# Copyright 2024-2026 Arm Limited and/or its affiliates.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.


from typing import List, Tuple, Union

import torch
from executorch.backends.arm.quantizer.arm_quantizer import (
    get_symmetric_a8w4_quantization_config,
)
from executorch.backends.arm.test import common
from executorch.backends.arm.test.tester.test_pipeline import (
    EthosU55PipelineINT,
    EthosU85PipelineINT,
    OpNotSupportedPipeline,
    TosaPipelineFP,
    TosaPipelineINT,
    VgfPipeline,
)


aten_op = "torch.ops.aten.conv2d.default"
exir_op = "executorch_exir_dialects_edge__ops_aten_convolution_default"


class Conv2d(torch.nn.Module):
    """Creates one or many chained 2D-convolutions.

    For multiple convolutions, the respective parameters are provided as lists.

    """

    def __init__(
        self,
        height=8,
        width=8,
        nbr_conv=1,  # Number of chained convs
        in_channels: Union[List, int, None] = None,
        out_channels: Union[List, int, None] = None,
        kernel_size: Union[List, Tuple, None] = None,
        stride: Union[List, Tuple, int, None] = None,
        padding: Union[List, Tuple, int, None] = None,
        dilation: Union[List, Tuple, int, None] = None,
        groups: Union[List, int, None] = None,
        bias: Union[List, bool, None] = None,
        padding_mode: Union[List, str, None] = None,
        batches=1,
        dtype=torch.float,
    ):
        super().__init__()
        self.nbr_convs = nbr_conv
        self.groups = groups

        # Handle default values
        in_channels = [2] * nbr_conv if in_channels is None else in_channels
        out_channels = [1 * nbr_conv] if out_channels is None else out_channels
        kernel_size = [(3, 3)] * nbr_conv if kernel_size is None else kernel_size
        stride = [(2, 2)] * nbr_conv if stride is None else stride
        padding = [(1, 1)] * nbr_conv if padding is None else padding
        dilation = [(1, 1)] * nbr_conv if dilation is None else dilation
        groups = [1] * nbr_conv if groups is None else groups
        bias = [True] * nbr_conv if bias is None else bias
        padding_mode = ["zeros"] * nbr_conv if padding_mode is None else padding_mode

        # This allows the input parameters to be either a single value or a list
        # as type hint implies
        if not isinstance(in_channels, List):
            in_channels = [in_channels]
        if not isinstance(out_channels, List):
            out_channels = [out_channels]
        if not isinstance(kernel_size, List):
            kernel_size = [kernel_size]
        if not isinstance(stride, List):
            stride = [stride]
        if not isinstance(padding, List):
            padding = [padding]
        if not isinstance(dilation, List):
            dilation = [dilation]
        if not isinstance(groups, List):
            groups = [groups]
        if not isinstance(bias, List):
            bias = [bias]
        if not isinstance(padding_mode, List):
            padding_mode = [padding_mode]

        self.batches = batches
        self.in_channels = in_channels
        self.height = height
        self.width = width
        self.dtype = dtype

        # Build chain of convs
        for i in range(self.nbr_convs):
            setattr(
                self,
                f"conv_{i}",
                torch.nn.Conv2d(
                    in_channels=in_channels[i],
                    out_channels=out_channels[i],
                    kernel_size=kernel_size[i],
                    stride=stride[i],
                    padding=padding[i],
                    dilation=dilation[i],
                    groups=groups[i],
                    bias=bias[i],
                    padding_mode=padding_mode[i],
                ).to(dtype),
            )

    def get_inputs(self):
        return (
            torch.randn(self.batches, self.in_channels[0], self.height, self.width).to(
                self.dtype
            ),
        )

    def forward(self, x):
        for i in range(self.nbr_convs):
            conv = getattr(self, f"conv_{i}")
            x = conv(x)
        return x


conv2d_2x2_3x2x14x14_nobias = Conv2d(
    in_channels=2,
    out_channels=3,
    kernel_size=(2, 2),
    stride=1,
    bias=False,
    padding=0,
    width=14,
    height=14,
    batches=2,
)

conv2d_3x3_1x3x24x24_st1 = Conv2d(
    in_channels=3,
    out_channels=10,
    kernel_size=(3, 3),
    stride=1,
    padding=0,
    width=24,
    height=24,
    batches=1,
)

