# 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 __future__ import annotations

import ctypes
import gc
import sys
from typing import Any

import numpy as np
import pytest
import tvm_ffi
import tvm_ffi.cpp

try:
    import torch
except ImportError:
    torch = None  # ty: ignore[invalid-assignment]

_HAS_TORCH_DLPACK_API = torch is not None and hasattr(torch.Tensor, "__dlpack_c_exchange_api__")


def test_echo() -> None:
    fecho = tvm_ffi.get_global_func("testing.echo")
    assert isinstance(fecho, tvm_ffi.Function)
    # test each type
    assert fecho(None) is None

    # test bool
    bool_result = fecho(True)
    assert isinstance(bool_result, bool)
    assert bool_result is True
    bool_result = fecho(False)
    assert isinstance(bool_result, bool)
    assert bool_result is False

    # test int/float
    assert fecho(1) == 1
    assert fecho(1.2) == 1.2

    # test str
    str_result = fecho("hello")
    assert isinstance(str_result, str)
    assert str_result == "hello"

    # test bytes
    bytes_result = fecho(b"abc")
    assert isinstance(bytes_result, bytes)
    assert bytes_result == b"abc"

    # test dtype
    dtype_result = fecho(tvm_ffi.dtype("float32"))
    assert isinstance(dtype_result, tvm_ffi.dtype)
    assert dtype_result == tvm_ffi.dtype("float32")

    c_dtype_result = fecho(tvm_ffi.core.DataType("float32"))
    assert isinstance(c_dtype_result, tvm_ffi.dtype)
    assert c_dtype_result == tvm_ffi.dtype("float32")

    # test device
    device_result = fecho(tvm_ffi.device("cuda:1"))
    assert isinstance(device_result, tvm_ffi.Device)
    assert device_result.dlpack_device_type() == tvm_ffi.DLDeviceType.kDLCUDA
    assert device_result.index == 1
    assert str(device_result) == "cuda:1"
    assert device_result.__repr__() == "device(type='cuda', index=1)"

    # test c_void_p
    c_void_p_result = fecho(ctypes.c_void_p(0x12345678))
    assert isinstance(c_void_p_result, ctypes.c_void_p)
    assert c_void_p_result.value == 0x12345678

    # test c_void_p for nullptr
    c_void_p_nullptr_result = fecho(ctypes.c_void_p(0))
    c_void_p_nullptr_result is None

    # test function: aka object
    fadd = tvm_ffi.convert(lambda a, b: a + b)
    fadd1 = fecho(fadd)
    assert fadd1(1, 2) == 3
    assert fadd1.same_as(fadd)

    def check_tensor() -> None:
        np_data = np.arange(10, dtype="int32")
        if not hasattr(np_data, "__dlpack__"):
            return
        # test Tensor
        x = tvm_ffi.from_dlpack(np_data)
        assert isinstance(x, tvm_ffi.Tensor)
        tensor_result = fecho(x)
        assert isinstance(tensor_result, tvm_ffi.Tensor)
        assert tensor_result.shape == (10,)
        assert tensor_result.dtype == tvm_ffi.dtype("int32")
        assert tensor_result.device.dlpack_device_type() == tvm_ffi.DLDeviceType.kDLCPU
        assert tensor_result.device.index == 0

    check_tensor()


def test_typed_device_arg_accepts_string() -> None:
    schema_id_device = tvm_ffi.get_global_func("testing.schema_id_device")
    result = schema_id_device("cpu")
    assert isinstance(result, tvm_ffi.Device)
    assert result == tvm_ffi.device("cpu", 0)

    result = schema_id_device("cuda:1")
    assert result == tvm_ffi.device("cuda", 1)


def test_return_raw_str_bytes() -> None:
    assert tvm_ffi.convert(lambda: "hello")() == "hello"
    assert tvm_ffi.convert(lambda: b"hello")() == b"hello"
    assert tvm_ffi.convert(lambda: bytearray(b"hello"))() == b"hello"


def test_string_bytes_passing() -> None:
    fecho = tvm_ffi.get_global_func("testing.echo")
    use_count = tvm_ffi.get_global_func("testing.object_use_count")
    # small string
    assert fecho("hello") == "hello"
    # large string
    x = "hello" * 100
    y = fecho(x)
    assert y == x
    assert y._tvm_ffi_cached_object is not None
    use_count(y) == 1
    # small bytes
    assert fecho(b"hello") == b"hello"
    # large bytes
    x2 = b"hello" * 100
    y2 = fecho(x2)
    assert y2 == x2
    assert y2._tvm_ffi_cached_object is not None
    fecho(y2) == 1


