# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

import math
import operator
import unittest
import weakref

from test_case_base import (
    TestCaseBase,
    test_instruction_translator_cache_context,
)

import paddle
from paddle.jit.sot.psdb import check_no_breakgraph


def dispatch_len(x: paddle.Tensor):
    return len(x.shape)


def dispatch_tensor_len(x: paddle.Tensor):
    return len(x)


def dispatch_reversed(x: paddle.Tensor | int, y: paddle.Tensor | int):
    return list(reversed([x + 1, y - 1, x * 10, y + 1000]))


def dispatch_bool(x: paddle.Tensor):
    return operator.truth(x.shape) and bool(x.shape)


def dispatch_ceil(x: paddle.Tensor | float):
    return math.ceil(x) + 1


def dispatch_floor(x: paddle.Tensor | float):
    return math.floor(x) + 1


def test_sum_tuple(x: paddle.Tensor | int, y: paddle.Tensor | int):
    return sum((x, y))


def test_sum_tuple2(
    x: paddle.Tensor | int | list[int] | list[paddle.Tensor],
    y: paddle.Tensor | int | list[int] | list[paddle.Tensor],
):
    return sum((x, y), x)


def test_sum_tuple3(x):
    return sum((), x)


def test_sum_list(x: paddle.Tensor | int, y: paddle.Tensor | int):
    return sum([x, y])


def test_sum_list2(
    x: paddle.Tensor | int | list[int] | list[paddle.Tensor],
    y: paddle.Tensor | int | list[int] | list[paddle.Tensor],
):
    return sum([x, y], x)


def test_sum_list3(x):
    return sum([], x)


def test_tensor_sum(x: paddle.Tensor):
    return sum(x)


def test_tensor_sum_api(x: paddle.Tensor):
    return x.sum()


def test_pow(x: paddle.Tensor | int, y: paddle.Tensor | int):
    return pow(x, y)


def test_pow2(x: paddle.Tensor | int, y: paddle.Tensor | int):
    return pow(x, y, 1)


def test_tensor_pow_api(x: paddle.Tensor, y: paddle.Tensor | int):
    return x.pow(y)


def test_math_pow(x: int, y: int):
    return math.pow(x, y)


def test_chr(x: int | hex | paddle.Tensor):
    return chr(x)


def test_ord(x: str):
    return ord(x)


@check_no_breakgraph
def test_min():
    return min(9, 8, 2, 4, 1, 7, 3, 5, 6)


@check_no_breakgraph
def test_max():
    return max(9, 8, 2, 4, 1, 7, 3, 5, 6)


@check_no_breakgraph
def test_sqrt(x: int):
    return math.sqrt(x)


@check_no_breakgraph
def test_log(x: int):
    return math.log(x)


@check_no_breakgraph
def test_any(var):
    return any(var)


@check_no_breakgraph
def test_any_iter(var):
    return any(iter(var))


@check_no_breakgraph
def test_all(var):
    return all(var)


@check_no_breakgraph
def test_all_iter(var):
    return all(iter(var))


@check_no_breakgraph
def test_builtin_type_check_eq():
    a = 1
    b = []
    c = ()
    d = {}
    eq_results = (
        a == b, a == c, a == d,
        b == a, b == c, b == d,
        c == a, c == b, c == d,
    )  # fmt: skip
    ne_results = (
        a != b, a != c, a != d,
        b != a, b != c, b != d,
        c != a, c != b, c != d,
    )  # fmt: skip
    return eq_results, ne_results


