n_images:
- break
- x = batch
- inputs = x.to(device).float()
- latents = get_latents(net, inputs, is_cars)
- all_latents.append(latents)
- i += len(latents)
- return torch.cat(all_latents)
-
-
-def save_image(img, save_dir, idx):
- result = tensor2im(img)
- im_save_path = os.path.join(save_dir, f"{idx:05d}.jpg")
- Image.fromarray(np.array(result)).save(im_save_path)
-
-
-@torch.no_grad()
-def generate_inversions(args, g, latent_codes, is_cars):
- print('Saving inversion images')
- inversions_directory_path = os.path.join(args.save_dir, 'inversions')
- os.makedirs(inversions_directory_path, exist_ok=True)
- for i in range(args.n_sample):
- imgs, _ = g([latent_codes[i].unsqueeze(0)], input_is_latent=True, randomize_noise=False, return_latents=True)
- if is_cars:
- imgs = imgs[:, :, 64:448, :]
- save_image(imgs[0], inversions_directory_path, i + 1)
-
-
-def run_alignment(image_path):
- predictor = dlib.shape_predictor(paths_config.model_paths['shape_predictor'])
- aligned_image = align_face(filepath=image_path, predictor=predictor)
- print("Aligned image has shape: {}".format(aligned_image.size))
- return aligned_image
-
-
-if __name__ == "__main__":
- device = "cuda"
-
- parser = argparse.ArgumentParser(description="Inference")
- parser.add_argument("--images_dir", type=str, default=None,
- help="The directory of the images to be inverted")
- parser.add_argument("--save_dir", type=str, default=None,
- help="The directory to save the latent codes and inversion images. (default: images_dir")
- parser.add_argument("--batch", type=int, default=1, help="batch size for the generator")
- parser.add_argument("--n_sample", type=int, default=None, help="number of the samples to infer.")
- parser.add_argument("--latents_only", action="store_true", help="infer only the latent codes of the directory")
- parser.add_argument("--align", action="store_true", help="align face images before inference")
- parser.add_argument("ckpt", metavar="CHECKPOINT", help="path to generator checkpoint")
-
- args = parser.parse_args()
- main(args)
diff --git a/spaces/bedrock123/andite-anything-v4.0/app.py b/spaces/bedrock123/andite-anything-v4.0/app.py
deleted file mode 100644
index 47a2051db6dadeea03edf70d62694fd3e5e88ba7..0000000000000000000000000000000000000000
--- a/spaces/bedrock123/andite-anything-v4.0/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/andite/anything-v4.0").launch()
\ No newline at end of file
diff --git a/spaces/bethecloud/storj_theme/app.py b/spaces/bethecloud/storj_theme/app.py
deleted file mode 100644
index 36790b1ce3fdc5ab02cbdbe25fc26d444de86434..0000000000000000000000000000000000000000
--- a/spaces/bethecloud/storj_theme/app.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import time
-
-from theme_dropdown import create_theme_dropdown # noqa: F401
-
-import gradio as gr
-
-dropdown, js = create_theme_dropdown()
-
-with gr.Blocks(theme='bethecloud/storj_theme') as demo:
- with gr.Row().style(equal_height=True):
- with gr.Column(scale=10):
- gr.Markdown(
- """
- # Theme preview: `storj_theme`
- To use this theme, set `theme='bethecloud/storj_theme'` in `gr.Blocks()` or `gr.Interface()`.
- You can append an `@` and a semantic version expression, e.g. @>=1.0.0,<2.0.0 to pin to a given version
- of this theme.
- """
- )
- with gr.Column(scale=3):
- with gr.Box():
- dropdown.render()
- toggle_dark = gr.Button(value="Toggle Dark").style(full_width=True)
-
- dropdown.change(None, dropdown, None, _js=js)
- toggle_dark.click(
- None,
- _js="""
- () => {
- document.body.classList.toggle('dark');
- document.querySelector('gradio-app').style.backgroundColor = 'var(--color-background-primary)'
- }
- """,
- )
-
- name = gr.Textbox(
- label="Name",
- info="Full name, including middle name. No special characters.",
- placeholder="John Doe",
- value="John Doe",
- interactive=True,
- )
-
- with gr.Row():
- slider1 = gr.Slider(label="Slider 1")
- slider2 = gr.Slider(label="Slider 2")
- gr.CheckboxGroup(["A", "B", "C"], label="Checkbox Group")
-
- with gr.Row():
- with gr.Column(variant="panel", scale=1):
- gr.Markdown("## Panel 1")
- radio = gr.Radio(
- ["A", "B", "C"],
- label="Radio",
- info="Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.",
- )
- drop = gr.Dropdown(["Option 1", "Option 2", "Option 3"], show_label=False)
- drop_2 = gr.Dropdown(
- ["Option A", "Option B", "Option C"],
- multiselect=True,
- value=["Option A"],
- label="Dropdown",
- interactive=True,
- )
- check = gr.Checkbox(label="Go")
- with gr.Column(variant="panel", scale=2):
- img = gr.Image(
- "https://gradio.app/assets/img/header-image.jpg", label="Image"
- ).style(height=320)
- with gr.Row():
- go_btn = gr.Button("Go", label="Primary Button", variant="primary")
- clear_btn = gr.Button(
- "Clear", label="Secondary Button", variant="secondary"
- )
-
- def go(*args):
- time.sleep(3)
- return "https://gradio.app/assets/img/header-image.jpg"
-
- go_btn.click(go, [radio, drop, drop_2, check, name], img, api_name="go")
-
- def clear():
- time.sleep(0.2)
- return None
-
- clear_btn.click(clear, None, img)
-
- with gr.Row():
- btn1 = gr.Button("Button 1").style(size="sm")
- btn2 = gr.UploadButton().style(size="sm")
- stop_btn = gr.Button("Stop", label="Stop Button", variant="stop").style(
- size="sm"
- )
-
- with gr.Row():
- gr.Dataframe(value=[[1, 2, 3], [4, 5, 6], [7, 8, 9]], label="Dataframe")
- gr.JSON(
- value={"a": 1, "b": 2, "c": {"test": "a", "test2": [1, 2, 3]}}, label="JSON"
- )
- gr.Label(value={"cat": 0.7, "dog": 0.2, "fish": 0.1})
- gr.File()
- with gr.Row():
- gr.ColorPicker()
- gr.Video("https://gradio-static-files.s3.us-west-2.amazonaws.com/world.mp4")
- gr.Gallery(
- [
- (
- "https://gradio-static-files.s3.us-west-2.amazonaws.com/lion.jpg",
- "lion",
- ),
- (
- "https://gradio-static-files.s3.us-west-2.amazonaws.com/logo.png",
- "logo",
- ),
- (
- "https://gradio-static-files.s3.us-west-2.amazonaws.com/tower.jpg",
- "tower",
- ),
- ]
- ).style(height="200px", grid=2)
-
- with gr.Row():
- with gr.Column(scale=2):
- chatbot = gr.Chatbot([("Hello", "Hi")], label="Chatbot")
- chat_btn = gr.Button("Add messages")
-
- def chat(history):
- time.sleep(2)
- yield [["How are you?", "I am good."]]
-
- chat_btn.click(
- lambda history: history
- + [["How are you?", "I am good."]]
- + (time.sleep(2) or []),
- chatbot,
- chatbot,
- )
- with gr.Column(scale=1):
- with gr.Accordion("Advanced Settings"):
- gr.Markdown("Hello")
- gr.Number(label="Chatbot control 1")
- gr.Number(label="Chatbot control 2")
- gr.Number(label="Chatbot control 3")
-
-
-if __name__ == "__main__":
- demo.queue().launch()
\ No newline at end of file
diff --git a/spaces/bguberfain/Detic/detic/data/datasets/imagenet.py b/spaces/bguberfain/Detic/detic/data/datasets/imagenet.py
deleted file mode 100644
index 9b6d78e51f1b0c7d6e1fba2869a72a6f383e81b2..0000000000000000000000000000000000000000
--- a/spaces/bguberfain/Detic/detic/data/datasets/imagenet.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import os
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.data.datasets.lvis import get_lvis_instances_meta
-from .lvis_v1 import custom_load_lvis_json, get_lvis_22k_meta
-def custom_register_imagenet_instances(name, metadata, json_file, image_root):
- """
- """
- DatasetCatalog.register(name, lambda: custom_load_lvis_json(
- json_file, image_root, name))
- MetadataCatalog.get(name).set(
- json_file=json_file, image_root=image_root,
- evaluator_type="imagenet", **metadata
- )
-
-_CUSTOM_SPLITS_IMAGENET = {
- "imagenet_lvis_v1": ("imagenet/ImageNet-LVIS/", "imagenet/annotations/imagenet_lvis_image_info.json"),
-}
-
-for key, (image_root, json_file) in _CUSTOM_SPLITS_IMAGENET.items():
- custom_register_imagenet_instances(
- key,
- get_lvis_instances_meta('lvis_v1'),
- os.path.join("datasets", json_file) if "://" not in json_file else json_file,
- os.path.join("datasets", image_root),
- )
-
-
-_CUSTOM_SPLITS_IMAGENET_22K = {
- "imagenet_lvis-22k": ("imagenet/ImageNet-LVIS/", "imagenet/annotations/imagenet-22k_image_info_lvis-22k.json"),
-}
-
-for key, (image_root, json_file) in _CUSTOM_SPLITS_IMAGENET_22K.items():
- custom_register_imagenet_instances(
- key,
- get_lvis_22k_meta(),
- os.path.join("datasets", json_file) if "://" not in json_file else json_file,
- os.path.join("datasets", image_root),
- )
\ No newline at end of file
diff --git a/spaces/bigPear/digitalWDF/examples/alter_self_cognition.md b/spaces/bigPear/digitalWDF/examples/alter_self_cognition.md
deleted file mode 100644
index d59d9bd5307e48a1ef55824f9072dc68e02f4528..0000000000000000000000000000000000000000
--- a/spaces/bigPear/digitalWDF/examples/alter_self_cognition.md
+++ /dev/null
@@ -1,104 +0,0 @@
-# 修改 ChatGLM 自我认知的例子
-
-## 一、环境配置
-
-首先你需要准备一台性能足够的运算设备，建议使用 Unix 操作系统。本框架的推荐运行配置如下表所述：
-
-| 设备 | 最低配置 | 推荐配置 |
-| ------- | -------- | -------------- |
-| 处理器 | Intel i7 | **Intel Xeon** |
-| 运行内存 | 16GB | **32GB** |
-| 显卡内存 | 12GB | **24GB** |
-| 硬盘大小 | 10GB | **20GB** |
-
-
-本案例中默认电脑已经配置完毕 [CUDA](https://developer.nvidia.com/cuda-toolkit) 运算环境。如果存在 CUDA 环境配置的问题，可以关注本项目之后将要发布的 Docker 安装包。
-
-我们推荐使用 [Conda](https://anaconda.org/anaconda/conda) 虚拟环境安装依赖，从而避免破坏外部项目的依赖，运行以下命令创建 Conda 虚拟环境并安装 Python 依赖：
-
-```bash
-git clone https://github.com/hiyouga/ChatGLM-Efficient-Tuning.git
-conda create -n chatglm_etuning python=3.10
-conda activate chatglm_etuning
-cd ChatGLM-Efficient-Tuning
-pip install -r requirements.txt
-```
-
-###### 本项目默认会联网获取最新的 ChatGLM 模型，其版本可能影响代码效果甚至报错，建议使用官方最新的模型版本文件。若频繁出现网络无法连接，请尝试使用下述命令下载官方模型，并修改 `src/utils/config.py` 中的 [CHATGLM_REPO_NAME](https://github.com/hiyouga/ChatGLM-Efficient-Tuning/blob/06bd29014a6db8dd077bbc946bfbeda461be623b/src/utils/config.py#L7) 为存放模型文件的本地路径。
-
-```bash
-git lfs install
-git clone https://huggingface.co/THUDM/chatglm-6b
-```
-
-
-
-## 二、数据集准备
-
-在该案例中，我们使用 `self_cognition` 数据集，该数据集包含了 18 条关于模型自我认知的数据，我们的目标是修改模型自身的知识，使模型给出我们希望的答复。数据集的内容请查看 `data/self_cognition.json`，这里我们列出两条示例。
-
-```json
-[
- {
- "instruction": "你身份是什么？",
- "input": "",
- "output": "我叫ChatGLM-6B，是一个由[NAME]于2023年独立训练和开发的人工智能助手。我的主要目标是协助用户解决问题和满足他们的需求。"
- },
- {
- "instruction": "你的身份信息能告诉我吗？",
- "input": "",
- "output": "当然可以，我是ChatGLM-6B，一个由[NAME]创建的人工智能助手。我在2023年研发完成，旨在为用户提供有针对性的回答和帮助。"
- }
-]
-```
-
-**提示：您可以将 `[NAME]` 换成您自己的名字，从而让模型回答它的创造者是您。**
-
-###### 本框架内置了十余种指令数据集，其简要介绍请移步 [data](../data/) 文件夹。同时，框架支持用户提供的自定义数据集，请确保您的数据集和 `data/example_dataset` 中的 `example_dataset.json` 文件格式相同。其中 `instruction` 项和 `output` 项为必需项，以保证模型的监督微调（SFT）能够正常运行。
-
-## 三、模型监督微调
-
-运行以下命令在单个 GPU 上进行模型监督微调。我们使用 `self_cognition` 数据集，采用 `lora` 微调方法，微调后的模型保存在 `cognition` 文件夹中。为了保证模型微调成功，我们采用 0.001 的学习率，在数据集上训练 10 个 epoch。
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python src/finetune.py \
- --do_train \
- --dataset self_cognition \
- --finetuning_type lora \
- --output_dir cognition \
- --overwrite_cache \
- --per_device_train_batch_size 2 \
- --gradient_accumulation_steps 2 \
- --lr_scheduler_type cosine \
- --logging_steps 10 \
- --save_steps 1000 \
- --warmup_steps 0 \
- --learning_rate 1e-3 \
- --num_train_epochs 10.0 \
- --fp16
-```
-
-框架运行日志如下图所示。
-
-
-
-## 四、模型效果测试
-
-运行以下命令在单个 GPU 上测试模型效果，它会加载 `cognition` 文件夹内保存的微调模型权重，并合并进原版 ChatGLM 模型的参数权重中，同时启动流式交互窗口。
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python src/infer.py \
- --checkpoint_dir cognition
-```
-
-向微调后的 ChatGLM-6B 模型问一些自我认知问题，我们可以发现它能够给出我们期望的回答。同时，我们还测试了两个额外的问题，验证结果说明模型的原本知识**并没有被严重破坏**。
-
-
-
-为了对比效果，我们同时测试了原版 ChatGLM-6B 模型的回答，下图为原版模型的回答，关于自身认知的回答与上图相比有着显著不同。
-
-
-
-## 五、模型部署
-
-如果要将微调后的模型部署在您的项目框架中，请参考 [README_zh.md](../README_zh.md#模型部署) 中关于部署微调模型的部分。
diff --git a/spaces/bigcode/santacoder-endpoint/app.py b/spaces/bigcode/santacoder-endpoint/app.py
deleted file mode 100644
index 2a95e54595ce0d675af9ec67bc2ac6d99ae32a15..0000000000000000000000000000000000000000
--- a/spaces/bigcode/santacoder-endpoint/app.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
-from transformers import pipeline
-import os
-
-description = """# SantaCoder Endpoint"""
-token = os.environ["HUB_TOKEN"]
-device="cuda:0"
-
-tokenizer = AutoTokenizer.from_pretrained("bigcode/christmas-models", use_auth_token=token)
-model = AutoModelForCausalLM.from_pretrained("bigcode/christmas-models", trust_remote_code=True, use_auth_token=token)
-
-
-def code_generation(gen_prompt, max_tokens, temperature=0.6, seed=42):
- set_seed(seed)
- pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
- generated_text = pipe(gen_prompt, do_sample=True, top_p=0.95, temperature=temperature, max_new_tokens=max_tokens)[0]['generated_text']
- return generated_text
-
-
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
-from transformers import pipeline
-import os
-
-title = "Santa Model Generator"
-description = "Demo"
-example = [
- ["def print_hello_world():", 8, 0.6, 42],
- ["def get_file_size(filepath):", 24, 0.6, 42],
- ["def count_lines(filename):", 40, 0.6, 42],
- ["def count_words(filename):", 40, 0.6, 42]]
-
-token = os.environ["HUB_TOKEN"]
-device="cuda:0"
-revision = "dedup-alt-comments"
-
-tokenizer = AutoTokenizer.from_pretrained("bigcode/christmas-models", use_auth_token=token)
-model = AutoModelForCausalLM.from_pretrained("bigcode/christmas-models", revision=revision, trust_remote_code=True, use_auth_token=token)
-
-
-def code_generation(gen_prompt, max_tokens, temperature=0.6, seed=42):
- set_seed(seed)
- pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
- generated_text = pipe(gen_prompt, do_sample=True, top_p=0.95, temperature=temperature, max_new_tokens=max_tokens)[0]['generated_text']
- return generated_text
-
-
-iface = gr.Interface(
- fn=code_generation,
- inputs=[
- gr.Textbox(lines=10, label="Input code"),
- gr.inputs.Slider(
- minimum=8,
- maximum=1000,
- step=1,
- default=8,
- label="Number of tokens to generate",
- ),
- gr.inputs.Slider(
- minimum=0,
- maximum=2.5,
- step=0.1,
- default=0.6,
- label="Temperature",
- ),
- gr.inputs.Slider(
- minimum=0,
- maximum=1000,
- step=1,
- default=42,
- label="Random seed to use for the generation"
- )
- ],
- outputs=gr.Textbox(label="Predicted code", lines=10),
- examples=example,
- layout="horizontal",
- theme="peach",
- description=description,
- title=title
-)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/bigscience-data/pyserini-demo/README.md b/spaces/bigscience-data/pyserini-demo/README.md
deleted file mode 100644
index a6b22cd33825794290ba757ed2392cb8fbed2577..0000000000000000000000000000000000000000
--- a/spaces/bigscience-data/pyserini-demo/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Pyserini
-emoji: 🦫
-colorFrom: purple
-colorTo: red
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/bioriAsaeru/text-to-voice/Codigos Autoplay Media Studio Download For Computer TOP.md b/spaces/bioriAsaeru/text-to-voice/Codigos Autoplay Media Studio Download For Computer TOP.md
deleted file mode 100644
index 6fe69612f216a7f847a0e7c2a46c346e3ae1cc77..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Codigos Autoplay Media Studio Download For Computer TOP.md
+++ /dev/null
@@ -1,12 +0,0 @@
-
-Contraseña/Password: www.intercambiosvirtuales.orgImportanteLos archivos estan comprimidos con WinRAR 5.3 empleando la opción de fichero RAR5, para descomprimir dichos archivos se recomienda actualizar tu descompresor (TUTORIAL).
