import json
import re
from typing import Dict, List, Sequence, Union

import partial_json_parser
from loguru import logger
from partial_json_parser.core.options import Allow

from aphrodite.common.utils import random_uuid
from aphrodite.endpoints.openai.protocol import (DeltaFunctionCall,
                                                 DeltaMessage, DeltaToolCall,
                                                 ExtractedToolCallInformation,
                                                 FunctionCall, ToolCall)
from aphrodite.endpoints.openai.tool_parsers.abstract_tool_parser import (
    ToolParser)
from aphrodite.endpoints.openai.tool_parsers.utils import (
    extract_intermediate_diff)
from aphrodite.transformers_utils.tokenizer import (AnyTokenizer,
                                                    MistralTokenizer)


class Hermes2ProToolParser(ToolParser):
    def __init__(self, tokenizer: AnyTokenizer):
        super().__init__(tokenizer)
        if isinstance(self.model_tokenizer, MistralTokenizer):
            logger.error("Detected Mistral tokenizer when using a Hermes model")
            self.model_tokenizer = self.model_tokenizer.tokenizer
        self.current_tool_name_sent: bool = False
        self.prev_tool_call_arr: List[Dict] = []
        self.current_tool_id: int = -1
        self.streamed_args_for_tool: List[
            str
        ] = []  # map what has been streamed for each tool so far to a list
        self.tool_call_start_token: str = "<tool_call>"
        self.tool_call_end_token: str = "</tool_call>"
        self.tool_call_regex = re.compile(
            r"<tool_call>(.*?)</tool_call>|<tool_call>(.*)", re.DOTALL
        )
        self.scratch_pad_regex = re.compile(
            r"<scratch_pad>(.*?)</scratch_pad>", re.DOTALL
        )
        if not self.model_tokenizer:
            raise ValueError(
                "The model tokenizer must be passed to the ToolParser "
                "constructor during construction."
            )
        self.tool_call_start_token_id: int = self.model_tokenizer.vocab[
            self.tool_call_start_token
        ]
        self.tool_call_end_token_id: int = self.model_tokenizer.vocab[
            self.tool_call_end_token
        ]
        if not self.tool_call_start_token_id or not self.tool_call_end_token_id:
            raise RuntimeError(
                "Hermes 2 Pro Tool parser could not locate tool call start/end "
                "tokens in the tokenizer!"
            )

    def extract_tool_calls(
        self, model_output: str
    ) -> ExtractedToolCallInformation:
        # sanity check; avoid unnecessary processing
        if self.tool_call_start_token not in model_output:
            return ExtractedToolCallInformation(
                tools_called=False, tool_calls=[], content=model_output
            )
        else:
            try:
                # there are two possible captures - between tags, or between a
                # tag and end-of-string so the result of
                # findall is an array of tuples where one is a function call and
                # the other is None
                function_call_tuples = self.tool_call_regex.findall(
                    model_output
                )
                # load the JSON, and then use it to build the Function and
                # Tool Call
                raw_function_calls = [
                    json.loads(match[0] if match[0] else match[1])
                    for match in function_call_tuples
                ]
                tool_calls = [
                    ToolCall(
                        type="function",
                        function=FunctionCall(
                            name=function_call["name"],
                            # function call args are JSON but as a string
                            arguments=json.dumps(function_call["arguments"]),
                        ),
                    )
                    for function_call in raw_function_calls
                ]
                content = model_output[
                    : model_output.find(self.tool_call_start_token)
                ]
                return ExtractedToolCallInformation(
                    tools_called=True,
                    tool_calls=tool_calls,
                    content=content if content else None,
                )
            except Exception as e:
                logger.error(f"Error in extracting tool call from response {e}")
                return ExtractedToolCallInformation(
                    tools_called=False, tool_calls=[], content=model_output
                )

