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ckpts/universal/global_step80/zero/18.post_attention_layernorm.weight/exp_avg.pt

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ckpts/universal/global_step80/zero/18.post_attention_layernorm.weight/exp_avg_sq.pt

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ckpts/universal/global_step80/zero/18.post_attention_layernorm.weight/fp32.pt

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ckpts/universal/global_step80/zero/20.attention.dense.weight/fp32.pt

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ckpts/universal/global_step80/zero/6.attention.dense.weight/exp_avg.pt

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ckpts/universal/global_step80/zero/6.attention.dense.weight/exp_avg_sq.pt

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venv/lib/python3.10/site-packages/pyarrow/tests/arrow_16597.py

@@ -0,0 +1,37 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# 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.
+
+# This file is called from a test in test_flight.py.
+import time
+
+import pyarrow as pa
+import pyarrow.flight as flight
+
+
+class Server(flight.FlightServerBase):
+    def do_put(self, context, descriptor, reader, writer):
+        time.sleep(1)
+        raise flight.FlightCancelledError("")
+
+
+if __name__ == "__main__":
+    server = Server("grpc://localhost:0")
+    client = flight.connect(f"grpc://localhost:{server.port}")
+    schema = pa.schema([])
+    writer, reader = client.do_put(
+        flight.FlightDescriptor.for_command(b""), schema)
+    writer.done_writing()

venv/lib/python3.10/site-packages/pyarrow/tests/extensions.pyx

@@ -0,0 +1,94 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# 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.
+
+# distutils: language=c++
+# cython: language_level = 3
+
+from pyarrow.lib cimport *
+
+cdef extern from * namespace "arrow::py" nogil:
+    """
+    #include "arrow/status.h"
+    #include "arrow/extension_type.h"
+    #include "arrow/ipc/json_simple.h"
+
+    namespace arrow {
+    namespace py {
+
+    class UuidArray : public ExtensionArray {
+    public:
+        using ExtensionArray::ExtensionArray;
+    };
+
+    class UuidType : public ExtensionType {
+    public:
+        UuidType() : ExtensionType(fixed_size_binary(16)) {}
+        std::string extension_name() const override { return "uuid"; }
+
+        bool ExtensionEquals(const ExtensionType& other) const override {
+            return other.extension_name() == this->extension_name();
+        }
+
+        std::shared_ptr<Array> MakeArray(std::shared_ptr<ArrayData> data) const override {
+            return std::make_shared<ExtensionArray>(data);
+        }
+
+        Result<std::shared_ptr<DataType>> Deserialize(
+            std::shared_ptr<DataType> storage_type,
+            const std::string& serialized) const override {
+            return std::make_shared<UuidType>();
+        }
+
+        std::string Serialize() const override { return ""; }
+    };
+
+
+    std::shared_ptr<DataType> MakeUuidType() {
+        return std::make_shared<UuidType>();
+    }
+
+    std::shared_ptr<Array> MakeUuidArray() {
+        auto uuid_type = MakeUuidType();
+        auto json = "[\\"abcdefghijklmno0\\", \\"0onmlkjihgfedcba\\"]";
+        auto result = ipc::internal::json::ArrayFromJSON(fixed_size_binary(16), json);
+        return ExtensionType::WrapArray(uuid_type, result.ValueOrDie());
+    }
+
+    std::once_flag uuid_registered;
+
+    static bool RegisterUuidType() {
+        std::call_once(uuid_registered, RegisterExtensionType,
+                       std::make_shared<UuidType>());
+        return true;
+    }
+
+    static auto uuid_type_registered = RegisterUuidType();
+
+    }  // namespace py
+    }  // namespace arrow
+    """
+
+    cdef shared_ptr[CDataType] CMakeUuidType" arrow::py::MakeUuidType"()
+    cdef shared_ptr[CArray] CMakeUuidArray" arrow::py::MakeUuidArray"()
+
+
+def _make_uuid_type():
+    return pyarrow_wrap_data_type(CMakeUuidType())
+
+
+def _make_uuid_array():
+    return pyarrow_wrap_array(CMakeUuidArray())

venv/lib/python3.10/site-packages/pyarrow/tests/interchange/__init__.py

@@ -0,0 +1,16 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# 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.

