import collections
import copy
import hashlib
import json
import logging
import os
import re
import threading
from dataclasses import dataclass
from datetime import datetime
from io import StringIO
from numbers import Number, Real
from typing import Any, Dict, List, Optional, Tuple, Union

import ray
import ray._private.services as services
from ray.autoscaler._private import constants
from ray.autoscaler._private.cli_logger import cli_logger
from ray.autoscaler._private.docker import validate_docker_config
from ray.autoscaler._private.local.config import prepare_local
from ray.autoscaler._private.providers import _get_default_config
from ray.autoscaler.tags import NODE_TYPE_LEGACY_HEAD, NODE_TYPE_LEGACY_WORKER

REQUIRED, OPTIONAL = True, False

PLACEMENT_GROUP_RESOURCE_BUNDLED_PATTERN = re.compile(
    r"(.+)_group_(\d+)_([0-9a-zA-Z]+)"
)
PLACEMENT_GROUP_RESOURCE_PATTERN = re.compile(r"(.+)_group_([0-9a-zA-Z]+)")

HEAD_TYPE_MAX_WORKERS_WARN_TEMPLATE = (
    "Setting `max_workers` for node type"
    " `{node_type}` to the global `max_workers` value of {max_workers}. To"
    " avoid spawning worker nodes of type `{node_type}`, explicitly set"
    " `max_workers: 0` for `{node_type}`.\n"
    "Note that `max_workers: 0` was the default value prior to Ray 1.3.0."
    " Your current version is Ray {version}.\n"
    "See the docs for more information:\n"
    "https://docs.ray.io/en/master/cluster/config.html"
    "#cluster-configuration-node-max-workers\n"
    "https://docs.ray.io/en/master/cluster/config.html#full-configuration"
)

ResourceBundle = Dict[str, Union[int, float]]

# A Dict and the count of how many times it occurred.
# Refer to freq_of_dicts() below.
DictCount = Tuple[Dict, Number]

# e.g., cpu_4_ondemand.
NodeType = str

# e.g., head, worker, unmanaged
NodeKind = str

# e.g., {"resources": ..., "max_workers": ...}.
NodeTypeConfigDict = Dict[str, Any]

# e.g., {"GPU": 1}.
ResourceDict = Dict[str, Real]

# e.g., "node-1".
NodeID = str

# e.g., "127.0.0.1".
NodeIP = str

# Number of nodes to launch
NodeCount = int

# e.g. "up-to-date", "update-failed"
# See autoscaler/tags.py for other status
# values used by the autoscaler.
NodeStatus = str

Usage = Dict[str, Tuple[Number, Number]]

logger = logging.getLogger(__name__)


def is_placement_group_resource(resource_name: str) -> bool:
    """
    Check if a resource name is structured like a placement group.
    """
    return bool(
        PLACEMENT_GROUP_RESOURCE_PATTERN.match(resource_name)
        or PLACEMENT_GROUP_RESOURCE_BUNDLED_PATTERN.match(resource_name)
    )


@dataclass
class LoadMetricsSummary:
    # Map of resource name (e.g. "memory") to pair of (Used, Available) numbers
    usage: Usage
    # Counts of demand bundles from task/actor demand.
    # e.g. [({"CPU": 1}, 5), ({"GPU":1}, 2)]
    resource_demand: List[DictCount]
    # Counts of pending placement groups
    pg_demand: List[DictCount]
    # Counts of demand bundles requested by autoscaler.sdk.request_resources
    request_demand: List[DictCount]
    node_types: List[DictCount]
    # Optionally included for backwards compatibility: IP of the head node. See
    # https://github.com/ray-project/ray/pull/20623 for details.
    head_ip: Optional[NodeIP] = None
    # Optionally included for backwards compatibility: Resource breakdown by
    # node. Mapping from node id to resource usage.
    usage_by_node: Optional[Dict[str, Usage]] = None
    # A mapping from node name (the same key as `usage_by_node`) to node type.
    # Optional for deployment modes which have the concept of node types and
    # backwards compatibility.
    node_type_mapping: Optional[Dict[str, str]] = None


class ConcurrentCounter:
    def __init__(self):
        self._lock = threading.RLock()
        self._counter = collections.defaultdict(int)

    def inc(self, key, count):
        with self._lock:
            self._counter[key] += count
            return self.value

    def dec(self, key, count):
        with self._lock:
            self._counter[key] -= count
            assert self._counter[key] >= 0, "counter cannot go negative"
            return self.value

    def breakdown(self):
        with self._lock:
            return dict(self._counter)

