import math
from typing import Callable

import torch
import torch.nn.functional as F
from torch import Tensor
from torch import nn as nn
from torch.nn.parameter import Parameter

from colossalai.context import seed
from colossalai.legacy.registry import LAYERS
from colossalai.nn import init as init
from colossalai.utils.cuda import get_current_device

from ..utils import to_2tuple


def drop_path(x, drop_prob: float = 0., training: bool = False):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
    'survival rate' as the argument.

    Args:
        drop_prob (float, optional): probability of dropping path, defaults 0.0.
        training (bool, optional): whether in training progress, defaults False.
    """
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)    # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()    # binarize
    output = x.div(keep_prob) * random_tensor
    return output


class DropPath(nn.Module):
    """
    Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    Adapted from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py

    Args:
        drop_prob (float, optional): probability of dropping path, defaults None.
    """

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


class WrappedDropout(nn.Module):
    r"""Same as torch.nn.Dropout. But it is wrapped with the context of seed manager. During training, randomly zeroes
    some elements of the input tensor with probability p using samples from a Bernoulli distribution. Each
    channel will be zeroed out independently on every forward call. Furthermore, the outputs are scaled by a factor of
    1/(1-p) during training. This means that during evaluation the module simply computes an identity function.

    Args:
        p (float, optional): probability of an element to be zeroed, defaults 0.5.
        inplace (bool, optional): whether to do dropout in-place, default to be False.
        mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode.

    Note:
        The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
        in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
    """

    def __init__(self, p: float = 0.5, inplace: bool = False, mode=None):
        super().__init__()
        if p < 0 or p > 1:
            raise ValueError("dropout probability has to be between 0 and 1, "
                             "but got {}".format(p))
        self.p = p
        self.inplace = inplace
        if mode is None:
            self.func = self.nonefunc
        else:
            self.func = self.normalfunc
            self.mode = mode

    def nonefunc(self, inputs):
        return F.dropout(inputs, self.p, self.training, self.inplace)

    def normalfunc(self, inputs):
        with seed(self.mode):
            return F.dropout(inputs, self.p, self.training, self.inplace)

    def forward(self, inputs):
        return self.func(inputs)


class WrappedDropPath(nn.Module):
    r"""Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    Here, it is wrapped with the context of seed manager.

    Args:
        p (float, optional): probability of dropping path, defaults 0.0.
        mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode.

    Note:
        The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
        in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
    """

    def __init__(self, p: float = 0., mode=None):
        super().__init__()
        self.p = p
        self.mode = mode
        if self.mode is None:
            self.func = self.nonefunc
        else:
            self.func = self.normalfunc
            self.mode = mode

    def nonefunc(self, inputs):
        return drop_path(inputs, self.p, self.training)

    def normalfunc(self, inputs):
        with seed(self.mode):
            return drop_path(inputs, self.p, self.training)

    def forward(self, inputs):
        return self.func(inputs)


@LAYERS.register_module
class VanillaPatchEmbedding(nn.Module):
    r"""
    2D Image to Patch Embedding

    Args:
        img_size (int): image size.
        patch_size (int): patch size.
        in_chans (int): number of channels of input image.
        embed_size (int): size of embedding.
        dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
        flatten (bool, optional): whether to flatten output tensor, defaults to True.
        weight_initializer (:class:`typing.Callable`, optional):
            The initializer of weight, defaults to kaiming uniform initializer.
        bias_initializer (:class:`typing.Callable`, optional):
            The initializer of bias, defaults to xavier uniform initializer.
        position_embed_initializer (:class:`typing.Callable`, optional):
            The initializer of position embedding, defaults to zeros initializer.

    More details about initializer please refer to
    `init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
    """

    def __init__(self,
                 img_size: int,
                 patch_size: int,
                 in_chans: int,
                 embed_size: int,
                 flatten: bool = True,
                 dtype: torch.dtype = None,
                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
                 position_embed_initializer: Callable = init.zeros_()):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
        self.num_patches = self.grid_size[0] * self.grid_size[1]
        self.flatten = flatten

        self.weight = nn.Parameter(
            torch.empty((embed_size, in_chans, *self.patch_size), device=get_current_device(), dtype=dtype))
        self.bias = nn.Parameter(torch.empty(embed_size, device=get_current_device(), dtype=dtype))
        self.cls_token = nn.Parameter(torch.zeros((1, 1, embed_size), device=get_current_device(), dtype=dtype))
        self.pos_embed = nn.Parameter(
            torch.zeros((1, self.num_patches + 1, embed_size), device=get_current_device(), dtype=dtype))

        self.reset_parameters(weight_initializer, bias_initializer, position_embed_initializer)

    def reset_parameters(self, weight_initializer, bias_initializer, position_embed_initializer):
        fan_in, fan_out = nn.init._calculate_fan_in_and_fan_out(self.weight)
        weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
        bias_initializer(self.bias, fan_in=fan_in)
        position_embed_initializer(self.pos_embed)

    def forward(self, input_: Tensor) -> Tensor:
        B, C, H, W = input_.shape
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        output = F.conv2d(input_, self.weight, self.bias, stride=self.patch_size)
        if self.flatten:
            output = output.flatten(2).transpose(1, 2)    # BCHW -> BNC

        cls_token = self.cls_token.expand(output.shape[0], -1, -1)
        output = torch.cat((cls_token, output), dim=1)
        output = output + self.pos_embed
        return output


