# Copyright The Lightning AI team.
#
# 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.
"""Utilities related to memory."""

import gc
from io import BytesIO
from typing import Any

import torch
from lightning_utilities.core.apply_func import apply_to_collection
from torch import Tensor
from torch.nn import Module


def recursive_detach(in_dict: Any, to_cpu: bool = False) -> Any:
    """Detach all tensors in `in_dict`.

    May operate recursively if some of the values in `in_dict` are dictionaries
    which contain instances of `Tensor`. Other types in `in_dict` are
    not affected by this utility function.

    Args:
        in_dict: Dictionary with tensors to detach
        to_cpu: Whether to move tensor to cpu

    Return:
        out_dict: Dictionary with detached tensors
    """

    def detach_and_move(t: Tensor, to_cpu: bool) -> Tensor:
        t = t.detach()
        if to_cpu:
            t = t.cpu()
        return t

    return apply_to_collection(in_dict, Tensor, detach_and_move, to_cpu=to_cpu)


def is_oom_error(exception: BaseException) -> bool:
    return is_cuda_out_of_memory(exception) or is_cudnn_snafu(exception) or is_out_of_cpu_memory(exception)


# based on https://github.com/BlackHC/toma/blob/master/toma/torch_cuda_memory.py
def is_cuda_out_of_memory(exception: BaseException) -> bool:
    return (
        isinstance(exception, RuntimeError)
        and len(exception.args) == 1
        and "CUDA" in exception.args[0]
        and "out of memory" in exception.args[0]
    )


# based on https://github.com/BlackHC/toma/blob/master/toma/torch_cuda_memory.py
def is_cudnn_snafu(exception: BaseException) -> bool:
    # For/because of https://github.com/pytorch/pytorch/issues/4107
    return (
        isinstance(exception, RuntimeError)
        and len(exception.args) == 1
        and "cuDNN error: CUDNN_STATUS_NOT_SUPPORTED." in exception.args[0]
    )


# based on https://github.com/BlackHC/toma/blob/master/toma/cpu_memory.py
def is_out_of_cpu_memory(exception: BaseException) -> bool:
    return (
        isinstance(exception, RuntimeError)
        and len(exception.args) == 1
        and "DefaultCPUAllocator: can't allocate memory" in exception.args[0]
    )


# based on https://github.com/BlackHC/toma/blob/master/toma/torch_cuda_memory.py
def garbage_collection_cuda() -> None:
    """Garbage collection Torch (CUDA) memory."""
    gc.collect()
    try:
        # This is the last thing that should cause an OOM error, but seemingly it can.
        torch.cuda.empty_cache()
    except RuntimeError as exception:
        if not is_oom_error(exception):
            # Only handle OOM errors
            raise


def get_model_size_mb(model: Module) -> float:
    """Calculates the size of a Module in megabytes.

    The computation includes everything in the :meth:`~torch.nn.Module.state_dict`,
    i.e., by default the parameters and buffers.

    Returns:
        Number of megabytes in the parameters of the input module.
    """
    model_size = BytesIO()
    torch.save(model.state_dict(), model_size)
    size_mb = model_size.getbuffer().nbytes / 1e6
    return size_mb
