Promote Colabcpp

Updated Model list and description in Read.md and GPU.ipynb markdown

.gitattributes

@@ -1,2 +1,3 @@
 *.min.lua linguist-vendored
 *documentation.html linguist-vendored
+/static/swagger-ui/* linguist-vendored

.gitignore

@@ -15,6 +15,7 @@ bin
 __pycache__
 *.log
 cache
+accelerate-disk-cache
 userscripts
 !userscripts/examples
 !userscripts/kaipreset_*.lua
@@ -24,6 +25,8 @@ softprompts
 models
 !models/models go here.txt
 Uninstall
+flask_session
+accelerate-disk-cache
 .ipynb_checkpoints
 
 # Ignore PyCharm project files.

README.md

@@ -48,33 +48,42 @@ If you would like to play KoboldAI online for free on a powerful computer you ca
 
 Each edition features different models and requires different hardware to run, this means that if you are unable to obtain a TPU or a GPU you might still be able to use the other version. The models you can use are listed underneath the edition. To open a Colab click the big link featuring the editions name.
 
-## [TPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb)
+## [Models the TPU can run:](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb)
 
-| Model | Size | Style | Description |
-| --- | --- | --- | --- |
-| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |
-| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |
-| [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |
-| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |
-| [Adventure](https://huggingface.co/KoboldAI/GPT-J-6B-Adventure) by VE\_FORBRYDERNE | 6B | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). |
-| [Lit](https://huggingface.co/hakurei/lit-6B) by Haru | 6B | NSFW | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. |
-| Neo(X) by EleutherAI | 20B | Generic | NeoX is the largest EleutherAI model currently available, being a generic model it is not particularly trained towards anything and can do a variety of writing, Q&A and coding tasks. 20B's performance is closely compared to the 13B models and it is worth trying both especially if you have a task that does not involve english writing. Its behavior will be similar to the GPT-J-6B model since they are trained on the same dataset but with more sensitivity towards repetition penalty and with more knowledge. |
-| [Fairseq Dense](https://huggingface.co/KoboldAI/fairseq-dense-13B) | 13B | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. |
-| [GPT-J-6B](https://huggingface.co/EleutherAI/gpt-j-6B) by EleutherAI | 6B | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. |
+| Model | Style | Description |
+| --- | --- | --- |
+| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |
+| [Erebus](https://huggingface.co/KoboldAI/OPT-13B-Erebus) by Mr Seeker | NSFW | Erebus is our community's flagship NSFW model, being a combination of multiple large datasets that include Literotica, Shinen and erotic novels from Nerys and featuring thourough tagging support it covers the vast majority of erotic writing styles. This model is capable of replacing both the Lit and Shinen models in terms of content and style and has been well received as (one of) the best NSFW models out there. If you wish to use this model for commercial or non research usage we recommend choosing the 20B version as that one is not subject to the restrictive OPT license. |
+| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |
+| [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | NSFW | Shinen is an NSFW model trained on a variety of stories from the website Sexstories it contains many different kinks. It has been merged into the larger (and better) Erebus model. |
+| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\_FORBRYDERNE | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |
+| [Adventure](https://huggingface.co/KoboldAI/GPT-J-6B-Adventure) by VE\_FORBRYDERNE | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). |
+| [Lit](https://huggingface.co/hakurei/lit-6B) ([V2](https://huggingface.co/hakurei/litv2-6B-rev3)) by Haru | NSFW | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. |
+| [OPT](https://huggingface.co/facebook/opt-13b) by Metaseq | Generic | OPT is considered one of the best base models as far as content goes, its behavior has the strengths of both GPT-Neo and Fairseq Dense. Compared to Neo duplicate and unnecessary content has been left out, while additional literature was added in similar to the Fairseq Dense model. The Fairseq Dense model however lacks the broader data that OPT does have. The biggest downfall of OPT is its license, which prohibits any commercial usage, or usage beyond research purposes. |
+| [Neo(X)](https://huggingface.co/EleutherAI/gpt-neox-20b) by EleutherAI | Generic | NeoX is the largest EleutherAI model currently available, being a generic model it is not particularly trained towards anything and can do a variety of writing, Q&A and coding tasks. 20B's performance is closely compared to the 13B models and it is worth trying both especially if you have a task that does not involve english writing. Its behavior will be similar to the GPT-J-6B model since they are trained on the same dataset but with more sensitivity towards repetition penalty and with more knowledge. |
+| [Fairseq Dense](https://huggingface.co/KoboldAI/fairseq-dense-13B) | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. Compared to other models the dataset focuses primarily on literature and contains little else. |
+| [GPT-J-6B](https://huggingface.co/EleutherAI/gpt-j-6B) by EleutherAI | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. |
 
-## [GPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/GPU.ipynb)
-
-| Model | Size | Style | Description |
-| --- | --- | --- | --- |
-| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |
-| [Janeway 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |
-| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on Neo 2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |
-| [AID 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |
-| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on Horni 2.7B and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |
-| [Horni 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |
-| [Shinen 2.7B ](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you Shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |
-| [Neo 2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |
+## [Models the Colab GPU can run:](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/GPU.ipynb)
 
+| Model | Style | Description |
+| --- | --- | --- |
+| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |
+| [Tiefighter 13B by KoboldAI](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) | Hybrid | Tiefighter 13B is a very versitile fiction Hybrid, it can write, chat and play adventure games and can also answer regular instructions (Although we do not recommend this model for factual use due to its fictional nature). This is an excellent starting model, for the best results avoid using Second person writing in your chats unless you are wanting it to become a text adventure.|
+| [Janeway](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |
+| [Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | Novel | Picard is a model trained for SFW Novels based on Neo 2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |
+| [AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |
+| [OPT](https://huggingface.co/facebook/opt-2.7b) by Metaseq | Generic | OPT is considered one of the best base models as far as content goes, its behavior has the strengths of both GPT-Neo and Fairseq Dense. Compared to Neo duplicate and unnecessary content has been left out, while additional literature was added in similar to the Fairseq Dense model. The Fairseq Dense model however lacks the broader data that OPT does have. The biggest downfall of OPT is its license, which prohibits any commercial usage, or usage beyond research purposes. |
+| [Fairseq Dense](https://huggingface.co/KoboldAI/fairseq-dense-2.7B) | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger models from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. Compared to other models the dataset focuses primarily on literature and contains little else. |
+| [MythoMax 13B](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ) by Gryphe | Roleplay | An improved, potentially even perfected variant of MythoMix, my MythoLogic-L2 and Huginn merge using a highly experimental tensor type merge technique¹. |
+| [Holomax 13B by KoboldAI](https://huggingface.co/KoboldAI/LLaMA2-13B-Holomax) | Adventure | This is an expansion merge to the well-praised MythoMax model from Gryphe (60%) using MrSeeker's KoboldAI Holodeck model (40%). The goal of this model is to enhance story-writing capabilities while preserving the desirable traits of the MythoMax model as much as possible (It does limit chat reply length). |
+| [Airoboros 13B](https://huggingface.co/jondurbin/airoboros-13b) by Jon Durbin | Generic | This is an instruction fine-tuned llama-2 model, using synthetic instructions generated by airoboros⁵. |
+| [Emerhyst 13B](https://huggingface.co/Undi95/Emerhyst-13B) by Undi | Roleplay | An attempt using BlockMerge_Gradient to get better result. In addition, LimaRP v3 was used⁷. |
+| [Chronos 13B](https://huggingface.co/elinas/chronos-13b) by Elinas | Generic | This model is primarily focused on chat, roleplay, and storywriting, but can accomplish other tasks such as simple reasoning and coding. Chronos generates very long outputs with coherent text, largely due to the human inputs it was trained on. |
+| [Spring Dragon by Henk717](https://huggingface.co/Henk717/spring-dragon) | Adventure | This model is a recreation attempt of the AI Dungeon 2 Dragon model. To achieve this, the "text_adventures.txt" dataset was used, which was bundled with the original AI Dungeon 2 GitHub release prior to the online service. It is worth noting that the same dataset file was used to create the Dragon model, where Dragon is a GPT-3 175B Davinci model from 2020. |
+| [Holodeck By KoboldAI](https://huggingface.co/KoboldAI/LLAMA2-13B-Holodeck-1) | Adventure |LLAMA2 13B-Holodeck is a finetune created using Meta's llama 2 model.The training data contains around 3000 ebooks in various genres. Most parts of the dataset have been prepended using the following text: [Genre: <genre1>, <genre2>|
+| [Neo](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |
+| [Various 2.7b models]() by various | Various smaller models are also possible to load in GPU colab. | |
 ### Styles
 
 | Type | Description |
@@ -100,7 +109,7 @@ KoboldAI has a large number of dependencies you will need to install on your com
 
 ### Downloading the latest version of KoboldAI
 
-KoboldAI is a rolling release on our github, the code you see is also the game. You can the software by clicking on the green Code button at the top of the page and clicking Download ZIP.
+KoboldAI is a rolling release on our github, the code you see is also the game. You can download the software by clicking on the green Code button at the top of the page and clicking Download ZIP, or use the `git clone` command instead. Then, on Windows you need to you run install_requirements.bat (using admin mode is recommanded to avoid errors), and once it's done, or if you're on Linux, either play.bat/sh or remote-play.bat/sh to run it.
 
 The easiest way for Windows users is to use the [offline installer](https://sourceforge.net/projects/koboldai/files/latest/download) below.
 
@@ -192,14 +201,21 @@ Lastly the all the features of our userscript API are documented inside the API
 
 For our TPU versions keep in mind that scripts modifying AI behavior relies on a different way of processing that is slower than if you leave these userscripts disabled even if your script only sporadically uses this modifier. If you want to partially use a script at its full speed than you can enable "No Gen Modifiers" to ensure that the parts that would make the TPU slow are not active.
 
+## API
+
+KoboldAI has a REST API that can be accessed by adding /api to the URL that Kobold provides you (For example http://127.0.0.1:5000/api).  
+When accessing this link in a browser you will be taken to the interactive documentation.
+
 ## Contributors
 
 This project contains work from the following contributors :
 
 *   The Gantian - Creator of KoboldAI, has created most features such as the interface, the different AI model / API integrations and in general the largest part of the project.
-*   VE FORBRYDERNE - Contributed many features such as the Editing overhaul, Adventure Mode, expansions to the world info section, breakmodel integration, scripting support, softpromtps and much more. As well as vastly improving the TPU compatibility and integrating external code into KoboldAI so we could use official versions of Transformers with virtually no downsides.
+*   VE FORBRYDERNE - Contributed many features such as the Editing overhaul, Adventure Mode, expansions to the world info section, breakmodel integration, scripting support, API, softpromtps and much more. As well as vastly improving the TPU compatibility and integrating external code into KoboldAI so we could use official versions of Transformers with virtually no downsides.
 *   Henk717 - Contributed the installation scripts, this readme, random story generator, the docker scripts, the foundation for the commandline interface and other smaller changes as well as integrating multiple parts of the code of different forks to unite it all. He also optimized the model loading so that downloaded models get converted to efficient offline models and that in future models are more likely to work out of the box. Not all code Github attributes to Henk717 is by Henk717 as some of it has been integrations of other people's work. We try to clarify this in the contributors list as much as we can.
-*   Ebolam - Automatic Saving
+*   Ebolam - Automatic Saving, back/redo, pinning, web loading of models
+*   one-some, Logits Viewer and Token Streaming
+*   db0, KoboldAI Horde
 *   Frogging101 - top\_k / tfs support (Part of this support was later redone by VE to integrate what was originally inside of finetuneanon's transformers)
 *   UWUplus (Ralf) - Contributed storage systems for community colabs, as well as cleaning up and integrating the website dependencies/code better. He is also the maintainer of flask-cloudflared which we use to generate the cloudflare links.
 *   Javalar - Initial Performance increases on the story\_refresh

breakmodel.py

@@ -4,7 +4,7 @@ https://github.com/arrmansa/Basic-UI-for-GPT-J-6B-with-low-vram/blob/main/GPT-J-
 The ORIGINAL version of the patch is released under the Apache License 2.0
 Copyright 2021 arrmansa
 Copyright 2021 finetuneanon
-Copyright 2018 The Hugging Face team
+Copyright 2018, 2022 The Hugging Face team
 
 
                                  Apache License
@@ -216,11 +216,13 @@ from torch import nn
 import torch.cuda.comm
 import copy
 import gc
+import os
 import sys
 import itertools
 import bisect
 import random
-from typing import Optional
+import utils
+from typing import Dict, List, Optional, Union
 
 from transformers.modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions
 
@@ -230,7 +232,100 @@ logger = logging.get_logger(__name__)
 
 breakmodel = True
 gpu_blocks = []
-primary_device = 0
+disk_blocks = 0
+primary_device = 0 if torch.cuda.device_count() > 0 else "cpu"
+
+
+if utils.HAS_ACCELERATE:
+    from accelerate.hooks import attach_align_device_hook_on_blocks
+    from accelerate.utils import OffloadedWeightsLoader, check_device_map, extract_submodules_state_dict, offload_state_dict
+    from accelerate import dispatch_model
+
+def dispatch_model_ex(
+    model: nn.Module,
+    device_map: Dict[str, Union[str, int, torch.device]],
+    main_device: Optional[torch.device] = None,
+    state_dict: Optional[Dict[str, torch.Tensor]] = None,
+    offload_dir: Union[str, os.PathLike] = None,
+    offload_buffers: bool = False,
+    **kwargs,
+):
+    """
+    This is a modified version of
+    https://github.com/huggingface/accelerate/blob/eeaba598f455fbd2c48661d7e816d3ff25ab050b/src/accelerate/big_modeling.py#L130
+    that still works when the main device is the CPU.
+
+    Dispatches a model according to a given device map. Layers of the model might be spread across GPUs, offloaded on
+    the CPU or even the disk.
+
+    Args:
+        model (`torch.nn.Module`):
+            The model to dispatch.
+        device_map (`Dict[str, Union[str, int, torch.device]]`):
+            A dictionary mapping module names in the models `state_dict` to the device they should go to. Note that
+            `"disk"` is accepted even if it's not a proper value for `torch.device`.
+        main_device (`str`, `int` or `torch.device`, *optional*):
+            The main execution device. Will default to the first device in the `device_map` different from `"cpu"` or
+            `"disk"`.
+        state_dict (`Dict[str, torch.Tensor]`, *optional*):
+            The state dict of the part of the model that will be kept on CPU.
+        offload_dir (`str` or `os.PathLike`):
+            The folder in which to offload the model weights (or where the model weights are already offloaded).
+        offload_buffers (`bool`, *optional*, defaults to `False`):
+            Whether or not to offload the buffers with the model parameters.
+        preload_module_classes (`List[str]`, *optional*):
+            A list of classes whose instances should load all their weights (even in the submodules) at the beginning
+            of the forward. This should only be used for classes that have submodules which are registered but not
+            called directly during the forward, for instance if a `dense` linear layer is registered, but at forward,
+            `dense.weight` and `dense.bias` are used in some operations instead of calling `dense` directly.
+    """
+    if main_device != "cpu":
+        return dispatch_model(model, device_map, main_device, state_dict, offload_dir=offload_dir, offload_buffers=offload_buffers, **kwargs)
+
+    # Error early if the device map is incomplete.
+    check_device_map(model, device_map)
+
+    offload_devices = ["cpu", "disk"] if main_device != "cpu" else ["disk"]
+
+    if main_device is None:
+        main_device = [d for d in device_map.values() if d not in offload_devices][0]
+
+    cpu_modules = [name for name, device in device_map.items() if device == "cpu"] if main_device != "cpu" else []
+    if state_dict is None and len(cpu_modules) > 0:
+        state_dict = extract_submodules_state_dict(model.state_dict(), cpu_modules)
+
+    disk_modules = [name for name, device in device_map.items() if device == "disk"]
+    if offload_dir is None and len(disk_modules) > 0:
+        raise ValueError(
+            "We need an `offload_dir` to dispatch this model according to this `device_map`, the following submodules "
+            f"need to be offloaded: {', '.join(disk_modules)}."
+        )
+    if len(disk_modules) > 0 and (
+        not os.path.isdir(offload_dir) or not os.path.isfile(os.path.join(offload_dir, "index.json"))
+    ):
+        disk_state_dict = extract_submodules_state_dict(model.state_dict(), disk_modules)
+        offload_state_dict(offload_dir, disk_state_dict)
+
+    execution_device = {
+        name: main_device if device in offload_devices else device for name, device in device_map.items()
+    }
+    offload = {name: device in offload_devices for name, device in device_map.items()}
+    save_folder = offload_dir if len(disk_modules) > 0 else None
+    if state_dict is not None or save_folder is not None:
+        weights_map = OffloadedWeightsLoader(state_dict=state_dict, save_folder=save_folder)
+    else:
+        weights_map = None
+
+    attach_align_device_hook_on_blocks(
+        model,
+        execution_device=execution_device,
+        offload=offload,
+        offload_buffers=offload_buffers,
+        weights_map=weights_map,
+        **kwargs,
+    )
+    model.hf_device_map = device_map
+    return model
 
 
 # Copied from transformers.models.bart.modeling_bart._expand_mask

colab/GPU.ipynb

@@ -1,23 +1,4 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "ColabKobold GPU",
-      "private_outputs": true,
-      "provenance": [],
-      "collapsed_sections": [],
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "accelerator": "GPU"
-  },
   "cells": [
     {
       "cell_type": "markdown",
@@ -35,52 +16,99 @@
         "id": "kX9y5koxa58q"
       },
       "source": [
+        "## [You can get faster generations and higher context with our Koboldcpp Notebook](https://koboldai.org/colabcpp)\n",
+        "\n",
         "# Welcome to KoboldAI on Google Colab, GPU Edition!\n",
         "KoboldAI is a powerful and easy way to use a variety of AI based text generation experiences. You can use it to write stories, blog posts, play a text adventure game, use it like a chatbot and more! In some cases it might even help you with an assignment or programming task (But always make sure the information the AI mentions is correct, it loves to make stuff up).\n",
         "\n",
         "For more information about KoboldAI check our our Github readme : https://github.com/KoboldAI/KoboldAI-Client/blob/main/readme.md\n",
         "\n",
-        "For the larger AI models (That are typically more coherent) check out our **[TPU edition](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb)**!"
+        "---\n",
+        "## How to load KoboldAI: Everything you need to know\n",
+        "1. On a phone? First put your browser in desktop mode because of a Google Colab bug. Otherwise nothing will happen when you click the play button. Then tap the play button next to \"<-- Tap This if you play on Mobile\", you will see an audio player. Keep the audio player playing so Colab does not get shut down in the background.\n",
+        "2. Select the desired model, you will find a description of all the available models further down the page.\n",
+        "3. Click the play button next to \"<-- Select your model below and then click this to start KoboldAI\".\n",
+        "4. Got a message saying no accelerator is available? Click cancel, and try again in a few minutes. If you do not manage to get a session when you frequently try again try at a different time of day, colab can be busy or your priority may have been lowered by frequent usage.\n",
+        "5. After everything is done loading you will get a link that you can use to open KoboldAI. In case of Localtunnel you will also be warned that some people are abusing Localtunnel for phishing, once you acknowledge this warning you will be taken to KoboldAI's interface. If you picked Cloudflare and get a 1033 error refresh the error page after waiting one minute.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "Further down the page you can find descriptions of the models, and tips to get the most out of your Google Colab experience.\n",
+        "\n",
+        "Make sure to keep this page open while you are using KoboldAI, and check back regularly to see if you got a Captcha. Failure to complete the captcha's in time can result in termination of your session or a lower priority towards the TPUs.\n",
+        "\n",
+        "Firefox users need to disable the enhanced tracking protection or use a different browser in order to be able to use Google Colab without errors (This is not something we can do anything about, the cookie blocker breaks the Google Drive integration because it uses different domains)."
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "ewkXkyiFP2Hq"
       },
+      "outputs": [],
       "source": [
         "#@title <-- Tap this if you play on Mobile { display-mode: \"form\" }\n",
         "%%html\n",
         "<b>Press play on the music player to keep the tab alive, then start KoboldAI below (Uses only 13MB of data)</b><br/>\n",
-        "<audio src=\"https://henk.tech/colabkobold/silence.m4a\" controls>"
-      ],
-      "execution_count": null,
-      "outputs": []
+        "<audio src=\"https://raw.githubusercontent.com/KoboldAI/KoboldAI-Client/main/colab/silence.m4a\" controls>"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
-        "id": "lVftocpwCoYw",
-        "cellView": "form"
+        "cellView": "form",
+        "id": "lVftocpwCoYw"
       },
+      "outputs": [],
       "source": [
         "#@title <b><-- Select your model below and then click this to start KoboldAI</b>\n",
         "#@markdown You can find a description of the models below along with instructions on how to start KoboldAI.\n",
         "\n",
-        "Model = \"Nerys 2.7B\" #@param [\"Nerys 2.7B\", \"AID 2.7B\", \"Erebus 2.7B\", \"Janeway 2.7B\", \"Picard 2.7B\", \"Horni LN 2.7B\", \"Horni 2.7B\", \"Shinen 2.7B\", \"Neo 2.7B\"] {allow-input: true}\n",
+        "Model = \"Nerys V2 6B\" #@param [\"Tiefighter 13B (United)\", \"Echidna 13B (United)\", \"HoloMax 13B (United)\", \"Emerhyst 13B (United)\", \"MythoMax 13B (United)\", \"Huginn 13B (United)\", \"Chronos 13B (United)\", \"Airoboros M2.0 13B (United)\", \"Holodeck 13B (United)\", \"Spring Dragon 13B (United)\", \"Nerys V2 6B\", \"Skein 6B\", \"Janeway 6B\", \"Adventure 6B\", \"Nerys 2.7B\", \"AID 2.7B\", \"Janeway 2.7B\", \"Picard 2.7B\", \"OPT 2.7B\", \"Fairseq Dense 2.7B\", \"Neo 2.7B\"] {allow-input: true}\n",
+        "Revision = \"\" #@param [\"\"]{allow-input: true}\n",
         "Version = \"Official\" #@param [\"Official\", \"United\"] {allow-input: true}\n",
-        "Provider = \"Localtunnel\" #@param [\"Localtunnel\", \"Cloudflare\"]\n",
+        "Provider = \"Cloudflare\" #@param [\"Localtunnel\", \"Cloudflare\"]\n",
+        "use_google_drive = True #@param {type:\"boolean\"}\n",
+        "\n",
+        "import os\n",
+        "if not os.path.isfile(\"/opt/bin/nvidia-smi\"):\n",
+        "  raise RuntimeError(\"⚠️Colab did not give you a GPU due to usage limits, this can take a few hours before they let you back in. Check out https://lite.koboldai.net for a free alternative (that does not provide an API link but can load KoboldAI saves and chat cards) or subscribe to Colab Pro for immediate access.⚠️\")\n",
         "\n",
         "!nvidia-smi\n",
         "from google.colab import drive\n",
-        "drive.mount('/content/drive/')\n",
+        "if use_google_drive:\n",
+        "  drive.mount('/content/drive/')\n",
+        "else:\n",
+        "  import os\n",
+        "  if not os.path.exists(\"/content/drive\"):\n",
+        "    os.mkdir(\"/content/drive\")\n",
+        "  if not os.path.exists(\"/content/drive/MyDrive/\"):\n",
+        "    os.mkdir(\"/content/drive/MyDrive/\")\n",
         "\n",
-        "if Model == \"Nerys 2.7B\":\n",
-        "  Model = \"KoboldAI/fairseq-dense-2.7B-Nerys\"\n",
+        "if Model == \"Nerys V2 6B\":\n",
+        "  Model = \"KoboldAI/OPT-6B-nerys-v2\"\n",
         "  path = \"\"\n",
         "  download = \"\"\n",
-        "elif Model == \"Erebus 2.7B\":\n",
-        "  Model = \"KoboldAI/OPT-2.7B-Erebus\"\n",
+        "elif Model == \"Skein 6B\":\n",
+        "  Model = \"KoboldAI/GPT-J-6B-Skein\"\n",
+        "  path = \"\"\n",
+        "  download = \"\"\n",
+        "elif Model == \"Janeway 6B\":\n",
+        "  Model = \"KoboldAI/GPT-J-6B-Janeway\"\n",
+        "  path = \"\"\n",
+        "  download = \"\"\n",
+        "elif Model == \"Adventure 6B\":\n",
+        "  Model = \"KoboldAI/GPT-J-6B-Adventure\"\n",
+        "  path = \"\"\n",
+        "  download = \"\"\n",
+        "elif Model == \"Shinen 6B\":\n",
+        "  Model = \"KoboldAI/GPT-J-6B-Shinen\"\n",
+        "  path = \"\"\n",
+        "  download = \"\"\n",
+        "elif Model == \"Nerys 2.7B\":\n",
+        "  Model = \"KoboldAI/fairseq-dense-2.7B-Nerys\"\n",
         "  path = \"\"\n",
         "  download = \"\"\n",
         "elif Model == \"Janeway 2.7B\":\n",
@@ -95,67 +123,107 @@
         "  Model = \"KoboldAI/GPT-Neo-2.7B-AID\"\n",
         "  path = \"\"\n",
         "  download = \"\"\n",
-        "elif Model == \"Horni LN 2.7B\":\n",
-        "  Model = \"KoboldAI/GPT-Neo-2.7B-Horni-LN\"\n",
+        "elif Model == \"Fairseq Dense 2.7B\":\n",
+        "  Model = \"KoboldAI/fairseq-dense-2.7B\"\n",
         "  path = \"\"\n",
         "  download = \"\"\n",
-        "elif Model == \"Horni 2.7B\":\n",
-        "  Model = \"KoboldAI/GPT-Neo-2.7B-Horni\"\n",
-        "  path = \"\"\n",
-        "  download = \"\"\n",
-        "elif Model == \"Shinen 2.7B\":\n",
-        "  Model = \"KoboldAI/GPT-Neo-2.7B-Shinen\"\n",
+        "elif Model == \"OPT 2.7B\":\n",
+        "  Model = \"facebook/opt-2.7b\"\n",
         "  path = \"\"\n",
         "  download = \"\"\n",
         "elif Model == \"Neo 2.7B\":\n",
         "  Model = \"EleutherAI/gpt-neo-2.7B\"\n",
         "  path = \"\"\n",
         "  download = \"\"\n",
+        "elif Model == \"Tiefighter 13B (United)\":\n",
+        "  Model = \"KoboldAI/LLaMA2-13B-Tiefighter\"\n",
+        "  path = \"\"\n",
+        "  download = \"\"\n",
+        "  Version = \"United\"\n",
+        "elif Model == \"Echidna 13B (United)\":\n",
+        "  Model = \"NeverSleep/Echidna-13b-v0.3\"\n",
+        "  path = \"\"\n",
+        "  download = \"\"\n",
+        "  Version = \"United\"\n",
+        "elif Model == \"Huginn 13B (United)\":\n",
+        "  Model = \"The-Face-Of-Goonery/Huginn-13b-v1.2\"\n",
+        "  path = \"\"\n",
+        "  download = \"\"\n",
+        "  Version = \"United\"\n",
+        "elif Model == \"Chronos 13B (United)\":\n",
+        "  Model = \"elinas/chronos-13b-v2\"\n",
+        "  path = \"\"\n",
+        "  download = \"\"\n",
+        "  Version = \"United\"\n",
+        "elif Model == \"Airoboros M2.0 13B (United)\":\n",
+        "  Model = \"jondurbin/airoboros-l2-13b-gpt4-m2.0\"\n",
+        "  path = \"\"\n",
+        "  download = \"\"\n",
+        "  Version = \"United\"\n",
+        "elif Model == \"Emerhyst 13B (United)\":\n",
+        "  Model = \"Undi95/Emerhyst-13B\"\n",
+        "  path = \"\"\n",
+        "  download = \"\"\n",
+        "  Version = \"United\"\n",
+        "elif Model == \"MythoMax 13B (United)\":\n",
+        "  Model = \"Gryphe/MythoMax-L2-13b\"\n",
+        "  Revision = \"\"\n",
+        "  path = \"\"\n",
+        "  download = \"\"\n",
+        "  Version = \"United\"\n",
+        "elif Model == \"Spring Dragon 13B (United)\":\n",
+        "  Model = \"Henk717/spring-dragon\"\n",
+        "  path = \"\"\n",
+        "  download = \"\"\n",
+        "  Version = \"United\"\n",
+        "elif Model == \"Holodeck 13B (United)\":\n",
+        "  Model = \"KoboldAI/LLAMA2-13B-Holodeck-1\"\n",
+        "  path = \"\"\n",
+        "  download = \"\"\n",
+        "  Version = \"United\"\n",
+        "elif Model == \"HoloMax 13B (United)\":\n",
+        "  Model = \"KoboldAI/LLaMA2-13B-Holomax\"\n",
+        "  path = \"\"\n",
+        "  download = \"\"\n",
+        "  Version = \"United\"\n",
         "\n",
         "if Provider == \"Localtunnel\":\n",
         "  tunnel = \"--localtunnel yes\"\n",
         "else:\n",
         "  tunnel = \"\"\n",
         "\n",
-        "!wget https://koboldai.org/ckds -O - | bash /dev/stdin -m $Model -g $Version $tunnel"
-      ],
-      "execution_count": null,
-      "outputs": []
+        "!wget https://koboldai.org/ckds -O - | bash /dev/stdin -m $Model -g $Version $Revision $tunnel"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "Lrm840I33hkC"
+      },
       "source": [
         "# GPU Edition Model Descriptions\n",
-        "| Model | Size | Style | Description |\n",
-        "| --- | --- | --- | --- |\n",
-        "| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
-        "| [Janeway 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
-        "| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on Neo 2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n",
-        "| [AID 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n",
-        "| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on Horni 2.7B and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n",
-        "| [Horni 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |\n",
-        "| [Shinen 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you Shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n",
-        "| [Neo 2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n",
-        "\n",
-        "# [TPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb)\n",
-        "\n",
-        "| Model | Size | Style | Description |\n",
-        "| --- | --- | --- | --- |\n",
-        "| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
-        "| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
-        "| [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |\n",
-        "| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |\n",
-        "| [Adventure](https://huggingface.co/KoboldAI/GPT-J-6B-Adventure) by VE\\_FORBRYDERNE | 6B | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). |\n",
-        "| [Lit](https://huggingface.co/hakurei/lit-6B) by Haru | 6B | NSFW | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. |\n",
-        "| Neo(X) by EleutherAI | 20B | Generic | NeoX is the largest EleutherAI model currently available, being a generic model it is not particularly trained towards anything and can do a variety of writing, Q&A and coding tasks. 20B's performance is closely compared to the 13B models and it is worth trying both especially if you have a task that does not involve english writing. Its behavior will be similar to the GPT-J-6B model since they are trained on the same dataset but with more sensitivity towards repetition penalty and with more knowledge. |\n",
-        "| [Fairseq Dense](https://huggingface.co/KoboldAI/fairseq-dense-13B) | 13B | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. |\n",
-        "| [GPT-J-6B](https://huggingface.co/EleutherAI/gpt-j-6B) by EleutherAI | 6B | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. |\n",
+        "| Model | Style | Description |\n",
+        "| --- | --- | --- |\n",
+        "| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
+        "| [Tiefighter 13B by KoboldAI](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) | Hybrid | Tiefighter 13B is a very versitile fiction Hybrid, it can write, chat and play adventure games and can also answer regular instructions (Although we do not recommend this model for factual use due to its fictional nature). This is an excellent starting model, for the best results avoid using Second person writing in your chats unless you are wanting it to become a text adventure.|\n",
+        "| [Janeway](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
+        "| [Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | Novel | Picard is a model trained for SFW Novels based on Neo 2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n",
+        "| [AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n",
+        "| [OPT](https://huggingface.co/facebook/opt-2.7b) by Metaseq | Generic | OPT is considered one of the best base models as far as content goes, its behavior has the strengths of both GPT-Neo and Fairseq Dense. Compared to Neo duplicate and unnecessary content has been left out, while additional literature was added in similar to the Fairseq Dense model. The Fairseq Dense model however lacks the broader data that OPT does have. The biggest downfall of OPT is its license, which prohibits any commercial usage, or usage beyond research purposes. |\n",
+        "| [Fairseq Dense](https://huggingface.co/KoboldAI/fairseq-dense-2.7B) | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger models from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. Compared to other models the dataset focuses primarily on literature and contains little else. |\n",
+        "| [MythoMax 13B](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ) by Gryphe | Roleplay | An improved, potentially even perfected variant of MythoMix, my MythoLogic-L2 and Huginn merge using a highly experimental tensor type merge technique¹. |\n",
+        "| [Holomax 13B by KoboldAI](https://huggingface.co/KoboldAI/LLaMA2-13B-Holomax) | Adventure | This is an expansion merge to the well-praised MythoMax model from Gryphe (60%) using MrSeeker's KoboldAI Holodeck model (40%). The goal of this model is to enhance story-writing capabilities while preserving the desirable traits of the MythoMax model as much as possible (It does limit chat reply length). |\n",
+        "| [Airoboros 13B](https://huggingface.co/jondurbin/airoboros-13b) by Jon Durbin | Generic | This is an instruction fine-tuned llama-2 model, using synthetic instructions generated by airoboros⁵. |\n",
+        "| [Emerhyst 13B](https://huggingface.co/Undi95/Emerhyst-13B) by Undi | Roleplay | An attempt using BlockMerge_Gradient to get better result. In addition, LimaRP v3 was used⁷. |\n",
+        "| [Chronos 13B](https://huggingface.co/elinas/chronos-13b) by Elinas | Generic | This model is primarily focused on chat, roleplay, and storywriting, but can accomplish other tasks such as simple reasoning and coding. Chronos generates very long outputs with coherent text, largely due to the human inputs it was trained on. |\n",
+        "| [Spring Dragon by Henk717](https://huggingface.co/Henk717/spring-dragon) | Adventure | This model is a recreation attempt of the AI Dungeon 2 Dragon model. To achieve this, the \"text_adventures.txt\" dataset was used, which was bundled with the original AI Dungeon 2 GitHub release prior to the online service. It is worth noting that the same dataset file was used to create the Dragon model, where Dragon is a GPT-3 175B Davinci model from 2020. |\n",
+        "| [Holodeck By KoboldAI](https://huggingface.co/KoboldAI/LLAMA2-13B-Holodeck-1) | Adventure |LLAMA2 13B-Holodeck is a finetune created using Meta's llama 2 model.The training data contains around 3000 ebooks in various genres. Most parts of the dataset have been prepended using the following text: [Genre: <genre1>, <genre2>|\n",
+        "| [Neo](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n",
         "\n",
         "\n",
         "| Style     | Description                                                  |\n",
         "| --------- | ------------------------------------------------------------ |\n",
         "| Novel     | For regular story writing, not compatible with Adventure mode or other specialty modes. |\n",
-        "| NSFW      | Indicates that the model is strongly biased towards NSFW content and is not suitable for children, work environments or livestreaming. Most NSFW models are also Novel models in nature. |\n",
         "| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel style model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. |\n",
         "| Generic   | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. |\n",
         "\n",
@@ -171,10 +239,39 @@
         "7. As you play KoboldAI, keep this Colab tab open in the background and check occationally for Captcha's so they do not shut your instance down. If you do get shut down you can always download a copy of your gamesave in the Save menu inside KoboldAI. Stories are never lost as long as you keep KoboldAI open in your browser.\n",
         "\n",
         "Get a error message saying you do not have access to a GPU/TPU instance? Do not continue and try again later, KoboldAI will not run correctly without them."
-      ],
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {
-        "id": "Lrm840I33hkC"
-      }
+        "cellView": "form",
+        "id": "5k8fK4F6UiTs"
+      },
+      "outputs": [],
+      "source": [
+        "#@title <b>Model Cleaner</b>\n",
+        "#@markdown Out of space? Run this to remove all cached models (Google Drive models are not effected).\n",
+        "!rm -rf /content/KoboldAI-Client/cache/*\n"
+      ]
     }
-  ]
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "ColabKobold GPU",
+      "private_outputs": true,
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }

colab/TPU.ipynb

@@ -46,7 +46,7 @@
         "#@title <-- Tap this if you play on Mobile { display-mode: \"form\" }\n",
         "%%html\n",
         "<b>Press play on the music player to keep the tab alive, then start KoboldAI below (Uses only 13MB of data)</b><br/>\n",
-        "<audio src=\"https://henk.tech/colabkobold/silence.m4a\" controls>"
+        "<audio src=\"https://raw.githubusercontent.com/KoboldAI/KoboldAI-Client/main/colab/silence.m4a\" controls>"
       ],
       "metadata": {
         "id": "ZIL7itnNaw5V"
@@ -66,9 +66,10 @@
         "#@title <b><-- Select your model below and then click this to start KoboldAI</b>\n",
         "#@markdown You can find a description of the models below along with instructions on how to start KoboldAI.\n",
         "\n",
-        "Model = \"Nerys 13B V2\" #@param [\"Nerys 13B V2\", \"Erebus 13B\", \"Janeway 13B\", \"Shinen 13B\", \"Skein 20B\", \"Erebus 20B\", \"Skein 6B\", \"Janeway 6B\", \"Adventure 6B\", \"Shinen 6B\", \"Lit V2 6B\", \"Lit 6B\", \"NeoX 20B\", \"OPT 13B\", \"Fairseq Dense 13B\", \"GPT-J-6B\"] {allow-input: true}\n",
+        "Model = \"Nerys 13B V2\" #@param [\"Nerys 13B V2\", \"Janeway 13B\", \"Skein 20B\", \"Skein 6B\", \"Janeway 6B\", \"Adventure 6B\", \"NeoX 20B\", \"OPT 13B\", \"Fairseq Dense 13B\", \"GPT-J-6B\"] {allow-input: true}\n",
         "Version = \"Official\" #@param [\"Official\", \"United\"] {allow-input: true}\n",
         "Provider = \"Cloudflare\" #@param [\"Localtunnel\", \"Cloudflare\"]\n",
+        "use_google_drive = True #@param {type:\"boolean\"}\n",
         "\n",
         "import os\n",
         "try:\n",
@@ -79,7 +80,16 @@
         "    raise RuntimeError(\"⚠️You can not run this notebook without the TPU accelerator, go to Runtime->Sessions, terminate your session and then try again.⚠️\")\n",
         "print('Now we will need your Google Drive to store settings and saves, you must login with the same account you used for Colab.')\n",
         "from google.colab import drive\n",
-        "drive.mount('/content/drive/')\n",
+        "if use_google_drive:\n",
+        "  drive.mount('/content/drive/')\n",
+        "else:\n",
+        "  import os\n",
+        "  if not os.path.exists(\"/content/drive\"):\n",
+        "    os.mkdir(\"/content/drive\")\n",
+        "  if not os.path.exists(\"/content/drive/MyDrive/\"):\n",
+        "    os.mkdir(\"/content/drive/MyDrive/\")\n",
+        "\n",
+        "Revision = \"\"\n",
         "\n",
         "if Model == \"Janeway 13B\":\n",
         "  Model = \"KoboldAI/fairseq-dense-13B-Janeway\"\n",
@@ -89,18 +99,6 @@
         "  Model = \"KoboldAI/OPT-13B-Nerys-v2\"\n",
         "  path = \"\"\n",
         "  download = \"\"\n",
-        "elif Model == \"Erebus 13B\":\n",
-        "  Model = \"KoboldAI/OPT-13B-Erebus\"\n",
-        "  path = \"\"\n",
-        "  download = \"\"\n",
-        "elif Model == \"Shinen 13B\":\n",
-        "  Model = \"KoboldAI/fairseq-dense-13B-Shinen\"\n",
-        "  path = \"\"\n",
-        "  download = \"\"\n",
-        "elif Model == \"Erebus 20B\":\n",
-        "  Model = \"KoboldAI/GPT-NeoX-20B-Erebus\"\n",
-        "  path = \"\"\n",
-        "  download = \"\"\n",
         "elif Model == \"Skein 20B\":\n",
         "  Model = \"KoboldAI/GPT-NeoX-20B-Skein\"\n",
         "  path = \"\"\n",
@@ -121,18 +119,6 @@
         "  Model = \"KoboldAI/GPT-J-6B-Adventure\"\n",
         "  path = \"\"\n",
         "  download = \"\"\n",
-        "elif Model == \"Lit V2 6B\":\n",
-        "  Model = \"hakurei/litv2-6B-rev3\"\n",
-        "  path = \"\"\n",
-        "  download = \"\"\n",
-        "elif Model == \"Lit 6B\":\n",
-        "  Model = \"hakurei/lit-6B\"\n",
-        "  path = \"\"\n",
-        "  download = \"\"\n",
-        "elif Model == \"Shinen 6B\":\n",
-        "  Model = \"KoboldAI/GPT-J-6B-Shinen\"\n",
-        "  path = \"\"\n",
-        "  download = \"\"\n",
         "elif Model == \"OPT 13B\":\n",
         "  Model = \"facebook/opt-13b\"\n",
         "  path = \"\"\n",
@@ -154,7 +140,7 @@
         "else:\n",
         "  tunnel = \"\"\n",
         "\n",
-        "!wget https://koboldai.org/ckds -O - | bash /dev/stdin $path$download -m $Model -g $Version $tunnel"
+        "!wget https://koboldai.org/ckds -O - | bash /dev/stdin $path$download -m $Model -g $Version $tunnel $Revision"
       ]
     },
     {
@@ -162,36 +148,33 @@
       "source": [
         "# TPU Edition Model Descriptions\n",
         "\n",
-        "| Model | Size | Style | Description |\n",
-        "| --- | --- | --- | --- |\n",
-        "| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
-        "| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
-        "| [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |\n",
-        "| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |\n",
-        "| [Adventure](https://huggingface.co/KoboldAI/GPT-J-6B-Adventure) by VE\\_FORBRYDERNE | 6B | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). |\n",
-        "| [Lit](https://huggingface.co/hakurei/lit-6B) by Haru | 6B | NSFW | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. |\n",
-        "| Neo(X) by EleutherAI | 20B | Generic | NeoX is the largest EleutherAI model currently available, being a generic model it is not particularly trained towards anything and can do a variety of writing, Q&A and coding tasks. 20B's performance is closely compared to the 13B models and it is worth trying both especially if you have a task that does not involve english writing. Its behavior will be similar to the GPT-J-6B model since they are trained on the same dataset but with more sensitivity towards repetition penalty and with more knowledge. |\n",
-        "| [Fairseq Dense](https://huggingface.co/KoboldAI/fairseq-dense-13B) | 13B | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. |\n",
-        "| [GPT-J-6B](https://huggingface.co/EleutherAI/gpt-j-6B) by EleutherAI | 6B | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. |\n",
-        "\n",
+        "| Model | Style | Description |\n",
+        "| --- | --- | --- |\n",
+        "| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
+        "| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
+        "| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\\_FORBRYDERNE | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |\n",
+        "| [Adventure](https://huggingface.co/KoboldAI/GPT-J-6B-Adventure) by VE\\_FORBRYDERNE | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). |\n",
+        "| [OPT](https://huggingface.co/facebook/opt-13b) by Metaseq | Generic | OPT is considered one of the best base models as far as content goes, its behavior has the strengths of both GPT-Neo and Fairseq Dense. Compared to Neo duplicate and unnecessary content has been left out, while additional literature was added in similar to the Fairseq Dense model. The Fairseq Dense model however lacks the broader data that OPT does have. The biggest downfall of OPT is its license, which prohibits any commercial usage, or usage beyond research purposes. |\n",
+        "| [Neo(X)](https://huggingface.co/EleutherAI/gpt-neox-20b) by EleutherAI | Generic | NeoX is the largest EleutherAI model currently available, being a generic model it is not particularly trained towards anything and can do a variety of writing, Q&A and coding tasks. 20B's performance is closely compared to the 13B models and it is worth trying both especially if you have a task that does not involve english writing. Its behavior will be similar to the GPT-J-6B model since they are trained on the same dataset but with more sensitivity towards repetition penalty and with more knowledge. |\n",
+        "| [Fairseq Dense](https://huggingface.co/KoboldAI/fairseq-dense-13B) | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. Compared to other models the dataset focuses primarily on literature and contains little else. |\n",
+        "| [GPT-J-6B](https://huggingface.co/EleutherAI/gpt-j-6B) by EleutherAI | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. |\n",
         "\n",
         "# [GPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/GPU.ipynb)\n",
         "\n",
-        "| Model | Size | Style | Description |\n",
-        "| --- | --- | --- | --- |\n",
-        "| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
-        "| [Janeway 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
-        "| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on Neo 2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n",
-        "| [AID 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n",
-        "| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on Horni 2.7B and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n",
-        "| [Horni 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |\n",
-        "| [Shinen 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you Shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n",
-        "| [Neo 2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n",
+        "| Model | Style | Description |\n",
+        "| --- | --- | --- |\n",
+        "| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
+        "| [Janeway](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
+        "| [Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | Novel | Picard is a model trained for SFW Novels based on Neo 2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n",
+        "| [AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n",
+        "| [OPT](https://huggingface.co/facebook/opt-2.7b) by Metaseq | Generic | OPT is considered one of the best base models as far as content goes, its behavior has the strengths of both GPT-Neo and Fairseq Dense. Compared to Neo duplicate and unnecessary content has been left out, while additional literature was added in similar to the Fairseq Dense model. The Fairseq Dense model however lacks the broader data that OPT does have. The biggest downfall of OPT is its license, which prohibits any commercial usage, or usage beyond research purposes. |\n",
+        "| [Fairseq Dense](https://huggingface.co/KoboldAI/fairseq-dense-2.7B) | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger models from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. Compared to other models the dataset focuses primarily on literature and contains little else. |\n",
+        "| [Neo](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n",
+        "\n",
         "\n",
         "| Style | Description |\n",
         "| --- | --- |\n",
         "| Novel | For regular story writing, not compatible with Adventure mode or other specialty modes. |\n",
-        "| NSFW | Indicates that the model is strongly biased towards NSFW content and is not suitable for children, work environments or livestreaming. Most NSFW models are also Novel models in nature. |\n",
         "| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel style model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. |\n",
         "| Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. |\n",
         "\n",
@@ -227,7 +210,6 @@
       "name": "ColabKobold TPU",
       "provenance": [],
       "private_outputs": true,
-      "collapsed_sections": [],
       "include_colab_link": true
     },
     "kernelspec": {

commandline.bat

@@ -1,5 +1,7 @@
 @echo off
 cd /D %~dp0
+SET CONDA_SHLVL=
+
 TITLE CMD for KoboldAI Runtime
 SET /P M=<loader.settings
 IF %M%==1 GOTO drivemap

customsettings_template.json

@@ -0,0 +1 @@
+{"aria2_port":null, "breakmodel":null, "breakmodel_disklayers":null, "breakmodel_gpulayers":null, "breakmodel_layers":null, "colab":null, "configname":null, "cpu":null, "host":null, "localtunnel":null, "lowmem":null, "model":null, "ngrok":null, "no_aria2":null, "noaimenu":null, "nobreakmodel":null, "override_delete":null, "override_rename":null, "path":null, "port":null, "quiet":null, "remote":null, "revision":null, "savemodel":null, "unblock":null}

docker-cuda/Dockerfile

@@ -6,4 +6,4 @@ WORKDIR /content/
 COPY env.yml /home/micromamba/env.yml
 RUN micromamba install -y -n base -f /home/micromamba/env.yml
 USER root
-RUN apt update && apt install xorg -y
+RUN apt update && apt install xorg aria2 -y

docker-cuda/docker-compose.yml

@@ -5,6 +5,8 @@ services:
     environment:
       - DISPLAY=${DISPLAY} 
     network_mode: "host"
+    security_opt:
+      - label:disable
     volumes:
       - /tmp/.X11-unix:/tmp/.X11-unix
       - /etc/protocols:/etc/protocols:ro

docker-rocm/Dockerfile

@@ -3,4 +3,4 @@ WORKDIR /content/
 COPY env.yml /home/micromamba/env.yml
 RUN micromamba install -y -n base -f /home/micromamba/env.yml
 USER root
-RUN apt update && apt install xorg libsqlite3-0 -y
+RUN apt update && apt install xorg libsqlite3-0 aria2 -y

docker-rocm/docker-compose.yml

@@ -5,6 +5,8 @@ services:
     environment:
       - DISPLAY=${DISPLAY} 
     network_mode: "host"
+    security_opt:
+      - label:disable
     volumes:
       - /tmp/.X11-unix:/tmp/.X11-unix
       - /etc/protocols:/etc/protocols:ro

docker-standalone/Dockerfile

@@ -0,0 +1,8 @@
+FROM debian
+RUN apt update && apt install wget aria2 git bzip2 -y
+RUN git clone https://github.com/koboldai/koboldai-client /opt/koboldai
+WORKDIR /opt/koboldai
+RUN ./install_requirements.sh cuda
+COPY docker-helper.sh /opt/koboldai/docker-helper.sh
+EXPOSE 5000/tcp
+CMD /opt/koboldai/docker-helper.sh

docker-standalone/Readme.txt

@@ -0,0 +1,17 @@
+These are the source files for the official versions of the standalone docker and are provided for completeness.
+Using these files you will not use any of the local modifications you make, instead it will use the latest github version of KoboldAI as the basis.
+
+If you wish to run KoboldAI containerised with access to the local directory you can do so using docker-cuda.sh or docker-rocm.sh instead.
+
+We do not support ROCm in the standalone docker as it is intended for cloud deployment on CUDA systems.
+If you wish to build a ROCm version instead, you can do so by modifying the Dockerfile and changing the install_requirements.sh from cuda to rocm.
+
+Similarly you need to modify the Dockerfile to specify which branch of KoboldAI the docker is being built for.
+
+Usage:
+This docker will automatically assume the persistent volume is mounted to /content and will by default not store models there.
+The following environment variables exist to adjust the behavior if desired.
+
+KOBOLDAI_DATADIR=/content , this can be used to specify a different default location for your stories, settings, userscripts, etc in case your provider does not let you change the mounted folder path.
+KOBOLDAI_MODELDIR= , This variable can be used to make model storage persistent, it can be the same location as your datadir but this is not required.
+KOBOLDAI_ARGS= , This variable is built in KoboldAI and can be used to override the default launch options. Right now the docker by default will launch in remote mode, with output hidden from the logs and file management enabled.

