test/test_rb.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import argparse
import contextlib
import importlib
import os
import pickle
import sys
from functools import partial
from unittest import mock

import numpy as np
import pytest
import torch

from _utils_internal import get_default_devices, make_tc
from packaging import version
from packaging.version import parse
from tensordict import (
    assert_allclose_td,
    is_tensor_collection,
    is_tensorclass,
    tensorclass,
    TensorDict,
    TensorDictBase,
)
from torch import multiprocessing as mp
from torch.utils._pytree import tree_flatten, tree_map
from torchrl.data import (
    PrioritizedReplayBuffer,
    RemoteTensorDictReplayBuffer,
    ReplayBuffer,
    ReplayBufferEnsemble,
    TensorDictPrioritizedReplayBuffer,
    TensorDictReplayBuffer,
)
from torchrl.data.replay_buffers import samplers, writers
from torchrl.data.replay_buffers.samplers import (
    PrioritizedSampler,
    PrioritizedSliceSampler,
    RandomSampler,
    SamplerEnsemble,
    SamplerWithoutReplacement,
    SliceSampler,
    SliceSamplerWithoutReplacement,
)

from torchrl.data.replay_buffers.storages import (
    LazyMemmapStorage,
    LazyTensorStorage,
    ListStorage,
    StorageEnsemble,
    TensorStorage,
)
from torchrl.data.replay_buffers.writers import (
    RoundRobinWriter,
    TensorDictMaxValueWriter,
    TensorDictRoundRobinWriter,
    WriterEnsemble,
)
from torchrl.envs.transforms.transforms import (
    BinarizeReward,
    CatFrames,
    CatTensors,
    CenterCrop,
    Compose,
    DiscreteActionProjection,
    DoubleToFloat,
    FiniteTensorDictCheck,
    FlattenObservation,
    GrayScale,
    gSDENoise,
    ObservationNorm,
    PinMemoryTransform,
    RenameTransform,
    Resize,
    RewardClipping,
    RewardScaling,
    SqueezeTransform,
    ToTensorImage,
    UnsqueezeTransform,
    VecNorm,
)

OLD_TORCH = parse(torch.__version__) < parse("2.0.0")
_has_tv = importlib.util.find_spec("torchvision") is not None
_has_snapshot = importlib.util.find_spec("torchsnapshot") is not None
_os_is_windows = sys.platform == "win32"

torch_2_3 = version.parse(
    ".".join([str(s) for s in version.parse(str(torch.__version__)).release])
) >= version.parse("2.3.0")


@pytest.mark.parametrize(
    "sampler", [samplers.RandomSampler, samplers.PrioritizedSampler]
)
@pytest.mark.parametrize(
    "writer", [writers.RoundRobinWriter, writers.TensorDictMaxValueWriter]
)
@pytest.mark.parametrize(
    "rb_type,storage,datatype",
    [
        [ReplayBuffer, ListStorage, None],
        [TensorDictReplayBuffer, ListStorage, "tensordict"],
        [RemoteTensorDictReplayBuffer, ListStorage, "tensordict"],
        [ReplayBuffer, LazyTensorStorage, "tensor"],
        [ReplayBuffer, LazyTensorStorage, "tensordict"],
        [ReplayBuffer, LazyTensorStorage, "pytree"],
        [TensorDictReplayBuffer, LazyTensorStorage, "tensordict"],
        [RemoteTensorDictReplayBuffer, LazyTensorStorage, "tensordict"],
        [ReplayBuffer, LazyMemmapStorage, "tensor"],
        [ReplayBuffer, LazyMemmapStorage, "tensordict"],
        [ReplayBuffer, LazyMemmapStorage, "pytree"],
        [TensorDictReplayBuffer, LazyMemmapStorage, "tensordict"],
        [RemoteTensorDictReplayBuffer, LazyMemmapStorage, "tensordict"],
    ],
)
@pytest.mark.parametrize("size", [3, 5, 100])
class TestComposableBuffers:
    def _get_rb(self, rb_type, size, sampler, writer, storage):

        if storage is not None:
            storage = storage(size)

        sampler_args = {}
        if sampler is samplers.PrioritizedSampler:
            sampler_args = {"max_capacity": size, "alpha": 0.8, "beta": 0.9}

        sampler = sampler(**sampler_args)
        writer = writer()
        rb = rb_type(storage=storage, sampler=sampler, writer=writer, batch_size=3)
        return rb

    def _get_datum(self, datatype):
        if datatype is None:
            data = torch.randint(100, (1,))
        elif datatype == "tensor":
            data = torch.randint(100, (1,))
        elif datatype == "tensordict":
            data = TensorDict(
                {"a": torch.randint(100, (1,)), "next": {"reward": torch.randn(1)}}, []
            )
        elif datatype == "pytree":
            data = {
                "a": torch.randint(100, (1,)),
                "b": {"c": [torch.zeros(3), (torch.ones(2),)]},
                30: torch.zeros(2),
            }
        else:
            raise NotImplementedError(datatype)
        return data

    def _get_data(self, datatype, size):
        if datatype is None:
            data = torch.randint(
                100,
                (
                    size,
                    1,
                ),
            )
        elif datatype == "tensor":
            data = torch.randint(
                100,
                (
                    size,
                    1,
                ),
            )
        elif datatype == "tensordict":
            data = TensorDict(
                {
                    "a": torch.randint(
                        100,
                        (
                            size,
                            1,
                        ),
                    ),
                    "next": {"reward": torch.randn(size, 1)},
                },
                [size],
            )
        elif datatype == "pytree":
            data = {
                "a": torch.randint(
                    100,
                    (
                        size,
                        1,
                    ),
                ),
                "b": {"c": [torch.zeros(size, 3), (torch.ones(size, 2),)]},
                30: torch.zeros(size, 2),
            }
        else:
            raise NotImplementedError(datatype)
        return data

    def test_add(self, rb_type, sampler, writer, storage, size, datatype):
        if rb_type is RemoteTensorDictReplayBuffer and _os_is_windows:
            pytest.skip(
                "Distributed package support on Windows is a prototype feature and is subject to changes."
            )
        torch.manual_seed(0)
        rb = self._get_rb(
            rb_type=rb_type, sampler=sampler, writer=writer, storage=storage, size=size
        )
        data = self._get_datum(datatype)
        if not is_tensor_collection(data) and writer is TensorDictMaxValueWriter:
            with pytest.raises(
                RuntimeError, match="expects data to be a tensor collection"
            ):
                rb.add(data)
            return
        rb.add(data)
        s = rb.sample(1)
        if isinstance(s, (torch.Tensor, TensorDictBase)):
            assert s.ndim, s
            s = s[0]
        else:

            def assert_ndim(tensor):
                assert tensor.shape[0] == 1

            tree_map(assert_ndim, s)
            s = tree_map(lambda s: s[0], s)
        if isinstance(s, TensorDictBase):
            s = s.select(*data.keys(True), strict=False)
            data = data.select(*s.keys(True), strict=False)
            assert (s == data).all()
            assert list(s.keys(True, True))
        else:
            flat_s = tree_flatten(s)[0]
            flat_data = tree_flatten(data)[0]
            assert all((_s == _data).all() for (_s, _data) in zip(flat_s, flat_data))

    def test_cursor_position(self, rb_type, sampler, writer, storage, size, datatype):
        storage = storage(size)
        writer = writer()
        writer.register_storage(storage)
        batch1 = self._get_data(datatype, size=5)
        cond = (
            OLD_TORCH
            and not isinstance(writer, TensorDictMaxValueWriter)
            and size < len(batch1)
            and isinstance(storage, TensorStorage)
        )

        if not is_tensor_collection(batch1) and isinstance(
            writer, TensorDictMaxValueWriter
        ):
            with pytest.raises(
                RuntimeError, match="expects data to be a tensor collection"
            ):
                writer.extend(batch1)
            return

        with pytest.warns(
            UserWarning,
            match="A cursor of length superior to the storage capacity was provided",
        ) if cond else contextlib.nullcontext():
            writer.extend(batch1)

        # Added less data than storage max size
        if size > 5:
            assert writer._cursor == 5
        # Added more data than storage max size
        elif size < 5:
            # if Max writer, we don't necessarily overwrite existing values so
            # we just check that the cursor is before the threshold
            if isinstance(writer, TensorDictMaxValueWriter):
                assert writer._cursor <= 5 - size
            else:
                assert writer._cursor == 5 - size
        # Added as data as storage max size
        else:
            assert writer._cursor == 0
            if not isinstance(writer, TensorDictMaxValueWriter):
                batch2 = self._get_data(datatype, size=size - 1)
                writer.extend(batch2)
                assert writer._cursor == size - 1

    def test_extend(self, rb_type, sampler, writer, storage, size, datatype):
        if rb_type is RemoteTensorDictReplayBuffer and _os_is_windows:
            pytest.skip(
                "Distributed package support on Windows is a prototype feature and is subject to changes."
            )
        torch.manual_seed(0)
        rb = self._get_rb(
            rb_type=rb_type, sampler=sampler, writer=writer, storage=storage, size=size
        )
        data_shape = 5
        data = self._get_data(datatype, size=data_shape)
        cond = (
            OLD_TORCH
            and writer is not TensorDictMaxValueWriter
            and size < len(data)
            and isinstance(rb._storage, TensorStorage)
        )
        if not is_tensor_collection(data) and writer is TensorDictMaxValueWriter:
            with pytest.raises(
                RuntimeError, match="expects data to be a tensor collection"
            ):
                rb.extend(data)
            return
        length = min(rb._storage.max_size, len(rb) + data_shape)
        if writer is TensorDictMaxValueWriter:
            data["next", "reward"][-length:] = 1_000_000
        with pytest.warns(
            UserWarning,
            match="A cursor of length superior to the storage capacity was provided",
        ) if cond else contextlib.nullcontext():
            rb.extend(data)
        length = len(rb)
        if is_tensor_collection(data):
            data_iter = data[-length:]
        else:

            def data_iter():
                for t in range(-length, -1):
                    yield tree_map(lambda x, t=t: x[t], data)

            data_iter = data_iter()
        for d in data_iter:
            for b in rb._storage:
                if isinstance(b, TensorDictBase):
                    keys = set(d.keys()).intersection(b.keys())
                    b = b.exclude("index").select(*keys, strict=False)
                    keys = set(d.keys()).intersection(b.keys())
                    d = d.select(*keys, strict=False)
                if isinstance(b, (torch.Tensor, TensorDictBase)):
                    value = b == d
                    value = value.all()
                else:
                    d_flat = tree_flatten(d)[0]
                    b_flat = tree_flatten(b)[0]
                    value = all((_b == _d).all() for (_b, _d) in zip(b_flat, d_flat))
                if value:
                    break
            else:
                raise RuntimeError("did not find match")

