# DO NOT EDIT THIS FILE!
#
# This file is generated from the CDP specification. If you need to make
# changes, edit the generator and regenerate all of the modules.
#
# CDP domain: HeapProfiler (experimental)
from __future__ import annotations
from .util import event_class, T_JSON_DICT
from dataclasses import dataclass
import enum
import typing
from . import runtime


class HeapSnapshotObjectId(str):
    '''
    Heap snapshot object id.
    '''
    def to_json(self) -> str:
        return self

    @classmethod
    def from_json(cls, json: str) -> HeapSnapshotObjectId:
        return cls(json)

    def __repr__(self):
        return 'HeapSnapshotObjectId({})'.format(super().__repr__())


@dataclass
class SamplingHeapProfileNode:
    '''
    Sampling Heap Profile node. Holds callsite information, allocation statistics and child nodes.
    '''
    #: Function location.
    call_frame: runtime.CallFrame

    #: Allocations size in bytes for the node excluding children.
    self_size: float

    #: Node id. Ids are unique across all profiles collected between startSampling and stopSampling.
    id_: int

    #: Child nodes.
    children: typing.List[SamplingHeapProfileNode]

    def to_json(self):
        json = dict()
        json['callFrame'] = self.call_frame.to_json()
        json['selfSize'] = self.self_size
        json['id'] = self.id_
        json['children'] = [i.to_json() for i in self.children]
        return json

    @classmethod
    def from_json(cls, json):
        return cls(
            call_frame=runtime.CallFrame.from_json(json['callFrame']),
            self_size=float(json['selfSize']),
            id_=int(json['id']),
            children=[SamplingHeapProfileNode.from_json(i) for i in json['children']],
        )


@dataclass
class SamplingHeapProfileSample:
    '''
    A single sample from a sampling profile.
    '''
    #: Allocation size in bytes attributed to the sample.
    size: float

    #: Id of the corresponding profile tree node.
    node_id: int

    #: Time-ordered sample ordinal number. It is unique across all profiles retrieved
    #: between startSampling and stopSampling.
    ordinal: float

    def to_json(self):
        json = dict()
        json['size'] = self.size
        json['nodeId'] = self.node_id
        json['ordinal'] = self.ordinal
        return json

    @classmethod
    def from_json(cls, json):
        return cls(
            size=float(json['size']),
            node_id=int(json['nodeId']),
            ordinal=float(json['ordinal']),
        )


@dataclass
class SamplingHeapProfile:
    '''
    Sampling profile.
    '''
    head: SamplingHeapProfileNode

    samples: typing.List[SamplingHeapProfileSample]

    def to_json(self):
        json = dict()
        json['head'] = self.head.to_json()
        json['samples'] = [i.to_json() for i in self.samples]
        return json

    @classmethod
    def from_json(cls, json):
        return cls(
            head=SamplingHeapProfileNode.from_json(json['head']),
            samples=[SamplingHeapProfileSample.from_json(i) for i in json['samples']],
        )


def add_inspected_heap_object(
        heap_object_id: HeapSnapshotObjectId
    ) -> typing.Generator[T_JSON_DICT,T_JSON_DICT,None]:
    '''
    Enables console to refer to the node with given id via $x (see Command Line API for more details
    $x functions).

    :param heap_object_id: Heap snapshot object id to be accessible by means of $x command line API.
    '''
    params: T_JSON_DICT = dict()
    params['heapObjectId'] = heap_object_id.to_json()
    cmd_dict: T_JSON_DICT = {
        'method': 'HeapProfiler.addInspectedHeapObject',
        'params': params,
    }
    json = yield cmd_dict


def collect_garbage() -> typing.Generator[T_JSON_DICT,T_JSON_DICT,None]:

    cmd_dict: T_JSON_DICT = {
        'method': 'HeapProfiler.collectGarbage',
    }
    json = yield cmd_dict


def disable() -> typing.Generator[T_JSON_DICT,T_JSON_DICT,None]:

    cmd_dict: T_JSON_DICT = {
        'method': 'HeapProfiler.disable',
    }
    json = yield cmd_dict


def enable() -> typing.Generator[T_JSON_DICT,T_JSON_DICT,None]:

    cmd_dict: T_JSON_DICT = {
        'method': 'HeapProfiler.enable',
    }
    json = yield cmd_dict


def get_heap_object_id(
        object_id: runtime.RemoteObjectId
    ) -> typing.Generator[T_JSON_DICT,T_JSON_DICT,HeapSnapshotObjectId]:
    '''
    :param object_id: Identifier of the object to get heap object id for.
    :returns: Id of the heap snapshot object corresponding to the passed remote object id.
    '''
    params: T_JSON_DICT = dict()
    params['objectId'] = object_id.to_json()
    cmd_dict: T_JSON_DICT = {
        'method': 'HeapProfiler.getHeapObjectId',
        'params': params,
    }
    json = yield cmd_dict
    return HeapSnapshotObjectId.from_json(json['heapSnapshotObjectId'])


