"""
DataDog Integration - sends logs to /api/v2/log

DD Reference API: https://docs.datadoghq.com/api/latest/logs

`async_log_success_event` - used by litellm proxy to send logs to datadog
`log_success_event` - sync version of logging to DataDog, only used on litellm Python SDK, if user opts in to using sync functions

async_log_success_event:  will store batch of DD_MAX_BATCH_SIZE in memory and flush to Datadog once it reaches DD_MAX_BATCH_SIZE or every 5 seconds

async_service_failure_hook: Logs failures from Redis, Postgres (Adjacent systems), as 'WARNING' on DataDog

For batching specific details see CustomBatchLogger class
"""

import asyncio
import datetime
import os
import sys
import traceback
import uuid
from datetime import datetime as datetimeObj
from typing import Any, Dict, List, Optional, Union

from httpx import Response

import litellm
from litellm._logging import verbose_logger
from litellm.integrations.custom_batch_logger import CustomBatchLogger
from litellm.llms.custom_httpx.http_handler import (
    _get_httpx_client,
    get_async_httpx_client,
    httpxSpecialProvider,
)
from litellm.types.services import ServiceLoggerPayload

from .types import DD_ERRORS, DatadogPayload, DataDogStatus
from .utils import make_json_serializable

DD_MAX_BATCH_SIZE = 1000  # max number of logs DD API can accept


class DataDogLogger(CustomBatchLogger):
    # Class variables or attributes
    def __init__(
        self,
        **kwargs,
    ):
        """
        Initializes the datadog logger, checks if the correct env variables are set

        Required environment variables:
        `DD_API_KEY` - your datadog api key
        `DD_SITE` - your datadog site, example = `"us5.datadoghq.com"`
        """
        try:
            verbose_logger.debug("Datadog: in init datadog logger")
            # check if the correct env variables are set
            if os.getenv("DD_API_KEY", None) is None:
                raise Exception("DD_API_KEY is not set, set 'DD_API_KEY=<>")
            if os.getenv("DD_SITE", None) is None:
                raise Exception("DD_SITE is not set in .env, set 'DD_SITE=<>")
            self.async_client = get_async_httpx_client(
                llm_provider=httpxSpecialProvider.LoggingCallback
            )
            self.DD_API_KEY = os.getenv("DD_API_KEY")
            self.intake_url = (
                f"https://http-intake.logs.{os.getenv('DD_SITE')}/api/v2/logs"
            )

            ###################################
            # OPTIONAL -only used for testing
            if os.getenv("_DATADOG_BASE_URL", None) is not None:
                _dd_base_url = os.getenv("_DATADOG_BASE_URL")
                self.intake_url = f"{_dd_base_url}/api/v2/logs"
            ###################################
            self.sync_client = _get_httpx_client()
            asyncio.create_task(self.periodic_flush())
            self.flush_lock = asyncio.Lock()
            super().__init__(
                **kwargs, flush_lock=self.flush_lock, batch_size=DD_MAX_BATCH_SIZE
            )
        except Exception as e:
            verbose_logger.exception(
                f"Datadog: Got exception on init Datadog client {str(e)}"
            )
            raise e

    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
        """
        Async Log success events to Datadog

        - Creates a Datadog payload
        - Adds the Payload to the in memory logs queue
        - Payload is flushed every 10 seconds or when batch size is greater than 100


        Raises:
            Raises a NON Blocking verbose_logger.exception if an error occurs
        """
        try:
            verbose_logger.debug(
                "Datadog: Logging - Enters logging function for model %s", kwargs
            )
            dd_payload = self.create_datadog_logging_payload(
                kwargs=kwargs,
                response_obj=response_obj,
                start_time=start_time,
                end_time=end_time,
            )

            self.log_queue.append(dd_payload)
            verbose_logger.debug(
                f"Datadog, event added to queue. Will flush in {self.flush_interval} seconds..."
            )

            if len(self.log_queue) >= self.batch_size:
                await self.async_send_batch()

        except Exception as e:
            verbose_logger.exception(
                f"Datadog Layer Error - {str(e)}\n{traceback.format_exc()}"
            )
            pass

    async def async_send_batch(self):
        """
        Sends the in memory logs queue to datadog api

        Logs sent to /api/v2/logs

        DD Ref: https://docs.datadoghq.com/api/latest/logs/

        Raises:
            Raises a NON Blocking verbose_logger.exception if an error occurs
        """
        try:
            if not self.log_queue:
                verbose_logger.exception("Datadog: log_queue does not exist")
                return

            verbose_logger.debug(
                "Datadog - about to flush %s events on %s",
                len(self.log_queue),
                self.intake_url,
            )

            response = await self.async_send_compressed_data(self.log_queue)
            if response.status_code == 413:
                verbose_logger.exception(DD_ERRORS.DATADOG_413_ERROR.value)
                return

            response.raise_for_status()
            if response.status_code != 202:
                raise Exception(
                    f"Response from datadog API status_code: {response.status_code}, text: {response.text}"
                )

            verbose_logger.debug(
                "Datadog: Response from datadog API status_code: %s, text: %s",
                response.status_code,
                response.text,
            )
        except Exception as e:
            verbose_logger.exception(
                f"Datadog Error sending batch API - {str(e)}\n{traceback.format_exc()}"
            )

    def log_success_event(self, kwargs, response_obj, start_time, end_time):
        """
        Sync Log success events to Datadog

