# Copyright (c) 2024, MeetKai Inc. All rights reserved.

from copy import deepcopy
import json
from typing import Any, Dict, List, Literal, Optional, Union

import jsonref
from pydantic import BaseModel, Field, model_validator
from typing_extensions import Self

from transformers.tokenization_utils_base import BatchEncoding
from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
from transformers.utils import TensorType, logging


logger = logging.get_logger(__name__)
SYSTEM_PROMPT = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. The assistant calls functions with appropriate input when necessary"""
CODE_INTERPRETER_SYSTEM_PROMPT = """When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 60.0 seconds. The drive at '/mnt/data' can be used to save and persist user files."""

class Function(BaseModel):
    name: str
    description: Optional[str] = Field(default="")
    parameters: Optional[dict] = None
    

class Tool(BaseModel):
    type: Literal["function", "code_interpreter"]
    function: Optional[Function] = None
    
    @model_validator(mode="after")
    def check_type_function_matches(self) -> Self:
        if self.type == "function":
            assert self.function is not None, '"function" must contain function description when `"type": "function"`'
        else:
            assert self.function is None, '"function" must not be provided when `"type": "code_interpreter"`'
        return self


def convert_data_type(param_type: str) -> str:
    """convert data_type to typescript data type

    Args:
        param_type (str): param_type

    Returns:
        str: param type in typescript
    """
    if param_type == "integer" or param_type == "float":
        return "number"
    return param_type


def get_param_type(param: Dict) -> str:
    """get param_type of parameter

    Args:
        param (Dict): param dict in properties

    Returns:
        str: _description_
    """
    param_type = "any"
    if "type" in param:
        raw_param_type = param["type"]
        if type(raw_param_type) is list:
            param_type = " | ".join(raw_param_type)
        else:
            param_type = raw_param_type

    else:  # in many cases, the json schema contains: oneOf instead of "type"
        if "oneOf" in param:
            one_of_types = []
            for item in param["oneOf"]:
                if "type" in item:
                    one_of_types.append(convert_data_type(item["type"]))
            one_of_types = list(set(one_of_types))
            param_type = " | ".join(one_of_types)
    return convert_data_type(param_type)


def get_format_param(param: Dict) -> Optional[str]:
    """Get "format" from param. There are cases where format is not directly in param but in oneOf

    Args:
        param (Dict): _description_

    Returns:
        Optional[str]: _description_
    """
    if "format" in param:
        return param["format"]
    if "oneOf" in param:
        formats = []
        for item in param["oneOf"]:
            if "format" in item:
                formats.append(item["format"])
        if len(formats) > 0:
            return " or ".join(formats)
    return None


def get_param_info(param: Dict) -> Optional[str]:
    """get additional information about parameter such as: format, default value, min, max, ...

    Args:
        param (Dict): _description_

    Returns:
        Optional[str]: _description_
    """
    param_type = param.get("type", "any")
    info_list = []
    if "description" in param:
        desc = param["description"]
        if not desc.endswith("."):
            desc += "."
        info_list.append(desc)

    if "default" in param:
        default_value = param["default"]
        if param_type == "string":
            default_value = f'"{default_value}"'  # if string --> add ""
        info_list.append(f"Default={default_value}.")

    format_param = get_format_param(param)
    if format_param is not None:
        info_list.append("Format=" + format_param)

    for field, field_name in [
        ("maximum", "Maximum"),
        ("minimum", "Minimum"),
        ("maxLength", "Maximum length"),
        ("minLength", "Minimum length"),
    ]:
        if field in param:
            info_list.append(f"{field_name}=" + str(param[field]))

    if len(info_list) > 0:
        result = "// " + " ".join(info_list)
        result = result.replace("\n", " ")
        return result
    return None


def append_new_param_info(
    info_list: List[str],
    param_declaration: str,
    comment_info: Optional[str],
    examples_info: List,
    depth: int,
):
    """Append a new parameter with comment to the info_list

