from enum import Enum
from typing import List

from core.model_runtime.entities.llm_entities import LLMResult
from core.model_runtime.entities.message_entities import PromptMessage, SystemPromptMessage, UserPromptMessage
from core.tools.model.tool_model_manager import ToolModelManager
from core.tools.tool.tool import Tool
from core.tools.utils.web_reader_tool import get_url

_SUMMARY_PROMPT = """You are a professional language researcher, you are interested in the language
and you can quickly aimed at the main point of an webpage and reproduce it in your own words but 
retain the original meaning and keep the key points. 
however, the text you got is too long, what you got is possible a part of the text.
Please summarize the text you got.
"""


class BuiltinTool(Tool):
    """
        Builtin tool

        :param meta: the meta data of a tool call processing
    """

    def invoke_model(
        self, user_id: str, prompt_messages: List[PromptMessage], stop: List[str]
    ) -> LLMResult:
        """
            invoke model

            :param model_config: the model config
            :param prompt_messages: the prompt messages
            :param stop: the stop words
            :return: the model result
        """
        # invoke model
        return ToolModelManager.invoke(
            user_id=user_id,
            tenant_id=self.runtime.tenant_id,
            tool_type='builtin',
            tool_name=self.identity.name,
            prompt_messages=prompt_messages,
        )
    
    def get_max_tokens(self) -> int:
        """
            get max tokens

            :param model_config: the model config
            :return: the max tokens
        """
        return ToolModelManager.get_max_llm_context_tokens(
            tenant_id=self.runtime.tenant_id,
        )

    def get_prompt_tokens(self, prompt_messages: List[PromptMessage]) -> int:
        """
            get prompt tokens

            :param prompt_messages: the prompt messages
            :return: the tokens
        """
        return ToolModelManager.calculate_tokens(
            tenant_id=self.runtime.tenant_id,
            prompt_messages=prompt_messages
        )
    
    def summary(self, user_id: str, content: str) -> str:
        max_tokens = self.get_max_tokens()

        if self.get_prompt_tokens(prompt_messages=[
            UserPromptMessage(content=content)
        ]) < max_tokens * 0.6:
            return content
        
        def get_prompt_tokens(content: str) -> int:
            return self.get_prompt_tokens(prompt_messages=[
                SystemPromptMessage(content=_SUMMARY_PROMPT),
                UserPromptMessage(content=content)
            ])
        
        def summarize(content: str) -> str:
            summary = self.invoke_model(user_id=user_id, prompt_messages=[
                SystemPromptMessage(content=_SUMMARY_PROMPT),
                UserPromptMessage(content=content)
            ], stop=[])

            return summary.message.content

        lines = content.split('\n')
        new_lines = []
        # split long line into multiple lines
        for i in range(len(lines)):
            line = lines[i]
            if not line.strip():
                continue
            if len(line) < max_tokens * 0.5:
                new_lines.append(line)
            elif get_prompt_tokens(line) > max_tokens * 0.7:
                while get_prompt_tokens(line) > max_tokens * 0.7:
                    new_lines.append(line[:int(max_tokens * 0.5)])
                    line = line[int(max_tokens * 0.5):]
                new_lines.append(line)
            else:
                new_lines.append(line)

        # merge lines into messages with max tokens
        messages: List[str] = []
        for i in new_lines:
            if len(messages) == 0:
                messages.append(i)
            else:
                if len(messages[-1]) + len(i) < max_tokens * 0.5:
                    messages[-1] += i
                if get_prompt_tokens(messages[-1] + i) > max_tokens * 0.7:
                    messages.append(i)
                else:
                    messages[-1] += i

        summaries = []
        for i in range(len(messages)):
            message = messages[i]
            summary = summarize(message)
            summaries.append(summary)

        result = '\n'.join(summaries)

        if self.get_prompt_tokens(prompt_messages=[
            UserPromptMessage(content=result)
        ]) > max_tokens * 0.7:
            return self.summary(user_id=user_id, content=result)
        
        return result
    
    def get_url(self, url: str, user_agent: str = None) -> str:
        """
            get url
        """
        return get_url(url, user_agent=user_agent)