diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json new file mode 100644 index 00000000..c2ad6c4e --- /dev/null +++ b/.devcontainer/devcontainer.json @@ -0,0 +1,49 @@ +// For format details, see https://aka.ms/devcontainer.json. For config options, see the +// README at: https://github.com/devcontainers/templates/tree/main/src/docker-existing-docker-compose +{ + "name": "Existing Docker Compose (Extend)", + + // Update the 'dockerComposeFile' list if you have more compose files or use different names. + // The .devcontainer/docker-compose.yml file contains any overrides you need/want to make. + "dockerComposeFile": [ + "../docker-compose.yml", + "docker-compose.yml" + ], + + // The 'service' property is the name of the service for the container that VS Code should + // use. Update this value and .devcontainer/docker-compose.yml to the real service name. + "service": "webui", + + // The optional 'workspaceFolder' property is the path VS Code should open by default when + // connected. This is typically a file mount in .devcontainer/docker-compose.yml + "workspaceFolder": "/workspaces/${localWorkspaceFolderBasename}", + "features": { + "ghcr.io/devcontainers/features/python:1": { + "installTools": true, + "optimize": true, + "version": "3.10" + }, + "ghcr.io/akhildevelops/devcontainer-features/pip:0": {} + } + + // Features to add to the dev container. More info: https://containers.dev/features. + // "features": {}, + + // Use 'forwardPorts' to make a list of ports inside the container available locally. + // "forwardPorts": [], + + // Uncomment the next line if you want start specific services in your Docker Compose config. + // "runServices": [], + + // Uncomment the next line if you want to keep your containers running after VS Code shuts down. + // "shutdownAction": "none", + + // Uncomment the next line to run commands after the container is created. + // "postCreateCommand": "cat /etc/os-release", + + // Configure tool-specific properties. + // "customizations": {}, + + // Uncomment to connect as an existing user other than the container default. More info: https://aka.ms/dev-containers-non-root. + // "remoteUser": "devcontainer" +} diff --git a/.devcontainer/docker-compose.yml b/.devcontainer/docker-compose.yml new file mode 100644 index 00000000..727a82a0 --- /dev/null +++ b/.devcontainer/docker-compose.yml @@ -0,0 +1,26 @@ +version: '3.8' +services: + # Update this to the name of the service you want to work with in your docker-compose.yml file + webui: + # Uncomment if you want to override the service's Dockerfile to one in the .devcontainer + # folder. Note that the path of the Dockerfile and context is relative to the *primary* + # docker-compose.yml file (the first in the devcontainer.json "dockerComposeFile" + # array). The sample below assumes your primary file is in the root of your project. + # + # build: + # context: . + # dockerfile: .devcontainer/Dockerfile + + volumes: + # Update this to wherever you want VS Code to mount the folder of your project + - ..:/workspaces:cached + + # Uncomment the next four lines if you will use a ptrace-based debugger like C++, Go, and Rust. + # cap_add: + # - SYS_PTRACE + # security_opt: + # - seccomp:unconfined + + # Overrides default command so things don't shut down after the process ends. + command: /bin/sh -c "while sleep 1000; do :; done" + diff --git a/api/config.py b/api/config.py index 41876fe7..ff59005c 100644 --- a/api/config.py +++ b/api/config.py @@ -6,7 +6,7 @@ # license : Apache 2.0 # Description : # A front end Flask application for llamacpp models. -# The official GPT4All Web ui +# The official LOLLMS Web ui # Made by the community for the community ###### import yaml diff --git a/app.py b/app.py index 97bf802b..b2035e76 100644 --- a/app.py +++ b/app.py @@ -5,7 +5,7 @@ # license : Apache 2.0 # Description : # A front end Flask application for llamacpp models. -# The official GPT4All Web ui +# The official LOLLMS Web ui # Made by the community for the community ###### @@ -436,6 +436,9 @@ class LoLLMsWebUI(LoLLMsAPPI): data = request.get_json() code = data["code"] + ASCIIColors.info("Executing python code:") + ASCIIColors.yellow(code) + def spawn_process(code): """Executes Python code and returns the output as JSON.""" diff --git a/convert.py b/convert.py deleted file mode 100644 index e59dad41..00000000 --- a/convert.py +++ /dev/null @@ -1,1152 +0,0 @@ -# Adapted from llamacpp conversion script -# Check https://github.com/ggerganov for more informations - -import argparse -import concurrent.futures -import copy -import enum -import faulthandler -import functools -import io -import itertools -import json -import math -import mmap -import pickle -import re -import signal -import struct -import sys -import zipfile -from abc import ABCMeta, abstractmethod -from dataclasses import dataclass -from pathlib import Path -from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Iterable, List, - Literal, Optional, Sequence, Tuple, TypeVar, Union) - -import numpy as np -from sentencepiece import SentencePieceProcessor # type: ignore - -if TYPE_CHECKING: - from typing_extensions import TypeAlias - -if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'): - faulthandler.register(signal.SIGUSR1) - -NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]' - - -@dataclass(frozen=True) -class UnquantizedDataType: - name: str - - -DT_F16 = UnquantizedDataType('F16') -DT_F32 = UnquantizedDataType('F32') -DT_I32 = UnquantizedDataType('I32') -DT_BF16 = UnquantizedDataType('BF16') - - -@dataclass(frozen=True) -class QuantizedDataType: - groupsize: int - have_addends: bool - have_g_idx: bool - - -DT_Q4_0 = QuantizedDataType(groupsize=32, have_addends=False, have_g_idx=False) -DT_Q4_1 = QuantizedDataType(groupsize=32, have_addends=True, have_g_idx=False) - -DataType = Union[UnquantizedDataType, QuantizedDataType] - -DATA_TYPE_TO_FTYPE: Dict[DataType, int] = { - DT_F32: 0, - DT_F16: 1, - DT_Q4_0: 2, - DT_Q4_1: 3, -} - -FTYPE_TO_DATA_TYPE: Dict[int, DataType] = \ - {ftype: dtype for (dtype, ftype) in DATA_TYPE_TO_FTYPE.items()} - -DATA_TYPE_TO_NUMPY: Dict[DataType, 'np.dtype[Any]'] = { - DT_F16: np.dtype(np.float16), - DT_F32: np.dtype(np.float32), - DT_I32: np.dtype(np.int32), -} - -NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = \ - {dtype: data_type for (data_type, dtype) in DATA_TYPE_TO_NUMPY.items()} - - -class GGMLFileType(enum.Enum): - AllF32 = 0 - MostlyF16 = 1 # except 1d tensors - MostlyQ4_0 = 2 # except 1d tensors - MostlyQ4_1 = 3 # except 1d tensors - PerLayerIsQ4_1 = 4 # but tok_embeddings.weight and output.weight are F16 - - def type_for_tensor(self, name: str, tensor: 'LazyTensor') -> DataType: - if len(tensor.shape) == 1: - # 1D tensors are always F32. - return DT_F32 - elif self == GGMLFileType.AllF32: - return DT_F32 - elif self == GGMLFileType.MostlyF16: - return DT_F16 - elif self == GGMLFileType.MostlyQ4_0: - return DT_Q4_0 - elif self == GGMLFileType.MostlyQ4_1: - return DT_Q4_1 - elif self == GGMLFileType.PerLayerIsQ4_1: - if name in ('output.weight', 'tok_embeddings.weight'): - return DT_F16 - else: - return DT_Q4_1 - else: - raise ValueError(self) - - -def make_tensors_list() -> List[str]: - ret = [ - 'tok_embeddings.weight', - 'norm.weight', - 'output.weight', - ] - for i in range(80): # maximum number of layer - ret += [ - f'layers.{i}.attention.wq.weight', - f'layers.{i}.attention.wk.weight', - f'layers.{i}.attention.wv.weight', - f'layers.{i}.attention.wo.weight', - f'layers.{i}.attention_norm.weight', - f'layers.{i}.feed_forward.w1.weight', - f'layers.{i}.feed_forward.w2.weight', - f'layers.{i}.feed_forward.w3.weight', - f'layers.{i}.atttention_norm.weight', - f'layers.{i}.ffn_norm.weight', - ] - return ret - - -TENSORS_LIST = make_tensors_list() -TENSORS_SET = set(TENSORS_LIST) - - -@dataclass -class Params: - n_vocab: int - n_embd: int - n_mult: int - n_head: int - n_layer: int - file_type: GGMLFileType - - @staticmethod - def guessed(model: 'LazyModel', file_type: GGMLFileType) -> 'Params': - n_vocab, n_embd = model["tok_embeddings.weight"].shape - - return Params( - n_vocab=n_vocab, - n_embd=n_embd, - n_mult=256, - n_head=n_embd // 128, - n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model), - file_type=file_type, - ) - - -class SentencePieceVocab: - def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None: - self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer)) - added_tokens: Dict[str, int] - if fname_added_tokens is not None: - added_tokens = json.load(open(fname_added_tokens)) - else: - added_tokens = {} - vocab_size: int = self.sentencepiece_tokenizer.vocab_size() - expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) - actual_ids = sorted(added_tokens.values()) - if expected_ids != actual_ids: - raise Exception(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}") - items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) - self.added_tokens_list = [text for (text, idx) in items] - self.vocab_size_base: int = vocab_size - self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list) - self.fname_tokenizer = fname_tokenizer - self.fname_added_tokens = fname_added_tokens - - def sentencepiece_tokens(self) -> Iterable[Tuple[bytes, float]]: - tokenizer = self.sentencepiece_tokenizer - for i in range(tokenizer.vocab_size()): - text: bytes - if tokenizer.is_unknown(i): - text = " \u2047 ".encode("utf-8") - elif tokenizer.is_control(i): - text = b"" - elif tokenizer.is_byte(i): - piece = tokenizer.id_to_piece(i) - if len(piece) != 6: - raise Exception(f"Invalid token: {piece}") - byte_value = int(piece[3:-1], 16) - text = struct.pack("B", byte_value) - else: - text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8") - score: float = tokenizer.get_score(i) - yield text, score - - def added_tokens(self) -> Iterable[Tuple[bytes, float]]: - for text in self.added_tokens_list: - score = -1000.0 - yield text.encode("utf-8"), score - - def all_tokens(self) -> Iterable[Tuple[bytes, float]]: - yield from self.sentencepiece_tokens() - yield from self.added_tokens() - - def __repr__(self) -> str: - return f"" - - -class GGMLVocab: - def __init__(self, tokens: List[Tuple[bytes, float]]): - self.tokens = tokens - self.vocab_size = len(tokens) - - def all_tokens(self) -> Iterable[Tuple[bytes, float]]: - return self.tokens - - def __repr__(self) -> str: - return f"" - - -Vocab = Union[SentencePieceVocab, GGMLVocab] - - -def permute(weights: NDArray, n_head: int) -> NDArray: - return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) - .swapaxes(1, 2) - .reshape(weights.shape)) - - -def dequantize_q4(qvalues_pack32: NDArray, scales: NDArray, addends: Optional[NDArray], g_idx: Optional[NDArray]) -> NDArray: - # First reinterpret each row from a list of int32s containing 8 values each - # to a list of uint8s containing 2 values each. - qvalues_pack8 = qvalues_pack32.view(np.uint8) - - # Then split out the two values per int8 (which requires an actual - # conversion because numpy doesn't natively support int4s). - qvalues = np.zeros([qvalues_pack8.shape[0], qvalues_pack8.shape[1] * 2], dtype=np.uint8) - qvalues[:, 0::2] = qvalues_pack8 & 0xf - qvalues[:, 1::2] = qvalues_pack8 >> 4 - - assert addends is None or addends.shape == scales.shape - assert qvalues.shape[0] == scales.shape[0] - assert qvalues.shape[1] % scales.shape[1] == 0 - if g_idx is None: - repeat_count = qvalues.shape[1] // scales.shape[1] - scales = scales[:, :, np.newaxis] - if addends is not None: - addends = addends[:, :, np.newaxis] - # Reshape so that the below computation broadcasts over scales and addends: - qvalues.shape = (qvalues.shape[0], scales.shape[1], int(repeat_count)) - else: - # In this case the scale and addend is selected for each column by g_idx: - assert addends is not None - scales = scales[:, g_idx] - addends = addends[:, g_idx] - if addends is None: - # Q4_0 - qvalues = qvalues.view(np.int8) - qvalues -= 8 - # And do the actual 'value = scale * qvalue + addend' computation. - values = scales * qvalues - if addends is not None: - values += addends - if g_idx is None: - values.shape = (values.shape[0], values.shape[1] * values.shape[2]) - return values - - -class Tensor(metaclass=ABCMeta): - data_type: DataType - - @abstractmethod - def astype(self, data_type: DataType) -> 'Tensor': ... - @abstractmethod - def permute(self, n_head: int) -> 'Tensor': ... - @abstractmethod - def to_ggml(self) -> 'GGMLCompatibleTensor': ... - - -class UnquantizedTensor(Tensor): - def __init__(self, ndarray: NDArray) -> None: - assert isinstance(ndarray, np.ndarray) - self.ndarray = ndarray - self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype] - - def astype(self, data_type: DataType) -> Tensor: - dtype = DATA_TYPE_TO_NUMPY[data_type] - return UnquantizedTensor(self.ndarray.astype(dtype)) - - def to_ggml(self) -> 'UnquantizedTensor': - return self - - def permute(self, n_head: int) -> 'UnquantizedTensor': - return UnquantizedTensor(permute(self.ndarray, n_head)) - - -def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, convert: bool = False) -> NDArray: - tensor = lazy_tensor.load() - assert isinstance(tensor, UnquantizedTensor) - - # double-check: - actual_shape = list(tensor.ndarray.shape) - assert actual_shape == lazy_tensor.shape, (actual_shape, lazy_tensor.shape) - if expected_dtype is not None and expected_dtype != tensor.ndarray.dtype: - if convert: - tensor.ndarray = tensor.ndarray.astype(expected_dtype) - else: - raise ValueError(f'expected this tensor to have dtype {expected_dtype}, got {tensor.ndarray.dtype}') - - return tensor.ndarray - - -class GGMLQuantizedTensor(Tensor): - data_type: QuantizedDataType - - def __init__(self, ndarray: NDArray, shape: List[int], data_type: DataType) -> None: - rows, columns = shape - assert data_type in (DT_Q4_1, DT_Q4_0) # for now - assert isinstance(data_type, QuantizedDataType) # redundant, but mypy complains without this - assert columns % data_type.groupsize == 0 - words_in_block = 6 if data_type == DT_Q4_1 else 5 - self.ndarray = ndarray.view(dtype=np.uint32).reshape((rows, columns // data_type.groupsize, words_in_block)) - self.shape = shape[:] - self.data_type = data_type - - def astype(self, data_type: DataType) -> Tensor: - if data_type == self.data_type: - return self - scales = self.ndarray[:, :, 0].view(np.float32) - if self.data_type.have_addends: - addends = self.ndarray[:, :, 1].view(np.float32) - else: - addends = None - qweights = self.ndarray[:, :, -4:].reshape([self.shape[0], self.shape[1] // 8]) - - dq = dequantize_q4(qweights, scales, addends, g_idx=None) - return UnquantizedTensor(dq).astype(data_type) - - def to_ggml(self) -> 'GGMLQuantizedTensor': - return self - - def permute(self, n_head: int) -> 'GGMLQuantizedTensor': - return GGMLQuantizedTensor(permute(self.ndarray, n_head), self.shape, self.data_type) - - -GGMLCompatibleTensor = Union[UnquantizedTensor, GGMLQuantizedTensor] - - -class DeferredPermutedTensor(Tensor): - def __init__(self, base: Tensor, n_head: int) -> None: - self.base = base - self.n_head = n_head - self.data_type = self.base.data_type - - def astype(self, data_type: DataType) -> Tensor: - return self.base.astype(data_type).permute(self.n_head) - - def to_ggml(self) -> GGMLCompatibleTensor: - return self.base.to_ggml().permute(self.n_head) - - def permute(self, n_head: int) -> Tensor: - raise Exception("shouldn't permute twice") - - -class GPTQForLLaMaQuantizedTensor(Tensor): - def __init__(self, model: 'LazyModel', namebase: str) -> None: - qweight = load_unquantized(model[f"{namebase}.qweight"], np.int32) - scales = load_unquantized(model[f"{namebase}.scales"], np.float32, convert=True) - - bias = model.get(f"{namebase}.bias") - if bias is not None: - # Q4_1 does not support bias; good thing the bias is always all zeros. - assert not np.any(load_unquantized(bias)) - - if f"{namebase}.zeros" in model: - zeros = load_unquantized(model[f"{namebase}.zeros"], np.float32) - else: - qzeros = load_unquantized(model[f"{namebase}.qzeros"], np.int32) - assert qzeros.dtype == np.int32 - zeros = dequantize_q4(qzeros, scales, scales, g_idx=None) - assert zeros.dtype == np.float32 - - assert zeros.shape == scales.shape - - # Output is transposed compared to the input, and addends have their sign flipped. - # Scales and zeros similarly must be transposed but only for newer - # versions of GPTQ-for-LLaMa; the older versions can be identified by - # having shape (n_embd, 1). - qweight = qweight.T - if scales.shape[1] != 1: - scales = scales.T - zeros = zeros.T - - # Output also has signs flipped for the addends. - self.qweight = qweight - self.scales = scales - self.addends = -zeros - - self.g_idx: Optional[NDArray] - if f"{namebase}.g_idx" in model: - self.g_idx = load_unquantized(model[f"{namebase}.g_idx"], np.int32) - assert self.g_idx.shape == (qweight.shape[1] * 8,) - else: - self.g_idx = None - - self.shape = [self.qweight.shape[0], self.qweight.shape[1] * 8] - self.data_type = QuantizedDataType(groupsize=self.groupsize(), have_addends=True, - have_g_idx=(self.g_idx is not None)) - - def inspect(self, row: int, col: int) -> None: - '''For debugging.''' - qweight = (self.qweight[row, col // 8] >> (4 * (col & 7))) & 0xf - if self.g_idx is not None: - group = self.g_idx[col] - else: - group = int(col // self.groupsize()) - scale = self.scales[row, group] - addend = self.addends[row, group] - with np.printoptions(precision=None, suppress=True): - print(f'scale:{scale} addend:{addend} qweight:{qweight}') - print('possible values:', np.arange(16) * scale + addend) - print('actual value:', qweight * scale + addend) - - def astype(self, data_type: DataType) -> Tensor: - if isinstance(data_type, QuantizedDataType): - assert self.g_idx is None and data_type.have_addends is True and data_type.have_g_idx is False - return self.regroup(data_type.groupsize) - - dequantized = dequantize_q4(np.ascontiguousarray(self.qweight), self.scales, self.addends, self.g_idx) - return UnquantizedTensor(dequantized).astype(data_type) - - def groupsize(self) -> int: - assert self.addends.shape == self.scales.shape - assert self.shape[1] % self.scales.shape[1] == 0 - return self.shape[1] // self.scales.shape[1] - - def regroup(self, new_groupsize: int = 32) -> 'GPTQForLLaMaQuantizedTensor': - # Old versions of GPTQ-for-LLaMa shared scales and addends between all the - # columns in a row. Newer versions share them between every set of N - # columns in a row, where N is the `groupsize` parameter, usually 128. The - # output format shares them between every set of 32 columns. To handle - # this, duplicate scales and addends for every smaller group. - # (In the above, 'row' and 'column' are in the sense of the output.) - assert self.g_idx is None - old_groupsize = self.groupsize() - assert old_groupsize >= new_groupsize and old_groupsize % new_groupsize == 0, old_groupsize - ret = copy.copy(self) - ret.addends = self.addends.repeat(old_groupsize // new_groupsize, axis=1) - ret.scales = self.scales.repeat(old_groupsize // new_groupsize, axis=1) - ret.data_type = QuantizedDataType(groupsize=new_groupsize, have_addends=True, have_g_idx=False) - return ret - - def permute(self, n_head: int) -> Tensor: - return DeferredPermutedTensor(self, n_head) - - def to_ggml(self) -> GGMLQuantizedTensor: - # The output format looks like this: - # For each row: - # For each group of 32 columns: - # - addend (float32, 4 bytes) - # - scale (float32, 4 bytes) - # - weights (int4 * 32, 16 bytes) - - if self.groupsize() != 32: - raise Exception("should have been regrouped before converting to ggml") - - # Since the output format is mixed between integers and floats, we have - # to hackily view the floats as int32s just so numpy will let us - # concatenate them. - addends_view = self.addends.view(dtype=np.int32)[:, :, np.newaxis] - scales_view = self.scales.view(dtype=np.int32)[:, :, np.newaxis] - - # Split into groups of 4 columns (i.e. 32 columns of quantized data): - grouped = self.qweight.reshape([self.qweight.shape[0], self.qweight.shape[1] // 4, 4]) - - # And concatenate: - grouped = np.concatenate([scales_view, addends_view, grouped], axis=2, casting='no') - - return GGMLQuantizedTensor(grouped, self.shape, DT_Q4_1) - - -@dataclass -class LazyTensor: - _load: Callable[[], Tensor] - shape: List[int] - data_type: DataType - description: str - - def load(self) -> Tensor: - ret = self._load() - assert ret.data_type == self.data_type, (self.data_type, ret.data_type, self.description) - return ret - - def astype(self, data_type: DataType) -> 'LazyTensor': - self.validate_conversion_to(data_type) - - def load() -> Tensor: - return self.load().astype(data_type) - return LazyTensor(load, self.shape, data_type, f'convert({data_type}) {self.description}') - - def validate_conversion_to(self, data_type: DataType) -> None: - if data_type == self.data_type: - return - if isinstance(data_type, QuantizedDataType): - if not isinstance(self.data_type, QuantizedDataType): - raise Exception(f"Can't turn an unquantized tensor into a quantized type ({data_type})") - if self.data_type.have_g_idx: - sys.stderr.write("Error: Input uses the newer GPTQ-for-LLaMa format (using g_idx), which is not yet natively supported by GGML. For now you can still convert this model by passing `--outtype f16` to dequantize, but that will result in a much larger output file for no quality benefit.