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# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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 collections
import datetime
import decimal
import itertools
import math
import re

import hypothesis as h
import numpy as np
import pytest

from pyarrow.pandas_compat import _pandas_api  # noqa
import pyarrow as pa
import pyarrow.tests.strategies as past


int_type_pairs = [
    (np.int8, pa.int8()),
    (np.int16, pa.int16()),
    (np.int32, pa.int32()),
    (np.int64, pa.int64()),
    (np.uint8, pa.uint8()),
    (np.uint16, pa.uint16()),
    (np.uint32, pa.uint32()),
    (np.uint64, pa.uint64())]


np_int_types, pa_int_types = zip(*int_type_pairs)


class StrangeIterable:
    def __init__(self, lst):
        self.lst = lst

    def __iter__(self):
        return self.lst.__iter__()


class MyInt:
    def __init__(self, value):
        self.value = value

    def __int__(self):
        return self.value


class MyBrokenInt:
    def __int__(self):
        1/0  # MARKER


def check_struct_type(ty, expected):
    """
    Check a struct type is as expected, but not taking order into account.
    """
    assert pa.types.is_struct(ty)
    assert set(ty) == set(expected)


def test_iterable_types():
    arr1 = pa.array(StrangeIterable([0, 1, 2, 3]))
    arr2 = pa.array((0, 1, 2, 3))

    assert arr1.equals(arr2)


def test_empty_iterable():
    arr = pa.array(StrangeIterable([]))
    assert len(arr) == 0
    assert arr.null_count == 0
    assert arr.type == pa.null()
    assert arr.to_pylist() == []


def test_limited_iterator_types():
    arr1 = pa.array(iter(range(3)), type=pa.int64(), size=3)
    arr2 = pa.array((0, 1, 2))
    assert arr1.equals(arr2)


def test_limited_iterator_size_overflow():
    arr1 = pa.array(iter(range(3)), type=pa.int64(), size=2)
    arr2 = pa.array((0, 1))
    assert arr1.equals(arr2)


def test_limited_iterator_size_underflow():
    arr1 = pa.array(iter(range(3)), type=pa.int64(), size=10)
    arr2 = pa.array((0, 1, 2))
    assert arr1.equals(arr2)


def test_iterator_without_size():
    expected = pa.array((0, 1, 2))
    arr1 = pa.array(iter(range(3)))
    assert arr1.equals(expected)
    # Same with explicit type
    arr1 = pa.array(iter(range(3)), type=pa.int64())
    assert arr1.equals(expected)


def test_infinite_iterator():
    expected = pa.array((0, 1, 2))
    arr1 = pa.array(itertools.count(0), size=3)
    assert arr1.equals(expected)
    # Same with explicit type
    arr1 = pa.array(itertools.count(0), type=pa.int64(), size=3)
    assert arr1.equals(expected)


def _as_list(xs):
    return xs


def _as_tuple(xs):
    return tuple(xs)


def _as_deque(xs):
    # deque is a sequence while neither tuple nor list
    return collections.deque(xs)


def _as_dict_values(xs):
    # a dict values object is not a sequence, just a regular iterable
    dct = {k: v for k, v in enumerate(xs)}
    return dct.values()


def _as_numpy_array(xs):
    arr = np.empty(len(xs), dtype=object)
    arr[:] = xs
    return arr


def _as_set(xs):
    return set(xs)


SEQUENCE_TYPES = [_as_list, _as_tuple, _as_numpy_array]
ITERABLE_TYPES = [_as_set, _as_dict_values] + SEQUENCE_TYPES
COLLECTIONS_TYPES = [_as_deque] + ITERABLE_TYPES

parametrize_with_iterable_types = pytest.mark.parametrize(
    "seq", ITERABLE_TYPES
)

parametrize_with_sequence_types = pytest.mark.parametrize(
    "seq", SEQUENCE_TYPES
)

parametrize_with_collections_types = pytest.mark.parametrize(
    "seq", COLLECTIONS_TYPES
)


@parametrize_with_collections_types
def test_sequence_types(seq):
    arr1 = pa.array(seq([1, 2, 3]))
    arr2 = pa.array([1, 2, 3])

    assert arr1.equals(arr2)


@parametrize_with_iterable_types
def test_nested_sequence_types(seq):
    arr1 = pa.array([seq([1, 2, 3])])
    arr2 = pa.array([[1, 2, 3]])

    assert arr1.equals(arr2)


@parametrize_with_sequence_types
def test_sequence_boolean(seq):
    expected = [True, None, False, None]
    arr = pa.array(seq(expected))
    assert len(arr) == 4
    assert arr.null_count == 2
    assert arr.type == pa.bool_()
    assert arr.to_pylist() == expected


@parametrize_with_sequence_types
def test_sequence_numpy_boolean(seq):
    expected = [np.bool_(True), None, np.bool_(False), None]
    arr = pa.array(seq(expected))
    assert arr.type == pa.bool_()
    assert arr.to_pylist() == [True, None, False, None]


@parametrize_with_sequence_types
def test_sequence_mixed_numpy_python_bools(seq):
    values = np.array([True, False])
    arr = pa.array(seq([values[0], None, values[1], True, False]))
    assert arr.type == pa.bool_()
    assert arr.to_pylist() == [True, None, False, True, False]


@parametrize_with_collections_types
def test_empty_list(seq):
    arr = pa.array(seq([]))
    assert len(arr) == 0
    assert arr.null_count == 0
    assert arr.type == pa.null()
    assert arr.to_pylist() == []


@parametrize_with_sequence_types
def test_nested_lists(seq):
    data = [[], [1, 2], None]
    arr = pa.array(seq(data))
    assert len(arr) == 3
    assert arr.null_count == 1
    assert arr.type == pa.list_(pa.int64())
    assert arr.to_pylist() == data
    # With explicit type
    arr = pa.array(seq(data), type=pa.list_(pa.int32()))
    assert len(arr) == 3
    assert arr.null_count == 1
    assert arr.type == pa.list_(pa.int32())
    assert arr.to_pylist() == data


@parametrize_with_sequence_types
def test_nested_large_lists(seq):
    data = [[], [1, 2], None]
    arr = pa.array(seq(data), type=pa.large_list(pa.int16()))
    assert len(arr) == 3
    assert arr.null_count == 1
    assert arr.type == pa.large_list(pa.int16())
    assert arr.to_pylist() == data


@parametrize_with_collections_types
def test_list_with_non_list(seq):
    # List types don't accept non-sequences
    with pytest.raises(TypeError):
        pa.array(seq([[], [1, 2], 3]), type=pa.list_(pa.int64()))
    with pytest.raises(TypeError):
        pa.array(seq([[], [1, 2], 3]), type=pa.large_list(pa.int64()))


@parametrize_with_sequence_types
def test_nested_arrays(seq):
    arr = pa.array(seq([np.array([], dtype=np.int64),
                        np.array([1, 2], dtype=np.int64), None]))
    assert len(arr) == 3
    assert arr.null_count == 1
    assert arr.type == pa.list_(pa.int64())
    assert arr.to_pylist() == [[], [1, 2], None]


@parametrize_with_sequence_types
def test_nested_fixed_size_list(seq):
    # sequence of lists
    data = [[1, 2], [3, None], None]
    arr = pa.array(seq(data), type=pa.list_(pa.int64(), 2))
    assert len(arr) == 3
    assert arr.null_count == 1
    assert arr.type == pa.list_(pa.int64(), 2)
    assert arr.to_pylist() == data

    # sequence of numpy arrays
    data = [np.array([1, 2], dtype='int64'), np.array([3, 4], dtype='int64'),
            None]
    arr = pa.array(seq(data), type=pa.list_(pa.int64(), 2))
    assert len(arr) == 3
    assert arr.null_count == 1
    assert arr.type == pa.list_(pa.int64(), 2)
    assert arr.to_pylist() == [[1, 2], [3, 4], None]

    # incorrect length of the lists or arrays
    data = [[1, 2, 4], [3, None], None]
    for data in [[[1, 2, 3]], [np.array([1, 2, 4], dtype='int64')]]:
        with pytest.raises(
                ValueError, match="Length of item not correct: expected 2"):
            pa.array(seq(data), type=pa.list_(pa.int64(), 2))

    # with list size of 0
    data = [[], [], None]
    arr = pa.array(seq(data), type=pa.list_(pa.int64(), 0))
    assert len(arr) == 3
    assert arr.null_count == 1
    assert arr.type == pa.list_(pa.int64(), 0)
    assert arr.to_pylist() == [[], [], None]


@parametrize_with_sequence_types
def test_sequence_all_none(seq):
    arr = pa.array(seq([None, None]))
    assert len(arr) == 2
    assert arr.null_count == 2
    assert arr.type == pa.null()
    assert arr.to_pylist() == [None, None]


@parametrize_with_sequence_types
@pytest.mark.parametrize("np_scalar_pa_type", int_type_pairs)
def test_sequence_integer(seq, np_scalar_pa_type):
    np_scalar, pa_type = np_scalar_pa_type
    expected = [1, None, 3, None,
                np.iinfo(np_scalar).min, np.iinfo(np_scalar).max]
    arr = pa.array(seq(expected), type=pa_type)
    assert len(arr) == 6
    assert arr.null_count == 2
    assert arr.type == pa_type
    assert arr.to_pylist() == expected


@parametrize_with_collections_types
@pytest.mark.parametrize("np_scalar_pa_type", int_type_pairs)
def test_sequence_integer_np_nan(seq, np_scalar_pa_type):
    # ARROW-2806: numpy.nan is a double value and thus should produce
    # a double array.
    _, pa_type = np_scalar_pa_type
    with pytest.raises(ValueError):
        pa.array(seq([np.nan]), type=pa_type, from_pandas=False)

    arr = pa.array(seq([np.nan]), type=pa_type, from_pandas=True)
    expected = [None]
    assert len(arr) == 1
    assert arr.null_count == 1
    assert arr.type == pa_type
    assert arr.to_pylist() == expected