    Args:
        info_lines (List[str]): current info_list
        param_declaration (str): param: type
        comment_info (Optional[str]): information of comment
        examples_info (List): information of examples given
        depth (int): level of nested param
    """
    offset = ""
    if depth >= 1:
        offset = "".join(["    " for _ in range(depth)])
    if comment_info is not None:
        # if depth == 0:  # format: //comment\nparam: type
        info_list.append(f"{offset}{comment_info}")
        if len(examples_info) > 0:
            for example in examples_info:
                info_list.append(f"{offset}{example}")
        info_list.append(f"{offset}{param_declaration}")
    # else:  # format: param: type  // comment
    #     info_list.append(f"{offset}{param_declaration}    {comment_info}")
    else:
        info_list.append(f"{offset}{param_declaration}")


def get_examples_info(param_name: str, examples: List) -> List:
    """get information about examples provided

    Args:
        param_name (str): _description_
        examples (List): _description_

    Returns:
        List: _description_
    """
    examples_list = [f"// Example {param_name}:"]
    for example in examples:
        if isinstance(example, dict) or isinstance(example, list):
            example_str = json.dumps(example, ensure_ascii=False).replace('\n', '\\n')
        else:
            example_str = str(example).replace('\n', '\\n')
        examples_list.append(f"// {example_str}")

    return examples_list


def get_enum_option_str(enum_options: List) -> str:
    """get enum option separated by: "|"

    Args:
        enum_options (List): list of options

    Returns:
        _type_: concatenation of options separated by "|"
    """
    # if each option is string --> add quote
    return " | ".join([f'"{v}"' if type(v) is str else str(v) for v in enum_options])


def get_array_typescript(
    param_name: Optional[str], param_dic: dict, depth: int = 0
) -> str:
    """recursive implementation for generating type script of array

    Args:
        param_name (Optional[str]): name of param, optional
        param_dic (dict): param_dic
        depth (int, optional): nested level. Defaults to 0.

    Returns:
        _type_: typescript of array
    """
    offset = ""
    if depth >= 1:
        offset = "".join(["    " for _ in range(depth)])
    items_info = param_dic.get("items", {})

    if len(items_info) == 0:
        if param_name is not None:
            return f"{offset}{param_name}: []"
        else:
            return "[]"
    array_type = get_param_type(items_info)
    if array_type == "object":
        info_lines = []
        child_lines = get_parameter_typescript(
            items_info.get("properties", {}), items_info.get("required", []), depth + 1
        )
        # if comment_info is not None:
        #    info_lines.append(f"{offset}{comment_info}")
        if param_name is not None:
            info_lines.append(f"{offset}{param_name}" + ": {")
        else:
            info_lines.append(f"{offset}" + "{")
        info_lines.extend(child_lines)
        info_lines.append(f"{offset}" + "}[]")
        return "\n".join(info_lines)

    elif array_type == "array":
        item_info = get_array_typescript(None, items_info, depth + 1)
        if param_name is None:
            return f"{item_info}[]"
        return f"{offset}{param_name}: {item_info.strip()}[]"

    else:
        if "enum" in items_info:
            item_type = get_enum_option_str(items_info["enum"])
            if param_name is None:
                return f"({item_type})[]"
            else:
                return f"{offset}{param_name}: ({item_type})[]"
        else:
            if param_name is None:
                return f"{array_type}[]"
            else:
                return f"{offset}{param_name}: {array_type}[],"


def get_parameter_typescript(properties, required_params, depth=0) -> List[str]:
    """Recursion, returning the information about parameters including data type, description and other information
    These kinds of information will be put into the prompt

    Args:
        properties (_type_): properties in parameters
        required_params (_type_): List of required parameters
        depth (int, optional): the depth of params (nested level). Defaults to 0.