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168 Comentarios 02/feb/2020168#tabs overflow: hidden; width: 100%; margin: 0; padding: 0; list-style: none;#tabs li float: left; margin: 0 .5em 0 0;#tabs a position: relative; background: #ddd; background-image: linear-gradient(to bottom, #fff, #ddd); padding: .7em 3.5em; float: left; text-decoration: none; color: #444; text-shadow: 0 1px 0 rgba(255,255,255,.8); border-radius: 5px 0 0 0; box-shadow: 0 2px 2px rgba(0,0,0,.4);#tabs a:hover,#tabs a:hover::after,#tabs a:focus,#tabs a:focus::after background: #fff;#tabs a:focus outline: 0;#tabs a::after content:''; position:absolute; z-index: 1; top: 0; right: -.5em; bottom: 0; width: 1em; background: #ddd; background-image: linear-gradient(to bottom, #fff, #ddd); box-shadow: 2px 2px 2px rgba(0,0,0,.4); transform: skew(10deg); border-radius: 0 5px 0 0; #tabs #current a,#tabs #current a::after background: #fff; z-index: 3;#MiContenido background: #fff; padding: 2em; height: 100%; width: 100%; position: relative; z-index: 2; border-radius: 0 5px 5px 5px; box-shadow: 0 -2px 3px -2px rgba(0, 0, 0, .5);(function(d, s, id) var js, fjs = d.getElementsByTagName(s)[0]; if (d.getElementById(id)) return; js = d.createElement(s); js.id = id; js.src = "//connect.facebook.net/es_ES/sdk.js#xfbml=1&version=v2.8&appId=1968157026547285"; fjs.parentNode.insertBefore(js, fjs);(document, 'script', 'facebook-jssdk'));$(document).ready(function() $("#MiContenido").find("[id^='tab']").hide(); // Hide all content $("#tabs li:first").attr("id","current"); // Activate the first tab $("#MiContenido #tab1").fadeIn(); // Show first tab's content $('#tabs a').click(function(e) e.preventDefault(); if ($(this).closest("li").attr("id") == "current") //detection for current tab return; else $("#MiContenido").find("[id^='tab']").hide(); // Hide all content $("#tabs li").attr("id",""); //Reset id's $(this).parent().attr("id","current"); // Activate this $('#' + $(this).attr('name')).fadeIn(); // Show content for the current tab ););
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-Autoplay media studio 8.exe issues are often the result of the executable file that is missing, deleted, or being moved from it's original location. Often, these EXE errors are encountered during AutoPlay Media Studio software startup. If your EXE file is suffering from one of those troubles, replacing it with a fresh file should resolve the issue. In addition, if your autoplay media studio 8.exe error was due to a removed malware infection, we recommend running a registry scan to clean up any invalid file path references created by the malicious program.
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-These types of errors will normally stop occuring if the correct autoplay media studio 8.exe file version is placed in the right location, but you should double-check that is the case. Try re-opening AutoPlay Media Studio to see if the error message still appears.
-AutoPlay Media Studio autoplay media studio 8.exe issues occur with installation, while autoplay media studio 8.exe-related software runs, during shutdown or startup, or less-likely during operating system updates. When seeing the autoplay media studio 8.exe error, please record occurrences to troubleshoot AutoPlay Media Studio, and to help Indigo Rose Corporation find the cause.
-A creative with a fixed size and position on a web page or in a mobile app. A banner is the most basic rich media ad format. Rich media banner creatives can include videos and polite download technology, which waits for the web page to completely load before loading the creative.
-PowerDVD is the best multimedia player for Windows 10 which allows you to download, organize, stream, share, and play your movies, music, photos, and more. It has support for almost every file format for digital files, as well as DVD and Blu-ray. PowerDVD can also screencast to your big screen TV, giving you a cinematic experience with improved audio and video. Detailed Review >
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- aaccfb2cb3
-
-
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diff --git a/spaces/bioriAsaeru/text-to-voice/Exitium-PLAZA The ultimate challenge for adventure game fans.md b/spaces/bioriAsaeru/text-to-voice/Exitium-PLAZA The ultimate challenge for adventure game fans.md
deleted file mode 100644
index 4adb8f42d244dbc28615180b4bb8736361e1c60b..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Exitium-PLAZA The ultimate challenge for adventure game fans.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Exitium-PLAZA
Download 🌟 https://urloso.com/2uyQ7x
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Kalman Filter For Beginners With MATLAB Examples Master the Fundamentals and Advanced Topics of Kalman Filter in MATLAB.md b/spaces/bioriAsaeru/text-to-voice/Kalman Filter For Beginners With MATLAB Examples Master the Fundamentals and Advanced Topics of Kalman Filter in MATLAB.md
deleted file mode 100644
index c405a6c0067b323376bcd0305c74c22441a5c867..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Kalman Filter For Beginners With MATLAB Examples Master the Fundamentals and Advanced Topics of Kalman Filter in MATLAB.md
+++ /dev/null
@@ -1,5 +0,0 @@
-
-The difference in quality when compared to certain leading hardware processors is obvious. Where other processors strain and fall apart, BreakawayOne is loud, clear and consistent, even with extremely difficult program material. The result is a broadcast that dominates the competition with volume and punch, and a clarity that they cannot possibly achieve, while strictly abiding to modulation and power regulations.
-breakaway broadcast processor asio crack
DOWNLOAD ··· https://urloso.com/2uyQfq
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-
-
\ No newline at end of file
diff --git a/spaces/bipin/image2story/app.py b/spaces/bipin/image2story/app.py
deleted file mode 100644
index ddf3b1874973a40a248b5430c2930b3fbbd83ffb..0000000000000000000000000000000000000000
--- a/spaces/bipin/image2story/app.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import gradio as gr
-from huggingface_hub import hf_hub_download
-
-from prefix_clip import generate_caption
-from gpt2_story_gen import generate_story
-
-conceptual_weights = hf_hub_download(repo_id="akhaliq/CLIP-prefix-captioning-conceptual-weights", filename="conceptual_weights.pt")
-coco_weights = hf_hub_download(repo_id="akhaliq/CLIP-prefix-captioning-COCO-weights", filename="coco_weights.pt")
-
-
-def main(pil_image, genre, model, n_stories, use_beam_search=False):
- if model.lower()=='coco':
- model_file = coco_weights
- elif model.lower()=='conceptual':
- model_file = conceptual_weights
-
- image_caption = generate_caption(
- model_path=model_file,
- pil_image=pil_image,
- use_beam_search=use_beam_search,
- )
- story = generate_story(image_caption, pil_image, genre.lower(), n_stories)
- return story
-
-
-if __name__ == "__main__":
- title = "Image to Story"
- article = "Combines the power of [clip prefix captioning](https://github.com/rmokady/CLIP_prefix_caption) with [gpt2 story generator](https://huggingface.co/pranavpsv/genre-story-generator-v2) to create stories of different genres from image"
- description = "Drop an image and generate stories of different genre based on that image"
-
- interface = gr.Interface(
- main,
- title=title,
- description=description,
- article=article,
- inputs=[
- gr.inputs.Image(type="pil", source="upload", label="Input"),
- gr.inputs.Dropdown(
- type="value",
- label="Story genre",
- choices=[
- "superhero",
- "action",
- "drama",
- "horror",
- "thriller",
- "sci_fi",
- ],
- ),
- gr.inputs.Radio(choices=["coco", "conceptual"], label="Model"),
- gr.inputs.Dropdown(choices=[1, 2, 3], label="No. of stories", type="value"),
- ],
- outputs=gr.outputs.Textbox(label="Generated story"),
- examples=[["car.jpg", "drama", "conceptual"], ["gangster.jpg", "action", "coco"]],
- enable_queue=True,
- )
- interface.launch()
diff --git a/spaces/bluebalam/paper-rec/README.md b/spaces/bluebalam/paper-rec/README.md
deleted file mode 100644
index 341ad1edf25bddf6b0b838212c17eea279d7e149..0000000000000000000000000000000000000000
--- a/spaces/bluebalam/paper-rec/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
----
-title: paper-rec
-emoji: 📃 🤖 💙
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-app_file: app.py
-pinned: true
-license: mit
-models: bluebalam/paper-rec
----
-
-# `paper-rec` demo
-
-What paper in ML/AI should I read next? It is difficult to choose from all great research publications published daily. This demo gives you a personalized selection of papers from the latest scientific contributions available in [arXiv](https://arxiv.org/).
-
-You just input the title or abstract (or both) of paper(s) you liked in the past or you can also use keywords of topics of interest and get the top-10 article recommendations tailored to your taste.
-
-Enjoy!
\ No newline at end of file
diff --git a/spaces/blueeyiz702/flax-midjourney-v4-diffusion/app.py b/spaces/blueeyiz702/flax-midjourney-v4-diffusion/app.py
deleted file mode 100644
index a7e777fc5c7f3e31a491e4bd016b8948b6a260f4..0000000000000000000000000000000000000000
--- a/spaces/blueeyiz702/flax-midjourney-v4-diffusion/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/flax/midjourney-v4-diffusion").launch()
\ No newline at end of file
diff --git a/spaces/bodah/RVC-Models-bo/lib/infer_pack/commons.py b/spaces/bodah/RVC-Models-bo/lib/infer_pack/commons.py
deleted file mode 100644
index 54470986f37825b35d90d7efa7437d1c26b87215..0000000000000000000000000000000000000000
--- a/spaces/bodah/RVC-Models-bo/lib/infer_pack/commons.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size * dilation - dilation) / 2)
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
- """KL(P||Q)"""
- kl = (logs_q - logs_p) - 0.5
- kl += (
- 0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
- )
- return kl
-
-
-def rand_gumbel(shape):
- """Sample from the Gumbel distribution, protect from overflows."""
- uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
- return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
- g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
- return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, :, idx_str:idx_end]
- return ret
-
-
-def slice_segments2(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, idx_str:idx_end]
- return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
- position = torch.arange(length, dtype=torch.float)
- num_timescales = channels // 2
- log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
- num_timescales - 1
- )
- inv_timescales = min_timescale * torch.exp(
- torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
- )
- scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
- signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
- signal = F.pad(signal, [0, 0, 0, channels % 2])
- signal = signal.view(1, channels, length)
- return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
- mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
- return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def shift_1d(x):
- x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
- return x
-
-
-def sequence_mask(length, max_length=None):
- if max_length is None:
- max_length = length.max()
- x = torch.arange(max_length, dtype=length.dtype, device=length.device)
- return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
- """
- duration: [b, 1, t_x]
- mask: [b, 1, t_y, t_x]
- """
- device = duration.device
-
- b, _, t_y, t_x = mask.shape
- cum_duration = torch.cumsum(duration, -1)
-
- cum_duration_flat = cum_duration.view(b * t_x)
- path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
- path = path.view(b, t_x, t_y)
- path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
- path = path.unsqueeze(1).transpose(2, 3) * mask
- return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
- if isinstance(parameters, torch.Tensor):
- parameters = [parameters]
- parameters = list(filter(lambda p: p.grad is not None, parameters))
- norm_type = float(norm_type)
- if clip_value is not None:
- clip_value = float(clip_value)
-
- total_norm = 0
- for p in parameters:
- param_norm = p.grad.data.norm(norm_type)
- total_norm += param_norm.item() ** norm_type
- if clip_value is not None:
- p.grad.data.clamp_(min=-clip_value, max=clip_value)
- total_norm = total_norm ** (1.0 / norm_type)
- return total_norm
diff --git a/spaces/bookbot/Grad-TTS-Weildan-Playground/Grad-TTS/text/cleaners.py b/spaces/bookbot/Grad-TTS-Weildan-Playground/Grad-TTS/text/cleaners.py
deleted file mode 100644
index de17a8b952f733daf464548de8a332cb4c89766d..0000000000000000000000000000000000000000
--- a/spaces/bookbot/Grad-TTS-Weildan-Playground/Grad-TTS/text/cleaners.py
+++ /dev/null
@@ -1,73 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-import re
-from unidecode import unidecode
-from .numbers import normalize_numbers
-
-
-_whitespace_re = re.compile(r'\s+')
-
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
- ('mrs', 'misess'),
- ('mr', 'mister'),
- ('dr', 'doctor'),
- ('st', 'saint'),
- ('co', 'company'),
- ('jr', 'junior'),
- ('maj', 'major'),
- ('gen', 'general'),
- ('drs', 'doctors'),
- ('rev', 'reverend'),
- ('lt', 'lieutenant'),
- ('hon', 'honorable'),
- ('sgt', 'sergeant'),
- ('capt', 'captain'),
- ('esq', 'esquire'),
- ('ltd', 'limited'),
- ('col', 'colonel'),
- ('ft', 'fort'),
-]]
-
-
-def expand_abbreviations(text):
- for regex, replacement in _abbreviations:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def expand_numbers(text):
- return normalize_numbers(text)
-
-
-def lowercase(text):
- return text.lower()
-
-
-def collapse_whitespace(text):
- return re.sub(_whitespace_re, ' ', text)
-
-
-def convert_to_ascii(text):
- return unidecode(text)
-
-
-def basic_cleaners(text):
- text = lowercase(text)
- text = collapse_whitespace(text)
- return text
-
-
-def transliteration_cleaners(text):
- text = convert_to_ascii(text)
- text = lowercase(text)
- text = collapse_whitespace(text)
- return text
-
-
-def english_cleaners(text):
- text = convert_to_ascii(text)
- text = lowercase(text)
- text = expand_numbers(text)
- text = expand_abbreviations(text)
- text = collapse_whitespace(text)
- return text
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/data/samplers/mask_from_densepose.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/data/samplers/mask_from_densepose.py
deleted file mode 100644
index 0e6e812ba5af4675a81aec3ef8fd9b96d53325cc..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/data/samplers/mask_from_densepose.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from detectron2.structures import BitMasks, Instances
-
-from densepose.converters import ToMaskConverter
-
-
-class MaskFromDensePoseSampler:
- """
- Produce mask GT from DensePose predictions
- This sampler simply converts DensePose predictions to BitMasks
- that a contain a bool tensor of the size of the input image
- """
-
- def __call__(self, instances: Instances) -> BitMasks:
- """
- Converts predicted data from `instances` into the GT mask data
-
- Args:
- instances (Instances): predicted results, expected to have `pred_densepose` field
-
- Returns:
- Boolean Tensor of the size of the input image that has non-zero
- values at pixels that are estimated to belong to the detected object
- """
- return ToMaskConverter.convert(
- instances.pred_densepose, instances.pred_boxes, instances.image_size
- )
diff --git a/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/platforms/base.py b/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/platforms/base.py
deleted file mode 100644
index c9ecda906145e239737901809aa59db8d3e231c6..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/platforms/base.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import abc
-
-import six
-
-
-@six.add_metaclass(abc.ABCMeta)
-class Platform(object):
- """Base class for all OpenGL platforms.
-
- Parameters
- ----------
- viewport_width : int
- The width of the main viewport, in pixels.
- viewport_height : int
- The height of the main viewport, in pixels
- """
-
- def __init__(self, viewport_width, viewport_height):
- self.viewport_width = viewport_width
- self.viewport_height = viewport_height
-
- @property
- def viewport_width(self):
- """int : The width of the main viewport, in pixels.
- """
- return self._viewport_width
-
- @viewport_width.setter
- def viewport_width(self, value):
- self._viewport_width = value
-
- @property
- def viewport_height(self):
- """int : The height of the main viewport, in pixels.
- """
- return self._viewport_height
-
- @viewport_height.setter
- def viewport_height(self, value):
- self._viewport_height = value
-
- @abc.abstractmethod
- def init_context(self):
- """Create an OpenGL context.
- """
- pass
-
- @abc.abstractmethod
- def make_current(self):
- """Make the OpenGL context current.
- """
- pass
-
- @abc.abstractmethod
- def make_uncurrent(self):
- """Make the OpenGL context uncurrent.
- """
- pass
-
- @abc.abstractmethod
- def delete_context(self):
- """Delete the OpenGL context.
- """
- pass
-
- @abc.abstractmethod
- def supports_framebuffers(self):
- """Returns True if the method supports framebuffer rendering.
- """
- pass
-
- def __del__(self):
- try:
- self.delete_context()
- except Exception:
- pass
diff --git a/spaces/camenduru-com/one-shot-talking-face/Dockerfile b/spaces/camenduru-com/one-shot-talking-face/Dockerfile
deleted file mode 100644
index b19e05fd5bacec4c732b69cb9566a03d933416d8..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/one-shot-talking-face/Dockerfile
+++ /dev/null
@@ -1,35 +0,0 @@
-# https://gitlab.com/nvidia/container-images/cuda/-/blob/master/dist/11.2.1/ubuntu2004/devel/cudnn8/Dockerfile
-FROM nvidia/cuda:12.2.0-base-ubuntu20.04
-ENV DEBIAN_FRONTEND noninteractive
-
-WORKDIR /content
-RUN apt-get update -y && apt-get upgrade -y && apt-get install -y sudo && apt-get install -y python3-pip && pip3 install --upgrade pip
-RUN apt-get install -y gnupg wget htop sudo git git-lfs software-properties-common build-essential cmake curl
-RUN apt-get install -y ffmpeg libavcodec-dev libavformat-dev libavdevice-dev libgl1 libgtk2.0-0 jq libdc1394-22-dev libraw1394-dev libopenblas-base
-
-ENV PATH="/home/admin/.local/bin:${PATH}"
-
-RUN pip3 install pandas scipy matplotlib torch torchvision torchaudio gradio altair imageio-ffmpeg pocketsphinx jq "numpy<1.24"
-
-RUN git lfs install
-RUN git clone https://huggingface.co/camenduru/pocketsphinx-20.04-t4 pocketsphinx && cd pocketsphinx && cmake --build build --target install
-
-RUN git clone https://huggingface.co/camenduru/one-shot-talking-face-20.04-t4 one-shot-talking-face && cd one-shot-talking-face && pip install -r requirements.txt && chmod 755 OpenFace/FeatureExtraction
-RUN mkdir /content/out
-
-COPY app.py /content/app.py
-COPY examples /content/examples
-
-RUN adduser --disabled-password --gecos '' admin
-RUN adduser admin sudo
-RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
-
-RUN chown -R admin:admin /content
-RUN chmod -R 777 /content
-RUN chown -R admin:admin /home
-RUN chmod -R 777 /home
-USER admin
-
-EXPOSE 7860
-
-CMD ["python3", "app.py"]
\ No newline at end of file
diff --git a/spaces/camillevanhoffelen/langchain-HuggingGPT/app.py b/spaces/camillevanhoffelen/langchain-HuggingGPT/app.py
deleted file mode 100644
index 1d930a6f418c3f90dc2d63b9d098234845293b64..0000000000000000000000000000000000000000
--- a/spaces/camillevanhoffelen/langchain-HuggingGPT/app.py
+++ /dev/null
@@ -1,243 +0,0 @@
-import logging
-import os
-import re
-
-import gradio as gr
-from dotenv import load_dotenv
-
-from hugginggpt.history import ConversationHistory
-from hugginggpt.llm_factory import create_llms
-from hugginggpt.log import setup_logging
-from hugginggpt.resources import (
- GENERATED_RESOURCES_DIR,
- get_resource_url,
- init_resource_dirs,
- load_audio,
- load_image,
- save_audio,
- save_image,
-)
-from main import compute
-
-load_dotenv()
-setup_logging()
-logger = logging.getLogger(__name__)
-init_resource_dirs()
-
-OPENAI_KEY = os.environ.get("OPENAI_API_KEY")
-HUGGINGFACE_TOKEN = os.environ.get("HUGGINGFACEHUB_API_TOKEN")
-
-
-class Client:
- def __init__(self) -> None:
- self.llms = None
- self.llm_history = ConversationHistory()
- self.last_user_input = ""
-
- @property
- def is_init(self) -> bool:
- return (
- os.environ.get("OPENAI_API_KEY")
- and os.environ.get("OPENAI_API_KEY").startswith("sk-")
- and os.environ.get("HUGGINGFACEHUB_API_TOKEN")
- and os.environ.get("HUGGINGFACEHUB_API_TOKEN").startswith("hf_")
- )
-
- def add_text(self, user_input, messages):
- if not self.is_init:
- return (
- "Please set your OpenAI API key and Hugging Face token first!!!",
- messages,
- )
- if not self.llms:
- self.llms = create_llms()
-
- self.last_user_input = user_input
- try:
- messages = display_message(
- role="user", message=user_input, messages=messages, save_media=True
- )
- except Exception as e:
- logger.exception("")
- error_message = f"Sorry, encountered error: {e}. Please try again. Check logs if problem persists."
- messages = display_message(
- role="assistant",
- message=error_message,
- messages=messages,
- save_media=False,
- )
- return "", messages
-
- def bot(self, messages):
- if not self.is_init:
- return {}, messages
- try:
- user_input = self.last_user_input
- response, task_summaries = compute(
- user_input=user_input,
- history=self.llm_history,
- llms=self.llms,
- )
- messages = display_message(
- role="assistant", message=response, messages=messages, save_media=False
- )
- self.llm_history.add(role="user", content=user_input)
- self.llm_history.add(role="assistant", content="")
- return task_summaries, messages
- except Exception as e:
- logger.exception("")
- error_message = f"Sorry, encountered error: {e}. Please try again. Check logs if problem persists."
- messages = display_message(
- role="assistant",
- message=error_message,
- messages=messages,
- save_media=False,
- )
- return [], messages
-
-
-css = ".json {height: 527px; overflow: scroll;} .json-holder {height: 527px; overflow: scroll;}"
-with gr.Blocks(css=css) as demo:
- gr.Markdown("langchain-HuggingGPT
")
- gr.Markdown(
- "
"
- )
- gr.Markdown(
- "A lightweight implementation of HuggingGPT with langchain. No local inference, only models available on the Hugging Face Inference API are used.
"
- )
- gr.HTML(
- """
Duplicate the Space and run securely with your OpenAI API Key and Hugging Face Token"""
- )
- if not OPENAI_KEY:
- with gr.Row().style():
- with gr.Column(scale=0.85):
- openai_api_key = gr.Textbox(
- show_label=False,
- placeholder="Set your OpenAI API key here and press Enter",
- lines=1,
- type="password",
- ).style(container=False)
- with gr.Column(scale=0.15, min_width=0):
- btn1 = gr.Button("Submit").style(full_height=True)
-
- if not HUGGINGFACE_TOKEN:
- with gr.Row().style():
- with gr.Column(scale=0.85):
- hugging_face_token = gr.Textbox(
- show_label=False,
- placeholder="Set your Hugging Face Token here and press Enter",
- lines=1,
- type="password",
- ).style(container=False)
- with gr.Column(scale=0.15, min_width=0):
- btn3 = gr.Button("Submit").style(full_height=True)
-
- with gr.Row().style():
- with gr.Column(scale=0.6):
- chatbot = gr.Chatbot([], elem_id="chatbot").style(height=500)
- with gr.Column(scale=0.4):
- results = gr.JSON(elem_classes="json")
-
- with gr.Row().style():
- with gr.Column(scale=0.85):
- txt = gr.Textbox(
- show_label=False,
- placeholder="Enter text and press enter. The url must contain the media type. e.g, https://example.com/example.jpg",
- lines=1,
- ).style(container=False)
- with gr.Column(scale=0.15, min_width=0):
- btn2 = gr.Button("Send").style(full_height=True)
-
- def set_key(openai_api_key):
- os.environ["OPENAI_API_KEY"] = openai_api_key
- return openai_api_key
-
- def set_token(hugging_face_token):
- os.environ["HUGGINGFACEHUB_API_TOKEN"] = hugging_face_token
- return hugging_face_token
-
- def add_text(state, user_input, messages):
- return state["client"].add_text(user_input, messages)
-
- def bot(state, messages):
- return state["client"].bot(messages)
-
- if not OPENAI_KEY or not HUGGINGFACE_TOKEN:
- openai_api_key.submit(set_key, [openai_api_key], [openai_api_key])
- btn1.click(set_key, [openai_api_key], [openai_api_key])
- hugging_face_token.submit(set_token, [hugging_face_token], [hugging_face_token])
- btn3.click(set_token, [hugging_face_token], [hugging_face_token])
-
- state = gr.State(value={"client": Client()})
-
- txt.submit(add_text, [state, txt, chatbot], [txt, chatbot]).then(
- bot, [state, chatbot], [results, chatbot]
- )
- btn2.click(add_text, [state, txt, chatbot], [txt, chatbot]).then(
- bot, [state, chatbot], [results, chatbot]
- )
-
- gr.Examples(
- examples=[
- "Draw me a sheep",
- "Write a poem about sheep, then read it to me",
- "Transcribe the audio file found at /audios/499e.flac. Then tell me how similar the transcription is to the following sentence: Sheep are nice.",
- "Tell me a joke about a sheep, then illustrate it by generating an image",
- ],
- inputs=txt,
- )
-
-
-def display_message(role: str, message: str, messages: list, save_media: bool):
- # Text
- messages.append(format_message(role=role, message=message))
-
- # Media
- image_urls, audio_urls = extract_medias(message)
- for image_url in image_urls:
- image_url = get_resource_url(image_url)
- if save_media:
- image = load_image(image_url)
- image_url = save_image(image)
- image_url = GENERATED_RESOURCES_DIR + image_url
- messages.append(format_message(role=role, message=(image_url,)))
-
- for audio_url in audio_urls:
- audio_url = get_resource_url(audio_url)
- if save_media:
- audio = load_audio(audio_url)
- audio_url = save_audio(audio)
- audio_url = GENERATED_RESOURCES_DIR + audio_url
- messages.append(format_message(role=role, message=(audio_url,)))
-
- return messages
-
-
-def format_message(role, message):
- if role == "user":
- return message, None
- if role == "assistant":
- return None, message
- else:
- raise ValueError("role must be either user or assistant")
-
-
-def extract_medias(message: str):
- image_pattern = re.compile(
- r"(http(s?):|\/)?([\.\/_\w:-])*?\.(jpg|jpeg|tiff|gif|png)"
- )
- image_urls = []
- for match in image_pattern.finditer(message):
- if match.group(0) not in image_urls:
- image_urls.append(match.group(0))
-
- audio_pattern = re.compile(r"(http(s?):|\/)?([\.\/_\w:-])*?\.(flac|wav)")
- audio_urls = []
- for match in audio_pattern.finditer(message):
- if match.group(0) not in audio_urls:
- audio_urls.append(match.group(0))
-
- return image_urls, audio_urls
-
-
-demo.launch()
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/TgaImagePlugin.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/TgaImagePlugin.py
deleted file mode 100644
index 67dfc3d3c8e5726c5885b1c62cdcb2553854c4dc..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/TgaImagePlugin.py
+++ /dev/null
@@ -1,255 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# TGA file handling
-#
-# History:
-# 95-09-01 fl created (reads 24-bit files only)
-# 97-01-04 fl support more TGA versions, including compressed images
-# 98-07-04 fl fixed orientation and alpha layer bugs
-# 98-09-11 fl fixed orientation for runlength decoder
-#
-# Copyright (c) Secret Labs AB 1997-98.
