# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
# pylint: disable=redefined-builtin,missing-docstring, invalid-name, unused-import, redefined-outer-name
# ruff: noqa: F401

from typing import Any, Optional, Union

from tvm.ir import Range
from tvm.relax import TensorType
from tvm.relax.distributed import DeviceMesh, DTensorType, Placement, device_mesh
from tvm.relax.script.builder.distributed import (
    annotate_sharding,
    call_tir,
    call_tir_local_view,
    const,
    redistribute,
    redistribute_replica_to_shard,
)
from tvm.script.ir_builder import IRBuilder
from tvm.script.ir_builder.ir import IRModuleFrame
from tvm.tirx import Expr

from .entry import TensorProxy, TypeProxy

############################### R.DTensor ###############################


class DTensorProxy(TypeProxy):
    tensor_ty_proxy: TensorProxy
    device_mesh: DeviceMesh
    placement: Placement

    def __init__(
        self,
        tensor_ty_proxy: TensorProxy,
        device_mesh: DeviceMesh,
        placement: Placement,
    ) -> None:
        self.device_mesh = device_mesh
        self.placement = placement
        self.tensor_ty_proxy = tensor_ty_proxy
        super().__init__()

    def get_symbolic_vars(self) -> set[str]:
        return self.tensor_ty_proxy.get_symbolic_vars()

    def as_ty(self, dict_globals: dict[str, Any] | None = None) -> DTensorType:
        return DTensorType(
            self.tensor_ty_proxy.as_ty(dict_globals),
            self.device_mesh,
            self.placement,
        )


def DTensor(
    shape: list[Expr | str] | None = None,
    dtype: str | None = None,
    device_mesh: DeviceMesh | str = DeviceMesh([], Range(0, 1)),
    placement: Placement | str = "",
    *,
    ndim: int = -1,
) -> DTensorProxy:
    # scalar tensor case
    if shape is not None and len(shape) == 0:
        shape = []
    if isinstance(shape, str) and dtype is None:
        dtype = shape
        shape = None

    if shape is not None and not isinstance(shape, tuple | list):
        raise ValueError(f"shape must be a list or tuple, but got: {shape}")
    if isinstance(device_mesh, str):
        if not IRBuilder.is_in_scope():
            return (
                DTensorProxy(
                    TensorProxy(shape, dtype, None, ndim), DeviceMesh([], Range(0, 1)), ""
                ),
            )
        name, index = device_mesh.split("[")
        index = int(index[:-1])
        frames = IRBuilder.current().frames
        for f in frames:
            if isinstance(f, IRModuleFrame):
                device_mesh = f.global_infos[name][index]
                break
        assert isinstance(device_mesh, DeviceMesh)
    if isinstance(placement, str):
        placement = Placement.from_text(placement)
    return DTensorProxy(TensorProxy(shape, dtype, None, ndim), device_mesh, placement)


__all__ = ["DTensor", "device_mesh"]