    Returns:
        _type_: list of lines containing information about all parameters
    """
    tp_lines = []
    for param_name, param in properties.items():
        # Sometimes properties have "required" field as a list of string.
        # Even though its supposed to be not under properties. So we skip it
        if not isinstance(param, dict):
            continue
        # Param Description
        comment_info = get_param_info(param)
        # Param Examples
        examples_info = []
        if "examples" in param:
            examples_info = get_examples_info(param_name, param["examples"])
        # Param Name declaration
        param_declaration = f"{param_name}"
        if isinstance(required_params, list):
            if param_name not in required_params:
                param_declaration += "?"
        param_type = get_param_type(param)

        offset = ""
        if depth >= 1:
            offset = "".join(["    " for _ in range(depth)])

        if param_type == "object":  # param_type is object
            child_lines = get_parameter_typescript(
                param.get("properties", {}), param.get("required", []), depth + 1
            )
            if comment_info is not None:
                tp_lines.append(f"{offset}{comment_info}")
            if len(examples_info) > 0:
                for example in examples_info:
                    tp_lines.append(f"{offset}{example}")

            param_declaration += ": {"
            tp_lines.append(f"{offset}{param_declaration}")
            tp_lines.extend(child_lines)
            tp_lines.append(f"{offset}" + "},")

        elif param_type == "array":  # param_type is an array
            item_info = param.get("items", {})
            if "type" not in item_info:  # don't know type of array
                param_declaration += ": [],"
                append_new_param_info(
                    tp_lines, param_declaration, comment_info, examples_info, depth
                )
            else:
                array_declaration = get_array_typescript(
                    param_declaration, param, depth
                )
                if not array_declaration.endswith(","):
                    array_declaration += ","
                if comment_info is not None:
                    tp_lines.append(f"{offset}{comment_info}")
                if len(examples_info) > 0:
                    for example in examples_info:
                        tp_lines.append(f"{offset}{example}")
                tp_lines.append(array_declaration)
        else:
            if "enum" in param:
                param_type = get_enum_option_str(param["enum"])
                # param_type = " | ".join([f'"{v}"' for v in param["enum"]])
            if "nullable" in param and param["nullable"] is True:
                param_type += " | null"
            param_declaration += f": {param_type},"
            append_new_param_info(
                tp_lines, param_declaration, comment_info, examples_info, depth
            )

    return tp_lines

def generate_schema_from_functions(
    functions: List[Function], namespace="functions"
) -> str:
    """
    Convert functions schema to a schema that language models can understand.
    """

    schema = "// Supported function definitions that should be called when necessary.\n"
    schema += f"namespace {namespace} {{\n\n"

    for function in functions:
        # Convert a Function object to dict, if necessary
        if not isinstance(function, dict):
            function = function.model_dump()
        function_name = function.get("name", None)
        if function_name is None:
            continue

        description = function.get("description", "")
        schema += f"// {description}\n"
        schema += f"type {function_name}"

        parameters = function.get("parameters", None)
        if parameters is not None and parameters.get("properties") is not None:
            parameters = deepcopy(jsonref.JsonRef.replace_refs(parameters))
            schema += " = (_: {\n"
            required_params = parameters.get("required", [])
            tp_lines = get_parameter_typescript(
                parameters.get("properties"),
                required_params,
                0,
            )
            schema += "\n".join(tp_lines)
            schema += "\n}) => any;\n\n"
        else:
            # Doesn't have any parameters
            schema += " = () => any;\n\n"

    schema += f"}} // namespace {namespace}"

    return schema

class FunctionaryTokenizer(PreTrainedTokenizerFast):
    def apply_chat_template(
        self,
        conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]], str],
        tools: Optional[List[Dict[str, Any]]],
        chat_template: Optional[str] = None,
        add_generation_prompt: bool = False,
        tokenize: bool = True,
        padding: bool = False,
        truncation: bool = False,
        max_length: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_dict: bool = False,
        tokenizer_kwargs: Optional[Dict[str, Any]] = None,
        **kwargs,
    ) -> Union[str, List[int], List[str], List[List[int]], BatchEncoding]:

