"""!!!DO NOT USE!!!

Distribution related class for JAX backend.

This is just a prototype and we might want to unify it
with other backends in the future.
"""

import jax
import numpy as np

from keras.utils import jax_utils


def list_devices(device_type=None):
    """Return all the available devices based on the device type.

    Note that this should return the global devices in a distributed setting.

    Args:
        device_type: string of `"cpu"`, `"gpu"` or `"tpu"`. Defaults to `"gpu"`
            or `"tpu"` if available when device_type is not provided. Otherwise
            will return the `"cpu"` devices.

    Return:
        List of devices that are available for distribute computation.
    """
    device_type = device_type.lower() if device_type else None
    jax_devices = jax.devices(backend=device_type)
    return [f"{device.platform}:{device.id}" for device in jax_devices]


def distribute_variable(value, layout):
    """Create a distributed variable for JAX.

    Since JAX doesn't have a variable class, this will just return a `jax.Array`
    with the corresponding layout/sharding specified.

    Note that this function should be used in eager context, not in jitted
    function.

    Args:
        value: the initial value of the variable.
        layout: `TensorLayout` for the created variable, or a
            `jax.sharding.Sharding` instance.

    Returns:
        jax.Array which is the distributed variable.
    """
    if not isinstance(layout, jax.sharding.Sharding):
        layout = _to_jax_layout(layout)
    if isinstance(
        value, (jax.Array, jax.numpy.ndarray)
    ) and value.sharding.is_equivalent_to(layout, ndim=len(value.shape)):
        # Skip the relayout if the value is already having the proper sharding
        return value

    if layout.is_fully_addressable:
        return jax.device_put(value, layout)
    else:
        # Need to only distribute the value to local addressible devices, and
        # repack them back into global format.
        mapping = layout.addressable_devices_indices_map(value.shape)
        local_values = jax.device_put(
            [value[i] for i in mapping.values()], list(mapping.keys())
        )
        global_value = jax.make_array_from_single_device_arrays(
            value.shape, layout, local_values
        )
        return global_value


def distribute_tensor(tensor, layout):
    """Distribute the tensor based on the layout.

    Note that this function can be used both in eager context, or within a
    jitted function.

    Args:
        tensor: `jax.Array` that need to be distributed.
        layout: `TensorLayout` for the distribution information, or a
            `jax.sharding.Sharding` instance.

    Returns:
        Distributed value.
    """
    if not isinstance(layout, jax.sharding.Sharding):
        layout = _to_jax_layout(layout)
    # TODO(scottzhu): This might not be a cheap check, we should consider
    # have some proper JAX API for doing this check.
    if jax_utils.is_in_jax_tracing_scope():
        return jax.lax.with_sharding_constraint(tensor, layout)

    if layout.is_fully_addressable:
        return jax.device_put(tensor, layout)
    else:
        # Need to only distribute the value to local addressible devices, and
        # repack them back into global format.
        mapping = layout.addressable_devices_indices_map(tensor.shape)
        local_values = jax.device_put(
            [tensor[i] for i in mapping.values()], list(mapping.keys())
        )
        global_value = jax.make_array_from_single_device_arrays(
            tensor.shape, layout, local_values
        )
        return global_value


def distribute_data_input(inputs, layout):
    """Distribute the input data with the corresponding layout.

    Note that the inputs here is a local worker batch. Within the local worker,
    the data need to be further partitioned to map to the each of the devices.

    Args:
        inputs: `jax.Array` that is already sharded to a local process size.
        layout: `TensorLayout` for the distribution information, or a
            `jax.sharding.Sharding` instance.

    Returns:
        Distributed inputs thats been properly put to local devices.
    """
    if not isinstance(layout, jax.sharding.Sharding):
        layout = _to_jax_layout(layout)
    if layout.is_fully_addressable:
        return jax.device_put(inputs, layout)

