# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
import os
from typing import Any, Dict, List, Optional, Type, Union
import functools
import asyncio
import random
import time

from openai import AsyncAzureOpenAI, AsyncStream, AzureOpenAI, Stream
from openai import OpenAIError, RateLimitError
from pydantic import BaseModel

from camel.configs import OPENAI_API_PARAMS, ChatGPTConfig
from camel.messages import OpenAIMessage
from camel.models.base_model import BaseModelBackend
from camel.types import (
    ChatCompletion,
    ChatCompletionChunk,
    ModelType,
)
from camel.utils import BaseTokenCounter, OpenAITokenCounter

def retry(max_retries=5, initial_delay=1, backoff_factor=2, exceptions=(Exception,), jitter=False):
    """
    A universal retry decorator with increasing delay, supporting both sync and async functions.
    """
    def decorator(func):
        @functools.wraps(func)
        async def async_wrapper(*args, **kwargs):
            delay = initial_delay
            for attempt in range(max_retries + 1):
                try:
                    return await func(*args, **kwargs)
                except exceptions as e:
                    if attempt < max_retries:
                        total_delay = delay + random.uniform(0, delay * 0.1) if jitter else delay
                        print(
                            f"Retry {attempt + 1} of {max_retries} after error: {e}. Waiting {total_delay} seconds...")
                        await asyncio.sleep(total_delay)
                        delay *= backoff_factor
                    else:
                        raise  # Re-raise the last exception if max retries exceeded

        @functools.wraps(func)
        def sync_wrapper(*args, **kwargs):
            delay = initial_delay
            for attempt in range(max_retries + 1):
                try:
                    return func(*args, **kwargs)
                except exceptions as e:
                    if attempt < max_retries:
                        total_delay = delay + random.uniform(0, delay * 0.1) if jitter else delay
                        print(
                            f"Retry {attempt + 1} of {max_retries} after error: {e}. Waiting {total_delay} seconds...")
                        time.sleep(total_delay)
                        delay *= backoff_factor
                    else:
                        raise  # Re-raise the last exception if max retries exceeded

        return async_wrapper if asyncio.iscoroutinefunction(func) else sync_wrapper
    return decorator

class AzureOpenAIModel(BaseModelBackend):
    r"""Azure OpenAI API in a unified BaseModelBackend interface.

    Args:
        model_type (Union[ModelType, str]): Model for which a backend is
            created, one of GPT_* series.
        model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
            that will be fed into:obj:`openai.ChatCompletion.create()`. If
            :obj:`None`, :obj:`ChatGPTConfig().as_dict()` will be used.
            (default: :obj:`None`)
        api_key (Optional[str], optional): The API key for authenticating with
            the OpenAI service. (default: :obj:`None`)
        url (Optional[str], optional): The url to the OpenAI service.
            (default: :obj:`None`)
        api_version (Optional[str], optional): The api version for the model.
            (default: :obj:`None`)
        azure_deployment_name (Optional[str], optional): The deployment name
            you chose when you deployed an azure model. (default: :obj:`None`)
        token_counter (Optional[BaseTokenCounter], optional): Token counter to
            use for the model. If not provided, :obj:`OpenAITokenCounter`
            will be used. (default: :obj:`None`)
        timeout (Optional[float], optional): The timeout value in seconds for
            API calls. If not provided, will fall back to the MODEL_TIMEOUT
            environment variable or default to 180 seconds.
            (default: :obj:`None`)

    References:
        https://learn.microsoft.com/en-us/azure/ai-services/openai/
    """

    def __init__(
        self,
        model_type: Union[ModelType, str],
        model_config_dict: Optional[Dict[str, Any]] = None,
        api_key: Optional[str] = None,
        url: Optional[str] = None,
        timeout: Optional[float] = None,
        token_counter: Optional[BaseTokenCounter] = None,
        api_version: Optional[str] = None,
        azure_deployment_name: Optional[str] = None,
    ) -> None:
        if model_config_dict is None:
            model_config_dict = ChatGPTConfig().as_dict()
        api_key = api_key or os.environ.get("AZURE_OPENAI_API_KEY")
        url = url or os.environ.get("AZURE_ENDPOINT")
        timeout = timeout or float(os.environ.get("MODEL_TIMEOUT", 180))
        super().__init__(
            model_type, model_config_dict, api_key, url, token_counter, timeout
        )

        self.api_version = api_version or os.environ.get("AZURE_API_VERSION")
        self.azure_deployment_name = azure_deployment_name or os.environ.get(
            "AZURE_DEPLOYMENT_NAME"
        )
        if self.api_version is None:
            raise ValueError(
                "Must provide either the `api_version` argument "
                "or `AZURE_API_VERSION` environment variable."
            )
        if self.azure_deployment_name is None:
            raise ValueError(
                "Must provide either the `azure_deployment_name` argument "
                "or `AZURE_DEPLOYMENT_NAME` environment variable."
            )

        self._client = AzureOpenAI(
            azure_endpoint=str(self._url),
            azure_deployment=self.azure_deployment_name,
            api_version=self.api_version,
            api_key=self._api_key,
            timeout=self._timeout,
            max_retries=3,
        )

        self._async_client = AsyncAzureOpenAI(
            azure_endpoint=str(self._url),
            azure_deployment=self.azure_deployment_name,
            api_version=self.api_version,
            api_key=self._api_key,
            timeout=self._timeout,
            max_retries=3,
        )

    @property
    def token_counter(self) -> BaseTokenCounter:
        r"""Initialize the token counter for the model backend.

