import warnings
from functools import reduce, singledispatch
from warnings import warn

import jax
import jax.numpy as jnp
import jax.scipy as jsp
import numpy as np
from numpy.random import Generator, RandomState
from numpy.random.bit_generator import _coerce_to_uint32_array

from aesara.compile.ops import DeepCopyOp, ViewOp
from aesara.configdefaults import config
from aesara.graph.fg import FunctionGraph
from aesara.ifelse import IfElse
from aesara.link.utils import fgraph_to_python
from aesara.raise_op import CheckAndRaise
from aesara.scalar import Softplus
from aesara.scalar.basic import Cast, Clip, Composite, Identity, ScalarOp, Second
from aesara.scalar.math import Erf, Erfc, Erfinv, Log1mexp, Psi
from aesara.scan.op import Scan
from aesara.scan.utils import ScanArgs
from aesara.tensor.basic import (
    Alloc,
    AllocDiag,
    AllocEmpty,
    ARange,
    ExtractDiag,
    Eye,
    Join,
    MakeVector,
    Rebroadcast,
    ScalarFromTensor,
    TensorFromScalar,
)
from aesara.tensor.blas import BatchedDot
from aesara.tensor.elemwise import CAReduce, DimShuffle, Elemwise
from aesara.tensor.extra_ops import (
    Bartlett,
    BroadcastTo,
    CumOp,
    FillDiagonal,
    FillDiagonalOffset,
    RavelMultiIndex,
    Repeat,
    Unique,
    UnravelIndex,
)
from aesara.tensor.math import Dot, MaxAndArgmax
from aesara.tensor.nlinalg import SVD, Det, Eig, Eigh, MatrixInverse, QRFull
from aesara.tensor.nnet.basic import LogSoftmax, Softmax, SoftmaxGrad
from aesara.tensor.random.op import RandomVariable
from aesara.tensor.shape import Reshape, Shape, Shape_i, SpecifyShape
from aesara.tensor.slinalg import Cholesky, Solve, SolveTriangular
from aesara.tensor.subtensor import (
    AdvancedIncSubtensor,
    AdvancedIncSubtensor1,
    AdvancedSubtensor,
    AdvancedSubtensor1,
    IncSubtensor,
    Subtensor,
    indices_from_subtensor,
)
from aesara.tensor.type_other import MakeSlice


# For use with JAX since JAX doesn't support 'str' arguments
numpy_bit_gens = {"MT19937": 0, "PCG64": 1, "Philox": 2, "SFC64": 3}


if config.floatX == "float64":
    jax.config.update("jax_enable_x64", True)
else:
    jax.config.update("jax_enable_x64", False)

# XXX: Enabling this will break some shape-based functionality, and severely
# limit the types of graphs that can be converted.
# See https://github.com/google/jax/blob/4d556837cc9003492f674c012689efc3d68fdf5f/design_notes/omnistaging.md
# Older versions < 0.2.0 do not have this flag so we don't need to set it.
try:
    jax.config.disable_omnistaging()
except AttributeError:
    pass
except Exception as e:
    # The version might be >= 0.2.12, which means that omnistaging can't be
    # disabled
    warnings.warn(f"JAX omnistaging couldn't be disabled: {e}")

subtensor_ops = (Subtensor, AdvancedSubtensor1, AdvancedSubtensor)
incsubtensor_ops = (IncSubtensor, AdvancedIncSubtensor1)


@singledispatch
def jax_typify(data, dtype=None, **kwargs):
    r"""Convert instances of Aesara `Type`\s to JAX types."""
    if dtype is None:
        return data
    else:
        return jnp.array(data, dtype=dtype)


@jax_typify.register(np.ndarray)
def jax_typify_ndarray(data, dtype=None, **kwargs):
    return jnp.array(data, dtype=dtype)


@jax_typify.register(RandomState)
def jax_typify_RandomState(state, **kwargs):
    state = state.get_state(legacy=False)
    state["bit_generator"] = numpy_bit_gens[state["bit_generator"]]
    # XXX: Is this a reasonable approach?
    state["jax_state"] = state["state"]["key"][0:2]
    return state


@jax_typify.register(Generator)
def jax_typify_Generator(rng, **kwargs):
    state = rng.__getstate__()
    state["bit_generator"] = numpy_bit_gens[state["bit_generator"]]

