import datetime
import logging
from collections import defaultdict
from typing import Any, Dict, List, Set, Tuple

from django.conf import settings
from django.db import transaction
from django.utils.timezone import now as timezone_now

from confirmation.models import one_click_unsubscribe_link
from zerver.context_processors import common_context
from zerver.lib.email_notifications import build_message_list
from zerver.lib.logging_util import log_to_file
from zerver.lib.message import get_last_message_id
from zerver.lib.queue import queue_json_publish
from zerver.lib.send_email import FromAddress, send_future_email
from zerver.lib.url_encoding import encode_stream
from zerver.models import (
    Message,
    Realm,
    RealmAuditLog,
    Recipient,
    Subscription,
    UserActivityInterval,
    UserProfile,
    get_active_streams,
)

logger = logging.getLogger(__name__)
log_to_file(logger, settings.DIGEST_LOG_PATH)

DIGEST_CUTOFF = 5

TopicKey = Tuple[int, str]

class DigestTopic:
    def __init__(self, topic_key: TopicKey) -> None:
        self.topic_key = topic_key
        self.human_senders: Set[str] = set()
        self.sample_messages: List[Message] = []
        self.num_human_messages = 0

    def stream_id(self) -> int:
        # topic_key is (stream_id, topic_name)
        return self.topic_key[0]

    def add_message(self, message: Message) -> None:
        if len(self.sample_messages) < 2:
            self.sample_messages.append(message)

        if message.sent_by_human():
            self.human_senders.add(message.sender.full_name)
            self.num_human_messages += 1

    def length(self) -> int:
        return self.num_human_messages

    def diversity(self) -> int:
        return len(self.human_senders)

    def teaser_data(self, user_profile: UserProfile) -> Dict[str, Any]:
        teaser_count = self.num_human_messages - len(self.sample_messages)
        first_few_messages = build_message_list(
            user_profile,
            self.sample_messages,
        )
        return {
            "participants": sorted(self.human_senders),
            "count": teaser_count,
            "first_few_messages": first_few_messages,
        }

# Digests accumulate 2 types of interesting traffic for a user:
# 1. New streams
# 2. Interesting stream traffic, as determined by the longest and most
#    diversely comment upon topics.

def should_process_digest(realm_str: str) -> bool:
    if realm_str in settings.SYSTEM_ONLY_REALMS:
        # Don't try to send emails to system-only realms
        return False
    return True

# Changes to this should also be reflected in
# zerver/worker/queue_processors.py:DigestWorker.consume()
def queue_digest_recipient(user_id: int, cutoff: datetime.datetime) -> None:
    # Convert cutoff to epoch seconds for transit.
    event = {
        "user_profile_id": user_id,
        "cutoff": cutoff.strftime('%s')
    }
    queue_json_publish("digest_emails", event)

def enqueue_emails(cutoff: datetime.datetime) -> None:
    if not settings.SEND_DIGEST_EMAILS:
        return

    weekday = timezone_now().weekday()
    for realm in Realm.objects.filter(deactivated=False, digest_emails_enabled=True, digest_weekday=weekday):
        if should_process_digest(realm.string_id):
            _enqueue_emails_for_realm(realm, cutoff)

def _enqueue_emails_for_realm(realm: Realm, cutoff: datetime.datetime) -> None:
    # This should only be called directly by tests.  Use enqueue_emails
    # to process all realms that are set up for processing on any given day.
    realm_user_ids = set(UserProfile.objects.filter(
        realm=realm,
        is_active=True,
        is_bot=False,
        enable_digest_emails=True,
    ).values_list('id', flat=True))

    twelve_hours_ago = timezone_now() - datetime.timedelta(hours=12)

    recent_user_ids = set(RealmAuditLog.objects.filter(
        realm_id=realm.id,
        event_type=RealmAuditLog.USER_DIGEST_EMAIL_CREATED,
        event_time__gt=twelve_hours_ago,
    ).values_list('modified_user_id', flat=True).distinct())

    realm_user_ids -= recent_user_ids

    active_user_ids = set(UserActivityInterval.objects.filter(
        user_profile_id__in=realm_user_ids,
        end__gt=cutoff,
    ).values_list('user_profile_id', flat=True).distinct())

