import os
import tempfile

import numpy as np

import pydantic
import pytest
from dgl import graphbolt as gb


def test_OnDiskDataset_TVTSet_exceptions():
    """Test excpetions thrown when parsing TVTSet."""
    with tempfile.TemporaryDirectory() as test_dir:
        yaml_file = os.path.join(test_dir, "test.yaml")

        # Case 1: ``format`` is invalid.
        yaml_content = """
        train_sets:
          - - type_name: paper
              format: torch_invalid
              path: set/paper-train.pt
        """
        yaml_file = os.path.join(test_dir, "test.yaml")
        with open(yaml_file, "w") as f:
            f.write(yaml_content)
        with pytest.raises(pydantic.ValidationError):
            _ = gb.OnDiskDataset(yaml_file)

        # Case 2: ``type_name`` is not specified while multiple TVT sets are specified.
        yaml_content = """
            train_sets:
              - - type_name: null
                  format: numpy
                  path: set/train.npy
                - type_name: null
                  format: numpy
                  path: set/train.npy
        """
        with open(yaml_file, "w") as f:
            f.write(yaml_content)
        with pytest.raises(
            AssertionError,
            match=r"Only one TVT set is allowed if type_name is not specified.",
        ):
            _ = gb.OnDiskDataset(yaml_file)


def test_OnDiskDataset_TVTSet_ItemSet_id_label():
    """Test TVTSet which returns ItemSet with IDs and labels."""
    with tempfile.TemporaryDirectory() as test_dir:
        train_ids = np.arange(1000)
        train_labels = np.random.randint(0, 10, size=1000)
        train_data = np.vstack([train_ids, train_labels]).T
        train_path = os.path.join(test_dir, "train.npy")
        np.save(train_path, train_data)

        validation_ids = np.arange(1000, 2000)
        validation_labels = np.random.randint(0, 10, size=1000)
        validation_data = np.vstack([validation_ids, validation_labels]).T
        validation_path = os.path.join(test_dir, "validation.npy")
        np.save(validation_path, validation_data)

        test_ids = np.arange(2000, 3000)
        test_labels = np.random.randint(0, 10, size=1000)
        test_data = np.vstack([test_ids, test_labels]).T
        test_path = os.path.join(test_dir, "test.npy")
        np.save(test_path, test_data)

        # Case 1:
        #   all TVT sets are specified.
        #   ``type_name`` is not specified or specified as ``null``.
        #   ``in_memory`` could be ``true`` and ``false``.
        yaml_content = f"""
            train_sets:
              - - type_name: null
                  format: numpy
                  in_memory: true
                  path: {train_path}
              - - type_name: null
                  format: numpy
                  path: {train_path}
            validation_sets:
              - - format: numpy
                  path: {validation_path}
              - - type_name: null
                  format: numpy
                  path: {validation_path}
            test_sets:
              - - type_name: null
                  format: numpy
                  in_memory: false
                  path: {test_path}
              - - type_name: null
                  format: numpy
                  path: {test_path}
        """
        yaml_file = os.path.join(test_dir, "test.yaml")
        with open(yaml_file, "w") as f:
            f.write(yaml_content)

        dataset = gb.OnDiskDataset(yaml_file)

        # Verify train set.
        train_sets = dataset.train_sets()
        assert len(train_sets) == 2
        for train_set in train_sets:
            assert len(train_set) == 1000
            assert isinstance(train_set, gb.ItemSet)
            for i, (id, label) in enumerate(train_set):
                assert id == train_ids[i]
                assert label == train_labels[i]
        train_sets = None

        # Verify validation set.
        validation_sets = dataset.validation_sets()
        assert len(validation_sets) == 2
        for validation_set in validation_sets:
            assert len(validation_set) == 1000
            assert isinstance(validation_set, gb.ItemSet)
            for i, (id, label) in enumerate(validation_set):
                assert id == validation_ids[i]
                assert label == validation_labels[i]
        validation_sets = None

        # Verify test set.
        test_sets = dataset.test_sets()
        assert len(test_sets) == 2
        for test_set in test_sets:
            assert len(test_set) == 1000
            assert isinstance(test_set, gb.ItemSet)
            for i, (id, label) in enumerate(test_set):
                assert id == test_ids[i]
                assert label == test_labels[i]
        test_sets = None
        dataset = None