    # XXX: Is this a reasonable approach?
    state["jax_state"] = _coerce_to_uint32_array(state["state"]["state"])[0:2]

    # The "state" and "inc" values in a NumPy `Generator` are 128 bits, which
    # JAX can't handle, so we split these values into arrays of 32 bit integers
    # and then combine the first two into a single 64 bit integers.
    #
    # XXX: Depending on how we expect these values to be used, is this approach
    # reasonable?
    #
    # TODO: We might as well remove these altogether, since this conversion
    # should only occur once (e.g. when the graph is converted/JAX-compiled),
    # and, from then on, we use the custom "jax_state" value.
    inc_32 = _coerce_to_uint32_array(state["state"]["inc"])
    state_32 = _coerce_to_uint32_array(state["state"]["state"])
    state["state"]["inc"] = inc_32[0] << 32 | inc_32[1]
    state["state"]["state"] = state_32[0] << 32 | state_32[1]
    return state


@singledispatch
def jax_funcify(op, node=None, storage_map=None, **kwargs):
    """Create a JAX compatible function from an Aesara `Op`."""
    raise NotImplementedError(f"No JAX conversion for the given `Op`: {op}")


@jax_funcify.register(MakeSlice)
def jax_funcify_MakeSlice(op, **kwargs):
    def makeslice(*x):
        return slice(*x)

    return makeslice


@jax_funcify.register(ScalarOp)
def jax_funcify_ScalarOp(op, **kwargs):
    func_name = op.nfunc_spec[0]

    if "." in func_name:
        jnp_func = reduce(getattr, [jax] + func_name.split("."))
    else:
        jnp_func = getattr(jnp, func_name)

    if hasattr(op, "nfunc_variadic"):
        # These are special cases that handle invalid arities due to the broken
        # Aesara `Op` type contract (e.g. binary `Op`s that also function as
        # their own variadic counterparts--even when those counterparts already
        # exist as independent `Op`s).
        jax_variadic_func = getattr(jnp, op.nfunc_variadic)

        def elemwise(*args):
            if len(args) > op.nfunc_spec[1]:
                return jax_variadic_func(
                    jnp.stack(jnp.broadcast_arrays(*args), axis=0), axis=0
                )
            else:
                return jnp_func(*args)

        return elemwise
    else:
        return jnp_func


@jax_funcify.register(Clip)
def jax_funcify_Clip(op, **kwargs):
    def clip(x, min, max):
        return jnp.where(x < min, min, jnp.where(x > max, max, x))

    return clip


@jax_funcify.register(Identity)
def jax_funcify_Identity(op, **kwargs):
    def identity(x):
        return x

    return identity


@jax_funcify.register(Softmax)
def jax_funcify_Softmax(op, **kwargs):
    axis = op.axis

    def softmax(x):
        return jax.nn.softmax(x, axis=axis)

    return softmax


@jax_funcify.register(SoftmaxGrad)
def jax_funcify_SoftmaxGrad(op, **kwargs):
    axis = op.axis

    def softmax_grad(dy, sm):
        dy_times_sm = dy * sm
        return dy_times_sm - jnp.sum(dy_times_sm, axis=axis, keepdims=True) * sm

    return softmax_grad


@jax_funcify.register(LogSoftmax)
def jax_funcify_LogSoftmax(op, **kwargs):
    axis = op.axis

    def log_softmax(x):
        return jax.nn.log_softmax(x, axis=axis)

    return log_softmax


@jax_funcify.register(Softplus)
def jax_funcify_Softplus(op, **kwargs):
    def softplus(x):
        # This expression is numerically equivalent to the Aesara one
        # It just contains one "speed" optimization less than the Aesara counterpart
        return jnp.where(
            x < -37.0, jnp.exp(x), jnp.where(x > 33.3, x, jnp.log1p(jnp.exp(x)))
        )

    return softplus


@jax_funcify.register(Second)
def jax_funcify_Second(op, **kwargs):
    def second(x, y):
        return jnp.broadcast_to(y, x.shape)

    return second


@jax_funcify.register(AllocDiag)
def jax_funcify_AllocDiag(op, **kwargs):
    offset = op.offset

    def allocdiag(v, offset=offset):
        return jnp.diag(v, k=offset)

    return allocdiag


@jax_funcify.register(AllocEmpty)
def jax_funcify_AllocEmpty(op, **kwargs):
    def allocempty(*shape):
        return jnp.empty(shape, dtype=op.dtype)