venv/lib/python3.10/site-packages/pyarrow/tests/interchange/test_conversion.py

@@ -0,0 +1,522 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# 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.
+
+from datetime import datetime as dt
+import numpy as np
+import pyarrow as pa
+from pyarrow.vendored.version import Version
+import pytest
+
+import pyarrow.interchange as pi
+from pyarrow.interchange.column import (
+    _PyArrowColumn,
+    ColumnNullType,
+    DtypeKind,
+)
+from pyarrow.interchange.from_dataframe import _from_dataframe
+
+try:
+    import pandas as pd
+    # import pandas.testing as tm
+except ImportError:
+    pass
+
+
+@pytest.mark.parametrize("unit", ['s', 'ms', 'us', 'ns'])
+@pytest.mark.parametrize("tz", ['', 'America/New_York', '+07:30', '-04:30'])
+def test_datetime(unit, tz):
+    dt_arr = [dt(2007, 7, 13), dt(2007, 7, 14), None]
+    table = pa.table({"A": pa.array(dt_arr, type=pa.timestamp(unit, tz=tz))})
+    col = table.__dataframe__().get_column_by_name("A")
+
+    assert col.size() == 3
+    assert col.offset == 0
+    assert col.null_count == 1
+    assert col.dtype[0] == DtypeKind.DATETIME
+    assert col.describe_null == (ColumnNullType.USE_BITMASK, 0)
+
+
+@pytest.mark.parametrize(
+    ["test_data", "kind"],
+    [
+        (["foo", "bar"], 21),
+        ([1.5, 2.5, 3.5], 2),
+        ([1, 2, 3, 4], 0),
+    ],
+)
+def test_array_to_pyarrowcolumn(test_data, kind):
+    arr = pa.array(test_data)
+    arr_column = _PyArrowColumn(arr)
+
+    assert arr_column._col == arr
+    assert arr_column.size() == len(test_data)
+    assert arr_column.dtype[0] == kind
+    assert arr_column.num_chunks() == 1
+    assert arr_column.null_count == 0
+    assert arr_column.get_buffers()["validity"] is None
+    assert len(list(arr_column.get_chunks())) == 1
+
+    for chunk in arr_column.get_chunks():
+        assert chunk == arr_column
+
+
+def test_offset_of_sliced_array():
+    arr = pa.array([1, 2, 3, 4])
+    arr_sliced = arr.slice(2, 2)
+
+    table = pa.table([arr], names=["arr"])
+    table_sliced = pa.table([arr_sliced], names=["arr_sliced"])
+
+    col = table_sliced.__dataframe__().get_column(0)
+    assert col.offset == 2
+
+    result = _from_dataframe(table_sliced.__dataframe__())
+    assert table_sliced.equals(result)
+    assert not table.equals(result)
+
+    # pandas hardcodes offset to 0:
+    # https://github.com/pandas-dev/pandas/blob/5c66e65d7b9fef47ccb585ce2fd0b3ea18dc82ea/pandas/core/interchange/from_dataframe.py#L247
+    # so conversion to pandas can't be tested currently
+
+    # df = pandas_from_dataframe(table)
+    # df_sliced = pandas_from_dataframe(table_sliced)
+
+    # tm.assert_series_equal(df["arr"][2:4], df_sliced["arr_sliced"],
+    #                        check_index=False, check_names=False)
+
+
+@pytest.mark.pandas
+@pytest.mark.parametrize(
+    "uint", [pa.uint8(), pa.uint16(), pa.uint32()]
+)
+@pytest.mark.parametrize(
+    "int", [pa.int8(), pa.int16(), pa.int32(), pa.int64()]
+)
+@pytest.mark.parametrize(
+    "float, np_float", [
+        # (pa.float16(), np.float16),   #not supported by pandas
+        (pa.float32(), np.float32),
+        (pa.float64(), np.float64)
+    ]
+)
+def test_pandas_roundtrip(uint, int, float, np_float):
+    if Version(pd.__version__) < Version("1.5.0"):
+        pytest.skip("__dataframe__ added to pandas in 1.5.0")
+
+    arr = [1, 2, 3]
+    table = pa.table(
+        {
+            "a": pa.array(arr, type=uint),
+            "b": pa.array(arr, type=int),
+            "c": pa.array(np.array(arr, dtype=np_float), type=float),
+            "d": [True, False, True],
+        }
+    )
+    from pandas.api.interchange import (