\n") - sys.exit(1) - assert not data_type.have_g_idx and self.data_type.have_addends and data_type.have_addends - - -LazyModel = Dict[str, LazyTensor] - - -@dataclass -class ModelPlus: - model: LazyModel - paths: List[Path] # Where this was read from. - format: Literal['ggml', 'torch', 'safetensors'] - vocab: Optional[Vocab] # For GGML models (which have vocab built in), the vocab. - - -def merge_sharded(models: List[LazyModel]) -> LazyModel: - # Original LLaMA models have each file contain one part of each tensor. - # Use a dict instead of a set to preserve order. - names = {name: None for model in models for name in model} - - def convert(name: str) -> LazyTensor: - lazy_tensors: List[LazyTensor] = [model[name] for model in models] - if len(lazy_tensors) == 1: - # only one file; don't go through this procedure since there might - # be quantized tensors - return lazy_tensors[0] - if len(lazy_tensors[0].shape) == 1: - # the tensor is just duplicated in every file - return lazy_tensors[0] - if name.startswith('tok_embeddings.') or \ - name.endswith('.attention.wo.weight') or \ - name.endswith('.feed_forward.w2.weight'): - # split by columns - axis = 1 - else: - # split by rows - axis = 0 - concatenated_shape = list(lazy_tensors[0].shape) - concatenated_shape[axis] = sum(tensor.shape[axis] for tensor in lazy_tensors) - - def load() -> UnquantizedTensor: - ndarrays = [load_unquantized(tensor) for tensor in lazy_tensors] - concatenated: NDArray = np.concatenate(ndarrays, axis=axis) - return UnquantizedTensor(concatenated) - description = 'concatenated[[' + '] | ['.join(lt.description for lt in lazy_tensors) + ']]' - return LazyTensor(load, concatenated_shape, lazy_tensors[0].data_type, description) - return {name: convert(name) for name in names} - - -def merge_multifile_models(models_plus: List[ModelPlus]) -> ModelPlus: - formats = set(mp.format for mp in models_plus) - assert len(formats) == 1, "different formats?" - format = formats.pop() - paths = [path for mp in models_plus for path in mp.paths] - # Use the first non-None vocab, if any. - try: - vocab = next(mp.vocab for mp in models_plus if mp.vocab is not None) - except StopIteration: - vocab = None - - if any("model.embed_tokens.weight" in mp.model for mp in models_plus): - # Transformers models put different tensors in different files, but - # don't split indivdual tensors between files. - model: LazyModel = {} - for mp in models_plus: - model.update(mp.model) - else: - model = merge_sharded([mp.model for mp in models_plus]) - - return ModelPlus(model, paths, format, vocab) - - -def permute_lazy(lazy_tensor: LazyTensor, n_head: int) -> LazyTensor: - def load() -> Tensor: - return lazy_tensor.load().permute(n_head) - return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description) - - -def convert_transformers_to_orig(model: LazyModel) -> LazyModel: - out: LazyModel = {} - out["tok_embeddings.weight"] = model["model.embed_tokens.weight"] - out["norm.weight"] = model["model.norm.weight"] - out["output.weight"] = model["lm_head.weight"] - - n_head = model["model.layers.0.self_attn.q_proj.weight"].shape[1] // 128 - for i in itertools.count(): - if f"model.layers.{i}.self_attn.q_proj.weight" not in model: - break - out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], n_head) - out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], n_head) - out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] - out[f"layers.{i}.attention.wo.weight"] = model[f"model.layers.{i}.self_attn.o_proj.weight"] - - out[f"layers.{i}.feed_forward.w1.weight"] = model[f"model.layers.{i}.mlp.gate_proj.weight"] - out[f"layers.{i}.feed_forward.w2.weight"] = model[f"model.layers.{i}.mlp.down_proj.weight"] - out[f"layers.{i}.feed_forward.w3.weight"] = model[f"model.layers.{i}.mlp.up_proj.weight"] - - out[f"layers.{i}.attention_norm.weight"] = model[f"model.layers.{i}.input_layernorm.weight"] - out[f"layers.{i}.ffn_norm.weight"] = model[f"model.layers.{i}.post_attention_layernorm.weight"] - return out - - -def handle_quantization(model: LazyModel) -> LazyModel: - '''Convert a model with entries for 'foo.qweight', 'foo.scales', etc. - (which resolve to UnquantizedTensors with the raw data) to one with entries - for 'foo.weight' (which resolve to QuantizedTensors). - ''' - def convert(name: str) -> Tuple[str, LazyTensor]: - if name.endswith(".qweight"): - namebase = name.rsplit('.', 1)[0] - orig_name = namebase + ".weight" - - lazy_tensor = model[name] - assert len(lazy_tensor.shape) == 2 - real_shape = [lazy_tensor.shape[1], lazy_tensor.shape[0] * 8] - - # Calculate type. This replicates the logic in - # GPTQForLLaMaQuantizedTensor (which is executed when the modelis - # actually loaded). - lazy_scales = model[f"{namebase}.scales"] - scales_width = 1 if lazy_scales.shape[1] == 1 else lazy_scales.shape[0] - assert real_shape[1] % scales_width == 0 - groupsize = real_shape[1] // scales_width - have_g_idx = f"{namebase}.g_idx" in model - data_type = QuantizedDataType(groupsize=groupsize, have_addends=True, have_g_idx=have_g_idx) - - def load() -> Tensor: - return GPTQForLLaMaQuantizedTensor(model, namebase) - - return (orig_name, LazyTensor(load, real_shape, data_type, '[quantized]')) - else: - return (name, model[name]) - return dict(convert(name) for name in model) - -# Functionality that simulates `torch.load` but where individual tensors are -# only loaded into memory on demand, not all at once. -# PyTorch can't do this natively as of time of writing: -# - https://github.com/pytorch/pytorch/issues/64327 -# This allows us to de-shard without multiplying RAM usage, and also -# conveniently drops the PyTorch dependency (though we still need numpy). - - -@dataclass -class LazyStorageKind: - data_type: DataType - - -@dataclass -class LazyStorage: - load: Callable[[int, int], NDArray] - kind: LazyStorageKind - description: str - - -class LazyUnpickler(pickle.Unpickler): - def __init__(self, fp: IO[bytes], data_base_path: str, zip_file: zipfile.ZipFile): - super().__init__(fp) - self.data_base_path = data_base_path - self.zip_file = zip_file - - def persistent_load(self, pid: Any) -> Any: - assert pid[0] == 'storage' - assert isinstance(pid[1], LazyStorageKind) - data_type = pid[1].data_type - filename_stem = pid[2] - filename = self.data_base_path + '/' + filename_stem - info = self.zip_file.getinfo(filename) - - def load(offset: int, elm_count: int) -> NDArray: - dtype = DATA_TYPE_TO_NUMPY.get(data_type) - if dtype is None: - raise Exception("tensor stored in unsupported format") - fp = self.zip_file.open(info) - fp.seek(offset * dtype.itemsize) - size = elm_count * dtype.itemsize - data = fp.read(size) - assert len(data) == size - return np.frombuffer(data, dtype) - description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}' - return LazyStorage(load=load, kind=pid[1], description=description) - - def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any, # pyright: ignore[reportSelfClsParameterName] - requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor: - assert isinstance(storage, LazyStorage) - - def load() -> UnquantizedTensor: - elm_count = stride[0] * size[0] - return UnquantizedTensor(storage.load(storage_offset, elm_count).reshape(size)) - description = f'pickled storage_offset={storage_offset} in {storage.description}' - return LazyTensor(load, list(size), storage.kind.data_type, description) - - CLASSES: Dict[Any, Any] = { - ('torch._utils', '_rebuild_tensor_v2'): lazy_rebuild_tensor_v2, - ('torch', 'BFloat16Storage'): LazyStorageKind(DT_BF16), - ('torch', 'HalfStorage'): LazyStorageKind(DT_F16), - ('torch', 'FloatStorage'): LazyStorageKind(DT_F32), - ('torch', 'IntStorage'): LazyStorageKind(DT_I32), - } - - def find_class(self, module: str, name: str) -> Any: - if not module.startswith('torch'): - return super().find_class(module, name) - return self.CLASSES[(module, name)] - - -def lazy_load_torch_file(outer_fp: IO[bytes], path: Path) -> ModelPlus: - zf = zipfile.ZipFile(outer_fp) - pickle_paths = [name for name in zf.namelist() if name.endswith('.pkl')] - assert len(pickle_paths) == 1, pickle_paths - pickle_fp = zf.open(pickle_paths[0], 'r') - unpickler = LazyUnpickler(pickle_fp, - data_base_path=pickle_paths[0][:-4], - zip_file=zf) - model = unpickler.load() - as_dict = dict(model.items()) - return ModelPlus(model=as_dict, paths=[path], format='torch', vocab=None) - - -SAFETENSORS_DATA_TYPES: Dict[str, DataType] = { - 'F16': DT_F16, - 'F32': DT_F32, - 'I32': DT_I32, -} - - -def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus: - header_size, = struct.unpack(' LazyTensor: - data_type = SAFETENSORS_DATA_TYPES[info['dtype']] - numpy_dtype = DATA_TYPE_TO_NUMPY[data_type] - shape: List[int] = info['shape'] - begin, end = info['data_offsets'] - assert 0 <= begin <= end <= len(byte_buf) - assert end - begin == math.prod(shape) * numpy_dtype.itemsize - buf = byte_buf[begin:end] - - def load() -> UnquantizedTensor: - return UnquantizedTensor(np.frombuffer(buf, dtype=numpy_dtype).reshape(shape)) - description = f'safetensors begin={begin} end={end} type={data_type} path={path}' - return LazyTensor(load, shape, data_type, description) - model = {name: convert(info) for (name, info) in header.items()} - return ModelPlus(model=model, paths=[path], format='safetensors', vocab=None) - - -def must_read(fp: IO[bytes], length: int) -> bytes: - ret = fp.read(length) - if len(ret) < length: - raise Exception("unexpectedly reached end of file") - return ret - - -def lazy_load_ggml_file(fp: io.BufferedReader, path: Path) -> ModelPlus: - magic = must_read(fp, 4)[::-1] - if magic in (b'ggmf', b'ggjt'): - version, = struct.unpack("i", must_read(fp, 4)) - assert version == 1 - else: - assert magic == b'ggml' - version = None - n_vocab, n_embd, n_mult, n_head, n_layer, rot, file_type = struct.unpack('<7i', must_read(fp, 28)) - - tokens: List[Tuple[bytes, float]] = [] - for i in range(n_vocab): - if i == 32000: - # HACK: GPT4All messed with the format without changing the magic - # number. Specifically, they changed the vocab section to contain - # `n_vocab - 1` tokens instead of `n_vocab` (i.e. omitting the - # extra pad token). Try to detect if we're reading a file like - # this. - orig_pos = fp.tell() - fp.seek(20, io.SEEK_CUR) - is_gpt4all = fp.read(21) == b'tok_embeddings.weight' - fp.seek(orig_pos) - if is_gpt4all: - break - - length, = struct.unpack("i", must_read(fp, 4)) - text = must_read(fp, length) - if magic != b'ggml': - score, = struct.unpack("f", must_read(fp, 4)) - tokens.append((text, score)) - vocab = GGMLVocab(tokens) if magic != b'ggml' else None - - model: LazyModel = {} - # Use mmap for the actual data to avoid race conditions with the file offset. - off = fp.raw.tell() - mapped = memoryview(mmap.mmap(fp.fileno(), 0, access=mmap.ACCESS_READ)) - fp.raw.seek(off) # needed on Windows - - def read_tensor() -> None: # this is a function so that variables captured in `load` don't change - shape_len, name_len, ftype = struct.unpack("iii", must_read(fp, 12)) - assert 0 <= shape_len <= 3 - shape: List[int] = list(struct.unpack(f"{shape_len}i", must_read(fp, 4 * shape_len))) - shape = shape[::-1] - name = must_read(fp, name_len).decode('utf-8') - data_type = FTYPE_TO_DATA_TYPE[ftype] - - if magic == b'ggjt': - fp.seek((fp.tell() + 31) & -32) - - if data_type == DT_Q4_1: - # See GPTQForLLaMaQuantizedTensor.ggml_ndarray() - size = 24 * (shape[1] // 32) * shape[0] - elif data_type == DT_Q4_0: - size = 20 * (shape[1] // 32) * shape[0] - else: - numpy_dtype = DATA_TYPE_TO_NUMPY[data_type] - elm_count = math.prod(shape) - size = elm_count * numpy_dtype.itemsize - offset = fp.tell() - buf = mapped[offset:offset+size] - fp.seek(size, io.SEEK_CUR) - - def load() -> Tensor: - if isinstance(data_type, QuantizedDataType): - ndarray = np.frombuffer(buf, dtype=np.uint32) - return GGMLQuantizedTensor(ndarray, shape, data_type) - else: - return UnquantizedTensor(np.frombuffer(buf, dtype=numpy_dtype).reshape(shape)) - description = f'ggml offset={offset} type={data_type} path={path}' - model[name] = LazyTensor(load, shape, data_type, description) - - while fp.read(1) != b'': - fp.seek(-1, io.SEEK_CUR) - read_tensor() - - return ModelPlus(model=model, paths=[path], format='ggml', vocab=vocab) - - -@functools.lru_cache(maxsize=None) -def lazy_load_file(path: Path) -> ModelPlus: - fp = open(path, 'rb') - first8 = fp.read(8) - fp.seek(0) - if first8[:2] == b'PK': - # A zip file, i.e. PyTorch format - return lazy_load_torch_file(fp, path) - elif first8[2:4] == b'gg': - # GGML format - return lazy_load_ggml_file(fp, path) - elif struct.unpack(' Iterable[Out]: - '''Parallel map, but with backpressure. If the caller doesn't call `next` - fast enough, this will stop calling `func` at some point rather than - letting results pile up in memory. Specifically, there is a max of one - output value buffered per thread.''' - with concurrent.futures.ThreadPoolExecutor() as executor: - futures: List[concurrent.futures.Future[Out]] = [] - items_rev = list(iterable)[::-1] - for i in range(min(concurrency, len(items_rev))): - futures.append(executor.submit(func, items_rev.pop())) - while futures: - result = futures.pop(0).result() - if items_rev: - futures.append(executor.submit(func, items_rev.pop())) - yield result - - -def check_vocab_size(params: Params, vocab: Vocab) -> None: - if params.n_vocab != vocab.vocab_size: - # GGMLVocab comes from the same file as the model so shouldn't mismatch: - assert isinstance(vocab, SentencePieceVocab) - if params.n_vocab == vocab.vocab_size_base: - print("Ignoring added_tokens.json since model matches vocab size without it.") - vocab.added_tokens_list = [] - vocab.vocab_size = vocab.vocab_size_base - return - msg = f"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer}" - if vocab.fname_added_tokens is not None: - msg += f" combined with {vocab.fname_added_tokens}" - msg += f" has {vocab.vocab_size})." - if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20 and vocab.fname_added_tokens is None: - msg += f" Most likely you are missing added_tokens.json (should be in {vocab.fname_tokenizer.parent})." - raise Exception(msg) - - -class OutputFile: - def __init__(self, fname_out: Path) -> None: - self.fout = open(fname_out, "wb") - - def write_file_header(self, params: Params) -> None: - self.fout.write(b"ggjt"[::-1]) # magic - values = [ - 1, # file version - params.n_vocab, - params.n_embd, - params.n_mult, - params.n_head, - params.n_layer, - params.n_embd // params.n_head, # rot (obsolete) - params.file_type.value, - ] - self.fout.write(struct.pack("i" * len(values), *values)) - - def write_tensor_header(self, name: str, shape: Sequence[int], data_type: DataType) -> None: - sname = name.encode('utf-8') - self.fout.write(struct.pack("iii", len(shape), len(sname), DATA_TYPE_TO_FTYPE[data_type])) - self.fout.write(struct.pack("i" * len(shape), *shape[::-1])) - self.fout.write(sname) - self.fout.seek((self.fout.tell() + 31) & -32) - - def write_vocab(self, vocab: Vocab) -> None: - for text, score in vocab.all_tokens(): - self.fout.write(struct.pack("i", len(text))) - self.fout.write(text) - self.fout.write(struct.pack("f", score)) - - @staticmethod - def write_vocab_only(fname_out: Path, vocab: Vocab) -> None: - of = OutputFile(fname_out) - params = Params(n_vocab=vocab.vocab_size, n_embd=0, n_mult=0, - n_head=1, n_layer=0, file_type=GGMLFileType.AllF32) - of = OutputFile(fname_out) - of.write_file_header(params) - of.write_vocab(vocab) - of.fout.close() - - @staticmethod - def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -> None: - check_vocab_size(params, vocab) - of = OutputFile(fname_out) - of.write_file_header(params) - print("Writing vocab...") - of.write_vocab(vocab) - - def do_item(item: Tuple[str, LazyTensor]) -> NDArray: - name, lazy_tensor = item - return lazy_tensor.load().to_ggml().ndarray - - ndarrays = bounded_parallel_map(do_item, model.items(), concurrency=8) - for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)): - size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape) - padi = len(str(len(model))) - print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type}") - of.write_tensor_header(name, lazy_tensor.shape, lazy_tensor.data_type) - ndarray.tofile(of.fout) - of.fout.close() - - -def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFileType: - wq_type = model["layers.0.attention.wq.weight"].data_type - if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32): - return GGMLFileType.AllF32 - if output_type_str == "f16" or (output_type_str is None and wq_type == DT_F16): - return GGMLFileType.MostlyF16 - if output_type_str == "q4_1" or (output_type_str is None and isinstance(wq_type, QuantizedDataType) and - wq_type.have_addends): - if isinstance(model["output.weight"].data_type, QuantizedDataType): - return GGMLFileType.MostlyQ4_1 - else: - return GGMLFileType.PerLayerIsQ4_1 - if output_type_str == "q4_0" or (output_type_str is None and isinstance(wq_type, QuantizedDataType)): - return GGMLFileType.MostlyQ4_0 - name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()} - raise Exception(f"Unexpected combination of types: {name_to_type}") - - -def do_necessary_conversions(model: LazyModel) -> LazyModel: - model = handle_quantization(model) - - if "lm_head.weight" in model: - model = convert_transformers_to_orig(model) - model = filter_and_sort_tensors(model) - - return model - - -def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel: - return {name: tensor.astype(output_type.type_for_tensor(name, tensor)) - for (name, tensor) in model.items()} - - -def nth_multifile_path(path: Path, n: int) -> Optional[Path]: - '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return - the nth path in the model. - ''' - # Support the following patterns: - patterns: List[Tuple[str, str]] = [ - # - x.00.pth, x.01.pth, etc. - (r'\.[0-9]{2}\.pth$', f'.{n:02}.pth'), - # - x-00001-of-00002.bin, x-00002-of-00002.bin, etc. - (r'-[0-9]{5}-of-(.*)$', fr'-{n:05}-of-\1'), - # x.bin, x.bin.1, etc. - (r'(\.[0-9]+)?$', r'\1' if n == 0 else fr'\1.{n}') - ] - for regex, replacement in patterns: - if re.search(regex, path.name): - new_path = path.with_name(re.sub(regex, replacement, path.name)) - if new_path.exists(): - return new_path - return None - - -def find_multifile_paths(path: Path) -> List[Path]: - '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return - the whole list of paths in the model. - ''' - ret: List[Path] = [] - for i in itertools.count(): - nth_path = nth_multifile_path(path, i) - if nth_path is None: - break - ret.append(nth_path) - if not ret: - # No matches. This should only happen if the file was named, e.g., - # foo.0, and there was no file named foo. Oh well, try to process it - # as a single file. - return [path] - return ret - - -def load_some_model(path: Path) -> ModelPlus: - '''Load a model of any supported format.''' - # Be extra-friendly and accept either a file or a directory: - if path.is_dir(): - globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt"] - files = [file for glob in globs for file in path.glob(glob)] - if not files: - # Try GGML too, but with lower priority, since if both a non-GGML - # model and a GGML model exist in the same directory, we assume the - # latter was converted from the former. - files = list(path.glob("ggml-model*.bin*")) - if not files: - raise Exception(f"Can't find model in directory {path}") - if len(files) > 1: - raise Exception(f"Found multiple models in {path}, not sure which to pick: {files}") - path = files[0] - - paths = find_multifile_paths(path) - models_plus: List[ModelPlus] = [] - for path in paths: - print(f"Loading model file {path}") - models_plus.append(lazy_load_file(path)) - - model_plus = merge_multifile_models(models_plus) - return model_plus - - -def filter_and_sort_tensors(model: LazyModel) -> LazyModel: - return {name: model[name] for name in TENSORS_LIST if name in model} - - -def load_vocab(path: Path) -> SentencePieceVocab: - # Be extra-friendly and accept either a file or a directory. Also, if it's - # a directory, it might be the model directory, and tokenizer.model might - # be in the parent of that. - if path.is_dir(): - path2 = path / "tokenizer.model" - # Use `.parent` instead of /.. to handle the symlink case better. - path3 = path.parent / "tokenizer.model" - if path2.exists(): - path = path2 - elif path3.exists(): - path = path3 - else: - raise FileNotFoundError(f"Could not find tokenizer.model in {path} or its parent; if it's in another directory, pass the directory as --vocab-dir") - added_tokens_path = path.parent / "added_tokens.json" - print(f"Loading vocab file {path}") - return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None) - - -def default_outfile(model_paths: List[Path], params: Params) -> Path: - namestr = { - GGMLFileType.AllF32: "f32", - GGMLFileType.MostlyF16: "f16", - GGMLFileType.MostlyQ4_0: "q4_0", - GGMLFileType.MostlyQ4_1: "q4_1", - GGMLFileType.PerLayerIsQ4_1: "q4_1", - }[params.file_type] - ret = model_paths[0].parent / f"ggml-model-{namestr}.bin" - if ret in model_paths: - sys.stderr.write(f"Error: Default output path ({ret}) would overwrite the input. Please explicitly specify a path using --outfile.