    return allocempty


@jax_funcify.register(Alloc)
def jax_funcify_Alloc(op, **kwargs):
    def alloc(x, *shape):
        res = jnp.broadcast_to(x, shape)
        return res

    return alloc


@jax_funcify.register(Dot)
def jax_funcify_Dot(op, **kwargs):
    def dot(x, y):
        return jnp.dot(x, y)

    return dot


@jax_funcify.register(ARange)
def jax_funcify_ARange(op, **kwargs):
    # XXX: This currently requires concrete arguments.
    def arange(start, stop, step):
        return jnp.arange(start, stop, step, dtype=op.dtype)

    return arange


def jnp_safe_copy(x):
    try:
        res = jnp.copy(x)
    except NotImplementedError:
        warn("`jnp.copy` is not implemented yet. " "Using the object's `copy` method.")
        if hasattr(x, "copy"):
            res = jnp.array(x.copy())
        else:
            warn(f"Object has no `copy` method: {x}")
            res = x

    return res


@jax_funcify.register(DeepCopyOp)
def jax_funcify_DeepCopyOp(op, **kwargs):
    def deepcopyop(x):
        return jnp_safe_copy(x)

    return deepcopyop


@jax_funcify.register(Shape)
def jax_funcify_Shape(op, **kwargs):
    def shape(x):
        return jnp.shape(x)

    return shape


@jax_funcify.register(Shape_i)
def jax_funcify_Shape_i(op, **kwargs):
    i = op.i

    def shape_i(x):
        return jnp.shape(x)[i]

    return shape_i


@jax_funcify.register(SpecifyShape)
def jax_funcify_SpecifyShape(op, **kwargs):
    def specifyshape(x, *shape):
        assert x.ndim == len(shape)
        assert jnp.all(x.shape == tuple(shape)), (
            "got shape",
            x.shape,
            "expected",
            shape,
        )
        return x

    return specifyshape


@jax_funcify.register(Rebroadcast)
def jax_funcify_Rebroadcast(op, **kwargs):
    op_axis = op.axis

    def rebroadcast(x):
        for axis, value in op_axis.items():
            if value and x.shape[axis] != 1:
                raise ValueError(
                    "Dimension %s in Rebroadcast's input was"
                    " supposed to be 1 (got %s instead)" % (axis, x.shape[axis])
                )
        return x

    return rebroadcast


@jax_funcify.register(ViewOp)
def jax_funcify_ViewOp(op, **kwargs):
    def viewop(x):
        return x

    return viewop


@jax_funcify.register(Cast)
def jax_funcify_Cast(op, **kwargs):
    def cast(x):
        return jnp.array(x).astype(op.o_type.dtype)

    return cast


@jax_funcify.register(TensorFromScalar)
def jax_funcify_TensorFromScalar(op, **kwargs):
    def tensor_from_scalar(x):
        return jnp.array(x)

    return tensor_from_scalar


@jax_funcify.register(ScalarFromTensor)
def jax_funcify_ScalarFromTensor(op, **kwargs):
    def scalar_from_tensor(x):
        return jnp.array(x).flatten()[0]

    return scalar_from_tensor


@jax_funcify.register(Elemwise)
def jax_funcify_Elemwise(op, **kwargs):
    scalar_op = op.scalar_op
    return jax_funcify(scalar_op, **kwargs)


@jax_funcify.register(Composite)
def jax_funcify_Composite(op, vectorize=True, **kwargs):
    jax_impl = jax_funcify(op.fgraph)

    def composite(*args):
        return jax_impl(*args)[0]

    return jnp.vectorize(composite)


@jax_funcify.register(Scan)
def jax_funcify_Scan(op, **kwargs):
    inner_fg = FunctionGraph(op.inputs, op.outputs)
    jax_at_inner_func = jax_funcify(inner_fg, **kwargs)

    def scan(*outer_inputs):
        scan_args = ScanArgs(
            list(outer_inputs), [None] * op.info.n_outs, op.inputs, op.outputs, op.info
        )

        # `outer_inputs` is a list with the following composite form:
        # [n_steps]
        # + outer_in_seqs
        # + outer_in_mit_mot
        # + outer_in_mit_sot
        # + outer_in_sit_sot
        # + outer_in_shared
        # + outer_in_nit_sot
        # + outer_in_non_seqs
        n_steps = scan_args.n_steps
        seqs = scan_args.outer_in_seqs

