# 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.
import itertools
from typing import Any, cast, Iterable, Iterator, List, Optional, Sized, Union

import torch
from torch import Tensor
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import BatchSampler, DistributedSampler, Sampler

import pytorch_lightning as pl
from lightning_fabric.utilities.distributed import _DatasetSamplerWrapper
from pytorch_lightning.overrides.base import _LightningModuleWrapperBase, _LightningPrecisionModuleWrapperBase


class LightningDistributedModule(_LightningModuleWrapperBase):
    def __init__(
        self,
        forward_module: Optional[Union["pl.LightningModule", _LightningPrecisionModuleWrapperBase]] = None,
        pl_module: Optional[Union["pl.LightningModule", _LightningPrecisionModuleWrapperBase]] = None,
    ) -> None:
        self._validate_init_arguments(pl_module, forward_module)
        super().__init__(forward_module=(pl_module or forward_module))


def _find_tensors(
    obj: Union[Tensor, list, tuple, dict, Any]
) -> Union[List[Tensor], itertools.chain]:  # pragma: no-cover
    """Recursively find all tensors contained in the specified object."""
    if isinstance(obj, Tensor):
        return [obj]
    if isinstance(obj, (list, tuple)):
        return itertools.chain(*map(_find_tensors, obj))
    if isinstance(obj, dict):
        return itertools.chain(*map(_find_tensors, obj.values()))
    return []


# In manual_optimization, we need to call reducer prepare_for_backward.
# Note: Keep track of PyTorch DDP and update if there is a change
# https://github.com/pytorch/pytorch/blob/v1.7.1/torch/nn/parallel/distributed.py#L626-L638
def prepare_for_backward(model: DistributedDataParallel, output: Any) -> None:
    # `prepare_for_backward` is `DistributedDataParallel` specific.
    if torch.is_grad_enabled() and model.require_backward_grad_sync:
        model.require_forward_param_sync = True
        # We'll return the output object verbatim since it is a freeform
        # object. We need to find any tensors in this object, though,
        # because we need to figure out which parameters were used during
        # this forward pass, to ensure we short circuit reduction for any
        # unused parameters. Only if `find_unused_parameters` is set.
        args = list(_find_tensors(output)) if model.find_unused_parameters else []
        reducer = cast(torch._C._distributed_c10d.Reducer, model.reducer)
        reducer._rebuild_buckets()  # avoids "INTERNAL ASSERT FAILED" with `find_unused_parameters=False`
        reducer.prepare_for_backward(args)
    else:
        model.require_forward_param_sync = False


class UnrepeatedDistributedSampler(DistributedSampler):
    """A fork of the PyTorch DistributedSampler that doesn't repeat data, instead allowing the number of batches
    per process to be off-by-one from each other. This makes this sampler usable for predictions (it's
    deterministic and doesn't require shuffling). It is potentially unsafe to use this sampler for training,
    because during training the DistributedDataParallel syncs buffers on each forward pass, so it could freeze if
    one of the processes runs one fewer batch. During prediction, buffers are only synced on the first batch, so
    this is safe to use as long as each process runs at least one batch. We verify this in an assert.

    Taken from https://github.com/jpuigcerver/PyLaia/blob/v1.0.0/laia/data/unpadded_distributed_sampler.py
    and https://github.com/pytorch/pytorch/issues/25162#issuecomment-634146002
    """

    def __init__(self, *args: Any, **kwargs: Any) -> None:
        super().__init__(*args, **kwargs)
        if not isinstance(self.dataset, Sized):
            raise TypeError("The given dataset must implement the `__len__` method.")
        self.num_samples = len(range(self.rank, len(self.dataset), self.num_replicas))
        self.total_size = len(self.dataset)
        # If any process has at least one batch, every other process needs to
        # have at least one batch, or the DistributedDataParallel could lock up.
        assert self.num_samples >= 1 or self.total_size == 0

    def __iter__(self) -> Iterator[List[int]]:
        if not isinstance(self.dataset, Sized):
            raise TypeError("The given dataset must implement the `__len__` method.")
        if self.shuffle:
            # deterministically shuffle based on epoch
            g = torch.Generator()
            g.manual_seed(self.epoch)
            indices = torch.randperm(len(self.dataset), generator=g).tolist()
        else:
            indices = list(range(len(self.dataset)))

        assert len(indices) == self.total_size

        # subsample
        indices = indices[self.rank : self.total_size : self.num_replicas]
        assert len(indices) == self.num_samples

        return iter(indices)


class UnrepeatedDistributedSamplerWrapper(UnrepeatedDistributedSampler):
    """Equivalent class to ``DistributedSamplerWrapper`` but for the ``UnrepeatedDistributedSampler``."""

    def __init__(self, sampler: Union[Sampler, Iterable], *args: Any, **kwargs: Any) -> None:
        super().__init__(_DatasetSamplerWrapper(sampler), *args, **kwargs)

    def __iter__(self) -> Iterator:
        self.dataset.reset()
        return (self.dataset[index] for index in super().__iter__())


class IndexBatchSamplerWrapper:
    """This class is used to wrap a :class:`torch.utils.data.BatchSampler` and capture its indices."""

    def __init__(self, sampler: BatchSampler) -> None:
        self.seen_batch_indices: List[List[int]] = []
        self._sampler = sampler

    def __iter__(self) -> Iterator[List[int]]:
        self.seen_batch_indices = []
        for batch in self._sampler:
            self.seen_batch_indices.append(batch)
            yield batch

    def __len__(self) -> int:
        return len(self._sampler)

    @property
    def drop_last(self) -> bool:
        return self._sampler.drop_last

    @property
    def batch_size(self) -> int:
        return self._sampler.batch_size

    @property
    def sampler(self) -> Union[Sampler, Iterable]:
        return self._sampler.sampler
