#   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.

import unittest
import numpy as np

from op_test import OpTest, convert_float_to_uint16
import paddle
import paddle.fluid as fluid
from paddle.static import Program, program_guard


# situation 1: have shape( list, no tensor), no actual shape(Tensor)
class TestReshapeOp(OpTest):

    def setUp(self):
        self.init_data()
        self.op_type = "reshape2"
        self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
        self.attrs = {"shape": self.new_shape}
        self.outputs = {
            "Out": self.inputs["X"].reshape(self.infered_shape),
            'XShape': np.random.random(self.ori_shape).astype("float32")
        }

    def init_data(self):
        self.ori_shape = (2, 60)
        self.new_shape = (12, 10)
        self.infered_shape = (12, 10)

    def test_check_output(self):
        self.check_output(no_check_set=['XShape'])

    def test_check_grad(self):
        self.check_grad(["X"], "Out")


class TestReshapeOp_ZeroDim1(OpTest):

    def init_data(self):
        self.ori_shape = ()
        self.new_shape = (1)
        self.infered_shape = (1)


class TestReshapeOp_ZeroDim2(OpTest):

    def init_data(self):
        self.ori_shape = ()
        self.new_shape = (-1)
        self.infered_shape = (1)


class TestReshapeOp_ZeroDim3(OpTest):

    def init_data(self):
        self.ori_shape = (1)
        self.new_shape = ()
        self.infered_shape = ()


class TestReshapeBF16Op(OpTest):

    def setUp(self):
        self.init_data()
        self.op_type = "reshape2"
        self.dtype = np.uint16
        x = np.random.random(self.ori_shape).astype("float32")
        out = x.reshape(self.infered_shape)
        self.inputs = {"X": convert_float_to_uint16(x)}
        self.attrs = {"shape": self.new_shape}
        self.outputs = {
            "Out":
            convert_float_to_uint16(out),
            'XShape':
            convert_float_to_uint16(
                np.random.random(self.ori_shape).astype("float32"))
        }

    def init_data(self):
        self.ori_shape = (2, 60)
        self.new_shape = (12, 10)
        self.infered_shape = (12, 10)

    def test_check_output(self):
        self.check_output(no_check_set=['XShape'])

    def test_check_grad(self):
        self.check_grad(["X"], "Out")


class TestReshapeOpDimInfer1(TestReshapeOp):

    def init_data(self):
        self.ori_shape = (5, 25)
        self.new_shape = (5, -1, 5)
        self.infered_shape = (5, -1, 5)


class TestReshapeOpDimInfer2(TestReshapeOp):

    def init_data(self):
        self.ori_shape = (10, 2, 6)
        self.new_shape = (10, 0, 3, -1)
        self.infered_shape = (10, 2, 3, -1)


# situation 2: have shape(list, no tensor), have actual shape(Tensor)
class TestReshapeOpWithInputShape(OpTest):

    def setUp(self):
        self.init_data()
        self.op_type = "reshape2"

        self.inputs = {
            "X": np.random.random(self.ori_shape).astype("float32"),
            "Shape": np.array(self.actual_shape, dtype="int32")
        }
        self.attrs = {"shape": self.new_shape}
        self.outputs = {
            "Out": self.inputs["X"].reshape(self.actual_shape),
            'XShape': np.random.random(self.ori_shape).astype("float32")
        }

    def init_data(self):
        self.ori_shape = (6, 20)
        self.new_shape = (0, -1, 20)
        self.actual_shape = (2, 3, 20)

    def test_check_output(self):
        self.check_output(no_check_set=['XShape'])

    def test_check_grad(self):
        self.check_grad(["X"], "Out")


# Situation 3: have shape(list, have tensor), no actual shape(Tensor)
class TestReshapeOp_attr_ShapeTensor(OpTest):

    def setUp(self):
        self.init_data()
        self.op_type = "reshape2"

        shape_tensor = []
        for index, ele in enumerate(self.new_shape):
            shape_tensor.append(("x" + str(index), np.ones(
                (1)).astype('int32') * ele))

        self.inputs = {
            "X": np.random.random(self.ori_shape).astype("float32"),
            'ShapeTensor': shape_tensor
        }
        self.attrs = {'shape': self.shape}
        self.outputs = {
            "Out": self.inputs["X"].reshape(self.infered_shape),
            'XShape': np.random.random(self.ori_shape).astype("float32")
        }

    def init_data(self):
        self.ori_shape = (4, 25)
        self.new_shape = (10, 10)
        self.infered_shape = (10, 10)
        self.shape = (-1, -1)

    def test_check_output(self):
        self.check_output(no_check_set=['XShape'])

    def test_check_grad(self):
        self.check_grad(["X"], "Out")


class TestReshapeOpDimInfer1_attr_ShapeTensor(TestReshapeOp_attr_ShapeTensor):

    def init_data(self):
        self.ori_shape = (5, 20)
        self.new_shape = (5, -1, 20)
        self.infered_shape = (5, -1, 20)
        self.shape = (5, -1, -1)


class TestReshapeOpDimInfer2_attr_ShapeTensor(TestReshapeOp_attr_ShapeTensor):

    def init_data(self):
        self.ori_shape = (10, 2, 6)
        self.new_shape = (10, 0, 3, -1)
        self.infered_shape = (10, 2, 3, -1)
        self.shape = (10, 0, 3, -1)