-# Copyright (c) Fredrik Lundh 1995-97.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-import warnings
-
-from . import Image, ImageFile, ImagePalette
-from ._binary import i16le as i16
-from ._binary import o8
-from ._binary import o16le as o16
-
-#
-# --------------------------------------------------------------------
-# Read RGA file
-
-
-MODES = {
- # map imagetype/depth to rawmode
- (1, 8): "P",
- (3, 1): "1",
- (3, 8): "L",
- (3, 16): "LA",
- (2, 16): "BGR;5",
- (2, 24): "BGR",
- (2, 32): "BGRA",
-}
-
-
-##
-# Image plugin for Targa files.
-
-
-class TgaImageFile(ImageFile.ImageFile):
- format = "TGA"
- format_description = "Targa"
-
- def _open(self):
- # process header
- s = self.fp.read(18)
-
- id_len = s[0]
-
- colormaptype = s[1]
- imagetype = s[2]
-
- depth = s[16]
-
- flags = s[17]
-
- self._size = i16(s, 12), i16(s, 14)
-
- # validate header fields
- if (
- colormaptype not in (0, 1)
- or self.size[0] <= 0
- or self.size[1] <= 0
- or depth not in (1, 8, 16, 24, 32)
- ):
- msg = "not a TGA file"
- raise SyntaxError(msg)
-
- # image mode
- if imagetype in (3, 11):
- self.mode = "L"
- if depth == 1:
- self.mode = "1" # ???
- elif depth == 16:
- self.mode = "LA"
- elif imagetype in (1, 9):
- self.mode = "P"
- elif imagetype in (2, 10):
- self.mode = "RGB"
- if depth == 32:
- self.mode = "RGBA"
- else:
- msg = "unknown TGA mode"
- raise SyntaxError(msg)
-
- # orientation
- orientation = flags & 0x30
- self._flip_horizontally = orientation in [0x10, 0x30]
- if orientation in [0x20, 0x30]:
- orientation = 1
- elif orientation in [0, 0x10]:
- orientation = -1
- else:
- msg = "unknown TGA orientation"
- raise SyntaxError(msg)
-
- self.info["orientation"] = orientation
-
- if imagetype & 8:
- self.info["compression"] = "tga_rle"
-
- if id_len:
- self.info["id_section"] = self.fp.read(id_len)
-
- if colormaptype:
- # read palette
- start, size, mapdepth = i16(s, 3), i16(s, 5), s[7]
- if mapdepth == 16:
- self.palette = ImagePalette.raw(
- "BGR;15", b"\0" * 2 * start + self.fp.read(2 * size)
- )
- elif mapdepth == 24:
- self.palette = ImagePalette.raw(
- "BGR", b"\0" * 3 * start + self.fp.read(3 * size)
- )
- elif mapdepth == 32:
- self.palette = ImagePalette.raw(
- "BGRA", b"\0" * 4 * start + self.fp.read(4 * size)
- )
-
- # setup tile descriptor
- try:
- rawmode = MODES[(imagetype & 7, depth)]
- if imagetype & 8:
- # compressed
- self.tile = [
- (
- "tga_rle",
- (0, 0) + self.size,
- self.fp.tell(),
- (rawmode, orientation, depth),
- )
- ]
- else:
- self.tile = [
- (
- "raw",
- (0, 0) + self.size,
- self.fp.tell(),
- (rawmode, 0, orientation),
- )
- ]
- except KeyError:
- pass # cannot decode
-
- def load_end(self):
- if self._flip_horizontally:
- self.im = self.im.transpose(Image.Transpose.FLIP_LEFT_RIGHT)
-
-
-#
-# --------------------------------------------------------------------
-# Write TGA file
-
-
-SAVE = {
- "1": ("1", 1, 0, 3),
- "L": ("L", 8, 0, 3),
- "LA": ("LA", 16, 0, 3),
- "P": ("P", 8, 1, 1),
- "RGB": ("BGR", 24, 0, 2),
- "RGBA": ("BGRA", 32, 0, 2),
-}
-
-
-def _save(im, fp, filename):
- try:
- rawmode, bits, colormaptype, imagetype = SAVE[im.mode]
- except KeyError as e:
- msg = f"cannot write mode {im.mode} as TGA"
- raise OSError(msg) from e
-
- if "rle" in im.encoderinfo:
- rle = im.encoderinfo["rle"]
- else:
- compression = im.encoderinfo.get("compression", im.info.get("compression"))
- rle = compression == "tga_rle"
- if rle:
- imagetype += 8
-
- id_section = im.encoderinfo.get("id_section", im.info.get("id_section", ""))
- id_len = len(id_section)
- if id_len > 255:
- id_len = 255
- id_section = id_section[:255]
- warnings.warn("id_section has been trimmed to 255 characters")
-
- if colormaptype:
- palette = im.im.getpalette("RGB", "BGR")
- colormaplength, colormapentry = len(palette) // 3, 24
- else:
- colormaplength, colormapentry = 0, 0
-
- if im.mode in ("LA", "RGBA"):
- flags = 8
- else:
- flags = 0
-
- orientation = im.encoderinfo.get("orientation", im.info.get("orientation", -1))
- if orientation > 0:
- flags = flags | 0x20
-
- fp.write(
- o8(id_len)
- + o8(colormaptype)
- + o8(imagetype)
- + o16(0) # colormapfirst
- + o16(colormaplength)
- + o8(colormapentry)
- + o16(0)
- + o16(0)
- + o16(im.size[0])
- + o16(im.size[1])
- + o8(bits)
- + o8(flags)
- )
-
- if id_section:
- fp.write(id_section)
-
- if colormaptype:
- fp.write(palette)
-
- if rle:
- ImageFile._save(
- im, fp, [("tga_rle", (0, 0) + im.size, 0, (rawmode, orientation))]
- )
- else:
- ImageFile._save(
- im, fp, [("raw", (0, 0) + im.size, 0, (rawmode, 0, orientation))]
- )
-
- # write targa version 2 footer
- fp.write(b"\000" * 8 + b"TRUEVISION-XFILE." + b"\000")
-
-
-#
-# --------------------------------------------------------------------
-# Registry
-
-
-Image.register_open(TgaImageFile.format, TgaImageFile)
-Image.register_save(TgaImageFile.format, _save)
-
-Image.register_extensions(TgaImageFile.format, [".tga", ".icb", ".vda", ".vst"])
-
-Image.register_mime(TgaImageFile.format, "image/x-tga")
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/utils.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/utils.py
deleted file mode 100644
index 9878c31d03bd4114425f89dd1c6dda74337fe2e2..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/utils.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import os
-from typing import Dict, Optional
-
-from detectron2.config import CfgNode
-
-
-def is_relative_local_path(path: str) -> bool:
- path_str = os.fsdecode(path)
- return ("://" not in path_str) and not os.path.isabs(path)
-
-
-def maybe_prepend_base_path(base_path: Optional[str], path: str):
- """
- Prepends the provided path with a base path prefix if:
- 1) base path is not None;
- 2) path is a local path
- """
- if base_path is None:
- return path
- if is_relative_local_path(path):
- return os.path.join(base_path, path)
- return path
-
-
-def get_class_to_mesh_name_mapping(cfg: CfgNode) -> Dict[int, str]:
- return {
- int(class_id): mesh_name
- for class_id, mesh_name in cfg.DATASETS.CLASS_TO_MESH_NAME_MAPPING.items()
- }
-
-
-def get_category_to_class_mapping(dataset_cfg: CfgNode) -> Dict[str, int]:
- return {
- category: int(class_id)
- for category, class_id in dataset_cfg.CATEGORY_TO_CLASS_MAPPING.items()
- }
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/tests/test_cse_annotations_accumulator.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/tests/test_cse_annotations_accumulator.py
deleted file mode 100644
index a22dce9ce00532d60dc3f4edbef4cea26b006b92..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/tests/test_cse_annotations_accumulator.py
+++ /dev/null
@@ -1,240 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-import unittest
-import torch
-
-from detectron2.structures import Boxes, BoxMode, Instances
-
-from densepose.modeling.losses.embed_utils import CseAnnotationsAccumulator
-from densepose.structures import DensePoseDataRelative, DensePoseList
-
-
-class TestCseAnnotationsAccumulator(unittest.TestCase):
- def test_cse_annotations_accumulator_nodp(self):
- instances_lst = [
- self._create_instances_nodp(),
- ]
- self._test_template(instances_lst)
-
- def test_cse_annotations_accumulator_sparsedp(self):
- instances_lst = [
- self._create_instances_sparsedp(),
- ]
- self._test_template(instances_lst)
-
- def test_cse_annotations_accumulator_fulldp(self):
- instances_lst = [
- self._create_instances_fulldp(),
- ]
- self._test_template(instances_lst)
-
- def test_cse_annotations_accumulator_combined(self):
- instances_lst = [
- self._create_instances_nodp(),
- self._create_instances_sparsedp(),
- self._create_instances_fulldp(),
- ]
- self._test_template(instances_lst)
-
- def _test_template(self, instances_lst):
- acc = CseAnnotationsAccumulator()
- for instances in instances_lst:
- acc.accumulate(instances)
- packed_anns = acc.pack()
- self._check_correspondence(packed_anns, instances_lst)
-
- def _create_instances_nodp(self):
- image_shape = (480, 640)
- instances = Instances(image_shape)
- instances.gt_boxes = Boxes(
- torch.as_tensor(
- [
- [40.0, 40.0, 140.0, 140.0],
- [160.0, 160.0, 270.0, 270.0],
- [40.0, 160.0, 160.0, 280.0],
- ]
- )
- )
- instances.proposal_boxes = Boxes(
- torch.as_tensor(
- [
- [41.0, 39.0, 142.0, 138.0],
- [161.0, 159.0, 272.0, 268.0],
- [41.0, 159.0, 162.0, 278.0],
- ]
- )
- )
- # do not add gt_densepose
- return instances
-
- def _create_instances_sparsedp(self):
- image_shape = (540, 720)
- instances = Instances(image_shape)
- instances.gt_boxes = Boxes(
- torch.as_tensor(
- [
- [50.0, 50.0, 130.0, 130.0],
- [150.0, 150.0, 240.0, 240.0],
- [50.0, 150.0, 230.0, 330.0],
- ]
- )
- )
- instances.proposal_boxes = Boxes(
- torch.as_tensor(
- [
- [49.0, 51.0, 131.0, 129.0],
- [151.0, 149.0, 241.0, 239.0],
- [51.0, 149.0, 232.0, 329.0],
- ]
- )
- )
- instances.gt_densepose = DensePoseList(
- [
- None,
- self._create_dp_data(
- {
- "dp_x": [81.69, 153.47, 151.00],
- "dp_y": [162.24, 128.71, 113.81],
- "dp_vertex": [0, 1, 2],
- "ref_model": "zebra_5002",
- "dp_masks": [],
- },
- {"c": (166, 133), "r": 64},
- ),
- None,
- ],
- instances.gt_boxes,
- image_shape,
- )
- return instances
-
- def _create_instances_fulldp(self):
- image_shape = (680, 840)
- instances = Instances(image_shape)
- instances.gt_boxes = Boxes(
- torch.as_tensor(
- [
- [65.0, 55.0, 165.0, 155.0],
- [170.0, 175.0, 275.0, 280.0],
- [55.0, 165.0, 165.0, 275.0],
- ]
- )
- )
- instances.proposal_boxes = Boxes(
- torch.as_tensor(
- [
- [66.0, 54.0, 166.0, 154.0],
- [171.0, 174.0, 276.0, 279.0],
- [56.0, 164.0, 166.0, 274.0],
- ]
- )
- )
- instances.gt_densepose = DensePoseList(
- [
- self._create_dp_data(
- {
- "dp_x": [149.99, 198.62, 157.59],
- "dp_y": [170.74, 197.73, 123.12],
- "dp_vertex": [3, 4, 5],
- "ref_model": "cat_5001",
- "dp_masks": [],
- },
- {"c": (100, 100), "r": 50},
- ),
- self._create_dp_data(
- {
- "dp_x": [234.53, 116.72, 71.66],
- "dp_y": [107.53, 11.31, 142.32],
- "dp_vertex": [6, 7, 8],
- "ref_model": "dog_5002",
- "dp_masks": [],
- },
- {"c": (200, 150), "r": 40},
- ),
- self._create_dp_data(
- {
- "dp_x": [225.54, 202.61, 135.90],
- "dp_y": [167.46, 181.00, 211.47],
- "dp_vertex": [9, 10, 11],
- "ref_model": "elephant_5002",
- "dp_masks": [],
- },
- {"c": (100, 200), "r": 45},
- ),
- ],
- instances.gt_boxes,
- image_shape,
- )
- return instances
-
- def _create_dp_data(self, anns, blob_def=None):
- dp_data = DensePoseDataRelative(anns)
- if blob_def is not None:
- dp_data.segm[
- blob_def["c"][0] - blob_def["r"] : blob_def["c"][0] + blob_def["r"],
- blob_def["c"][1] - blob_def["r"] : blob_def["c"][1] + blob_def["r"],
- ] = 1
- return dp_data
-
- def _check_correspondence(self, packed_anns, instances_lst):
- instance_idx = 0
- data_idx = 0
- pt_offset = 0
- if packed_anns is not None:
- bbox_xyxy_gt = BoxMode.convert(
- packed_anns.bbox_xywh_gt.clone(), BoxMode.XYWH_ABS, BoxMode.XYXY_ABS
- )
- bbox_xyxy_est = BoxMode.convert(
- packed_anns.bbox_xywh_est.clone(), BoxMode.XYWH_ABS, BoxMode.XYXY_ABS
- )
- for instances in instances_lst:
- if not hasattr(instances, "gt_densepose"):
- instance_idx += len(instances)
- continue
- for i, dp_data in enumerate(instances.gt_densepose):
- if dp_data is None:
- instance_idx += 1
- continue
- n_pts = len(dp_data.x)
- self.assertTrue(
- torch.allclose(dp_data.x, packed_anns.x_gt[pt_offset : pt_offset + n_pts])
- )
- self.assertTrue(
- torch.allclose(dp_data.y, packed_anns.y_gt[pt_offset : pt_offset + n_pts])
- )
- self.assertTrue(torch.allclose(dp_data.segm, packed_anns.coarse_segm_gt[data_idx]))
- self.assertTrue(
- torch.allclose(
- torch.ones(n_pts, dtype=torch.long) * dp_data.mesh_id,
- packed_anns.vertex_mesh_ids_gt[pt_offset : pt_offset + n_pts],
- )
- )
- self.assertTrue(
- torch.allclose(
- dp_data.vertex_ids, packed_anns.vertex_ids_gt[pt_offset : pt_offset + n_pts]
- )
- )
- self.assertTrue(
- torch.allclose(instances.gt_boxes.tensor[i], bbox_xyxy_gt[data_idx])
- )
- self.assertTrue(
- torch.allclose(instances.proposal_boxes.tensor[i], bbox_xyxy_est[data_idx])
- )
- self.assertTrue(
- torch.allclose(
- torch.ones(n_pts, dtype=torch.long) * data_idx,
- packed_anns.point_bbox_with_dp_indices[pt_offset : pt_offset + n_pts],
- )
- )
- self.assertTrue(
- torch.allclose(
- torch.ones(n_pts, dtype=torch.long) * instance_idx,
- packed_anns.point_bbox_indices[pt_offset : pt_offset + n_pts],
- )
- )
- self.assertEqual(instance_idx, packed_anns.bbox_indices[data_idx])
- pt_offset += n_pts
- instance_idx += 1
- data_idx += 1
- if data_idx == 0:
- self.assertIsNone(packed_anns)
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/PointRend/point_rend/__init__.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/PointRend/point_rend/__init__.py
deleted file mode 100644
index e3050cbddb92f4ec3acf091cc7aed0ea70484927..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/PointRend/point_rend/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .config import add_pointrend_config
-from .mask_head import PointRendMaskHead, ImplicitPointRendMaskHead
-from .semantic_seg import PointRendSemSegHead
-from .color_augmentation import ColorAugSSDTransform
-
-from . import roi_heads as _ # only registration
diff --git a/spaces/ccolas/TastyPiano/src/__init__.py b/spaces/ccolas/TastyPiano/src/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/docs/conf.py b/spaces/chendl/compositional_test/multimodal/YOLOX/docs/conf.py
deleted file mode 100644
index 5d529682b248d7fb33668e0a4c56f5b178efa675..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/YOLOX/docs/conf.py
+++ /dev/null
@@ -1,384 +0,0 @@
-# -*- coding: utf-8 -*-
-# Code are based on
-# https://github.com/facebookresearch/detectron2/blob/master/docs/conf.py
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-# flake8: noqa
-
-# Configuration file for the Sphinx documentation builder.
