# Copyright 2021 DeepMind Technologies Limited
#
# 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
#
#      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.

"""Specialized mapping functions."""

import functools
import inspect

from typing import Any, Callable, Optional, Sequence, Union

import haiku as hk
import jax
import jax.numpy as jnp


PYTREE = Any
PYTREE_JAX_ARRAY = Any

partial = functools.partial
PROXY = object()


def _maybe_slice(array, i, slice_size, axis):
  if axis is PROXY:
    return array
  else:
    return jax.lax.dynamic_slice_in_dim(
        array, i, slice_size=slice_size, axis=axis)


def _maybe_get_size(array, axis):
  if axis == PROXY:
    return -1
  else:
    return array.shape[axis]


def _expand_axes(axes, values, name='sharded_apply'):
  values_tree_def = jax.tree_flatten(values)[1]
  flat_axes = jax.api_util.flatten_axes(name, values_tree_def, axes)
  # Replace None's with PROXY
  flat_axes = [PROXY if x is None else x for x in flat_axes]
  return jax.tree_unflatten(values_tree_def, flat_axes)


def sharded_map(
    fun: Callable[..., PYTREE_JAX_ARRAY],
    shard_size: Union[int, None] = 1,
    in_axes: Union[int, PYTREE] = 0,
    out_axes: Union[int, PYTREE] = 0) -> Callable[..., PYTREE_JAX_ARRAY]:
  """Sharded vmap.

  Maps `fun` over axes, in a way similar to vmap, but does so in shards of
  `shard_size`. This allows a smooth trade-off between memory usage
  (as in a plain map) vs higher throughput (as in a vmap).

  Args:
    fun: Function to apply smap transform to.
    shard_size: Integer denoting shard size.
    in_axes: Either integer or pytree describing which axis to map over for each
      input to `fun`, None denotes broadcasting.
    out_axes: integer or pytree denoting to what axis in the output the mapped
      over axis maps.

  Returns:
    function with smap applied.
  """
  if 'split_rng' in inspect.signature(hk.vmap).parameters:
    vmapped_fun = hk.vmap(fun, in_axes, out_axes, split_rng=False)
  else:
    # TODO(tomhennigan): Remove this when older versions of Haiku aren't used.
    vmapped_fun = hk.vmap(fun, in_axes, out_axes)
  return sharded_apply(vmapped_fun, shard_size, in_axes, out_axes)


def sharded_apply(
    fun: Callable[..., PYTREE_JAX_ARRAY],  # pylint: disable=g-bare-generic
    shard_size: Union[int, None] = 1,
    in_axes: Union[int, PYTREE] = 0,
    out_axes: Union[int, PYTREE] = 0,
    new_out_axes: bool = False) -> Callable[..., PYTREE_JAX_ARRAY]:
  """Sharded apply.

  Applies `fun` over shards to axes, in a way similar to vmap,
  but does so in shards of `shard_size`. Shards are stacked after.
  This allows a smooth trade-off between
  memory usage (as in a plain map) vs higher throughput (as in a vmap).

  Args:
    fun: Function to apply smap transform to.
    shard_size: Integer denoting shard size.
    in_axes: Either integer or pytree describing which axis to map over for each
      input to `fun`, None denotes broadcasting.
    out_axes: integer or pytree denoting to what axis in the output the mapped
      over axis maps.
    new_out_axes: whether to stack outputs on new axes. This assumes that the
      output sizes for each shard (including the possible remainder shard) are
      the same.

  Returns:
    function with smap applied.
  """
  docstr = ('Mapped version of {fun}. Takes similar arguments to {fun} '
            'but with additional array axes over which {fun} is mapped.')
  if new_out_axes:
    raise NotImplementedError('New output axes not yet implemented.')