# Situation 4: have shape(Tensor), no actual shape(Tensor)
class TestReshapeOp_attr_OnlyShape(OpTest):

    def setUp(self):
        self.init_data()
        self.op_type = "reshape2"

        self.inputs = {
            "X": np.random.random(self.ori_shape).astype("float32"),
            "Shape": np.array(self.new_shape, dtype="int32")
        }
        self.attrs = {}
        self.outputs = {
            "Out": self.inputs["X"].reshape(self.infered_shape),
            'XShape': np.random.random(self.ori_shape).astype("float32")
        }

    def init_data(self):
        self.ori_shape = (4, 25)
        self.new_shape = (10, 10)
        self.infered_shape = (10, 10)

    def test_check_output(self):
        self.check_output(no_check_set=['XShape'])

    def test_check_grad(self):
        self.check_grad(["X"], "Out")


class TestReshapeOpDimInfer1_attr_OnlyShape(TestReshapeOp_attr_OnlyShape):

    def init_data(self):
        self.ori_shape = (5, 20)
        self.new_shape = (5, -1, 10)
        self.infered_shape = (5, -1, 10)
        self.shape = (5, -1, -1)


class TestReshapeOpDimInfer2_attr_OnlyShape(TestReshapeOp_attr_OnlyShape):

    def init_data(self):
        self.ori_shape = (10, 2, 6)
        self.new_shape = (10, 0, 3, -1)
        self.infered_shape = (10, 2, 3, -1)
        self.shape = (10, 0, 3, -1)


# test int8 data type on CPU
class TestReshapeInt8Op(OpTest):

    def setUp(self):
        self.init_dtype()
        self.init_data()
        self.use_mkldnn = True
        self._cpu_only = True
        self.op_type = "reshape2"
        input = np.random.randint(0, 127, self.ori_shape).astype(self.dtype)
        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
        self.attrs = {
            'shape': self.new_shape,
            'use_mkldnn': self.use_mkldnn,
        }
        self.outputs = {
            "Out": self.inputs["X"].reshape(self.infered_shape),
            'XShape': np.random.random(self.ori_shape).astype(np.float32)
        }

    def init_dtype(self):
        self.dtype = np.int8

    def init_data(self):
        self.ori_shape = (10, 2, 6)
        self.new_shape = (10, 0, 3, -1)
        self.infered_shape = (10, 2, 3, -1)

    def test_check_output(self):
        self.check_output_with_place(fluid.core.CPUPlace(),
                                     atol=1e-5,
                                     no_check_set=['XShape'])

    def test_check_grad(self):
        pass


# test unt8 data type on CPU
class TestReshapeUint8Op(TestReshapeInt8Op):

    def init_dtype(self):
        self.dtype = np.uint8


class TestReshapeOpBool(TestReshapeOp):

    def setUp(self):
        self.init_data()
        self.op_type = "reshape2"
        self.inputs = {
            "X": np.random.choice([True, False], size=self.ori_shape)
        }
        self.attrs = {"shape": self.new_shape}
        self.outputs = {
            "Out": self.inputs["X"].reshape(self.infered_shape),
            'XShape': np.random.random(self.ori_shape).astype("float32")
        }

    def test_check_grad(self):
        pass


# Test python API
class TestReshapeAPI(unittest.TestCase):

    def _set_paddle_api(self):
        self.fill_constant = paddle.fluid.layers.fill_constant
        self.data = paddle.static.data
        self.to_tensor = paddle.to_tensor
        self._executed_api()

    def _executed_api(self):
        self.reshape = paddle.reshape

    def _set_fluid_api(self):
        self.fill_constant = fluid.layers.fill_constant
        self.data = paddle.static.data
        self.reshape = fluid.layers.reshape

    def _test_api(self):
        paddle.enable_static()
        input = np.random.random([2, 25]).astype("float32")
        shape = [2, 5, 5]
        main_prog = Program()
        with program_guard(main_prog, Program()):
            positive_five = self.fill_constant([1], "int32", 5)
            x = self.data(name="x", shape=[2, 25], dtype="float32")

            actual_shape = self.data(name="shape", shape=[3], dtype="int32")