@parametrize_with_sequence_types
@pytest.mark.parametrize("np_scalar_pa_type", int_type_pairs)
def test_sequence_integer_nested_np_nan(seq, np_scalar_pa_type):
    # ARROW-2806: numpy.nan is a double value and thus should produce
    # a double array.
    _, pa_type = np_scalar_pa_type
    with pytest.raises(ValueError):
        pa.array(seq([[np.nan]]), type=pa.list_(pa_type), from_pandas=False)

    arr = pa.array(seq([[np.nan]]), type=pa.list_(pa_type), from_pandas=True)
    expected = [[None]]
    assert len(arr) == 1
    assert arr.null_count == 0
    assert arr.type == pa.list_(pa_type)
    assert arr.to_pylist() == expected


@parametrize_with_sequence_types
def test_sequence_integer_inferred(seq):
    expected = [1, None, 3, None]
    arr = pa.array(seq(expected))
    assert len(arr) == 4
    assert arr.null_count == 2
    assert arr.type == pa.int64()
    assert arr.to_pylist() == expected


@parametrize_with_sequence_types
@pytest.mark.parametrize("np_scalar_pa_type", int_type_pairs)
def test_sequence_numpy_integer(seq, np_scalar_pa_type):
    np_scalar, pa_type = np_scalar_pa_type
    expected = [np_scalar(1), None, np_scalar(3), None,
                np_scalar(np.iinfo(np_scalar).min),
                np_scalar(np.iinfo(np_scalar).max)]
    arr = pa.array(seq(expected), type=pa_type)
    assert len(arr) == 6
    assert arr.null_count == 2
    assert arr.type == pa_type
    assert arr.to_pylist() == expected


@parametrize_with_sequence_types
@pytest.mark.parametrize("np_scalar_pa_type", int_type_pairs)
def test_sequence_numpy_integer_inferred(seq, np_scalar_pa_type):
    np_scalar, pa_type = np_scalar_pa_type
    expected = [np_scalar(1), None, np_scalar(3), None]
    expected += [np_scalar(np.iinfo(np_scalar).min),
                 np_scalar(np.iinfo(np_scalar).max)]
    arr = pa.array(seq(expected))
    assert len(arr) == 6
    assert arr.null_count == 2
    assert arr.type == pa_type
    assert arr.to_pylist() == expected


@parametrize_with_sequence_types
def test_sequence_custom_integers(seq):
    expected = [0, 42, 2**33 + 1, -2**63]
    data = list(map(MyInt, expected))
    arr = pa.array(seq(data), type=pa.int64())
    assert arr.to_pylist() == expected


@parametrize_with_collections_types
def test_broken_integers(seq):
    data = [MyBrokenInt()]
    with pytest.raises(pa.ArrowInvalid, match="tried to convert to int"):
        pa.array(seq(data), type=pa.int64())


def test_numpy_scalars_mixed_type():
    # ARROW-4324
    data = [np.int32(10), np.float32(0.5)]
    arr = pa.array(data)
    expected = pa.array([10, 0.5], type="float64")
    assert arr.equals(expected)

    # ARROW-9490
    data = [np.int8(10), np.float32(0.5)]
    arr = pa.array(data)
    expected = pa.array([10, 0.5], type="float32")
    assert arr.equals(expected)


@pytest.mark.xfail(reason="Type inference for uint64 not implemented",
                   raises=OverflowError)
def test_uint64_max_convert():
    data = [0, np.iinfo(np.uint64).max]

    arr = pa.array(data, type=pa.uint64())
    expected = pa.array(np.array(data, dtype='uint64'))
    assert arr.equals(expected)

    arr_inferred = pa.array(data)
    assert arr_inferred.equals(expected)


@pytest.mark.parametrize("bits", [8, 16, 32, 64])
def test_signed_integer_overflow(bits):
    ty = getattr(pa, "int%d" % bits)()
    # XXX ideally would always raise OverflowError
    with pytest.raises((OverflowError, pa.ArrowInvalid)):
        pa.array([2 ** (bits - 1)], ty)
    with pytest.raises((OverflowError, pa.ArrowInvalid)):
        pa.array([-2 ** (bits - 1) - 1], ty)


@pytest.mark.parametrize("bits", [8, 16, 32, 64])
def test_unsigned_integer_overflow(bits):
    ty = getattr(pa, "uint%d" % bits)()
    # XXX ideally would always raise OverflowError
    with pytest.raises((OverflowError, pa.ArrowInvalid)):
        pa.array([2 ** bits], ty)
    with pytest.raises((OverflowError, pa.ArrowInvalid)):
        pa.array([-1], ty)


@parametrize_with_collections_types
@pytest.mark.parametrize("typ", pa_int_types)
def test_integer_from_string_error(seq, typ):
    # ARROW-9451: pa.array(['1'], type=pa.uint32()) should not succeed
    with pytest.raises(pa.ArrowInvalid):
        pa.array(seq(['1']), type=typ)


def test_convert_with_mask():
    data = [1, 2, 3, 4, 5]
    mask = np.array([False, True, False, False, True])

    result = pa.array(data, mask=mask)
    expected = pa.array([1, None, 3, 4, None])

    assert result.equals(expected)

    # Mask wrong length
    with pytest.raises(ValueError):
        pa.array(data, mask=mask[1:])


def test_garbage_collection():
    import gc

    # Force the cyclic garbage collector to run
    gc.collect()

    bytes_before = pa.total_allocated_bytes()
    pa.array([1, None, 3, None])
    gc.collect()
    assert pa.total_allocated_bytes() == bytes_before


def test_sequence_double():
    data = [1.5, 1., None, 2.5, None, None]
    arr = pa.array(data)
    assert len(arr) == 6
    assert arr.null_count == 3
    assert arr.type == pa.float64()
    assert arr.to_pylist() == data


def test_double_auto_coerce_from_integer():
    # Done as part of ARROW-2814
    data = [1.5, 1., None, 2.5, None, None]
    arr = pa.array(data)

    data2 = [1.5, 1, None, 2.5, None, None]
    arr2 = pa.array(data2)

    assert arr.equals(arr2)

    data3 = [1, 1.5, None, 2.5, None, None]
    arr3 = pa.array(data3)

    data4 = [1., 1.5, None, 2.5, None, None]
    arr4 = pa.array(data4)

    assert arr3.equals(arr4)


def test_double_integer_coerce_representable_range():
    valid_values = [1.5, 1, 2, None, 1 << 53, -(1 << 53)]
    invalid_values = [1.5, 1, 2, None, (1 << 53) + 1]
    invalid_values2 = [1.5, 1, 2, None, -((1 << 53) + 1)]

    # it works
    pa.array(valid_values)

    # it fails
    with pytest.raises(ValueError):
        pa.array(invalid_values)

    with pytest.raises(ValueError):
        pa.array(invalid_values2)


def test_float32_integer_coerce_representable_range():
    f32 = np.float32
    valid_values = [f32(1.5), 1 << 24, -(1 << 24)]
    invalid_values = [f32(1.5), (1 << 24) + 1]
    invalid_values2 = [f32(1.5), -((1 << 24) + 1)]

    # it works
    pa.array(valid_values, type=pa.float32())

    # it fails
    with pytest.raises(ValueError):
        pa.array(invalid_values, type=pa.float32())

    with pytest.raises(ValueError):
        pa.array(invalid_values2, type=pa.float32())


def test_mixed_sequence_errors():
    with pytest.raises(ValueError, match="tried to convert to boolean"):
        pa.array([True, 'foo'], type=pa.bool_())

    with pytest.raises(ValueError, match="tried to convert to float32"):
        pa.array([1.5, 'foo'], type=pa.float32())

    with pytest.raises(ValueError, match="tried to convert to double"):
        pa.array([1.5, 'foo'])


@parametrize_with_sequence_types
@pytest.mark.parametrize("np_scalar,pa_type", [
    (np.float16, pa.float16()),
    (np.float32, pa.float32()),
    (np.float64, pa.float64())
])
@pytest.mark.parametrize("from_pandas", [True, False])
def test_sequence_numpy_double(seq, np_scalar, pa_type, from_pandas):
    data = [np_scalar(1.5), np_scalar(1), None, np_scalar(2.5), None, np.nan]
    arr = pa.array(seq(data), from_pandas=from_pandas)
    assert len(arr) == 6
    if from_pandas:
        assert arr.null_count == 3
    else:
        assert arr.null_count == 2
    if from_pandas:
        # The NaN is skipped in type inference, otherwise it forces a
        # float64 promotion
        assert arr.type == pa_type
    else:
        assert arr.type == pa.float64()

    assert arr.to_pylist()[:4] == data[:4]
    if from_pandas:
        assert arr.to_pylist()[5] is None
    else:
        assert np.isnan(arr.to_pylist()[5])


@pytest.mark.parametrize("from_pandas", [True, False])
@pytest.mark.parametrize("inner_seq", [np.array, list])
def test_ndarray_nested_numpy_double(from_pandas, inner_seq):
    # ARROW-2806
    data = np.array([
        inner_seq([1., 2.]),
        inner_seq([1., 2., 3.]),
        inner_seq([np.nan]),
        None
    ], dtype=object)
    arr = pa.array(data, from_pandas=from_pandas)
    assert len(arr) == 4
    assert arr.null_count == 1
    assert arr.type == pa.list_(pa.float64())
    if from_pandas:
        assert arr.to_pylist() == [[1.0, 2.0], [1.0, 2.0, 3.0], [None], None]
    else:
        np.testing.assert_equal(arr.to_pylist(),
                                [[1., 2.], [1., 2., 3.], [np.nan], None])


def test_nested_ndarray_in_object_array():
    # ARROW-4350
    arr = np.empty(2, dtype=object)
    arr[:] = [np.array([1, 2], dtype=np.int64),
              np.array([2, 3], dtype=np.int64)]

    arr2 = np.empty(2, dtype=object)
    arr2[0] = [3, 4]
    arr2[1] = [5, 6]

    expected_type = pa.list_(pa.list_(pa.int64()))
    assert pa.infer_type([arr]) == expected_type

    result = pa.array([arr, arr2])
    expected = pa.array([[[1, 2], [2, 3]], [[3, 4], [5, 6]]],
                        type=expected_type)

    assert result.equals(expected)