    @property
    def value(self):
        with self._lock:
            return sum(self._counter.values())


def validate_config(config: Dict[str, Any]) -> None:
    """Required Dicts indicate that no extra fields can be introduced."""
    if not isinstance(config, dict):
        raise ValueError("Config {} is not a dictionary".format(config))

    schema_path = os.path.join(
        os.path.dirname(ray.autoscaler.__file__), "ray-schema.json"
    )
    with open(schema_path) as f:
        schema = json.load(f)

    try:
        import jsonschema
    except (ModuleNotFoundError, ImportError) as e:
        # Don't log a warning message here. Logging be handled by upstream.
        raise e from None

    try:
        jsonschema.validate(config, schema)
    except jsonschema.ValidationError as e:
        raise e from None

    # Detect out of date defaults. This happens when the autoscaler that filled
    # out the default values is older than the version of the autoscaler that
    # is running on the cluster.
    if "cluster_synced_files" not in config:
        raise RuntimeError(
            "Missing 'cluster_synced_files' field in the cluster "
            "configuration. This is likely due to the Ray version running "
            "in the cluster {ray_version} is greater than the Ray version "
            "running on your laptop. Please try updating Ray on your local "
            "machine and make sure the versions match.".format(
                ray_version=ray.__version__
            )
        )

    if "available_node_types" in config:
        if "head_node_type" not in config:
            raise ValueError(
                "You must specify `head_node_type` if `available_node_types is set."
            )
        if config["head_node_type"] not in config["available_node_types"]:
            raise ValueError("`head_node_type` must be one of `available_node_types`.")

        sum_min_workers = sum(
            config["available_node_types"][node_type].get("min_workers", 0)
            for node_type in config["available_node_types"]
        )
        if sum_min_workers > config["max_workers"]:
            raise ValueError(
                "The specified global `max_workers` is smaller than the "
                "sum of `min_workers` of all the available node types."
            )


def check_legacy_fields(config: Dict[str, Any]) -> None:
    """For use in providers that have completed the migration to
    available_node_types.

    Warns user that head_node and worker_nodes fields are being ignored.
    Throws an error if available_node_types and head_node_type aren't
    specified.
    """
    # log warning if non-empty head_node field
    if "head_node" in config and config["head_node"]:
        cli_logger.warning(
            "The `head_node` field is deprecated and will be ignored. "
            "Use `head_node_type` and `available_node_types` instead."
        )
    # log warning if non-empty worker_nodes field
    if "worker_nodes" in config and config["worker_nodes"]:
        cli_logger.warning(
            "The `worker_nodes` field is deprecated and will be ignored. "
            "Use `available_node_types` instead."
        )
    if "available_node_types" not in config:
        cli_logger.error("`available_node_types` not specified in config")
        raise ValueError("`available_node_types` not specified in config")
    if "head_node_type" not in config:
        cli_logger.error("`head_node_type` not specified in config")
        raise ValueError("`head_node_type` not specified in config")


def prepare_config(config: Dict[str, Any]) -> Dict[str, Any]:
    """
    The returned config has the following properties:
    - Uses the multi-node-type autoscaler configuration.
    - Merged with the appropriate defaults.yaml
    - Has a valid Docker configuration if provided.
    - Has max_worker set for each node type.
    """
    is_local = config.get("provider", {}).get("type") == "local"
    if is_local:
        config = prepare_local(config)

    with_defaults = fillout_defaults(config)
    merge_setup_commands(with_defaults)
    validate_docker_config(with_defaults)
    fill_node_type_min_max_workers(with_defaults)
    return with_defaults


def translate_trivial_legacy_config(config: Dict[str, Any]):
    """
    Drop empty deprecated fields ("head_node" and "worker_node").
    """

    REMOVABLE_FIELDS = ["head_node", "worker_nodes"]

    for field in REMOVABLE_FIELDS:
        if field in config and not config[field]:
            logger.warning(
                f"Dropping the empty legacy field {field}. {field}"
                "is not supported for ray>=2.0.0. It is recommended to remove"
                f"{field} from the cluster config."
            )
            del config[field]


def fillout_defaults(config: Dict[str, Any]) -> Dict[str, Any]:
    defaults = _get_default_config(config["provider"])
    defaults.update(config)