        - Creates a Datadog payload
        - instantly logs it on DD API
        """
        try:
            verbose_logger.debug(
                "Datadog: Logging - Enters logging function for model %s", kwargs
            )
            dd_payload = self.create_datadog_logging_payload(
                kwargs=kwargs,
                response_obj=response_obj,
                start_time=start_time,
                end_time=end_time,
            )

            response = self.sync_client.post(
                url=self.intake_url,
                json=dd_payload,  # type: ignore
                headers={
                    "DD-API-KEY": self.DD_API_KEY,
                },
            )

            response.raise_for_status()
            if response.status_code != 202:
                raise Exception(
                    f"Response from datadog API status_code: {response.status_code}, text: {response.text}"
                )

            verbose_logger.debug(
                "Datadog: Response from datadog API status_code: %s, text: %s",
                response.status_code,
                response.text,
            )

        except Exception as e:
            verbose_logger.exception(
                f"Datadog Layer Error - {str(e)}\n{traceback.format_exc()}"
            )
            pass
        pass

    def create_datadog_logging_payload(
        self,
        kwargs: Union[dict, Any],
        response_obj: Any,
        start_time: datetime.datetime,
        end_time: datetime.datetime,
    ) -> DatadogPayload:
        """
        Helper function to create a datadog payload for logging

        Args:
            kwargs (Union[dict, Any]): request kwargs
            response_obj (Any): llm api response
            start_time (datetime.datetime): start time of request
            end_time (datetime.datetime): end time of request

        Returns:
            DatadogPayload: defined in types.py
        """
        import json

        litellm_params = kwargs.get("litellm_params", {})
        metadata = (
            litellm_params.get("metadata", {}) or {}
        )  # if litellm_params['metadata'] == None
        messages = kwargs.get("messages")
        optional_params = kwargs.get("optional_params", {})
        call_type = kwargs.get("call_type", "litellm.completion")
        cache_hit = kwargs.get("cache_hit", False)
        usage = response_obj["usage"]
        id = response_obj.get("id", str(uuid.uuid4()))
        usage = dict(usage)
        try:
            response_time = (end_time - start_time).total_seconds() * 1000
        except Exception:
            response_time = None

        try:
            response_obj = dict(response_obj)
        except Exception:
            response_obj = response_obj

        # Clean Metadata before logging - never log raw metadata
        # the raw metadata can contain circular references which leads to infinite recursion
        # we clean out all extra litellm metadata params before logging
        clean_metadata = {}
        if isinstance(metadata, dict):
            for key, value in metadata.items():
                # clean litellm metadata before logging
                if key in [
                    "endpoint",
                    "caching_groups",
                    "previous_models",
                ]:
                    continue
                else:
                    clean_metadata[key] = value

        # Build the initial payload
        payload = {
            "id": id,
            "call_type": call_type,
            "cache_hit": cache_hit,
            "start_time": start_time,
            "end_time": end_time,
            "response_time": response_time,
            "model": kwargs.get("model", ""),
            "user": kwargs.get("user", ""),
            "model_parameters": optional_params,
            "spend": kwargs.get("response_cost", 0),
            "messages": messages,
            "response": response_obj,
            "usage": usage,
            "metadata": clean_metadata,
        }

        make_json_serializable(payload)
        json_payload = json.dumps(payload)

        verbose_logger.debug("Datadog: Logger - Logging payload = %s", json_payload)

        dd_payload = DatadogPayload(
            ddsource=os.getenv("DD_SOURCE", "litellm"),
            ddtags="",
            hostname="",
            message=json_payload,
            service="litellm-server",
            status=DataDogStatus.INFO,
        )
        return dd_payload

    async def async_send_compressed_data(self, data: List) -> Response:
        """
        Async helper to send compressed data to datadog self.intake_url

        Datadog recommends using gzip to compress data
        https://docs.datadoghq.com/api/latest/logs/

        "Datadog recommends sending your logs compressed. Add the Content-Encoding: gzip header to the request when sending"
        """
        import gzip
        import json

        compressed_data = gzip.compress(json.dumps(data).encode("utf-8"))
        response = await self.async_client.post(
            url=self.intake_url,
            data=compressed_data,  # type: ignore
            headers={
                "DD-API-KEY": self.DD_API_KEY,
                "Content-Encoding": "gzip",
                "Content-Type": "application/json",
            },
        )
        return response

    async def async_service_failure_hook(
        self,
        payload: ServiceLoggerPayload,
        error: Optional[str] = "",
        parent_otel_span: Optional[Any] = None,
        start_time: Optional[Union[datetimeObj, float]] = None,
        end_time: Optional[Union[float, datetimeObj]] = None,
        event_metadata: Optional[dict] = None,
    ):
        """
        Logs failures from Redis, Postgres (Adjacent systems), as 'WARNING' on DataDog

        - example - Redis is failing / erroring, will be logged on DataDog
        """

        try:
            import json

            _payload_dict = payload.model_dump()
            _dd_message_str = json.dumps(_payload_dict)
            _dd_payload = DatadogPayload(
                ddsource="litellm",
                ddtags="",
                hostname="",
                message=_dd_message_str,
                service="litellm-server",
                status=DataDogStatus.WARN,
            )

            self.log_queue.append(_dd_payload)

        except Exception as e:
            verbose_logger.exception(
                f"Datadog: Logger - Exception in async_service_failure_hook: {e}"
            )
        pass

    async def async_service_success_hook(
        self,
        payload: ServiceLoggerPayload,
        error: Optional[str] = "",
        parent_otel_span: Optional[Any] = None,
        start_time: Optional[Union[datetimeObj, float]] = None,
        end_time: Optional[Union[float, datetimeObj]] = None,
        event_metadata: Optional[dict] = None,
    ):
        """
        Logs success from Redis, Postgres (Adjacent systems), as 'INFO' on DataDog

        No user has asked for this so far, this might be spammy on datatdog. If need arises we can implement this
        """
        return