    # test case for len-1 arrays to ensure they are interpreted as
    # sublists and not scalars
    arr = np.empty(2, dtype=object)
    arr[:] = [np.array([1]), np.array([2])]
    result = pa.array([arr, arr])
    assert result.to_pylist() == [[[1], [2]], [[1], [2]]]


@pytest.mark.xfail(reason=("Type inference for multidimensional ndarray "
                           "not yet implemented"),
                   raises=AssertionError)
def test_multidimensional_ndarray_as_nested_list():
    # TODO(wesm): see ARROW-5645
    arr = np.array([[1, 2], [2, 3]], dtype=np.int64)
    arr2 = np.array([[3, 4], [5, 6]], dtype=np.int64)

    expected_type = pa.list_(pa.list_(pa.int64()))
    assert pa.infer_type([arr]) == expected_type

    result = pa.array([arr, arr2])
    expected = pa.array([[[1, 2], [2, 3]], [[3, 4], [5, 6]]],
                        type=expected_type)

    assert result.equals(expected)


@pytest.mark.parametrize(('data', 'value_type'), [
    ([True, False], pa.bool_()),
    ([None, None], pa.null()),
    ([1, 2, None], pa.int8()),
    ([1, 2., 3., None], pa.float32()),
    ([datetime.date.today(), None], pa.date32()),
    ([None, datetime.date.today()], pa.date64()),
    ([datetime.time(1, 1, 1), None], pa.time32('s')),
    ([None, datetime.time(2, 2, 2)], pa.time64('us')),
    ([datetime.datetime.now(), None], pa.timestamp('us')),
    ([datetime.timedelta(seconds=10)], pa.duration('s')),
    ([b"a", b"b"], pa.binary()),
    ([b"aaa", b"bbb", b"ccc"], pa.binary(3)),
    ([b"a", b"b", b"c"], pa.large_binary()),
    (["a", "b", "c"], pa.string()),
    (["a", "b", "c"], pa.large_string()),
    (
        [{"a": 1, "b": 2}, None, {"a": 5, "b": None}],
        pa.struct([('a', pa.int8()), ('b', pa.int16())])
    )
])
def test_list_array_from_object_ndarray(data, value_type):
    ty = pa.list_(value_type)
    ndarray = np.array(data, dtype=object)
    arr = pa.array([ndarray], type=ty)
    assert arr.type.equals(ty)
    assert arr.to_pylist() == [data]


@pytest.mark.parametrize(('data', 'value_type'), [
    ([[1, 2], [3]], pa.list_(pa.int64())),
    ([[1, 2], [3, 4]], pa.list_(pa.int64(), 2)),
    ([[1], [2, 3]], pa.large_list(pa.int64()))
])
def test_nested_list_array_from_object_ndarray(data, value_type):
    ndarray = np.empty(len(data), dtype=object)
    ndarray[:] = [np.array(item, dtype=object) for item in data]

    ty = pa.list_(value_type)
    arr = pa.array([ndarray], type=ty)
    assert arr.type.equals(ty)
    assert arr.to_pylist() == [data]


def test_array_ignore_nan_from_pandas():
    # See ARROW-4324, this reverts logic that was introduced in
    # ARROW-2240
    with pytest.raises(ValueError):
        pa.array([np.nan, 'str'])

    arr = pa.array([np.nan, 'str'], from_pandas=True)
    expected = pa.array([None, 'str'])
    assert arr.equals(expected)


def test_nested_ndarray_different_dtypes():
    data = [
        np.array([1, 2, 3], dtype='int64'),
        None,
        np.array([4, 5, 6], dtype='uint32')
    ]

    arr = pa.array(data)
    expected = pa.array([[1, 2, 3], None, [4, 5, 6]],
                        type=pa.list_(pa.int64()))
    assert arr.equals(expected)

    t2 = pa.list_(pa.uint32())
    arr2 = pa.array(data, type=t2)
    expected2 = expected.cast(t2)
    assert arr2.equals(expected2)


def test_sequence_unicode():
    data = ['foo', 'bar', None, 'mañana']
    arr = pa.array(data)
    assert len(arr) == 4
    assert arr.null_count == 1
    assert arr.type == pa.string()
    assert arr.to_pylist() == data


def check_array_mixed_unicode_bytes(binary_type, string_type):
    values = ['qux', b'foo', bytearray(b'barz')]
    b_values = [b'qux', b'foo', b'barz']
    u_values = ['qux', 'foo', 'barz']

    arr = pa.array(values)
    expected = pa.array(b_values, type=pa.binary())
    assert arr.type == pa.binary()
    assert arr.equals(expected)

    arr = pa.array(values, type=binary_type)
    expected = pa.array(b_values, type=binary_type)
    assert arr.type == binary_type
    assert arr.equals(expected)

    arr = pa.array(values, type=string_type)
    expected = pa.array(u_values, type=string_type)
    assert arr.type == string_type
    assert arr.equals(expected)


def test_array_mixed_unicode_bytes():
    check_array_mixed_unicode_bytes(pa.binary(), pa.string())
    check_array_mixed_unicode_bytes(pa.large_binary(), pa.large_string())


@pytest.mark.large_memory
@pytest.mark.parametrize("ty", [pa.large_binary(), pa.large_string()])
def test_large_binary_array(ty):
    # Construct a large binary array with more than 4GB of data
    s = b"0123456789abcdefghijklmnopqrstuvwxyz" * 10
    nrepeats = math.ceil((2**32 + 5) / len(s))
    data = [s] * nrepeats
    arr = pa.array(data, type=ty)
    assert isinstance(arr, pa.Array)
    assert arr.type == ty
    assert len(arr) == nrepeats


@pytest.mark.slow
@pytest.mark.large_memory
@pytest.mark.parametrize("ty", [pa.large_binary(), pa.large_string()])
def test_large_binary_value(ty):
    # Construct a large binary array with a single value larger than 4GB
    s = b"0123456789abcdefghijklmnopqrstuvwxyz"
    nrepeats = math.ceil((2**32 + 5) / len(s))
    arr = pa.array([b"foo", s * nrepeats, None, b"bar"], type=ty)
    assert isinstance(arr, pa.Array)
    assert arr.type == ty
    assert len(arr) == 4
    buf = arr[1].as_buffer()
    assert len(buf) == len(s) * nrepeats


@pytest.mark.large_memory
@pytest.mark.parametrize("ty", [pa.binary(), pa.string()])
def test_string_too_large(ty):
    # Construct a binary array with a single value larger than 4GB
    s = b"0123456789abcdefghijklmnopqrstuvwxyz"
    nrepeats = math.ceil((2**32 + 5) / len(s))
    with pytest.raises(pa.ArrowCapacityError):
        pa.array([b"foo", s * nrepeats, None, b"bar"], type=ty)


def test_sequence_bytes():
    u1 = b'ma\xc3\xb1ana'

    data = [b'foo',
            memoryview(b'dada'),
            memoryview(b'd-a-t-a')[::2],  # non-contiguous is made contiguous
            u1.decode('utf-8'),  # unicode gets encoded,
            bytearray(b'bar'),
            None]
    for ty in [None, pa.binary(), pa.large_binary()]:
        arr = pa.array(data, type=ty)
        assert len(arr) == 6
        assert arr.null_count == 1
        assert arr.type == ty or pa.binary()
        assert arr.to_pylist() == [b'foo', b'dada', b'data', u1, b'bar', None]


@pytest.mark.parametrize("ty", [pa.string(), pa.large_string()])
def test_sequence_utf8_to_unicode(ty):
    # ARROW-1225
    data = [b'foo', None, b'bar']
    arr = pa.array(data, type=ty)
    assert arr.type == ty
    assert arr[0].as_py() == 'foo'

    # test a non-utf8 unicode string
    val = ('mañana').encode('utf-16-le')
    with pytest.raises(pa.ArrowInvalid):
        pa.array([val], type=ty)


def test_sequence_fixed_size_bytes():
    data = [b'foof', None, bytearray(b'barb'), b'2346']
    arr = pa.array(data, type=pa.binary(4))
    assert len(arr) == 4
    assert arr.null_count == 1
    assert arr.type == pa.binary(4)
    assert arr.to_pylist() == [b'foof', None, b'barb', b'2346']


def test_fixed_size_bytes_does_not_accept_varying_lengths():
    data = [b'foo', None, b'barb', b'2346']
    with pytest.raises(pa.ArrowInvalid):
        pa.array(data, type=pa.binary(4))


def test_fixed_size_binary_length_check():
    # ARROW-10193
    data = [b'\x19h\r\x9e\x00\x00\x00\x00\x01\x9b\x9fA']
    assert len(data[0]) == 12
    ty = pa.binary(12)
    arr = pa.array(data, type=ty)
    assert arr.to_pylist() == data


def test_sequence_date():
    data = [datetime.date(2000, 1, 1), None, datetime.date(1970, 1, 1),
            datetime.date(2040, 2, 26)]
    arr = pa.array(data)
    assert len(arr) == 4
    assert arr.type == pa.date32()
    assert arr.null_count == 1
    assert arr[0].as_py() == datetime.date(2000, 1, 1)
    assert arr[1].as_py() is None
    assert arr[2].as_py() == datetime.date(1970, 1, 1)
    assert arr[3].as_py() == datetime.date(2040, 2, 26)


@pytest.mark.parametrize('input',
                         [(pa.date32(), [10957, None]),
                          (pa.date64(), [10957 * 86400000, None])])
def test_sequence_explicit_types(input):
    t, ex_values = input
    data = [datetime.date(2000, 1, 1), None]
    arr = pa.array(data, type=t)
    arr2 = pa.array(ex_values, type=t)

    for x in [arr, arr2]:
        assert len(x) == 2
        assert x.type == t
        assert x.null_count == 1
        assert x[0].as_py() == datetime.date(2000, 1, 1)
        assert x[1].as_py() is None


def test_date32_overflow():
    # Overflow
    data3 = [2**32, None]
    with pytest.raises((OverflowError, pa.ArrowException)):
        pa.array(data3, type=pa.date32())


