# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# 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,
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"""Helper functions for running models in a distributed setting."""

import json
import os
import tensorflow as tf


def _collective_communication(all_reduce_alg):
  """Return a CollectiveCommunication based on all_reduce_alg.

  Args:
    all_reduce_alg: a string specifying which collective communication to pick,
      or None.

  Returns:
    tf.distribute.experimental.CollectiveCommunication object

  Raises:
    ValueError: if `all_reduce_alg` not in [None, "ring", "nccl"]
  """
  collective_communication_options = {
      None: tf.distribute.experimental.CollectiveCommunication.AUTO,
      "ring": tf.distribute.experimental.CollectiveCommunication.RING,
      "nccl": tf.distribute.experimental.CollectiveCommunication.NCCL
  }
  if all_reduce_alg not in collective_communication_options:
    raise ValueError(
        "When used with `multi_worker_mirrored`, valid values for "
        "all_reduce_alg are [`ring`, `nccl`].  Supplied value: {}".format(
            all_reduce_alg))
  return collective_communication_options[all_reduce_alg]


def _mirrored_cross_device_ops(all_reduce_alg, num_packs):
  """Return a CrossDeviceOps based on all_reduce_alg and num_packs.

  Args:
    all_reduce_alg: a string specifying which cross device op to pick, or None.
    num_packs: an integer specifying number of packs for the cross device op.

  Returns:
    tf.distribute.CrossDeviceOps object or None.

  Raises:
    ValueError: if `all_reduce_alg` not in [None, "nccl", "hierarchical_copy"].
  """
  if all_reduce_alg is None:
    return None
  mirrored_all_reduce_options = {
      "nccl": tf.distribute.NcclAllReduce,
      "hierarchical_copy": tf.distribute.HierarchicalCopyAllReduce
  }
  if all_reduce_alg not in mirrored_all_reduce_options:
    raise ValueError(
        "When used with `mirrored`, valid values for all_reduce_alg are "
        "[`nccl`, `hierarchical_copy`].  Supplied value: {}".format(
            all_reduce_alg))
  cross_device_ops_class = mirrored_all_reduce_options[all_reduce_alg]
  return cross_device_ops_class(num_packs=num_packs)


def tpu_initialize(tpu_address):
  """Initializes TPU for TF 2.x training.

  Args:
    tpu_address: string, bns address of master TPU worker.

  Returns:
    A TPUClusterResolver.
  """
  cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
      tpu=tpu_address)
  if tpu_address not in ("", "local"):
    tf.config.experimental_connect_to_cluster(cluster_resolver)
  tf.tpu.experimental.initialize_tpu_system(cluster_resolver)
  return cluster_resolver


def get_distribution_strategy(distribution_strategy="mirrored",
                              num_gpus=0,
                              all_reduce_alg=None,
                              num_packs=1,
                              tpu_address=None,
                              **kwargs):
  """Return a DistributionStrategy for running the model.

  Args:
    distribution_strategy: a string specifying which distribution strategy to
      use. Accepted values are "off", "one_device", "mirrored",
      "parameter_server", "multi_worker_mirrored", and "tpu" -- case
      insensitive. "tpu" means to use TPUStrategy using `tpu_address`.
      "off" means to use the default strategy which is obtained from
      tf.distribute.get_strategy (for details on the default strategy, see
      https://www.tensorflow.org/guide/distributed_training#default_strategy).
    num_gpus: Number of GPUs to run this model.
    all_reduce_alg: Optional. Specifies which algorithm to use when performing
      all-reduce. For `MirroredStrategy`, valid values are "nccl" and
      "hierarchical_copy". For `MultiWorkerMirroredStrategy`, valid values are
      "ring" and "nccl".  If None, DistributionStrategy will choose based on
      device topology.
    num_packs: Optional.  Sets the `num_packs` in `tf.distribute.NcclAllReduce`
      or `tf.distribute.HierarchicalCopyAllReduce` for `MirroredStrategy`.
    tpu_address: Optional. String that represents TPU to connect to. Must not be
      None if `distribution_strategy` is set to `tpu`.
    **kwargs: Additional kwargs for internal usages.

