"""
Collection of conversion functions for linear / conv2d structured pruning
Also contains utilities for bias propagation
"""
from typing import cast, Optional, Callable, Tuple

import torch
from torch import nn, Tensor
from torch.nn.utils import parametrize
from torch.nn.utils.parametrize import ParametrizationList
from .parametrization import FakeStructuredSparsity, BiasHook

# BIAS PROPAGATION
def _remove_bias_handles(module: nn.Module) -> None:
    if hasattr(module, "_forward_hooks"):
        bias_hooks = []
        for key, hook in module._forward_hooks.items():
            if isinstance(hook, BiasHook):
                bias_hooks.append(key)

        for key in bias_hooks:
            del module._forward_hooks[key]


def _get_adjusted_next_layer_bias(
    next_layer: nn.Module, pruned_biases: Tensor, mask: Tensor
) -> nn.Parameter:
    r"""Returns new adjusted bias for the second supported module"""
    if parametrize.is_parametrized(next_layer):
        # need to access original weight
        parametrization_dict = cast(nn.ModuleDict, next_layer.parametrizations)
        weight_parameterizations = cast(
            ParametrizationList, parametrization_dict.weight
        )
        next_weight = weight_parameterizations.original
    else:
        next_weight = cast(Tensor, next_layer.weight)

    scaling_weight = next_weight[:, ~mask]
    if isinstance(next_layer, nn.Conv2d):  # checking for Conv2d
        # Propagating first layer pruned biases and calculating the new second layer bias
        # involves more steps since the Conv2d scaling weight has extra dimensions,
        # so adding bias involves broadcasting, logically:
        # for each channel k in range(oC):
        #     scaled_biases = sum(first_bias[pruned_idx] @ next_weight[k, pruned_idx, :, :].T)
        #     new_next_bias[k] = old_next_bias[k] + scaled_biases
        scaling_product = torch.matmul(
            pruned_biases.reshape(1, -1), torch.transpose(scaling_weight, 1, 2)
        )
        sum_range = list(range(len(scaling_product.shape)))[
            1:
        ]  # all but the first dimension
        scaled_biases = torch.sum(scaling_product, sum_range)
    elif isinstance(next_layer, nn.Linear):  # Linear
        scaled_biases = torch.matmul(
            pruned_biases, torch.transpose(scaling_weight, 0, 1)
        )  # recall b2_new = b1 @ w2.T + b2
    else:
        raise NotImplementedError(f"Type {type(next_layer)} not supported yet.")

    if (
        parametrize.is_parametrized(next_layer)
        and getattr(next_layer, "_bias", None) is not None
    ):  # next_layer is parametrized & has original bias ._bias
        adjusted_bias = nn.Parameter(scaled_biases + next_layer._bias)
    elif (
        not parametrize.is_parametrized(next_layer) and next_layer.bias is not None
    ):  # next_layer not parametrized & has .bias
        adjusted_bias = nn.Parameter(scaled_biases + next_layer.bias)
    else:  # next_layer has no bias
        adjusted_bias = nn.Parameter(scaled_biases)
    return adjusted_bias


def _prune_module_bias(module: nn.Module, mask: Tensor) -> None:
    r"""Applies mask to given modules bias"""
    # prune bias along with weights, discard pruned indices of bias
    original_bias = cast(Tensor, getattr(module, "_bias", module.bias))
    if original_bias is not None:
        module.bias = nn.Parameter(original_bias[mask])

    #  remove _bias parameter
    if hasattr(module, "_bias"):
        delattr(module, "_bias")


def _propogate_module_bias(module: nn.Module, mask: Tensor) -> Optional[Tensor]:
    r"""
    In the case that we need to propagate biases, this function will return the biases we need
    """
    # set current module bias
    if module.bias is not None:
        module.bias = nn.Parameter(cast(Tensor, module.bias)[mask])
    elif getattr(module, "_bias", None) is not None:
        module.bias = nn.Parameter(cast(Tensor, module._bias)[mask])

    # get pruned biases to propagate to subsequent layer
    if getattr(module, "_bias", None) is not None:
        pruned_biases = cast(Tensor, module._bias)[~mask]
    else:
        pruned_biases = None

    if hasattr(module, "_bias"):
        delattr(module, "_bias")

    return pruned_biases


# LINEAR
def _prune_linear_helper(linear: nn.Linear) -> Tensor:
    # expects linear to be a parameterized linear module
    parametrization_dict = cast(nn.ModuleDict, linear.parametrizations)
    weight_parameterizations = cast(ParametrizationList, parametrization_dict.weight)
    for p in weight_parameterizations:
        if isinstance(p, FakeStructuredSparsity):
            mask = cast(Tensor, p.mask)