        # Case 2: Some TVT sets are None.
        yaml_content = f"""
            train_sets:
              - - type_name: null
                  format: numpy
                  path: {train_path}
        """
        yaml_file = os.path.join(test_dir, "test.yaml")
        with open(yaml_file, "w") as f:
            f.write(yaml_content)

        dataset = gb.OnDiskDataset(yaml_file)
        assert dataset.train_sets() is not None
        assert dataset.validation_sets() is None
        assert dataset.test_sets() is None
        dataset = None


def test_OnDiskDataset_TVTSet_ItemSet_node_pair_label():
    """Test TVTSet which returns ItemSet with IDs and labels."""
    with tempfile.TemporaryDirectory() as test_dir:
        train_pairs = (np.arange(1000), np.arange(1000, 2000))
        train_labels = np.random.randint(0, 10, size=1000)
        train_data = np.vstack([train_pairs, train_labels]).T
        train_path = os.path.join(test_dir, "train.npy")
        np.save(train_path, train_data)

        validation_pairs = (np.arange(1000, 2000), np.arange(2000, 3000))
        validation_labels = np.random.randint(0, 10, size=1000)
        validation_data = np.vstack([validation_pairs, validation_labels]).T
        validation_path = os.path.join(test_dir, "validation.npy")
        np.save(validation_path, validation_data)

        test_pairs = (np.arange(2000, 3000), np.arange(3000, 4000))
        test_labels = np.random.randint(0, 10, size=1000)
        test_data = np.vstack([test_pairs, test_labels]).T
        test_path = os.path.join(test_dir, "test.npy")
        np.save(test_path, test_data)

        yaml_content = f"""
            train_sets:
              - - type_name: null
                  format: numpy
                  in_memory: true
                  path: {train_path}
              - - type_name: null
                  format: numpy
                  path: {train_path}
            validation_sets:
              - - format: numpy
                  path: {validation_path}
              - - type_name: null
                  format: numpy
                  path: {validation_path}
            test_sets:
              - - type_name: null
                  format: numpy
                  in_memory: false
                  path: {test_path}
              - - type_name: null
                  format: numpy
                  path: {test_path}
        """
        yaml_file = os.path.join(test_dir, "test.yaml")
        with open(yaml_file, "w") as f:
            f.write(yaml_content)

        dataset = gb.OnDiskDataset(yaml_file)

        # Verify train set.
        train_sets = dataset.train_sets()
        assert len(train_sets) == 2
        for train_set in train_sets:
            assert len(train_set) == 1000
            assert isinstance(train_set, gb.ItemSet)
            for i, (src, dst, label) in enumerate(train_set):
                assert src == train_pairs[0][i]
                assert dst == train_pairs[1][i]
                assert label == train_labels[i]
        train_sets = None

        # Verify validation set.
        validation_sets = dataset.validation_sets()
        assert len(validation_sets) == 2
        for validation_set in validation_sets:
            assert len(validation_set) == 1000
            assert isinstance(validation_set, gb.ItemSet)
            for i, (src, dst, label) in enumerate(validation_set):
                assert src == validation_pairs[0][i]
                assert dst == validation_pairs[1][i]
                assert label == validation_labels[i]
        validation_sets = None

        # Verify test set.
        test_sets = dataset.test_sets()
        assert len(test_sets) == 2
        for test_set in test_sets:
            assert len(test_set) == 1000
            assert isinstance(test_set, gb.ItemSet)
            for i, (src, dst, label) in enumerate(test_set):
                assert src == test_pairs[0][i]
                assert dst == test_pairs[1][i]
                assert label == test_labels[i]
        test_sets = None
        dataset = None


def test_OnDiskDataset_TVTSet_ItemSetDict_id_label():
    """Test TVTSet which returns ItemSetDict with IDs and labels."""
    with tempfile.TemporaryDirectory() as test_dir:
        train_ids = np.arange(1000)
        train_labels = np.random.randint(0, 10, size=1000)
        train_data = np.vstack([train_ids, train_labels]).T
        train_path = os.path.join(test_dir, "train.npy")
        np.save(train_path, train_data)

        validation_ids = np.arange(1000, 2000)
        validation_labels = np.random.randint(0, 10, size=1000)
        validation_data = np.vstack([validation_ids, validation_labels]).T
        validation_path = os.path.join(test_dir, "validation.npy")
        np.save(validation_path, validation_data)

        test_ids = np.arange(2000, 3000)
        test_labels = np.random.randint(0, 10, size=1000)
        test_data = np.vstack([test_ids, test_labels]).T
        test_path = os.path.join(test_dir, "test.npy")
        np.save(test_path, test_data)

        yaml_content = f"""
            train_sets:
              - - type_name: paper
                  format: numpy
                  in_memory: true
                  path: {train_path}
              - - type_name: author
                  format: numpy
                  path: {train_path}
            validation_sets:
              - - type_name: paper
                  format: numpy
                  path: {validation_path}
              - - type_name: author
                  format: numpy
                  path: {validation_path}
            test_sets:
              - - type_name: paper
                  format: numpy
                  in_memory: false
                  path: {test_path}
              - - type_name: author
                  format: numpy
                  path: {test_path}
        """
        yaml_file = os.path.join(test_dir, "test.yaml")
        with open(yaml_file, "w") as f:
            f.write(yaml_content)

        dataset = gb.OnDiskDataset(yaml_file)