            # situation 1: have shape( list, no tensor), no actual shape(Tensor)
            out_1 = self.reshape(x, shape)

            # situation 2: have shape(list, no tensor), have actual shape(Tensor)
            out_2 = fluid.layers.reshape(x,
                                         shape=shape,
                                         actual_shape=actual_shape)

            # Situation 3: have shape(list, have tensor), no actual shape(Tensor)
            out_3 = self.reshape(x, shape=[positive_five, 10])

            # Situation 4: have shape(Tensor), no actual shape(Tensor)
            out_4 = self.reshape(x, shape=actual_shape)

        exe = paddle.static.Executor(place=paddle.CPUPlace())
        res_1, res_2, res_3, res_4 = exe.run(
            main_prog,
            feed={
                "x": input,
                "shape": np.array([2, 5, 5]).astype("int32")
            },
            fetch_list=[out_1, out_2, out_3, out_4])

        assert np.array_equal(res_1, input.reshape(shape))
        assert np.array_equal(res_2, input.reshape(shape))
        assert np.array_equal(res_3, input.reshape([5, 10]))
        assert np.array_equal(res_4, input.reshape(shape))

    def test_paddle_api(self):
        self._set_paddle_api()
        self._test_api()

    def test_fluid_api(self):
        self._set_fluid_api()
        self._test_api()

    def test_imperative(self):
        self._set_paddle_api()
        input = np.random.random([2, 25]).astype("float32")
        shape = [2, 5, 5]
        with fluid.dygraph.guard():
            x = self.to_tensor(input)
            positive_five = self.fill_constant([1], "int32", 5)

            out_1 = self.reshape(x, shape)

            out_2 = self.reshape(x, shape=[positive_five, 10])

            shape_tensor = self.to_tensor(np.array([2, 5, 5]).astype("int32"))
            out_3 = self.reshape(x, shape=shape_tensor)

        assert np.array_equal(out_1.numpy(), input.reshape(shape))
        assert np.array_equal(out_2.numpy(), input.reshape([5, 10]))
        assert np.array_equal(out_3.numpy(), input.reshape(shape))


class TestStaticReshape_(TestReshapeAPI):

    def _executed_api(self):
        self.reshape = paddle.reshape_

    def test_imperative(self):
        self._set_paddle_api()
        input = np.random.random([2, 25]).astype("float32")
        shape = [2, 5, 5]
        with fluid.dygraph.guard():
            x = self.to_tensor(input)
            positive_five = self.fill_constant([1], "int32", 5)

            out_1 = self.reshape(x, shape)

            out_2 = self.reshape(x, shape=[positive_five, 10])

            shape_tensor = self.to_tensor(np.array([2, 5, 5]).astype("int32"))
            out_3 = self.reshape(x, shape=shape_tensor)

        assert np.array_equal(out_1.numpy(), input.reshape(shape))
        assert np.array_equal(out_2.numpy(), input.reshape(shape))
        assert np.array_equal(out_3.numpy(), input.reshape(shape))


# Test Input Error
class TestReshapeOpError(unittest.TestCase):

    def _set_paddle_api(self):
        self.data = paddle.static.data
        self.reshape = paddle.reshape

    def _set_fluid_api(self):
        self.data = fluid.data
        self.reshape = fluid.layers.reshape

    def _test_errors(self):
        with program_guard(Program(), Program()):
            # The x type of reshape_op must be Variable.
            def test_x_type():
                x1 = fluid.create_lod_tensor(np.array([[-1]]), [[1]],
                                             paddle.CPUPlace())
                self.reshape(x1, shape=[1])

            self.assertRaises(TypeError, test_x_type)

            # The x dtype of reshape_op must be float16, float32, float64, int32 or int64.
            def test_x_dtype():
                x2 = self.data(name="x2", shape=[2, 25], dtype="int8")
                self.reshape(x2, shape=[2, 5, 5])

            self.assertRaises(TypeError, test_x_dtype)

            def test_x_dtype_float16():
                x_float16 = self.data(name="x_float16",
                                      shape=[2, 25],
                                      dtype="float16")
                self.reshape(x_float16, shape=[2, 5, 5])

            test_x_dtype_float16()

            x3 = self.data(name="x3", shape=[2, 25], dtype="float32")

            # The argument shape's type of reshape_op must be list, tuple or Variable.
            def test_shape_type():
                self.reshape(x3, shape=1)

            self.assertRaises(TypeError, test_shape_type)

            # The argument actual_shape's type of reshape_op must be Variable or None.
            def test_actual_shape_type():
                self.reshape(x3, shape=[25, 2], actual_shape=1)

            self.assertRaises(TypeError, test_actual_shape_type)

            # The argument shape have more than one -1.
            def test_shape_1():
                self.reshape(x3, shape=[-1, -1, 5])

            self.assertRaises(AssertionError, test_shape_1)

