# Copyright 2021 AlQuraishi Laboratory
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
from typing import Any, Tuple, List, Callable, Optional

deepspeed_is_installed = importlib.util.find_spec("deepspeed") is not None
if(deepspeed_is_installed):
    import deepspeed

import torch
import torch.utils.checkpoint


BLOCK_ARG = Any
BLOCK_ARGS = List[BLOCK_ARG]


def get_checkpoint_fn():
    deepspeed_is_configured = (
        deepspeed_is_installed and
        deepspeed.checkpointing.is_configured()
    )
    if(deepspeed_is_configured):
        checkpoint = deepspeed.checkpointing.checkpoint
    else:
        checkpoint = torch.utils.checkpoint.checkpoint

    return checkpoint


@torch.jit.ignore
def checkpoint_blocks(
    blocks: List[Callable],
    args: BLOCK_ARGS,
    blocks_per_ckpt: Optional[int],
) -> BLOCK_ARGS:
    """
    Chunk a list of blocks and run each chunk with activation
    checkpointing. We define a "block" as a callable whose only inputs are
    the outputs of the previous block.

    Implements Subsection 1.11.8  #. about checkpointing details

    Args:
        blocks:
            List of blocks
        args:
            Tuple of arguments for the first block.
        blocks_per_ckpt:
            Size of each chunk. A higher value corresponds to fewer 
            checkpoints, and trades memory for speed. If None, no checkpointing 
            is performed.
    Returns:
        The output of the final block
    """
    # print("In checkpoint_blocks:")  # DEBUG
    # print("torch.is_grad_enabled():", torch.is_grad_enabled())  # DEBUG
    # print("blocks_per_ckpt:", blocks_per_ckpt)  # DEBUG

    def wrap(a):
        return (a,) if type(a) is not tuple else a

    def exec(b, a):
        for block in b:
            a = wrap(block(*a))
        return a

    def chunker(s, e):
        def exec_sliced(*a):
            return exec(blocks[s:e], a)

        return exec_sliced

    # Avoids mishaps when the blocks take just one argument
    args = wrap(args)

    if blocks_per_ckpt is None or not torch.is_grad_enabled():
        # print("No Checkpointing performed")
        return exec(blocks, args)
    elif blocks_per_ckpt < 1 or blocks_per_ckpt > len(blocks):
        raise ValueError("blocks_per_ckpt must be between 1 and len(blocks)")

    checkpoint = get_checkpoint_fn() 

    for s in range(0, len(blocks), blocks_per_ckpt):
        e = s + blocks_per_ckpt
        args = checkpoint(chunker(s, e), *args)
        args = wrap(args)

    return args
