Nahs / tests /test_protocol_on_cpu.py
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# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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 random

import numpy as np
import pytest
import tensordict
import torch
from packaging.version import parse as parse_version
from tensordict import TensorDict

from verl import DataProto
from verl.protocol import (
    deserialize_single_tensor,
    deserialize_tensordict,
    serialize_single_tensor,
    serialize_tensordict,
    union_numpy_dict,
    union_tensor_dict,
)
from verl.utils import tensordict_utils as tu


def test_union_tensor_dict():
    obs = torch.randn(100, 10)

    data1 = TensorDict({"obs": obs, "act": torch.randn(100, 3)}, batch_size=[100])
    data2 = TensorDict({"obs": obs, "next_obs": torch.randn(100, 10), "rew": torch.randn(100)}, batch_size=[100])

    data_with_copied_obs = TensorDict(
        {"obs": obs.clone(), "next_obs": torch.randn(100, 10), "rew": torch.randn(100)}, batch_size=[100]
    )

    union_tensor_dict(data1, data2)
    with pytest.raises(AssertionError):
        union_tensor_dict(data1, data_with_copied_obs)


def test_union_numpy_dict():
    """
    A comprehensive test suite for union_numpy_dict, covering standard use
    cases, N-dimensional arrays, object-dtype arrays, and NaN value handling.
    """
    arr_3d = np.arange(8).reshape((2, 2, 2))
    union_numpy_dict({"a": arr_3d}, {"a": arr_3d})
    arr1 = np.array([1, "hello", np.array([2, 3])], dtype=object)
    arr2 = np.array([1, "hello", np.array([2, 3])], dtype=object)
    union_numpy_dict({"a": arr1}, {"a": arr2})
    # --- Test Case 1: The original test with mixed object/float types ---
    # This test case from the original test file is preserved.
    data = np.random.random(100)
    # This array intentionally mixes float('nan') and the string 'nan'
    nan_data = [float("nan") for _ in range(99)]
    nan_data.append("nan")
    nan_data_arr = np.array(nan_data, dtype=object)

    dict1 = {"a": data, "b": nan_data_arr}
    dict2_same = {"a": data.copy(), "b": nan_data_arr.copy()}
    dict3_different = {"a": np.random.random(100)}

    union_numpy_dict(dict1, dict2_same)  # Should pass
    with pytest.raises(AssertionError):
        union_numpy_dict(dict1, dict3_different)

    # --- Test Case 2: Standard 3D arrays (fixes the core bug) ---
    arr_3d = np.arange(24, dtype=np.int32).reshape((2, 3, 4))
    dict_3d_1 = {"nd_array": arr_3d}
    dict_3d_2_same = {"nd_array": arr_3d.copy()}
    dict_3d_3_different = {"nd_array": arr_3d + 1}

    union_numpy_dict(dict_3d_1, dict_3d_2_same)  # Should pass
    with pytest.raises(AssertionError, match="`nd_array` in tensor_dict1 and tensor_dict2 are not the same object."):
        union_numpy_dict(dict_3d_1, dict_3d_3_different)

    # --- Test Case 3: Nested 2D and 4D object-dtype arrays ---
    sub_arr1 = np.array([1, 2])
    sub_arr2 = np.array([3.0, 4.0])
    # 2D object array
    arr_2d_obj = np.array([[sub_arr1, "text"], [sub_arr2, None]], dtype=object)
    arr_2d_obj_diff = np.array([[sub_arr1, "text"], [sub_arr2, "other"]], dtype=object)

    union_numpy_dict({"data": arr_2d_obj}, {"data": arr_2d_obj.copy()})  # Should pass
    with pytest.raises(AssertionError):
        union_numpy_dict({"data": arr_2d_obj}, {"data": arr_2d_obj_diff})

    # 4D object array to ensure deep recursion is robust
    arr_4d_obj = np.array([[[[sub_arr1]]], [[[sub_arr2]]]], dtype=object)
    arr_4d_obj_diff = np.array([[[[sub_arr1]]], [[[np.array([9, 9])]]]], dtype=object)

    union_numpy_dict({"data": arr_4d_obj}, {"data": arr_4d_obj.copy()})  # Should pass
    with pytest.raises(AssertionError):
        union_numpy_dict({"data": arr_4d_obj}, {"data": arr_4d_obj_diff})

    # --- Test Case 4: Explicit NaN value comparison ---
    # This verifies that our new _deep_equal logic correctly handles NaNs.
    nan_arr = np.array([1.0, np.nan, 3.0])
    dict_nan_1 = {"data": nan_arr}
    dict_nan_2_same = {"data": np.array([1.0, np.nan, 3.0])}  # A new array with same values
    dict_nan_3_different_val = {"data": np.array([1.0, 2.0, 3.0])}
    dict_nan_4_different_pos = {"data": np.array([np.nan, 1.0, 3.0])}

    # NaNs in the same position should be considered equal for merging.
    union_numpy_dict(dict_nan_1, dict_nan_2_same)  # Should pass

    with pytest.raises(AssertionError):
        union_numpy_dict(dict_nan_1, dict_nan_3_different_val)
    with pytest.raises(AssertionError):
        union_numpy_dict(dict_nan_1, dict_nan_4_different_pos)

    # --- Test Case 5: Circular reference handling ---
    # Create two separate, but structurally identical, circular references.
    # This should pass without a RecursionError.
    circ_arr_1 = np.array([None], dtype=object)
    circ_arr_1[0] = circ_arr_1

    circ_arr_2 = np.array([None], dtype=object)
    circ_arr_2[0] = circ_arr_2

    union_numpy_dict({"data": circ_arr_1}, {"data": circ_arr_2})  # Should pass

    # Create a circular reference and a non-circular one.
    # This should fail with an AssertionError because they are different.
    non_circ_arr = np.array([None], dtype=object)

    with pytest.raises(AssertionError):
        union_numpy_dict({"data": circ_arr_1}, {"data": non_circ_arr})


def test_tensor_dict_constructor():
    obs = torch.randn(100, 10)
    act = torch.randn(100, 10, 3)
    data = DataProto.from_dict(tensors={"obs": obs, "act": act})

    assert data.batch.batch_size == torch.Size([100])

    with pytest.raises(AssertionError):
        data = DataProto.from_dict(tensors={"obs": obs, "act": act}, num_batch_dims=2)

    with pytest.raises(AssertionError):
        data = DataProto.from_dict(tensors={"obs": obs, "act": act}, num_batch_dims=3)


def test_tensor_dict_make_iterator():
    obs = torch.randn(100, 10)
    labels = [random.choice(["abc", "cde"]) for _ in range(100)]
    dataset = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels})

    data_iter_1 = dataset.make_iterator(mini_batch_size=10, epochs=2, seed=1)
    data_list_1 = []
    for data in data_iter_1:
        data_list_1.append(data)

    data_iter_2 = dataset.make_iterator(mini_batch_size=10, epochs=2, seed=1)
    data_list_2 = []
    for data in data_iter_2:
        data_list_2.append(data)

