from open_webui.utils.task import prompt_template, prompt_variables_template
from open_webui.utils.misc import (
    deep_update,
    add_or_update_system_message,
    replace_system_message_content,
)

from typing import Callable, Optional
import copy
import json


# inplace function: form_data is modified
def apply_system_prompt_to_body(
    system: Optional[str],
    form_data: dict,
    metadata: Optional[dict] = None,
    user=None,
    replace: bool = False,
) -> dict:
    if not system:
        return form_data

    # Metadata (WebUI Usage)
    if metadata:
        variables = metadata.get("variables", {})
        if variables:
            system = prompt_variables_template(system, variables)

    # Legacy (API Usage)
    system = prompt_template(system, user)

    if replace:
        form_data["messages"] = replace_system_message_content(
            system, form_data.get("messages", [])
        )
    else:
        form_data["messages"] = add_or_update_system_message(
            system, form_data.get("messages", [])
        )

    return form_data


# inplace function: form_data is modified
def apply_model_params_to_body(
    params: dict, form_data: dict, mappings: dict[str, Callable]
) -> dict:
    if not params:
        return form_data

    for key, value in params.items():
        if value is not None:
            if key in mappings:
                cast_func = mappings[key]
                if isinstance(cast_func, Callable):
                    form_data[key] = cast_func(value)
            else:
                form_data[key] = value

    return form_data


def remove_open_webui_params(params: dict) -> dict:
    """
    Removes OpenWebUI specific parameters from the provided dictionary.

    Args:
        params (dict): The dictionary containing parameters.

    Returns:
        dict: The modified dictionary with OpenWebUI parameters removed.
    """
    open_webui_params = {
        "stream_response": bool,
        "stream_delta_chunk_size": int,
        "function_calling": str,
        "reasoning_tags": list,
        "system": str,
    }

    for key in list(params.keys()):
        if key in open_webui_params:
            del params[key]

    return params


# inplace function: form_data is modified
def apply_model_params_to_body_openai(params: dict, form_data: dict) -> dict:
    params = remove_open_webui_params(params)

    custom_params = params.pop("custom_params", {})
    if custom_params:
        # Attempt to parse custom_params if they are strings
        for key, value in custom_params.items():
            if isinstance(value, str):
                try:
                    # Attempt to parse the string as JSON
                    custom_params[key] = json.loads(value)
                except json.JSONDecodeError:
                    # If it fails, keep the original string
                    pass

        # If there are custom parameters, we need to apply them first
        params = deep_update(params, custom_params)

    mappings = {
        "temperature": float,
        "top_p": float,
        "min_p": float,
        "max_tokens": int,
        "frequency_penalty": float,
        "presence_penalty": float,
        "reasoning_effort": str,
        "seed": lambda x: x,
        "stop": lambda x: [bytes(s, "utf-8").decode("unicode_escape") for s in x],
        "logit_bias": lambda x: x,
        "response_format": dict,
    }
    return apply_model_params_to_body(params, form_data, mappings)


def apply_model_params_to_body_ollama(params: dict, form_data: dict) -> dict:
    params = remove_open_webui_params(params)

    custom_params = params.pop("custom_params", {})
    if custom_params:
        # Attempt to parse custom_params if they are strings
        for key, value in custom_params.items():
            if isinstance(value, str):
                try:
                    # Attempt to parse the string as JSON
                    custom_params[key] = json.loads(value)
                except json.JSONDecodeError:
                    # If it fails, keep the original string
                    pass

        # If there are custom parameters, we need to apply them first
        params = deep_update(params, custom_params)

    # Convert OpenAI parameter names to Ollama parameter names if needed.
    name_differences = {
        "max_tokens": "num_predict",
    }

    for key, value in name_differences.items():
        if (param := params.get(key, None)) is not None:
            # Copy the parameter to new name then delete it, to prevent Ollama warning of invalid option provided
            params[value] = params[key]
            del params[key]