        # TODO: mit_mots
        mit_mot_in_slices = []

        mit_sot_in_slices = []
        for tap, seq in zip(scan_args.mit_sot_in_slices, scan_args.outer_in_mit_sot):
            neg_taps = [abs(t) for t in tap if t < 0]
            pos_taps = [abs(t) for t in tap if t > 0]
            max_neg = max(neg_taps) if neg_taps else 0
            max_pos = max(pos_taps) if pos_taps else 0
            init_slice = seq[: max_neg + max_pos]
            mit_sot_in_slices.append(init_slice)

        sit_sot_in_slices = [seq[0] for seq in scan_args.outer_in_sit_sot]

        init_carry = (
            mit_mot_in_slices,
            mit_sot_in_slices,
            sit_sot_in_slices,
            scan_args.outer_in_shared,
            scan_args.outer_in_non_seqs,
        )

        def jax_args_to_inner_scan(op, carry, x):
            # `carry` contains all inner-output taps, non_seqs, and shared
            # terms
            (
                inner_in_mit_mot,
                inner_in_mit_sot,
                inner_in_sit_sot,
                inner_in_shared,
                inner_in_non_seqs,
            ) = carry

            # `x` contains the in_seqs
            inner_in_seqs = x

            # `inner_scan_inputs` is a list with the following composite form:
            # inner_in_seqs
            # + sum(inner_in_mit_mot, [])
            # + sum(inner_in_mit_sot, [])
            # + inner_in_sit_sot
            # + inner_in_shared
            # + inner_in_non_seqs
            inner_in_mit_sot_flatten = []
            for array, index in zip(inner_in_mit_sot, scan_args.mit_sot_in_slices):
                inner_in_mit_sot_flatten.extend(array[jnp.array(index)])

            inner_scan_inputs = sum(
                [
                    inner_in_seqs,
                    inner_in_mit_mot,
                    inner_in_mit_sot_flatten,
                    inner_in_sit_sot,
                    inner_in_shared,
                    inner_in_non_seqs,
                ],
                [],
            )

            return inner_scan_inputs

        def inner_scan_outs_to_jax_outs(
            op,
            old_carry,
            inner_scan_outs,
        ):
            (
                inner_in_mit_mot,
                inner_in_mit_sot,
                inner_in_sit_sot,
                inner_in_shared,
                inner_in_non_seqs,
            ) = old_carry

            def update_mit_sot(mit_sot, new_val):
                return jnp.concatenate([mit_sot[1:], new_val[None, ...]], axis=0)

            inner_out_mit_sot = [
                update_mit_sot(mit_sot, new_val)
                for mit_sot, new_val in zip(inner_in_mit_sot, inner_scan_outs)
            ]

            # This should contain all inner-output taps, non_seqs, and shared
            # terms
            if not inner_in_sit_sot:
                inner_out_sit_sot = []
            else:
                inner_out_sit_sot = inner_scan_outs
            new_carry = (
                inner_in_mit_mot,
                inner_out_mit_sot,
                inner_out_sit_sot,
                inner_in_shared,
                inner_in_non_seqs,
            )

            return new_carry

        def jax_inner_func(carry, x):
            inner_args = jax_args_to_inner_scan(op, carry, x)
            inner_scan_outs = list(jax_at_inner_func(*inner_args))
            new_carry = inner_scan_outs_to_jax_outs(op, carry, inner_scan_outs)
            return new_carry, inner_scan_outs

        _, scan_out = jax.lax.scan(jax_inner_func, init_carry, seqs, length=n_steps)

        # We need to prepend the initial values so that the JAX output will
        # match the raw `Scan` `Op` output and, thus, work with a downstream
        # `Subtensor` `Op` introduced by the `scan` helper function.
        def append_scan_out(scan_in_part, scan_out_part):
            return jnp.concatenate([scan_in_part[:-n_steps], scan_out_part], axis=0)

        if scan_args.outer_in_mit_sot:
            scan_out_final = [
                append_scan_out(init, out)
                for init, out in zip(scan_args.outer_in_mit_sot, scan_out)
            ]
        elif scan_args.outer_in_sit_sot:
            scan_out_final = [
                append_scan_out(init, out)
                for init, out in zip(scan_args.outer_in_sit_sot, scan_out)
            ]