docker-standalone/docker-helper.sh

@@ -0,0 +1,47 @@
+#!/bin/bash
+cd /opt/koboldai
+git pull
+#./install_requirements.sh cuda
+
+if [[ ! -v KOBOLDAI_DATADIR ]];then
+	mkdir /content
+	KOBOLDAI_DATADIR=/content
+fi
+
+mkdir $KOBOLDAI_DATADIR/stories
+if [[ -v KOBOLDAI_MODELDIR ]];then
+	mkdir $KOBOLDAI_MODELDIR/models
+fi
+mkdir $KOBOLDAI_DATADIR/settings
+mkdir $KOBOLDAI_DATADIR/softprompts
+mkdir $KOBOLDAI_DATADIR/userscripts
+#mkdir $KOBOLDAI_MODELDIR/cache
+
+cp -rn stories/* $KOBOLDAI_DATADIR/stories/
+cp -rn userscripts/* $KOBOLDAI_DATADIR/userscripts/
+cp -rn softprompts/* $KOBOLDAI_DATADIR/softprompts/
+
+rm stories
+rm -rf stories/
+rm userscripts
+rm -rf userscripts/
+rm softprompts
+rm -rf softprompts/
+
+if [[ -v KOBOLDAI_MODELDIR ]];then
+	rm models
+	rm -rf models/
+	#rm cache
+	#rm -rf cache/
+fi
+
+ln -s $KOBOLDAI_DATADIR/stories/ stories
+ln -s $KOBOLDAI_DATADIR/settings/ settings
+ln -s $KOBOLDAI_DATADIR/softprompts/ softprompts
+ln -s $KOBOLDAI_DATADIR/userscripts/ userscripts
+if [[ -v KOBOLDAI_MODELDIR ]];then
+	ln -s $KOBOLDAI_MODELDIR/models/ models
+	#ln -s $KOBOLDAI_MODELDIR/cache/ cache
+fi
+
+PYTHONUNBUFFERED=1 ./play.sh --remote --quiet --override_delete --override_rename

environments/finetuneanon.yml

@@ -1,22 +0,0 @@
-name: koboldai
-channels:
-  - pytorch
-  - conda-forge
-  - defaults
-dependencies:
-  - colorama
-  - flask-socketio
-  - pytorch
-  - cudatoolkit=11.1
-  - tensorflow-gpu
-  - python=3.8.*
-  - eventlet
-  - markdown
-  - bleach=4.1.0
-  - pip
-  - git=2.35.1
-  - pip:
-    - git+https://github.com/finetuneanon/transformers@gpt-neo-localattention3-rp-b
-    - flask-cloudflared
-    - flask-ngrok
-    - lupa==1.10

environments/huggingface.yml

@@ -5,20 +5,33 @@ channels:
   - defaults
 dependencies:
   - colorama
-  - flask-socketio
+  - flask=2.2.3
+  - flask-socketio=5.3.2
+  - flask-session=0.4.0
+  - python-socketio=5.7.2
   - pytorch=1.11.*
   - python=3.8.*
   - cudatoolkit=11.1
-  - eventlet
+  - eventlet=0.33.3
+  - dnspython=2.2.1
   - markdown
   - bleach=4.1.0
   - pip
   - git=2.35.1
   - sentencepiece
   - protobuf
+  - marshmallow>=3.13
+  - apispec-webframeworks
+  - loguru
+  - termcolor
+  - psutil
   - pip:
-    - flask-cloudflared
+    - flask-cloudflared==0.0.10
     - flask-ngrok
+    - Werkzeug==2.3.7
     - lupa==1.10
-    - transformers>=4.20.1
-    - accelerate
+    - transformers==4.24.0
+    - huggingface_hub==0.12.1
+    - safetensors
+    - accelerate
+    - git+https://github.com/VE-FORBRYDERNE/mkultra

environments/rocm-finetune.yml

@@ -1,21 +0,0 @@
-name: koboldai-ft
-channels:
-  - conda-forge
-  - defaults
-dependencies:
-  - colorama
-  - flask-socketio
-  - python=3.8.*
-  - eventlet
-  - markdown
-  - bleach=4.1.0
-  - pip
-  - git=2.35.1
-  - pip:
-    - --find-links https://download.pytorch.org/whl/rocm4.2/torch_stable.html
-    - torch
-    - torchvision==0.11.1
-    - flask-cloudflared
-    - git+https://github.com/finetuneanon/transformers@gpt-neo-localattention3-rp-b
-    - flask-ngrok
-    - lupa==1.10

environments/rocm.yml

@@ -4,21 +4,33 @@ channels:
   - defaults
 dependencies:
   - colorama
-  - flask-socketio
+  - flask=2.2.3
+  - flask-socketio=5.3.2
+  - flask-session=0.4.0
+  - python-socketio=5.7.2
   - python=3.8.*
-  - eventlet
+  - eventlet=0.33.3
+  - dnspython=2.2.1
   - markdown
   - bleach=4.1.0
   - pip
   - git=2.35.1
   - sentencepiece
   - protobuf
+  - marshmallow>=3.13
+  - apispec-webframeworks
+  - loguru
+  - termcolor
+  - psutil
   - pip:
-    - --find-links https://download.pytorch.org/whl/rocm4.2/torch_stable.html
-    - torch==1.10.*
-    - torchvision
-    - flask-cloudflared
+    - --extra-index-url https://download.pytorch.org/whl/rocm5.1.1
+    - torch==1.12.1+rocm5.1.1
+    - flask-cloudflared==0.0.10
     - flask-ngrok
+    - Werkzeug==2.3.7
     - lupa==1.10
-    - transformers>=4.20.1
+    - transformers==4.24.0
+    - huggingface_hub==0.12.1
+    - safetensors
     - accelerate
+    - git+https://github.com/VE-FORBRYDERNE/mkultra

fileops.py

@@ -3,6 +3,7 @@ from typing import Tuple, Union, Optional
 import os
 import json
 import zipfile
+from logger import logger
 
 #==================================================================#
 #  Generic Method for prompting for file path
@@ -85,7 +86,7 @@ def uspath(filename):
 def getstoryfiles():
     list = []
     for file in listdir("stories"):
-        if file.endswith(".json"):
+        if file.endswith(".json") and not file.endswith(".v2.json"):
             ob = {}
             ob["name"] = file.replace(".json", "")
             f = open("stories/"+file, "r")
@@ -149,16 +150,16 @@ def getspfiles(model_dimension: int):
             continue
         z, version, shape, fortran_order, dtype = checksp(file, model_dimension)
         if z == 1:
-            print(f"Browser SP loading error: {file} is malformed or not a soft prompt ZIP file.")
+            logger.warning(f"Softprompt {file} is malformed or not a soft prompt ZIP file.")
             continue
         if z == 2:
-            print(f"Browser SP loading error: {file} tensor.npy has unsupported dtype '{dtype.name}'.")
+            logger.warning(f"Softprompt {file} tensor.npy has unsupported dtype '{dtype.name}'.")
             continue
         if z == 3:
-            print(f"Browser SP loading error: {file} tensor.npy has model dimension {shape[1]} which does not match your model's model dimension of {model_dimension}. This usually means this soft prompt is not compatible with your model.")
+            logger.debug(f"Softprompt {file} tensor.npy has model dimension {shape[1]} which does not match your model's model dimension of {model_dimension}. This usually means this soft prompt is not compatible with your model.")
             continue
         if z == 4:
-            print(f"Browser SP loading error: {file} tensor.npy has {shape[0]} tokens but it is supposed to have less than 2048 tokens.")
+            logger.warning(f"Softprompt {file} tensor.npy has {shape[0]} tokens but it is supposed to have less than 2048 tokens.")
             continue
         assert isinstance(z, zipfile.ZipFile)
         try:

gensettings.py

@@ -230,6 +230,50 @@ gensettingstf = [
 	"default": 0,
   "tooltip": "Disables userscript generation modifiers."
 	},
+	{
+	"uitype": "toggle",
+	"unit": "bool",
+	"label": "Full Determinism",
+	"id": "setfulldeterminism",
+	"min": 0,
+	"max": 1,
+	"step": 1,
+	"default": 0,
+  "tooltip": "Causes generation to be fully deterministic -- the model will always output the same thing as long as your story, settings and RNG seed are the same. If this is off, only the sequence of outputs that the model makes will be deterministic."
+	},
+    {
+	"uitype": "toggle",
+	"unit": "bool",
+	"label": "Token Streaming",
+	"id": "setoutputstreaming",
+	"min": 0,
+	"max": 1,
+	"step": 1,
+	"default": 0,
+  "tooltip": "Shows outputs to you as they are made. Does not work with more than one gens per action."
+	},
+    {
+	"uitype": "toggle",
+	"unit": "bool",
+	"label": "Probability Viewer",
+	"id": "setshowprobs",
+	"min": 0,
+	"max": 1,
+	"step": 1,
+	"default": 0,
+  "tooltip": "Shows token selection probabilities. Does not work with more than one gens per action."
+	},
+    {
+	"uitype": "toggle",
+	"unit": "bool",
+	"label": "Show Field Budget",
+	"id": "setshowbudget",
+	"min": 0,
+	"max": 1,
+	"step": 1,
+	"default": 0,
+  "tooltip": "Shows token usage when typing in relevant text boxes. <b>May lag slower devices.</b>"
+	},
     {
 	"uitype": "toggle",
 	"unit": "bool",
@@ -240,7 +284,7 @@ gensettingstf = [
 	"step": 1,
 	"default": 0,
   "tooltip": "Show debug info"
-	}
+	},
 ]
 
 gensettingsik =[{
@@ -404,9 +448,9 @@ formatcontrols = [{
     "tooltip": "Remove special characters (@,#,%,^, etc)"
     },
     {
-    "label": "Add sentence spacing",
+    "label": "Automatic spacing",
     "id": "frmtadsnsp",
-    "tooltip": "If the last action ended with punctuation, add a space to the beginning of the next action."
+    "tooltip": "Add spaces automatically if needed"
     },
     {
     "label": "Single Line",

install_requirements.bat

@@ -8,6 +8,7 @@ echo.
 
 Reg add "HKLM\SYSTEM\CurrentControlSet\Control\FileSystem" /v "LongPathsEnabled" /t REG_DWORD /d "1" /f 2>nul
 cd /D %~dp0
+SET CONDA_SHLVL=
 
 if exist miniconda3\ (
   echo Delete existing installation?

install_requirements.sh

@@ -1,12 +1,12 @@
 #!/bin/bash
-if [[ $1 = "cuda" ]]; then
+if [[ $1 = "cuda" || $1 = "CUDA" ]]; then
 wget -qO- https://micromamba.snakepit.net/api/micromamba/linux-64/latest | tar -xvj bin/micromamba
 bin/micromamba create -f environments/huggingface.yml -r runtime -n koboldai -y
 # Weird micromamba bug causes it to fail the first time, running it twice just to be safe, the second time is much faster
 bin/micromamba create -f environments/huggingface.yml -r runtime -n koboldai -y
 exit
 fi
-if [[ $1 = "rocm" ]]; then
+if [[ $1 = "rocm" || $1 = "ROCM" ]]; then
 wget -qO- https://micromamba.snakepit.net/api/micromamba/linux-64/latest | tar -xvj bin/micromamba
 bin/micromamba create -f environments/rocm.yml -r runtime -n koboldai-rocm -y
 # Weird micromamba bug causes it to fail the first time, running it twice just to be safe, the second time is much faster

logger.py

@@ -0,0 +1,99 @@
+import sys
+from functools import partialmethod
+from loguru import logger
+
+STDOUT_LEVELS = ["GENERATION", "PROMPT"]
+INIT_LEVELS = ["INIT", "INIT_OK", "INIT_WARN", "INIT_ERR"]
+MESSAGE_LEVELS = ["MESSAGE"]
+# By default we're at error level or higher
+verbosity = 20
+quiet = 0
+
+def set_logger_verbosity(count):
+    global verbosity
+    # The count comes reversed. So count = 0 means minimum verbosity
+    # While count 5 means maximum verbosity
+    # So the more count we have, the lowe we drop the versbosity maximum
+    verbosity = 20 - (count * 10)
+
+def quiesce_logger(count):
+    global quiet
+    # The bigger the count, the more silent we want our logger
+    quiet = count * 10
+
+def is_stdout_log(record):
+    if record["level"].name not in STDOUT_LEVELS:
+        return(False)
+    if record["level"].no < verbosity + quiet:
+        return(False)
+    return(True)
+
+def is_init_log(record):
+    if record["level"].name not in INIT_LEVELS:
+        return(False)
+    if record["level"].no < verbosity + quiet:
+        return(False)
+    return(True)
+
+def is_msg_log(record):
+    if record["level"].name not in MESSAGE_LEVELS:
+        return(False)
+    if record["level"].no < verbosity + quiet:
+        return(False)
+    return(True)
+
+def is_stderr_log(record):
+    if record["level"].name in STDOUT_LEVELS + INIT_LEVELS + MESSAGE_LEVELS:
+        return(False)
+    if record["level"].no < verbosity + quiet:
+        return(False)
+    return(True)
+
+def test_logger():
+    logger.generation("This is a generation message\nIt is typically multiline\nThee Lines".encode("unicode_escape").decode("utf-8"))
+    logger.prompt("This is a prompt message")
+    logger.debug("Debug Message")
+    logger.info("Info Message")
+    logger.warning("Info Warning")
+    logger.error("Error Message")
+    logger.critical("Critical Message")
+    logger.init("This is an init message", status="Starting")
+    logger.init_ok("This is an init message", status="OK")
+    logger.init_warn("This is an init message", status="Warning")
+    logger.init_err("This is an init message", status="Error")
+    logger.message("This is user message")
+    sys.exit()
+
+
+logfmt = "<level>{level: <10}</level> | <green>{name}</green>:<green>{function}</green>:<green>{line}</green> - <level>{message}</level>"
+genfmt = "<level>{level: <10}</level> @ <green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{message}</level>"
+initfmt = "<magenta>INIT      </magenta> | <level>{extra[status]: <10}</level> | <magenta>{message}</magenta>"
+msgfmt = "<level>{level: <10}</level> | <level>{message}</level>"
+
+logger.level("GENERATION", no=24, color="<cyan>")
+logger.level("PROMPT", no=23, color="<yellow>")
+logger.level("INIT", no=31, color="<white>")
+logger.level("INIT_OK", no=31, color="<green>")
+logger.level("INIT_WARN", no=31, color="<yellow>")
+logger.level("INIT_ERR", no=31, color="<red>")
+# Messages contain important information without which this application might not be able to be used
+# As such, they have the highest priority
+logger.level("MESSAGE", no=61, color="<green>")
+
+logger.__class__.generation = partialmethod(logger.__class__.log, "GENERATION")
+logger.__class__.prompt = partialmethod(logger.__class__.log, "PROMPT")
+logger.__class__.init = partialmethod(logger.__class__.log, "INIT")
+logger.__class__.init_ok = partialmethod(logger.__class__.log, "INIT_OK")
+logger.__class__.init_warn = partialmethod(logger.__class__.log, "INIT_WARN")
+logger.__class__.init_err = partialmethod(logger.__class__.log, "INIT_ERR")
+logger.__class__.message = partialmethod(logger.__class__.log, "MESSAGE")
+
+config = {
+    "handlers": [
+        {"sink": sys.stderr, "format": logfmt, "colorize":True, "filter": is_stderr_log},
+        {"sink": sys.stdout, "format": genfmt, "level": "PROMPT", "colorize":True, "filter": is_stdout_log},
+        {"sink": sys.stdout, "format": initfmt, "level": "INIT", "colorize":True, "filter": is_init_log},
+        {"sink": sys.stdout, "format": msgfmt, "level": "MESSAGE", "colorize":True, "filter": is_msg_log}
+    ],
+}
+logger.configure(**config)

maps/bloom.json

@@ -0,0 +1,30 @@
+{
+  "mtj_compat": "bloom",
+  "mtj_pe": "alibi",
+  "mtj_config_map": {
+    "d_model": "n_embed",
+    "n_heads": "num_attention_heads",
+    "layers": "n_layer"
+  },
+  "static_weights": {
+    "word_embeddings.weight": {"mtj": {"module": "embedding_shard/~/linear", "param": "w", "transforms": ["no_transpose", "vocab_pad"]}},
+    "word_embeddings_layernorm.weight": {"mtj": {"module": "embedding_shard/~/replicated_layer_norm", "param": "scale"}},
+    "word_embeddings_layernorm.bias": {"mtj": {"module": "embedding_shard/~/replicated_layer_norm", "param": "offset"}},
+    "ln_f.weight": {"mtj": {"module": "projection_shard/~/replicated_layer_norm", "param": "scale"}},
+    "ln_f.bias": {"mtj": {"module": "projection_shard/~/replicated_layer_norm", "param": "offset"}}
+  },
+  "layer_weights": {
+    "h.{layer}.self_attention.query_key_value.weight": {"mtj": {"module": "layer_{layer}/~/combined_qkv", "param": "w"}},
+    "h.{layer}.self_attention.query_key_value.bias": {"mtj": {"module": "layer_{layer}/~/combined_qkv", "param": "b"}},
+    "h.{layer}.self_attention.dense.weight": {"mtj": {"module": "layer_{layer}/~/linear_3", "param": "w"}},
+    "h.{layer}.self_attention.dense.bias": {"mtj": {"module": "layer_{layer}/~/linear_3", "param": "b", "transforms": ["divide_by_shards"]}},
+    "h.{layer}.mlp.dense_h_to_4h.weight": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "w"}},
+    "h.{layer}.mlp.dense_h_to_4h.bias": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "b"}},
+    "h.{layer}.mlp.dense_4h_to_h.weight": {"mtj": {"module": "layer_{layer}/~/linear_5", "param": "w"}},
+    "h.{layer}.mlp.dense_4h_to_h.bias": {"mtj": {"module": "layer_{layer}/~/linear_5", "param": "b", "transforms": ["divide_by_shards"]}},
+    "h.{layer}.input_layernorm.weight": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm", "param": "scale"}},
+    "h.{layer}.input_layernorm.bias": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm", "param": "offset"}},
+    "h.{layer}.post_attention_layernorm.weight": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm_1", "param": "scale"}},
+    "h.{layer}.post_attention_layernorm.bias": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm_1", "param": "offset"}}
+  }
+}

maps/gptj.json

@@ -9,11 +9,11 @@
   },
   "static_weights": {
     "transformer.wte.weight": {"mtj": {"module": "embedding_shard/~/linear", "param": "w", "transforms": ["no_transpose", "vocab_pad"]}},
-    "transformer.wte.bias": {"mtj": {"module": "embedding_shard/~/linear", "param": "b"}},
+    "transformer.wte.bias": {"mtj": {"module": "embedding_shard/~/linear", "param": "b", "transforms": ["vocab_pad"]}},
     "transformer.ln_f.weight": {"mtj": {"module": "projection_shard/~/replicated_layer_norm", "param": "scale"}},
     "transformer.ln_f.bias": {"mtj": {"module": "projection_shard/~/replicated_layer_norm", "param": "offset"}},
     "lm_head.weight": {"mtj": {"module": "projection_shard/~/linear", "param": "w", "transforms": ["vocab_pad"]}},
-    "lm_head.bias": {"mtj": {"module": "projection_shard/~/linear", "param": "b"}}
+    "lm_head.bias": {"mtj": {"module": "projection_shard/~/linear", "param": "b", "transforms": ["vocab_pad"]}}
   },
   "layer_weights": {
     "transformer.h.{layer}.attn.bias": {},

play.bat

@@ -1,5 +1,9 @@
 @echo off
 cd /D %~dp0
+SET CONDA_SHLVL=
+
+rmdir /S /Q flask_session
+
 TITLE KoboldAI - Server
 SET /P M=<loader.settings
 IF %M%==1 GOTO drivemap

pytest.ini

@@ -0,0 +1,2 @@
+[pytest]
+addopts = --ignore=miniconda3 --ignore=runtime --html=unit_test_report.html --self-contained-html -v

requirements.txt

@@ -1,14 +1,25 @@
-transformers>=4.20.1
-Flask
-Flask-SocketIO
+transformers==4.24.0
+huggingface_hub==0.12.1
+Flask==2.2.3
+Flask-SocketIO==5.3.2
+Werkzeug==2.3.7
+python-socketio==5.7.2
 requests
-torch==1.11
-flask-cloudflared
+torch >= 1.9, < 1.13
+flask-cloudflared==0.0.10
 flask-ngrok
-eventlet
+eventlet==0.33.3
+dnspython==2.2.1
 lupa==1.10
 markdown
 bleach==4.1.0
 sentencepiece
 protobuf
 accelerate
+flask-session==0.4.0
+marshmallow>=3.13
+apispec-webframeworks
+loguru
+termcolor
+safetensors
+git+https://github.com/VE-FORBRYDERNE/mkultra

requirements_mtj.txt

@@ -1,18 +1,27 @@
-torch >= 1.9, <= 1.11
+torch >= 1.9, < 1.13
 numpy
 tqdm
 requests
-dm-haiku == 0.0.5
-jax == 0.2.21
-jaxlib >= 0.1.69, <= 0.3.7
-transformers >= 4.20.1
+dm-haiku==0.0.9
+jax==0.3.25
+jaxlib==0.3.25
+chex == 0.1.5
+transformers == 4.24.0
+huggingface_hub==0.12.1
 progressbar2
 git+https://github.com/VE-FORBRYDERNE/mesh-transformer-jax@ck
-flask
-Flask-SocketIO
-flask-cloudflared >= 0.0.5
+Flask==2.2.3
+Flask-SocketIO==5.3.2
+python-socketio==5.7.2
+flask-cloudflared==0.0.10
 flask-ngrok
-eventlet
+Werkzeug==2.3.7
+eventlet==0.33.3
+dnspython==2.2.1
 lupa==1.10
 markdown
 bleach==4.1.0
+flask-session==0.4.0
+marshmallow>=3.13
+apispec-webframeworks
+loguru

static/custom.css

@@ -291,7 +291,7 @@ body.connected #formatmenu, #formatmenu.always-available {
 	align-items: center;
 }
 
-#popup {
+#popup_old {
 	width: 75%;
 	min-width: 500px;
 	max-width: 1000px;
@@ -369,14 +369,14 @@ body.connected #popupfooter, #popupfooter.always-available {
 	margin-top: 200px;
 }
 
-#loadpopup {
+.loadpopup {
 	width: 500px;
 	background-color: #262626;
 	margin-top: 100px;
 }
 
 @media (max-width: 768px) {
-	#loadpopup {
+	.loadpopup {
 		width: 100%;
 		background-color: #262626;
 		margin-top: 100px;
@@ -473,7 +473,7 @@ body.connected #popupfooter, #popupfooter.always-available {
 }
 
 #samplerslist {
-	height: 300px;
+	height: 310px;
 	overflow-y: scroll;
 	overflow-wrap: anywhere;
 }
@@ -1056,7 +1056,7 @@ body.connected .statusiconlabel, .statusiconlabel.always-available {
 }
 
 .loadlistitem {
-	padding: 5px 10px 5px 10px;
+	padding: 0px 0px 0px 0px;
 	display: flex;
 	flex-grow: 1;
 	color: #ffffff;
@@ -1072,6 +1072,28 @@ body.connected .statusiconlabel, .statusiconlabel.always-available {
 	background-color: #688f1f;
 }
 
+.breadcrumbitem {
+	padding: 5px 10px 5px 10px;
+	color: #ffffff;
+	background-color: transparent;
+	border: none;
+	
+	-moz-transition: background-color 0.25s ease-in;
+	-o-transition: background-color 0.25s ease-in;
+	-webkit-transition: background-color 0.25s ease-in;
+	transition: background-color 0.25s ease-in;
+}
+
+.breadcrumbitem:hover {
+	cursor: pointer;
+	background-color: #688f1f;
+}
+
+hr {
+    padding: 0px;
+    margin: 0px;    
+}
+
 .loadlistpadding {
 	padding-right: 10px;
 }
@@ -1463,3 +1485,240 @@ body.connected .popupfooter, .popupfooter.always-available {
 	overflow: hidden;
 	font-size: 12pt;
 }
+
+.model_layers {
+	width: 3ch;
+	background-color: inherit;
+	border: none; 
+	outline: none;
+}
+
+.model_layers:focus {
+	color: #cdf;
+}
+
+.menu_icon {
+	position: fixed;
+	top:10px;
+	left: 5px;
+	z-index:100;
+	display: inline-block;
+	cursor: pointer;
+}
+
+.SideMenu {
+  height: 100%;
+  width: 0;
+  position: fixed;
+  z-index: 1;
+  top: 0;
+  left: 0;
+  background-color: #111;
+  overflow-x: hidden;
+  transition: 0.5s;
+  padding-top: 60px;
+}
+
+.SideMenu.open {
+  width: 450px;
+}
+
+@media only screen and (max-width: 768px) {
+	.SideMenu.open {
+	  width: 100%;
+	}
+}
+
+
+.menubar1, .menubar2, .menubar3 {
+  width: 21px;
+  height: 3px;
+  background-color: #999;
+  margin: 3px 0;
+  transition: 0.4s;
+}
+
+.change .menubar1 {
+  transform: translate(0px, 6px) rotate(-45deg);
+}
+
+.change .menubar2 {opacity: 0;}
+
+.change .menubar3 {
+  transform: translate(0px, -6px) rotate(45deg);
+}
+
+
+/*---------------------------------- Popup -------------------------------------------------*/
+.new_popup {
+	position: absolute;
+	top: 10vh;
+	left: 10%;
+	z-index: 999;
+	width: 80%;
+	height: 80vh;
+	background-color: black;
+	display: flex;
+	flex-direction: column;
+	background-color: #474B4F;
+	color: white;
+}
+
+.new_popup .title {
+	width: 100%;
+	background-color: #337AB7;
+	text-align: center;
+	font-size: 1.3em;
+}
+
+.new_popup .popup_list_area {
+	height: 70vh;
+	overflow-x: hidden;
+}
+.new_popup .item {
+	width: 100%;
+	background-color: #262626;
+	padding: 2px;
+	display: grid;
+	grid-template-areas: "folder_icon delete_icon edit_icon rename_icon file";
+	grid-template-columns: 20px 20px 20px 20px auto;
+	
+}
+
+.new_popup .item .folder_icon {
+	grid-area: folder_icon;
+}
+
+.new_popup .item .edit_icon {
+	grid-area: edit_icon;
+}
+
+.new_popup .item .rename_icon {
+	grid-area: rename_icon;
+}
+
+.new_popup .item .delete_icon {
+	grid-area: delete_icon;
+}
+
+.new_popup .item .file {
+	grid-area: file;
+}
+
+.new_popup .item .file:hover {
+	background-color: #688f1f;
+}
+
+.new_popup textarea {
+	grid-area: textarea;
+	background-color: #404040;
+	color: white;
+	resize: none;
+	width: 100%;
+}
+
+.new_popup .popup_load_cancel {
+	text-align: center;
+	background-color: #285070;
+}
+
+.popup_load_cancel_button {
+	vertical-align: bottom;
+	display: inline;
+}
+
+.popup_load_cancel_button.btn-secondary {
+	color: rgb(51, 51, 51);
+	background-color: #686c68;
+}
+
+.breadcrumbitem {
+	padding: 5px 10px 5px 10px;
+	color: #ffffff;
+	background-color: transparent;
+	border: none;
+	
+	-moz-transition: background-color 0.25s ease-in;
+	-o-transition: background-color 0.25s ease-in;
+	-webkit-transition: background-color 0.25s ease-in;
+	transition: background-color 0.25s ease-in;
+}
+
+.breadcrumbitem:hover {
+	cursor: pointer;
+	background-color: #688f1f;
+}
+
+#token_prob_menu {
+	color: white;
+	background-color: #262626;
+}
+
+.token-probs {
+	display: inline-block;
+	text-align: center;
+	margin-right: 5px;
+}
+
+.token-probs > table {
+	width: 100%;
+}
+
+.token-probs > table > tbody > tr > td {
+	border: 1px solid #262626;
+	border-collapse: collapse;
+	padding: 2px 15px;
+}
+
+.token-probs > table > tbody > tr {
+	background-color: #3e3e3e;
+}
+
+.token-probs > table > tbody > tr:nth-child(2n) {
+	background-color: #575757;
+}
+
+.token-probs-final-token {
+	font-weight: bold;
+	text-decoration: underline;
+}
+
+.token-probs-final-token > td {
+	background: #5c8a5a;
+}
+
+.token-probs-header {
+	display: block;
+}
+
+#token_prob_container {
+	overflow-x: auto;
+	white-space: nowrap;
+}
+
+.tokens-counted {
+	position: relative;
+}
+
+.input-token-usage {
+	color: white;
+	position: absolute;
+	font-size: 10px;
+	bottom: 2px;
+	right: 5px;
+
+	-webkit-user-select: none;
+	-moz-user-select: none;
+	-ms-user-select: none;
+	user-select: none;
+}
+
+/* Override needed here due to the 10px right padding on inputrowleft; add 10 px. */
+#inputrowleft > .input-token-usage {
+	right: 15px;
+	bottom: 1px;
+}
+
+.wientry > .input-token-usage {
+	bottom: 8px;
+}

static/favicon.js

@@ -0,0 +1,70 @@
+// Global Definitions
+var fav_icon2 = "data:image/x-icon;base64,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";
+var fav_icon1 = "data:image/png;base64,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";
+var fav_icon = "data:image/png;base64,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"
+var submit_start;
+
+var favicon = {
+
+	// Change the Page Icon and Title.
+	change: function(iconURL) {
+		this.addLink(iconURL, "icon");
+		this.addLink(iconURL, "shortcut icon");
+	},
+
+	addLink: function(iconURL, relValue) {
+		var link = document.createElement("link");
+		link.type = "image/x-icon";
+		link.rel = relValue;
+		link.href = iconURL;
+		this.removeLink(relValue);
+		this.docHead.appendChild(link);
+	},
+
+	removeLink: function(relValue) {
+		var links = this.docHead.getElementsByTagName("link");
+		for (var i = 0; i < links.length; i++) {
+			var link = links[i];
+			if (link.type == "image/x-icon" && link.rel == relValue) {
+				this.docHead.removeChild(link);
+				return; // Assuming only one match at most.
+			}
+		}
+	},
+	
+	swapLink: function() {
+		if (this.run == true) {
+			if (this.icon == 1) {
+				this.change(fav_icon2);
+				this.icon = 2;
+			} else {
+				this.change(fav_icon1);
+				this.icon = 1;
+			}
+		}
+	},
+	
+	auto_swap: function() {
+		if (this.run == true) {
+			this.swapLink();
+			setTimeout(() => {  this.auto_swap(); }, 1000);
+		}
+	},
+	
+	start_swap: function() {
+		this.run = true;
+		this.auto_swap();
+		submit_start = Date.now();
+	},
+	
+	stop_swap: function() {
+		this.run = false;
+		this.change(fav_icon);
+		if (typeof submit_start !== 'undefined') {
+			$("#runtime")[0].innerHTML = `Execution time: ${Math.round((Date.now() - submit_start)/1000)} sec`;
+			delete submit_start;
+		}
+	},
+	
+	docHead:document.getElementsByTagName("head")[0]
+}