\n") - sys.exit(1) - return ret - - -def do_dump_model(model_plus: ModelPlus) -> None: - print(f"model_plus.paths = {model_plus.paths!r}") - print(f"model_plus.format = {model_plus.format!r}") - print(f"model_plus.vocab = {model_plus.vocab!r}") - for name, lazy_tensor in model_plus.model.items(): - print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}") - - -def main(args_in: Optional[List[str]] = None) -> None: - parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file") - parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") - parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") - parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outtype", choices=["f32", "f16", "q4_1", "q4_0"], help="output format (default: based on input)") - parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") - args = parser.parse_args(args_in) - - vocab: Vocab - if args.dump_single: - model_plus = lazy_load_file(args.model) - do_dump_model(model_plus) - elif args.vocab_only: - vocab = load_vocab(args.vocab_dir or args.model) - assert args.outfile, "need --outfile if using --vocab-only" - outfile = args.outfile - OutputFile.write_vocab_only(outfile, vocab) - print(f"Wrote {outfile}") - else: - model_plus = load_some_model(args.model) - if args.dump: - do_dump_model(model_plus) - return - if model_plus.vocab is not None and args.vocab_dir is None: - vocab = model_plus.vocab - else: - vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent - vocab = load_vocab(vocab_dir) - model = model_plus.model - model = do_necessary_conversions(model) - output_type = pick_output_type(model, args.outtype) - model = convert_to_output_type(model, output_type) - params = Params.guessed(model, output_type) - outfile = args.outfile or default_outfile(model_plus.paths, params) - OutputFile.write_all(outfile, params, model, vocab) - print(f"Wrote {outfile}") - - -if __name__ == '__main__': - main() diff --git a/docs/dev/new_ui_dev.md b/docs/dev/new_ui_dev.md index 25b80d51..44105e00 100644 --- a/docs/dev/new_ui_dev.md +++ b/docs/dev/new_ui_dev.md @@ -17,7 +17,7 @@ npm run dev ``` > Note -> To run the developmen environment you need to create copy of the `.env` file and name it either `.env.development` or if that dont work then `.env.dev`. Set `VITE_GPT4ALL_API_BASEURL = /api/ ` in the `.env.development`. +> To run the developmen environment you need to create copy of the `.env` file and name it either `.env.development` or if that dont work then `.env.dev`. Set `VITE_LoLLMs_API_BASEURL = /api/ ` in the `.env.development`. > Run your gpt binding by launching `webui.bat` or bash `webui.sh`. ## Building frontend - UI diff --git a/docs/dev/server_endpoints.md b/docs/dev/server_endpoints.md index 2e225abe..47604869 100644 --- a/docs/dev/server_endpoints.md +++ b/docs/dev/server_endpoints.md @@ -21,9 +21,9 @@ This Flask server provides various endpoints to manage and interact with the cha "ggml-vicuna-13b-4bit-rev1.bin", "ggml-vicuna-7b-4bit-rev1.bin", "ggml-vicuna-7b-4bit.bin", - "gpt4all-lora-quantized-ggml.bin", - "gpt4all-lora-quantized.bin", - "gpt4all-lora-unfiltered-quantized.bin" + "lollms-lora-quantized-ggml.bin", + "lollms-lora-quantized.bin", + "lollms-lora-unfiltered-quantized.bin" ] ``` - "/list_personalities_languages": GET request endpoint to list all the available personality languages. @@ -120,7 +120,7 @@ This Flask server provides various endpoints to manage and interact with the cha ``` [ { - "content": "##Instructions:\\nGPT4All is a smart and helpful Assistant built by Nomic-AI. It can discuss with humans and assist them.\n", + "content": "##Instructions:\\nLoLLMs is a smart and helpful Assistant built by Nomic-AI. It can discuss with humans and assist them.\n", "id": 23, "parent": 0, "rank": 0, @@ -128,11 +128,11 @@ This Flask server provides various endpoints to manage and interact with the cha "type": 1 }, { - "content": "Welcome! I am GPT4All A free and open assistant. What can I do for you today?", + "content": "Welcome! I am LoLLMs A free and open assistant. What can I do for you today?", "id": 24, "parent": 23, "rank": 0, - "sender": "gpt4all", + "sender": "lollms", "type": 0 }, { @@ -148,7 +148,7 @@ This Flask server provides various endpoints to manage and interact with the cha "id": 26, "parent": 25, "rank": 0, - "sender": "gpt4all", + "sender": "lollms", "type": 0 } ] @@ -173,7 +173,7 @@ This Flask server provides various endpoints to manage and interact with the cha "debug": false, "host": "localhost", "language": "en-US", - "model": "gpt4all-lora-quantized-ggml.bin", + "model": "lollms-lora-quantized-ggml.bin", "n_predict": 1024, "n_threads": 8, "nb_messages_to_remember": 5, @@ -199,15 +199,15 @@ This Flask server provides various endpoints to manage and interact with the cha ``` { "personality": { - "ai_message_prefix": "###gpt4all:\n", + "ai_message_prefix": "###lollms:\n", "anti_prompts": [ "###user", "### user", - "###gpt4all", - "### gpt4all" + "###lollms", + "### lollms" ], "assets_list": [ - "personalities\\english\\generic\\gpt4all\\assets\\logo.png" + "personalities\\english\\generic\\lollms\\assets\\logo.png" ], "author": "ParisNeo", "category": "General", @@ -221,13 +221,13 @@ This Flask server provides various endpoints to manage and interact with the cha "model_temperature": 0.6, "model_top_k": 50, "model_top_p": 0.9, - "name": "gpt4all", - "personality_conditioning": "## Information:\nAssistant's name is gpt4all\nToday's date is {{date}}\n## Instructions:\nYour mission is to assist user to perform various tasks and answer his questions\n", + "name": "lollms", + "personality_conditioning": "## Information:\nAssistant's name is lollms\nToday's date is {{date}}\n## Instructions:\nYour mission is to assist user to perform various tasks and answer his questions\n", "personality_description": "This personality is a helpful and Kind AI ready to help you solve your problems \n", "user_message_prefix": "###user:\n", "user_name": "user", "version": "1.0.0", - "welcome_message": "Welcome! My name is gpt4all.\nHow can I help you today?\n" + "welcome_message": "Welcome! My name is lollms.\nHow can I help you today?\n" } } ``` diff --git a/docs/tutorials/noobs_tutorial.md b/docs/tutorials/noobs_tutorial.md index 8575c7b7..c3f23df8 100644 --- a/docs/tutorials/noobs_tutorial.md +++ b/docs/tutorials/noobs_tutorial.md @@ -11,7 +11,7 @@ Welcome to the LOLLMS WebUI tutorial! In this tutorial, we will walk you through 1. Visit the GitHub repository page at [github.com/ParisNeo/lollms-webui](https://github.com/ParisNeo/lollms-webui). 2. Click on the "Latest Release" button. 3. Depending on your platform, download either `webui.bat` for Windows or `webui.sh` for Linux. -4. Choose a folder on your system to install the application launcher. For example, you can create a folder named `gpt4all-webui` in your `ai` directory. +4. Choose a folder on your system to install the application launcher. For example, you can create a folder named `lollms-webui` in your `ai` directory. 5. Run the downloaded script (application launcher). Note: Some antivirus programs or Windows Defender might display a warning due to the tool's reputation. This warning is a false positive caused by the tool being relatively new. You can ignore the warning and proceed with the installation. 6. The installer will no longer prompt you to install the default model. This step will be performed in the UI, making it easier for you. @@ -31,7 +31,7 @@ Welcome to the LOLLMS WebUI tutorial! In this tutorial, we will walk you through ## Starting a Discussion 1. Return to the discussions view. 2. Click the "+" button to create a new discussion. -3. You will see a predefined welcome message based on the selected personality configuration. By default, the GPT4All personality is used, which aims to be helpful. +3. You will see a predefined welcome message based on the selected personality configuration. By default, the LoLLMs personality is used, which aims to be helpful. 4. Enter your query or prompt. For example, you can ask, "Who is Abraham Lincoln?" 5. You can stop the generation at any time by clicking the "Stop Generating" button. diff --git a/docs/tutorials/personalities_tutorial.md b/docs/tutorials/personalities_tutorial.md index 39ee8bd5..002fc5f1 100644 --- a/docs/tutorials/personalities_tutorial.md +++ b/docs/tutorials/personalities_tutorial.md @@ -1,10 +1,10 @@ # Personalities and What You Can Do with Them -In this tutorial, we will explore the concept of personalities and their capabilities within the GPT4All webui. +In this tutorial, we will explore the concept of personalities and their capabilities within the LoLLMs webui. ## Introduction -The GPT4All webui utilizes the PyAIPersonality library, which provides a standardized way to define AI simulations and integrate AI personalities with other tools, applications, and data. Before diving into the details, let's familiarize ourselves with some key concepts that will help us understand the inner workings of these tools. +The LoLLMs webui utilizes the PyAIPersonality library, which provides a standardized way to define AI simulations and integrate AI personalities with other tools, applications, and data. Before diving into the details, let's familiarize ourselves with some key concepts that will help us understand the inner workings of these tools. ## Large Language Models (LLMs) @@ -53,13 +53,13 @@ Personality settings are defined in a YAML file, which contains parameters and c Let's take a closer look at the GPT for Art personality, which specializes in generating descriptions of artwork and even transforming descriptions into actual images using the stable diffusion generator. -To use the GPT for Art personality, you need to follow the custom installation steps outlined in the documentation. Once installed, you can leverage its capabilities through the GPT4All webui. +To use the GPT for Art personality, you need to follow the custom installation steps outlined in the documentation. Once installed, you can leverage its capabilities through the LoLLMs webui. -## Using the GPT4All Webui with the GPT for Art Personality +## Using the LoLLMs Webui with the GPT for Art Personality -To select and apply a personality in the GPT4All webui, follow these steps: +To select and apply a personality in the LoLLMs webui, follow these steps: -1. Open the GPT4All webui and navigate to the "Personality" section. +1. Open the LoLLMs webui and navigate to the "Personality" section. 2. Select the GPT for Art personality from the available options. 3. Start a conversation with the AI agent. @@ -77,9 +77,9 @@ By interacting with the AI agent, users can request specific changes or addition ## Conclusion -In this tutorial, we explored the concept of personalities and their integration within the GPT4All webui. We discussed the hardware and software layers, text processing and tokenization, sampling techniques, iterative text generation, and the customization of personality settings. +In this tutorial, we explored the concept of personalities and their integration within the LoLLMs webui. We discussed the hardware and software layers, text processing and tokenization, sampling techniques, iterative text generation, and the customization of personality settings. -We also delved into the GPT for Art personality, its installation steps, and how to apply it in the GPT4All webui. Through an example discussion with the artbot, we witnessed the collaborative creative process between users and AI. +We also delved into the GPT for Art personality, its installation steps, and how to apply it in the LoLLMs webui. Through an example discussion with the artbot, we witnessed the collaborative creative process between users and AI. -The GPT4All webui, coupled with AI personalities, opens up a world of possibilities for generating personalized and contextually relevant text. With further enhancements and customization, these tools have the potential to revolutionize various industries and creative endeavors. +The LoLLMs webui, coupled with AI personalities, opens up a world of possibilities for generating personalized and contextually relevant text. With further enhancements and customization, these tools have the potential to revolutionize various industries and creative endeavors. diff --git a/docs/usage/Build_extensions.md b/docs/usage/Build_extensions.md index 4a4acfa8..608d1bbf 100644 --- a/docs/usage/Build_extensions.md +++ b/docs/usage/Build_extensions.md @@ -6,19 +6,19 @@ Extensions are little projects built by the community that can be plugged to the There are many types of extensions: 1 - pipeline extensions These extensions have no UI, they only intercept the communication between the user and the AI, perform some modifications or operations, then submit them to the discussion to enritch it. For example: -- Net enabled GPT4All (under construction at https://github.com/ParisNeo/Net_enabled-GPT4All-Extension) : An extension that offers a special personality that indicates to the chatbot that whenever the user is asking a question it has no answer to, it should invoke a search function. The extension intercepts this keyword, do the research on the net then mirror it back to the AI. The AI can then use those inputs to formulate an answer. -- Image enabled GPT4All : An extension that uses Blip to convert an image into text that can be interpreted by the AI and used in the discussion. +- Net enabled LoLLMs (under construction at https://github.com/ParisNeo/Net_enabled-LoLLMs-Extension) : An extension that offers a special personality that indicates to the chatbot that whenever the user is asking a question it has no answer to, it should invoke a search function. The extension intercepts this keyword, do the research on the net then mirror it back to the AI. The AI can then use those inputs to formulate an answer. +- Image enabled LoLLMs : An extension that uses Blip to convert an image into text that can be interpreted by the AI and used in the discussion. The extension should offer a yaml file that describes it to allow the system to integrate it. ```yaml -# This is a gpt4all extension project +# This is a lollms extension project # Project name : Models tester # Author : ParisNeo # Description : # This extension allows applying the model on a bunch of questions at once and recover answers in a text file -name: GPT4All-Models-Tester-Extension +name: LoLLMs-Models-Tester-Extension author: ParisNeo description: | This extension allows applying the model on a bunch of questions at once and recover answers in a text file diff --git a/docs/usage/Linux_Osx_Install.md b/docs/usage/Linux_Osx_Install.md index 52f32b62..ec2fdf2a 100644 --- a/docs/usage/Linux_Osx_Install.md +++ b/docs/usage/Linux_Osx_Install.md @@ -1,4 +1,4 @@ -# Installing GPT4All-Webui on Linux or macOS: +# Installing lollms-webui on Linux or macOS: \- Make sure you have all the dependencies for requirements `python3.11 -m pip install cmake` diff --git a/docs/usage/Linux_Osx_Usage.md b/docs/usage/Linux_Osx_Usage.md index b40e641e..3d5e2a93 100644 --- a/docs/usage/Linux_Osx_Usage.md +++ b/docs/usage/Linux_Osx_Usage.md @@ -1,4 +1,4 @@ -# Using GPT4All-Webui on Linux or macOS: +# Using lollms-webui on Linux or macOS: To run the Flask server, execute the following command: diff --git a/docs/youtube/script_install.md b/docs/youtube/script_install.md index a1ba0e59..20c31683 100644 --- a/docs/youtube/script_install.md +++ b/docs/youtube/script_install.md @@ -10,7 +10,7 @@ Before starting, let me tell you what this project is made for. This project is This project is under Apache 2.0 licence which is an open source licence that can be used commercially, so people can built things from this and use it in their business. -Also, please don't confuse the GPT4All application built by Nomic AI which is an interesting more professional application that you can find on their website gpt4all.io. It has a great community and I encourage you to check it up. +Also, please don't confuse the LoLLMs application built by Nomic AI which is an interesting more professional application that you can find on their website lollms.io. It has a great community and I encourage you to check it up. I have built this ui to explore new things and build on top of it. I am not building a company out of this, this is a side project. I just want to give back to the open source community and help make this technology available for all (hence the name). @@ -23,7 +23,7 @@ Before installing this tool you need to install python 3.10 or higher as well as Now let's cut to the chace. Let's start by installing the tool. First, go to the github repository page at github.com/ParisNeo/lollms-webui then press the latest release button. Depending on your platform download webui.bat for windows or webui.sh for linux. -We call this file, the application launcher. Make sure you install the launcher in a folder you choose. For example I'll put it in my ai folder at gpt4all-webui. +We call this file, the application launcher. Make sure you install the launcher in a folder you choose. For example I'll put it in my ai folder at lollms-webui. Now let's run the script. You may encounter a warning from some antivirus or windows defender warining you about the script. It is a false positive caused by the reputation condition in some antiviruses. This means if a program is not used by enough users, some antiviruses consider it dangerous. This is true for this tool as it is new and not enough people as using it as of now so I have to wait for it to become more accepted. @@ -47,7 +47,7 @@ Notice that applying modifications does not save the configuration, so You need Now your model is selected and you are ready to start your first discussion. -Let's go back to discussions view. To create a new discussion, press the + button. You should see the personality welcome message. This is a predefined welcome message that you can find in the personality configuration file. by default, we use the GPT4All personality which is conditioned to be a helpful personality. Let's ask it something. For example, who is Abraham Lincoln? +Let's go back to discussions view. To create a new discussion, press the + button. You should see the personality welcome message. This is a predefined welcome message that you can find in the personality configuration file. by default, we use the LoLLMs personality which is conditioned to be a helpful personality. Let's ask it something. For example, who is Abraham Lincoln? You can stop the generation at any time by pressing the Stop Generating button. diff --git a/docs/youtube/script_lollms.md b/docs/youtube/script_lollms.md index 710237a8..769f4ce2 100644 --- a/docs/youtube/script_lollms.md +++ b/docs/youtube/script_lollms.md @@ -1,5 +1,5 @@ Hi Every one. -This is a new video about Lord of Large language models, formally known as GPT4All webui. +This is a new video about Lord of Large language models, formally known as LoLLMs webui. In this video, we start by presenting the tool, its phylosophy and it's main goals. Then, we discuss how to install and use it, we dive deep into its different use cases and how you can harness the power of Large language models in one tool. We will also do some interesting tests and comparisons of models and bindings, and we'll finish by some thoughts about AI, its benefits and dangers. @@ -33,7 +33,7 @@ You will be asked to select a personal folder. This folder will contain: - the discussion database Make sure to put this folder to a partition that has enough space as models may be heavy sometimes. Here I just press enter to choose the default location which is my documents folder. -The first time you run this application, you are prompted to select the binding. bindings are bridge modules that allows lollms to talk to different libraries that can run language models. If you are using a mac, I would recommend using gpt4all binding. If you have a powerful GPU and want to use as many models as possible then you go with ctransformers. The fastest for llama models is the official llama cpp binding. The Pyllamacpp is a tiny stable binding that runs with only llama models but can run on any pc seamlessly. As of today, GPTQ binding can run but it is still in experimental stage. Maybe use it in few weeks. I have a GPU, and want to test many models, so I'll go with CTransformers. +The first time you run this application, you are prompted to select the binding. bindings are bridge modules that allows lollms to talk to different libraries that can run language models. If you are using a mac, I would recommend using lollms binding. If you have a powerful GPU and want to use as many models as possible then you go with ctransformers. The fastest for llama models is the official llama cpp binding. The Pyllamacpp is a tiny stable binding that runs with only llama models but can run on any pc seamlessly. As of today, GPTQ binding can run but it is still in experimental stage. Maybe use it in few weeks. I have a GPU, and want to test many models, so I'll go with CTransformers. This may take few minutes to complete as it should install many modules. Let's fastforward. Once the binding is installed, you need to select a first model. You have the choice between installing a model from the internet or link to a local model file. This allows you tu mutualize models with other tools like Gpt4all or oobbabooga's text generation webui. diff --git a/docs/youtube/script_personalities.md b/docs/youtube/script_personalities.md index 86ec3852..53e3147d 100644 --- a/docs/youtube/script_personalities.md +++ b/docs/youtube/script_personalities.md @@ -1,6 +1,6 @@ Hi there. In this video, we are going to talk about the personalities and what you can do with them. -The GPT4All webui uses my PyAIPersonality library under the hood. I have buit this library to create a standard way to define AI simulations and integrate the AI personality with other tools, applications and data. Before starting, I want