        if len(scan_out_final) == 1:
            scan_out_final = scan_out_final[0]
        return scan_out_final

    return scan


@jax_funcify.register(IfElse)
def jax_funcify_IfElse(op, **kwargs):
    n_outs = op.n_outs

    def ifelse(cond, *args, n_outs=n_outs):
        res = jax.lax.cond(
            cond, lambda _: args[:n_outs], lambda _: args[n_outs:], operand=None
        )
        return res if n_outs > 1 else res[0]

    return ifelse


@jax_funcify.register(CheckAndRaise)
def jax_funcify_CheckAndRaise(op, **kwargs):

    raise NotImplementedError(
        f"""This exception is raised because you tried to convert an aesara graph with a `CheckAndRaise` Op (message: {op.msg}) to JAX.

        JAX uses tracing to jit-compile functions, and assertions typically
        don't do well with tracing. The appropriate workaround depends on what
        you intended to do with the assertions in the first place.

        Note that all assertions can be removed from the graph by adding
        `local_remove_all_assert` to the rewrites."""
    )


@jax_funcify.register(Subtensor)
def jax_funcify_Subtensor(op, **kwargs):

    idx_list = getattr(op, "idx_list", None)

    def subtensor(x, *ilists):

        indices = indices_from_subtensor(ilists, idx_list)

        if len(indices) == 1:
            indices = indices[0]

        return x.__getitem__(indices)

    return subtensor


_ = [jax_funcify.register(op, jax_funcify_Subtensor) for op in subtensor_ops]


def jax_funcify_IncSubtensor(op, **kwargs):

    idx_list = getattr(op, "idx_list", None)

    if getattr(op, "set_instead_of_inc", False):
        jax_fn = getattr(jax.ops, "index_update", None)

        if jax_fn is None:

            def jax_fn(x, indices, y):
                return x.at[indices].set(y)

    else:
        jax_fn = getattr(jax.ops, "index_add", None)

        if jax_fn is None:

            def jax_fn(x, indices, y):
                return x.at[indices].add(y)

    def incsubtensor(x, y, *ilist, jax_fn=jax_fn, idx_list=idx_list):
        indices = indices_from_subtensor(ilist, idx_list)
        if len(indices) == 1:
            indices = indices[0]

        return jax_fn(x, indices, y)

    return incsubtensor


_ = [jax_funcify.register(op, jax_funcify_IncSubtensor) for op in incsubtensor_ops]


@jax_funcify.register(AdvancedIncSubtensor)
def jax_funcify_AdvancedIncSubtensor(op, **kwargs):

    if getattr(op, "set_instead_of_inc", False):
        jax_fn = getattr(jax.ops, "index_update", None)

        if jax_fn is None:

            def jax_fn(x, indices, y):
                return x.at[indices].set(y)

    else:
        jax_fn = getattr(jax.ops, "index_add", None)

        if jax_fn is None:

            def jax_fn(x, indices, y):
                return x.at[indices].add(y)

    def advancedincsubtensor(x, y, *ilist, jax_fn=jax_fn):
        return jax_fn(x, ilist, y)

    return advancedincsubtensor


@jax_funcify.register(FunctionGraph)
def jax_funcify_FunctionGraph(
    fgraph,
    node=None,
    fgraph_name="jax_funcified_fgraph",
    **kwargs,
):
    return fgraph_to_python(
        fgraph,
        jax_funcify,
        type_conversion_fn=jax_typify,
        fgraph_name=fgraph_name,
        **kwargs,
    )


@jax_funcify.register(CAReduce)
def jax_funcify_CAReduce(op, **kwargs):
    axis = op.axis
    op_nfunc_spec = getattr(op, "nfunc_spec", None)
    scalar_nfunc_spec = getattr(op.scalar_op, "nfunc_spec", None)
    scalar_op_name = getattr(op.scalar_op, "name", None)
    scalar_op_identity = getattr(op.scalar_op, "identity", None)
    acc_dtype = getattr(op, "acc_dtype", None)

    def careduce(x):
        nonlocal axis, op_nfunc_spec, scalar_nfunc_spec, scalar_op_name, scalar_op_identity, acc_dtype

        if axis is None:
            axis = list(range(x.ndim))

        if acc_dtype is None:
            acc_dtype = x.dtype.type

        if op_nfunc_spec:
            jax_op = getattr(jnp, op_nfunc_spec[0])
            return jax_op(x, axis=axis).astype(acc_dtype)