def get_object_by_heap_object_id(
        object_id: HeapSnapshotObjectId,
        object_group: typing.Optional[str] = None
    ) -> typing.Generator[T_JSON_DICT,T_JSON_DICT,runtime.RemoteObject]:
    '''
    :param object_id:
    :param object_group: *(Optional)* Symbolic group name that can be used to release multiple objects.
    :returns: Evaluation result.
    '''
    params: T_JSON_DICT = dict()
    params['objectId'] = object_id.to_json()
    if object_group is not None:
        params['objectGroup'] = object_group
    cmd_dict: T_JSON_DICT = {
        'method': 'HeapProfiler.getObjectByHeapObjectId',
        'params': params,
    }
    json = yield cmd_dict
    return runtime.RemoteObject.from_json(json['result'])


def get_sampling_profile() -> typing.Generator[T_JSON_DICT,T_JSON_DICT,SamplingHeapProfile]:
    '''


    :returns: Return the sampling profile being collected.
    '''
    cmd_dict: T_JSON_DICT = {
        'method': 'HeapProfiler.getSamplingProfile',
    }
    json = yield cmd_dict
    return SamplingHeapProfile.from_json(json['profile'])


def start_sampling(
        sampling_interval: typing.Optional[float] = None,
        include_objects_collected_by_major_gc: typing.Optional[bool] = None,
        include_objects_collected_by_minor_gc: typing.Optional[bool] = None
    ) -> typing.Generator[T_JSON_DICT,T_JSON_DICT,None]:
    '''
    :param sampling_interval: *(Optional)* Average sample interval in bytes. Poisson distribution is used for the intervals. The default value is 32768 bytes.
    :param include_objects_collected_by_major_gc: *(Optional)* By default, the sampling heap profiler reports only objects which are still alive when the profile is returned via getSamplingProfile or stopSampling, which is useful for determining what functions contribute the most to steady-state memory usage. This flag instructs the sampling heap profiler to also include information about objects discarded by major GC, which will show which functions cause large temporary memory usage or long GC pauses.
    :param include_objects_collected_by_minor_gc: *(Optional)* By default, the sampling heap profiler reports only objects which are still alive when the profile is returned via getSamplingProfile or stopSampling, which is useful for determining what functions contribute the most to steady-state memory usage. This flag instructs the sampling heap profiler to also include information about objects discarded by minor GC, which is useful when tuning a latency-sensitive application for minimal GC activity.
    '''
    params: T_JSON_DICT = dict()
    if sampling_interval is not None:
        params['samplingInterval'] = sampling_interval
    if include_objects_collected_by_major_gc is not None:
        params['includeObjectsCollectedByMajorGC'] = include_objects_collected_by_major_gc
    if include_objects_collected_by_minor_gc is not None:
        params['includeObjectsCollectedByMinorGC'] = include_objects_collected_by_minor_gc
    cmd_dict: T_JSON_DICT = {
        'method': 'HeapProfiler.startSampling',
        'params': params,
    }
    json = yield cmd_dict


def start_tracking_heap_objects(
        track_allocations: typing.Optional[bool] = None
    ) -> typing.Generator[T_JSON_DICT,T_JSON_DICT,None]:
    '''
    :param track_allocations: *(Optional)*
    '''
    params: T_JSON_DICT = dict()
    if track_allocations is not None:
        params['trackAllocations'] = track_allocations
    cmd_dict: T_JSON_DICT = {
        'method': 'HeapProfiler.startTrackingHeapObjects',
        'params': params,
    }
    json = yield cmd_dict


def stop_sampling() -> typing.Generator[T_JSON_DICT,T_JSON_DICT,SamplingHeapProfile]:
    '''


