from collections.abc import Sequence

from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.chat import ChatPromptTemplate
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.tools.render import ToolsRenderer, render_text_description

from langchain_classic.agents.format_scratchpad import format_log_to_messages
from langchain_classic.agents.json_chat.prompt import TEMPLATE_TOOL_RESPONSE
from langchain_classic.agents.output_parsers import JSONAgentOutputParser


def create_json_chat_agent(
    llm: BaseLanguageModel,
    tools: Sequence[BaseTool],
    prompt: ChatPromptTemplate,
    stop_sequence: bool | list[str] = True,  # noqa: FBT001,FBT002
    tools_renderer: ToolsRenderer = render_text_description,
    template_tool_response: str = TEMPLATE_TOOL_RESPONSE,
) -> Runnable:
    r"""Create an agent that uses JSON to format its logic, build for Chat Models.

    Args:
        llm: LLM to use as the agent.
        tools: Tools this agent has access to.
        prompt: The prompt to use. See Prompt section below for more.
        stop_sequence: bool or list of str.
            If `True`, adds a stop token of "Observation:" to avoid hallucinates.
            If `False`, does not add a stop token.
            If a list of str, uses the provided list as the stop tokens.

            You may to set this to False if the LLM you are using does not support stop
            sequences.
        tools_renderer: This controls how the tools are converted into a string and
            then passed into the LLM.
        template_tool_response: Template prompt that uses the tool response
            (observation) to make the LLM generate the next action to take.

    Returns:
        A Runnable sequence representing an agent. It takes as input all the same input
        variables as the prompt passed in does. It returns as output either an
        AgentAction or AgentFinish.

    Raises:
        ValueError: If the prompt is missing required variables.
        ValueError: If the template_tool_response is missing
            the required variable 'observation'.

    Example:
        ```python
        from langchain_classic import hub
        from langchain_openai import ChatOpenAI
        from langchain_classic.agents import AgentExecutor, create_json_chat_agent

        prompt = hub.pull("hwchase17/react-chat-json")
        model = ChatOpenAI()
        tools = ...

        agent = create_json_chat_agent(model, tools, prompt)
        agent_executor = AgentExecutor(agent=agent, tools=tools)

        agent_executor.invoke({"input": "hi"})

        # Using with chat history
        from langchain_core.messages import AIMessage, HumanMessage

        agent_executor.invoke(
            {
                "input": "what's my name?",
                "chat_history": [
                    HumanMessage(content="hi! my name is bob"),
                    AIMessage(content="Hello Bob! How can I assist you today?"),
                ],
            }
        )
        ```

    Prompt:

        The prompt must have input keys:
            * `tools`: contains descriptions and arguments for each tool.
            * `tool_names`: contains all tool names.
            * `agent_scratchpad`: must be a MessagesPlaceholder. Contains previous
                agent actions and tool outputs as messages.

        Here's an example:

        ```python
        from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

        system = '''Assistant is a large language model trained by OpenAI.

        Assistant is designed to be able to assist with a wide range of tasks, from answering
        simple questions to providing in-depth explanations and discussions on a wide range of
        topics. As a language model, Assistant is able to generate human-like text based on
        the input it receives, allowing it to engage in natural-sounding conversations and
        provide responses that are coherent and relevant to the topic at hand.

        Assistant is constantly learning and improving, and its capabilities are constantly
        evolving. It is able to process and understand large amounts of text, and can use this
        knowledge to provide accurate and informative responses to a wide range of questions.
        Additionally, Assistant is able to generate its own text based on the input it
        receives, allowing it to engage in discussions and provide explanations and
        descriptions on a wide range of topics.

        Overall, Assistant is a powerful system that can help with a wide range of tasks
        and provide valuable insights and information on a wide range of topics. Whether
        you need help with a specific question or just want to have a conversation about
        a particular topic, Assistant is here to assist.'''

        human = '''TOOLS
        ------
        Assistant can ask the user to use tools to look up information that may be helpful in
        answering the users original question. The tools the human can use are:

        {tools}

        RESPONSE FORMAT INSTRUCTIONS
        ----------------------------

        When responding to me, please output a response in one of two formats:

        **Option 1:**
        Use this if you want the human to use a tool.
        Markdown code snippet formatted in the following schema:

        ```json
        {{
            "action": string, \\\\ The action to take. Must be one of {tool_names}
            "action_input": string \\\\ The input to the action
        }}
        ```

        **Option #2:**
        Use this if you want to respond directly to the human. Markdown code snippet formatted
        in the following schema:

        ```json
        {{
            "action": "Final Answer",
            "action_input": string \\\\ You should put what you want to return to use here
        }}
        ```

        USER'S INPUT
        --------------------
        Here is the user's input (remember to respond with a markdown code snippet of a json
        blob with a single action, and NOTHING else):

        {input}'''

        prompt = ChatPromptTemplate.from_messages(
            [
                ("system", system),
                MessagesPlaceholder("chat_history", optional=True),
                ("human", human),
                MessagesPlaceholder("agent_scratchpad"),
            ]
        )

        ```
    """  # noqa: E501
    missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference(
        prompt.input_variables + list(prompt.partial_variables),
    )
    if missing_vars:
        msg = f"Prompt missing required variables: {missing_vars}"
        raise ValueError(msg)

    if "{observation}" not in template_tool_response:
        msg = "Template tool response missing required variable 'observation'"
        raise ValueError(msg)

    prompt = prompt.partial(
        tools=tools_renderer(list(tools)),
        tool_names=", ".join([t.name for t in tools]),
    )
    if stop_sequence:
        stop = ["\nObservation"] if stop_sequence is True else stop_sequence
        llm_to_use = llm.bind(stop=stop)
    else:
        llm_to_use = llm

    return (
        RunnablePassthrough.assign(
            agent_scratchpad=lambda x: format_log_to_messages(
                x["intermediate_steps"],
                template_tool_response=template_tool_response,
            ),
        )
        | prompt
        | llm_to_use
        | JSONAgentOutputParser()
    )