    # Just for clarity:
    merged_config = copy.deepcopy(defaults)

    # Fill auth field to avoid key errors.
    # This field is accessed when calling NodeUpdater but is not relevant to
    # certain node providers and is thus left out of some cluster launching
    # configs.
    merged_config["auth"] = merged_config.get("auth", {})

    # A legacy config is one which doesn't have available_node_types,
    # but has at least one of head_node or worker_nodes.
    is_legacy_config = ("available_node_types" not in config) and (
        "head_node" in config or "worker_nodes" in config
    )
    # Do merging logic for legacy configs.
    if is_legacy_config:
        merged_config = merge_legacy_yaml_with_defaults(merged_config)
    # Take care of this here, in case a config does not specify any of head,
    # workers, node types, but does specify min workers:
    merged_config.pop("min_workers", None)

    translate_trivial_legacy_config(merged_config)

    return merged_config


def merge_legacy_yaml_with_defaults(merged_config: Dict[str, Any]) -> Dict[str, Any]:
    """Rewrite legacy config's available node types after it has been merged
    with defaults yaml.
    """
    cli_logger.warning(
        "Converting legacy cluster config to a multi node type cluster "
        "config. Multi-node-type cluster configs are the recommended "
        "format for configuring Ray clusters. "
        "See the docs for more information:\n"
        "https://docs.ray.io/en/master/cluster/config.html#full-configuration"
    )

    # Get default head and worker types.
    default_head_type = merged_config["head_node_type"]
    # Default configs are assumed to have two node types -- one for the head
    # and one for the workers.
    assert len(merged_config["available_node_types"].keys()) == 2
    default_worker_type = (
        merged_config["available_node_types"].keys() - {default_head_type}
    ).pop()

    if merged_config["head_node"]:
        # User specified a head node in legacy config.
        # Convert it into data for the head's node type.
        head_node_info = {
            "node_config": merged_config["head_node"],
            "resources": merged_config["head_node"].get("resources") or {},
            "min_workers": 0,
            "max_workers": 0,
        }
    else:
        # Use default data for the head's node type.
        head_node_info = merged_config["available_node_types"][default_head_type]
    if merged_config["worker_nodes"]:
        # User specified a worker node in legacy config.
        # Convert it into data for the workers' node type.
        worker_node_info = {
            "node_config": merged_config["worker_nodes"],
            "resources": merged_config["worker_nodes"].get("resources") or {},
            "min_workers": merged_config.get("min_workers", 0),
            "max_workers": merged_config["max_workers"],
        }
    else:
        # Use default data for the workers' node type.
        worker_node_info = merged_config["available_node_types"][default_worker_type]

    # Rewrite available_node_types.
    merged_config["available_node_types"] = {
        NODE_TYPE_LEGACY_HEAD: head_node_info,
        NODE_TYPE_LEGACY_WORKER: worker_node_info,
    }
    merged_config["head_node_type"] = NODE_TYPE_LEGACY_HEAD

    # Resources field in head/worker fields cause node launch to fail.
    merged_config["head_node"].pop("resources", None)
    merged_config["worker_nodes"].pop("resources", None)

    return merged_config


def merge_setup_commands(config):
    config["head_setup_commands"] = (
        config["setup_commands"] + config["head_setup_commands"]
    )
    config["worker_setup_commands"] = (
        config["setup_commands"] + config["worker_setup_commands"]
    )
    return config


def fill_node_type_min_max_workers(config):
    """Sets default per-node max workers to global max_workers.
    This equivalent to setting the default per-node max workers to infinity,
    with the only upper constraint coming from the global max_workers.
    Sets default per-node min workers to zero.
    Also sets default max_workers for the head node to zero.
    """
    assert "max_workers" in config, "Global max workers should be set."
    node_types = config["available_node_types"]
    for node_type_name in node_types:
        node_type_data = node_types[node_type_name]

        node_type_data.setdefault("min_workers", 0)
        if "max_workers" not in node_type_data:
            if node_type_name == config["head_node_type"]:
                logger.info("setting max workers for head node type to 0")
                node_type_data.setdefault("max_workers", 0)
            else:
                global_max_workers = config["max_workers"]
                logger.info(
                    f"setting max workers for {node_type_name} to "
                    f"{global_max_workers}"
                )
                node_type_data.setdefault("max_workers", global_max_workers)


def with_envs(cmds: List[str], kv: Dict[str, str]) -> str:
    """
    Returns a list of commands with the given environment variables set.