            # The argument shape have element 0 whose index exceed the input dimension.
            def test_shape_2():
                self.reshape(x3, [2, 5, 5, 0])

            self.assertRaises(AssertionError, test_shape_2)

            # The argument shape have more than one negative value.
            def test_shape_3():
                self.reshape(x3, [-1, -2, 5])

            self.assertRaises(AssertionError, test_shape_3)

    def test_paddle_api_error(self):
        self._set_paddle_api()
        self._test_errors()

    def test_fluid_api_error(self):
        self._set_fluid_api()
        self._test_errors()


class TestDygraphReshapeAPI(unittest.TestCase):

    def setUp(self):
        self.executed_api()

    def executed_api(self):
        self.reshape = paddle.reshape

    def test_out(self):
        paddle.disable_static()
        input_1 = np.random.random([5, 1, 10]).astype("int32")
        input = paddle.to_tensor(input_1)
        output = self.reshape(x=input, shape=[5, 10])
        out_np = output.numpy()
        expected_out = np.reshape(input_1, newshape=[5, 10])
        np.testing.assert_allclose(expected_out, out_np, rtol=1e-05)

    def test_out_uint8(self):
        paddle.disable_static()
        input_1 = np.random.random([5, 1, 10]).astype("uint8")
        input = paddle.to_tensor(input_1)
        output = self.reshape(x=input, shape=[5, 10])
        out_np = output.numpy()
        expected_out = np.reshape(input_1, newshape=[5, 10])
        np.testing.assert_allclose(expected_out, out_np, rtol=1e-05)

    def test_out_float32(self):
        paddle.disable_static()
        input_1 = np.random.random([5, 1, 10]).astype("float32")
        input = paddle.to_tensor(input_1)
        output = self.reshape(x=input, shape=[5, 10])
        out_np = output.numpy()
        expected_out = np.reshape(input_1, newshape=[5, 10])
        np.testing.assert_allclose(expected_out, out_np, rtol=1e-05)


class TestDygraphReshapeInplaceAPI(TestDygraphReshapeAPI):

    def executed_api(self):
        self.reshape = paddle.reshape_


class TestReshapeZeroTensor(unittest.TestCase):

    def test_reshape_zero_tensor_success(self):
        zero_tensor = paddle.zeros([0, 2, 3])
        # since we use "0" as the dimension copy semantically in reshape,
        # we need to copy the 0 dim in the src tensor in order to make a successful zero tensor reshape
        zero_tensor = zero_tensor.reshape([0, 6])
        self.assertTrue(list(zero_tensor.shape) == [0, 6])

    def test_reshape_zero_tensor_error(self):
        zero_tensor = paddle.zeros([0, 2, 3])
        with self.assertRaises(ValueError):
            zero_tensor.reshape([2, 3])


class TestReshapeAPI_ZeroDim(unittest.TestCase):

    def test_dygraph(self):
        paddle.disable_static()
        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
        x = paddle.rand([])
        x.stop_gradient = False

        out = paddle.reshape(x, [1])
        out.backward()
        self.assertEqual(x.grad.shape, [])
        self.assertEqual(out.shape, [1])
        self.assertEqual(out.grad.shape, [1])

        out = paddle.reshape(x, [-1, 1])
        out.backward()
        self.assertEqual(x.grad.shape, [])
        self.assertEqual(out.shape, [1, 1])
        self.assertEqual(out.grad.shape, [1, 1])

        x = paddle.rand([1])
        x.stop_gradient = False
        out = paddle.reshape(x, [])
        out.backward()
        self.assertEqual(x.grad.shape, [1])
        self.assertEqual(out.shape, [])
        self.assertEqual(out.grad.shape, [])

        paddle.enable_static()

    def test_static(self):
        main_prog = fluid.Program()
        with fluid.program_guard(main_prog, fluid.Program()):
            x = paddle.rand([])
            x.stop_gradient = False
            out = paddle.reshape(x, [-1])
            fluid.backward.append_backward(out)

            prog = paddle.static.default_main_program()
            block = prog.global_block()

            x_grad = block.var(fluid.framework.grad_var_name(x.name))
            out_grad = block.var(fluid.framework.grad_var_name(out.name))

            # Test compile shape
            self.assertEqual(x.shape, ())
            self.assertEqual(out.shape, (1, ))
            self.assertEqual(x_grad.shape, ())
            self.assertEqual(out_grad.shape, (1, ))

            exe = fluid.Executor()
            result = exe.run(main_prog, fetch_list=[x, out, x_grad, out_grad])

            # Test runtime shape
            self.assertEqual(result[0].shape, ())
            self.assertEqual(result[1].shape, (1, ))
            self.assertEqual(result[2].shape, ())
            self.assertEqual(result[3].shape, (1, ))


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