to explain some concepts to make it easy for you to understand the inner workings of these tools. Let's dive right in. +The LoLLMs webui uses my PyAIPersonality library under the hood. I have buit this library to create a standard way to define AI simulations and integrate the AI personality with other tools, applications and data. Before starting, I want to explain some concepts to make it easy for you to understand the inner workings of these tools. Let's dive right in. Large Language Models (LLMs) are powerful text processing models based on machine learning techniques. As their name suggests, these models are characterized by their substantial size and versatility in handling various text-based tasks. In the context of this work, we focus specifically on text generation models. @@ -124,7 +124,7 @@ As we can see, the model did the requested changes. Keep in mind that this tool is still in its early stages of development, and there's plenty of room for improvement. One way to enhance its performance is by adjusting the default sampler to an Euler sampler, which can potentially yield even better results. Additionally, you have the flexibility to explore a wide range of models available on Hugging Face repositories. With thousands of models at your disposal, you can experiment and choose the one that aligns best with your specific needs and preferences. By making these adjustments, you can take this tool to new heights and unlock its full potential. -Please note that all the generated images bear a watermark with the GPT4All signature, serving as a clear indication that they were created by AI using the stable diffusion WatermarkEncoder. This step is crucial to promote responsible AI usage and ensure that each generated work is properly identified as an AI creation. +Please note that all the generated images bear a watermark with the LoLLMs signature, serving as a clear indication that they were created by AI using the stable diffusion WatermarkEncoder. This step is crucial to promote responsible AI usage and ensure that each generated work is properly identified as an AI creation. It's important to emphasize that this tool is intended for appreciating art, fostering creative exploration, and sparking new ideas. It is not meant for malicious purposes or spreading misinformation. We firmly stand against such misuse. @@ -161,7 +161,7 @@ Let's put GPT 4 Internet to the test with a current affairs question: Who is the As you can observe, the personality performed its intended function flawlessly. It intelligently crafted a well-tailored query, conducted the search seamlessly behind the scenes, and swiftly presented the desired information along with proper source attribution. This showcases the power of leveraging the internet to enhance the AI's capabilities and provide you with accurate and reliable answers. -Finally, to install the personalities, go to the root of your gpt4all webui application and open a terminal. Then type installation/add_personality.bat or add_personality.sh depending on your operating system. you'll be prompted to choose a language, then a category, and finally the personality you want to install. Once installed, your personality will apear in the zoo. +Finally, to install the personalities, go to the root of your lollms webui application and open a terminal. Then type installation/add_personality.bat or add_personality.sh depending on your operating system. you'll be prompted to choose a language, then a category, and finally the personality you want to install. Once installed, your personality will apear in the zoo. Alright, let's wrap up here to keep the video concise. With over 250 personalities to explore, we've only scratched the surface of what GPT 4 All has to offer. While not all personalities have been fully adapted to the new format, a majority of them are already functional and ready for testing. diff --git a/installations/add_backend.bat b/installations/add_backend.bat deleted file mode 100644 index 5bdb3c08..00000000 --- a/installations/add_backend.bat +++ /dev/null @@ -1,3 +0,0 @@ -call ../env/Scripts/activate.bat -python install_binding.py %* -pause \ No newline at end of file diff --git a/installations/add_backend.sh b/installations/add_backend.sh deleted file mode 100644 index 7e6757f1..00000000 --- a/installations/add_backend.sh +++ /dev/null @@ -1,4 +0,0 @@ -#!/bin/bash -source ../env/bin/activate -python install_binding.py "$@" -read -p "Press any key to continue..." diff --git a/installations/add_personality.bat b/installations/add_personality.bat deleted file mode 100644 index 096bd676..00000000 --- a/installations/add_personality.bat +++ /dev/null @@ -1,55 +0,0 @@ -@echo off -setlocal enabledelayedexpansion - -REM Clone the repository to a tmp folder -set "REPO_URL=https://github.com/ParisNeo/PyAIPersonality.git" -set "TMP_FOLDER=%temp%\PyAIPersonality" -git clone %REPO_URL% %TMP_FOLDER% - -REM List the available languages and prompt user to select one -set "LANGUAGES_FOLDER=%TMP_FOLDER%\personalities_zoo" -set "LANGUAGE_INDEX=0" -for /d %%d in ("%LANGUAGES_FOLDER%\*") do ( - set /a "LANGUAGE_INDEX+=1" - set "LANGUAGES[!LANGUAGE_INDEX!]=%%~nxd" - echo !LANGUAGE_INDEX!. %%~nxd -) -set /p "SELECTED_LANGUAGE=Enter the number of the desired language: " -set "LANGUAGE_FOLDER=%LANGUAGES_FOLDER%\!LANGUAGES[%SELECTED_LANGUAGE%]!" - -REM List the available categories and prompt user to select one -set "CATEGORIES_FOLDER=%LANGUAGE_FOLDER%" -set "CATEGORY_INDEX=0" -for /d %%d in ("%CATEGORIES_FOLDER%\*") do ( - set /a "CATEGORY_INDEX+=1" - set "CATEGORIES[!CATEGORY_INDEX!]=%%~nxd" - echo !CATEGORY_INDEX!. %%~nxd -) -set /p "SELECTED_CATEGORY=Enter the number of the desired category: " -set "CATEGORY_FOLDER=%CATEGORIES_FOLDER%\!CATEGORIES[%SELECTED_CATEGORY%]!" - -REM List the available personalities and prompt user to select one -set "PERSONALITIES_FOLDER=%CATEGORY_FOLDER%" -set "PERSONALITY_INDEX=0" -for /d %%d in ("%PERSONALITIES_FOLDER%\*") do ( - set /a "PERSONALITY_INDEX+=1" - set "PERSONALITIES[!PERSONALITY_INDEX!]=%%~nxd" - echo !PERSONALITY_INDEX!. %%~nxd -) -set /p "SELECTED_PERSONALITY=Enter the number of the desired personality: " -set "PERSONALITY_FOLDER=%PERSONALITIES_FOLDER%\!PERSONALITIES[%SELECTED_PERSONALITY%]!" - -REM Copy the selected personality folder to personalities/language/category folder -set "OUTPUT_FOLDER=%CD%\personalities\!LANGUAGES[%SELECTED_LANGUAGE%]!\!CATEGORIES[%SELECTED_CATEGORY%]!\!PERSONALITIES[%SELECTED_PERSONALITY%]!" -if not exist "%OUTPUT_FOLDER%" mkdir "%OUTPUT_FOLDER%" -xcopy /e /y "%PERSONALITY_FOLDER%" "%OUTPUT_FOLDER%" - -REM cleaning -if exist "./tmp" ( -echo Cleaning tmp folder -rd /s /q "./tmp" -) -REM Remove the tmp folder -rd /s /q "%TMP_FOLDER%" -echo Done -pause \ No newline at end of file diff --git a/installations/add_personality.sh b/installations/add_personality.sh deleted file mode 100755 index 756ae803..00000000 --- a/installations/add_personality.sh +++ /dev/null @@ -1,53 +0,0 @@ -#!/usr/bin/env bash - -# Clone the repository to a tmp folder -REPO_URL="https://github.com/ParisNeo/PyAIPersonality.git" -TMP_FOLDER=$(mktemp -d) -git clone "$REPO_URL" "$TMP_FOLDER" - -# List the available languages and prompt user to select one -LANGUAGES_FOLDER="$TMP_FOLDER/personalities_zoo" -LANGUAGE_INDEX=0 -for d in "$LANGUAGES_FOLDER"/*; do - LANGUAGE_INDEX=$((LANGUAGE_INDEX+1)) - LANGUAGES[$LANGUAGE_INDEX]=$(basename "$d") - echo "$LANGUAGE_INDEX. ${LANGUAGES[$LANGUAGE_INDEX]}" -done -read -p "Enter the number of the desired language: " SELECTED_LANGUAGE -LANGUAGE_FOLDER="$LANGUAGES_FOLDER/${LANGUAGES[$SELECTED_LANGUAGE]}" - -# List the available categories and prompt user to select one -CATEGORIES_FOLDER="$LANGUAGE_FOLDER" -CATEGORY_INDEX=0 -for d in "$CATEGORIES_FOLDER"/*; do - CATEGORY_INDEX=$((CATEGORY_INDEX+1)) - CATEGORIES[$CATEGORY_INDEX]=$(basename "$d") - echo "$CATEGORY_INDEX. ${CATEGORIES[$CATEGORY_INDEX]}" -done -read -p "Enter the number of the desired category: " SELECTED_CATEGORY -CATEGORY_FOLDER="$CATEGORIES_FOLDER/${CATEGORIES[$SELECTED_CATEGORY]}" - -# List the available personalities and prompt user to select one -PERSONALITIES_FOLDER="$CATEGORY_FOLDER" -PERSONALITY_INDEX=0 -for d in "$PERSONALITIES_FOLDER"/*; do - PERSONALITY_INDEX=$((PERSONALITY_INDEX+1)) - PERSONALITIES[$PERSONALITY_INDEX]=$(basename "$d") - echo "$PERSONALITY_INDEX. ${PERSONALITIES[$PERSONALITY_INDEX]}" -done -read -p "Enter the number of the desired personality: " SELECTED_PERSONALITY -PERSONALITY_FOLDER="$PERSONALITIES_FOLDER/${PERSONALITIES[$SELECTED_PERSONALITY]}" - -# Copy the selected personality folder to personalities/language/category folder -OUTPUT_FOLDER="$(pwd)/personalities/${LANGUAGES[$SELECTED_LANGUAGE]}/${CATEGORIES[$SELECTED_CATEGORY]}/${PERSONALITIES[$SELECTED_PERSONALITY]}" -mkdir -p "$OUTPUT_FOLDER" -cp -r "$PERSONALITY_FOLDER/." "$OUTPUT_FOLDER" - -# Cleaning -if [[ -d "./tmp" ]]; then - echo "Cleaning tmp folder" - rm -rf "./tmp" -fi - -# Remove the tmp folder -rm -rf "$TMP_FOLDER" diff --git a/installations/backendlist.yaml b/installations/backendlist.yaml deleted file mode 100644 index 4ccff39e..00000000 --- a/installations/backendlist.yaml +++ /dev/null @@ -1 +0,0 @@ -GPT4All_GPTJ_binding : https://github.com/ParisNeo/GPT4All_GPTJ_binding \ No newline at end of file diff --git a/installations/download_all_personalities.bat b/installations/download_all_personalities.bat deleted file mode 100644 index 255a209a..00000000 --- a/installations/download_all_personalities.bat +++ /dev/null @@ -1,27 +0,0 @@ -@echo off - -rem Set the environment name -set environment_name=env - -rem Activate the virtual environment -call %environment_name%\Scripts\activate.bat - -rem Change to the installations subfolder - -rem Run the Python script -python installations/download_all_personalities.py - -rem Deactivate the virtual environment -echo deactivating -call %environment_name%\Scripts\deactivate.bat - -rem Remove tmp folder -set "folder=tmp" - -if exist "%folder%" ( - echo Folder exists. Deleting... - rd /s /q "%folder%" - echo Folder deleted. -) else ( - echo Folder does not exist. -) \ No newline at end of file diff --git a/installations/download_all_personalities.py b/installations/download_all_personalities.py deleted file mode 100644 index 45415190..00000000 --- a/installations/download_all_personalities.py +++ /dev/null @@ -1,57 +0,0 @@ -import os -import shutil -from pathlib import Path - -def copy_files(source_path, destination_path): - for item in os.listdir(source_path): - source_item = source_path / item - destination_item = destination_path / item - - if source_item.is_file(): - # Remove destination file if it already exists - try: - if destination_item.exists(): - destination_item.unlink() - # Copy file from source to destination - shutil.copy2(str(source_item), str(destination_item)) - except: - print(f"Couldn't install personality {item}") - - elif source_item.is_dir(): - # Create destination directory if it does not exist - destination_item.mkdir(parents=True, exist_ok=True) - - # Recursively copy files in subdirectories - copy_files(source_item, destination_item) - -import subprocess - -def clone_and_copy_repository(repo_url): - tmp_folder = Path("tmp/git_clone") - personalities_folder = Path("personalities") - subfolder_name = "personalities_zoo" - - # Clone the repository to a temporary folder - subprocess.run(["git", "clone", repo_url, str(tmp_folder)]) - - # Check if the repository was cloned successfully - if not tmp_folder.exists(): - print("Failed to clone the repository.") - return - - # Construct the source and destination paths for copying the subfolder - subfolder_path = tmp_folder / subfolder_name - destination_path = Path.cwd() / personalities_folder - - # Copy files and folders recursively - print(f"copying") - copy_files(subfolder_path, destination_path) - - # Remove the temporary folder - shutil.rmtree(str(tmp_folder)) - - print("Repository clone and copy completed successfully.") - -# Example usage -repo_url = "https://github.com/ParisNeo/PyAIPersonality.git" -clone_and_copy_repository(repo_url) diff --git a/installations/download_all_personalities.sh b/installations/download_all_personalities.sh deleted file mode 100644 index ba8a9be3..00000000 --- a/installations/download_all_personalities.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash - -# Set the environment name -environment_name="env" - -# Activate the virtual environment -source "$environment_name/bin/activate" - -# Change to the installations subfolder - -# Run the Python script -python installations/download_all_personalities.py - -# Deactivate the virtual environment -echo "deactivating" -deactivate - -# Remove tmp folder -folder="tmp" - -if [ -d "$folder" ]; then - echo "Folder exists. Deleting..." - rm -r "$folder" - echo "Folder deleted." -else - echo "Folder does not exist." -fi \ No newline at end of file diff --git a/installations/install_backend.py b/installations/install_backend.py deleted file mode 100644 index 57a61da4..00000000 --- a/installations/install_backend.py +++ /dev/null @@ -1,60 +0,0 @@ -import argparse -import subprocess -import shutil -import yaml -from pathlib import Path - - -def install_binding(binding_name): - # Load the list of available bindings from bindinglist.yaml - with open('bindinglist.yaml', 'r') as f: - binding_list = yaml.safe_load(f) - - # Get the Github repository URL for the selected binding - try: - binding_url = binding_list[binding_name] - except KeyError: - print(f"Binding '{binding_name}' not found in bindinglist.yaml") - return - - # Clone the Github repository to a tmp folder - tmp_folder = Path('tmp') - if tmp_folder.exists(): - shutil.rmtree(tmp_folder) - subprocess.run(['git', 'clone', binding_url, tmp_folder]) - - # Install the requirements.txt from the cloned project - requirements_file = tmp_folder / 'requirements.txt' - subprocess.run(['pip', 'install', '-r', str(requirements_file)]) - - # Copy the folder found inside the binding to ../bindings - folders = [f for f in tmp_folder.iterdir() if f.is_dir() and not f.stem.startswith(".")] - src_folder = folders[0] - dst_folder = Path('../bindings') / src_folder.stem - print(f"coipying from {src_folder} to {dst_folder}") - # Delete the destination directory if it already exists - if dst_folder.exists(): - shutil.rmtree(dst_folder) - - shutil.copytree(src_folder, dst_folder) - - # Create an empty folder in ../models with the same name - models_folder = Path('../models') - models_folder.mkdir(exist_ok=True) - (models_folder / binding_name).mkdir(exist_ok=True, parents=True) - if tmp_folder.exists(): - shutil.rmtree(tmp_folder) - - -if __name__ == '__main__': - # Load the list of available bindings from bindinglist.yaml - with open('bindinglist.yaml', 'r') as f: - binding_list = yaml.safe_load(f) - - # Print the list of available bindings and prompt the user to select one - print("Available bindings:") - for binding_id, binding_name in enumerate(binding_list): - print(f" {binding_id} - {binding_name}") - binding_id = int(input("Select a binding to install: ")) - - install_binding(list(binding_list.keys())[binding_id]) diff --git a/installations/install_backend_gpu.bat b/installations/install_backend_gpu.bat deleted file mode 100644 index 4e3b66f6..00000000 --- a/installations/install_backend_gpu.bat +++ /dev/null @@ -1,7 +0,0 @@ -echo this will recompile llapacpp to use your hardware with gpu enabled. -pip uninstall llama-cpp-python -y -rem First we need to purge any old installation -pip cache purge -set CMAKE_ARGS=-DLLAMA_CUBLAS=on -set FORCE_CMAKE=1 -pip install llama-cpp-python --upgrade \ No newline at end of file diff --git a/installations/install_backend_gpu.sh b/installations/install_backend_gpu.sh deleted file mode 100644 index cd4f35e4..00000000 --- a/installations/install_backend_gpu.sh +++ /dev/null @@ -1,7 +0,0 @@ -echo "this will recompile llapacpp to use your hardware with gpu enabled." -pip uninstall llama-cpp-python -y -# First we need to purge any old installation -pip cache purge -export CMAKE_ARGS="-DLLAMA_CUBLAS=on" -export FORCE_CMAKE=1 -pip install llama-cpp-python --upgrade diff --git a/presets/make_programming_project.yaml b/presets/make_programming_project.yaml new file mode 100644 index 00000000..8e3d6ba4 --- /dev/null +++ b/presets/make_programming_project.yaml @@ -0,0 +1,27 @@ +name: Make programming project +content: | + ```@@ + # project: @@ + # author: @@ + # description: @@@@ + ``` + --------- + Extra information: + Licence: apache 2.0 + Program type: Stand alone. + Documentation: + Make README.md with the following table of contents: + ## Description + ## Installation + ## Usage + ## Licence + ## Contribute + ## Ethical guidelines + Instructions: + Write a user side README.md + Stick to the provided code content and do not invent extra information. + Make sure all sections of the table of contents are present in the file. + ---- + README.md: + ```markdown@@ + ```", diff --git a/presets/original.json b/presets/original.json index e36f4534..15751077 100644 --- a/presets/original.json +++ b/presets/original.json @@ -1,6 +1,6 @@ { "Build a Latex Book": "@@\n```latex\n\\documentclass[12pt]{book}\n\\usepackage{url}\n\\begin{document}\n\\title{@@}\n\\author{@<Author name>@} % Author\n\\date{\\today} % Date\n\\maketitle\n\\tableofcontents\n\\chapter{Introduction}\n@<generation_placeholder>@\n\\end{document}\n```", - "Simple Book writing":"Once apon a time", + "Simple Book writing":"#@<Title of the book:The advantures of Gandalf and Darth Vador>@\n@<Start the story:Once apon a time in middle earth>@", "Simple Question Answer":"User:@<What is your question>@\nAssistant:@<generation_placeholder>@", "Question Answer with conditionning":"Assistant is a highly developed AI capable of answering any question about any subject.\nUser:@<What's your question?>\nAssistant:@<generation_placeholder>@", "Instruct mode": "Instructions:\n@<Give instructions to the AI>@\nAnswer:@<generation_placeholder>@", diff --git a/presets/simple_book_writing.yaml b/presets/simple_book_writing.yaml index c8fa5da5..ed108c5a 100644 --- a/presets/simple_book_writing.yaml +++ b/presets/simple_book_writing.yaml @@ -1,2 +1,4 @@ name: Simple Book writing -content: Once apon a time \ No newline at end of file +content: | + # @<Title of the book:The advantures of Gandalf and Darth Vador>@ + @<Start the story:Once apon a time in middle earth>@@<generation_placeholder>@ \ No newline at end of file diff --git a/presets/simple_question_nswer.yaml b/presets/simple_question_answer.yaml similarity index 100% rename from presets/simple_question_nswer.yaml rename to presets/simple_question_answer.yaml diff --git a/presets/translate_text.yaml b/presets/translate_text.yaml new file mode 100644 index 00000000..170f4749 --- /dev/null +++ b/presets/translate_text.yaml @@ -0,0 +1,8 @@ +name: Translate text +content: | + ```@<Source language:all_language_options>@ + @<Text to translate>@ + ``` + ```@<Destination language:all_language_options>@ + @<generation_placeholder>@ + ``` \ No newline at end of file diff --git a/requirements_dev.txt b/requirements_dev.txt index 90443df1..d5a6b87b 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -1,13 +1,17 @@ tqdm +psutil flask flask_socketio -nomic pytest pyyaml markdown -pyllamacpp==2.0.0 -gpt4all-j -gpt4all -transformers -pyaipersonality>=0.0.11 -git \ No newline at end of file +gevent +gevent-websocket +lollms +langchain +requests +eventlet +websocket-client +GitPython +setuptools +numpy \ No newline at end of file diff --git a/scripts/convert model_ggml.bat b/scripts/convert model_ggml.bat index 68fd3eb8..cde93c86 100644 --- a/scripts/convert model_ggml.bat +++ b/scripts/convert model_ggml.bat @@ -9,4 +9,4 @@ cd tmp\llama.cpp git checkout 6c248707f51c8a50f7792e7f7787ec481881db88 cd ../.. echo Converting ... -python tmp\llama.cpp\convert-gpt4all-to-ggml.py "%filename%" "%tokenizer%" \ No newline at end of file +python tmp\llama.cpp\convert-lollms-to-ggml.py "%filename%" "%tokenizer%" \ No newline at end of file diff --git a/scripts/convert_model_ggml.sh b/scripts/convert_model_ggml.sh index b1453276..b1627a99 100644 --- a/scripts/convert_model_ggml.sh +++ b/scripts/convert_model_ggml.sh @@ -10,4 +10,4 @@ cd tmp\llama.cpp $(git checkout 6c248707f51c8a50f7792e7f7787ec481881db88) cd ../.. echo Converting ... -python -c tmp\llama.cpp\convert-gpt4all-to-ggml.py \"$FILENAME\" \"$TOKENIZER\" \ No newline at end of file +python -c tmp\llama.cpp\convert-lollms-to-ggml.py \"$FILENAME\" \"$TOKENIZER\" \ No newline at end of file diff --git a/templates/help.html b/templates/help.html index 1e63e36c..ff8e0d5f 100644 --- a/templates/help.html +++ b/templates/help.html @@ -43,7 +43,7 @@ <p class="mb-4">Here are the developers who worked on this website:</p> <ul class="list-disc list-inside mb-4"> <li>@ParisNeo : Creator of the project and Lead developer</li> - <li>@AndriyMulyar : CEO of Nomic-ai who offered to link the project as their official ui for GPT4All</li> + <li>@AndriyMulyar : CEO of Nomic-ai who offered to link the project as their official ui for LoLLMs</li> <li><a href="https://github.com/ParisNeo/lollms-webui/graphs/contributors" target="_blank" class="text-blue-900 dark:text-blue-600">A number of very talented open-source developers without whom this project wouldn't be as awesome as it is.</a></li> <li> We also appreciate the support of the users of this tool who have helped us in various ways.</li> </ul> diff --git a/templates/index.html b/templates/index.html index 0265484a..7f2a13df 100644 --- a/templates/index.html +++ b/templates/index.html @@ -2,7 +2,7 @@ <html> <head> <meta charset="utf-8"> - <title>GPT4All - WEBUI + LoLLMs - WEBUI diff --git a/templates/settings.html b/templates/settings.html index 9741e696..c94ddade 100644 --- a/templates/settings.html +++ b/templates/settings.html @@ -22,7 +22,7 @@