    Args:
        cmds (List[str]): List of commands to set environment variables for.
        kv (Dict[str, str]): Dictionary of environment variables to set.

    Returns:
        List[str]: List of commands with the given environment variables set.

    Example:
        with_envs(["echo $FOO"], {"FOO": "BAR"})
            -> ["export FOO=BAR; echo $FOO"]
    """
    out_cmds = []
    for cmd in cmds:
        kv_str = ""
        for k, v in kv.items():
            # We will need to do export here so that it works correctly with
            # shell if the cmd args uses the argument.
            kv_str += f"export {k}={v}; "

        out_cmds.append(f"{kv_str}{cmd}")
    return out_cmds


def with_head_node_ip(cmds, head_ip=None):
    if head_ip is None:
        head_ip = services.get_node_ip_address()
    return with_envs(cmds, {"RAY_HEAD_IP": head_ip})


def hash_launch_conf(node_conf, auth):
    hasher = hashlib.sha1()
    # For hashing, we replace the path to the key with the
    # key itself. This is to make sure the hashes are the
    # same even if keys live at different locations on different
    # machines.
    full_auth = auth.copy()
    for key_type in ["ssh_private_key", "ssh_public_key"]:
        if key_type in auth:
            with open(os.path.expanduser(auth[key_type])) as key:
                full_auth[key_type] = key.read()
    hasher.update(json.dumps([node_conf, full_auth], sort_keys=True).encode("utf-8"))
    return hasher.hexdigest()


# Cache the file hashes to avoid rescanning it each time. Also, this avoids
# inadvertently restarting workers if the file mount content is mutated on the
# head node.
_hash_cache = {}


def hash_runtime_conf(
    file_mounts,
    cluster_synced_files,
    extra_objs,
    generate_file_mounts_contents_hash=False,
):
    """Returns two hashes, a runtime hash and file_mounts_content hash.

    The runtime hash is used to determine if the configuration or file_mounts
    contents have changed. It is used at launch time (ray up) to determine if
    a restart is needed.

    The file_mounts_content hash is used to determine if the file_mounts or
    cluster_synced_files contents have changed. It is used at monitor time to
    determine if additional file syncing is needed.
    """
    runtime_hasher = hashlib.sha1()
    contents_hasher = hashlib.sha1()

    def add_content_hashes(path, allow_non_existing_paths: bool = False):
        def add_hash_of_file(fpath):
            with open(fpath, "rb") as f:
                for chunk in iter(lambda: f.read(2**20), b""):
                    contents_hasher.update(chunk)

        path = os.path.expanduser(path)
        if allow_non_existing_paths and not os.path.exists(path):
            return
        if os.path.isdir(path):
            dirs = []
            for dirpath, _, filenames in os.walk(path):
                dirs.append((dirpath, sorted(filenames)))
            for dirpath, filenames in sorted(dirs):
                contents_hasher.update(dirpath.encode("utf-8"))
                for name in filenames:
                    contents_hasher.update(name.encode("utf-8"))
                    fpath = os.path.join(dirpath, name)
                    add_hash_of_file(fpath)
        else:
            add_hash_of_file(path)

    conf_str = json.dumps(file_mounts, sort_keys=True).encode("utf-8") + json.dumps(
        extra_objs, sort_keys=True
    ).encode("utf-8")

    # Only generate a contents hash if generate_contents_hash is true or
    # if we need to generate the runtime_hash
    if conf_str not in _hash_cache or generate_file_mounts_contents_hash:
        for local_path in sorted(file_mounts.values()):
            add_content_hashes(local_path)
        head_node_contents_hash = contents_hasher.hexdigest()

        # Generate a new runtime_hash if its not cached
        # The runtime hash does not depend on the cluster_synced_files hash
        # because we do not want to restart nodes only if cluster_synced_files
        # contents have changed.
        if conf_str not in _hash_cache:
            runtime_hasher.update(conf_str)
            runtime_hasher.update(head_node_contents_hash.encode("utf-8"))
            _hash_cache[conf_str] = runtime_hasher.hexdigest()