  # shard size None denotes no sharding
  if shard_size is None:
    return fun

  @jax.util.wraps(fun, docstr=docstr)
  def mapped_fn(*args):
    # Expand in axes and Determine Loop range
    in_axes_ = _expand_axes(in_axes, args)

    in_sizes = jax.tree_multimap(_maybe_get_size, args, in_axes_)
    flat_sizes = jax.tree_flatten(in_sizes)[0]
    in_size = max(flat_sizes)
    assert all(i in {in_size, -1} for i in flat_sizes)

    num_extra_shards = (in_size - 1) // shard_size

    # Fix Up if necessary
    last_shard_size = in_size % shard_size
    last_shard_size = shard_size if last_shard_size == 0 else last_shard_size

    def apply_fun_to_slice(slice_start, slice_size):
      input_slice = jax.tree_multimap(
          lambda array, axis: _maybe_slice(array, slice_start, slice_size, axis
                                          ), args, in_axes_)
      return fun(*input_slice)

    remainder_shape_dtype = hk.eval_shape(
        partial(apply_fun_to_slice, 0, last_shard_size))
    out_dtypes = jax.tree_map(lambda x: x.dtype, remainder_shape_dtype)
    out_shapes = jax.tree_map(lambda x: x.shape, remainder_shape_dtype)
    out_axes_ = _expand_axes(out_axes, remainder_shape_dtype)

    if num_extra_shards > 0:
      regular_shard_shape_dtype = hk.eval_shape(
          partial(apply_fun_to_slice, 0, shard_size))
      shard_shapes = jax.tree_map(lambda x: x.shape, regular_shard_shape_dtype)

      def make_output_shape(axis, shard_shape, remainder_shape):
        return shard_shape[:axis] + (
            shard_shape[axis] * num_extra_shards +
            remainder_shape[axis],) + shard_shape[axis + 1:]

      out_shapes = jax.tree_multimap(make_output_shape, out_axes_, shard_shapes,
                                     out_shapes)

    # Calls dynamic Update slice with different argument order
    # This is here since tree_multimap only works with positional arguments
    def dynamic_update_slice_in_dim(full_array, update, axis, i):
      return jax.lax.dynamic_update_slice_in_dim(full_array, update, i, axis)

    def compute_shard(outputs, slice_start, slice_size):
      slice_out = apply_fun_to_slice(slice_start, slice_size)
      update_slice = partial(
          dynamic_update_slice_in_dim, i=slice_start)
      return jax.tree_multimap(update_slice, outputs, slice_out, out_axes_)

    def scan_iteration(outputs, i):
      new_outputs = compute_shard(outputs, i, shard_size)
      return new_outputs, ()

    slice_starts = jnp.arange(0, in_size - shard_size + 1, shard_size)

    def allocate_buffer(dtype, shape):
      return jnp.zeros(shape, dtype=dtype)

    outputs = jax.tree_multimap(allocate_buffer, out_dtypes, out_shapes)

    if slice_starts.shape[0] > 0:
      outputs, _ = hk.scan(scan_iteration, outputs, slice_starts)

    if last_shard_size != shard_size:
      remainder_start = in_size - last_shard_size
      outputs = compute_shard(outputs, remainder_start, last_shard_size)

    return outputs

  return mapped_fn


def inference_subbatch(
    module: Callable[..., PYTREE_JAX_ARRAY],
    subbatch_size: int,
    batched_args: Sequence[PYTREE_JAX_ARRAY],
    nonbatched_args: Sequence[PYTREE_JAX_ARRAY],
    low_memory: bool = True,
    input_subbatch_dim: int = 0,
    output_subbatch_dim: Optional[int] = None) -> PYTREE_JAX_ARRAY:
  """Run through subbatches (like batch apply but with split and concat)."""
  assert len(batched_args) > 0  # pylint: disable=g-explicit-length-test

  if not low_memory:
    args = list(batched_args) + list(nonbatched_args)
    return module(*args)

  if output_subbatch_dim is None:
    output_subbatch_dim = input_subbatch_dim

  def run_module(*batched_args):
    args = list(batched_args) + list(nonbatched_args)
    return module(*args)
  sharded_module = sharded_apply(run_module,
                                 shard_size=subbatch_size,
                                 in_axes=input_subbatch_dim,
                                 out_axes=output_subbatch_dim)
  return sharded_module(*batched_args)