  Returns:
    tf.distribute.DistibutionStrategy object.
  Raises:
    ValueError: if `distribution_strategy` is "off" or "one_device" and
      `num_gpus` is larger than 1; or `num_gpus` is negative or if
      `distribution_strategy` is `tpu` but `tpu_address` is not specified.
  """
  del kwargs
  if num_gpus < 0:
    raise ValueError("`num_gpus` can not be negative.")

  if not isinstance(distribution_strategy, str):
    msg = ("distribution_strategy must be a string but got: %s." %
           (distribution_strategy,))
    if distribution_strategy == False:  # pylint: disable=singleton-comparison,g-explicit-bool-comparison
      msg += (" If you meant to pass the string 'off', make sure you add "
              "quotes around 'off' so that yaml interprets it as a string "
              "instead of a bool.")
    raise ValueError(msg)

  distribution_strategy = distribution_strategy.lower()
  if distribution_strategy == "off":
    if num_gpus > 1:
      raise ValueError(f"When {num_gpus} GPUs are specified, "
                       "distribution_strategy flag cannot be set to `off`.")
    # Return the default distribution strategy.
    return tf.distribute.get_strategy()

  if distribution_strategy == "tpu":
    # When tpu_address is an empty string, we communicate with local TPUs.
    cluster_resolver = tpu_initialize(tpu_address)
    return tf.distribute.TPUStrategy(cluster_resolver)

  if distribution_strategy == "multi_worker_mirrored":
    return tf.distribute.experimental.MultiWorkerMirroredStrategy(
        communication=_collective_communication(all_reduce_alg))

  if distribution_strategy == "one_device":
    if num_gpus == 0:
      return tf.distribute.OneDeviceStrategy("device:CPU:0")
    if num_gpus > 1:
      raise ValueError("`OneDeviceStrategy` can not be used for more than "
                       "one device.")
    return tf.distribute.OneDeviceStrategy("device:GPU:0")

  if distribution_strategy == "mirrored":
    if num_gpus == 0:
      devices = ["device:CPU:0"]
    else:
      devices = ["device:GPU:%d" % i for i in range(num_gpus)]
    return tf.distribute.MirroredStrategy(
        devices=devices,
        cross_device_ops=_mirrored_cross_device_ops(all_reduce_alg, num_packs))

  if distribution_strategy == "parameter_server":
    cluster_resolver = tf.distribute.cluster_resolver.TFConfigClusterResolver()
    return tf.distribute.experimental.ParameterServerStrategy(cluster_resolver)

  raise ValueError("Unrecognized Distribution Strategy: %r" %
                   distribution_strategy)


def configure_cluster(worker_hosts=None, task_index=-1):
  """Set multi-worker cluster spec in TF_CONFIG environment variable.

  Args:
    worker_hosts: comma-separated list of worker ip:port pairs.
    task_index: index of the worker.

  Returns:
    Number of workers in the cluster.
  """
  tf_config = json.loads(os.environ.get("TF_CONFIG", "{}"))
  if tf_config:
    num_workers = (
        len(tf_config["cluster"].get("chief", [])) +
        len(tf_config["cluster"].get("worker", [])))
  elif worker_hosts:
    workers = worker_hosts.split(",")
    num_workers = len(workers)
    if num_workers > 1 and task_index < 0:
      raise ValueError("Must specify task_index when number of workers > 1")
    task_index = 0 if num_workers == 1 else task_index
    os.environ["TF_CONFIG"] = json.dumps({
        "cluster": {
            "worker": workers
        },
        "task": {
            "type": "worker",
            "index": task_index
        }
    })
  else:
    num_workers = 1
  return num_workers


def get_strategy_scope(strategy):
  if strategy:
    strategy_scope = strategy.scope()
  else:
    strategy_scope = DummyContextManager()

  return strategy_scope


class DummyContextManager(object):

  def __enter__(self):
    pass

  def __exit__(self, *args):
    pass