    with torch.no_grad():
        parametrize.remove_parametrizations(linear, "weight", leave_parametrized=True)
        linear.weight = nn.Parameter(linear.weight[mask])
    linear.out_features = linear.weight.shape[0]
    _remove_bias_handles(linear)

    return mask


def prune_linear(linear: nn.Linear) -> None:
    mask = _prune_linear_helper(linear)
    if getattr(linear, "prune_bias", False):
        _prune_module_bias(linear, mask)


def prune_linear_linear(linear1: nn.Linear, linear2: nn.Linear) -> None:
    prune_linear_activation_linear(linear1, None, linear2)


def prune_linear_activation_linear(
    linear1: nn.Linear,
    activation: Optional[Callable[[Tensor], Tensor]],
    linear2: nn.Linear,
):
    mask = _prune_linear_helper(linear1)
    if getattr(linear1, "prune_bias", False):
        _prune_module_bias(linear1, mask)
    else:
        pruned_biases = _propogate_module_bias(linear1, mask)
        if pruned_biases is not None:
            if activation:
                pruned_biases = activation(pruned_biases)
            linear2.bias = _get_adjusted_next_layer_bias(linear2, pruned_biases, mask)

    with torch.no_grad():
        if parametrize.is_parametrized(linear2):
            parametrization_dict = cast(nn.ModuleDict, linear2.parametrizations)
            weight_parameterizations = cast(
                ParametrizationList, parametrization_dict.weight
            )

            weight_parameterizations.original = nn.Parameter(
                weight_parameterizations.original[:, mask]
            )
            linear2.in_features = weight_parameterizations.original.shape[1]
        else:
            linear2.weight = nn.Parameter(linear2.weight[:, mask])
            linear2.in_features = linear2.weight.shape[1]


# CONV2D
def _prune_conv2d_helper(conv2d: nn.Conv2d) -> Tensor:
    parametrization_dict = cast(nn.ModuleDict, conv2d.parametrizations)
    weight_parameterizations = cast(ParametrizationList, parametrization_dict.weight)
    for p in weight_parameterizations:
        if isinstance(p, FakeStructuredSparsity):
            mask = cast(Tensor, p.mask)

    with torch.no_grad():
        parametrize.remove_parametrizations(conv2d, "weight", leave_parametrized=True)
        conv2d.weight = nn.Parameter(conv2d.weight[mask])
    conv2d.out_channels = conv2d.weight.shape[0]

    _remove_bias_handles(conv2d)
    return mask


def prune_conv2d_padded(conv2d_1: nn.Conv2d) -> None:
    parametrization_dict = cast(nn.ModuleDict, conv2d_1.parametrizations)
    weight_parameterizations = cast(ParametrizationList, parametrization_dict.weight)
    for p in weight_parameterizations:
        if isinstance(p, FakeStructuredSparsity):
            mask = cast(Tensor, p.mask)

    with torch.no_grad():
        parametrize.remove_parametrizations(conv2d_1, "weight", leave_parametrized=True)

    if getattr(conv2d_1, "_bias", None) is not None:
        if (
            conv2d_1.bias is not None
        ):  # conv2d_1 has original bias and bias propagated from previous layer
            new_bias = torch.zeros(conv2d_1.bias.shape)
            new_bias[mask] = conv2d_1.bias[mask]
            # adjusted bias that to keep in conv2d_1
            new_bias[~mask] = cast(Tensor, conv2d_1._bias)[~mask]
            # pruned biases that are kept instead of propagated
            conv2d_1.bias = nn.Parameter(new_bias)
        else:  # conv2d_1 has only original bias
            conv2d_1.bias = nn.Parameter(cast(Tensor, conv2d_1._bias))
    else:
        # no original bias, only propagated bias
        if (
            conv2d_1.bias is not None
        ):  # conv2d_1 has bias propagated from previous layer
            conv2d_1.bias.data[~mask] = 0

    if hasattr(conv2d_1, "_bias"):
        delattr(conv2d_1, "_bias")


def prune_conv2d(conv2d: nn.Conv2d) -> None:
    mask = _prune_conv2d_helper(conv2d)
    if getattr(conv2d, "prune_bias", False):
        _prune_module_bias(conv2d, mask)


def prune_conv2d_conv2d(conv2d_1: nn.Conv2d, conv2d_2: nn.Conv2d) -> None:
    prune_conv2d_activation_conv2d(conv2d_1, None, conv2d_2)


def prune_conv2d_activation_conv2d(
    conv2d_1: nn.Conv2d,
    activation: Optional[Callable[[Tensor], Tensor]],
    conv2d_2: nn.Conv2d,
):
    r"""
    Fusion Pattern for conv2d -> some activation module / function -> conv2d layers
    """
    parametrization_dict = cast(nn.ModuleDict, conv2d_1.parametrizations)
    weight_parameterizations = cast(ParametrizationList, parametrization_dict.weight)
    for p in weight_parameterizations:
        if isinstance(p, FakeStructuredSparsity):
            mask = cast(Tensor, p.mask)