    for data1, data2 in zip(data_list_1, data_list_2, strict=True):
        assert isinstance(data1, DataProto)
        assert isinstance(data2, DataProto)
        result = torch.all(torch.eq(data1.batch["obs"], data2.batch["obs"]))
        if not result.item():
            print(data1.batch["obs"])
            print(data2.batch["obs"])
            raise AssertionError()
        non_tensor_result = np.all(np.equal(data1.non_tensor_batch["labels"], data2.non_tensor_batch["labels"]))
        if not non_tensor_result.item():
            print(data1.non_tensor_batch["labels"])
            print(data2.non_tensor_batch["labels"])


def test_reorder():
    obs = torch.tensor([1, 2, 3, 4, 5, 6])
    labels = ["a", "b", "c", "d", "e", "f"]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"name": "abdce"})
    data.reorder(torch.tensor([3, 4, 2, 0, 1, 5]))

    assert torch.all(torch.eq(data.batch["obs"], torch.tensor([4, 5, 3, 1, 2, 6])))
    assert np.all(data.non_tensor_batch["labels"] == np.array(["d", "e", "c", "a", "b", "f"]))
    assert data.meta_info == {"name": "abdce"}


def test_chunk_concat():
    obs = torch.tensor([1, 2, 3, 4, 5, 6])
    labels = ["a", "b", "c", "d", "e", "f"]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"name": "abdce"})

    with pytest.raises(AssertionError):
        data.chunk(5)

    data_split = data.chunk(2)
    assert len(data_split) == 2
    assert torch.all(torch.eq(data_split[0].batch["obs"], torch.tensor([1, 2, 3])))
    assert np.all(data_split[0].non_tensor_batch["labels"] == np.array(["a", "b", "c"]))
    assert data_split[0].meta_info == {"name": "abdce"}

    assert torch.all(torch.eq(data_split[1].batch["obs"], torch.tensor([4, 5, 6])))
    assert np.all(data_split[1].non_tensor_batch["labels"] == np.array(["d", "e", "f"]))
    assert data_split[1].meta_info == {"name": "abdce"}

    concat_data = DataProto.concat(data_split)
    assert torch.all(torch.eq(concat_data.batch["obs"], data.batch["obs"]))
    assert np.all(concat_data.non_tensor_batch["labels"] == data.non_tensor_batch["labels"])
    assert concat_data.meta_info == data.meta_info


def test_concat_metrics_from_multiple_workers():
    """Test that concat() properly merges metrics from all workers in distributed training."""
    # Simulate 3 workers each with their own metrics
    obs1 = torch.tensor([1, 2])
    obs2 = torch.tensor([3, 4])
    obs3 = torch.tensor([5, 6])

    # Each worker has different metrics (as list of dict format)
    worker1_metrics = [{"loss": 0.5, "accuracy": 0.9}]
    worker2_metrics = [{"loss": 0.6, "accuracy": 0.85}]
    worker3_metrics = [{"loss": 0.55, "accuracy": 0.88}]

    data1 = DataProto.from_dict(tensors={"obs": obs1}, meta_info={"metrics": worker1_metrics, "config_flag": True})
    data2 = DataProto.from_dict(tensors={"obs": obs2}, meta_info={"metrics": worker2_metrics, "config_flag": True})
    data3 = DataProto.from_dict(tensors={"obs": obs3}, meta_info={"metrics": worker3_metrics, "config_flag": True})

    # Concat all workers' data
    concat_data = DataProto.concat([data1, data2, data3])

    # Verify tensors are concatenated
    assert torch.all(torch.eq(concat_data.batch["obs"], torch.tensor([1, 2, 3, 4, 5, 6])))

    # Verify ALL workers' metrics are flattened to dict of lists
    expected_metrics = {"loss": [0.5, 0.6, 0.55], "accuracy": [0.9, 0.85, 0.88]}
    assert concat_data.meta_info["metrics"] == expected_metrics

    # Verify config flags are preserved from first worker
    assert concat_data.meta_info["config_flag"] is True


def test_concat_with_empty_and_non_list_meta_info():
    """Test concat() handles edge cases: empty meta_info, non-list values, and None."""
    obs1 = torch.tensor([1, 2])
    obs2 = torch.tensor([3, 4])

    # Worker 1 has metrics, worker 2 doesn't
    data1 = DataProto.from_dict(tensors={"obs": obs1}, meta_info={"metrics": [{"loss": 0.5}], "flag": True})
    data2 = DataProto.from_dict(tensors={"obs": obs2}, meta_info={"flag": True})

    concat_data = DataProto.concat([data1, data2])

    # Should flatten worker1's metrics to dict of lists
    assert concat_data.meta_info["metrics"] == {"loss": [0.5]}
    assert concat_data.meta_info["flag"] is True

    # Test with non-list meta_info value
    data3 = DataProto.from_dict(tensors={"obs": obs1}, meta_info={"single_value": 42})
    data4 = DataProto.from_dict(tensors={"obs": obs2}, meta_info={"single_value": 42})

    concat_data2 = DataProto.concat([data3, data4])
    assert concat_data2.meta_info["single_value"] == 42


def test_concat_first_worker_missing_metrics():
    """Test that metrics from other workers are preserved even when first worker has no metrics.
    This is a critical edge case - the old buggy implementation only checked data[0].meta_info
    and would lose all metrics if the first worker didn't have any.
    """
    obs1 = torch.tensor([1, 2])
    obs2 = torch.tensor([3, 4])
    obs3 = torch.tensor([5, 6])

    # First worker has NO metrics, but workers 2 and 3 do
    data1 = DataProto.from_dict(tensors={"obs": obs1}, meta_info={"config_flag": True})
    data2 = DataProto.from_dict(tensors={"obs": obs2}, meta_info={"metrics": {"loss": 0.6}, "config_flag": True})
    data3 = DataProto.from_dict(tensors={"obs": obs3}, meta_info={"metrics": {"loss": 0.55}, "config_flag": True})

    concat_data = DataProto.concat([data1, data2, data3])

    # Should flatten metrics from workers 2 and 3 into dict of lists
    expected_metrics = {"loss": [0.6, 0.55]}
    assert concat_data.meta_info["metrics"] == expected_metrics
    assert concat_data.meta_info["config_flag"] is True


def test_concat_non_list_metrics():
    """Test that concat() handles non-list metrics (single dict) correctly.
    In some cases, metrics might be a single dict instead of a list.
    The implementation should flatten them into a dict of lists.
    """
    obs1 = torch.tensor([1, 2])
    obs2 = torch.tensor([3, 4])

    # Metrics as single dict (not wrapped in list)
    data1 = DataProto.from_dict(tensors={"obs": obs1}, meta_info={"metrics": {"loss": 0.5, "accuracy": 0.9}})
    data2 = DataProto.from_dict(tensors={"obs": obs2}, meta_info={"metrics": {"loss": 0.6, "accuracy": 0.85}})

    concat_data = DataProto.concat([data1, data2])