@LAYERS.register_module
class VanillaClassifier(nn.Module):
    r"""Dense linear classifier.

    Args:
        in_features (int): size of each input sample.
        num_classes (int): number of classes.
        weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None.
        dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
        flatten (bool, optional): whether to flatten output tensor, defaults to True.
        weight_initializer (:class:`typing.Callable`, optional):
            The initializer of weight, defaults to kaiming uniform initializer.
        bias_initializer (:class:`typing.Callable`, optional):
            The initializer of bias, defaults to xavier uniform initializer.

    More details about initializer please refer to
    `init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
    """

    def __init__(self,
                 in_features: int,
                 num_classes: int,
                 weight: nn.Parameter = None,
                 bias: bool = True,
                 dtype: torch.dtype = None,
                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
        super().__init__()
        self.in_features = in_features
        self.num_classes = num_classes

        if weight is not None:
            self.weight = weight
            self.has_weight = False
        else:
            self.weight = nn.Parameter(
                torch.empty(self.num_classes, self.in_features, device=get_current_device(), dtype=dtype))
            self.has_weight = True
        if bias:
            self.bias = nn.Parameter(torch.zeros(self.num_classes, device=get_current_device(), dtype=dtype))
        else:
            self.bias = None

        self.reset_parameters(weight_initializer, bias_initializer)

    def reset_parameters(self, weight_initializer, bias_initializer):
        fan_in, fan_out = self.in_features, self.num_classes

        if self.has_weight:
            weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)

        if self.bias is not None:
            bias_initializer(self.bias, fan_in=fan_in)

    def forward(self, input_: Tensor) -> Tensor:
        return F.linear(input_, self.weight, self.bias)


@LAYERS.register_module
class VanillaLayerNorm(nn.Module):
    r"""
    Layer Normalization for colossalai

    Args:
        normalized_shape (int): input shape from an expected input of size.
            :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1]
            \times \ldots \times \text{normalized_shape}[-1]]`
            If a single integer is used, it is treated as a singleton list, and this module will
            normalize over the last dimension which is expected to be of that specific size.
        eps (float): a value added to the denominator for numerical stability, defaults to 1e-05.
        bias (bool, optional): Whether to add a bias, defaults to ``True``.
        dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
    """

    def __init__(self, normalized_shape: int, eps=1e-05, bias=True, dtype=None):
        super().__init__()

        self.normalized_shape = (normalized_shape,)
        self.variance_epsilon = eps

        factory_kwargs = {'device': get_current_device(), 'dtype': dtype}

        self.weight = nn.Parameter(torch.ones(normalized_shape, **factory_kwargs))
        if bias:
            self.bias = nn.Parameter(torch.zeros(normalized_shape, **factory_kwargs))
        else:
            self.bias = None

    def forward(self, x: Tensor) -> Tensor:
        return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.variance_epsilon)


@LAYERS.register_module
class VanillaLinear(nn.Module):
    """Linear layer.

    Args:
        in_features (int): size of each input sample.
        out_features (int): size of each output sample.
        bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
        dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
        skip_bias_add: bool (optional, default to be false).
        weight_initializer (:class:`typing.Callable`, optional):
            The initializer of weight, defaults to kaiming uniform initializer.
        bias_initializer (:class:`typing.Callable`, optional):
            The initializer of bias, defaults to xavier uniform initializer.

    More details about ``initializer`` please refer to
    `init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
    """

    def __init__(self,
                 in_features: int,
                 out_features: int,
                 bias: bool = True,
                 dtype: torch.dtype = None,
                 skip_bias_add: bool = False,
                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
                 **kwargs) -> None:
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.skip_bias_add = skip_bias_add
        factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
        self.weight = Parameter(torch.empty(self.out_features, self.in_features, **factory_kwargs))
        if bias:
            self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs))
        else:
            self.bias = None
        weight_initializer(self.weight, fan_in=in_features, fan_out=out_features)
        if self.bias is not None:
            bias_initializer(self.bias, fan_in=in_features)

    def forward(self, input: Tensor) -> Tensor:
        if not self.skip_bias_add:
            return F.linear(input, self.weight, self.bias)
        else:
            return F.linear(input, self.weight), self.bias
