# Copyright (c) 2020 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.

import unittest

import numpy as np
from op_test import (
    OpTest,
    convert_float_to_uint16,
    get_device_place,
    is_custom_device,
)
from utils import dygraph_guard

import paddle
from paddle import base
from paddle.base import Program, program_guard


def call_nonzero(x):
    input = paddle.to_tensor(x)
    return paddle.nonzero(x=input)


class TestNonZeroAPI(unittest.TestCase):
    def test_nonzero_api_as_tuple(self):
        paddle.enable_static()
        data = np.array([[1, 0], [0, 1]], dtype='float32')
        with program_guard(Program(), Program()):
            x = paddle.static.data(name='x', shape=[-1, 2], dtype='float32')
            if not paddle.framework.use_pir_api():
                x.desc.set_need_check_feed(False)
            y = paddle.nonzero(x, as_tuple=True)
            self.assertEqual(type(y), tuple)
            self.assertEqual(len(y), 2)
            z = paddle.concat(list(y), axis=0)
            exe = base.Executor(base.CPUPlace())

            (res,) = exe.run(
                feed={'x': data}, fetch_list=[z], return_numpy=False
            )
        expect_out = np.array([0, 1, 0, 1])
        np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)

        data = np.array([1, 1, 0], dtype="float32")
        with program_guard(Program(), Program()):
            x = paddle.static.data(name='x', shape=[-1], dtype='float32')
            if not paddle.framework.use_pir_api():
                x.desc.set_need_check_feed(False)
            y = paddle.nonzero(x, as_tuple=True)
            self.assertEqual(type(y), tuple)
            self.assertEqual(len(y), 1)
            z = paddle.concat(list(y), axis=0)
            exe = base.Executor(base.CPUPlace())
            (res,) = exe.run(
                feed={'x': data}, fetch_list=[z], return_numpy=False
            )
        expect_out = np.array([0, 1])
        np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)

        data = np.zeros([10, 3, 0], dtype="float32")
        with program_guard(Program(), Program()):
            x = paddle.static.data(name='x', shape=[10, 3, 0], dtype='float32')
            if not paddle.framework.use_pir_api():
                x.desc.set_need_check_feed(False)
            y = paddle.nonzero(x, as_tuple=True)
            self.assertEqual(type(y), tuple)
            self.assertEqual(len(y), 3)
            expect_out = np.zeros([0])
            for item in y:
                np.testing.assert_array_equal(expect_out, item)

    def test_nonzero_api(self):
        paddle.enable_static()
        data = np.array([[1, 0], [0, 1]], dtype="float32")
        with program_guard(Program(), Program()):
            x = paddle.static.data(name='x', shape=[-1, 2], dtype='float32')
            if not paddle.framework.use_pir_api():
                x.desc.set_need_check_feed(False)
            y = paddle.nonzero(x)
            exe = base.Executor(base.CPUPlace())
            (res,) = exe.run(
                feed={'x': data}, fetch_list=[y], return_numpy=False
            )
        expect_out = np.array([[0, 0], [1, 1]])
        np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)

        data = np.array([1, 1, 0], dtype="float32")
        with program_guard(Program(), Program()):
            x = paddle.static.data(name='x', shape=[-1], dtype='float32')
            if not paddle.framework.use_pir_api():
                x.desc.set_need_check_feed(False)
            y = paddle.nonzero(x)
            exe = base.Executor(base.CPUPlace())
            (res,) = exe.run(
                feed={'x': data}, fetch_list=[y], return_numpy=False
            )
        expect_out = np.array([[0], [1]])
        np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)

    def test_dygraph_api(self):
        data_x = np.array([[True, False], [False, True]])
        with base.dygraph.guard():
            x = paddle.to_tensor(data_x)
            z = paddle.nonzero(x)
            np_z = z.numpy()
        expect_out = np.array([[0, 0], [1, 1]])