        # Add cluster_synced_files to the file_mounts_content hash
        if cluster_synced_files is not None:
            for local_path in sorted(cluster_synced_files):
                # For cluster_synced_files, we let the path be non-existant
                # because its possible that the source directory gets set up
                # anytime over the life of the head node.
                add_content_hashes(local_path, allow_non_existing_paths=True)

        file_mounts_contents_hash = contents_hasher.hexdigest()

    else:
        file_mounts_contents_hash = None

    return (_hash_cache[conf_str], file_mounts_contents_hash)


def add_prefix(info_string, prefix):
    """Prefixes each line of info_string, except the first, by prefix."""
    lines = info_string.split("\n")
    prefixed_lines = [lines[0]]
    for line in lines[1:]:
        prefixed_line = ":".join([prefix, line])
        prefixed_lines.append(prefixed_line)
    prefixed_info_string = "\n".join(prefixed_lines)
    return prefixed_info_string


def format_pg(pg):
    strategy = pg["strategy"]
    bundles = pg["bundles"]
    shape_strs = []
    for bundle, count in bundles:
        shape_strs.append(f"{bundle} * {count}")
    bundles_str = ", ".join(shape_strs)
    return f"{bundles_str} ({strategy})"


def parse_placement_group_resource_str(
    placement_group_resource_str: str,
) -> Tuple[str, Optional[str]]:
    """Parse placement group resource in the form of following 3 cases:
    {resource_name}_group_{bundle_id}_{group_name};
    -> This case is ignored as it is duplicated to the case below.
    {resource_name}_group_{group_name};
    {resource_name}

    Returns:
        Tuple of (resource_name, placement_group_name, is_countable_resource).
        placement_group_name could be None if its not a placement group
        resource. is_countable_resource is True if the resource
        doesn't contain bundle index. We shouldn't count resources
        with bundle index because it will
        have duplicated resource information as
        wildcard resources (resource name without bundle index).
    """
    result = PLACEMENT_GROUP_RESOURCE_BUNDLED_PATTERN.match(
        placement_group_resource_str
    )
    if result:
        return (result.group(1), result.group(3), False)
    result = PLACEMENT_GROUP_RESOURCE_PATTERN.match(placement_group_resource_str)
    if result:
        return (result.group(1), result.group(2), True)
    return (placement_group_resource_str, None, True)


MEMORY_SUFFIXES = [
    ("TiB", 2**40),
    ("GiB", 2**30),
    ("MiB", 2**20),
    ("KiB", 2**10),
]


def format_memory(mem_bytes: Number) -> str:
    """Formats memory in bytes in friendly unit. E.g. (2**30 + 1) bytes should
    be displayed as 1GiB but 1 byte should be displayed as 1B, (as opposed to
    rounding it to 0GiB).
    """
    for suffix, bytes_per_unit in MEMORY_SUFFIXES:
        if mem_bytes >= bytes_per_unit:
            mem_in_unit = mem_bytes / bytes_per_unit
            return f"{mem_in_unit:.2f}{suffix}"

    return f"{int(mem_bytes)}B"


def parse_usage(usage: Usage, verbose: bool) -> List[str]:
    # first collect resources used in placement groups
    placement_group_resource_usage = {}
    placement_group_resource_total = collections.defaultdict(float)
    for resource, (used, total) in usage.items():
        (pg_resource_name, pg_name, is_countable) = parse_placement_group_resource_str(
            resource
        )
        if pg_name:
            if pg_resource_name not in placement_group_resource_usage:
                placement_group_resource_usage[pg_resource_name] = 0
            if is_countable:
                placement_group_resource_usage[pg_resource_name] += used
                placement_group_resource_total[pg_resource_name] += total
            continue
    usage_lines = []
    for resource, (used, total) in sorted(usage.items()):
        if "node:" in resource:
            continue  # Skip the auto-added per-node "node:<ip>" resource.

        (_, pg_name, _) = parse_placement_group_resource_str(resource)
        if pg_name:
            continue  # Skip resource used by placement groups

        pg_used = 0
        pg_total = 0
        used_in_pg = resource in placement_group_resource_usage
        if used_in_pg:
            pg_used = placement_group_resource_usage[resource]
            pg_total = placement_group_resource_total[resource]
            # Used includes pg_total because when pgs are created
            # it allocates resources.
            # To get the real resource usage, we should subtract the pg
            # reserved resources from the usage and add pg used instead.
            used = used - pg_total + pg_used