    # Should flatten to dict of lists
    expected_metrics = {"loss": [0.5, 0.6], "accuracy": [0.9, 0.85]}
    assert concat_data.meta_info["metrics"] == expected_metrics


def test_concat_merge_different_non_metric_keys():
    """Test that concat() merges non-metric meta_info keys from all workers.
    When different workers have different non-metric keys, all keys should be preserved.
    This prevents silent data loss and aligns with the docstring stating meta_info is "merged".
    """
    obs1 = torch.tensor([1, 2])
    obs2 = torch.tensor([3, 4])
    obs3 = torch.tensor([5, 6])

    # Each worker has some unique non-metric keys
    data1 = DataProto.from_dict(tensors={"obs": obs1}, meta_info={"config": "A", "shared_key": "X"})
    data2 = DataProto.from_dict(tensors={"obs": obs2}, meta_info={"extra_key": "B", "shared_key": "X"})
    data3 = DataProto.from_dict(tensors={"obs": obs3}, meta_info={"another_key": "C", "shared_key": "X"})

    concat_data = DataProto.concat([data1, data2, data3])

    # All unique keys should be preserved
    assert concat_data.meta_info["config"] == "A"
    assert concat_data.meta_info["extra_key"] == "B"
    assert concat_data.meta_info["another_key"] == "C"
    assert concat_data.meta_info["shared_key"] == "X"


def test_concat_conflicting_non_metric_keys():
    """Test that concat() raises an assertion error when non-metric keys have conflicting values.
    This ensures data integrity by catching cases where workers have different values
    for what should be the same configuration parameter.
    """
    obs1 = torch.tensor([1, 2])
    obs2 = torch.tensor([3, 4])

    # Same key "config" but different values
    data1 = DataProto.from_dict(tensors={"obs": obs1}, meta_info={"config": "A"})
    data2 = DataProto.from_dict(tensors={"obs": obs2}, meta_info={"config": "B"})

    # Should raise an assertion error due to conflicting values
    with pytest.raises(AssertionError, match="Conflicting values for meta_info key 'config'"):
        DataProto.concat([data1, data2])


def test_pop():
    obs = torch.randn(100, 10)
    act = torch.randn(100, 3)
    dataset = DataProto.from_dict({"obs": obs, "act": act}, meta_info={"2": 2, "1": 1})
    poped_dataset = dataset.pop(batch_keys=["obs"], meta_info_keys=["2"])

    assert poped_dataset.batch.keys() == {"obs"}
    assert poped_dataset.meta_info.keys() == {"2"}

    assert dataset.batch.keys() == {"act"}
    assert dataset.meta_info.keys() == {"1"}


def test_repeat():
    # Create a DataProto object with some batch and non-tensor data
    obs = torch.tensor([[1, 2], [3, 4], [5, 6]])
    labels = ["a", "b", "c"]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"})

    # Test interleave=True
    repeated_data_interleave = data.repeat(repeat_times=2, interleave=True)
    expected_obs_interleave = torch.tensor([[1, 2], [1, 2], [3, 4], [3, 4], [5, 6], [5, 6]])
    expected_labels_interleave = ["a", "a", "b", "b", "c", "c"]

    assert torch.all(torch.eq(repeated_data_interleave.batch["obs"], expected_obs_interleave))
    assert (repeated_data_interleave.non_tensor_batch["labels"] == expected_labels_interleave).all()
    assert repeated_data_interleave.meta_info == {"info": "test_info"}

    # Test interleave=False
    repeated_data_no_interleave = data.repeat(repeat_times=2, interleave=False)
    expected_obs_no_interleave = torch.tensor([[1, 2], [3, 4], [5, 6], [1, 2], [3, 4], [5, 6]])
    expected_labels_no_interleave = ["a", "b", "c", "a", "b", "c"]

    assert torch.all(torch.eq(repeated_data_no_interleave.batch["obs"], expected_obs_no_interleave))
    assert (repeated_data_no_interleave.non_tensor_batch["labels"] == expected_labels_no_interleave).all()
    assert repeated_data_no_interleave.meta_info == {"info": "test_info"}


def test_dataproto_pad_unpad():
    obs = torch.tensor([[1, 2], [3, 4], [5, 6]])
    labels = ["a", "b", "c"]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"})

    from verl.protocol import pad_dataproto_to_divisor, unpad_dataproto

    padded_data, pad_size = pad_dataproto_to_divisor(data, size_divisor=2)
    assert pad_size == 1

    expected_obs = torch.tensor([[1, 2], [3, 4], [5, 6], [1, 2]])
    expected_labels = ["a", "b", "c", "a"]

    assert torch.all(torch.eq(padded_data.batch["obs"], expected_obs))
    assert (padded_data.non_tensor_batch["labels"] == expected_labels).all()
    assert padded_data.meta_info == {"info": "test_info"}

    unpadd_data = unpad_dataproto(padded_data, pad_size=pad_size)
    assert torch.all(torch.eq(unpadd_data.batch["obs"], obs))
    assert (unpadd_data.non_tensor_batch["labels"] == labels).all()
    assert unpadd_data.meta_info == {"info": "test_info"}

    padded_data, pad_size = pad_dataproto_to_divisor(data, size_divisor=3)
    assert pad_size == 0

    expected_obs = torch.tensor([[1, 2], [3, 4], [5, 6]])
    expected_labels = ["a", "b", "c"]

    assert torch.all(torch.eq(padded_data.batch["obs"], expected_obs))
    assert (padded_data.non_tensor_batch["labels"] == expected_labels).all()
    assert padded_data.meta_info == {"info": "test_info"}

    unpadd_data = unpad_dataproto(padded_data, pad_size=pad_size)
    assert torch.all(torch.eq(unpadd_data.batch["obs"], obs))
    assert (unpadd_data.non_tensor_batch["labels"] == labels).all()
    assert unpadd_data.meta_info == {"info": "test_info"}

    padded_data, pad_size = pad_dataproto_to_divisor(data, size_divisor=7)
    assert pad_size == 4

    expected_obs = torch.tensor([[1, 2], [3, 4], [5, 6], [1, 2], [3, 4], [5, 6], [1, 2]])
    expected_labels = ["a", "b", "c", "a", "b", "c", "a"]
    assert torch.all(torch.eq(padded_data.batch["obs"], expected_obs))
    assert (padded_data.non_tensor_batch["labels"] == expected_labels).all()
    assert padded_data.meta_info == {"info": "test_info"}

    unpadd_data = unpad_dataproto(padded_data, pad_size=pad_size)
    assert torch.all(torch.eq(unpadd_data.batch["obs"], obs))
    assert (unpadd_data.non_tensor_batch["labels"] == labels).all()
    assert unpadd_data.meta_info == {"info": "test_info"}


def test_dataproto_fold_unfold():
    from verl.protocol import DataProto, fold_batch_dim, unfold_batch_dim

    obs = torch.tensor([[1, 2], [3, 4], [5, 6]])
    labels = ["a", "b", "c"]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"})