conv2d_3x3_1x3x12x12_st2_pd1 = Conv2d(
    in_channels=3,
    out_channels=4,
    kernel_size=(3, 3),
    stride=2,
    padding=1,
    width=12,
    height=12,
    batches=1,
)

conv2d_1x1_1x2x16x16_st1 = Conv2d(
    in_channels=2,
    out_channels=1,
    kernel_size=(1, 1),
    stride=1,
    padding=0,
    width=16,
    height=16,
    batches=1,
)

conv2d_2x2_2x1x14x13_st2 = Conv2d(
    in_channels=2,
    out_channels=1,
    kernel_size=(2, 2),
    stride=2,
    padding=0,
    width=14,
    height=13,
    batches=1,
)

conv2d_5x5_3x2x24x24_st1 = Conv2d(
    in_channels=2,
    out_channels=3,
    kernel_size=(5, 5),
    stride=1,
    padding=0,
    width=24,
    height=24,
    batches=2,
)

conv2d_3x3_1x3x28x28_st2_pd1 = Conv2d(
    in_channels=3,
    out_channels=16,
    kernel_size=(3, 3),
    stride=2,
    padding=1,
    width=28,
    height=28,
    batches=1,
)

conv2d_5x5_1x3x14x15_st3_pd1 = Conv2d(
    in_channels=3,
    out_channels=16,
    kernel_size=(5, 5),
    stride=3,
    padding=1,
    width=14,
    height=15,
    batches=1,
)

conv2d_7x7_1x3x16x16_st2_pd1_dl2 = Conv2d(
    in_channels=3,
    out_channels=3,
    kernel_size=(7, 7),
    stride=2,
    padding=1,
    dilation=2,
    width=16,
    height=16,
    batches=1,
)

conv2d_7x7_1x3x15x15_st1_pd0_dl1 = Conv2d(
    in_channels=3,
    out_channels=3,
    kernel_size=(7, 7),
    stride=1,
    padding=0,
    dilation=1,
    width=15,
    height=15,
    batches=1,
)

conv2d_5x5_1x3x14x14_st5_pd0_dl1 = Conv2d(
    in_channels=3,
    out_channels=3,
    kernel_size=(5, 5),
    stride=5,
    padding=0,
    dilation=1,
    width=14,
    height=14,
    batches=1,
)

conv2d_5x5_1x3x9x9_st5_pd0_dl1 = Conv2d(
    in_channels=3,
    out_channels=3,
    kernel_size=(5, 5),
    stride=5,
    padding=0,
    dilation=1,
    width=9,
    height=9,
    batches=1,
)

conv2d_3x3_1x3x8x9_st3_pd0_dl1 = Conv2d(
    in_channels=3,
    out_channels=3,
    kernel_size=(3, 3),
    stride=3,
    padding=0,
    dilation=1,
    width=8,
    height=9,
    batches=1,
)

conv2d_3x3_1x3x9x8_st3_pd0_dl1 = Conv2d(
    in_channels=3,
    out_channels=3,
    kernel_size=(3, 3),
    stride=3,
    padding=0,
    dilation=1,
    width=8,
    height=9,
    batches=1,
)

conv2d_3x4_1x3x7x7_st3_pd0_dl1 = Conv2d(
    in_channels=3,
    out_channels=3,
    kernel_size=(3, 4),
    stride=3,
    padding=0,
    dilation=1,
    width=7,
    height=7,
    batches=1,
)

conv2d_4x3_1x3x7x7_st3_pd0_dl1 = Conv2d(
    in_channels=3,
    out_channels=3,
    kernel_size=(4, 3),
    stride=3,
    padding=0,
    dilation=1,
    width=7,
    height=7,
    batches=1,
)

two_conv2d_nobias = Conv2d(
    nbr_conv=2,
    width=32,
    height=32,
    in_channels=[3, 10],
    out_channels=[10, 15],
    kernel_size=[(5, 5), (5, 5)],
    stride=[1, 1],
    padding=[0, 0],
    bias=[False, False],
    batches=1,
)