-
diff --git a/tests/end_point_tests/endpoints.http b/tests/end_point_tests/endpoints.http index 9a4f470f..82147c7f 100644 --- a/tests/end_point_tests/endpoints.http +++ b/tests/end_point_tests/endpoints.http @@ -83,7 +83,7 @@ Content-Type: application/json { "language": "english", "category": "generic", - "folder": "gpt4all" + "folder": "lollms" } ############################################ ### Unmount personality diff --git a/web/dist/assets/index-8f2cef47.js b/web/dist/assets/index-170c73a7.js similarity index 91% rename from web/dist/assets/index-8f2cef47.js rename to web/dist/assets/index-170c73a7.js index 29948d9e..1afde139 100644 --- a/web/dist/assets/index-8f2cef47.js +++ b/web/dist/assets/index-170c73a7.js @@ -1,4 +1,4 @@ -(function(){const e=document.createElement("link").relList;if(e&&e.supports&&e.supports("modulepreload"))return;for(const o of document.querySelectorAll('link[rel="modulepreload"]'))s(o);new MutationObserver(o=>{for(const r of 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r(){s||(s=!0,u(),n.close(),n=null)}const i=h=>{const f=new Error("probe error: "+h);f.transport=n.name,r(),this.emitReserved("upgradeError",f)};function a(){i("transport closed")}function l(){i("socket closed")}function c(h){n&&h.name!==n.name&&r()}const u=()=>{n.removeListener("open",o),n.removeListener("error",i),n.removeListener("close",a),this.off("close",l),this.off("upgrading",c)};n.once("open",o),n.once("error",i),n.once("close",a),this.once("close",l),this.once("upgrading",c),n.open()}onOpen(){if(this.readyState="open",ps.priorWebsocketSuccess=this.transport.name==="websocket",this.emitReserved("open"),this.flush(),this.readyState==="open"&&this.opts.upgrade){let e=0;const n=this.upgrades.length;for(;e{this.onClose("ping 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s=="function"&&(o=s,s=null),this.readyState==="closing"||this.readyState==="closed")return;s=s||{},s.compress=s.compress!==!1;const r={type:e,data:n,options:s};this.emitReserved("packetCreate",r),this.writeBuffer.push(r),o&&this.once("flush",o),this.flush()}close(){const e=()=>{this.onClose("forced close"),this.transport.close()},n=()=>{this.off("upgrade",n),this.off("upgradeError",n),e()},s=()=>{this.once("upgrade",n),this.once("upgradeError",n)};return(this.readyState==="opening"||this.readyState==="open")&&(this.readyState="closing",this.writeBuffer.length?this.once("drain",()=>{this.upgrading?s():e()}):this.upgrading?s():e()),this}onError(e){ps.priorWebsocketSuccess=!1,this.emitReserved("error",e),this.onClose("transport error",e)}onClose(e,n){(this.readyState==="opening"||this.readyState==="open"||this.readyState==="closing")&&(this.clearTimeoutFn(this.pingTimeoutTimer),this.transport.removeAllListeners("close"),this.transport.close(),this.transport.removeAllListeners(),typeof removeEventListener=="function"&&(removeEventListener("beforeunload",this.beforeunloadEventListener,!1),removeEventListener("offline",this.offlineEventListener,!1)),this.readyState="closed",this.id=null,this.emitReserved("close",e,n),this.writeBuffer=[],this.prevBufferLen=0)}filterUpgrades(e){const n=[];let s=0;const o=e.length;for(;stypeof ArrayBuffer.isView=="function"?ArrayBuffer.isView(t):t.buffer instanceof ArrayBuffer,Xp=Object.prototype.toString,K2=typeof Blob=="function"||typeof Blob<"u"&&Xp.call(Blob)==="[object BlobConstructor]",W2=typeof File=="function"||typeof File<"u"&&Xp.call(File)==="[object FileConstructor]";function sc(t){return V2&&(t instanceof ArrayBuffer||G2(t))||K2&&t instanceof Blob||W2&&t instanceof File}function pr(t,e){if(!t||typeof t!="object")return!1;if(Array.isArray(t)){for(let n=0,s=t.length;n=0&&t.num{delete this.acks[e];for(let i=0;i{this.io.clearTimeoutFn(r),n.apply(this,[null,...i])}}emitWithAck(e,...n){const s=this.flags.timeout!==void 0||this._opts.ackTimeout!==void 0;return new Promise((o,r)=>{n.push((i,a)=>s?i?r(i):o(a):o(i)),this.emit(e,...n)})}_addToQueue(e){let n;typeof e[e.length-1]=="function"&&(n=e.pop());const s={id:this._queueSeq++,tryCount:0,pending:!1,args:e,flags:Object.assign({fromQueue:!0},this.flags)};e.push((o,...r)=>s!==this._queue[0]?void 0:(o!==null?s.tryCount>this._opts.retries&&(this._queue.shift(),n&&n(o)):(this._queue.shift(),n&&n(null,...r)),s.pending=!1,this._drainQueue())),this._queue.push(s),this._drainQueue()}_drainQueue(e=!1){if(!this.connected||this._queue.length===0)return;const n=this._queue[0];n.pending&&!e||(n.pending=!0,n.tryCount++,this.flags=n.flags,this.emit.apply(this,n.args))}packet(e){e.nsp=this.nsp,this.io._packet(e)}onopen(){typeof this.auth=="function"?this.auth(e=>{this._sendConnectPacket(e)}):this._sendConnectPacket(this.auth)}_sendConnectPacket(e){this.packet({type:Fe.CONNECT,data:this._pid?Object.assign({pid:this._pid,offset:this._lastOffset},e):e})}onerror(e){this.connected||this.emitReserved("connect_error",e)}onclose(e,n){this.connected=!1,delete this.id,this.emitReserved("disconnect",e,n)}onpacket(e){if(e.nsp===this.nsp)switch(e.type){case Fe.CONNECT:e.data&&e.data.sid?this.onconnect(e.data.sid,e.data.pid):this.emitReserved("connect_error",new Error("It seems you are trying to reach a Socket.IO server in v2.x with a v3.x client, but they are not compatible (more information here: https://socket.io/docs/v3/migrating-from-2-x-to-3-0/)"));break;case Fe.EVENT:case Fe.BINARY_EVENT:this.onevent(e);break;case Fe.ACK:case Fe.BINARY_ACK:this.onack(e);break;case Fe.DISCONNECT:this.ondisconnect();break;case Fe.CONNECT_ERROR:this.destroy();const s=new Error(e.data.message);s.data=e.data.data,this.emitReserved("connect_error",s);break}}onevent(e){const n=e.data||[];e.id!=null&&n.push(this.ack(e.id)),this.connected?this.emitEvent(n):this.receiveBuffer.push(Object.freeze(n))}emitEvent(e){if(this._anyListeners&&this._anyListeners.length){const n=this._anyListeners.slice();for(const s of n)s.apply(this,e)}super.emit.apply(this,e),this._pid&&e.length&&typeof e[e.length-1]=="string"&&(this._lastOffset=e[e.length-1])}ack(e){const n=this;let s=!1;return function(...o){s||(s=!0,n.packet({type:Fe.ACK,id:e,data:o}))}}onack(e){const n=this.acks[e.id];typeof n=="function"&&(n.apply(this,e.data),delete this.acks[e.id])}onconnect(e,n){this.id=e,this.recovered=n&&this._pid===n,this._pid=n,this.connected=!0,this.emitBuffered(),this.emitReserved("connect"),this._drainQueue(!0)}emitBuffered(){this.receiveBuffer.forEach(e=>this.emitEvent(e)),this.receiveBuffer=[],this.sendBuffer.forEach(e=>{this.notifyOutgoingListeners(e),this.packet(e)}),this.sendBuffer=[]}ondisconnect(){this.destroy(),this.onclose("io server disconnect")}destroy(){this.subs&&(this.subs.forEach(e=>e()),this.subs=void 0),this.io._destroy(this)}disconnect(){return this.connected&&this.packet({type:Fe.DISCONNECT}),this.destroy(),this.connected&&this.onclose("io client disconnect"),this}close(){return this.disconnect()}compress(e){return this.flags.compress=e,this}get volatile(){return this.flags.volatile=!0,this}timeout(e){return this.flags.timeout=e,this}onAny(e){return this._anyListeners=this._anyListeners||[],this._anyListeners.push(e),this}prependAny(e){return this._anyListeners=this._anyListeners||[],this._anyListeners.unshift(e),this}offAny(e){if(!this._anyListeners)return this;if(e){const n=this._anyListeners;for(let s=0;s0&&t.jitter<=1?t.jitter:0,this.attempts=0}Gs.prototype.duration=function(){var t=this.ms*Math.pow(this.factor,this.attempts++);if(this.jitter){var e=Math.random(),n=Math.floor(e*this.jitter*t);t=Math.floor(e*10)&1?t+n:t-n}return Math.min(t,this.max)|0};Gs.prototype.reset=function(){this.attempts=0};Gs.prototype.setMin=function(t){this.ms=t};Gs.prototype.setMax=function(t){this.max=t};Gs.prototype.setJitter=function(t){this.jitter=t};class dl extends tt{constructor(e,n){var s;super(),this.nsps={},this.subs=[],e&&typeof e=="object"&&(n=e,e=void 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i=this._timeout;i===0&&o();const a=this.setTimeoutFn(()=>{o(),n.close(),n.emit("error",new Error("timeout"))},i);this.opts.autoUnref&&a.unref(),this.subs.push(function(){clearTimeout(a)})}return this.subs.push(o),this.subs.push(r),this}connect(e){return this.open(e)}onopen(){this.cleanup(),this._readyState="open",this.emitReserved("open");const e=this.engine;this.subs.push(Dt(e,"ping",this.onping.bind(this)),Dt(e,"data",this.ondata.bind(this)),Dt(e,"error",this.onerror.bind(this)),Dt(e,"close",this.onclose.bind(this)),Dt(this.decoder,"decoded",this.ondecoded.bind(this)))}onping(){this.emitReserved("ping")}ondata(e){try{this.decoder.add(e)}catch(n){this.onclose("parse error",n)}}ondecoded(e){Jp(()=>{this.emitReserved("packet",e)},this.setTimeoutFn)}onerror(e){this.emitReserved("error",e)}socket(e,n){let s=this.nsps[e];return s?this._autoConnect&&!s.active&&s.connect():(s=new eg(this,e,n),this.nsps[e]=s),s}_destroy(e){const n=Object.keys(this.nsps);for(const s of 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location<"u"&&location.port?location.port:this.secure?"443":"80"),this.transports=n.transports||["polling","websocket"],this.writeBuffer=[],this.prevBufferLen=0,this.opts=Object.assign({path:"/engine.io",agent:!1,withCredentials:!1,upgrade:!0,timestampParam:"t",rememberUpgrade:!1,addTrailingSlash:!0,rejectUnauthorized:!0,perMessageDeflate:{threshold:1024},transportOptions:{},closeOnBeforeunload:!0},n),this.opts.path=this.opts.path.replace(/\/$/,"")+(this.opts.addTrailingSlash?"/":""),typeof this.opts.query=="string"&&(this.opts.query=N2(this.opts.query)),this.id=null,this.upgrades=null,this.pingInterval=null,this.pingTimeout=null,this.pingTimeoutTimer=null,typeof addEventListener=="function"&&(this.opts.closeOnBeforeunload&&(this.beforeunloadEventListener=()=>{this.transport&&(this.transport.removeAllListeners(),this.transport.close())},addEventListener("beforeunload",this.beforeunloadEventListener,!1)),this.hostname!=="localhost"&&(this.offlineEventListener=()=>{this.onClose("transport close",{description:"network connection lost"})},addEventListener("offline",this.offlineEventListener,!1))),this.open()}createTransport(e){const n=Object.assign({},this.opts.query);n.EIO=qp,n.transport=e,this.id&&(n.sid=this.id);const s=Object.assign({},this.opts.transportOptions[e],this.opts,{query:n,socket:this,hostname:this.hostname,secure:this.secure,port:this.port});return new $2[e](s)}open(){let e;if(this.opts.rememberUpgrade&&ps.priorWebsocketSuccess&&this.transports.indexOf("websocket")!==-1)e="websocket";else if(this.transports.length===0){this.setTimeoutFn(()=>{this.emitReserved("error","No transports available")},0);return}else e=this.transports[0];this.readyState="opening";try{e=this.createTransport(e)}catch{this.transports.shift(),this.open();return}e.open(),this.setTransport(e)}setTransport(e){this.transport&&this.transport.removeAllListeners(),this.transport=e,e.on("drain",this.onDrain.bind(this)).on("packet",this.onPacket.bind(this)).on("error",this.onError.bind(this)).on("close",n=>this.onClose("transport close",n))}probe(e){let n=this.createTransport(e),s=!1;ps.priorWebsocketSuccess=!1;const o=()=>{s||(n.send([{type:"ping",data:"probe"}]),n.once("packet",h=>{if(!s)if(h.type==="pong"&&h.data==="probe"){if(this.upgrading=!0,this.emitReserved("upgrading",n),!n)return;ps.priorWebsocketSuccess=n.name==="websocket",this.transport.pause(()=>{s||this.readyState!=="closed"&&(u(),this.setTransport(n),n.send([{type:"upgrade"}]),this.emitReserved("upgrade",n),n=null,this.upgrading=!1,this.flush())})}else{const f=new Error("probe error");f.transport=n.name,this.emitReserved("upgradeError",f)}}))};function r(){s||(s=!0,u(),n.close(),n=null)}const i=h=>{const f=new Error("probe error: "+h);f.transport=n.name,r(),this.emitReserved("upgradeError",f)};function a(){i("transport closed")}function l(){i("socket closed")}function c(h){n&&h.name!==n.name&&r()}const u=()=>{n.removeListener("open",o),n.removeListener("error",i),n.removeListener("close",a),this.off("close",l),this.off("upgrading",c)};n.once("open",o),n.once("error",i),n.once("close",a),this.once("close",l),this.once("upgrading",c),n.open()}onOpen(){if(this.readyState="open",ps.priorWebsocketSuccess=this.transport.name==="websocket",this.emitReserved("open"),this.flush(),this.readyState==="open"&&this.opts.upgrade){let e=0;const n=this.upgrades.length;for(;e{this.onClose("ping timeout")},this.pingInterval+this.pingTimeout),this.opts.autoUnref&&this.pingTimeoutTimer.unref()}onDrain(){this.writeBuffer.splice(0,this.prevBufferLen),this.prevBufferLen=0,this.writeBuffer.length===0?this.emitReserved("drain"):this.flush()}flush(){if(this.readyState!=="closed"&&this.transport.writable&&!this.upgrading&&this.writeBuffer.length){const e=this.getWritablePackets();this.transport.send(e),this.prevBufferLen=e.length,this.emitReserved("flush")}}getWritablePackets(){if(!(this.maxPayload&&this.transport.name==="polling"&&this.writeBuffer.length>1))return this.writeBuffer;let n=1;for(let s=0;s0&&n>this.maxPayload)return this.writeBuffer.slice(0,s);n+=2}return this.writeBuffer}write(e,n,s){return this.sendPacket("message",e,n,s),this}send(e,n,s){return this.sendPacket("message",e,n,s),this}sendPacket(e,n,s,o){if(typeof n=="function"&&(o=n,n=void 0),typeof s=="function"&&(o=s,s=null),this.readyState==="closing"||this.readyState==="closed")return;s=s||{},s.compress=s.compress!==!1;const r={type:e,data:n,options:s};this.emitReserved("packetCreate",r),this.writeBuffer.push(r),o&&this.once("flush",o),this.flush()}close(){const e=()=>{this.onClose("forced close"),this.transport.close()},n=()=>{this.off("upgrade",n),this.off("upgradeError",n),e()},s=()=>{this.once("upgrade",n),this.once("upgradeError",n)};return(this.readyState==="opening"||this.readyState==="open")&&(this.readyState="closing",this.writeBuffer.length?this.once("drain",()=>{this.upgrading?s():e()}):this.upgrading?s():e()),this}onError(e){ps.priorWebsocketSuccess=!1,this.emitReserved("error",e),this.onClose("transport error",e)}onClose(e,n){(this.readyState==="opening"||this.readyState==="open"||this.readyState==="closing")&&(this.clearTimeoutFn(this.pingTimeoutTimer),this.transport.removeAllListeners("close"),this.transport.close(),this.transport.removeAllListeners(),typeof removeEventListener=="function"&&(removeEventListener("beforeunload",this.beforeunloadEventListener,!1),removeEventListener("offline",this.offlineEventListener,!1)),this.readyState="closed",this.id=null,this.emitReserved("close",e,n),this.writeBuffer=[],this.prevBufferLen=0)}filterUpgrades(e){const n=[];let s=0;const o=e.length;for(;stypeof ArrayBuffer.isView=="function"?ArrayBuffer.isView(t):t.buffer instanceof ArrayBuffer,Xp=Object.prototype.toString,K2=typeof Blob=="function"||typeof Blob<"u"&&Xp.call(Blob)==="[object BlobConstructor]",W2=typeof File=="function"||typeof File<"u"&&Xp.call(File)==="[object FileConstructor]";function sc(t){return V2&&(t instanceof ArrayBuffer||G2(t))||K2&&t instanceof Blob||W2&&t instanceof File}function pr(t,e){if(!t||typeof t!="object")return!1;if(Array.isArray(t)){for(let n=0,s=t.length;n=0&&t.num{delete this.acks[e];for(let i=0;i{this.io.clearTimeoutFn(r),n.apply(this,[null,...i])}}emitWithAck(e,...n){const s=this.flags.timeout!==void 0||this._opts.ackTimeout!==void 0;return new Promise((o,r)=>{n.push((i,a)=>s?i?r(i):o(a):o(i)),this.emit(e,...n)})}_addToQueue(e){let n;typeof e[e.length-1]=="function"&&(n=e.pop());const s={id:this._queueSeq++,tryCount:0,pending:!1,args:e,flags:Object.assign({fromQueue:!0},this.flags)};e.push((o,...r)=>s!==this._queue[0]?void 0:(o!==null?s.tryCount>this._opts.retries&&(this._queue.shift(),n&&n(o)):(this._queue.shift(),n&&n(null,...r)),s.pending=!1,this._drainQueue())),this._queue.push(s),this._drainQueue()}_drainQueue(e=!1){if(!this.connected||this._queue.length===0)return;const n=this._queue[0];n.pending&&!e||(n.pending=!0,n.tryCount++,this.flags=n.flags,this.emit.apply(this,n.args))}packet(e){e.nsp=this.nsp,this.io._packet(e)}onopen(){typeof this.auth=="function"?this.auth(e=>{this._sendConnectPacket(e)}):this._sendConnectPacket(this.auth)}_sendConnectPacket(e){this.packet({type:Fe.CONNECT,data:this._pid?Object.assign({pid:this._pid,offset:this._lastOffset},e):e})}onerror(e){this.connected||this.emitReserved("connect_error",e)}onclose(e,n){this.connected=!1,delete this.id,this.emitReserved("disconnect",e,n)}onpacket(e){if(e.nsp===this.nsp)switch(e.type){case Fe.CONNECT:e.data&&e.data.sid?this.onconnect(e.data.sid,e.data.pid):this.emitReserved("connect_error",new Error("It seems you are trying to reach a Socket.IO server in v2.x with a v3.x client, but they are not compatible (more information here: https://socket.io/docs/v3/migrating-from-2-x-to-3-0/)"));break;case Fe.EVENT:case Fe.BINARY_EVENT:this.onevent(e);break;case Fe.ACK:case Fe.BINARY_ACK:this.onack(e);break;case Fe.DISCONNECT:this.ondisconnect();break;case Fe.CONNECT_ERROR:this.destroy();const s=new Error(e.data.message);s.data=e.data.data,this.emitReserved("connect_error",s);break}}onevent(e){const n=e.data||[];e.id!=null&&n.push(this.ack(e.id)),this.connected?this.emitEvent(n):this.receiveBuffer.push(Object.freeze(n))}emitEvent(e){if(this._anyListeners&&this._anyListeners.length){const n=this._anyListeners.slice();for(const s of n)s.apply(this,e)}super.emit.apply(this,e),this._pid&&e.length&&typeof e[e.length-1]=="string"&&(this._lastOffset=e[e.length-1])}ack(e){const n=this;let s=!1;return function(...o){s||(s=!0,n.packet({type:Fe.ACK,id:e,data:o}))}}onack(e){const n=this.acks[e.id];typeof n=="function"&&(n.apply(this,e.data),delete this.acks[e.id])}onconnect(e,n){this.id=e,this.recovered=n&&this._pid===n,this._pid=n,this.connected=!0,this.emitBuffered(),this.emitReserved("connect"),this._drainQueue(!0)}emitBuffered(){this.receiveBuffer.forEach(e=>this.emitEvent(e)),this.receiveBuffer=[],this.sendBuffer.forEach(e=>{this.notifyOutgoingListeners(e),this.packet(e)}),this.sendBuffer=[]}ondisconnect(){this.destroy(),this.onclose("io server disconnect")}destroy(){this.subs&&(this.subs.forEach(e=>e()),this.subs=void 0),this.io._destroy(this)}disconnect(){return this.connected&&this.packet({type:Fe.DISCONNECT}),this.destroy(),this.connected&&this.onclose("io client disconnect"),this}close(){return this.disconnect()}compress(e){return this.flags.compress=e,this}get volatile(){return this.flags.volatile=!0,this}timeout(e){return this.flags.timeout=e,this}onAny(e){return this._anyListeners=this._anyListeners||[],this._anyListeners.push(e),this}prependAny(e){return 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0),n=n||{},n.path=n.path||"/socket.io",this.opts=n,li(this,n),this.reconnection(n.reconnection!==!1),this.reconnectionAttempts(n.reconnectionAttempts||1/0),this.reconnectionDelay(n.reconnectionDelay||1e3),this.reconnectionDelayMax(n.reconnectionDelayMax||5e3),this.randomizationFactor((s=n.randomizationFactor)!==null&&s!==void 0?s:.5),this.backoff=new Gs({min:this.reconnectionDelay(),max:this.reconnectionDelayMax(),jitter:this.randomizationFactor()}),this.timeout(n.timeout==null?2e4:n.timeout),this._readyState="closed",this.uri=e;const o=n.parser||ev;this.encoder=new o.Encoder,this.decoder=new o.Decoder,this._autoConnect=n.autoConnect!==!1,this._autoConnect&&this.open()}reconnection(e){return arguments.length?