If you are looking for a powerful and versatile CAD/CAM software for mold, die, and tool design and manufacturing, you might have heard of Cimatron E12. This software is one of the most popular and widely used solutions in the industry, offering a comprehensive set of features and benefits for various applications. However, you might also know that Cimatron E12 is not cheap, and you might not be able to afford it or justify its cost. That's why you might be interested in finding a way to get Cimatron E12 crack serial download, which can allow you to use the software for free without any limitations. In this article, we will explain what Cimatron E12 is, what a crack serial is and why you need it, and how to download and install Cimatron E12 crack serial safely and easily. Let's get started!
-Cimatron E12 is a CAD/CAM software that provides a complete solution for mold, die, and tool design and manufacturing. It enables you to design complex parts and assemblies, create high-quality molds and dies, optimize machining processes, and manage your projects efficiently. With Cimatron E12, you can benefit from:
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To run Cimatron E12 smoothly on your computer, you need to meet the following minimum system requirements:
-| Operating system | Windows 7/8/10 (64-bit) |
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| Processor | Intel Core i5 or higher |
| Memory | 8 GB RAM or higher |
| Graphics card | NVIDIA Quadro or AMD FirePro with 2 GB VRAM or higher |
| Hard disk space | 20 GB or higher |
| Internet connection | Required for activation and updates |
Cimatron E12 is compatible with various file formats, such as IGES, STEP, DXF/DWG, STL, Parasolid, CATIA V4/V5/V6/3DEXPERIENCE, SolidWorks, Solid Edge, NX, Creo, Inventor, and more. You can import and export files easily using the built-in translators.
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-If you are a fan of Football Manager 2008, you might want to spice up your game with some extra leagues and teams. One of the most popular patches for FM 2008 is the Cyprus Patch, which adds the Cypriot First and Second Division, as well as the Cup and Super Cup competitions. In this article, we will show you how to download and install the Cyprus Patch for Football Manager 2008.
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Make sure you download the correct version of the patch for your version of Football Manager 2008. The patch is compatible with both PC and Mac versions of the game.
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-You need to copy the "graphics" folder from the patch to the "graphics" folder in your FM 2008 folder. If you don't have a "graphics" folder in your FM 2008 folder, you can create one.
-You also need to copy the "editor data" folder from the patch to the "editor data" folder in your FM 2008 folder. If you don't have an "editor data" folder in your FM 2008 folder, you can create one.
-Now you are ready to start a new game with the Cyprus Patch. Launch Football Manager 2008 and click on "New Game". In the database selection screen, make sure you tick the box next to "Cyprus Patch 08". You can also choose other databases and custom files if you want.
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- - -If you want to make your game more realistic and immersive, you can also download some extra files to enhance the Cyprus Patch. For example, you can download logos, kits, faces, and stadiums for the Cypriot teams and players. You can find these files on various websites and forums dedicated to Football Manager 2008.
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-Have you ever wondered how to solve the Rubik's Cube, the world's most famous and challenging puzzle? If you have, you are not alone. Millions of people around the world have tried to crack this colorful cube, but only a few have succeeded. Some people spend hours, days, or even years trying to figure out the right moves, while others give up in frustration.
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But what if we told you that there is an easier way to solve the Rubik's Cube, using only your Android device? Yes, you read that right. Thanks to technology, you can now download and install a Cube Rubik Solver APK, an application that will guide you step by step to solve your cube in minutes or even seconds. Sounds amazing, right?
-In this article, we will explain what a Rubik's Cube is and why it is so popular, what a Cube Rubik Solver APK is and how it works, and how to download and install one on your device. We will also compare some of the most popular Cube Rubik Solver APKs available on the market, and give you some tips on how to use them effectively. By the end of this article, you will be able to solve any Rubik's Cube with ease and impress your friends and family.
-A Rubik's Cube is a three-dimensional puzzle that consists of six faces, each divided into nine smaller squares of one of six colors: white, yellow, red, blue, green, and orange. The goal of the puzzle is to twist and turn the cube until each face has only one color.
-The Rubik's Cube was invented in 1974 by Ernő Rubik, a Hungarian professor of architecture and design. He originally created it as a model to demonstrate three-dimensional geometry, but soon realized that it could also be used as a toy. He patented his invention in 1975 and named it the "Magic Cube".