        Returns:
            BaseTokenCounter: The token counter following the model's
                tokenization style.
        """
        if not self._token_counter:
            self._token_counter = OpenAITokenCounter(self.model_type)
        return self._token_counter

    def _run(
        self,
        messages: List[OpenAIMessage],
        response_format: Optional[Type[BaseModel]] = None,
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
        r"""Runs inference of Azure OpenAI chat completion.

        Args:
            messages (List[OpenAIMessage]): Message list with the chat history
                in OpenAI API format.
            response_format (Optional[Type[BaseModel]]): The format of the
                response.
            tools (Optional[List[Dict[str, Any]]]): The schema of the tools to
                use for the request.

        Returns:
            Union[ChatCompletion, Stream[ChatCompletionChunk]]:
                `ChatCompletion` in the non-stream mode, or
                `Stream[ChatCompletionChunk]` in the stream mode.
        """
        response_format = response_format or self.model_config_dict.get(
            "response_format", None
        )
        if response_format:
            return self._request_parse(messages, response_format, tools)
        else:
            return self._request_chat_completion(messages, tools)

    async def _arun(
        self,
        messages: List[OpenAIMessage],
        response_format: Optional[Type[BaseModel]] = None,
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
        r"""Runs inference of Azure OpenAI chat completion.

        Args:
            messages (List[OpenAIMessage]): Message list with the chat history
                in OpenAI API format.
            response_format (Optional[Type[BaseModel]]): The format of the
                response.
            tools (Optional[List[Dict[str, Any]]]): The schema of the tools to
                use for the request.

        Returns:
            Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
                `ChatCompletion` in the non-stream mode, or
                `AsyncStream[ChatCompletionChunk]` in the stream mode.
        """
        response_format = response_format or self.model_config_dict.get(
            "response_format", None
        )
        if response_format:
            return await self._arequest_parse(messages, response_format, tools)
        else:
            return await self._arequest_chat_completion(messages, tools)

    @retry(max_retries=16, initial_delay=8, backoff_factor=1.41, exceptions=(OpenAIError, RateLimitError))
    def _request_chat_completion(
        self,
        messages: List[OpenAIMessage],
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
        request_config = self.model_config_dict.copy()

        if tools:
            request_config["tools"] = tools

        return self._client.chat.completions.create(
            messages=messages,
            model=self.azure_deployment_name,  # type:ignore[arg-type]
            **request_config,
        )

    @retry(max_retries=16, initial_delay=8, backoff_factor=1.41, exceptions=(OpenAIError, RateLimitError))
    async def _arequest_chat_completion(
        self,
        messages: List[OpenAIMessage],
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
        request_config = self.model_config_dict.copy()

        if tools:
            request_config["tools"] = tools

        return await self._async_client.chat.completions.create(
            messages=messages,
            model=self.azure_deployment_name,  # type:ignore[arg-type]
            **request_config,
        )

    def _request_parse(
        self,
        messages: List[OpenAIMessage],
        response_format: Type[BaseModel],
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> ChatCompletion:
        import copy

        request_config = copy.deepcopy(self.model_config_dict)

        request_config["response_format"] = response_format
        # Remove stream from request config since OpenAI does not support it
        # with structured response
        request_config.pop("stream", None)
        if tools is not None:
            request_config["tools"] = tools

        return self._client.beta.chat.completions.parse(
            messages=messages,
            model=self.azure_deployment_name,  # type:ignore[arg-type]
            **request_config,
        )

    async def _arequest_parse(
        self,
        messages: List[OpenAIMessage],
        response_format: Type[BaseModel],
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> ChatCompletion:
        import copy

        request_config = copy.deepcopy(self.model_config_dict)

        request_config["response_format"] = response_format
        # Remove stream from request config since OpenAI does not support it
        # with structured response
        request_config.pop("stream", None)
        if tools is not None:
            request_config["tools"] = tools

        return await self._async_client.beta.chat.completions.parse(
            messages=messages,
            model=self.azure_deployment_name,  # type:ignore[arg-type]
            **request_config,
        )

    def check_model_config(self):
        r"""Check whether the model configuration contains any
        unexpected arguments to Azure OpenAI API.

        Raises:
            ValueError: If the model configuration dictionary contains any
                unexpected arguments to Azure OpenAI API.
        """
        for param in self.model_config_dict:
            if param not in OPENAI_API_PARAMS:
                raise ValueError(
                    f"Unexpected argument `{param}` is "
                    "input into Azure OpenAI model backend."
                )

    @property
    def stream(self) -> bool:
        r"""Returns whether the model is in stream mode,
        which sends partial results each time.

        Returns:
            bool: Whether the model is in stream mode.
        """
        return self.model_config_dict.get("stream", False)
