#!/usr/bin/env python3

# Copyright (c) 2021 CINN 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 sys
import unittest

import cinn
import numpy as np
from cinn.common import *
from cinn.frontend import *
from op_test import OpTest, OpTestTool

import paddle


class TestBroadcastToOp(OpTest):
    def setUp(self):
        self.init_case()

    def init_case(self):
        self.inputs = {"x": np.random.random([6]).astype("float32")}
        self.out_shape = [4, 5, 6]
        self.broadcast_axes = [2]

    def build_paddle_program(self, target):
        x = paddle.to_tensor(self.inputs["x"], stop_gradient=True)
        out = paddle.broadcast_to(x, shape=self.out_shape)

        self.paddle_outputs = [out]

    # Note: If the forward and backward operators are run in the same program,
    # the forward result will be incorrect.
    def build_cinn_program(self, target):
        builder = NetBuilder("BroadcastTo")
        x = builder.create_input(Float(32), self.inputs["x"].shape, "x")
        out = builder.broadcast_to(
            x, out_shape=self.out_shape, broadcast_axes=self.broadcast_axes
        )

        prog = builder.build()
        res = self.get_cinn_output(prog, target, [x], [self.inputs["x"]], [out])

        self.cinn_outputs = res

    def test_check_results(self):
        self.check_outputs_and_grads(all_equal=True)


class TestBroadcastToCase1(TestBroadcastToOp):
    def init_case(self):
        self.inputs = {"x": np.random.random([1, 1, 3]).astype("float32")}
        self.out_shape = [4, 5, 3]
        self.broadcast_axes = [0, 1, 2]


class TestBroadcastToCase2(TestBroadcastToOp):
    def init_case(self):
        self.inputs = {"x": np.random.random([5, 3]).astype("float32")}
        self.out_shape = [4, 5, 3]
        self.broadcast_axes = [1, 2]


class TestBroadcastToCase3(TestBroadcastToOp):
    def init_case(self):
        self.inputs = {"x": np.random.random([4, 3]).astype("float32")}
        self.out_shape = [4, 5, 3]
        self.broadcast_axes = [0, 2]

    def test_check_results(self):
        self.build_cinn_program(self.target)
        # because paddle and numpy do not support discontinuous broadcast,
        # so here we just pass the check until we know how to compose
        pass


class TestBroadcastToCase4(TestBroadcastToOp):
    def init_case(self):
        self.inputs = {"x": np.random.random([5]).astype("float32")}
        self.out_shape = [4, 5, 3]
        self.broadcast_axes = [1]

    def test_check_results(self):
        self.build_cinn_program(self.target)
        # because paddle and numpy do not support discontinuous broadcast,
        # so here we just pass the check until we know how to compose
        pass


class TestBroadcastToOpNoAxes(OpTest):
    def setUp(self):
        self.init_case()

    def init_case(self):
        self.inputs = {"x": np.random.random([6]).astype("float32")}
        self.out_shape = [4, 5, 6]

    def build_paddle_program(self, target):
        x = paddle.to_tensor(self.inputs["x"], stop_gradient=True)
        out = paddle.broadcast_to(x, shape=self.out_shape)

        self.paddle_outputs = [out]

    # Note: If the forward and backward operators are run in the same program,
    # the forward result will be incorrect.
    def build_cinn_program(self, target):
        builder = NetBuilder("BroadcastTo")
        x = builder.create_input(Float(32), self.inputs["x"].shape, "x")
        out = builder.broadcast_to(x, out_shape=self.out_shape)

        prog = builder.build()
        res = self.get_cinn_output(prog, target, [x], [self.inputs["x"]], [out])

        self.cinn_outputs = res

    def test_check_results(self):
        self.check_outputs_and_grads(all_equal=True)


class TestBroadcastToNoAxesCase1(TestBroadcastToOpNoAxes):
    def init_case(self):
        self.inputs = {"x": np.random.random([1, 1, 3]).astype("float32")}
        self.out_shape = [4, 5, 3]


class TestBroadcastToNoAxesCase2(TestBroadcastToOpNoAxes):
    def init_case(self):
        self.inputs = {"x": np.random.random([5, 3]).astype("float32")}
        self.out_shape = [4, 5, 3]


class TestBroadcastToNoAxesCase3(TestBroadcastToOpNoAxes):
    def init_case(self):
        self.inputs = {"x": np.random.random([4, 1, 3]).astype("float32")}
        self.out_shape = [4, 5, 3]


class TestBroadcastToNoAxesCase4(TestBroadcastToOpNoAxes):
    def init_case(self):
        self.inputs = {"x": np.random.random([1, 1, 1]).astype("float32")}
        self.out_shape = [4, 5, 3]


class TestBroadcastToNoAxesCase5(TestBroadcastToOpNoAxes):
    def init_case(self):
        self.inputs = {"x": np.random.random([5]).astype("float32")}
        self.out_shape = [4, 5, 3]

    def test_check_results(self):
        self.build_cinn_program(self.target)
        # because paddle and numpy do not support discontinuous broadcast,
        # so here we just pass the check until we know how to compose
        pass


class TestBroadcastToNoAxesCase6(TestBroadcastToOpNoAxes):
    def init_case(self):
        self.inputs = {"x": np.random.random([1]).astype("float32")}
        self.out_shape = [5]


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