two_conv2d = Conv2d(
    nbr_conv=2,
    width=32,
    height=32,
    in_channels=[3, 10],
    out_channels=[10, 15],
    kernel_size=[(5, 5), (5, 5)],
    stride=[1, 1],
    padding=[0, 0],
    bias=[True, True],
    batches=1,
)

conv2d_groups = Conv2d(
    in_channels=12,
    out_channels=9,
    kernel_size=(3, 3),
    stride=1,
    padding=0,
    dilation=1,
    width=7,
    height=7,
    batches=1,
    groups=3,
    bias=False,
)

conv2d_groups_bias = Conv2d(
    in_channels=15,
    out_channels=5,
    kernel_size=(3, 3),
    stride=1,
    padding=0,
    dilation=1,
    width=7,
    height=7,
    batches=1,
    groups=5,
    bias=True,
)

# Shenanigan to get a nicer output when test fails. With unittest it looks like:
# FAIL: test_convolution_2d_tosa_INT_2_3x3_1x3x12x12_st2_pd1
test_data_FP = {
    "2x2_3x2x14x14_nobias": lambda: conv2d_2x2_3x2x14x14_nobias,
    "3x3_1x3x24x24_st1": lambda: conv2d_3x3_1x3x24x24_st1,
    "3x3_1x3x12x12_st2_pd1": lambda: conv2d_3x3_1x3x12x12_st2_pd1,
    "1x1_1x2x16x16_st1": lambda: conv2d_1x1_1x2x16x16_st1,
    "2x2_2x1x14x13_st2_needs_adjust_pass": lambda: conv2d_2x2_2x1x14x13_st2,
    "5x5_1x3x14x15_st3_pd1_needs_adjust_pass": lambda: conv2d_5x5_1x3x14x15_st3_pd1,
    "7x7_1x3x16x16_st2_pd1_dl2_needs_adjust_pass": lambda: conv2d_7x7_1x3x16x16_st2_pd1_dl2,
    "7x7_1x3x15x15_st1_pd0_dl1_needs_adjust_pass": lambda: conv2d_7x7_1x3x15x15_st1_pd0_dl1,
    "5x5_1x3x14x14_st5_pd0_dl1_needs_adjust_pass": lambda: conv2d_5x5_1x3x14x14_st5_pd0_dl1,
    "5x5_1x3x9x9_st5_pd0_dl1_needs_adjust_pass": lambda: conv2d_5x5_1x3x9x9_st5_pd0_dl1,
    "3x3_1x3x9x8_st3_pd0_dl1_needs_adjust_pass": lambda: conv2d_3x3_1x3x9x8_st3_pd0_dl1,
    "3x3_1x3x8x9_st3_pd0_dl1_needs_adjust_pass": lambda: conv2d_3x3_1x3x8x9_st3_pd0_dl1,
    "3x4_1x3x7x7_st3_pd0_dl1_needs_adjust_pass": lambda: conv2d_3x4_1x3x7x7_st3_pd0_dl1,
    "4x3_1x3x7x7_st3_pd0_dl1_needs_adjust_pass": lambda: conv2d_4x3_1x3x7x7_st3_pd0_dl1,
    "5x5_3x2x24x24_st1": lambda: conv2d_5x5_3x2x24x24_st1,
    "3x3_1x3x28x28_st2_pd1": lambda: conv2d_3x3_1x3x28x28_st2_pd1,
    "two_conv2d_nobias": lambda: two_conv2d_nobias,
    "two_conv2d": lambda: two_conv2d,
    "groups": lambda: conv2d_groups,
    "groups_bias": lambda: conv2d_groups_bias,
}

test_data_FP_bf16 = {
    "bf16_3x3": lambda: Conv2d(
        height=12,
        width=12,
        in_channels=3,
        out_channels=4,
        kernel_size=(3, 3),
        stride=(1, 1),
        padding=(1, 1),
        bias=True,
        dtype=torch.bfloat16,
    ),
    "bf16_1x1": lambda: Conv2d(
        height=8,
        width=8,
        in_channels=2,
        out_channels=2,
        kernel_size=(1, 1),
        stride=(2, 1),
        padding=(0, 3),
        bias=False,
        dtype=torch.bfloat16,
    ),
}
test_data_FP_fp16 = {
    "fp16_3x3": lambda: Conv2d(
        height=12,
        width=12,
        in_channels=3,
        out_channels=4,
        kernel_size=(3, 3),
        stride=(1, 1),
        padding=(1, 1),
        bias=True,
        dtype=torch.float16,
    ),
    "fp16_1x1": lambda: Conv2d(
        height=8,
        width=8,
        in_channels=2,
        out_channels=2,
        kernel_size=(1, 1),
        stride=(2, 1),
        padding=(0, 3),
        bias=False,
        dtype=torch.float16,
    ),
}

