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# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
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# under the License.
# Prepare test library for standalone wasm runtime test.

import os

import tvm
from tvm import relax, te
from tvm.contrib import tvmjs
from tvm.script import relax as R


def prepare_relax_lib(base_path):
    pipeline = relax.get_pipeline()

    @tvm.script.ir_module
    class Mod:
        @R.function
        def main(x: R.Tensor(["n"], "float32"), y: R.Tensor(["n"], "float32")):
            lv0 = R.add(x, y)
            return lv0

    target = tvm.target.Target({"kind": "llvm", "mtriple": "wasm32-unknown-unknown-wasm"})

    mod = pipeline(Mod)
    ex = relax.build(mod, target)
    wasm_path = os.path.join(base_path, "test_relax.wasm")
    ex.export_library(wasm_path, fcompile=tvmjs.create_tvmjs_wasm)


def prepare_tir_lib(base_path):
    target = {"kind": "llvm", "mtriple": "wasm32-unknown-unknown-wasm"}
    if not tvm.runtime.enabled("llvm"):
        raise RuntimeError(f"Target {target} is not enbaled")
    n = te.var("n")
    A = te.placeholder((n,), name="A")
    B = te.compute(A.shape, lambda *i: A(*i) + 1.0, name="B")
    mod = tvm.IRModule.from_expr(
        te.create_prim_func([A, B]).with_attr("global_symbol", "add_one")
    ).with_attr("system_lib_prefix", "")

    fadd = tvm.build(mod, target)
    wasm_path = os.path.join(base_path, "test_addone.wasm")
    fadd.export_library(wasm_path, fcompile=tvmjs.create_tvmjs_wasm)


if __name__ == "__main__":
    curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
    base_path = os.path.join(curr_path, "../../dist/wasm")
    prepare_tir_lib(base_path)
    prepare_relax_lib(base_path)