static/open-iconic/css/open-iconic-bootstrap.css

@@ -0,0 +1,952 @@
+/* Bootstrap */
+
+@font-face {
+  font-family: 'Icons';
+  src: url('../fonts/open-iconic.eot');
+  src: url('../fonts/open-iconic.eot?#iconic-sm') format('embedded-opentype'), url('../fonts/open-iconic.woff') format('woff'), url('../fonts/open-iconic.ttf') format('truetype'), url('../fonts/open-iconic.otf') format('opentype'), url('../fonts/open-iconic.svg#iconic-sm') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+
+.oi {
+  position: relative;
+  top: 1px;
+  display: inline-block;
+  speak:none;
+  font-family: 'Icons';
+  font-style: normal;
+  font-weight: normal;
+  line-height: 1;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+
+.oi:empty:before {
+  width: 1em;
+  text-align: center;
+  box-sizing: content-box;
+}
+
+.oi.oi-align-center:before {
+  text-align: center;
+}
+
+.oi.oi-align-left:before {
+  text-align: left;
+}
+
+.oi.oi-align-right:before {
+  text-align: right;
+}
+
+
+.oi.oi-flip-horizontal:before {
+  -webkit-transform: scale(-1, 1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, 1);
+}
+
+.oi.oi-flip-vertical:before {
+  -webkit-transform: scale(1, -1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(1, -1);
+}
+
+.oi.oi-flip-horizontal-vertical:before {
+  -webkit-transform: scale(-1, -1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, -1);
+}
+
+
+.oi-account-login:before {
+  content:'\e000';
+}
+
+.oi-account-logout:before {
+  content:'\e001';
+}
+
+.oi-action-redo:before {
+  content:'\e002';
+}
+
+.oi-action-undo:before {
+  content:'\e003';
+}
+
+.oi-align-center:before {
+  content:'\e004';
+}
+
+.oi-align-left:before {
+  content:'\e005';
+}
+
+.oi-align-right:before {
+  content:'\e006';
+}
+
+.oi-aperture:before {
+  content:'\e007';
+}
+
+.oi-arrow-bottom:before {
+  content:'\e008';
+}
+
+.oi-arrow-circle-bottom:before {
+  content:'\e009';
+}
+
+.oi-arrow-circle-left:before {
+  content:'\e00a';
+}
+
+.oi-arrow-circle-right:before {
+  content:'\e00b';
+}
+
+.oi-arrow-circle-top:before {
+  content:'\e00c';
+}
+
+.oi-arrow-left:before {
+  content:'\e00d';
+}
+
+.oi-arrow-right:before {
+  content:'\e00e';
+}
+
+.oi-arrow-thick-bottom:before {
+  content:'\e00f';
+}
+
+.oi-arrow-thick-left:before {
+  content:'\e010';
+}
+
+.oi-arrow-thick-right:before {
+  content:'\e011';
+}
+
+.oi-arrow-thick-top:before {
+  content:'\e012';
+}
+
+.oi-arrow-top:before {
+  content:'\e013';
+}
+
+.oi-audio-spectrum:before {
+  content:'\e014';
+}
+
+.oi-audio:before {
+  content:'\e015';
+}
+
+.oi-badge:before {
+  content:'\e016';
+}
+
+.oi-ban:before {
+  content:'\e017';
+}
+
+.oi-bar-chart:before {
+  content:'\e018';
+}
+
+.oi-basket:before {
+  content:'\e019';
+}
+
+.oi-battery-empty:before {
+  content:'\e01a';
+}
+
+.oi-battery-full:before {
+  content:'\e01b';
+}
+
+.oi-beaker:before {
+  content:'\e01c';
+}
+
+.oi-bell:before {
+  content:'\e01d';
+}
+
+.oi-bluetooth:before {
+  content:'\e01e';
+}
+
+.oi-bold:before {
+  content:'\e01f';
+}
+
+.oi-bolt:before {
+  content:'\e020';
+}
+
+.oi-book:before {
+  content:'\e021';
+}
+
+.oi-bookmark:before {
+  content:'\e022';
+}
+
+.oi-box:before {
+  content:'\e023';
+}
+
+.oi-briefcase:before {
+  content:'\e024';
+}
+
+.oi-british-pound:before {
+  content:'\e025';
+}
+
+.oi-browser:before {
+  content:'\e026';
+}
+
+.oi-brush:before {
+  content:'\e027';
+}
+
+.oi-bug:before {
+  content:'\e028';
+}
+
+.oi-bullhorn:before {
+  content:'\e029';
+}
+
+.oi-calculator:before {
+  content:'\e02a';
+}
+
+.oi-calendar:before {
+  content:'\e02b';
+}
+
+.oi-camera-slr:before {
+  content:'\e02c';
+}
+
+.oi-caret-bottom:before {
+  content:'\e02d';
+}
+
+.oi-caret-left:before {
+  content:'\e02e';
+}
+
+.oi-caret-right:before {
+  content:'\e02f';
+}
+
+.oi-caret-top:before {
+  content:'\e030';
+}
+
+.oi-cart:before {
+  content:'\e031';
+}
+
+.oi-chat:before {
+  content:'\e032';
+}
+
+.oi-check:before {
+  content:'\e033';
+}
+
+.oi-chevron-bottom:before {
+  content:'\e034';
+}
+
+.oi-chevron-left:before {
+  content:'\e035';
+}
+
+.oi-chevron-right:before {
+  content:'\e036';
+}
+
+.oi-chevron-top:before {
+  content:'\e037';
+}
+
+.oi-circle-check:before {
+  content:'\e038';
+}
+
+.oi-circle-x:before {
+  content:'\e039';
+}
+
+.oi-clipboard:before {
+  content:'\e03a';
+}
+
+.oi-clock:before {
+  content:'\e03b';
+}
+
+.oi-cloud-download:before {
+  content:'\e03c';
+}
+
+.oi-cloud-upload:before {
+  content:'\e03d';
+}
+
+.oi-cloud:before {
+  content:'\e03e';
+}
+
+.oi-cloudy:before {
+  content:'\e03f';
+}
+
+.oi-code:before {
+  content:'\e040';
+}
+
+.oi-cog:before {
+  content:'\e041';
+}
+
+.oi-collapse-down:before {
+  content:'\e042';
+}
+
+.oi-collapse-left:before {
+  content:'\e043';
+}
+
+.oi-collapse-right:before {
+  content:'\e044';
+}
+
+.oi-collapse-up:before {
+  content:'\e045';
+}
+
+.oi-command:before {
+  content:'\e046';
+}
+
+.oi-comment-square:before {
+  content:'\e047';
+}
+
+.oi-compass:before {
+  content:'\e048';
+}
+
+.oi-contrast:before {
+  content:'\e049';
+}
+
+.oi-copywriting:before {
+  content:'\e04a';
+}
+
+.oi-credit-card:before {
+  content:'\e04b';
+}
+
+.oi-crop:before {
+  content:'\e04c';
+}
+
+.oi-dashboard:before {
+  content:'\e04d';
+}
+
+.oi-data-transfer-download:before {
+  content:'\e04e';
+}
+
+.oi-data-transfer-upload:before {
+  content:'\e04f';
+}
+
+.oi-delete:before {
+  content:'\e050';
+}
+
+.oi-dial:before {
+  content:'\e051';
+}
+
+.oi-document:before {
+  content:'\e052';
+}
+
+.oi-dollar:before {
+  content:'\e053';
+}
+
+.oi-double-quote-sans-left:before {
+  content:'\e054';
+}
+
+.oi-double-quote-sans-right:before {
+  content:'\e055';
+}
+
+.oi-double-quote-serif-left:before {
+  content:'\e056';
+}
+
+.oi-double-quote-serif-right:before {
+  content:'\e057';
+}
+
+.oi-droplet:before {
+  content:'\e058';
+}
+
+.oi-eject:before {
+  content:'\e059';
+}
+
+.oi-elevator:before {
+  content:'\e05a';
+}
+
+.oi-ellipses:before {
+  content:'\e05b';
+}
+
+.oi-envelope-closed:before {
+  content:'\e05c';
+}
+
+.oi-envelope-open:before {
+  content:'\e05d';
+}
+
+.oi-euro:before {
+  content:'\e05e';
+}
+
+.oi-excerpt:before {
+  content:'\e05f';
+}
+
+.oi-expand-down:before {
+  content:'\e060';
+}
+
+.oi-expand-left:before {
+  content:'\e061';
+}
+
+.oi-expand-right:before {
+  content:'\e062';
+}
+
+.oi-expand-up:before {
+  content:'\e063';
+}
+
+.oi-external-link:before {
+  content:'\e064';
+}
+
+.oi-eye:before {
+  content:'\e065';
+}
+
+.oi-eyedropper:before {
+  content:'\e066';
+}
+
+.oi-file:before {
+  content:'\e067';
+}
+
+.oi-fire:before {
+  content:'\e068';
+}
+
+.oi-flag:before {
+  content:'\e069';
+}
+
+.oi-flash:before {
+  content:'\e06a';
+}
+
+.oi-folder:before {
+  content:'\e06b';
+}
+
+.oi-fork:before {
+  content:'\e06c';
+}
+
+.oi-fullscreen-enter:before {
+  content:'\e06d';
+}
+
+.oi-fullscreen-exit:before {
+  content:'\e06e';
+}
+
+.oi-globe:before {
+  content:'\e06f';
+}
+
+.oi-graph:before {
+  content:'\e070';
+}
+
+.oi-grid-four-up:before {
+  content:'\e071';
+}
+
+.oi-grid-three-up:before {
+  content:'\e072';
+}
+
+.oi-grid-two-up:before {
+  content:'\e073';
+}
+
+.oi-hard-drive:before {
+  content:'\e074';
+}
+
+.oi-header:before {
+  content:'\e075';
+}
+
+.oi-headphones:before {
+  content:'\e076';
+}
+
+.oi-heart:before {
+  content:'\e077';
+}
+
+.oi-home:before {
+  content:'\e078';
+}
+
+.oi-image:before {
+  content:'\e079';
+}
+
+.oi-inbox:before {
+  content:'\e07a';
+}
+
+.oi-infinity:before {
+  content:'\e07b';
+}
+
+.oi-info:before {
+  content:'\e07c';
+}
+
+.oi-italic:before {
+  content:'\e07d';
+}
+
+.oi-justify-center:before {
+  content:'\e07e';
+}
+
+.oi-justify-left:before {
+  content:'\e07f';
+}
+
+.oi-justify-right:before {
+  content:'\e080';
+}
+
+.oi-key:before {
+  content:'\e081';
+}
+
+.oi-laptop:before {
+  content:'\e082';
+}
+
+.oi-layers:before {
+  content:'\e083';
+}
+
+.oi-lightbulb:before {
+  content:'\e084';
+}
+
+.oi-link-broken:before {
+  content:'\e085';
+}
+
+.oi-link-intact:before {
+  content:'\e086';
+}
+
+.oi-list-rich:before {
+  content:'\e087';
+}
+
+.oi-list:before {
+  content:'\e088';
+}
+
+.oi-location:before {
+  content:'\e089';
+}
+
+.oi-lock-locked:before {
+  content:'\e08a';
+}
+
+.oi-lock-unlocked:before {
+  content:'\e08b';
+}
+
+.oi-loop-circular:before {
+  content:'\e08c';
+}
+
+.oi-loop-square:before {
+  content:'\e08d';
+}
+
+.oi-loop:before {
+  content:'\e08e';
+}
+
+.oi-magnifying-glass:before {
+  content:'\e08f';
+}
+
+.oi-map-marker:before {
+  content:'\e090';
+}
+
+.oi-map:before {
+  content:'\e091';
+}
+
+.oi-media-pause:before {
+  content:'\e092';
+}
+
+.oi-media-play:before {
+  content:'\e093';
+}
+
+.oi-media-record:before {
+  content:'\e094';
+}
+
+.oi-media-skip-backward:before {
+  content:'\e095';
+}
+
+.oi-media-skip-forward:before {
+  content:'\e096';
+}
+
+.oi-media-step-backward:before {
+  content:'\e097';
+}
+
+.oi-media-step-forward:before {
+  content:'\e098';
+}
+
+.oi-media-stop:before {
+  content:'\e099';
+}
+
+.oi-medical-cross:before {
+  content:'\e09a';
+}
+
+.oi-menu:before {
+  content:'\e09b';
+}
+
+.oi-microphone:before {
+  content:'\e09c';
+}
+
+.oi-minus:before {
+  content:'\e09d';
+}
+
+.oi-monitor:before {
+  content:'\e09e';
+}
+
+.oi-moon:before {
+  content:'\e09f';
+}
+
+.oi-move:before {
+  content:'\e0a0';
+}
+
+.oi-musical-note:before {
+  content:'\e0a1';
+}
+
+.oi-paperclip:before {
+  content:'\e0a2';
+}
+
+.oi-pencil:before {
+  content:'\e0a3';
+}
+
+.oi-people:before {
+  content:'\e0a4';
+}
+
+.oi-person:before {
+  content:'\e0a5';
+}
+
+.oi-phone:before {
+  content:'\e0a6';
+}
+
+.oi-pie-chart:before {
+  content:'\e0a7';
+}
+
+.oi-pin:before {
+  content:'\e0a8';
+}
+
+.oi-play-circle:before {
+  content:'\e0a9';
+}
+
+.oi-plus:before {
+  content:'\e0aa';
+}
+
+.oi-power-standby:before {
+  content:'\e0ab';
+}
+
+.oi-print:before {
+  content:'\e0ac';
+}
+
+.oi-project:before {
+  content:'\e0ad';
+}
+
+.oi-pulse:before {
+  content:'\e0ae';
+}
+
+.oi-puzzle-piece:before {
+  content:'\e0af';
+}
+
+.oi-question-mark:before {
+  content:'\e0b0';
+}
+
+.oi-rain:before {
+  content:'\e0b1';
+}
+
+.oi-random:before {
+  content:'\e0b2';
+}
+
+.oi-reload:before {
+  content:'\e0b3';
+}
+
+.oi-resize-both:before {
+  content:'\e0b4';
+}
+
+.oi-resize-height:before {
+  content:'\e0b5';
+}
+
+.oi-resize-width:before {
+  content:'\e0b6';
+}
+
+.oi-rss-alt:before {
+  content:'\e0b7';
+}
+
+.oi-rss:before {
+  content:'\e0b8';
+}
+
+.oi-script:before {
+  content:'\e0b9';
+}
+
+.oi-share-boxed:before {
+  content:'\e0ba';
+}
+
+.oi-share:before {
+  content:'\e0bb';
+}
+
+.oi-shield:before {
+  content:'\e0bc';
+}
+
+.oi-signal:before {
+  content:'\e0bd';
+}
+
+.oi-signpost:before {
+  content:'\e0be';
+}
+
+.oi-sort-ascending:before {
+  content:'\e0bf';
+}
+
+.oi-sort-descending:before {
+  content:'\e0c0';
+}
+
+.oi-spreadsheet:before {
+  content:'\e0c1';
+}
+
+.oi-star:before {
+  content:'\e0c2';
+}
+
+.oi-sun:before {
+  content:'\e0c3';
+}
+
+.oi-tablet:before {
+  content:'\e0c4';
+}
+
+.oi-tag:before {
+  content:'\e0c5';
+}
+
+.oi-tags:before {
+  content:'\e0c6';
+}
+
+.oi-target:before {
+  content:'\e0c7';
+}
+
+.oi-task:before {
+  content:'\e0c8';
+}
+
+.oi-terminal:before {
+  content:'\e0c9';
+}
+
+.oi-text:before {
+  content:'\e0ca';
+}
+
+.oi-thumb-down:before {
+  content:'\e0cb';
+}
+
+.oi-thumb-up:before {
+  content:'\e0cc';
+}
+
+.oi-timer:before {
+  content:'\e0cd';
+}
+
+.oi-transfer:before {
+  content:'\e0ce';
+}
+
+.oi-trash:before {
+  content:'\e0cf';
+}
+
+.oi-underline:before {
+  content:'\e0d0';
+}
+
+.oi-vertical-align-bottom:before {
+  content:'\e0d1';
+}
+
+.oi-vertical-align-center:before {
+  content:'\e0d2';
+}
+
+.oi-vertical-align-top:before {
+  content:'\e0d3';
+}
+
+.oi-video:before {
+  content:'\e0d4';
+}
+
+.oi-volume-high:before {
+  content:'\e0d5';
+}
+
+.oi-volume-low:before {
+  content:'\e0d6';
+}
+
+.oi-volume-off:before {
+  content:'\e0d7';
+}
+
+.oi-warning:before {
+  content:'\e0d8';
+}
+
+.oi-wifi:before {
+  content:'\e0d9';
+}
+
+.oi-wrench:before {
+  content:'\e0da';
+}
+
+.oi-x:before {
+  content:'\e0db';
+}
+
+.oi-yen:before {
+  content:'\e0dc';
+}
+
+.oi-zoom-in:before {
+  content:'\e0dd';
+}
+
+.oi-zoom-out:before {
+  content:'\e0de';
+}

static/open-iconic/css/open-iconic-bootstrap.less

@@ -0,0 +1,960 @@
+/* Bootstrap */
+
+/* Override Bootstrap default variable */
+//@icon-font-path: "../fonts/";
+
+@font-face {
+  font-family: 'Icons';
+  src: ~"url('@{icon-font-path}open-iconic.eot')";
+  src: ~"url('@{icon-font-path}open-iconic.eot?#iconic-sm') format('embedded-opentype')",
+       ~"url('@{icon-font-path}open-iconic.woff') format('woff')",
+       ~"url('@{icon-font-path}open-iconic.ttf') format('truetype')",
+       ~"url('@{icon-font-path}open-iconic.svg#iconic-sm') format('svg')";
+  font-weight: normal;
+  font-style: normal;
+}
+
+// Catchall baseclass
+.oi {
+  position: relative;
+  top: 1px;
+  display: inline-block;
+  font-family: 'Icons';
+  font-style: normal;
+  font-weight: normal;
+  line-height: 1;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+
+  &:empty:before {
+    width: 1em;
+    text-align: center;
+    box-sizing: content-box;
+  }
+
+  &.oi-align-center:before {
+    text-align: center;
+  }
+
+  &.oi-align-left:before {
+    text-align: left;
+  }
+
+  &.oi-align-right:before {
+    text-align: right;
+  }
+
+
+  &.oi-flip-horizontal:before {
+    -webkit-transform: scale(-1, 1);
+    -ms-transform: scale(-1, 1);
+    transform: scale(-1, 1);
+  }
+
+  &.oi-flip-vertical:before {
+    -webkit-transform: scale(1, -1);
+    -ms-transform: scale(-1, 1);
+    transform: scale(1, -1);
+  }
+
+  &.oi-flip-horizontal-vertical:before {
+    -webkit-transform: scale(-1, -1);
+    -ms-transform: scale(-1, 1);
+    transform: scale(-1, -1);
+  }
+}
+
+
+
+.oi-account-login:before {
+  content:"\e000";
+}
+
+.oi-account-logout:before {
+  content:"\e001";
+}
+
+.oi-action-redo:before {
+  content:"\e002";
+}
+
+.oi-action-undo:before {
+  content:"\e003";
+}
+
+.oi-align-center:before {
+  content:"\e004";
+}
+
+.oi-align-left:before {
+  content:"\e005";
+}
+
+.oi-align-right:before {
+  content:"\e006";
+}
+
+.oi-aperture:before {
+  content:"\e007";
+}
+
+.oi-arrow-bottom:before {
+  content:"\e008";
+}
+
+.oi-arrow-circle-bottom:before {
+  content:"\e009";
+}
+
+.oi-arrow-circle-left:before {
+  content:"\e00a";
+}
+
+.oi-arrow-circle-right:before {
+  content:"\e00b";
+}
+
+.oi-arrow-circle-top:before {
+  content:"\e00c";
+}
+
+.oi-arrow-left:before {
+  content:"\e00d";
+}
+
+.oi-arrow-right:before {
+  content:"\e00e";
+}
+
+.oi-arrow-thick-bottom:before {
+  content:"\e00f";
+}
+
+.oi-arrow-thick-left:before {
+  content:"\e010";
+}
+
+.oi-arrow-thick-right:before {
+  content:"\e011";
+}
+
+.oi-arrow-thick-top:before {
+  content:"\e012";
+}
+
+.oi-arrow-top:before {
+  content:"\e013";
+}
+
+.oi-audio-spectrum:before {
+  content:"\e014";
+}
+
+.oi-audio:before {
+  content:"\e015";
+}
+
+.oi-badge:before {
+  content:"\e016";
+}
+
+.oi-ban:before {
+  content:"\e017";
+}
+
+.oi-bar-chart:before {
+  content:"\e018";
+}
+
+.oi-basket:before {
+  content:"\e019";
+}
+
+.oi-battery-empty:before {
+  content:"\e01a";
+}
+
+.oi-battery-full:before {
+  content:"\e01b";
+}
+
+.oi-beaker:before {
+  content:"\e01c";
+}
+
+.oi-bell:before {
+  content:"\e01d";
+}
+
+.oi-bluetooth:before {
+  content:"\e01e";
+}
+
+.oi-bold:before {
+  content:"\e01f";
+}
+
+.oi-bolt:before {
+  content:"\e020";
+}
+
+.oi-book:before {
+  content:"\e021";
+}
+
+.oi-bookmark:before {
+  content:"\e022";
+}
+
+.oi-box:before {
+  content:"\e023";
+}
+
+.oi-briefcase:before {
+  content:"\e024";
+}
+
+.oi-british-pound:before {
+  content:"\e025";
+}
+
+.oi-browser:before {
+  content:"\e026";
+}
+
+.oi-brush:before {
+  content:"\e027";
+}
+
+.oi-bug:before {
+  content:"\e028";
+}
+
+.oi-bullhorn:before {
+  content:"\e029";
+}
+
+.oi-calculator:before {
+  content:"\e02a";
+}
+
+.oi-calendar:before {
+  content:"\e02b";
+}
+
+.oi-camera-slr:before {
+  content:"\e02c";
+}
+
+.oi-caret-bottom:before {
+  content:"\e02d";
+}
+
+.oi-caret-left:before {
+  content:"\e02e";
+}
+
+.oi-caret-right:before {
+  content:"\e02f";
+}
+
+.oi-caret-top:before {
+  content:"\e030";
+}
+
+.oi-cart:before {
+  content:"\e031";
+}
+
+.oi-chat:before {
+  content:"\e032";
+}
+
+.oi-check:before {
+  content:"\e033";
+}
+
+.oi-chevron-bottom:before {
+  content:"\e034";
+}
+
+.oi-chevron-left:before {
+  content:"\e035";
+}
+
+.oi-chevron-right:before {
+  content:"\e036";
+}
+
+.oi-chevron-top:before {
+  content:"\e037";
+}
+
+.oi-circle-check:before {
+  content:"\e038";
+}
+
+.oi-circle-x:before {
+  content:"\e039";
+}
+
+.oi-clipboard:before {
+  content:"\e03a";
+}
+
+.oi-clock:before {
+  content:"\e03b";
+}
+
+.oi-cloud-download:before {
+  content:"\e03c";
+}
+
+.oi-cloud-upload:before {
+  content:"\e03d";
+}
+
+.oi-cloud:before {
+  content:"\e03e";
+}
+
+.oi-cloudy:before {
+  content:"\e03f";
+}
+
+.oi-code:before {
+  content:"\e040";
+}
+
+.oi-cog:before {
+  content:"\e041";
+}
+
+.oi-collapse-down:before {
+  content:"\e042";
+}
+
+.oi-collapse-left:before {
+  content:"\e043";
+}
+
+.oi-collapse-right:before {
+  content:"\e044";
+}
+
+.oi-collapse-up:before {
+  content:"\e045";
+}
+
+.oi-command:before {
+  content:"\e046";
+}
+
+.oi-comment-square:before {
+  content:"\e047";
+}
+
+.oi-compass:before {
+  content:"\e048";
+}
+
+.oi-contrast:before {
+  content:"\e049";
+}
+
+.oi-copywriting:before {
+  content:"\e04a";
+}
+
+.oi-credit-card:before {
+  content:"\e04b";
+}
+
+.oi-crop:before {
+  content:"\e04c";
+}
+
+.oi-dashboard:before {
+  content:"\e04d";
+}
+
+.oi-data-transfer-download:before {
+  content:"\e04e";
+}
+
+.oi-data-transfer-upload:before {
+  content:"\e04f";
+}
+
+.oi-delete:before {
+  content:"\e050";
+}
+
+.oi-dial:before {
+  content:"\e051";
+}
+
+.oi-document:before {
+  content:"\e052";
+}
+
+.oi-dollar:before {
+  content:"\e053";
+}
+
+.oi-double-quote-sans-left:before {
+  content:"\e054";
+}
+
+.oi-double-quote-sans-right:before {
+  content:"\e055";
+}
+
+.oi-double-quote-serif-left:before {
+  content:"\e056";
+}
+
+.oi-double-quote-serif-right:before {
+  content:"\e057";
+}
+
+.oi-droplet:before {
+  content:"\e058";
+}
+
+.oi-eject:before {
+  content:"\e059";
+}
+
+.oi-elevator:before {
+  content:"\e05a";
+}
+
+.oi-ellipses:before {
+  content:"\e05b";
+}
+
+.oi-envelope-closed:before {
+  content:"\e05c";
+}
+
+.oi-envelope-open:before {
+  content:"\e05d";
+}
+
+.oi-euro:before {
+  content:"\e05e";
+}
+
+.oi-excerpt:before {
+  content:"\e05f";
+}
+
+.oi-expand-down:before {
+  content:"\e060";
+}
+
+.oi-expand-left:before {
+  content:"\e061";
+}
+
+.oi-expand-right:before {
+  content:"\e062";
+}
+
+.oi-expand-up:before {
+  content:"\e063";
+}
+
+.oi-external-link:before {
+  content:"\e064";
+}
+
+.oi-eye:before {
+  content:"\e065";
+}
+
+.oi-eyedropper:before {
+  content:"\e066";
+}
+
+.oi-file:before {
+  content:"\e067";
+}
+
+.oi-fire:before {
+  content:"\e068";
+}
+
+.oi-flag:before {
+  content:"\e069";
+}
+
+.oi-flash:before {
+  content:"\e06a";
+}
+
+.oi-folder:before {
+  content:"\e06b";
+}
+
+.oi-fork:before {
+  content:"\e06c";
+}
+
+.oi-fullscreen-enter:before {
+  content:"\e06d";
+}
+
+.oi-fullscreen-exit:before {
+  content:"\e06e";
+}
+
+.oi-globe:before {
+  content:"\e06f";
+}
+
+.oi-graph:before {
+  content:"\e070";
+}
+
+.oi-grid-four-up:before {
+  content:"\e071";
+}
+
+.oi-grid-three-up:before {
+  content:"\e072";
+}
+
+.oi-grid-two-up:before {
+  content:"\e073";
+}
+
+.oi-hard-drive:before {
+  content:"\e074";
+}
+
+.oi-header:before {
+  content:"\e075";
+}
+
+.oi-headphones:before {
+  content:"\e076";
+}
+
+.oi-heart:before {
+  content:"\e077";
+}
+
+.oi-home:before {
+  content:"\e078";
+}
+
+.oi-image:before {
+  content:"\e079";
+}
+
+.oi-inbox:before {
+  content:"\e07a";
+}
+
+.oi-infinity:before {
+  content:"\e07b";
+}
+
+.oi-info:before {
+  content:"\e07c";
+}
+
+.oi-italic:before {
+  content:"\e07d";
+}
+
+.oi-justify-center:before {
+  content:"\e07e";
+}
+
+.oi-justify-left:before {
+  content:"\e07f";
+}
+
+.oi-justify-right:before {
+  content:"\e080";
+}
+
+.oi-key:before {
+  content:"\e081";
+}
+
+.oi-laptop:before {
+  content:"\e082";
+}
+
+.oi-layers:before {
+  content:"\e083";
+}
+
+.oi-lightbulb:before {
+  content:"\e084";
+}
+
+.oi-link-broken:before {
+  content:"\e085";
+}
+
+.oi-link-intact:before {
+  content:"\e086";
+}
+
+.oi-list-rich:before {
+  content:"\e087";
+}
+
+.oi-list:before {
+  content:"\e088";
+}
+
+.oi-location:before {
+  content:"\e089";
+}
+
+.oi-lock-locked:before {
+  content:"\e08a";
+}
+
+.oi-lock-unlocked:before {
+  content:"\e08b";
+}
+
+.oi-loop-circular:before {
+  content:"\e08c";
+}
+
+.oi-loop-square:before {
+  content:"\e08d";
+}
+
+.oi-loop:before {
+  content:"\e08e";
+}
+
+.oi-magnifying-glass:before {
+  content:"\e08f";
+}
+
+.oi-map-marker:before {
+  content:"\e090";
+}
+
+.oi-map:before {
+  content:"\e091";
+}
+
+.oi-media-pause:before {
+  content:"\e092";
+}
+
+.oi-media-play:before {
+  content:"\e093";
+}
+
+.oi-media-record:before {
+  content:"\e094";
+}
+
+.oi-media-skip-backward:before {
+  content:"\e095";
+}
+
+.oi-media-skip-forward:before {
+  content:"\e096";
+}
+
+.oi-media-step-backward:before {
+  content:"\e097";
+}
+
+.oi-media-step-forward:before {
+  content:"\e098";
+}
+
+.oi-media-stop:before {
+  content:"\e099";
+}
+
+.oi-medical-cross:before {
+  content:"\e09a";
+}
+
+.oi-menu:before {
+  content:"\e09b";
+}
+
+.oi-microphone:before {
+  content:"\e09c";
+}
+
+.oi-minus:before {
+  content:"\e09d";
+}
+
+.oi-monitor:before {
+  content:"\e09e";
+}
+
+.oi-moon:before {
+  content:"\e09f";
+}
+
+.oi-move:before {
+  content:"\e0a0";
+}
+
+.oi-musical-note:before {
+  content:"\e0a1";
+}
+
+.oi-paperclip:before {
+  content:"\e0a2";
+}
+
+.oi-pencil:before {
+  content:"\e0a3";
+}
+
+.oi-people:before {
+  content:"\e0a4";
+}
+
+.oi-person:before {
+  content:"\e0a5";
+}
+
+.oi-phone:before {
+  content:"\e0a6";
+}
+
+.oi-pie-chart:before {
+  content:"\e0a7";
+}
+
+.oi-pin:before {
+  content:"\e0a8";
+}
+
+.oi-play-circle:before {
+  content:"\e0a9";
+}
+
+.oi-plus:before {
+  content:"\e0aa";
+}
+
+.oi-power-standby:before {
+  content:"\e0ab";
+}
+
+.oi-print:before {
+  content:"\e0ac";
+}
+
+.oi-project:before {
+  content:"\e0ad";
+}
+
+.oi-pulse:before {
+  content:"\e0ae";
+}
+
+.oi-puzzle-piece:before {
+  content:"\e0af";
+}
+
+.oi-question-mark:before {
+  content:"\e0b0";
+}
+
+.oi-rain:before {
+  content:"\e0b1";
+}
+
+.oi-random:before {
+  content:"\e0b2";
+}
+
+.oi-reload:before {
+  content:"\e0b3";
+}
+
+.oi-resize-both:before {
+  content:"\e0b4";
+}
+
+.oi-resize-height:before {
+  content:"\e0b5";
+}
+
+.oi-resize-width:before {
+  content:"\e0b6";
+}
+
+.oi-rss-alt:before {
+  content:"\e0b7";
+}
+
+.oi-rss:before {
+  content:"\e0b8";
+}
+
+.oi-script:before {
+  content:"\e0b9";
+}
+
+.oi-share-boxed:before {
+  content:"\e0ba";
+}
+
+.oi-share:before {
+  content:"\e0bb";
+}
+
+.oi-shield:before {
+  content:"\e0bc";
+}
+
+.oi-signal:before {
+  content:"\e0bd";
+}
+
+.oi-signpost:before {
+  content:"\e0be";
+}
+
+.oi-sort-ascending:before {
+  content:"\e0bf";
+}
+
+.oi-sort-descending:before {
+  content:"\e0c0";
+}
+
+.oi-spreadsheet:before {
+  content:"\e0c1";
+}
+
+.oi-star:before {
+  content:"\e0c2";
+}
+
+.oi-sun:before {
+  content:"\e0c3";
+}
+
+.oi-tablet:before {
+  content:"\e0c4";
+}
+
+.oi-tag:before {
+  content:"\e0c5";
+}
+
+.oi-tags:before {
+  content:"\e0c6";
+}
+
+.oi-target:before {
+  content:"\e0c7";
+}
+
+.oi-task:before {
+  content:"\e0c8";
+}
+
+.oi-terminal:before {
+  content:"\e0c9";
+}
+
+.oi-text:before {
+  content:"\e0ca";
+}
+
+.oi-thumb-down:before {
+  content:"\e0cb";
+}
+
+.oi-thumb-up:before {
+  content:"\e0cc";
+}
+
+.oi-timer:before {
+  content:"\e0cd";
+}
+
+.oi-transfer:before {
+  content:"\e0ce";
+}
+
+.oi-trash:before {
+  content:"\e0cf";
+}
+
+.oi-underline:before {
+  content:"\e0d0";
+}
+
+.oi-vertical-align-bottom:before {
+  content:"\e0d1";
+}
+
+.oi-vertical-align-center:before {
+  content:"\e0d2";
+}
+
+.oi-vertical-align-top:before {
+  content:"\e0d3";
+}
+
+.oi-video:before {
+  content:"\e0d4";
+}
+
+.oi-volume-high:before {
+  content:"\e0d5";
+}
+
+.oi-volume-low:before {
+  content:"\e0d6";
+}
+
+.oi-volume-off:before {
+  content:"\e0d7";
+}
+
+.oi-warning:before {
+  content:"\e0d8";
+}
+
+.oi-wifi:before {
+  content:"\e0d9";
+}
+
+.oi-wrench:before {
+  content:"\e0da";
+}
+
+.oi-x:before {
+  content:"\e0db";
+}
+
+.oi-yen:before {
+  content:"\e0dc";
+}
+
+.oi-zoom-in:before {
+  content:"\e0dd";
+}
+
+.oi-zoom-out:before {
+  content:"\e0de";
+}
+

static/open-iconic/css/open-iconic-bootstrap.scss

@@ -0,0 +1,958 @@
+/* Bootstrap */
+
+/* Override Bootstrap default variable */
+$icon-font-path: '../fonts/' !default;
+
+@font-face {
+  font-family: 'Icons';
+  src: url('#{$icon-font-path}open-iconic.eot');
+  src: url('#{$icon-font-path}open-iconic.eot?#iconic-sm') format('embedded-opentype'), url('#{$icon-font-path}open-iconic.woff') format('woff'), url('#{$icon-font-path}open-iconic.ttf') format('truetype'), url('#{$icon-font-path}open-iconic.svg#iconic-sm') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+
+// Catchall baseclass
+.oi {
+  position: relative;
+  top: 1px;
+  display: inline-block;
+  font-family: 'Icons';
+  font-style: normal;
+  font-weight: normal;
+  line-height: 1;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+
+
+  &:empty:before {
+    width: 1em;
+    text-align: center;
+    box-sizing: content-box;
+  }
+
+  &.oi-align-center:before {
+    text-align: center;
+  }
+
+  &.oi-align-left:before {
+    text-align: left;
+  }
+
+  &.oi-align-right:before {
+    text-align: right;
+  }
+
+
+  &.oi-flip-horizontal:before {
+    -webkit-transform: scale(-1, 1);
+    -ms-transform: scale(-1, 1);
+    transform: scale(-1, 1);
+  }
+
+  &.oi-flip-vertical:before {
+    -webkit-transform: scale(1, -1);
+    -ms-transform: scale(-1, 1);
+    transform: scale(1, -1);
+  }
+
+  &.oi-flip-horizontal-vertical:before {
+    -webkit-transform: scale(-1, -1);
+    -ms-transform: scale(-1, 1);
+    transform: scale(-1, -1);
+  }
+}
+
+
+
+.oi-account-login:before {
+  content:'\e000';
+}
+
+.oi-account-logout:before {
+  content:'\e001';
+}
+
+.oi-action-redo:before {
+  content:'\e002';
+}
+
+.oi-action-undo:before {
+  content:'\e003';
+}
+
+.oi-align-center:before {
+  content:'\e004';
+}
+
+.oi-align-left:before {
+  content:'\e005';
+}
+
+.oi-align-right:before {
+  content:'\e006';
+}
+
+.oi-aperture:before {
+  content:'\e007';
+}
+
+.oi-arrow-bottom:before {
+  content:'\e008';
+}
+
+.oi-arrow-circle-bottom:before {
+  content:'\e009';
+}
+
+.oi-arrow-circle-left:before {
+  content:'\e00a';
+}
+
+.oi-arrow-circle-right:before {
+  content:'\e00b';
+}
+
+.oi-arrow-circle-top:before {
+  content:'\e00c';
+}
+
+.oi-arrow-left:before {
+  content:'\e00d';
+}
+
+.oi-arrow-right:before {
+  content:'\e00e';
+}
+
+.oi-arrow-thick-bottom:before {
+  content:'\e00f';
+}
+
+.oi-arrow-thick-left:before {
+  content:'\e010';
+}
+
+.oi-arrow-thick-right:before {
+  content:'\e011';
+}
+
+.oi-arrow-thick-top:before {
+  content:'\e012';
+}
+
+.oi-arrow-top:before {
+  content:'\e013';
+}
+
+.oi-audio-spectrum:before {
+  content:'\e014';
+}
+
+.oi-audio:before {
+  content:'\e015';
+}
+
+.oi-badge:before {
+  content:'\e016';
+}
+
+.oi-ban:before {
+  content:'\e017';
+}
+
+.oi-bar-chart:before {
+  content:'\e018';
+}
+
+.oi-basket:before {
+  content:'\e019';
+}
+
+.oi-battery-empty:before {
+  content:'\e01a';
+}
+
+.oi-battery-full:before {
+  content:'\e01b';
+}
+
+.oi-beaker:before {
+  content:'\e01c';
+}
+
+.oi-bell:before {
+  content:'\e01d';
+}
+
+.oi-bluetooth:before {
+  content:'\e01e';
+}
+
+.oi-bold:before {
+  content:'\e01f';
+}
+
+.oi-bolt:before {
+  content:'\e020';
+}
+
+.oi-book:before {
+  content:'\e021';
+}
+
+.oi-bookmark:before {
+  content:'\e022';
+}
+
+.oi-box:before {
+  content:'\e023';
+}
+
+.oi-briefcase:before {
+  content:'\e024';
+}
+
+.oi-british-pound:before {
+  content:'\e025';
+}
+
+.oi-browser:before {
+  content:'\e026';
+}
+
+.oi-brush:before {
+  content:'\e027';
+}
+
+.oi-bug:before {
+  content:'\e028';
+}
+
+.oi-bullhorn:before {
+  content:'\e029';
+}
+
+.oi-calculator:before {
+  content:'\e02a';
+}
+
+.oi-calendar:before {
+  content:'\e02b';
+}
+
+.oi-camera-slr:before {
+  content:'\e02c';
+}
+
+.oi-caret-bottom:before {
+  content:'\e02d';
+}
+
+.oi-caret-left:before {
+  content:'\e02e';
+}
+
+.oi-caret-right:before {
+  content:'\e02f';
+}
+
+.oi-caret-top:before {
+  content:'\e030';
+}
+
+.oi-cart:before {
+  content:'\e031';
+}
+
+.oi-chat:before {
+  content:'\e032';
+}
+
+.oi-check:before {
+  content:'\e033';
+}
+
+.oi-chevron-bottom:before {
+  content:'\e034';
+}
+
+.oi-chevron-left:before {
+  content:'\e035';
+}
+
+.oi-chevron-right:before {
+  content:'\e036';
+}
+
+.oi-chevron-top:before {
+  content:'\e037';
+}
+
+.oi-circle-check:before {
+  content:'\e038';
+}
+
+.oi-circle-x:before {
+  content:'\e039';
+}
+
+.oi-clipboard:before {
+  content:'\e03a';
+}
+
+.oi-clock:before {
+  content:'\e03b';
+}
+
+.oi-cloud-download:before {
+  content:'\e03c';
+}
+
+.oi-cloud-upload:before {
+  content:'\e03d';
+}
+
+.oi-cloud:before {
+  content:'\e03e';
+}
+
+.oi-cloudy:before {
+  content:'\e03f';
+}
+
+.oi-code:before {
+  content:'\e040';
+}
+
+.oi-cog:before {
+  content:'\e041';
+}
+
+.oi-collapse-down:before {
+  content:'\e042';
+}
+
+.oi-collapse-left:before {
+  content:'\e043';
+}
+
+.oi-collapse-right:before {
+  content:'\e044';
+}
+
+.oi-collapse-up:before {
+  content:'\e045';
+}
+
+.oi-command:before {
+  content:'\e046';
+}
+
+.oi-comment-square:before {
+  content:'\e047';
+}
+
+.oi-compass:before {
+  content:'\e048';
+}
+
+.oi-contrast:before {
+  content:'\e049';
+}
+
+.oi-copywriting:before {
+  content:'\e04a';
+}
+
+.oi-credit-card:before {
+  content:'\e04b';
+}
+
+.oi-crop:before {
+  content:'\e04c';
+}
+
+.oi-dashboard:before {
+  content:'\e04d';
+}
+
+.oi-data-transfer-download:before {
+  content:'\e04e';
+}
+
+.oi-data-transfer-upload:before {
+  content:'\e04f';
+}
+
+.oi-delete:before {
+  content:'\e050';
+}
+
+.oi-dial:before {
+  content:'\e051';
+}
+
+.oi-document:before {
+  content:'\e052';
+}
+
+.oi-dollar:before {
+  content:'\e053';
+}
+
+.oi-double-quote-sans-left:before {
+  content:'\e054';
+}
+
+.oi-double-quote-sans-right:before {
+  content:'\e055';
+}
+
+.oi-double-quote-serif-left:before {
+  content:'\e056';
+}
+
+.oi-double-quote-serif-right:before {
+  content:'\e057';
+}
+
+.oi-droplet:before {
+  content:'\e058';
+}
+
+.oi-eject:before {
+  content:'\e059';
+}
+
+.oi-elevator:before {
+  content:'\e05a';
+}
+
+.oi-ellipses:before {
+  content:'\e05b';
+}
+
+.oi-envelope-closed:before {
+  content:'\e05c';
+}
+
+.oi-envelope-open:before {
+  content:'\e05d';
+}
+
+.oi-euro:before {
+  content:'\e05e';
+}
+
+.oi-excerpt:before {
+  content:'\e05f';
+}
+
+.oi-expand-down:before {
+  content:'\e060';
+}
+
+.oi-expand-left:before {
+  content:'\e061';
+}
+
+.oi-expand-right:before {
+  content:'\e062';
+}
+
+.oi-expand-up:before {
+  content:'\e063';
+}
+
+.oi-external-link:before {
+  content:'\e064';
+}
+
+.oi-eye:before {
+  content:'\e065';
+}
+
+.oi-eyedropper:before {
+  content:'\e066';
+}
+
+.oi-file:before {
+  content:'\e067';
+}
+
+.oi-fire:before {
+  content:'\e068';
+}
+
+.oi-flag:before {
+  content:'\e069';
+}
+
+.oi-flash:before {
+  content:'\e06a';
+}
+
+.oi-folder:before {
+  content:'\e06b';
+}
+
+.oi-fork:before {
+  content:'\e06c';
+}
+
+.oi-fullscreen-enter:before {
+  content:'\e06d';
+}
+
+.oi-fullscreen-exit:before {
+  content:'\e06e';
+}
+
+.oi-globe:before {
+  content:'\e06f';
+}
+
+.oi-graph:before {
+  content:'\e070';
+}
+
+.oi-grid-four-up:before {
+  content:'\e071';
+}
+
+.oi-grid-three-up:before {
+  content:'\e072';
+}
+
+.oi-grid-two-up:before {
+  content:'\e073';
+}
+
+.oi-hard-drive:before {
+  content:'\e074';
+}
+
+.oi-header:before {
+  content:'\e075';
+}
+
+.oi-headphones:before {
+  content:'\e076';
+}
+
+.oi-heart:before {
+  content:'\e077';
+}
+
+.oi-home:before {
+  content:'\e078';
+}
+
+.oi-image:before {
+  content:'\e079';
+}
+
+.oi-inbox:before {
+  content:'\e07a';
+}
+
+.oi-infinity:before {
+  content:'\e07b';
+}
+
+.oi-info:before {
+  content:'\e07c';
+}
+
+.oi-italic:before {
+  content:'\e07d';
+}
+
+.oi-justify-center:before {
+  content:'\e07e';
+}
+
+.oi-justify-left:before {
+  content:'\e07f';
+}
+
+.oi-justify-right:before {
+  content:'\e080';
+}
+
+.oi-key:before {
+  content:'\e081';
+}
+
+.oi-laptop:before {
+  content:'\e082';
+}
+
+.oi-layers:before {
+  content:'\e083';
+}
+
+.oi-lightbulb:before {
+  content:'\e084';
+}
+
+.oi-link-broken:before {
+  content:'\e085';
+}
+
+.oi-link-intact:before {
+  content:'\e086';
+}
+
+.oi-list-rich:before {
+  content:'\e087';
+}
+
+.oi-list:before {
+  content:'\e088';
+}
+
+.oi-location:before {
+  content:'\e089';
+}
+
+.oi-lock-locked:before {
+  content:'\e08a';
+}
+
+.oi-lock-unlocked:before {
+  content:'\e08b';
+}
+
+.oi-loop-circular:before {
+  content:'\e08c';
+}
+
+.oi-loop-square:before {
+  content:'\e08d';
+}
+
+.oi-loop:before {
+  content:'\e08e';
+}
+
+.oi-magnifying-glass:before {
+  content:'\e08f';
+}
+
+.oi-map-marker:before {
+  content:'\e090';
+}
+
+.oi-map:before {
+  content:'\e091';
+}
+
+.oi-media-pause:before {
+  content:'\e092';
+}
+
+.oi-media-play:before {
+  content:'\e093';
+}
+
+.oi-media-record:before {
+  content:'\e094';
+}
+
+.oi-media-skip-backward:before {
+  content:'\e095';
+}
+
+.oi-media-skip-forward:before {
+  content:'\e096';
+}
+
+.oi-media-step-backward:before {
+  content:'\e097';
+}
+
+.oi-media-step-forward:before {
+  content:'\e098';
+}
+
+.oi-media-stop:before {
+  content:'\e099';
+}
+
+.oi-medical-cross:before {
+  content:'\e09a';
+}
+
+.oi-menu:before {
+  content:'\e09b';
+}
+
+.oi-microphone:before {
+  content:'\e09c';
+}
+
+.oi-minus:before {
+  content:'\e09d';
+}
+
+.oi-monitor:before {
+  content:'\e09e';
+}
+
+.oi-moon:before {
+  content:'\e09f';
+}
+
+.oi-move:before {
+  content:'\e0a0';
+}
+
+.oi-musical-note:before {
+  content:'\e0a1';
+}
+
+.oi-paperclip:before {
+  content:'\e0a2';
+}
+
+.oi-pencil:before {
+  content:'\e0a3';
+}
+
+.oi-people:before {
+  content:'\e0a4';
+}
+
+.oi-person:before {
+  content:'\e0a5';
+}
+
+.oi-phone:before {
+  content:'\e0a6';
+}
+
+.oi-pie-chart:before {
+  content:'\e0a7';
+}
+
+.oi-pin:before {
+  content:'\e0a8';
+}
+
+.oi-play-circle:before {
+  content:'\e0a9';
+}
+
+.oi-plus:before {
+  content:'\e0aa';
+}
+
+.oi-power-standby:before {
+  content:'\e0ab';
+}
+
+.oi-print:before {
+  content:'\e0ac';
+}
+
+.oi-project:before {
+  content:'\e0ad';
+}
+
+.oi-pulse:before {
+  content:'\e0ae';
+}
+
+.oi-puzzle-piece:before {
+  content:'\e0af';
+}
+
+.oi-question-mark:before {
+  content:'\e0b0';
+}
+
+.oi-rain:before {
+  content:'\e0b1';
+}
+
+.oi-random:before {
+  content:'\e0b2';
+}
+
+.oi-reload:before {
+  content:'\e0b3';
+}
+
+.oi-resize-both:before {
+  content:'\e0b4';
+}
+
+.oi-resize-height:before {
+  content:'\e0b5';
+}
+
+.oi-resize-width:before {
+  content:'\e0b6';
+}
+
+.oi-rss-alt:before {
+  content:'\e0b7';
+}
+
+.oi-rss:before {
+  content:'\e0b8';
+}
+
+.oi-script:before {
+  content:'\e0b9';
+}
+
+.oi-share-boxed:before {
+  content:'\e0ba';
+}
+
+.oi-share:before {
+  content:'\e0bb';
+}
+
+.oi-shield:before {
+  content:'\e0bc';
+}
+
+.oi-signal:before {
+  content:'\e0bd';
+}
+
+.oi-signpost:before {
+  content:'\e0be';
+}
+
+.oi-sort-ascending:before {
+  content:'\e0bf';
+}
+
+.oi-sort-descending:before {
+  content:'\e0c0';
+}
+
+.oi-spreadsheet:before {
+  content:'\e0c1';
+}
+
+.oi-star:before {
+  content:'\e0c2';
+}
+
+.oi-sun:before {
+  content:'\e0c3';
+}
+
+.oi-tablet:before {
+  content:'\e0c4';
+}
+
+.oi-tag:before {
+  content:'\e0c5';
+}
+
+.oi-tags:before {
+  content:'\e0c6';
+}
+
+.oi-target:before {
+  content:'\e0c7';
+}
+
+.oi-task:before {
+  content:'\e0c8';
+}
+
+.oi-terminal:before {
+  content:'\e0c9';
+}
+
+.oi-text:before {
+  content:'\e0ca';
+}
+
+.oi-thumb-down:before {
+  content:'\e0cb';
+}
+
+.oi-thumb-up:before {
+  content:'\e0cc';
+}
+
+.oi-timer:before {
+  content:'\e0cd';
+}
+
+.oi-transfer:before {
+  content:'\e0ce';
+}
+
+.oi-trash:before {
+  content:'\e0cf';
+}
+
+.oi-underline:before {
+  content:'\e0d0';
+}
+
+.oi-vertical-align-bottom:before {
+  content:'\e0d1';
+}
+
+.oi-vertical-align-center:before {
+  content:'\e0d2';
+}
+
+.oi-vertical-align-top:before {
+  content:'\e0d3';
+}
+
+.oi-video:before {
+  content:'\e0d4';
+}
+
+.oi-volume-high:before {
+  content:'\e0d5';
+}
+
+.oi-volume-low:before {
+  content:'\e0d6';
+}
+
+.oi-volume-off:before {
+  content:'\e0d7';
+}
+
+.oi-warning:before {
+  content:'\e0d8';
+}
+
+.oi-wifi:before {
+  content:'\e0d9';
+}
+
+.oi-wrench:before {
+  content:'\e0da';
+}
+
+.oi-x:before {
+  content:'\e0db';
+}
+
+.oi-yen:before {
+  content:'\e0dc';
+}
+
+.oi-zoom-in:before {
+  content:'\e0dd';
+}
+
+.oi-zoom-out:before {
+  content:'\e0de';
+}
+