(this._reconnection=!!e,this):this._reconnection}reconnectionAttempts(e){return e===void 0?this._reconnectionAttempts:(this._reconnectionAttempts=e,this)}reconnectionDelay(e){var n;return e===void 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i=this._timeout;i===0&&o();const a=this.setTimeoutFn(()=>{o(),n.close(),n.emit("error",new Error("timeout"))},i);this.opts.autoUnref&&a.unref(),this.subs.push(function(){clearTimeout(a)})}return this.subs.push(o),this.subs.push(r),this}connect(e){return this.open(e)}onopen(){this.cleanup(),this._readyState="open",this.emitReserved("open");const e=this.engine;this.subs.push(Dt(e,"ping",this.onping.bind(this)),Dt(e,"data",this.ondata.bind(this)),Dt(e,"error",this.onerror.bind(this)),Dt(e,"close",this.onclose.bind(this)),Dt(this.decoder,"decoded",this.ondecoded.bind(this)))}onping(){this.emitReserved("ping")}ondata(e){try{this.decoder.add(e)}catch(n){this.onclose("parse error",n)}}ondecoded(e){Jp(()=>{this.emitReserved("packet",e)},this.setTimeoutFn)}onerror(e){this.emitReserved("error",e)}socket(e,n){let s=this.nsps[e];return s?this._autoConnect&&!s.active&&s.connect():(s=new eg(this,e,n),this.nsps[e]=s),s}_destroy(e){const n=Object.keys(this.nsps);for(const s of 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="⤪",oK="ß",rK=" ",iK="⌖",aK="Τ",lK="τ",cK="⎴",dK="Ť",uK="ť",hK="Ţ",fK="ţ",pK="Т",gK="т",mK="⃛",_K="⌕",bK="𝔗",yK="𝔱",vK="∴",wK="∴",xK="∴",kK="Θ",EK="θ",CK="ϑ",AK="ϑ",SK="≈",TK="∼",MK="  ",OK=" ",RK=" ",NK="≈",DK="∼",LK="Þ",IK="þ",PK="˜",FK="∼",BK="≃",$K="≅",jK="≈",zK="⨱",UK="⊠",qK="×",HK="⨰",VK="∭",GK="⤨",KK="⌶",WK="⫱",ZK="⊤",YK="𝕋",JK="𝕥",QK="⫚",XK="⤩",eW="‴",tW="™",nW="™",sW="▵",oW="▿",rW="◃",iW="⊴",aW="≜",lW="▹",cW="⊵",dW="◬",uW="≜",hW="⨺",fW="⃛",pW="⨹",gW="⧍",mW="⨻",_W="⏢",bW="𝒯",yW="𝓉",vW="Ц",wW="ц",xW="Ћ",kW="ћ",EW="Ŧ",CW="ŧ",AW="≬",SW="↞",TW="↠",MW="Ú",OW="ú",RW="↑",NW="↟",DW="⇑",LW="⥉",IW="Ў",PW="ў",FW="Ŭ",BW="ŭ",$W="Û",jW="û",zW="У",UW="у",qW="⇅",HW="Ű",VW="ű",GW="⥮",KW="⥾",WW="𝔘",ZW="𝔲",YW="Ù",JW="ù",QW="⥣",XW="↿",eZ="↾",tZ="▀",nZ="⌜",sZ="⌜",oZ="⌏",rZ="◸",iZ="Ū",aZ="ū",lZ="¨",cZ="_",dZ="⏟",uZ="⎵",hZ="⏝",fZ="⋃",pZ="⊎",gZ="Ų",mZ="ų",_Z="𝕌",bZ="𝕦",yZ="⤒",vZ="↑",wZ="↑",xZ="⇑",kZ="⇅",EZ="↕",CZ="↕",AZ="⇕",SZ="⥮",TZ="↿",MZ="↾",OZ="⊎",RZ="↖",NZ="↗",DZ="υ",LZ="ϒ",IZ="ϒ",PZ="Υ",FZ="υ",BZ="↥",$Z="⊥",jZ="⇈",zZ="⌝",UZ="⌝",qZ="⌎",HZ="Ů",VZ="ů",GZ="◹",KZ="𝒰",WZ="𝓊",ZZ="⋰",YZ="Ũ",JZ="ũ",QZ="▵",XZ="▴",eY="⇈",tY="Ü",nY="ü",sY="⦧",oY="⦜",rY="ϵ",iY="ϰ",aY="∅",lY="ϕ",cY="ϖ",dY="∝",uY="↕",hY="⇕",fY="ϱ",pY="ς",gY="⊊︀",mY="⫋︀",_Y="⊋︀",bY="⫌︀",yY="ϑ",vY="⊲",wY="⊳",xY="⫨",kY="⫫",EY="⫩",CY="В",AY="в",SY="⊢",TY="⊨",MY="⊩",OY="⊫",RY="⫦",NY="⊻",DY="∨",LY="⋁",IY="≚",PY="⋮",FY="|",BY="‖",$Y="|",jY="‖",zY="∣",UY="|",qY="❘",HY="≀",VY=" ",GY="𝔙",KY="𝔳",WY="⊲",ZY="⊂⃒",YY="⊃⃒",JY="𝕍",QY="𝕧",XY="∝",eJ="⊳",tJ="𝒱",nJ="𝓋",sJ="⫋︀",oJ="⊊︀",rJ="⫌︀",iJ="⊋︀",aJ="⊪",lJ="⦚",cJ="Ŵ",dJ="ŵ",uJ="⩟",hJ="∧",fJ="⋀",pJ="≙",gJ="℘",mJ="𝔚",_J="𝔴",bJ="𝕎",yJ="𝕨",vJ="℘",wJ="≀",xJ="≀",kJ="𝒲",EJ="𝓌",CJ="⋂",AJ="◯",SJ="⋃",TJ="▽",MJ="𝔛",OJ="𝔵",RJ="⟷",NJ="⟺",DJ="Ξ",LJ="ξ",IJ="⟵",PJ="⟸",FJ="⟼",BJ="⋻",$J="⨀",jJ="𝕏",zJ="𝕩",UJ="⨁",qJ="⨂",HJ="⟶",VJ="⟹",GJ="𝒳",KJ="𝓍",WJ="⨆",ZJ="⨄",YJ="△",JJ="⋁",QJ="⋀",XJ="Ý",eQ="ý",tQ="Я",nQ="я",sQ="Ŷ",oQ="ŷ",rQ="Ы",iQ="ы",aQ="¥",lQ="𝔜",cQ="𝔶",dQ="Ї",uQ="ї",hQ="𝕐",fQ="𝕪",pQ="𝒴",gQ="𝓎",mQ="Ю",_Q="ю",bQ="ÿ",yQ="Ÿ",vQ="Ź",wQ="ź",xQ="Ž",kQ="ž",EQ="З",CQ="з",AQ="Ż",SQ="ż",TQ="ℨ",MQ="​",OQ="Ζ",RQ="ζ",NQ="𝔷",DQ="ℨ",LQ="Ж",IQ="ж",PQ="⇝",FQ="𝕫",BQ="ℤ",$Q="𝒵",jQ="𝓏",zQ="‍",UQ="‌",qQ={Aacute:Av,aacute:Sv,Abreve:Tv,abreve:Mv,ac:Ov,acd:Rv,acE:Nv,Acirc:Dv,acirc:Lv,acute:Iv,Acy:Pv,acy:Fv,AElig:Bv,aelig:$v,af:jv,Afr:zv,afr:Uv,Agrave:qv,agrave:Hv,alefsym:Vv,aleph:Gv,Alpha:Kv,alpha:Wv,Amacr:Zv,amacr:Yv,amalg:Jv,amp:Qv,AMP:Xv,andand:ew,And:tw,and:nw,andd:sw,andslope:ow,andv:rw,ang:iw,ange:aw,angle:lw,angmsdaa:cw,angmsdab:dw,angmsdac:uw,angmsdad:hw,angmsdae:fw,angmsdaf:pw,angmsdag:gw,angmsdah:mw,angmsd:_w,angrt:bw,angrtvb:yw,angrtvbd:vw,angsph:ww,angst:xw,angzarr:kw,Aogon:Ew,aogon:Cw,Aopf:Aw,aopf:Sw,apacir:Tw,ap:Mw,apE:Ow,ape:Rw,apid:Nw,apos:Dw,ApplyFunction:Lw,approx:Iw,approxeq:Pw,Aring:Fw,aring:Bw,Ascr:$w,ascr:jw,Assign:zw,ast:Uw,asymp:qw,asympeq:Hw,Atilde:Vw,atilde:Gw,Auml:Kw,auml:Ww,awconint:Zw,awint:Yw,backcong:Jw,backepsilon:Qw,backprime:Xw,backsim:ex,backsimeq:tx,Backslash:nx,Barv:sx,barvee:ox,barwed:rx,Barwed:ix,barwedge:ax,bbrk:lx,bbrktbrk:cx,bcong:dx,Bcy:ux,bcy:hx,bdquo:fx,becaus:px,because:gx,Because:mx,bemptyv:_x,bepsi:bx,bernou:yx,Bernoullis:vx,Beta:wx,beta:xx,beth:kx,between:Ex,Bfr:Cx,bfr:Ax,bigcap:Sx,bigcirc:Tx,bigcup:Mx,bigodot:Ox,bigoplus:Rx,bigotimes:Nx,bigsqcup:Dx,bigstar:Lx,bigtriangledown:Ix,bigtriangleup:Px,biguplus:Fx,bigvee:Bx,bigwedge:$x,bkarow:jx,blacklozenge:zx,blacksquare:Ux,blacktriangle:qx,blacktriangledown:Hx,blacktriangleleft:Vx,blacktriangleright:Gx,blank:Kx,blk12:Wx,blk14:Zx,blk34:Yx,block:Jx,bne:Qx,bnequiv:Xx,bNot:ek,bnot:tk,Bopf:nk,bopf:sk,bot:ok,bottom:rk,bowtie:ik,boxbox:ak,boxdl:lk,boxdL:ck,boxDl:dk,boxDL:uk,boxdr:hk,boxdR:fk,boxDr:pk,boxDR:gk,boxh:mk,boxH:_k,boxhd:bk,boxHd:yk,boxhD:vk,boxHD:wk,boxhu:xk,boxHu:kk,boxhU:Ek,boxHU:Ck,boxminus:Ak,boxplus:Sk,boxtimes:Tk,boxul:Mk,boxuL:Ok,boxUl:Rk,boxUL:Nk,boxur:Dk,boxuR:Lk,boxUr:Ik,boxUR:Pk,boxv:Fk,boxV:Bk,boxvh:$k,boxvH:jk,boxVh:zk,boxVH:Uk,boxvl:qk,boxvL:Hk,boxVl:Vk,boxVL:Gk,boxvr:Kk,boxvR:Wk,boxVr:Zk,boxVR:Yk,bprime:Jk,breve:Qk,Breve:Xk,brvbar:e5,bscr:t5,Bscr:n5,bsemi:s5,bsim:o5,bsime:r5,bsolb:i5,bsol:a5,bsolhsub:l5,bull:c5,bullet:d5,bump:u5,bumpE:h5,bumpe:f5,Bumpeq:p5,bumpeq:g5,Cacute:m5,cacute:_5,capand:b5,capbrcup:y5,capcap:v5,cap:w5,Cap:x5,capcup:k5,capdot:E5,CapitalDifferentialD:C5,caps:A5,caret:S5,caron:T5,Cayleys:M5,ccaps:O5,Ccaron:R5,ccaron:N5,Ccedil:D5,ccedil:L5,Ccirc:I5,ccirc:P5,Cconint:F5,ccups:B5,ccupssm:$5,Cdot:j5,cdot:z5,cedil:U5,Cedilla:q5,cemptyv:H5,cent:V5,centerdot:G5,CenterDot:K5,cfr:W5,Cfr:Z5,CHcy:Y5,chcy:J5,check:Q5,checkmark:X5,Chi:eE,chi:tE,circ:nE,circeq:sE,circlearrowleft:oE,circlearrowright:rE,circledast:iE,circledcirc:aE,circleddash:lE,CircleDot:cE,circledR:dE,circledS:uE,CircleMinus:hE,CirclePlus:fE,CircleTimes:pE,cir:gE,cirE:mE,cire:_E,cirfnint:bE,cirmid:yE,cirscir:vE,ClockwiseContourIntegral:wE,CloseCurlyDoubleQuote:xE,CloseCurlyQuote:kE,clubs:EE,clubsuit:CE,colon:AE,Colon:SE,Colone:TE,colone:ME,coloneq:OE,comma:RE,commat:NE,comp:DE,compfn:LE,complement:IE,complexes:PE,cong:FE,congdot:BE,Congruent:$E,conint:jE,Conint:zE,ContourIntegral:UE,copf:qE,Copf:HE,coprod:VE,Coproduct:GE,copy:KE,COPY:WE,copysr:ZE,CounterClockwiseContourIntegral:YE,crarr:JE,cross:QE,Cross:XE,Cscr:e4,cscr:t4,csub:n4,csube:s4,csup:o4,csupe:r4,ctdot:i4,cudarrl:a4,cudarrr:l4,cuepr:c4,cuesc:d4,cularr:u4,cularrp:h4,cupbrcap:f4,cupcap:p4,CupCap:g4,cup:m4,Cup:_4,cupcup:b4,cupdot:y4,cupor:v4,cups:w4,curarr:x4,curarrm:k4,curlyeqprec:E4,curlyeqsucc:C4,curlyvee:A4,curlywedge:S4,curren:T4,curvearrowleft:M4,curvearrowright:O4,cuvee:R4,cuwed:N4,cwconint:D4,cwint:L4,cylcty:I4,dagger:P4,Dagger:F4,daleth:B4,darr:$4,Darr:j4,dArr:z4,dash:U4,Dashv:q4,dashv:H4,dbkarow:V4,dblac:G4,Dcaron:K4,dcaron:W4,Dcy:Z4,dcy:Y4,ddagger:J4,ddarr:Q4,DD:X4,dd:e8,DDotrahd:t8,ddotseq:n8,deg:s8,Del:o8,Delta:r8,delta:i8,demptyv:a8,dfisht:l8,Dfr:c8,dfr:d8,dHar:u8,dharl:h8,dharr:f8,DiacriticalAcute:p8,DiacriticalDot:g8,DiacriticalDoubleAcute:m8,DiacriticalGrave:_8,DiacriticalTilde:b8,diam:y8,diamond:v8,Diamond:w8,diamondsuit:x8,diams:k8,die:E8,DifferentialD:C8,digamma:A8,disin:S8,div:T8,divide:M8,divideontimes:O8,divonx:R8,DJcy:N8,djcy:D8,dlcorn:L8,dlcrop:I8,dollar:P8,Dopf:F8,dopf:B8,Dot:$8,dot:j8,DotDot:z8,doteq:U8,doteqdot:q8,DotEqual:H8,dotminus:V8,dotplus:G8,dotsquare:K8,doublebarwedge:W8,DoubleContourIntegral:Z8,DoubleDot:Y8,DoubleDownArrow:J8,DoubleLeftArrow:Q8,DoubleLeftRightArrow:X8,DoubleLeftTee:eC,DoubleLongLeftArrow:tC,DoubleLongLeftRightArrow:nC,DoubleLongRightArrow:sC,DoubleRightArrow:oC,DoubleRightTee:rC,DoubleUpArrow:iC,DoubleUpDownArrow:aC,DoubleVerticalBar:lC,DownArrowBar:cC,downarrow:dC,DownArrow:uC,Downarrow:hC,DownArrowUpArrow:fC,DownBreve:pC,downdownarrows:gC,downharpoonleft:mC,downharpoonright:_C,DownLeftRightVector:bC,DownLeftTeeVector:yC,DownLeftVectorBar:vC,DownLeftVector:wC,DownRightTeeVector:xC,DownRightVectorBar:kC,DownRightVector:EC,DownTeeArrow:CC,DownTee:AC,drbkarow:SC,drcorn:TC,drcrop:MC,Dscr:OC,dscr:RC,DScy:NC,dscy:DC,dsol:LC,Dstrok:IC,dstrok:PC,dtdot:FC,dtri:BC,dtrif:$C,duarr:jC,duhar:zC,dwangle:UC,DZcy:qC,dzcy:HC,dzigrarr:VC,Eacute:GC,eacute:KC,easter:WC,Ecaron:ZC,ecaron:YC,Ecirc:JC,ecirc:QC,ecir:XC,ecolon:e3,Ecy:t3,ecy:n3,eDDot:s3,Edot:o3,edot:r3,eDot:i3,ee:a3,efDot:l3,Efr:c3,efr:d3,eg:u3,Egrave:h3,egrave:f3,egs:p3,egsdot:g3,el:m3,Element:_3,elinters:b3,ell:y3,els:v3,elsdot:w3,Emacr:x3,emacr:k3,empty:E3,emptyset:C3,EmptySmallSquare:A3,emptyv:S3,EmptyVerySmallSquare:T3,emsp13:M3,emsp14:O3,emsp:R3,ENG:N3,eng:D3,ensp:L3,Eogon:I3,eogon:P3,Eopf:F3,eopf:B3,epar:$3,eparsl:j3,eplus:z3,epsi:U3,Epsilon:q3,epsilon:H3,epsiv:V3,eqcirc:G3,eqcolon:K3,eqsim:W3,eqslantgtr:Z3,eqslantless:Y3,Equal:J3,equals:Q3,EqualTilde:X3,equest:e9,Equilibrium:t9,equiv:n9,equivDD:s9,eqvparsl:o9,erarr:r9,erDot:i9,escr:a9,Escr:l9,esdot:c9,Esim:d9,esim:u9,Eta:h9,eta:f9,ETH:p9,eth:g9,Euml:m9,euml:_9,euro:b9,excl:y9,exist:v9,Exists:w9,expectation:x9,exponentiale:k9,ExponentialE:E9,fallingdotseq:C9,Fcy:A9,fcy:S9,female:T9,ffilig:M9,fflig:O9,ffllig:R9,Ffr:N9,ffr:D9,filig:L9,FilledSmallSquare:I9,FilledVerySmallSquare:P9,fjlig:F9,flat:B9,fllig:$9,fltns:j9,fnof:z9,Fopf:U9,fopf:q9,forall:H9,ForAll:V9,fork:G9,forkv:K9,Fouriertrf:W9,fpartint:Z9,frac12:Y9,frac13:J9,frac14:Q9,frac15:X9,frac16:e6,frac18:t6,frac23:n6,frac25:s6,frac34:o6,frac35:r6,frac38:i6,frac45:a6,frac56:l6,frac58:c6,frac78:d6,frasl:u6,frown:h6,fscr:f6,Fscr:p6,gacute:g6,Gamma:m6,gamma:_6,Gammad:b6,gammad:y6,gap:v6,Gbreve:w6,gbreve:x6,Gcedil:k6,Gcirc:E6,gcirc:C6,Gcy:A6,gcy:S6,Gdot:T6,gdot:M6,ge:O6,gE:R6,gEl:N6,gel:D6,geq:L6,geqq:I6,geqslant:P6,gescc:F6,ges:B6,gesdot:$6,gesdoto:j6,gesdotol:z6,gesl:U6,gesles:q6,Gfr:H6,gfr:V6,gg:G6,Gg:K6,ggg:W6,gimel:Z6,GJcy:Y6,gjcy:J6,gla:Q6,gl:X6,glE:eA,glj:tA,gnap:nA,gnapprox:sA,gne:oA,gnE:rA,gneq:iA,gneqq:aA,gnsim:lA,Gopf:cA,gopf:dA,grave:uA,GreaterEqual:hA,GreaterEqualLess:fA,GreaterFullEqual:pA,GreaterGreater:gA,GreaterLess:mA,GreaterSlantEqual:_A,GreaterTilde:bA,Gscr:yA,gscr:vA,gsim:wA,gsime:xA,gsiml:kA,gtcc:EA,gtcir:CA,gt:AA,GT:SA,Gt:TA,gtdot:MA,gtlPar:OA,gtquest:RA,gtrapprox:NA,gtrarr:DA,gtrdot:LA,gtreqless:IA,gtreqqless:PA,gtrless:FA,gtrsim:BA,gvertneqq:$A,gvnE:jA,Hacek:zA,hairsp:UA,half:qA,hamilt:HA,HARDcy:VA,hardcy:GA,harrcir:KA,harr:WA,hArr:ZA,harrw:YA,Hat:JA,hbar:QA,Hcirc:XA,hcirc:eS,hearts:tS,heartsuit:nS,hellip:sS,hercon:oS,hfr:rS,Hfr:iS,HilbertSpace:aS,hksearow:lS,hkswarow:cS,hoarr:dS,homtht:uS,hookleftarrow:hS,hookrightarrow:fS,hopf:pS,Hopf:gS,horbar:mS,HorizontalLine:_S,hscr:bS,Hscr:yS,hslash:vS,Hstrok:wS,hstrok:xS,HumpDownHump:kS,HumpEqual:ES,hybull:CS,hyphen:AS,Iacute:SS,iacute:TS,ic:MS,Icirc:OS,icirc:RS,Icy:NS,icy:DS,Idot:LS,IEcy:IS,iecy:PS,iexcl:FS,iff:BS,ifr:$S,Ifr:jS,Igrave:zS,igrave:US,ii:qS,iiiint:HS,iiint:VS,iinfin:GS,iiota:KS,IJlig:WS,ijlig:ZS,Imacr:YS,imacr:JS,image:QS,ImaginaryI:XS,imagline:eT,imagpart:tT,imath:nT,Im:sT,imof:oT,imped:rT,Implies:iT,incare:aT,in:"∈",infin:lT,infintie:cT,inodot:dT,intcal:uT,int:hT,Int:fT,integers:pT,Integral:gT,intercal:mT,Intersection:_T,intlarhk:bT,intprod:yT,InvisibleComma:vT,InvisibleTimes:wT,IOcy:xT,iocy:kT,Iogon:ET,iogon:CT,Iopf:AT,iopf:ST,Iota:TT,iota:MT,iprod:OT,iquest:RT,iscr:NT,Iscr:DT,isin:LT,isindot:IT,isinE:PT,isins:FT,isinsv:BT,isinv:$T,it:jT,Itilde:zT,itilde:UT,Iukcy:qT,iukcy:HT,Iuml:VT,iuml:GT,Jcirc:KT,jcirc:WT,Jcy:ZT,jcy:YT,Jfr:JT,jfr:QT,jmath:XT,Jopf:e7,jopf:t7,Jscr:n7,jscr:s7,Jsercy:o7,jsercy:r7,Jukcy:i7,jukcy:a7,Kappa:l7,kappa:c7,kappav:d7,Kcedil:u7,kcedil:h7,Kcy:f7,kcy:p7,Kfr:g7,kfr:m7,kgreen:_7,KHcy:b7,khcy:y7,KJcy:v7,kjcy:w7,Kopf:x7,kopf:k7,Kscr:E7,kscr:C7,lAarr:A7,Lacute:S7,lacute:T7,laemptyv:M7,lagran:O7,Lambda:R7,lambda:N7,lang:D7,Lang:L7,langd:I7,langle:P7,lap:F7,Laplacetrf:B7,laquo:$7,larrb:j7,larrbfs:z7,larr:U7,Larr:q7,lArr:H7,larrfs:V7,larrhk:G7,larrlp:K7,larrpl:W7,larrsim:Z7,larrtl:Y7,latail:J7,lAtail:Q7,lat:X7,late:eM,lates:tM,lbarr:nM,lBarr:sM,lbbrk:oM,lbrace:rM,lbrack:iM,lbrke:aM,lbrksld:lM,lbrkslu:cM,Lcaron:dM,lcaron:uM,Lcedil:hM,lcedil:fM,lceil:pM,lcub:gM,Lcy:mM,lcy:_M,ldca:bM,ldquo:yM,ldquor:vM,ldrdhar:wM,ldrushar:xM,ldsh:kM,le:EM,lE:CM,LeftAngleBracket:AM,LeftArrowBar:SM,leftarrow:TM,LeftArrow:MM,Leftarrow:OM,LeftArrowRightArrow:RM,leftarrowtail:NM,LeftCeiling:DM,LeftDoubleBracket:LM,LeftDownTeeVector:IM,LeftDownVectorBar:PM,LeftDownVector:FM,LeftFloor:BM,leftharpoondown:$M,leftharpoonup:jM,leftleftarrows:zM,leftrightarrow:UM,LeftRightArrow:qM,Leftrightarrow:HM,leftrightarrows:VM,leftrightharpoons:GM,leftrightsquigarrow:KM,LeftRightVector:WM,LeftTeeArrow:ZM,LeftTee:YM,LeftTeeVector:JM,leftthreetimes:QM,LeftTriangleBar:XM,LeftTriangle:eO,LeftTriangleEqual:tO,LeftUpDownVector:nO,LeftUpTeeVector:sO,LeftUpVectorBar:oO,LeftUpVector:rO,LeftVectorBar:iO,LeftVector:aO,lEg:lO,leg:cO,leq:dO,leqq:uO,leqslant:hO,lescc:fO,les:pO,lesdot:gO,lesdoto:mO,lesdotor:_O,lesg:bO,lesges:yO,lessapprox:vO,lessdot:wO,lesseqgtr:xO,lesseqqgtr:kO,LessEqualGreater:EO,LessFullEqual:CO,LessGreater:AO,lessgtr:SO,LessLess:TO,lesssim:MO,LessSlantEqual:OO,LessTilde:RO,lfisht:NO,lfloor:DO,Lfr:LO,lfr:IO,lg:PO,lgE:FO,lHar:BO,lhard:$O,lharu:jO,lharul:zO,lhblk:UO,LJcy:qO,ljcy:HO,llarr:VO,ll:GO,Ll:KO,llcorner:WO,Lleftarrow:ZO,llhard:YO,lltri:JO,Lmidot:QO,lmidot:XO,lmoustache:eR,lmoust:tR,lnap:nR,lnapprox:sR,lne:oR,lnE:rR,lneq:iR,lneqq:aR,lnsim:lR,loang:cR,loarr:dR,lobrk:uR,longleftarrow:hR,LongLeftArrow:fR,Longleftarrow:pR,longleftrightarrow:gR,LongLeftRightArrow:mR,Longleftrightarrow:_R,longmapsto:bR,longrightarrow:yR,LongRightArrow:vR,Longrightarrow:wR,looparrowleft:xR,looparrowright:kR,lopar:ER,Lopf:CR,lopf:AR,loplus:SR,lotimes:TR,lowast:MR,lowbar:OR,LowerLeftArrow:RR,LowerRightArrow:NR,loz:DR,lozenge:LR,lozf:IR,lpar:PR,lparlt:FR,lrarr:BR,lrcorner:$R,lrhar:jR,lrhard:zR,lrm:UR,lrtri:qR,lsaquo:HR,lscr:VR,Lscr:GR,lsh:KR,Lsh:WR,lsim:ZR,lsime:YR,lsimg:JR,lsqb:QR,lsquo:XR,lsquor:eN,Lstrok:tN,lstrok:nN,ltcc:sN,ltcir:oN,lt:rN,LT:iN,Lt:aN,ltdot:lN,lthree:cN,ltimes:dN,ltlarr:uN,ltquest:hN,ltri:fN,ltrie:pN,ltrif:gN,ltrPar:mN,lurdshar:_N,luruhar:bN,lvertneqq:yN,lvnE:vN,macr:wN,male:xN,malt:kN,maltese:EN,Map:"⤅",map:CN,mapsto:AN,mapstodown:SN,mapstoleft:TN,mapstoup:MN,marker:ON,mcomma:RN,Mcy:NN,mcy:DN,mdash:LN,mDDot:IN,measuredangle:PN,MediumSpace:FN,Mellintrf:BN,Mfr:$N,mfr:jN,mho:zN,micro:UN,midast:qN,midcir:HN,mid:VN,middot:GN,minusb:KN,minus:WN,minusd:ZN,minusdu:YN,MinusPlus:JN,mlcp:QN,mldr:XN,mnplus:eD,models:tD,Mopf:nD,mopf:sD,mp:oD,mscr:rD,Mscr:iD,mstpos:aD,Mu:lD,mu:cD,multimap:dD,mumap:uD,nabla:hD,Nacute:fD,nacute:pD,nang:gD,nap:mD,napE:_D,napid:bD,napos:yD,napprox:vD,natural:wD,naturals:xD,natur:kD,nbsp:ED,nbump:CD,nbumpe:AD,ncap:SD,Ncaron:TD,ncaron:MD,Ncedil:OD,ncedil:RD,ncong:ND,ncongdot:DD,ncup:LD,Ncy:ID,ncy:PD,ndash:FD,nearhk:BD,nearr:$D,neArr:jD,nearrow:zD,ne:UD,nedot:qD,NegativeMediumSpace:HD,NegativeThickSpace:VD,NegativeThinSpace:GD,NegativeVeryThinSpace:KD,nequiv:WD,nesear:ZD,nesim:YD,NestedGreaterGreater:JD,NestedLessLess:QD,NewLine:XD,nexist:eL,nexists:tL,Nfr:nL,nfr:sL,ngE:oL,nge:rL,ngeq:iL,ngeqq:aL,ngeqslant:lL,nges:cL,nGg:dL,ngsim:uL,nGt:hL,ngt:fL,ngtr:pL,nGtv:gL,nharr:mL,nhArr:_L,nhpar:bL,ni:yL,nis:vL,nisd:wL,niv:xL,NJcy:kL,njcy:EL,nlarr:CL,nlArr:AL,nldr:SL,nlE:TL,nle:ML,nleftarrow:OL,nLeftarrow:RL,nleftrightarrow:NL,nLeftrightarrow:DL,nleq:LL,nleqq:IL,nleqslant:PL,nles:FL,nless:BL,nLl:$L,nlsim:jL,nLt:zL,nlt:UL,nltri:qL,nltrie:HL,nLtv:VL,nmid:GL,NoBreak:KL,NonBreakingSpace:WL,nopf:ZL,Nopf:YL,Not:JL,not:QL,NotCongruent:XL,NotCupCap:eI,NotDoubleVerticalBar:tI,NotElement:nI,NotEqual:sI,NotEqualTilde:oI,NotExists:rI,NotGreater:iI,NotGreaterEqual:aI,NotGreaterFullEqual:lI,NotGreaterGreater:cI,NotGreaterLess:dI,NotGreaterSlantEqual:uI,NotGreaterTilde:hI,NotHumpDownHump:fI,NotHumpEqual:pI,notin:gI,notindot:mI,notinE:_I,notinva:bI,notinvb:yI,notinvc:vI,NotLeftTriangleBar:wI,NotLeftTriangle:xI,NotLeftTriangleEqual:kI,NotLess:EI,NotLessEqual:CI,NotLessGreater:AI,NotLessLess:SI,NotLessSlantEqual:TI,NotLessTilde:MI,NotNestedGreaterGreater:OI,NotNestedLessLess:RI,notni:NI,notniva:DI,notnivb:LI,notnivc:II,NotPrecedes:PI,NotPrecedesEqual:FI,NotPrecedesSlantEqual:BI,NotReverseElement:$I,NotRightTriangleBar:jI,NotRightTriangle:zI,NotRightTriangleEqual:UI,NotSquareSubset:qI,NotSquareSubsetEqual:HI,NotSquareSuperset:VI,NotSquareSupersetEqual:GI,NotSubset:KI,NotSubsetEqual:WI,NotSucceeds:ZI,NotSucceedsEqual:YI,NotSucceedsSlantEqual:JI,NotSucceedsTilde:QI,NotSuperset:XI,NotSupersetEqual:eP,NotTilde:tP,NotTildeEqual:nP,NotTildeFullEqual:sP,NotTildeTilde:oP,NotVerticalBar:rP,nparallel:iP,npar:aP,nparsl:lP,npart:cP,npolint:dP,npr:uP,nprcue:hP,nprec:fP,npreceq:pP,npre:gP,nrarrc:mP,nrarr:_P,nrArr:bP,nrarrw:yP,nrightarrow:vP,nRightarrow:wP,nrtri:xP,nrtrie:kP,nsc:EP,nsccue:CP,nsce:AP,Nscr:SP,nscr:TP,nshortmid:MP,nshortparallel:OP,nsim:RP,nsime:NP,nsimeq:DP,nsmid:LP,nspar:IP,nsqsube:PP,nsqsupe:FP,nsub:BP,nsubE:$P,nsube:jP,nsubset:zP,nsubseteq:UP,nsubseteqq:qP,nsucc:HP,nsucceq:VP,nsup:GP,nsupE:KP,nsupe:WP,nsupset:ZP,nsupseteq:YP,nsupseteqq:JP,ntgl:QP,Ntilde:XP,ntilde:eF,ntlg:tF,ntriangleleft:nF,ntrianglelefteq:sF,ntriangleright:oF,ntrianglerighteq:rF,Nu:iF,nu:aF,num:lF,numero:cF,numsp:dF,nvap:uF,nvdash:hF,nvDash:fF,nVdash:pF,nVDash:gF,nvge:mF,nvgt:_F,nvHarr:bF,nvinfin:yF,nvlArr:vF,nvle:wF,nvlt:xF,nvltrie:kF,nvrArr:EF,nvrtrie:CF,nvsim:AF,nwarhk:SF,nwarr:TF,nwArr:MF,nwarrow:OF,nwnear:RF,Oacute:NF,oacute:DF,oast:LF,Ocirc:IF,ocirc:PF,ocir:FF,Ocy:BF,ocy:$F,odash:jF,Odblac:zF,odblac:UF,odiv:qF,odot:HF,odsold:VF,OElig:GF,oelig:KF,ofcir:WF,Ofr:ZF,ofr:YF,ogon:JF,Ograve:QF,ograve:XF,ogt:eB,ohbar:tB,ohm:nB,oint:sB,olarr:oB,olcir:rB,olcross:iB,oline:aB,olt:lB,Omacr:cB,omacr:dB,Omega:uB,omega:hB,Omicron:fB,omicron:pB,omid:gB,ominus:mB,Oopf:_B,oopf:bB,opar:yB,OpenCurlyDoubleQuote:vB,OpenCurlyQuote:wB,operp:xB,oplus:kB,orarr:EB,Or:CB,or:AB,ord:SB,order:TB,orderof:MB,ordf:OB,ordm:RB,origof:NB,oror:DB,orslope:LB,orv:IB,oS:PB,Oscr:FB,oscr:BB,Oslash:$B,oslash:jB,osol:zB,Otilde:UB,otilde:qB,otimesas:HB,Otimes:VB,otimes:GB,Ouml:KB,ouml:WB,ovbar:ZB,OverBar:YB,OverBrace:JB,OverBracket:QB,OverParenthesis:XB,para:e$,parallel:t$,par:n$,parsim:s$,parsl:o$,part:r$,PartialD:i$,Pcy:a$,pcy:l$,percnt:c$,period:d$,permil:u$,perp:h$,pertenk:f$,Pfr:p$,pfr:g$,Phi:m$,phi:_$,phiv:b$,phmmat:y$,phone:v$,Pi:w$,pi:x$,pitchfork:k$,piv:E$,planck:C$,planckh:A$,plankv:S$,plusacir:T$,plusb:M$,pluscir:O$,plus:R$,plusdo:N$,plusdu:D$,pluse:L$,PlusMinus:I$,plusmn:P$,plussim:F$,plustwo:B$,pm:$$,Poincareplane:j$,pointint:z$,popf:U$,Popf:q$,pound:H$,prap:V$,Pr:G$,pr:K$,prcue:W$,precapprox:Z$,prec:Y$,preccurlyeq:J$,Precedes:Q$,PrecedesEqual:X$,PrecedesSlantEqual:ej,PrecedesTilde:tj,preceq:nj,precnapprox:sj,precneqq:oj,precnsim:rj,pre:ij,prE:aj,precsim:lj,prime:cj,Prime:dj,primes:uj,prnap:hj,prnE:fj,prnsim:pj,prod:gj,Product:mj,profalar:_j,profline:bj,profsurf:yj,prop:vj,Proportional:wj,Proportion:xj,propto:kj,prsim:Ej,prurel:Cj,Pscr:Aj,pscr:Sj,Psi:Tj,psi:Mj,puncsp:Oj,Qfr:Rj,qfr:Nj,qint:Dj,qopf:Lj,Qopf:Ij,qprime:Pj,Qscr:Fj,qscr:Bj,quaternions:$j,quatint:jj,quest:zj,questeq:Uj,quot:qj,QUOT:Hj,rAarr:Vj,race:Gj,Racute:Kj,racute:Wj,radic:Zj,raemptyv:Yj,rang:Jj,Rang:Qj,rangd:Xj,range:ez,rangle:tz,raquo:nz,rarrap:sz,rarrb:oz,rarrbfs:rz,rarrc:iz,rarr:az,Rarr:lz,rArr:cz,rarrfs:dz,rarrhk:uz,rarrlp:hz,rarrpl:fz,rarrsim:pz,Rarrtl:gz,rarrtl:mz,rarrw:_z,ratail:bz,rAtail:yz,ratio:vz,rationals:wz,rbarr:xz,rBarr:kz,RBarr:Ez,rbbrk:Cz,rbrace:Az,rbrack:Sz,rbrke:Tz,rbrksld:Mz,rbrkslu:Oz,Rcaron:Rz,rcaron:Nz,Rcedil:Dz,rcedil:Lz,rceil:Iz,rcub:Pz,Rcy:Fz,rcy:Bz,rdca:$z,rdldhar:jz,rdquo:zz,rdquor:Uz,rdsh:qz,real:Hz,realine:Vz,realpart:Gz,reals:Kz,Re:Wz,rect:Zz,reg:Yz,REG:Jz,ReverseElement:Qz,ReverseEquilibrium:Xz,ReverseUpEquilibrium:eU,rfisht:tU,rfloor:nU,rfr:sU,Rfr:oU,rHar:rU,rhard:iU,rharu:aU,rharul:lU,Rho:cU,rho:dU,rhov:uU,RightAngleBracket:hU,RightArrowBar:fU,rightarrow:pU,RightArrow:gU,Rightarrow:mU,RightArrowLeftArrow:_U,rightarrowtail:bU,RightCeiling:yU,RightDoubleBracket:vU,RightDownTeeVector:wU,RightDownVectorBar:xU,RightDownVector:kU,RightFloor:EU,rightharpoondown:CU,rightharpoonup:AU,rightleftarrows:SU,rightleftharpoons:TU,rightrightarrows:MU,rightsquigarrow:OU,RightTeeArrow:RU,RightTee:NU,RightTeeVector:DU,rightthreetimes:LU,RightTriangleBar:IU,RightTriangle:PU,RightTriangleEqual:FU,RightUpDownVector:BU,RightUpTeeVector:$U,RightUpVectorBar:jU,RightUpVector:zU,RightVectorBar:UU,RightVector:qU,ring:HU,risingdotseq:VU,rlarr:GU,rlhar:KU,rlm:WU,rmoustache:ZU,rmoust:YU,rnmid:JU,roang:QU,roarr:XU,robrk:eq,ropar:tq,ropf:nq,Ropf:sq,roplus:oq,rotimes:rq,RoundImplies:iq,rpar:aq,rpargt:lq,rppolint:cq,rrarr:dq,Rrightarrow:uq,rsaquo:hq,rscr:fq,Rscr:pq,rsh:gq,Rsh:mq,rsqb:_q,rsquo:bq,rsquor:yq,rthree:vq,rtimes:wq,rtri:xq,rtrie:kq,rtrif:Eq,rtriltri:Cq,RuleDelayed:Aq,ruluhar:Sq,rx:Tq,Sacute:Mq,sacute:Oq,sbquo:Rq,scap:Nq,Scaron:Dq,scaron:Lq,Sc:Iq,sc:Pq,sccue:Fq,sce:Bq,scE:$q,Scedil:jq,scedil:zq,Scirc:Uq,scirc:qq,scnap:Hq,scnE:Vq,scnsim:Gq,scpolint:Kq,scsim:Wq,Scy:Zq,scy:Yq,sdotb:Jq,sdot:Qq,sdote:Xq,searhk:eH,searr:tH,seArr:nH,searrow:sH,sect:oH,semi:rH,seswar:iH,setminus:aH,setmn:lH,sext:cH,Sfr:dH,sfr:uH,sfrown:hH,sharp:fH,SHCHcy:pH,shchcy:gH,SHcy:mH,shcy:_H,ShortDownArrow:bH,ShortLeftArrow:yH,shortmi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Failed to enable unknown rule(s): "+s);return this};Mt.prototype.disable=function(t,e){var n=[];Array.isArray(t)||(t=[t]),["core","block","inline"].forEach(function(o){n=n.concat(this[o].ruler.disable(t,!0))},this),n=n.concat(this.inline.ruler2.disable(t,!0));var s=t.filter(function(o){return n.indexOf(o)<0});if(s.length&&!e)throw new Error("MarkdownIt. Failed to disable unknown rule(s): "+s);return this};Mt.prototype.use=function(t){var e=[this].concat(Array.prototype.slice.call(arguments,1));return t.apply(t,e),this};Mt.prototype.parse=function(t,e){if(typeof t!