    data1 = data.repeat(repeat_times=2, interleave=True)

    data2 = fold_batch_dim(data1, new_batch_size=3)

    torch.testing.assert_close(data2.batch["obs"], torch.tensor([[[1, 2], [1, 2]], [[3, 4], [3, 4]], [[5, 6], [5, 6]]]))
    assert (data2.non_tensor_batch["labels"] == [["a", "a"], ["b", "b"], ["c", "c"]]).all()

    data2.reorder(indices=torch.tensor([1, 2, 0]))

    data3 = unfold_batch_dim(data2, batch_dims=2)

    torch.testing.assert_close(data3.batch["obs"], torch.tensor([[3, 4], [3, 4], [5, 6], [5, 6], [1, 2], [1, 2]]))
    assert (data3.non_tensor_batch["labels"] == ["b", "b", "c", "c", "a", "a"]).all()
    assert data3.meta_info == {"info": "test_info"}


def test_torch_save_data_proto():
    obs = torch.tensor([[1, 2], [3, 4], [5, 6]])
    labels = ["a", "b", "c"]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"})
    data.save_to_disk("test_data.pt")
    loaded_data = DataProto.load_from_disk("test_data.pt")

    assert torch.all(torch.eq(loaded_data.batch["obs"], data.batch["obs"]))
    assert (loaded_data.non_tensor_batch["labels"] == data.non_tensor_batch["labels"]).all()
    assert loaded_data.meta_info == data.meta_info

    import os

    os.remove("test_data.pt")


def test_len():
    obs = torch.tensor([[1, 2], [3, 4], [5, 6]])
    labels = np.array(["a", "b", "c"], dtype=object)
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"})

    assert len(data) == 3

    data = DataProto(batch=None, non_tensor_batch={"labels": labels}, meta_info={"info": "test_info"})

    assert len(data) == 3

    data = DataProto(batch=None, non_tensor_batch={}, meta_info={"info": "test_info"})

    assert len(data) == 0

    data = DataProto(batch=None, non_tensor_batch=None, meta_info={"info": "test_info"})

    assert len(data) == 0


def test_dataproto_index():
    data_len = 100
    idx_num = 10

    obs = torch.randn(data_len, 10)
    labels = [random.choice(["abc", "cde"]) for _ in range(data_len)]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels})
    labels_np = np.array(labels)

    idx_np_int = np.random.randint(0, data_len, size=(idx_num,))
    result_np_int = data[idx_np_int]
    assert result_np_int.batch.keys() == data.batch.keys()
    assert result_np_int.non_tensor_batch.keys() == data.non_tensor_batch.keys()
    assert result_np_int.batch["obs"].shape[0] == idx_num
    assert result_np_int.non_tensor_batch["labels"].shape[0] == idx_num
    assert np.array_equal(result_np_int.batch["obs"].cpu().numpy(), obs[idx_np_int].numpy())
    assert np.array_equal(result_np_int.non_tensor_batch["labels"], labels_np[idx_np_int])

    idx_torch_int = torch.randint(0, data_len, size=(idx_num,))
    result_torch_int = data[idx_torch_int]
    assert result_torch_int.batch.keys() == data.batch.keys()
    assert result_torch_int.non_tensor_batch.keys() == data.non_tensor_batch.keys()
    assert result_torch_int.batch["obs"].shape[0] == idx_num
    assert result_torch_int.non_tensor_batch["labels"].shape[0] == idx_num
    assert np.array_equal(result_torch_int.batch["obs"].cpu().numpy(), obs[idx_torch_int].cpu().numpy())
    assert np.array_equal(result_torch_int.non_tensor_batch["labels"], labels_np[idx_torch_int.cpu().numpy()])

    idx_list_int = [np.random.randint(0, data_len) for _ in range(idx_num)]
    result_list_int = data[idx_list_int]
    assert result_list_int.batch.keys() == data.batch.keys()
    assert result_list_int.non_tensor_batch.keys() == data.non_tensor_batch.keys()
    assert result_list_int.batch["obs"].shape[0] == idx_num
    assert result_list_int.non_tensor_batch["labels"].shape[0] == idx_num
    assert np.array_equal(result_list_int.batch["obs"].cpu().numpy(), obs[idx_list_int].cpu().numpy())
    assert np.array_equal(result_list_int.non_tensor_batch["labels"], labels_np[idx_list_int])

    idx_np_bool = np.random.randint(0, 2, size=(data_len,), dtype=bool)
    result_np_bool = data[idx_np_bool]
    assert result_np_bool.batch.keys() == data.batch.keys()
    assert result_np_bool.non_tensor_batch.keys() == data.non_tensor_batch.keys()
    assert result_np_bool.batch["obs"].shape[0] == idx_np_bool.sum()
    assert result_np_bool.non_tensor_batch["labels"].shape[0] == idx_np_bool.sum()
    assert np.array_equal(result_np_bool.batch["obs"].cpu().numpy(), obs[idx_np_bool].cpu().numpy())
    assert np.array_equal(result_np_bool.non_tensor_batch["labels"], labels_np[idx_np_bool])

    idx_torch_bool = torch.randint(0, 2, size=(data_len,), dtype=torch.bool)
    result_torch_bool = data[idx_torch_bool]
    assert result_torch_bool.batch.keys() == data.batch.keys()
    assert result_torch_bool.non_tensor_batch.keys() == data.non_tensor_batch.keys()
    assert result_torch_bool.batch["obs"].shape[0] == idx_torch_bool.sum().item()
    assert result_torch_bool.non_tensor_batch["labels"].shape[0] == idx_torch_bool.sum().item()
    assert np.array_equal(result_torch_bool.batch["obs"].cpu().numpy(), obs[idx_torch_bool].cpu().numpy())
    assert np.array_equal(result_torch_bool.non_tensor_batch["labels"], labels_np[idx_torch_bool])

    idx_list_bool = [np.random.randint(0, 2, dtype=bool) for _ in range(data_len)]
    result_list_bool = data[idx_list_bool]
    assert result_list_bool.batch.keys() == data.batch.keys()
    assert result_list_bool.non_tensor_batch.keys() == data.non_tensor_batch.keys()
    assert result_list_bool.batch["obs"].shape[0] == sum(idx_list_bool)
    assert result_list_bool.non_tensor_batch["labels"].shape[0] == sum(idx_list_bool)
    assert np.array_equal(result_list_bool.batch["obs"].cpu().numpy(), obs[idx_list_bool].cpu().numpy())
    assert np.array_equal(result_list_bool.non_tensor_batch["labels"], labels_np[idx_list_bool])


def test_old_vs_new_from_single_dict():
    class CustomProto(DataProto):
        """Uses the new, fixed from_single_dict."""

        pass

    class OriginProto(DataProto):
        """Mimics the *old* from_single_dict (always returns a DataProto)."""

