"""CloudWatch Logs module."""

from __future__ import annotations

import datetime
import logging
import time
from typing import Any, Dict, List, cast

import boto3

import awswrangler.pandas as pd
from awswrangler import _utils, exceptions
from awswrangler._config import apply_configs

_logger: logging.Logger = logging.getLogger(__name__)

_QUERY_WAIT_POLLING_DELAY: float = 1.0  # SECONDS


def _validate_args(
    start_timestamp: int,
    end_timestamp: int,
) -> None:
    if start_timestamp < 0:
        raise exceptions.InvalidArgument("`start_time` cannot be a negative value.")
    if start_timestamp >= end_timestamp:
        raise exceptions.InvalidArgumentCombination("`start_time` must be inferior to `end_time`.")


def start_query(
    query: str,
    log_group_names: list[str],
    start_time: datetime.datetime | None = None,
    end_time: datetime.datetime | None = None,
    limit: int | None = None,
    boto3_session: boto3.Session | None = None,
) -> str:
    """Run a query against AWS CloudWatchLogs Insights.

    https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/CWL_QuerySyntax.html

    Parameters
    ----------
    query
        The query string.
    log_group_names
        The list of log group names or ARNs to be queried. You can include up to 50 log groups.
    start_time
        The beginning of the time range to query.
    end_time
        The end of the time range to query.
    limit
        The maximum number of log events to return in the query.
    boto3_session
        The default boto3 session will be used if **boto3_session** is ``None``.

    Returns
    -------
        Query ID.

    Examples
    --------
    >>> import awswrangler as wr
    >>> query_id = wr.cloudwatch.start_query(
    ...     log_group_names=["loggroup"],
    ...     query="fields @timestamp, @message | sort @timestamp desc | limit 5",
    ... )

    """
    _logger.debug("log_group_names: %s", log_group_names)

    start_time = (
        start_time if start_time else datetime.datetime(year=1970, month=1, day=1, tzinfo=datetime.timezone.utc)
    )
    end_time = end_time if end_time else datetime.datetime.utcnow()

    start_timestamp: int = int(1000 * start_time.timestamp())
    end_timestamp: int = int(1000 * end_time.timestamp())
    _logger.debug("start_timestamp: %s", start_timestamp)
    _logger.debug("end_timestamp: %s", end_timestamp)

    _validate_args(
        start_timestamp=start_timestamp,
        end_timestamp=end_timestamp,
    )
    args: dict[str, Any] = {
        "logGroupIdentifiers": log_group_names,
        "startTime": start_timestamp,
        "endTime": end_timestamp,
        "queryString": query,
    }
    if limit is not None:
        args["limit"] = limit

    client_logs = _utils.client(service_name="logs", session=boto3_session)
    response = client_logs.start_query(**args)
    return response["queryId"]


@apply_configs
def wait_query(
    query_id: str,
    boto3_session: boto3.Session | None = None,
    cloudwatch_query_wait_polling_delay: float = _QUERY_WAIT_POLLING_DELAY,
) -> dict[str, Any]:
    """Wait query ends.

    https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/CWL_QuerySyntax.html

    Parameters
    ----------
    query_id
        Query ID.
    boto3_session
        The default boto3 session will be used if **boto3_session** is ``None``.
    cloudwatch_query_wait_polling_delay
        Interval in seconds for how often the function will check if the CloudWatch query has completed.

    Returns
    -------
        Query result payload.

    Examples
    --------
    >>> import awswrangler as wr
    >>> query_id = wr.cloudwatch.start_query(
    ...     log_group_names=["loggroup"],
    ...     query="fields @timestamp, @message | sort @timestamp desc | limit 5",
    ... )
    ... response = wr.cloudwatch.wait_query(query_id=query_id)

    """
    final_states: list[str] = ["Complete", "Failed", "Cancelled"]
    client_logs = _utils.client(service_name="logs", session=boto3_session)
    response = client_logs.get_query_results(queryId=query_id)
    status = response["status"]
    while status not in final_states:
        time.sleep(cloudwatch_query_wait_polling_delay)
        response = client_logs.get_query_results(queryId=query_id)
        status = response["status"]
    _logger.debug("status: %s", status)
    if status == "Failed":
        raise exceptions.QueryFailed(f"query ID: {query_id}")
    if status == "Cancelled":
        raise exceptions.QueryCancelled(f"query ID: {query_id}")
    return cast(Dict[str, Any], response)


def run_query(
    query: str,
    log_group_names: list[str],
    start_time: datetime.datetime | None = None,
    end_time: datetime.datetime | None = None,
    limit: int | None = None,
    boto3_session: boto3.Session | None = None,
) -> list[list[dict[str, str]]]:
    """Run a query against AWS CloudWatchLogs Insights and wait the results.

    https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/CWL_QuerySyntax.html

    Parameters
    ----------
    query
        The query string.
    log_group_names
        The list of log group names or ARNs to be queried. You can include up to 50 log groups.
    start_time
        The beginning of the time range to query.
    end_time
        The end of the time range to query.
    limit
        The maximum number of log events to return in the query.
    boto3_session
        The default boto3 session will be used if **boto3_session** is ``None``.