        # The Aesara `Op` didn't tell us which NumPy equivalent to use (or
        # there isn't one), so we use this fallback approach
        if scalar_nfunc_spec:
            scalar_fn_name = scalar_nfunc_spec[0]
        elif scalar_op_name:
            scalar_fn_name = scalar_op_name

        to_reduce = reversed(sorted(axis))

        if to_reduce:
            # In this case, we need to use the `jax.lax` function (if there
            # is one), and not the `jnp` version.
            jax_op = getattr(jax.lax, scalar_fn_name)
            init_value = jnp.array(scalar_op_identity, dtype=acc_dtype)
            return jax.lax.reduce(x, init_value, jax_op, to_reduce).astype(acc_dtype)
        else:
            return x

    return careduce


@jax_funcify.register(MakeVector)
def jax_funcify_MakeVector(op, **kwargs):
    def makevector(*x):
        return jnp.array(x, dtype=op.dtype)

    return makevector


@jax_funcify.register(Reshape)
def jax_funcify_Reshape(op, **kwargs):
    def reshape(x, shape):
        return jnp.reshape(x, shape)

    return reshape


@jax_funcify.register(DimShuffle)
def jax_funcify_DimShuffle(op, **kwargs):
    def dimshuffle(x):

        res = jnp.transpose(x, op.transposition)

        shape = list(res.shape[: len(op.shuffle)])

        for augm in op.augment:
            shape.insert(augm, 1)

        res = jnp.reshape(res, shape)

        if not op.inplace:
            res = jnp_safe_copy(res)

        return res

    return dimshuffle


@jax_funcify.register(Join)
def jax_funcify_Join(op, **kwargs):
    def join(axis, *tensors):
        # tensors could also be tuples, and in this case they don't have a ndim
        tensors = [jnp.asarray(tensor) for tensor in tensors]
        view = op.view
        if (view != -1) and all(
            tensor.shape[axis] == 0 for tensor in tensors[0:view] + tensors[view + 1 :]
        ):
            return tensors[view]

        else:
            return jnp.concatenate(tensors, axis=axis)

    return join


@jax_funcify.register(MaxAndArgmax)
def jax_funcify_MaxAndArgmax(op, **kwargs):
    axis = op.axis

    def maxandargmax(x, axis=axis):
        if axis is None:
            axes = tuple(range(x.ndim))
        else:
            axes = tuple(int(ax) for ax in axis)

        max_res = jnp.max(x, axis)

        # NumPy does not support multiple axes for argmax; this is a
        # work-around
        keep_axes = jnp.array(
            [i for i in range(x.ndim) if i not in axes], dtype="int64"
        )
        # Not-reduced axes in front
        transposed_x = jnp.transpose(
            x, jnp.concatenate((keep_axes, jnp.array(axes, dtype="int64")))
        )
        kept_shape = transposed_x.shape[: len(keep_axes)]
        reduced_shape = transposed_x.shape[len(keep_axes) :]

        # Numpy.prod returns 1.0 when arg is empty, so we cast it to int64
        # Otherwise reshape would complain citing float arg
        new_shape = kept_shape + (
            jnp.prod(jnp.array(reduced_shape, dtype="int64"), dtype="int64"),
        )
        reshaped_x = transposed_x.reshape(new_shape)

        max_idx_res = jnp.argmax(reshaped_x, axis=-1).astype("int64")

        return max_res, max_idx_res

    return maxandargmax


@jax_funcify.register(ExtractDiag)
def jax_funcify_ExtractDiag(op, **kwargs):
    offset = op.offset
    axis1 = op.axis1
    axis2 = op.axis2

    def extract_diag(x, offset=offset, axis1=axis1, axis2=axis2):
        return jnp.diagonal(x, offset=offset, axis1=axis1, axis2=axis2)

    return extract_diag


@jax_funcify.register(Cholesky)
def jax_funcify_Cholesky(op, **kwargs):
    lower = op.lower

    def cholesky(a, lower=lower):
        return jsp.linalg.cholesky(a, lower=lower).astype(a.dtype)

    return cholesky


@jax_funcify.register(Solve)
def jax_funcify_Solve(op, **kwargs):

    if op.assume_a != "gen" and op.lower:
        lower = True
    else:
        lower = False