    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],
    ) -> Union[DeltaMessage, None]:
        logger.debug(f"delta_text: {delta_text}")
        logger.debug(f"delta_token_ids: {delta_token_ids}")
        # check to see if we should be streaming a tool call - is there a
        if self.tool_call_start_token_id not in current_token_ids:
            logger.debug("No tool call tokens found!")
            return DeltaMessage(content=delta_text)
        try:
            # figure out where we are in the parsing by counting tool call
            # start & end tags
            prev_tool_start_count = previous_token_ids.count(
                self.tool_call_start_token_id
            )
            prev_tool_end_count = previous_token_ids.count(
                self.tool_call_end_token_id
            )
            cur_tool_start_count = current_token_ids.count(
                self.tool_call_start_token_id
            )
            cur_tool_end_count = current_token_ids.count(
                self.tool_call_end_token_id
            )
            # case: if we're generating text, OR rounding out a tool call
            if (
                cur_tool_start_count == cur_tool_end_count
                and prev_tool_end_count == cur_tool_end_count
            ):
                logger.debug("Generating text content! skipping tool parsing.")
                if delta_text != self.tool_call_end_token:
                    return DeltaMessage(content=delta_text)
            # case: if tool open & close tag counts don't match, we're doing
            # imaginary "else" block here
            # something with tools with this diff.
            # flags for partial JSON parting. exported constants from
            # "Allow" are handled via BIT MASK
            flags = (
                Allow.ALL
                if self.current_tool_name_sent
                else Allow.ALL & ~Allow.STR
            )
            # case -- we're starting a new tool call
            if (
                cur_tool_start_count > cur_tool_end_count
                and cur_tool_start_count > prev_tool_start_count
            ):
                if len(delta_token_ids) > 1:
                    tool_call_portion = current_text.split(
                        self.tool_call_start_token
                    )[-1]
                else:
                    tool_call_portion = None
                    delta = None
                text_portion = None
                # set cursors and state appropriately
                self.current_tool_id += 1
                self.current_tool_name_sent = False
                self.streamed_args_for_tool.append("")
                logger.debug(f"Starting on a new tool {self.current_tool_id}")
            # case -- we're updating an existing tool call
            elif (
                cur_tool_start_count > cur_tool_end_count
                and cur_tool_start_count == prev_tool_start_count
            ):
                # get the portion of the text that's the tool call
                tool_call_portion = current_text.split(
                    self.tool_call_start_token
                )[-1]
                text_portion = None
            # case -- the current tool call is being closed.
            elif (
                cur_tool_start_count == cur_tool_end_count
                and cur_tool_end_count > prev_tool_end_count
            ):
                diff = self.prev_tool_call_arr[self.current_tool_id].get(
                    "arguments"
                )
                if diff:
                    diff = json.dumps(diff).replace(
                        self.streamed_args_for_tool[self.current_tool_id], ""
                    )
                    logger.debug(
                        f"Finishing tool and found diff that had not "
                        f"been streamed yet: {diff}"
                    )
                    self.streamed_args_for_tool[self.current_tool_id] += diff
                    return DeltaMessage(
                        tool_calls=[
                            DeltaToolCall(
                                index=self.current_tool_id,
                                function=DeltaFunctionCall(
                                    arguments=diff
                                ).model_dump(exclude_none=True),
                            )
                        ]
                    )
            # case -- otherwise we're just generating text
            else:
                text = delta_text.replace(self.tool_call_start_token, "")
                text = text.replace(self.tool_call_end_token, "")
                delta = DeltaMessage(tool_calls=[], content=text)
                return delta
            try:
                current_tool_call = (
                    partial_json_parser.loads(tool_call_portion or "{}", flags)
                    if tool_call_portion
                    else None
                )
                logger.debug(f"Parsed tool call {current_tool_call}")
            except partial_json_parser.core.exceptions.MalformedJSON:
                logger.debug("not enough tokens to parse into JSON yet")
                return None
            # case - we haven't sent the tool name yet. If it's available, send
            #   it. otherwise, wait until it's available.
            if not self.current_tool_name_sent:
                function_name: Union[str, None] = current_tool_call.get("name")
                if function_name:
                    self.current_tool_name_sent = True
                    return DeltaMessage(tool_calls=[
                        DeltaToolCall(index=self.current_tool_id,
                                      type="function",
                                      id=f"chatcmpl-tool-{random_uuid()}",
                                      function=DeltaFunctionCall(
                                          name=function_name).model_dump(
                                              exclude_none=True))
                    ])
                else:
                    return None
            # case -- otherwise, send the tool call delta
            # if the tool call portion is None, send the delta as text
            if tool_call_portion is None:
                # if there's text but not tool calls, send that -
                # otherwise None to skip chunk
                delta = (
                    DeltaMessage(content=delta_text)
                    if text_portion is not None
                    else None
                )
                return delta
            # now, the nitty-gritty of tool calls
            # now we have the portion to parse as tool call.
            logger.debug(
                "Trying to parse current tool call with ID "
                f"{self.current_tool_id}"
            )
            # if we're starting a new tool call, push an empty object in as
            #   a placeholder for the arguments
            if len(self.prev_tool_call_arr) <= self.current_tool_id:
                self.prev_tool_call_arr.append({})
            # main logic for tool parsing here - compare prev. partially-parsed
            #   JSON to the current partially-parsed JSON
            prev_arguments = self.prev_tool_call_arr[self.current_tool_id].get(
                "arguments"
            )
            cur_arguments = current_tool_call.get("arguments")
            logger.debug(f"diffing old arguments: {prev_arguments}")
            logger.debug(f"against new ones: {cur_arguments}")
            # case -- no arguments have been created yet. skip sending a delta.
            if not cur_arguments and not prev_arguments:
                logger.debug(f"Skipping text {delta_text} - no arguments")
                delta = None
            # case -- prev arguments are defined, but non are now.
            #   probably impossible, but not a fatal error - just keep going
            elif not cur_arguments and prev_arguments:
                logger.error(
                    "should be impossible to have arguments reset "
                    "mid-call. skipping streaming anything."
                )
                delta = None
            # case -- we now have the first info about arguments available from
            #   autocompleting the JSON
            elif cur_arguments and not prev_arguments:
                cur_arguments_json = json.dumps(cur_arguments)
                logger.debug(
                    f"finding {delta_text} in {cur_arguments_json}"
                )
                # get the location where previous args differ from current
                args_delta_start_loc = cur_arguments_json.index(
                    delta_text
                ) + len(delta_text)
                # use that to find the actual delta
                arguments_delta = cur_arguments_json[:args_delta_start_loc]
                logger.debug(
                    f"First tokens in arguments received: {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
            # last case -- we have an update to existing arguments.
            elif cur_arguments and prev_arguments:
                cur_args_json = json.dumps(cur_arguments)
                prev_args_json = json.dumps(prev_arguments)
                logger.debug(f"Searching for diff between\n{cur_args_json}")
                logger.debug(f"and\n{prev_args_json}")
                argument_diff = extract_intermediate_diff(
                    cur_args_json, prev_args_json
                )
                logger.debug(f"got argument diff {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
            # handle saving the state for the current tool into
            # the "prev" list for use in diffing for the next iteration
            if self.current_tool_id == len(self.prev_tool_call_arr) - 1:
                self.prev_tool_call_arr[
                    self.current_tool_id
                ] = current_tool_call
            else:
                self.prev_tool_call_arr.append(current_tool_call)
            return delta
        except Exception as e:
            logger.error(f"Error trying to handle streaming tool call: {e}")
            return None  # do not stream a delta. skip this token ID.