        data2 = self._get_data(datatype, size=2 * size + 2)
        cond = (
            OLD_TORCH
            and writer is not TensorDictMaxValueWriter
            and size < len(data2)
            and isinstance(rb._storage, TensorStorage)
        )
        with pytest.warns(
            UserWarning,
            match="A cursor of length superior to the storage capacity was provided",
        ) if cond else contextlib.nullcontext():
            rb.extend(data2)

    def test_sample(self, rb_type, sampler, writer, storage, size, datatype):
        if rb_type is RemoteTensorDictReplayBuffer and _os_is_windows:
            pytest.skip(
                "Distributed package support on Windows is a prototype feature and is subject to changes."
            )
        torch.manual_seed(0)
        rb = self._get_rb(
            rb_type=rb_type, sampler=sampler, writer=writer, storage=storage, size=size
        )
        data = self._get_data(datatype, size=5)
        cond = (
            OLD_TORCH
            and writer is not TensorDictMaxValueWriter
            and size < len(data)
            and isinstance(rb._storage, TensorStorage)
        )
        if not is_tensor_collection(data) and writer is TensorDictMaxValueWriter:
            with pytest.raises(
                RuntimeError, match="expects data to be a tensor collection"
            ):
                rb.extend(data)
            return
        with pytest.warns(
            UserWarning,
            match="A cursor of length superior to the storage capacity was provided",
        ) if cond else contextlib.nullcontext():
            rb.extend(data)
        rb_sample = rb.sample()
        # if not isinstance(new_data, (torch.Tensor, TensorDictBase)):
        #     new_data = new_data[0]

        if is_tensor_collection(data) or isinstance(data, torch.Tensor):
            rb_sample_iter = rb_sample
        else:

            def data_iter_func(maxval, data=data):
                for t in range(maxval):
                    yield tree_map(lambda x, t=t: x[t], data)

            rb_sample_iter = data_iter_func(rb._batch_size, rb_sample)

        for single_sample in rb_sample_iter:

            if is_tensor_collection(data) or isinstance(data, torch.Tensor):
                data_iter = data
            else:
                data_iter = data_iter_func(5, data)

            for data_sample in data_iter:
                if isinstance(data_sample, TensorDictBase):
                    keys = set(single_sample.keys()).intersection(data_sample.keys())
                    data_sample = data_sample.exclude("index").select(
                        *keys, strict=False
                    )
                    keys = set(single_sample.keys()).intersection(data_sample.keys())
                    single_sample = single_sample.select(*keys, strict=False)

                if isinstance(data_sample, (torch.Tensor, TensorDictBase)):
                    value = data_sample == single_sample
                    value = value.all()
                else:
                    d_flat = tree_flatten(single_sample)[0]
                    b_flat = tree_flatten(data_sample)[0]
                    value = all((_b == _d).all() for (_b, _d) in zip(b_flat, d_flat))

                if value:
                    break
            else:
                raise RuntimeError("did not find match")

    def test_index(self, rb_type, sampler, writer, storage, size, datatype):
        if rb_type is RemoteTensorDictReplayBuffer and _os_is_windows:
            pytest.skip(
                "Distributed package support on Windows is a prototype feature and is subject to changes."
            )
        torch.manual_seed(0)
        rb = self._get_rb(
            rb_type=rb_type, sampler=sampler, writer=writer, storage=storage, size=size
        )
        data = self._get_data(datatype, size=5)
        cond = (
            OLD_TORCH
            and writer is not TensorDictMaxValueWriter
            and size < len(data)
            and isinstance(rb._storage, TensorStorage)
        )
        if not is_tensor_collection(data) and writer is TensorDictMaxValueWriter:
            with pytest.raises(
                RuntimeError, match="expects data to be a tensor collection"
            ):
                rb.extend(data)
            return
        with pytest.warns(
            UserWarning,
            match="A cursor of length superior to the storage capacity was provided",
        ) if cond else contextlib.nullcontext():
            rb.extend(data)
        d1 = rb[2]
        d2 = rb._storage[2]
        if type(d1) is not type(d2):
            d1 = d1[0]
        if is_tensor_collection(data) or isinstance(data, torch.Tensor):
            b = d1 == d2
            if not isinstance(b, bool):
                b = b.all()
        else:
            d1_flat = tree_flatten(d1)[0]
            d2_flat = tree_flatten(d2)[0]
            b = all((_d1 == _d2).all() for (_d1, _d2) in zip(d1_flat, d2_flat))
        assert b

    def test_pickable(self, rb_type, sampler, writer, storage, size, datatype):

        rb = self._get_rb(
            rb_type=rb_type, sampler=sampler, writer=writer, storage=storage, size=size
        )
        serialized = pickle.dumps(rb)
        rb2 = pickle.loads(serialized)
        assert rb.__dict__.keys() == rb2.__dict__.keys()
        for key in sorted(rb.__dict__.keys()):
            assert isinstance(rb.__dict__[key], type(rb2.__dict__[key]))


class TestStorages:
    def _get_tensor(self):
        return torch.randn(10, 11)

    def _get_tensordict(self):
        return TensorDict(
            {"data": torch.randn(10, 11), ("nested", "data"): torch.randn(10, 11, 3)},
            [10, 11],
        )

    def _get_pytree(self):
        return {
            "a": torch.randint(100, (10, 11, 1)),
            "b": {"c": [torch.zeros(10, 11), (torch.ones(10, 11),)]},
            30: torch.zeros(10, 11),
        }

    def _get_tensorclass(self):
        data = self._get_tensordict()
        return make_tc(data)(**data, batch_size=data.shape)

    @pytest.mark.parametrize("storage_type", [TensorStorage])
    def test_errors(self, storage_type):
        with pytest.raises(ValueError, match="Expected storage to be non-null"):
            storage_type(None)
        data = torch.randn(3)
        with pytest.raises(
            ValueError, match="The max-size and the storage shape mismatch"
        ):
            storage_type(data, max_size=4)

    @pytest.mark.parametrize(
        "data_type", ["tensor", "tensordict", "tensorclass", "pytree"]
    )
    @pytest.mark.parametrize("storage_type", [TensorStorage])
    def test_get_set(self, storage_type, data_type):
        if data_type == "tensor":
            data = self._get_tensor()
        elif data_type == "tensorclass":
            data = self._get_tensorclass()
        elif data_type == "tensordict":
            data = self._get_tensordict()
        elif data_type == "pytree":
            data = self._get_pytree()
        else:
            raise NotImplementedError
        storage = storage_type(data)
        if data_type == "pytree":
            storage.set(range(10), tree_map(torch.zeros_like, data))

            def check(x):
                assert (x == 0).all()

            tree_map(check, storage.get(range(10)))
        else:
            storage.set(range(10), torch.zeros_like(data))
            assert (storage.get(range(10)) == 0).all()

    @pytest.mark.parametrize(
        "data_type", ["tensor", "tensordict", "tensorclass", "pytree"]
    )
    @pytest.mark.parametrize("storage_type", [TensorStorage])
    def test_state_dict(self, storage_type, data_type):
        if data_type == "tensor":
            data = self._get_tensor()
        elif data_type == "tensorclass":
            data = self._get_tensorclass()
        elif data_type == "tensordict":
            data = self._get_tensordict()
        elif data_type == "pytree":
            data = self._get_pytree()
        else:
            raise NotImplementedError
        storage = storage_type(data)
        if data_type == "pytree":
            with pytest.raises(TypeError, match="are not supported by"):
                storage.state_dict()
            return
        sd = storage.state_dict()
        storage2 = storage_type(torch.zeros_like(data))
        storage2.load_state_dict(sd)
        assert (storage.get(range(10)) == storage2.get(range(10))).all()
        assert type(storage.get(range(10))) is type(  # noqa: E721
            storage2.get(range(10))
        )

    @pytest.mark.skipif(
        not torch.cuda.device_count(),
        reason="not cuda device found to test rb storage.",
    )
    @pytest.mark.parametrize(
        "device_data,device_storage",
        [
            [torch.device("cuda"), torch.device("cpu")],
            [torch.device("cpu"), torch.device("cuda")],
            [torch.device("cpu"), "auto"],
            [torch.device("cuda"), "auto"],
        ],
    )
    @pytest.mark.parametrize("storage_type", [LazyMemmapStorage, LazyTensorStorage])
    @pytest.mark.parametrize("data_type", ["tensor", "tc", "td"])
    def test_storage_device(self, device_data, device_storage, storage_type, data_type):
        @tensorclass
        class TC:
            a: torch.Tensor

        if data_type == "tensor":
            data = torch.randn(3, device=device_data)
        elif data_type == "td":
            data = TensorDict(
                {"a": torch.randn(3, device=device_data)}, [], device=device_data
            )
        elif data_type == "tc":
            data = TC(
                a=torch.randn(3, device=device_data),
                batch_size=[],
                device=device_data,
            )
        else:
            raise NotImplementedError
        storage = storage_type(max_size=10, device=device_storage)
        if device_storage == "auto":
            device_storage = device_data
        if storage_type is LazyMemmapStorage and device_storage.type == "cuda":
            with pytest.warns(
                DeprecationWarning, match="Support for Memmap device other than CPU"
            ):
                # this is rather brittle and will fail with some indices
                # when both device (storage and data) don't match (eg, range())
                storage.set(0, data)
        else:
            storage.set(0, data)
        assert storage.get(0).device.type == device_storage.type

    @pytest.mark.parametrize("storage_in", ["tensor", "memmap"])
    @pytest.mark.parametrize("storage_out", ["tensor", "memmap"])
    @pytest.mark.parametrize("init_out", [True, False])
    @pytest.mark.parametrize(
        "backend", ["torch"] + (["torchsnapshot"] if _has_snapshot else [])
    )
    def test_storage_state_dict(self, storage_in, storage_out, init_out, backend):
        os.environ["CKPT_BACKEND"] = backend
        buffer_size = 100
        if storage_in == "memmap":
            storage_in = LazyMemmapStorage(buffer_size, device="cpu")
        elif storage_in == "tensor":
            storage_in = LazyTensorStorage(buffer_size, device="cpu")
        if storage_out == "memmap":
            storage_out = LazyMemmapStorage(buffer_size, device="cpu")
        elif storage_out == "tensor":
            storage_out = LazyTensorStorage(buffer_size, device="cpu")

        replay_buffer = TensorDictReplayBuffer(
            pin_memory=False, prefetch=3, storage=storage_in, batch_size=3
        )
        # fill replay buffer with random data
        transition = TensorDict(
            {
                "observation": torch.ones(1, 4),
                "action": torch.ones(1, 2),
                "reward": torch.ones(1, 1),
                "dones": torch.ones(1, 1),
                "next": {"observation": torch.ones(1, 4)},
            },
            batch_size=1,
        )
        for _ in range(3):
            replay_buffer.extend(transition)

        state_dict = replay_buffer.state_dict()

        new_replay_buffer = TensorDictReplayBuffer(
            pin_memory=False,
            prefetch=3,
            storage=storage_out,
            batch_size=state_dict["_batch_size"],
        )
        if init_out:
            new_replay_buffer.extend(transition)

        new_replay_buffer.load_state_dict(state_dict)
        s = new_replay_buffer.sample()
        assert (s.exclude("index") == 1).all()