    def solve(a, b, lower=lower):
        return jsp.linalg.solve(a, b, lower=lower)

    return solve


@jax_funcify.register(SolveTriangular)
def jax_funcify_SolveTriangular(op, **kwargs):
    lower = op.lower
    trans = op.trans
    unit_diagonal = op.unit_diagonal
    check_finite = op.check_finite

    def solve_triangular(A, b):
        return jsp.linalg.solve_triangular(
            A,
            b,
            lower=lower,
            trans=trans,
            unit_diagonal=unit_diagonal,
            check_finite=check_finite,
        )

    return solve_triangular


@jax_funcify.register(Det)
def jax_funcify_Det(op, **kwargs):
    def det(x):
        return jnp.linalg.det(x)

    return det


@jax_funcify.register(Eig)
def jax_funcify_Eig(op, **kwargs):
    def eig(x):
        return jnp.linalg.eig(x)

    return eig


@jax_funcify.register(Eigh)
def jax_funcify_Eigh(op, **kwargs):
    uplo = op.UPLO

    def eigh(x, uplo=uplo):
        return jnp.linalg.eigh(x, UPLO=uplo)

    return eigh


@jax_funcify.register(MatrixInverse)
def jax_funcify_MatrixInverse(op, **kwargs):
    def matrix_inverse(x):
        return jnp.linalg.inv(x)

    return matrix_inverse


@jax_funcify.register(QRFull)
def jax_funcify_QRFull(op, **kwargs):
    mode = op.mode

    def qr_full(x, mode=mode):
        return jnp.linalg.qr(x, mode=mode)

    return qr_full


@jax_funcify.register(SVD)
def jax_funcify_SVD(op, **kwargs):
    full_matrices = op.full_matrices
    compute_uv = op.compute_uv

    def svd(x, full_matrices=full_matrices, compute_uv=compute_uv):
        return jnp.linalg.svd(x, full_matrices=full_matrices, compute_uv=compute_uv)

    return svd


@jax_funcify.register(CumOp)
def jax_funcify_CumOp(op, **kwargs):
    axis = op.axis
    mode = op.mode

    def cumop(x, axis=axis, mode=mode):
        if mode == "add":
            return jnp.cumsum(x, axis=axis)
        else:
            return jnp.cumprod(x, axis=axis)

    return cumop


@jax_funcify.register(Repeat)
def jax_funcify_Repeat(op, **kwargs):
    axis = op.axis

    def repeatop(x, repeats, axis=axis):
        return jnp.repeat(x, repeats, axis=axis)

    return repeatop


@jax_funcify.register(Bartlett)
def jax_funcify_Bartlett(op, **kwargs):
    def bartlett(x):
        return jnp.bartlett(x)

    return bartlett


@jax_funcify.register(FillDiagonal)
def jax_funcify_FillDiagonal(op, **kwargs):

    # def filldiagonal(a, val):
    #     if a.ndim == 2:
    #         step = a.shape[1] + 1
    #         end = a.shape[1] * a.shape[1]
    #         a.flat[:end:step] = val
    #     else:
    #         jnp.fill_diagonal(a, val)
    #
    #     return a
    #
    # return filldiagonal

    raise NotImplementedError("flatiter not implemented in JAX")


@jax_funcify.register(FillDiagonalOffset)
def jax_funcify_FillDiagonalOffset(op, **kwargs):

    # def filldiagonaloffset(a, val, offset):
    #     height, width = a.shape
    #
    #     if offset >= 0:
    #         start = offset
    #         num_of_step = min(min(width, height), width - offset)
    #     else:
    #         start = -offset * a.shape[1]
    #         num_of_step = min(min(width, height), height + offset)
    #
    #     step = a.shape[1] + 1
    #     end = start + step * num_of_step
    #     a.flat[start:end:step] = val
    #
    #     return a
    #
    # return filldiagonaloffset

    raise NotImplementedError("flatiter not implemented in JAX")