    prune_bias = getattr(conv2d_1, "prune_bias", False)
    if (
        hasattr(conv2d_2, "padding")
        and cast(Tuple[int], conv2d_2.padding) > (0, 0)
        and (conv2d_1.bias is not None or getattr(conv2d_1, "_bias", None) is not None)
    ):
        prune_conv2d_padded(conv2d_1)
    else:
        mask = _prune_conv2d_helper(conv2d_1)
        if prune_bias:
            _prune_module_bias(conv2d_1, mask)
        else:
            pruned_biases = _propogate_module_bias(conv2d_1, mask)
            if pruned_biases is not None:
                if activation:
                    pruned_biases = activation(pruned_biases)
                conv2d_2.bias = _get_adjusted_next_layer_bias(
                    conv2d_2, pruned_biases, mask
                )

        if (
            not (
                hasattr(conv2d_2, "padding")
                and cast(Tuple[int], conv2d_2.padding) > (0, 0)
            )
            or conv2d_1.bias is None
        ):
            with torch.no_grad():
                if parametrize.is_parametrized(conv2d_2):
                    parametrization_dict = cast(
                        nn.ModuleDict, conv2d_2.parametrizations
                    )
                    weight_parameterizations = cast(
                        ParametrizationList, parametrization_dict.weight
                    )
                    weight_parameterizations.original = nn.Parameter(
                        weight_parameterizations.original[:, mask]
                    )
                    conv2d_2.in_channels = weight_parameterizations.original.shape[1]
                else:
                    conv2d_2.weight = nn.Parameter(conv2d_2.weight[:, mask])
                    conv2d_2.in_channels = conv2d_2.weight.shape[1]


def prune_conv2d_pool_activation_conv2d(
    c1: nn.Conv2d,
    pool: nn.Module,
    activation: Optional[Callable[[Tensor], Tensor]],
    c2: nn.Conv2d,
) -> None:
    prune_conv2d_activation_conv2d(c1, activation, c2)


def prune_conv2d_activation_pool_conv2d(
    c1: nn.Conv2d,
    activation: Optional[Callable[[Tensor], Tensor]],
    pool: nn.Module,
    c2: nn.Conv2d,
) -> None:
    prune_conv2d_activation_conv2d(c1, activation, c2)


def prune_conv2d_pool_flatten_linear(
    conv2d: nn.Conv2d,
    pool: nn.Module,
    flatten: Optional[Callable[[Tensor], Tensor]],
    linear: nn.Linear,
) -> None:
    mask = _prune_conv2d_helper(conv2d)

    # We map the pruned indices of the Conv2d output to the flattened indices of the Linear following the Flatten layer.
    # we determine the flattening scale (h * w), and readjust `first_pruned_indices`
    # (each idx maps to range idx * h * w to (idx+1) * h * w), `first_valid_indices`,
    # and `pruned_biases` (repeat each bias by h * w).
    if parametrize.is_parametrized(linear):
        parametrization_dict = cast(nn.ModuleDict, linear.parametrizations)
        weight_parameterizations = cast(
            ParametrizationList, parametrization_dict.weight
        )
        linear_ic = weight_parameterizations.original.shape[1]
    else:
        linear_ic = linear.weight.shape[1]

    conv2d_oc = len(mask)
    assert (
        linear_ic % conv2d_oc == 0
    ), f"Flattening from dimensions {conv2d_oc} to {linear_ic} not supported"

    flatten_scale = linear_ic // conv2d_oc
    flattened_mask = torch.tensor(
        [[val] * flatten_scale for val in mask], dtype=torch.bool, device=mask.device
    ).flatten()

    if getattr(conv2d, "prune_bias", False):
        _prune_module_bias(conv2d, mask)
    else:
        pruned_biases = cast(Tensor, _propogate_module_bias(conv2d, mask))
        flattened_pruned_biases = torch.tensor(
            [[bias] * flatten_scale for bias in pruned_biases], device=mask.device
        ).flatten()
        linear.bias = _get_adjusted_next_layer_bias(
            linear, flattened_pruned_biases, flattened_mask
        )

    with torch.no_grad():
        if parametrize.is_parametrized(linear):
            parametrization_dict = cast(nn.ModuleDict, linear.parametrizations)
            weight_parameterizations = cast(
                ParametrizationList, parametrization_dict.weight
            )
            weight_parameterizations.original = nn.Parameter(
                weight_parameterizations.original[:, flattened_mask]
            )
            linear.in_features = weight_parameterizations.original.shape[1]
        else:
            linear.weight = nn.Parameter(linear.weight[:, flattened_mask])
            linear.in_features = linear.weight.shape[1]