        if resource in ["memory", "object_store_memory"]:
            formatted_used = format_memory(used)
            formatted_total = format_memory(total)
            line = f"{formatted_used}/{formatted_total} {resource}"
            if used_in_pg:
                formatted_pg_used = format_memory(pg_used)
                formatted_pg_total = format_memory(pg_total)
                line = line + (
                    f" ({formatted_pg_used} used of "
                    f"{formatted_pg_total} " + "reserved in placement groups)"
                )
            usage_lines.append(line)
        elif resource.startswith("accelerator_type:") and not verbose:
            # We made a judgement call not to show this.
            # https://github.com/ray-project/ray/issues/33272
            pass
        else:
            line = f"{used}/{total} {resource}"
            if used_in_pg:
                line += (
                    f" ({pg_used} used of " f"{pg_total} reserved in placement groups)"
                )
            usage_lines.append(line)
    return usage_lines


def get_usage_report(lm_summary: LoadMetricsSummary, verbose: bool) -> str:
    usage_lines = parse_usage(lm_summary.usage, verbose)

    sio = StringIO()
    for line in usage_lines:
        print(f" {line}", file=sio)
    return sio.getvalue()


def format_resource_demand_summary(
    resource_demand: List[Tuple[ResourceBundle, int]]
) -> List[str]:
    def filter_placement_group_from_bundle(bundle: ResourceBundle):
        """filter placement group from bundle resource name. returns
        filtered bundle and a bool indicate if the bundle is using
        placement group.

        Example: {"CPU_group_groupid": 1} returns {"CPU": 1}, True
                 {"memory": 1} return {"memory": 1}, False
        """
        using_placement_group = False
        result_bundle = dict()
        for pg_resource_str, resource_count in bundle.items():
            (resource_name, pg_name, _) = parse_placement_group_resource_str(
                pg_resource_str
            )
            result_bundle[resource_name] = resource_count
            if pg_name:
                using_placement_group = True
        return (result_bundle, using_placement_group)

    bundle_demand = collections.defaultdict(int)
    pg_bundle_demand = collections.defaultdict(int)

    for bundle, count in resource_demand:
        (
            pg_filtered_bundle,
            using_placement_group,
        ) = filter_placement_group_from_bundle(bundle)

        # bundle is a special keyword for placement group ready tasks
        # do not report the demand for this.
        if "bundle" in pg_filtered_bundle.keys():
            continue

        bundle_demand[tuple(sorted(pg_filtered_bundle.items()))] += count
        if using_placement_group:
            pg_bundle_demand[tuple(sorted(pg_filtered_bundle.items()))] += count

    demand_lines = []
    for bundle, count in bundle_demand.items():
        line = f" {dict(bundle)}: {count}+ pending tasks/actors"
        if bundle in pg_bundle_demand:
            line += f" ({pg_bundle_demand[bundle]}+ using placement groups)"
        demand_lines.append(line)
    return demand_lines


def get_demand_report(lm_summary: LoadMetricsSummary):
    demand_lines = []
    if lm_summary.resource_demand:
        demand_lines.extend(format_resource_demand_summary(lm_summary.resource_demand))
    for entry in lm_summary.pg_demand:
        pg, count = entry
        pg_str = format_pg(pg)
        line = f" {pg_str}: {count}+ pending placement groups"
        demand_lines.append(line)
    for bundle, count in lm_summary.request_demand:
        line = f" {bundle}: {count}+ from request_resources()"
        demand_lines.append(line)
    if len(demand_lines) > 0:
        demand_report = "\n".join(demand_lines)
    else:
        demand_report = " (no resource demands)"
    return demand_report


def get_per_node_breakdown(
    lm_summary: LoadMetricsSummary,
    node_type_mapping: Optional[Dict[str, float]],
    verbose: bool,
) -> str:
    sio = StringIO()

    if node_type_mapping is None:
        node_type_mapping = {}

    print(file=sio)
    for node_ip, usage in lm_summary.usage_by_node.items():
        print(file=sio)  # Print a newline.
        node_string = f"Node: {node_ip}"
        if node_ip in node_type_mapping:
            node_type = node_type_mapping[node_ip]
            node_string += f" ({node_type})"

        print(node_string, file=sio)
        print(" Usage:", file=sio)
        for line in parse_usage(usage, verbose):
            print(f"  {line}", file=sio)

    return sio.getvalue()