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The Magic Cube was first sold in Hungary in 1977, but it was not until 1980 that it became an international sensation. That year, it was renamed the "Rubik's Cube" and licensed by Ideal Toy Corp., an American company that marketed it worldwide. The Rubik's Cube quickly became a best-selling toy, winning several awards and breaking records. By 1982, more than 100 million cubes had been sold.
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-A Cube Rubik Solver APK is an application that can be downloaded and installed on an Android device, such as a smartphone or a tablet. It is designed to help users solve the Rubik's Cube by providing them with step-by-step instructions and animations.
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-If you want to use a Cube Rubik Solver APK on your Android device, you need to download and install it first. Here are the steps you need to follow:
-The first thing you need to do is to find a trustworthy website that offers the APK file of the Cube Rubik Solver app you want to use. There are many websites that claim to provide free and safe APK files, but some of them may contain viruses, malware, or spyware that can harm your device or steal your personal information.
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-The next thing you need to do is to allow your device to install apps from unknown sources. This is because most Android devices have a default setting that prevents them from installing apps that are not downloaded from the official app stores, such as Google Play Store or Amazon Appstore.
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-Now that you have downloaded and installed a Cube Rubik Solver APK on your Android device, you may be wondering how to use it to solve your Rubik's Cube. Don't worry, it's very easy and intuitive. Here are the steps you need to follow:
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-Note: The scanning process may vary depending on the app you are using. Some apps may require you to scan only one face at a time, while others may allow you to scan multiple faces at once. Some apps may also have different color schemes or orientations for the cube. You can check the app's settings or help section for more details.
-The next thing you need to do is to choose a solution mode and follow the instructions. To do this, you need to follow these steps:
-Note: The solution mode and instructions may vary depending on the app you are using. Some apps may have different modes or levels of difficulty, such as easy, normal, hard, or expert. Some apps may also have different notations or formats for the instructions, such as arrows, symbols, or colors. You can check the app's settings or help section for more details.
-The final thing you need to do is to enjoy solving your cube in minutes or seconds. To do this, you need to follow these steps:
-You have now learned how to use a Cube Rubik Solver APK to solve the Rubik's Cube with your Android device. We hope you enjoyed this article and found it useful and informative. If you have any questions or feedback, please feel free to leave a comment below. Happy cubing!
-As we mentioned earlier, there are many Cube Rubik Solver APKs available on the market, each with its own features and advantages. To help you choose the best one for your needs and preferences, we have compared some of the most popular ones in terms of their ratings, downloads, size, and functionality. Here is a table that summarizes our comparison:
-| Cube Rubik Solver APK | -Rating | -Downloads | -Size | -Functionality | -
|---|---|---|---|---|
| AZ Rubik's Cube Solver | -4.5/5 | -1M+ | -8.9 MB | -- Supports 2x2x2 to 7x7x7 cubes - Offers beginner to expert modes - Allows manual or automatic scanning - Shows text and 3D animations - Has customizable settings and themes - Includes a timer and a leaderboard |
-
| Rubik's Solver | -4.4/5 | -500K+ | -6.8 MB | -- Supports 3x3x3 cubes only - Offers beginner to advanced modes - Requires manual scanning - Shows text and 2D animations - Has simple settings and interface - Includes a timer and a history |
-
| Online Rubik's Cube Solver | -4.3/5 | -100K+ | -4.1 MB | -- Supports 2x2x2 to 6x6x6 cubes - Offers easy to expert modes - Allows manual or automatic scanning - Shows text and 3D animations - Has adjustable settings and colors - Includes a timer and a statistics |
-
Note: The information in this table is based on the data available at the time of writing this article. It may change or vary depending on the updates or changes made by the app developers or providers.
-In this article, we have covered the following topics:
-We have also provided you with some tips, resources, and examples to help you solve the Rubik's Cube with ease and speed using your Android device.
-If you are interested in trying out a Cube Rubik Solver APK, we recommend you to download one of the apps we have compared in this article and follow our instructions on how to use it. You can also explore other apps that may suit your needs and preferences better.
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-Thank you for reading this article and happy cubing!
-Here are some of the most common questions that people ask about Cube Rubik Solver APKs:
-A: No, using a A: No, using a Cube Rubik Solver APK is not cheating. It is a tool that can help you learn and improve your skills in solving the Rubik's Cube. It can also provide you with fun and entertainment. However, if you are participating in a competition or a challenge, you should not use the app, as it may be considered unfair or dishonest by the organizers or the other participants.