    user_ids = realm_user_ids - active_user_ids

    for user_id in user_ids:
        queue_digest_recipient(user_id, cutoff)
        logger.info(
            "User %s is inactive, queuing for potential digest",
            user_id,
        )

def get_recent_topics(
    stream_ids: List[int],
    cutoff_date: datetime.datetime,
) -> List[DigestTopic]:
    # Gather information about topic conversations, then
    # classify by:
    #   * topic length
    #   * number of senders

    messages = Message.objects.filter(
        recipient__type=Recipient.STREAM,
        recipient__type_id__in=stream_ids,
        date_sent__gt=cutoff_date,
    ).order_by(
        'id',  # we will sample the first few messages
    ).select_related(
        'recipient',  # we need stream_id
        'sender',  # we need the sender's full name
        'sending_client'  # for Message.sent_by_human
    )

    digest_topic_map: Dict[TopicKey, DigestTopic] = {}
    for message in messages:
        topic_key = (message.recipient.type_id, message.topic_name())

        if topic_key not in digest_topic_map:
            digest_topic_map[topic_key] = DigestTopic(topic_key)

        digest_topic_map[topic_key].add_message(message)

    topics = list(digest_topic_map.values())

    return topics

def get_hot_topics(
    all_topics: List[DigestTopic],
    stream_ids: Set[int],
) -> List[DigestTopic]:
    topics = [
        topic for topic in all_topics
        if topic.stream_id() in stream_ids
    ]
    topics_by_diversity = sorted(topics, key=lambda dt: dt.diversity())
    topics_by_length = sorted(topics, key=lambda dt: dt.length())

    # Start with the two most diverse topics.
    hot_topics = topics_by_diversity[:2]

    # Pad out our list up to 4 items, using the topics' length (aka message
    # count) as the secondary filter.
    for topic in topics_by_length:
        if topic not in hot_topics:
            hot_topics.append(topic)
        if len(hot_topics) >= 4:
            break

    return hot_topics

def gather_new_streams(user_profile: UserProfile,
                       threshold: datetime.datetime) -> Tuple[int, Dict[str, List[str]]]:
    if user_profile.is_guest:
        new_streams = list(get_active_streams(user_profile.realm).filter(
            is_web_public=True, date_created__gt=threshold))

    elif user_profile.can_access_public_streams():
        new_streams = list(get_active_streams(user_profile.realm).filter(
            invite_only=False, date_created__gt=threshold))

    base_url = f"{user_profile.realm.uri}/#narrow/stream/"

    streams_html = []
    streams_plain = []

    for stream in new_streams:
        narrow_url = base_url + encode_stream(stream.id, stream.name)
        stream_link = f"<a href='{narrow_url}'>{stream.name}</a>"
        streams_html.append(stream_link)
        streams_plain.append(stream.name)

    return len(new_streams), {"html": streams_html, "plain": streams_plain}

def enough_traffic(hot_conversations: str, new_streams: int) -> bool:
    return bool(hot_conversations or new_streams)

def bulk_get_digest_context(users: List[UserProfile], cutoff: float) -> Dict[int, Dict[str, Any]]:
    # Convert from epoch seconds to a datetime object.
    cutoff_date = datetime.datetime.fromtimestamp(int(cutoff), tz=datetime.timezone.utc)

    result: Dict[int, Dict[str, Any]] = {}

    user_ids = [user.id for user in users]

    def get_stream_map(user_ids: List[int]) -> Dict[int, Set[int]]:
        rows = Subscription.objects.filter(
            user_profile_id__in=user_ids,
            recipient__type=Recipient.STREAM,
            active=True,
            is_muted=False,
        ).values('user_profile_id', 'recipient__type_id')