    # See https://github.com/ollama/ollama/blob/main/docs/api.md#request-8
    mappings = {
        "temperature": float,
        "top_p": float,
        "seed": lambda x: x,
        "mirostat": int,
        "mirostat_eta": float,
        "mirostat_tau": float,
        "num_ctx": int,
        "num_batch": int,
        "num_keep": int,
        "num_predict": int,
        "repeat_last_n": int,
        "top_k": int,
        "min_p": float,
        "repeat_penalty": float,
        "presence_penalty": float,
        "frequency_penalty": float,
        "stop": lambda x: [bytes(s, "utf-8").decode("unicode_escape") for s in x],
        "num_gpu": int,
        "use_mmap": bool,
        "use_mlock": bool,
        "num_thread": int,
    }

    def parse_json(value: str) -> dict:
        """
        Parses a JSON string into a dictionary, handling potential JSONDecodeError.
        """
        try:
            return json.loads(value)
        except Exception as e:
            return value

    ollama_root_params = {
        "format": lambda x: parse_json(x),
        "keep_alive": lambda x: parse_json(x),
        "think": bool,
    }

    for key, value in ollama_root_params.items():
        if (param := params.get(key, None)) is not None:
            # Copy the parameter to new name then delete it, to prevent Ollama warning of invalid option provided
            form_data[key] = value(param)
            del params[key]

    # Unlike OpenAI, Ollama does not support params directly in the body
    form_data["options"] = apply_model_params_to_body(
        params, (form_data.get("options", {}) or {}), mappings
    )
    return form_data


def convert_messages_openai_to_ollama(messages: list[dict]) -> list[dict]:
    ollama_messages = []

    for message in messages:
        # Initialize the new message structure with the role
        new_message = {"role": message["role"]}

        content = message.get("content", [])
        tool_calls = message.get("tool_calls", None)
        tool_call_id = message.get("tool_call_id", None)

        # Check if the content is a string (just a simple message)
        if isinstance(content, str) and not tool_calls:
            # If the content is a string, it's pure text
            new_message["content"] = content

            # If message is a tool call, add the tool call id to the message
            if tool_call_id:
                new_message["tool_call_id"] = tool_call_id

        elif tool_calls:
            # If tool calls are present, add them to the message
            ollama_tool_calls = []
            for tool_call in tool_calls:
                ollama_tool_call = {
                    "index": tool_call.get("index", 0),
                    "id": tool_call.get("id", None),
                    "function": {
                        "name": tool_call.get("function", {}).get("name", ""),
                        "arguments": json.loads(
                            tool_call.get("function", {}).get("arguments", {})
                        ),
                    },
                }
                ollama_tool_calls.append(ollama_tool_call)
            new_message["tool_calls"] = ollama_tool_calls

            # Put the content to empty string (Ollama requires an empty string for tool calls)
            new_message["content"] = ""

        else:
            # Otherwise, assume the content is a list of dicts, e.g., text followed by an image URL
            content_text = ""
            images = []

            # Iterate through the list of content items
            for item in content:
                # Check if it's a text type
                if item.get("type") == "text":
                    content_text += item.get("text", "")

                # Check if it's an image URL type
                elif item.get("type") == "image_url":
                    img_url = item.get("image_url", {}).get("url", "")
                    if img_url:
                        # If the image url starts with data:, it's a base64 image and should be trimmed
                        if img_url.startswith("data:"):
                            img_url = img_url.split(",")[-1]
                        images.append(img_url)

            # Add content text (if any)
            if content_text:
                new_message["content"] = content_text.strip()

            # Add images (if any)
            if images:
                new_message["images"] = images

        # Append the new formatted message to the result
        ollama_messages.append(new_message)

    return ollama_messages


def convert_payload_openai_to_ollama(openai_payload: dict) -> dict:
    """
    Converts a payload formatted for OpenAI's API to be compatible with Ollama's API endpoint for chat completions.