        @classmethod
        def from_single_dict(cls, data, meta_info=None, auto_padding=False):
            tensors, non_tensors = {}, {}
            for k, v in data.items():
                if torch.is_tensor(v):
                    tensors[k] = v
                else:
                    non_tensors[k] = v
            # always calls DataProto.from_dict, ignoring `cls`
            return DataProto.from_dict(
                tensors=tensors,
                non_tensors=non_tensors,
                meta_info=meta_info,
                auto_padding=auto_padding,
            )

    sample = {"x": torch.tensor([0])}

    orig = OriginProto.from_single_dict(sample)
    # old behavior: always DataProto, not a CustomOriginProto
    assert type(orig) is DataProto
    assert type(orig) is not OriginProto

    cust = CustomProto.from_single_dict(sample)
    # new behavior: respects subclass
    assert type(cust) is CustomProto


def test_dataproto_no_batch():
    labels = ["a", "b", "c"]
    data = DataProto.from_dict(non_tensors={"labels": labels}, meta_info={"info": "test_info"})
    selected = data.select(non_tensor_batch_keys=["labels"])
    assert (selected.non_tensor_batch["labels"] == labels).all()
    pop_data = data.pop(non_tensor_batch_keys=["labels"])
    assert (pop_data.non_tensor_batch["labels"] == labels).all()
    assert data.non_tensor_batch == {}


def test_sample_level_repeat():
    # Create a DataProto object with some batch and non-tensor data
    obs = torch.tensor([[1, 2], [3, 4], [5, 6]])
    labels = ["a", "b", "c"]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"})

    # list
    repeated_data_interleave = data.sample_level_repeat(repeat_times=[3, 1, 2])
    expected_obs_interleave = torch.tensor([[1, 2], [1, 2], [1, 2], [3, 4], [5, 6], [5, 6]])
    expected_labels_interleave = ["a", "a", "a", "b", "c", "c"]

    assert torch.all(torch.eq(repeated_data_interleave.batch["obs"], expected_obs_interleave))
    assert (repeated_data_interleave.non_tensor_batch["labels"] == expected_labels_interleave).all()
    assert repeated_data_interleave.meta_info == {"info": "test_info"}

    # torch.tensor
    repeated_data_no_interleave = data.sample_level_repeat(repeat_times=torch.tensor([1, 2, 3]))
    expected_obs_no_interleave = torch.tensor([[1, 2], [3, 4], [3, 4], [5, 6], [5, 6], [5, 6]])
    expected_labels_no_interleave = ["a", "b", "b", "c", "c", "c"]

    assert torch.all(torch.eq(repeated_data_no_interleave.batch["obs"], expected_obs_no_interleave))
    assert (repeated_data_no_interleave.non_tensor_batch["labels"] == expected_labels_no_interleave).all()
    assert repeated_data_no_interleave.meta_info == {"info": "test_info"}


def test_dataproto_unfold_column_chunks():
    obs1 = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
    obs2 = torch.tensor([[1, 2], [5, 6], [9, 10]])

    labels = ["a", "b", "c"]
    data = DataProto.from_dict(
        tensors={"obs1": obs1, "obs2": obs2}, non_tensors={"labels": labels}, meta_info={"name": "abc"}
    )
    ret = data.unfold_column_chunks(2, split_keys=["obs1"])

    expect_obs1 = torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])
    expect_obs2 = torch.tensor([[1, 2], [1, 2], [5, 6], [5, 6], [9, 10], [9, 10]])
    expect_labels = ["a", "a", "b", "b", "c", "c"]
    assert torch.all(torch.eq(ret.batch["obs1"], expect_obs1))
    assert torch.all(torch.eq(ret.batch["obs2"], expect_obs2))
    assert (ret.non_tensor_batch["labels"] == expect_labels).all()
    assert ret.meta_info == {"name": "abc"}

    obs1 = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
    obs2 = torch.tensor([[1, 2], [5, 6], [9, 10]])

    labels = [["a1", "a2"], ["b1", "b2"], ["c1", "c2"]]
    data = DataProto.from_dict(
        tensors={"obs1": obs1, "obs2": obs2}, non_tensors={"labels": labels}, meta_info={"name": "abc"}
    )
    ret = data.unfold_column_chunks(2, split_keys=["obs1", "labels"])

    expect_obs1 = torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])
    expect_obs2 = torch.tensor([[1, 2], [1, 2], [5, 6], [5, 6], [9, 10], [9, 10]])
    expect_labels = [["a1"], ["a2"], ["b1"], ["b2"], ["c1"], ["c2"]]
    assert torch.all(torch.eq(ret.batch["obs1"], expect_obs1))
    assert torch.all(torch.eq(ret.batch["obs2"], expect_obs2))
    assert (ret.non_tensor_batch["labels"] == expect_labels).all()
    assert ret.meta_info == {"name": "abc"}

    obs1 = torch.tensor(
        [[[1, 1], [2, 2], [3, 3], [4, 4]], [[5, 5], [6, 6], [7, 7], [8, 8]], [[9, 9], [10, 10], [11, 11], [12, 12]]]
    )
    obs2 = torch.tensor([[[1, 1], [2, 2]], [[5, 5], [6, 6]], [[9, 9], [10, 10]]])

    labels = ["a", "b", "c"]
    data = DataProto.from_dict(
        tensors={"obs1": obs1, "obs2": obs2}, non_tensors={"labels": labels}, meta_info={"name": "abc"}
    )
    ret = data.unfold_column_chunks(2, split_keys=["obs1"])

    expect_obs1 = torch.tensor(
        [
            [[1, 1], [2, 2]],
            [[3, 3], [4, 4]],
            [[5, 5], [6, 6]],
            [[7, 7], [8, 8]],
            [[9, 9], [10, 10]],
            [[11, 11], [12, 12]],
        ]
    )
    expect_obs2 = torch.tensor(
        [[[1, 1], [2, 2]], [[1, 1], [2, 2]], [[5, 5], [6, 6]], [[5, 5], [6, 6]], [[9, 9], [10, 10]], [[9, 9], [10, 10]]]
    )
    expect_labels = ["a", "a", "b", "b", "c", "c"]
    assert torch.all(torch.eq(ret.batch["obs1"], expect_obs1))
    assert torch.all(torch.eq(ret.batch["obs2"], expect_obs2))
    assert (ret.non_tensor_batch["labels"] == expect_labels).all()
    assert ret.meta_info == {"name": "abc"}


def test_dataproto_chunk_after_index():
    data_len = 4
    obs = torch.randn(data_len, 4)
    labels = [f"label_{i}" for i in range(data_len)]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"name": "abc"})

    # Test with boolean numpy array
    bool_mask = np.array([True, False, True, False])
    selected = data[bool_mask]
    assert isinstance(selected.batch.batch_size, torch.Size)
    assert all(isinstance(d, int) for d in selected.batch.batch_size)  # int or List[int]

    # Test with integer numpy array
    int_mask = np.array([0, 2])
    selected = data[int_mask]
    assert isinstance(selected.batch.batch_size, torch.Size)
    assert all(isinstance(d, int) for d in selected.batch.batch_size)

