import json
from moto.core.utils import iso_8601_datetime_with_milliseconds
from moto.core import BaseBackend, BaseModel
from moto.core.exceptions import RESTError
import boto.ec2.cloudwatch
from datetime import datetime, timedelta
from dateutil.tz import tzutc
from .utils import make_arn_for_dashboard

DEFAULT_ACCOUNT_ID = 123456789012

_EMPTY_LIST = tuple()


class Dimension(object):
    def __init__(self, name, value):
        self.name = name
        self.value = value


def daterange(start, stop, step=timedelta(days=1), inclusive=False):
    """
    This method will iterate from `start` to `stop` datetimes with a timedelta step of `step`
    (supports iteration forwards or backwards in time)

    :param start: start datetime
    :param stop: end datetime
    :param step: step size as a timedelta
    :param inclusive: if True, last item returned will be as step closest to `end` (or `end` if no remainder).
    """

    # inclusive=False to behave like range by default
    total_step_secs = step.total_seconds()
    assert total_step_secs != 0

    if total_step_secs > 0:
        while start < stop:
            yield start
            start = start + step
    else:
        while stop < start:
            yield start
            start = start + step

    if inclusive and start == stop:
        yield start


class FakeAlarm(BaseModel):
    def __init__(
        self,
        name,
        namespace,
        metric_name,
        comparison_operator,
        evaluation_periods,
        period,
        threshold,
        statistic,
        description,
        dimensions,
        alarm_actions,
        ok_actions,
        insufficient_data_actions,
        unit,
    ):
        self.name = name
        self.namespace = namespace
        self.metric_name = metric_name
        self.comparison_operator = comparison_operator
        self.evaluation_periods = evaluation_periods
        self.period = period
        self.threshold = threshold
        self.statistic = statistic
        self.description = description
        self.dimensions = [
            Dimension(dimension["name"], dimension["value"]) for dimension in dimensions
        ]
        self.alarm_actions = alarm_actions
        self.ok_actions = ok_actions
        self.insufficient_data_actions = insufficient_data_actions
        self.unit = unit
        self.configuration_updated_timestamp = datetime.utcnow()

        self.history = []

        self.state_reason = ""
        self.state_reason_data = "{}"
        self.state_value = "OK"
        self.state_updated_timestamp = datetime.utcnow()

    def update_state(self, reason, reason_data, state_value):
        # History type, that then decides what the rest of the items are, can be one of ConfigurationUpdate | StateUpdate | Action
        self.history.append(
            (
                "StateUpdate",
                self.state_reason,
                self.state_reason_data,
                self.state_value,
                self.state_updated_timestamp,
            )
        )

        self.state_reason = reason
        self.state_reason_data = reason_data
        self.state_value = state_value
        self.state_updated_timestamp = datetime.utcnow()


class MetricDatum(BaseModel):
    def __init__(self, namespace, name, value, dimensions, timestamp):
        self.namespace = namespace
        self.name = name
        self.value = value
        self.timestamp = timestamp or datetime.utcnow().replace(tzinfo=tzutc())
        self.dimensions = [
            Dimension(dimension["Name"], dimension["Value"]) for dimension in dimensions
        ]


class Dashboard(BaseModel):
    def __init__(self, name, body):
        # Guaranteed to be unique for now as the name is also the key of a dictionary where they are stored
        self.arn = make_arn_for_dashboard(DEFAULT_ACCOUNT_ID, name)
        self.name = name
        self.body = body
        self.last_modified = datetime.now()

    @property
    def last_modified_iso(self):
        return self.last_modified.isoformat()

    @property
    def size(self):
        return len(self)

    def __len__(self):
        return len(self.body)

    def __repr__(self):
        return "<CloudWatchDashboard {0}>".format(self.name)


class Statistics:
    def __init__(self, stats, dt):
        self.timestamp = iso_8601_datetime_with_milliseconds(dt)
        self.values = []
        self.stats = stats

    @property
    def sample_count(self):
        if "SampleCount" not in self.stats:
            return None

        return len(self.values)

    @property
    def unit(self):
        return None

    @property
    def sum(self):
        if "Sum" not in self.stats:
            return None

        return sum(self.values)

    @property
    def minimum(self):
        if "Minimum" not in self.stats:
            return None

        return min(self.values)

    @property
    def maximum(self):
        if "Maximum" not in self.stats:
            return None

        return max(self.values)

    @property
    def average(self):
        if "Average" not in self.stats:
            return None

        # when moto is 3.4+ we can switch to the statistics module
        return sum(self.values) / len(self.values)


class CloudWatchBackend(BaseBackend):
    def __init__(self):
        self.alarms = {}
        self.dashboards = {}
        self.metric_data = []

    def put_metric_alarm(
        self,
        name,
        namespace,
        metric_name,
        comparison_operator,
        evaluation_periods,
        period,
        threshold,
        statistic,
        description,
        dimensions,
        alarm_actions,
        ok_actions,
        insufficient_data_actions,
        unit,
    ):
        alarm = FakeAlarm(
            name,
            namespace,
            metric_name,
            comparison_operator,
            evaluation_periods,
            period,
            threshold,
            statistic,
            description,
            dimensions,
            alarm_actions,
            ok_actions,
            insufficient_data_actions,
            unit,
        )
        self.alarms[name] = alarm
        return alarm

    def get_all_alarms(self):
        return self.alarms.values()