    @pytest.mark.parametrize("device_data", get_default_devices())
    @pytest.mark.parametrize("storage_type", [LazyMemmapStorage, LazyTensorStorage])
    @pytest.mark.parametrize("data_type", ["tensor", "tc", "td", "pytree"])
    @pytest.mark.parametrize("isinit", [True, False])
    def test_storage_dumps_loads(
        self, device_data, storage_type, data_type, isinit, tmpdir
    ):
        dir_rb = tmpdir / "rb"
        dir_save = tmpdir / "save"
        dir_rb.mkdir()
        dir_save.mkdir()
        torch.manual_seed(0)

        @tensorclass
        class TC:
            tensor: torch.Tensor
            td: TensorDict
            text: str

        if data_type == "tensor":
            data = torch.randint(10, (3,), device=device_data)
        elif data_type == "pytree":
            data = {
                "a": torch.randint(10, (3,), device=device_data),
                "b": {"c": [torch.ones(3), (-torch.ones(3, 2),)]},
                30: -torch.ones(3, 1),
            }
        elif data_type == "td":
            data = TensorDict(
                {
                    "a": torch.randint(10, (3,), device=device_data),
                    "b": TensorDict(
                        {"c": torch.randint(10, (3,), device=device_data)},
                        batch_size=[3],
                    ),
                },
                batch_size=[3],
                device=device_data,
            )
        elif data_type == "tc":
            data = TC(
                tensor=torch.randint(10, (3,), device=device_data),
                td=TensorDict(
                    {"c": torch.randint(10, (3,), device=device_data)}, batch_size=[3]
                ),
                text="some text",
                batch_size=[3],
                device=device_data,
            )
        else:
            raise NotImplementedError
        if storage_type in (LazyMemmapStorage,):
            storage = storage_type(max_size=10, scratch_dir=dir_rb)
        else:
            storage = storage_type(max_size=10)
        # We cast the device to CPU as CUDA isn't automatically cast to CPU when using range() index
        if data_type == "pytree":
            storage.set(range(3), tree_map(lambda x: x.cpu(), data))
        else:
            storage.set(range(3), data.cpu())
        storage.dumps(dir_save)
        # check we can dump twice
        storage.dumps(dir_save)

        storage_recover = storage_type(max_size=10)
        if isinit:
            if data_type == "pytree":
                storage_recover.set(range(3), tree_map(lambda x: x.cpu().zero_(), data))
            else:
                storage_recover.set(range(3), data.cpu().zero_())

        if data_type in ("tensor", "pytree") and not isinit:
            with pytest.raises(
                RuntimeError,
                match="Cannot fill a non-initialized pytree-based TensorStorage",
            ):
                storage_recover.loads(dir_save)
            return
        storage_recover.loads(dir_save)
        # tree_map with more than one pytree is only available in torch >= 2.3
        if torch_2_3:
            if data_type in ("tensor", "pytree"):
                tree_map(
                    torch.testing.assert_close,
                    tree_flatten(storage._storage)[0],
                    tree_flatten(storage_recover._storage)[0],
                )
            else:
                assert_allclose_td(storage._storage, storage_recover._storage)
        if data == "tc":
            assert storage._storage.text == storage_recover._storage.text


@pytest.mark.parametrize("max_size", [1000])
@pytest.mark.parametrize("shape", [[3, 4]])
@pytest.mark.parametrize("storage", [LazyTensorStorage, LazyMemmapStorage])
class TestLazyStorages:
    def _get_nested_tensorclass(self, shape):
        @tensorclass
        class NestedTensorClass:
            key1: torch.Tensor
            key2: torch.Tensor

        @tensorclass
        class TensorClass:
            key1: torch.Tensor
            key2: torch.Tensor
            next: NestedTensorClass

        return TensorClass(
            key1=torch.ones(*shape),
            key2=torch.ones(*shape),
            next=NestedTensorClass(
                key1=torch.ones(*shape), key2=torch.ones(*shape), batch_size=shape
            ),
            batch_size=shape,
        )

    def _get_nested_td(self, shape):
        nested_td = TensorDict(
            {
                "key1": torch.ones(*shape),
                "key2": torch.ones(*shape),
                "next": TensorDict(
                    {
                        "key1": torch.ones(*shape),
                        "key2": torch.ones(*shape),
                    },
                    shape,
                ),
            },
            shape,
        )
        return nested_td

    def test_init(self, max_size, shape, storage):
        td = self._get_nested_td(shape)
        mystorage = storage(max_size=max_size)
        mystorage._init(td)
        assert mystorage._storage.shape == (max_size, *shape)

    def test_set(self, max_size, shape, storage):
        td = self._get_nested_td(shape)
        mystorage = storage(max_size=max_size)
        mystorage.set(list(range(td.shape[0])), td)
        assert mystorage._storage.shape == (max_size, *shape[1:])
        idx = list(range(1, td.shape[0] - 1))
        tc_sample = mystorage.get(idx)
        assert tc_sample.shape == torch.Size([td.shape[0] - 2, *td.shape[1:]])

    def test_init_tensorclass(self, max_size, shape, storage):
        tc = self._get_nested_tensorclass(shape)
        mystorage = storage(max_size=max_size)
        mystorage._init(tc)
        assert is_tensorclass(mystorage._storage)
        assert mystorage._storage.shape == (max_size, *shape)

    def test_set_tensorclass(self, max_size, shape, storage):
        tc = self._get_nested_tensorclass(shape)
        mystorage = storage(max_size=max_size)
        mystorage.set(list(range(tc.shape[0])), tc)
        assert mystorage._storage.shape == (max_size, *shape[1:])
        idx = list(range(1, tc.shape[0] - 1))
        tc_sample = mystorage.get(idx)
        assert tc_sample.shape == torch.Size([tc.shape[0] - 2, *tc.shape[1:]])


@pytest.mark.parametrize("priority_key", ["pk", "td_error"])
@pytest.mark.parametrize("contiguous", [True, False])
@pytest.mark.parametrize("device", get_default_devices())
def test_ptdrb(priority_key, contiguous, device):
    torch.manual_seed(0)
    np.random.seed(0)
    rb = TensorDictReplayBuffer(
        sampler=samplers.PrioritizedSampler(5, alpha=0.7, beta=0.9),
        priority_key=priority_key,
        batch_size=5,
    )
    td1 = TensorDict(
        source={
            "a": torch.randn(3, 1),
            priority_key: torch.rand(3, 1) / 10,
            "_idx": torch.arange(3).view(3, 1),
        },
        batch_size=[3],
        device=device,
    )
    rb.extend(td1)
    s = rb.sample()
    assert s.batch_size == torch.Size([5])
    assert (td1[s.get("_idx").squeeze()].get("a") == s.get("a")).all()
    assert_allclose_td(td1[s.get("_idx").squeeze()].select("a"), s.select("a"))

    # test replacement
    td2 = TensorDict(
        source={
            "a": torch.randn(5, 1),
            priority_key: torch.rand(5, 1) / 10,
            "_idx": torch.arange(5).view(5, 1),
        },
        batch_size=[5],
        device=device,
    )
    rb.extend(td2)
    s = rb.sample()
    assert s.batch_size == torch.Size([5])
    assert (td2[s.get("_idx").squeeze()].get("a") == s.get("a")).all()
    assert_allclose_td(td2[s.get("_idx").squeeze()].select("a"), s.select("a"))

    # test strong update
    # get all indices that match first item
    idx = s.get("_idx")
    idx_match = (idx == idx[0]).nonzero()[:, 0]
    s.set_at_(
        priority_key,
        torch.ones(idx_match.numel(), 1, device=device) * 100000000,
        idx_match,
    )
    val = s.get("a")[0]

    idx0 = s.get("_idx")[0]
    rb.update_tensordict_priority(s)
    s = rb.sample()
    assert (val == s.get("a")).sum() >= 1
    torch.testing.assert_close(td2[idx0].get("a").view(1), s.get("a").unique().view(1))

    # test updating values of original td
    td2.set_("a", torch.ones_like(td2.get("a")))
    s = rb.sample()
    torch.testing.assert_close(td2[idx0].get("a").view(1), s.get("a").unique().view(1))


@pytest.mark.parametrize("stack", [False, True])
@pytest.mark.parametrize("datatype", ["tc", "tb"])
@pytest.mark.parametrize("reduction", ["min", "max", "median", "mean"])
def test_replay_buffer_trajectories(stack, reduction, datatype):
    traj_td = TensorDict(
        {"obs": torch.randn(3, 4, 5), "actions": torch.randn(3, 4, 2)},
        batch_size=[3, 4],
    )
    if datatype == "tc":
        c = make_tc(traj_td)
        traj_td = c(**traj_td, batch_size=traj_td.batch_size)
        assert is_tensorclass(traj_td)
    elif datatype != "tb":
        raise NotImplementedError

    if stack:
        traj_td = torch.stack(list(traj_td), 0)

    rb = TensorDictReplayBuffer(
        sampler=samplers.PrioritizedSampler(
            5,
            alpha=0.7,
            beta=0.9,
            reduction=reduction,
        ),
        priority_key="td_error",
        batch_size=3,
    )
    rb.extend(traj_td)
    sampled_td = rb.sample()
    if datatype == "tc":
        assert is_tensorclass(traj_td)
        return

    sampled_td.set("td_error", torch.rand(sampled_td.shape))
    rb.update_tensordict_priority(sampled_td)
    sampled_td = rb.sample(include_info=True)
    assert (sampled_td.get("_weight") > 0).all()
    assert sampled_td.batch_size == torch.Size([3, 4])

    # # set back the trajectory length
    # sampled_td_filtered = sampled_td.to_tensordict().exclude(
    #     "_weight", "index", "td_error"
    # )
    # sampled_td_filtered.batch_size = [3, 4]