# Base case
class TestNonzeroOp(OpTest):
    def setUp(self):
        '''Test where_index op with random value'''
        np.random.seed(2023)
        self.op_type = "where_index"
        self.python_api = call_nonzero
        self.init_shape()
        self.init_dtype()

        self.inputs = self.create_inputs()
        self.outputs = self.return_outputs()

    def test_check_output(self):
        self.check_output(check_pir=True, check_symbol_infer=False)

    def init_shape(self):
        self.shape = [8, 8]

    def init_dtype(self):
        self.dtype = np.float64

    def create_inputs(self):
        return {
            'Condition': np.random.randint(5, size=self.shape).astype(
                self.dtype
            )
        }

    def return_outputs(self):
        return {'Out': np.transpose(np.nonzero(self.inputs['Condition']))}


class TestNonzeroComplex64Op(TestNonzeroOp):
    def init_shape(self):
        self.shape = [1, 2, 3]

    def init_dtype(self):
        self.dtype = np.complex64


class TestNonzeroComplex128Op(TestNonzeroOp):
    def init_shape(self):
        self.shape = [1, 2, 3]

    def init_dtype(self):
        self.dtype = np.complex128


class TestNonzeroFP32Op(TestNonzeroOp):
    def init_shape(self):
        self.shape = [2, 10, 2]

    def init_dtype(self):
        self.dtype = np.float32


class TestNonzeroFP16Op(TestNonzeroOp):
    def init_shape(self):
        self.shape = [3, 4, 7]

    def init_dtype(self):
        self.dtype = np.float16


class TestNonzeroBF16(OpTest):
    def setUp(self):
        '''Test where_index op with bfloat16 dtype'''
        np.random.seed(2023)
        self.op_type = "where_index"
        self.python_api = call_nonzero
        self.init_shape()
        self.init_dtype()

        self.inputs = self.create_inputs()
        self.outputs = self.return_outputs()

    def test_check_output(self):
        self.check_output(check_pir=True, check_symbol_infer=False)

    def init_shape(self):
        self.shape = [12, 9]

    def init_dtype(self):
        self.dtype = np.uint16

    def create_inputs(self):
        return {
            'Condition': convert_float_to_uint16(
                np.random.randint(5, size=self.shape).astype(np.float32)
            )
        }

    def return_outputs(self):
        return {'Out': np.transpose(np.nonzero(self.inputs['Condition']))}


class TestZeroSizeOp(TestNonzeroOp):
    def init_shape(self):
        self.shape = [0, 10]

    def init_dtype(self):
        self.dtype = np.float64


class TestZeroSizeOpCase2(TestNonzeroOp):
    def init_shape(self):
        self.shape = [0, 10]

    def init_dtype(self):
        self.dtype = np.float64

    def test_check_output(self):
        self.check_output(check_pir=True, check_symbol_infer=True)


class TestNonzeroCompatibility(unittest.TestCase):
    def setUp(self):
        self.places = [paddle.CPUPlace()]
        if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
            self.places.append(get_device_place())
        self.input_data = [[1, 0, 3], [0, 5, 0], [7, 0, 9]]
        self.expected_indices = np.array(
            [[0, 0], [0, 2], [1, 1], [2, 0], [2, 2]]
        )

    def test_nonzero_with_param_aliases(self):
        with dygraph_guard():
            for place in self.places:
                paddle.device.set_device(place)
                input_tensor = paddle.to_tensor(
                    self.input_data, dtype='float32'
                )
                for param_name in ['x', 'input']:
                    for as_tuple in [False, True]:
                        kwargs = {
                            param_name: input_tensor,
                            'as_tuple': as_tuple,
                        }
                        result = paddle.nonzero(**kwargs)
                        if as_tuple:
                            combined = np.stack(
                                [r.numpy() for r in result], axis=1
                            )
                            np.testing.assert_array_equal(
                                combined, self.expected_indices
                            )
                        else:
                            np.testing.assert_array_equal(
                                result.numpy(), self.expected_indices
                            )

    def test_nonzero_with_out(self):
        def run_nonzero(test_type):
            x = paddle.to_tensor(self.input_data, dtype='float32')
            x.stop_gradient = False
            out_shape = [len(self.expected_indices), 2]
            out = (
                paddle.zeros(out_shape, dtype='int64')
                if test_type in ["with_out", "both"]
                else None
            )
            if test_type == "return":
                out = paddle.nonzero(x, out=None)
            elif test_type == "with_out":
                paddle.nonzero(x, out=out)
            elif test_type == "both":
                out = paddle.nonzero(x, out=out)
            expected = paddle._C_ops.nonzero(x)
            np.testing.assert_array_equal(out.numpy(), expected.numpy())
            loss = out.sum().astype('float32')
            loss.backward()
            return out, x.grad

        with dygraph_guard():
            for place in self.places:
                paddle.device.set_device(place)
                out1, _ = run_nonzero("return")
                out2, _ = run_nonzero("with_out")
                out3, _ = run_nonzero("both")
                for out in [out2, out3]:
                    np.testing.assert_allclose(
                        out1.numpy(), out.numpy(), rtol=1e-10
                    )


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