# Generate a new test set paired with per_channel_quant=True/False.
test_data_INT = {
    f"{k},per_channel_quant={q}": (lambda v=v, q=q: (v(), q))
    for (k, v) in test_data_FP.items()
    for q in [True, False]
}

input_t = Tuple[torch.Tensor]


def _get_dtype_count(model: torch.nn.Module):
    # Set nbr_conv to be the amount of groups set if necessary.
    nbr_convs: int = model.nbr_convs if model.groups is None else model.groups  # noqa
    return {
        "CONST": {"INT4": nbr_convs * 2},  # One for the weight, one for the zp.
        "CONV2D": {"INT32": nbr_convs},
        "RESCALE": {"INT8": nbr_convs},
    }


@common.parametrize(
    "test_data",
    test_data_INT,
)
def test_convolution_2d_vgf_quant_a8w4(test_data):
    model, per_channel_quantization = test_data()
    pipeline = VgfPipeline[input_t](
        model,
        model.get_inputs(),
        aten_op,
        exir_op,
    )
    pipeline.quantizer.set_global(
        get_symmetric_a8w4_quantization_config(is_per_channel=per_channel_quantization)
    )
    pipeline.run()


@common.parametrize("test_data", test_data_FP | test_data_FP_bf16 | test_data_FP_fp16)
def test_convolution_2d_tosa_FP(test_data):
    model = test_data()
    pipeline = TosaPipelineFP[input_t](
        model,
        model.get_inputs(),
        aten_op,
        exir_op,
        tosa_extensions=["bf16"],
    )
    pipeline.run()


@common.parametrize("test_data", test_data_INT)
def test_convolution_2d_tosa_INT(test_data):
    model, per_channel_quantization = test_data()
    pipeline = TosaPipelineINT[input_t](
        model,
        model.get_inputs(),
        aten_op,
        exir_op,
        per_channel_quantization=per_channel_quantization,
        qtol=1,
    )
    pipeline.run()


@common.parametrize("test_data", test_data_INT)
def test_convolution_2d_tosa_INT_a8w4(test_data):
    model, per_channel_quantization = test_data()
    pipeline = TosaPipelineINT[input_t](
        model,
        model.get_inputs(),
        aten_op,
        exir_op,
        tosa_extensions=["int4"],
        frobenius_threshold=0.4,
    )
    pipeline.quantizer.set_global(
        get_symmetric_a8w4_quantization_config(is_per_channel=per_channel_quantization)
    )
    pipeline.add_stage_after(
        "to_edge_transform_and_lower",
        pipeline.tester.check_dtype_count,
        _get_dtype_count(model),
    )
    pipeline.run()


@common.parametrize("test_data", test_data_INT)
@common.XfailIfNoCorstone300
def test_convolution_2d_u55_INT(test_data):
    model, per_channel_quantization = test_data()
    pipeline = EthosU55PipelineINT[input_t](
        model,
        model.get_inputs(),
        aten_op,
        exir_op,
        per_channel_quantization=per_channel_quantization,
    )
    pipeline.run()


@common.parametrize("test_data", test_data_INT)
@common.XfailIfNoCorstone300
def test_convolution_2d_u55_INT_a8w4(test_data):
    model, per_channel_quantization = test_data()
    pipeline = EthosU55PipelineINT[input_t](
        model,
        model.get_inputs(),
        aten_op,
        exir_op,
    )
    pipeline.quantizer.set_global(
        get_symmetric_a8w4_quantization_config(is_per_channel=per_channel_quantization)
    )
    pipeline.run()


@common.parametrize("test_data", test_data_INT)
@common.XfailIfNoCorstone320
def test_convolution_u85_INT(test_data):
    model, per_channel_quantization = test_data()
    pipeline = EthosU85PipelineINT[input_t](
        model,
        model.get_inputs(),
        aten_op,
        exir_op,
        per_channel_quantization=per_channel_quantization,
    )
    pipeline.run()