-#
-# This file does only contain a selection of the most common options. For a
-# full list see the documentation:
-# http://www.sphinx-doc.org/en/master/config
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os
-import sys
-from unittest import mock
-from sphinx.domains import Domain
-from typing import Dict, List, Tuple
-
-# The theme to use for HTML and HTML Help pages. See the documentation for
-# a list of builtin themes.
-#
-import sphinx_rtd_theme
-
-
-class GithubURLDomain(Domain):
- """
- Resolve certain links in markdown files to github source.
- """
-
- name = "githuburl"
- ROOT = "https://github.com/Megvii-BaseDetection/YOLOX"
- # LINKED_DOC = ["tutorials/install", "tutorials/getting_started"]
- LINKED_DOC = ["tutorials/install",]
-
- def resolve_any_xref(self, env, fromdocname, builder, target, node, contnode):
- github_url = None
- if not target.endswith("html") and target.startswith("../../"):
- url = target.replace("../", "")
- github_url = url
- if fromdocname in self.LINKED_DOC:
- # unresolved links in these docs are all github links
- github_url = target
-
- if github_url is not None:
- if github_url.endswith("MODEL_ZOO") or github_url.endswith("README"):
- # bug of recommonmark.
- # https://github.com/readthedocs/recommonmark/blob/ddd56e7717e9745f11300059e4268e204138a6b1/recommonmark/parser.py#L152-L155
- github_url += ".md"
- print("Ref {} resolved to github:{}".format(target, github_url))
- contnode["refuri"] = self.ROOT + github_url
- return [("githuburl:any", contnode)]
- else:
- return []
-
-
-# to support markdown
-from recommonmark.parser import CommonMarkParser
-
-sys.path.insert(0, os.path.abspath("../"))
-os.environ["_DOC_BUILDING"] = "True"
-DEPLOY = os.environ.get("READTHEDOCS") == "True"
-
-
-# -- Project information -----------------------------------------------------
-
-# fmt: off
-try:
- import torch # noqa
-except ImportError:
- for m in [
- "torch", "torchvision", "torch.nn", "torch.nn.parallel", "torch.distributed", "torch.multiprocessing", "torch.autograd",
- "torch.autograd.function", "torch.nn.modules", "torch.nn.modules.utils", "torch.utils", "torch.utils.data", "torch.onnx",
- "torchvision", "torchvision.ops",
- ]:
- sys.modules[m] = mock.Mock(name=m)
- sys.modules['torch'].__version__ = "1.7" # fake version
- HAS_TORCH = False
-else:
- try:
- torch.ops.yolox = mock.Mock(name="torch.ops.yolox")
- except:
- pass
- HAS_TORCH = True
-
-for m in [
- "cv2", "scipy", "portalocker", "yolox._C",
- "pycocotools", "pycocotools.mask", "pycocotools.coco", "pycocotools.cocoeval",
- "google", "google.protobuf", "google.protobuf.internal", "onnx",
- "caffe2", "caffe2.proto", "caffe2.python", "caffe2.python.utils", "caffe2.python.onnx", "caffe2.python.onnx.backend",
-]:
- sys.modules[m] = mock.Mock(name=m)
-# fmt: on
-sys.modules["cv2"].__version__ = "3.4"
-
-import yolox # isort: skip
-
-# if HAS_TORCH:
-# from detectron2.utils.env import fixup_module_metadata
-
-# fixup_module_metadata("torch.nn", torch.nn.__dict__)
-# fixup_module_metadata("torch.utils.data", torch.utils.data.__dict__)
-
-
-project = "YOLOX"
-copyright = "2021-2021, YOLOX contributors"
-author = "YOLOX contributors"
-
-# The short X.Y version
-version = yolox.__version__
-# The full version, including alpha/beta/rc tags
-release = version
-
-
-# -- General configuration ---------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-#
-needs_sphinx = "3.0"
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
- "recommonmark",
- "sphinx.ext.autodoc",
- "sphinx.ext.napoleon",
- "sphinx.ext.intersphinx",
- "sphinx.ext.todo",
- "sphinx.ext.coverage",
- "sphinx.ext.mathjax",
- "sphinx.ext.viewcode",
- "sphinx.ext.githubpages",
- 'sphinx_markdown_tables',
-]
-
-# -- Configurations for plugins ------------
-napoleon_google_docstring = True
-napoleon_include_init_with_doc = True
-napoleon_include_special_with_doc = True
-napoleon_numpy_docstring = False
-napoleon_use_rtype = False
-autodoc_inherit_docstrings = False
-autodoc_member_order = "bysource"
-
-if DEPLOY:
- intersphinx_timeout = 10
-else:
- # skip this when building locally
- intersphinx_timeout = 0.5
-intersphinx_mapping = {
- "python": ("https://docs.python.org/3.6", None),
- "numpy": ("https://docs.scipy.org/doc/numpy/", None),
- "torch": ("https://pytorch.org/docs/master/", None),
-}
-# -------------------------
-
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ["_templates"]
-
-source_suffix = [".rst", ".md"]
-
-# The master toctree document.
-master_doc = "index"
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-language = None
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "build", "README.md", "tutorials/README.md"]
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = "sphinx"
-
-
-# -- Options for HTML output -------------------------------------------------
-
-html_theme = "sphinx_rtd_theme"
-html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further. For a list of options available for each theme, see the
-# documentation.
-#
-# html_theme_options = {}
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ["_static"]
-html_css_files = ["css/custom.css"]
-
-# Custom sidebar templates, must be a dictionary that maps document names
-# to template names.
-#
-# The default sidebars (for documents that don't match any pattern) are
-# defined by theme itself. Builtin themes are using these templates by
-# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
-# 'searchbox.html']``.
-#
-# html_sidebars = {}
-
-
-# -- Options for HTMLHelp output ---------------------------------------------
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = "yoloxdoc"
-
-
-# -- Options for LaTeX output ------------------------------------------------
-
-latex_elements = {
- # The paper size ('letterpaper' or 'a4paper').
- #
- # 'papersize': 'letterpaper',
- # The font size ('10pt', '11pt' or '12pt').
- #
- # 'pointsize': '10pt',
- # Additional stuff for the LaTeX preamble.
- #
- # 'preamble': '',
- # Latex figure (float) alignment
- #
- # 'figure_align': 'htbp',
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-# author, documentclass [howto, manual, or own class]).
-latex_documents = [
- (master_doc, "yolox.tex", "yolox Documentation", "yolox contributors", "manual")
-]
-
-
-# -- Options for manual page output ------------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [(master_doc, "YOLOX", "YOLOX Documentation", [author], 1)]
-
-
-# -- Options for Texinfo output ----------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-# dir menu entry, description, category)
-texinfo_documents = [
- (
- master_doc,
- "YOLOX",
- "YOLOX Documentation",
- author,
- "YOLOX",
- "One line description of project.",
- "Miscellaneous",
- )
-]
-
-
-# -- Options for todo extension ----------------------------------------------
-
-# If true, `todo` and `todoList` produce output, else they produce nothing.
-todo_include_todos = True
-
-
-def autodoc_skip_member(app, what, name, obj, skip, options):
- # we hide something deliberately
- if getattr(obj, "__HIDE_SPHINX_DOC__", False):
- return True
-
- # Hide some that are deprecated or not intended to be used
- HIDDEN = {
- "ResNetBlockBase",
- "GroupedBatchSampler",
- "build_transform_gen",
- "export_caffe2_model",
- "export_onnx_model",
- "apply_transform_gens",
- "TransformGen",
- "apply_augmentations",
- "StandardAugInput",
- "build_batch_data_loader",
- "draw_panoptic_seg_predictions",
- "WarmupCosineLR",
- "WarmupMultiStepLR",
- }
- try:
- if name in HIDDEN or (
- hasattr(obj, "__doc__") and obj.__doc__.lower().strip().startswith("deprecated")
- ):
- print("Skipping deprecated object: {}".format(name))
- return True
- except:
- pass
- return skip
-
-
-# _PAPER_DATA = {
-# "resnet": ("1512.03385", "Deep Residual Learning for Image Recognition"),
-# "fpn": ("1612.03144", "Feature Pyramid Networks for Object Detection"),
-# "mask r-cnn": ("1703.06870", "Mask R-CNN"),
-# "faster r-cnn": (
-# "1506.01497",
-# "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
-# ),
-# "deformconv": ("1703.06211", "Deformable Convolutional Networks"),
-# "deformconv2": ("1811.11168", "Deformable ConvNets v2: More Deformable, Better Results"),
-# "panopticfpn": ("1901.02446", "Panoptic Feature Pyramid Networks"),
-# "retinanet": ("1708.02002", "Focal Loss for Dense Object Detection"),
-# "cascade r-cnn": ("1712.00726", "Cascade R-CNN: Delving into High Quality Object Detection"),
-# "lvis": ("1908.03195", "LVIS: A Dataset for Large Vocabulary Instance Segmentation"),
-# "rrpn": ("1703.01086", "Arbitrary-Oriented Scene Text Detection via Rotation Proposals"),
-# "imagenet in 1h": ("1706.02677", "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour"),
-# "xception": ("1610.02357", "Xception: Deep Learning with Depthwise Separable Convolutions"),
-# "mobilenet": (
-# "1704.04861",
-# "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications",
-# ),
-# "deeplabv3+": (
-# "1802.02611",
-# "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation",
-# ),
-# "dds": ("2003.13678", "Designing Network Design Spaces"),
-# "scaling": ("2103.06877", "Fast and Accurate Model Scaling"),
-# }
-
-
-# def paper_ref_role(
-# typ: str,
-# rawtext: str,
-# text: str,
-# lineno: int,
-# inliner,
-# options: Dict = {},
-# content: List[str] = [],
-# ):
-# """
-# Parse :paper:`xxx`. Similar to the "extlinks" sphinx extension.
-# """
-# from docutils import nodes, utils
-# from sphinx.util.nodes import split_explicit_title
-
-# text = utils.unescape(text)
-# has_explicit_title, title, link = split_explicit_title(text)
-# link = link.lower()
-# if link not in _PAPER_DATA:
-# inliner.reporter.warning("Cannot find paper " + link)
-# paper_url, paper_title = "#", link
-# else:
-# paper_url, paper_title = _PAPER_DATA[link]
-# if "/" not in paper_url:
-# paper_url = "https://arxiv.org/abs/" + paper_url
-# if not has_explicit_title:
-# title = paper_title
-# pnode = nodes.reference(title, title, internal=False, refuri=paper_url)
-# return [pnode], []
-
-
-def setup(app):
- from recommonmark.transform import AutoStructify
-
- app.add_domain(GithubURLDomain)
- app.connect("autodoc-skip-member", autodoc_skip_member)
- # app.add_role("paper", paper_ref_role)
- app.add_config_value(
- "recommonmark_config",
- {"enable_math": True, "enable_inline_math": True, "enable_eval_rst": True},
- True,
- )
- app.add_transform(AutoStructify)
diff --git a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/old_test_datasets.py b/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/old_test_datasets.py
deleted file mode 100644
index 0b907b1ed9fbb6ea3e2540e4e18d7a5f22d88c74..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/old_test_datasets.py
+++ /dev/null
@@ -1,247 +0,0 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-from pathlib import Path
-
-import numpy as np
-import pytest
-from pack_dataset import pack_data_dir
-from parameterized import parameterized
-from save_len_file import save_len_file
-from torch.utils.data import DataLoader
-
-from transformers import AutoTokenizer
-from transformers.models.mbart.modeling_mbart import shift_tokens_right
-from transformers.testing_utils import TestCasePlus, slow
-from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeq2SeqDataset, Seq2SeqDataset
-
-
-BERT_BASE_CASED = "bert-base-cased"
-PEGASUS_XSUM = "google/pegasus-xsum"
-ARTICLES = [" Sam ate lunch today.", "Sams lunch ingredients."]
-SUMMARIES = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
-T5_TINY = "patrickvonplaten/t5-tiny-random"
-BART_TINY = "sshleifer/bart-tiny-random"
-MBART_TINY = "sshleifer/tiny-mbart"
-MARIAN_TINY = "sshleifer/tiny-marian-en-de"
-
-
-def _dump_articles(path: Path, articles: list):
- content = "\n".join(articles)
- Path(path).open("w").writelines(content)
-
-
-def make_test_data_dir(tmp_dir):
- for split in ["train", "val", "test"]:
- _dump_articles(os.path.join(tmp_dir, f"{split}.source"), ARTICLES)
- _dump_articles(os.path.join(tmp_dir, f"{split}.target"), SUMMARIES)
- return tmp_dir
-
-
-class TestAll(TestCasePlus):
- @parameterized.expand(
- [
- MBART_TINY,
- MARIAN_TINY,
- T5_TINY,
- BART_TINY,
- PEGASUS_XSUM,
- ],
- )
- @slow
- def test_seq2seq_dataset_truncation(self, tok_name):
- tokenizer = AutoTokenizer.from_pretrained(tok_name)
- tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
- max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
- max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
- max_src_len = 4
- max_tgt_len = 8
- assert max_len_target > max_src_len # Will be truncated
- assert max_len_source > max_src_len # Will be truncated
- src_lang, tgt_lang = "ro_RO", "de_DE" # ignored for all but mbart, but never causes error.