+        from_dataframe as pandas_from_dataframe
+    )
+    pandas_df = pandas_from_dataframe(table)
+    result = pi.from_dataframe(pandas_df)
+    assert table.equals(result)
+
+    table_protocol = table.__dataframe__()
+    result_protocol = result.__dataframe__()
+
+    assert table_protocol.num_columns() == result_protocol.num_columns()
+    assert table_protocol.num_rows() == result_protocol.num_rows()
+    assert table_protocol.num_chunks() == result_protocol.num_chunks()
+    assert table_protocol.column_names() == result_protocol.column_names()
+
+
+@pytest.mark.pandas
+def test_pandas_roundtrip_string():
+    # See https://github.com/pandas-dev/pandas/issues/50554
+    if Version(pd.__version__) < Version("1.6"):
+        pytest.skip("Column.size() bug in pandas")
+
+    arr = ["a", "", "c"]
+    table = pa.table({"a": pa.array(arr)})
+
+    from pandas.api.interchange import (
+        from_dataframe as pandas_from_dataframe
+    )
+
+    pandas_df = pandas_from_dataframe(table)
+    result = pi.from_dataframe(pandas_df)
+
+    assert result["a"].to_pylist() == table["a"].to_pylist()
+    assert pa.types.is_string(table["a"].type)
+    assert pa.types.is_large_string(result["a"].type)
+
+    table_protocol = table.__dataframe__()
+    result_protocol = result.__dataframe__()
+
+    assert table_protocol.num_columns() == result_protocol.num_columns()
+    assert table_protocol.num_rows() == result_protocol.num_rows()
+    assert table_protocol.num_chunks() == result_protocol.num_chunks()
+    assert table_protocol.column_names() == result_protocol.column_names()
+
+
+@pytest.mark.pandas
+def test_pandas_roundtrip_large_string():
+    # See https://github.com/pandas-dev/pandas/issues/50554
+    if Version(pd.__version__) < Version("1.6"):
+        pytest.skip("Column.size() bug in pandas")
+
+    arr = ["a", "", "c"]
+    table = pa.table({"a_large": pa.array(arr, type=pa.large_string())})
+
+    from pandas.api.interchange import (
+        from_dataframe as pandas_from_dataframe
+    )
+
+    if Version(pd.__version__) >= Version("2.0.1"):
+        pandas_df = pandas_from_dataframe(table)
+        result = pi.from_dataframe(pandas_df)
+
+        assert result["a_large"].to_pylist() == table["a_large"].to_pylist()
+        assert pa.types.is_large_string(table["a_large"].type)
+        assert pa.types.is_large_string(result["a_large"].type)
+
+        table_protocol = table.__dataframe__()
+        result_protocol = result.__dataframe__()
+
+        assert table_protocol.num_columns() == result_protocol.num_columns()
+        assert table_protocol.num_rows() == result_protocol.num_rows()
+        assert table_protocol.num_chunks() == result_protocol.num_chunks()
+        assert table_protocol.column_names() == result_protocol.column_names()
+
+    else:
+        # large string not supported by pandas implementation for
+        # older versions of pandas
+        # https://github.com/pandas-dev/pandas/issues/52795
+        with pytest.raises(AssertionError):
+            pandas_from_dataframe(table)
+
+
+@pytest.mark.pandas
+def test_pandas_roundtrip_string_with_missing():
+    # See https://github.com/pandas-dev/pandas/issues/50554
+    if Version(pd.__version__) < Version("1.6"):
+        pytest.skip("Column.size() bug in pandas")
+
+    arr = ["a", "", "c", None]
+    table = pa.table({"a": pa.array(arr),
+                      "a_large": pa.array(arr, type=pa.large_string())})
+
+    from pandas.api.interchange import (
+        from_dataframe as pandas_from_dataframe
+    )
+
+    if Version(pd.__version__) >= Version("2.0.2"):
+        pandas_df = pandas_from_dataframe(table)