@check_no_breakgraph
def test_is(x, y):
    return x is y


class TestBuiltinDispatch(TestCaseBase):
    def test_dispatch_len(self):
        self.assert_results(dispatch_len, paddle.to_tensor([1, 2, 3]))

    def test_dispatch_bool(self):
        self.assert_results(dispatch_bool, paddle.to_tensor([1, 2, 3]))

    def test_dispatch_tensor_len(self):
        with test_instruction_translator_cache_context() as ctx:
            self.assert_results(
                dispatch_tensor_len, paddle.to_tensor([1, 2, 3])
            )
            self.assertEqual(ctx.translate_count, 1)
            self.assert_results(
                dispatch_tensor_len, paddle.to_tensor([4, 5, 6])
            )
            self.assertEqual(ctx.translate_count, 1)

    def test_dispatch_list_reversed(self):
        self.assert_results(dispatch_reversed, paddle.to_tensor(1), 2)
        self.assert_results(dispatch_reversed, 2, paddle.to_tensor(1))

    def test_dispatch_tensor_reversed(self):
        self.assert_results(
            dispatch_reversed,
            paddle.to_tensor([1, 2]),
            paddle.to_tensor([3, 4]),
        )

    def test_not_dispatch_tensor_ceil(self):
        # ceil should break graph, since it returns a int rather than a tensor
        self.assert_results(dispatch_ceil, paddle.to_tensor(1.2))

    def test_dispatch_float_ceil(self):
        self.assert_results(dispatch_ceil, 1.2)

    def test_not_dispatch_tensor_floor(self):
        # floor should break graph, since it returns a int rather than a tensor
        self.assert_results(dispatch_floor, paddle.to_tensor(1.2))

    def test_dispatch_float_floor(self):
        self.assert_results(dispatch_floor, 1.2)

    def test_dispatch_sum(self):
        self.assert_results(test_sum_tuple, 1, 1)
        self.assert_results(test_sum_tuple, paddle.to_tensor(1), 1)
        self.assert_results(
            test_sum_tuple, paddle.to_tensor(1), paddle.to_tensor(1)
        )
        self.assert_results(
            test_sum_tuple, paddle.to_tensor([1, 2]), paddle.to_tensor(1)
        )
        self.assert_results(
            test_sum_tuple, paddle.to_tensor([1, 2]), paddle.to_tensor([1, 3])
        )
        self.assert_results(test_sum_tuple2, 1, 1)
        self.assert_results(test_sum_tuple2, [1, 2], [3, 4])
        self.assert_results(test_sum_tuple2, paddle.to_tensor(1), 1)
        self.assert_results(
            test_sum_tuple2, paddle.to_tensor(1), paddle.to_tensor(1)
        )
        self.assert_results(
            test_sum_tuple2,
            [paddle.to_tensor(1), paddle.to_tensor(2)],
            [paddle.to_tensor(3), paddle.to_tensor(4)],
        )
        self.assert_results(
            test_sum_tuple2, paddle.to_tensor([1, 2]), paddle.to_tensor(1)
        )
        self.assert_results(
            test_sum_tuple2, paddle.to_tensor([1, 2]), paddle.to_tensor([1, 3])
        )
        self.assert_results(test_sum_tuple3, 1)
        self.assert_results(test_sum_tuple3, paddle.to_tensor(1))
        self.assert_results(test_sum_list, 1, 1)
        self.assert_results(test_sum_list, paddle.to_tensor(1), 1)
        self.assert_results(
            test_sum_list, paddle.to_tensor(1), paddle.to_tensor(1)
        )
        self.assert_results(
            test_sum_list, paddle.to_tensor([1, 2]), paddle.to_tensor(1)
        )
        self.assert_results(
            test_sum_list, paddle.to_tensor([1, 2]), paddle.to_tensor([1, 3])
        )
        self.assert_results(test_sum_list2, 1, 1)
        self.assert_results(test_sum_list2, [1, 2], [3, 4])
        self.assert_results(test_sum_list2, paddle.to_tensor(1), 1)
        self.assert_results(
            test_sum_list2, paddle.to_tensor(1), paddle.to_tensor(1)
        )
        self.assert_results(
            test_sum_list2,
            [paddle.to_tensor(1), paddle.to_tensor(2)],
            [paddle.to_tensor(3), paddle.to_tensor(4)],
        )
        self.assert_results(
            test_sum_list2, paddle.to_tensor([1, 2]), paddle.to_tensor(1)
        )
        self.assert_results(
            test_sum_list2, paddle.to_tensor([1, 2]), paddle.to_tensor([1, 3])
        )
        self.assert_results(test_sum_list3, 1)
        self.assert_results(test_sum_list3, paddle.to_tensor(1))
        self.assert_results(test_tensor_sum, paddle.to_tensor([1, 2]))
        self.assert_results(test_tensor_sum, paddle.to_tensor((1, 2)))
        self.assert_results(test_tensor_sum_api, paddle.to_tensor([1, 2]))
        self.assert_results(test_tensor_sum_api, paddle.to_tensor((1, 2)))