@common.parametrize("test_data", test_data_INT)
@common.XfailIfNoCorstone320
def test_convolution_2d_u85_INT_a8w4(test_data):
    model, per_channel_quantization = test_data()
    pipeline = EthosU85PipelineINT[input_t](
        model,
        model.get_inputs(),
        aten_op,
        exir_op,
    )
    pipeline.quantizer.set_global(
        get_symmetric_a8w4_quantization_config(is_per_channel=per_channel_quantization)
    )
    pipeline.run()


@common.parametrize("test_data", test_data_FP | test_data_FP_fp16)
@common.SkipIfNoModelConverter
def test_convolution_2d_vgf_no_quant(test_data):
    model = test_data()
    pipeline = VgfPipeline[input_t](
        model,
        model.get_inputs(),
        aten_op,
        exir_op,
        quantize=False,
    )
    pipeline.run()


@common.parametrize("test_data", test_data_INT)
@common.SkipIfNoModelConverter
def test_convolution_2d_vgf_quant(test_data):
    model, per_channel_quantization = test_data()
    pipeline = VgfPipeline[input_t](
        model,
        model.get_inputs(),
        aten_op,
        exir_op,
        per_channel_quantization=per_channel_quantization,
        quantize=True,
    )
    pipeline.run()


reject_suite = {
    "large_stride": lambda: Conv2d(
        in_channels=1,
        out_channels=1,
        kernel_size=(2, 4),
        stride=(2, 4),
        padding=1,
        width=10,
        height=14,
        batches=1,
    ),
    "large_kernel_height": lambda: Conv2d(
        in_channels=1,
        out_channels=1,
        kernel_size=(2, 65),
        stride=(1, 1),
        padding=0,
        width=70,
        height=70,
        batches=1,
    ),
    "large_kernel": lambda: Conv2d(
        in_channels=1,
        out_channels=1,
        kernel_size=(70, 60),
        stride=(1,),
        padding=0,
        width=80,
        height=80,
        batches=1,
    ),
}


@common.parametrize("module", reject_suite)
def test_convolution_2d_u55_INT_not_delegated(module: Conv2d):
    OpNotSupportedPipeline(
        module(),
        module().get_inputs(),
        {"executorch_exir_dialects_edge__ops_aten_convolution_default": 1},
        quantize=True,
        u55_subset=True,
    ).run()


@common.parametrize("test_data", test_data_INT)
def test_convolution_2d_tosa_INT_a16w8(test_data: input_t):
    """Test conv2d with 16A8W quantization for TOSA INT."""
    model, per_channel_quantization = test_data()
    pipeline = TosaPipelineINT[input_t](
        model,
        model.get_inputs(),
        aten_op,
        exir_op,
        tosa_extensions=["int16"],
        per_channel_quantization=per_channel_quantization,
    )
    pipeline.run()


@common.parametrize("test_data", test_data_INT)
@common.XfailIfNoCorstone300
def test_convolution_2d_u55_INT_a16w8(test_data: input_t):
    """Test conv2d with 16A8W quantization on U55 (16-bit activations, 8-bit
    weights)
    """
    model, per_channel_quantization = test_data()
    pipeline = EthosU55PipelineINT[input_t](
        model,
        model.get_inputs(),
        aten_op,
        exir_op,
        a16w8_quantization=True,
        use_to_edge_transform_and_lower=True,
        per_channel_quantization=per_channel_quantization,
    )
    pipeline.run()


@common.parametrize("test_data", test_data_INT)
@common.XfailIfNoCorstone320
def test_convolution_2d_u85_INT_a16w8(test_data: input_t):
    """Test conv2d with 16A8W quantization on U85 (16-bit activations, 8-bit
    weights)
    """
    model, per_channel_quantization = test_data()
    pipeline = EthosU85PipelineINT[input_t](
        model,
        model.get_inputs(),
        aten_op,
        exir_op,
        a16w8_quantization=True,
        use_to_edge_transform_and_lower=True,
        per_channel_quantization=per_channel_quantization,
    )
    pipeline.run()