@pytest.mark.parametrize(
    "rbtype,storage",
    [
        (ReplayBuffer, None),
        (ReplayBuffer, ListStorage),
        (PrioritizedReplayBuffer, None),
        (PrioritizedReplayBuffer, ListStorage),
        (TensorDictReplayBuffer, None),
        (TensorDictReplayBuffer, ListStorage),
        (TensorDictReplayBuffer, LazyTensorStorage),
        (TensorDictReplayBuffer, LazyMemmapStorage),
        (TensorDictPrioritizedReplayBuffer, None),
        (TensorDictPrioritizedReplayBuffer, ListStorage),
        (TensorDictPrioritizedReplayBuffer, LazyTensorStorage),
        (TensorDictPrioritizedReplayBuffer, LazyMemmapStorage),
    ],
)
@pytest.mark.parametrize("size", [3, 5, 100])
@pytest.mark.parametrize("prefetch", [0])
class TestBuffers:
    _default_params_rb = {}
    _default_params_td_rb = {}
    _default_params_prb = {"alpha": 0.8, "beta": 0.9}
    _default_params_td_prb = {"alpha": 0.8, "beta": 0.9}

    def _get_rb(self, rbtype, size, storage, prefetch):
        if storage is not None:
            storage = storage(size)
        if rbtype is ReplayBuffer:
            params = self._default_params_rb
        elif rbtype is PrioritizedReplayBuffer:
            params = self._default_params_prb
        elif rbtype is TensorDictReplayBuffer:
            params = self._default_params_td_rb
        elif rbtype is TensorDictPrioritizedReplayBuffer:
            params = self._default_params_td_prb
        else:
            raise NotImplementedError(rbtype)
        rb = rbtype(storage=storage, prefetch=prefetch, batch_size=3, **params)
        return rb

    def _get_datum(self, rbtype):
        if rbtype is ReplayBuffer:
            data = torch.randint(100, (1,))
        elif rbtype is PrioritizedReplayBuffer:
            data = torch.randint(100, (1,))
        elif rbtype is TensorDictReplayBuffer:
            data = TensorDict({"a": torch.randint(100, (1,))}, [])
        elif rbtype is TensorDictPrioritizedReplayBuffer:
            data = TensorDict({"a": torch.randint(100, (1,))}, [])
        else:
            raise NotImplementedError(rbtype)
        return data

    def _get_data(self, rbtype, size):
        if rbtype is ReplayBuffer:
            data = [torch.randint(100, (1,)) for _ in range(size)]
        elif rbtype is PrioritizedReplayBuffer:
            data = [torch.randint(100, (1,)) for _ in range(size)]
        elif rbtype is TensorDictReplayBuffer:
            data = TensorDict(
                {
                    "a": torch.randint(100, (size,)),
                    "b": TensorDict({"c": torch.randint(100, (size,))}, [size]),
                },
                [size],
            )
        elif rbtype is TensorDictPrioritizedReplayBuffer:
            data = TensorDict(
                {
                    "a": torch.randint(100, (size,)),
                    "b": TensorDict({"c": torch.randint(100, (size,))}, [size]),
                },
                [size],
            )
        else:
            raise NotImplementedError(rbtype)
        return data

    def test_cursor_position2(self, rbtype, storage, size, prefetch):
        torch.manual_seed(0)
        rb = self._get_rb(rbtype, storage=storage, size=size, prefetch=prefetch)
        batch1 = self._get_data(rbtype, size=5)
        cond = (
            OLD_TORCH and size < len(batch1) and isinstance(rb._storage, TensorStorage)
        )
        with pytest.warns(
            UserWarning,
            match="A cursor of length superior to the storage capacity was provided",
        ) if cond else contextlib.nullcontext():
            rb.extend(batch1)

        # Added fewer data than storage max size
        if size > 5 or storage is None:
            assert rb._writer._cursor == 5
        # Added more data than storage max size
        elif size < 5:
            assert rb._writer._cursor == 5 - size
        # Added as data as storage max size
        else:
            assert rb._writer._cursor == 0
            batch2 = self._get_data(rbtype, size=size - 1)
            rb.extend(batch2)
            assert rb._writer._cursor == size - 1

    def test_add(self, rbtype, storage, size, prefetch):
        torch.manual_seed(0)
        rb = self._get_rb(rbtype, storage=storage, size=size, prefetch=prefetch)
        data = self._get_datum(rbtype)
        rb.add(data)
        s = rb.sample(1)[0]
        if isinstance(s, TensorDictBase):
            s = s.select(*data.keys(True), strict=False)
            data = data.select(*s.keys(True), strict=False)
            assert (s == data).all()
            assert list(s.keys(True, True))
        else:
            assert (s == data).all()

    def test_empty(self, rbtype, storage, size, prefetch):
        torch.manual_seed(0)
        rb = self._get_rb(rbtype, storage=storage, size=size, prefetch=prefetch)
        data = self._get_datum(rbtype)
        for _ in range(2):
            rb.add(data)
            s = rb.sample(1)[0]
            if isinstance(s, TensorDictBase):
                s = s.select(*data.keys(True), strict=False)
                data = data.select(*s.keys(True), strict=False)
                assert (s == data).all()
                assert list(s.keys(True, True))
            else:
                assert (s == data).all()
            rb.empty()
            with pytest.raises(
                RuntimeError, match="Cannot sample from an empty storage"
            ):
                rb.sample()

    def test_extend(self, rbtype, storage, size, prefetch):
        torch.manual_seed(0)
        rb = self._get_rb(rbtype, storage=storage, size=size, prefetch=prefetch)
        data = self._get_data(rbtype, size=5)
        cond = OLD_TORCH and size < len(data) and isinstance(rb._storage, TensorStorage)
        with pytest.warns(
            UserWarning,
            match="A cursor of length superior to the storage capacity was provided",
        ) if cond else contextlib.nullcontext():
            rb.extend(data)
        length = len(rb)
        for d in data[-length:]:
            found_similar = False
            for b in rb._storage:
                if isinstance(b, TensorDictBase):
                    keys = set(d.keys()).intersection(b.keys())
                    b = b.exclude("index").select(*keys, strict=False)
                    keys = set(d.keys()).intersection(b.keys())
                    d = d.select(*keys, strict=False)

                value = b == d
                if isinstance(value, (torch.Tensor, TensorDictBase)):
                    value = value.all()
                if value:
                    break
            else:
                raise RuntimeError("did not find match")

    def test_sample(self, rbtype, storage, size, prefetch):
        torch.manual_seed(0)
        rb = self._get_rb(rbtype, storage=storage, size=size, prefetch=prefetch)
        data = self._get_data(rbtype, size=5)
        cond = OLD_TORCH and size < len(data) and isinstance(rb._storage, TensorStorage)
        with pytest.warns(
            UserWarning,
            match="A cursor of length superior to the storage capacity was provided",
        ) if cond else contextlib.nullcontext():
            rb.extend(data)
        new_data = rb.sample()
        if not isinstance(new_data, (torch.Tensor, TensorDictBase)):
            new_data = new_data[0]

        for d in new_data:
            found_similar = False
            for b in data:
                if isinstance(b, TensorDictBase):
                    keys = set(d.keys()).intersection(b.keys())
                    b = b.exclude("index").select(*keys, strict=False)
                    keys = set(d.keys()).intersection(b.keys())
                    d = d.select(*keys, strict=False)

                value = b == d
                if isinstance(value, (torch.Tensor, TensorDictBase)):
                    value = value.all()
                if value:
                    break
            else:
                raise RuntimeError("did not find matching value")

    def test_index(self, rbtype, storage, size, prefetch):
        torch.manual_seed(0)
        rb = self._get_rb(rbtype, storage=storage, size=size, prefetch=prefetch)
        data = self._get_data(rbtype, size=5)
        cond = OLD_TORCH and size < len(data) and isinstance(rb._storage, TensorStorage)
        with pytest.warns(
            UserWarning,
            match="A cursor of length superior to the storage capacity was provided",
        ) if cond else contextlib.nullcontext():
            rb.extend(data)
        d1 = rb[2]
        d2 = rb._storage[2]
        if type(d1) is not type(d2):
            d1 = d1[0]
        b = d1 == d2
        if not isinstance(b, bool):
            b = b.all()
        assert b

    def test_index_nonfull(self, rbtype, storage, size, prefetch):
        # checks that indexing the buffer before it's full gives the accurate view of the data
        rb = self._get_rb(rbtype, storage=storage, size=size, prefetch=prefetch)
        data = self._get_data(rbtype, size=size - 1)
        rb.extend(data)
        assert len(rb[: size - 1]) == size - 1
        assert len(rb[size - 2 :]) == 1


def test_multi_loops():
    """Tests that one can iterate multiple times over a buffer without rep."""
    rb = ReplayBuffer(
        batch_size=5, storage=ListStorage(10), sampler=SamplerWithoutReplacement()
    )
    rb.extend(torch.zeros(10))
    for i, d in enumerate(rb):  # noqa: B007
        assert (d == 0).all()
    assert i == 1
    for i, d in enumerate(rb):  # noqa: B007
        assert (d == 0).all()
    assert i == 1


def test_batch_errors():
    """Tests error messages related to batch-size"""
    rb = ReplayBuffer(
        storage=ListStorage(10), sampler=SamplerWithoutReplacement(drop_last=False)
    )
    rb.extend(torch.zeros(10))
    rb.sample(3)  # that works
    with pytest.raises(
        RuntimeError,
        match="Cannot iterate over the replay buffer. Batch_size was not specified",
    ):
        for _ in rb:
            pass
    with pytest.raises(RuntimeError, match="batch_size not specified"):
        rb.sample()
    with pytest.raises(ValueError, match="Samplers with drop_last=True"):
        ReplayBuffer(
            storage=ListStorage(10), sampler=SamplerWithoutReplacement(drop_last=True)
        )
    # that works
    ReplayBuffer(
        storage=ListStorage(10),
    )
    rb = ReplayBuffer(
        storage=ListStorage(10),
        sampler=SamplerWithoutReplacement(drop_last=False),
        batch_size=3,
    )
    rb.extend(torch.zeros(10))
    for _ in rb:
        pass
    rb.sample()


@pytest.mark.parametrize("priority_key", ["pk", "td_error"])
@pytest.mark.parametrize("contiguous", [True, False])
@pytest.mark.parametrize("device", get_default_devices())
def test_prb(priority_key, contiguous, device):
    torch.manual_seed(0)
    np.random.seed(0)
    rb = TensorDictPrioritizedReplayBuffer(
        alpha=0.7,
        beta=0.9,
        priority_key=priority_key,
        storage=ListStorage(5),
        batch_size=5,
    )
    td1 = TensorDict(
        source={
            "a": torch.randn(3, 1),
            priority_key: torch.rand(3, 1) / 10,
            "_idx": torch.arange(3).view(3, 1),
        },
        batch_size=[3],
    ).to(device)

    rb.extend(td1)
    s = rb.sample()
    assert s.batch_size == torch.Size([5])
    assert (td1[s.get("_idx").squeeze()].get("a") == s.get("a")).all()
    assert_allclose_td(td1[s.get("_idx").squeeze()].select("a"), s.select("a"))

    # test replacement
    td2 = TensorDict(
        source={
            "a": torch.randn(5, 1),
            priority_key: torch.rand(5, 1) / 10,
            "_idx": torch.arange(5).view(5, 1),
        },
        batch_size=[5],
    ).to(device)
    rb.extend(td2)
    s = rb.sample()
    assert s.batch_size == torch.Size([5])
    assert (td2[s.get("_idx").squeeze()].get("a") == s.get("a")).all()
    assert_allclose_td(td2[s.get("_idx").squeeze()].select("a"), s.select("a"))