    def test_dispatch_pow(self):
        self.assert_results(test_pow, 2, 3)
        self.assert_results(test_pow, paddle.to_tensor(2), 3)
        self.assert_results(test_pow, paddle.to_tensor(2), paddle.to_tensor(3))
        self.assert_results(test_pow2, 2, 3)
        self.assert_results(test_math_pow, 2, 3)
        self.assert_results(test_tensor_pow_api, paddle.to_tensor(2), 3)
        self.assert_results(
            test_tensor_pow_api, paddle.to_tensor(2), paddle.to_tensor(3)
        )

    def test_dispatch_chr(self):
        self.assert_results(test_chr, 65)
        self.assert_results(test_chr, 0x41)
        self.assert_results(test_chr, paddle.to_tensor(65))
        self.assert_results(test_chr, paddle.to_tensor(0x41))

    def test_dispatch_ord(self):
        self.assert_results(test_ord, "a")

    def test_dispatch_sqrt(self):
        self.assert_results(test_sqrt, 9)

    def test_dispatch_log(self):
        self.assert_results(test_log, math.e)

    def test_dispatch_min(self):
        self.assert_results(test_min)

    def test_dispatch_max(self):
        self.assert_results(test_max)

    def test_dispatch_builtin_type_check_eq(self):
        self.assert_results(test_builtin_type_check_eq)

    def test_dispatch_any(self):
        l_pure_true = [1, True, 5, 6]
        l_pure_false = [False, 0, 0]
        l_true_and_false = [1, False, 0, 3]
        d_true = {"a": 1}
        d_false = {}
        self.assert_results(test_any, l_pure_true)
        self.assert_results(test_any, l_pure_false)
        self.assert_results(test_any, l_true_and_false)
        self.assert_results(test_any, d_true)
        self.assert_results(test_any, d_false)
        self.assert_results(test_any_iter, l_true_and_false)

    def test_dispatch_all(self):
        l_pure_true = [1, True, 5, 6]
        l_pure_false = [False, 0, 0]
        l_true_and_false = [1, False, 0, 3]
        d_true = {"a": 1}
        d_false = {}
        self.assert_results(test_all, l_pure_true)
        self.assert_results(test_all, l_pure_false)
        self.assert_results(test_all, l_true_and_false)
        self.assert_results(test_all, d_true)
        self.assert_results(test_all, d_false)
        self.assert_results(test_all_iter, l_true_and_false)

    def test_dispatch_is(self):
        x = paddle.ones(shape=[1, 2])
        y = paddle.ones(shape=[1, 2])
        # TODO(wangmingkai02): support comparison of same tensor object
        # self.assert_results(test_is, x, x)
        # self.assert_results(test_is, [x], [x])
        self.assert_results(test_is, x, y)
        self.assert_results(test_is, x, None)
        self.assert_results(test_is, [x], x)
        self.assert_results(test_is, None, x)
        self.assert_results(test_is, [x], None)
        self.assert_results(test_is, None, [x])
        self.assert_results(test_is, None, None)


def run_getattr(x: paddle.Tensor):
    attr = 'dtype'
    out = getattr(x, attr)
    return out


class TestGetattr(TestCaseBase):
    def test_getattr(self):
        x = paddle.to_tensor(4)
        self.assert_results(run_getattr, x)


def tensor_hasattr(x: paddle.Tensor):
    return (
        hasattr(x, "dtype"),
        hasattr(x, "stop_gradient"),
        hasattr(x, "abs"),
        hasattr(x, "non_tensor_attr"),
    )


class ObjectHasattr:
    def __init__(self):
        attr1 = 1
        attr2 = "2"
        attr3 = [3]