def test_nested_container_passing() -> None:
    # test and make sure our ref counting is correct
    fecho = tvm_ffi.get_global_func("testing.echo")
    use_count = tvm_ffi.get_global_func("testing.object_use_count")
    obj = tvm_ffi.convert((1, 2, 3))
    assert use_count(obj) == 1
    y = fecho([obj, {"a": 1, "b": obj}])
    assert use_count(y) == 1
    assert use_count(obj) == 3
    assert use_count(y[1]) == 2


def test_pyfunc_convert() -> None:
    def add(a: int, b: int) -> int:
        return a + b

    fadd = tvm_ffi.convert(add)
    assert isinstance(fadd, tvm_ffi.Function)
    assert fadd(1, 2) == 3

    def fapply(f: Any, *args: Any) -> Any:
        return f(*args)

    fapply = tvm_ffi.convert(fapply)
    assert fapply(add, 1, 3.3) == 4.3


def test_global_func() -> None:
    @tvm_ffi.register_global_func("mytest.echo")
    def echo(x: Any) -> Any:
        return x

    f = tvm_ffi.get_global_func("mytest.echo")
    assert f.same_as(echo)
    assert f(1) == 1

    assert "mytest.echo" in tvm_ffi.registry.list_global_func_names()

    tvm_ffi.registry.remove_global_func("mytest.echo")
    assert "mytest.echo" not in tvm_ffi.registry.list_global_func_names()
    assert tvm_ffi.get_global_func("mytest.echo", allow_missing=True) is None


def test_rvalue_ref() -> None:
    use_count = tvm_ffi.get_global_func("testing.object_use_count")

    def callback(x: Any, expected_count: int) -> Any:
        # The use count of TVM FFI objects is decremented as part of
        # `ObjectRef.__del__`, which runs when the Python object is
        # destructed.  However, Python object destruction is not
        # deterministic, and even CPython's reference-counting is
        # considered an implementation detail.  Therefore, to ensure
        # correct results from this test, `gc.collect()` must be
        # explicitly called.
        gc.collect()
        assert expected_count == use_count(x)
        return x._move()

    f = tvm_ffi.convert(callback)

    def check0() -> None:
        x = tvm_ffi.convert([1, 2])
        assert use_count(x) == 1
        f(x, 2)
        f(x._move(), 1)
        assert x.__ctypes_handle__().value is None

    def check1() -> None:
        x = tvm_ffi.convert([1, 2])
        assert use_count(x) == 1
        y = f(x, 2)
        f(x._move(), 2)
        assert x.__ctypes_handle__().value is None
        assert y.__ctypes_handle__().value is not None

    check0()
    check1()


def test_echo_with_opaque_object() -> None:
    class MyObject:
        def __init__(self, value: Any) -> None:
            self.value = value

    fecho = tvm_ffi.get_global_func("testing.echo")
    x = MyObject("hello")
    assert sys.getrefcount(x) == 2
    y = fecho(x)
    assert isinstance(y, MyObject)
    assert y is x
    assert sys.getrefcount(x) == 3

    def py_callback(z: Any) -> Any:
        """Python callback with opaque object."""
        assert z is x
        return z

    fcallback = tvm_ffi.convert(py_callback)
    z = fcallback(x)
    assert z is x
    assert sys.getrefcount(x) == 4


def test_function_from_c_symbol() -> None:
    add_one_c_symbol = tvm_ffi.get_global_func("testing.get_add_one_c_symbol")()
    fadd_one = tvm_ffi.Function.__from_extern_c__(add_one_c_symbol)
    assert fadd_one(1) == 2
    assert fadd_one(2) == 3

    with pytest.raises(TypeError):
        fadd_one(None)

    keep_alive = [1, 2, 3]
    base_ref_count = sys.getrefcount(keep_alive)
    fadd_one = tvm_ffi.Function.__from_extern_c__(add_one_c_symbol, keep_alive_object=keep_alive)
    assert fadd_one(1) == 2
    assert fadd_one(2) == 3
    assert sys.getrefcount(keep_alive) == base_ref_count + 1
    fadd_one = None
    assert sys.getrefcount(keep_alive) == base_ref_count


def test_function_from_mlir_packed_safe_call() -> None:
    add_one_c_symbol = tvm_ffi.get_global_func("testing.get_mlir_add_one_c_symbol")()
    fadd_one = tvm_ffi.Function.__from_mlir_packed_safe_call__(add_one_c_symbol)
    assert fadd_one(1) == 2
    assert fadd_one(2) == 3

    keep_alive = [1, 2, 3]
    base_ref_count = sys.getrefcount(keep_alive)
    fadd_one = tvm_ffi.Function.__from_mlir_packed_safe_call__(
        add_one_c_symbol, keep_alive_object=keep_alive
    )

    with pytest.raises(TypeError):
        fadd_one(None)

    assert fadd_one(1) == 2
    assert fadd_one(2) == 3
    assert sys.getrefcount(keep_alive) == base_ref_count + 1
    fadd_one = None
    assert sys.getrefcount(keep_alive) == base_ref_count


def test_function_subclass() -> None:
    class JitFunction:
        def __init__(self, metadata: Any) -> None:
            self.metadata = metadata

    class MyFunction(tvm_ffi.Function, JitFunction):
        def __init__(self, metadata: Any) -> None:
            # Explicitly initialize the mixin. `super()` is not used because `tvm_ffi.Function`
            # is an extension type without a standard `__init__`.
            JitFunction.__init__(self, metadata)