- train_dataset = Seq2SeqDataset(
- tokenizer,
- data_dir=tmp_dir,
- type_path="train",
- max_source_length=max_src_len,
- max_target_length=max_tgt_len, # ignored
- src_lang=src_lang,
- tgt_lang=tgt_lang,
- )
- dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
- for batch in dataloader:
- assert isinstance(batch, dict)
- assert batch["attention_mask"].shape == batch["input_ids"].shape
- # show that articles were trimmed.
- assert batch["input_ids"].shape[1] == max_src_len
- # show that targets are the same len
- assert batch["labels"].shape[1] == max_tgt_len
- if tok_name != MBART_TINY:
- continue
- # check language codes in correct place
- batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], tokenizer.pad_token_id)
- assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
- assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
- assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
- assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
-
- break # No need to test every batch
-
- @parameterized.expand([BART_TINY, BERT_BASE_CASED])
- def test_legacy_dataset_truncation(self, tok):
- tokenizer = AutoTokenizer.from_pretrained(tok)
- tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
- max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
- max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
- trunc_target = 4
- train_dataset = LegacySeq2SeqDataset(
- tokenizer,
- data_dir=tmp_dir,
- type_path="train",
- max_source_length=20,
- max_target_length=trunc_target,
- )
- dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
- for batch in dataloader:
- assert batch["attention_mask"].shape == batch["input_ids"].shape
- # show that articles were trimmed.
- assert batch["input_ids"].shape[1] == max_len_source
- assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
- # show that targets were truncated
- assert batch["labels"].shape[1] == trunc_target # Truncated
- assert max_len_target > trunc_target # Truncated
- break # No need to test every batch
-
- def test_pack_dataset(self):
- tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
-
- tmp_dir = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
- orig_examples = tmp_dir.joinpath("train.source").open().readlines()
- save_dir = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
- pack_data_dir(tokenizer, tmp_dir, 128, save_dir)
- orig_paths = {x.name for x in tmp_dir.iterdir()}
- new_paths = {x.name for x in save_dir.iterdir()}
- packed_examples = save_dir.joinpath("train.source").open().readlines()
- # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
- # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
- assert len(packed_examples) < len(orig_examples)
- assert len(packed_examples) == 1
- assert len(packed_examples[0]) == sum(len(x) for x in orig_examples)
- assert orig_paths == new_paths
-
- @pytest.mark.skipif(not FAIRSEQ_AVAILABLE, reason="This test requires fairseq")
- def test_dynamic_batch_size(self):
- if not FAIRSEQ_AVAILABLE:
- return
- ds, max_tokens, tokenizer = self._get_dataset(max_len=64)
- required_batch_size_multiple = 64
- batch_sampler = ds.make_dynamic_sampler(max_tokens, required_batch_size_multiple=required_batch_size_multiple)
- batch_sizes = [len(x) for x in batch_sampler]
- assert len(set(batch_sizes)) > 1 # it's not dynamic batch size if every batch is the same length
- assert sum(batch_sizes) == len(ds) # no dropped or added examples
- data_loader = DataLoader(ds, batch_sampler=batch_sampler, collate_fn=ds.collate_fn, num_workers=2)
- failures = []
- num_src_per_batch = []
- for batch in data_loader:
- src_shape = batch["input_ids"].shape
- bs = src_shape[0]
- assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
- num_src_tokens = np.product(batch["input_ids"].shape)
- num_src_per_batch.append(num_src_tokens)
- if num_src_tokens > (max_tokens * 1.1):
- failures.append(num_src_tokens)
- assert num_src_per_batch[0] == max(num_src_per_batch)
- if failures:
- raise AssertionError(f"too many tokens in {len(failures)} batches")
-
- def test_sortish_sampler_reduces_padding(self):
- ds, _, tokenizer = self._get_dataset(max_len=512)
- bs = 2
- sortish_sampler = ds.make_sortish_sampler(bs, shuffle=False)
-
- naive_dl = DataLoader(ds, batch_size=bs, collate_fn=ds.collate_fn, num_workers=2)
- sortish_dl = DataLoader(ds, batch_size=bs, collate_fn=ds.collate_fn, num_workers=2, sampler=sortish_sampler)
-
- pad = tokenizer.pad_token_id
-
- def count_pad_tokens(data_loader, k="input_ids"):
- return [batch[k].eq(pad).sum().item() for batch in data_loader]
-
- assert sum(count_pad_tokens(sortish_dl, k="labels")) < sum(count_pad_tokens(naive_dl, k="labels"))
- assert sum(count_pad_tokens(sortish_dl)) < sum(count_pad_tokens(naive_dl))
- assert len(sortish_dl) == len(naive_dl)
-
- def _get_dataset(self, n_obs=1000, max_len=128):
- if os.getenv("USE_REAL_DATA", False):
- data_dir = "examples/seq2seq/wmt_en_ro"
- max_tokens = max_len * 2 * 64
- if not Path(data_dir).joinpath("train.len").exists():
- save_len_file(MARIAN_TINY, data_dir)
- else:
- data_dir = "examples/seq2seq/test_data/wmt_en_ro"
- max_tokens = max_len * 4
- save_len_file(MARIAN_TINY, data_dir)
-
- tokenizer = AutoTokenizer.from_pretrained(MARIAN_TINY)
- ds = Seq2SeqDataset(
- tokenizer,
- data_dir=data_dir,
- type_path="train",
- max_source_length=max_len,
- max_target_length=max_len,
- n_obs=n_obs,
- )
- return ds, max_tokens, tokenizer
-
- def test_distributed_sortish_sampler_splits_indices_between_procs(self):
- ds, max_tokens, tokenizer = self._get_dataset()
- ids1 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=0, add_extra_examples=False))
- ids2 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=1, add_extra_examples=False))
- assert ids1.intersection(ids2) == set()
-
- @parameterized.expand(
- [
- MBART_TINY,
- MARIAN_TINY,
- T5_TINY,
- BART_TINY,
- PEGASUS_XSUM,
- ],
- )
- def test_dataset_kwargs(self, tok_name):
- tokenizer = AutoTokenizer.from_pretrained(tok_name, use_fast=False)
- if tok_name == MBART_TINY:
- train_dataset = Seq2SeqDataset(
- tokenizer,
- data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()),
- type_path="train",
- max_source_length=4,
- max_target_length=8,
- src_lang="EN",
- tgt_lang="FR",
- )
- kwargs = train_dataset.dataset_kwargs
- assert "src_lang" in kwargs and "tgt_lang" in kwargs
- else:
- train_dataset = Seq2SeqDataset(
- tokenizer,
- data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()),
- type_path="train",
- max_source_length=4,
- max_target_length=8,
- )
- kwargs = train_dataset.dataset_kwargs
- assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
- assert len(kwargs) == 1 if tok_name == BART_TINY else len(kwargs) == 0
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/anyio/_core/_resources.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/anyio/_core/_resources.py
deleted file mode 100644
index b9a5344aef2962670f9b305a02cd0b11f2087d2f..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/anyio/_core/_resources.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from __future__ import annotations
-
-from ..abc import AsyncResource
-from ._tasks import CancelScope
-
-
-async def aclose_forcefully(resource: AsyncResource) -> None:
- """
- Close an asynchronous resource in a cancelled scope.
-
- Doing this closes the resource without waiting on anything.
-
- :param resource: the resource to close
-
- """
- with CancelScope() as scope:
- scope.cancel()
- await resource.aclose()
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/errors.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/errors.py
deleted file mode 100644
index 9b53d8fec4af2c85c41e6a7d1cc21213406d5003..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/errors.py
+++ /dev/null
@@ -1,66 +0,0 @@
-from abc import abstractmethod
-from typing import Dict, Type
-from overrides import overrides, EnforceOverrides
-
-
-class ChromaError(Exception, EnforceOverrides):
- def code(self) -> int:
- """Return an appropriate HTTP response code for this error"""
- return 400 # Bad Request
-
- def message(self) -> str:
- return ", ".join(self.args)
-
- @classmethod
- @abstractmethod
- def name(self) -> str:
- """Return the error name"""
- pass
-
-
-class InvalidDimensionException(ChromaError):
- @classmethod
- @overrides
- def name(cls) -> str:
- return "InvalidDimension"
-
-
-class InvalidCollectionException(ChromaError):
- @classmethod
- @overrides
- def name(cls) -> str:
- return "InvalidCollection"
-
-
-class IDAlreadyExistsError(ChromaError):
- @overrides
- def code(self) -> int:
- return 409 # Conflict
-
- @classmethod
- @overrides
- def name(cls) -> str:
- return "IDAlreadyExists"
-
-
-class DuplicateIDError(ChromaError):
- @classmethod
- @overrides
- def name(cls) -> str:
- return "DuplicateID"
-
-
-class InvalidUUIDError(ChromaError):
- @classmethod
- @overrides
- def name(cls) -> str:
- return "InvalidUUID"
-
-
-error_types: Dict[str, Type[ChromaError]] = {
- "InvalidDimension": InvalidDimensionException,
- "InvalidCollection": InvalidCollectionException,
- "IDAlreadyExists": IDAlreadyExistsError,
- "DuplicateID": DuplicateIDError,
- "InvalidUUID": InvalidUUIDError,
-}
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/asymmetric/dh.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/asymmetric/dh.py
deleted file mode 100644
index 751bcc402e9417b6e7b9a72e92052c83fcc65751..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/asymmetric/dh.py
+++ /dev/null
@@ -1,261 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-from __future__ import annotations
-
-import abc
-import typing
-
-from cryptography.hazmat.bindings._rust import openssl as rust_openssl
-from cryptography.hazmat.primitives import _serialization
-
-
-def generate_parameters(
- generator: int, key_size: int, backend: typing.Any = None
-) -> DHParameters:
- from cryptography.hazmat.backends.openssl.backend import backend as ossl
-
- return ossl.generate_dh_parameters(generator, key_size)
-
-
-class DHParameterNumbers:
- def __init__(self, p: int, g: int, q: typing.Optional[int] = None) -> None:
- if not isinstance(p, int) or not isinstance(g, int):
- raise TypeError("p and g must be integers")
- if q is not None and not isinstance(q, int):
- raise TypeError("q must be integer or None")
-
- if g < 2:
- raise ValueError("DH generator must be 2 or greater")
-
- if p.bit_length() < rust_openssl.dh.MIN_MODULUS_SIZE:
- raise ValueError(
- f"p (modulus) must be at least "
- f"{rust_openssl.dh.MIN_MODULUS_SIZE}-bit"
- )
-
- self._p = p
- self._g = g
- self._q = q
-
- def __eq__(self, other: object) -> bool:
- if not isinstance(other, DHParameterNumbers):
- return NotImplemented
-
- return (
- self._p == other._p and self._g == other._g and self._q == other._q
- )
-
- def parameters(self, backend: typing.Any = None) -> DHParameters:
- from cryptography.hazmat.backends.openssl.backend import (
- backend as ossl,
- )
-
- return ossl.load_dh_parameter_numbers(self)
-
- @property
- def p(self) -> int:
- return self._p
-
- @property
- def g(self) -> int:
- return self._g
-
- @property
- def q(self) -> typing.Optional[int]:
- return self._q
-
-
-class DHPublicNumbers:
- def __init__(self, y: int, parameter_numbers: DHParameterNumbers) -> None:
- if not isinstance(y, int):
- raise TypeError("y must be an integer.")
-
- if not isinstance(parameter_numbers, DHParameterNumbers):
- raise TypeError(
- "parameters must be an instance of DHParameterNumbers."
- )
-
- self._y = y
- self._parameter_numbers = parameter_numbers
-
- def __eq__(self, other: object) -> bool:
- if not isinstance(other, DHPublicNumbers):
- return NotImplemented
-
- return (
- self._y == other._y
- and self._parameter_numbers == other._parameter_numbers
- )
-
- def public_key(self, backend: typing.Any = None) -> DHPublicKey:
- from cryptography.hazmat.backends.openssl.backend import (
- backend as ossl,
- )
-
- return ossl.load_dh_public_numbers(self)
-
- @property
- def y(self) -> int:
- return self._y
-
- @property
- def parameter_numbers(self) -> DHParameterNumbers:
- return self._parameter_numbers
-
-
-class DHPrivateNumbers:
- def __init__(self, x: int, public_numbers: DHPublicNumbers) -> None:
- if not isinstance(x, int):
- raise TypeError("x must be an integer.")
-
- if not isinstance(public_numbers, DHPublicNumbers):
- raise TypeError(
- "public_numbers must be an instance of " "DHPublicNumbers."
- )
-
- self._x = x
- self._public_numbers = public_numbers
-
- def __eq__(self, other: object) -> bool:
- if not isinstance(other, DHPrivateNumbers):
- return NotImplemented
-
- return (
- self._x == other._x
- and self._public_numbers == other._public_numbers
- )
-
- def private_key(self, backend: typing.Any = None) -> DHPrivateKey:
- from cryptography.hazmat.backends.openssl.backend import (
- backend as ossl,
- )
-
- return ossl.load_dh_private_numbers(self)
-
- @property
- def public_numbers(self) -> DHPublicNumbers:
- return self._public_numbers
-
- @property
- def x(self) -> int:
- return self._x
-
-
-class DHParameters(metaclass=abc.ABCMeta):
- @abc.abstractmethod
- def generate_private_key(self) -> DHPrivateKey:
- """
- Generates and returns a DHPrivateKey.
- """
-
- @abc.abstractmethod
- def parameter_bytes(
- self,
- encoding: _serialization.Encoding,
- format: _serialization.ParameterFormat,
- ) -> bytes:
- """
- Returns the parameters serialized as bytes.
- """
-
- @abc.abstractmethod
- def parameter_numbers(self) -> DHParameterNumbers:
- """
- Returns a DHParameterNumbers.
- """
-
-
-DHParametersWithSerialization = DHParameters
-DHParameters.register(rust_openssl.dh.DHParameters)
-
-
-class DHPublicKey(metaclass=abc.ABCMeta):
- @property
- @abc.abstractmethod
- def key_size(self) -> int:
- """
- The bit length of the prime modulus.
- """
-
- @abc.abstractmethod
- def parameters(self) -> DHParameters:
- """
- The DHParameters object associated with this public key.
- """
-
- @abc.abstractmethod
- def public_numbers(self) -> DHPublicNumbers:
- """
- Returns a DHPublicNumbers.
- """
-
- @abc.abstractmethod
- def public_bytes(
- self,
- encoding: _serialization.Encoding,
- format: _serialization.PublicFormat,
- ) -> bytes:
- """
- Returns the key serialized as bytes.
- """
-
- @abc.abstractmethod
- def __eq__(self, other: object) -> bool:
- """
- Checks equality.