        # Verify train set.
        train_sets = dataset.train_sets()
        assert len(train_sets) == 2
        for train_set in train_sets:
            assert len(train_set) == 1000
            assert isinstance(train_set, gb.ItemSetDict)
            for i, item in enumerate(train_set):
                assert isinstance(item, dict)
                assert len(item) == 1
                key = list(item.keys())[0]
                assert key in ["paper", "author"]
                id, label = item[key]
                assert id == train_ids[i]
                assert label == train_labels[i]
        train_sets = None

        # Verify validation set.
        validation_sets = dataset.validation_sets()
        assert len(validation_sets) == 2
        for validation_set in validation_sets:
            assert len(validation_set) == 1000
            assert isinstance(train_set, gb.ItemSetDict)
            for i, item in enumerate(validation_set):
                assert isinstance(item, dict)
                assert len(item) == 1
                key = list(item.keys())[0]
                assert key in ["paper", "author"]
                id, label = item[key]
                assert id == validation_ids[i]
                assert label == validation_labels[i]
        validation_sets = None

        # Verify test set.
        test_sets = dataset.test_sets()
        assert len(test_sets) == 2
        for test_set in test_sets:
            assert len(test_set) == 1000
            assert isinstance(train_set, gb.ItemSetDict)
            for i, item in enumerate(test_set):
                assert isinstance(item, dict)
                assert len(item) == 1
                key = list(item.keys())[0]
                assert key in ["paper", "author"]
                id, label = item[key]
                assert id == test_ids[i]
                assert label == test_labels[i]
        test_sets = None
        dataset = None


def test_OnDiskDataset_TVTSet_ItemSetDict_node_pair_label():
    """Test TVTSet which returns ItemSetDict with node pairs and labels."""
    with tempfile.TemporaryDirectory() as test_dir:
        train_pairs = (np.arange(1000), np.arange(1000, 2000))
        train_labels = np.random.randint(0, 10, size=1000)
        train_data = np.vstack([train_pairs, train_labels]).T
        train_path = os.path.join(test_dir, "train.npy")
        np.save(train_path, train_data)

        validation_pairs = (np.arange(1000, 2000), np.arange(2000, 3000))
        validation_labels = np.random.randint(0, 10, size=1000)
        validation_data = np.vstack([validation_pairs, validation_labels]).T
        validation_path = os.path.join(test_dir, "validation.npy")
        np.save(validation_path, validation_data)

        test_pairs = (np.arange(2000, 3000), np.arange(3000, 4000))
        test_labels = np.random.randint(0, 10, size=1000)
        test_data = np.vstack([test_pairs, test_labels]).T
        test_path = os.path.join(test_dir, "test.npy")
        np.save(test_path, test_data)

        yaml_content = f"""
            train_sets:
              - - type_name: paper
                  format: numpy
                  in_memory: true
                  path: {train_path}
              - - type_name: author
                  format: numpy
                  path: {train_path}
            validation_sets:
              - - type_name: paper
                  format: numpy
                  path: {validation_path}
              - - type_name: author
                  format: numpy
                  path: {validation_path}
            test_sets:
              - - type_name: paper
                  format: numpy
                  in_memory: false
                  path: {test_path}
              - - type_name: author
                  format: numpy
                  path: {test_path}
        """
        yaml_file = os.path.join(test_dir, "test.yaml")
        with open(yaml_file, "w") as f:
            f.write(yaml_content)

        dataset = gb.OnDiskDataset(yaml_file)

        # Verify train set.
        train_sets = dataset.train_sets()
        assert len(train_sets) == 2
        for train_set in train_sets:
            assert len(train_set) == 1000
            assert isinstance(train_set, gb.ItemSetDict)
            for i, item in enumerate(train_set):
                assert isinstance(item, dict)
                assert len(item) == 1
                key = list(item.keys())[0]
                assert key in ["paper", "author"]
                src, dst, label = item[key]
                assert src == train_pairs[0][i]
                assert dst == train_pairs[1][i]
                assert label == train_labels[i]
        train_sets = None

        # Verify validation set.
        validation_sets = dataset.validation_sets()
        assert len(validation_sets) == 2
        for validation_set in validation_sets:
            assert len(validation_set) == 1000
            assert isinstance(train_set, gb.ItemSetDict)
            for i, item in enumerate(validation_set):
                assert isinstance(item, dict)
                assert len(item) == 1
                key = list(item.keys())[0]
                assert key in ["paper", "author"]
                src, dst, label = item[key]
                assert src == validation_pairs[0][i]
                assert dst == validation_pairs[1][i]
                assert label == validation_labels[i]
        validation_sets = None

        # Verify test set.
        test_sets = dataset.test_sets()
        assert len(test_sets) == 2
        for test_set in test_sets:
            assert len(test_set) == 1000
            assert isinstance(train_set, gb.ItemSetDict)
            for i, item in enumerate(test_set):
                assert isinstance(item, dict)
                assert len(item) == 1
                key = list(item.keys())[0]
                assert key in ["paper", "author"]
                src, dst, label = item[key]
                assert src == test_pairs[0][i]
                assert dst == test_pairs[1][i]
                assert label == test_labels[i]
        test_sets = None
        dataset = None