static/open-iconic/css/open-iconic-bootstrap.styl

@@ -0,0 +1,954 @@
+/* Bootstrap */
+
+@font-face
+  font-family 'Icons'
+  src url('../fonts/open-iconic.eot')
+  src url('../fonts/open-iconic.eot?#iconic-sm') format('embedded-opentype'), url('../fonts/open-iconic.woff') format('woff'), url('../fonts/open-iconic.ttf') format('truetype'), url('../fonts/open-iconic.svg#iconic-sm') format('svg')
+  font-weight normal
+  font-style normal
+
+
+// Catchall baseclass
+.oi
+  position relative
+  top 1px
+  display inline-block
+  font-family 'Icons'
+  font-style normal
+  font-weight normal
+  line-height 1
+  -webkit-font-smoothing antialiased
+  -moz-osx-font-smoothing grayscale
+
+
+  &:empty:before
+    width 1em
+    text-align center
+    box-sizing content-box
+
+  &.oi-align-center:before
+    text-align center
+
+
+  &.oi-align-left:before
+    text-align left
+
+
+  &.oi-align-right:before
+    text-align right
+
+
+
+  &.oi-flip-horizontal:before
+    -webkit-transform scale(-1, 1)
+    -ms-transform scale(-1, 1)
+    transform scale(-1, 1)
+
+
+  &.oi-flip-vertical:before
+    -webkit-transform scale(1, -1)
+    -ms-transform scale(-1, 1)
+    transform scale(1, -1)
+
+
+  &.oi-flip-horizontal-vertical:before
+    -webkit-transform scale(-1, -1)
+    -ms-transform scale(-1, 1)
+    transform scale(-1, -1)
+
+
+
+
+
+.oi-account-login:before {
+  content'\e000'
+}
+
+.oi-account-logout:before {
+  content'\e001'
+}
+
+.oi-action-redo:before {
+  content'\e002'
+}
+
+.oi-action-undo:before {
+  content'\e003'
+}
+
+.oi-align-center:before {
+  content'\e004'
+}
+
+.oi-align-left:before {
+  content'\e005'
+}
+
+.oi-align-right:before {
+  content'\e006'
+}
+
+.oi-aperture:before {
+  content'\e007'
+}
+
+.oi-arrow-bottom:before {
+  content'\e008'
+}
+
+.oi-arrow-circle-bottom:before {
+  content'\e009'
+}
+
+.oi-arrow-circle-left:before {
+  content'\e00a'
+}
+
+.oi-arrow-circle-right:before {
+  content'\e00b'
+}
+
+.oi-arrow-circle-top:before {
+  content'\e00c'
+}
+
+.oi-arrow-left:before {
+  content'\e00d'
+}
+
+.oi-arrow-right:before {
+  content'\e00e'
+}
+
+.oi-arrow-thick-bottom:before {
+  content'\e00f'
+}
+
+.oi-arrow-thick-left:before {
+  content'\e010'
+}
+
+.oi-arrow-thick-right:before {
+  content'\e011'
+}
+
+.oi-arrow-thick-top:before {
+  content'\e012'
+}
+
+.oi-arrow-top:before {
+  content'\e013'
+}
+
+.oi-audio-spectrum:before {
+  content'\e014'
+}
+
+.oi-audio:before {
+  content'\e015'
+}
+
+.oi-badge:before {
+  content'\e016'
+}
+
+.oi-ban:before {
+  content'\e017'
+}
+
+.oi-bar-chart:before {
+  content'\e018'
+}
+
+.oi-basket:before {
+  content'\e019'
+}
+
+.oi-battery-empty:before {
+  content'\e01a'
+}
+
+.oi-battery-full:before {
+  content'\e01b'
+}
+
+.oi-beaker:before {
+  content'\e01c'
+}
+
+.oi-bell:before {
+  content'\e01d'
+}
+
+.oi-bluetooth:before {
+  content'\e01e'
+}
+
+.oi-bold:before {
+  content'\e01f'
+}
+
+.oi-bolt:before {
+  content'\e020'
+}
+
+.oi-book:before {
+  content'\e021'
+}
+
+.oi-bookmark:before {
+  content'\e022'
+}
+
+.oi-box:before {
+  content'\e023'
+}
+
+.oi-briefcase:before {
+  content'\e024'
+}
+
+.oi-british-pound:before {
+  content'\e025'
+}
+
+.oi-browser:before {
+  content'\e026'
+}
+
+.oi-brush:before {
+  content'\e027'
+}
+
+.oi-bug:before {
+  content'\e028'
+}
+
+.oi-bullhorn:before {
+  content'\e029'
+}
+
+.oi-calculator:before {
+  content'\e02a'
+}
+
+.oi-calendar:before {
+  content'\e02b'
+}
+
+.oi-camera-slr:before {
+  content'\e02c'
+}
+
+.oi-caret-bottom:before {
+  content'\e02d'
+}
+
+.oi-caret-left:before {
+  content'\e02e'
+}
+
+.oi-caret-right:before {
+  content'\e02f'
+}
+
+.oi-caret-top:before {
+  content'\e030'
+}
+
+.oi-cart:before {
+  content'\e031'
+}
+
+.oi-chat:before {
+  content'\e032'
+}
+
+.oi-check:before {
+  content'\e033'
+}
+
+.oi-chevron-bottom:before {
+  content'\e034'
+}
+
+.oi-chevron-left:before {
+  content'\e035'
+}
+
+.oi-chevron-right:before {
+  content'\e036'
+}
+
+.oi-chevron-top:before {
+  content'\e037'
+}
+
+.oi-circle-check:before {
+  content'\e038'
+}
+
+.oi-circle-x:before {
+  content'\e039'
+}
+
+.oi-clipboard:before {
+  content'\e03a'
+}
+
+.oi-clock:before {
+  content'\e03b'
+}
+
+.oi-cloud-download:before {
+  content'\e03c'
+}
+
+.oi-cloud-upload:before {
+  content'\e03d'
+}
+
+.oi-cloud:before {
+  content'\e03e'
+}
+
+.oi-cloudy:before {
+  content'\e03f'
+}
+
+.oi-code:before {
+  content'\e040'
+}
+
+.oi-cog:before {
+  content'\e041'
+}
+
+.oi-collapse-down:before {
+  content'\e042'
+}
+
+.oi-collapse-left:before {
+  content'\e043'
+}
+
+.oi-collapse-right:before {
+  content'\e044'
+}
+
+.oi-collapse-up:before {
+  content'\e045'
+}
+
+.oi-command:before {
+  content'\e046'
+}
+
+.oi-comment-square:before {
+  content'\e047'
+}
+
+.oi-compass:before {
+  content'\e048'
+}
+
+.oi-contrast:before {
+  content'\e049'
+}
+
+.oi-copywriting:before {
+  content'\e04a'
+}
+
+.oi-credit-card:before {
+  content'\e04b'
+}
+
+.oi-crop:before {
+  content'\e04c'
+}
+
+.oi-dashboard:before {
+  content'\e04d'
+}
+
+.oi-data-transfer-download:before {
+  content'\e04e'
+}
+
+.oi-data-transfer-upload:before {
+  content'\e04f'
+}
+
+.oi-delete:before {
+  content'\e050'
+}
+
+.oi-dial:before {
+  content'\e051'
+}
+
+.oi-document:before {
+  content'\e052'
+}
+
+.oi-dollar:before {
+  content'\e053'
+}
+
+.oi-double-quote-sans-left:before {
+  content'\e054'
+}
+
+.oi-double-quote-sans-right:before {
+  content'\e055'
+}
+
+.oi-double-quote-serif-left:before {
+  content'\e056'
+}
+
+.oi-double-quote-serif-right:before {
+  content'\e057'
+}
+
+.oi-droplet:before {
+  content'\e058'
+}
+
+.oi-eject:before {
+  content'\e059'
+}
+
+.oi-elevator:before {
+  content'\e05a'
+}
+
+.oi-ellipses:before {
+  content'\e05b'
+}
+
+.oi-envelope-closed:before {
+  content'\e05c'
+}
+
+.oi-envelope-open:before {
+  content'\e05d'
+}
+
+.oi-euro:before {
+  content'\e05e'
+}
+
+.oi-excerpt:before {
+  content'\e05f'
+}
+
+.oi-expand-down:before {
+  content'\e060'
+}
+
+.oi-expand-left:before {
+  content'\e061'
+}
+
+.oi-expand-right:before {
+  content'\e062'
+}
+
+.oi-expand-up:before {
+  content'\e063'
+}
+
+.oi-external-link:before {
+  content'\e064'
+}
+
+.oi-eye:before {
+  content'\e065'
+}
+
+.oi-eyedropper:before {
+  content'\e066'
+}
+
+.oi-file:before {
+  content'\e067'
+}
+
+.oi-fire:before {
+  content'\e068'
+}
+
+.oi-flag:before {
+  content'\e069'
+}
+
+.oi-flash:before {
+  content'\e06a'
+}
+
+.oi-folder:before {
+  content'\e06b'
+}
+
+.oi-fork:before {
+  content'\e06c'
+}
+
+.oi-fullscreen-enter:before {
+  content'\e06d'
+}
+
+.oi-fullscreen-exit:before {
+  content'\e06e'
+}
+
+.oi-globe:before {
+  content'\e06f'
+}
+
+.oi-graph:before {
+  content'\e070'
+}
+
+.oi-grid-four-up:before {
+  content'\e071'
+}
+
+.oi-grid-three-up:before {
+  content'\e072'
+}
+
+.oi-grid-two-up:before {
+  content'\e073'
+}
+
+.oi-hard-drive:before {
+  content'\e074'
+}
+
+.oi-header:before {
+  content'\e075'
+}
+
+.oi-headphones:before {
+  content'\e076'
+}
+
+.oi-heart:before {
+  content'\e077'
+}
+
+.oi-home:before {
+  content'\e078'
+}
+
+.oi-image:before {
+  content'\e079'
+}
+
+.oi-inbox:before {
+  content'\e07a'
+}
+
+.oi-infinity:before {
+  content'\e07b'
+}
+
+.oi-info:before {
+  content'\e07c'
+}
+
+.oi-italic:before {
+  content'\e07d'
+}
+
+.oi-justify-center:before {
+  content'\e07e'
+}
+
+.oi-justify-left:before {
+  content'\e07f'
+}
+
+.oi-justify-right:before {
+  content'\e080'
+}
+
+.oi-key:before {
+  content'\e081'
+}
+
+.oi-laptop:before {
+  content'\e082'
+}
+
+.oi-layers:before {
+  content'\e083'
+}
+
+.oi-lightbulb:before {
+  content'\e084'
+}
+
+.oi-link-broken:before {
+  content'\e085'
+}
+
+.oi-link-intact:before {
+  content'\e086'
+}
+
+.oi-list-rich:before {
+  content'\e087'
+}
+
+.oi-list:before {
+  content'\e088'
+}
+
+.oi-location:before {
+  content'\e089'
+}
+
+.oi-lock-locked:before {
+  content'\e08a'
+}
+
+.oi-lock-unlocked:before {
+  content'\e08b'
+}
+
+.oi-loop-circular:before {
+  content'\e08c'
+}
+
+.oi-loop-square:before {
+  content'\e08d'
+}
+
+.oi-loop:before {
+  content'\e08e'
+}
+
+.oi-magnifying-glass:before {
+  content'\e08f'
+}
+
+.oi-map-marker:before {
+  content'\e090'
+}
+
+.oi-map:before {
+  content'\e091'
+}
+
+.oi-media-pause:before {
+  content'\e092'
+}
+
+.oi-media-play:before {
+  content'\e093'
+}
+
+.oi-media-record:before {
+  content'\e094'
+}
+
+.oi-media-skip-backward:before {
+  content'\e095'
+}
+
+.oi-media-skip-forward:before {
+  content'\e096'
+}
+
+.oi-media-step-backward:before {
+  content'\e097'
+}
+
+.oi-media-step-forward:before {
+  content'\e098'
+}
+
+.oi-media-stop:before {
+  content'\e099'
+}
+
+.oi-medical-cross:before {
+  content'\e09a'
+}
+
+.oi-menu:before {
+  content'\e09b'
+}
+
+.oi-microphone:before {
+  content'\e09c'
+}
+
+.oi-minus:before {
+  content'\e09d'
+}
+
+.oi-monitor:before {
+  content'\e09e'
+}
+
+.oi-moon:before {
+  content'\e09f'
+}
+
+.oi-move:before {
+  content'\e0a0'
+}
+
+.oi-musical-note:before {
+  content'\e0a1'
+}
+
+.oi-paperclip:before {
+  content'\e0a2'
+}
+
+.oi-pencil:before {
+  content'\e0a3'
+}
+
+.oi-people:before {
+  content'\e0a4'
+}
+
+.oi-person:before {
+  content'\e0a5'
+}
+
+.oi-phone:before {
+  content'\e0a6'
+}
+
+.oi-pie-chart:before {
+  content'\e0a7'
+}
+
+.oi-pin:before {
+  content'\e0a8'
+}
+
+.oi-play-circle:before {
+  content'\e0a9'
+}
+
+.oi-plus:before {
+  content'\e0aa'
+}
+
+.oi-power-standby:before {
+  content'\e0ab'
+}
+
+.oi-print:before {
+  content'\e0ac'
+}
+
+.oi-project:before {
+  content'\e0ad'
+}
+
+.oi-pulse:before {
+  content'\e0ae'
+}
+
+.oi-puzzle-piece:before {
+  content'\e0af'
+}
+
+.oi-question-mark:before {
+  content'\e0b0'
+}
+
+.oi-rain:before {
+  content'\e0b1'
+}
+
+.oi-random:before {
+  content'\e0b2'
+}
+
+.oi-reload:before {
+  content'\e0b3'
+}
+
+.oi-resize-both:before {
+  content'\e0b4'
+}
+
+.oi-resize-height:before {
+  content'\e0b5'
+}
+
+.oi-resize-width:before {
+  content'\e0b6'
+}
+
+.oi-rss-alt:before {
+  content'\e0b7'
+}
+
+.oi-rss:before {
+  content'\e0b8'
+}
+
+.oi-script:before {
+  content'\e0b9'
+}
+
+.oi-share-boxed:before {
+  content'\e0ba'
+}
+
+.oi-share:before {
+  content'\e0bb'
+}
+
+.oi-shield:before {
+  content'\e0bc'
+}
+
+.oi-signal:before {
+  content'\e0bd'
+}
+
+.oi-signpost:before {
+  content'\e0be'
+}
+
+.oi-sort-ascending:before {
+  content'\e0bf'
+}
+
+.oi-sort-descending:before {
+  content'\e0c0'
+}
+
+.oi-spreadsheet:before {
+  content'\e0c1'
+}
+
+.oi-star:before {
+  content'\e0c2'
+}
+
+.oi-sun:before {
+  content'\e0c3'
+}
+
+.oi-tablet:before {
+  content'\e0c4'
+}
+
+.oi-tag:before {
+  content'\e0c5'
+}
+
+.oi-tags:before {
+  content'\e0c6'
+}
+
+.oi-target:before {
+  content'\e0c7'
+}
+
+.oi-task:before {
+  content'\e0c8'
+}
+
+.oi-terminal:before {
+  content'\e0c9'
+}
+
+.oi-text:before {
+  content'\e0ca'
+}
+
+.oi-thumb-down:before {
+  content'\e0cb'
+}
+
+.oi-thumb-up:before {
+  content'\e0cc'
+}
+
+.oi-timer:before {
+  content'\e0cd'
+}
+
+.oi-transfer:before {
+  content'\e0ce'
+}
+
+.oi-trash:before {
+  content'\e0cf'
+}
+
+.oi-underline:before {
+  content'\e0d0'
+}
+
+.oi-vertical-align-bottom:before {
+  content'\e0d1'
+}
+
+.oi-vertical-align-center:before {
+  content'\e0d2'
+}
+
+.oi-vertical-align-top:before {
+  content'\e0d3'
+}
+
+.oi-video:before {
+  content'\e0d4'
+}
+
+.oi-volume-high:before {
+  content'\e0d5'
+}
+
+.oi-volume-low:before {
+  content'\e0d6'
+}
+
+.oi-volume-off:before {
+  content'\e0d7'
+}
+
+.oi-warning:before {
+  content'\e0d8'
+}
+
+.oi-wifi:before {
+  content'\e0d9'
+}
+
+.oi-wrench:before {
+  content'\e0da'
+}
+
+.oi-x:before {
+  content'\e0db'
+}
+
+.oi-yen:before {
+  content'\e0dc'
+}
+
+.oi-zoom-in:before {
+  content'\e0dd'
+}
+
+.oi-zoom-out:before {
+  content'\e0de'
+}
+

static/open-iconic/css/open-iconic.css

@@ -0,0 +1,511 @@
+
+@font-face {
+  font-family: 'Icons';
+  src: url('../fonts/open-iconic.eot');
+  src: url('../fonts/open-iconic.eot?#iconic-sm') format('embedded-opentype'), url('../fonts/open-iconic.woff') format('woff'), url('../fonts/open-iconic.ttf') format('truetype'), url('../fonts/open-iconic.otf') format('opentype'), url('../fonts/open-iconic.svg#iconic-sm') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+
+.oi[data-glyph].oi-text-replace {
+  font-size: 0;
+  line-height: 0;
+}
+
+.oi[data-glyph].oi-text-replace:before {
+  width: 1em;
+  text-align: center;
+}
+
+.oi[data-glyph]:before {
+  font-family: 'Icons';
+  display: inline-block;
+  speak: none;
+  line-height: 1;
+  vertical-align: baseline;
+  font-weight: normal;
+  font-style: normal;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+
+.oi[data-glyph]:empty:before {
+  width: 1em;
+  text-align: center;
+  box-sizing: content-box;
+}
+
+.oi[data-glyph].oi-align-left:before {
+  text-align: left;
+}
+
+.oi[data-glyph].oi-align-right:before {
+  text-align: right;
+}
+
+.oi[data-glyph].oi-align-center:before {
+  text-align: center;
+}
+
+.oi[data-glyph].oi-flip-horizontal:before {
+  -webkit-transform: scale(-1, 1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, 1);
+}
+.oi[data-glyph].oi-flip-vertical:before {
+  -webkit-transform: scale(1, -1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(1, -1);
+}
+.oi[data-glyph].oi-flip-horizontal-vertical:before {
+  -webkit-transform: scale(-1, -1);
+  -ms-transform: scale(-1, 1);
+  transform: scale(-1, -1);
+}
+
+
+.oi[data-glyph=account-login]:before { content:'\e000'; }
+
+.oi[data-glyph=account-logout]:before { content:'\e001'; }
+
+.oi[data-glyph=action-redo]:before { content:'\e002'; }
+
+.oi[data-glyph=action-undo]:before { content:'\e003'; }
+
+.oi[data-glyph=align-center]:before { content:'\e004'; }
+
+.oi[data-glyph=align-left]:before { content:'\e005'; }
+
+.oi[data-glyph=align-right]:before { content:'\e006'; }
+
+.oi[data-glyph=aperture]:before { content:'\e007'; }
+
+.oi[data-glyph=arrow-bottom]:before { content:'\e008'; }
+
+.oi[data-glyph=arrow-circle-bottom]:before { content:'\e009'; }
+
+.oi[data-glyph=arrow-circle-left]:before { content:'\e00a'; }
+
+.oi[data-glyph=arrow-circle-right]:before { content:'\e00b'; }
+
+.oi[data-glyph=arrow-circle-top]:before { content:'\e00c'; }
+
+.oi[data-glyph=arrow-left]:before { content:'\e00d'; }
+
+.oi[data-glyph=arrow-right]:before { content:'\e00e'; }
+
+.oi[data-glyph=arrow-thick-bottom]:before { content:'\e00f'; }
+
+.oi[data-glyph=arrow-thick-left]:before { content:'\e010'; }
+
+.oi[data-glyph=arrow-thick-right]:before { content:'\e011'; }
+
+.oi[data-glyph=arrow-thick-top]:before { content:'\e012'; }
+
+.oi[data-glyph=arrow-top]:before { content:'\e013'; }
+
+.oi[data-glyph=audio-spectrum]:before { content:'\e014'; }
+
+.oi[data-glyph=audio]:before { content:'\e015'; }
+
+.oi[data-glyph=badge]:before { content:'\e016'; }
+
+.oi[data-glyph=ban]:before { content:'\e017'; }
+
+.oi[data-glyph=bar-chart]:before { content:'\e018'; }
+
+.oi[data-glyph=basket]:before { content:'\e019'; }
+
+.oi[data-glyph=battery-empty]:before { content:'\e01a'; }
+
+.oi[data-glyph=battery-full]:before { content:'\e01b'; }
+
+.oi[data-glyph=beaker]:before { content:'\e01c'; }
+
+.oi[data-glyph=bell]:before { content:'\e01d'; }
+
+.oi[data-glyph=bluetooth]:before { content:'\e01e'; }
+
+.oi[data-glyph=bold]:before { content:'\e01f'; }
+
+.oi[data-glyph=bolt]:before { content:'\e020'; }
+
+.oi[data-glyph=book]:before { content:'\e021'; }
+
+.oi[data-glyph=bookmark]:before { content:'\e022'; }
+
+.oi[data-glyph=box]:before { content:'\e023'; }
+
+.oi[data-glyph=briefcase]:before { content:'\e024'; }
+
+.oi[data-glyph=british-pound]:before { content:'\e025'; }
+
+.oi[data-glyph=browser]:before { content:'\e026'; }
+
+.oi[data-glyph=brush]:before { content:'\e027'; }
+
+.oi[data-glyph=bug]:before { content:'\e028'; }
+
+.oi[data-glyph=bullhorn]:before { content:'\e029'; }
+
+.oi[data-glyph=calculator]:before { content:'\e02a'; }
+
+.oi[data-glyph=calendar]:before { content:'\e02b'; }
+
+.oi[data-glyph=camera-slr]:before { content:'\e02c'; }
+
+.oi[data-glyph=caret-bottom]:before { content:'\e02d'; }
+
+.oi[data-glyph=caret-left]:before { content:'\e02e'; }
+
+.oi[data-glyph=caret-right]:before { content:'\e02f'; }
+
+.oi[data-glyph=caret-top]:before { content:'\e030'; }
+
+.oi[data-glyph=cart]:before { content:'\e031'; }
+
+.oi[data-glyph=chat]:before { content:'\e032'; }
+
+.oi[data-glyph=check]:before { content:'\e033'; }
+
+.oi[data-glyph=chevron-bottom]:before { content:'\e034'; }
+
+.oi[data-glyph=chevron-left]:before { content:'\e035'; }
+
+.oi[data-glyph=chevron-right]:before { content:'\e036'; }
+
+.oi[data-glyph=chevron-top]:before { content:'\e037'; }
+
+.oi[data-glyph=circle-check]:before { content:'\e038'; }
+
+.oi[data-glyph=circle-x]:before { content:'\e039'; }
+
+.oi[data-glyph=clipboard]:before { content:'\e03a'; }
+
+.oi[data-glyph=clock]:before { content:'\e03b'; }
+
+.oi[data-glyph=cloud-download]:before { content:'\e03c'; }
+
+.oi[data-glyph=cloud-upload]:before { content:'\e03d'; }
+
+.oi[data-glyph=cloud]:before { content:'\e03e'; }
+
+.oi[data-glyph=cloudy]:before { content:'\e03f'; }
+
+.oi[data-glyph=code]:before { content:'\e040'; }
+
+.oi[data-glyph=cog]:before { content:'\e041'; }
+
+.oi[data-glyph=collapse-down]:before { content:'\e042'; }
+
+.oi[data-glyph=collapse-left]:before { content:'\e043'; }
+
+.oi[data-glyph=collapse-right]:before { content:'\e044'; }
+
+.oi[data-glyph=collapse-up]:before { content:'\e045'; }
+
+.oi[data-glyph=command]:before { content:'\e046'; }
+
+.oi[data-glyph=comment-square]:before { content:'\e047'; }
+
+.oi[data-glyph=compass]:before { content:'\e048'; }
+
+.oi[data-glyph=contrast]:before { content:'\e049'; }
+
+.oi[data-glyph=copywriting]:before { content:'\e04a'; }
+
+.oi[data-glyph=credit-card]:before { content:'\e04b'; }
+
+.oi[data-glyph=crop]:before { content:'\e04c'; }
+
+.oi[data-glyph=dashboard]:before { content:'\e04d'; }
+
+.oi[data-glyph=data-transfer-download]:before { content:'\e04e'; }
+
+.oi[data-glyph=data-transfer-upload]:before { content:'\e04f'; }
+
+.oi[data-glyph=delete]:before { content:'\e050'; }
+
+.oi[data-glyph=dial]:before { content:'\e051'; }
+
+.oi[data-glyph=document]:before { content:'\e052'; }
+
+.oi[data-glyph=dollar]:before { content:'\e053'; }
+
+.oi[data-glyph=double-quote-sans-left]:before { content:'\e054'; }
+
+.oi[data-glyph=double-quote-sans-right]:before { content:'\e055'; }
+
+.oi[data-glyph=double-quote-serif-left]:before { content:'\e056'; }
+
+.oi[data-glyph=double-quote-serif-right]:before { content:'\e057'; }
+
+.oi[data-glyph=droplet]:before { content:'\e058'; }
+
+.oi[data-glyph=eject]:before { content:'\e059'; }
+
+.oi[data-glyph=elevator]:before { content:'\e05a'; }
+
+.oi[data-glyph=ellipses]:before { content:'\e05b'; }
+
+.oi[data-glyph=envelope-closed]:before { content:'\e05c'; }
+
+.oi[data-glyph=envelope-open]:before { content:'\e05d'; }
+
+.oi[data-glyph=euro]:before { content:'\e05e'; }
+
+.oi[data-glyph=excerpt]:before { content:'\e05f'; }
+
+.oi[data-glyph=expand-down]:before { content:'\e060'; }
+
+.oi[data-glyph=expand-left]:before { content:'\e061'; }
+
+.oi[data-glyph=expand-right]:before { content:'\e062'; }
+
+.oi[data-glyph=expand-up]:before { content:'\e063'; }
+
+.oi[data-glyph=external-link]:before { content:'\e064'; }
+
+.oi[data-glyph=eye]:before { content:'\e065'; }
+
+.oi[data-glyph=eyedropper]:before { content:'\e066'; }
+
+.oi[data-glyph=file]:before { content:'\e067'; }
+
+.oi[data-glyph=fire]:before { content:'\e068'; }
+
+.oi[data-glyph=flag]:before { content:'\e069'; }
+
+.oi[data-glyph=flash]:before { content:'\e06a'; }
+
+.oi[data-glyph=folder]:before { content:'\e06b'; }
+
+.oi[data-glyph=fork]:before { content:'\e06c'; }
+
+.oi[data-glyph=fullscreen-enter]:before { content:'\e06d'; }
+
+.oi[data-glyph=fullscreen-exit]:before { content:'\e06e'; }
+
+.oi[data-glyph=globe]:before { content:'\e06f'; }
+
+.oi[data-glyph=graph]:before { content:'\e070'; }
+
+.oi[data-glyph=grid-four-up]:before { content:'\e071'; }
+
+.oi[data-glyph=grid-three-up]:before { content:'\e072'; }
+
+.oi[data-glyph=grid-two-up]:before { content:'\e073'; }
+
+.oi[data-glyph=hard-drive]:before { content:'\e074'; }
+
+.oi[data-glyph=header]:before { content:'\e075'; }
+
+.oi[data-glyph=headphones]:before { content:'\e076'; }
+
+.oi[data-glyph=heart]:before { content:'\e077'; }
+
+.oi[data-glyph=home]:before { content:'\e078'; }
+
+.oi[data-glyph=image]:before { content:'\e079'; }
+
+.oi[data-glyph=inbox]:before { content:'\e07a'; }
+
+.oi[data-glyph=infinity]:before { content:'\e07b'; }
+
+.oi[data-glyph=info]:before { content:'\e07c'; }
+
+.oi[data-glyph=italic]:before { content:'\e07d'; }
+
+.oi[data-glyph=justify-center]:before { content:'\e07e'; }
+
+.oi[data-glyph=justify-left]:before { content:'\e07f'; }
+
+.oi[data-glyph=justify-right]:before { content:'\e080'; }
+
+.oi[data-glyph=key]:before { content:'\e081'; }
+
+.oi[data-glyph=laptop]:before { content:'\e082'; }
+
+.oi[data-glyph=layers]:before { content:'\e083'; }
+
+.oi[data-glyph=lightbulb]:before { content:'\e084'; }
+
+.oi[data-glyph=link-broken]:before { content:'\e085'; }
+
+.oi[data-glyph=link-intact]:before { content:'\e086'; }
+
+.oi[data-glyph=list-rich]:before { content:'\e087'; }
+
+.oi[data-glyph=list]:before { content:'\e088'; }
+
+.oi[data-glyph=location]:before { content:'\e089'; }
+
+.oi[data-glyph=lock-locked]:before { content:'\e08a'; }
+
+.oi[data-glyph=lock-unlocked]:before { content:'\e08b'; }
+
+.oi[data-glyph=loop-circular]:before { content:'\e08c'; }
+
+.oi[data-glyph=loop-square]:before { content:'\e08d'; }
+
+.oi[data-glyph=loop]:before { content:'\e08e'; }
+
+.oi[data-glyph=magnifying-glass]:before { content:'\e08f'; }
+
+.oi[data-glyph=map-marker]:before { content:'\e090'; }
+
+.oi[data-glyph=map]:before { content:'\e091'; }
+
+.oi[data-glyph=media-pause]:before { content:'\e092'; }
+
+.oi[data-glyph=media-play]:before { content:'\e093'; }
+
+.oi[data-glyph=media-record]:before { content:'\e094'; }
+
+.oi[data-glyph=media-skip-backward]:before { content:'\e095'; }
+
+.oi[data-glyph=media-skip-forward]:before { content:'\e096'; }
+
+.oi[data-glyph=media-step-backward]:before { content:'\e097'; }
+
+.oi[data-glyph=media-step-forward]:before { content:'\e098'; }
+
+.oi[data-glyph=media-stop]:before { content:'\e099'; }
+
+.oi[data-glyph=medical-cross]:before { content:'\e09a'; }
+
+.oi[data-glyph=menu]:before { content:'\e09b'; }
+
+.oi[data-glyph=microphone]:before { content:'\e09c'; }
+
+.oi[data-glyph=minus]:before { content:'\e09d'; }
+
+.oi[data-glyph=monitor]:before { content:'\e09e'; }
+
+.oi[data-glyph=moon]:before { content:'\e09f'; }
+
+.oi[data-glyph=move]:before { content:'\e0a0'; }
+
+.oi[data-glyph=musical-note]:before { content:'\e0a1'; }
+
+.oi[data-glyph=paperclip]:before { content:'\e0a2'; }
+
+.oi[data-glyph=pencil]:before { content:'\e0a3'; }
+
+.oi[data-glyph=people]:before { content:'\e0a4'; }
+
+.oi[data-glyph=person]:before { content:'\e0a5'; }
+
+.oi[data-glyph=phone]:before { content:'\e0a6'; }
+
+.oi[data-glyph=pie-chart]:before { content:'\e0a7'; }
+
+.oi[data-glyph=pin]:before { content:'\e0a8'; }
+
+.oi[data-glyph=play-circle]:before { content:'\e0a9'; }
+
+.oi[data-glyph=plus]:before { content:'\e0aa'; }
+
+.oi[data-glyph=power-standby]:before { content:'\e0ab'; }
+
+.oi[data-glyph=print]:before { content:'\e0ac'; }
+
+.oi[data-glyph=project]:before { content:'\e0ad'; }
+
+.oi[data-glyph=pulse]:before { content:'\e0ae'; }
+
+.oi[data-glyph=puzzle-piece]:before { content:'\e0af'; }
+
+.oi[data-glyph=question-mark]:before { content:'\e0b0'; }
+
+.oi[data-glyph=rain]:before { content:'\e0b1'; }
+
+.oi[data-glyph=random]:before { content:'\e0b2'; }
+
+.oi[data-glyph=reload]:before { content:'\e0b3'; }
+
+.oi[data-glyph=resize-both]:before { content:'\e0b4'; }
+
+.oi[data-glyph=resize-height]:before { content:'\e0b5'; }
+
+.oi[data-glyph=resize-width]:before { content:'\e0b6'; }
+
+.oi[data-glyph=rss-alt]:before { content:'\e0b7'; }
+
+.oi[data-glyph=rss]:before { content:'\e0b8'; }
+
+.oi[data-glyph=script]:before { content:'\e0b9'; }
+
+.oi[data-glyph=share-boxed]:before { content:'\e0ba'; }
+
+.oi[data-glyph=share]:before { content:'\e0bb'; }
+
+.oi[data-glyph=shield]:before { content:'\e0bc'; }
+
+.oi[data-glyph=signal]:before { content:'\e0bd'; }
+
+.oi[data-glyph=signpost]:before { content:'\e0be'; }
+
+.oi[data-glyph=sort-ascending]:before { content:'\e0bf'; }
+
+.oi[data-glyph=sort-descending]:before { content:'\e0c0'; }
+
+.oi[data-glyph=spreadsheet]:before { content:'\e0c1'; }
+
+.oi[data-glyph=star]:before { content:'\e0c2'; }
+
+.oi[data-glyph=sun]:before { content:'\e0c3'; }
+
+.oi[data-glyph=tablet]:before { content:'\e0c4'; }
+
+.oi[data-glyph=tag]:before { content:'\e0c5'; }
+
+.oi[data-glyph=tags]:before { content:'\e0c6'; }
+
+.oi[data-glyph=target]:before { content:'\e0c7'; }
+
+.oi[data-glyph=task]:before { content:'\e0c8'; }
+
+.oi[data-glyph=terminal]:before { content:'\e0c9'; }
+
+.oi[data-glyph=text]:before { content:'\e0ca'; }
+
+.oi[data-glyph=thumb-down]:before { content:'\e0cb'; }
+
+.oi[data-glyph=thumb-up]:before { content:'\e0cc'; }
+
+.oi[data-glyph=timer]:before { content:'\e0cd'; }
+
+.oi[data-glyph=transfer]:before { content:'\e0ce'; }
+
+.oi[data-glyph=trash]:before { content:'\e0cf'; }
+
+.oi[data-glyph=underline]:before { content:'\e0d0'; }
+
+.oi[data-glyph=vertical-align-bottom]:before { content:'\e0d1'; }
+
+.oi[data-glyph=vertical-align-center]:before { content:'\e0d2'; }
+
+.oi[data-glyph=vertical-align-top]:before { content:'\e0d3'; }
+
+.oi[data-glyph=video]:before { content:'\e0d4'; }
+
+.oi[data-glyph=volume-high]:before { content:'\e0d5'; }
+
+.oi[data-glyph=volume-low]:before { content:'\e0d6'; }
+
+.oi[data-glyph=volume-off]:before { content:'\e0d7'; }
+
+.oi[data-glyph=warning]:before { content:'\e0d8'; }
+
+.oi[data-glyph=wifi]:before { content:'\e0d9'; }
+
+.oi[data-glyph=wrench]:before { content:'\e0da'; }
+
+.oi[data-glyph=x]:before { content:'\e0db'; }
+
+.oi[data-glyph=yen]:before { content:'\e0dc'; }
+
+.oi[data-glyph=zoom-in]:before { content:'\e0dd'; }
+
+.oi[data-glyph=zoom-out]:before { content:'\e0de'; }