    :returns: Recorded sampling heap profile.
    '''
    cmd_dict: T_JSON_DICT = {
        'method': 'HeapProfiler.stopSampling',
    }
    json = yield cmd_dict
    return SamplingHeapProfile.from_json(json['profile'])


def stop_tracking_heap_objects(
        report_progress: typing.Optional[bool] = None,
        treat_global_objects_as_roots: typing.Optional[bool] = None,
        capture_numeric_value: typing.Optional[bool] = None,
        expose_internals: typing.Optional[bool] = None
    ) -> typing.Generator[T_JSON_DICT,T_JSON_DICT,None]:
    '''
    :param report_progress: *(Optional)* If true 'reportHeapSnapshotProgress' events will be generated while snapshot is being taken when the tracking is stopped.
    :param treat_global_objects_as_roots: *(Optional)* Deprecated in favor of ```exposeInternals```.
    :param capture_numeric_value: *(Optional)* If true, numerical values are included in the snapshot
    :param expose_internals: **(EXPERIMENTAL)** *(Optional)* If true, exposes internals of the snapshot.
    '''
    params: T_JSON_DICT = dict()
    if report_progress is not None:
        params['reportProgress'] = report_progress
    if treat_global_objects_as_roots is not None:
        params['treatGlobalObjectsAsRoots'] = treat_global_objects_as_roots
    if capture_numeric_value is not None:
        params['captureNumericValue'] = capture_numeric_value
    if expose_internals is not None:
        params['exposeInternals'] = expose_internals
    cmd_dict: T_JSON_DICT = {
        'method': 'HeapProfiler.stopTrackingHeapObjects',
        'params': params,
    }
    json = yield cmd_dict


def take_heap_snapshot(
        report_progress: typing.Optional[bool] = None,
        treat_global_objects_as_roots: typing.Optional[bool] = None,
        capture_numeric_value: typing.Optional[bool] = None,
        expose_internals: typing.Optional[bool] = None
    ) -> typing.Generator[T_JSON_DICT,T_JSON_DICT,None]:
    '''
    :param report_progress: *(Optional)* If true 'reportHeapSnapshotProgress' events will be generated while snapshot is being taken.
    :param treat_global_objects_as_roots: *(Optional)* If true, a raw snapshot without artificial roots will be generated. Deprecated in favor of ```exposeInternals```.
    :param capture_numeric_value: *(Optional)* If true, numerical values are included in the snapshot
    :param expose_internals: **(EXPERIMENTAL)** *(Optional)* If true, exposes internals of the snapshot.
    '''
    params: T_JSON_DICT = dict()
    if report_progress is not None:
        params['reportProgress'] = report_progress
    if treat_global_objects_as_roots is not None:
        params['treatGlobalObjectsAsRoots'] = treat_global_objects_as_roots
    if capture_numeric_value is not None:
        params['captureNumericValue'] = capture_numeric_value
    if expose_internals is not None:
        params['exposeInternals'] = expose_internals
    cmd_dict: T_JSON_DICT = {
        'method': 'HeapProfiler.takeHeapSnapshot',
        'params': params,
    }
    json = yield cmd_dict


@event_class('HeapProfiler.addHeapSnapshotChunk')
@dataclass
class AddHeapSnapshotChunk:
    chunk: str

    @classmethod
    def from_json(cls, json: T_JSON_DICT) -> AddHeapSnapshotChunk:
        return cls(
            chunk=str(json['chunk'])
        )


@event_class('HeapProfiler.heapStatsUpdate')
@dataclass
class HeapStatsUpdate:
    '''
    If heap objects tracking has been started then backend may send update for one or more fragments
    '''
    #: An array of triplets. Each triplet describes a fragment. The first integer is the fragment
    #: index, the second integer is a total count of objects for the fragment, the third integer is
    #: a total size of the objects for the fragment.
    stats_update: typing.List[int]

    @classmethod
    def from_json(cls, json: T_JSON_DICT) -> HeapStatsUpdate:
        return cls(
            stats_update=[int(i) for i in json['statsUpdate']]
        )


@event_class('HeapProfiler.lastSeenObjectId')
@dataclass
class LastSeenObjectId:
    '''
    If heap objects tracking has been started then backend regularly sends a current value for last
    seen object id and corresponding timestamp. If the were changes in the heap since last event
    then one or more heapStatsUpdate events will be sent before a new lastSeenObjectId event.
    '''
    last_seen_object_id: int
    timestamp: float

    @classmethod
    def from_json(cls, json: T_JSON_DICT) -> LastSeenObjectId:
        return cls(
            last_seen_object_id=int(json['lastSeenObjectId']),
            timestamp=float(json['timestamp'])
        )


@event_class('HeapProfiler.reportHeapSnapshotProgress')
@dataclass
class ReportHeapSnapshotProgress:
    done: int
    total: int
    finished: typing.Optional[bool]

    @classmethod
    def from_json(cls, json: T_JSON_DICT) -> ReportHeapSnapshotProgress:
        return cls(
            done=int(json['done']),
            total=int(json['total']),
            finished=bool(json['finished']) if 'finished' in json else None
        )


@event_class('HeapProfiler.resetProfiles')
@dataclass
class ResetProfiles:


    @classmethod
    def from_json(cls, json: T_JSON_DICT) -> ResetProfiles:
        return cls(

        )