@pytest.mark.parametrize(('time_type', 'unit', 'int_type'), [
    (pa.time32, 's', 'int32'),
    (pa.time32, 'ms', 'int32'),
    (pa.time64, 'us', 'int64'),
    (pa.time64, 'ns', 'int64'),
])
def test_sequence_time_with_timezone(time_type, unit, int_type):
    def expected_integer_value(t):
        # only use with utc time object because it doesn't adjust with the
        # offset
        units = ['s', 'ms', 'us', 'ns']
        multiplier = 10**(units.index(unit) * 3)
        if t is None:
            return None
        seconds = (
            t.hour * 3600 +
            t.minute * 60 +
            t.second +
            t.microsecond * 10**-6
        )
        return int(seconds * multiplier)

    def expected_time_value(t):
        # only use with utc time object because it doesn't adjust with the
        # time objects tzdata
        if unit == 's':
            return t.replace(microsecond=0)
        elif unit == 'ms':
            return t.replace(microsecond=(t.microsecond // 1000) * 1000)
        else:
            return t

    # only timezone naive times are supported in arrow
    data = [
        datetime.time(8, 23, 34, 123456),
        datetime.time(5, 0, 0, 1000),
        None,
        datetime.time(1, 11, 56, 432539),
        datetime.time(23, 10, 0, 437699)
    ]

    ty = time_type(unit)
    arr = pa.array(data, type=ty)
    assert len(arr) == 5
    assert arr.type == ty
    assert arr.null_count == 1

    # test that the underlying integers are UTC values
    values = arr.cast(int_type)
    expected = list(map(expected_integer_value, data))
    assert values.to_pylist() == expected

    # test that the scalars are datetime.time objects with UTC timezone
    assert arr[0].as_py() == expected_time_value(data[0])
    assert arr[1].as_py() == expected_time_value(data[1])
    assert arr[2].as_py() is None
    assert arr[3].as_py() == expected_time_value(data[3])
    assert arr[4].as_py() == expected_time_value(data[4])

    def tz(hours, minutes=0):
        offset = datetime.timedelta(hours=hours, minutes=minutes)
        return datetime.timezone(offset)


def test_sequence_timestamp():
    data = [
        datetime.datetime(2007, 7, 13, 1, 23, 34, 123456),
        None,
        datetime.datetime(2006, 1, 13, 12, 34, 56, 432539),
        datetime.datetime(2010, 8, 13, 5, 46, 57, 437699)
    ]
    arr = pa.array(data)
    assert len(arr) == 4
    assert arr.type == pa.timestamp('us')
    assert arr.null_count == 1
    assert arr[0].as_py() == datetime.datetime(2007, 7, 13, 1,
                                               23, 34, 123456)
    assert arr[1].as_py() is None
    assert arr[2].as_py() == datetime.datetime(2006, 1, 13, 12,
                                               34, 56, 432539)
    assert arr[3].as_py() == datetime.datetime(2010, 8, 13, 5,
                                               46, 57, 437699)


@pytest.mark.parametrize('timezone', [
    None,
    'UTC',
    'Etc/GMT-1',
    'Europe/Budapest',
])
@pytest.mark.parametrize('unit', [
    's',
    'ms',
    'us',
    'ns'
])
def test_sequence_timestamp_with_timezone(timezone, unit):
    pytz = pytest.importorskip("pytz")

    def expected_integer_value(dt):
        units = ['s', 'ms', 'us', 'ns']
        multiplier = 10**(units.index(unit) * 3)
        if dt is None:
            return None
        else:
            # avoid float precision issues
            ts = decimal.Decimal(str(dt.timestamp()))
            return int(ts * multiplier)

    def expected_datetime_value(dt):
        if dt is None:
            return None

        if unit == 's':
            dt = dt.replace(microsecond=0)
        elif unit == 'ms':
            dt = dt.replace(microsecond=(dt.microsecond // 1000) * 1000)

        # adjust the timezone
        if timezone is None:
            # make datetime timezone unaware
            return dt.replace(tzinfo=None)
        else:
            # convert to the expected timezone
            return dt.astimezone(pytz.timezone(timezone))

    data = [
        datetime.datetime(2007, 7, 13, 8, 23, 34, 123456),  # naive
        pytz.utc.localize(
            datetime.datetime(2008, 1, 5, 5, 0, 0, 1000)
        ),
        None,
        pytz.timezone('US/Eastern').localize(
            datetime.datetime(2006, 1, 13, 12, 34, 56, 432539)
        ),
        pytz.timezone('Europe/Moscow').localize(
            datetime.datetime(2010, 8, 13, 5, 0, 0, 437699)
        ),
    ]
    utcdata = [
        pytz.utc.localize(data[0]),
        data[1],
        None,
        data[3].astimezone(pytz.utc),
        data[4].astimezone(pytz.utc),
    ]

    ty = pa.timestamp(unit, tz=timezone)
    arr = pa.array(data, type=ty)
    assert len(arr) == 5
    assert arr.type == ty
    assert arr.null_count == 1

    # test that the underlying integers are UTC values
    values = arr.cast('int64')
    expected = list(map(expected_integer_value, utcdata))
    assert values.to_pylist() == expected

    # test that the scalars are datetimes with the correct timezone
    for i in range(len(arr)):
        assert arr[i].as_py() == expected_datetime_value(utcdata[i])


@pytest.mark.parametrize('timezone', [
    None,
    'UTC',
    'Etc/GMT-1',
    'Europe/Budapest',
])
def test_pyarrow_ignore_timezone_environment_variable(monkeypatch, timezone):
    # note that any non-empty value will evaluate to true
    pytest.importorskip("pytz")
    import pytz

    monkeypatch.setenv("PYARROW_IGNORE_TIMEZONE", "1")
    data = [
        datetime.datetime(2007, 7, 13, 8, 23, 34, 123456),  # naive
        pytz.utc.localize(
            datetime.datetime(2008, 1, 5, 5, 0, 0, 1000)
        ),
        pytz.timezone('US/Eastern').localize(
            datetime.datetime(2006, 1, 13, 12, 34, 56, 432539)
        ),
        pytz.timezone('Europe/Moscow').localize(
            datetime.datetime(2010, 8, 13, 5, 0, 0, 437699)
        ),
    ]

    expected = [dt.replace(tzinfo=None) for dt in data]
    if timezone is not None:
        tzinfo = pytz.timezone(timezone)
        expected = [tzinfo.fromutc(dt) for dt in expected]

    ty = pa.timestamp('us', tz=timezone)
    arr = pa.array(data, type=ty)
    assert arr.to_pylist() == expected


def test_sequence_timestamp_with_timezone_inference():
    pytest.importorskip("pytz")
    import pytz

    data = [
        datetime.datetime(2007, 7, 13, 8, 23, 34, 123456),  # naive
        pytz.utc.localize(
            datetime.datetime(2008, 1, 5, 5, 0, 0, 1000)
        ),
        None,
        pytz.timezone('US/Eastern').localize(
            datetime.datetime(2006, 1, 13, 12, 34, 56, 432539)
        ),
        pytz.timezone('Europe/Moscow').localize(
            datetime.datetime(2010, 8, 13, 5, 0, 0, 437699)
        ),
    ]
    expected = [
        pa.timestamp('us', tz=None),
        pa.timestamp('us', tz='UTC'),
        pa.timestamp('us', tz=None),
        pa.timestamp('us', tz='US/Eastern'),
        pa.timestamp('us', tz='Europe/Moscow')
    ]
    for dt, expected_type in zip(data, expected):
        prepended = [dt] + data
        arr = pa.array(prepended)
        assert arr.type == expected_type


@pytest.mark.pandas
def test_sequence_timestamp_from_mixed_builtin_and_pandas_datetimes():
    pytest.importorskip("pytz")
    import pytz
    import pandas as pd

    data = [
        pd.Timestamp(1184307814123456123, tz=pytz.timezone('US/Eastern'),
                     unit='ns'),
        datetime.datetime(2007, 7, 13, 8, 23, 34, 123456),  # naive
        pytz.utc.localize(
            datetime.datetime(2008, 1, 5, 5, 0, 0, 1000)
        ),
        None,
    ]
    utcdata = [
        data[0].astimezone(pytz.utc),
        pytz.utc.localize(data[1]),
        data[2].astimezone(pytz.utc),
        None,
    ]

    arr = pa.array(data)
    assert arr.type == pa.timestamp('us', tz='US/Eastern')

    values = arr.cast('int64')
    expected = [int(dt.timestamp() * 10**6) if dt else None for dt in utcdata]
    assert values.to_pylist() == expected


def test_sequence_timestamp_out_of_bounds_nanosecond():
    # https://issues.apache.org/jira/browse/ARROW-9768
    # datetime outside of range supported for nanosecond resolution
    data = [datetime.datetime(2262, 4, 12)]
    with pytest.raises(ValueError, match="out of bounds"):
        pa.array(data, type=pa.timestamp('ns'))

    # with microsecond resolution it works fine
    arr = pa.array(data, type=pa.timestamp('us'))
    assert arr.to_pylist() == data