    Returns
    -------
        Result.

    Examples
    --------
    >>> import awswrangler as wr
    >>> result = wr.cloudwatch.run_query(
    ...     log_group_names=["loggroup"],
    ...     query="fields @timestamp, @message | sort @timestamp desc | limit 5",
    ... )

    """
    query_id: str = start_query(
        query=query,
        log_group_names=log_group_names,
        start_time=start_time,
        end_time=end_time,
        limit=limit,
        boto3_session=boto3_session,
    )
    response: dict[str, Any] = wait_query(query_id=query_id, boto3_session=boto3_session)
    return cast(List[List[Dict[str, str]]], response["results"])


def read_logs(
    query: str,
    log_group_names: list[str],
    start_time: datetime.datetime | None = None,
    end_time: datetime.datetime | None = None,
    limit: int | None = None,
    boto3_session: boto3.Session | None = None,
) -> pd.DataFrame:
    """Run a query against AWS CloudWatchLogs Insights and convert the results to Pandas DataFrame.

    https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/CWL_QuerySyntax.html

    Parameters
    ----------
    query:
        The query string.
    log_group_names
        The list of log group names or ARNs to be queried. You can include up to 50 log groups.
    start_time
        The beginning of the time range to query.
    end_time
        The end of the time range to query.
    limit
        The maximum number of log events to return in the query.
    boto3_session
        The default boto3 session will be used if **boto3_session** is ``None``.

    Returns
    -------
        Result as a Pandas DataFrame.

    Examples
    --------
    >>> import awswrangler as wr
    >>> df = wr.cloudwatch.read_logs(
    ...     log_group_names=["loggroup"],
    ...     query="fields @timestamp, @message | sort @timestamp desc | limit 5",
    ... )

    """
    results: list[list[dict[str, str]]] = run_query(
        query=query,
        log_group_names=log_group_names,
        start_time=start_time,
        end_time=end_time,
        limit=limit,
        boto3_session=boto3_session,
    )
    pre_df: list[dict[str, str]] = []
    for row in results:
        new_row: dict[str, str] = {}
        for col in row:
            if col["field"].startswith("@"):
                col_name = col["field"].replace("@", "", 1)
            else:
                col_name = col["field"]
            new_row[col_name] = col["value"]
        pre_df.append(new_row)
    df: pd.DataFrame = pd.DataFrame(pre_df, dtype="string")
    if "timestamp" in df.columns:
        df["timestamp"] = pd.to_datetime(df["timestamp"])
    return df


def describe_log_streams(
    log_group_name: str,
    log_stream_name_prefix: str | None = None,
    order_by: str | None = "LogStreamName",
    descending: bool | None = False,
    limit: int | None = 50,
    boto3_session: boto3.Session | None = None,
) -> pd.DataFrame:
    """List the log streams for the specified log group, return results as a Pandas DataFrame.

    https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/logs.html#CloudWatchLogs.Client.describe_log_streams

    Parameters
    ----------
    log_group_name
        The name of the log group.
    log_stream_name_prefix
        The prefix to match log streams' name
    order_by
        If the value is LogStreamName , the results are ordered by log stream name.
        If the value is LastEventTime , the results are ordered by the event time.
        The default value is LogStreamName .
    descending
        If the value is True, results are returned in descending order.
        If the value is to False, results are returned in ascending order.
        The default value is False.
    limit
        The maximum number of items returned. The default is up to 50 items.
    boto3_session
        The default boto3 session will be used if **boto3_session** is ``None``.

    Returns
    -------
        Result as a Pandas DataFrame.

    Examples
    --------
    >>> import awswrangler as wr
    >>> df = wr.cloudwatch.describe_log_streams(
    ...     log_group_name="aws_sdk_pandas_log_group",
    ...     log_stream_name_prefix="aws_sdk_pandas_log_stream",
    ... )

    """
    client_logs = _utils.client(service_name="logs", session=boto3_session)
    args: dict[str, Any] = {
        "logGroupName": log_group_name,
        "descending": descending,
        "orderBy": order_by,
        "limit": limit,
    }
    if log_stream_name_prefix and order_by == "LogStreamName":
        args["logStreamNamePrefix"] = log_stream_name_prefix
    elif log_stream_name_prefix and order_by == "LastEventTime":
        raise exceptions.InvalidArgumentCombination(
            "Cannot call describe_log_streams with both `log_stream_name_prefix` and order_by equal 'LastEventTime'"
        )
    log_streams: list[dict[str, Any]] = []
    response = client_logs.describe_log_streams(**args)

    log_streams += cast(List[Dict[str, Any]], response["logStreams"])
    while "nextToken" in response:
        response = client_logs.describe_log_streams(
            **args,
            nextToken=response["nextToken"],
        )
        log_streams += cast(List[Dict[str, Any]], response["logStreams"])
    if log_streams:
        df: pd.DataFrame = pd.DataFrame(log_streams)
        df["logGroupName"] = log_group_name
        return df
    return pd.DataFrame()