="string")throw new Error("Input data should be a String");var n=new this.core.State(t,this,e);return this.core.process(n),n.tokens};Mt.prototype.render=function(t,e){return e=e||{},this.renderer.render(this.parse(t,e),this.options,e)};Mt.prototype.parseInline=function(t,e){var n=new this.core.State(t,this,e);return n.inlineMode=!0,this.core.process(n),n.tokens};Mt.prototype.renderInline=function(t,e){return e=e||{},this.renderer.render(this.parseInline(t,e),this.options,e)};var Rte=Mt,Nte=Rte;const Dte=is(Nte),Lte="😀",Ite="😃",Pte="😄",Fte="😁",Bte="😆",$te="😆",jte="😅",zte="🤣",Ute="😂",qte="🙂",Hte="🙃",Vte="😉",Gte="😊",Kte="😇",Wte="🥰",Zte="😍",Yte="🤩",Jte="😘",Qte="😗",Xte="☺️",ene="😚",tne="😙",nne="🥲",sne="😋",one="😛",rne="😜",ine="🤪",ane="😝",lne="🤑",cne="🤗",dne="🤭",une="🤫",hne="🤔",fne="🤐",pne="🤨",gne="😐",mne="😑",_ne="😶",bne="😏",yne="😒",vne="🙄",wne="😬",xne="🤥",kne="😌",Ene="😔",Cne="😪",Ane="🤤",Sne="😴",Tne="😷",Mne="🤒",One="🤕",Rne="🤢",Nne="🤮",Dne="🤧",Lne="🥵",Ine="🥶",Pne="🥴",Fne="😵",Bne="🤯",$ne="🤠",jne="🥳",zne="🥸",Une="😎",qne="🤓",Hne="🧐",Vne="😕",Gne="😟",Kne="🙁",Wne="☹️",Zne="😮",Yne="😯",Jne="😲",Qne="😳",Xne="🥺",ese="😦",tse="😧",nse="😨",sse="😰",ose="😥",rse="😢",ise="😭",ase="😱",lse="😖",cse="😣",dse="😞",use="😓",hse="😩",fse="😫",pse="🥱",gse="😤",mse="😡",_se="😡",bse="😠",yse="🤬",vse="😈",wse="👿",xse="💀",kse="☠️",Ese="💩",Cse="💩",Ase="💩",Sse="🤡",Tse="👹",Mse="👺",Ose="👻",Rse="👽",Nse="👾",Dse="🤖",Lse="😺",Ise="😸",Pse="😹",Fse="😻",Bse="😼",$se="😽",jse="🙀",zse="😿",Use="😾",qse="🙈",Hse="🙉",Vse="🙊",Gse="💋",Kse="💌",Wse="💘",Zse="💝",Yse="💖",Jse="💗",Qse="💓",Xse="💞",eoe="💕",toe="💟",noe="❣️",soe="💔",ooe="❤️",roe="🧡",ioe="💛",aoe="💚",loe="💙",coe="💜",doe="🤎",uoe="🖤",hoe="🤍",foe="💢",poe="💥",goe="💥",moe="💫",_oe="💦",boe="💨",yoe="🕳️",voe="💣",woe="💬",xoe="👁️‍🗨️",koe="🗨️",Eoe="🗯️",Coe="💭",Aoe="💤",Soe="👋",Toe="🤚",Moe="🖐️",Ooe="✋",Roe="✋",Noe="🖖",Doe="👌",Loe="🤌",Ioe="🤏",Poe="✌️",Foe="🤞",Boe="🤟",$oe="🤘",joe="🤙",zoe="👈",Uoe="👉",qoe="👆",Hoe="🖕",Voe="🖕",Goe="👇",Koe="☝️",Woe="👍",Zoe="👎",Yoe="✊",Joe="✊",Qoe="👊",Xoe="👊",ere="👊",tre="🤛",nre="🤜",sre="👏",ore="🙌",rre="👐",ire="🤲",are="🤝",lre="🙏",cre="✍️",dre="💅",ure="🤳",hre="💪",fre="🦾",pre="🦿",gre="🦵",mre="🦶",_re="👂",bre="🦻",yre="👃",vre="🧠",wre="🫀",xre="🫁",kre="🦷",Ere="🦴",Cre="👀",Are="👁️",Sre="👅",Tre="👄",Mre="👶",Ore="🧒",Rre="👦",Nre="👧",Dre="🧑",Lre="👱",Ire="👨",Pre="🧔",Fre="👨‍🦰",Bre="👨‍🦱",$re="👨‍🦳",jre="👨‍🦲",zre="👩",Ure="👩‍🦰",qre="🧑‍🦰",Hre="👩‍🦱",Vre="🧑‍🦱",Gre="👩‍🦳",Kre="🧑‍🦳",Wre="👩‍🦲",Zre="🧑‍🦲",Yre="👱‍♀️",Jre="👱‍♀️",Qre="👱‍♂️",Xre="🧓",eie="👴",tie="👵",nie="🙍",sie="🙍‍♂️",oie="🙍‍♀️",rie="🙎",iie="🙎‍♂️",aie="🙎‍♀️",lie="🙅",cie="🙅‍♂️",die="🙅‍♂️",uie="🙅‍♀️",hie="🙅‍♀️",fie="🙆",pie="🙆‍♂️",gie="🙆‍♀️",mie="💁",_ie="💁",bie="💁‍♂️",yie="💁‍♂️",vie="💁‍♀️",wie="💁‍♀️",xie="🙋",kie="🙋‍♂️",Eie="🙋‍♀️",Cie="🧏",Aie="🧏‍♂️",Sie="🧏‍♀️",Tie="🙇",Mie="🙇‍♂️",Oie="🙇‍♀️",Rie="🤦",Nie="🤦‍♂️",Die="🤦‍♀️",Lie="🤷",Iie="🤷‍♂️",Pie="🤷‍♀️",Fie="🧑‍⚕️",Bie="👨‍⚕️",$ie="👩‍⚕️",jie="🧑‍🎓",zie="👨‍🎓",Uie="👩‍🎓",qie="🧑‍🏫",Hie="👨‍🏫",Vie="👩‍🏫",Gie="🧑‍⚖️",Kie="👨‍⚖️",Wie="👩‍⚖️",Zie="🧑‍🌾",Yie="👨‍🌾",Jie="👩‍🌾",Qie="🧑‍🍳",Xie="👨‍🍳",eae="👩‍🍳",tae="🧑‍🔧",nae="👨‍🔧",sae="👩‍🔧",oae="🧑‍🏭",rae="👨‍🏭",iae="👩‍🏭",aae="🧑‍💼",lae="👨‍💼",cae="👩‍💼",dae="🧑‍🔬",uae="👨‍🔬",hae="👩‍🔬",fae="🧑‍💻",pae="👨‍💻",gae="👩‍💻",mae="🧑‍🎤",_ae="👨‍🎤",bae="👩‍🎤",yae="🧑‍🎨",vae="👨‍🎨",wae="👩‍🎨",xae="🧑‍✈️",kae="👨‍✈️",Eae="👩‍✈️",Cae="🧑‍🚀",Aae="👨‍🚀",Sae="👩‍🚀",Tae="🧑‍🚒",Mae="👨‍🚒",Oae="👩‍🚒",Rae="👮",Nae="👮",Dae="👮‍♂️",Lae="👮‍♀️",Iae="🕵️",Pae="🕵️‍♂️",Fae="🕵️‍♀️",Bae="💂",$ae="💂‍♂️",jae="💂‍♀️",zae="🥷",Uae="👷",qae="👷‍♂️",Hae="👷‍♀️",Vae="🤴",Gae="👸",Kae="👳",Wae="👳‍♂️",Zae="👳‍♀️",Yae="👲",Jae="🧕",Qae="🤵",Xae="🤵‍♂️",ele="🤵‍♀️",tle="👰",nle="👰‍♂️",sle="👰‍♀️",ole="👰‍♀️",rle="🤰",ile="🤱",ale="👩‍🍼",lle="👨‍🍼",cle="🧑‍🍼",dle="👼",ule="🎅",hle="🤶",fle="🧑‍🎄",ple="🦸",gle="🦸‍♂️",mle="🦸‍♀️",_le="🦹",ble="🦹‍♂️",yle="🦹‍♀️",vle="🧙",wle="🧙‍♂️",xle="🧙‍♀️",kle="🧚",Ele="🧚‍♂️",Cle="🧚‍♀️",Ale="🧛",Sle="🧛‍♂️",Tle="🧛‍♀️",Mle="🧜",Ole="🧜‍♂️",Rle="🧜‍♀️",Nle="🧝",Dle="🧝‍♂️",Lle="🧝‍♀️",Ile="🧞",Ple="🧞‍♂️",Fle="🧞‍♀️",Ble="🧟",$le="🧟‍♂️",jle="🧟‍♀️",zle="💆",Ule="💆‍♂️",qle="💆‍♀️",Hle="💇",Vle="💇‍♂️",Gle="💇‍♀️",Kle="🚶",Wle="🚶‍♂️",Zle="🚶‍♀️",Yle="🧍",Jle="🧍‍♂️",Qle="🧍‍♀️",Xle="🧎",ece="🧎‍♂️",tce="🧎‍♀️",nce="🧑‍🦯",sce="👨‍🦯",oce="👩‍🦯",rce="🧑‍🦼",ice="👨‍🦼",ace="👩‍🦼",lce="🧑‍🦽",cce="👨‍🦽",dce="👩‍🦽",uce="🏃",hce="🏃",fce="🏃‍♂️",pce="🏃‍♀️",gce="💃",mce="💃",_ce="🕺",bce="🕴️",yce="👯",vce="👯‍♂️",wce="👯‍♀️",xce="🧖",kce="🧖‍♂️",Ece="🧖‍♀️",Cce="🧗",Ace="🧗‍♂️",Sce="🧗‍♀️",Tce="🤺",Mce="🏇",Oce="⛷️",Rce="🏂",Nce="🏌️",Dce="🏌️‍♂️",Lce="🏌️‍♀️",Ice="🏄",Pce="🏄‍♂️",Fce="🏄‍♀️",Bce="🚣",$ce="🚣‍♂️",jce="🚣‍♀️",zce="🏊",Uce="🏊‍♂️",qce="🏊‍♀️",Hce="⛹️",Vce="⛹️‍♂️",Gce="⛹️‍♂️",Kce="⛹️‍♀️",Wce="⛹️‍♀️",Zce="🏋️",Yce="🏋️‍♂️",Jce="🏋️‍♀️",Qce="🚴",Xce="🚴‍♂️",ede="🚴‍♀️",tde="🚵",nde="🚵‍♂️",sde="🚵‍♀️",ode="🤸",rde="🤸‍♂️",ide="🤸‍♀️",ade="🤼",lde="🤼‍♂️",cde="🤼‍♀️",dde="🤽",ude="🤽‍♂️",hde="🤽‍♀️",fde="🤾",pde="🤾‍♂️",gde="🤾‍♀️",mde="🤹",_de="🤹‍♂️",bde="🤹‍♀️",yde="🧘",vde="🧘‍♂️",wde="🧘‍♀️",xde="🛀",kde="🛌",Ede="🧑‍🤝‍🧑",Cde="👭",Ade="👫",Sde="👬",Tde="💏",Mde="👩‍❤️‍💋‍👨",Ode="👨‍❤️‍💋‍👨",Rde="👩‍❤️‍💋‍👩",Nde="💑",Dde="👩‍❤️‍👨",Lde="👨‍❤️‍👨",Ide="👩‍❤️‍👩",Pde="👪",Fde="👨‍👩‍👦",Bde="👨‍👩‍👧",$de="👨‍👩‍👧‍👦",jde="👨‍👩‍👦‍👦",zde="👨‍👩‍👧‍👧",Ude="👨‍👨‍👦",qde="👨‍👨‍👧",Hde="👨‍👨‍👧‍👦",Vde="👨‍👨‍👦‍👦",Gde="👨‍👨‍👧‍👧",Kde="👩‍👩‍👦",Wde="👩‍👩‍👧",Zde="👩‍👩‍👧‍👦",Yde="👩‍👩‍👦‍👦",Jde="👩‍👩‍👧‍👧",Qde="👨‍👦",Xde="👨‍👦‍👦",eue="👨‍👧",tue="👨‍👧‍👦",nue="👨‍👧‍👧",sue="👩‍👦",oue="👩‍👦‍👦",rue="👩‍👧",iue="👩‍👧‍👦",aue="👩‍👧‍👧",lue="🗣️",cue="👤",due="👥",uue="🫂",hue="👣",fue="🐵",pue="🐒",gue="🦍",mue="🦧",_ue="🐶",bue="🐕",yue="🦮",vue="🐕‍🦺",wue="🐩",xue="🐺",kue="🦊",Eue="🦝",Cue="🐱",Aue="🐈",Sue="🐈‍⬛",Tue="🦁",Mue="🐯",Oue="🐅",Rue="🐆",Nue="🐴",Due="🐎",L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"📻",nve="🎷",sve="🪗",ove="🎸",rve="🎹",ive="🎺",ave="🎻",lve="🪕",cve="🥁",dve="🪘",uve="📱",hve="📲",fve="☎️",pve="☎️",gve="📞",mve="📟",_ve="📠",bve="🔋",yve="🔌",vve="💻",wve="🖥️",xve="🖨️",kve="⌨️",Eve="🖱️",Cve="🖲️",Ave="💽",Sve="💾",Tve="💿",Mve="📀",Ove="🧮",Rve="🎥",Nve="🎞️",Dve="📽️",Lve="🎬",Ive="📺",Pve="📷",Fve="📸",Bve="📹",$ve="📼",jve="🔍",zve="🔎",Uve="🕯️",qve="💡",Hve="🔦",Vve="🏮",Gve="🏮",Kve="🪔",Wve="📔",Zve="📕",Yve="📖",Jve="📖",Qve="📗",Xve="📘",ewe="📙",twe="📚",nwe="📓",swe="📒",owe="📃",rwe="📜",iwe="📄",awe="📰",lwe="🗞️",cwe="📑",dwe="🔖",uwe="🏷️",hwe="💰",fwe="🪙",pwe="💴",gwe="💵",mwe="💶",_we="💷",bwe="💸",ywe="💳",vwe="🧾",wwe="💹",xwe="✉️",kwe="📧",Ewe="📨",Cwe="📩",Awe="📤",Swe="📥",Twe="📫",Mwe="📪",Owe="📬",Rwe="📭",Nwe="📮",Dwe="🗳️",Lwe="✏️",Iwe="✒️",Pwe="🖋️",Fwe="🖊️",Bwe="🖌️",$we="🖍️",jwe="📝",zwe="📝",Uwe="💼",qwe="📁",Hwe="📂",Vwe="🗂️",Gwe="📅",Kwe="📆",Wwe="🗒️",Zwe="🗓️",Ywe="📇",Jwe="📈",Qwe="📉",Xwe="📊",exe="📋",txe="📌",nxe="📍",sxe="📎",oxe="🖇️",rxe="📏",ixe="📐",axe="✂️",lxe="🗃️",cxe="🗄️",dxe="🗑️",uxe="🔒",hxe="🔓",fxe="🔏",pxe="🔐",gxe="🔑",mxe="🗝️",_xe="🔨",bxe="🪓",yxe="⛏️",vxe="⚒️",wxe="🛠️",xxe="🗡️",kxe="⚔️",Exe="🔫",Cxe="🪃",Axe="🏹",Sxe="🛡️",Txe="🪚",Mxe="🔧",Oxe="🪛",Rxe="🔩",Nxe="⚙️",Dxe="🗜️",Lxe="⚖️",Ixe="🦯",Pxe="🔗",Fxe="⛓️",Bxe="🪝",$xe="🧰",jxe="🧲",zxe="🪜",Uxe="⚗️",qxe="🧪",Hxe="🧫",Vxe="🧬",Gxe="🔬",Kxe="🔭",Wxe="📡",Zxe="💉",Yxe="🩸",Jxe="💊",Qxe="🩹",Xxe="🩺",eke="🚪",tke="🛗",nke="🪞",ske="🪟",oke="🛏️",rke="🛋️",ike="🪑",ake="🚽",lke="🪠",cke="🚿",dke="🛁",uke="🪤",hke="🪒",fke="🧴",pke="🧷",gke="🧹",mke="🧺",_ke="🧻",bke="🪣",yke="🧼",vke="🪥",wke="🧽",xke="🧯",kke="🛒",Eke="🚬",Cke="⚰️",Ake="🪦",Ske="⚱️",Tke="🗿",Mke="🪧",Oke="🏧",Rke="🚮",Nke="🚰",Dke="♿",Lke="🚹",Ike="🚺",Pke="🚻",Fke="🚼",Bke="🚾",$ke="🛂",jke="🛃",zke="🛄",Uke="🛅",qke="⚠️",Hke="🚸",Vke="⛔",Gke="🚫",Kke="🚳",Wke="🚭",Zke="🚯",Yke="🚷",Jke="📵",Qke="🔞",Xke="☢️",e5e="☣️",t5e="⬆️",n5e="↗️",s5e="➡️",o5e="↘️",r5e="⬇️",i5e="↙️",a5e="⬅️",l5e="↖️",c5e="↕️",d5e="↔️",u5e="↩️",h5e="↪️",f5e="⤴️",p5e="⤵️",g5e="🔃",m5e="🔄",_5e="🔙",b5e="🔚",y5e="🔛",v5e="🔜",w5e="🔝",x5e="🛐",k5e="⚛️",E5e="🕉️",C5e="✡️",A5e="☸️",S5e="☯️",T5e="✝️",M5e="☦️",O5e="☪️",R5e="☮️",N5e="🕎",D5e="🔯",L5e="♈",I5e="♉",P5e="♊",F5e="♋",B5e="♌",$5e="♍",j5e="♎",z5e="♏",U5e="♐",q5e="♑",H5e="♒",V5e="♓",G5e="⛎",K5e="🔀",W5e="🔁",Z5e="🔂",Y5e="▶️",J5e="⏩",Q5e="⏭️",X5e="⏯️",eEe="◀️",tEe="⏪",nEe="⏮️",sEe="🔼",oEe="⏫",rEe="🔽",iEe="⏬",aEe="⏸️",lEe="⏹️",cEe="⏺️",dEe="⏏️",uEe="🎦",hEe="🔅",fEe="🔆",pEe="📶",gEe="📳",mEe="📴",_Ee="♀️",bEe="♂️",yEe="⚧️",vEe="✖️",wEe="➕",xEe="➖",kEe="➗",EEe="♾️",CEe="‼️",AEe="⁉️",SEe="❓",TEe="❔",MEe="❕",OEe="❗",REe="❗",NEe="〰️",DEe="💱",LEe="💲",IEe="⚕️",PEe="♻️",FEe="⚜️",BEe="🔱",$Ee="📛",jEe="🔰",zEe="⭕",UEe="✅",qEe="☑️",HEe="✔️",VEe="❌",GEe="❎",KEe="➰",WEe="➿",ZEe="〽️",YEe="✳️",JEe="✴️",QEe="❇️",XEe="©️",e4e="®️",t4e="™️",n4e="#️⃣",s4e="*️⃣",o4e="0️⃣",r4e="1️⃣",i4e="2️⃣",a4e="3️⃣",l4e="4️⃣",c4e="5️⃣",d4e="6️⃣",u4e="7️⃣",h4e="8️⃣",f4e="9️⃣",p4e="🔟",g4e="🔠",m4e="🔡",_4e="🔣",b4e="🔤",y4e="🅰️",v4e="🆎",w4e="🅱️",x4e="🆑",k4e="🆒",E4e="🆓",C4e="ℹ️",A4e="🆔",S4e="Ⓜ️",T4e="🆖",M4e="🅾️",O4e="🆗",R4e="🅿️",N4e="🆘",D4e="🆙",L4e="🆚",I4e="🈁",P4e="🈂️",F4e="🉐",B4e="🉑",$4e="㊗️",j4e="㊙️",z4e="🈵",U4e="🔴",q4e="🟠",H4e="🟡",V4e="🟢",G4e="🔵",K4e="🟣",W4e="🟤",Z4e="⚫",Y4e="⚪",J4e="🟥",Q4e="🟧",X4e="🟨",e8e="🟩",t8e="🟦",n8e="🟪",s8e="🟫",o8e="⬛",r8e="⬜",i8e="◼️",a8e="◻️",l8e="◾",c8e="◽",d8e="▪️",u8e="▫️",h8e="🔶",f8e="🔷",p8e="🔸",g8e="🔹",m8e="🔺",_8e="🔻",b8e="💠",y8e="🔘",v8e="🔳",w8e="🔲",x8e="🏁",k8e="🚩",E8e="🎌",C8e="🏴",A8e="🏳️",S8e="🏳️‍🌈",T8e="🏳️‍⚧️",M8e="🏴‍☠️",O8e="🇦🇨",R8e="🇦🇩",N8e="🇦🇪",D8e="🇦🇫",L8e="🇦🇬",I8e="🇦🇮",P8e="🇦🇱",F8e="🇦🇲",B8e="🇦🇴",$8e="🇦🇶",j8e="🇦🇷",z8e="🇦🇸",U8e="🇦🇹",q8e="🇦🇺",H8e="🇦🇼",V8e="🇦🇽",G8e="🇦🇿",K8e="🇧🇦",W8e="🇧🇧",Z8e="🇧🇩",Y8e="🇧🇪",J8e="🇧🇫",Q8e="🇧🇬",X8e="🇧🇭",eCe="🇧🇮",tCe="🇧🇯",nCe="🇧🇱",sCe="🇧🇲",oCe="🇧🇳",rCe="🇧🇴",iCe="🇧🇶",aCe="🇧🇷",lCe="🇧🇸",cCe="🇧🇹",dCe="🇧🇻",uCe="🇧🇼",hCe="🇧🇾",fCe="🇧🇿",pCe="🇨🇦",gCe="🇨🇨",mCe="🇨🇩",_Ce="🇨🇫",bCe="🇨🇬",yCe="🇨🇭",vCe="🇨🇮",wCe="🇨🇰",xCe="🇨🇱",kCe="🇨🇲",ECe="🇨🇳",CCe="🇨🇴",ACe="🇨🇵",SCe="🇨🇷",TCe="🇨🇺",MCe="🇨🇻",OCe="🇨🇼",RCe="🇨🇽",NCe="🇨🇾",DCe="🇨🇿",LCe="🇩🇪",ICe="🇩🇬",PCe="🇩🇯",FCe="🇩🇰",BCe="🇩🇲",$Ce="🇩🇴",jCe="🇩🇿",zCe="🇪🇦",UCe="🇪🇨",qCe="🇪🇪",HCe="🇪🇬",VCe="🇪🇭",GCe="🇪🇷",KCe="🇪🇸",WCe="🇪🇹",ZCe="🇪🇺",YCe="🇪🇺",JCe="🇫🇮",QCe="🇫🇯",XCe="🇫🇰",e3e="🇫🇲",t3e="🇫🇴",n3e="🇫🇷",s3e="🇬🇦",o3e="🇬🇧",r3e="🇬🇧",i3e="🇬🇩",a3e="🇬🇪",l3e="🇬🇫",c3e="🇬🇬",d3e="🇬🇭",u3e="🇬🇮",h3e="🇬🇱",f3e="🇬🇲",p3e="🇬🇳",g3e="🇬🇵",m3e="🇬🇶",_3e="🇬🇷",b3e="🇬🇸",y3e="🇬🇹",v3e="🇬🇺",w3e="🇬🇼",x3e="🇬🇾",k3e="🇭🇰",E3e="🇭🇲",C3e="🇭🇳",A3e="🇭🇷",S3e="🇭🇹",T3e="🇭🇺",M3e="🇮🇨",O3e="🇮🇩",R3e="🇮🇪",N3e="🇮🇱",D3e="🇮🇲",L3e="🇮🇳",I3e="🇮🇴",P3e="🇮🇶",F3e="🇮🇷",B3e="🇮🇸",$3e="🇮🇹",j3e="🇯🇪",z3e="🇯🇲",U3e="🇯🇴",q3e="🇯🇵",H3e="🇰🇪",V3e="🇰🇬",G3e="🇰🇭",K3e="🇰🇮",W3e="🇰🇲",Z3e="🇰🇳",Y3e="🇰🇵",J3e="🇰🇷",Q3e="🇰🇼",X3e="🇰🇾",e9e="🇰🇿",t9e="🇱🇦",n9e="🇱🇧",s9e="🇱🇨",o9e="🇱🇮",r9e="🇱🇰",i9e="🇱🇷",a9e="🇱🇸",l9e="🇱🇹",c9e="🇱🇺",d9e="🇱🇻",u9e="🇱🇾",h9e="🇲🇦",f9e="🇲🇨",p9e="🇲🇩",g9e="🇲🇪",m9e="🇲🇫",_9e="🇲🇬",b9e="🇲🇭",y9e="🇲🇰",v9e="🇲🇱",w9e="🇲🇲",x9e="🇲🇳",k9e="🇲🇴",E9e="🇲🇵",C9e="🇲🇶",A9e="🇲🇷",S9e="🇲🇸",T9e="🇲🇹",M9e="🇲🇺",O9e="🇲🇻",R9e="🇲🇼",N9e="🇲🇽",D9e="🇲🇾",L9e="🇲🇿",I9e="🇳🇦",P9e="🇳🇨",F9e="🇳🇪",B9e="🇳🇫",$9e="🇳🇬",j9e="🇳🇮",z9e="🇳🇱",U9e="🇳🇴",q9e="🇳🇵",H9e="🇳🇷",V9e="🇳🇺"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Failed to enable unknown rule(s): "+s);return this};Mt.prototype.disable=function(t,e){var n=[];Array.isArray(t)||(t=[t]),["core","block","inline"].forEach(function(o){n=n.concat(this[o].ruler.disable(t,!0))},this),n=n.concat(this.inline.ruler2.disable(t,!0));var s=t.filter(function(o){return n.indexOf(o)<0});if(s.length&&!e)throw new Error("MarkdownIt. Failed to disable unknown rule(s): "+s);return this};Mt.prototype.use=function(t){var e=[this].concat(Array.prototype.slice.call(arguments,1));return t.apply(t,e),this};Mt.prototype.parse=function(t,e){if(typeof t!="string")throw new Error("Input data should be a String");var n=new this.core.State(t,this,e);return this.core.process(n),n.tokens};Mt.prototype.render=function(t,e){return e=e||{},this.renderer.render(this.parse(t,e),this.options,e)};Mt.prototype.parseInline=function(t,e){var n=new this.core.State(t,this,e);return n.inlineMode=!0,this.core.process(n),n.tokens};Mt.prototype.renderInline=function(t,e){return e=e||{},this.renderer.render(this.parseInline(t,e),this.options,e)};var Rte=Mt,Nte=Rte;const Dte=is(Nte),Lte="😀",Ite="😃",Pte="😄",Fte="😁",Bte="😆",$te="😆",jte="😅",zte="🤣",Ute="😂",qte="🙂",Hte="🙃",Vte="😉",Gte="😊",Kte="😇",Wte="🥰",Zte="😍",Yte="🤩",Jte="😘",Qte="😗",Xte="☺️",ene="😚",tne="😙",nne="🥲",sne="😋",one="😛",rne="😜",ine="🤪",ane="😝",lne="🤑",cne="🤗",dne="🤭",une="🤫",hne="🤔",fne="🤐",pne="🤨",gne="😐",mne="😑",_ne="😶",bne="😏",yne="😒",vne="🙄",wne="😬",xne="🤥",kne="😌",Ene="😔",Cne="😪",Ane="🤤",Sne="😴",Tne="😷",Mne="🤒",One="🤕",Rne="🤢",Nne="🤮",Dne="🤧",Lne="🥵",Ine="🥶",Pne="🥴",Fne="😵",Bne="🤯",$ne="🤠",jne="🥳",zne="🥸",Une="😎",qne="🤓",Hne="🧐",Vne="😕",Gne="😟",Kne="🙁",Wne="☹️",Zne="😮",Yne="😯",Jne="😲",Qne="😳",Xne="🥺",ese="😦",tse="😧",nse="😨",sse="😰",ose="😥",rse="😢",ise="😭",ase="😱",lse="😖",cse="😣",dse="😞",use="😓",hse="😩",fse="😫",pse="🥱",gse="😤",mse="😡",_se="😡",bse="😠",yse="🤬",vse="😈",wse="👿",xse="💀",kse="☠️",Ese="💩",Cse="💩",Ase="💩",Sse="🤡",Tse="👹",Mse="👺",Ose="👻",Rse="👽",Nse="👾",Dse="🤖",Lse="😺",Ise="😸",Pse="😹",Fse="😻",Bse="😼",$se="😽",jse="🙀",zse="😿",Use="😾",qse="🙈",Hse="🙉",Vse="🙊",Gse="💋",Kse="💌",Wse="💘",Zse="💝",Yse="💖",Jse="💗",Qse="💓",Xse="💞",eoe="💕",toe="💟",noe="❣️",soe="💔",ooe="❤️",roe="🧡",ioe="💛",aoe="💚",loe="💙",coe="💜",doe="🤎",uoe="🖤",hoe="🤍",foe="💢",poe="💥",goe="💥",moe="💫",_oe="💦",boe="💨",yoe="🕳️",voe="💣",woe="💬",xoe="👁️‍🗨️",koe="🗨️",Eoe="🗯️",Coe="💭",Aoe="💤",Soe="👋",Toe="🤚",Moe="🖐️",Ooe="✋",Roe="✋",Noe="🖖",Doe="👌",Loe="🤌",Ioe="🤏",Poe="✌️",Foe="🤞",Boe="🤟",$oe="🤘",joe="🤙",zoe="👈",Uoe="👉",qoe="👆",Hoe="🖕",Voe="🖕",Goe="👇",Koe="☝️",Woe="👍",Zoe="👎",Yoe="✊",Joe="✊",Qoe="👊",Xoe="👊",ere="👊",tre="🤛",nre="🤜",sre="👏",ore="🙌",rre="👐",ire="🤲",are="🤝",lre="🙏",cre="✍️",dre="💅",ure="🤳",hre="💪",fre="🦾",pre="🦿",gre="🦵",mre="🦶",_re="👂",bre="🦻",yre="👃",vre="🧠",wre="🫀",xre="🫁",kre="🦷",Ere="🦴",Cre="👀",Are="👁️",Sre="👅",Tre="👄",Mre="👶",Ore="🧒",Rre="👦",Nre="👧",Dre="🧑",Lre="👱",Ire="👨",Pre="🧔",Fre="👨‍🦰",Bre="👨‍🦱",$re="👨‍🦳",jre="👨‍🦲",zre="👩",Ure="👩‍🦰",qre="🧑‍🦰",Hre="👩‍🦱",Vre="🧑‍🦱",Gre="👩‍🦳",Kre="🧑‍🦳",Wre="👩‍🦲",Zre="🧑‍🦲",Yre="👱‍♀️",Jre="👱‍♀️",Qre="👱‍♂️",Xre="🧓",eie="👴",tie="👵",nie="🙍",sie="🙍‍♂️",oie="🙍‍♀️",rie="🙎",iie="🙎‍♂️",aie="🙎‍♀️",lie="🙅",cie="🙅‍♂️",die="🙅‍♂️",uie="🙅‍♀️",hie="🙅‍♀️",fie="🙆",pie="🙆‍♂️",gie="🙆‍♀️",mie="💁",_ie="💁",bie="💁‍♂️",yie="💁‍♂️",vie="💁‍♀️",wie="💁‍♀️",xie="🙋",kie="🙋‍♂️",Eie="🙋‍♀️",Cie="🧏",Aie="🧏‍♂️",Sie="🧏‍♀️",Tie="🙇",Mie="🙇‍♂️",Oie="🙇‍♀️",Rie="🤦",Nie="🤦‍♂️",Die="🤦‍♀️",Lie="🤷",Iie="🤷‍♂️",Pie="🤷‍♀️",Fie="🧑‍⚕️",Bie="👨‍⚕️",$ie="👩‍⚕️",jie="🧑‍🎓",zie="👨‍🎓",Uie="👩‍🎓",qie="🧑‍🏫",Hie="👨‍🏫",Vie="👩‍🏫",Gie="🧑‍⚖️",Kie="👨‍⚖️",Wie="👩‍⚖️",Zie="🧑‍🌾",Yie="👨‍🌾",Jie="👩‍🌾",Qie="🧑‍🍳",Xie="👨‍🍳",eae="👩‍🍳",tae="🧑‍🔧",nae="👨‍🔧",sae="👩‍🔧",oae="🧑‍🏭",rae="👨‍🏭",iae="👩‍🏭",aae="🧑‍💼",lae="👨‍💼",cae="👩‍💼",dae="🧑‍🔬",uae="👨‍🔬",hae="👩‍🔬",fae="🧑‍💻",pae="👨‍💻",gae="👩‍💻",mae="🧑‍🎤",_ae="👨‍🎤",bae="👩‍🎤",yae="🧑‍🎨",vae="👨‍🎨",wae="👩‍🎨",xae="🧑‍✈️",kae="👨‍✈️",Eae="👩‍✈️",Cae="🧑‍🚀",Aae="👨‍🚀",Sae="👩‍🚀",Tae="🧑‍🚒",Mae="👨‍🚒",Oae="👩‍🚒",Rae="👮",Nae="👮",Dae="👮‍♂️",Lae="👮‍♀️",Iae="🕵️",Pae="🕵️‍♂️",Fae="🕵️‍♀️",Bae="💂",$ae="💂‍♂️",jae="💂‍♀️",zae="🥷",Uae="👷",qae="👷‍♂️",Hae="👷‍♀️",Vae="🤴",Gae="👸",Kae="👳",Wae="👳‍♂️",Zae="👳‍♀️",Yae="👲",Jae="🧕",Qae="🤵",Xae="🤵‍♂️",ele="🤵‍♀️",tle="👰",nle="👰‍♂️",sle="👰‍♀️",ole="👰‍♀️",rle="🤰",ile="🤱",ale="👩‍🍼",lle="👨‍🍼",cle="🧑‍🍼",dle="👼",ule="🎅",hle="🤶",fle="🧑‍🎄",ple="🦸",gle="🦸‍♂️",mle="🦸‍♀️",_le="🦹",ble="🦹‍♂️",yle="🦹‍♀️",vle="🧙",wle="🧙‍♂️",xle="🧙‍♀️",kle="🧚",Ele="🧚‍♂️",Cle="🧚‍♀️",Ale="🧛",Sle="🧛‍♂️",Tle="🧛‍♀️",Mle="🧜",Ole="🧜‍♂️",Rle="🧜‍♀️",Nle="🧝",Dle="🧝‍♂️",Lle="🧝‍♀️",Ile="🧞",Ple="🧞‍♂️",Fle="🧞‍♀️",Ble="🧟",$le="🧟‍♂️",jle="🧟‍♀️",zle="💆",Ule="💆‍♂️",qle="💆‍♀️",Hle="💇",Vle="💇‍♂️",Gle="💇‍♀️",Kle="🚶",Wle="🚶‍♂️",Zle="🚶‍♀️",Yle="🧍",Jle="🧍‍♂️",Qle="🧍‍♀️",Xle="🧎",ece="🧎‍♂️",tce="🧎‍♀️",nce="🧑‍🦯",sce="👨‍🦯",oce="👩‍🦯",rce="🧑‍🦼",ice="👨‍🦼",ace="👩‍🦼",lce="🧑‍🦽",cce="👨‍🦽",dce="👩‍🦽",uce="🏃",hce="🏃",fce="🏃‍♂️",pce="🏃‍♀️",gce="💃",mce="💃",_ce="🕺",bce="🕴️",yce="👯",vce="👯‍♂️",wce="👯‍♀️",xce="🧖",kce="🧖‍♂️",Ece="🧖‍♀️",Cce="🧗",Ace="🧗‍♂️",Sce="🧗‍♀️",Tce="🤺",Mce="🏇",Oce="⛷️",Rce="🏂",Nce="🏌️",Dce="🏌️‍♂️",Lce="🏌️‍♀️",Ice="🏄",Pce="🏄‍♂️",Fce="🏄‍♀️",Bce="🚣",$ce="🚣‍♂️",jce="🚣‍♀️",zce="🏊",Uce="🏊‍♂️",qce="🏊‍♀️",Hce="⛹️",Vce="⛹️‍♂️",Gce="⛹️‍♂️",Kce="⛹️‍♀️",Wce="⛹️‍♀️",Zce="🏋️",Yce="🏋️‍♂️",Jce="🏋️‍♀️",Qce="🚴",Xce="🚴‍♂️",ede="🚴‍♀️",tde="🚵",nde="🚵‍♂️",sde="🚵‍♀️",ode="🤸",rde="🤸‍♂️",ide="🤸‍♀️",ade="🤼",lde="🤼‍♂️",cde="🤼‍♀️",dde="🤽",ude="🤽‍♂️",hde="🤽‍♀️",fde="🤾",pde="🤾‍♂️",gde="🤾‍♀️",mde="🤹",_de="🤹‍♂️",bde="🤹‍♀️",yde="🧘",vde="🧘‍♂️",wde="🧘‍♀️",xde="🛀",kde="🛌",Ede="🧑‍🤝‍🧑",Cde="👭",Ade="👫",Sde="👬",Tde="💏",Mde="👩‍❤️‍💋‍👨",Ode="👨‍❤️‍💋‍👨",Rde="👩‍❤️‍💋‍👩",Nde="💑",Dde="👩‍❤️‍👨",Lde="👨‍❤️‍👨",Ide="👩‍❤️‍👩",Pde="👪",Fde="👨‍👩‍👦",Bde="👨‍👩‍👧",$de="👨‍👩‍👧‍👦",jde="👨‍👩‍👦‍👦",zde="👨‍👩‍👧‍👧",Ude="👨‍👨‍👦",qde="👨‍👨‍👧",Hde="👨‍👨‍👧‍👦",Vde="👨‍👨‍👦‍👦",Gde="👨‍👨‍👧‍👧",Kde="👩‍👩‍👦",Wde="👩‍👩‍👧",Zde="👩‍👩‍👧‍👦",Yde="👩‍👩‍👦‍👦",Jde="👩‍👩‍👧‍👧",Qde="👨‍👦",Xde="👨‍👦‍👦",eue="👨‍👧",tue="👨‍👧‍👦",nue="👨‍👧‍👧",sue="👩‍👦",oue="👩‍👦‍👦",rue="👩‍👧",iue="👩‍👧‍👦",aue="👩‍👧‍👧",lue="🗣️",cue="👤",due="👥",uue="🫂",hue="👣",fue="🐵",pue="🐒",gue="🦍",mue="🦧",_ue="🐶",bue="🐕",yue="🦮",vue="🐕‍🦺",wue="🐩",xue="🐺",kue="🦊",Eue="🦝",Cue="🐱",Aue="🐈",Sue="🐈‍⬛",Tue="🦁",Mue="🐯",Oue="🐅",Rue="🐆",Nue="🐴",Due="🐎",L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"📻",nve="🎷",sve="🪗",ove="🎸",rve="🎹",ive="🎺",ave="🎻",lve="🪕",cve="🥁",dve="🪘",uve="📱",hve="📲",fve="☎️",pve="☎️",gve="📞",mve="📟",_ve="📠",bve="🔋",yve="🔌",vve="💻",wve="🖥️",xve="🖨️",kve="⌨️",Eve="🖱️",Cve="🖲️",Ave="💽",Sve="💾",Tve="💿",Mve="📀",Ove="🧮",Rve="🎥",Nve="🎞️",Dve="📽️",Lve="🎬",Ive="📺",Pve="📷",Fve="📸",Bve="📹",$ve="📼",jve="🔍",zve="🔎",Uve="🕯️",qve="💡",Hve="🔦",Vve="🏮",Gve="🏮",Kve="🪔",Wve="📔",Zve="📕",Yve="📖",Jve="📖",Qve="📗",Xve="📘",ewe="📙",twe="📚",nwe="📓",swe="📒",owe="📃",rwe="📜",iwe="📄",awe="📰",lwe="🗞️",cwe="📑",dwe="🔖",uwe="🏷️",hwe="💰",fwe="🪙",pwe="💴",gwe="💵",mwe="💶",_we="💷",bwe="💸",ywe="💳",vwe="🧾",wwe="💹",xwe="✉️",kwe="📧",Ewe="📨",Cwe="📩",Awe="📤",Swe="📥",Twe="📫",Mwe="📪",Owe="📬",Rwe="📭",Nwe="📮",Dwe="🗳️",Lwe="✏️",Iwe="✒️",Pwe="🖋️",Fwe="🖊️",Bwe="🖌️",$we="🖍️",jwe="📝",zwe="📝",Uwe="💼",qwe="📁",Hwe="📂",Vwe="🗂️",Gwe="📅",Kwe="📆",Wwe="🗒️",Zwe="🗓️",Ywe="📇",Jwe="📈",Qwe="📉",Xwe="📊",exe="📋",txe="📌",nxe="📍",sxe="📎",oxe="🖇️",rxe="📏",ixe="📐",axe="✂️",lxe="🗃️",cxe="🗄️",dxe="🗑️",uxe="🔒",hxe="🔓",fxe="🔏",pxe="🔐",gxe="🔑",mxe="🗝️",_xe="🔨",bxe="🪓",yxe="⛏️",vxe="⚒️",wxe="🛠️",xxe="🗡️",kxe="⚔️",Exe="🔫",Cxe="🪃",Axe="🏹",Sxe="🛡️",Txe="🪚",Mxe="🔧",Oxe="🪛",Rxe="🔩",Nxe="⚙️",Dxe="🗜️",Lxe="⚖️",Ixe="🦯",Pxe="🔗",Fxe="⛓️",Bxe="🪝",$xe="🧰",jxe="🧲",zxe="🪜",Uxe="⚗️",qxe="🧪",Hxe="🧫",Vxe="🧬",Gxe="🔬",Kxe="🔭",Wxe="📡",Zxe="💉",Yxe="🩸",Jxe="💊",Qxe="🩹",Xxe="🩺",eke="🚪",tke="🛗",nke="🪞",ske="🪟",oke="🛏️",rke="🛋️",ike="🪑",ake="🚽",lke="🪠",cke="🚿",dke="🛁",uke="🪤",hke="🪒",fke="🧴",pke="🧷",gke="🧹",mke="🧺",_ke="🧻",bke="🪣",yke="🧼",vke="🪥",wke="🧽",xke="🧯",kke="🛒",Eke="🚬",Cke="⚰️",Ake="🪦",Ske="⚱️",Tke="🗿",Mke="🪧",Oke="🏧",Rke="🚮",Nke="🚰",Dke="♿",Lke="🚹",Ike="🚺",Pke="🚻",Fke="🚼",Bke="🚾",$ke="🛂",jke="🛃",zke="🛄",Uke="🛅",qke="⚠️",Hke="🚸",Vke="⛔",Gke="🚫",Kke="🚳",Wke="🚭",Zke="🚯",Yke="🚷",Jke="📵",Qke="🔞",Xke="☢️",e5e="☣️",t5e="⬆️",n5e="↗️",s5e="➡️",o5e="↘️",r5e="⬇️",i5e="↙️",a5e="⬅️",l5e="↖️",c5e="↕️",d5e="↔️",u5e="↩️",h5e="↪️",f5e="⤴️",p5e="⤵️",g5e="🔃",m5e="🔄",_5e="🔙",b5e="🔚",y5e="🔛",v5e="🔜",w5e="🔝",x5e="🛐",k5e="⚛️",E5e="🕉️",C5e="✡️",A5e="☸️",S5e="☯️",T5e="✝️",M5e="☦️",O5e="☪️",R5e="☮️",N5e="🕎",D5e="🔯",L5e="♈",I5e="♉",P5e="♊",F5e="♋",B5e="♌",$5e="♍",j5e="♎",z5e="♏",U5e="♐",q5e="♑",H5e="♒",V5e="♓",G5e="⛎",K5e="🔀",W5e="🔁",Z5e="🔂",Y5e="▶️",J5e="⏩",Q5e="⏭️",X5e="⏯️",eEe="◀️",tEe="⏪",nEe="⏮️",sEe="🔼",oEe="⏫",rEe="🔽",iEe="⏬",aEe="⏸️",lEe="⏹️",cEe="⏺️",dEe="⏏️",uEe="🎦",hEe="🔅",fEe="🔆",pEe="📶",gEe="📳",mEe="📴",_Ee="♀️",bEe="♂️",yEe="⚧️",vEe="✖️",wEe="➕",xEe="➖",kEe="➗",EEe="♾️",CEe="‼️",AEe="⁉️",SEe="❓",TEe="❔",MEe="❕",OEe="❗",REe="❗",NEe="〰️",DEe="💱",LEe="💲",IEe="⚕️",PEe="♻️",FEe="⚜️",BEe="🔱",$Ee="📛",jEe="🔰",zEe="⭕",UEe="✅",qEe="☑️",HEe="✔️",VEe="❌",GEe="❎",KEe="➰",WEe="➿",ZEe="〽️",YEe="✳️",JEe="✴️",QEe="❇️",XEe="©️",e4e="®️",t4e="™️",n4e="#️⃣",s4e="*️⃣",o4e="0️⃣",r4e="1️⃣",i4e="2️⃣",a4e="3️⃣",l4e="4️⃣",c4e="5️⃣",d4e="6️⃣",u4e="7️⃣",h4e="8️⃣",f4e="9️⃣",p4e="🔟",g4e="🔠",m4e="🔡",_4e="🔣",b4e="🔤",y4e="🅰️",v4e="🆎",w4e="🅱️",x4e="🆑",k4e="🆒",E4e="🆓",C4e="ℹ️",A4e="🆔",S4e="Ⓜ️",T4e="🆖",M4e="🅾️",O4e="🆗",R4e="🅿️",N4e="🆘",D4e="🆙",L4e="🆚",I4e="🈁",P4e="🈂️",F4e="🉐",B4e="🉑",$4e="㊗️",j4e="㊙️",z4e="🈵",U4e="🔴",q4e="🟠",H4e="🟡",V4e="🟢",G4e="🔵",K4e="🟣",W4e="🟤",Z4e="⚫",Y4e="⚪",J4e="🟥",Q4e="🟧",X4e="🟨",e8e="🟩",t8e="🟦",n8e="🟪",s8e="🟫",o8e="⬛",r8e="⬜",i8e="◼️",a8e="◻️",l8e="◾",c8e="◽",d8e="▪️",u8e="▫️",h8e="🔶",f8e="🔷",p8e="🔸",g8e="🔹",m8e="🔺",_8e="🔻",b8e="💠",y8e="🔘",v8e="🔳",w8e="🔲",x8e="🏁",k8e="🚩",E8e="🎌",C8e="🏴",A8e="🏳️",S8e="🏳️‍🌈",T8e="🏳️‍⚧️",M8e="🏴‍☠️",O8e="🇦🇨",R8e="🇦🇩",N8e="🇦🇪",D8e="🇦🇫",L8e="🇦🇬",I8e="🇦🇮",P8e="🇦🇱",F8e="🇦🇲",B8e="🇦🇴",$8e="🇦🇶",j8e="🇦🇷",z8e="🇦🇸",U8e="🇦🇹",q8e="🇦🇺",H8e="🇦🇼",V8e="🇦🇽",G8e="🇦🇿",K8e="🇧🇦",W8e="🇧🇧",Z8e="🇧🇩",Y8e="🇧🇪",J8e="🇧🇫",Q8e="🇧🇬",X8e="🇧🇭",eCe="🇧🇮",tCe="🇧🇯",nCe="🇧🇱",sCe="🇧🇲",oCe="🇧🇳",rCe="🇧🇴",iCe="🇧🇶",aCe="🇧🇷",lCe="🇧🇸",cCe="🇧🇹",dCe="🇧🇻",uCe="🇧🇼",hCe="🇧🇾",fCe="🇧🇿",pCe="🇨🇦",gCe="🇨🇨",mCe="🇨🇩",_Ce="🇨🇫",bCe="🇨🇬",yCe="🇨🇭",vCe="🇨🇮",wCe="🇨🇰",xCe="🇨🇱",kCe="🇨🇲",ECe="🇨🇳",CCe="🇨🇴",ACe="🇨🇵",SCe="🇨🇷",TCe="🇨🇺",MCe="🇨🇻",OCe="🇨🇼",RCe="🇨🇽",NCe="🇨🇾",DCe="🇨🇿",LCe="🇩🇪",ICe="🇩🇬",PCe="🇩🇯",FCe="🇩🇰",BCe="🇩🇲",$Ce="🇩🇴",jCe="🇩🇿",zCe="🇪🇦",UCe="🇪🇨",qCe="🇪🇪",HCe="🇪🇬",VCe="🇪🇭",GCe="🇪🇷",KCe="🇪🇸",WCe="🇪🇹",ZCe="🇪🇺",YCe="🇪🇺",JCe="🇫🇮",QCe="🇫🇯",XCe="🇫🇰",e3e="🇫🇲",t3e="🇫🇴",n3e="🇫🇷",s3e="🇬🇦",o3e="🇬🇧",r3e="🇬🇧",i3e="🇬🇩",a3e="🇬🇪",l3e="🇬🇫",c3e="🇬🇬",d3e="🇬🇭",u3e="🇬🇮",h3e="🇬🇱",f3e="🇬🇲",p3e="🇬🇳",g3e="🇬🇵",m3e="🇬🇶",_3e="🇬🇷",b3e="🇬🇸",y3e="🇬🇹",v3e="🇬🇺",w3e="🇬🇼",x3e="🇬🇾",k3e="🇭🇰",E3e="🇭🇲",C3e="🇭🇳",A3e="🇭🇷",S3e="🇭🇹",T3e="🇭🇺",M3e="🇮🇨",O3e="🇮🇩",R3e="🇮🇪",N3e="🇮🇱",D3e="🇮🇲",L3e="🇮🇳",I3e="🇮🇴",P3e="🇮🇶",F3e="🇮🇷",B3e="🇮🇸",$3e="🇮🇹",j3e="🇯🇪",z3e="🇯🇲",U3e="🇯🇴",q3e="🇯🇵",H3e="🇰🇪",V3e="🇰🇬",G3e="🇰🇭",K3e="🇰🇮",W3e="🇰🇲",Z3e="🇰🇳",Y3e="🇰🇵",J3e="🇰🇷",Q3e="🇰🇼",X3e="🇰🇾",e9e="🇰🇿",t9e="🇱🇦",n9e="🇱🇧",s9e="🇱🇨",o9e="🇱🇮",r9e="🇱🇰",i9e="🇱🇷",a9e="🇱🇸",l9e="🇱🇹",c9e="🇱🇺",d9e="🇱🇻",u9e="🇱🇾",h9e="🇲🇦",f9e="🇲🇨",p9e="🇲🇩",g9e="🇲🇪",m9e="🇲🇫",_9e="🇲🇬",b9e="🇲🇭",y9e="🇲🇰",v9e="🇲🇱",w9e="🇲🇲",x9e="🇲🇳",k9e="🇲🇴",E9e="🇲🇵",C9e="🇲🇶",A9e="🇲🇷",S9e="🇲🇸",T9e="🇲🇹",M9e="🇲🇺",O9e="🇲🇻",R9e="🇲🇼",N9e="🇲🇽",D9e="🇲🇾",L9e="🇲🇿",I9e="🇳🇦",P9e="🇳🇨",F9e="🇳🇪",B9e="🇳🇫",$9e="🇳🇬",j9e="🇳🇮",z9e="🇳🇱",U9e="🇳🇴",q9e="🇳🇵",H9e="🇳🇷",V9e="🇳🇺"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Credits

This project is developed by ParisNeo With help from the community.

Check out the full list of developers here and show them some love.

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")}}},vi=t=>(ns("data-v-6f1a11a2"),t=t(),ss(),t),vMe={class:"container mx-auto p-4 bg-bg-light-tone dark:bg-bg-dark-tone shadow-lg"},wMe=vi(()=>d("h2",{class:"text-2xl font-bold mb-2"},"About Lord of large Language Models",-1)),xMe={class:"mb-4"},kMe=vi(()=>d("p",null,[xe("Discord link: "),d("a",{class:"text-blue-500 hover:text-blue-400 duration-150",href:"https://discord.gg/C73K7hjy"},"https://discord.gg/C73K7hjy")],-1)),EMe={class:"mb-8 overflow-y-auto max-h-96 scrollbar"},CMe=vi(()=>d("h2",{class:"text-2xl font-bold mb-2"},"Frequently Asked Questions",-1)),AMe={class:"list-disc pl-4"},SMe={class:"text-xl font-bold mb-1"},TMe=["innerHTML"],MMe=vi(()=>d("div",null,[d("h2",{class:"text-2xl font-bold mb-2"},"Contact Us"),d("p",{class:"mb-4"},"If you have any further questions or need assistance, feel free to reach out to me."),d("p",null,[xe("Discord link: "),d("a",{class:"text-blue-500 hover:text-blue-400 duration-150",href:"https://discord.gg/C73K7hjy"},"https://discord.gg/C73K7hjy")])],-1)),OMe={class:"mt-8"},RMe=os('

Credits

This project is developed by ParisNeo With help from the community.

Check out the full list of developers here and show them some love.