    # test strong update
    # get all indices that match first item
    idx = s.get("_idx")
    idx_match = (idx == idx[0]).nonzero()[:, 0]
    s.set_at_(
        priority_key,
        torch.ones(idx_match.numel(), 1, device=device) * 100000000,
        idx_match,
    )
    val = s.get("a")[0]

    idx0 = s.get("_idx")[0]
    rb.update_tensordict_priority(s)
    s = rb.sample()
    assert (val == s.get("a")).sum() >= 1
    torch.testing.assert_close(td2[idx0].get("a").view(1), s.get("a").unique().view(1))

    # test updating values of original td
    td2.set_("a", torch.ones_like(td2.get("a")))
    s = rb.sample()
    torch.testing.assert_close(td2[idx0].get("a").view(1), s.get("a").unique().view(1))


@pytest.mark.parametrize("stack", [False, True])
@pytest.mark.parametrize("reduction", ["min", "max", "mean", "median"])
def test_rb_trajectories(stack, reduction):
    traj_td = TensorDict(
        {"obs": torch.randn(3, 4, 5), "actions": torch.randn(3, 4, 2)},
        batch_size=[3, 4],
    )
    if stack:
        traj_td = torch.stack([td.to_tensordict() for td in traj_td], 0)

    rb = TensorDictPrioritizedReplayBuffer(
        alpha=0.7,
        beta=0.9,
        priority_key="td_error",
        storage=ListStorage(5),
        batch_size=3,
    )
    rb.extend(traj_td)
    sampled_td = rb.sample()
    sampled_td.set("td_error", torch.rand(3, 4))
    rb.update_tensordict_priority(sampled_td)
    sampled_td = rb.sample(include_info=True)
    assert (sampled_td.get("_weight") > 0).all()
    assert sampled_td.batch_size == torch.Size([3, 4])

    # set back the trajectory length
    sampled_td_filtered = sampled_td.to_tensordict().exclude(
        "_weight", "index", "td_error"
    )
    sampled_td_filtered.batch_size = [3, 4]


def test_shared_storage_prioritized_sampler():
    n = 100

    storage = LazyMemmapStorage(n)
    writer = RoundRobinWriter()
    sampler0 = RandomSampler()
    sampler1 = PrioritizedSampler(max_capacity=n, alpha=0.7, beta=1.1)

    rb0 = ReplayBuffer(storage=storage, writer=writer, sampler=sampler0, batch_size=10)
    rb1 = ReplayBuffer(storage=storage, writer=writer, sampler=sampler1, batch_size=10)

    data = TensorDict({"a": torch.arange(50)}, [50])

    # Extend rb0. rb1 should be aware of changes to storage.
    rb0.extend(data)

    assert len(rb0) == 50
    assert len(storage) == 50
    assert len(rb1) == 50

    rb0.sample()
    rb1.sample()

    assert rb1._sampler._sum_tree.query(0, 10) == 10
    assert rb1._sampler._sum_tree.query(0, 50) == 50
    assert rb1._sampler._sum_tree.query(0, 70) == 50


class TestTransforms:
    def test_append_transform(self):
        rb = ReplayBuffer(collate_fn=lambda x: torch.stack(x, 0), batch_size=1)
        td = TensorDict(
            {
                "observation": torch.randn(2, 4, 3, 16),
                "observation2": torch.randn(2, 4, 3, 16),
            },
            [],
        )
        rb.add(td)
        flatten = CatTensors(
            in_keys=["observation", "observation2"], out_key="observation_cat"
        )

        rb.append_transform(flatten)

        sampled = rb.sample()
        assert sampled.get("observation_cat").shape[-1] == 32

    def test_init_transform(self):
        flatten = FlattenObservation(
            -2, -1, in_keys=["observation"], out_keys=["flattened"]
        )

        rb = ReplayBuffer(
            collate_fn=lambda x: torch.stack(x, 0), transform=flatten, batch_size=1
        )

        td = TensorDict({"observation": torch.randn(2, 4, 3, 16)}, [])
        rb.add(td)
        sampled = rb.sample()
        assert sampled.get("flattened").shape[-1] == 48

    def test_insert_transform(self):
        flatten = FlattenObservation(
            -2, -1, in_keys=["observation"], out_keys=["flattened"]
        )
        rb = ReplayBuffer(
            collate_fn=lambda x: torch.stack(x, 0), transform=flatten, batch_size=1
        )
        td = TensorDict({"observation": torch.randn(2, 4, 3, 16, 1)}, [])
        rb.add(td)

        rb.insert_transform(0, SqueezeTransform(-1, in_keys=["observation"]))

        sampled = rb.sample()
        assert sampled.get("flattened").shape[-1] == 48

        with pytest.raises(ValueError):
            rb.insert_transform(10, SqueezeTransform(-1, in_keys=["observation"]))

    transforms = [
        ToTensorImage,
        pytest.param(
            partial(RewardClipping, clamp_min=0.1, clamp_max=0.9), id="RewardClipping"
        ),
        BinarizeReward,
        pytest.param(
            partial(Resize, w=2, h=2),
            id="Resize",
            marks=pytest.mark.skipif(
                not _has_tv, reason="needs torchvision dependency"
            ),
        ),
        pytest.param(
            partial(CenterCrop, w=1),
            id="CenterCrop",
            marks=pytest.mark.skipif(
                not _has_tv, reason="needs torchvision dependency"
            ),
        ),
        pytest.param(
            partial(UnsqueezeTransform, unsqueeze_dim=-1), id="UnsqueezeTransform"
        ),
        pytest.param(partial(SqueezeTransform, squeeze_dim=-1), id="SqueezeTransform"),
        GrayScale,
        pytest.param(partial(ObservationNorm, loc=1, scale=2), id="ObservationNorm"),
        pytest.param(partial(CatFrames, dim=-3, N=4), id="CatFrames"),
        pytest.param(partial(RewardScaling, loc=1, scale=2), id="RewardScaling"),
        DoubleToFloat,
        VecNorm,
    ]

    @pytest.mark.parametrize("transform", transforms)
    def test_smoke_replay_buffer_transform(self, transform):
        rb = TensorDictReplayBuffer(
            transform=transform(in_keys=["observation"]), batch_size=1
        )

        # td = TensorDict({"observation": torch.randn(3, 3, 3, 16, 1), "action": torch.randn(3)}, [])
        td = TensorDict({"observation": torch.randn(3, 3, 3, 16, 3)}, [])
        rb.add(td)

        m = mock.Mock()
        m.side_effect = [td.unsqueeze(0)]
        rb._transform.forward = m
        # rb._transform.__len__ = lambda *args: 3
        rb.sample()
        assert rb._transform.forward.called

        # was_called = [False]
        # forward = rb._transform.forward
        # def new_forward(*args, **kwargs):
        #     was_called[0] = True
        #     return forward(*args, **kwargs)
        # rb._transform.forward = new_forward
        # rb.sample()
        # assert was_called[0]

    transforms2 = [
        partial(DiscreteActionProjection, num_actions_effective=1, max_actions=3),
        FiniteTensorDictCheck,
        gSDENoise,
        PinMemoryTransform,
    ]

    @pytest.mark.parametrize("transform", transforms2)
    def test_smoke_replay_buffer_transform_no_inkeys(self, transform):
        if transform == PinMemoryTransform and not torch.cuda.is_available():
            raise pytest.skip("No CUDA device detected, skipping PinMemory")
        rb = ReplayBuffer(
            collate_fn=lambda x: torch.stack(x, 0), transform=transform(), batch_size=1
        )

        action = torch.zeros(3)
        action[..., 0] = 1
        td = TensorDict(
            {"observation": torch.randn(3, 3, 3, 16, 1), "action": action}, []
        )
        rb.add(td)
        rb.sample()

        rb._transform = mock.MagicMock()
        rb._transform.__len__ = lambda *args: 3
        rb.sample()
        assert rb._transform.called

    @pytest.mark.parametrize("at_init", [True, False])
    def test_transform_nontensor(self, at_init):
        def t(x):
            return tree_map(lambda y: y * 0, x)

        if at_init:
            rb = ReplayBuffer(storage=LazyMemmapStorage(100), transform=t)
        else:
            rb = ReplayBuffer(storage=LazyMemmapStorage(100))
            rb.append_transform(t)
        data = {
            "a": torch.randn(3),
            "b": {"c": (torch.zeros(2), [torch.ones(1)])},
            30: -torch.ones(()),
        }
        rb.add(data)

        def assert0(x):
            assert (x == 0).all()

        s = rb.sample(10)
        tree_map(assert0, s)


@pytest.mark.parametrize("size", [10, 15, 20])
@pytest.mark.parametrize("samples", [5, 9, 11, 14, 16])
@pytest.mark.parametrize("drop_last", [True, False])
def test_samplerwithoutrep(size, samples, drop_last):
    torch.manual_seed(0)
    storage = ListStorage(size)
    storage.set(range(size), range(size))
    assert len(storage) == size
    sampler = SamplerWithoutReplacement(drop_last=drop_last)
    visited = False
    for _ in range(10):
        _n_left = (
            sampler._sample_list.numel() if sampler._sample_list is not None else size
        )
        if samples > size and drop_last:
            with pytest.raises(
                ValueError,
                match=r"The batch size .* is greater than the storage capacity",
            ):
                idx, _ = sampler.sample(storage, samples)
            break
        idx, _ = sampler.sample(storage, samples)
        if drop_last or _n_left >= samples:
            assert idx.numel() == samples
            assert idx.unique().numel() == idx.numel()
        else:
            assert idx.numel() == _n_left
            visited = True
    if not drop_last and (size % samples > 0):
        assert visited
    else:
        assert not visited