    # Test with boolean list
    list_mask = [True, False, True, False]
    selected = data[list_mask]
    assert isinstance(selected.batch.batch_size, torch.Size)
    assert all(isinstance(d, int) for d in selected.batch.batch_size)

    # Test with list
    list_mask = [0, 2]
    selected = data[list_mask]
    assert isinstance(selected.batch.batch_size, torch.Size)
    assert all(isinstance(d, int) for d in selected.batch.batch_size)

    # Test with torch tensor (bool)
    torch_bool_mask = torch.tensor([True, False, True, False])
    selected = data[torch_bool_mask]
    assert isinstance(selected.batch.batch_size, torch.Size)
    assert all(isinstance(d, int) for d in selected.batch.batch_size)

    # Test with torch tensor (int)
    torch_int_mask = torch.tensor([0, 2])
    selected = data[torch_int_mask]
    assert isinstance(selected.batch.batch_size, torch.Size)
    assert all(isinstance(d, int) for d in selected.batch.batch_size)


@pytest.mark.skipif(
    parse_version(tensordict.__version__) < parse_version("0.10"), reason="requires at least tensordict 0.10"
)
def test_to_tensordict():
    obs = torch.tensor([1, 2, 3, 4, 5, 6])
    labels = ["a", "b", "c", "d", "e", "f"]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"name": "abdce"})
    output = data.to_tensordict()

    assert torch.all(torch.eq(output["obs"], obs)).item()
    assert output["labels"] == labels
    assert output["name"] == "abdce"


@pytest.mark.skipif(
    parse_version(tensordict.__version__) < parse_version("0.10"), reason="requires at least tensordict 0.10"
)
def test_from_tensordict():
    tensor_dict = {
        "obs": torch.tensor([1, 2, 3, 4, 5, 6]),
        "labels": ["a", "b", "c", "d", "e", "f"],
    }
    non_tensor_dict = {"name": "abdce"}
    tensordict = tu.get_tensordict(tensor_dict, non_tensor_dict)
    data = DataProto.from_tensordict(tensordict)

    assert data.non_tensor_batch["labels"].tolist() == tensor_dict["labels"]
    assert torch.all(torch.eq(data.batch["obs"], tensor_dict["obs"])).item()
    assert data.meta_info["name"] == "abdce"


@pytest.mark.skipif(
    parse_version(tensordict.__version__) < parse_version("0.10"), reason="requires at least tensordict 0.10"
)
def test_to_tensordict_with_nested_lists():
    """Test converting DataProto with nested lists to TensorDict (lists of lists)."""
    obs = torch.tensor([1, 2, 3])
    # Simulate turn_scores or tool_rewards: array of lists with varying lengths
    turn_scores = [[], [0.5, 0.8], [0.9]]

    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"turn_scores": turn_scores})

    # This should not raise an error
    tensordict_output = data.to_tensordict()

    # Verify the data is preserved
    assert torch.all(torch.eq(tensordict_output["obs"], obs)).item()
    # Verify nested structure is accessible (TensorDict wraps NonTensorStack as LinkedList)
    retrieved_scores = tensordict_output["turn_scores"]
    assert len(retrieved_scores) == len(turn_scores)
    # Verify content matches
    assert list(retrieved_scores[0]) == []
    assert list(retrieved_scores[1]) == [0.5, 0.8]
    assert list(retrieved_scores[2]) == [0.9]


@pytest.mark.skipif(
    parse_version(tensordict.__version__) < parse_version("0.10"), reason="requires at least tensordict 0.10"
)
def test_to_tensordict_with_nested_dicts():
    """Test converting DataProto with lists of dicts to TensorDict."""
    obs = torch.tensor([1, 2, 3])
    # Simulate reward_extra_info: array of dicts
    reward_extra_info = [{"acc": 1.0}, {"acc": 0.0}, {"acc": 1.0}]

    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"reward_extra_info": reward_extra_info})

    # This should not raise an error - this was the original bug
    tensordict_output = data.to_tensordict()

    # Verify the data is preserved
    assert torch.all(torch.eq(tensordict_output["obs"], obs)).item()
    # Verify nested dicts are accessible
    retrieved_info = tensordict_output["reward_extra_info"]
    assert len(retrieved_info) == len(reward_extra_info)
    # Verify content matches
    for i, expected_dict in enumerate(reward_extra_info):
        assert dict(retrieved_info[i]) == expected_dict


@pytest.mark.skipif(
    parse_version(tensordict.__version__) < parse_version("0.10"), reason="requires at least tensordict 0.10"
)
def test_to_tensordict_with_complex_nested_structures():
    """Test converting DataProto with complex nested structures (lists of lists of dicts)."""
    obs = torch.tensor([1, 2, 3])
    # Simulate raw_prompt: array of lists containing dicts
    raw_prompt = [
        [{"content": "Question 1", "role": "user"}],
        [{"content": "Question 2", "role": "user"}, {"content": "Answer 2", "role": "assistant"}],
        [{"content": "Question 3", "role": "user"}],
    ]

    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"raw_prompt": raw_prompt})

    # This should not raise an error
    tensordict_output = data.to_tensordict()

    # Verify the data is preserved
    assert torch.all(torch.eq(tensordict_output["obs"], obs)).item()
    # Verify complex nested structure is accessible
    retrieved_prompt = tensordict_output["raw_prompt"]
    assert len(retrieved_prompt) == len(raw_prompt)
    # Spot check: verify first prompt has correct structure
    assert len(retrieved_prompt[0]) == 1
    assert dict(retrieved_prompt[0][0]) == {"content": "Question 1", "role": "user"}


@pytest.mark.skipif(
    parse_version(tensordict.__version__) < parse_version("0.10"), reason="requires at least tensordict 0.10"
)
def test_to_tensordict_and_back_with_nested_data():
    """Test round-trip conversion: DataProto → TensorDict → DataProto with nested structures."""
    obs = torch.tensor([1, 2, 3, 4])
    labels = ["a", "b", "c", "d"]

    # Multiple types of nested structures
    turn_scores = [[], [0.5], [0.8, 0.9], [0.7]]
    reward_extra_info = [
        {"acc": 1.0, "loss": 0.1},
        {"acc": 0.5, "loss": 0.3},
        {"acc": 1.0, "loss": 0.05},
        {"acc": 0.0, "loss": 0.9},
    ]
    raw_prompt = [
        [{"content": "Q1", "role": "user"}],
        [{"content": "Q2", "role": "user"}],
        [{"content": "Q3", "role": "user"}, {"content": "A3", "role": "assistant"}],
        [{"content": "Q4", "role": "user"}],
    ]

    # Create original DataProto
    original_data = DataProto.from_dict(
        tensors={"obs": obs},
        non_tensors={
            "labels": labels,
            "turn_scores": turn_scores,
            "reward_extra_info": reward_extra_info,
            "raw_prompt": raw_prompt,
        },
        meta_info={"experiment": "test_nested"},
    )