static/open-iconic/css/open-iconic.less

@@ -0,0 +1,962 @@
+@iconic-font-path: '../fonts/';
+
+@font-face {
+  font-family: 'Icons';
+  src: url('@{iconic-font-path}open-iconic.eot');
+  src: url('@{iconic-font-path}open-iconic.eot?#iconic-sm') format('embedded-opentype'), url('@{iconic-font-path}open-iconic.woff') format('woff'), url('@{iconic-font-path}open-iconic.ttf') format('truetype'), url('@{iconic-font-path}open-iconic.otf') format('opentype'), url('@{iconic-font-path}open-iconic.svg#iconic-sm') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+
+.oi[data-glyph].oi-text-replace {
+  font-size: 0;
+  line-height: 0;
+}
+
+.oi[data-glyph].oi-text-replace:before {
+  width: 1em;
+  text-align: center;
+}
+
+.oi[data-glyph] {
+  &:before {
+    position: relative;
+    top: 1px;
+    font-family: 'Icons';
+    display: inline-block;
+    speak: none;
+    line-height: 1;
+    vertical-align: baseline;
+    font-weight: normal;
+    font-style: normal;
+    -webkit-font-smoothing: antialiased;
+    -moz-osx-font-smoothing: grayscale;
+  }
+
+  &:empty:before {
+    width: 1em;
+    text-align: center;
+    box-sizing: content-box;
+  }
+
+  &.oi-align-left:before {
+    text-align: left;
+  }
+  &.oi-align-right:before {
+    text-align: right;
+  }
+  &.oi-align-center:before {
+    text-align: center;
+  }
+
+  &.oi-flip-horizontal:before {
+    -webkit-transform: scale(-1, 1);
+    -ms-transform: scale(-1, 1);
+    transform: scale(-1, 1);
+  }
+
+  &.oi-flip-vertical:before {
+    -webkit-transform: scale(1, -1);
+    -ms-transform: scale(-1, 1);
+    transform: scale(1, -1);
+  }
+
+  &.oi-flip-horizontal-vertical:before {
+    -webkit-transform: scale(-1, -1);
+    -ms-transform: scale(-1, 1);
+    transform: scale(-1, -1);
+  }
+}
+
+
+.oi[data-glyph=account-login]:before {
+  content: '\e000';
+}
+
+.oi[data-glyph=account-logout]:before {
+  content: '\e001';
+}
+
+.oi[data-glyph=action-redo]:before {
+  content: '\e002';
+}
+
+.oi[data-glyph=action-undo]:before {
+  content: '\e003';
+}
+
+.oi[data-glyph=align-center]:before {
+  content: '\e004';
+}
+
+.oi[data-glyph=align-left]:before {
+  content: '\e005';
+}
+
+.oi[data-glyph=align-right]:before {
+  content: '\e006';
+}
+
+.oi[data-glyph=aperture]:before {
+  content: '\e007';
+}
+
+.oi[data-glyph=arrow-bottom]:before {
+  content: '\e008';
+}
+
+.oi[data-glyph=arrow-circle-bottom]:before {
+  content: '\e009';
+}
+
+.oi[data-glyph=arrow-circle-left]:before {
+  content: '\e00a';
+}
+
+.oi[data-glyph=arrow-circle-right]:before {
+  content: '\e00b';
+}
+
+.oi[data-glyph=arrow-circle-top]:before {
+  content: '\e00c';
+}
+
+.oi[data-glyph=arrow-left]:before {
+  content: '\e00d';
+}
+
+.oi[data-glyph=arrow-right]:before {
+  content: '\e00e';
+}
+
+.oi[data-glyph=arrow-thick-bottom]:before {
+  content: '\e00f';
+}
+
+.oi[data-glyph=arrow-thick-left]:before {
+  content: '\e010';
+}
+
+.oi[data-glyph=arrow-thick-right]:before {
+  content: '\e011';
+}
+
+.oi[data-glyph=arrow-thick-top]:before {
+  content: '\e012';
+}
+
+.oi[data-glyph=arrow-top]:before {
+  content: '\e013';
+}
+
+.oi[data-glyph=audio-spectrum]:before {
+  content: '\e014';
+}
+
+.oi[data-glyph=audio]:before {
+  content: '\e015';
+}
+
+.oi[data-glyph=badge]:before {
+  content: '\e016';
+}
+
+.oi[data-glyph=ban]:before {
+  content: '\e017';
+}
+
+.oi[data-glyph=bar-chart]:before {
+  content: '\e018';
+}
+
+.oi[data-glyph=basket]:before {
+  content: '\e019';
+}
+
+.oi[data-glyph=battery-empty]:before {
+  content: '\e01a';
+}
+
+.oi[data-glyph=battery-full]:before {
+  content: '\e01b';
+}
+
+.oi[data-glyph=beaker]:before {
+  content: '\e01c';
+}
+
+.oi[data-glyph=bell]:before {
+  content: '\e01d';
+}
+
+.oi[data-glyph=bluetooth]:before {
+  content: '\e01e';
+}
+
+.oi[data-glyph=bold]:before {
+  content: '\e01f';
+}
+
+.oi[data-glyph=bolt]:before {
+  content: '\e020';
+}
+
+.oi[data-glyph=book]:before {
+  content: '\e021';
+}
+
+.oi[data-glyph=bookmark]:before {
+  content: '\e022';
+}
+
+.oi[data-glyph=box]:before {
+  content: '\e023';
+}
+
+.oi[data-glyph=briefcase]:before {
+  content: '\e024';
+}
+
+.oi[data-glyph=british-pound]:before {
+  content: '\e025';
+}
+
+.oi[data-glyph=browser]:before {
+  content: '\e026';
+}
+
+.oi[data-glyph=brush]:before {
+  content: '\e027';
+}
+
+.oi[data-glyph=bug]:before {
+  content: '\e028';
+}
+
+.oi[data-glyph=bullhorn]:before {
+  content: '\e029';
+}
+
+.oi[data-glyph=calculator]:before {
+  content: '\e02a';
+}
+
+.oi[data-glyph=calendar]:before {
+  content: '\e02b';
+}
+
+.oi[data-glyph=camera-slr]:before {
+  content: '\e02c';
+}
+
+.oi[data-glyph=caret-bottom]:before {
+  content: '\e02d';
+}
+
+.oi[data-glyph=caret-left]:before {
+  content: '\e02e';
+}
+
+.oi[data-glyph=caret-right]:before {
+  content: '\e02f';
+}
+
+.oi[data-glyph=caret-top]:before {
+  content: '\e030';
+}
+
+.oi[data-glyph=cart]:before {
+  content: '\e031';
+}
+
+.oi[data-glyph=chat]:before {
+  content: '\e032';
+}
+
+.oi[data-glyph=check]:before {
+  content: '\e033';
+}
+
+.oi[data-glyph=chevron-bottom]:before {
+  content: '\e034';
+}
+
+.oi[data-glyph=chevron-left]:before {
+  content: '\e035';
+}
+
+.oi[data-glyph=chevron-right]:before {
+  content: '\e036';
+}
+
+.oi[data-glyph=chevron-top]:before {
+  content: '\e037';
+}
+
+.oi[data-glyph=circle-check]:before {
+  content: '\e038';
+}
+
+.oi[data-glyph=circle-x]:before {
+  content: '\e039';
+}
+
+.oi[data-glyph=clipboard]:before {
+  content: '\e03a';
+}
+
+.oi[data-glyph=clock]:before {
+  content: '\e03b';
+}
+
+.oi[data-glyph=cloud-download]:before {
+  content: '\e03c';
+}
+
+.oi[data-glyph=cloud-upload]:before {
+  content: '\e03d';
+}
+
+.oi[data-glyph=cloud]:before {
+  content: '\e03e';
+}
+
+.oi[data-glyph=cloudy]:before {
+  content: '\e03f';
+}
+
+.oi[data-glyph=code]:before {
+  content: '\e040';
+}
+
+.oi[data-glyph=cog]:before {
+  content: '\e041';
+}
+
+.oi[data-glyph=collapse-down]:before {
+  content: '\e042';
+}
+
+.oi[data-glyph=collapse-left]:before {
+  content: '\e043';
+}
+
+.oi[data-glyph=collapse-right]:before {
+  content: '\e044';
+}
+
+.oi[data-glyph=collapse-up]:before {
+  content: '\e045';
+}
+
+.oi[data-glyph=command]:before {
+  content: '\e046';
+}
+
+.oi[data-glyph=comment-square]:before {
+  content: '\e047';
+}
+
+.oi[data-glyph=compass]:before {
+  content: '\e048';
+}
+
+.oi[data-glyph=contrast]:before {
+  content: '\e049';
+}
+
+.oi[data-glyph=copywriting]:before {
+  content: '\e04a';
+}
+
+.oi[data-glyph=credit-card]:before {
+  content: '\e04b';
+}
+
+.oi[data-glyph=crop]:before {
+  content: '\e04c';
+}
+
+.oi[data-glyph=dashboard]:before {
+  content: '\e04d';
+}
+
+.oi[data-glyph=data-transfer-download]:before {
+  content: '\e04e';
+}
+
+.oi[data-glyph=data-transfer-upload]:before {
+  content: '\e04f';
+}
+
+.oi[data-glyph=delete]:before {
+  content: '\e050';
+}
+
+.oi[data-glyph=dial]:before {
+  content: '\e051';
+}
+
+.oi[data-glyph=document]:before {
+  content: '\e052';
+}
+
+.oi[data-glyph=dollar]:before {
+  content: '\e053';
+}
+
+.oi[data-glyph=double-quote-sans-left]:before {
+  content: '\e054';
+}
+
+.oi[data-glyph=double-quote-sans-right]:before {
+  content: '\e055';
+}
+
+.oi[data-glyph=double-quote-serif-left]:before {
+  content: '\e056';
+}
+
+.oi[data-glyph=double-quote-serif-right]:before {
+  content: '\e057';
+}
+
+.oi[data-glyph=droplet]:before {
+  content: '\e058';
+}
+
+.oi[data-glyph=eject]:before {
+  content: '\e059';
+}
+
+.oi[data-glyph=elevator]:before {
+  content: '\e05a';
+}
+
+.oi[data-glyph=ellipses]:before {
+  content: '\e05b';
+}
+
+.oi[data-glyph=envelope-closed]:before {
+  content: '\e05c';
+}
+
+.oi[data-glyph=envelope-open]:before {
+  content: '\e05d';
+}
+
+.oi[data-glyph=euro]:before {
+  content: '\e05e';
+}
+
+.oi[data-glyph=excerpt]:before {
+  content: '\e05f';
+}
+
+.oi[data-glyph=expand-down]:before {
+  content: '\e060';
+}
+
+.oi[data-glyph=expand-left]:before {
+  content: '\e061';
+}
+
+.oi[data-glyph=expand-right]:before {
+  content: '\e062';
+}
+
+.oi[data-glyph=expand-up]:before {
+  content: '\e063';
+}
+
+.oi[data-glyph=external-link]:before {
+  content: '\e064';
+}
+
+.oi[data-glyph=eye]:before {
+  content: '\e065';
+}
+
+.oi[data-glyph=eyedropper]:before {
+  content: '\e066';
+}
+
+.oi[data-glyph=file]:before {
+  content: '\e067';
+}
+
+.oi[data-glyph=fire]:before {
+  content: '\e068';
+}
+
+.oi[data-glyph=flag]:before {
+  content: '\e069';
+}
+
+.oi[data-glyph=flash]:before {
+  content: '\e06a';
+}
+
+.oi[data-glyph=folder]:before {
+  content: '\e06b';
+}
+
+.oi[data-glyph=fork]:before {
+  content: '\e06c';
+}
+
+.oi[data-glyph=fullscreen-enter]:before {
+  content: '\e06d';
+}
+
+.oi[data-glyph=fullscreen-exit]:before {
+  content: '\e06e';
+}
+
+.oi[data-glyph=globe]:before {
+  content: '\e06f';
+}
+
+.oi[data-glyph=graph]:before {
+  content: '\e070';
+}
+
+.oi[data-glyph=grid-four-up]:before {
+  content: '\e071';
+}
+
+.oi[data-glyph=grid-three-up]:before {
+  content: '\e072';
+}
+
+.oi[data-glyph=grid-two-up]:before {
+  content: '\e073';
+}
+
+.oi[data-glyph=hard-drive]:before {
+  content: '\e074';
+}
+
+.oi[data-glyph=header]:before {
+  content: '\e075';
+}
+
+.oi[data-glyph=headphones]:before {
+  content: '\e076';
+}
+
+.oi[data-glyph=heart]:before {
+  content: '\e077';
+}
+
+.oi[data-glyph=home]:before {
+  content: '\e078';
+}
+
+.oi[data-glyph=image]:before {
+  content: '\e079';
+}
+
+.oi[data-glyph=inbox]:before {
+  content: '\e07a';
+}
+
+.oi[data-glyph=infinity]:before {
+  content: '\e07b';
+}
+
+.oi[data-glyph=info]:before {
+  content: '\e07c';
+}
+
+.oi[data-glyph=italic]:before {
+  content: '\e07d';
+}
+
+.oi[data-glyph=justify-center]:before {
+  content: '\e07e';
+}
+
+.oi[data-glyph=justify-left]:before {
+  content: '\e07f';
+}
+
+.oi[data-glyph=justify-right]:before {
+  content: '\e080';
+}
+
+.oi[data-glyph=key]:before {
+  content: '\e081';
+}
+
+.oi[data-glyph=laptop]:before {
+  content: '\e082';
+}
+
+.oi[data-glyph=layers]:before {
+  content: '\e083';
+}
+
+.oi[data-glyph=lightbulb]:before {
+  content: '\e084';
+}
+
+.oi[data-glyph=link-broken]:before {
+  content: '\e085';
+}
+
+.oi[data-glyph=link-intact]:before {
+  content: '\e086';
+}
+
+.oi[data-glyph=list-rich]:before {
+  content: '\e087';
+}
+
+.oi[data-glyph=list]:before {
+  content: '\e088';
+}
+
+.oi[data-glyph=location]:before {
+  content: '\e089';
+}
+
+.oi[data-glyph=lock-locked]:before {
+  content: '\e08a';
+}
+
+.oi[data-glyph=lock-unlocked]:before {
+  content: '\e08b';
+}
+
+.oi[data-glyph=loop-circular]:before {
+  content: '\e08c';
+}
+
+.oi[data-glyph=loop-square]:before {
+  content: '\e08d';
+}
+
+.oi[data-glyph=loop]:before {
+  content: '\e08e';
+}
+
+.oi[data-glyph=magnifying-glass]:before {
+  content: '\e08f';
+}
+
+.oi[data-glyph=map-marker]:before {
+  content: '\e090';
+}
+
+.oi[data-glyph=map]:before {
+  content: '\e091';
+}
+
+.oi[data-glyph=media-pause]:before {
+  content: '\e092';
+}
+
+.oi[data-glyph=media-play]:before {
+  content: '\e093';
+}
+
+.oi[data-glyph=media-record]:before {
+  content: '\e094';
+}
+
+.oi[data-glyph=media-skip-backward]:before {
+  content: '\e095';
+}
+
+.oi[data-glyph=media-skip-forward]:before {
+  content: '\e096';
+}
+
+.oi[data-glyph=media-step-backward]:before {
+  content: '\e097';
+}
+
+.oi[data-glyph=media-step-forward]:before {
+  content: '\e098';
+}
+
+.oi[data-glyph=media-stop]:before {
+  content: '\e099';
+}
+
+.oi[data-glyph=medical-cross]:before {
+  content: '\e09a';
+}
+
+.oi[data-glyph=menu]:before {
+  content: '\e09b';
+}
+
+.oi[data-glyph=microphone]:before {
+  content: '\e09c';
+}
+
+.oi[data-glyph=minus]:before {
+  content: '\e09d';
+}
+
+.oi[data-glyph=monitor]:before {
+  content: '\e09e';
+}
+
+.oi[data-glyph=moon]:before {
+  content: '\e09f';
+}
+
+.oi[data-glyph=move]:before {
+  content: '\e0a0';
+}
+
+.oi[data-glyph=musical-note]:before {
+  content: '\e0a1';
+}
+
+.oi[data-glyph=paperclip]:before {
+  content: '\e0a2';
+}
+
+.oi[data-glyph=pencil]:before {
+  content: '\e0a3';
+}
+
+.oi[data-glyph=people]:before {
+  content: '\e0a4';
+}
+
+.oi[data-glyph=person]:before {
+  content: '\e0a5';
+}
+
+.oi[data-glyph=phone]:before {
+  content: '\e0a6';
+}
+
+.oi[data-glyph=pie-chart]:before {
+  content: '\e0a7';
+}
+
+.oi[data-glyph=pin]:before {
+  content: '\e0a8';
+}
+
+.oi[data-glyph=play-circle]:before {
+  content: '\e0a9';
+}
+
+.oi[data-glyph=plus]:before {
+  content: '\e0aa';
+}
+
+.oi[data-glyph=power-standby]:before {
+  content: '\e0ab';
+}
+
+.oi[data-glyph=print]:before {
+  content: '\e0ac';
+}
+
+.oi[data-glyph=project]:before {
+  content: '\e0ad';
+}
+
+.oi[data-glyph=pulse]:before {
+  content: '\e0ae';
+}
+
+.oi[data-glyph=puzzle-piece]:before {
+  content: '\e0af';
+}
+
+.oi[data-glyph=question-mark]:before {
+  content: '\e0b0';
+}
+
+.oi[data-glyph=rain]:before {
+  content: '\e0b1';
+}
+
+.oi[data-glyph=random]:before {
+  content: '\e0b2';
+}
+
+.oi[data-glyph=reload]:before {
+  content: '\e0b3';
+}
+
+.oi[data-glyph=resize-both]:before {
+  content: '\e0b4';
+}
+
+.oi[data-glyph=resize-height]:before {
+  content: '\e0b5';
+}
+
+.oi[data-glyph=resize-width]:before {
+  content: '\e0b6';
+}
+
+.oi[data-glyph=rss-alt]:before {
+  content: '\e0b7';
+}
+
+.oi[data-glyph=rss]:before {
+  content: '\e0b8';
+}
+
+.oi[data-glyph=script]:before {
+  content: '\e0b9';
+}
+
+.oi[data-glyph=share-boxed]:before {
+  content: '\e0ba';
+}
+
+.oi[data-glyph=share]:before {
+  content: '\e0bb';
+}
+
+.oi[data-glyph=shield]:before {
+  content: '\e0bc';
+}
+
+.oi[data-glyph=signal]:before {
+  content: '\e0bd';
+}
+
+.oi[data-glyph=signpost]:before {
+  content: '\e0be';
+}
+
+.oi[data-glyph=sort-ascending]:before {
+  content: '\e0bf';
+}
+
+.oi[data-glyph=sort-descending]:before {
+  content: '\e0c0';
+}
+
+.oi[data-glyph=spreadsheet]:before {
+  content: '\e0c1';
+}
+
+.oi[data-glyph=star]:before {
+  content: '\e0c2';
+}
+
+.oi[data-glyph=sun]:before {
+  content: '\e0c3';
+}
+
+.oi[data-glyph=tablet]:before {
+  content: '\e0c4';
+}
+
+.oi[data-glyph=tag]:before {
+  content: '\e0c5';
+}
+
+.oi[data-glyph=tags]:before {
+  content: '\e0c6';
+}
+
+.oi[data-glyph=target]:before {
+  content: '\e0c7';
+}
+
+.oi[data-glyph=task]:before {
+  content: '\e0c8';
+}
+
+.oi[data-glyph=terminal]:before {
+  content: '\e0c9';
+}
+
+.oi[data-glyph=text]:before {
+  content: '\e0ca';
+}
+
+.oi[data-glyph=thumb-down]:before {
+  content: '\e0cb';
+}
+
+.oi[data-glyph=thumb-up]:before {
+  content: '\e0cc';
+}
+
+.oi[data-glyph=timer]:before {
+  content: '\e0cd';
+}
+
+.oi[data-glyph=transfer]:before {
+  content: '\e0ce';
+}
+
+.oi[data-glyph=trash]:before {
+  content: '\e0cf';
+}
+
+.oi[data-glyph=underline]:before {
+  content: '\e0d0';
+}
+
+.oi[data-glyph=vertical-align-bottom]:before {
+  content: '\e0d1';
+}
+
+.oi[data-glyph=vertical-align-center]:before {
+  content: '\e0d2';
+}
+
+.oi[data-glyph=vertical-align-top]:before {
+  content: '\e0d3';
+}
+
+.oi[data-glyph=video]:before {
+  content: '\e0d4';
+}
+
+.oi[data-glyph=volume-high]:before {
+  content: '\e0d5';
+}
+
+.oi[data-glyph=volume-low]:before {
+  content: '\e0d6';
+}
+
+.oi[data-glyph=volume-off]:before {
+  content: '\e0d7';
+}
+
+.oi[data-glyph=warning]:before {
+  content: '\e0d8';
+}
+
+.oi[data-glyph=wifi]:before {
+  content: '\e0d9';
+}
+
+.oi[data-glyph=wrench]:before {
+  content: '\e0da';
+}
+
+.oi[data-glyph=x]:before {
+  content: '\e0db';
+}
+
+.oi[data-glyph=yen]:before {
+  content: '\e0dc';
+}
+
+.oi[data-glyph=zoom-in]:before {
+  content: '\e0dd';
+}
+
+.oi[data-glyph=zoom-out]:before {
+  content: '\e0de';
+}

static/open-iconic/css/open-iconic.scss

@@ -0,0 +1,963 @@
+$iconic-font-path: '../fonts/' !default;
+
+@font-face {
+  font-family: 'Icons';
+  src: url('#{$iconic-font-path}open-iconic.eot');
+  src: url('#{$iconic-font-path}open-iconic.eot?#iconic-sm') format('embedded-opentype'), url('#{$iconic-font-path}open-iconic.woff') format('woff'), url('#{$iconic-font-path}open-iconic.ttf') format('truetype'), url('#{$iconic-font-path}open-iconic.otf') format('opentype'), url('#{$iconic-font-path}open-iconic.svg#iconic-sm') format('svg');
+  font-weight: normal;
+  font-style: normal;
+}
+
+.oi[data-glyph].oi-text-replace {
+  font-size: 0;
+  line-height: 0;
+}
+
+.oi[data-glyph].oi-text-replace:before {
+  width: 1em;
+  text-align: center;
+}
+
+.oi[data-glyph] {
+  &:before {
+    position: relative;
+    top: 1px;
+    font-family: 'Icons';
+    display: inline-block;
+    speak: none;
+    line-height: 1;
+    vertical-align: baseline;
+    font-weight: normal;
+    font-style: normal;
+    -webkit-font-smoothing: antialiased;
+    -moz-osx-font-smoothing: grayscale;
+  }
+
+  &:empty:before {
+    width: 1em;
+    text-align: center;
+    box-sizing: content-box;
+  }
+
+  &.oi-align-left:before {
+    text-align: left;
+  }
+  &.oi-align-right:before {
+    text-align: right;
+  }
+  &.oi-align-center:before {
+    text-align: center;
+  }
+
+  &.oi-flip-horizontal:before {
+    -webkit-transform: scale(-1, 1);
+    -ms-transform: scale(-1, 1);
+    transform: scale(-1, 1);
+  }
+
+  &.oi-flip-vertical:before {
+    -webkit-transform: scale(1, -1);
+    -ms-transform: scale(-1, 1);
+    transform: scale(1, -1);
+  }
+
+  &.oi-flip-horizontal-vertical:before {
+    -webkit-transform: scale(-1, -1);
+    -ms-transform: scale(-1, 1);
+    transform: scale(-1, -1);
+  }
+}
+
+
+.oi[data-glyph=account-login]:before {
+  content: '\e000';
+}
+
+.oi[data-glyph=account-logout]:before {
+  content: '\e001';
+}
+
+.oi[data-glyph=action-redo]:before {
+  content: '\e002';
+}
+
+.oi[data-glyph=action-undo]:before {
+  content: '\e003';
+}
+
+.oi[data-glyph=align-center]:before {
+  content: '\e004';
+}
+
+.oi[data-glyph=align-left]:before {
+  content: '\e005';
+}
+
+.oi[data-glyph=align-right]:before {
+  content: '\e006';
+}
+
+.oi[data-glyph=aperture]:before {
+  content: '\e007';
+}
+
+.oi[data-glyph=arrow-bottom]:before {
+  content: '\e008';
+}
+
+.oi[data-glyph=arrow-circle-bottom]:before {
+  content: '\e009';
+}
+
+.oi[data-glyph=arrow-circle-left]:before {
+  content: '\e00a';
+}
+
+.oi[data-glyph=arrow-circle-right]:before {
+  content: '\e00b';
+}
+
+.oi[data-glyph=arrow-circle-top]:before {
+  content: '\e00c';
+}
+
+.oi[data-glyph=arrow-left]:before {
+  content: '\e00d';
+}
+
+.oi[data-glyph=arrow-right]:before {
+  content: '\e00e';
+}
+
+.oi[data-glyph=arrow-thick-bottom]:before {
+  content: '\e00f';
+}
+
+.oi[data-glyph=arrow-thick-left]:before {
+  content: '\e010';
+}
+
+.oi[data-glyph=arrow-thick-right]:before {
+  content: '\e011';
+}
+
+.oi[data-glyph=arrow-thick-top]:before {
+  content: '\e012';
+}
+
+.oi[data-glyph=arrow-top]:before {
+  content: '\e013';
+}
+
+.oi[data-glyph=audio-spectrum]:before {
+  content: '\e014';
+}
+
+.oi[data-glyph=audio]:before {
+  content: '\e015';
+}
+
+.oi[data-glyph=badge]:before {
+  content: '\e016';
+}
+
+.oi[data-glyph=ban]:before {
+  content: '\e017';
+}
+
+.oi[data-glyph=bar-chart]:before {
+  content: '\e018';
+}
+
+.oi[data-glyph=basket]:before {
+  content: '\e019';
+}
+
+.oi[data-glyph=battery-empty]:before {
+  content: '\e01a';
+}
+
+.oi[data-glyph=battery-full]:before {
+  content: '\e01b';
+}
+
+.oi[data-glyph=beaker]:before {
+  content: '\e01c';
+}
+
+.oi[data-glyph=bell]:before {
+  content: '\e01d';
+}
+
+.oi[data-glyph=bluetooth]:before {
+  content: '\e01e';
+}
+
+.oi[data-glyph=bold]:before {
+  content: '\e01f';
+}
+
+.oi[data-glyph=bolt]:before {
+  content: '\e020';
+}
+
+.oi[data-glyph=book]:before {
+  content: '\e021';
+}
+
+.oi[data-glyph=bookmark]:before {
+  content: '\e022';
+}
+
+.oi[data-glyph=box]:before {
+  content: '\e023';
+}
+
+.oi[data-glyph=briefcase]:before {
+  content: '\e024';
+}
+
+.oi[data-glyph=british-pound]:before {
+  content: '\e025';
+}
+
+.oi[data-glyph=browser]:before {
+  content: '\e026';
+}
+
+.oi[data-glyph=brush]:before {
+  content: '\e027';
+}
+
+.oi[data-glyph=bug]:before {
+  content: '\e028';
+}
+
+.oi[data-glyph=bullhorn]:before {
+  content: '\e029';
+}
+
+.oi[data-glyph=calculator]:before {
+  content: '\e02a';
+}
+
+.oi[data-glyph=calendar]:before {
+  content: '\e02b';
+}
+
+.oi[data-glyph=camera-slr]:before {
+  content: '\e02c';
+}
+
+.oi[data-glyph=caret-bottom]:before {
+  content: '\e02d';
+}
+
+.oi[data-glyph=caret-left]:before {
+  content: '\e02e';
+}
+
+.oi[data-glyph=caret-right]:before {
+  content: '\e02f';
+}
+
+.oi[data-glyph=caret-top]:before {
+  content: '\e030';
+}
+
+.oi[data-glyph=cart]:before {
+  content: '\e031';
+}
+
+.oi[data-glyph=chat]:before {
+  content: '\e032';
+}
+
+.oi[data-glyph=check]:before {
+  content: '\e033';
+}
+
+.oi[data-glyph=chevron-bottom]:before {
+  content: '\e034';
+}
+
+.oi[data-glyph=chevron-left]:before {
+  content: '\e035';
+}
+
+.oi[data-glyph=chevron-right]:before {
+  content: '\e036';
+}
+
+.oi[data-glyph=chevron-top]:before {
+  content: '\e037';
+}
+
+.oi[data-glyph=circle-check]:before {
+  content: '\e038';
+}
+
+.oi[data-glyph=circle-x]:before {
+  content: '\e039';
+}
+
+.oi[data-glyph=clipboard]:before {
+  content: '\e03a';
+}
+
+.oi[data-glyph=clock]:before {
+  content: '\e03b';
+}
+
+.oi[data-glyph=cloud-download]:before {
+  content: '\e03c';
+}
+
+.oi[data-glyph=cloud-upload]:before {
+  content: '\e03d';
+}
+
+.oi[data-glyph=cloud]:before {
+  content: '\e03e';
+}
+
+.oi[data-glyph=cloudy]:before {
+  content: '\e03f';
+}
+
+.oi[data-glyph=code]:before {
+  content: '\e040';
+}
+
+.oi[data-glyph=cog]:before {
+  content: '\e041';
+}
+
+.oi[data-glyph=collapse-down]:before {
+  content: '\e042';
+}
+
+.oi[data-glyph=collapse-left]:before {
+  content: '\e043';
+}
+
+.oi[data-glyph=collapse-right]:before {
+  content: '\e044';
+}
+
+.oi[data-glyph=collapse-up]:before {
+  content: '\e045';
+}
+
+.oi[data-glyph=command]:before {
+  content: '\e046';
+}
+
+.oi[data-glyph=comment-square]:before {
+  content: '\e047';
+}
+
+.oi[data-glyph=compass]:before {
+  content: '\e048';
+}
+
+.oi[data-glyph=contrast]:before {
+  content: '\e049';
+}
+
+.oi[data-glyph=copywriting]:before {
+  content: '\e04a';
+}
+
+.oi[data-glyph=credit-card]:before {
+  content: '\e04b';
+}
+
+.oi[data-glyph=crop]:before {
+  content: '\e04c';
+}
+
+.oi[data-glyph=dashboard]:before {
+  content: '\e04d';
+}
+
+.oi[data-glyph=data-transfer-download]:before {
+  content: '\e04e';
+}
+
+.oi[data-glyph=data-transfer-upload]:before {
+  content: '\e04f';
+}
+
+.oi[data-glyph=delete]:before {
+  content: '\e050';
+}
+
+.oi[data-glyph=dial]:before {
+  content: '\e051';
+}
+
+.oi[data-glyph=document]:before {
+  content: '\e052';
+}
+
+.oi[data-glyph=dollar]:before {
+  content: '\e053';
+}
+
+.oi[data-glyph=double-quote-sans-left]:before {
+  content: '\e054';
+}
+
+.oi[data-glyph=double-quote-sans-right]:before {
+  content: '\e055';
+}
+
+.oi[data-glyph=double-quote-serif-left]:before {
+  content: '\e056';
+}
+
+.oi[data-glyph=double-quote-serif-right]:before {
+  content: '\e057';
+}
+
+.oi[data-glyph=droplet]:before {
+  content: '\e058';
+}
+
+.oi[data-glyph=eject]:before {
+  content: '\e059';
+}
+
+.oi[data-glyph=elevator]:before {
+  content: '\e05a';
+}
+
+.oi[data-glyph=ellipses]:before {
+  content: '\e05b';
+}
+
+.oi[data-glyph=envelope-closed]:before {
+  content: '\e05c';
+}
+
+.oi[data-glyph=envelope-open]:before {
+  content: '\e05d';
+}
+
+.oi[data-glyph=euro]:before {
+  content: '\e05e';
+}
+
+.oi[data-glyph=excerpt]:before {
+  content: '\e05f';
+}
+
+.oi[data-glyph=expand-down]:before {
+  content: '\e060';
+}
+
+.oi[data-glyph=expand-left]:before {
+  content: '\e061';
+}
+
+.oi[data-glyph=expand-right]:before {
+  content: '\e062';
+}
+
+.oi[data-glyph=expand-up]:before {
+  content: '\e063';
+}
+
+.oi[data-glyph=external-link]:before {
+  content: '\e064';
+}
+
+.oi[data-glyph=eye]:before {
+  content: '\e065';
+}
+
+.oi[data-glyph=eyedropper]:before {
+  content: '\e066';
+}
+
+.oi[data-glyph=file]:before {
+  content: '\e067';
+}
+
+.oi[data-glyph=fire]:before {
+  content: '\e068';
+}
+
+.oi[data-glyph=flag]:before {
+  content: '\e069';
+}
+
+.oi[data-glyph=flash]:before {
+  content: '\e06a';
+}
+
+.oi[data-glyph=folder]:before {
+  content: '\e06b';
+}
+
+.oi[data-glyph=fork]:before {
+  content: '\e06c';
+}
+
+.oi[data-glyph=fullscreen-enter]:before {
+  content: '\e06d';
+}
+
+.oi[data-glyph=fullscreen-exit]:before {
+  content: '\e06e';
+}
+
+.oi[data-glyph=globe]:before {
+  content: '\e06f';
+}
+
+.oi[data-glyph=graph]:before {
+  content: '\e070';
+}
+
+.oi[data-glyph=grid-four-up]:before {
+  content: '\e071';
+}
+
+.oi[data-glyph=grid-three-up]:before {
+  content: '\e072';
+}
+
+.oi[data-glyph=grid-two-up]:before {
+  content: '\e073';
+}
+
+.oi[data-glyph=hard-drive]:before {
+  content: '\e074';
+}
+
+.oi[data-glyph=header]:before {
+  content: '\e075';
+}
+
+.oi[data-glyph=headphones]:before {
+  content: '\e076';
+}
+
+.oi[data-glyph=heart]:before {
+  content: '\e077';
+}
+
+.oi[data-glyph=home]:before {
+  content: '\e078';
+}
+
+.oi[data-glyph=image]:before {
+  content: '\e079';
+}
+
+.oi[data-glyph=inbox]:before {
+  content: '\e07a';
+}
+
+.oi[data-glyph=infinity]:before {
+  content: '\e07b';
+}
+
+.oi[data-glyph=info]:before {
+  content: '\e07c';
+}
+
+.oi[data-glyph=italic]:before {
+  content: '\e07d';
+}
+
+.oi[data-glyph=justify-center]:before {
+  content: '\e07e';
+}
+
+.oi[data-glyph=justify-left]:before {
+  content: '\e07f';
+}
+
+.oi[data-glyph=justify-right]:before {
+  content: '\e080';
+}
+
+.oi[data-glyph=key]:before {
+  content: '\e081';
+}
+
+.oi[data-glyph=laptop]:before {
+  content: '\e082';
+}
+
+.oi[data-glyph=layers]:before {
+  content: '\e083';
+}
+
+.oi[data-glyph=lightbulb]:before {
+  content: '\e084';
+}
+
+.oi[data-glyph=link-broken]:before {
+  content: '\e085';
+}
+
+.oi[data-glyph=link-intact]:before {
+  content: '\e086';
+}
+
+.oi[data-glyph=list-rich]:before {
+  content: '\e087';
+}
+
+.oi[data-glyph=list]:before {
+  content: '\e088';
+}
+
+.oi[data-glyph=location]:before {
+  content: '\e089';
+}
+
+.oi[data-glyph=lock-locked]:before {
+  content: '\e08a';
+}
+
+.oi[data-glyph=lock-unlocked]:before {
+  content: '\e08b';
+}
+
+.oi[data-glyph=loop-circular]:before {
+  content: '\e08c';
+}
+
+.oi[data-glyph=loop-square]:before {
+  content: '\e08d';
+}
+
+.oi[data-glyph=loop]:before {
+  content: '\e08e';
+}
+
+.oi[data-glyph=magnifying-glass]:before {
+  content: '\e08f';
+}
+
+.oi[data-glyph=map-marker]:before {
+  content: '\e090';
+}
+
+.oi[data-glyph=map]:before {
+  content: '\e091';
+}
+
+.oi[data-glyph=media-pause]:before {
+  content: '\e092';
+}
+
+.oi[data-glyph=media-play]:before {
+  content: '\e093';
+}
+
+.oi[data-glyph=media-record]:before {
+  content: '\e094';
+}
+
+.oi[data-glyph=media-skip-backward]:before {
+  content: '\e095';
+}
+
+.oi[data-glyph=media-skip-forward]:before {
+  content: '\e096';
+}
+
+.oi[data-glyph=media-step-backward]:before {
+  content: '\e097';
+}
+
+.oi[data-glyph=media-step-forward]:before {
+  content: '\e098';
+}
+
+.oi[data-glyph=media-stop]:before {
+  content: '\e099';
+}
+
+.oi[data-glyph=medical-cross]:before {
+  content: '\e09a';
+}
+
+.oi[data-glyph=menu]:before {
+  content: '\e09b';
+}
+
+.oi[data-glyph=microphone]:before {
+  content: '\e09c';
+}
+
+.oi[data-glyph=minus]:before {
+  content: '\e09d';
+}
+
+.oi[data-glyph=monitor]:before {
+  content: '\e09e';
+}
+
+.oi[data-glyph=moon]:before {
+  content: '\e09f';
+}
+
+.oi[data-glyph=move]:before {
+  content: '\e0a0';
+}
+
+.oi[data-glyph=musical-note]:before {
+  content: '\e0a1';
+}
+
+.oi[data-glyph=paperclip]:before {
+  content: '\e0a2';
+}
+
+.oi[data-glyph=pencil]:before {
+  content: '\e0a3';
+}
+
+.oi[data-glyph=people]:before {
+  content: '\e0a4';
+}
+
+.oi[data-glyph=person]:before {
+  content: '\e0a5';
+}
+
+.oi[data-glyph=phone]:before {
+  content: '\e0a6';
+}
+
+.oi[data-glyph=pie-chart]:before {
+  content: '\e0a7';
+}
+
+.oi[data-glyph=pin]:before {
+  content: '\e0a8';
+}
+
+.oi[data-glyph=play-circle]:before {
+  content: '\e0a9';
+}
+
+.oi[data-glyph=plus]:before {
+  content: '\e0aa';
+}
+
+.oi[data-glyph=power-standby]:before {
+  content: '\e0ab';
+}
+
+.oi[data-glyph=print]:before {
+  content: '\e0ac';
+}
+
+.oi[data-glyph=project]:before {
+  content: '\e0ad';
+}
+
+.oi[data-glyph=pulse]:before {
+  content: '\e0ae';
+}
+
+.oi[data-glyph=puzzle-piece]:before {
+  content: '\e0af';
+}
+
+.oi[data-glyph=question-mark]:before {
+  content: '\e0b0';
+}
+
+.oi[data-glyph=rain]:before {
+  content: '\e0b1';
+}
+
+.oi[data-glyph=random]:before {
+  content: '\e0b2';
+}
+
+.oi[data-glyph=reload]:before {
+  content: '\e0b3';
+}
+
+.oi[data-glyph=resize-both]:before {
+  content: '\e0b4';
+}
+
+.oi[data-glyph=resize-height]:before {
+  content: '\e0b5';
+}
+
+.oi[data-glyph=resize-width]:before {
+  content: '\e0b6';
+}
+
+.oi[data-glyph=rss-alt]:before {
+  content: '\e0b7';
+}
+
+.oi[data-glyph=rss]:before {
+  content: '\e0b8';
+}
+
+.oi[data-glyph=script]:before {
+  content: '\e0b9';
+}
+
+.oi[data-glyph=share-boxed]:before {
+  content: '\e0ba';
+}
+
+.oi[data-glyph=share]:before {
+  content: '\e0bb';
+}
+
+.oi[data-glyph=shield]:before {
+  content: '\e0bc';
+}
+
+.oi[data-glyph=signal]:before {
+  content: '\e0bd';
+}
+
+.oi[data-glyph=signpost]:before {
+  content: '\e0be';
+}
+
+.oi[data-glyph=sort-ascending]:before {
+  content: '\e0bf';
+}
+
+.oi[data-glyph=sort-descending]:before {
+  content: '\e0c0';
+}
+
+.oi[data-glyph=spreadsheet]:before {
+  content: '\e0c1';
+}
+
+.oi[data-glyph=star]:before {
+  content: '\e0c2';
+}
+
+.oi[data-glyph=sun]:before {
+  content: '\e0c3';
+}
+
+.oi[data-glyph=tablet]:before {
+  content: '\e0c4';
+}
+
+.oi[data-glyph=tag]:before {
+  content: '\e0c5';
+}
+
+.oi[data-glyph=tags]:before {
+  content: '\e0c6';
+}
+
+.oi[data-glyph=target]:before {
+  content: '\e0c7';
+}
+
+.oi[data-glyph=task]:before {
+  content: '\e0c8';
+}
+
+.oi[data-glyph=terminal]:before {
+  content: '\e0c9';
+}
+
+.oi[data-glyph=text]:before {
+  content: '\e0ca';
+}
+
+.oi[data-glyph=thumb-down]:before {
+  content: '\e0cb';
+}
+
+.oi[data-glyph=thumb-up]:before {
+  content: '\e0cc';
+}
+
+.oi[data-glyph=timer]:before {
+  content: '\e0cd';
+}
+
+.oi[data-glyph=transfer]:before {
+  content: '\e0ce';
+}
+
+.oi[data-glyph=trash]:before {
+  content: '\e0cf';
+}
+
+.oi[data-glyph=underline]:before {
+  content: '\e0d0';
+}
+
+.oi[data-glyph=vertical-align-bottom]:before {
+  content: '\e0d1';
+}
+
+.oi[data-glyph=vertical-align-center]:before {
+  content: '\e0d2';
+}
+
+.oi[data-glyph=vertical-align-top]:before {
+  content: '\e0d3';
+}
+
+.oi[data-glyph=video]:before {
+  content: '\e0d4';
+}
+
+.oi[data-glyph=volume-high]:before {
+  content: '\e0d5';
+}
+
+.oi[data-glyph=volume-low]:before {
+  content: '\e0d6';
+}
+
+.oi[data-glyph=volume-off]:before {
+  content: '\e0d7';
+}
+
+.oi[data-glyph=warning]:before {
+  content: '\e0d8';
+}
+
+.oi[data-glyph=wifi]:before {
+  content: '\e0d9';
+}
+
+.oi[data-glyph=wrench]:before {
+  content: '\e0da';
+}
+
+.oi[data-glyph=x]:before {
+  content: '\e0db';
+}
+
+.oi[data-glyph=yen]:before {
+  content: '\e0dc';
+}
+
+.oi[data-glyph=zoom-in]:before {
+  content: '\e0dd';
+}
+
+.oi[data-glyph=zoom-out]:before {
+  content: '\e0de';
+}
+

static/open-iconic/css/open-iconic.styl

@@ -0,0 +1,733 @@
+@font-face
+  font-family 'Icons'
+  src url('../fonts/open-iconic.eot')
+  src url('../fonts/open-iconic.eot?#iconic-sm') format('embedded-opentype'), url('../fonts/open-iconic.woff') format('woff'), url('../fonts/open-iconic.ttf') format('truetype'), url('../fonts/open-iconic.otf') format('opentype'), url('../fonts/open-iconic.svg#iconic-sm') format('svg')
+  font-weight normal
+  font-style normal
+
+
+.oi[data-glyph].oi-text-replace
+  font-size 0
+  line-height 0
+
+.oi[data-glyph].oi-text-replace:before
+  width 1em
+  text-align center
+
+.oi[data-glyph]
+  &:before
+    position relative
+    top 1px
+    font-family 'Icons'
+    display inline-block
+    speak none
+    line-height 1
+    vertical-align baseline
+    font-weight normal
+    font-style normal
+    -webkit-font-smoothing antialiased
+    -moz-osx-font-smoothing grayscale
+
+  &:empty:before
+    width 1em
+    text-align center
+    box-sizing content-box
+
+  &.oi-align-left:before
+    text-align left
+
+  &.oi-align-right:before
+    text-align right
+
+  &.oi-align-center:before
+    text-align center
+
+
+  &.oi-flip-horizontal:before
+    -webkit-transform scale(-1, 1)
+    -ms-transform scale(-1, 1)
+    transform scale(-1, 1)
+
+
+  &.oi-flip-vertical:before
+    -webkit-transform scale(1, -1)
+    -ms-transform scale(-1, 1)
+    transform scale(1, -1)
+
+
+  &.oi-flip-horizontal-vertical:before
+    -webkit-transform scale(-1, -1)
+    -ms-transform scale(-1, 1)
+    transform scale(-1, -1)
+
+
+
+
+.oi[data-glyph=account-login]:before
+  content '\e000'
+
+.oi[data-glyph=account-logout]:before
+  content '\e001'
+
+.oi[data-glyph=action-redo]:before
+  content '\e002'
+
+.oi[data-glyph=action-undo]:before
+  content '\e003'
+
+.oi[data-glyph=align-center]:before
+  content '\e004'
+
+.oi[data-glyph=align-left]:before
+  content '\e005'
+
+.oi[data-glyph=align-right]:before
+  content '\e006'
+
+.oi[data-glyph=aperture]:before
+  content '\e007'
+
+.oi[data-glyph=arrow-bottom]:before
+  content '\e008'
+
+.oi[data-glyph=arrow-circle-bottom]:before
+  content '\e009'
+
+.oi[data-glyph=arrow-circle-left]:before
+  content '\e00a'
+
+.oi[data-glyph=arrow-circle-right]:before
+  content '\e00b'
+
+.oi[data-glyph=arrow-circle-top]:before
+  content '\e00c'
+
+.oi[data-glyph=arrow-left]:before
+  content '\e00d'
+
+.oi[data-glyph=arrow-right]:before
+  content '\e00e'
+
+.oi[data-glyph=arrow-thick-bottom]:before
+  content '\e00f'
+
+.oi[data-glyph=arrow-thick-left]:before
+  content '\e010'
+
+.oi[data-glyph=arrow-thick-right]:before
+  content '\e011'
+
+.oi[data-glyph=arrow-thick-top]:before
+  content '\e012'
+
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+  content '\e013'
+
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+  content '\e014'
+
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+  content '\e015'
+
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+  content '\e016'
+
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+  content '\e017'
+
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+  content '\e018'
+
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+  content '\e019'
+
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+  content '\e01a'
+
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+  content '\e01b'
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+  content '\e01c'
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+  content '\e01d'
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+  content '\e01e'
+
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+  content '\e01f'
+
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+  content '\e020'
+
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+  content '\e021'
+
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+  content '\e022'
+
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+  content '\e023'
+
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+  content '\e024'
+
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+  content '\e025'
+
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+  content '\e026'
+
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+  content '\e027'
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+  content '\e028'
+
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+  content '\e029'
+
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+  content '\e02a'
+
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+  content '\e02b'
+
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+  content '\e02c'
+
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+  content '\e02d'
+
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+  content '\e02e'
+
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+  content '\e02f'
+
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+  content '\e030'
+
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+  content '\e031'
+
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+  content '\e032'
+
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+  content '\e033'
+
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+  content '\e034'
+
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+  content '\e035'
+
+.oi[data-glyph=chevron-right]:before
+  content '\e036'
+
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+  content '\e037'
+
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+  content '\e038'
+
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+  content '\e039'
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+  content '\e03a'
+
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+  content '\e03b'
+
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+  content '\e03c'
+
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+  content '\e03d'
+
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+  content '\e03e'
+
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+  content '\e03f'
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+  content '\e040'
+
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+  content '\e041'
+
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+  content '\e042'
+
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+  content '\e043'
+
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+  content '\e044'
+
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+  content '\e045'
+
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+  content '\e046'
+
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+  content '\e047'
+
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+  content '\e048'
+
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+  content '\e049'
+
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+  content '\e04a'
+
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+  content '\e04b'
+
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+  content '\e04c'
+
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+  content '\e04d'
+
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+  content '\e04e'
+
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+  content '\e04f'
+
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+  content '\e050'
+
+.oi[data-glyph=dial]:before
+  content '\e051'
+
+.oi[data-glyph=document]:before
+  content '\e052'
+
+.oi[data-glyph=dollar]:before
+  content '\e053'
+
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+  content '\e054'
+
+.oi[data-glyph=double-quote-sans-right]:before
+  content '\e055'
+
+.oi[data-glyph=double-quote-serif-left]:before
+  content '\e056'
+
+.oi[data-glyph=double-quote-serif-right]:before
+  content '\e057'
+
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+  content '\e058'
+
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+  content '\e059'
+
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+  content '\e05a'
+
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+  content '\e05b'
+
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+  content '\e05c'
+
+.oi[data-glyph=envelope-open]:before
+  content '\e05d'
+
+.oi[data-glyph=euro]:before
+  content '\e05e'
+
+.oi[data-glyph=excerpt]:before
+  content '\e05f'
+
+.oi[data-glyph=expand-down]:before
+  content '\e060'
+
+.oi[data-glyph=expand-left]:before
+  content '\e061'
+
+.oi[data-glyph=expand-right]:before
+  content '\e062'
+
+.oi[data-glyph=expand-up]:before
+  content '\e063'
+
+.oi[data-glyph=external-link]:before
+  content '\e064'
+
+.oi[data-glyph=eye]:before
+  content '\e065'
+
+.oi[data-glyph=eyedropper]:before
+  content '\e066'
+
+.oi[data-glyph=file]:before
+  content '\e067'
+
+.oi[data-glyph=fire]:before
+  content '\e068'
+
+.oi[data-glyph=flag]:before
+  content '\e069'
+
+.oi[data-glyph=flash]:before
+  content '\e06a'
+
+.oi[data-glyph=folder]:before
+  content '\e06b'
+
+.oi[data-glyph=fork]:before
+  content '\e06c'
+
+.oi[data-glyph=fullscreen-enter]:before
+  content '\e06d'
+
+.oi[data-glyph=fullscreen-exit]:before
+  content '\e06e'
+
+.oi[data-glyph=globe]:before
+  content '\e06f'
+
+.oi[data-glyph=graph]:before
+  content '\e070'
+
+.oi[data-glyph=grid-four-up]:before
+  content '\e071'
+
+.oi[data-glyph=grid-three-up]:before
+  content '\e072'
+
+.oi[data-glyph=grid-two-up]:before
+  content '\e073'
+
+.oi[data-glyph=hard-drive]:before
+  content '\e074'
+
+.oi[data-glyph=header]:before
+  content '\e075'
+
+.oi[data-glyph=headphones]:before
+  content '\e076'
+
+.oi[data-glyph=heart]:before
+  content '\e077'
+
+.oi[data-glyph=home]:before
+  content '\e078'
+
+.oi[data-glyph=image]:before
+  content '\e079'
+
+.oi[data-glyph=inbox]:before
+  content '\e07a'
+
+.oi[data-glyph=infinity]:before
+  content '\e07b'
+
+.oi[data-glyph=info]:before
+  content '\e07c'
+
+.oi[data-glyph=italic]:before
+  content '\e07d'
+
+.oi[data-glyph=justify-center]:before
+  content '\e07e'
+
+.oi[data-glyph=justify-left]:before
+  content '\e07f'
+
+.oi[data-glyph=justify-right]:before
+  content '\e080'
+
+.oi[data-glyph=key]:before
+  content '\e081'
+
+.oi[data-glyph=laptop]:before
+  content '\e082'
+
+.oi[data-glyph=layers]:before
+  content '\e083'
+
+.oi[data-glyph=lightbulb]:before
+  content '\e084'
+
+.oi[data-glyph=link-broken]:before
+  content '\e085'
+
+.oi[data-glyph=link-intact]:before
+  content '\e086'
+
+.oi[data-glyph=list-rich]:before
+  content '\e087'
+
+.oi[data-glyph=list]:before
+  content '\e088'
+
+.oi[data-glyph=location]:before
+  content '\e089'
+
+.oi[data-glyph=lock-locked]:before
+  content '\e08a'
+
+.oi[data-glyph=lock-unlocked]:before
+  content '\e08b'
+
+.oi[data-glyph=loop-circular]:before
+  content '\e08c'
+
+.oi[data-glyph=loop-square]:before
+  content '\e08d'
+
+.oi[data-glyph=loop]:before
+  content '\e08e'
+
+.oi[data-glyph=magnifying-glass]:before
+  content '\e08f'
+
+.oi[data-glyph=map-marker]:before
+  content '\e090'
+
+.oi[data-glyph=map]:before
+  content '\e091'
+
+.oi[data-glyph=media-pause]:before
+  content '\e092'
+
+.oi[data-glyph=media-play]:before
+  content '\e093'
+
+.oi[data-glyph=media-record]:before
+  content '\e094'
+
+.oi[data-glyph=media-skip-backward]:before
+  content '\e095'
+
+.oi[data-glyph=media-skip-forward]:before
+  content '\e096'
+
+.oi[data-glyph=media-step-backward]:before
+  content '\e097'
+
+.oi[data-glyph=media-step-forward]:before
+  content '\e098'
+
+.oi[data-glyph=media-stop]:before
+  content '\e099'
+
+.oi[data-glyph=medical-cross]:before
+  content '\e09a'
+
+.oi[data-glyph=menu]:before
+  content '\e09b'
+
+.oi[data-glyph=microphone]:before
+  content '\e09c'
+
+.oi[data-glyph=minus]:before
+  content '\e09d'
+
+.oi[data-glyph=monitor]:before
+  content '\e09e'
+
+.oi[data-glyph=moon]:before
+  content '\e09f'
+
+.oi[data-glyph=move]:before
+  content '\e0a0'
+
+.oi[data-glyph=musical-note]:before
+  content '\e0a1'
+
+.oi[data-glyph=paperclip]:before
+  content '\e0a2'
+
+.oi[data-glyph=pencil]:before
+  content '\e0a3'
+
+.oi[data-glyph=people]:before
+  content '\e0a4'
+
+.oi[data-glyph=person]:before
+  content '\e0a5'
+
+.oi[data-glyph=phone]:before
+  content '\e0a6'
+
+.oi[data-glyph=pie-chart]:before
+  content '\e0a7'
+
+.oi[data-glyph=pin]:before
+  content '\e0a8'
+
+.oi[data-glyph=play-circle]:before
+  content '\e0a9'
+
+.oi[data-glyph=plus]:before
+  content '\e0aa'
+
+.oi[data-glyph=power-standby]:before
+  content '\e0ab'
+
+.oi[data-glyph=print]:before
+  content '\e0ac'
+
+.oi[data-glyph=project]:before
+  content '\e0ad'
+
+.oi[data-glyph=pulse]:before
+  content '\e0ae'
+
+.oi[data-glyph=puzzle-piece]:before
+  content '\e0af'
+
+.oi[data-glyph=question-mark]:before
+  content '\e0b0'
+
+.oi[data-glyph=rain]:before
+  content '\e0b1'
+
+.oi[data-glyph=random]:before
+  content '\e0b2'
+
+.oi[data-glyph=reload]:before
+  content '\e0b3'
+
+.oi[data-glyph=resize-both]:before
+  content '\e0b4'
+
+.oi[data-glyph=resize-height]:before
+  content '\e0b5'
+
+.oi[data-glyph=resize-width]:before
+  content '\e0b6'
+
+.oi[data-glyph=rss-alt]:before
+  content '\e0b7'
+
+.oi[data-glyph=rss]:before
+  content '\e0b8'
+
+.oi[data-glyph=script]:before
+  content '\e0b9'
+
+.oi[data-glyph=share-boxed]:before
+  content '\e0ba'
+
+.oi[data-glyph=share]:before
+  content '\e0bb'
+
+.oi[data-glyph=shield]:before
+  content '\e0bc'
+
+.oi[data-glyph=signal]:before
+  content '\e0bd'
+
+.oi[data-glyph=signpost]:before
+  content '\e0be'
+
+.oi[data-glyph=sort-ascending]:before
+  content '\e0bf'
+
+.oi[data-glyph=sort-descending]:before
+  content '\e0c0'
+
+.oi[data-glyph=spreadsheet]:before
+  content '\e0c1'
+
+.oi[data-glyph=star]:before
+  content '\e0c2'
+
+.oi[data-glyph=sun]:before
+  content '\e0c3'
+
+.oi[data-glyph=tablet]:before
+  content '\e0c4'
+
+.oi[data-glyph=tag]:before
+  content '\e0c5'
+
+.oi[data-glyph=tags]:before
+  content '\e0c6'
+
+.oi[data-glyph=target]:before
+  content '\e0c7'
+
+.oi[data-glyph=task]:before
+  content '\e0c8'
+
+.oi[data-glyph=terminal]:before
+  content '\e0c9'
+
+.oi[data-glyph=text]:before
+  content '\e0ca'
+
+.oi[data-glyph=thumb-down]:before
+  content '\e0cb'
+
+.oi[data-glyph=thumb-up]:before
+  content '\e0cc'
+
+.oi[data-glyph=timer]:before
+  content '\e0cd'
+
+.oi[data-glyph=transfer]:before
+  content '\e0ce'
+
+.oi[data-glyph=trash]:before
+  content '\e0cf'
+
+.oi[data-glyph=underline]:before
+  content '\e0d0'
+
+.oi[data-glyph=vertical-align-bottom]:before
+  content '\e0d1'
+
+.oi[data-glyph=vertical-align-center]:before
+  content '\e0d2'
+
+.oi[data-glyph=vertical-align-top]:before
+  content '\e0d3'
+
+.oi[data-glyph=video]:before
+  content '\e0d4'
+
+.oi[data-glyph=volume-high]:before
+  content '\e0d5'
+
+.oi[data-glyph=volume-low]:before
+  content '\e0d6'
+
+.oi[data-glyph=volume-off]:before
+  content '\e0d7'
+
+.oi[data-glyph=warning]:before
+  content '\e0d8'
+
+.oi[data-glyph=wifi]:before
+  content '\e0d9'
+
+.oi[data-glyph=wrench]:before
+  content '\e0da'
+
+.oi[data-glyph=x]:before
+  content '\e0db'
+
+.oi[data-glyph=yen]:before
+  content '\e0dc'
+
+.oi[data-glyph=zoom-in]:before
+  content '\e0dd'
+
+.oi[data-glyph=zoom-out]:before
+  content '\e0de'