+        result = pi.from_dataframe(pandas_df)
+
+        assert result["a"].to_pylist() == table["a"].to_pylist()
+        assert pa.types.is_string(table["a"].type)
+        assert pa.types.is_large_string(result["a"].type)
+
+        assert result["a_large"].to_pylist() == table["a_large"].to_pylist()
+        assert pa.types.is_large_string(table["a_large"].type)
+        assert pa.types.is_large_string(result["a_large"].type)
+    else:
+        # older versions of pandas do not have bitmask support
+        # https://github.com/pandas-dev/pandas/issues/49888
+        with pytest.raises(NotImplementedError):
+            pandas_from_dataframe(table)
+
+
+@pytest.mark.pandas
+def test_pandas_roundtrip_categorical():
+    if Version(pd.__version__) < Version("2.0.2"):
+        pytest.skip("Bitmasks not supported in pandas interchange implementation")
+
+    arr = ["Mon", "Tue", "Mon", "Wed", "Mon", "Thu", "Fri", "Sat", None]
+    table = pa.table(
+        {"weekday": pa.array(arr).dictionary_encode()}
+    )
+
+    from pandas.api.interchange import (
+        from_dataframe as pandas_from_dataframe
+    )
+    pandas_df = pandas_from_dataframe(table)
+    result = pi.from_dataframe(pandas_df)
+
+    assert result["weekday"].to_pylist() == table["weekday"].to_pylist()
+    assert pa.types.is_dictionary(table["weekday"].type)
+    assert pa.types.is_dictionary(result["weekday"].type)
+    assert pa.types.is_string(table["weekday"].chunk(0).dictionary.type)
+    assert pa.types.is_large_string(result["weekday"].chunk(0).dictionary.type)
+    assert pa.types.is_int32(table["weekday"].chunk(0).indices.type)
+    assert pa.types.is_int8(result["weekday"].chunk(0).indices.type)
+
+    table_protocol = table.__dataframe__()
+    result_protocol = result.__dataframe__()
+
+    assert table_protocol.num_columns() == result_protocol.num_columns()
+    assert table_protocol.num_rows() == result_protocol.num_rows()
+    assert table_protocol.num_chunks() == result_protocol.num_chunks()
+    assert table_protocol.column_names() == result_protocol.column_names()
+
+    col_table = table_protocol.get_column(0)
+    col_result = result_protocol.get_column(0)
+
+    assert col_result.dtype[0] == DtypeKind.CATEGORICAL
+    assert col_result.dtype[0] == col_table.dtype[0]
+    assert col_result.size() == col_table.size()
+    assert col_result.offset == col_table.offset
+
+    desc_cat_table = col_result.describe_categorical
+    desc_cat_result = col_result.describe_categorical
+
+    assert desc_cat_table["is_ordered"] == desc_cat_result["is_ordered"]
+    assert desc_cat_table["is_dictionary"] == desc_cat_result["is_dictionary"]
+    assert isinstance(desc_cat_result["categories"]._col, pa.Array)
+
+
+@pytest.mark.pandas
+@pytest.mark.parametrize("unit", ['s', 'ms', 'us', 'ns'])
+def test_pandas_roundtrip_datetime(unit):
+    if Version(pd.__version__) < Version("1.5.0"):
+        pytest.skip("__dataframe__ added to pandas in 1.5.0")
+    from datetime import datetime as dt
+
+    # timezones not included as they are not yet supported in
+    # the pandas implementation
+    dt_arr = [dt(2007, 7, 13), dt(2007, 7, 14), dt(2007, 7, 15)]
+    table = pa.table({"a": pa.array(dt_arr, type=pa.timestamp(unit))})
+
+    if Version(pd.__version__) < Version("1.6"):
+        # pandas < 2.0 always creates datetime64 in "ns"
+        # resolution
+        expected = pa.table({"a": pa.array(dt_arr, type=pa.timestamp('ns'))})
+    else:
+        expected = table
+
+    from pandas.api.interchange import (
+        from_dataframe as pandas_from_dataframe
+    )
+    pandas_df = pandas_from_dataframe(table)
