# 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

from typing import List

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

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


class SplitSizeModel(torch.nn.Module):
    def __init__(self, split_size: int, dim: int = 0):
        super().__init__()
        self.split_size = split_size
        self.dim = dim

    def forward(self, x):
        return torch.split(x, self.split_size, dim=self.dim)


class SplitSectionsModel(torch.nn.Module):
    def __init__(self, sections: List[int], dim: int = 0):
        super().__init__()
        self.sections = sections
        self.dim = dim

    def forward(self, x):
        return torch.split(x, self.sections, dim=self.dim)


@operator_test
class Split(OperatorTest):
    @dtype_test
    def test_split_dtype(self, flow: TestFlow, dtype) -> None:
        self._test_op(
            SplitSizeModel(split_size=2),
            (torch.rand(6, 4).to(dtype),),
            flow,
        )

    def test_split_size_dimensions(self, flow: TestFlow) -> None:
        self._test_op(
            SplitSizeModel(split_size=2, dim=0),
            (torch.randn(6, 4),),
            flow,
        )

        self._test_op(
            SplitSizeModel(split_size=2, dim=1),
            (torch.randn(4, 6),),
            flow,
        )

        self._test_op(
            SplitSizeModel(split_size=2, dim=2),
            (torch.randn(3, 4, 6),),
            flow,
        )

    def test_split_size_uneven(self, flow: TestFlow) -> None:
        self._test_op(
            SplitSizeModel(split_size=3),
            (torch.randn(7, 4),),
            flow,
        )

        self._test_op(
            SplitSizeModel(split_size=3, dim=1),
            (torch.randn(4, 7),),
            flow,
        )

    def test_split_sections_dimensions(self, flow: TestFlow) -> None:
        self._test_op(
            SplitSectionsModel(sections=[2, 3, 1], dim=0),
            (torch.randn(6, 4),),
            flow,
        )

        self._test_op(
            SplitSectionsModel(sections=[2, 3, 1], dim=1),
            (torch.randn(4, 6),),
            flow,
        )

        self._test_op(
            SplitSectionsModel(sections=[2, 3, 1], dim=2),
            (torch.randn(3, 4, 6),),
            flow,
        )

    def test_split_negative_dim(self, flow: TestFlow) -> None:
        self._test_op(
            SplitSizeModel(split_size=2, dim=-1),
            (torch.randn(4, 6),),
            flow,
        )

        self._test_op(
            SplitSizeModel(split_size=2, dim=-2),
            (torch.randn(4, 6),),
            flow,
        )

        self._test_op(
            SplitSectionsModel(sections=[2, 3, 1], dim=-1),
            (torch.randn(4, 6),),
            flow,
        )