    # case where the naive is within bounds, but converted to UTC not
    tz = datetime.timezone(datetime.timedelta(hours=-1))
    data = [datetime.datetime(2262, 4, 11, 23, tzinfo=tz)]
    with pytest.raises(ValueError, match="out of bounds"):
        pa.array(data, type=pa.timestamp('ns'))

    arr = pa.array(data, type=pa.timestamp('us'))
    assert arr.to_pylist()[0] == datetime.datetime(2262, 4, 12)


def test_sequence_numpy_timestamp():
    data = [
        np.datetime64(datetime.datetime(2007, 7, 13, 1, 23, 34, 123456)),
        None,
        np.datetime64(datetime.datetime(2006, 1, 13, 12, 34, 56, 432539)),
        np.datetime64(datetime.datetime(2010, 8, 13, 5, 46, 57, 437699))
    ]
    arr = pa.array(data)
    assert len(arr) == 4
    assert arr.type == pa.timestamp('us')
    assert arr.null_count == 1
    assert arr[0].as_py() == datetime.datetime(2007, 7, 13, 1,
                                               23, 34, 123456)
    assert arr[1].as_py() is None
    assert arr[2].as_py() == datetime.datetime(2006, 1, 13, 12,
                                               34, 56, 432539)
    assert arr[3].as_py() == datetime.datetime(2010, 8, 13, 5,
                                               46, 57, 437699)


class MyDate(datetime.date):
    pass


class MyDatetime(datetime.datetime):
    pass


class MyTimedelta(datetime.timedelta):
    pass


def test_datetime_subclassing():
    data = [
        MyDate(2007, 7, 13),
    ]
    date_type = pa.date32()
    arr_date = pa.array(data, type=date_type)
    assert len(arr_date) == 1
    assert arr_date.type == date_type
    assert arr_date[0].as_py() == datetime.date(2007, 7, 13)

    data = [
        MyDatetime(2007, 7, 13, 1, 23, 34, 123456),
    ]

    s = pa.timestamp('s')
    ms = pa.timestamp('ms')
    us = pa.timestamp('us')

    arr_s = pa.array(data, type=s)
    assert len(arr_s) == 1
    assert arr_s.type == s
    assert arr_s[0].as_py() == datetime.datetime(2007, 7, 13, 1,
                                                 23, 34, 0)

    arr_ms = pa.array(data, type=ms)
    assert len(arr_ms) == 1
    assert arr_ms.type == ms
    assert arr_ms[0].as_py() == datetime.datetime(2007, 7, 13, 1,
                                                  23, 34, 123000)

    arr_us = pa.array(data, type=us)
    assert len(arr_us) == 1
    assert arr_us.type == us
    assert arr_us[0].as_py() == datetime.datetime(2007, 7, 13, 1,
                                                  23, 34, 123456)

    data = [
        MyTimedelta(123, 456, 1002),
    ]

    s = pa.duration('s')
    ms = pa.duration('ms')
    us = pa.duration('us')

    arr_s = pa.array(data)
    assert len(arr_s) == 1
    assert arr_s.type == us
    assert arr_s[0].as_py() == datetime.timedelta(123, 456, 1002)

    arr_s = pa.array(data, type=s)
    assert len(arr_s) == 1
    assert arr_s.type == s
    assert arr_s[0].as_py() == datetime.timedelta(123, 456)

    arr_ms = pa.array(data, type=ms)
    assert len(arr_ms) == 1
    assert arr_ms.type == ms
    assert arr_ms[0].as_py() == datetime.timedelta(123, 456, 1000)

    arr_us = pa.array(data, type=us)
    assert len(arr_us) == 1
    assert arr_us.type == us
    assert arr_us[0].as_py() == datetime.timedelta(123, 456, 1002)


@pytest.mark.xfail(not _pandas_api.have_pandas,
                   reason="pandas required for nanosecond conversion")
def test_sequence_timestamp_nanoseconds():
    inputs = [
        [datetime.datetime(2007, 7, 13, 1, 23, 34, 123456)],
        [MyDatetime(2007, 7, 13, 1, 23, 34, 123456)]
    ]

    for data in inputs:
        ns = pa.timestamp('ns')
        arr_ns = pa.array(data, type=ns)
        assert len(arr_ns) == 1
        assert arr_ns.type == ns
        assert arr_ns[0].as_py() == datetime.datetime(2007, 7, 13, 1,
                                                      23, 34, 123456)


@pytest.mark.pandas
def test_sequence_timestamp_from_int_with_unit():
    # TODO(wesm): This test might be rewritten to assert the actual behavior
    # when pandas is not installed

    data = [1]

    s = pa.timestamp('s')
    ms = pa.timestamp('ms')
    us = pa.timestamp('us')
    ns = pa.timestamp('ns')

    arr_s = pa.array(data, type=s)
    assert len(arr_s) == 1
    assert arr_s.type == s
    assert repr(arr_s[0]) == (
        "<pyarrow.TimestampScalar: datetime.datetime(1970, 1, 1, 0, 0, 1)>"
    )
    assert str(arr_s[0]) == "1970-01-01 00:00:01"

    arr_ms = pa.array(data, type=ms)
    assert len(arr_ms) == 1
    assert arr_ms.type == ms
    assert repr(arr_ms[0].as_py()) == (
        "datetime.datetime(1970, 1, 1, 0, 0, 0, 1000)"
    )
    assert str(arr_ms[0]) == "1970-01-01 00:00:00.001000"

    arr_us = pa.array(data, type=us)
    assert len(arr_us) == 1
    assert arr_us.type == us
    assert repr(arr_us[0].as_py()) == (
        "datetime.datetime(1970, 1, 1, 0, 0, 0, 1)"
    )
    assert str(arr_us[0]) == "1970-01-01 00:00:00.000001"

    arr_ns = pa.array(data, type=ns)
    assert len(arr_ns) == 1
    assert arr_ns.type == ns
    assert repr(arr_ns[0].as_py()) == (
        "Timestamp('1970-01-01 00:00:00.000000001')"
    )
    assert str(arr_ns[0]) == "1970-01-01 00:00:00.000000001"

    expected_exc = TypeError

    class CustomClass():
        pass

    for ty in [ns, pa.date32(), pa.date64()]:
        with pytest.raises(expected_exc):
            pa.array([1, CustomClass()], type=ty)


@pytest.mark.parametrize('np_scalar', [True, False])
def test_sequence_duration(np_scalar):
    td1 = datetime.timedelta(2, 3601, 1)
    td2 = datetime.timedelta(1, 100, 1000)
    if np_scalar:
        data = [np.timedelta64(td1), None, np.timedelta64(td2)]
    else:
        data = [td1, None, td2]

    arr = pa.array(data)
    assert len(arr) == 3
    assert arr.type == pa.duration('us')
    assert arr.null_count == 1
    assert arr[0].as_py() == td1
    assert arr[1].as_py() is None
    assert arr[2].as_py() == td2


@pytest.mark.parametrize('unit', ['s', 'ms', 'us', 'ns'])
def test_sequence_duration_with_unit(unit):
    data = [
        datetime.timedelta(3, 22, 1001),
    ]
    expected = {'s': datetime.timedelta(3, 22),
                'ms': datetime.timedelta(3, 22, 1000),
                'us': datetime.timedelta(3, 22, 1001),
                'ns': datetime.timedelta(3, 22, 1001)}

    ty = pa.duration(unit)

    arr_s = pa.array(data, type=ty)
    assert len(arr_s) == 1
    assert arr_s.type == ty
    assert arr_s[0].as_py() == expected[unit]


@pytest.mark.parametrize('unit', ['s', 'ms', 'us', 'ns'])
def test_sequence_duration_from_int_with_unit(unit):
    data = [5]

    ty = pa.duration(unit)
    arr = pa.array(data, type=ty)
    assert len(arr) == 1
    assert arr.type == ty
    assert arr[0].value == 5


def test_sequence_duration_nested_lists():
    td1 = datetime.timedelta(1, 1, 1000)
    td2 = datetime.timedelta(1, 100)

    data = [[td1, None], [td1, td2]]

    arr = pa.array(data)
    assert len(arr) == 2
    assert arr.type == pa.list_(pa.duration('us'))
    assert arr.to_pylist() == data

    arr = pa.array(data, type=pa.list_(pa.duration('ms')))
    assert len(arr) == 2
    assert arr.type == pa.list_(pa.duration('ms'))
    assert arr.to_pylist() == data


def test_sequence_duration_nested_lists_numpy():
    td1 = datetime.timedelta(1, 1, 1000)
    td2 = datetime.timedelta(1, 100)

    data = [[np.timedelta64(td1), None],
            [np.timedelta64(td1), np.timedelta64(td2)]]

    arr = pa.array(data)
    assert len(arr) == 2
    assert arr.type == pa.list_(pa.duration('us'))
    assert arr.to_pylist() == [[td1, None], [td1, td2]]

    data = [np.array([np.timedelta64(td1), None], dtype='timedelta64[us]'),
            np.array([np.timedelta64(td1), np.timedelta64(td2)])]

    arr = pa.array(data)
    assert len(arr) == 2
    assert arr.type == pa.list_(pa.duration('us'))
    assert arr.to_pylist() == [[td1, None], [td1, td2]]


def test_sequence_nesting_levels():
    data = [1, 2, None]
    arr = pa.array(data)
    assert arr.type == pa.int64()
    assert arr.to_pylist() == data

    data = [[1], [2], None]
    arr = pa.array(data)
    assert arr.type == pa.list_(pa.int64())
    assert arr.to_pylist() == data

    data = [[1], [2, 3, 4], [None]]
    arr = pa.array(data)
    assert arr.type == pa.list_(pa.int64())
    assert arr.to_pylist() == data

    data = [None, [[None, 1]], [[2, 3, 4], None], [None]]
    arr = pa.array(data)
    assert arr.type == pa.list_(pa.list_(pa.int64()))
    assert arr.to_pylist() == data

    exceptions = (pa.ArrowInvalid, pa.ArrowTypeError)