+    result = pi.from_dataframe(pandas_df)
+
+    assert expected.equals(result)
+
+    expected_protocol = expected.__dataframe__()
+    result_protocol = result.__dataframe__()
+
+    assert expected_protocol.num_columns() == result_protocol.num_columns()
+    assert expected_protocol.num_rows() == result_protocol.num_rows()
+    assert expected_protocol.num_chunks() == result_protocol.num_chunks()
+    assert expected_protocol.column_names() == result_protocol.column_names()
+
+
+@pytest.mark.pandas
+@pytest.mark.parametrize(
+    "np_float", [np.float32, np.float64]
+)
+def test_pandas_to_pyarrow_with_missing(np_float):
+    if Version(pd.__version__) < Version("1.5.0"):
+        pytest.skip("__dataframe__ added to pandas in 1.5.0")
+
+    np_array = np.array([0, np.nan, 2], dtype=np_float)
+    datetime_array = [None, dt(2007, 7, 14), dt(2007, 7, 15)]
+    df = pd.DataFrame({
+        "a": np_array,   # float, ColumnNullType.USE_NAN
+        "dt": datetime_array  # ColumnNullType.USE_SENTINEL
+    })
+    expected = pa.table({
+        "a": pa.array(np_array, from_pandas=True),
+        "dt": pa.array(datetime_array, type=pa.timestamp("ns"))
+    })
+    result = pi.from_dataframe(df)
+
+    assert result.equals(expected)
+
+
+@pytest.mark.pandas
+def test_pandas_to_pyarrow_float16_with_missing():
+    if Version(pd.__version__) < Version("1.5.0"):
+        pytest.skip("__dataframe__ added to pandas in 1.5.0")
+
+    # np.float16 errors if ps.is_nan is used
+    # pyarrow.lib.ArrowNotImplementedError: Function 'is_nan' has no kernel
+    # matching input types (halffloat)
+    np_array = np.array([0, np.nan, 2], dtype=np.float16)
+    df = pd.DataFrame({"a": np_array})
+
+    with pytest.raises(NotImplementedError):
+        pi.from_dataframe(df)
+
+
+@pytest.mark.parametrize(
+    "uint", [pa.uint8(), pa.uint16(), pa.uint32()]
+)
+@pytest.mark.parametrize(
+    "int", [pa.int8(), pa.int16(), pa.int32(), pa.int64()]
+)
+@pytest.mark.parametrize(
+    "float, np_float", [
+        (pa.float16(), np.float16),
+        (pa.float32(), np.float32),
+        (pa.float64(), np.float64)
+    ]
+)
+@pytest.mark.parametrize("unit", ['s', 'ms', 'us', 'ns'])
+@pytest.mark.parametrize("tz", ['America/New_York', '+07:30', '-04:30'])
+@pytest.mark.parametrize("offset, length", [(0, 3), (0, 2), (1, 2), (2, 1)])
+def test_pyarrow_roundtrip(uint, int, float, np_float,
+                           unit, tz, offset, length):
+
+    from datetime import datetime as dt
+    arr = [1, 2, None]
+    dt_arr = [dt(2007, 7, 13), None, dt(2007, 7, 15)]
+
+    table = pa.table(
+        {
+            "a": pa.array(arr, type=uint),
+            "b": pa.array(arr, type=int),
+            "c": pa.array(np.array(arr, dtype=np_float),
+                          type=float, from_pandas=True),
+            "d": [True, False, True],
+            "e": [True, False, None],
+            "f": ["a", None, "c"],
+            "g": pa.array(dt_arr, type=pa.timestamp(unit, tz=tz))
+        }
+    )
+    table = table.slice(offset, length)
+    result = _from_dataframe(table.__dataframe__())
+
+    assert table.equals(result)
+
+    table_protocol = table.__dataframe__()
+    result_protocol = result.__dataframe__()
+
+    assert table_protocol.num_columns() == result_protocol.num_columns()
+    assert table_protocol.num_rows() == result_protocol.num_rows()
+    assert table_protocol.num_chunks() == result_protocol.num_chunks()
+    assert table_protocol.column_names() == result_protocol.column_names()
+
+
+@pytest.mark.parametrize("offset, length", [(0, 10), (0, 2), (7, 3), (2, 1)])
+def test_pyarrow_roundtrip_categorical(offset, length):