    # Convert to TensorDict
    tensordict_output = original_data.to_tensordict()

    # Convert back to DataProto
    reconstructed_data = DataProto.from_tensordict(tensordict_output)

    # Verify tensors are preserved
    assert torch.all(torch.eq(reconstructed_data.batch["obs"], obs)).item()

    # Verify non-tensor data is preserved
    assert reconstructed_data.non_tensor_batch["labels"].tolist() == labels

    # Verify nested structures are preserved
    assert len(reconstructed_data.non_tensor_batch["turn_scores"]) == len(turn_scores)
    for orig, recon in zip(turn_scores, reconstructed_data.non_tensor_batch["turn_scores"], strict=True):
        assert list(orig) == list(recon)

    assert len(reconstructed_data.non_tensor_batch["reward_extra_info"]) == len(reward_extra_info)
    for orig, recon in zip(reward_extra_info, reconstructed_data.non_tensor_batch["reward_extra_info"], strict=True):
        assert orig == recon

    assert len(reconstructed_data.non_tensor_batch["raw_prompt"]) == len(raw_prompt)
    for orig, recon in zip(raw_prompt, reconstructed_data.non_tensor_batch["raw_prompt"], strict=True):
        assert orig == list(recon)

    # Verify meta_info is preserved
    assert reconstructed_data.meta_info["experiment"] == "test_nested"


@pytest.mark.skipif(
    parse_version(tensordict.__version__) < parse_version("0.10"), reason="requires at least tensordict 0.10"
)
def test_to_tensordict_agent_loop_scenario():
    """Test the exact scenario from agent loop: DataProto with tool rewards, acc, etc.
    This test reproduces the exact error from the agent loop where nested structures
    (lists of lists, lists of dicts) failed to convert to TensorDict.
    """
    # Simulate real agent loop data structure
    prompts = torch.tensor([[1, 2, 3], [4, 5, 6]])
    responses = torch.tensor([[7, 8], [9, 10]])

    # Non-tensor data with nested structures from agent loop
    data_source = ["lighteval/MATH", "lighteval/MATH"]
    uid = ["uuid-1", "uuid-2"]
    num_turns = np.array([2, 4], dtype=np.int32)
    acc = np.array([1.0, 0.0])
    turn_scores = [[], [0.5, 0.8]]  # Lists of varying lengths
    reward_extra_info = [{"acc": 1.0}, {"acc": 0.0}]  # List of dicts
    raw_prompt = [
        [{"content": "Compute 4 @ 2", "role": "user"}],
        [{"content": "Compute 8 @ 7", "role": "user"}],
    ]
    tool_rewards = [[0.0], []]  # List of lists

    data = DataProto.from_dict(
        tensors={"prompts": prompts, "responses": responses},
        non_tensors={
            "data_source": data_source,
            "uid": uid,
            "num_turns": num_turns,
            "acc": acc,
            "turn_scores": turn_scores,
            "reward_extra_info": reward_extra_info,
            "raw_prompt": raw_prompt,
            "tool_rewards": tool_rewards,
        },
        meta_info={"global_steps": 42},
    )

    # THE KEY TEST: This should not raise ValueError about TensorDict conversion
    tensordict_output = data.to_tensordict()

    # Verify tensors are accessible
    assert torch.all(torch.eq(tensordict_output["prompts"], prompts)).item()
    assert torch.all(torch.eq(tensordict_output["responses"], responses)).item()

    # Verify all nested structures are accessible (content check, not type check)
    assert len(tensordict_output["turn_scores"]) == 2
    assert list(tensordict_output["turn_scores"][0]) == []
    assert list(tensordict_output["turn_scores"][1]) == [0.5, 0.8]

    assert len(tensordict_output["reward_extra_info"]) == 2
    assert dict(tensordict_output["reward_extra_info"][0]) == {"acc": 1.0}

    assert len(tensordict_output["raw_prompt"]) == 2
    assert dict(tensordict_output["raw_prompt"][0][0]) == {"content": "Compute 4 @ 2", "role": "user"}

    assert len(tensordict_output["tool_rewards"]) == 2
    assert list(tensordict_output["tool_rewards"][0]) == [0.0]
    assert list(tensordict_output["tool_rewards"][1]) == []

    # Verify round-trip conversion works perfectly
    reconstructed = DataProto.from_tensordict(tensordict_output)
    assert len(reconstructed) == 2
    assert reconstructed.meta_info["global_steps"] == 42
    assert torch.all(torch.eq(reconstructed.batch["prompts"], prompts)).item()


def test_serialize_deserialize_single_tensor():
    """Test serialization and deserialization of a single tensor"""
    # Create test tensor
    original_tensor = torch.randn(3, 4, 5)

    # Serialize
    dtype, shape, data = serialize_single_tensor(original_tensor)

    # Deserialize
    reconstructed_tensor = deserialize_single_tensor((dtype, shape, data))

    # Verify results
    assert torch.allclose(original_tensor, reconstructed_tensor)
    assert original_tensor.shape == reconstructed_tensor.shape
    assert original_tensor.dtype == reconstructed_tensor.dtype


def test_serialize_deserialize_tensordict_regular_tensors():
    """Test serialization and deserialization of TensorDict with regular tensors"""
    # Create test data
    batch_size = (5, 3)
    tensor1 = torch.randn(*batch_size, 4)
    tensor2 = torch.randint(0, 10, (*batch_size, 2))

    # Create TensorDict
    original_tensordict = TensorDict({"tensor1": tensor1, "tensor2": tensor2}, batch_size=batch_size)

    # Serialize
    batch_size_serialized, device, encoded_items = serialize_tensordict(original_tensordict)

    # Deserialize
    reconstructed_tensordict = deserialize_tensordict((batch_size_serialized, device, encoded_items))

    # Verify results
    assert original_tensordict.batch_size == reconstructed_tensordict.batch_size
    assert set(original_tensordict.keys()) == set(reconstructed_tensordict.keys())

    for key in original_tensordict.keys():
        original_tensor = original_tensordict[key]
        reconstructed_tensor = reconstructed_tensordict[key]

        assert torch.allclose(original_tensor, reconstructed_tensor)
        assert original_tensor.shape == reconstructed_tensor.shape
        assert original_tensor.dtype == reconstructed_tensor.dtype


def test_serialize_deserialize_tensordict_nested_tensors():
    """Test serialization and deserialization of TensorDict with nested tensors"""
    # Create nested tensor
    tensor_list = [torch.randn(2, 3), torch.randn(3, 4), torch.randn(1, 5)]
    nested_tensor = torch.nested.as_nested_tensor(tensor_list)

    # Create regular tensor for comparison
    regular_tensor = torch.randn(3, 4, 5)

    # Create TensorDict
    original_tensordict = TensorDict({"nested": nested_tensor, "regular": regular_tensor}, batch_size=(3,))

    # Serialize
    batch_size_serialized, device, encoded_items = serialize_tensordict(original_tensordict)

    # Deserialize
    reconstructed_tensordict = deserialize_tensordict((batch_size_serialized, device, encoded_items))

    # Verify results
    assert original_tensordict.batch_size == reconstructed_tensordict.batch_size
    assert set(original_tensordict.keys()) == set(reconstructed_tensordict.keys())

