# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import json
from collections.abc import Sequence
from random import choices
from string import ascii_letters, digits
from typing import Union

import partial_json_parser
import regex as re
from partial_json_parser.core.options import Allow
from pydantic import Field

from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                              DeltaFunctionCall, DeltaMessage,
                                              DeltaToolCall,
                                              ExtractedToolCallInformation,
                                              FunctionCall, ToolCall)
from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
    ToolParser, ToolParserManager)
from vllm.entrypoints.openai.tool_parsers.utils import (
    extract_intermediate_diff)
from vllm.logger import init_logger
from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer

logger = init_logger(__name__)

ALPHANUMERIC = ascii_letters + digits


class MistralToolCall(ToolCall):
    id: str = Field(
        default_factory=lambda: MistralToolCall.generate_random_id())

    @staticmethod
    def generate_random_id():
        # Mistral Tool Call Ids must be alphanumeric with a length of 9.
        # https://github.com/mistralai/mistral-common/blob/21ee9f6cee3441e9bb1e6ed2d10173f90bd9b94b/src/mistral_common/protocol/instruct/validator.py#L299
        return "".join(choices(ALPHANUMERIC, k=9))

    @staticmethod
    def is_valid_id(id: str) -> bool:
        return id.isalnum() and len(id) == 9


def _is_fn_name_regex_support(model_tokenizer: AnyTokenizer) -> bool:
    return isinstance(model_tokenizer, MistralTokenizer) \
        and model_tokenizer.version >= 11


@ToolParserManager.register_module("mistral")
class MistralToolParser(ToolParser):
    """
    Tool call parser for Mistral 7B Instruct v0.3, intended for use with
    - [`mistral_common`](https://github.com/mistralai/mistral-common/)
    - the examples/tool_chat_template_mistral.jinja template.

    Used when --enable-auto-tool-choice --tool-call-parser mistral are all set
    """

    def __init__(self, tokenizer: AnyTokenizer):
        super().__init__(tokenizer)

        if not isinstance(self.model_tokenizer, MistralTokenizer):
            logger.info("Non-Mistral tokenizer detected when using a Mistral "
                        "model...")

        # initialize properties used for state when parsing tool calls in
        # streaming mode
        self.prev_tool_call_arr: list[dict] = []
        self.current_tool_id: int = -1
        self.current_tool_name_sent: bool = False
        self.streamed_args_for_tool: list[str] = [
        ]  # map what has been streamed for each tool so far to a list
        self.bot_token = "[TOOL_CALLS]"
        self.bot_token_id = self.vocab.get(self.bot_token)
        self.tool_call_regex = re.compile(r"\[{.*}\]", re.DOTALL)
        if _is_fn_name_regex_support(self.model_tokenizer):
            self.fn_name_regex = re.compile(
                r'([a-zA-Z0-9_-]+)(\{[\s\S]*?\})(?=\s*$|,|\s)', re.DOTALL)
        else:
            self.fn_name_regex = None

        if self.bot_token_id is None:
            raise RuntimeError(
                "Mistral Tool Parser could not locate the tool call token in "
                "the tokenizer!")

    def adjust_request(
            self, request: ChatCompletionRequest) -> ChatCompletionRequest:
        if not isinstance(
                self.model_tokenizer, MistralTokenizer
        ) and request.tools and request.tool_choice != 'none':
            # Do not skip special tokens when using chat template
            # with Mistral parser as TOOL_CALL token is needed
            # for tool detection.
            # Note: we don't want skip_special_tokens=False
            # with MistralTokenizer as it is incompatible
            request.skip_special_tokens = False
        return request

    def extract_tool_calls(
        self,
        model_output: str,
        request: ChatCompletionRequest,
    ) -> ExtractedToolCallInformation:
        """
        Extract the tool calls from a complete model response. Requires
        find-and-replacing single quotes with double quotes for JSON parsing,
        make sure your tool call arguments don't ever include quotes!
        """

        # case -- if a tool call token is not present, return a text response
        if self.bot_token not in model_output:
            return ExtractedToolCallInformation(tools_called=False,
                                                tool_calls=[],
                                                content=model_output)

