# Copyright 2024-2025 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 Tuple

import torch

from executorch.backends.arm.test import common
from executorch.backends.arm.test.tester.test_pipeline import (
    EthosU55PipelineINT,
    EthosU85PipelineINT,
    TosaPipelineFP,
    TosaPipelineINT,
    VgfPipeline,
)

exir_op = "executorch_exir_dialects_edge__ops_aten_split_with_sizes_copy_default"
input_t1 = Tuple[torch.Tensor]  # Input x


class Split(torch.nn.Module):
    test_data = {
        "split_1d_2_size_0_dim": lambda: (torch.rand(10), 2, 0),
        "split_2d_3_size_1_dim": lambda: (torch.rand(10, 10), 3, 1),
        "split_2d_2_size_4_dim": lambda: (torch.rand(10, 10), 4, -1),
        "split_4d_2_size_2_dim": lambda: (torch.rand(4, 4, 4, 4), 2, 0),
    }

    test_data_list = {
        "split_3d_2_sizes_dim": lambda: (torch.rand(10, 15, 10), [2, 2, 11], 1),
        "split_4d_2_sizes_dim_neg": lambda: (torch.rand(4, 4, 4, 4), [1, 1, 1, 1], -2),
    }

    def forward(
        self, x: torch.Tensor, split_size_or_sections: int | list[int], dim: int
    ):
        return x.split(split_size=split_size_or_sections, dim=dim)


class SplitWithSizes(torch.nn.Module):
    def forward(self, x: torch.Tensor, split_sizes: list[int], dim: int):
        return x.split_with_sizes(split_sizes=split_sizes, dim=dim)


class SplitSingleOut(torch.nn.Module):
    def forward(
        self, x: torch.Tensor, split_size_or_sections: int | list[int], dim: int
    ):
        return x.split(split_size=split_size_or_sections, dim=dim)[1]


class SplitTwoOut(torch.nn.Module):
    def forward(
        self, x: torch.Tensor, split_size_or_sections: int | list[int], dim: int
    ):
        return x.split(split_size=split_size_or_sections, dim=dim)[1:3]


class SplitCopy(torch.nn.Module):
    aten_op = "torch.ops.aten.split_copy.Tensor"
    exir_op = "executorch_exir_dialects_edge__ops_aten_split_copy_Tensor"

    def forward(
        self,
        x: torch.Tensor,
        split_size: int,
        dim: int,
    ):
        return torch.split_copy(x, split_size=split_size, dim=dim)


@common.parametrize(
    "test_data",
    (Split.test_data | Split.test_data_list),
)
def test_split_with_sizes_tosa_FP(test_data: input_t1):
    pipeline = TosaPipelineFP[input_t1](
        Split(),
        test_data(),
        aten_op=[],
        exir_op=exir_op,
    )
    pipeline.run()


@common.parametrize("test_data", Split.test_data_list)
def test_split_with_sizes_tosa_FP_2(test_data: input_t1):
    pipeline = TosaPipelineFP[input_t1](
        SplitWithSizes(),
        test_data(),
        aten_op=[],
        exir_op=exir_op,
    )
    pipeline.run()


@common.parametrize(
    "test_data",
    (Split.test_data | Split.test_data_list),
)
def test_split_with_sizes_tosa_FP_one_out(test_data: input_t1):
    pipeline = TosaPipelineFP[input_t1](
        SplitSingleOut(),
        test_data(),
        aten_op=[],
        exir_op=exir_op,
    )
    pipeline.run()


@common.parametrize(
    "test_data",
    (Split.test_data | Split.test_data_list),
)
def test_split_with_sizes_tosa_FP_two_out(test_data: input_t1):
    pipeline = TosaPipelineFP[input_t1](
        SplitTwoOut(),
        test_data(),
        aten_op=[],
        exir_op=exir_op,
    )
    pipeline.run()


@common.parametrize(
    "test_data",
    (Split.test_data | Split.test_data_list),
)
def test_split_with_sizes_tosa_INT(test_data: input_t1):
    pipeline = TosaPipelineINT[input_t1](
        Split(),
        test_data(),
        aten_op=[],
        exir_op=exir_op,
    )
    pipeline.run()


@common.parametrize("test_data", (Split.test_data | Split.test_data_list))
@common.XfailIfNoCorstone300
def test_split_with_sizes_u55_INT(test_data: input_t1):
    pipeline = EthosU55PipelineINT[input_t1](
        Split(),
        test_data(),
        aten_ops=[],
        exir_ops=exir_op,
    )
    pipeline.run()