static/open-iconic/fonts/open-iconic.svg

@@ -0,0 +1,543 @@
+<?xml version="1.0" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
+<!--
+2014-7-1: Created.
+-->
+<svg xmlns="http://www.w3.org/2000/svg">
+<metadata>
+Created by FontForge 20120731 at Tue Jul  1 20:39:22 2014
+ By P.J. Onori
+Created by P.J. Onori with FontForge 2.0 (http://fontforge.sf.net)
+</metadata>
+<defs>
+<font id="open-iconic" horiz-adv-x="800" >
+  <font-face 
+    font-family="Icons"
+    font-weight="400"
+    font-stretch="normal"
+    units-per-em="800"
+    panose-1="2 0 5 3 0 0 0 0 0 0"
+    ascent="800"
+    descent="0"
+    bbox="-0.5 -101 802 800.126"
+    underline-thickness="50"
+    underline-position="-100"
+    unicode-range="U+E000-E0DE"
+  />
+    <missing-glyph />
+    <glyph glyph-name="" unicode="&#xe000;" 
+d="M300 700h500v-700h-500v100h400v500h-400v100zM400 500l200 -150l-200 -150v100h-400v100h400v100z" />
+    <glyph glyph-name="1" unicode="&#xe001;" 
+d="M300 700h500v-700h-500v100h400v500h-400v100zM200 500v-100h400v-100h-400v-100l-200 150z" />
+    <glyph glyph-name="2" unicode="&#xe002;" 
+d="M350 700c193 0 350 -157 350 -350v-50h100l-200 -200l-200 200h100v50c0 138 -112 250 -250 250s-250 -112 -250 -250c0 193 157 350 350 350z" />
+    <glyph glyph-name="3" unicode="&#xe003;" 
+d="M450 700c193 0 350 -157 350 -350c0 138 -112 250 -250 250s-250 -112 -250 -250v-50h100l-200 -200l-200 200h100v50c0 193 157 350 350 350z" />
+    <glyph glyph-name="4" unicode="&#xe004;" 
+d="M0 700h800v-100h-800v100zM100 500h600v-100h-600v100zM0 300h800v-100h-800v100zM100 100h600v-100h-600v100z" />
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+d="M0 700h800v-100h-800v100zM0 500h600v-100h-600v100zM0 300h800v-100h-800v100zM0 100h600v-100h-600v100z" />
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+d="M0 700h800v-100h-800v100zM200 500h600v-100h-600v100zM0 300h800v-100h-800v100zM200 100h600v-100h-600v100z" />
+    <glyph glyph-name="7" unicode="&#xe007;" 
+d="M400 700c75 0 146 -23 206 -59l-75 -225l-322 234c57 31 122 50 191 50zM125 588l191 -138l-310 -222c-4 24 -6 47 -6 72c0 114 49 215 125 288zM688 575c69 -72 112 -168 112 -275c0 -35 -8 -68 -16 -100h-218zM216 253l112 -347c-128 23 -232 109 -287 222zM372 100
+h372c-64 -109 -177 -185 -310 -197z" />
+    <glyph glyph-name="8" unicode="&#xe008;" horiz-adv-x="600" 
+d="M200 800h100v-500h200l-247 -300l-253 300h200v500z" />
+    <glyph glyph-name="9" unicode="&#xe009;" 
+d="M400 800c221 0 400 -179 400 -400s-179 -400 -400 -400s-400 179 -400 400s179 400 400 400zM300 700v-300h-200l300 -300l300 300h-200v300h-200z" />
+    <glyph glyph-name="a" unicode="&#xe00a;" 
+d="M400 800c221 0 400 -179 400 -400s-179 -400 -400 -400s-400 179 -400 400s179 400 400 400zM400 700l-300 -300l300 -300v200h300v200h-300v200z" />
+    <glyph glyph-name="b" unicode="&#xe00b;" 
+d="M400 800c221 0 400 -179 400 -400s-179 -400 -400 -400s-400 179 -400 400s179 400 400 400zM400 700v-200h-300v-200h300v-200l300 300z" />
+    <glyph glyph-name="c" unicode="&#xe00c;" 
+d="M400 800c221 0 400 -179 400 -400s-179 -400 -400 -400s-400 179 -400 400s179 400 400 400zM400 700l-300 -300h200v-300h200v300h200z" />
+    <glyph glyph-name="d" unicode="&#xe00d;" 
+d="M300 600v-200h500v-100h-500v-200l-300 247z" />
+    <glyph glyph-name="e" unicode="&#xe00e;" 
+d="M500 600l300 -247l-300 -253v200h-500v100h500v200z" />
+    <glyph glyph-name="f" unicode="&#xe00f;" horiz-adv-x="600" 
+d="M200 800h200v-500h200l-297 -300l-303 300h200v500z" />
+    <glyph glyph-name="10" unicode="&#xe010;" 
+d="M300 700v-200h500v-200h-500v-200l-300 297z" />
+    <glyph glyph-name="11" unicode="&#xe011;" 
+d="M500 700l300 -297l-300 -303v200h-500v200h500v200z" />
+    <glyph glyph-name="12" unicode="&#xe012;" horiz-adv-x="600" 
+d="M297 800l303 -300h-200v-500h-200v500h-200z" />
+    <glyph glyph-name="13" unicode="&#xe013;" horiz-adv-x="600" 
+d="M247 800l253 -300h-200v-500h-100v500h-200z" />
+    <glyph glyph-name="14" unicode="&#xe014;" 
+d="M400 800h100v-800h-100v800zM200 700h100v-600h-100v600zM600 600h100v-400h-100v400zM0 500h100v-200h-100v200z" />
+    <glyph glyph-name="15" unicode="&#xe015;" 
+d="M116 600l72 -72c-54 -54 -88 -126 -88 -209s34 -159 88 -213l-72 -72c-72 72 -116 175 -116 285s44 209 116 281zM684 600c72 -72 116 -171 116 -281s-44 -213 -116 -285l-72 72c54 54 88 130 88 213s-34 155 -88 209zM259 460l69 -72c-18 -18 -28 -41 -28 -69
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+    <glyph glyph-name="16" unicode="&#xe016;" horiz-adv-x="400" 
+d="M200 800c110 0 200 -90 200 -200s-90 -200 -200 -200s-200 90 -200 200s90 200 200 200zM100 319c31 -11 65 -19 100 -19s68 8 100 19v-319l-100 100l-100 -100v319z" />
+    <glyph glyph-name="17" unicode="&#xe017;" 
+d="M400 800c220 0 400 -180 400 -400s-180 -400 -400 -400s-400 180 -400 400s180 400 400 400zM400 700c-166 0 -300 -134 -300 -300c0 -66 21 -126 56 -175l419 419c-49 35 -109 56 -175 56zM644 575l-419 -419c49 -35 109 -56 175 -56c166 0 300 134 300 300
+c0 66 -21 126 -56 175z" />
+    <glyph glyph-name="18" unicode="&#xe018;" 
+d="M0 700h100v-600h700v-100h-800v700zM500 700h200v-500h-200v500zM200 500h200v-300h-200v300z" />
+    <glyph glyph-name="19" unicode="&#xe019;" 
+d="M397 800c13 1 23 -4 34 -13c2 -2 214 -254 241 -287h128v-100h-100v-366c0 -18 -16 -34 -34 -34h-532c-18 0 -34 16 -34 34v366h-100v100h128l234 281c9 11 22 18 35 19zM400 672l-144 -172h288zM250 300c-28 0 -50 -22 -50 -50v-100c0 -28 22 -50 50 -50s50 22 50 50
+v100c0 28 -22 50 -50 50zM550 300c-28 0 -50 -22 -50 -50v-100c0 -28 22 -50 50 -50s50 22 50 50v100c0 28 -22 50 -50 50z" />
+    <glyph glyph-name="1a" unicode="&#xe01a;" 
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static/swagger-ui/LICENSE

@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
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static/swagger-ui/SwaggerDark.css

@@ -0,0 +1,853 @@
+/*!
+ * MIT License
+ * 
+ * Copyright (c) 2020 Romans Pokrovskis
+ * 
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ * 
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ * 
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+a { color: #8c8cfa; }
+
+::-webkit-scrollbar-track-piece { background-color: rgba(255, 255, 255, .2) !important; }
+
+::-webkit-scrollbar-track { background-color: rgba(255, 255, 255, .3) !important; }
+
+::-webkit-scrollbar-thumb { background-color: rgba(255, 255, 255, .5) !important; }
+
+embed[type="application/pdf"] { filter: invert(90%); }
+
+html {
+    background: #1f1f1f !important;
+    box-sizing: border-box;
+    filter: contrast(100%) brightness(100%) saturate(100%);
+    overflow-y: scroll;
+}
+
+body {
+    background: #1f1f1f;
+    background-color: #1f1f1f;
+    background-image: none !important;
+}
+
+button, input, select, textarea {
+    background-color: #1f1f1f;
+    color: #bfbfbf;
+}
+
+font, html { color: #bfbfbf; }
+
+.swagger-ui, .swagger-ui section h3 { color: #b5bac9; }
+
+.swagger-ui a { background-color: transparent; }
+
+.swagger-ui mark {
+    background-color: #664b00;
+    color: #bfbfbf;
+}
+
+.swagger-ui legend { color: inherit; }
+
+.swagger-ui .debug * { outline: #e6da99 solid 1px; }
+
+.swagger-ui .debug-white * { outline: #fff solid 1px; }
+
+.swagger-ui .debug-black * { outline: #bfbfbf solid 1px; }
+
+.swagger-ui .debug-grid { background: url(data:image/png;base64,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) 0 0; }
+
+.swagger-ui .debug-grid-16 { background: url(data:image/png;base64,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) 0 0; }
+
+.swagger-ui .debug-grid-8-solid { background: url(data:image/jpeg;base64,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) 0 0 #1c1c21; }
+
+.swagger-ui .debug-grid-16-solid { background: url(data:image/png;base64,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) 0 0 #1c1c21; }
+
+.swagger-ui .b--black { border-color: #000; }
+
+.swagger-ui .b--near-black { border-color: #121212; }
+
+.swagger-ui .b--dark-gray { border-color: #333; }
+
+.swagger-ui .b--mid-gray { border-color: #545454; }
+
+.swagger-ui .b--gray { border-color: #787878; }
+
+.swagger-ui .b--silver { border-color: #999; }
+
+.swagger-ui .b--light-silver { border-color: #6e6e6e; }
+
+.swagger-ui .b--moon-gray { border-color: #4d4d4d; }
+
+.swagger-ui .b--light-gray { border-color: #2b2b2b; }
+
+.swagger-ui .b--near-white { border-color: #242424; }
+
+.swagger-ui .b--white { border-color: #1c1c21; }
+
+.swagger-ui .b--white-90 { border-color: rgba(28, 28, 33, .9); }
+
+.swagger-ui .b--white-80 { border-color: rgba(28, 28, 33, .8); }
+
+.swagger-ui .b--white-70 { border-color: rgba(28, 28, 33, .7); }
+
+.swagger-ui .b--white-60 { border-color: rgba(28, 28, 33, .6); }
+
+.swagger-ui .b--white-50 { border-color: rgba(28, 28, 33, .5); }
+
+.swagger-ui .b--white-40 { border-color: rgba(28, 28, 33, .4); }
+
+.swagger-ui .b--white-30 { border-color: rgba(28, 28, 33, .3); }
+
+.swagger-ui .b--white-20 { border-color: rgba(28, 28, 33, .2); }
+
+.swagger-ui .b--white-10 { border-color: rgba(28, 28, 33, .1); }
+
+.swagger-ui .b--white-05 { border-color: rgba(28, 28, 33, .05); }
+
+.swagger-ui .b--white-025 { border-color: rgba(28, 28, 33, .024); }
+
+.swagger-ui .b--white-0125 { border-color: rgba(28, 28, 33, .01); }
+
+.swagger-ui .b--black-90 { border-color: rgba(0, 0, 0, .9); }
+
+.swagger-ui .b--black-80 { border-color: rgba(0, 0, 0, .8); }
+
+.swagger-ui .b--black-70 { border-color: rgba(0, 0, 0, .7); }
+
+.swagger-ui .b--black-60 { border-color: rgba(0, 0, 0, .6); }
+
+.swagger-ui .b--black-50 { border-color: rgba(0, 0, 0, .5); }
+
+.swagger-ui .b--black-40 { border-color: rgba(0, 0, 0, .4); }
+
+.swagger-ui .b--black-30 { border-color: rgba(0, 0, 0, .3); }
+
+.swagger-ui .b--black-20 { border-color: rgba(0, 0, 0, .2); }
+
+.swagger-ui .b--black-10 { border-color: rgba(0, 0, 0, .1); }
+
+.swagger-ui .b--black-05 { border-color: rgba(0, 0, 0, .05); }
+
+.swagger-ui .b--black-025 { border-color: rgba(0, 0, 0, .024); }
+
+.swagger-ui .b--black-0125 { border-color: rgba(0, 0, 0, .01); }
+
+.swagger-ui .b--dark-red { border-color: #bc2f36; }
+
+.swagger-ui .b--red { border-color: #c83932; }
+
+.swagger-ui .b--light-red { border-color: #ab3c2b; }
+
+.swagger-ui .b--orange { border-color: #cc6e33; }
+
+.swagger-ui .b--purple { border-color: #5e2ca5; }
+
+.swagger-ui .b--light-purple { border-color: #672caf; }
+
+.swagger-ui .b--dark-pink { border-color: #ab2b81; }
+
+.swagger-ui .b--hot-pink { border-color: #c03086; }
+
+.swagger-ui .b--pink { border-color: #8f2464; }
+
+.swagger-ui .b--light-pink { border-color: #721d4d; }
+
+.swagger-ui .b--dark-green { border-color: #1c6e50; }
+
+.swagger-ui .b--green { border-color: #279b70; }
+
+.swagger-ui .b--light-green { border-color: #228762; }
+
+.swagger-ui .b--navy { border-color: #0d1d35; }
+
+.swagger-ui .b--dark-blue { border-color: #20497e; }
+
+.swagger-ui .b--blue { border-color: #4380d0; }
+
+.swagger-ui .b--light-blue { border-color: #20517e; }
+
+.swagger-ui .b--lightest-blue { border-color: #143a52; }
+
+.swagger-ui .b--washed-blue { border-color: #0c312d; }
+
+.swagger-ui .b--washed-green { border-color: #0f3d2c; }
+
+.swagger-ui .b--washed-red { border-color: #411010; }
+
+.swagger-ui .b--transparent { border-color: transparent; }
+
+.swagger-ui .b--gold, .swagger-ui .b--light-yellow, .swagger-ui .b--washed-yellow, .swagger-ui .b--yellow { border-color: #664b00; }
+
+.swagger-ui .shadow-1 { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
+
+.swagger-ui .shadow-2 { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
+
+.swagger-ui .shadow-3 { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
+
+.swagger-ui .shadow-4 { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
+
+.swagger-ui .shadow-5 { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
+
+@media screen and (min-width: 30em) {
+    .swagger-ui .shadow-1-ns { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
+
+    .swagger-ui .shadow-2-ns { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
+
+    .swagger-ui .shadow-3-ns { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
+
+    .swagger-ui .shadow-4-ns { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
+
+    .swagger-ui .shadow-5-ns { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
+}
+
+@media screen and (max-width: 60em) and (min-width: 30em) {
+    .swagger-ui .shadow-1-m { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
+
+    .swagger-ui .shadow-2-m { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
+
+    .swagger-ui .shadow-3-m { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
+
+    .swagger-ui .shadow-4-m { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
+
+    .swagger-ui .shadow-5-m { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
+}
+
+@media screen and (min-width: 60em) {
+    .swagger-ui .shadow-1-l { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
+
+    .swagger-ui .shadow-2-l { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
+
+    .swagger-ui .shadow-3-l { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
+
+    .swagger-ui .shadow-4-l { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
+
+    .swagger-ui .shadow-5-l { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
+}
+
+.swagger-ui .black-05 { color: rgba(191, 191, 191, .05); }
+
+.swagger-ui .bg-black-05 { background-color: rgba(0, 0, 0, .05); }
+
+.swagger-ui .black-90, .swagger-ui .hover-black-90:focus, .swagger-ui .hover-black-90:hover { color: rgba(191, 191, 191, .9); }
+
+.swagger-ui .black-80, .swagger-ui .hover-black-80:focus, .swagger-ui .hover-black-80:hover { color: rgba(191, 191, 191, .8); }
+
+.swagger-ui .black-70, .swagger-ui .hover-black-70:focus, .swagger-ui .hover-black-70:hover { color: rgba(191, 191, 191, .7); }
+
+.swagger-ui .black-60, .swagger-ui .hover-black-60:focus, .swagger-ui .hover-black-60:hover { color: rgba(191, 191, 191, .6); }
+
+.swagger-ui .black-50, .swagger-ui .hover-black-50:focus, .swagger-ui .hover-black-50:hover { color: rgba(191, 191, 191, .5); }
+
+.swagger-ui .black-40, .swagger-ui .hover-black-40:focus, .swagger-ui .hover-black-40:hover { color: rgba(191, 191, 191, .4); }
+
+.swagger-ui .black-30, .swagger-ui .hover-black-30:focus, .swagger-ui .hover-black-30:hover { color: rgba(191, 191, 191, .3); }
+
+.swagger-ui .black-20, .swagger-ui .hover-black-20:focus, .swagger-ui .hover-black-20:hover { color: rgba(191, 191, 191, .2); }
+
+.swagger-ui .black-10, .swagger-ui .hover-black-10:focus, .swagger-ui .hover-black-10:hover { color: rgba(191, 191, 191, .1); }
+
+.swagger-ui .hover-white-90:focus, .swagger-ui .hover-white-90:hover, .swagger-ui .white-90 { color: rgba(255, 255, 255, .9); }
+
+.swagger-ui .hover-white-80:focus, .swagger-ui .hover-white-80:hover, .swagger-ui .white-80 { color: rgba(255, 255, 255, .8); }
+
+.swagger-ui .hover-white-70:focus, .swagger-ui .hover-white-70:hover, .swagger-ui .white-70 { color: rgba(255, 255, 255, .7); }
+
+.swagger-ui .hover-white-60:focus, .swagger-ui .hover-white-60:hover, .swagger-ui .white-60 { color: rgba(255, 255, 255, .6); }
+
+.swagger-ui .hover-white-50:focus, .swagger-ui .hover-white-50:hover, .swagger-ui .white-50 { color: rgba(255, 255, 255, .5); }
+
+.swagger-ui .hover-white-40:focus, .swagger-ui .hover-white-40:hover, .swagger-ui .white-40 { color: rgba(255, 255, 255, .4); }
+
+.swagger-ui .hover-white-30:focus, .swagger-ui .hover-white-30:hover, .swagger-ui .white-30 { color: rgba(255, 255, 255, .3); }
+
+.swagger-ui .hover-white-20:focus, .swagger-ui .hover-white-20:hover, .swagger-ui .white-20 { color: rgba(255, 255, 255, .2); }
+
+.swagger-ui .hover-white-10:focus, .swagger-ui .hover-white-10:hover, .swagger-ui .white-10 { color: rgba(255, 255, 255, .1); }
+
+.swagger-ui .hover-moon-gray:focus, .swagger-ui .hover-moon-gray:hover, .swagger-ui .moon-gray { color: #ccc; }
+
+.swagger-ui .hover-light-gray:focus, .swagger-ui .hover-light-gray:hover, .swagger-ui .light-gray { color: #ededed; }
+
+.swagger-ui .hover-near-white:focus, .swagger-ui .hover-near-white:hover, .swagger-ui .near-white { color: #f5f5f5; }
+
+.swagger-ui .dark-red, .swagger-ui .hover-dark-red:focus, .swagger-ui .hover-dark-red:hover { color: #e6999d; }
+
+.swagger-ui .hover-red:focus, .swagger-ui .hover-red:hover, .swagger-ui .red { color: #e69d99; }
+
+.swagger-ui .hover-light-red:focus, .swagger-ui .hover-light-red:hover, .swagger-ui .light-red { color: #e6a399; }
+
+.swagger-ui .hover-orange:focus, .swagger-ui .hover-orange:hover, .swagger-ui .orange { color: #e6b699; }
+
+.swagger-ui .gold, .swagger-ui .hover-gold:focus, .swagger-ui .hover-gold:hover { color: #e6d099; }
+
+.swagger-ui .hover-yellow:focus, .swagger-ui .hover-yellow:hover, .swagger-ui .yellow { color: #e6da99; }
+
+.swagger-ui .hover-light-yellow:focus, .swagger-ui .hover-light-yellow:hover, .swagger-ui .light-yellow { color: #ede6b6; }
+
+.swagger-ui .hover-purple:focus, .swagger-ui .hover-purple:hover, .swagger-ui .purple { color: #b99ae4; }
+
+.swagger-ui .hover-light-purple:focus, .swagger-ui .hover-light-purple:hover, .swagger-ui .light-purple { color: #bb99e6; }
+
+.swagger-ui .dark-pink, .swagger-ui .hover-dark-pink:focus, .swagger-ui .hover-dark-pink:hover { color: #e699cc; }
+
+.swagger-ui .hot-pink, .swagger-ui .hover-hot-pink:focus, .swagger-ui .hover-hot-pink:hover, .swagger-ui .hover-pink:focus, .swagger-ui .hover-pink:hover, .swagger-ui .pink { color: #e699c7; }
+
+.swagger-ui .hover-light-pink:focus, .swagger-ui .hover-light-pink:hover, .swagger-ui .light-pink { color: #edb6d5; }
+
+.swagger-ui .dark-green, .swagger-ui .green, .swagger-ui .hover-dark-green:focus, .swagger-ui .hover-dark-green:hover, .swagger-ui .hover-green:focus, .swagger-ui .hover-green:hover { color: #99e6c9; }
+
+.swagger-ui .hover-light-green:focus, .swagger-ui .hover-light-green:hover, .swagger-ui .light-green { color: #a1e8ce; }
+
+.swagger-ui .hover-navy:focus, .swagger-ui .hover-navy:hover, .swagger-ui .navy { color: #99b8e6; }
+
+.swagger-ui .blue, .swagger-ui .dark-blue, .swagger-ui .hover-blue:focus, .swagger-ui .hover-blue:hover, .swagger-ui .hover-dark-blue:focus, .swagger-ui .hover-dark-blue:hover { color: #99bae6; }
+
+.swagger-ui .hover-light-blue:focus, .swagger-ui .hover-light-blue:hover, .swagger-ui .light-blue { color: #a9cbea; }
+
+.swagger-ui .hover-lightest-blue:focus, .swagger-ui .hover-lightest-blue:hover, .swagger-ui .lightest-blue { color: #d6e9f5; }
+
+.swagger-ui .hover-washed-blue:focus, .swagger-ui .hover-washed-blue:hover, .swagger-ui .washed-blue { color: #f7fdfc; }
+
+.swagger-ui .hover-washed-green:focus, .swagger-ui .hover-washed-green:hover, .swagger-ui .washed-green { color: #ebfaf4; }
+
+.swagger-ui .hover-washed-yellow:focus, .swagger-ui .hover-washed-yellow:hover, .swagger-ui .washed-yellow { color: #fbf9ef; }
+
+.swagger-ui .hover-washed-red:focus, .swagger-ui .hover-washed-red:hover, .swagger-ui .washed-red { color: #f9e7e7; }
+
+.swagger-ui .color-inherit, .swagger-ui .hover-inherit:focus, .swagger-ui .hover-inherit:hover { color: inherit; }
+
+.swagger-ui .bg-black-90, .swagger-ui .hover-bg-black-90:focus, .swagger-ui .hover-bg-black-90:hover { background-color: rgba(0, 0, 0, .9); }
+
+.swagger-ui .bg-black-80, .swagger-ui .hover-bg-black-80:focus, .swagger-ui .hover-bg-black-80:hover { background-color: rgba(0, 0, 0, .8); }
+
+.swagger-ui .bg-black-70, .swagger-ui .hover-bg-black-70:focus, .swagger-ui .hover-bg-black-70:hover { background-color: rgba(0, 0, 0, .7); }
+
+.swagger-ui .bg-black-60, .swagger-ui .hover-bg-black-60:focus, .swagger-ui .hover-bg-black-60:hover { background-color: rgba(0, 0, 0, .6); }
+
+.swagger-ui .bg-black-50, .swagger-ui .hover-bg-black-50:focus, .swagger-ui .hover-bg-black-50:hover { background-color: rgba(0, 0, 0, .5); }
+
+.swagger-ui .bg-black-40, .swagger-ui .hover-bg-black-40:focus, .swagger-ui .hover-bg-black-40:hover { background-color: rgba(0, 0, 0, .4); }
+
+.swagger-ui .bg-black-30, .swagger-ui .hover-bg-black-30:focus, .swagger-ui .hover-bg-black-30:hover { background-color: rgba(0, 0, 0, .3); }
+
+.swagger-ui .bg-black-20, .swagger-ui .hover-bg-black-20:focus, .swagger-ui .hover-bg-black-20:hover { background-color: rgba(0, 0, 0, .2); }
+
+.swagger-ui .bg-white-90, .swagger-ui .hover-bg-white-90:focus, .swagger-ui .hover-bg-white-90:hover { background-color: rgba(28, 28, 33, .9); }
+
+.swagger-ui .bg-white-80, .swagger-ui .hover-bg-white-80:focus, .swagger-ui .hover-bg-white-80:hover { background-color: rgba(28, 28, 33, .8); }
+
+.swagger-ui .bg-white-70, .swagger-ui .hover-bg-white-70:focus, .swagger-ui .hover-bg-white-70:hover { background-color: rgba(28, 28, 33, .7); }
+
+.swagger-ui .bg-white-60, .swagger-ui .hover-bg-white-60:focus, .swagger-ui .hover-bg-white-60:hover { background-color: rgba(28, 28, 33, .6); }
+
+.swagger-ui .bg-white-50, .swagger-ui .hover-bg-white-50:focus, .swagger-ui .hover-bg-white-50:hover { background-color: rgba(28, 28, 33, .5); }
+
+.swagger-ui .bg-white-40, .swagger-ui .hover-bg-white-40:focus, .swagger-ui .hover-bg-white-40:hover { background-color: rgba(28, 28, 33, .4); }
+
+.swagger-ui .bg-white-30, .swagger-ui .hover-bg-white-30:focus, .swagger-ui .hover-bg-white-30:hover { background-color: rgba(28, 28, 33, .3); }
+
+.swagger-ui .bg-white-20, .swagger-ui .hover-bg-white-20:focus, .swagger-ui .hover-bg-white-20:hover { background-color: rgba(28, 28, 33, .2); }
+
+.swagger-ui .bg-black, .swagger-ui .hover-bg-black:focus, .swagger-ui .hover-bg-black:hover { background-color: #000; }
+
+.swagger-ui .bg-near-black, .swagger-ui .hover-bg-near-black:focus, .swagger-ui .hover-bg-near-black:hover { background-color: #121212; }
+
+.swagger-ui .bg-dark-gray, .swagger-ui .hover-bg-dark-gray:focus, .swagger-ui .hover-bg-dark-gray:hover { background-color: #333; }
+
+.swagger-ui .bg-mid-gray, .swagger-ui .hover-bg-mid-gray:focus, .swagger-ui .hover-bg-mid-gray:hover { background-color: #545454; }
+
+.swagger-ui .bg-gray, .swagger-ui .hover-bg-gray:focus, .swagger-ui .hover-bg-gray:hover { background-color: #787878; }
+
+.swagger-ui .bg-silver, .swagger-ui .hover-bg-silver:focus, .swagger-ui .hover-bg-silver:hover { background-color: #999; }
+
+.swagger-ui .bg-white, .swagger-ui .hover-bg-white:focus, .swagger-ui .hover-bg-white:hover { background-color: #1c1c21; }
+
+.swagger-ui .bg-transparent, .swagger-ui .hover-bg-transparent:focus, .swagger-ui .hover-bg-transparent:hover { background-color: transparent; }
+
+.swagger-ui .bg-dark-red, .swagger-ui .hover-bg-dark-red:focus, .swagger-ui .hover-bg-dark-red:hover { background-color: #bc2f36; }
+
+.swagger-ui .bg-red, .swagger-ui .hover-bg-red:focus, .swagger-ui .hover-bg-red:hover { background-color: #c83932; }
+
+.swagger-ui .bg-light-red, .swagger-ui .hover-bg-light-red:focus, .swagger-ui .hover-bg-light-red:hover { background-color: #ab3c2b; }
+
+.swagger-ui .bg-orange, .swagger-ui .hover-bg-orange:focus, .swagger-ui .hover-bg-orange:hover { background-color: #cc6e33; }
+
+.swagger-ui .bg-gold, .swagger-ui .bg-light-yellow, .swagger-ui .bg-washed-yellow, .swagger-ui .bg-yellow, .swagger-ui .hover-bg-gold:focus, .swagger-ui .hover-bg-gold:hover, .swagger-ui .hover-bg-light-yellow:focus, .swagger-ui .hover-bg-light-yellow:hover, .swagger-ui .hover-bg-washed-yellow:focus, .swagger-ui .hover-bg-washed-yellow:hover, .swagger-ui .hover-bg-yellow:focus, .swagger-ui .hover-bg-yellow:hover { background-color: #664b00; }
+
+.swagger-ui .bg-purple, .swagger-ui .hover-bg-purple:focus, .swagger-ui .hover-bg-purple:hover { background-color: #5e2ca5; }
+
+.swagger-ui .bg-light-purple, .swagger-ui .hover-bg-light-purple:focus, .swagger-ui .hover-bg-light-purple:hover { background-color: #672caf; }
+
+.swagger-ui .bg-dark-pink, .swagger-ui .hover-bg-dark-pink:focus, .swagger-ui .hover-bg-dark-pink:hover { background-color: #ab2b81; }
+
+.swagger-ui .bg-hot-pink, .swagger-ui .hover-bg-hot-pink:focus, .swagger-ui .hover-bg-hot-pink:hover { background-color: #c03086; }
+
+.swagger-ui .bg-pink, .swagger-ui .hover-bg-pink:focus, .swagger-ui .hover-bg-pink:hover { background-color: #8f2464; }
+
+.swagger-ui .bg-light-pink, .swagger-ui .hover-bg-light-pink:focus, .swagger-ui .hover-bg-light-pink:hover { background-color: #721d4d; }
+
+.swagger-ui .bg-dark-green, .swagger-ui .hover-bg-dark-green:focus, .swagger-ui .hover-bg-dark-green:hover { background-color: #1c6e50; }
+
+.swagger-ui .bg-green, .swagger-ui .hover-bg-green:focus, .swagger-ui .hover-bg-green:hover { background-color: #279b70; }
+
+.swagger-ui .bg-light-green, .swagger-ui .hover-bg-light-green:focus, .swagger-ui .hover-bg-light-green:hover { background-color: #228762; }
+
+.swagger-ui .bg-navy, .swagger-ui .hover-bg-navy:focus, .swagger-ui .hover-bg-navy:hover { background-color: #0d1d35; }
+
+.swagger-ui .bg-dark-blue, .swagger-ui .hover-bg-dark-blue:focus, .swagger-ui .hover-bg-dark-blue:hover { background-color: #20497e; }
+
+.swagger-ui .bg-blue, .swagger-ui .hover-bg-blue:focus, .swagger-ui .hover-bg-blue:hover { background-color: #4380d0; }
+
+.swagger-ui .bg-light-blue, .swagger-ui .hover-bg-light-blue:focus, .swagger-ui .hover-bg-light-blue:hover { background-color: #20517e; }
+
+.swagger-ui .bg-lightest-blue, .swagger-ui .hover-bg-lightest-blue:focus, .swagger-ui .hover-bg-lightest-blue:hover { background-color: #143a52; }
+
+.swagger-ui .bg-washed-blue, .swagger-ui .hover-bg-washed-blue:focus, .swagger-ui .hover-bg-washed-blue:hover { background-color: #0c312d; }
+
+.swagger-ui .bg-washed-green, .swagger-ui .hover-bg-washed-green:focus, .swagger-ui .hover-bg-washed-green:hover { background-color: #0f3d2c; }
+
+.swagger-ui .bg-washed-red, .swagger-ui .hover-bg-washed-red:focus, .swagger-ui .hover-bg-washed-red:hover { background-color: #411010; }
+
+.swagger-ui .bg-inherit, .swagger-ui .hover-bg-inherit:focus, .swagger-ui .hover-bg-inherit:hover { background-color: inherit; }
+
+.swagger-ui .shadow-hover { transition: all .5s cubic-bezier(.165, .84, .44, 1) 0s; }
+
+.swagger-ui .shadow-hover::after {
+    border-radius: inherit;
+    box-shadow: rgba(0, 0, 0, .2) 0 0 16px 2px;
+    content: "";
+    height: 100%;
+    left: 0;
+    opacity: 0;
+    position: absolute;
+    top: 0;
+    transition: opacity .5s cubic-bezier(.165, .84, .44, 1) 0s;
+    width: 100%;
+    z-index: -1;
+}
+
+.swagger-ui .bg-animate, .swagger-ui .bg-animate:focus, .swagger-ui .bg-animate:hover { transition: background-color .15s ease-in-out 0s; }
+
+.swagger-ui .nested-links a {
+    color: #99bae6;
+    transition: color .15s ease-in 0s;
+}
+
+.swagger-ui .nested-links a:focus, .swagger-ui .nested-links a:hover {
+    color: #a9cbea;
+    transition: color .15s ease-in 0s;
+}
+
+.swagger-ui .opblock-tag {
+    border-bottom: 1px solid rgba(58, 64, 80, .3);
+    color: #b5bac9;
+    transition: all .2s ease 0s;
+}
+
+.swagger-ui .opblock-tag svg, .swagger-ui section.models h4 svg { transition: all .4s ease 0s; }
+
+.swagger-ui .opblock {
+    border: 1px solid #000;
+    border-radius: 4px;
+    box-shadow: rgba(0, 0, 0, .19) 0 0 3px;
+    margin: 0 0 15px;
+}
+
+.swagger-ui .opblock .tab-header .tab-item.active h4 span::after { background: gray; }
+
+.swagger-ui .opblock.is-open .opblock-summary { border-bottom: 1px solid #000; }
+
+.swagger-ui .opblock .opblock-section-header {
+    background: rgba(28, 28, 33, .8);
+    box-shadow: rgba(0, 0, 0, .1) 0 1px 2px;
+}
+
+.swagger-ui .opblock .opblock-section-header > label > span { padding: 0 10px 0 0; }
+
+.swagger-ui .opblock .opblock-summary-method {
+    background: #000;
+    color: #fff;
+    text-shadow: rgba(0, 0, 0, .1) 0 1px 0;
+}
+
+.swagger-ui .opblock.opblock-post {
+    background: rgba(72, 203, 144, .1);
+    border-color: #48cb90;
+}
+
+.swagger-ui .opblock.opblock-post .opblock-summary-method, .swagger-ui .opblock.opblock-post .tab-header .tab-item.active h4 span::after { background: #48cb90; }
+
+.swagger-ui .opblock.opblock-post .opblock-summary { border-color: #48cb90; }
+
+.swagger-ui .opblock.opblock-put {
+    background: rgba(213, 157, 88, .1);
+    border-color: #d59d58;
+}
+
+.swagger-ui .opblock.opblock-put .opblock-summary-method, .swagger-ui .opblock.opblock-put .tab-header .tab-item.active h4 span::after { background: #d59d58; }
+
+.swagger-ui .opblock.opblock-put .opblock-summary { border-color: #d59d58; }
+
+.swagger-ui .opblock.opblock-delete {
+    background: rgba(200, 50, 50, .1);
+    border-color: #c83232;
+}
+
+.swagger-ui .opblock.opblock-delete .opblock-summary-method, .swagger-ui .opblock.opblock-delete .tab-header .tab-item.active h4 span::after { background: #c83232; }
+
+.swagger-ui .opblock.opblock-delete .opblock-summary { border-color: #c83232; }
+
+.swagger-ui .opblock.opblock-get {
+    background: rgba(42, 105, 167, .1);
+    border-color: #2a69a7;
+}
+
+.swagger-ui .opblock.opblock-get .opblock-summary-method, .swagger-ui .opblock.opblock-get .tab-header .tab-item.active h4 span::after { background: #2a69a7; }
+
+.swagger-ui .opblock.opblock-get .opblock-summary { border-color: #2a69a7; }
+
+.swagger-ui .opblock.opblock-patch {
+    background: rgba(92, 214, 188, .1);
+    border-color: #5cd6bc;
+}
+
+.swagger-ui .opblock.opblock-patch .opblock-summary-method, .swagger-ui .opblock.opblock-patch .tab-header .tab-item.active h4 span::after { background: #5cd6bc; }
+
+.swagger-ui .opblock.opblock-patch .opblock-summary { border-color: #5cd6bc; }
+
+.swagger-ui .opblock.opblock-head {
+    background: rgba(140, 63, 207, .1);
+    border-color: #8c3fcf;
+}
+
+.swagger-ui .opblock.opblock-head .opblock-summary-method, .swagger-ui .opblock.opblock-head .tab-header .tab-item.active h4 span::after { background: #8c3fcf; }
+
+.swagger-ui .opblock.opblock-head .opblock-summary { border-color: #8c3fcf; }
+
+.swagger-ui .opblock.opblock-options {
+    background: rgba(36, 89, 143, .1);
+    border-color: #24598f;
+}
+
+.swagger-ui .opblock.opblock-options .opblock-summary-method, .swagger-ui .opblock.opblock-options .tab-header .tab-item.active h4 span::after { background: #24598f; }
+
+.swagger-ui .opblock.opblock-options .opblock-summary { border-color: #24598f; }
+
+.swagger-ui .opblock.opblock-deprecated {
+    background: rgba(46, 46, 46, .1);
+    border-color: #2e2e2e;
+    opacity: .6;
+}
+
+.swagger-ui .opblock.opblock-deprecated .opblock-summary-method, .swagger-ui .opblock.opblock-deprecated .tab-header .tab-item.active h4 span::after { background: #2e2e2e; }
+
+.swagger-ui .opblock.opblock-deprecated .opblock-summary { border-color: #2e2e2e; }
+
+.swagger-ui .filter .operation-filter-input { border: 2px solid #2b3446; }
+
+.swagger-ui .tab li:first-of-type::after { background: rgba(0, 0, 0, .2); }
+
+.swagger-ui .download-contents {
+    background: #7c8192;
+    color: #fff;
+}
+
+.swagger-ui .scheme-container {
+    background: #1c1c21;
+    box-shadow: rgba(0, 0, 0, .15) 0 1px 2px 0;
+}
+
+.swagger-ui .loading-container .loading::before {
+    animation: 1s linear 0s infinite normal none running rotation, .5s ease 0s 1 normal none running opacity;
+    border-color: rgba(0, 0, 0, .6) rgba(84, 84, 84, .1) rgba(84, 84, 84, .1);
+}
+
+.swagger-ui .response-control-media-type--accept-controller select { border-color: #196619; }
+
+.swagger-ui .response-control-media-type__accept-message { color: #99e699; }
+
+.swagger-ui .version-pragma__message code { background-color: #3b3b3b; }
+
+.swagger-ui .btn {
+    background: 0 0;
+    border: 2px solid gray;
+    box-shadow: rgba(0, 0, 0, .1) 0 1px 2px;
+    color: #b5bac9;
+}
+
+.swagger-ui .btn:hover { box-shadow: rgba(0, 0, 0, .3) 0 0 5px; }
+
+.swagger-ui .btn.authorize, .swagger-ui .btn.cancel {
+    background-color: transparent;
+    border-color: #a72a2a;
+    color: #e69999;
+}
+
+.swagger-ui .btn.authorize {
+    border-color: #48cb90;
+    color: #9ce3c3;
+}
+
+.swagger-ui .btn.authorize svg { fill: #9ce3c3; }
+
+.swagger-ui .btn.execute {
+    background-color: #5892d5;
+    border-color: #5892d5;
+    color: #fff;
+}
+
+.swagger-ui .copy-to-clipboard { background: #7c8192; }
+
+.swagger-ui .copy-to-clipboard button { background: url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"16\" height=\"16\" aria-hidden=\"true\"><path fill=\"%23fff\" fill-rule=\"evenodd\" d=\"M2 13h4v1H2v-1zm5-6H2v1h5V7zm2 3V8l-3 3 3 3v-2h5v-2H9zM4.5 9H2v1h2.5V9zM2 12h2.5v-1H2v1zm9 1h1v2c-.02.28-.11.52-.3.7-.19.18-.42.28-.7.3H1c-.55 0-1-.45-1-1V4c0-.55.45-1 1-1h3c0-1.11.89-2 2-2 1.11 0 2 .89 2 2h3c.55 0 1 .45 1 1v5h-1V6H1v9h10v-2zM2 5h8c0-.55-.45-1-1-1H8c-.55 0-1-.45-1-1s-.45-1-1-1-1 .45-1 1-.45 1-1 1H3c-.55 0-1 .45-1 1z\"/></svg>") 50% center no-repeat; }
+
+.swagger-ui select {
+    background: url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 20 20\"><path d=\"M13.418 7.859a.695.695 0 01.978 0 .68.68 0 010 .969l-3.908 3.83a.697.697 0 01-.979 0l-3.908-3.83a.68.68 0 010-.969.695.695 0 01.978 0L10 11l3.418-3.141z\"/></svg>") right 10px center/20px no-repeat #212121;
+    background: url(data:image/svg+xml;base64,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) right 10px center/20px no-repeat #1c1c21;
+    border: 2px solid #41444e;
+}
+
+.swagger-ui select[multiple] { background: #212121; }
+
+.swagger-ui button.invalid, .swagger-ui input[type=email].invalid, .swagger-ui input[type=file].invalid, .swagger-ui input[type=password].invalid, .swagger-ui input[type=search].invalid, .swagger-ui input[type=text].invalid, .swagger-ui select.invalid, .swagger-ui textarea.invalid {
+    background: #390e0e;
+    border-color: #c83232;
+}
+
+.swagger-ui input[type=email], .swagger-ui input[type=file], .swagger-ui input[type=password], .swagger-ui input[type=search], .swagger-ui input[type=text], .swagger-ui textarea {
+    background: #1c1c21;
+    border: 1px solid #404040;
+}
+
+.swagger-ui textarea {
+    background: rgba(28, 28, 33, .8);
+    color: #b5bac9;
+}
+
+.swagger-ui input[disabled], .swagger-ui select[disabled] {
+    background-color: #1f1f1f;
+    color: #bfbfbf;
+}
+
+.swagger-ui textarea[disabled] {
+    background-color: #41444e;
+    color: #fff;
+}
+
+.swagger-ui select[disabled] { border-color: #878787; }
+
+.swagger-ui textarea:focus { border: 2px solid #2a69a7; }
+
+.swagger-ui .checkbox input[type=checkbox] + label > .item {
+    background: #303030;
+    box-shadow: #303030 0 0 0 2px;
+}
+
+.swagger-ui .checkbox input[type=checkbox]:checked + label > .item { background: url("data:image/svg+xml;charset=utf-8,<svg width=\"10\" height=\"8\" viewBox=\"3 7 10 8\" xmlns=\"http://www.w3.org/2000/svg\"><path fill=\"%2341474E\" fill-rule=\"evenodd\" d=\"M6.333 15L3 11.667l1.333-1.334 2 2L11.667 7 13 8.333z\"/></svg>") 50% center no-repeat #303030; }
+
+.swagger-ui .dialog-ux .backdrop-ux { background: rgba(0, 0, 0, .8); }
+
+.swagger-ui .dialog-ux .modal-ux {
+    background: #1c1c21;
+    border: 1px solid #2e2e2e;
+    box-shadow: rgba(0, 0, 0, .2) 0 10px 30px 0;
+}
+
+.swagger-ui .dialog-ux .modal-ux-header .close-modal { background: 0 0; }
+
+.swagger-ui .model .deprecated span, .swagger-ui .model .deprecated td { color: #bfbfbf !important; }
+
+.swagger-ui .model-toggle::after { background: url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"24\" height=\"24\"><path d=\"M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z\"/></svg>") 50% center/100% no-repeat; }
+
+.swagger-ui .model-hint {
+    background: rgba(0, 0, 0, .7);
+    color: #ebebeb;
+}
+
+.swagger-ui section.models { border: 1px solid rgba(58, 64, 80, .3); }
+
+.swagger-ui section.models.is-open h4 { border-bottom: 1px solid rgba(58, 64, 80, .3); }
+
+.swagger-ui section.models .model-container { background: rgba(0, 0, 0, .05); }
+
+.swagger-ui section.models .model-container:hover { background: rgba(0, 0, 0, .07); }
+
+.swagger-ui .model-box { background: rgba(0, 0, 0, .1); }
+
+.swagger-ui .prop-type { color: #aaaad4; }
+
+.swagger-ui table thead tr td, .swagger-ui table thead tr th {
+    border-bottom: 1px solid rgba(58, 64, 80, .2);
+    color: #b5bac9;
+}
+
+.swagger-ui .parameter__name.required::after { color: rgba(230, 153, 153, .6); }
+
+.swagger-ui .topbar .download-url-wrapper .select-label { color: #f0f0f0; }
+
+.swagger-ui .topbar .download-url-wrapper .download-url-button {
+    background: #63a040;
+    color: #fff;
+}
+
+.swagger-ui .info .title small { background: #7c8492; }
+
+.swagger-ui .info .title small.version-stamp { background-color: #7a9b27; }
+
+.swagger-ui .auth-container .errors {
+    background-color: #350d0d;
+    color: #b5bac9;
+}
+
+.swagger-ui .errors-wrapper {
+    background: rgba(200, 50, 50, .1);
+    border: 2px solid #c83232;
+}
+
+.swagger-ui .markdown code, .swagger-ui .renderedmarkdown code {
+    background: rgba(0, 0, 0, .05);
+    color: #c299e6;
+}
+
+.swagger-ui .model-toggle:after { background: url(data:image/svg+xml;base64,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) 50% no-repeat; }
+
+.swagger-ui .expand-operation svg, .swagger-ui section.models h4 svg { fill: #fff; }
+
+::-webkit-scrollbar-track { background-color: #646464 !important; }
+
+::-webkit-scrollbar-thumb {
+    background-color: #242424 !important;
+    border: 2px solid #3e4346 !important;
+}
+
+::-webkit-scrollbar-button:vertical:start:decrement {
+    background: linear-gradient(130deg, #696969 40%, rgba(255, 0, 0, 0) 41%), linear-gradient(230deg, #696969 40%, transparent 41%), linear-gradient(0deg, #696969 40%, transparent 31%);
+    background-color: #b6b6b6;
+}
+
+::-webkit-scrollbar-button:vertical:end:increment {
+    background: linear-gradient(310deg, #696969 40%, transparent 41%), linear-gradient(50deg, #696969 40%, transparent 41%), linear-gradient(180deg, #696969 40%, transparent 31%);
+    background-color: #b6b6b6;
+}
+
+::-webkit-scrollbar-button:horizontal:end:increment {
+    background: linear-gradient(210deg, #696969 40%, transparent 41%), linear-gradient(330deg, #696969 40%, transparent 41%), linear-gradient(90deg, #696969 30%, transparent 31%);
+    background-color: #b6b6b6;
+}
+
+::-webkit-scrollbar-button:horizontal:start:decrement {
+    background: linear-gradient(30deg, #696969 40%, transparent 41%), linear-gradient(150deg, #696969 40%, transparent 41%), linear-gradient(270deg, #696969 30%, transparent 31%);
+    background-color: #b6b6b6;
+}
+
+::-webkit-scrollbar-button, ::-webkit-scrollbar-track-piece { background-color: #3e4346 !important; }
+
+.swagger-ui .black, .swagger-ui .checkbox, .swagger-ui .dark-gray, .swagger-ui .download-url-wrapper .loading, .swagger-ui .errors-wrapper .errors small, .swagger-ui .fallback, .swagger-ui .filter .loading, .swagger-ui .gray, .swagger-ui .hover-black:focus, .swagger-ui .hover-black:hover, .swagger-ui .hover-dark-gray:focus, .swagger-ui .hover-dark-gray:hover, .swagger-ui .hover-gray:focus, .swagger-ui .hover-gray:hover, .swagger-ui .hover-light-silver:focus, .swagger-ui .hover-light-silver:hover, .swagger-ui .hover-mid-gray:focus, .swagger-ui .hover-mid-gray:hover, .swagger-ui .hover-near-black:focus, .swagger-ui .hover-near-black:hover, .swagger-ui .hover-silver:focus, .swagger-ui .hover-silver:hover, .swagger-ui .light-silver, .swagger-ui .markdown pre, .swagger-ui .mid-gray, .swagger-ui .model .property, .swagger-ui .model .property.primitive, .swagger-ui .model-title, .swagger-ui .near-black, .swagger-ui .parameter__extension, .swagger-ui .parameter__in, .swagger-ui .prop-format, .swagger-ui .renderedmarkdown pre, .swagger-ui .response-col_links .response-undocumented, .swagger-ui .response-col_status .response-undocumented, .swagger-ui .silver, .swagger-ui section.models h4, .swagger-ui section.models h5, .swagger-ui span.token-not-formatted, .swagger-ui span.token-string, .swagger-ui table.headers .header-example, .swagger-ui table.model tr.description, .swagger-ui table.model tr.extension { color: #bfbfbf; }
+
+.swagger-ui .hover-white:focus, .swagger-ui .hover-white:hover, .swagger-ui .info .title small pre, .swagger-ui .topbar a, .swagger-ui .white { color: #fff; }
+
+.swagger-ui .bg-black-10, .swagger-ui .hover-bg-black-10:focus, .swagger-ui .hover-bg-black-10:hover, .swagger-ui .stripe-dark:nth-child(2n + 1) { background-color: rgba(0, 0, 0, .1); }
+
+.swagger-ui .bg-white-10, .swagger-ui .hover-bg-white-10:focus, .swagger-ui .hover-bg-white-10:hover, .swagger-ui .stripe-light:nth-child(2n + 1) { background-color: rgba(28, 28, 33, .1); }
+
+.swagger-ui .bg-light-silver, .swagger-ui .hover-bg-light-silver:focus, .swagger-ui .hover-bg-light-silver:hover, .swagger-ui .striped--light-silver:nth-child(2n + 1) { background-color: #6e6e6e; }
+
+.swagger-ui .bg-moon-gray, .swagger-ui .hover-bg-moon-gray:focus, .swagger-ui .hover-bg-moon-gray:hover, .swagger-ui .striped--moon-gray:nth-child(2n + 1) { background-color: #4d4d4d; }
+
+.swagger-ui .bg-light-gray, .swagger-ui .hover-bg-light-gray:focus, .swagger-ui .hover-bg-light-gray:hover, .swagger-ui .striped--light-gray:nth-child(2n + 1) { background-color: #2b2b2b; }
+
+.swagger-ui .bg-near-white, .swagger-ui .hover-bg-near-white:focus, .swagger-ui .hover-bg-near-white:hover, .swagger-ui .striped--near-white:nth-child(2n + 1) { background-color: #242424; }
+
+.swagger-ui .opblock-tag:hover, .swagger-ui section.models h4:hover { background: rgba(0, 0, 0, .02); }
+
+.swagger-ui .checkbox p, .swagger-ui .dialog-ux .modal-ux-content h4, .swagger-ui .dialog-ux .modal-ux-content p, .swagger-ui .dialog-ux .modal-ux-header h3, .swagger-ui .errors-wrapper .errors h4, .swagger-ui .errors-wrapper hgroup h4, .swagger-ui .info .base-url, .swagger-ui .info .title, .swagger-ui .info h1, .swagger-ui .info h2, .swagger-ui .info h3, .swagger-ui .info h4, .swagger-ui .info h5, .swagger-ui .info li, .swagger-ui .info p, .swagger-ui .info table, .swagger-ui .loading-container .loading::after, .swagger-ui .model, .swagger-ui .opblock .opblock-section-header h4, .swagger-ui .opblock .opblock-section-header > label, .swagger-ui .opblock .opblock-summary-description, .swagger-ui .opblock .opblock-summary-operation-id, .swagger-ui .opblock .opblock-summary-path, .swagger-ui .opblock .opblock-summary-path__deprecated, .swagger-ui .opblock-description-wrapper, .swagger-ui .opblock-description-wrapper h4, .swagger-ui .opblock-description-wrapper p, .swagger-ui .opblock-external-docs-wrapper, .swagger-ui .opblock-external-docs-wrapper h4, .swagger-ui .opblock-external-docs-wrapper p, .swagger-ui .opblock-tag small, .swagger-ui .opblock-title_normal, .swagger-ui .opblock-title_normal h4, .swagger-ui .opblock-title_normal p, .swagger-ui .parameter__name, .swagger-ui .parameter__type, .swagger-ui .response-col_links, .swagger-ui .response-col_status, .swagger-ui .responses-inner h4, .swagger-ui .responses-inner h5, .swagger-ui .scheme-container .schemes > label, .swagger-ui .scopes h2, .swagger-ui .servers > label, .swagger-ui .tab li, .swagger-ui label, .swagger-ui select, .swagger-ui table.headers td { color: #b5bac9; }
+
+.swagger-ui .download-url-wrapper .failed, .swagger-ui .filter .failed, .swagger-ui .model-deprecated-warning, .swagger-ui .parameter__deprecated, .swagger-ui .parameter__name.required span, .swagger-ui table.model tr.property-row .star { color: #e69999; }
+
+.swagger-ui .opblock-body pre.microlight, .swagger-ui textarea.curl {
+    background: #41444e;
+    border-radius: 4px;
+    color: #fff;
+}
+
+.swagger-ui .expand-methods svg, .swagger-ui .expand-methods:hover svg { fill: #bfbfbf; }
+
+.swagger-ui .auth-container, .swagger-ui .dialog-ux .modal-ux-header { border-bottom: 1px solid #2e2e2e; }
+
+.swagger-ui .topbar .download-url-wrapper .select-label select, .swagger-ui .topbar .download-url-wrapper input[type=text] { border: 2px solid #63a040; }
+
+.swagger-ui .info a, .swagger-ui .info a:hover, .swagger-ui .scopes h2 a { color: #99bde6; }
+
+/* Dark Scrollbar */
+::-webkit-scrollbar {
+    width: 14px;
+    height: 14px;
+}
+
+::-webkit-scrollbar-button {
+    background-color: #3e4346 !important;
+}
+
+::-webkit-scrollbar-track {
+    background-color: #646464 !important;
+}
+
+::-webkit-scrollbar-track-piece {
+    background-color: #3e4346 !important;
+}
+
+::-webkit-scrollbar-thumb {
+    height: 50px;
+    background-color: #242424 !important;
+    border: 2px solid #3e4346 !important;
+}
+
+::-webkit-scrollbar-corner {}
+
+::-webkit-resizer {}
+
+::-webkit-scrollbar-button:vertical:start:decrement {
+    background:
+        linear-gradient(130deg, #696969 40%, rgba(255, 0, 0, 0) 41%),
+        linear-gradient(230deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
+        linear-gradient(0deg, #696969 40%, rgba(0, 0, 0, 0) 31%);
+    background-color: #b6b6b6;
+}
+
+::-webkit-scrollbar-button:vertical:end:increment {
+    background:
+        linear-gradient(310deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
+        linear-gradient(50deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
+        linear-gradient(180deg, #696969 40%, rgba(0, 0, 0, 0) 31%);
+    background-color: #b6b6b6;
+}
+
+::-webkit-scrollbar-button:horizontal:end:increment {
+    background:
+        linear-gradient(210deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
+        linear-gradient(330deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
+        linear-gradient(90deg, #696969 30%, rgba(0, 0, 0, 0) 31%);
+    background-color: #b6b6b6;
+}
+
+::-webkit-scrollbar-button:horizontal:start:decrement {
+    background:
+        linear-gradient(30deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
+        linear-gradient(150deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
+        linear-gradient(270deg, #696969 30%, rgba(0, 0, 0, 0) 31%);
+    background-color: #b6b6b6;
+}

