core: Implement codegen for indexing into ndarray
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0d5c53e60c
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@ -2,7 +2,7 @@ use std::{collections::HashMap, convert::TryInto, iter::once, iter::zip};
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use crate::{
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codegen::{
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classes::{ListValue, RangeValue},
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classes::{ListValue, NDArrayValue, RangeValue},
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concrete_type::{ConcreteFuncArg, ConcreteTypeEnum, ConcreteTypeStore},
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gen_in_range_check,
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get_llvm_type,
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@ -1190,6 +1190,213 @@ pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
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}
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}
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/// Generates code for a subscript expression on an `ndarray`.
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///
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/// * `ty` - The `Type` of the `NDArray` elements.
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/// * `ndims` - The `Type` of the `NDArray` number-of-dimensions `Literal`.
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/// * `v` - The `NDArray` value.
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/// * `slice` - The slice expression used to subscript into the `ndarray`.
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fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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ty: Type,
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ndims: Type,
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v: NDArrayValue<'ctx>,
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slice: &Expr<Option<Type>>,
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) -> Result<Option<ValueEnum<'ctx>>, String> {
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let llvm_void = ctx.ctx.void_type();
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let llvm_i1 = ctx.ctx.bool_type();
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let llvm_i8 = ctx.ctx.i8_type();
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let llvm_usize = generator.get_size_type(ctx.ctx);
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let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
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let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) else {
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unreachable!()
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};
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let ndims = values.iter()
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.map(|ndim| match *ndim {
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SymbolValue::U64(v) => Ok(v),
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SymbolValue::U32(v) => Ok(v as u64),
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SymbolValue::I32(v) => u64::try_from(v)
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.map_err(|_| format!("Expected non-negative literal for TNDArray.ndims, got {v}")),
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SymbolValue::I64(v) => u64::try_from(v)
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.map_err(|_| format!("Expected non-negative literal for TNDArray.ndims, got {v}")),
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_ => unreachable!(),
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})
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.collect::<Result<Vec<_>, _>>()?;
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assert!(!ndims.is_empty());
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let ndarray_ty_enum = TypeEnum::TNDArray {
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ty,
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ndims: ctx.unifier.get_fresh_literal(
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ndims.iter().map(|v| SymbolValue::U64(v - 1)).collect(),
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None,
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),
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};
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let ndarray_ty = ctx.unifier.add_ty(ndarray_ty_enum);
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let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
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let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
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let llvm_ndarray_data_t = ctx.get_llvm_type(generator, ty).as_basic_type_enum();
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// Check that len is non-zero
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let len = v.load_ndims(ctx);
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ctx.make_assert(
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generator,
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ctx.builder.build_int_compare(IntPredicate::SGT, len, llvm_usize.const_zero(), ""),
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"0:IndexError",
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"too many indices for array: array is {0}-dimensional but 1 were indexed",
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[Some(len), None, None],
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slice.location,
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);
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if ndims.len() == 1 && ndims[0] == 1 {
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// Accessing an element from a 1-dimensional `ndarray`
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if let ExprKind::Slice { .. } = &slice.node {
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return Err(String::from("subscript operator for ndarray not implemented"))
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}
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let index = if let Some(v) = generator.gen_expr(ctx, slice)? {
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v.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value()
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} else {
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return Ok(None)
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};
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Ok(Some(v.get_data()
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.get_const(
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ctx,
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generator,
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ctx.ctx.i32_type().const_array(&[index]),
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None,
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)
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.into()))
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} else {
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// Accessing an element from a multi-dimensional `ndarray`
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if let ExprKind::Slice { .. } = &slice.node {
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return Err(String::from("subscript operator for ndarray not implemented"))
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}
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let index = if let Some(v) = generator.gen_expr(ctx, slice)? {
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v.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value()
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} else {
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return Ok(None)
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};
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// Create a new array, remove the top dimension from the dimension-size-list, and copy the
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// elements over
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let subscripted_ndarray = generator.gen_var_alloc(
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ctx,
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llvm_ndarray_t.into(),
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None
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)?;
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let ndarray = NDArrayValue::from_ptr_val(
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subscripted_ndarray,
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llvm_usize,