    Args:
        openai_payload (dict): The payload originally designed for OpenAI API usage.

    Returns:
        dict: A modified payload compatible with the Ollama API.
    """
    openai_payload = copy.deepcopy(openai_payload)
    ollama_payload = {}

    # Mapping basic model and message details
    ollama_payload["model"] = openai_payload.get("model")
    ollama_payload["messages"] = convert_messages_openai_to_ollama(
        openai_payload.get("messages")
    )
    ollama_payload["stream"] = openai_payload.get("stream", False)
    if "tools" in openai_payload:
        ollama_payload["tools"] = openai_payload["tools"]

    if "max_tokens" in openai_payload:
        ollama_payload["num_predict"] = openai_payload["max_tokens"]
        del openai_payload["max_tokens"]

    # If there are advanced parameters in the payload, format them in Ollama's options field
    if openai_payload.get("options"):
        ollama_payload["options"] = openai_payload["options"]
        ollama_options = openai_payload["options"]

        def parse_json(value: str) -> dict:
            """
            Parses a JSON string into a dictionary, handling potential JSONDecodeError.
            """
            try:
                return json.loads(value)
            except Exception as e:
                return value

        ollama_root_params = {
            "format": lambda x: parse_json(x),
            "keep_alive": lambda x: parse_json(x),
            "think": bool,
        }

        # Ollama's options field can contain parameters that should be at the root level.
        for key, value in ollama_root_params.items():
            if (param := ollama_options.get(key, None)) is not None:
                # Copy the parameter to new name then delete it, to prevent Ollama warning of invalid option provided
                ollama_payload[key] = value(param)
                del ollama_options[key]

        # Re-Mapping OpenAI's `max_tokens` -> Ollama's `num_predict`
        if "max_tokens" in ollama_options:
            ollama_options["num_predict"] = ollama_options["max_tokens"]
            del ollama_options["max_tokens"]

        # Ollama lacks a "system" prompt option. It has to be provided as a direct parameter, so we copy it down.
        # Comment: Not sure why this is needed, but we'll keep it for compatibility.
        if "system" in ollama_options:
            ollama_payload["system"] = ollama_options["system"]
            del ollama_options["system"]

        ollama_payload["options"] = ollama_options

    # If there is the "stop" parameter in the openai_payload, remap it to the ollama_payload.options
    if "stop" in openai_payload:
        ollama_options = ollama_payload.get("options", {})
        ollama_options["stop"] = openai_payload.get("stop")
        ollama_payload["options"] = ollama_options

    if "metadata" in openai_payload:
        ollama_payload["metadata"] = openai_payload["metadata"]

    if "response_format" in openai_payload:
        response_format = openai_payload["response_format"]
        format_type = response_format.get("type", None)

        schema = response_format.get(format_type, None)
        if schema:
            format = schema.get("schema", None)
            ollama_payload["format"] = format

    return ollama_payload


def convert_embedding_payload_openai_to_ollama(openai_payload: dict) -> dict:
    """
    Convert an embeddings request payload from OpenAI format to Ollama format.

    Args:
        openai_payload (dict): The original payload designed for OpenAI API usage.

    Returns:
        dict: A payload compatible with the Ollama API embeddings endpoint.
    """
    ollama_payload = {"model": openai_payload.get("model")}
    input_value = openai_payload.get("input")

    # Ollama expects 'input' as a list, and 'prompt' as a single string.
    if isinstance(input_value, list):
        ollama_payload["input"] = input_value
        ollama_payload["prompt"] = "\n".join(str(x) for x in input_value)
    else:
        ollama_payload["input"] = [input_value]
        ollama_payload["prompt"] = str(input_value)

    # Optionally forward other fields if present
    for optional_key in ("options", "truncate", "keep_alive"):
        if optional_key in openai_payload:
            ollama_payload[optional_key] = openai_payload[optional_key]

    return ollama_payload
