from collections import Counter, defaultdict
from datetime import datetime, timedelta
from pprint import pprint

from pymongo import MongoClient
from zoneinfo import ZoneInfo

timezone = ZoneInfo("America/Argentina/Buenos_Aires")
utctz = ZoneInfo("UTC")

client = MongoClient()
db = client.deskmeter
switches = db.switch


task_file = "/home/mariano/LETRAS/adm/task/main"


def task_or_none(task=None):
    if not task:
        task = read_and_extract(task_file)

    if task == "all":
        task = None

    return task


def now():
    return datetime.now(timezone)


def convert_seconds(seconds, use_days=False):
    days = seconds // 86400
    hours = (seconds % 86400) // 3600
    minutes = (seconds % 3600) // 60
    remaining_seconds = seconds % 60

    if use_days:
        return "{} days, {:02d}:{:02d}:{:02d}".format(
            days, hours, minutes, remaining_seconds
        )
    return "{:02d}:{:02d}:{:02d}".format(hours + days * 24, minutes, remaining_seconds)


def extract(line):
    if line.rstrip().endswith("*"):
        pipe_index = line.find("|")
        if pipe_index != -1 and len(line) > pipe_index + 8:
            value = line[pipe_index + 1 : pipe_index + 9]
            return value
    return None


def read_and_extract(file_path):
    with open(file_path, "r") as file:
        for line in file:
            value = extract(line)
            if value:
                return value
    return None


def get_period_totals(start, end, task=None):
    task_query = {"$in": task.split(",")} if task else {}

    match_query = {"date": {"$gte": start, "$lte": end}}
    if task_query:
        match_query["task"] = task_query

    pipeline = [
        {"$match": match_query},
        {"$sort": {"date": 1}},
        {
            "$group": {
                "_id": None,
                "documents_in_range": {"$push": "$$ROOT"},
                "first_doc": {"$first": "$$ROOT"},
                "last_doc": {"$last": "$$ROOT"},
            }
        },
        {
            "$lookup": {
                "from": "switch",
                "let": {"first_date": "$first_doc.date", "task": "$first_doc.task"},
                "pipeline": [
                    {
                        "$match": {
                            "$expr": {
                                "$and": [
                                    {
                                        "$lt": ["$date", "$$first_date"]
                                    },  # Only before the first date
                                    {
                                        "$eq": ["$task", "$$task"]
                                    },  # Must have the same task
                                ]
                            }
                        }
                    },
                    {"$sort": {"date": -1}},  # Get the most recent (closest) document
                    {"$limit": 1},  # Only the immediate previous document
                ],
                "as": "before_first",
            }
        },
        {
            "$project": {
                "documents": {
                    "$concatArrays": [
                        {"$ifNull": ["$before_first", []]},  # Add only if found
                        "$documents_in_range",
                    ]
                }
            }
        },
        {"$unwind": "$documents"},
        {"$replaceRoot": {"newRoot": "$documents"}},
        {
            "$group": {
                "_id": "$workspace",
                "total": {"$sum": "$delta"},
            }
        },
    ]

    results = list(switches.aggregate(pipeline))

    if not results:
        return [{"ws": "No Data", "total": ""}]

    pipeline_before_after = [
        # Match documents within the date range
        {"$match": match_query},
        {"$sort": {"date": 1}},
        {
            "$group": {
                "_id": None,
                "first_doc": {"$first": "$$ROOT"},
                "last_doc": {"$last": "$$ROOT"},
            }
        },
        # Lookup to get one document before the first document in the range
        {
            "$lookup": {
                "from": "switch",
                "let": {"first_date": "$first_doc.date", "task": "$first_doc.task"},
                "pipeline": [
                    {
                        "$match": {
                            "$expr": {
                                "$and": [
                                    {
                                        "$lt": ["$date", "$$first_date"]
                                    },  # Only before the first date
                                    {
                                        "$eq": ["$task", "$$task"]
                                    },  # Must have the same task
                                ]
                            }
                        }
                    },
                    {"$sort": {"date": -1}},  # Get the most recent (closest) document
                    {"$limit": 1},  # Only the immediate previous document
                ],
                "as": "before_first",
            }
        },
        {
            "$project": {
                "before_first": {
                    "$ifNull": [{"$arrayElemAt": ["$before_first", 0]}, ""]
                },
                "last_doc": "$last_doc",  # Include the last_doc from the matched period
            }
        },
    ]

    aux_results = list(switches.aggregate(pipeline_before_after))

    bfirst = aux_results[0]["before_first"]

    if bfirst:
        bfdate = bfirst["date"].replace(tzinfo=utctz)
        start_delta = round((start - bfdate.astimezone(timezone)).total_seconds())

    ldoc = aux_results[0]["last_doc"]
    lastdate = ldoc["date"].replace(tzinfo=utctz)
    end_delta = round((end - lastdate.astimezone(timezone)).total_seconds())

    rows = []
    active_vs_idle = {"Active": 0, "Idle": 0}

    for result in results:
        if bfirst:
            if result["_id"] == bfirst["workspace"]:
                result["total"] -= start_delta

        if end < now():
            if result["_id"] == ldoc["workspace"]:
                result["total"] -= ldoc["delta"] - end_delta

    for result in results:
        if result["total"] > 0:
            rows.append(
                {"ws": result["_id"], "total": convert_seconds(result["total"])}
            )
            if result["_id"] in ["Think", "Plan", "Work"]:
                active_vs_idle["Active"] += result["total"]
            if result["_id"] in ["Away", "Other"]:
                active_vs_idle["Idle"] += result["total"]

    order = ["Plan", "Think", "Work", "Other", "Away", "Active", "Idle"]

    rows = sorted(rows, key=lambda x: order.index(x["ws"]))

    for k, v in active_vs_idle.items():
        rows.append({"ws": k, "total": convert_seconds(v)})

    return rows


# print(
#     get_period_totals(
#         datetime.today().replace(hour=0, minute=0, second=0, tzinfo=timezone)
#         - timedelta(days=1),
#         datetime.today().replace(hour=23, minute=59, second=59, tzinfo=timezone)
#         - timedelta(days=1),
#         # "ffbe198e",
#     )
# )

# print(
#     get_period_totals(
#         datetime.today().replace(hour=0, minute=0, second=0, tzinfo=timezone),
#         datetime.today().replace(hour=23, minute=59, second=59, tzinfo=timezone),
#         "5fc751ec",
#     )
# )