    # Verify regular tensor
    original_regular = original_tensordict["regular"]
    reconstructed_regular = reconstructed_tensordict["regular"]

    assert torch.allclose(original_regular, reconstructed_regular)
    assert original_regular.shape == reconstructed_regular.shape
    assert original_regular.dtype == reconstructed_regular.dtype

    # Verify nested tensor
    original_nested = original_tensordict["nested"]
    reconstructed_nested = reconstructed_tensordict["nested"]

    # Check if it's a nested tensor
    assert original_nested.is_nested
    assert reconstructed_nested.is_nested

    # Check layout
    assert original_nested.layout == reconstructed_nested.layout

    # Check each tensor after unbinding
    original_unbind = original_nested.unbind()
    reconstructed_unbind = reconstructed_nested.unbind()

    assert len(original_unbind) == len(reconstructed_unbind)

    for orig, recon in zip(original_unbind, reconstructed_unbind, strict=False):
        assert torch.allclose(orig, recon)
        assert orig.shape == recon.shape
        assert orig.dtype == recon.dtype


def test_serialize_deserialize_tensordict_mixed_types():
    """Test serialization and deserialization of TensorDict with mixed tensor types"""
    # Create tensors with different data types
    float_tensor = torch.randn(2, 3).float()
    double_tensor = torch.randn(2, 3).double()
    int_tensor = torch.randint(0, 10, (2, 3)).int()
    long_tensor = torch.randint(0, 10, (2, 3)).long()
    bool_tensor = torch.tensor([[True, False], [False, True]])
    bfloat16_tensor = torch.randn(2, 3).bfloat16()

    # Add fp8 tensor (if available)
    # Note: FP8 is not natively supported in all PyTorch versions
    # We'll check if it's available and conditionally include it
    has_fp8 = hasattr(torch, "float8_e5m2") or hasattr(torch, "float8_e4m3fn")
    if has_fp8:
        try:
            # Try to create an FP8 tensor (implementation may vary)
            # This is a placeholder - actual FP8 support might require specific hardware
            fp8_tensor = torch.randn(2, 3)
            if hasattr(torch, "float8_e5m2"):
                fp8_tensor = fp8_tensor.to(torch.float8_e5m2)
            elif hasattr(torch, "float8_e4m3fn"):
                fp8_tensor = fp8_tensor.to(torch.float8_e4m3fn)
        except Exception:
            has_fp8 = False

    # Create nested tensor
    tensor_list = [
        torch.randn(2, 3),
        torch.randn(3, 4),
    ]
    nested_tensor = torch.nested.as_nested_tensor(tensor_list)

    # Create TensorDict with all available types
    tensordict_data = {
        "float": float_tensor,
        "double": double_tensor,
        "int": int_tensor,
        "long": long_tensor,
        "bool": bool_tensor,
        "bfloat16": bfloat16_tensor,
        "nested": nested_tensor,
    }

    # Conditionally add fp8 tensor if available
    if has_fp8:
        tensordict_data["fp8"] = fp8_tensor

    original_tensordict = TensorDict(
        tensordict_data,
        batch_size=(2,),
    )

    # Serialize
    batch_size_serialized, device, encoded_items = serialize_tensordict(original_tensordict)

    # Deserialize
    reconstructed_tensordict = deserialize_tensordict((batch_size_serialized, device, encoded_items))

    # Verify results
    assert original_tensordict.batch_size == reconstructed_tensordict.batch_size
    assert set(original_tensordict.keys()) == set(reconstructed_tensordict.keys())

    for key in original_tensordict.keys():
        original_tensor = original_tensordict[key]
        reconstructed_tensor = reconstructed_tensordict[key]

        if original_tensor.is_nested:
            # For nested tensors, check each tensor after unbinding
            original_unbind = original_tensor.unbind()
            reconstructed_unbind = reconstructed_tensor.unbind()

            assert len(original_unbind) == len(reconstructed_unbind)

            for orig, recon in zip(original_unbind, reconstructed_unbind, strict=False):
                assert torch.allclose(orig, recon, equal_nan=True)
                assert orig.shape == recon.shape
                assert orig.dtype == recon.dtype
        else:
            # For regular tensors, compare directly
            assert torch.all(original_tensor == reconstructed_tensor)
            assert original_tensor.shape == reconstructed_tensor.shape
            assert original_tensor.dtype == reconstructed_tensor.dtype


def test_serialize_deserialize_tensordict_with_device():
    """Test serialization and deserialization of TensorDict with device information"""
    # Create test data
    batch_size = (2, 3)
    tensor1 = torch.randn(*batch_size, 4)
    tensor2 = torch.randint(0, 10, (*batch_size, 2))

    # Create TensorDict with device information
    device = "cpu"
    original_tensordict = TensorDict({"tensor1": tensor1, "tensor2": tensor2}, batch_size=batch_size, device=device)

    # Serialize
    batch_size_serialized, device_serialized, encoded_items = serialize_tensordict(original_tensordict)

    # Deserialize
    reconstructed_tensordict = deserialize_tensordict((batch_size_serialized, device_serialized, encoded_items))

    # Verify results
    assert original_tensordict.batch_size == reconstructed_tensordict.batch_size
    assert str(original_tensordict.device) == str(reconstructed_tensordict.device)
    assert set(original_tensordict.keys()) == set(reconstructed_tensordict.keys())

    for key in original_tensordict.keys():
        original_tensor = original_tensordict[key]
        reconstructed_tensor = reconstructed_tensordict[key]

        assert torch.allclose(original_tensor.cpu(), reconstructed_tensor.cpu())
        assert original_tensor.shape == reconstructed_tensor.shape
        assert original_tensor.dtype == reconstructed_tensor.dtype


def test_serialize_dataproto_with_empty_tensordict():
    """Tests that serializing a DataProto with an empty TensorDict does not crash.
    This test verifies the fix for the torch.cat error that occurs when calling
    consolidate() on an empty TensorDict during serialization.
    """
    import pickle

    # This test requires tensordict >= 0.5.0 to trigger the code path
    if parse_version(tensordict.__version__) < parse_version("0.5.0"):
        pytest.skip("Test requires tensordict>=0.5.0")

    # Create a DataProto with an empty TensorDict but with a batch size
    empty_td = TensorDict({}, batch_size=[10])
    data = DataProto(batch=empty_td)

    # This would crash before the fix with:
    # RuntimeError: torch.cat(): expected a non-empty list of Tensors
    try:
        serialized_data = pickle.dumps(data)
    except Exception as e:
        pytest.fail(f"Serializing DataProto with empty TensorDict failed with: {e}")

    # Verify deserialization works as expected
    deserialized_data = pickle.loads(serialized_data)
    assert len(deserialized_data.batch.keys()) == 0
    assert deserialized_data.batch.batch_size == torch.Size([10])