def _filter_log_events(
    log_group_name: str,
    log_stream_names: list[str],
    start_timestamp: int | None = None,
    end_timestamp: int | None = None,
    filter_pattern: str | None = None,
    limit: int | None = 10000,
    boto3_session: boto3.Session | None = None,
) -> list[dict[str, Any]]:
    client_logs = _utils.client(service_name="logs", session=boto3_session)
    events: list[dict[str, Any]] = []
    args: dict[str, Any] = {
        "logGroupName": log_group_name,
        "logStreamNames": log_stream_names,
        "limit": limit,
    }
    if start_timestamp:
        args["startTime"] = start_timestamp
    if end_timestamp:
        args["endTime"] = end_timestamp
    if filter_pattern:
        args["filterPattern"] = filter_pattern
    response = client_logs.filter_log_events(**args)
    events += cast(List[Dict[str, Any]], response["events"])
    while "nextToken" in response:
        response = client_logs.filter_log_events(
            **args,
            nextToken=response["nextToken"],
        )
        events += cast(List[Dict[str, Any]], response["events"])
    return events


def filter_log_events(
    log_group_name: str,
    log_stream_name_prefix: str | None = None,
    log_stream_names: list[str] | None = None,
    filter_pattern: str | None = None,
    start_time: datetime.datetime | None = None,
    end_time: datetime.datetime | None = None,
    boto3_session: boto3.Session | None = None,
) -> pd.DataFrame:
    """List log events from the specified log group. The results are returned as Pandas DataFrame.

    https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/logs.html#CloudWatchLogs.Client.filter_log_events

    Note
    ----
    Cannot call ``filter_log_events`` with both ``log_stream_names`` and ``log_stream_name_prefix``.

    Parameters
    ----------
    log_group_name
        The name of the log group.
    log_stream_name_prefix
        Filters the results to include only events from log streams that have names starting with this prefix.
    log_stream_names
        Filters the results to only logs from the log streams in this list.
    filter_pattern
        The filter pattern to use. If not provided, all the events are matched.
    start_time
        Events with a timestamp before this time are not returned.
    end_time
        Events with a timestamp later than this time are not returned.
    boto3_session
        The default boto3 session will be used if **boto3_session** is ``None``.

    Returns
    -------
        Result as a Pandas DataFrame.

    Examples
    --------
    Get all log events from log group 'aws_sdk_pandas_log_group' that have log stream prefix 'aws_sdk_pandas_log_stream'

    >>> import awswrangler as wr
    >>> df = wr.cloudwatch.filter_log_events(
    ...     log_group_name="aws_sdk_pandas_log_group",
    ...     log_stream_name_prefix="aws_sdk_pandas_log_stream",
    ... )

    Get all log events contains 'REPORT' from log stream
    'aws_sdk_pandas_log_stream_one' and 'aws_sdk_pandas_log_stream_two'
    from log group 'aws_sdk_pandas_log_group'

    >>> import awswrangler as wr
    >>> df = wr.cloudwatch.filter_log_events(
    ...     log_group_name="aws_sdk_pandas_log_group",
    ...     log_stream_names=["aws_sdk_pandas_log_stream_one","aws_sdk_pandas_log_stream_two"],
    ...     filter_pattern="REPORT",
    ... )

    """
    if log_stream_name_prefix and log_stream_names:
        raise exceptions.InvalidArgumentCombination(
            "Cannot call `filter_log_events` with both `log_stream_names` and `log_stream_name_prefix`"
        )
    _logger.debug("log_group_name: %s", log_group_name)

    events: list[dict[str, Any]] = []
    if not log_stream_names:
        describe_log_streams_args: dict[str, Any] = {
            "log_group_name": log_group_name,
        }
        if boto3_session:
            describe_log_streams_args["boto3_session"] = boto3_session
        if log_stream_name_prefix:
            describe_log_streams_args["log_stream_name_prefix"] = log_stream_name_prefix
        log_streams = describe_log_streams(**describe_log_streams_args)
        log_stream_names = log_streams["logStreamName"].tolist() if len(log_streams.index) else []

    args: dict[str, Any] = {
        "log_group_name": log_group_name,
    }
    if start_time:
        args["start_timestamp"] = int(1000 * start_time.timestamp())
    if end_time:
        args["end_timestamp"] = int(1000 * end_time.timestamp())
    if filter_pattern:
        args["filter_pattern"] = filter_pattern
    if boto3_session:
        args["boto3_session"] = boto3_session
    chunked_log_streams_size: int = 50

    for i in range(0, len(log_stream_names), chunked_log_streams_size):
        log_streams = log_stream_names[i : i + chunked_log_streams_size]
        events += _filter_log_events(**args, log_stream_names=log_streams)
    if events:
        df: pd.DataFrame = pd.DataFrame(events)
        df["logGroupName"] = log_group_name
        return df
    return pd.DataFrame()