        # maps user_id -> {stream_id, stream_id, ...}
        dct: Dict[int, Set[int]] = defaultdict(set)
        for row in rows:
            dct[row['user_profile_id']].add(row['recipient__type_id'])

        return dct

    stream_map = get_stream_map(user_ids)

    long_term_idle_user_ids = [user.id for user in users if user.long_term_idle]

    recently_modified_streams = get_modified_streams(long_term_idle_user_ids, cutoff_date)

    all_stream_ids = set()

    for user in users:
        stream_ids = stream_map[user.id]

        if user.long_term_idle:
            stream_ids -= recently_modified_streams.get(user.id, set())

        all_stream_ids |= stream_ids

    # Get all the recent topics for all the users.  This does the heavy
    # lifting of making an expensive query to the Message table.  Then
    # for each user, we filter to just the streams they care about.
    recent_topics = get_recent_topics(sorted(list(all_stream_ids)), cutoff_date)

    for user in users:
        stream_ids = stream_map[user.id]

        hot_topics = get_hot_topics(recent_topics, stream_ids)

        context = common_context(user)

        # Start building email template data.
        unsubscribe_link = one_click_unsubscribe_link(user, "digest")
        context.update(unsubscribe_link=unsubscribe_link)

        # Get context data for hot conversations.
        context["hot_conversations"] = [
            hot_topic.teaser_data(user)
            for hot_topic in hot_topics
        ]

        # Gather new streams.
        new_streams_count, new_streams = gather_new_streams(user, cutoff_date)
        context["new_streams"] = new_streams
        context["new_streams_count"] = new_streams_count

        result[user.id] = context

    return result

def get_digest_context(user: UserProfile, cutoff: float) -> Dict[str, Any]:
    return bulk_get_digest_context([user], cutoff)[user.id]

@transaction.atomic
def bulk_handle_digest_email(user_ids: List[int], cutoff: float) -> None:
    # We go directly to the database to get user objects,
    # since inactive users are likely to not be in the cache.
    users = UserProfile.objects.filter(id__in=user_ids).order_by('id')
    context_map = bulk_get_digest_context(users, cutoff)

    digest_users = []

    for user in users:
        context = context_map[user.id]

        # We don't want to send emails containing almost no information.
        if enough_traffic(context["hot_conversations"], context["new_streams_count"]):
            digest_users.append(user)
            logger.info("Sending digest email for user %s", user.id)
            # Send now, as a ScheduledEmail
            send_future_email(
                'zerver/emails/digest',
                user.realm,
                to_user_ids=[user.id],
                from_name="Zulip Digest",
                from_address=FromAddress.no_reply_placeholder,
                context=context,
            )

    bulk_write_realm_audit_logs(digest_users)

def bulk_write_realm_audit_logs(users: List[UserProfile]) -> None:
    if not users:
        return

    # We write RealmAuditLog rows for auditing, and we will also
    # use these rows during the next run to possibly exclude the
    # users (if insufficient time has passed).
    last_message_id = get_last_message_id()
    now = timezone_now()

    log_rows = [
        RealmAuditLog(
            realm_id=user.realm_id,
            modified_user_id=user.id,
            event_last_message_id=last_message_id,
            event_time=now,
            event_type=RealmAuditLog.USER_DIGEST_EMAIL_CREATED,
        )
        for user in users
    ]

    RealmAuditLog.objects.bulk_create(log_rows)

def handle_digest_email(user_id: int, cutoff: float) -> None:
    bulk_handle_digest_email([user_id], cutoff)

def get_modified_streams(user_ids: List[int], cutoff_date: datetime.datetime) -> Dict[int, Set[int]]:
    events = [
        RealmAuditLog.SUBSCRIPTION_CREATED,
        RealmAuditLog.SUBSCRIPTION_ACTIVATED,
        RealmAuditLog.SUBSCRIPTION_DEACTIVATED,
    ]

    # Get rows where the users' subscriptions have changed.
    rows = RealmAuditLog.objects.filter(
        modified_user_id__in=user_ids,
        event_time__gt=cutoff_date,
        event_type__in=events,
    ).values("modified_user_id", "modified_stream_id").distinct()

    result: Dict[int, Set[int]] = defaultdict(set)

    for row in rows:
        user_id = row["modified_user_id"]
        stream_id = row["modified_stream_id"]
        result[user_id].add(stream_id)

    return result