    # Mixed nesting levels are rejected
    with pytest.raises(exceptions):
        pa.array([1, 2, [1]])

    with pytest.raises(exceptions):
        pa.array([1, 2, []])

    with pytest.raises(exceptions):
        pa.array([[1], [2], [None, [1]]])


def test_sequence_mixed_types_fails():
    data = ['a', 1, 2.0]
    with pytest.raises(pa.ArrowTypeError):
        pa.array(data)


def test_sequence_mixed_types_with_specified_type_fails():
    data = ['-10', '-5', {'a': 1}, '0', '5', '10']

    type = pa.string()
    with pytest.raises(TypeError):
        pa.array(data, type=type)


def test_sequence_decimal():
    data = [decimal.Decimal('1234.183'), decimal.Decimal('8094.234')]
    for type in [pa.decimal128, pa.decimal256]:
        arr = pa.array(data, type=type(precision=7, scale=3))
        assert arr.to_pylist() == data


def test_sequence_decimal_different_precisions():
    data = [
        decimal.Decimal('1234234983.183'), decimal.Decimal('80943244.234')
    ]
    for type in [pa.decimal128, pa.decimal256]:
        arr = pa.array(data, type=type(precision=13, scale=3))
        assert arr.to_pylist() == data


def test_sequence_decimal_no_scale():
    data = [decimal.Decimal('1234234983'), decimal.Decimal('8094324')]
    for type in [pa.decimal128, pa.decimal256]:
        arr = pa.array(data, type=type(precision=10))
        assert arr.to_pylist() == data


def test_sequence_decimal_negative():
    data = [decimal.Decimal('-1234.234983'), decimal.Decimal('-8.094324')]
    for type in [pa.decimal128, pa.decimal256]:
        arr = pa.array(data, type=type(precision=10, scale=6))
        assert arr.to_pylist() == data


def test_sequence_decimal_no_whole_part():
    data = [decimal.Decimal('-.4234983'), decimal.Decimal('.0103943')]
    for type in [pa.decimal128, pa.decimal256]:
        arr = pa.array(data, type=type(precision=7, scale=7))
        assert arr.to_pylist() == data


def test_sequence_decimal_large_integer():
    data = [decimal.Decimal('-394029506937548693.42983'),
            decimal.Decimal('32358695912932.01033')]
    for type in [pa.decimal128, pa.decimal256]:
        arr = pa.array(data, type=type(precision=23, scale=5))
        assert arr.to_pylist() == data


def test_sequence_decimal_from_integers():
    data = [0, 1, -39402950693754869342983]
    expected = [decimal.Decimal(x) for x in data]
    for type in [pa.decimal128, pa.decimal256]:
        arr = pa.array(data, type=type(precision=28, scale=5))
        assert arr.to_pylist() == expected


def test_sequence_decimal_too_high_precision():
    # ARROW-6989 python decimal has too high precision
    with pytest.raises(ValueError, match="precision out of range"):
        pa.array([decimal.Decimal('1' * 80)])


def test_sequence_decimal_infer():
    for data, typ in [
        # simple case
        (decimal.Decimal('1.234'), pa.decimal128(4, 3)),
        # trailing zeros
        (decimal.Decimal('12300'), pa.decimal128(5, 0)),
        (decimal.Decimal('12300.0'), pa.decimal128(6, 1)),
        # scientific power notation
        (decimal.Decimal('1.23E+4'), pa.decimal128(5, 0)),
        (decimal.Decimal('123E+2'), pa.decimal128(5, 0)),
        (decimal.Decimal('123E+4'), pa.decimal128(7, 0)),
        # leading zeros
        (decimal.Decimal('0.0123'), pa.decimal128(4, 4)),
        (decimal.Decimal('0.01230'), pa.decimal128(5, 5)),
        (decimal.Decimal('1.230E-2'), pa.decimal128(5, 5)),
    ]:
        assert pa.infer_type([data]) == typ
        arr = pa.array([data])
        assert arr.type == typ
        assert arr.to_pylist()[0] == data


def test_sequence_decimal_infer_mixed():
    # ARROW-12150 - ensure mixed precision gets correctly inferred to
    # common type that can hold all input values
    cases = [
        ([decimal.Decimal('1.234'), decimal.Decimal('3.456')],
         pa.decimal128(4, 3)),
        ([decimal.Decimal('1.234'), decimal.Decimal('456.7')],
         pa.decimal128(6, 3)),
        ([decimal.Decimal('123.4'), decimal.Decimal('4.567')],
         pa.decimal128(6, 3)),
        ([decimal.Decimal('123e2'), decimal.Decimal('4567e3')],
         pa.decimal128(7, 0)),
        ([decimal.Decimal('123e4'), decimal.Decimal('4567e2')],
         pa.decimal128(7, 0)),
        ([decimal.Decimal('0.123'), decimal.Decimal('0.04567')],
         pa.decimal128(5, 5)),
        ([decimal.Decimal('0.001'), decimal.Decimal('1.01E5')],
         pa.decimal128(9, 3)),
    ]
    for data, typ in cases:
        assert pa.infer_type(data) == typ
        arr = pa.array(data)
        assert arr.type == typ
        assert arr.to_pylist() == data


def test_sequence_decimal_given_type():
    for data, typs, wrong_typs in [
        # simple case
        (
            decimal.Decimal('1.234'),
            [pa.decimal128(4, 3), pa.decimal128(5, 3), pa.decimal128(5, 4)],
            [pa.decimal128(4, 2), pa.decimal128(4, 4)]
        ),
        # trailing zeros
        (
            decimal.Decimal('12300'),
            [pa.decimal128(5, 0), pa.decimal128(6, 0), pa.decimal128(3, -2)],
            [pa.decimal128(4, 0), pa.decimal128(3, -3)]
        ),
        # scientific power notation
        (
            decimal.Decimal('1.23E+4'),
            [pa.decimal128(5, 0), pa.decimal128(6, 0), pa.decimal128(3, -2)],
            [pa.decimal128(4, 0), pa.decimal128(3, -3)]
        ),
    ]:
        for typ in typs:
            arr = pa.array([data], type=typ)
            assert arr.type == typ
            assert arr.to_pylist()[0] == data
        for typ in wrong_typs:
            with pytest.raises(ValueError):
                pa.array([data], type=typ)


def test_range_types():
    arr1 = pa.array(range(3))
    arr2 = pa.array((0, 1, 2))
    assert arr1.equals(arr2)


def test_empty_range():
    arr = pa.array(range(0))
    assert len(arr) == 0
    assert arr.null_count == 0
    assert arr.type == pa.null()
    assert arr.to_pylist() == []


def test_structarray():
    arr = pa.StructArray.from_arrays([], names=[])
    assert arr.type == pa.struct([])
    assert len(arr) == 0
    assert arr.to_pylist() == []

    ints = pa.array([None, 2, 3], type=pa.int64())
    strs = pa.array(['a', None, 'c'], type=pa.string())
    bools = pa.array([True, False, None], type=pa.bool_())
    arr = pa.StructArray.from_arrays(
        [ints, strs, bools],
        ['ints', 'strs', 'bools'])

    expected = [
        {'ints': None, 'strs': 'a', 'bools': True},
        {'ints': 2, 'strs': None, 'bools': False},
        {'ints': 3, 'strs': 'c', 'bools': None},
    ]

    pylist = arr.to_pylist()
    assert pylist == expected, (pylist, expected)

    # len(names) != len(arrays)
    with pytest.raises(ValueError):
        pa.StructArray.from_arrays([ints], ['ints', 'strs'])


def test_struct_from_dicts():
    ty = pa.struct([pa.field('a', pa.int32()),
                    pa.field('b', pa.string()),
                    pa.field('c', pa.bool_())])
    arr = pa.array([], type=ty)
    assert arr.to_pylist() == []

    data = [{'a': 5, 'b': 'foo', 'c': True},
            {'a': 6, 'b': 'bar', 'c': False}]
    arr = pa.array(data, type=ty)
    assert arr.to_pylist() == data

    # With omitted values
    data = [{'a': 5, 'c': True},
            None,
            {},
            {'a': None, 'b': 'bar'}]
    arr = pa.array(data, type=ty)
    expected = [{'a': 5, 'b': None, 'c': True},
                None,
                {'a': None, 'b': None, 'c': None},
                {'a': None, 'b': 'bar', 'c': None}]
    assert arr.to_pylist() == expected


def test_struct_from_dicts_bytes_keys():
    # ARROW-6878
    ty = pa.struct([pa.field('a', pa.int32()),
                    pa.field('b', pa.string()),
                    pa.field('c', pa.bool_())])
    arr = pa.array([], type=ty)
    assert arr.to_pylist() == []

    data = [{b'a': 5, b'b': 'foo'},
            {b'a': 6, b'c': False}]
    arr = pa.array(data, type=ty)
    assert arr.to_pylist() == [
        {'a': 5, 'b': 'foo', 'c': None},
        {'a': 6, 'b': None, 'c': False},
    ]


def test_struct_from_tuples():
    ty = pa.struct([pa.field('a', pa.int32()),
                    pa.field('b', pa.string()),
                    pa.field('c', pa.bool_())])

    data = [(5, 'foo', True),
            (6, 'bar', False)]
    expected = [{'a': 5, 'b': 'foo', 'c': True},
                {'a': 6, 'b': 'bar', 'c': False}]
    arr = pa.array(data, type=ty)

    data_as_ndarray = np.empty(len(data), dtype=object)
    data_as_ndarray[:] = data
    arr2 = pa.array(data_as_ndarray, type=ty)
    assert arr.to_pylist() == expected

    assert arr.equals(arr2)

    # With omitted values
    data = [(5, 'foo', None),
            None,
            (6, None, False)]
    expected = [{'a': 5, 'b': 'foo', 'c': None},
                None,
                {'a': 6, 'b': None, 'c': False}]
    arr = pa.array(data, type=ty)
    assert arr.to_pylist() == expected

    # Invalid tuple size
    for tup in [(5, 'foo'), (), ('5', 'foo', True, None)]:
        with pytest.raises(ValueError, match="(?i)tuple size"):
            pa.array([tup], type=ty)


def test_struct_from_list_of_pairs():
    ty = pa.struct([
        pa.field('a', pa.int32()),
        pa.field('b', pa.string()),
        pa.field('c', pa.bool_())
    ])
    data = [
        [('a', 5), ('b', 'foo'), ('c', True)],
        [('a', 6), ('b', 'bar'), ('c', False)],
        None
    ]
    arr = pa.array(data, type=ty)
    assert arr.to_pylist() == [
        {'a': 5, 'b': 'foo', 'c': True},
        {'a': 6, 'b': 'bar', 'c': False},
        None
    ]

    # test with duplicated field names
    ty = pa.struct([
        pa.field('a', pa.int32()),
        pa.field('a', pa.string()),
        pa.field('b', pa.bool_())
    ])
    data = [
        [('a', 5), ('a', 'foo'), ('b', True)],
        [('a', 6), ('a', 'bar'), ('b', False)],
    ]
    arr = pa.array(data, type=ty)
    with pytest.raises(ValueError):
        # TODO(kszucs): ARROW-9997
        arr.to_pylist()