def object_hasattr(x: ObjectHasattr):
    return (
        hasattr(x, "attr1"),
        hasattr(x, "attr2"),
        hasattr(x, "attr3"),
        hasattr(x, "non_obj_attr"),
    )


def layer_hasattr(layer: paddle.nn.Layer):
    return (
        hasattr(layer, "parameters"),
        hasattr(layer, "sublayers"),
        hasattr(layer, "non_layer_attr"),
    )


class TestHasattr(TestCaseBase):
    def test_tensor_hasattr(self):
        x = paddle.to_tensor(4)
        self.assert_results(tensor_hasattr, x)

    def test_object_hasattr(self):
        x = ObjectHasattr()
        self.assert_results(object_hasattr, x)

    def test_layer_hasattr(self):
        x = paddle.nn.Layer()
        self.assert_results(layer_hasattr, x)


class WeakrefableObject: ...


def weakref_breakgraph(obj):
    return weakref.ref(obj)


class TestWeakref(TestCaseBase):
    def test_weakref_breakgraph(self):
        obj = WeakrefableObject()
        self.assert_results(weakref_breakgraph, obj)


def test_builtin_type_conversion_breakgraph(x):
    return int(x), bool(x), float(x)


class TestBuiltinTypeConversion(TestCaseBase):
    def test_builtin_type_conversion_breakgraph(self):
        self.assert_results(
            test_builtin_type_conversion_breakgraph, paddle.to_tensor(1.2)
        )
        self.assert_results(
            test_builtin_type_conversion_breakgraph, paddle.to_tensor(0)
        )


@check_no_breakgraph
def test_native_code_function():
    res1 = paddle.base.libpaddle.is_compiled_with_avx()
    res2 = paddle.base.libpaddle.is_compiled_with_cuda()
    res3 = paddle.base.libpaddle.is_compiled_with_cudnn_frontend()
    res4 = paddle.base.libpaddle.is_compiled_with_rocm()
    res5 = paddle.base.libpaddle.is_compiled_with_custom_device("npu")
    res6 = paddle.base.libpaddle.is_compiled_with_ipu()
    res7 = paddle.base.libpaddle.is_compiled_with_xpu()
    res8_deprecated = (
        paddle.base.libpaddle.is_compiled_with_mkldnn()
    )  # Paddle 3.3 deprecated
    res8 = paddle.base.libpaddle.is_compiled_with_onednn()
    res9 = paddle.base.libpaddle.is_compiled_with_nccl()
    res10 = paddle.base.libpaddle.is_compiled_with_mpi()
    res11 = paddle.base.libpaddle.is_compiled_with_mpi_aware()
    res12 = paddle.base.libpaddle.is_compiled_with_cinn()
    res13 = paddle.base.libpaddle.is_compiled_with_distribute()
    res14 = paddle.base.libpaddle.is_compiled_with_brpc()
    res15 = paddle.base.libpaddle.is_compiled_with_dist()

    return (
        res1,
        res2,
        res3,
        res4,
        res5,
        res6,
        res7,
        res8_deprecated,
        res8,
        res9,
        res10,
        res11,
        res12,
        res13,
        res14,
        res15,
    )


@check_no_breakgraph
def test_native_code_function_gpu_only():
    # Directly returning device_properties causes BreakGraph due to FallbackError:
    # "ObjectVariable does not implement '_reconstruct' method"
    # Therefore, we return individual properties as primitive types instead
    device_properties = paddle.device.cuda.get_device_properties()
    return (
        device_properties.name,
        device_properties.major,
        device_properties.minor,
        device_properties.total_memory,
        device_properties.multi_processor_count,
    )


class TestNativeCodeFunction(TestCaseBase):
    def test_native_code_function(self):
        self.assert_results(test_native_code_function)

    @unittest.skipUnless(paddle.device.is_compiled_with_cuda(), "requires CUDA")
    def test_native_code_function_gpu_only(self):
        self.assert_results(test_native_code_function_gpu_only)


if __name__ == "__main__":
    unittest.main()