def prune_lstm_output_linear(
    lstm: nn.LSTM, getitem: Callable, linear: nn.Linear
) -> None:
    prune_lstm_output_layernorm_linear(lstm, getitem, None, linear)


def prune_lstm_output_layernorm_linear(
    lstm: nn.LSTM,
    getitem: Callable,
    layernorm: Optional[nn.LayerNorm],
    linear: nn.Linear,
) -> None:
    for i in range(lstm.num_layers):
        if parametrize.is_parametrized(lstm, f"weight_ih_l{i}"):
            parametrization_dict = cast(nn.ModuleDict, lstm.parametrizations)
            weight_parameterizations = cast(
                ParametrizationList, parametrization_dict[f"weight_ih_l{i}"]
            )
            mask = weight_parameterizations[0].mask

            with torch.no_grad():
                parametrize.remove_parametrizations(
                    lstm, f"weight_ih_l{i}", leave_parametrized=True
                )
                setattr(
                    lstm,
                    f"weight_ih_l{i}",
                    nn.Parameter(getattr(lstm, f"weight_ih_l{i}")[mask]),
                )
                setattr(
                    lstm,
                    f"bias_ih_l{i}",
                    nn.Parameter(getattr(lstm, f"bias_ih_l{i}")[mask]),
                )

        if parametrize.is_parametrized(lstm, f"weight_hh_l{i}"):
            parametrization_dict = cast(nn.ModuleDict, lstm.parametrizations)
            weight_parameterizations = cast(
                ParametrizationList, parametrization_dict[f"weight_hh_l{i}"]
            )
            mask = weight_parameterizations[0].mask

            with torch.no_grad():
                parametrize.remove_parametrizations(
                    lstm, f"weight_hh_l{i}", leave_parametrized=True
                )
                # splitting out hidden-hidden masks
                W_hi, W_hf, W_hg, W_ho = torch.split(
                    getattr(lstm, f"weight_hh_l{i}"), lstm.hidden_size
                )
                M_hi, M_hf, M_hg, M_ho = torch.split(mask, lstm.hidden_size)

                # resize each individual weight separately
                W_hi = W_hi[M_hi][:, M_hi]
                W_hf = W_hf[M_hf][:, M_hf]
                W_hg = W_hg[M_hg][:, M_hg]
                W_ho = W_ho[M_ho][:, M_ho]

                # concat, use this as new weight
                new_weight = torch.cat((W_hi, W_hf, W_hg, W_ho))
                setattr(lstm, f"weight_hh_l{i}", nn.Parameter(new_weight))
                setattr(
                    lstm,
                    f"bias_hh_l{i}",
                    nn.Parameter(getattr(lstm, f"bias_hh_l{i}")[mask]),
                )

            # If this is the final layer, then we need to prune linear layer columns
            if i + 1 == lstm.num_layers:
                lstm.hidden_size = int(M_hi.sum())
                with torch.no_grad():
                    if parametrize.is_parametrized(linear):
                        parametrization_dict = cast(
                            nn.ModuleDict, linear.parametrizations
                        )
                        weight_parameterizations = cast(
                            ParametrizationList, parametrization_dict.weight
                        )

                        weight_parameterizations.original = nn.Parameter(
                            weight_parameterizations.original[:, M_ho]
                        )
                        linear.in_features = weight_parameterizations.original.shape[1]
                    else:
                        linear.weight = nn.Parameter(linear.weight[:, M_ho])
                        linear.in_features = linear.weight.shape[1]

                    # if layernorm module, prune weight and bias
                    if layernorm is not None:
                        layernorm.normalized_shape = (linear.in_features,)
                        layernorm.weight = nn.Parameter(layernorm.weight[M_ho])
                        layernorm.bias = nn.Parameter(layernorm.bias[M_ho])

            # otherwise need to prune the columns of the input of the next LSTM layer
            else:
                with torch.no_grad():
                    if parametrize.is_parametrized(lstm, f"weight_ih_l{i+1}"):
                        parametrization_dict = cast(
                            nn.ModuleDict, lstm.parametrizations
                        )
                        weight_parameterizations = cast(
                            ParametrizationList,
                            getattr(parametrization_dict, f"weight_ih_l{i+1}"),
                        )

                        weight_parameterizations.original = nn.Parameter(
                            weight_parameterizations.original[:, M_ho]
                        )
                    else:
                        next_layer_weight = getattr(lstm, f"weight_ih_l{i+1}")
                        setattr(
                            lstm,
                            f"weight_ih_l{i+1}",
                            nn.Parameter(next_layer_weight[:, M_ho]),
                        )