static/swagger-ui/index.css

@@ -0,0 +1,17 @@
+/*! Swagger UI 4.13.2 | https://swagger.io/tools/swagger-ui/ | Apache License 2.0 (license file can be found at ./LICENSE) */
+html {
+    box-sizing: border-box;
+    overflow: -moz-scrollbars-vertical;
+    overflow-y: scroll;
+}
+
+*,
+*:before,
+*:after {
+    box-sizing: inherit;
+}
+
+body {
+    margin: 0;
+    background: #fafafa;
+}

static/swagger-ui/oauth2-redirect.html

@@ -0,0 +1,79 @@
+<!doctype html>
+<html lang="en-US">
+<head>
+    <title>Swagger UI: OAuth2 Redirect</title>
+</head>
+<body>
+<script>
+    'use strict';
+    function run () {
+        var oauth2 = window.opener.swaggerUIRedirectOauth2;
+        var sentState = oauth2.state;
+        var redirectUrl = oauth2.redirectUrl;
+        var isValid, qp, arr;
+
+        if (/code|token|error/.test(window.location.hash)) {
+            qp = window.location.hash.substring(1);
+        } else {
+            qp = location.search.substring(1);
+        }
+
+        arr = qp.split("&");
+        arr.forEach(function (v,i,_arr) { _arr[i] = '"' + v.replace('=', '":"') + '"';});
+        qp = qp ? JSON.parse('{' + arr.join() + '}',
+                function (key, value) {
+                    return key === "" ? value : decodeURIComponent(value);
+                }
+        ) : {};
+
+        isValid = qp.state === sentState;
+
+        if ((
+          oauth2.auth.schema.get("flow") === "accessCode" ||
+          oauth2.auth.schema.get("flow") === "authorizationCode" ||
+          oauth2.auth.schema.get("flow") === "authorization_code"
+        ) && !oauth2.auth.code) {
+            if (!isValid) {
+                oauth2.errCb({
+                    authId: oauth2.auth.name,
+                    source: "auth",
+                    level: "warning",
+                    message: "Authorization may be unsafe, passed state was changed in server. The passed state wasn't returned from auth server."
+                });
+            }
+
+            if (qp.code) {
+                delete oauth2.state;
+                oauth2.auth.code = qp.code;
+                oauth2.callback({auth: oauth2.auth, redirectUrl: redirectUrl});
+            } else {
+                let oauthErrorMsg;
+                if (qp.error) {
+                    oauthErrorMsg = "["+qp.error+"]: " +
+                        (qp.error_description ? qp.error_description+ ". " : "no accessCode received from the server. ") +
+                        (qp.error_uri ? "More info: "+qp.error_uri : "");
+                }
+
+                oauth2.errCb({
+                    authId: oauth2.auth.name,
+                    source: "auth",
+                    level: "error",
+                    message: oauthErrorMsg || "[Authorization failed]: no accessCode received from the server."
+                });
+            }
+        } else {
+            oauth2.callback({auth: oauth2.auth, token: qp, isValid: isValid, redirectUrl: redirectUrl});
+        }
+        window.close();
+    }
+
+    if (document.readyState !== 'loading') {
+        run();
+    } else {
+        document.addEventListener('DOMContentLoaded', function () {
+            run();
+        });
+    }
+</script>
+</body>
+</html>

templates/index.html

@@ -9,7 +9,8 @@
 	<link rel="stylesheet" href="static/bootstrap.min.css">
 	<link rel="stylesheet" href="static/bootstrap-toggle.min.css">
 	<link rel="stylesheet" href="static/open-iconic-bootstrap.min.css">
-	<link rel="stylesheet" href="static/custom.css?ver=1.18.1a">
+	<link href="static/open-iconic/css/open-iconic.css" rel="stylesheet">
+	<link rel="stylesheet" href="static/custom.css?ver=1.18.1c">
 
 	<script src="static/jquery-3.6.0.min.js"></script>
 	<script src="static/jquery-ui.sortable.min.js"></script>
@@ -17,7 +18,8 @@
 	<script src="static/bootstrap.min.js"></script>
 	<script src="static/bootstrap-toggle.min.js"></script>
 	<script src="static/rangy-core.min.js"></script>
-	<script src="static/application.js?ver=1.18.1d"></script>
+	<script src="static/application.js?ver=1.18.1f"></script>
+	<script src="static/favicon.js"></script>
 </head>
 <body>
 	<input type="file" id="remote-save-select" accept="application/json" style="display:none">
@@ -33,6 +35,11 @@
 						</button>
 						<div class="collapse navbar-collapse" id="navbarNavDropdown">
 							<ul class="nav navbar-nav">
+								{% if not hide_ai_menu %}
+								<li class="nav-item">
+									<a class="nav-link" href="#" id="btn_loadmodel">AI</a>
+								</li>
+								{% endif %}
 								<li class="nav-item dropdown">
 									<a class="nav-link dropdown-toggle" href="#" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">New Game</a>
 									<div class="dropdown-menu">
@@ -86,6 +93,7 @@
 				</div>
 				<div id="connectstatusdiv" class="flex-row-container">
 					<span id="connectstatus" class="color_orange flex-row">Waiting for connection...</span>
+					<div class="layer-container status-container flex-push-left" style="color: #FFFFFF;" id="runtime"></div>
 					<div class="layer-container status-container flex-push-right">
 						<span class="oi oi-puzzle-piece statusicon layer-bottom" aria-hidden="true">
 							<div class="statustext statustext-wide">
@@ -108,6 +116,11 @@
 		</div>
 		<div class="row" id="formatmenu">
 		</div>
+
+		<div id="token_prob_menu" class="row hidden">
+			<div id="token_prob_container"></div>
+		</div>
+
 		<div class="layer-container">
 			<div class="layer-bottom row" id="gamescreen">
 				<span id="gametext" contenteditable="true"><p>...</p></span>
@@ -141,7 +154,7 @@
 				<div id="inputrowmode">
 					<button type="button" class="btn btn-secondary hidden" id="btnmode">Mode:<br/><b id="btnmode_label">Story</b></button>
 				</div>
-				<div id="inputrowleft">
+				<div id="inputrowleft" class="tokens-counted">
 					<textarea class="form-control" id="input_text" placeholder="Enter text here"></textarea>
 				</div>
 				<div id="inputrowright">
@@ -154,7 +167,7 @@
 						<div class="anotelabel no-padding">
 							Author's Note
 						</div>
-						<div class="anotefield">
+						<div class="anotefield tokens-counted">
 							<textarea class="form-control" placeholder="Author's Note" id="anoteinput"></textarea>
 						</div>
 					</div>
@@ -195,7 +208,7 @@
 		</div>
 	</div>
 	<div class="hidden" id="popupcontainer">
-		<div id="popup">
+		<div id="popup_old">
 			<div id="popuptitlebar">
 				<div id="popuptitletext">Select an Adventure to Import</div>
 			</div>
@@ -252,7 +265,7 @@
 		</div>
 	</div>
 	<div class="popupcontainer hidden" id="loadcontainer">
-		<div id="loadpopup">
+		<div class="loadpopup" id="loadpopup">
 			<div class="popuptitlebar">
 				<div class="popuptitletext">Select A Story To Load</div>
 			</div>
@@ -268,10 +281,64 @@
 			</div>
 		</div>
 	</div>
+	<div class="popupcontainer hidden" id="loadmodelcontainer">
+		<div class="loadpopup">
+			<div class="popuptitlebar">
+				<div class="popuptitletext">Select A Model To Load</div>
+			</div>
+			<div id="loadmodellistbreadcrumbs">
+				
+			</div>
+			<div id="loadmodellistcontent" style="overflow: auto; height: 300px;">
+			</div>
+			<div class="popupfooter">
+				<input class="form-control hidden" type="text" placeholder="Enter the URL of the server (For example a trycloudflare link)" id="modelurl" onchange="check_enable_model_load()">
+				<input class="form-control hidden" type="text" placeholder="key" id="modelkey" onblur="socket.send({'cmd': 'OAI_Key_Update', 'key': $('#modelkey')[0].value});">
+				<input class="form-control hidden" type="text" placeholder="Model Path or Hugging Face Name" id="custommodelname" menu="" onblur="socket.send({'cmd': 'selectmodel', 'data': $(this).attr('menu'), 'path_modelname': $('#custommodelname')[0].value});">
+			</div>
+			<div class="popupfooter">
+				<select class="form-control hidden" id="oaimodel"><option value="">Select Model(s)</option></select>
+			</div>
+			<div class="popupfooter hidden" id=modellayers>
+				<div class='settingitem' style="width:100%">
+					<div class='settinglabel'>
+						<div class="justifyleft">
+							GPU/Disk Layers
+							<span class="helpicon">?
+								<span class="helptext">Number of layers to assign to GPUs and to disk cache. Remaining layers will be put into CPU RAM.</span>
+							</span>
+						</div>
+						<div class="justifyright" id="gpu_layers_current">0</div>
+					</div>
+					<div id=model_layer_bars style="color: white">
+						
+					</div>
+					<input type=hidden id='gpu_count' value=0/>
+					<div class="settingminmax">
+						<div class="justifyleft">
+							0
+						</div>
+						<div class="justifyright" id="gpu_layers_max">
+							24
+						</div>
+					</div>
+				</div>
+			</div>
+			<div class="popupfooter">
+				<button type="button" class="btn btn-primary" id="btn_loadmodelaccept">Load</button>
+				<button type="button" class="btn btn-primary" id="btn_loadmodelclose">Cancel</button>
+				<div class="box flex-push-right hidden" id=use_gpu_div>
+					<input type="checkbox" data-toggle="toggle" data-onstyle="success" id="use_gpu" checked>
+					<div class="box-label">Use GPU</div>
+				</div>
+			</div>
+		</div>
+	</div>
 	<div class="popupcontainer hidden" id="spcontainer">
 		<div id="sppopup">
 			<div class="popuptitlebar">
 				<div class="popuptitletext">Select A Soft Prompt To Use</div>
+				<button class="btn btn-primary" onclick="socket.emit('show_folder_soft_prompt', {});"><span class="oi" style="color: white;" data-glyph="folder"></span></button>
 			</div>
 			<div id="splistcontent">
 			</div>
@@ -285,6 +352,7 @@
 		<div id="uspopup">
 			<div class="popuptitlebar">
 				<div class="popuptitletext">Select userscripts to load; drag-and-drop to reorder</div>
+				<button class="btn btn-primary" onclick="socket.emit('show_folder_usersripts', {});"><span class="oi" style="color: white;" data-glyph="folder"></span></button>
 			</div>
 			<div class="usheadergrid">
 				<div>[AVAILABLE]</div>
@@ -367,6 +435,19 @@
 			</div>
 		</div>
 	</div>
+	<div class="popupcontainer hidden flex" id="showmodelnamecontainer" style="center;">
+		<div class="loadpopup">
+			<div class="popuptitlebar" style="width:50% center;">
+				<div class="popuptitletext">Model Info</div>
+			</div>
+			<div id=showmodelnamecontent style="width:50%;">
+				Model Info Missing
+			</div>
+			<div class="popupfooter" style="width:50% center;">
+				
+			</div>
+		</div>
+	</div>
 	<div class="popupcontainer hidden" id="rndgamecontainer">
 		<div id="rspopup">
 			<div class="popuptitlebar">
@@ -393,5 +474,26 @@
 			</div>
 		</div>
 	</div>
+	
+	<!------------- Pop-Up ------------------------------->
+	<div class="popupcontainer hidden" id="popup">
+		<div class="new_popup">
+			<div style="height:100%;">
+				<div class="title" id="popup_title">
+					Popup Title
+				</div>
+				<div id="popup_breadcrumbs"></div>
+				<div class="popup_list_area" id="popup_list"></div>
+				<div class="popup_load_cancel hidden" id="popup_upload">
+				<input type=file id="popup_upload_file">
+			</div>
+			<div style="background-color: black">Drag file(s) above or click here to Upload File<input id="popup_upload_input" type=file onchange="upload_file(this)"></div>
+			<div class="popup_load_cancel" id="popup_load_cancel">
+					<button class="btn btn-secondary popup_load_cancel_button" id="popup_accept">Load</button>
+					<button class="btn btn-primary popup_load_cancel_button" id="popup_cancel" onclick='document.getElementById("popup").classList.add("hidden");'>Cancel</button>
+				</div>
+			</div>
+		</div>
+	</div>
 </body>
 </html>

templates/swagger-ui.html

@@ -0,0 +1,35 @@
+{# This is the HTML template for Swagger UI (the GUI for the API documentation at /api/latest/docs) #}
+<!DOCTYPE html>
+<html lang="en">
+	<head>
+		<title>KoboldAI API</title>
+		<meta charset="UTF-8">
+		<link rel="stylesheet" type="text/css" href="/static/swagger-ui/swagger-ui.css" />
+		<link rel="stylesheet" type="text/css" href="/static/swagger-ui/index.css" />
+		<script>
+			if (window.matchMedia && window.matchMedia("(prefers-color-scheme: dark)").matches) document.write('<link rel="stylesheet" type="text/css" href="/static/swagger-ui/SwaggerDark.css" />');
+		</script>
+	</head>
+	<body>
+		<div id="swagger-ui"></div>
+		<script src="/static/swagger-ui/swagger-ui-bundle.js" charset="UTF-8"></script>
+		<script>
+			window.onload = function() {
+				window.ui = SwaggerUIBundle({
+					url: "{{ url }}",
+					oauth2RedirectUrl: "/static/swagger-ui/oauth2-redirect.html",
+					dom_id: "#swagger-ui",
+					deepLinking: true,
+					defaultModelsExpandDepth: 0,  // Causes the "Schemas" section at the bottom to be collapsed by default
+					presets: [
+						SwaggerUIBundle.presets.apis
+					],
+					plugins: [
+						SwaggerUIBundle.plugins.DownloadUrl
+					],
+					layout: "BaseLayout"
+				});
+			};
+		</script>
+	</body>
+</html>

test_aiserver.py

@@ -0,0 +1,226 @@
+import pytest, time
+import aiserver
+
+#Test Model List:
+test_models = [
+                ('EleutherAI/gpt-neo-1.3B', {'key': False, 'gpu': False, 'layer_count': 24, 'breakmodel': True, 'url': False}), 
+                ('gpt2', {'key': False, 'gpu': False, 'layer_count': 12, 'breakmodel': True, 'url': False}), 
+                ('facebook/opt-350m', {'key': False, 'gpu': False, 'layer_count': 24, 'breakmodel': True, 'url': False})
+              ]
+
+@pytest.fixture
+def client_data():
+    app = aiserver.app
+    #app.test_client_class = FlaskLoginClient
+    client_conn = app.test_client()
+    socketio_client = aiserver.socketio.test_client(app, flask_test_client=client_conn)
+    #Clear out the connection message
+    response = socketio_client.get_received()
+    return (client_conn, app, socketio_client)
+
+
+def get_model_menu(model):
+    for menu in aiserver.model_menu:
+        for item in aiserver.model_menu[menu]:
+            if item[1] == model:
+                for main_menu_line in aiserver.model_menu['mainmenu']:
+                    if main_menu_line[1] == menu:
+                        return (menu, main_menu_line, item)
+    return None
+    
+def generate_story_data(client_data):
+    (client, app, socketio_client) = client_data
+    socketio_client.emit('message',{'cmd': 'submit', 'allowabort': False, 'actionmode': 0, 'chatname': None, 'data': ''})
+    
+    #wait until the game state turns back to start
+    state = 'wait'
+    new_text = None
+    start_time = time.time()
+    timeout = time.time() + 60*1
+    while state == 'wait':
+        if time.time() > timeout:
+            break
+        responses = socketio_client.get_received()
+        for response in responses:
+            response = response['args'][0]
+            print(response)
+            if response['cmd'] == 'setgamestate':
+                state = response['data']
+            elif response['cmd'] == 'updatechunk' or response['cmd'] == 'genseqs':
+                new_text = response['data']
+        time.sleep(0.1)
+    
+    assert new_text is not None
+
+def test_basic_connection(client_data):
+    (client, app, socketio_client) = client_data
+    response = client.get("/")
+    assert response.status_code == 200
+
+def test_load_story_from_web_ui(client_data):
+    (client, app, socketio_client) = client_data
+    
+    #List out the stories and make sure we have the sample story
+    socketio_client.emit('message',{'cmd': 'loadlistrequest', 'data': ''})
+    response = socketio_client.get_received()[0]['args'][0]['data']
+    found_sample_story = False
+    for story in response:
+        if story['name'] == 'sample_story':
+            found_sample_story = True
+    assert found_sample_story
+    
+    #Click on the sample story, then click load
+    socketio_client.emit('message',{'cmd': 'loadselect', 'data': 'sample_story'})
+    socketio_client.emit('message',{'cmd': 'loadrequest', 'data': ''})
+    
+    #Wait until we get the data back from the load
+    loaded_story = False
+    timeout = time.time() + 60*2
+    while not loaded_story:
+        if time.time() > timeout:
+            break
+        responses = socketio_client.get_received()
+        for response in responses:
+            response = response['args'][0]
+            if 'cmd' not in response:
+                print(response)
+                assert False
+            if response['cmd'] == 'updatescreen':
+                loaded_story = True
+                story_text = response['data']
+                break
+    assert loaded_story
+    
+    #Verify that it's the right story data
+    assert story_text == '<chunk n="0" id="n0" tabindex="-1">Niko the kobold stalked carefully down the alley, his small scaly figure obscured by a dusky cloak that fluttered lightly in the cold winter breeze. Holding up his tail to keep it from dragging in the dirty snow that covered the cobblestone, he waited patiently for the butcher to turn his attention from his stall so that he could pilfer his next meal: a tender-looking</chunk><chunk n="1" id="n1" tabindex="-1"> chicken. He crouched just slightly as he neared the stall to ensure that no one was watching, not that anyone would be dumb enough to hassle a small kobold. What else was there for a lowly kobold to</chunk><chunk n="2" id="n2" tabindex="-1"> do in a city? All that Niko needed to know was</chunk><chunk n="3" id="n3" tabindex="-1"> where to find the chicken and then how to make off with it.<br/><br/>A soft thud caused Niko to quickly lift his head. Standing behind the stall where the butcher had been cutting his chicken,</chunk>'  
+
+@pytest.mark.parametrize("model, expected_load_options", test_models)
+def test_load_model_from_web_ui(client_data, model, expected_load_options):
+    (client, app, socketio_client) = client_data
+    
+    #Clear out any old messages
+    response = socketio_client.get_received()
+    
+    (menu, menu_line, model_line) = get_model_menu(model)
+    
+    #Send the ai load model menu option
+    socketio_client.emit('message',{'cmd': 'list_model', 'data': 'mainmenu'})
+    response = socketio_client.get_received()[0]['args'][0]['data']
+    assert menu_line in response
+    
+    #Send the click model menu option
+    socketio_client.emit('message',{'cmd': 'list_model', 'data': menu, 'pretty_name': ""})
+    response = socketio_client.get_received()[0]['args'][0]['data']
+    assert model_line in response
+    
+    #Click the model
+    socketio_client.emit('message',{'cmd': 'selectmodel', 'data': model})
+    response = socketio_client.get_received()[0]['args'][0]
+    #Check that we're getting the right load options
+    print(response)
+    assert response['key'] == expected_load_options['key']
+    assert response['gpu'] == expected_load_options['gpu']
+    assert response['layer_count'] == expected_load_options['layer_count']
+    assert response['breakmodel'] == expected_load_options['breakmodel']
+    assert response['url'] == expected_load_options['url']
+    
+    #Now send the load 
+    socketio_client.emit('message',{'cmd': 'load_model', 'use_gpu': True, 'key': '', 'gpu_layers': str(expected_load_options['layer_count']), 'disk_layers': '0', 'url': '', 'online_model': ''})
+    #wait until the game state turns back to start
+    state = 'wait'
+    start_time = time.time()
+    timeout = time.time() + 60*2
+    while state == 'wait':
+        if time.time() > timeout:
+            break
+        responses = socketio_client.get_received()
+        for response in responses:
+            response = response['args'][0]
+            if response['cmd'] == 'setgamestate':
+                state = response['data']
+        time.sleep(0.1)
+    
+    #Give it a second to get all of the settings, etc and clear out the messages
+    responses = socketio_client.get_received()
+    
+    #check the model info to see if it's loaded
+    socketio_client.emit('message',{'cmd': 'show_model', 'data': ''})
+    response = socketio_client.get_received()[0]['args'][0]
+    assert response == {'cmd': 'show_model_name', 'data': model}
+    
+    generate_story_data(client_data)
+  
+def test_load_GooseAI_from_web_ui(client_data):
+    
+    pytest.skip("unsupported configuration")
+
+@pytest.mark.parametrize("model, expected_load_options", test_models)
+def test_load_model_from_command_line(client_data, model, expected_load_options):
+    (client, app, socketio_client) = client_data
+    
+    #Clear out any old messages
+    response = socketio_client.get_received()
+    
+    (menu, menu_line, model_line) = get_model_menu(model)
+    
+    aiserver.general_startup("--model {}".format(model))
+    
+    aiserver.load_model(initial_load=True)
+    
+    #check the model info to see if it's loaded
+    socketio_client.emit('message',{'cmd': 'show_model', 'data': ''})
+    response = socketio_client.get_received()[0]['args'][0]
+    assert response == {'cmd': 'show_model_name', 'data': model}
+    
+    generate_story_data(client_data)
+
+def test_back_redo(client_data):
+    (client, app, socketio_client) = client_data
+    
+    
+    #Make sure we have known story in the ui
+    test_load_story_from_web_ui(client_data)
+    
+    #Clear out any old messages
+    response = socketio_client.get_received()
+    
+    #run a back action
+    socketio_client.emit('message',{'cmd': 'back', 'data': ''})
+    response = socketio_client.get_received()[0]['args'][0]
+    assert response == {'cmd': 'removechunk', 'data': 3}
+    
+    #Run a redo action
+    socketio_client.emit('message',{'cmd': 'redo', 'data': ''})
+    response = socketio_client.get_received()[0]['args'][0]
+    assert response == {'cmd': 'updatechunk', 'data': {'index': 3, 'html': '<chunk n="3" id="n3" tabindex="-1"> where to find the chicken and then how to make off with it.<br/><br/>A soft thud caused Niko to quickly lift his head. Standing behind the stall where the butcher had been cutting his chicken,</chunk>'}}
+    
+    #Go all the way back, then all the way forward
+    socketio_client.emit('message',{'cmd': 'back', 'data': ''})
+    response = socketio_client.get_received()[0]['args'][0]
+    assert response == {'cmd': 'removechunk', 'data': 3}
+    socketio_client.emit('message',{'cmd': 'back', 'data': ''})
+    response = socketio_client.get_received()[0]['args'][0]
+    assert response == {'cmd': 'removechunk', 'data': 2}
+    socketio_client.emit('message',{'cmd': 'back', 'data': ''})
+    response = socketio_client.get_received()[0]['args'][0]
+    assert response == {'cmd': 'removechunk', 'data': 1}
+    socketio_client.emit('message',{'cmd': 'back', 'data': ''})
+    response = socketio_client.get_received()[0]['args'][0]
+    assert response == {'cmd': 'errmsg', 'data': 'Cannot delete the prompt.'}
+    socketio_client.emit('message',{'cmd': 'redo', 'data': ''})
+    response = socketio_client.get_received()
+    assert response == [{'name': 'from_server', 'args': [{'cmd': 'updatescreen', 'gamestarted': True, 'data': '<chunk n="0" id="n0" tabindex="-1">Niko the kobold stalked carefully down the alley, his small scaly figure obscured by a dusky cloak that fluttered lightly in the cold winter breeze. Holding up his tail to keep it from dragging in the dirty snow that covered the cobblestone, he waited patiently for the butcher to turn his attention from his stall so that he could pilfer his next meal: a tender-looking</chunk><chunk n="1" id="n1" tabindex="-1"> chicken. He crouched just slightly as he neared the stall to ensure that no one was watching, not that anyone would be dumb enough to hassle a small kobold. What else was there for a lowly kobold to</chunk>'}], 'namespace': '/'}, 
+                        {'name': 'from_server', 'args': [{'cmd': 'texteffect', 'data': 1}], 'namespace': '/'}]
+    socketio_client.emit('message',{'cmd': 'redo', 'data': ''})
+    response = socketio_client.get_received()
+    assert response == [{'name': 'from_server', 'args': [{'cmd': 'updatechunk', 'data': {'index': 2, 'html': '<chunk n="2" id="n2" tabindex="-1"> do in a city? All that Niko needed to know was</chunk>'}}], 'namespace': '/'}, 
+                        {'name': 'from_server', 'args': [{'cmd': 'texteffect', 'data': 2}], 'namespace': '/'}]
+    socketio_client.emit('message',{'cmd': 'redo', 'data': ''})
+    response = socketio_client.get_received()
+    assert response == [{'name': 'from_server', 'args': [{'cmd': 'updatechunk', 'data': {'index': 3, 'html': '<chunk n="3" id="n3" tabindex="-1"> where to find the chicken and then how to make off with it.<br/><br/>A soft thud caused Niko to quickly lift his head. Standing behind the stall where the butcher had been cutting his chicken,</chunk>'}}], 'namespace': '/'}, 
+                        {'name': 'from_server', 'args': [{'cmd': 'texteffect', 'data': 3}], 'namespace': '/'}]
+    
+    
+    
+
+    

torch_lazy_loader.py

@@ -50,8 +50,13 @@ import itertools
 import zipfile
 import pickle
 import torch
+import numpy as np
+import collections
+import _codecs
+import utils
+import os
 from torch.nn import Module
-from typing import Any, Callable, Dict, Optional, Tuple, Union
+from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
 