    @staticmethod
    def _list_element_starts_with(items, needle):
        """True of any of the list elements starts with needle"""
        for item in items:
            if item.startswith(needle):
                return True
        return False

    def get_alarms_by_action_prefix(self, action_prefix):
        return [
            alarm
            for alarm in self.alarms.values()
            if CloudWatchBackend._list_element_starts_with(
                alarm.alarm_actions, action_prefix
            )
        ]

    def get_alarms_by_alarm_name_prefix(self, name_prefix):
        return [
            alarm
            for alarm in self.alarms.values()
            if alarm.name.startswith(name_prefix)
        ]

    def get_alarms_by_alarm_names(self, alarm_names):
        return [alarm for alarm in self.alarms.values() if alarm.name in alarm_names]

    def get_alarms_by_state_value(self, target_state):
        return filter(
            lambda alarm: alarm.state_value == target_state, self.alarms.values()
        )

    def delete_alarms(self, alarm_names):
        for alarm_name in alarm_names:
            self.alarms.pop(alarm_name, None)

    def put_metric_data(self, namespace, metric_data):
        for metric_member in metric_data:
            # Preserve "datetime" for get_metric_statistics comparisons
            timestamp = metric_member.get("Timestamp")
            if timestamp is not None and type(timestamp) != datetime:
                timestamp = datetime.strptime(timestamp, "%Y-%m-%dT%H:%M:%S.%fZ")
                timestamp = timestamp.replace(tzinfo=tzutc())
            self.metric_data.append(
                MetricDatum(
                    namespace,
                    metric_member["MetricName"],
                    float(metric_member.get("Value", 0)),
                    metric_member.get("Dimensions.member", _EMPTY_LIST),
                    timestamp,
                )
            )

    def get_metric_statistics(
        self, namespace, metric_name, start_time, end_time, period, stats
    ):
        period_delta = timedelta(seconds=period)
        filtered_data = [
            md
            for md in self.metric_data
            if md.namespace == namespace
            and md.name == metric_name
            and start_time <= md.timestamp <= end_time
        ]

        # earliest to oldest
        filtered_data = sorted(filtered_data, key=lambda x: x.timestamp)
        if not filtered_data:
            return []

        idx = 0
        data = list()
        for dt in daterange(
            filtered_data[0].timestamp,
            filtered_data[-1].timestamp + period_delta,
            period_delta,
        ):
            s = Statistics(stats, dt)
            while idx < len(filtered_data) and filtered_data[idx].timestamp < (
                dt + period_delta
            ):
                s.values.append(filtered_data[idx].value)
                idx += 1

            if not s.values:
                continue

            data.append(s)

        return data

    def get_all_metrics(self):
        return self.metric_data

    def put_dashboard(self, name, body):
        self.dashboards[name] = Dashboard(name, body)

    def list_dashboards(self, prefix=""):
        for key, value in self.dashboards.items():
            if key.startswith(prefix):
                yield value

    def delete_dashboards(self, dashboards):
        to_delete = set(dashboards)
        all_dashboards = set(self.dashboards.keys())

        left_over = to_delete - all_dashboards
        if len(left_over) > 0:
            # Some dashboards are not found
            return (
                False,
                "The specified dashboard does not exist. [{0}]".format(
                    ", ".join(left_over)
                ),
            )

        for dashboard in to_delete:
            del self.dashboards[dashboard]

        return True, None

    def get_dashboard(self, dashboard):
        return self.dashboards.get(dashboard)

    def set_alarm_state(self, alarm_name, reason, reason_data, state_value):
        try:
            if reason_data is not None:
                json.loads(reason_data)
        except ValueError:
            raise RESTError("InvalidFormat", "StateReasonData is invalid JSON")

        if alarm_name not in self.alarms:
            raise RESTError(
                "ResourceNotFound", "Alarm {0} not found".format(alarm_name), status=404
            )

        if state_value not in ("OK", "ALARM", "INSUFFICIENT_DATA"):
            raise RESTError(
                "InvalidParameterValue",
                "StateValue is not one of OK | ALARM | INSUFFICIENT_DATA",
            )

        self.alarms[alarm_name].update_state(reason, reason_data, state_value)


class LogGroup(BaseModel):
    def __init__(self, spec):
        # required
        self.name = spec["LogGroupName"]
        # optional
        self.tags = spec.get("Tags", [])

    @classmethod
    def create_from_cloudformation_json(
        cls, resource_name, cloudformation_json, region_name
    ):
        properties = cloudformation_json["Properties"]
        spec = {"LogGroupName": properties["LogGroupName"]}
        optional_properties = "Tags".split()
        for prop in optional_properties:
            if prop in properties:
                spec[prop] = properties[prop]
        return LogGroup(spec)


cloudwatch_backends = {}
for region in boto.ec2.cloudwatch.regions():
    cloudwatch_backends[region.name] = CloudWatchBackend()
