# ========= 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

from pydantic import BaseModel

from camel.configs import ANTHROPIC_API_PARAMS, AnthropicConfig
from camel.messages import OpenAIMessage
from camel.models.base_model import BaseModelBackend
from camel.types import ChatCompletion, ModelType
from camel.utils import (
    AnthropicTokenCounter,
    BaseTokenCounter,
    api_keys_required,
    dependencies_required,
)


class AnthropicModel(BaseModelBackend):
    r"""Anthropic API in a unified BaseModelBackend interface.

    Args:
        model_type (Union[ModelType, str]): Model for which a backend is
            created, one of CLAUDE_* series.
        model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
            that will be fed into `openai.ChatCompletion.create()`.  If
            :obj:`None`, :obj:`AnthropicConfig().as_dict()` will be used.
            (default: :obj:`None`)
        api_key (Optional[str], optional): The API key for authenticating with
            the Anthropic service. (default: :obj:`None`)
        url (Optional[str], optional): The url to the Anthropic service.
            (default: :obj:`https://api.anthropic.com/v1/`)
        token_counter (Optional[BaseTokenCounter], optional): Token counter to
            use for the model. If not provided, :obj:`AnthropicTokenCounter`
            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`)
    """

    @api_keys_required(
        [
            ("api_key", "ANTHROPIC_API_KEY"),
        ]
    )
    @dependencies_required('anthropic')
    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,
        token_counter: Optional[BaseTokenCounter] = None,
        timeout: Optional[float] = None,
    ) -> None:
        from openai import AsyncOpenAI, OpenAI

        if model_config_dict is None:
            model_config_dict = AnthropicConfig().as_dict()
        api_key = api_key or os.environ.get("ANTHROPIC_API_KEY")
        url = (
            url
            or os.environ.get("ANTHROPIC_API_BASE_URL")
            or "https://api.anthropic.com/v1/"
        )
        timeout = timeout or float(os.environ.get("MODEL_TIMEOUT", 180))
        super().__init__(
            model_type, model_config_dict, api_key, url, token_counter, timeout
        )
        self.client = OpenAI(
            base_url=self._url, api_key=self._api_key, timeout=self._timeout
        )

        self.async_client = AsyncOpenAI(
            api_key=self._api_key, base_url=self._url, timeout=self._timeout
        )

    @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 = AnthropicTokenCounter(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,
    ):
        r"""Run inference of Anthropic chat completion.

        Args:
            messages (List[OpenAIMessage]): Message list with the chat history
                in OpenAI API format.

        Returns:
            ChatCompletion: Response in the OpenAI API format.
        """
        response = self.client.chat.completions.create(
            model=self.model_type,
            messages=messages,
            **self.model_config_dict,
            tools=tools,  # type: ignore[arg-type]
        )

        return response

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

        Args:
            messages (List[OpenAIMessage]): Message list with the chat history
                in OpenAI API format.

        Returns:
            ChatCompletion: Response in the OpenAI API format.
        """
        response = await self.async_client.chat.completions.create(
            model=self.model_type,
            messages=messages,
            **self.model_config_dict,
            tools=tools,  # type: ignore[arg-type]
        )

        return response

    def check_model_config(self):
        r"""Check whether the model configuration is valid for anthropic
        model backends.

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