#!/usr/bin/env python3

from __future__ import annotations

import argparse
import os

import yaml

from torchgen.code_template import CodeTemplate
from torchgen.selective_build.selector import SelectiveBuilder


# Safely load fast C Yaml loader/dumper if they are available
try:
    from yaml import CSafeLoader as Loader
except ImportError:
    from yaml import SafeLoader as Loader  # type: ignore[assignment, misc]


if_condition_template_str = """if (kernel_tag_sv.compare("$kernel_tag_name") == 0) {
  return $dtype_checks;
}"""
if_condition_template = CodeTemplate(if_condition_template_str)

selected_kernel_dtypes_h_template_str = """
#include <c10/core/ScalarType.h>
#include <c10/macros/Macros.h>
#include <string_view>

namespace at {
inline constexpr bool should_include_kernel_dtype(
  const char *kernel_tag_str,
  at::ScalarType scalar_type
) {
  [[maybe_unused]] auto kernel_tag_sv =
      std::string_view(kernel_tag_str);
  $body return false;
}
}
"""
selected_kernel_dtypes_h_template = CodeTemplate(selected_kernel_dtypes_h_template_str)

selected_mobile_ops_preamble = """#pragma once
/**
 * Generated by gen_selected_mobile_ops_header.py
 */

"""


def extract_root_operators(selective_builder: SelectiveBuilder) -> set[str]:
    ops = []
    for op_name, op in selective_builder.operators.items():
        if op.is_root_operator:
            ops.append(op_name)
    return set(ops)


def get_selected_kernel_dtypes_code(
    selective_builder: SelectiveBuilder,
) -> str:
    # See https://www.internalfb.com/intern/paste/P153411698/ for an example of the
    # generated code in case all kernel dtypes are selected and in case some kernel
    # dtypes are selected (i.e. both cases).
    #
    body = "return true;"
    if (
        selective_builder.include_all_operators is False
        and selective_builder.include_all_non_op_selectives is False
    ):
        body_parts = []
        for kernel_tag, dtypes in selective_builder.kernel_metadata.items():
            conditions = ["scalar_type == at::ScalarType::" + x for x in dtypes]
            body_parts.append(
                # pyrefly: ignore [bad-argument-type]
                if_condition_template.substitute(
                    kernel_tag_name=kernel_tag,
                    dtype_checks=" || ".join(conditions),
                ),
            )
        body = " else ".join(body_parts)

    header_contents = selected_kernel_dtypes_h_template.substitute(body=body)
    return header_contents


# Write the file selected_mobile_ops.h with optionally:
# 1. The selected root operators
# 2. The selected kernel dtypes
def write_selected_mobile_ops(
    output_file_path: str,
    selective_builder: SelectiveBuilder,
) -> None:
    root_ops = extract_root_operators(selective_builder)
    custom_classes = selective_builder.custom_classes
    build_features = selective_builder.build_features
    with open(output_file_path, "wb") as out_file:
        body_parts = [selected_mobile_ops_preamble]
        # This condition checks if we are in selective build.
        # if these lists are not defined the corresponding selective build macros trivially return the item in question was selected
        if not selective_builder.include_all_operators:
            body_parts.append(
                "#define TORCH_OPERATOR_WHITELIST "
                + (";".join(sorted(root_ops)))
                + ";\n\n"
            )
            # This condition checks if we are in tracing based selective build
            if selective_builder.include_all_non_op_selectives is False:
                body_parts.append(
                    "#define TORCH_CUSTOM_CLASS_ALLOWLIST "
                    + (";".join(sorted(custom_classes)))
                    + ";\n\n"
                )
                body_parts.append(
                    "#define TORCH_BUILD_FEATURE_ALLOWLIST "
                    + (";".join(sorted(build_features)))
                    + ";\n\n"
                )

        body_parts.append(get_selected_kernel_dtypes_code(selective_builder))
        header_contents = "".join(body_parts)
        out_file.write(header_contents.encode("utf-8"))


# root_ops: a set of selected root operators for selective build
# Write the file selected_mobile_ops.h with optionally:
# 1. The selected root operators from root_ops
# 2. All kernel dtypes
def write_selected_mobile_ops_with_all_dtypes(
    output_file_path: str,
    root_ops: set[str],
) -> None:
    with open(output_file_path, "wb") as out_file:
        body_parts = [selected_mobile_ops_preamble]
        body_parts.append(
            "#define TORCH_OPERATOR_WHITELIST " + (";".join(sorted(root_ops))) + ";\n\n"
        )

        selective_builder = SelectiveBuilder.get_nop_selector()
        body_parts.append(get_selected_kernel_dtypes_code(selective_builder))

        header_contents = "".join(body_parts)
        out_file.write(header_contents.encode("utf-8"))


def main() -> None:
    parser = argparse.ArgumentParser(
        description="Generate selected_mobile_ops.h for selective build."
    )
    parser.add_argument(
        "-p",
        "--yaml-file-path",
        "--yaml_file_path",
        type=str,
        required=True,
        help="Path to the yaml file with a list of operators used by the model.",
    )
    parser.add_argument(
        "-o",
        "--output-file-path",
        "--output_file_path",
        type=str,
        required=True,
        help="Path to destinationfolder where selected_mobile_ops.h will be written.",
    )
    parsed_args = parser.parse_args()
    model_file_name = parsed_args.yaml_file_path

    print("Loading yaml file: ", model_file_name)
    loaded_model = {}
    with open(model_file_name, "rb") as model_file:
        loaded_model = yaml.load(model_file, Loader=Loader)

    root_operators_set = set(loaded_model)
    print("Writing header file selected_mobile_ops.h: ", parsed_args.output_file_path)
    write_selected_mobile_ops_with_all_dtypes(
        os.path.join(parsed_args.output_file_path, "selected_mobile_ops.h"),
        root_operators_set,
    )


if __name__ == "__main__":
    main()
