# mypy: allow-untyped-defs
from typing import Callable, Optional, Union

import torch

from .base_structured_sparsifier import BaseStructuredSparsifier


__all__ = ["FPGMPruner"]


class FPGMPruner(BaseStructuredSparsifier):
    r"""Filter Pruning via Geometric Median (FPGM) Structured Pruner
    This sparsifier prune fliter (row) in a tensor according to distances among filters according to
    `Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration <https://arxiv.org/abs/1811.00250>`_.

    This sparsifier is controlled by three variables:
    1. `sparsity_level` defines the number of filters (rows) that are zeroed-out.
    2. `dist` defines the distance measurement type. Default: 3 (L2 distance).
    Available options are: [1, 2, (custom callable distance function)].

    Note::
        Inputs should be a 4D convolutional tensor of shape (N, C, H, W).
            - N: output channels size
            - C: input channels size
            - H: height of kernel
            - W: width of kernel
    """

    def __init__(
        self, sparsity_level: float = 0.5, dist: Optional[Union[Callable, int]] = None
    ):
        defaults = {
            "sparsity_level": sparsity_level,
        }

        if dist is None:
            dist = 2

        if callable(dist):
            self.dist_fn = dist
        elif dist == 1:
            self.dist_fn = lambda x: torch.cdist(x, x, p=1)
        elif dist == 2:
            self.dist_fn = lambda x: torch.cdist(x, x, p=2)
        else:
            raise NotImplementedError("Distance function is not yet implemented.")
        super().__init__(defaults=defaults)

    def _compute_distance(self, t):
        r"""Compute distance across all entries in tensor `t` along all dimension
        except for the one identified by dim.
        Args:
            t (torch.Tensor): tensor representing the parameter to prune
        Returns:
            distance (torch.Tensor): distance computed across filtters
        """
        dim = 0  # prune filter (row)

        size = t.size(dim)
        slc = [slice(None)] * t.dim()

        # flatten the tensor along the dimension
        t_flatten = [
            t[tuple(slc[:dim] + [slice(i, i + 1)] + slc[dim + 1 :])].reshape(-1)
            for i in range(size)
        ]
        t_flatten = torch.stack(t_flatten)

        # distance measurement
        dist_matrix = self.dist_fn(t_flatten)

        # more similar with other filter indicates large in the sum of row
        distance = torch.sum(torch.abs(dist_matrix), 1)

        return distance

    def update_mask(  # type: ignore[override]
        self, module, tensor_name, sparsity_level, **kwargs
    ):
        tensor_weight = getattr(module, tensor_name)
        mask = getattr(module.parametrizations, tensor_name)[0].mask

        if sparsity_level <= 0:
            mask.data = torch.ones_like(mask).bool()
        elif sparsity_level >= 1.0:
            mask.data = torch.zeros_like(mask).bool()
        else:
            distance = self._compute_distance(tensor_weight)

            tensor_size = tensor_weight.shape[0]  # prune filter (row)
            nparams_toprune = round(sparsity_level * tensor_size)
            nparams_toprune = min(
                max(nparams_toprune, 0), tensor_size
            )  # clamp to [0, tensor_size]
            topk = torch.topk(distance, k=nparams_toprune, largest=False)
            mask[topk.indices] = False