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this.$store.state.config},mountedPers(){return this.$store.state.mountedPers},allDiscussionPersonalities(){if(this.discussionList.length>0){let t=[];for(let e=0;e{const s={filename:t.name,fileData:n.result};Ee.on("file_received",o=>{if(o.status){console.log(o.filename);let r=this.fileList.findIndex(i=>i.name===o.filename);r>=0?(this.isFileSentList[r]=!0,console.log(this.isFileSentList)):console.log("Not found"),this.onShowToastMessage("File uploaded successfully",4,!0)}else this.onShowToastMessage(`Couldn't upload file -`+o.error,4,!1);Ee.off("file_received")}),Ee.emit("send_file",s)},n.readAsDataURL(t)},startSpeechRecognition(){"SpeechRecognition"in window||"webkitSpeechRecognition"in 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be(()=>{ve.replace()}),Gt(t)},removeItem(t){this.fileList=this.fileList.filter(e=>e!=t)},sendMessageEvent(t){this.fileList=[],this.$emit("messageSentEvent",t)},submitOnEnter(t){t.which===13&&(t.preventDefault(),t.repeat||(this.sendMessageEvent(this.message),this.message=""))},submit(){this.message&&(this.sendMessageEvent(this.message),this.message="")},stopGenerating(){this.$emit("stopGenerating")},addFiles(t){this.fileList=this.fileList.concat([...t.target.files]),this.isFileSentList=this.isFileSentList.concat([!1]*this.fileList.length),this.send_file(this.fileList[this.fileList.length-1])}},watch:{showFileList(){be(()=>{ve.replace()})},loading(t,e){be(()=>{ve.replace()})},fileList:{handler(t,e){let n=0;if(t.length>0)for(let s=0;s{ve.replace()})},activated(){be(()=>{ve.replace()})}},ft=t=>(ns("data-v-55bd190c"),t=t(),ss(),t),ZUe={class:"absolute bottom-0 min-w-96 w-full justify-center text-center p-4"},YUe={key:0,class:"flex items-center justify-center w-full"},JUe={class:"flex 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Lord of Large Language Models

One tool to rule them all

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Logo

Lord of Large Language Models

One tool to rule them all

Welcome

Please create a new discussion or select existing one to start

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gap-3"},fGe=Ke(()=>d("i",{"data-feather":"log-out"},null,-1)),pGe=[fGe],gGe=Ke(()=>d("i",{"data-feather":"list"},null,-1)),mGe=[gGe],_Ge={class:"z-5"},bGe={class:"relative flex flex-row flex-grow mb-10 z-0"},yGe={key:1,class:"gap-2 py-2 my-2 hover:shadow-md hover:bg-primary-light dark:hover:bg-primary rounded-md p-2 duration-75 group cursor-pointer"},vGe=Ke(()=>d("p",{class:"px-3"},"No discussions are found",-1)),wGe=[vGe],xGe=Ke(()=>d("div",{class:"sticky bottom-0 bg-gradient-to-t pointer-events-none from-bg-light-tone dark:from-bg-dark-tone flex flex-grow"},null,-1)),kGe={class:"z-20 h-max"},EGe={class:"container pt-4 pb-10 mb-28"},CGe=Ke(()=>d("div",{class:"absolute w-full bottom-0 bg-transparent p-10 pt-16 bg-gradient-to-t from-bg-light dark:from-bg-dark from-5% via-bg-light dark:via-bg-dark via-10% to-transparent to-100%"},null,-1)),AGe={key:0,class:"bottom-0 container flex flex-row items-center justify-center"},SGe={setup(){},data(){return{msgTypes:{MSG_TYPE_CHUNK:0,MSG_TYPE_FULL:1,MSG_TYPE_FULL_INVISIBLE_TO_AI:2,MSG_TYPE_FULL_INVISIBLE_TO_USER:3,MSG_TYPE_EXCEPTION:4,MSG_TYPE_WARNING:5,MSG_TYPE_INFO:6,MSG_TYPE_STEP:7,MSG_TYPE_STEP_START:8,MSG_TYPE_STEP_PROGRESS:9,MSG_TYPE_STEP_END:10,MSG_TYPE_JSON_INFOS:11,MSG_TYPE_REF:12,MSG_TYPE_CODE:13,MSG_TYPE_UI:14,MSG_TYPE_NEW_MESSAGE:15,MSG_TYPE_FINISHED_MESSAGE:17},senderTypes:{SENDER_TYPES_USER:0,SENDER_TYPES_AI:1,SENDER_TYPES_SYSTEM:2},version:"5.0",list:[],tempList:[],currentDiscussion:{},discussionArr:[],loading:!1,filterTitle:"",filterInProgress:!1,isCreated:!1,isGenerating:!1,isCheckbox:!1,isSelectAll:!1,showConfirmation:!1,chime:new Audio("chime_aud.wav"),showToast:!1,isSearch:!1,isDiscussionBottom:!1,personalityAvatars:[],fileList:[],isDragOverDiscussion:!1,isDragOverChat:!1,panelCollapsed:!1,isOpen:!1}},methods:{save_configuration(){this.showConfirmation=!1,ye.post("/save_settings",{}).then(t=>{if(t)return t.status?this.$refs.toast.showToast("Settings saved!",4,!0):this.$refs.messageBox.showMessage("Error: Couldn't save settings!"),t.data}).catch(t=>(console.log(t.message,"save_configuration"),this.$refs.messageBox.showMessage("Couldn't save settings!"),{status:!1}))},showToastMessage(t,e,n){console.log("sending",t),this.$refs.toast.showToast(t,e,n)},togglePanel(){this.panelCollapsed=!this.panelCollapsed},toggleDropdown(){this.isOpen=!this.isOpen},importChatGPT(){},async api_get_req(t){try{const e=await ye.get("/"+t);if(e)return e.data}catch(e){console.log(e.message,"api_get_req");return}},async list_discussions(){try{const t=await ye.get("/list_discussions");if(t)return this.createDiscussionList(t.data),t.data}catch(t){return console.log("Error: Could not list discussions",t.message),[]}},load_discussion(t,e){t&&(console.log("Loading discussion",t),this.loading=!0,this.discussionArr=[],this.setDiscussionLoading(t,this.loading),Ee.on("discussion",n=>{this.loading=!1,this.setDiscussionLoading(t,this.loading),n&&(console.log("received discussion"),console.log(n),this.discussionArr=n.filter(s=>s.message_type==this.msgTypes.MSG_TYPE_CHUNK||s.message_type==this.msgTypes.MSG_TYPE_FULL||s.message_type==this.msgTypes.MSG_TYPE_FULL_INVISIBLE_TO_AI||s.message_type==this.msgTypes.MSG_TYPE_CODE||s.message_type==this.msgTypes.MSG_TYPE_JSON_INFOS||s.message_type==this.msgTypes.MSG_TYPE_UI),console.log("this.discussionArr"),console.log(this.discussionArr),e&&e()),Ee.off("discussion")}),Ee.emit("load_discussion",{id:t}))},new_discussion(t){try{this.loading=!0,Ee.on("discussion_created",e=>{Ee.off("discussion_created"),this.list_discussions().then(()=>{const n=this.list.findIndex(o=>o.id==e.id),s=this.list[n];this.selectDiscussion(s),this.load_discussion(e.id,()=>{this.loading=!1,be(()=>{const o=document.getElementById("dis-"+e.id);this.scrollToElement(o),console.log("Scrolling tp "+o)})})})}),console.log("new_discussion ",t),Ee.emit("new_discussion",{title:t})}catch(e){return console.log("Error: Could not create new discussion",e.message),{}}},async delete_discussion(t){try{t&&(this.loading=!0,this.setDiscussionLoading(t,this.loading),await ye.post("/delete_discussion",{client_id:this.client_id,id:t}),this.loading=!1,this.setDiscussionLoading(t,this.loading))}catch(e){console.log("Error: Could not delete discussion",e.message),this.loading=!1,this.setDiscussionLoading(t,this.loading)}},async edit_title(t,e){try{if(t){this.loading=!0,this.setDiscussionLoading(t,this.loading);const n=await ye.post("/edit_title",{client_id:this.client_id,id:t,title:e});if(this.loading=!1,this.setDiscussionLoading(t,this.loading),n.status==200){const s=this.list.findIndex(r=>r.id==t),o=this.list[s];o.title=e,this.tempList=this.list}}}catch(n){console.log("Error: Could not edit title",n.message),this.loading=!1,this.setDiscussionLoading(t,this.loading)}},async delete_message(t){try{const e=await ye.get("/delete_message",{params:{client_id:this.client_id,id:t}});if(e)return e.data}catch(e){return console.log("Error: Could delete message",e.message),{}}},async stop_gen(){try{if(Ee.emit("cancel_generation"),res)return res.data}catch(t){return console.log("Error: Could not stop generating",t.message),{}}},async message_rank_up(t){try{const e=await ye.get("/message_rank_up",{params:{client_id:this.client_id,id:t}});if(e)return e.data}catch(e){return console.log("Error: Could not rank up message",e.message),{}}},async message_rank_down(t){try{const e=await ye.get("/message_rank_down",{params:{client_id:this.client_id,id:t}});if(e)return e.data}catch(e){return console.log("Error: Could not rank down message",e.message),{}}},async edit_message(t,e){try{const n=await ye.get("/edit_message",{params:{client_id:this.client_id,id:t,message:e}});if(n)return n.data}catch(n){return console.log("Error: Could not update message",n.message),{}}},async export_multiple_discussions(t){try{if(t.length>0){const e=await ye.post("/export_multiple_discussions",{discussion_ids:t});if(e)return e.data}}catch(e){return console.log("Error: Could not export multiple discussions",e.message),{}}},async import_multiple_discussions(t){try{if(t.length>0){console.log("sending import",t);const e=await ye.post("/import_multiple_discussions",{jArray:t});if(e)return console.log("import response",e.data),e.data}}catch(e){console.log("Error: Could not import multiple discussions",e.message);return}},filterDiscussions(){this.filterInProgress||(this.filterInProgress=!0,setTimeout(()=>{this.filterTitle?this.list=this.tempList.filter(t=>t.title&&t.title.includes(this.filterTitle)):this.list=this.tempList,this.filterInProgress=!1},100))},async selectDiscussion(t){t&&(console.log("this.currentDiscussion",this.currentDiscussion),this.currentDiscussion===void 0?(this.currentDiscussion=t,this.setPageTitle(t),localStorage.setItem("selected_discussion",this.currentDiscussion.id),this.load_discussion(t.id,()=>{this.discussionArr.length>1&&(this.currentDiscussion.title===""||this.currentDiscussion.title===null)&&this.changeTitleUsingUserMSG(this.currentDiscussion.id,this.discussionArr[1].content)})):this.currentDiscussion.id!=t.id&&(this.currentDiscussion=t,this.setPageTitle(t),localStorage.setItem("selected_discussion",this.currentDiscussion.id),this.load_discussion(t.id,()=>{this.discussionArr.length>1&&(this.currentDiscussion.title===""||this.currentDiscussion.title===null)&&this.changeTitleUsingUserMSG(this.currentDiscussion.id,this.discussionArr[1].content)})),be(()=>{const e=document.getElementById("dis-"+this.currentDiscussion.id);this.scrollToElementInContainer(e,"leftPanel");const n=document.getElementById("messages-list");this.scrollBottom(n)}))},scrollToElement(t){t?t.scrollIntoView({behavior:"smooth",block:"start",inline:"nearest"}):console.log("Error: scrollToElement")},scrollToElementInContainer(t,e){try{const n=t.offsetTop;document.getElementById(e).scrollTo({top:n,behavior:"smooth"})}catch{}},scrollBottom(t){t?t.scrollTo({top:t.scrollHeight,behavior:"smooth"}):console.log("Error: scrollBottom")},scrollTop(t){t?t.scrollTo({top:0,behavior:"smooth"}):console.log("Error: scrollTop")},createUserMsg(t){let e={content:t.message,id:t.id,rank:0,sender:t.user,created_at:t.created_at,steps:[],html_js_s:[]};this.discussionArr.push(e),be(()=>{const n=document.getElementById("messages-list");this.scrollBottom(n)})},updateLastUserMsg(t){const e=this.discussionArr.indexOf(s=>s.id=t.user_id),n={binding:t.binding,content:t.message,created_at:t.created_at,type:t.type,finished_generating_at:t.finished_generating_at,id:t.user_id,model:t.model,personality:t.personality,sender:t.user,steps:[]};e!==-1&&(this.discussionArr[e]=n)},socketIOConnected(){return console.log("socketIOConnected"),this.$store.state.isConnected=!0,!0},socketIODisconnected(){return console.log("socketIOConnected"),this.$store.state.isConnected=!1,!0},new_message(t){console.log("create bot",t);let e={sender:t.sender,message_type:t.message_type,sender_type:t.sender_type,content:t.content,id:t.id,parent_id:t.parent_id,binding:t.binding,model:t.model,personality:t.personality,created_at:t.created_at,finished_generating_at:t.finished_generating_at,rank:0,steps:[],parameters:t.parameters,metadata:t.metadata};console.log(e),this.discussionArr.push(e),(this.currentDiscussion.title===""||this.currentDiscussion.title===null)&&this.changeTitleUsingUserMSG(this.currentDiscussion.id,t.message),console.log("infos",t)},talk(t){this.isGenerating=!0,this.setDiscussionLoading(this.currentDiscussion.id,this.isGenerating),ye.get("/get_generation_status",{}).then(e=>{e&&(e.data.status?console.log("Already generating"):(console.log("Generating message from ",e.data.status),Ee.emit("generate_msg_from",{id:-1}),this.discussionArr.length>0&&Number(this.discussionArr[this.discussionArr.length-1].id)+1))}).catch(e=>{console.log("Error: Could not get generation status",e)})},sendMsg(t){if(!t){this.$refs.toast.showToast("Message contains no content!",4,!1);return}this.isGenerating=!0,this.setDiscussionLoading(this.currentDiscussion.id,this.isGenerating),ye.get("/get_generation_status",{}).then(e=>{if(e)if(e.data.status)console.log("Already generating");else{Ee.emit("generate_msg",{prompt:t});let n=0;this.discussionArr.length>0&&(n=Number(this.discussionArr[this.discussionArr.length-1].id)+1);let s={message:t,id:n,rank:0,user:this.$store.state.config.user_name,created_at:new Date().toLocaleString(),sender:this.$store.state.config.user_name,message_type:this.msgTypes.MSG_TYPE_FULL,sender_type:this.senderTypes.SENDER_TYPES_USER,content:t,id:n,parent_id:n,binding:"",model:"",personality:"",created_at:new Date().toLocaleString(),finished_generating_at:new Date().toLocaleString(),rank:0,steps:[],parameters:null,metadata:[]};this.createUserMsg(s)}}).catch(e=>{console.log("Error: Could not get generation status",e)})},notify(t){self.isGenerating=!1,this.setDiscussionLoading(this.currentDiscussion.id,this.isGenerating),be(()=>{const e=document.getElementById("messages-list");this.scrollBottom(e)}),this.$refs.toast.showToast(t.content,5,t.status),this.chime.play()},streamMessageContent(t){const e=t.discussion_id;if(this.setDiscussionLoading(e,!0),this.currentDiscussion.id==e){this.isGenerating=!0;const n=this.discussionArr.findIndex(o=>o.id==t.id),s=this.discussionArr[n];if(s&&(t.message_type==this.msgTypes.MSG_TYPE_FULL||t.message_type==this.msgTypes.MSG_TYPE_FULL_INVISIBLE_TO_AI))s.content=t.content,s.finished_generating_at=t.finished_generating_at;else if(s&&t.message_type==this.msgTypes.MSG_TYPE_CHUNK)s.content+=t.content;else if(t.message_type==this.msgTypes.MSG_TYPE_STEP_START)s.steps.push({message:t.content,done:!1,status:!0});else if(t.message_type==this.msgTypes.MSG_TYPE_STEP_END){const o=s.steps.find(r=>r.message===t.content);if(o){o.done=!0;try{console.log(t.parameters);const r=t.parameters;o.status=r.status,console.log(r)}catch(r){console.error("Error parsing JSON:",r.message)}}}else t.message_type==this.msgTypes.MSG_TYPE_JSON_INFOS?(console.log("JSON message"),console.log(t.metadata),s.metadata=t.metadata):t.message_type==this.msgTypes.MSG_TYPE_EXCEPTION&&this.$refs.toast.showToast(t.content,5,!1)}this.$nextTick(()=>{ve.replace()})},async changeTitleUsingUserMSG(t,e){const n=this.list.findIndex(o=>o.id==t),s=this.list[n];e&&(s.title=e,this.tempList=this.list,await this.edit_title(t,e))},async createNewDiscussion(){this.new_discussion(null)},loadLastUsedDiscussion(){console.log("Loading last discussion");const t=localStorage.getItem("selected_discussion");if(console.log("Last discussion id: ",t),t){const e=this.list.findIndex(s=>s.id==t),n=this.list[e];n&&this.selectDiscussion(n)}},async deleteDiscussion(t){await this.delete_discussion(t),this.currentDiscussion.id==t&&(this.currentDiscussion={},this.discussionArr=[],this.setPageTitle()),this.list.splice(this.list.findIndex(e=>e.id==t),1),this.createDiscussionList(this.list)},async deleteDiscussionMulti(){const t=this.selectedDiscussions;for(let e=0;es.id==n.id),1)}this.tempList=this.list,this.isCheckbox=!1,this.$refs.toast.showToast("Removed ("+t.length+") items",4,!0),this.showConfirmation=!1,console.log("Multi delete done")},async deleteMessage(t){await this.delete_message(t).then(()=>{this.discussionArr.splice(this.discussionArr.findIndex(e=>e.id==t),1)}).catch(()=>{this.$refs.toast.showToast("Could not remove message",4,!1),console.log("Error: Could not delete message")})},async editTitle(t){const e=this.list.findIndex(s=>s.id==t.id),n=this.list[e];n.title=t.title,n.loading=!0,await this.edit_title(t.id,t.title),n.loading=!1},checkUncheckDiscussion(t,e){const n=this.list.findIndex(o=>o.id==e),s=this.list[n];s.checkBoxValue=t.target.checked,this.tempList=this.list},selectAllDiscussions(){this.isSelectAll=!this.tempList.filter(t=>t.checkBoxValue==!1).length>0;for(let t=0;t({id:n.id,title:n.title,selected:!1,loading:!1,checkBoxValue:!1})).sort(function(n,s){return s.id-n.id});this.list=e,this.tempList=e,console.log("List created")}},setDiscussionLoading(t,e){const n=this.list.findIndex(o=>o.id==t),s=this.list[n];s.loading=e},setPageTitle(t){if(t)if(t.id){const e=t.title?t.title==="untitled"?"New discussion":t.title:"New discussion";document.title="LoLLMS WebUI - "+e}else{const e=t||"Welcome";document.title="LoLLMS WebUI - "+e}else{const e=t||"Welcome";document.title="LoLLMS WebUI - "+e}},async rankUpMessage(t){await this.message_rank_up(t).then(e=>{const n=this.discussionArr[this.discussionArr.findIndex(s=>s.id==t)];n.rank=e.new_rank}).catch(()=>{this.$refs.toast.showToast("Could not rank up message",4,!1),console.log("Error: Could not rank up message")})},async rankDownMessage(t){await this.message_rank_down(t).then(e=>{const n=this.discussionArr[this.discussionArr.findIndex(s=>s.id==t)];n.rank=e.new_rank}).catch(()=>{this.$refs.toast.showToast("Could not rank down message",4,!1),console.log("Error: Could not rank down message")})},async updateMessage(t,e){await this.edit_message(t,e).then(()=>{const n=this.discussionArr[this.discussionArr.findIndex(s=>s.id==t)];n.content=e}).catch(()=>{this.$refs.toast.showToast("Could not update message",4,!1),console.log("Error: Could not update message")})},resendMessage(t,e){be(()=>{ve.replace()}),this.isGenerating=!0,this.setDiscussionLoading(this.currentDiscussion.id,this.isGenerating),ye.get("/get_generation_status",{}).then(n=>{n&&(console.log("--------------------"),console.log(t),n.data.status?console.log("Already generating"):(console.log("generate_msg_from"),Ee.emit("generate_msg_from",{prompt:e,id:t})))}).catch(n=>{console.log("Error: Could not get generation status",n)})},continueMessage(t,e){be(()=>{ve.replace()}),this.isGenerating=!0,this.setDiscussionLoading(this.currentDiscussion.id,this.isGenerating),ye.get("/get_generation_status",{}).then(n=>{n&&(console.log(n),n.data.status?console.log("Already generating"):Ee.emit("continue_generate_msg_from",{prompt:e,id:t}))}).catch(n=>{console.log("Error: Could not get generation status",n)})},stopGenerating(){this.stop_gen(),this.isGenerating=!1,this.setDiscussionLoading(this.currentDiscussion.id,this.isGenerating),console.log("Stopped generating"),be(()=>{const t=document.getElementById("messages-list");this.scrollBottom(t)})},finalMsgEvent(t){console.log("final",t),t.parent_id;const e=t.discussion_id;if(this.currentDiscussion.id==e){const n=this.discussionArr.findIndex(s=>s.id==t.id);this.discussionArr[n].content=t.content,this.discussionArr[n].finished_generating_at=t.finished_generating_at}be(()=>{const n=document.getElementById("messages-list");this.scrollBottom(n)}),this.isGenerating=!1,this.setDiscussionLoading(this.currentDiscussion.id,this.isGenerating),this.chime.play()},copyToClipBoard(t){this.$refs.toast.showToast("Copied to clipboard successfully",4,!0);let e="";t.message.binding&&(e=`Binding: ${t.message.binding}`);let n="";t.message.personality&&(n=` Personality: ${t.message.personality}`);let s="";t.created_at_parsed&&(s=` Created: ${t.created_at_parsed}`);let o="";t.message.content&&(o=t.message.content);let r="";t.message.model&&(r=`Model: ${t.message.model}`);let i="";t.message.seed&&(i=`Seed: ${t.message.seed}`);let a="";t.time_spent&&(a=` Time spent: ${t.time_spent}`);let l="";l=`${e} ${r} ${i} ${a}`.trim();const c=`${t.message.sender}${n}${s} @@ -197,4 +197,4 @@ ${o} ${l}`;navigator.clipboard.writeText(c),be(()=>{ve.replace()})},closeToast(){this.showToast=!1},saveJSONtoFile(t,e){e=e||"data.json";const n=document.createElement("a");n.href=URL.createObjectURL(new Blob([JSON.stringify(t,null,2)],{type:"text/plain"})),n.setAttribute("download",e),document.body.appendChild(n),n.click(),document.body.removeChild(n)},parseJsonObj(t){try{return JSON.parse(t)}catch(e){return this.$refs.toast.showToast(`Could not parse JSON. `+e.message,4,!1),null}},async parseJsonFile(t){return new Promise((e,n)=>{const s=new FileReader;s.onload=o=>e(this.parseJsonObj(o.target.result)),s.onerror=o=>n(o),s.readAsText(t)})},async exportDiscussions(){const t=this.list.filter(e=>e.checkBoxValue==!0).map(e=>e.id);if(t.length>0){console.log("export",t);let e=new Date;const n=e.getFullYear(),s=(e.getMonth()+1).toString().padStart(2,"0"),o=e.getDate().toString().padStart(2,"0"),r=e.getHours().toString().padStart(2,"0"),i=e.getMinutes().toString().padStart(2,"0"),a=e.getSeconds().toString().padStart(2,"0"),c="discussions_export_"+(n+"."+s+"."+o+"."+r+i+a)+".json";this.loading=!0;const u=await this.export_multiple_discussions(t);u?(this.saveJSONtoFile(u,c),this.$refs.toast.showToast("Successfully exported",4,!0),this.isCheckbox=!1):this.$refs.toast.showToast("Failed to export discussions",4,!1),this.loading=!1}},async importDiscussions(t){const e=await this.parseJsonFile(t.target.files[0]);await this.import_multiple_discussions(e)?(this.$refs.toast.showToast("Successfully imported ("+e.length+")",4,!0),await this.list_discussions()):this.$refs.toast.showToast("Failed to import discussions",4,!1)},async getPersonalityAvatars(){for(;this.$store.state.personalities===null;)await new Promise(e=>setTimeout(e,100));let t=this.$store.state.personalities;this.personalityAvatars=t.map(e=>({name:e.name,avatar:e.avatar}))},getAvatar(t){if(t.toLowerCase().trim()==this.$store.state.config.user_name.toLowerCase().trim())return"user_infos/"+this.$store.state.config.user_avatar;const e=this.personalityAvatars.findIndex(s=>s.name===t),n=this.personalityAvatars[e];if(n)return console.log("Avatar",n.avatar),n.avatar},setFileListChat(t){try{this.$refs.chatBox.fileList=this.$refs.chatBox.fileList.concat(t)}catch(e){this.$refs.toast.showToast(`Failed to set filelist in chatbox -`+e.message,4,!1)}this.isDragOverChat=!1},setDropZoneChat(){this.isDragOverChat=!0,this.$refs.dragdropChat.show=!0},async setFileListDiscussion(t){if(t.length>1){this.$refs.toast.showToast("Failed to import discussions. 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xi=X1(p2);console.log("Loaded main.js");const SGe=A0({state(){return{ready:!1,settingsChanged:!1,isConnected:!1,config:null,mountedPers:null,mountedPersArr:null,bindingsArr:null,modelsArr:null,models_zoo:null,personalities:null,diskUsage:null,ramUsage:null,vramUsage:null,extensionsZoo:null}},mutations:{setIsConnected(t,e){t.isConnected=e},setConfig(t,e){t.config=e},setPersonalities(t,e){t.personalities=e},setMountedPers(t,e){t.mountedPers=e},setMountedPersArr(t,e){t.mountedPersArr=e},setBindingsArr(t,e){t.bindingsArr=e},setModelsArr(t,e){t.modelsArr=e},setDiskUsage(t,e){t.diskUsage=e},setRamUsage(t,e){t.ramUsage=e},setVramUsage(t,e){t.vramUsage=e},setExtensionsZoo(t,e){t.extensionsZoo=e},setModelsZoo(t,e){t.models_zoo=e}},getters:{getIsConnected(t){return t.isConnected},getConfig(t){return t.config},getPersonalities(t){return t.personalities},getMountedPersArr(t){return t.mountedPersArr},getMountedPers(t){return t.mountedPers},getbindingsArr(t){return t.bindingsArr},getModelsArr(t){return t.modelsArr},getDiskUsage(t){return t.diskUsage},getRamUsage(t){return t.ramUsage},getVramUsage(t){return t.vramUsage},getModelsZoo(t){return t.models_zoo},getExtensionsZoo(t){return t.extensionsZoo}},actions:{async refreshConfig({commit:t}){console.log("Fetching configuration");try{const e=await _n("get_config");let n=e.personalities[e.active_personality_id].split("/");e.personality_category=n[0],e.personality_folder=n[1],console.log("Recovered config"),console.log(e),t("setConfig",e)}catch(e){console.log(e.message,"refreshConfig")}},async refreshPersonalitiesArr({commit:t}){let e=[];const n=await _n("get_all_personalities"),s=Object.keys(n);console.log("Personalities recovered:"+this.state.config.personalities);for(let o=0;o{const c=this.state.config.personalities.includes(r+"/"+l.folder);let u={};return u=l,u.category=r,u.full_path=r+"/"+l.folder,u.isMounted=c,u});e.length==0?e=a:e=e.concat(a)}e.sort((o,r)=>o.name.localeCompare(r.name)),t("setPersonalities",e),console.log("Done loading personalities")},refreshMountedPersonalities({commit:t}){let e=[];for(let n=0;ni.full_path==s),r=this.state.personalities[o];r?e.push(r):e.push(this.state.personalities[this.state.personalities.findIndex(i=>i.full_path=="generic/lollms")])}console.log("Personalities list",this.state.personalities),t("setMountedPersArr",e),console.log("active_personality_id",this.state.config.active_personality_id),console.log("selected pers",this.state.config.personalities[this.state.config.active_personality_id]),this.state.mountedPers=this.state.personalities[this.state.personalities.findIndex(n=>n.full_path==this.state.config.personalities[this.state.config.active_personality_id])],console.log("selected pers",this.state.mountedPers)},async refreshBindings({commit:t}){let e=await _n("list_bindings");t("setBindingsArr",e)},async refreshModels({commit:t}){let e=await _n("list_models");t("setModelsArr",e)},async refreshExtensionsZoo({commit:t}){let e=await _n("list_extensions");t("setExtensionsZoo",e)},async refreshDiskUsage({commit:t}){this.state.diskUsage=await _n("disk_usage")},async refreshRamUsage({commit:t}){this.state.ramUsage=await _n("ram_usage")},async refreshVramUsage({commit:t}){console.log("getting gpu data");const e=await _n("vram_usage"),n=[];if(e.nb_gpus>0){for(let o=0;o{console.log("found models");let n=e.data;n.sort((s,o)=>s.title.localeCompare(o.title));for(let s=0;si.title==o)==-1){let i={};i.title=o,i.path=o,i.icon="",i.isCustomModel=!0,i.isInstalled=!0,n.push(i)}}n.sort((s,o)=>s.isInstalled&&!o.isInstalled?-1:!s.isInstalled&&o.isInstalled?1:0),n.forEach(s=>{s.title==this.state.config.model_name?s.selected=!0:s.selected=!1}),t("setModelsZoo",n),console.log("Models zoo loaded successfully")}).catch(e=>{console.log(e.message,"fetchModels")})},fetchCustomModels({commit:t}){ye.get("/list_models").then(e=>{}).catch(e=>{console.log(e.message,"fetchCustomModels")})}}});async function _n(t){try{const e=await ye.get("/"+t);if(e)return e.data}catch(e){throw console.log(e.message,"api_get_req"),e}}let zh=!1;xi.mixin({created(){zh||(zh=!0,console.log("Calling"),this.$store.dispatch("refreshConfig").then(()=>{console.log("recovered config"),this.$store.dispatch("refreshPersonalitiesArr").then(()=>{this.$store.dispatch("refreshMountedPersonalities"),this.$store.dispatch("refreshBindings"),this.$store.dispatch("refreshModels"),this.$store.dispatch("refreshDiskUsage"),this.$store.dispatch("refreshRamUsage"),this.$store.dispatch("refreshVramUsage"),this.$store.dispatch("refreshModelsZoo"),this.$store.dispatch("refreshExtensionsZoo"),this.$store.state.ready=!0,console.log("done loading data")})}))},beforeMount(){}});xi.use(AGe);xi.use(SGe);xi.mount("#app"); +`+e.message,4,!1)}this.isDragOverChat=!1},setDropZoneChat(){this.isDragOverChat=!0,this.$refs.dragdropChat.show=!0},async setFileListDiscussion(t){if(t.length>1){this.$refs.toast.showToast("Failed to import discussions. 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xi=X1(p2);console.log("Loaded main.js");const RGe=A0({state(){return{ready:!1,settingsChanged:!1,isConnected:!1,config:null,mountedPers:null,mountedPersArr:null,bindingsArr:null,modelsArr:null,models_zoo:null,personalities:null,diskUsage:null,ramUsage:null,vramUsage:null,extensionsZoo:null}},mutations:{setIsConnected(t,e){t.isConnected=e},setConfig(t,e){t.config=e},setPersonalities(t,e){t.personalities=e},setMountedPers(t,e){t.mountedPers=e},setMountedPersArr(t,e){t.mountedPersArr=e},setBindingsArr(t,e){t.bindingsArr=e},setModelsArr(t,e){t.modelsArr=e},setDiskUsage(t,e){t.diskUsage=e},setRamUsage(t,e){t.ramUsage=e},setVramUsage(t,e){t.vramUsage=e},setExtensionsZoo(t,e){t.extensionsZoo=e},setModelsZoo(t,e){t.models_zoo=e}},getters:{getIsConnected(t){return t.isConnected},getConfig(t){return t.config},getPersonalities(t){return t.personalities},getMountedPersArr(t){return t.mountedPersArr},getMountedPers(t){return t.mountedPers},getbindingsArr(t){return t.bindingsArr},getModelsArr(t){return t.modelsArr},getDiskUsage(t){return t.diskUsage},getRamUsage(t){return t.ramUsage},getVramUsage(t){return t.vramUsage},getModelsZoo(t){return t.models_zoo},getExtensionsZoo(t){return t.extensionsZoo}},actions:{async refreshConfig({commit:t}){console.log("Fetching configuration");try{const e=await _n("get_config");let n=e.personalities[e.active_personality_id].split("/");e.personality_category=n[0],e.personality_folder=n[1],console.log("Recovered config"),console.log(e),t("setConfig",e)}catch(e){console.log(e.message,"refreshConfig")}},async refreshPersonalitiesArr({commit:t}){let e=[];const n=await _n("get_all_personalities"),s=Object.keys(n);console.log("Personalities recovered:"+this.state.config.personalities);for(let o=0;o{const c=this.state.config.personalities.includes(r+"/"+l.folder);let u={};return u=l,u.category=r,u.full_path=r+"/"+l.folder,u.isMounted=c,u});e.length==0?e=a:e=e.concat(a)}e.sort((o,r)=>o.name.localeCompare(r.name)),t("setPersonalities",e),console.log("Done loading personalities")},refreshMountedPersonalities({commit:t}){let e=[];for(let n=0;ni.full_path==s),r=this.state.personalities[o];r?e.push(r):e.push(this.state.personalities[this.state.personalities.findIndex(i=>i.full_path=="generic/lollms")])}console.log("Personalities list",this.state.personalities),t("setMountedPersArr",e),console.log("active_personality_id",this.state.config.active_personality_id),console.log("selected pers",this.state.config.personalities[this.state.config.active_personality_id]),this.state.mountedPers=this.state.personalities[this.state.personalities.findIndex(n=>n.full_path==this.state.config.personalities[this.state.config.active_personality_id])],console.log("selected pers",this.state.mountedPers)},async refreshBindings({commit:t}){let e=await _n("list_bindings");t("setBindingsArr",e)},async refreshModels({commit:t}){let e=await _n("list_models");t("setModelsArr",e)},async refreshExtensionsZoo({commit:t}){let e=await _n("list_extensions");t("setExtensionsZoo",e)},async refreshDiskUsage({commit:t}){this.state.diskUsage=await _n("disk_usage")},async refreshRamUsage({commit:t}){this.state.ramUsage=await _n("ram_usage")},async refreshVramUsage({commit:t}){console.log("getting gpu data");const e=await _n("vram_usage"),n=[];if(e.nb_gpus>0){for(let o=0;o{console.log("found models");let n=e.data;n.sort((s,o)=>s.title.localeCompare(o.title));for(let s=0;si.title==o)==-1){let i={};i.title=o,i.path=o,i.icon="",i.isCustomModel=!0,i.isInstalled=!0,n.push(i)}}n.sort((s,o)=>s.isInstalled&&!o.isInstalled?-1:!s.isInstalled&&o.isInstalled?1:0),n.forEach(s=>{s.title==this.state.config.model_name?s.selected=!0:s.selected=!1}),t("setModelsZoo",n),console.log("Models zoo loaded successfully")}).catch(e=>{console.log(e.message,"fetchModels")})},fetchCustomModels({commit:t}){ye.get("/list_models").then(e=>{}).catch(e=>{console.log(e.message,"fetchCustomModels")})}}});async function _n(t){try{const e=await ye.get("/"+t);if(e)return e.data}catch(e){throw console.log(e.message,"api_get_req"),e}}let zh=!1;xi.mixin({created(){zh||(zh=!0,console.log("Calling"),this.$store.dispatch("refreshConfig").then(()=>{console.log("recovered config"),this.$store.dispatch("refreshPersonalitiesArr").then(()=>{this.$store.dispatch("refreshMountedPersonalities"),this.$store.dispatch("refreshBindings"),this.$store.dispatch("refreshModels"),this.$store.dispatch("refreshDiskUsage"),this.$store.dispatch("refreshRamUsage"),this.$store.dispatch("refreshVramUsage"),this.$store.dispatch("refreshModelsZoo"),this.$store.dispatch("refreshExtensionsZoo"),this.$store.state.ready=!0,console.log("done loading data")})}))},beforeMount(){}});xi.use(OGe);xi.use(RGe);xi.mount("#app"); diff --git a/web/dist/index.html b/web/dist/index.html index 34ebd949..085ab932 100644 --- a/web/dist/index.html +++ b/web/dist/index.html @@ -6,7 +6,7 @@ LoLLMS WebUI - Welcome - + diff --git a/web/src/components/InteractiveMenu.vue b/web/src/components/InteractiveMenu.vue index abc3ba21..ecb5fa00 100644 --- a/web/src/components/InteractiveMenu.vue +++ b/web/src/components/InteractiveMenu.vue @@ -1,7 +1,9 @@