#!/usr/bin/env python3

# Copyright 2019 Shigeki Karita
#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

"""Repeat the same layer definition."""

import torch


class MultiSequential(torch.nn.Sequential):
    """Multi-input multi-output torch.nn.Sequential."""

    def __init__(self, *args, layer_drop_rate=0.0):
        """Initialize MultiSequential with layer_drop.

        Args:
            layer_drop_rate (float): Probability of dropping out each fn (layer).

        """
        super().__init__(*args)
        self.layer_drop_rate = layer_drop_rate

    def forward(self, *args):
        """Repeat."""
        _probs = torch.empty(len(self)).uniform_()
        for idx, m in enumerate(self):
            if not self.training or (_probs[idx] >= self.layer_drop_rate):
                args = m(*args)
        return args


def repeat(N, fn, layer_drop_rate=0.0):
    """Repeat module N times.

    Args:
        N (int): Number of repeat time.
        fn (Callable): Function to generate module.
        layer_drop_rate (float): Probability of dropping out each fn (layer).

    Returns:
        MultiSequential: Repeated model instance.

    """
    return MultiSequential(*[fn(n) for n in range(N)], layer_drop_rate=layer_drop_rate)
