# Copyright (C) 2018-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import numpy as np
import pytest
import torch

from pytorch_layer_test_class import PytorchLayerTest


@pytest.mark.parametrize('test_input', [(np.array([[1, 2], [3, 4]], dtype=np.float32),
                                         np.array([[1, 1], [2, 2]], dtype=np.float32),),
                                        (np.array([[1, 2], [3, 4]], dtype=np.float32),
                                         np.array([2, 3], dtype=np.float32),),
                                        (np.array([[1, 2], [3, 4]], dtype=np.float32),
                                         np.array([2], dtype=np.float32),),
                                        (np.array([5, 6], dtype=np.float32),
                                         np.array([[1, 2], [3, 4]], dtype=np.float32),),
                                        (np.array([5], dtype=np.float32),
                                         np.array([[1, 2], [3, 4]], dtype=np.float32),)])
class TestPow(PytorchLayerTest):
    """
    Input test data contains five test cases - elementwise power, broadcast exponent, one exponent,
    broadcast base, one base.
    """

    def _prepare_input(self):
        return self.test_input

    def create_model(self):
        class aten_pow(torch.nn.Module):

            def forward(self, input_data, exponent):
                return torch.pow(input_data, exponent)

        ref_net = None

        return aten_pow(), ref_net, "aten::pow"

    @pytest.mark.nightly
    @pytest.mark.precommit
    def test_pow(self, ie_device, precision, ir_version, test_input):
        self.test_input = test_input
        self._test(*self.create_model(), ie_device, precision, ir_version, use_convert_model=True)


class TestPowMixedTypes(PytorchLayerTest):
    def _prepare_input(self):
        if len(self.lhs_shape) == 0:
            return (torch.randn(self.rhs_shape) * 2 + 0.6).to(self.rhs_type).numpy(),
        elif len(self.rhs_shape) == 0:
            return (torch.randint(1, 3, self.lhs_shape).to(self.lhs_type).numpy(),)
        return (torch.randint(1, 3, self.lhs_shape).to(self.lhs_type).numpy(),
                (torch.randn(self.rhs_shape) * 2 + 0.6).to(self.rhs_type).numpy())

    def create_model(self, lhs_type, lhs_shape, rhs_type, rhs_shape):

        class aten_pow(torch.nn.Module):
            def __init__(self, lhs_type, lhs_shape, rhs_type, rhs_shape):
                super().__init__()
                self.lhs_type = lhs_type
                self.rhs_type = rhs_type
                if len(lhs_shape) == 0:
                    self.forward = self.forward1
                elif len(rhs_shape) == 0:
                    self.forward = self.forward2
                else:
                    self.forward = self.forward3

            def forward1(self, rhs):
                return torch.pow(torch.tensor(3).to(self.lhs_type), rhs.to(self.rhs_type))

            def forward2(self, lhs):
                return torch.pow(lhs.to(self.lhs_type), torch.tensor(3).to(self.rhs_type))

            def forward3(self, lhs, rhs):
                return torch.pow(lhs.to(self.lhs_type), rhs.to(self.rhs_type))

        ref_net = None

        return aten_pow(lhs_type, lhs_shape, rhs_type, rhs_shape), ref_net, "aten::pow"

    @pytest.mark.parametrize(("lhs_type", "rhs_type"),
                             [[torch.int32, torch.int64],
                              [torch.int32, torch.float32],
                              [torch.int32, torch.float64],
                              [torch.int64, torch.int32],
                              [torch.int64, torch.float32],
                              [torch.int64, torch.float64],
                              [torch.float32, torch.int32],
                              [torch.float32, torch.int64],
                              [torch.float32, torch.float64],
                              ])
    @pytest.mark.parametrize(("lhs_shape", "rhs_shape"), [([2, 3], [2, 3]),
                                                          ([2, 3], []),
                                                          ([], [2, 3]),
                                                          ])
    @pytest.mark.nightly
    @pytest.mark.precommit
    def test_pow_mixed_types(self, ie_device, precision, ir_version, lhs_type, lhs_shape, rhs_type, rhs_shape):
        self.lhs_type = lhs_type
        self.lhs_shape = lhs_shape
        self.rhs_type = rhs_type
        self.rhs_shape = rhs_shape
        self._test(*self.create_model(lhs_type, lhs_shape, rhs_type, rhs_shape),
                   ie_device, precision, ir_version)

class TestPowMixedTypesScalars(PytorchLayerTest):
    def _prepare_input(self):
        return (torch.randn([1,2,3,4]).numpy(),)

    def create_model(self):

        class aten_pow(torch.nn.Module):
            def forward(self, x):
                return torch.pow(x.size(2), -0.5)

        ref_net = None

        return aten_pow(), ref_net, "aten::pow"

    @pytest.mark.nightly
    @pytest.mark.precommit
    def test_pow_mixed_types(self, ie_device, precision, ir_version):
        self._test(*self.create_model(), ie_device, precision, ir_version)