#!/usr/bin/env python3

# Copyright (c) 2023 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 paddle
import random
import unittest
from op_mapper_test import OpMapperTest


class TestClipOp(OpMapperTest):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2], "float32", -1.0, 1.0),
        }
        self.min_val = -random.random()
        self.max_val = random.random()

    def set_op_type(self):
        return "clip"

    def set_op_inputs(self):
        x = paddle.static.data(
            name='x',
            shape=self.feed_data['x'].shape,
            dtype=self.feed_data['x'].dtype)
        return {'X': [x]}

    def set_op_attrs(self):
        return {"min": self.min_val, "max": self.max_val}

    def set_op_outputs(self):
        return {'Out': [str(self.feed_data['x'].dtype)]}

    def test_check_results(self):
        self.check_outputs_and_grads()


class TestClipOp2D(TestClipOp):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3], "float32", -1.0, 1.0),
        }
        self.min_val = -random.random()
        self.max_val = random.random()


class TestClipOp3D(TestClipOp):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3, 4], "float32", -1.0, 1.0),
        }
        self.min_val = -random.random()
        self.max_val = random.random()


class TestClipOp4D(TestClipOp):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3, 4, 5], "float32", -1.0, 1.0),
        }
        self.min_val = -random.random()
        self.max_val = random.random()


class TestClipOpSpecialCaseWithOne(TestClipOp):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 1, 4, 5], "float32", -1.0, 1.0),
        }
        self.min_val = -random.random()
        self.max_val = random.random()


class TestClipOpSpecialCaseAllOne(TestClipOp):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([1, 1, 1, 1], "float32", -1.0, 1.0),
        }
        self.min_val = -random.random()
        self.max_val = random.random()


class TestClipOpSpecialCaseLessThan1024(TestClipOp):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3, 512, 5], "float32", -1.0, 1.0),
        }
        self.min_val = -random.random()
        self.max_val = random.random()


class TestClipOpSpecialCaseGreaterThan1024(TestClipOp):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3, 4, 2048], "float32", -1.0, 1.0),
        }
        self.min_val = -random.random()
        self.max_val = random.random()


class TestClipOpMaxTensor(TestClipOp):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3, 4], "float32", -1.0, 1.0),
            'max_input': self.random([1], "float32")
        }
        self.min_val = -random.random()
        self.max_val = random.random()

    def set_op_inputs(self):
        x = paddle.static.data(
            name='x',
            shape=self.feed_data['x'].shape,
            dtype=self.feed_data['x'].dtype)
        max_input = paddle.static.data(
            name='max_input',
            shape=self.feed_data['max_input'].shape,
            dtype=self.feed_data['max_input'].dtype)
        return {'X': [x], 'Max': [max_input]}


class TestClipOpMaxTensorInt32(TestClipOpMaxTensor):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3, 4], "int32"),
            'max_input': self.random([1], "int32")
        }
        self.min_val = -random.random()
        self.max_val = random.random()


class TestClipOpMaxTensorFloat64(TestClipOpMaxTensor):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3, 4], "float64"),
            'max_input': self.random([1], "float64")
        }
        self.min_val = -random.random()
        self.max_val = random.random()


class TestClipOpMaxTensorTypeCast(TestClipOpMaxTensor):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3, 4], "float64"),
            'max_input': self.random([1], "float32")
        }
        self.min_val = -random.random()
        self.max_val = random.random()


class TestClipOpMinTensor(TestClipOp):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3, 4], "float32"),
            'min_input': self.random([1], "float32")
        }
        self.min_val = -random.random()
        self.max_val = random.random()

    def set_op_inputs(self):
        x = paddle.static.data(
            name='x',
            shape=self.feed_data['x'].shape,
            dtype=self.feed_data['x'].dtype)
        min_input = paddle.static.data(
            name='min_input',
            shape=self.feed_data['min_input'].shape,
            dtype=self.feed_data['min_input'].dtype)
        return {'X': [x], 'Min': [min_input]}

    def set_op_attrs(self):
        return {"min": 0.0, "max": 1.0}


class TestClipOpMinTensorInt32(TestClipOpMinTensor):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3, 4], "int32"),
            'min_input': self.random([1], "int32")
        }
        self.min_val = -random.random()
        self.max_val = random.random()


class TestClipOpMinTensorFloat64(TestClipOpMinTensor):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3, 4], "float64"),
            'min_input': self.random([1], "float64")
        }
        self.min_val = -random.random()
        self.max_val = random.random()


class TestClipOpMinTensorTypeCast(TestClipOpMinTensor):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3, 4], "float64"),
            'min_input': self.random([1], "float32")
        }
        self.min_val = -random.random()
        self.max_val = random.random()


class TestClipOpFloat64(TestClipOp):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3, 4], "float64"),
        }
        self.min_val = -random.random()
        self.max_val = random.random()


class TestClipOpInt32(TestClipOp):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3, 4], "int32", low=0, high=10),
        }
        self.min_val = -random.random()
        self.max_val = random.random()

    def set_op_attrs(self):
        return {"min": 3.0, "max": 7.0}

    def set_op_outputs(self):
        return {'Out': [str(self.feed_data['x'].dtype)]}

    def test_check_results(self):
        self.check_outputs_and_grads()


class TestClipOpInt64(TestClipOpInt32):
    def init_input_data(self):
        self.feed_data = {
            'x': self.random([2, 3, 4], "int64", low=0, high=10),
        }
        self.min_val = -random.random()
        self.max_val = random.random()


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