# *****************************************************************************
#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
#  Redistribution and use in source and binary forms, with or without
#  modification, are permitted provided that the following conditions are met:
#      * Redistributions of source code must retain the above copyright
#        notice, this list of conditions and the following disclaimer.
#      * Redistributions in binary form must reproduce the above copyright
#        notice, this list of conditions and the following disclaimer in the
#        documentation and/or other materials provided with the distribution.
#      * Neither the name of the NVIDIA CORPORATION nor the
#        names of its contributors may be used to endorse or promote products
#        derived from this software without specific prior written permission.
#
#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# *****************************************************************************

import logging
import os
import shutil
from typing import Callable, List, Tuple

import torch
from torch import Tensor


def save_checkpoint(state, is_best, filename):
    r"""Save the model to a temporary file first, then copy it to filename,
    in case signals interrupt the torch.save() process.
    """
    torch.save(state, filename)
    logging.info(f"Checkpoint saved to {filename}")

    if is_best:
        path, best_filename = os.path.split(filename)
        best_filename = os.path.join(path, "best_" + best_filename)
        shutil.copyfile(filename, best_filename)
        logging.info(f"Current best checkpoint saved to {best_filename}")


def pad_sequences(batch: List[Tensor]) -> Tuple[Tensor, Tensor]:
    r"""Right zero-pad all one-hot text sequences to max input length.

    Modified from https://github.com/NVIDIA/DeepLearningExamples.
    """
    input_lengths, ids_sorted_decreasing = torch.sort(torch.LongTensor([len(x) for x in batch]), dim=0, descending=True)
    max_input_len = input_lengths[0]

    text_padded = torch.LongTensor(len(batch), max_input_len)
    text_padded.zero_()
    for i in range(len(ids_sorted_decreasing)):
        text = batch[ids_sorted_decreasing[i]]
        text_padded[i, : text.size(0)] = text

    return text_padded, input_lengths


def prepare_input_sequence(texts: List[str], text_processor: Callable[[str], List[int]]) -> Tuple[Tensor, Tensor]:
    d = []
    for text in texts:
        d.append(torch.IntTensor(text_processor(text)[:]))

    text_padded, input_lengths = pad_sequences(d)
    return text_padded, input_lengths

# *****************************************************************************
#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
#  Redistribution and use in source and binary forms, with or without
#  modification, are permitted provided that the following conditions are met:
#      * Redistributions of source code must retain the above copyright
#        notice, this list of conditions and the following disclaimer.
#      * Redistributions in binary form must reproduce the above copyright
#        notice, this list of conditions and the following disclaimer in the
#        documentation and/or other materials provided with the distribution.
#      * Neither the name of the NVIDIA CORPORATION nor the
#        names of its contributors may be used to endorse or promote products
#        derived from this software without specific prior written permission.
#
#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# *****************************************************************************

import logging
import os
import shutil
from typing import List, Tuple, Callable

import torch
from torch import Tensor


def save_checkpoint(state, is_best, filename):
    r"""Save the model to a temporary file first, then copy it to filename,
    in case signals interrupt the torch.save() process.
    """
    torch.save(state, filename)
    logging.info(f"Checkpoint saved to {filename}")

    if is_best:
        path, best_filename = os.path.split(filename)
        best_filename = os.path.join(path, "best_" + best_filename)
        shutil.copyfile(filename, best_filename)
        logging.info(f"Current best checkpoint saved to {best_filename}")


def pad_sequences(batch: List[Tensor]) -> Tuple[Tensor, Tensor]:
    r"""Right zero-pad all one-hot text sequences to max input length.

    Modified from https://github.com/NVIDIA/DeepLearningExamples.
    """
    input_lengths, ids_sorted_decreasing = torch.sort(torch.LongTensor([len(x) for x in batch]), dim=0, descending=True)
    max_input_len = input_lengths[0]

    text_padded = torch.LongTensor(len(batch), max_input_len)
    text_padded.zero_()
    for i in range(len(ids_sorted_decreasing)):
        text = batch[ids_sorted_decreasing[i]]
        text_padded[i, : text.size(0)] = text

    return text_padded, input_lengths


def prepare_input_sequence(texts: List[str], text_processor: Callable[[str], List[int]]) -> Tuple[Tensor, Tensor]:
    d = []
    for text in texts:
        d.append(torch.IntTensor(text_processor(text)[:]))

    text_padded, input_lengths = pad_sequences(d)
    return text_padded, input_lengths