    # test with empty elements
    ty = pa.struct([
        pa.field('a', pa.int32()),
        pa.field('b', pa.string()),
        pa.field('c', pa.bool_())
    ])
    data = [
        [],
        [('a', 5), ('b', 'foo'), ('c', True)],
        [('a', 2), ('b', 'baz')],
        [('a', 1), ('b', 'bar'), ('c', False), ('d', 'julia')],
    ]
    expected = [
        {'a': None, 'b': None, 'c': None},
        {'a': 5, 'b': 'foo', 'c': True},
        {'a': 2, 'b': 'baz', 'c': None},
        {'a': 1, 'b': 'bar', 'c': False},
    ]
    arr = pa.array(data, type=ty)
    assert arr.to_pylist() == expected


def test_struct_from_list_of_pairs_errors():
    ty = pa.struct([
        pa.field('a', pa.int32()),
        pa.field('b', pa.string()),
        pa.field('c', pa.bool_())
    ])

    # test that it raises if the key doesn't match the expected field name
    data = [
        [],
        [('a', 5), ('c', True), ('b', None)],
    ]
    msg = "The expected field name is `b` but `c` was given"
    with pytest.raises(ValueError, match=msg):
        pa.array(data, type=ty)

    # test various errors both at the first position and after because of key
    # type inference
    template = (
        r"Could not convert {} with type {}: was expecting tuple of "
        r"(key, value) pair"
    )
    cases = [
        tuple(),  # empty key-value pair
        tuple('a',),  # missing value
        tuple('unknown-key',),  # not known field name
        'string',  # not a tuple
    ]
    for key_value_pair in cases:
        msg = re.escape(template.format(
            repr(key_value_pair), type(key_value_pair).__name__
        ))

        with pytest.raises(TypeError, match=msg):
            pa.array([
                [key_value_pair],
                [('a', 5), ('b', 'foo'), ('c', None)],
            ], type=ty)

        with pytest.raises(TypeError, match=msg):
            pa.array([
                [('a', 5), ('b', 'foo'), ('c', None)],
                [key_value_pair],
            ], type=ty)


def test_struct_from_mixed_sequence():
    # It is forbidden to mix dicts and tuples when initializing a struct array
    ty = pa.struct([pa.field('a', pa.int32()),
                    pa.field('b', pa.string()),
                    pa.field('c', pa.bool_())])
    data = [(5, 'foo', True),
            {'a': 6, 'b': 'bar', 'c': False}]
    with pytest.raises(TypeError):
        pa.array(data, type=ty)


def test_struct_from_dicts_inference():
    expected_type = pa.struct([pa.field('a', pa.int64()),
                               pa.field('b', pa.string()),
                               pa.field('c', pa.bool_())])
    data = [{'a': 5, 'b': 'foo', 'c': True},
            {'a': 6, 'b': 'bar', 'c': False}]

    arr = pa.array(data)
    check_struct_type(arr.type, expected_type)
    assert arr.to_pylist() == data

    # With omitted values
    data = [{'a': 5, 'c': True},
            None,
            {},
            {'a': None, 'b': 'bar'}]
    expected = [{'a': 5, 'b': None, 'c': True},
                None,
                {'a': None, 'b': None, 'c': None},
                {'a': None, 'b': 'bar', 'c': None}]

    arr = pa.array(data)
    data_as_ndarray = np.empty(len(data), dtype=object)
    data_as_ndarray[:] = data
    arr2 = pa.array(data)

    check_struct_type(arr.type, expected_type)
    assert arr.to_pylist() == expected
    assert arr.equals(arr2)

    # Nested
    expected_type = pa.struct([
        pa.field('a', pa.struct([pa.field('aa', pa.list_(pa.int64())),
                                 pa.field('ab', pa.bool_())])),
        pa.field('b', pa.string())])
    data = [{'a': {'aa': [5, 6], 'ab': True}, 'b': 'foo'},
            {'a': {'aa': None, 'ab': False}, 'b': None},
            {'a': None, 'b': 'bar'}]
    arr = pa.array(data)

    assert arr.to_pylist() == data

    # Edge cases
    arr = pa.array([{}])
    assert arr.type == pa.struct([])
    assert arr.to_pylist() == [{}]

    # Mixing structs and scalars is rejected
    with pytest.raises((pa.ArrowInvalid, pa.ArrowTypeError)):
        pa.array([1, {'a': 2}])


def test_structarray_from_arrays_coerce():
    # ARROW-1706
    ints = [None, 2, 3]
    strs = ['a', None, 'c']
    bools = [True, False, None]
    ints_nonnull = [1, 2, 3]

    arrays = [ints, strs, bools, ints_nonnull]
    result = pa.StructArray.from_arrays(arrays,
                                        ['ints', 'strs', 'bools',
                                         'int_nonnull'])
    expected = pa.StructArray.from_arrays(
        [pa.array(ints, type='int64'),
         pa.array(strs, type='utf8'),
         pa.array(bools),
         pa.array(ints_nonnull, type='int64')],
        ['ints', 'strs', 'bools', 'int_nonnull'])

    with pytest.raises(ValueError):
        pa.StructArray.from_arrays(arrays)

    assert result.equals(expected)


def test_decimal_array_with_none_and_nan():
    values = [decimal.Decimal('1.234'), None, np.nan, decimal.Decimal('nan')]

    with pytest.raises(TypeError):
        # ARROW-6227: Without from_pandas=True, NaN is considered a float
        array = pa.array(values)

    array = pa.array(values, from_pandas=True)
    assert array.type == pa.decimal128(4, 3)
    assert array.to_pylist() == values[:2] + [None, None]

    array = pa.array(values, type=pa.decimal128(10, 4), from_pandas=True)
    assert array.to_pylist() == [decimal.Decimal('1.2340'), None, None, None]


def test_map_from_dicts():
    data = [[{'key': b'a', 'value': 1}, {'key': b'b', 'value': 2}],
            [{'key': b'c', 'value': 3}],
            [{'key': b'd', 'value': 4}, {'key': b'e', 'value': 5},
             {'key': b'f', 'value': None}],
            [{'key': b'g', 'value': 7}]]
    expected = [[(d['key'], d['value']) for d in entry] for entry in data]

    arr = pa.array(expected, type=pa.map_(pa.binary(), pa.int32()))

    assert arr.to_pylist() == expected

    # With omitted values
    data[1] = None
    expected[1] = None

    arr = pa.array(expected, type=pa.map_(pa.binary(), pa.int32()))

    assert arr.to_pylist() == expected

    # Invalid dictionary
    for entry in [[{'value': 5}], [{}], [{'k': 1, 'v': 2}]]:
        with pytest.raises(ValueError, match="Invalid Map"):
            pa.array([entry], type=pa.map_('i4', 'i4'))

    # Invalid dictionary types
    for entry in [[{'key': '1', 'value': 5}], [{'key': {'value': 2}}]]:
        with pytest.raises(pa.ArrowInvalid, match="tried to convert to int"):
            pa.array([entry], type=pa.map_('i4', 'i4'))


def test_map_from_tuples():
    expected = [[(b'a', 1), (b'b', 2)],
                [(b'c', 3)],
                [(b'd', 4), (b'e', 5), (b'f', None)],
                [(b'g', 7)]]

    arr = pa.array(expected, type=pa.map_(pa.binary(), pa.int32()))

    assert arr.to_pylist() == expected

    # With omitted values
    expected[1] = None

    arr = pa.array(expected, type=pa.map_(pa.binary(), pa.int32()))

    assert arr.to_pylist() == expected

    # Invalid tuple size
    for entry in [[(5,)], [()], [('5', 'foo', True)]]:
        with pytest.raises(ValueError, match="(?i)tuple size"):
            pa.array([entry], type=pa.map_('i4', 'i4'))


def test_dictionary_from_boolean():
    typ = pa.dictionary(pa.int8(), value_type=pa.bool_())
    a = pa.array([False, False, True, False, True], type=typ)
    assert isinstance(a.type, pa.DictionaryType)
    assert a.type.equals(typ)

    expected_indices = pa.array([0, 0, 1, 0, 1], type=pa.int8())
    expected_dictionary = pa.array([False, True], type=pa.bool_())
    assert a.indices.equals(expected_indices)
    assert a.dictionary.equals(expected_dictionary)


@pytest.mark.parametrize('value_type', [
    pa.int8(),
    pa.int16(),
    pa.int32(),
    pa.int64(),
    pa.uint8(),
    pa.uint16(),
    pa.uint32(),
    pa.uint64(),
    pa.float32(),
    pa.float64(),
])
def test_dictionary_from_integers(value_type):
    typ = pa.dictionary(pa.int8(), value_type=value_type)
    a = pa.array([1, 2, 1, 1, 2, 3], type=typ)
    assert isinstance(a.type, pa.DictionaryType)
    assert a.type.equals(typ)

    expected_indices = pa.array([0, 1, 0, 0, 1, 2], type=pa.int8())
    expected_dictionary = pa.array([1, 2, 3], type=value_type)
    assert a.indices.equals(expected_indices)
    assert a.dictionary.equals(expected_dictionary)


@pytest.mark.parametrize('input_index_type', [
    pa.int8(),
    pa.int16(),
    pa.int32(),
    pa.int64()
])
def test_dictionary_index_type(input_index_type):
    # dictionary array is constructed using adaptive index type builder,
    # but the input index type is considered as the minimal width type to use

    typ = pa.dictionary(input_index_type, value_type=pa.int64())
    arr = pa.array(range(10), type=typ)
    assert arr.type.equals(typ)


def test_dictionary_is_always_adaptive():
    # dictionary array is constructed using adaptive index type builder,
    # meaning that the output index type may be wider than the given index type
    # since it depends on the input data
    typ = pa.dictionary(pa.int8(), value_type=pa.int64())

    a = pa.array(range(2**7), type=typ)
    expected = pa.dictionary(pa.int8(), pa.int64())
    assert a.type.equals(expected)

    a = pa.array(range(2**7 + 1), type=typ)
    expected = pa.dictionary(pa.int16(), pa.int64())
    assert a.type.equals(expected)


def test_dictionary_from_strings():
    for value_type in [pa.binary(), pa.string()]:
        typ = pa.dictionary(pa.int8(), value_type)
        a = pa.array(["", "a", "bb", "a", "bb", "ccc"], type=typ)

        assert isinstance(a.type, pa.DictionaryType)

        expected_indices = pa.array([0, 1, 2, 1, 2, 3], type=pa.int8())
        expected_dictionary = pa.array(["", "a", "bb", "ccc"], type=value_type)
        assert a.indices.equals(expected_indices)
        assert a.dictionary.equals(expected_dictionary)