        # When subclassing a Cython cdef class and overriding `__init__`,
        # special methods like `__call__` may not be inherited automatically.
        # This explicit assignment ensures the subclass remains callable.
        __call__ = tvm_ffi.Function.__call__

    f = tvm_ffi.convert(lambda x: x)
    assert isinstance(f, tvm_ffi.Function)
    f_sub = MyFunction(128)
    # move handle from f to f_sub an existing function
    f_sub.__move_handle_from__(f)
    assert isinstance(f_sub, MyFunction)
    assert isinstance(f_sub, JitFunction)
    assert f_sub.metadata == 128

    y: int = f_sub(2)
    assert y == 2
    echo = tvm_ffi.get_global_func("testing.echo")
    fechoed = echo(f_sub)
    assert isinstance(fechoed, tvm_ffi.Function)
    assert fechoed.__chandle__() == f_sub.__chandle__()
    assert fechoed(10) == 10


def test_function_with_opaque_ptr_protocol() -> None:
    class MyObject:
        def __init__(self, value: Any) -> None:
            self.value = value

        def __tvm_ffi_opaque_ptr__(self) -> Any:
            return self.value

    fecho = tvm_ffi.get_global_func("testing.echo")
    x = MyObject(10)
    y = fecho(x)
    assert isinstance(y, ctypes.c_void_p)
    assert y.value == 10


def test_function_with_dlpack_data_type_protocol() -> None:
    class DLPackDataTypeProtocol:
        def __init__(self, dlpack_data_type: tuple[int, int, int]) -> None:
            self.dlpack_data_type = dlpack_data_type

        def __dlpack_data_type__(self) -> tuple[int, int, int]:
            return self.dlpack_data_type

    dtype = tvm_ffi.dtype("float32")
    fecho = tvm_ffi.get_global_func("testing.echo")
    x = DLPackDataTypeProtocol((dtype.type_code, dtype.bits, dtype.lanes))
    y = fecho(x)
    assert y == dtype
    converted_y = tvm_ffi.convert(x)
    assert converted_y == dtype


def test_function_with_dlpack_device_protocol() -> None:
    device = tvm_ffi.device("cuda:1")

    class DLPackDeviceProtocol:
        def __init__(self, device: tvm_ffi.Device) -> None:
            self.device = device

        def __dlpack_device__(self) -> tuple[int, int]:
            return (self.device.dlpack_device_type(), self.device.index)

    fecho = tvm_ffi.get_global_func("testing.echo")
    x = DLPackDeviceProtocol(device)
    y = fecho(x)
    assert y == device


def test_integral_float_variants_passing() -> None:
    fecho = tvm_ffi.get_global_func("testing.echo")
    y = fecho(np.int32(1))
    assert isinstance(y, int)
    assert y == 1

    y = fecho(np.float64(2.0))
    assert isinstance(y, float)
    assert y == 2.0

    class IntProtocol:
        def __init__(self, value: int) -> None:
            self.value = value

        def __tvm_ffi_int__(self) -> int:
            return self.value

    y = fecho(IntProtocol(10))
    assert isinstance(y, int)
    assert y == 10

    class FloatProtocol:
        def __init__(self, value: float) -> None:
            self.value = value

        def __tvm_ffi_float__(self) -> float:
            return self.value

    y = fecho(FloatProtocol(10))
    assert isinstance(y, float)
    assert y == 10


def test_function_with_value_protocol() -> None:
    class ValueProtocol:
        def __init__(self, value: Any) -> None:
            self.value = value

        def __tvm_ffi_value__(self) -> Any:
            return self.value

    fecho = tvm_ffi.get_global_func("testing.echo")
    assert fecho(ValueProtocol(10)) == 10
    assert tuple(fecho(ValueProtocol([1, 2, 3]))) == (1, 2, 3)
    assert tuple(fecho(ValueProtocol([1, 2, ValueProtocol(3)]))) == (1, 2, 3)
    nested_value_protocol = ValueProtocol(ValueProtocol(ValueProtocol(10)))
    assert fecho(nested_value_protocol) == 10

    nested_value_protocol = ValueProtocol([ValueProtocol(1), ValueProtocol(2), ValueProtocol(3)])
    assert tuple(fecho(nested_value_protocol)) == (1, 2, 3)


def test_convert_func_tensor_cls_missing_attribute() -> None:
    """Passing a class without __dlpack_c_exchange_api__ raises TypeError."""