        # first remove the BOT token
        tool_content = model_output.replace(self.bot_token, "").strip()

        try:
            # we first try to directly load the json as parsing very nested
            # jsons is difficult
            try:
                if self.fn_name_regex:
                    matches = self.fn_name_regex.findall(tool_content)

                    function_call_arr = []
                    for match in matches:
                        fn_name = match[0]
                        args = match[1]

                        # fn_name is encoded outside serialized json dump
                        # only arguments are serialized
                        function_call_arr.append({
                            "name": fn_name,
                            "arguments": json.loads(args)
                        })
                else:
                    function_call_arr = json.loads(tool_content)
            except json.JSONDecodeError:
                # use a regex to find the part corresponding to the tool call.
                # NOTE: This use case should not happen if the model is trained
                # correctly. It's a easy possible fix so it's included, but
                # can be brittle for very complex / highly nested tool calls
                raw_tool_call = self.tool_call_regex.findall(tool_content)[0]
                function_call_arr = json.loads(raw_tool_call)

            # Tool Call
            tool_calls: list[MistralToolCall] = [
                MistralToolCall(
                    type="function",
                    function=FunctionCall(
                        name=raw_function_call["name"],
                        # function call args are JSON but as a string
                        arguments=json.dumps(raw_function_call["arguments"],
                                             ensure_ascii=False)))
                for raw_function_call in function_call_arr
            ]

            # get any content before  the tool call
            content = model_output.split(self.bot_token)[0]
            return ExtractedToolCallInformation(
                tools_called=True,
                tool_calls=tool_calls,
                content=content if len(content) > 0 else None)

        except Exception:
            logger.exception("Error in extracting tool call from response.")
            # return information to just treat the tool call as regular JSON
            return ExtractedToolCallInformation(tools_called=False,
                                                tool_calls=[],
                                                content=tool_content)

    def extract_tool_calls_streaming(
        self,
        previous_text: str,
        current_text: str,
        delta_text: str,
        previous_token_ids: Sequence[int],
        current_token_ids: Sequence[int],
        delta_token_ids: Sequence[int],
        request: ChatCompletionRequest,
    ) -> Union[DeltaMessage, None]:

        # if the tool call token is not in the tokens generated so far, append
        # output to contents since it's not a tool
        if self.bot_token not in current_text:
            return DeltaMessage(content=delta_text)

        # if the tool call token ID IS in the tokens generated so far, that
        # means we're parsing as tool calls now

        # handle if we detected the BOT token which means the start of tool
        # calling
        if (self.bot_token_id in delta_token_ids
                and len(delta_token_ids) == 1):
            # if it's the only token, return None, so we don't send a chat
            # completion any don't send a control token
            return None

        # bit mask flags for partial JSON parsing. If the name hasn't been
        # sent yet, don't allow sending
        # an incomplete string since OpenAI only ever (as far as I have
        # seen) allows sending the entire tool/ function name at once.
        flags = Allow.ALL if self.current_tool_name_sent \
            else Allow.ALL & ~Allow.STR
        try:

            # replace BOT token with empty string, and convert single quotes
            # to double to allow parsing as JSON since mistral uses single
            # quotes instead of double for tool calls
            parsable_arr = current_text.split(self.bot_token)[-1]

            # tool calls are generated in an array, so do partial JSON
            # parsing on the entire array
            try:
                tool_call_arr: list[dict] = partial_json_parser.loads(
                    parsable_arr, flags)
            except partial_json_parser.core.exceptions.MalformedJSON:
                logger.debug('not enough tokens to parse into JSON yet')
                return None

            # select as the current tool call the one we're on the state at

            current_tool_call: dict = tool_call_arr[self.current_tool_id] \
                if len(tool_call_arr) > 0 else {}

            # case -- if no tokens have been streamed for the tool, e.g.
            #   only the array brackets, stream nothing
            if len(tool_call_arr) == 0:
                return None

            # case: we are starting a new tool in the array
            #   -> array has > 0 length AND length has moved past cursor
            elif (len(tool_call_arr) > 0
                  and len(tool_call_arr) > self.current_tool_id + 1):