    # fixed size binary type
    typ = pa.dictionary(pa.int8(), pa.binary(3))
    a = pa.array(["aaa", "aaa", "bbb", "ccc", "bbb"], type=typ)
    assert isinstance(a.type, pa.DictionaryType)

    expected_indices = pa.array([0, 0, 1, 2, 1], type=pa.int8())
    expected_dictionary = pa.array(["aaa", "bbb", "ccc"], type=pa.binary(3))
    assert a.indices.equals(expected_indices)
    assert a.dictionary.equals(expected_dictionary)


@pytest.mark.parametrize(('unit', 'expected'), [
    ('s', datetime.timedelta(seconds=-2147483000)),
    ('ms', datetime.timedelta(milliseconds=-2147483000)),
    ('us', datetime.timedelta(microseconds=-2147483000)),
    ('ns', datetime.timedelta(microseconds=-2147483))
])
def test_duration_array_roundtrip_corner_cases(unit, expected):
    # Corner case discovered by hypothesis: there were implicit conversions to
    # unsigned values resulting wrong values with wrong signs.
    ty = pa.duration(unit)
    arr = pa.array([-2147483000], type=ty)
    restored = pa.array(arr.to_pylist(), type=ty)
    assert arr.equals(restored)

    expected_list = [expected]
    if unit == 'ns':
        # if pandas is available then a pandas Timedelta is returned
        try:
            import pandas as pd
        except ImportError:
            pass
        else:
            expected_list = [pd.Timedelta(-2147483000, unit='ns')]

    assert restored.to_pylist() == expected_list


@pytest.mark.pandas
def test_roundtrip_nanosecond_resolution_pandas_temporal_objects():
    # corner case discovered by hypothesis: preserving the nanoseconds on
    # conversion from a list of Timedelta and Timestamp objects
    import pandas as pd

    ty = pa.duration('ns')
    arr = pa.array([9223371273709551616], type=ty)
    data = arr.to_pylist()
    assert isinstance(data[0], pd.Timedelta)
    restored = pa.array(data, type=ty)
    assert arr.equals(restored)
    assert restored.to_pylist() == [
        pd.Timedelta(9223371273709551616, unit='ns')
    ]

    ty = pa.timestamp('ns')
    arr = pa.array([9223371273709551616], type=ty)
    data = arr.to_pylist()
    assert isinstance(data[0], pd.Timestamp)
    restored = pa.array(data, type=ty)
    assert arr.equals(restored)
    assert restored.to_pylist() == [
        pd.Timestamp(9223371273709551616, unit='ns')
    ]

    ty = pa.timestamp('ns', tz='US/Eastern')
    value = 1604119893000000000
    arr = pa.array([value], type=ty)
    data = arr.to_pylist()
    assert isinstance(data[0], pd.Timestamp)
    restored = pa.array(data, type=ty)
    assert arr.equals(restored)
    assert restored.to_pylist() == [
        pd.Timestamp(value, unit='ns').tz_localize(
            "UTC").tz_convert('US/Eastern')
    ]


@h.given(past.all_arrays)
def test_array_to_pylist_roundtrip(arr):
    seq = arr.to_pylist()
    restored = pa.array(seq, type=arr.type)
    assert restored.equals(arr)


@pytest.mark.large_memory
def test_auto_chunking_binary_like():
    # single chunk
    v1 = b'x' * 100000000
    v2 = b'x' * 147483646

    # single chunk
    one_chunk_data = [v1] * 20 + [b'', None, v2]
    arr = pa.array(one_chunk_data, type=pa.binary())
    assert isinstance(arr, pa.Array)
    assert len(arr) == 23
    assert arr[20].as_py() == b''
    assert arr[21].as_py() is None
    assert arr[22].as_py() == v2

    # two chunks
    two_chunk_data = one_chunk_data + [b'two']
    arr = pa.array(two_chunk_data, type=pa.binary())
    assert isinstance(arr, pa.ChunkedArray)
    assert arr.num_chunks == 2
    assert len(arr.chunk(0)) == 23
    assert len(arr.chunk(1)) == 1
    assert arr.chunk(0)[20].as_py() == b''
    assert arr.chunk(0)[21].as_py() is None
    assert arr.chunk(0)[22].as_py() == v2
    assert arr.chunk(1).to_pylist() == [b'two']

    # three chunks
    three_chunk_data = one_chunk_data * 2 + [b'three', b'three']
    arr = pa.array(three_chunk_data, type=pa.binary())
    assert isinstance(arr, pa.ChunkedArray)
    assert arr.num_chunks == 3
    assert len(arr.chunk(0)) == 23
    assert len(arr.chunk(1)) == 23
    assert len(arr.chunk(2)) == 2
    for i in range(2):
        assert arr.chunk(i)[20].as_py() == b''
        assert arr.chunk(i)[21].as_py() is None
        assert arr.chunk(i)[22].as_py() == v2
    assert arr.chunk(2).to_pylist() == [b'three', b'three']


@pytest.mark.large_memory
def test_auto_chunking_list_of_binary():
    # ARROW-6281
    vals = [['x' * 1024]] * ((2 << 20) + 1)
    arr = pa.array(vals)
    assert isinstance(arr, pa.ChunkedArray)
    assert arr.num_chunks == 2
    assert len(arr.chunk(0)) == 2**21 - 1
    assert len(arr.chunk(1)) == 2
    assert arr.chunk(1).to_pylist() == [['x' * 1024]] * 2


@pytest.mark.large_memory
def test_auto_chunking_list_like():
    item = np.ones((2**28,), dtype='uint8')
    data = [item] * (2**3 - 1)
    arr = pa.array(data, type=pa.list_(pa.uint8()))
    assert isinstance(arr, pa.Array)
    assert len(arr) == 7

    item = np.ones((2**28,), dtype='uint8')
    data = [item] * 2**3
    arr = pa.array(data, type=pa.list_(pa.uint8()))
    assert isinstance(arr, pa.ChunkedArray)
    assert arr.num_chunks == 2
    assert len(arr.chunk(0)) == 7
    assert len(arr.chunk(1)) == 1
    chunk = arr.chunk(1)
    scalar = chunk[0]
    assert isinstance(scalar, pa.ListScalar)
    expected = pa.array(item, type=pa.uint8())
    assert scalar.values == expected


@pytest.mark.slow
@pytest.mark.large_memory
def test_auto_chunking_map_type():
    # takes ~20 minutes locally
    ty = pa.map_(pa.int8(), pa.int8())
    item = [(1, 1)] * 2**28
    data = [item] * 2**3
    arr = pa.array(data, type=ty)
    assert isinstance(arr, pa.ChunkedArray)
    assert len(arr.chunk(0)) == 7
    assert len(arr.chunk(1)) == 1


@pytest.mark.large_memory
@pytest.mark.parametrize(('ty', 'char'), [
    (pa.string(), 'x'),
    (pa.binary(), b'x'),
])
def test_nested_auto_chunking(ty, char):
    v1 = char * 100000000
    v2 = char * 147483646

    struct_type = pa.struct([
        pa.field('bool', pa.bool_()),
        pa.field('integer', pa.int64()),
        pa.field('string-like', ty),
    ])

    data = [{'bool': True, 'integer': 1, 'string-like': v1}] * 20
    data.append({'bool': True, 'integer': 1, 'string-like': v2})
    arr = pa.array(data, type=struct_type)
    assert isinstance(arr, pa.Array)

    data.append({'bool': True, 'integer': 1, 'string-like': char})
    arr = pa.array(data, type=struct_type)
    assert isinstance(arr, pa.ChunkedArray)
    assert arr.num_chunks == 2
    assert len(arr.chunk(0)) == 21
    assert len(arr.chunk(1)) == 1
    assert arr.chunk(1)[0].as_py() == {
        'bool': True,
        'integer': 1,
        'string-like': char
    }


@pytest.mark.large_memory
def test_array_from_pylist_data_overflow():
    # Regression test for ARROW-12983
    # Data buffer overflow - should result in chunked array
    items = [b'a' * 4096] * (2 ** 19)
    arr = pa.array(items, type=pa.string())
    assert isinstance(arr, pa.ChunkedArray)
    assert len(arr) == 2**19
    assert len(arr.chunks) > 1

    mask = np.zeros(2**19, bool)
    arr = pa.array(items, mask=mask, type=pa.string())
    assert isinstance(arr, pa.ChunkedArray)
    assert len(arr) == 2**19
    assert len(arr.chunks) > 1

    arr = pa.array(items, type=pa.binary())
    assert isinstance(arr, pa.ChunkedArray)
    assert len(arr) == 2**19
    assert len(arr.chunks) > 1


@pytest.mark.slow
@pytest.mark.large_memory
def test_array_from_pylist_offset_overflow():
    # Regression test for ARROW-12983
    # Offset buffer overflow - should result in chunked array
    # Note this doesn't apply to primitive arrays
    items = [b'a'] * (2 ** 31)
    arr = pa.array(items, type=pa.string())
    assert isinstance(arr, pa.ChunkedArray)
    assert len(arr) == 2**31
    assert len(arr.chunks) > 1

    mask = np.zeros(2**31, bool)
    arr = pa.array(items, mask=mask, type=pa.string())
    assert isinstance(arr, pa.ChunkedArray)
    assert len(arr) == 2**31
    assert len(arr.chunks) > 1

    arr = pa.array(items, type=pa.binary())
    assert isinstance(arr, pa.ChunkedArray)
    assert len(arr) == 2**31
    assert len(arr.chunks) > 1