-A: Cube Rubik Solver APKs are generally accurate and reliable, as they use mathematical algorithms and formulas to find the optimal solution for the cube. However, some factors may affect their accuracy and reliability, such as the quality of the device camera, the lighting conditions, the color recognition, and the scanning process. Therefore, you should always check the scanned cube and the solution on the screen before following them on your real cube. You should also make sure that the app is updated and compatible with your device.
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-DOWNLOAD ::: https://jinyurl.com/2uNK0M
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-Download ⇔ https://jinyurl.com/2uNMgU
But wait, there's more. You can also download the mod apk version of the game, which will give you access to unlimited money, free craft, god mode, and other features that will make your gameplay more fun and easy. In this article, we will tell you everything you need to know about Last Pirate Island Survival and how to download the mod apk terbaru version for your Android device. Let's get started!
-Last Pirate Island Survival is a survival and pirate game developed by RetroStyle Games UA. It was released in 2019 and has since gained over 10 million downloads on Google Play Store. The game is set in a post-apocalyptic world, where a deadly virus has turned most people into zombies. You are one of the few survivors who managed to escape to an island, where you hope to find a safe haven. However, you soon realize that the island is not as peaceful as it seems. You will have to face many dangers and enemies, such as:
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Last Pirate Island Survival is not an easy game. It has many challenges that will test your skills and patience. Some of them are:
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-In this article, we have covered the following main points:
-Here are some frequently asked questions about Last Pirate Island Survival and its mod apk version:
-
- 
-
-GitHub | Project Report -
- -
-
- 
-
- 
-
- 
-
-
-
-
-
-
-
-
-
-
', 16.020000457763672: '
"}
- for i, size in enumerate(font_sizes):
- if size > p_size:
- size_tag[size] = f" 1. Stella Shania Mintara - 2301860596
- | 2. David Samuel - 2301850304 | 3. Egivenia - 2301850134 | 4. Aurelius Va
- nnes Leander - 2301862102 | 5. Juanrico Alvaro - 2301847316 ||']
- """
- texts = []
- previous_s = {}
- block_string = ""
- for b in [b for page in doc for b in page.get_text("dict")["blocks"] if b['type'] == 0]:
- block_string = ""
- for l in b["lines"]:
- for s in l["spans"]:
- text = re.sub(r"[^\w\s]", '', s["text"]).strip()
- if text:
- if not previous_s: # First Span
- previous_s = s
- block_string = size_tag[s['size']] + s['text']
- elif s['size'] == previous_s['size']:
- if not block_string or (block_string and all((c == "|") for c in block_string)): # New block
- block_string = size_tag[s['size']] + s['text']
- else: # in the same block, so concatenate strings
- block_string += f" {s['text']}"
- else:
- texts.append(block_string)
- block_string = size_tag[s['size']] + s['text']
- previous_s = s
- if block_string:
- block_string += "|"
- # if block_string:
- texts.append(block_string)
- return texts
-
- @staticmethod
- def get_slides(texts):
- """
- Returns the tagged texts into a slide format dictionary where the page is the
- key and the value is a list contaning the component of that page.
-
- Args:
- texts (List[str]): PDF text with element tags.
-
- Returns:
- Dict: The text of the PDF seperated by the header 1 tags.
- Examples: {'Page 1': [('h1', 'Group Members:'),
- ['p', '1. Stella Shania Mintara - 2301860596 2. David Samuel -
- 2301850304 3. Egivenia - 2301850134 4. Aurelius Vannes Leander -
- 2301862102 5.
