#   Copyright (c) 2018 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 print_function

import unittest
import numpy as np

import paddle
import paddle.fluid.core as core
import paddle.fluid as fluid
from paddle.fluid import compiler, Program, program_guard
from op_test import OpTest, convert_uint16_to_float, convert_float_to_uint16
from paddle.fluid.framework import _test_eager_guard


class TestCastOpFp32ToFp64(OpTest):

    def setUp(self):
        ipt = np.random.random(size=[10, 10])
        self.inputs = {'X': ipt.astype('float32')}
        self.outputs = {'Out': ipt.astype('float64')}
        self.attrs = {
            'in_dtype': int(core.VarDesc.VarType.FP32),
            'out_dtype': int(core.VarDesc.VarType.FP64)
        }
        self.op_type = 'cast'

    def test_check_output(self):
        self.check_output()

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


class TestCastOpFp16ToFp32(OpTest):

    def setUp(self):
        ipt = np.random.random(size=[10, 10])
        self.inputs = {'X': ipt.astype('float16')}
        self.outputs = {'Out': ipt.astype('float32')}
        self.attrs = {
            'in_dtype': int(core.VarDesc.VarType.FP16),
            'out_dtype': int(core.VarDesc.VarType.FP32)
        }
        self.op_type = 'cast'
        self.__class__.no_need_check_grad = True

    def test_check_output(self):
        self.check_output(atol=1e-3)


class TestCastOpFp32ToFp16(OpTest):

    def setUp(self):
        ipt = np.random.random(size=[10, 10])
        self.inputs = {'X': ipt.astype('float32')}
        self.outputs = {'Out': ipt.astype('float16')}
        self.attrs = {
            'in_dtype': int(core.VarDesc.VarType.FP32),
            'out_dtype': int(core.VarDesc.VarType.FP16)
        }
        self.op_type = 'cast'
        self.__class__.no_need_check_grad = True

    def test_check_output(self):
        self.check_output(atol=1e-3)


class TestCastOpBf16ToFp32(OpTest):

    def setUp(self):
        ipt = np.array(np.random.randint(10, size=[10, 10])).astype('uint16')
        self.inputs = {'X': ipt}
        self.outputs = {'Out': convert_uint16_to_float(ipt)}
        self.attrs = {
            'in_dtype': int(core.VarDesc.VarType.BF16),
            'out_dtype': int(core.VarDesc.VarType.FP32)
        }
        self.op_type = 'cast'
        self.__class__.no_need_check_grad = True

    def test_check_output(self):
        self.check_output()


class TestCastOpFp32ToBf16(OpTest):

    def setUp(self):
        ipt = np.random.random(size=[10, 10]).astype('float32')
        self.inputs = {'X': ipt}
        self.outputs = {'Out': convert_float_to_uint16(ipt)}
        self.attrs = {
            'in_dtype': int(core.VarDesc.VarType.FP32),
            'out_dtype': int(core.VarDesc.VarType.BF16)
        }
        self.op_type = 'cast'
        self.__class__.no_need_check_grad = True

    def test_check_output(self):
        self.check_output()


class TestCastOpError(unittest.TestCase):

    def test_errors(self):
        with program_guard(Program(), Program()):
            # The input type of cast_op must be Variable.
            x1 = fluid.create_lod_tensor(np.array([[-1]]), [[1]],
                                         fluid.CPUPlace())
            self.assertRaises(TypeError, fluid.layers.cast, x1, 'int32')


class TestCastOpEager(unittest.TestCase):

    def test_eager(self):
        with paddle.fluid.dygraph.base.guard():
            with _test_eager_guard():
                x = paddle.ones([2, 2], dtype="float16")
                x.stop_gradient = False
                out = paddle.cast(x, "float32")
                self.assertTrue(
                    np.array_equal(out,
                                   np.ones([2, 2]).astype("float32")))
                out.backward()
                self.assertTrue(np.array_equal(x.gradient(), x.numpy()))
                self.assertTrue(x.gradient().dtype == np.float16)


if __name__ == '__main__':
    paddle.enable_static()
    unittest.main()