# Copyright (c) 2021 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 math
import numpy as np
from scipy.special import psi
import paddle
import paddle.fluid as fluid
import paddle.static as static
from op_test import OpTest
from paddle.fluid.framework import _test_eager_guard


class TestDigammaOp(OpTest):

    def setUp(self):
        # switch to static
        paddle.enable_static()

        self.op_type = 'digamma'
        self.python_api = paddle.digamma
        self.init_dtype_type()
        shape = (5, 32)
        data = np.random.random(shape).astype(self.dtype) + 1
        self.inputs = {'X': data}
        result = np.ones(shape).astype(self.dtype)
        result = psi(data)
        self.outputs = {'Out': result}

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

    def test_check_output(self):
        self.check_output(check_eager=True)

    def test_check_grad_normal(self):
        self.check_grad(['X'], 'Out', check_eager=True)


class TestDigammaOpFp32(TestDigammaOp):

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

    def test_check_grad_normal(self):
        self.check_grad(['X'], 'Out')


class TestDigammaAPI(unittest.TestCase):

    def setUp(self):
        # switch to static
        paddle.enable_static()
        # prepare test attrs
        self.dtypes = ["float32", "float64"]
        self.places = [paddle.CPUPlace()]
        if paddle.is_compiled_with_cuda():
            self.places.append(paddle.CUDAPlace(0))
        self._shape = [8, 3, 32, 32]

    def test_in_static_mode(self):

        def init_input_output(dtype):
            input = np.random.random(self._shape).astype(dtype)
            return {'x': input}, psi(input)

        for dtype in self.dtypes:
            input_dict, sc_res = init_input_output(dtype)
            for place in self.places:
                with static.program_guard(static.Program()):
                    x = static.data(name="x", shape=self._shape, dtype=dtype)
                    out = paddle.digamma(x)

                    exe = static.Executor(place)
                    out_value = exe.run(feed=input_dict, fetch_list=[out.name])
                    np.testing.assert_allclose(out_value[0], sc_res, rtol=1e-05)

    def test_in_dynamic_mode(self):
        for dtype in self.dtypes:
            input = np.random.random(self._shape).astype(dtype)
            sc_res = psi(input)
            for place in self.places:
                # it is more convenient to use `guard` than `enable/disable_**` here
                with fluid.dygraph.guard(place):
                    input_t = paddle.to_tensor(input)
                    res = paddle.digamma(input_t).numpy()
                    np.testing.assert_allclose(res, sc_res, rtol=1e-05)

    def test_in_eager_dynamic_mode(self):
        with _test_eager_guard():
            self.test_in_dynamic_mode()

    def test_name_argument(self):
        with static.program_guard(static.Program()):
            x = static.data(name="x", shape=self._shape, dtype=self.dtypes[0])
            out = paddle.digamma(x, name="digamma_res")
            self.assertTrue("digamma_res" in out.name)

    def test_dtype_error(self):
        # in static mode
        with self.assertRaises(TypeError):
            with static.program_guard(static.Program()):
                x = static.data(name="x", shape=self._shape, dtype="int32")
                out = paddle.digamma(x, name="digamma_res")

        # in dynamic mode
        with self.assertRaises(RuntimeError):
            with fluid.dygraph.guard():
                input = np.random.random(self._shape).astype("int32")
                input_t = paddle.to_tensor(input)
                res = paddle.digamma(input_t)

        with self.assertRaises(RuntimeError):
            with fluid.dygraph.guard():
                with _test_eager_guard():
                    input = np.random.random(self._shape).astype("int32")
                    input_t = paddle.to_tensor(input)
                    res = paddle.digamma(input_t)


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