@pytest.mark.parametrize("size", [10, 15, 20])
@pytest.mark.parametrize("drop_last", [True, False])
def test_replay_buffer_iter(size, drop_last):
    torch.manual_seed(0)
    storage = ListStorage(size)
    sampler = SamplerWithoutReplacement(drop_last=drop_last)
    writer = RoundRobinWriter()

    rb = ReplayBuffer(storage=storage, sampler=sampler, writer=writer, batch_size=3)
    rb.extend([torch.randint(100, (1,)) for _ in range(size)])

    for i, _ in enumerate(rb):
        if i == 20:
            # guard against infinite loop if error is introduced
            raise RuntimeError("Iteration didn't terminate")

    if drop_last:
        assert i == size // 3 - 1
    else:
        assert i == (size - 1) // 3


class TestMaxValueWriter:
    @pytest.mark.parametrize("size", [20, 25, 30])
    @pytest.mark.parametrize("batch_size", [1, 10, 15])
    @pytest.mark.parametrize("reward_ranges", [(0.25, 0.5, 1.0)])
    @pytest.mark.parametrize("device", get_default_devices())
    def test_max_value_writer(self, size, batch_size, reward_ranges, device):
        torch.manual_seed(0)
        rb = TensorDictReplayBuffer(
            storage=LazyTensorStorage(size, device=device),
            sampler=SamplerWithoutReplacement(),
            batch_size=batch_size,
            writer=TensorDictMaxValueWriter(rank_key="key"),
        )

        max_reward1, max_reward2, max_reward3 = reward_ranges

        td = TensorDict(
            {
                "key": torch.clamp_max(torch.rand(size), max=max_reward1),
                "obs": torch.rand(size),
            },
            batch_size=size,
            device=device,
        )
        rb.extend(td)
        sample = rb.sample()
        assert (sample.get("key") <= max_reward1).all()
        assert (0 <= sample.get("key")).all()
        assert len(sample.get("index").unique()) == len(sample.get("index"))

        td = TensorDict(
            {
                "key": torch.clamp(torch.rand(size), min=max_reward1, max=max_reward2),
                "obs": torch.rand(size),
            },
            batch_size=size,
            device=device,
        )
        rb.extend(td)
        sample = rb.sample()
        assert (sample.get("key") <= max_reward2).all()
        assert (max_reward1 <= sample.get("key")).all()
        assert len(sample.get("index").unique()) == len(sample.get("index"))

        td = TensorDict(
            {
                "key": torch.clamp(torch.rand(size), min=max_reward2, max=max_reward3),
                "obs": torch.rand(size),
            },
            batch_size=size,
            device=device,
        )

        for sample in td:
            rb.add(sample)

        sample = rb.sample()
        assert (sample.get("key") <= max_reward3).all()
        assert (max_reward2 <= sample.get("key")).all()
        assert len(sample.get("index").unique()) == len(sample.get("index"))

        # Finally, test the case when no obs should be added
        td = TensorDict(
            {
                "key": torch.zeros(size),
                "obs": torch.rand(size),
            },
            batch_size=size,
            device=device,
        )
        rb.extend(td)
        sample = rb.sample()
        assert (sample.get("key") != 0).all()

    @pytest.mark.parametrize("size", [20, 25, 30])
    @pytest.mark.parametrize("batch_size", [1, 10, 15])
    @pytest.mark.parametrize("reward_ranges", [(0.25, 0.5, 1.0)])
    @pytest.mark.parametrize("device", get_default_devices())
    def test_max_value_writer_serialize(
        self, size, batch_size, reward_ranges, device, tmpdir
    ):
        rb = TensorDictReplayBuffer(
            storage=LazyTensorStorage(size, device=device),
            sampler=SamplerWithoutReplacement(),
            batch_size=batch_size,
            writer=TensorDictMaxValueWriter(rank_key="key"),
        )

        max_reward1, max_reward2, max_reward3 = reward_ranges

        td = TensorDict(
            {
                "key": torch.clamp_max(torch.rand(size), max=max_reward1),
                "obs": torch.rand(size),
            },
            batch_size=size,
            device=device,
        )
        rb.extend(td)
        rb._writer.dumps(tmpdir)
        # check we can dump twice
        rb._writer.dumps(tmpdir)
        other = TensorDictMaxValueWriter(rank_key="key")
        other.loads(tmpdir)
        assert len(rb._writer._current_top_values) == len(other._current_top_values)
        torch.testing.assert_close(
            torch.tensor(rb._writer._current_top_values),
            torch.tensor(other._current_top_values),
        )

    @pytest.mark.parametrize("size", [[], [1], [2, 3]])
    @pytest.mark.parametrize("device", get_default_devices())
    @pytest.mark.parametrize("reduction", ["max", "min", "mean", "median", "sum"])
    def test_max_value_writer_reduce(self, size, device, reduction):
        torch.manual_seed(0)
        batch_size = 4
        rb = TensorDictReplayBuffer(
            storage=LazyTensorStorage(1, device=device),
            sampler=SamplerWithoutReplacement(),
            batch_size=batch_size,
            writer=TensorDictMaxValueWriter(rank_key="key", reduction=reduction),
        )

        key = torch.rand(batch_size, *size, device=device)
        obs = torch.rand(batch_size, *size, device=device)
        td = TensorDict(
            {"key": key, "obs": obs},
            batch_size=batch_size,
            device=device,
        )
        rb.extend(td)
        sample = rb.sample()
        if reduction == "max":
            rank_key = torch.stack([k.max() for k in key.unbind(0)])
        elif reduction == "min":
            rank_key = torch.stack([k.min() for k in key.unbind(0)])
        elif reduction == "mean":
            rank_key = torch.stack([k.mean() for k in key.unbind(0)])
        elif reduction == "median":
            rank_key = torch.stack([k.median() for k in key.unbind(0)])
        elif reduction == "sum":
            rank_key = torch.stack([k.sum() for k in key.unbind(0)])

        top_rank = torch.argmax(rank_key)
        assert (sample.get("obs") == obs[top_rank]).all()


class TestMultiProc:
    @staticmethod
    def worker(rb, q0, q1):
        td = TensorDict({"a": torch.ones(10), "next": {"reward": torch.ones(10)}}, [10])
        rb.extend(td)
        q0.put("extended")
        extended = q1.get(timeout=5)
        assert extended == "extended"
        assert len(rb) == 21, len(rb)
        assert (rb["a"][:9] == 2).all()
        q0.put("finish")

    def exec_multiproc_rb(
        self,
        storage_type=LazyMemmapStorage,
        init=True,
        writer_type=TensorDictRoundRobinWriter,
        sampler_type=RandomSampler,
    ):
        rb = TensorDictReplayBuffer(
            storage=storage_type(21), writer=writer_type(), sampler=sampler_type()
        )
        if init:
            td = TensorDict(
                {"a": torch.zeros(10), "next": {"reward": torch.ones(10)}}, [10]
            )
            rb.extend(td)
        q0 = mp.Queue(1)
        q1 = mp.Queue(1)
        proc = mp.Process(target=self.worker, args=(rb, q0, q1))
        proc.start()
        try:
            extended = q0.get(timeout=100)
            assert extended == "extended"
            assert len(rb) == 20
            assert (rb["a"][10:20] == 1).all()
            td = TensorDict({"a": torch.zeros(10) + 2}, [10])
            rb.extend(td)
            q1.put("extended")
            finish = q0.get(timeout=5)
            assert finish == "finish"
        finally:
            proc.join()

    def test_multiproc_rb(self):
        return self.exec_multiproc_rb()

    def test_error_list(self):
        # list storage cannot be shared
        with pytest.raises(RuntimeError, match="Cannot share a storage of type"):
            self.exec_multiproc_rb(storage_type=ListStorage)

    def test_error_nonshared(self):
        # non shared tensor storage cannot be shared
        with pytest.raises(
            RuntimeError, match="The storage must be place in shared memory"
        ):
            self.exec_multiproc_rb(storage_type=LazyTensorStorage)

    def test_error_maxwriter(self):
        # TensorDictMaxValueWriter cannot be shared
        with pytest.raises(RuntimeError, match="cannot be shared between processes"):
            self.exec_multiproc_rb(writer_type=TensorDictMaxValueWriter)

    def test_error_prb(self):
        # PrioritizedSampler cannot be shared
        with pytest.raises(RuntimeError, match="cannot be shared between processes"):
            self.exec_multiproc_rb(
                sampler_type=lambda: PrioritizedSampler(21, alpha=1.1, beta=0.5)
            )

    def test_error_noninit(self):
        # list storage cannot be shared
        with pytest.raises(RuntimeError, match="it has not been initialized yet"):
            self.exec_multiproc_rb(init=False)


class TestSamplers:
    @pytest.mark.parametrize(
        "backend", ["torch"] + (["torchsnapshot"] if _has_snapshot else [])
    )
    def test_sampler_without_rep_state_dict(self, backend):
        os.environ["CKPT_BACKEND"] = backend
        torch.manual_seed(0)

        n_samples = 3
        buffer_size = 100
        storage_in = LazyTensorStorage(buffer_size, device="cpu")
        storage_out = LazyTensorStorage(buffer_size, device="cpu")

        replay_buffer = TensorDictReplayBuffer(
            storage=storage_in,
            sampler=SamplerWithoutReplacement(),
        )
        # fill replay buffer with random data
        transition = TensorDict(
            {
                "observation": torch.ones(1, 4),
                "action": torch.ones(1, 2),
                "reward": torch.ones(1, 1),
                "dones": torch.ones(1, 1),
                "next": {"observation": torch.ones(1, 4)},
            },
            batch_size=1,
        )
        for _ in range(n_samples):
            replay_buffer.extend(transition.clone())
        for _ in range(n_samples):
            s = replay_buffer.sample(batch_size=1)
            assert (s.exclude("index") == 1).all()

        replay_buffer.extend(torch.zeros_like(transition))

        state_dict = replay_buffer.state_dict()

        new_replay_buffer = TensorDictReplayBuffer(
            storage=storage_out,
            batch_size=state_dict["_batch_size"],
            sampler=SamplerWithoutReplacement(),
        )

        new_replay_buffer.load_state_dict(state_dict)
        s = new_replay_buffer.sample(batch_size=1)
        assert (s.exclude("index") == 0).all()

    @pytest.mark.parametrize(
        "batch_size,num_slices,slice_len,prioritized",
        [
            [100, 20, None, True],
            [100, 20, None, False],
            [120, 30, None, False],
            [100, None, 5, False],
            [120, None, 4, False],
            [101, None, 101, False],
        ],
    )
    @pytest.mark.parametrize("episode_key", ["episode", ("some", "episode")])
    @pytest.mark.parametrize("done_key", ["done", ("some", "done")])
    @pytest.mark.parametrize("match_episode", [True, False])
    @pytest.mark.parametrize("_data_prefix", [True, False])
    @pytest.mark.parametrize("device", get_default_devices())
    def test_slice_sampler(
        self,
        batch_size,
        num_slices,
        slice_len,
        prioritized,
        episode_key,
        done_key,
        match_episode,
        _data_prefix,
        device,
    ):
        torch.manual_seed(0)
        storage = LazyMemmapStorage(100)
        episode = torch.zeros(100, dtype=torch.int, device=device)
        episode[:30] = 1
        episode[30:55] = 2
        episode[55:70] = 3
        episode[70:] = 4
        steps = torch.cat(
            [torch.arange(30), torch.arange(25), torch.arange(15), torch.arange(30)], 0
        )

        done = torch.zeros(100, 1, dtype=torch.bool)
        done[torch.tensor([29, 54, 69])] = 1

        data = TensorDict(
            {
                # we only use episode_key if we want the sampler to access it
                episode_key if match_episode else "whatever_episode": episode,
                "another_episode": episode,
                "obs": torch.randn((3, 4, 5)).expand(100, 3, 4, 5),
                "act": torch.randn((20,)).expand(100, 20),
                "steps": steps,
                "count": torch.arange(100),
                "other": torch.randn((20, 50)).expand(100, 20, 50),
                done_key: done,
            },
            [100],
            device=device,
        )
        if _data_prefix:
            data = TensorDict({"_data": data}, [100])
        storage.set(range(100), data)
        if slice_len is not None and slice_len > 15:
            # we may have to sample trajs shorter than slice_len
            strict_length = False
        else:
            strict_length = True