@jax_funcify.register(Unique)
def jax_funcify_Unique(op, **kwargs):
    axis = op.axis

    if axis is not None:
        raise NotImplementedError(
            "jax.numpy.unique is not implemented for the axis argument"
        )

    return_index = op.return_index
    return_inverse = op.return_inverse
    return_counts = op.return_counts

    def unique(
        x,
        return_index=return_index,
        return_inverse=return_inverse,
        return_counts=return_counts,
        axis=axis,
    ):
        ret = jnp.lax_numpy._unique1d(x, return_index, return_inverse, return_counts)
        if len(ret) == 1:
            return ret[0]
        else:
            return ret

    return unique


@jax_funcify.register(UnravelIndex)
def jax_funcify_UnravelIndex(op, **kwargs):
    order = op.order

    warn("JAX ignores the `order` parameter in `unravel_index`.")

    def unravelindex(indices, dims, order=order):
        return jnp.unravel_index(indices, dims)

    return unravelindex


@jax_funcify.register(RavelMultiIndex)
def jax_funcify_RavelMultiIndex(op, **kwargs):
    mode = op.mode
    order = op.order

    def ravelmultiindex(*inp, mode=mode, order=order):
        multi_index, dims = inp[:-1], inp[-1]
        return jnp.ravel_multi_index(multi_index, dims, mode=mode, order=order)

    return ravelmultiindex


@jax_funcify.register(Eye)
def jax_funcify_Eye(op, **kwargs):
    dtype = op.dtype

    def eye(N, M, k):
        return jnp.eye(N, M, k, dtype=dtype)

    return eye


@jax_funcify.register(BatchedDot)
def jax_funcify_BatchedDot(op, **kwargs):
    def batched_dot(a, b):
        if a.shape[0] != b.shape[0]:
            raise TypeError("Shapes must match in the 0-th dimension")
        if a.ndim == 2 or b.ndim == 2:
            return jnp.einsum("n...j,nj...->n...", a, b)
        return jnp.einsum("nij,njk->nik", a, b)

    return batched_dot


@jax_funcify.register(RandomVariable)
def jax_funcify_RandomVariable(op, node, **kwargs):
    name = op.name

    if not hasattr(jax.random, name):
        raise NotImplementedError(
            f"No JAX conversion for the given distribution: {name}"
        )

    dtype = node.outputs[1].dtype

    def random_variable(rng, size, dtype_num, *args):
        if not op.inplace:
            rng = rng.copy()
        prng = rng["jax_state"]
        data = getattr(jax.random, name)(key=prng, shape=size)
        smpl_value = jnp.array(data, dtype=dtype)
        rng["jax_state"] = jax.random.split(prng, num=1)[0]
        return (rng, smpl_value)

    return random_variable


@jax_funcify.register(Erf)
def jax_funcify_Erf(op, node, **kwargs):
    def erf(x):
        return jax.scipy.special.erf(x)

    return erf


@jax_funcify.register(Erfc)
def jax_funcify_Erfc(op, **kwargs):
    def erfc(x):
        return jax.scipy.special.erfc(x)

    return erfc


@jax_funcify.register(Log1mexp)
def jax_funcify_Log1mexp(op, node, **kwargs):
    def log1mexp(x):
        return jnp.where(
            x < jnp.log(0.5), jnp.log1p(-jnp.exp(x)), jnp.log(-jnp.expm1(x))
        )

    return log1mexp


# Commented out because jax.scipy does not have erfcx,
# but leaving the implementation in here just in case we ever see
# a JAX implementation of Erfcx.
# See https://github.com/google/jax/issues/1987 for context.
# @jax_funcify.register(Erfcx)
# def jax_funcify_Erfcx(op, **kwargs):
#     def erfcx(x):
#         return jax.scipy.special.erfcx(x)
#     return erfcx


@jax_funcify.register(Erfinv)
def jax_funcify_Erfinv(op, **kwargs):
    def erfinv(x):
        return jax.scipy.special.erfinv(x)

    return erfinv


# Commented out because jax.scipy does not have Erfcinv,
# but leaving the implementation in here just in case we ever see
# a JAX implementation of Erfcinv.
# @jax_funcify.register(Erfcinv)
# def jax_funcify_Erfcinv(op, **kwargs):
#     def erfcinv(x):
#         return jax.scipy.special.erfcinv(x)
#     return erfcinv


@jax_funcify.register(Psi)
def jax_funcify_Psi(op, node, **kwargs):
    def psi(x):
        return jax.scipy.special.digamma(x)

    return psi


@jax_funcify.register(BroadcastTo)
def jax_funcify_BroadcastTo(op, **kwargs):
    def broadcast_to(x, *shape):
        return jnp.broadcast_to(x, shape)

    return broadcast_to