def format_info_string(
    lm_summary,
    autoscaler_summary,
    time=None,
    gcs_request_time: Optional[float] = None,
    non_terminated_nodes_time: Optional[float] = None,
    autoscaler_update_time: Optional[float] = None,
    verbose: bool = False,
):
    if time is None:
        time = datetime.now()
    header = "=" * 8 + f" Autoscaler status: {time} " + "=" * 8
    separator = "-" * len(header)
    if verbose:
        header += "\n"
        if gcs_request_time:
            header += f"GCS request time: {gcs_request_time:3f}s\n"
        if non_terminated_nodes_time:
            header += (
                "Node Provider non_terminated_nodes time: "
                f"{non_terminated_nodes_time:3f}s\n"
            )
        if autoscaler_update_time:
            header += "Autoscaler iteration time: " f"{autoscaler_update_time:3f}s\n"

    available_node_report_lines = []
    for node_type, count in autoscaler_summary.active_nodes.items():
        line = f" {count} {node_type}"
        available_node_report_lines.append(line)
    available_node_report = "\n".join(available_node_report_lines)

    pending_lines = []
    for node_type, count in autoscaler_summary.pending_launches.items():
        line = f" {node_type}, {count} launching"
        pending_lines.append(line)
    for ip, node_type, status in autoscaler_summary.pending_nodes:
        line = f" {ip}: {node_type}, {status.lower()}"
        pending_lines.append(line)
    if pending_lines:
        pending_report = "\n".join(pending_lines)
    else:
        pending_report = " (no pending nodes)"

    failure_lines = []
    for ip, node_type in autoscaler_summary.failed_nodes:
        line = f" {node_type}: NodeTerminated (ip: {ip})"
        failure_lines.append(line)
    if autoscaler_summary.node_availability_summary:
        records = sorted(
            autoscaler_summary.node_availability_summary.node_availabilities.values(),
            key=lambda record: record.last_checked_timestamp,
        )
        for record in records:
            if record.is_available:
                continue
            assert record.unavailable_node_information is not None
            node_type = record.node_type
            category = record.unavailable_node_information.category
            description = record.unavailable_node_information.description
            attempted_time = datetime.fromtimestamp(record.last_checked_timestamp)
            formatted_time = (
                # This `:02d` funny business is python syntax for printing a 2
                # digit number with a leading zero as padding if needed.
                f"{attempted_time.hour:02d}:"
                f"{attempted_time.minute:02d}:"
                f"{attempted_time.second:02d}"
            )
            line = f" {node_type}: {category} (latest_attempt: {formatted_time})"
            if verbose:
                line += f" - {description}"
            failure_lines.append(line)

    failure_lines = failure_lines[: -constants.AUTOSCALER_MAX_FAILURES_DISPLAYED : -1]
    failure_report = "Recent failures:\n"
    if failure_lines:
        failure_report += "\n".join(failure_lines)
    else:
        failure_report += " (no failures)"

    usage_report = get_usage_report(lm_summary, verbose)
    demand_report = get_demand_report(lm_summary)

    formatted_output = f"""{header}
Node status
{separator}
Healthy:
{available_node_report}
Pending:
{pending_report}
{failure_report}

Resources
{separator}
{"Total " if verbose else ""}Usage:
{usage_report}
{"Total " if verbose else ""}Demands:
{demand_report}"""

    if verbose and lm_summary.usage_by_node:
        formatted_output += get_per_node_breakdown(
            lm_summary, autoscaler_summary.node_type_mapping, verbose
        )

    return formatted_output.strip()


def format_readonly_node_type(node_id: str):
    """The anonymous node type for readonly node provider nodes."""
    return "node_{}".format(node_id)


def format_no_node_type_string(node_type: dict):
    placement_group_resource_usage = {}
    regular_resource_usage = collections.defaultdict(float)
    for resource, total in node_type.items():
        (pg_resource_name, pg_name, is_countable) = parse_placement_group_resource_str(
            resource
        )
        if pg_name:
            if not is_countable:
                continue
            if pg_resource_name not in placement_group_resource_usage:
                placement_group_resource_usage[pg_resource_name] = 0
            placement_group_resource_usage[pg_resource_name] += total
        else:
            regular_resource_usage[resource] += total

    output_lines = [""]
    for resource, total in regular_resource_usage.items():
        output_line = f"{resource}: {total}"
        if resource in placement_group_resource_usage:
            pg_resource = placement_group_resource_usage[resource]
            output_line += f" ({pg_resource} reserved in placement groups)"
        output_lines.append(output_line)

    return "\n  ".join(output_lines)