    class DummyTensor:
        pass

    with pytest.raises(TypeError, match="__dlpack_c_exchange_api__"):
        tvm_ffi.convert_func(lambda x: x, tensor_cls=DummyTensor)

    with pytest.raises(TypeError, match="__dlpack_c_exchange_api__"):
        tvm_ffi.convert_func(lambda x: x, tensor_cls=object)


def test_convert_func_raises_propagates() -> None:
    """An exception raised inside the callback propagates out to the caller."""

    def raises(x: int) -> None:
        raise ValueError(f"boom {x}")

    f = tvm_ffi.convert_func(raises)
    with pytest.raises(ValueError, match="boom 42"):
        f(42)


@pytest.mark.skipif(
    not _HAS_TORCH_DLPACK_API,
    reason="torch.Tensor.__dlpack_c_exchange_api__ not available",
)
def test_convert_func_with_torch_tensor_cls() -> None:
    """tensor_cls=torch.Tensor delivers torch.Tensor instances to the callback.

    Asserts the type *inside* the callback (which runs on the C++ -> Python
    side of the FFI boundary) — the return value's Python type depends on
    the outer caller's conversion path, so we verify shape survives the
    round-trip rather than isinstance on the return.
    """
    assert torch is not None
    torch_mod = torch
    calls = 0

    def callback(a: Any, b: Any, c: Any) -> Any:
        nonlocal calls
        calls += 1
        assert isinstance(a, torch_mod.Tensor)
        assert isinstance(b, torch_mod.Tensor)
        assert isinstance(c, torch_mod.Tensor)
        assert list(a.shape) == [2]
        assert list(b.shape) == [3]
        assert list(c.shape) == [4]
        return b

    f = tvm_ffi.convert_func(callback, tensor_cls=torch_mod.Tensor)
    a = torch_mod.zeros(2)
    b = torch_mod.ones(3)
    c = torch_mod.full((4,), 2.0)
    out = f(a, b, c)
    assert calls == 1
    assert tuple(out.shape) == (3,)


def test_callback_rawstr_and_bytearrayptr_args() -> None:
    """Regression: C++ -> Python callback with kTVMFFIRawStr / kTVMFFIByteArrayPtr args.

    When C++ invokes a Python callback with non-owning RawStr or ByteArrayPtr
    arg shapes, the callback arg setter must materialise a Python str / bytes
    directly rather than hitting the ``raise ValueError`` guard that formerly
    existed in ``TVMFFICyCallbackArgSetterFactory``.

    Two trampolines are compiled via cpp.load_inline:
    - ``invoke_with_raw_str(callback)``    — calls ``callback("hello rawstr")``
      using a C-string literal, which the TypeTraits pack as kTVMFFIRawStr.
    - ``invoke_with_byte_array_ptr(callback)`` — calls ``callback(&byte_arr)``
      where ``byte_arr`` is a ``TVMFFIByteArray`` on the stack, packed as
      kTVMFFIByteArrayPtr.
    """
    mod = tvm_ffi.cpp.load_inline(
        name="test_callback_rawstr_bytearrayptr",
        cpp_sources=r"""
            void invoke_with_raw_str(tvm::ffi::Function callback) {
              // Passing a string literal packs as kTVMFFIRawStr (const char* TypeTraits).
              callback("hello rawstr");
            }

            void invoke_with_byte_array_ptr(tvm::ffi::Function callback) {
              // Passing a TVMFFIByteArray* packs as kTVMFFIByteArrayPtr.
              static const char kData[] = "hello bytearrayptr";
              TVMFFIByteArray byte_arr{kData, sizeof(kData) - 1};
              callback(&byte_arr);
            }
        """,
        functions=["invoke_with_raw_str", "invoke_with_byte_array_ptr"],
    )

    # --- kTVMFFIRawStr path ---
    str_received: list[Any] = []
    str_cb = tvm_ffi.convert(str_received.append)
    mod.invoke_with_raw_str(str_cb)
    assert len(str_received) == 1
    assert isinstance(str_received[0], str), f"expected str, got {type(str_received[0])}"
    assert str_received[0] == "hello rawstr"

    # --- kTVMFFIByteArrayPtr path ---
    bytes_received: list[Any] = []
    bytes_cb = tvm_ffi.convert(bytes_received.append)
    mod.invoke_with_byte_array_ptr(bytes_cb)
    assert len(bytes_received) == 1
    assert isinstance(bytes_received[0], bytes), f"expected bytes, got {type(bytes_received[0])}"
    assert bytes_received[0] == b"hello bytearrayptr"