 
 _EXTRA_STATE_KEY_SUFFIX = '_extra_state'
@@ -89,12 +94,16 @@ class LazyTensor:
     def __repr__(self):
         return self.__view(repr)
 
-    def materialize(self, checkpoint: Union[zipfile.ZipFile, zipfile.ZipExtFile], map_location=None, no_grad=True) -> torch.Tensor:
+    def materialize(self, checkpoint: Union[zipfile.ZipFile, zipfile.ZipExtFile], map_location=None, no_grad=True, filename="pytorch_model.bin") -> torch.Tensor:
+        filename = os.path.basename(os.path.normpath(filename)).split('.')[0]
         size = reduce(lambda x, y: x * y, self.shape, 1)
         dtype = self.dtype
         nbytes = size if dtype is torch.bool else size * ((torch.finfo if dtype.is_floating_point else torch.iinfo)(dtype).bits >> 3)
         if isinstance(checkpoint, zipfile.ZipFile):
-            f = checkpoint.open(f"archive/data/{self.key}", "r")
+            try:
+                f = checkpoint.open(f"archive/data/{self.key}", "r")
+            except:
+                f = checkpoint.open(f"{filename}/data/{self.key}", "r")
             f.read(self.seek_offset)
         else:
             f = checkpoint
@@ -110,8 +119,50 @@ class LazyTensor:
         tensor._backward_hooks = self.backward_hooks
         return tensor
 
+class RestrictedUnpickler(pickle.Unpickler):
+    def original_persistent_load(self, saved_id):
+        return super().persistent_load(saved_id)
 
-class _LazyUnpickler(pickle.Unpickler):
+    def forced_persistent_load(self, saved_id):
+        if saved_id[0] != "storage":
+            raise pickle.UnpicklingError("`saved_id[0]` must be 'storage'")
+        return self.original_persistent_load(saved_id)
+
+    def find_class(self, module, name):
+        if module == "collections" and name == "OrderedDict":
+            return collections.OrderedDict
+        elif module == "torch._utils" and name == "_rebuild_tensor_v2":
+            return torch._utils._rebuild_tensor_v2
+        elif module == "torch" and name in (
+            "DoubleStorage",
+            "FloatStorage",
+            "HalfStorage",
+            "LongStorage",
+            "IntStorage",
+            "ShortStorage",
+            "CharStorage",
+            "ByteStorage",
+            "BoolStorage",
+            "BFloat16Storage",
+        ):
+            return getattr(torch, name)
+        elif module == "numpy.core.multiarray" and name == "scalar":
+            return np.core.multiarray.scalar
+        elif module == "numpy" and name == "dtype":
+            return np.dtype
+        elif module == "_codecs" and name == "encode":
+            return _codecs.encode
+        else:
+            # Forbid everything else.
+            qualified_name = name if module == "__builtin__" else f"{module}.{name}"
+            raise pickle.UnpicklingError(f"`{qualified_name}` is forbidden; the model you are loading probably contains malicious code")
+
+    def load(self, *args, **kwargs):
+        self.original_persistent_load = getattr(self, "persistent_load", pickle.Unpickler.persistent_load)
+        self.persistent_load = self.forced_persistent_load
+        return super().load(*args, **kwargs)
+
+class _LazyUnpickler(RestrictedUnpickler):
     lazy_loaded_storages: Dict[str, LazyTensor]
 
     def __init__(self, *args, **kwargs):
@@ -126,7 +177,6 @@ class _LazyUnpickler(pickle.Unpickler):
         return LazyTensor(storage_type, key, location)
 
     def load(self, *args, **kwargs):
-        self.persistent_load = self.forced_persistent_load
         retval = super().load(*args, **kwargs)
         self.lazy_loaded_storages = {}
         return retval
@@ -213,15 +263,32 @@ def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, miss
 
 
 @contextlib.contextmanager
-def use_lazy_torch_load(enable=True, callback: Optional[Callable] = None, dematerialized_modules=False):
+def use_custom_unpickler(unpickler: Type[pickle.Unpickler] = RestrictedUnpickler):
+    try:
+        old_unpickler = pickle.Unpickler
+        pickle.Unpickler = unpickler
+
+        old_pickle_load = pickle.load
+
+        def new_pickle_load(*args, **kwargs):
+            return pickle.Unpickler(*args, **kwargs).load()
+
+        pickle.load = new_pickle_load
+
+        yield
+
+    finally:
+        pickle.Unpickler = old_unpickler
+        pickle.load = old_pickle_load
+
+@contextlib.contextmanager
+def use_lazy_torch_load(enable=True, callback: Optional[Callable] = None, dematerialized_modules=False, use_accelerate_init_empty_weights=False):
     if not enable:
-        yield False
+        with use_custom_unpickler(RestrictedUnpickler):
+            yield False
         return
 
     try:
-        old_unpickler = pickle.Unpickler
-        pickle.Unpickler = _LazyUnpickler
-
         old_rebuild_tensor = torch._utils._rebuild_tensor
         torch._utils._rebuild_tensor = _rebuild_tensor
 
@@ -236,33 +303,41 @@ def use_lazy_torch_load(enable=True, callback: Optional[Callable] = None, demate
         torch.load = torch_load
 
         if dematerialized_modules:
-            old_linear_init = torch.nn.Linear.__init__
-            old_embedding_init = torch.nn.Embedding.__init__
-            old_layernorm_init = torch.nn.LayerNorm.__init__
+            if use_accelerate_init_empty_weights and utils.HAS_ACCELERATE:
+                import accelerate
+                init_empty_weights = accelerate.init_empty_weights()
+                init_empty_weights.__enter__()
+            else:
+                old_linear_init = torch.nn.Linear.__init__
+                old_embedding_init = torch.nn.Embedding.__init__
+                old_layernorm_init = torch.nn.LayerNorm.__init__
 
-            def linear_init(self, *args, device=None, **kwargs):
-                return old_linear_init(self, *args, device="meta", **kwargs)
+                def linear_init(self, *args, device=None, **kwargs):
+                    return old_linear_init(self, *args, device="meta", **kwargs)
 
-            def embedding_init(self, *args, device=None, **kwargs):
-                return old_embedding_init(self, *args, device="meta", **kwargs)
+                def embedding_init(self, *args, device=None, **kwargs):
+                    return old_embedding_init(self, *args, device="meta", **kwargs)
 
-            def layernorm_init(self, *args, device=None, **kwargs):
-                return old_layernorm_init(self, *args, device="meta", **kwargs)
+                def layernorm_init(self, *args, device=None, **kwargs):
+                    return old_layernorm_init(self, *args, device="meta", **kwargs)
 
-            torch.nn.Linear.__init__ = linear_init
-            torch.nn.Embedding.__init__ = embedding_init
-            torch.nn.LayerNorm.__init__ = layernorm_init
-            old_load_from_state_dict = torch.nn.Module._load_from_state_dict
-            torch.nn.Module._load_from_state_dict = _load_from_state_dict
+                torch.nn.Linear.__init__ = linear_init
+                torch.nn.Embedding.__init__ = embedding_init
+                torch.nn.LayerNorm.__init__ = layernorm_init
+                old_load_from_state_dict = torch.nn.Module._load_from_state_dict
+                torch.nn.Module._load_from_state_dict = _load_from_state_dict
 
-        yield True
+        with use_custom_unpickler(_LazyUnpickler):
+            yield True
 
     finally:
-        pickle.Unpickler = old_unpickler
         torch._utils._rebuild_tensor = old_rebuild_tensor
         torch.load = old_torch_load
         if dematerialized_modules:
-            torch.nn.Linear.__init__ = old_linear_init
-            torch.nn.Embedding.__init__ = old_embedding_init
-            torch.nn.LayerNorm.__init__ = old_layernorm_init
-            torch.nn.Module._load_from_state_dict = old_load_from_state_dict
+            if use_accelerate_init_empty_weights and utils.HAS_ACCELERATE:
+                init_empty_weights.__exit__(None, None, None)
+            else:
+                torch.nn.Linear.__init__ = old_linear_init
+                torch.nn.Embedding.__init__ = old_embedding_init
+                torch.nn.LayerNorm.__init__ = old_layernorm_init
+                torch.nn.Module._load_from_state_dict = old_load_from_state_dict

tpu_mtj_backend.py

@@ -46,7 +46,7 @@ from jax.experimental import maps
 import jax.numpy as jnp
 import numpy as np
 import haiku as hk
-from transformers import AutoTokenizer, GPT2TokenizerFast, AutoModelForCausalLM, GPTNeoForCausalLM
+from transformers import AutoTokenizer, GPT2Tokenizer, AutoModelForCausalLM, GPTNeoForCausalLM
 from tokenizers import Tokenizer
 from mesh_transformer.checkpoint import read_ckpt_lowmem
 from mesh_transformer.transformer_shard import CausalTransformer, CausalTransformerShard, PlaceholderTensor
@@ -55,6 +55,31 @@ from mesh_transformer.util import to_bf16
 
 params: Dict[str, Any] = {}
 
+__seed = random.randrange(2**64)
+rng = random.Random(__seed)
+
+
+def get_rng_seed():
+    return __seed
+
+def set_rng_seed(seed: int):
+    global __seed, rng
+    rng = random.Random(seed)
+    __seed = seed
+    return seed
+
+def randomize_rng_seed():
+    return set_rng_seed(random.randrange(2**64))
+
+def get_rng_state():
+    return rng
+
+def set_rng_state(state):
+    global rng
+    rng = state
+
+def new_rng_state(seed: int):
+    return random.Random(seed)
 
 def warper_callback(logits) -> np.array:
     raise NotImplementedError("`tpu_mtj_backend.warper_callback()` needs to be defined")
@@ -167,7 +192,7 @@ def apply_repetition_penalty_dynamic(logits, tokens, repetition_penalty, generat
     logits[tokens] = penalty_logits
     return logits
 
-def kobold_sample_dynamic(key, logits, sampler_order: Optional[np.ndarray] = None, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typical=1.0, top_a=0.0):
+def kobold_sample_dynamic(key, logits, rpargs, sampler_order: Optional[np.ndarray] = None, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typical=1.0, top_a=0.0):
     '''
     This gets called by generate_loop_fn to apply a series of 6 filters
     to the logits (top-k, then top-a, then top-p, then TFS, then typical, then temperature)
@@ -303,6 +328,7 @@ def kobold_sample_dynamic(key, logits, sampler_order: Optional[np.ndarray] = Non
         if k == 3 and tfs < 1.0: logits = tail_free_filter(logits)
         if k == 4 and typical < 1.0: logits = typical_filter(logits)
         if k == 5 and temp != 1.0: logits = temp_filter(logits)
+        if k == 6 and rpargs[1] != 1.0: logits = apply_repetition_penalty_dynamic(logits, *rpargs)
     # Finally, pick one token using the softmax thingy again (it gives
     # an array whose elements sum to 1 so it can be used nicely as a
     # probability distribution)
@@ -353,7 +379,7 @@ def apply_repetition_penalty_static(logits, tokens, repetition_penalty, generate
     # positions in the logits array
     return logits.at[tokens].set(penalty_logits)
 
-def kobold_sample_static(key, logits, sampler_order: Optional[np.ndarray] = None, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typical=1.0, top_a=0.0):
+def kobold_sample_static(key, logits, rpargs, sampler_order: Optional[np.ndarray] = None, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typical=1.0, top_a=0.0):
     '''
     This gets called by generate_loop_fn to apply a series of 6 filters
     to the logits (top-k, then top-a, then top-p, then TFS, then typical, then temperature)
@@ -488,6 +514,7 @@ def kobold_sample_static(key, logits, sampler_order: Optional[np.ndarray] = None
         logits = jax.lax.cond(jnp.logical_and(k == 3, tfs < 1.0), tail_free_filter, lambda x: x, logits)
         logits = jax.lax.cond(jnp.logical_and(k == 4, typical < 1.0), typical_filter, lambda x: x, logits)
         logits = jax.lax.cond(jnp.logical_and(k == 5, temp != 1.0), temp_filter, lambda x: x, logits)
+        logits = jax.lax.cond(jnp.logical_and(k == 6, rpargs[1] != 1.0), lambda x: apply_repetition_penalty_static(*x), lambda x: x[0], (logits, *rpargs))
     # Finally, pick one token using the softmax thingy again (it gives
     # an array whose elements sum to 1 so it can be used nicely as a
     # probability distribution)
@@ -504,17 +531,6 @@ def sample_func(data, key, numseqs_aux, badwords, repetition_penalty, generated_
         # Get the pseudo-random number generator key that will
         # be used by kobold_sample_dynamic to randomly pick a token
         sample_key, new_key = jax.random.split(sample_key, num=2)
-        # Apply repetition penalty to all tokens that are
-        # currently inside the "generated" array
-        logits = apply_repetition_penalty_dynamic(
-            logits,
-            generated,
-            repetition_penalty,
-            generated_index, 
-            gen_length,
-            rpslope,
-            rprange,
-        )
         # Remove any tokens in the badwords list by setting
         # their logits to negative infinity which effectively
         # makes their probabilities of being chosen zero
@@ -526,6 +542,14 @@ def sample_func(data, key, numseqs_aux, badwords, repetition_penalty, generated_
         next_token = kobold_sample_dynamic(
             sample_key,
             logits,
+            (
+                generated,
+                repetition_penalty,
+                generated_index, 
+                gen_length,
+                rpslope,
+                rprange,
+            ),
             **sampler_options,
         )
         # Remember what token was picked
@@ -597,18 +621,6 @@ class PenalizingCausalTransformer(CausalTransformer):
                     assert logits.shape == (1, config["n_vocab"])
                     # Flatten it into a 1D array to make it easier to use
                     logits = logits[0]
-                    # Apply repetition penalty to all tokens that are
-                    # currently inside the "generated" array
-                    if repetition_penalty is not None:
-                        logits = apply_repetition_penalty_static(
-                            logits,
-                            generated,
-                            repetition_penalty,
-                            generated_index,
-                            gen_length,
-                            rpslope,
-                            rprange,
-                        )
                     # Remove any tokens in the badwords list by setting
                     # their logits to negative infinity which effectively
                     # makes their probabilities of being chosen zero
@@ -620,6 +632,14 @@ class PenalizingCausalTransformer(CausalTransformer):
                     next_token = kobold_sample_static(
                         sample_key,
                         logits,
+                        (
+                            generated,
+                            repetition_penalty,
+                            generated_index,
+                            gen_length,
+                            rpslope,
+                            rprange,
+                        ),
                         **sampler_options,
                     )
                     # Remember what token was picked
@@ -735,7 +755,7 @@ class PenalizingCausalTransformer(CausalTransformer):
         assert not return_logits
         assert gen_length.ndim == 1
         assert soft_embeddings is not None
-        key = hk.PRNGSequence(random.randint(0, 2 ** 60))
+        key = hk.PRNGSequence(rng.randint(0, 2 ** 60))
         batch_size = ctx.shape[0]
         self.batch_size = batch_size
         _numseqs_aux = jnp.empty((batch_size, numseqs), dtype=np.uint32)
@@ -783,7 +803,7 @@ class PenalizingCausalTransformer(CausalTransformer):
         return sample_data, n_generated, regeneration_required, halt
     def generate_static(self, ctx, ctx_length, gen_length, numseqs, sampler_options, return_logits=False, soft_embeddings=None):
         assert not return_logits
-        key = hk.PRNGSequence(random.randint(0, 2 ** 60))
+        key = hk.PRNGSequence(rng.randint(0, 2 ** 60))
         batch_size = ctx.shape[0]
         self.batch_size = batch_size
         started_compiling_callback()
@@ -854,6 +874,9 @@ def infer_static(
     maps.thread_resources.env = thread_resources_env
     if sampler_order is None:
         sampler_order = utils.default_sampler_order.copy()
+    sampler_order = sampler_order[:]
+    if len(sampler_order) < 7:  # Add repetition penalty at beginning if it's not present
+        sampler_order = [6] + sampler_order
     sampler_order = np.uint32(sampler_order)
     total_batch = 1
     tokens = context
@@ -932,6 +955,7 @@ def read_neox_checkpoint(state, path, config, checkpoint_shards=2):
 
     import torch
     import torch.utils.dlpack
+    import torch_lazy_loader
     from tqdm.auto import tqdm
 
     move_xmap = jax.experimental.maps.xmap(
@@ -973,8 +997,9 @@ def read_neox_checkpoint(state, path, config, checkpoint_shards=2):
             continue
         layer = checkpoint_layer - 2
         shards = []
-        for checkpoint_shard in range(checkpoint_shards):
-            shards.append(torch.load(path_template.format(layer=checkpoint_layer, shard=checkpoint_shard), map_location="cpu"))
+        with torch_lazy_loader.use_custom_unpickler(torch_lazy_loader.RestrictedUnpickler):
+            for checkpoint_shard in range(checkpoint_shards):
+                shards.append(torch.load(path_template.format(layer=checkpoint_layer, shard=checkpoint_shard), map_location="cpu"))
         for key in shards[0]:
             if key == "attention.rotary_emb.inv_freq":
                 continue
@@ -1024,8 +1049,13 @@ def read_neox_checkpoint(state, path, config, checkpoint_shards=2):
                 raise RuntimeError(error)
 
 
-def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpoint=False, **kwargs) -> None:
-    global thread_resources_env, seq, tokenizer, network, params
+def load_model(path: str, driver_version="tpu_driver_20221109", hf_checkpoint=False, socketio_queue=None, initial_load=False, logger=None, **kwargs) -> None:
+    global thread_resources_env, seq, tokenizer, network, params, pad_token_id
+
+    if "pad_token_id" in kwargs:
+        pad_token_id = kwargs["pad_token_id"]
+    elif "eos_token_id" in kwargs:
+        pad_token_id = kwargs["eos_token_id"]
 
     if not hasattr(vars, "sampler_order") or not vars.sampler_order:
         vars.sampler_order = utils.default_sampler_order.copy()
@@ -1042,7 +1072,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
         "pe_rotary_dims": 64,
         "seq": 2048,
         "cores_per_replica": 8,
-        "tokenizer_class": "GPT2TokenizerFast",
+        "tokenizer_class": "GPT2Tokenizer",
         "tokenizer": "gpt2",
     }
     params = kwargs
@@ -1060,7 +1090,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
             "pe_rotary_dims": 24,
             "seq": 2048,
             "cores_per_replica": 8,
-            "tokenizer_class": "GPT2TokenizerFast",
+            "tokenizer_class": "GPT2Tokenizer",
             "tokenizer": "gpt2",
         }
 
@@ -1118,6 +1148,10 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
         if param not in params:
             params[param] = default_params[param]
 
+    # Use an optimization that will allow us to avoid one extra transpose operation
+    if hf_checkpoint:
+        params["transposed_linear"] = True
+
     # Load tokenizer
     if vars.model == "TPUMeshTransformerGPTNeoX":
         tokenizer = Tokenizer.from_file(os.path.join(path, "20B_tokenizer.json"))
@@ -1161,10 +1195,6 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
     thread_resources_env = maps.ResourceEnv(maps.Mesh(devices, ('dp', 'mp')), ())
     maps.thread_resources.env = thread_resources_env
 
-    global shard_xmap, batch_xmap
-    shard_xmap = __shard_xmap()
-    batch_xmap = __batch_xmap(shard_dim=cores_per_replica)
-
     global badwords
     # These are the tokens that we don't want the AI to ever write
     badwords = jnp.array(vars.badwordsids).squeeze()
@@ -1210,6 +1240,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
     from tqdm.auto import tqdm
     import functools
 
+
     def callback(model_dict, f, **_):
         if callback.nested:
             return
@@ -1217,6 +1248,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
         with zipfile.ZipFile(f, "r") as z:
             try:
                 last_storage_key = None
+                zipfolder = os.path.basename(os.path.normpath(f)).split('.')[0]
                 f = None
                 current_offset = 0
                 if utils.current_shard == 0:
@@ -1249,7 +1281,10 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
                         last_storage_key = storage_key
                         if isinstance(f, zipfile.ZipExtFile):
                             f.close()
-                        f = z.open(f"archive/data/{storage_key}")
+                        try:
+                            f = z.open(f"archive/data/{storage_key}")
+                        except:
+                            f = z.open(f"{zipfolder}/data/{storage_key}")
                         current_offset = 0
                     if current_offset != model_dict[key].seek_offset:
                         f.read(model_dict[key].seek_offset - current_offset)
@@ -1274,23 +1309,25 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
                     if "divide_by_shards" in transforms:
                         tensor /= params["cores_per_replica"]
                     if "vocab_pad" in transforms:
-                        tensor = torch.nn.functional.pad(tensor, (0, 0, 0, params["n_vocab_padding"]))
-                    if "no_transpose" not in transforms and tensor.ndim == 2:
-                        tensor = tensor.T
+                        tensor = torch.nn.functional.pad(tensor, (0,) * (tensor.ndim * 2 - 1) + (params["n_vocab_padding"],))
+                    # We don't need to transpose linear module weights anymore because MTJ will do it for us if `transposed_linear` is set to True in the config
+                    #if "no_transpose" not in transforms and tensor.ndim == 2:
+                    #    tensor = tensor.T
                     tensor.unsqueeze_(0)
-                    if tensor.dtype is torch.float16 or tensor.dtype is torch.float32:
-                        tensor = tensor.bfloat16()
+                    
 
                     # Shard the tensor so that parts of the tensor can be used
                     # on different TPU cores
+                    tensor = reshard_reverse(
+                        tensor,
+                        params["cores_per_replica"],
+                        network.state["params"][spec["module"]][spec["param"]].shape,
+                    )
+                    tensor = jnp.array(tensor.detach())
+                    if tensor.dtype is torch.float16 or tensor.dtype is torch.float32:
+                        tensor = tensor.bfloat16()
                     network.state["params"][spec["module"]][spec["param"]] = move_xmap(
-                        jax.dlpack.from_dlpack(torch.utils.dlpack.to_dlpack(
-                            reshard_reverse(
-                                tensor,
-                                params["cores_per_replica"],
-                                network.state["params"][spec["module"]][spec["param"]].shape,
-                            )
-                        )).copy(),
+                        tensor,
                         np.empty(params["cores_per_replica"]),
                     )
 
@@ -1331,52 +1368,52 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
     print("\n", flush=True)
     with torch_lazy_loader.use_lazy_torch_load(callback=callback, dematerialized_modules=True):
         if(os.path.isdir(vars.custmodpth)):
-            try:
-                tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
-            except Exception as e:
-                pass
             try:
                 tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache", use_fast=False)
             except Exception as e:
                 try:
-                    tokenizer = GPT2TokenizerFast.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
+                    tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
                 except Exception as e:
-                    tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
+                    try:
+                        tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
+                    except Exception as e:
+                        tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
             try:
                 model     = AutoModelForCausalLM.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
             except Exception as e:
                 model     = GPTNeoForCausalLM.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
         elif(os.path.isdir("models/{}".format(vars.model.replace('/', '_')))):
-            try:
-                tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
-            except Exception as e:
-                pass
             try:
                 tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache", use_fast=False)
             except Exception as e:
                 try:
-                    tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
+                    tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
                 except Exception as e:
-                    tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
+                    try:
+                        tokenizer = GPT2Tokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
+                    except Exception as e:
+                        tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
             try:
                 model     = AutoModelForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
             except Exception as e:
                 model     = GPTNeoForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
         else:
-            try:
-                tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
-            except Exception as e:
-                pass
             try:
                 tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache", use_fast=False)
             except Exception as e:
                 try:
-                    tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
+                    tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
                 except Exception as e:
-                    tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
+                    try:
+                        tokenizer = GPT2Tokenizer.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
+                    except Exception as e:
+                        tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
             try:
                 model     = AutoModelForCausalLM.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
             except Exception as e:
                 model     = GPTNeoForCausalLM.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
 
     #network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))
+    global shard_xmap, batch_xmap
+    shard_xmap = __shard_xmap()
+    batch_xmap = __batch_xmap(shard_dim=cores_per_replica)

update-koboldai.bat

@@ -1,5 +1,7 @@
 @echo off
 cd /d %~dp0
+SET CONDA_SHLVL=
+
 TITLE KoboldAI - Updater
 SET /P M=<loader.settings
 IF %M%==1 GOTO drivemap

userscripts/api_documentation.html

@@ -500,6 +500,7 @@
 <li>kwargs? (<code>table&lt;string, any&gt;</code>): Table of optional keyword arguments from the following list. Defaults to <code>{}</code>.
 <ul>
 <li>scan_story? (<code>boolean</code>): Whether or not to scan the past few actions of the story for world info keys in addition to the submission like how world info normally behaves. If this is set to <code>false</code>, only the <code>submission</code> is scanned for world info keys. Defaults to <code>true</code>.</li>
+<li>include_anote? (<code>boolean</code>): Whether to include the author's note in the story.  Defaults to <code>true</code>, pass <code>false</code> to suppress including the author's note.</li>
 </ul>
 </li>
 </ul>
@@ -574,6 +575,7 @@
 <li>kwargs? (<code>table&lt;string, any&gt;</code>): Table of optional keyword arguments from the following list. Defaults to <code>{}</code>.
 <ul>
 <li>scan_story? (<code>boolean</code>): Whether or not to scan the past few actions of the story for world info keys in addition to the submission like how world info normally behaves. If this is set to <code>false</code>, only the <code>submission</code> is scanned for world info keys. Defaults to <code>true</code>.</li>
+<li>include_anote? (<code>boolean</code>): Whether to include the author's note in the story.  Defaults to <code>true</code>, pass <code>false</code> to suppress including the author's note.</li>
 </ul>
 </li>
 </ul>
@@ -687,6 +689,7 @@
 <li>kwargs? (<code>table&lt;string, any&gt;</code>): Table of optional keyword arguments from the following list. Defaults to <code>{}</code>.
 <ul>
 <li>scan_story? (<code>boolean</code>): Whether or not to scan the past few actions of the story for world info keys in addition to the submission like how world info normally behaves. If this is set to <code>false</code>, only the <code>submission</code> is scanned for world info keys. Defaults to <code>true</code>.</li>
+<li>include_anote? (<code>boolean</code>): Whether to include the author's note in the story.  Defaults to <code>true</code>, pass <code>false</code> to suppress including the author's note.</li>
 </ul>
 </li>
 </ul>

userscripts/api_documentation.md

@@ -538,6 +538,7 @@ Computes the context that would be sent to the generator with the user's current
 * entries? (`KoboldWorldInfoEntry|table<any, KoboldWorldInfoEntry>`): A `KoboldWorldInfoEntry` or table thereof that indicates an allowed subset of world info entries to include in the context. Defaults to all world info entries.
 * kwargs? (`table<string, any>`): Table of optional keyword arguments from the following list. Defaults to `{}`.
     * scan_story? (`boolean`): Whether or not to scan the past few actions of the story for world info keys in addition to the submission like how world info normally behaves. If this is set to `false`, only the `submission` is scanned for world info keys. Defaults to `true`.
+    * include_anote? (`boolean`): Whether to include the author's note in the story.  Defaults to `true`, pass `false` to suppress including the author's note.
 
 ### Returns
 
@@ -636,6 +637,7 @@ The same as calling `kobold.worldinfo:compute_context()` with this world info en
 * submission (`string`): String to use as simulated user's input after being formatted by input formatting.
 * kwargs? (`table<string, any>`): Table of optional keyword arguments from the following list. Defaults to `{}`.
     * scan_story? (`boolean`): Whether or not to scan the past few actions of the story for world info keys in addition to the submission like how world info normally behaves. If this is set to `false`, only the `submission` is scanned for world info keys. Defaults to `true`.
+    * include_anote? (`boolean`): Whether to include the author's note in the story.  Defaults to `true`, pass `false` to suppress including the author's note.
 
 ### Returns
 
@@ -819,6 +821,7 @@ Unlike `kobold.worldinfo:compute_context()`, this function doesn't include world
 * entries? (`KoboldWorldInfoEntry|table<any, KoboldWorldInfoEntry>`): A `KoboldWorldInfoEntry` or table thereof that indicates an allowed subset of world info entries to include in the context. Entries that are not inside of the folder are still not included. Defaults to all world info entries in the folder.
 * kwargs? (`table<string, any>`): Table of optional keyword arguments from the following list. Defaults to `{}`.
     * scan_story? (`boolean`): Whether or not to scan the past few actions of the story for world info keys in addition to the submission like how world info normally behaves. If this is set to `false`, only the `submission` is scanned for world info keys. Defaults to `true`.
+    * include_anote? (`boolean`): Whether to include the author's note in the story.  Defaults to `true`, pass `false` to suppress including the author's note.
 
 ### Returns
 

utils.py

@@ -8,6 +8,9 @@ from urllib.error import HTTPError
 import requests
 import requests.adapters
 import time
+from transformers import __version__ as transformers_version
+from transformers import PreTrainedModel
+import packaging.version
 from tqdm.auto import tqdm
 import os
 import itertools
@@ -15,9 +18,16 @@ import hashlib
 import huggingface_hub
 import packaging.version
 from pathlib import Path
-from typing import Optional
+from typing import List, Optional
+
+HAS_ACCELERATE = packaging.version.parse(transformers_version) >= packaging.version.parse("4.20.0.dev0")
+try:
+    import accelerate
+except ImportError:
+    HAS_ACCELERATE = False
 
 vars = None
+args = None
 num_shards: Optional[int] = None
 current_shard = 0
 from_pretrained_model_name = ""
@@ -25,7 +35,13 @@ from_pretrained_index_filename: Optional[str] = None
 from_pretrained_kwargs = {}
 bar = None
 
-default_sampler_order = [0, 1, 2, 3, 4, 5]
+layers_module_names: Optional[List[str]] = None
+module_names: Optional[List[str]] = None
+named_buffers: Optional[List[tuple]] = None
+
+default_sampler_order = [6, 0, 1, 2, 3, 4, 5]
+
+emit = None
 
 #==================================================================#
 # Decorator to prevent a function's actions from being run until
@@ -111,7 +127,7 @@ def addsentencespacing(txt, vars):
     else:
         action = vars.prompt
         lastchar = action[-1] if len(action) else ""
-    if(lastchar == "." or lastchar == "!" or lastchar == "?" or lastchar == "," or lastchar == ";" or lastchar == ":"):
+    if(lastchar != " "):
         txt = " " + txt
     return txt
 	
@@ -159,13 +175,33 @@ def decodenewlines(txt):
 #  Returns number of layers given an HF model config
 #==================================================================#
 def num_layers(config):
-    return config.num_layers if hasattr(config, "num_layers") else config.n_layer if hasattr(config, "n_layer") else config.num_hidden_layers
+    return config["n_layer"] if isinstance(config, dict) else config.num_layers if hasattr(config, "num_layers") else config.n_layer if hasattr(config, "n_layer") else config.num_hidden_layers if hasattr(config, 'num_hidden_layers') else None
 
 #==================================================================#
 #  Downloads huggingface checkpoints using aria2c if possible
 #==================================================================#
+from flask_socketio import emit
+            
 def _download_with_aria2(aria2_config: str, total_length: int, directory: str = ".", user_agent=None, force_download=False, use_auth_token=None):
+    class Send_to_socketio(object):
+        def write(self, bar):
+            bar = bar.replace("\r", "").replace("\n", "")
+            
+            if bar != "":
+                try:
+                    print('\r' + bar, end='')
+                    try:
+                        emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", " ")}, broadcast=True)
+                    except:
+                        pass
+                    eventlet.sleep(seconds=0)
+                except:
+                    pass
+        def flush(self):
+            pass
+    
     import transformers
+    aria2_port = 6799 if vars is None else vars.aria2_port
     lengths = {}
     s = requests.Session()
     s.mount("http://", requests.adapters.HTTPAdapter(max_retries=requests.adapters.Retry(total=120, backoff_factor=1)))
@@ -176,9 +212,9 @@ def _download_with_aria2(aria2_config: str, total_length: int, directory: str =
         with tempfile.NamedTemporaryFile("w+b", delete=False) as f:
             f.write(aria2_config)
             f.flush()
-            p = subprocess.Popen(["aria2c", "-x", "10", "-s", "10", "-j", "10", "--enable-rpc=true", f"--rpc-secret={secret}", "--rpc-listen-port", str(vars.aria2_port), "--disable-ipv6", "--file-allocation=trunc", "--allow-overwrite", "--auto-file-renaming=false", "-d", directory, "-i", f.name, "-U", transformers.file_utils.http_user_agent(user_agent)] + (["-c"] if not force_download else []) + ([f"--header='Authorization: Bearer {use_auth_token}'"] if use_auth_token else []), stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+            p = subprocess.Popen(["aria2c", "-x", "10", "-s", "10", "-j", "10", "--enable-rpc=true", f"--rpc-secret={secret}", "--rpc-listen-port", str(aria2_port), "--disable-ipv6", "--file-allocation=trunc", "--allow-overwrite", "--auto-file-renaming=false", "-d", directory, "-i", f.name, "-U", transformers.file_utils.http_user_agent(user_agent)] + (["-c"] if not force_download else []) + ([f"--header='Authorization: Bearer {use_auth_token}'"] if use_auth_token else []), stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
             while p.poll() is None:
-                r = s.post(f"http://localhost:{vars.aria2_port}/jsonrpc", json={"jsonrpc": "2.0", "id": "kai", "method": "aria2.tellActive", "params": [f"token:{secret}"]}).json()["result"]
+                r = s.post(f"http://localhost:{aria2_port}/jsonrpc", json={"jsonrpc": "2.0", "id": "kai", "method": "aria2.tellActive", "params": [f"token:{secret}"]}).json()["result"]
                 if not r:
                     s.close()
                     if bar is not None:
@@ -188,7 +224,7 @@ def _download_with_aria2(aria2_config: str, total_length: int, directory: str =
                     done = True
                     break
                 if bar is None:
-                    bar = tqdm(total=total_length, desc=f"[aria2] Downloading model", unit="B", unit_scale=True, unit_divisor=1000)
+                    bar = tqdm(total=total_length, desc=f"[aria2] Downloading model", unit="B", unit_scale=True, unit_divisor=1000, file=Send_to_socketio())
                 visited = set()
                 for x in r:
                     filename = x["files"][0]["path"]
@@ -225,7 +261,7 @@ def _transformers22_aria2_hook(pretrained_model_name_or_path: str, force_downloa
             if token is None:
                 raise EnvironmentError("You specified use_auth_token=True, but a huggingface token was not found.")
     _cache_dir = str(cache_dir) if cache_dir is not None else transformers.TRANSFORMERS_CACHE
-    _revision = revision if revision is not None else huggingface_hub.constants.DEFAULT_REVISION
+    _revision = args.revision if args.revision is not None else huggingface_hub.constants.DEFAULT_REVISION
     sharded = False
     headers = {"user-agent": transformers.file_utils.http_user_agent(user_agent)}
     if use_auth_token:
@@ -236,7 +272,7 @@ def _transformers22_aria2_hook(pretrained_model_name_or_path: str, force_downloa
 
     def is_cached(filename):
         try:
-            huggingface_hub.hf_hub_download(pretrained_model_name_or_path, filename, cache_dir=cache_dir, local_files_only=True)
+            huggingface_hub.hf_hub_download(pretrained_model_name_or_path, filename, cache_dir=cache_dir, local_files_only=True, revision=_revision)
         except ValueError:
             return False
         return True
@@ -245,7 +281,7 @@ def _transformers22_aria2_hook(pretrained_model_name_or_path: str, force_downloa
             filename = transformers.modeling_utils.WEIGHTS_INDEX_NAME if sharded else transformers.modeling_utils.WEIGHTS_NAME
         except AttributeError:
             return
-        url = huggingface_hub.hf_hub_url(pretrained_model_name_or_path, filename, revision=revision)
+        url = huggingface_hub.hf_hub_url(pretrained_model_name_or_path, filename, revision=_revision)
         if is_cached(filename) or requests.head(url, allow_redirects=True, proxies=proxies, headers=headers):
             break
         if sharded:
@@ -259,7 +295,7 @@ def _transformers22_aria2_hook(pretrained_model_name_or_path: str, force_downloa
         with open(map_filename) as f:
             map_data = json.load(f)
         filenames = set(map_data["weight_map"].values())
-    urls = [huggingface_hub.hf_hub_url(pretrained_model_name_or_path, n, revision=revision) for n in filenames]
+    urls = [huggingface_hub.hf_hub_url(pretrained_model_name_or_path, n, revision=_revision) for n in filenames]
     if not force_download:
         urls = [u for u, n in zip(urls, filenames) if not is_cached(n)]
         if not urls:
@@ -406,7 +442,7 @@ def _transformers22_aria2_hook(pretrained_model_name_or_path: str, force_downloa
     headers = [requests.head(u, headers=headers, allow_redirects=True, proxies=proxies, timeout=10).headers for u in urls]
 
     for n in filenames:
-        prefix, suffix = n.rsplit("/", 1)
+        prefix, suffix = n.rsplit(os.sep, 1)
         path = os.path.join(prefix, "kai-tempfile." + suffix + ".aria2")
         if os.path.exists(path):
             os.remove(path)
@@ -414,16 +450,17 @@ def _transformers22_aria2_hook(pretrained_model_name_or_path: str, force_downloa
         if os.path.exists(path):
             os.remove(path)
     total_length = sum(int(h["Content-Length"]) for h in headers)
-    aria2_config = "\n".join(f"{u}\n  out={os.path.join(prefix, 'kai-tempfile.' + suffix)}" for u, n in zip(urls, filenames) for prefix, suffix in [n.rsplit("/", 1)]).encode()
+    aria2_config = "\n".join(f"{u}\n  out={os.path.join(prefix, 'kai-tempfile.' + suffix)}" for u, n in zip(urls, filenames) for prefix, suffix in [n.rsplit(os.sep, 1)]).encode()
     _download_with_aria2(aria2_config, total_length, use_auth_token=token if use_auth_token else None, user_agent=user_agent, force_download=force_download)
     for u, n in zip(urls, filenames):
-        prefix, suffix = n.rsplit("/", 1)
+        prefix, suffix = n.rsplit(os.sep, 1)
         os.rename(os.path.join(prefix, "kai-tempfile." + suffix), os.path.join(prefix, suffix))
 
 def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_dir=None, proxies=None, resume_download=False, local_files_only=False, use_auth_token=None, user_agent=None, revision=None, **kwargs):
     import transformers
     import transformers.modeling_utils
     from huggingface_hub import HfFolder
+    _revision = args.revision if args.revision is not None else huggingface_hub.constants.DEFAULT_REVISION
     if shutil.which("aria2c") is None:  # Don't do anything if aria2 is not installed
         return
     if local_files_only:  # If local_files_only is true, we obviously don't need to download anything
@@ -458,7 +495,7 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d
             filename = transformers.modeling_utils.WEIGHTS_INDEX_NAME if sharded else transformers.modeling_utils.WEIGHTS_NAME
         except AttributeError:
             return
-        url = huggingface_hub.hf_hub_url(pretrained_model_name_or_path, filename, revision=revision)
+        url = huggingface_hub.hf_hub_url(pretrained_model_name_or_path, filename, revision=_revision)
         if is_cached(url) or requests.head(url, allow_redirects=True, proxies=proxies, headers=headers):
             break
         if sharded:
@@ -472,7 +509,7 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d
         with open(map_filename) as f:
             map_data = json.load(f)
         filenames = set(map_data["weight_map"].values())
-    urls = [huggingface_hub.hf_hub_url(pretrained_model_name_or_path, n, revision=revision) for n in filenames]
+    urls = [huggingface_hub.hf_hub_url(pretrained_model_name_or_path, n, revision=_revision) for n in filenames]
     if not force_download:
         urls = [u for u in urls if not is_cached(u)]
         if not urls:
@@ -519,5 +556,56 @@ def get_num_shards(filename):
 def get_sharded_checkpoint_num_tensors(pretrained_model_name_or_path, filename, cache_dir=None, force_download=False, proxies=None, resume_download=False, local_files_only=False, use_auth_token=None, user_agent=None, revision=None, **kwargs):
     import transformers.modeling_utils
     import torch
-    shard_paths, _ = transformers.modeling_utils.get_checkpoint_shard_files(pretrained_model_name_or_path, filename, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent, revision=revision)
+    _revision = args.revision if args.revision is not None else huggingface_hub.constants.DEFAULT_REVISION
+    shard_paths, _ = transformers.modeling_utils.get_checkpoint_shard_files(pretrained_model_name_or_path, filename, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent, revision=_revision)
     return list(itertools.chain(*(torch.load(p, map_location="cpu").keys() for p in shard_paths)))
+
+#==================================================================#
+#  Given a PreTrainedModel, returns the list of module names that correspond
+#  to the model's hidden layers.
+#==================================================================#
+def get_layers_module_names(model: PreTrainedModel) -> List[str]:
+    names: List[str] = []
+    def recurse(module, head=""):
+        for c in module.named_children():
+            name = head + c[0]
+            if c[0].isnumeric() and any(c[1].__class__.__name__.endswith(suffix) for suffix in ("Block", "Layer")):
+                names.append(name)
+            else:
+                recurse(c[1], head=name + ".")
+    recurse(model)
+    return names
+
+#==================================================================#
+#  Given a PreTrainedModel, returns the module name that corresponds
+#  to the model's input embeddings.
+#==================================================================#
+def get_input_embeddings_module_name(model: PreTrainedModel) -> str:
+    embeddings = model.get_input_embeddings()
+    def recurse(module, head=""):
+        for c in module.named_children():
+            name = head + c[0]
+            if c[1] is embeddings:
+                return name
+            else:
+                return recurse(c[1], head=name + ".")
+    return recurse(model)
+
+#==================================================================#
+#  Given a PreTrainedModel and a list of module names, returns a list
+#  of module names such that the union of the set of modules given as input
+#  and the set of modules returned as output contains all modules in the model.
+#==================================================================#
+def get_missing_module_names(model: PreTrainedModel, names: List[str]) -> List[str]:
+    missing_names: List[str] = []
+    def recurse(module, head=""):
+        for c in module.named_children():
+            name = head + c[0]
+            if any(name.startswith(n) for n in names):
+                continue
+            if next(c[1].named_children(), None) is None:
+                missing_names.append(name)
+            else:
+                recurse(c[1], head=name + ".")
+    recurse(model)
+    return missing_names

warpers.py

@@ -28,10 +28,10 @@ SOFTWARE.
 '''
 
 import torch
-from transformers import LogitsWarper, LogitsProcessor
+from transformers import LogitsWarper
 
 
-class AdvancedRepetitionPenaltyLogitsProcessor(LogitsProcessor):
+class AdvancedRepetitionPenaltyLogitsProcessor(LogitsWarper):
     def __init__(self, *args, **kwargs):
         pass