        if prioritized:
            num_steps = data.shape[0]
            sampler = PrioritizedSliceSampler(
                max_capacity=num_steps,
                alpha=0.7,
                beta=0.9,
                num_slices=num_slices,
                traj_key=episode_key,
                end_key=done_key,
                slice_len=slice_len,
                strict_length=strict_length,
            )
            index = torch.arange(0, num_steps, 1)
            sampler.extend(index)
        else:
            sampler = SliceSampler(
                num_slices=num_slices,
                traj_key=episode_key,
                end_key=done_key,
                slice_len=slice_len,
                strict_length=strict_length,
            )
        if slice_len is not None:
            num_slices = batch_size // slice_len
        trajs_unique_id = set()
        too_short = False
        count_unique = set()
        for _ in range(30):
            index, info = sampler.sample(storage, batch_size=batch_size)
            if _data_prefix:
                samples = storage._storage["_data"][index]
            else:
                samples = storage._storage[index]
            if strict_length:
                # check that trajs are ok
                samples = samples.view(num_slices, -1)

                assert samples["another_episode"].unique(
                    dim=1
                ).squeeze().shape == torch.Size([num_slices])
                assert (
                    samples["steps"][..., 1:] - 1 == samples["steps"][..., :-1]
                ).all()
            too_short = too_short or index.numel() < batch_size
            trajs_unique_id = trajs_unique_id.union(
                samples["another_episode"].view(-1).tolist()
            )
            count_unique = count_unique.union(samples.get("count").view(-1).tolist())
            if len(count_unique) == 100:
                # all items have been sampled
                break
        else:
            raise AssertionError(
                f"Not all items can be sampled: {set(range(100))-count_unique} are missing"
            )

        if strict_length:
            assert not too_short
        else:
            assert too_short

        assert len(trajs_unique_id) == 4
        truncated = info[("next", "truncated")]
        assert truncated.view(num_slices, -1)[:, -1].all()

    def test_slice_sampler_errors(self):
        device = "cpu"
        _data_prefix = False
        batch_size, num_slices = 100, 20

        episode = torch.zeros(100, dtype=torch.int, device=device)
        episode[:30] = 1
        episode[30:55] = 2
        episode[55:70] = 3
        episode[70:] = 4
        steps = torch.cat(
            [torch.arange(30), torch.arange(25), torch.arange(15), torch.arange(30)], 0
        )

        done = torch.zeros(100, 1, dtype=torch.bool)
        done[torch.tensor([29, 54, 69])] = 1

        data = TensorDict(
            {
                # we only use episode_key if we want the sampler to access it
                "episode": episode,
                "another_episode": episode,
                "obs": torch.randn((3, 4, 5)).expand(100, 3, 4, 5),
                "act": torch.randn((20,)).expand(100, 20),
                "steps": steps,
                "other": torch.randn((20, 50)).expand(100, 20, 50),
                ("next", "done"): done,
            },
            [100],
            device=device,
        )
        if _data_prefix:
            data = TensorDict({"_data": data}, [100])

        data_wrong_done = data.clone(False)
        data_wrong_done.rename_key_("episode", "_")
        data_wrong_done["next", "done"] = done.unsqueeze(1).expand(100, 5, 1)
        storage = LazyMemmapStorage(100)
        storage.set(range(100), data_wrong_done)
        sampler = SliceSampler(num_slices=num_slices)
        with pytest.raises(
            RuntimeError,
            match="Expected the end-of-trajectory signal to be 1-dimensional",
        ):
            index, _ = sampler.sample(storage, batch_size=batch_size)

        storage = ListStorage(100)
        storage.set(range(100), data)
        sampler = SliceSampler(num_slices=num_slices)
        with pytest.raises(
            RuntimeError,
            match="Could not get a tensordict out of the storage, which is required for SliceSampler to compute the trajectories.",
        ):
            index, _ = sampler.sample(storage, batch_size=batch_size)

    @pytest.mark.parametrize("batch_size,num_slices", [[20, 4], [4, 2]])
    @pytest.mark.parametrize("episode_key", ["episode", ("some", "episode")])
    @pytest.mark.parametrize("done_key", ["done", ("some", "done")])
    @pytest.mark.parametrize("match_episode", [True, False])
    @pytest.mark.parametrize("_data_prefix", [True, False])
    @pytest.mark.parametrize("device", get_default_devices())
    def test_slice_sampler_without_replacement(
        self,
        batch_size,
        num_slices,
        episode_key,
        done_key,
        match_episode,
        _data_prefix,
        device,
    ):
        torch.manual_seed(0)
        storage = LazyMemmapStorage(100)
        episode = torch.zeros(100, dtype=torch.int, device=device)
        steps = []
        done = torch.zeros(100, 1, dtype=torch.bool)
        for i in range(0, 100, 5):
            episode[i : i + 5] = i // 5
            steps.append(torch.arange(5))
            done[i + 4] = 1
        steps = torch.cat(steps)

        data = TensorDict(
            {
                # we only use episode_key if we want the sampler to access it
                episode_key if match_episode else "whatever_episode": episode,
                "another_episode": episode,
                "obs": torch.randn((3, 4, 5)).expand(100, 3, 4, 5),
                "act": torch.randn((20,)).expand(100, 20),
                "steps": steps,
                "other": torch.randn((20, 50)).expand(100, 20, 50),
                done_key: done,
            },
            [100],
            device=device,
        )
        if _data_prefix:
            data = TensorDict({"_data": data}, [100])
        storage.set(range(100), data)
        sampler = SliceSamplerWithoutReplacement(
            num_slices=num_slices, traj_key=episode_key, end_key=done_key
        )
        trajs_unique_id = set()
        for i in range(5):
            index, info = sampler.sample(storage, batch_size=batch_size)
            if _data_prefix:
                samples = storage._storage["_data"][index]
            else:
                samples = storage._storage[index]

            # check that trajs are ok
            samples = samples.view(num_slices, -1)
            assert samples["another_episode"].unique(
                dim=1
            ).squeeze().shape == torch.Size([num_slices])
            assert (samples["steps"][..., 1:] - 1 == samples["steps"][..., :-1]).all()
            cur_episodes = samples["another_episode"].view(-1).tolist()
            for ep in cur_episodes:
                assert ep not in trajs_unique_id, i
            trajs_unique_id = trajs_unique_id.union(
                cur_episodes,
            )
        truncated = info[("next", "truncated")]
        assert truncated.view(num_slices, -1)[:, -1].all()


def test_prioritized_slice_sampler_doc_example():
    sampler = PrioritizedSliceSampler(max_capacity=9, num_slices=3, alpha=0.7, beta=0.9)
    rb = TensorDictReplayBuffer(
        storage=LazyMemmapStorage(9), sampler=sampler, batch_size=6
    )
    data = TensorDict(
        {
            "observation": torch.randn(9, 16),
            "action": torch.randn(9, 1),
            "episode": torch.tensor([0, 0, 0, 1, 1, 1, 2, 2, 2], dtype=torch.long),
            "steps": torch.tensor([0, 1, 2, 0, 1, 2, 0, 1, 2], dtype=torch.long),
            ("next", "observation"): torch.randn(9, 16),
            ("next", "reward"): torch.randn(9, 1),
            ("next", "done"): torch.tensor(
                [0, 0, 1, 0, 0, 1, 0, 0, 1], dtype=torch.bool
            ).unsqueeze(1),
        },
        batch_size=[9],
    )
    rb.extend(data)
    sample, info = rb.sample(return_info=True)
    # print("episode", sample["episode"].tolist())
    # print("steps", sample["steps"].tolist())
    # print("weight", info["_weight"].tolist())

    priority = torch.tensor([0, 3, 3, 0, 0, 0, 1, 1, 1])
    rb.update_priority(torch.arange(0, 9, 1), priority=priority)
    sample, info = rb.sample(return_info=True)
    # print("episode", sample["episode"].tolist())
    # print("steps", sample["steps"].tolist())
    # print("weight", info["_weight"].tolist())


@pytest.mark.parametrize("device", get_default_devices())
def test_prioritized_slice_sampler_episodes(device):
    num_slices = 10
    batch_size = 20

    episode = torch.zeros(100, dtype=torch.int, device=device)
    episode[:30] = 1
    episode[30:55] = 2
    episode[55:70] = 3
    episode[70:] = 4
    steps = torch.cat(
        [torch.arange(30), torch.arange(25), torch.arange(15), torch.arange(30)], 0
    )
    done = torch.zeros(100, 1, dtype=torch.bool)
    done[torch.tensor([29, 54, 69])] = 1

    data = TensorDict(
        {
            "observation": torch.randn(100, 16),
            "action": torch.randn(100, 4),
            "episode": episode,
            "steps": steps,
            ("next", "observation"): torch.randn(100, 16),
            ("next", "reward"): torch.randn(100, 1),
            ("next", "done"): done,
        },
        batch_size=[100],
        device=device,
    )

    num_steps = data.shape[0]
    sampler = PrioritizedSliceSampler(
        max_capacity=num_steps,
        alpha=0.7,
        beta=0.9,
        num_slices=num_slices,
    )

    rb = TensorDictReplayBuffer(
        storage=LazyMemmapStorage(100),
        sampler=sampler,
        batch_size=batch_size,
    )
    rb.extend(data)

    episodes = []
    for _ in range(10):
        sample = rb.sample()
        episodes.append(sample["episode"])
    assert {1, 2, 3, 4} == set(
        torch.cat(episodes).cpu().tolist()
    ), "all episodes are expected to be sampled at least once"

    index = torch.arange(0, num_steps, 1)
    new_priorities = torch.cat(
        [torch.ones(30), torch.zeros(25), torch.ones(15), torch.zeros(30)], 0
    )
    sampler.update_priority(index, new_priorities)

    episodes = []
    for _ in range(10):
        sample = rb.sample()
        episodes.append(sample["episode"])
    assert {1, 3} == set(
        torch.cat(episodes).cpu().tolist()
    ), "after priority update, only episode 1 and 3 are expected to be sampled"


class TestEnsemble:
    def _make_data(self, data_type):
        if data_type is torch.Tensor:
            return torch.ones(90)
        if data_type is TensorDict:
            return TensorDict(
                {
                    "root": torch.arange(90),
                    "nested": TensorDict(
                        {"data": torch.arange(180).view(90, 2)}, batch_size=[90, 2]
                    ),
                },
                batch_size=[90],
            )
        raise NotImplementedError

    def _make_sampler(self, sampler_type):
        if sampler_type is SamplerWithoutReplacement:
            return SamplerWithoutReplacement(drop_last=True)
        if sampler_type is RandomSampler:
            return RandomSampler()
        raise NotImplementedError

    def _make_storage(self, storage_type, data_type):
        if storage_type is LazyMemmapStorage:
            return LazyMemmapStorage(max_size=100)
        if storage_type is TensorStorage:
            if data_type is TensorDict:
                return TensorStorage(TensorDict({}, [100]))
            elif data_type is torch.Tensor:
                return TensorStorage(torch.zeros(100))
            else:
                raise NotImplementedError
        if storage_type is ListStorage:
            return ListStorage(max_size=100)
        raise NotImplementedError

    def _make_collate(self, storage_type):
        if storage_type is ListStorage:
            return torch.stack
        else:
            return self._robust_stack