@common.parametrize("test_data", (Split.test_data | Split.test_data_list))
@common.XfailIfNoCorstone320
def test_split_with_sizes_u85_INT(test_data: input_t1):
    pipeline = EthosU85PipelineINT[input_t1](
        Split(),
        test_data(),
        aten_ops=[],
        exir_ops=exir_op,
    )
    pipeline.run()


@common.parametrize(
    "test_data",
    (Split.test_data | Split.test_data_list),
)
@common.SkipIfNoModelConverter
def test_split_with_sizes_vgf_no_quant(test_data: input_t1):
    pipeline = VgfPipeline[input_t1](
        Split(),
        test_data(),
        aten_op=[],
        exir_op=exir_op,
        quantize=False,
    )
    pipeline.run()


@common.parametrize("test_data", Split.test_data_list)
@common.SkipIfNoModelConverter
def test_split_with_sizes_vgf_no_quant_2(test_data: input_t1):
    pipeline = VgfPipeline[input_t1](
        SplitWithSizes(),
        test_data(),
        aten_op=[],
        exir_op=exir_op,
        quantize=False,
    )
    pipeline.run()


@common.parametrize(
    "test_data",
    (Split.test_data | Split.test_data_list),
)
@common.SkipIfNoModelConverter
def test_split_with_sizes_vgf_no_quant_one_out(test_data: input_t1):
    pipeline = VgfPipeline[input_t1](
        SplitSingleOut(),
        test_data(),
        aten_op=[],
        exir_op=exir_op,
        quantize=False,
    )
    pipeline.run()


@common.parametrize(
    "test_data",
    (Split.test_data | Split.test_data_list),
)
@common.SkipIfNoModelConverter
def test_split_with_sizes_vgf_no_quant_two_out(test_data: input_t1):
    pipeline = VgfPipeline[input_t1](
        SplitTwoOut(),
        test_data(),
        aten_op=[],
        exir_op=exir_op,
        quantize=False,
    )
    pipeline.run()


@common.parametrize(
    "test_data",
    (Split.test_data | Split.test_data_list),
)
@common.SkipIfNoModelConverter
def test_split_with_sizes_vgf_quant(test_data: input_t1):
    pipeline = VgfPipeline[input_t1](
        Split(),
        test_data(),
        aten_op=[],
        exir_op=exir_op,
        quantize=True,
    )
    pipeline.run()


@common.parametrize("test_data", Split.test_data)
def test_split_tensor_tosa_FP(test_data: Tuple):
    pipeline = TosaPipelineFP[input_t1](
        SplitCopy(),
        test_data(),
        aten_op=SplitCopy.aten_op,
        exir_op=SplitCopy.exir_op,
    )
    pipeline.run()


@common.parametrize("test_data", Split.test_data)
def test_split_tensor_tosa_INT(test_data: Tuple):
    pipeline = TosaPipelineINT[input_t1](
        SplitCopy(),
        test_data(),
        aten_op=SplitCopy.aten_op,
        exir_op=SplitCopy.exir_op,
    )
    pipeline.run()


@common.XfailIfNoCorstone300
@common.parametrize("test_data", Split.test_data)
def test_split_tensor_u55_INT(test_data: Tuple):
    pipeline = EthosU55PipelineINT[input_t1](
        SplitCopy(),
        test_data(),
        aten_ops=SplitCopy.aten_op,
        exir_ops=SplitCopy.exir_op,
    )
    pipeline.run()


@common.XfailIfNoCorstone320
@common.parametrize("test_data", Split.test_data)
def test_split_tensor_u85_INT(test_data: Tuple):
    pipeline = EthosU85PipelineINT[input_t1](
        SplitCopy(),
        test_data(),
        aten_ops=SplitCopy.aten_op,
        exir_ops=SplitCopy.exir_op,
    )
    pipeline.run()


@common.parametrize("test_data", Split.test_data)
@common.SkipIfNoModelConverter
def test_split_tensor_vgf_no_quant(test_data: Tuple):
    pipeline = VgfPipeline[input_t1](
        SplitCopy(),
        test_data(),
        aten_op=SplitCopy.aten_op,
        exir_op=SplitCopy.exir_op,
        quantize=False,
    )
    pipeline.run()


@common.parametrize("test_data", Split.test_data)
@common.SkipIfNoModelConverter
def test_split_tensor_vgf_quant(test_data: Tuple):
    pipeline = VgfPipeline[input_t1](
        SplitCopy(),
        test_data(),
        aten_op=SplitCopy.aten_op,
        exir_op=SplitCopy.exir_op,
        quantize=True,
    )
    pipeline.run()