    # We need the jax mesh information to determine how to place the data
    # on to each of the worker.
    jax_mesh = layout.mesh
    mesh_rank = len(jax_mesh.shape)
    per_process_batch_size = inputs.shape[0]
    if mesh_rank == 1:
        # This is data parallel mesh only. We will split the full data
        # across the batch dim.
        num_split = jax.local_device_count()
        per_replica_batch_size = per_process_batch_size // num_split
        if per_process_batch_size % per_replica_batch_size != 0:
            raise ValueError(
                f"The local batch size {per_process_batch_size} is not"
                "divisible by the number of local replicas "
                f"{num_split}"
            )
        global_batch_size = per_process_batch_size * jax.process_count()
        per_replica_batches = jax.numpy.split(inputs, num_split, axis=0)
    elif mesh_rank == 2:
        # Data+Model parallel
        # In this case, we need to check if the mesh batch dim shape is large
        # than number of local devices, so that we can decide whether a split
        # is needed for the data, or a repeat/copy of the data is needed for
        # each of the device.
        # TODO(scottzhu): The mesh batch dim name is not available here, since
        # we only have jax Mesh. We assume the first dim is for batch, and
        # second dim is for model for now.
        mesh_batch_dim_size = list(jax_mesh.shape.values())[0]
        local_device_count = jax.local_device_count()
        if mesh_batch_dim_size < local_device_count:
            # No split needed, we only need to repeat here.
            global_batch_size = per_process_batch_size
            per_replica_batches = [inputs for _ in range(local_device_count)]
        else:
            # Note that global batch size is not simply per_process_batch_size *
            # num_process. It actually depends on the model dim size.
            global_batch_size = per_process_batch_size * (
                mesh_batch_dim_size // local_device_count
            )
            per_replica_batches = jax.numpy.split(
                inputs, local_device_count, axis=0
            )
    else:
        raise ValueError(
            "Only 1D or 2D mesh is supported at the moment. "
            f"Received mesh shape = {jax_mesh.shape}"
        )

    global_shape = (global_batch_size,) + inputs.shape[1:]
    global_batch_array = jax.make_array_from_single_device_arrays(
        global_shape,
        layout,
        arrays=[
            jax.device_put(batch, device)
            for batch, device in zip(
                per_replica_batches, layout.addressable_devices
            )
        ],
    )
    return global_batch_array


def initialize(job_addresses, num_processes, process_id):
    if job_addresses and "," in job_addresses:
        # When user provide all the job addresses, we will split and get the
        # first one, which is the coordinator.
        job_addresses = job_addresses.split(",")
        # Do a sanity check to make sure the number of addresses also match
        # the num_processes.
        if num_processes is not None and num_processes != len(job_addresses):
            raise ValueError(
                f"The provided job_addresses {job_addresses} has "
                f"{len(job_addresses)} jobs, but num_processes is "
                f"{num_processes}"
            )
        corrdinator_address = job_addresses[0]
    else:
        corrdinator_address = job_addresses

    jax.distributed.initialize(
        corrdinator_address=corrdinator_address,
        num_processes=num_processes,
        process_id=process_id,
    )


def num_processes():
    """Return the number of processes for the current distribution setting."""
    return jax.process_count()


def process_id():
    """Return the current process ID for the distribution setting."""
    return jax.process_index()


def _to_jax_device(device_id):
    if isinstance(device_id, jax.Device):
        return device_id
    device_type, index = device_id.split(":")
    index = int(index)
    devices = jax.devices(backend=device_type)
    if index >= len(devices):
        raise ValueError(f"Unknown device: {device_id}")
    return devices[index]


def _to_jax_mesh(device_mesh):
    """Convert the DeviceMesh to JAX backend specific Mesh.

    Args:
        device_mesh: DeviceMesh instance to convert.

    Returns:
        A `jax.sharding.Mesh` instance.
    """
    shape = device_mesh.devices.shape
    devices = [_to_jax_device(d) for d in device_mesh.devices.flatten()]
    devices = np.array(devices).reshape(shape)
    return jax.sharding.Mesh(devices, device_mesh.axis_names)


def _to_jax_layout(tensor_layout):
    """Convert the TensorLayout to JAX backend specific Sharding.

    Args:
        tensor_layout: TensorLayout instance to convert.

    Returns:
        A `jax.sharding.NamedSharding` instance.
    """
    if tensor_layout.device_mesh is None:
        raise ValueError(
            "Cannot create sharding when device mesh is not set "
            "for TensorLayout."
        )
    partition_spec = jax.sharding.PartitionSpec(*tensor_layout.axes)
    jax_mesh = _to_jax_mesh(tensor_layout.device_mesh)
    return jax.sharding.NamedSharding(jax_mesh, partition_spec)
