """
    Like other fourier feats but sines only and absoluted.

    Inspired by:
    Tancik, M., Srinivasan, P.P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R.,
    Barron, J.T. and Ng, R. (2020) ‘Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional
     Domains’, arXiv [cs.CV]. Available at: http://arxiv.org/abs/2006.10739.

    Some notes:
    * we'll put the frequencies at powers of 1/2 rather than random Gaussian samples; this means it will match the
        Binarizer quite closely but be a bit smoother.
    """

    def __init__(self):

        num_freqs = int(np.ceil(np.log2(self.MAX_COUNT_INT))) + 2

        freqs = (0.5 ** torch.arange(num_freqs, dtype=torch.float32))[2:]
        freqs_time_2pi = 2 * np.pi * freqs

        super().__init__(embedding_dim=freqs_time_2pi.shape[0])

        # we will define the features at this frequency up front (as we only will ever see a fixed number of counts):
        combo_of_sinusoid_args = (
            torch.arange(self.MAX_COUNT_INT, dtype=torch.float32)[:, None]
            * freqs_time_2pi[None, :]
        )
        # ^ shape:  MAX_COUNT_INT x 2 * num_freqs
        self.int_to_feat_matrix = nn.Parameter(
            torch.abs(torch.sin(combo_of_sinusoid_args)).float()
        )
        self.int_to_feat_matrix.requires_grad = False


class RBFFeaturizer(IntFeaturizer):
    """
    A featurizer that puts radial basis functions evenly between 0 and max_count-1. These will have a width of
    (max_count-1) / (num_funcs) to decay to about 0.6 of its original height at reaching the next func.

    """

    def __init__(self, num_funcs=32):
        """
        :param num_funcs: number of radial basis functions to use: their width will automatically be chosen -- see class
                            docstring.
        """
        super().__init__(embedding_dim=num_funcs)
        width = (self.MAX_COUNT_INT - 1) / num_funcs
        centers = torch.linspace(0, self.MAX_COUNT_INT - 1, num_funcs)

        pre_exponential_terms = (
            -0.5
            * ((torch.arange(self.MAX_COUNT_INT)[:, None] - centers[None, :]) / width)
            ** 2
        )
        # ^ shape: MAX_COUNT_INT x num_funcs
        feats = torch.exp(pre_exponential_terms)

        self.int_to_feat_matrix = nn.Parameter(feats.float())
        self.int_to_feat_matrix.requires_grad = False


class OneHotFeaturizer(IntFeaturizer):
    """
    A featurizer that turns integers into their one hot encoding.

    Represents:
     - 0 as 1000000000...
     - 1 as 0100000000...
     - 2 as 0010000000...
     and so on.
    """

    def __init__(self):
        super().__init__(embedding_dim=self.MAX_COUNT_INT)
        feats = torch.eye(self.MAX_COUNT_INT)
        self.int_to_feat_matrix = nn.Parameter(feats.float())
        self.int_to_feat_matrix.requires_grad = False


class LearnedFeaturizer(IntFeaturizer):
    """
    Learns the features for the different integers.

    Pretty much `nn.Embedding` but we get to use the forward of the superclass which behaves a bit differently.
    """

    def __init__(self, feature_dim=32):
        super().__init__(embedding_dim=feature_dim)
        weights = torch.zeros(self.MAX_COUNT_INT, feature_dim)
        self.int_to_feat_matrix = nn.Parameter(weights, requires_grad=True)
        nn.init.normal_(self.int_to_feat_matrix, 0.0, 1.0)


class FloatFeaturizer(IntFeaturizer):
    """
    Norms the features
    """

    def __init__(self):
        # Norm vec
        # Placeholder..
        super().__init__(embedding_dim=1)
        self.norm_vec = torch.from_numpy(common.NORM_VEC).float()
        self.norm_vec = nn.Parameter(self.norm_vec)
        self.norm_vec.requires_grad = False

    def forward(self, tensor):
        """
        Convert the integer `tensor` into its new representation -- note that it gets stacked along final dimension.
        """
        tens_shape = tensor.shape
        out_shape = [1] * (len(tens_shape) - 1) + [-1]
        return tensor / self.norm_vec.reshape(*out_shape)

    @property
    def num_dim(self):
        return 1


def get_embedder(embedder):
    if embedder == "fourier":
        embedder = FourierFeaturizer()
    elif embedder == "rbf":
        embedder = RBFFeaturizer()
    elif embedder == "one-hot":
        embedder = OneHotFeaturizer()
    elif embedder == "learnt":
        embedder = LearnedFeaturizer()
    elif embedder == "float":
        embedder = FloatFeaturizer()
    elif embedder == "fourier-sines":
        embedder = FourierFeaturizerSines()
    elif embedder == "abs-sines":
        embedder = FourierFeaturizerAbsoluteSines()
    else:
        raise NotImplementedError
    return embedder