        if return_dict and not tokenize:
            raise ValueError(
                "`return_dict=True` is incompatible with `tokenize=False`, because there is no dict "
                "of tokenizer outputs to return."
            )

        if tokenizer_kwargs is None:
            tokenizer_kwargs = {}

        using_default_template = False
        
        # First, handle the cases when the model has a dict of multiple templates
        if isinstance(self.chat_template, dict) or (
            self.chat_template is None and isinstance(self.default_chat_template, dict)
        ):
            if self.chat_template is not None:
                template_dict = self.chat_template
                using_default_dict = False
            else:
                template_dict = self.default_chat_template
                using_default_dict = True
            if chat_template is not None and chat_template in template_dict:
                # The user can pass the name of a template to the chat template argument instead of an entire template
                chat_template = template_dict[chat_template]
                if using_default_dict:
                    using_default_template = True
            elif chat_template is None and "default" in template_dict:
                chat_template = template_dict["default"]
                if using_default_dict:
                    using_default_template = True
            elif chat_template is None:
                raise ValueError(
                    "This model has multiple chat templates with no default specified! Please either pass a chat "
                    "template or the name of the template you wish to use to the `chat_template` argument. Available "
                    f"template names are {sorted(template_dict.keys())}."
                )
        elif chat_template is None:
            # These are the cases when the model has a single template
            # priority: `chat_template` argument > `tokenizer.chat_template` > `tokenizer.default_chat_template
            if self.chat_template is not None:
                chat_template = self.chat_template
            else:
                chat_template = self.default_chat_template
                using_default_template = True
                
        if using_default_template:
            logger.warning_once(
                "No chat template is set for this tokenizer, falling back to a default class-level template. This is "
                "very error-prone, because models are often trained with templates different from the class default! "
                "Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which "
                "point any code depending on them will stop working. We recommend setting a valid chat template before "
                "then to ensure that this model continues working without issues."
            )
            
        # Prepare tools/functions into schema
        functions_pydantic_to_render = []
        has_code_interpreter = False
        for i in range(len(tools)):
            tool_pydantic = Tool.model_validate(tools[i])
            if tool_pydantic.type == "function":
                functions_pydantic_to_render.append(tool_pydantic.function)
            else:
                has_code_interpreter = True
        conversation.insert(0, {"role": "system", "content": generate_schema_from_functions(functions_pydantic_to_render)})
        # Insert system prompt
        system_prompt_to_use = SYSTEM_PROMPT if not has_code_interpreter else CODE_INTERPRETER_SYSTEM_PROMPT
        conversation.insert(1, {"role": "system", "content": system_prompt_to_use})

        # Compilation function uses a cache to avoid recompiling the same template
        compiled_template = self._compile_jinja_template(chat_template)
        
        if isinstance(conversation, (list, tuple)) and (
            isinstance(conversation[0], (list, tuple)) or hasattr(conversation[0], "messages")
        ):
            conversations = conversation
            is_batched = True
        else:
            conversations = [conversation]
            is_batched = False

        rendered = []
        template_kwargs = {**self.special_tokens_map, **kwargs}  # kwargs overwrite special tokens if both are present
        for chat in conversations:
            if hasattr(chat, "messages"):
                # Indicates it's a Conversation object
                chat = chat.messages
            rendered_chat = compiled_template.render(
                messages=chat, add_generation_prompt=add_generation_prompt, **template_kwargs
            )
            rendered.append(rendered_chat)

        if not is_batched:
            rendered = rendered[0]

        if tokenize:
            out = self(
                rendered,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                add_special_tokens=False,
                return_tensors=return_tensors,
                **tokenizer_kwargs,
            )
            if return_dict:
                return out
            else:
                return out["input_ids"]
        else:
            return rendered