# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

# pyre-unsafe


import torch
from executorch.backends.test.suite.flow import TestFlow

from executorch.backends.test.suite.operators import (
    dtype_test,
    operator_test,
    OperatorTest,
)


class Model(torch.nn.Module):
    def __init__(
        self,
        kernel_size=3,
        stride=None,
        padding=0,
        ceil_mode=False,
        count_include_pad=True,
    ):
        super().__init__()

        # Create the avgpool layer with the given parameters
        # torch.nn.AvgPool2d accepts both int and tuple types for kernel_size, stride, and padding
        self.avgpool = torch.nn.AvgPool2d(
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            ceil_mode=ceil_mode,
            count_include_pad=count_include_pad,
        )

    def forward(self, x):
        return self.avgpool(x)


@operator_test
class AvgPool2d(OperatorTest):
    @dtype_test
    def test_avgpool2d_dtype(self, flow: TestFlow, dtype) -> None:
        # Input shape: (batch_size, channels, height, width)
        self._test_op(
            Model().to(dtype),
            ((torch.rand(1, 8, 20, 20) * 10).to(dtype),),
            flow,
        )

    def test_avgpool2d_kernel_size(self, flow: TestFlow) -> None:
        # Test with different kernel sizes
        self._test_op(
            Model(kernel_size=1),
            (torch.randn(1, 8, 20, 20),),
            flow,
        )
        self._test_op(
            Model(kernel_size=5),
            (torch.randn(1, 8, 20, 20),),
            flow,
        )
        self._test_op(
            Model(kernel_size=(3, 2)),
            (torch.randn(1, 8, 20, 20),),
            flow,
        )

    def test_avgpool2d_stride(self, flow: TestFlow) -> None:
        # Test with different stride values
        self._test_op(
            Model(stride=2),
            (torch.randn(1, 8, 20, 20),),
            flow,
        )
        self._test_op(
            Model(stride=(2, 1)),
            (torch.randn(1, 8, 20, 20),),
            flow,
        )

    def test_avgpool2d_padding(self, flow: TestFlow) -> None:
        # Test with different padding values
        self._test_op(
            Model(padding=1),
            (torch.randn(1, 8, 20, 20),),
            flow,
        )
        self._test_op(
            Model(padding=(1, 2)),
            (torch.randn(1, 8, 20, 20),),
            flow,
        )

    def test_avgpool2d_ceil_mode(self, flow: TestFlow) -> None:
        # Test with ceil_mode=True
        self._test_op(
            Model(ceil_mode=True),
            (torch.randn(1, 8, 20, 20),),
            flow,
        )

    def test_avgpool2d_count_include_pad(self, flow: TestFlow) -> None:
        # Test with count_include_pad=False
        self._test_op(
            Model(padding=1, count_include_pad=False),
            (torch.randn(1, 8, 20, 20),),
            flow,
        )

    def test_avgpool2d_batch_sizes(self, flow: TestFlow) -> None:
        # Test with batch inputs
        self._test_op(
            Model(),
            (torch.randn(2, 8, 20, 20),),
            flow,
        )
        self._test_op(
            Model(),
            (torch.randn(8, 8, 20, 20),),
            flow,
        )
        self._test_op(
            Model(),
            (torch.randn(16, 8, 20, 20),),
            flow,
        )

    def test_avgpool2d_input_sizes(self, flow: TestFlow) -> None:
        # Test with different input sizes
        self._test_op(
            Model(),
            (torch.randn(1, 4, 20, 20),),
            flow,
        )
        self._test_op(
            Model(),
            (torch.randn(1, 16, 20, 20),),
            flow,
        )

    def test_avgpool2d_combinations(self, flow: TestFlow) -> None:
        # Test with combinations of parameters
        self._test_op(
            Model(kernel_size=2, stride=2, padding=1),
            (torch.randn(1, 8, 20, 20),),
            flow,
        )
        self._test_op(
            Model(kernel_size=3, stride=2, padding=1, ceil_mode=True),
            (torch.randn(1, 8, 21, 21),),
            flow,
        )
        self._test_op(
            Model(
                kernel_size=(2, 3),
                stride=(2, 1),
                padding=(1, 0),
                count_include_pad=False,
            ),
            (torch.randn(1, 8, 20, 20),),
            flow,
        )