# Copyright 2024 The JAX Authors.
#
# Licensed 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
#
#     https://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.

from functools import partial, wraps
from typing import Any

from jax._src import config
from jax._src import core
from jax._src import dispatch
from jax._src import tree_util
from jax._src import xla_metadata_lib
from jax._src.interpreters import ad, batching, mlir
from jax._src.lib import xla_client
from jax._src.lib.mlir import ir

config_ext = xla_client._xla.config


class _XlaMetadataWrapper:
  """A wrapper class to allow XlaMetadataContextManager to be used as a decorator.

  When XlaMetadataContextManager is used as a decorator on a function `f`, it
  returns an instance of this class. This wrapper ensures that when `f` is
  called, it runs within the metadata context. It also forwards attribute
  access to `f` via `__getattr__`, and if an attribute of `f` is callable (e.g.,
  the `.lower()` method of a jitted function), it wraps that attribute so it
  too runs within the metadata context when called. This allows decorated
  functions to be used seamlessly with JAX transformations like `jax.jit`.
  """

  def __init__(self, f, ctx):
    self._f = f
    self._ctx = ctx
    wraps(f)(self)

  def __call__(self, *args, **kwargs):
    with self._ctx:
      return self._f(*args, **kwargs)

  def __getattr__(self, name):
    attr = getattr(self._f, name)
    if not callable(attr):
      return attr

    @wraps(attr)
    def wrapper(*args, **kwargs):
      with self._ctx:
        return attr(*args, **kwargs)

    return wrapper


class XlaMetadataContextManager:
  __slots__ = ["prev", "updates"]

  def __init__(self, updates):
    self.updates = updates

  def __enter__(self):
    if not self.updates:
      return

    self.prev = config.xla_metadata_context_manager.get_local()
    config.xla_metadata_context_manager.set_local(
        xla_metadata_lib.update_metadata(self.prev, self.updates)
    )

  def __exit__(self, exc_type, exc_value, traceback):
    if not self.updates:
      return
    config.xla_metadata_context_manager.set_local(self.prev)

  def __call__(self, f):
    return _XlaMetadataWrapper(f, self)


def set_xla_metadata(x=None, **kwargs):
  if x is None:
    return XlaMetadataContextManager(kwargs)
  else:
    hashable_metadata = tuple(sorted(kwargs.items()))
    return tree_util.tree_map(
        lambda v: xla_metadata_value_p.bind(
            v, xla_metadata_kvs=hashable_metadata
        ),
        x,
    )


# `xla_metadata_value_p` is an identity primitive for attaching frontend_attributes
# to the primitive's producing (parent/owner) op.
xla_metadata_value_p = core.Primitive("xla_metadata_value")
xla_metadata_value_p.def_impl(
    partial(dispatch.apply_primitive, xla_metadata_value_p)
)
xla_metadata_value_p.def_abstract_eval(lambda aval, *, xla_metadata_kvs: aval)
batching.defvectorized(xla_metadata_value_p)
# TODO(nbasile): Implement tagging gradient ops with metadata.
ad.deflinear2(xla_metadata_value_p, lambda ct, _, **kwargs: (ct,))


def _xla_metadata_value_lowering_rule(
    ctx: mlir.LoweringRuleContext, val: ir.Value, *, xla_metadata_kvs
):
  xla_metadata = dict(xla_metadata_kvs)
  op_to_attach_metadata = _target_op_to_attach_metadata(val)
  if op_to_attach_metadata is not None:
    _attach_xla_metadata_to_op(xla_metadata, op_to_attach_metadata)
  return [val]


# If we leave `cacheable=True`, when we are in the lowering rule, the `val.owner`
# becomes a cached `FuncOp`. FuncOp.owners are Blocks, which we can't tag.
mlir.register_lowering(
    xla_metadata_value_p, _xla_metadata_value_lowering_rule, cacheable=False
)


def _target_op_to_attach_metadata(value_mlir: ir.Value) -> ir.Operation | None:
  op = value_mlir.owner
  if op is None or isinstance(op, ir.Block):
    return None
  return op


def _attach_xla_metadata_to_op(
    xla_metadata: dict[str, Any], op: ir.Operation
) -> None:
  if xla_metadata:
    ctx_attributes, existing_attributes = {}, {}
    for k, v in xla_metadata.items():
      ctx_attributes[k] = ir.StringAttr.get(str(v).lower())
    # Combine with existing mhlo.frontend_attributes
    for attr in op.attributes:
      if attr == "mhlo.frontend_attributes":
        for a in op.attributes[attr]:
          existing_attributes[a.name] = a.attr
    op.attributes["mhlo.frontend_attributes"] = ir.DictAttr.get(
        ctx_attributes | existing_attributes
    )