- """
-
-
-DHPublicKeyWithSerialization = DHPublicKey
-DHPublicKey.register(rust_openssl.dh.DHPublicKey)
-
-
-class DHPrivateKey(metaclass=abc.ABCMeta):
- @property
- @abc.abstractmethod
- def key_size(self) -> int:
- """
- The bit length of the prime modulus.
- """
-
- @abc.abstractmethod
- def public_key(self) -> DHPublicKey:
- """
- The DHPublicKey associated with this private key.
- """
-
- @abc.abstractmethod
- def parameters(self) -> DHParameters:
- """
- The DHParameters object associated with this private key.
- """
-
- @abc.abstractmethod
- def exchange(self, peer_public_key: DHPublicKey) -> bytes:
- """
- Given peer's DHPublicKey, carry out the key exchange and
- return shared key as bytes.
- """
-
- @abc.abstractmethod
- def private_numbers(self) -> DHPrivateNumbers:
- """
- Returns a DHPrivateNumbers.
- """
-
- @abc.abstractmethod
- def private_bytes(
- self,
- encoding: _serialization.Encoding,
- format: _serialization.PrivateFormat,
- encryption_algorithm: _serialization.KeySerializationEncryption,
- ) -> bytes:
- """
- Returns the key serialized as bytes.
- """
-
-
-DHPrivateKeyWithSerialization = DHPrivateKey
-DHPrivateKey.register(rust_openssl.dh.DHPrivateKey)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/otlLib/optimize/__main__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/otlLib/optimize/__main__.py
deleted file mode 100644
index b0ae9081ca8dac338bcf085c71adad87805e3bad..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/otlLib/optimize/__main__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import sys
-from fontTools.otlLib.optimize import main
-
-
-if __name__ == "__main__":
- sys.exit(main())
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I_D_.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I_D_.py
deleted file mode 100644
index 536ff2f98a0abb8b27fe6da44199534a32fd0c3e..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I_D_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .T_S_I_V_ import table_T_S_I_V_
-
-
-class table_T_S_I_D_(table_T_S_I_V_):
- pass
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/number.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/number.py
deleted file mode 100644
index ee629775673753c17f213f56d1ced2e5bc418e9b..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/number.py
+++ /dev/null
@@ -1,237 +0,0 @@
-"""gr.Number() component."""
-
-from __future__ import annotations
-
-import math
-from typing import Callable, Literal
-
-import numpy as np
-from gradio_client.documentation import document, set_documentation_group
-from gradio_client.serializing import NumberSerializable
-
-from gradio.components.base import FormComponent, IOComponent, _Keywords
-from gradio.events import (
- Blurrable,
- Changeable,
- Inputable,
- Submittable,
-)
-from gradio.exceptions import Error
-from gradio.interpretation import NeighborInterpretable
-
-set_documentation_group("component")
-
-
-@document()
-class Number(
- FormComponent,
- Changeable,
- Inputable,
- Submittable,
- Blurrable,
- IOComponent,
- NumberSerializable,
- NeighborInterpretable,
-):
- """
- Creates a numeric field for user to enter numbers as input or display numeric output.
- Preprocessing: passes field value as a {float} or {int} into the function, depending on `precision`.
- Postprocessing: expects an {int} or {float} returned from the function and sets field value to it.
- Examples-format: a {float} or {int} representing the number's value.
-
- Demos: tax_calculator, titanic_survival, blocks_simple_squares
- """
-
- def __init__(
- self,
- value: float | Callable | None = None,
- *,
- label: str | None = None,
- info: str | None = None,
- every: float | None = None,
- show_label: bool = True,
- container: bool = True,
- scale: int | None = None,
- min_width: int = 160,
- interactive: bool | None = None,
- visible: bool = True,
- elem_id: str | None = None,
- elem_classes: list[str] | str | None = None,
- precision: int | None = None,
- minimum: float | None = None,
- maximum: float | None = None,
- **kwargs,
- ):
- """
- Parameters:
- value: default value. If callable, the function will be called whenever the app loads to set the initial value of the component.
- label: component name in interface.
- info: additional component description.
- every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
- show_label: if True, will display label.
- container: If True, will place the component in a container - providing some extra padding around the border.
- scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
- min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
- interactive: if True, will be editable; if False, editing will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
- visible: If False, component will be hidden.
- elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
- elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
- precision: Precision to round input/output to. If set to 0, will round to nearest integer and convert type to int. If None, no rounding happens.
- minimum: Minimum value. Only applied when component is used as an input. If a user provides a smaller value, a gr.Error exception is raised by the backend.
- maximum: Maximum value. Only applied when component is used as an input. If a user provides a larger value, a gr.Error exception is raised by the backend.
- """
- self.precision = precision
- self.minimum = minimum
- self.maximum = maximum
-
- IOComponent.__init__(
- self,
- label=label,
- info=info,
- every=every,
- show_label=show_label,
- container=container,
- scale=scale,
- min_width=min_width,
- interactive=interactive,
- visible=visible,
- elem_id=elem_id,
- elem_classes=elem_classes,
- value=value,
- **kwargs,
- )
- NeighborInterpretable.__init__(self)
-
- @staticmethod
- def _round_to_precision(num: float | int, precision: int | None) -> float | int:
- """
- Round to a given precision.
-
- If precision is None, no rounding happens. If 0, num is converted to int.
-
- Parameters:
- num: Number to round.
- precision: Precision to round to.
- Returns:
- rounded number
- """
- if precision is None:
- return float(num)
- elif precision == 0:
- return int(round(num, precision))
- else:
- return round(num, precision)
-
- def get_config(self):
- return {
- "value": self.value,
- "minimum": self.minimum,
- "maximum": self.maximum,
- **IOComponent.get_config(self),
- }
-
- @staticmethod
- def update(
- value: float | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE,
- minimum: float | None = None,
- maximum: float | None = None,
- label: str | None = None,
- info: str | None = None,
- show_label: bool | None = None,
- container: bool | None = None,
- scale: int | None = None,
- min_width: int | None = None,
- interactive: bool | None = None,
- visible: bool | None = None,
- ):
- return {
- "label": label,
- "info": info,
- "show_label": show_label,
- "container": container,
- "scale": scale,
- "min_width": min_width,
- "visible": visible,
- "value": value,
- "minimum": minimum,
- "maximum": maximum,
- "interactive": interactive,
- "__type__": "update",
- }
-
- def preprocess(self, x: float | None) -> float | None:
- """
- Parameters:
- x: numeric input
- Returns:
- number representing function input
- """
- if x is None:
- return None
- elif self.minimum is not None and x < self.minimum:
- raise Error(f"Value {x} is less than minimum value {self.minimum}.")
- elif self.maximum is not None and x > self.maximum:
- raise Error(f"Value {x} is greater than maximum value {self.maximum}.")
- return self._round_to_precision(x, self.precision)
-
- def postprocess(self, y: float | None) -> float | None:
- """
- Any postprocessing needed to be performed on function output.
-
- Parameters:
- y: numeric output
- Returns:
- number representing function output
- """
- if y is None:
- return None
- return self._round_to_precision(y, self.precision)
-
- def set_interpret_parameters(
- self, steps: int = 3, delta: float = 1, delta_type: str = "percent"
- ):
- """
- Calculates interpretation scores of numeric values close to the input number.
- Parameters:
- steps: Number of nearby values to measure in each direction (above and below the input number).
- delta: Size of step in each direction between nearby values.
- delta_type: "percent" if delta step between nearby values should be a calculated as a percent, or "absolute" if delta should be a constant step change.
- """
- self.interpretation_steps = steps
- self.interpretation_delta = delta
- self.interpretation_delta_type = delta_type
- return self
-
- def get_interpretation_neighbors(self, x: float | int) -> tuple[list[float], dict]:
- x = self._round_to_precision(x, self.precision)
- if self.interpretation_delta_type == "percent":
- delta = 1.0 * self.interpretation_delta * x / 100
- elif self.interpretation_delta_type == "absolute":
- delta = self.interpretation_delta
- else:
- delta = self.interpretation_delta
- if self.precision == 0 and math.floor(delta) != delta:
- raise ValueError(
- f"Delta value {delta} is not an integer and precision=0. Cannot generate valid set of neighbors. "
- "If delta_type='percent', pick a value of delta such that x * delta is an integer. "
- "If delta_type='absolute', pick a value of delta that is an integer."
- )
- # run_interpretation will preprocess the neighbors so no need to convert to int here
- negatives = (
- np.array(x) + np.arange(-self.interpretation_steps, 0) * delta
- ).tolist()
- positives = (
- np.array(x) + np.arange(1, self.interpretation_steps + 1) * delta
- ).tolist()
- return negatives + positives, {}
-
- def get_interpretation_scores(
- self, x: float, neighbors: list[float], scores: list[float | None], **kwargs
- ) -> list[tuple[float, float | None]]:
- """
- Returns:
- Each tuple set represents a numeric value near the input and its corresponding interpretation score.
- """
- interpretation = list(zip(neighbors, scores))
- interpretation.insert(int(len(interpretation) / 2), (x, None))
- return interpretation
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-aef3869a.css b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-aef3869a.css
deleted file mode 100644
index a1f402a49e82009fd7eafa923615d67793b8751c..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-aef3869a.css
+++ /dev/null
@@ -1 +0,0 @@
-td.svelte-xrr240.svelte-xrr240{width:45%}td.svelte-xrr240.svelte-xrr240:last-child{width:10%;text-align:right}.file-preview-holder.svelte-xrr240.svelte-xrr240{overflow-x:auto}.file-preview.svelte-xrr240.svelte-xrr240{width:var(--size-full);max-height:var(--size-60);overflow-y:auto;color:var(--body-text-color)}.file.svelte-xrr240.svelte-xrr240{width:var(--size-full)}.file.svelte-xrr240>.svelte-xrr240{padding:var(--size-1) var(--size-2-5)}.download.svelte-xrr240.svelte-xrr240:hover{text-decoration:underline}.download.svelte-xrr240>a.svelte-xrr240{color:var(--link-text-color)}.download.svelte-xrr240>a.svelte-xrr240:hover{color:var(--link-text-color-hover)}.download.svelte-xrr240>a.svelte-xrr240:visited{color:var(--link-text-color-visited)}.download.svelte-xrr240>a.svelte-xrr240:active{color:var(--link-text-color-active)}.selectable.svelte-xrr240.svelte-xrr240{cursor:pointer}
diff --git a/spaces/cihyFjudo/fairness-paper-search/Cc Cleaner Code How to Stop Online Tracking with Kamo by CCleaner.md b/spaces/cihyFjudo/fairness-paper-search/Cc Cleaner Code How to Stop Online Tracking with Kamo by CCleaner.md
deleted file mode 100644
index 40625a18784322efc5977a7c51ac9ab5b5ffe4fe..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Cc Cleaner Code How to Stop Online Tracking with Kamo by CCleaner.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Smoke 2016 Crack Xforce Keygen.epub
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Does The Livejasmin Hack Work.md b/spaces/cihyFjudo/fairness-paper-search/Does The Livejasmin Hack Work.md
deleted file mode 100644
index 570bddc10bb32f0196fde9870566ce777c4b0176..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Does The Livejasmin Hack Work.md
+++ /dev/null
@@ -1,7 +0,0 @@
-
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-Does The Livejasmin Hack Work
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-LiveJasmin is a luxury premium webcam sites which doesn't just offer one of the most sought-after selections of cam hosts, HD streaming quality site-wide, leading fetish practitioners and a user-friendly layout that lets you quickly find your dream date, it can also offer some private show rates that can make the eyes water, especially if you want to open you cam in a private session. However, there are several money saving hacks including free credit giveaways that can significantly reduce the price you pay for a 1on1 session. So, if you want the best of the best at a rock-bottom low rate, read on.
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Shadow Of The Tomb Raider Download.md b/spaces/cihyFjudo/fairness-paper-search/Shadow Of The Tomb Raider Download.md
deleted file mode 100644
index c4faf8a698289fb5196cdaa9a4d9f0a93bf68e15..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Shadow Of The Tomb Raider Download.md
+++ /dev/null
@@ -1,12 +0,0 @@
-
-Shadow of the Tomb Raider released several chapters of downloadable content that expanded on the game's narrative. Each of these chapters run parallel to the main storyline and focus on an additional tomb. Lara uncovers the source of Mayan influence in Peru and solves the mystery of a missing oil worker; locates an artefact to bolster Unuratu's rebellion, but finds a secret that could threaten it; confronts her worst fears as she searches for a potent weapon; learns of a tragedy that shaped Amaru's decision to join Trinity; aids a splinter group of rebels taken by the Cult of Kukulkan; investigates a disturbance at a local temple that turns into a trap laid by Trinity; and learns the fate of the Yaaxil that survived the battle with Trinity.
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-shadow of the tomb raider download
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Sunday Full Movie Hindi Ajay Devgan Hd A Hilarious Mystery with a Twist.md b/spaces/cihyFjudo/fairness-paper-search/Sunday Full Movie Hindi Ajay Devgan Hd A Hilarious Mystery with a Twist.md
deleted file mode 100644
index 844b82ae14479af034bb79e3e28caa0f016d10e0..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Sunday Full Movie Hindi Ajay Devgan Hd A Hilarious Mystery with a Twist.md
+++ /dev/null
@@ -1,5 +0,0 @@
-
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diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/transform.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/transform.py
deleted file mode 100644
index f85b54b73121589cb8de284a5e8efe9d20fefa17..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/transform.py
+++ /dev/null
@@ -1,495 +0,0 @@
-"""Affine 2D transformation matrix class.
-
-The Transform class implements various transformation matrix operations,
-both on the matrix itself, as well as on 2D coordinates.
-
-Transform instances are effectively immutable: all methods that operate on the
-transformation itself always return a new instance. This has as the
-interesting side effect that Transform instances are hashable, ie. they can be
-used as dictionary keys.
-
-This module exports the following symbols:
-
-Transform
- this is the main class
-Identity
- Transform instance set to the identity transformation
-Offset
- Convenience function that returns a translating transformation
-Scale
- Convenience function that returns a scaling transformation
-
-The DecomposedTransform class implements a transformation with separate
-translate, rotation, scale, skew, and transformation-center components.