    @staticmethod
    def _robust_stack(tensor_list):
        if not isinstance(tensor_list, (tuple, list)):
            return tensor_list
        if all(tensor.shape == tensor_list[0].shape for tensor in tensor_list[1:]):
            return torch.stack(list(tensor_list))
        if is_tensor_collection(tensor_list[0]):
            return torch.cat(list(tensor_list))
        return torch.nested.nested_tensor(list(tensor_list))

    @pytest.mark.parametrize(
        "storage_type", [LazyMemmapStorage, TensorStorage, ListStorage]
    )
    @pytest.mark.parametrize("data_type", [torch.Tensor, TensorDict])
    @pytest.mark.parametrize("p", [[0.0, 0.9, 0.1], None])
    @pytest.mark.parametrize("num_buffer_sampled", [3, 16, None])
    @pytest.mark.parametrize("batch_size", [48, None])
    @pytest.mark.parametrize("sampler_type", [RandomSampler, SamplerWithoutReplacement])
    def test_rb(
        self, storage_type, sampler_type, data_type, p, num_buffer_sampled, batch_size
    ):

        storages = [self._make_storage(storage_type, data_type) for _ in range(3)]
        collate_fn = self._make_collate(storage_type)
        data = [self._make_data(data_type) for _ in range(3)]
        samplers = [self._make_sampler(sampler_type) for _ in range(3)]
        sub_batch_size = (
            batch_size // 3
            if issubclass(sampler_type, SamplerWithoutReplacement)
            and batch_size is not None
            else None
        )
        error_catcher = (
            pytest.raises(
                ValueError,
                match="Samplers with drop_last=True must work with a predictible batch-size",
            )
            if batch_size is None
            and issubclass(sampler_type, SamplerWithoutReplacement)
            else contextlib.nullcontext()
        )
        rbs = None
        with error_catcher:
            rbs = (rb0, rb1, rb2) = [
                ReplayBuffer(
                    storage=storage,
                    sampler=sampler,
                    collate_fn=collate_fn,
                    batch_size=sub_batch_size,
                )
                for (storage, sampler) in zip(storages, samplers)
            ]
        if rbs is None:
            return
        for datum, rb in zip(data, rbs):
            rb.extend(datum)
        rb = ReplayBufferEnsemble(
            *rbs, p=p, num_buffer_sampled=num_buffer_sampled, batch_size=batch_size
        )
        if batch_size is not None:
            for batch_iter in rb:
                assert isinstance(batch_iter, (torch.Tensor, TensorDictBase))
                break
            batch_sample, info = rb.sample(return_info=True)
        else:
            batch_iter = None
            batch_sample, info = rb.sample(48, return_info=True)
        assert isinstance(batch_sample, (torch.Tensor, TensorDictBase))
        if isinstance(batch_sample, TensorDictBase):
            assert "root" in batch_sample.keys()
            assert "nested" in batch_sample.keys()
            assert ("nested", "data") in batch_sample.keys(True)
            if p is not None:
                if batch_iter is not None:
                    buffer_ids = batch_iter.get(("index", "buffer_ids"))
                    assert isinstance(buffer_ids, torch.Tensor), batch_iter
                    assert 0 not in buffer_ids.unique().tolist()

                buffer_ids = batch_sample.get(("index", "buffer_ids"))
                assert isinstance(buffer_ids, torch.Tensor), buffer_ids
                assert 0 not in buffer_ids.unique().tolist()
            if num_buffer_sampled is not None:
                if batch_iter is not None:
                    assert batch_iter.shape == torch.Size(
                        [num_buffer_sampled, 48 // num_buffer_sampled]
                    )
                assert batch_sample.shape == torch.Size(
                    [num_buffer_sampled, 48 // num_buffer_sampled]
                )
            else:
                if batch_iter is not None:
                    assert batch_iter.shape == torch.Size([3, 16])
                assert batch_sample.shape == torch.Size([3, 16])

    def _prepare_dual_replay_buffer(self, explicit=False):
        torch.manual_seed(0)
        rb0 = TensorDictReplayBuffer(
            storage=LazyMemmapStorage(10),
            transform=Compose(
                ToTensorImage(in_keys=["pixels", ("next", "pixels")]),
                Resize(32, in_keys=["pixels", ("next", "pixels")]),
                RenameTransform([("some", "key")], ["renamed"]),
            ),
        )
        rb1 = TensorDictReplayBuffer(
            storage=LazyMemmapStorage(10),
            transform=Compose(
                ToTensorImage(in_keys=["pixels", ("next", "pixels")]),
                Resize(32, in_keys=["pixels", ("next", "pixels")]),
                RenameTransform(["another_key"], ["renamed"]),
            ),
        )
        if explicit:
            storages = StorageEnsemble(
                rb0._storage, rb1._storage, transforms=[rb0._transform, rb1._transform]
            )
            writers = WriterEnsemble(rb0._writer, rb1._writer)
            samplers = SamplerEnsemble(rb0._sampler, rb1._sampler, p=[0.5, 0.5])
            collate_fns = [rb0._collate_fn, rb1._collate_fn]
            rb = ReplayBufferEnsemble(
                storages=storages,
                samplers=samplers,
                writers=writers,
                collate_fns=collate_fns,
                transform=Resize(33, in_keys=["pixels"], out_keys=["pixels33"]),
            )
        else:
            rb = ReplayBufferEnsemble(
                rb0,
                rb1,
                p=[0.5, 0.5],
                transform=Resize(33, in_keys=["pixels"], out_keys=["pixels33"]),
            )
        data0 = TensorDict(
            {
                "pixels": torch.randint(255, (10, 244, 244, 3)),
                ("next", "pixels"): torch.randint(255, (10, 244, 244, 3)),
                ("some", "key"): torch.randn(10),
            },
            batch_size=[10],
        )
        data1 = TensorDict(
            {
                "pixels": torch.randint(255, (10, 64, 64, 3)),
                ("next", "pixels"): torch.randint(255, (10, 64, 64, 3)),
                "another_key": torch.randn(10),
            },
            batch_size=[10],
        )
        rb0.extend(data0)
        rb1.extend(data1)
        return rb, rb0, rb1

    @pytest.mark.skipif(not _has_tv, reason="torchvision not found")
    def test_rb_transform(self):
        rb, rb0, rb1 = self._prepare_dual_replay_buffer()
        for _ in range(2):
            sample = rb.sample(10)
            assert sample["next", "pixels"].shape == torch.Size([2, 5, 3, 32, 32])
            assert sample["pixels"].shape == torch.Size([2, 5, 3, 32, 32])
            assert sample["pixels33"].shape == torch.Size([2, 5, 3, 33, 33])
            assert sample["renamed"].shape == torch.Size([2, 5])

    @pytest.mark.skipif(not _has_tv, reason="torchvision not found")
    @pytest.mark.parametrize("explicit", [False, True])
    def test_rb_indexing(self, explicit):
        rb, rb0, rb1 = self._prepare_dual_replay_buffer(explicit=explicit)
        if explicit:
            # indirect checks
            assert rb[0]._storage is rb0._storage
            assert rb[1]._storage is rb1._storage
        else:
            assert rb[0] is rb0
            assert rb[1] is rb1
        assert rb[:] is rb

        torch.manual_seed(0)
        sample1 = rb.sample(6)
        # tensor
        torch.manual_seed(0)
        sample0 = rb[torch.tensor([0, 1])].sample(6)
        assert_allclose_td(sample0, sample1)
        # slice
        torch.manual_seed(0)
        sample0 = rb[:2].sample(6)
        assert_allclose_td(sample0, sample1)
        # np.ndarray
        torch.manual_seed(0)
        sample0 = rb[np.array([0, 1])].sample(6)
        assert_allclose_td(sample0, sample1)
        # list
        torch.manual_seed(0)
        sample0 = rb[[0, 1]].sample(6)
        assert_allclose_td(sample0, sample1)

        # direct indexing
        sample1 = rb[:, :3]
        # tensor
        sample0 = rb[torch.tensor([0, 1]), :3]
        assert_allclose_td(sample0, sample1)
        # slice
        torch.manual_seed(0)
        sample0 = rb[:2, :3]
        assert_allclose_td(sample0, sample1)
        # np.ndarray
        torch.manual_seed(0)
        sample0 = rb[np.array([0, 1]), :3]
        assert_allclose_td(sample0, sample1)
        # list
        torch.manual_seed(0)
        sample0 = rb[[0, 1], :3]
        assert_allclose_td(sample0, sample1)

        # check indexing of components
        assert isinstance(rb._storage[:], StorageEnsemble)
        assert isinstance(rb._storage[:2], StorageEnsemble)
        assert isinstance(rb._storage[torch.tensor([0, 1])], StorageEnsemble)
        assert isinstance(rb._storage[np.array([0, 1])], StorageEnsemble)
        assert isinstance(rb._storage[[0, 1]], StorageEnsemble)
        assert isinstance(rb._storage[1], LazyMemmapStorage)

        rb._storage[:, :3]
        rb._storage[:2, :3]
        rb._storage[torch.tensor([0, 1]), :3]
        rb._storage[np.array([0, 1]), :3]
        rb._storage[[0, 1], :3]

        assert isinstance(rb._sampler[:], SamplerEnsemble)
        assert isinstance(rb._sampler[:2], SamplerEnsemble)
        assert isinstance(rb._sampler[torch.tensor([0, 1])], SamplerEnsemble)
        assert isinstance(rb._sampler[np.array([0, 1])], SamplerEnsemble)
        assert isinstance(rb._sampler[[0, 1]], SamplerEnsemble)
        assert isinstance(rb._sampler[1], RandomSampler)

        assert isinstance(rb._writer[:], WriterEnsemble)
        assert isinstance(rb._writer[:2], WriterEnsemble)
        assert isinstance(rb._writer[torch.tensor([0, 1])], WriterEnsemble)
        assert isinstance(rb._writer[np.array([0, 1])], WriterEnsemble)
        assert isinstance(rb._writer[[0, 1]], WriterEnsemble)
        assert isinstance(rb._writer[0], RoundRobinWriter)


if __name__ == "__main__":
    args, unknown = argparse.ArgumentParser().parse_known_args()
    pytest.main([__file__, "--capture", "no", "--exitfirst"] + unknown)