+    arr = ["Mon", "Tue", "Mon", "Wed", "Mon", "Thu", "Fri", None, "Sun"]
+    table = pa.table(
+        {"weekday": pa.array(arr).dictionary_encode()}
+    )
+    table = table.slice(offset, length)
+    result = _from_dataframe(table.__dataframe__())
+
+    assert table.equals(result)
+
+    table_protocol = table.__dataframe__()
+    result_protocol = result.__dataframe__()
+
+    assert table_protocol.num_columns() == result_protocol.num_columns()
+    assert table_protocol.num_rows() == result_protocol.num_rows()
+    assert table_protocol.num_chunks() == result_protocol.num_chunks()
+    assert table_protocol.column_names() == result_protocol.column_names()
+
+    col_table = table_protocol.get_column(0)
+    col_result = result_protocol.get_column(0)
+
+    assert col_result.dtype[0] == DtypeKind.CATEGORICAL
+    assert col_result.dtype[0] == col_table.dtype[0]
+    assert col_result.size() == col_table.size()
+    assert col_result.offset == col_table.offset
+
+    desc_cat_table = col_table.describe_categorical
+    desc_cat_result = col_result.describe_categorical
+
+    assert desc_cat_table["is_ordered"] == desc_cat_result["is_ordered"]
+    assert desc_cat_table["is_dictionary"] == desc_cat_result["is_dictionary"]
+    assert isinstance(desc_cat_result["categories"]._col, pa.Array)
+
+
+@pytest.mark.large_memory
+def test_pyarrow_roundtrip_large_string():
+
+    data = np.array([b'x'*1024]*(3*1024**2), dtype='object')  # 3GB bytes data
+    arr = pa.array(data, type=pa.large_string())
+    table = pa.table([arr], names=["large_string"])
+
+    result = _from_dataframe(table.__dataframe__())
+    col = result.__dataframe__().get_column(0)
+
+    assert col.size() == 3*1024**2
+    assert pa.types.is_large_string(table[0].type)
+    assert pa.types.is_large_string(result[0].type)
+
+    assert table.equals(result)
+
+
+def test_nan_as_null():
+    table = pa.table({"a": [1, 2, 3, 4]})
+    with pytest.raises(RuntimeError):
+        table.__dataframe__(nan_as_null=True)
+
+
+@pytest.mark.pandas
+def test_allow_copy_false():
+    if Version(pd.__version__) < Version("1.5.0"):
+        pytest.skip("__dataframe__ added to pandas in 1.5.0")
+
+    # Test that an error is raised when a copy is needed
+    # to create a bitmask
+
+    df = pd.DataFrame({"a": [0, 1.0, 2.0]})
+    with pytest.raises(RuntimeError):
+        pi.from_dataframe(df, allow_copy=False)
+
+    df = pd.DataFrame({
+        "dt": [None, dt(2007, 7, 14), dt(2007, 7, 15)]
+    })
+    with pytest.raises(RuntimeError):
+        pi.from_dataframe(df, allow_copy=False)
+
+
+@pytest.mark.pandas
+def test_allow_copy_false_bool_categorical():
+    if Version(pd.__version__) < Version("1.5.0"):
+        pytest.skip("__dataframe__ added to pandas in 1.5.0")
+
+    # Test that an error is raised for boolean
+    # and categorical dtype (copy is always made)
+
+    df = pd.DataFrame({"a": [None, False, True]})
+    with pytest.raises(RuntimeError):
+        pi.from_dataframe(df, allow_copy=False)
+
+    df = pd.DataFrame({"a": [True, False, True]})
+    with pytest.raises(RuntimeError):
+        pi.from_dataframe(df, allow_copy=False)
+
+    df = pd.DataFrame({"weekday": ["a", "b", None]})
+    df = df.astype("category")
+    with pytest.raises(RuntimeError):
+        pi.from_dataframe(df, allow_copy=False)
+
+    df = pd.DataFrame({"weekday": ["a", "b", "c"]})
+    df = df.astype("category")
+    with pytest.raises(RuntimeError):
+        pi.from_dataframe(df, allow_copy=False)
+
+
+def test_empty_dataframe():
+    schema = pa.schema([('col1', pa.int8())])
+    df = pa.table([[]], schema=schema)
+    dfi = df.__dataframe__()
+    assert pi.from_dataframe(dfi) == df