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// DO NOT EDIT! This file was auto-generated by crates/re_types_builder/src/codegen/rust/api.rs
// Based on "crates/re_types/definitions/rerun/components/tensor_data.fbs".

#![allow(trivial_numeric_casts)]
#![allow(unused_imports)]
#![allow(unused_parens)]
#![allow(clippy::clone_on_copy)]
#![allow(clippy::cloned_instead_of_copied)]
#![allow(clippy::iter_on_single_items)]
#![allow(clippy::map_flatten)]
#![allow(clippy::match_wildcard_for_single_variants)]
#![allow(clippy::needless_question_mark)]
#![allow(clippy::new_without_default)]
#![allow(clippy::redundant_closure)]
#![allow(clippy::too_many_arguments)]
#![allow(clippy::too_many_lines)]
#![allow(clippy::unnecessary_cast)]

use ::re_types_core::external::arrow2;
use ::re_types_core::ComponentName;
use ::re_types_core::SerializationResult;
use ::re_types_core::{ComponentBatch, MaybeOwnedComponentBatch};
use ::re_types_core::{DeserializationError, DeserializationResult};

/// **Component**: A multi-dimensional `Tensor` of data.
///
/// The number of dimensions and their respective lengths is specified by the `shape` field.
/// The dimensions are ordered from outermost to innermost. For example, in the common case of
/// a 2D RGB Image, the shape would be `[height, width, channel]`.
///
/// These dimensions are combined with an index to look up values from the `buffer` field,
/// which stores a contiguous array of typed values.
///
/// Note that the buffer may be encoded in a compressed format such as `jpeg` or
/// in a format with downsampled chroma, such as NV12 or YUY2.
/// For file formats, the shape is used as a hint, for chroma downsampled format
/// the shape has to be the shape of the decoded image.
#[derive(Clone, Debug, PartialEq)]
#[repr(transparent)]
pub struct TensorData(pub crate::datatypes::TensorData);

impl ::re_types_core::SizeBytes for TensorData {
    #[inline]
    fn heap_size_bytes(&self) -> u64 {
        self.0.heap_size_bytes()
    }

    #[inline]
    fn is_pod() -> bool {
        <crate::datatypes::TensorData>::is_pod()
    }
}

impl<T: Into<crate::datatypes::TensorData>> From<T> for TensorData {
    fn from(v: T) -> Self {
        Self(v.into())
    }
}

impl std::borrow::Borrow<crate::datatypes::TensorData> for TensorData {
    #[inline]
    fn borrow(&self) -> &crate::datatypes::TensorData {
        &self.0
    }
}

impl std::ops::Deref for TensorData {
    type Target = crate::datatypes::TensorData;

    #[inline]
    fn deref(&self) -> &crate::datatypes::TensorData {
        &self.0
    }
}

::re_types_core::macros::impl_into_cow!(TensorData);

impl ::re_types_core::Loggable for TensorData {
    type Name = ::re_types_core::ComponentName;

    #[inline]
    fn name() -> Self::Name {
        "rerun.components.TensorData".into()
    }

    #[allow(clippy::wildcard_imports)]
    #[inline]
    fn arrow_datatype() -> arrow2::datatypes::DataType {
        use arrow2::datatypes::*;
        DataType::Struct(std::sync::Arc::new(vec![
            Field::new(
                "shape",
                DataType::List(std::sync::Arc::new(Field::new(
                    "item",
                    <crate::datatypes::TensorDimension>::arrow_datatype(),
                    false,
                ))),
                false,
            ),
            Field::new(
                "buffer",
                <crate::datatypes::TensorBuffer>::arrow_datatype(),
                false,
            ),
        ]))
    }

    #[allow(clippy::wildcard_imports)]
    fn to_arrow_opt<'a>(
        data: impl IntoIterator<Item = Option<impl Into<::std::borrow::Cow<'a, Self>>>>,
    ) -> SerializationResult<Box<dyn arrow2::array::Array>>
    where
        Self: Clone + 'a,
    {
        use ::re_types_core::{Loggable as _, ResultExt as _};
        use arrow2::{array::*, datatypes::*};
        Ok({
            let (somes, data0): (Vec<_>, Vec<_>) = data
                .into_iter()
                .map(|datum| {
                    let datum: Option<::std::borrow::Cow<'a, Self>> = datum.map(Into::into);
                    let datum = datum.map(|datum| datum.into_owned().0);
                    (datum.is_some(), datum)
                })
                .unzip();
            let data0_bitmap: Option<arrow2::bitmap::Bitmap> = {
                let any_nones = somes.iter().any(|some| !*some);
                any_nones.then(|| somes.into())
            };
            {
                _ = data0_bitmap;
                crate::datatypes::TensorData::to_arrow_opt(data0)?
            }
        })
    }

    #[allow(clippy::wildcard_imports)]
    fn from_arrow_opt(
        arrow_data: &dyn arrow2::array::Array,
    ) -> DeserializationResult<Vec<Option<Self>>>
    where
        Self: Sized,
    {
        use ::re_types_core::{Loggable as _, ResultExt as _};
        use arrow2::{array::*, buffer::*, datatypes::*};
        Ok(crate::datatypes::TensorData::from_arrow_opt(arrow_data)
            .with_context("rerun.components.TensorData#data")?
            .into_iter()
            .map(|v| v.ok_or_else(DeserializationError::missing_data))
            .map(|res| res.map(|v| Some(Self(v))))
            .collect::<DeserializationResult<Vec<Option<_>>>>()
            .with_context("rerun.components.TensorData#data")
            .with_context("rerun.components.TensorData")?)
    }
}