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None
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);
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let num_dims = v.load_ndims(ctx);
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ndarray.store_ndims(
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ctx,
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generator,
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ctx.builder.build_int_sub(num_dims, llvm_usize.const_int(1, false), ""),
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);
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let ndarray_num_dims = ndarray.load_ndims(ctx);
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ndarray.create_dims(ctx, llvm_usize, ndarray_num_dims);
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let memcpy_fn_name = format!(
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"llvm.memcpy.p0i8.p0i8.i{}",
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generator.get_size_type(ctx.ctx).get_bit_width(),
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);
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let memcpy_fn = ctx.module.get_function(memcpy_fn_name.as_str()).unwrap_or_else(|| {
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let fn_type = llvm_void.fn_type(
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&[
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llvm_pi8.into(),
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llvm_pi8.into(),
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llvm_usize.into(),
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llvm_i1.into(),
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],
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false,
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);
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ctx.module.add_function(memcpy_fn_name.as_str(), fn_type, None)
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});
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let ndarray_num_dims = ndarray.load_ndims(ctx);
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let v_dims_src_ptr = v.get_dims().ptr_offset(
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ctx,
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generator,
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llvm_usize.const_int(1, false),
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None,
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);
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ctx.builder.build_call(
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memcpy_fn,
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&[
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ctx.builder.build_bitcast(
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ndarray.get_dims().get_ptr(ctx),
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llvm_pi8,
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"",
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).into(),
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ctx.builder.build_bitcast(
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v_dims_src_ptr,
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llvm_pi8,
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"",
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).into(),
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ctx.builder.build_int_mul(
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ndarray_num_dims.into(),
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llvm_usize.size_of(),
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"",
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).into(),
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llvm_i1.const_zero().into(),
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],
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"",
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);
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let ndarray_num_elems = call_ndarray_calc_size(
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generator,
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ctx,
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ndarray.load_ndims(ctx),
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ndarray.get_dims().get_ptr(ctx),
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);
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ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
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let v_data_src_ptr = v.get_data().ptr_offset_const(
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ctx,
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generator,
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ctx.ctx.i32_type().const_array(&[index]),
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None
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);
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ctx.builder.build_call(
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memcpy_fn,
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&[
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ctx.builder.build_bitcast(
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ndarray.get_data().get_ptr(ctx),
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llvm_pi8,
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"",
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).into(),
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ctx.builder.build_bitcast(
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v_data_src_ptr,
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llvm_pi8,
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"",
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).into(),
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ctx.builder.build_int_mul(
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ndarray_num_elems.into(),
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llvm_ndarray_data_t.size_of().unwrap(),
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"",
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).into(),
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llvm_i1.const_zero().into(),
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],
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"",
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);
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Ok(Some(v.get_ptr().into()))
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}
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}
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/// See [`CodeGenerator::gen_expr`].
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pub fn gen_expr<'ctx, G: CodeGenerator>(
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generator: &mut G,
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@ -1810,8 +2017,22 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
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v.get_data().get(ctx, generator, index, None).into()
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}
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}
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TypeEnum::TNDArray { .. } => {
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return Err(String::from("subscript operator for ndarray not implemented"))
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TypeEnum::TNDArray { ty, ndims } => {
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let v = if let Some(v) = generator.gen_expr(ctx, value)? {
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v.to_basic_value_enum(ctx, generator, value.custom.unwrap())?.into_pointer_value()
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} else {
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return Ok(None)
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};
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let v = NDArrayValue::from_ptr_val(v, usize, None);
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return gen_ndarray_subscript_expr(
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generator,
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ctx,
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*ty,
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*ndims,
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v,
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&*slice,
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)
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}
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TypeEnum::TTuple { .. } => {
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let index: u32 =
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