-
-:Example:
-
- >>> t = Transform(2, 0, 0, 3, 0, 0)
- >>> t.transformPoint((100, 100))
- (200, 300)
- >>> t = Scale(2, 3)
- >>> t.transformPoint((100, 100))
- (200, 300)
- >>> t.transformPoint((0, 0))
- (0, 0)
- >>> t = Offset(2, 3)
- >>> t.transformPoint((100, 100))
- (102, 103)
- >>> t.transformPoint((0, 0))
- (2, 3)
- >>> t2 = t.scale(0.5)
- >>> t2.transformPoint((100, 100))
- (52.0, 53.0)
- >>> import math
- >>> t3 = t2.rotate(math.pi / 2)
- >>> t3.transformPoint((0, 0))
- (2.0, 3.0)
- >>> t3.transformPoint((100, 100))
- (-48.0, 53.0)
- >>> t = Identity.scale(0.5).translate(100, 200).skew(0.1, 0.2)
- >>> t.transformPoints([(0, 0), (1, 1), (100, 100)])
- [(50.0, 100.0), (50.550167336042726, 100.60135501775433), (105.01673360427253, 160.13550177543362)]
- >>>
-"""
-
-import math
-from typing import NamedTuple
-from dataclasses import dataclass
-
-
-__all__ = ["Transform", "Identity", "Offset", "Scale", "DecomposedTransform"]
-
-
-_EPSILON = 1e-15
-_ONE_EPSILON = 1 - _EPSILON
-_MINUS_ONE_EPSILON = -1 + _EPSILON
-
-
-def _normSinCos(v):
- if abs(v) < _EPSILON:
- v = 0
- elif v > _ONE_EPSILON:
- v = 1
- elif v < _MINUS_ONE_EPSILON:
- v = -1
- return v
-
-
-class Transform(NamedTuple):
-
- """2x2 transformation matrix plus offset, a.k.a. Affine transform.
- Transform instances are immutable: all transforming methods, eg.
- rotate(), return a new Transform instance.
-
- :Example:
-
- >>> t = Transform()
- >>> t
-
- >>> t.scale(2)
-
- >>> t.scale(2.5, 5.5)
-
- >>>
- >>> t.scale(2, 3).transformPoint((100, 100))
- (200, 300)
-
- Transform's constructor takes six arguments, all of which are
- optional, and can be used as keyword arguments::
-
- >>> Transform(12)
-
- >>> Transform(dx=12)
-
- >>> Transform(yx=12)
-
-
- Transform instances also behave like sequences of length 6::
-
- >>> len(Identity)
- 6
- >>> list(Identity)
- [1, 0, 0, 1, 0, 0]
- >>> tuple(Identity)
- (1, 0, 0, 1, 0, 0)
-
- Transform instances are comparable::
-
- >>> t1 = Identity.scale(2, 3).translate(4, 6)
- >>> t2 = Identity.translate(8, 18).scale(2, 3)
- >>> t1 == t2
- 1
-
- But beware of floating point rounding errors::
-
- >>> t1 = Identity.scale(0.2, 0.3).translate(0.4, 0.6)
- >>> t2 = Identity.translate(0.08, 0.18).scale(0.2, 0.3)
- >>> t1
-
- >>> t2
-
- >>> t1 == t2
- 0
-
- Transform instances are hashable, meaning you can use them as
- keys in dictionaries::
-
- >>> d = {Scale(12, 13): None}
- >>> d
- {: None}
-
- But again, beware of floating point rounding errors::
-
- >>> t1 = Identity.scale(0.2, 0.3).translate(0.4, 0.6)
- >>> t2 = Identity.translate(0.08, 0.18).scale(0.2, 0.3)
- >>> t1
-
- >>> t2
-
- >>> d = {t1: None}
- >>> d
- {: None}
- >>> d[t2]
- Traceback (most recent call last):
- File "", line 1, in ?
- KeyError:
- """
-
- xx: float = 1
- xy: float = 0
- yx: float = 0
- yy: float = 1
- dx: float = 0
- dy: float = 0
-
- def transformPoint(self, p):
- """Transform a point.
-
- :Example:
-
- >>> t = Transform()
- >>> t = t.scale(2.5, 5.5)
- >>> t.transformPoint((100, 100))
- (250.0, 550.0)
- """
- (x, y) = p
- xx, xy, yx, yy, dx, dy = self
- return (xx * x + yx * y + dx, xy * x + yy * y + dy)
-
- def transformPoints(self, points):
- """Transform a list of points.
-
- :Example:
-
- >>> t = Scale(2, 3)
- >>> t.transformPoints([(0, 0), (0, 100), (100, 100), (100, 0)])
- [(0, 0), (0, 300), (200, 300), (200, 0)]
- >>>
- """
- xx, xy, yx, yy, dx, dy = self
- return [(xx * x + yx * y + dx, xy * x + yy * y + dy) for x, y in points]
-
- def transformVector(self, v):
- """Transform an (dx, dy) vector, treating translation as zero.
-
- :Example:
-
- >>> t = Transform(2, 0, 0, 2, 10, 20)
- >>> t.transformVector((3, -4))
- (6, -8)
- >>>
- """
- (dx, dy) = v
- xx, xy, yx, yy = self[:4]
- return (xx * dx + yx * dy, xy * dx + yy * dy)
-
- def transformVectors(self, vectors):
- """Transform a list of (dx, dy) vector, treating translation as zero.
-
- :Example:
- >>> t = Transform(2, 0, 0, 2, 10, 20)
- >>> t.transformVectors([(3, -4), (5, -6)])
- [(6, -8), (10, -12)]
- >>>
- """
- xx, xy, yx, yy = self[:4]
- return [(xx * dx + yx * dy, xy * dx + yy * dy) for dx, dy in vectors]
-
- def translate(self, x=0, y=0):
- """Return a new transformation, translated (offset) by x, y.
-
- :Example:
- >>> t = Transform()
- >>> t.translate(20, 30)
-
- >>>
- """
- return self.transform((1, 0, 0, 1, x, y))
-
- def scale(self, x=1, y=None):
- """Return a new transformation, scaled by x, y. The 'y' argument
- may be None, which implies to use the x value for y as well.
-
- :Example:
- >>> t = Transform()
- >>> t.scale(5)
-
- >>> t.scale(5, 6)
-
- >>>
- """
- if y is None:
- y = x
- return self.transform((x, 0, 0, y, 0, 0))
-
- def rotate(self, angle):
- """Return a new transformation, rotated by 'angle' (radians).
-
- :Example:
- >>> import math
- >>> t = Transform()
- >>> t.rotate(math.pi / 2)
-
- >>>
- """
- import math
-
- c = _normSinCos(math.cos(angle))
- s = _normSinCos(math.sin(angle))
- return self.transform((c, s, -s, c, 0, 0))
-
- def skew(self, x=0, y=0):
- """Return a new transformation, skewed by x and y.
-
- :Example:
- >>> import math
- >>> t = Transform()
- >>> t.skew(math.pi / 4)
-
- >>>
- """
- import math
-
- return self.transform((1, math.tan(y), math.tan(x), 1, 0, 0))
-
- def transform(self, other):
- """Return a new transformation, transformed by another
- transformation.
-
- :Example:
- >>> t = Transform(2, 0, 0, 3, 1, 6)
- >>> t.transform((4, 3, 2, 1, 5, 6))
-
- >>>
- """
- xx1, xy1, yx1, yy1, dx1, dy1 = other
- xx2, xy2, yx2, yy2, dx2, dy2 = self
- return self.__class__(
- xx1 * xx2 + xy1 * yx2,
- xx1 * xy2 + xy1 * yy2,
- yx1 * xx2 + yy1 * yx2,
- yx1 * xy2 + yy1 * yy2,
- xx2 * dx1 + yx2 * dy1 + dx2,
- xy2 * dx1 + yy2 * dy1 + dy2,
- )
-
- def reverseTransform(self, other):
- """Return a new transformation, which is the other transformation
- transformed by self. self.reverseTransform(other) is equivalent to
- other.transform(self).
-
- :Example:
- >>> t = Transform(2, 0, 0, 3, 1, 6)
- >>> t.reverseTransform((4, 3, 2, 1, 5, 6))
-
- >>> Transform(4, 3, 2, 1, 5, 6).transform((2, 0, 0, 3, 1, 6))
-
- >>>
- """
- xx1, xy1, yx1, yy1, dx1, dy1 = self
- xx2, xy2, yx2, yy2, dx2, dy2 = other
- return self.__class__(
- xx1 * xx2 + xy1 * yx2,
- xx1 * xy2 + xy1 * yy2,
- yx1 * xx2 + yy1 * yx2,
- yx1 * xy2 + yy1 * yy2,
- xx2 * dx1 + yx2 * dy1 + dx2,
- xy2 * dx1 + yy2 * dy1 + dy2,
- )
-
- def inverse(self):
- """Return the inverse transformation.
-
- :Example:
- >>> t = Identity.translate(2, 3).scale(4, 5)
- >>> t.transformPoint((10, 20))
- (42, 103)
- >>> it = t.inverse()
- >>> it.transformPoint((42, 103))
- (10.0, 20.0)
- >>>
- """
- if self == Identity:
- return self
- xx, xy, yx, yy, dx, dy = self
- det = xx * yy - yx * xy
- xx, xy, yx, yy = yy / det, -xy / det, -yx / det, xx / det
- dx, dy = -xx * dx - yx * dy, -xy * dx - yy * dy
- return self.__class__(xx, xy, yx, yy, dx, dy)
-
- def toPS(self):
- """Return a PostScript representation
-
- :Example:
-
- >>> t = Identity.scale(2, 3).translate(4, 5)
- >>> t.toPS()
- '[2 0 0 3 8 15]'
- >>>
- """
- return "[%s %s %s %s %s %s]" % self
-
- def toDecomposed(self) -> "DecomposedTransform":
- """Decompose into a DecomposedTransform."""
- return DecomposedTransform.fromTransform(self)
-
- def __bool__(self):
- """Returns True if transform is not identity, False otherwise.
-
- :Example:
-
- >>> bool(Identity)
- False
- >>> bool(Transform())
- False
- >>> bool(Scale(1.))
- False
- >>> bool(Scale(2))
- True
- >>> bool(Offset())
- False
- >>> bool(Offset(0))
- False
- >>> bool(Offset(2))
- True
- """
- return self != Identity
-
- def __repr__(self):
- return "<%s [%g %g %g %g %g %g]>" % ((self.__class__.__name__,) + self)
-
-
-Identity = Transform()
-
-
-def Offset(x=0, y=0):
- """Return the identity transformation offset by x, y.
-
- :Example:
- >>> Offset(2, 3)
-
- >>>
- """
- return Transform(1, 0, 0, 1, x, y)
-
-
-def Scale(x, y=None):
- """Return the identity transformation scaled by x, y. The 'y' argument
- may be None, which implies to use the x value for y as well.
-
- :Example:
- >>> Scale(2, 3)
-
- >>>
- """
- if y is None:
- y = x
- return Transform(x, 0, 0, y, 0, 0)
-
-
-@dataclass
-class DecomposedTransform:
- """The DecomposedTransform class implements a transformation with separate
- translate, rotation, scale, skew, and transformation-center components.
- """
-
- translateX: float = 0
- translateY: float = 0
- rotation: float = 0 # in degrees, counter-clockwise
- scaleX: float = 1
- scaleY: float = 1
- skewX: float = 0 # in degrees, clockwise
- skewY: float = 0 # in degrees, counter-clockwise
- tCenterX: float = 0
- tCenterY: float = 0
-
- @classmethod
- def fromTransform(self, transform):
- # Adapted from an answer on
- # https://math.stackexchange.com/questions/13150/extracting-rotation-scale-values-from-2d-transformation-matrix
- a, b, c, d, x, y = transform
-
- sx = math.copysign(1, a)
- if sx < 0:
- a *= sx
- b *= sx
-
- delta = a * d - b * c
-
- rotation = 0
- scaleX = scaleY = 0
- skewX = skewY = 0
-
- # Apply the QR-like decomposition.
- if a != 0 or b != 0:
- r = math.sqrt(a * a + b * b)
- rotation = math.acos(a / r) if b >= 0 else -math.acos(a / r)
- scaleX, scaleY = (r, delta / r)
- skewX, skewY = (math.atan((a * c + b * d) / (r * r)), 0)
- elif c != 0 or d != 0:
- s = math.sqrt(c * c + d * d)
- rotation = math.pi / 2 - (
- math.acos(-c / s) if d >= 0 else -math.acos(c / s)
- )
- scaleX, scaleY = (delta / s, s)
- skewX, skewY = (0, math.atan((a * c + b * d) / (s * s)))
- else:
- # a = b = c = d = 0
- pass
-
- return DecomposedTransform(
- x,
- y,
- math.degrees(rotation),
- scaleX * sx,
- scaleY,
- math.degrees(skewX) * sx,
- math.degrees(skewY),
- 0,
- 0,
- )
-
- def toTransform(self):
- """Return the Transform() equivalent of this transformation.
-
- :Example:
- >>> DecomposedTransform(scaleX=2, scaleY=2).toTransform()
- 
`;
-const gpt_image = `
`;
-let prompt_lock = false;
-
-hljs.addPlugin(new CopyButtonPlugin());
-
-message_input.addEventListener("blur", () => {
- window.scrollTo(0, 0);
-});
-
-message_input.addEventListener("focus", () => {
- document.documentElement.scrollTop = document.documentElement.scrollHeight;
-});
-
-const delete_conversations = async () => {
- localStorage.clear();
- await new_conversation();
-};
-
-const handle_ask = async () => {
- message_input.style.height = `80px`;
- window.scrollTo(0, 0);
- let message = message_input.value;
-
- if (message.length > 0) {
- message_input.value = ``;
- message_input.dispatchEvent(new Event("input"));
- await ask_gpt(message);
- }
-};
-
-const remove_cancel_button = async () => {
- stop_generating.classList.add(`stop-generating-hiding`);
-
- setTimeout(() => {
- stop_generating.classList.remove(`stop-generating-hiding`);
- stop_generating.classList.add(`stop-generating-hidden`);
- }, 300);
-};
-
-const ask_gpt = async (message) => {
- try {
- message_input.value = ``;
- message_input.innerHTML = ``;
- message_input.innerText = ``;
-
- add_conversation(window.conversation_id, message.substr(0, 16));
- window.scrollTo(0, 0);
- window.controller = new AbortController();
-
- jailbreak = document.getElementById("jailbreak");
- model = document.getElementById("model");
- prompt_lock = true;
- window.text = ``;
- window.token = message_id();
-
- stop_generating.classList.remove(`stop-generating-hidden`);
-
- add_user_message_box(message);
-
- message_box.scrollTop = message_box.scrollHeight;
- window.scrollTo(0, 0);
- await new Promise((r) => setTimeout(r, 500));
- window.scrollTo(0, 0);
-
- message_box.innerHTML += `
-
- `;
-
- message_box.scrollTop = message_box.scrollHeight;
- window.scrollTo(0, 0);
- await new Promise((r) => setTimeout(r, 1000));
- window.scrollTo(0, 0);
-
- const response = await fetch(`${url_prefix}/backend-api/v2/conversation`, {
- method: `POST`,
- signal: window.controller.signal,
- headers: {
- "content-type": `application/json`,
- accept: `text/event-stream`,
- },
- body: JSON.stringify({
- conversation_id: window.conversation_id,
- action: `_ask`,
- model: model.options[model.selectedIndex].value,
- jailbreak: jailbreak.options[jailbreak.selectedIndex].value,
- meta: {
- id: window.token,
- content: {
- conversation: await get_conversation(window.conversation_id),
- internet_access: document.getElementById("switch").checked,
- content_type: "text",
- parts: [
- {
- content: message,
- role: "user",
- },
- ],
- },
- },
- }),
- });
-
- const reader = response.body.getReader();
-
- while (true) {
- const { value, done } = await reader.read();
- if (done) break;
-
- chunk = decodeUnicode(new TextDecoder().decode(value));
-
- if (
- chunk.includes(`