                # if we're moving on to a new call, first make sure we
                # haven't missed anything in the previous one that was
                # auto-generated due to JSON completions, but wasn't
                # streamed to the client yet.
                if self.current_tool_id >= 0:
                    diff: Union[str, None] = current_tool_call.get("arguments")

                    if diff:
                        diff = json.dumps(diff, ensure_ascii=False).replace(
                            self.streamed_args_for_tool[self.current_tool_id],
                            "")
                        delta = DeltaMessage(tool_calls=[
                            DeltaToolCall(index=self.current_tool_id,
                                          function=DeltaFunctionCall(
                                              arguments=diff).model_dump(
                                                  exclude_none=True))
                        ])
                        self.streamed_args_for_tool[
                            self.current_tool_id] += diff
                    else:
                        delta = None
                else:
                    delta = None
                # re-set stuff pertaining to progress in the current tool
                self.current_tool_id = len(tool_call_arr) - 1
                self.current_tool_name_sent = False
                self.streamed_args_for_tool.append("")
                logger.debug("starting on new tool %d", self.current_tool_id)
                return delta

            # case: update an existing tool - this is handled below

            # if the current tool name hasn't been sent, send if available
            # - otherwise send nothing
            if not self.current_tool_name_sent:
                function_name = current_tool_call.get("name")
                if function_name:

                    delta = DeltaMessage(tool_calls=[
                        DeltaToolCall(index=self.current_tool_id,
                                      type="function",
                                      id=MistralToolCall.generate_random_id(),
                                      function=DeltaFunctionCall(
                                          name=function_name).model_dump(
                                              exclude_none=True))
                    ])
                    self.current_tool_name_sent = True
                else:
                    delta = None

            # now we know we're on the same tool call and we're streaming
            # arguments
            else:

                prev_arguments = self.prev_tool_call_arr[
                    self.current_tool_id].get("arguments")
                cur_arguments = current_tool_call.get("arguments")

                new_text = delta_text.replace("\'", "\"")
                if ('"}' in new_text):
                    new_text = new_text[:new_text.rindex('"}')]

                if not cur_arguments and not prev_arguments:

                    delta = None
                elif not cur_arguments and prev_arguments:
                    logger.error(
                        "INVARIANT - impossible to have arguments reset "
                        "mid-arguments")
                    delta = None
                elif cur_arguments and not prev_arguments:
                    cur_arguments_json = json.dumps(cur_arguments,
                                                    ensure_ascii=False)[:-2]
                    logger.debug("finding %s in %s", new_text,
                                 cur_arguments_json)

                    if (new_text not in cur_arguments_json):
                        return None
                    arguments_delta = cur_arguments_json[:cur_arguments_json.
                                                         rindex(new_text) +
                                                         len(new_text)]
                    logger.debug("First tokens in arguments received: %s",
                                 arguments_delta)
                    delta = DeltaMessage(tool_calls=[
                        DeltaToolCall(index=self.current_tool_id,
                                      function=DeltaFunctionCall(
                                          arguments=arguments_delta).
                                      model_dump(exclude_none=True))
                    ])
                    self.streamed_args_for_tool[
                        self.current_tool_id] += arguments_delta

                elif cur_arguments and prev_arguments:
                    cur_args_json = json.dumps(cur_arguments,
                                               ensure_ascii=False)
                    prev_args_json = json.dumps(prev_arguments,
                                                ensure_ascii=False)
                    logger.debug("Searching for diff between \n%s\n%s",
                                 cur_args_json, prev_args_json)

                    argument_diff = extract_intermediate_diff(
                        cur_args_json, prev_args_json)
                    logger.debug("got arguments diff: %s", argument_diff)
                    delta = DeltaMessage(tool_calls=[
                        DeltaToolCall(index=self.current_tool_id,
                                      function=DeltaFunctionCall(
                                          arguments=argument_diff).model_dump(
                                              exclude_none=True))
                    ])
                    self.streamed_args_for_tool[
                        self.current_tool_id] += argument_diff
                else:
                    # try parsing it with regular JSON - if it works we're
                    # at the end, and we need to send the difference between
                    # tokens streamed so far and the valid JSON
                    delta = None

            # check to see if the name is defined and has been sent. if so,
            # stream the name - otherwise keep waiting
            # finish by setting old and returning None as base case
            self.prev_tool_call_arr = tool_call_arr
            return delta

        except Exception:
            logger.exception("Error trying to handle streaming tool call.")
            logger.debug(
                "Skipping chunk as a result of tool streaming extraction "
                "error")
            return None