- Juanrico Alvaro - 2301847316']],
- 'Page 2': [('h1', 'Case Problem'),
- ['p', FreshMart is an established large-scale supermarket with branc
- hes in popular areas across Jakarta and big cities]]}
- """
- slides = {}
- section = []
- page = 1
-
- current_header = ""
- for text, next_text in zip(texts, texts[1:] + [None]):
- tag_match = re.search(r'(?<=<)(.*?)(?=>)', text)
- if tag_match:
- tag = tag_match.group()
- if tag == 'h1':
- section = []
- section.append(('h1', re.sub(r'<.*?>|\|', '', text).strip()))
- elif tag.startswith('h'): # non h1 headers
- # Remove tag and pipes from the text
- section.append((tag, re.sub(r'<.*?>|\|', '', text).strip()))
- elif tag.startswith('p'):
- text = re.split("((\|){2,})", text) # If encounter more than 1 pipe than split that text into different paragraphs
- for paragraph in text:
- paragraph = re.sub(r'<.*?>|\|', '', paragraph).strip() # Remove any pipe
- paragraph = re.sub(' +', ' ', paragraph) # Remove any double or more spaces into single space
- if paragraph and paragraph[0].islower(): # If a pargraph in a different block is found and the first character isn't an uppercase then concanate with last paragraph
- section[-1][1] += f" {paragraph}"
- elif paragraph:
- section.append([tag, paragraph])
- try:
- if tag_match.group() == 'h1': # Create new page when current text is a type 1 header or title
- slides[f"Page {page}"] = section
- page += 1
- except:
- continue
- return slides
-
\ No newline at end of file
diff --git a/spaces/DeeeTeeee01/SentimentAnalysis/README.md b/spaces/DeeeTeeee01/SentimentAnalysis/README.md
deleted file mode 100644
index 5db310f296f718e742e0146a9fe3cb43a04d5481..0000000000000000000000000000000000000000
--- a/spaces/DeeeTeeee01/SentimentAnalysis/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: SentimentAnalysis
-emoji: 🐠
-colorFrom: gray
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/DragGan/DragGan/gradio_utils/__init__.py b/spaces/DragGan/DragGan/gradio_utils/__init__.py
deleted file mode 100644
index 6a54920c53b4373690fd0ca59ee59159d33d1f92..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/gradio_utils/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from .utils import (ImageMask, draw_mask_on_image, draw_points_on_image,
- get_latest_points_pair, get_valid_mask,
- on_change_single_global_state)
-
-__all__ = [
- 'draw_mask_on_image', 'draw_points_on_image',
- 'on_change_single_global_state', 'get_latest_points_pair',
- 'get_valid_mask', 'ImageMask'
-]
diff --git a/spaces/DynoKevin/img-cap-for-vision-mate/app.py b/spaces/DynoKevin/img-cap-for-vision-mate/app.py
deleted file mode 100644
index 117022763b74b7bf0a3aaa834e799885f84d7105..0000000000000000000000000000000000000000
--- a/spaces/DynoKevin/img-cap-for-vision-mate/app.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import streamlit as st
-import os
-import io
-import IPython.display
-from PIL import Image
-import base64
-from transformers import pipeline
-
-# Load the image captioning model
-get_completion = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
-
-def image_to_base64_str(pil_image):
- byte_arr = io.BytesIO()
- pil_image.save(byte_arr, format='PNG')
- byte_arr = byte_arr.getvalue()
- return str(base64.b64encode(byte_arr).decode('utf-8'))
-
-# Streamlit app
-st.title("Image Captioning demo for Vision Mate bot")
-
-# Upload image
-image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
-
-if image:
- # Display the uploaded image with a smaller width
- st.image(image, caption="Uploaded Image", width=400) # Adjust the width as needed
-
-
- # Convert the uploaded image to a PIL image
- pil_image = Image.open(image)
-
- # Convert the PIL image to a base64 string
- base64_image = image_to_base64_str(pil_image)
-
- # Generate caption using the image-to-text pipeline
- caption = get_completion(base64_image)
-
- # Display the generated caption
- st.success(caption[0]['generated_text'])
diff --git a/spaces/EsoCode/text-generation-webui/extensions/ngrok/script.py b/spaces/EsoCode/text-generation-webui/extensions/ngrok/script.py
deleted file mode 100644
index 782deeac0c7062e193fa2cf096e861956f194df4..0000000000000000000000000000000000000000
--- a/spaces/EsoCode/text-generation-webui/extensions/ngrok/script.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# Adds ngrok ingress, to use add `--extension ngrok` to the command line options
-#
-# Parameters can be customized in settings.json of webui, e.g.:
-# {"ngrok": {"basic_auth":"user:password"} }
-# or
-# {"ngrok": {"oauth_provider":"google", "oauth_allow_emails":["asdf@asdf.com"]} }
-#
-# See this example for full list of options: https://github.com/ngrok/ngrok-py/blob/main/examples/ngrok-connect-full.py
-# or the README.md in this directory.
-
-import logging
-from modules import shared
-
-# Pick up host/port command line arguments
-host = shared.args.listen_host if shared.args.listen_host and shared.args.listen else '127.0.0.1'
-port = shared.args.listen_port if shared.args.listen_port else '7860'
-
-# Default options
-options = {
- 'addr': f"{host}:{port}",
- 'authtoken_from_env': True,
- 'session_metadata': 'text-generation-webui',
-}
-
-def ui():
- settings = shared.settings.get("ngrok")
- if settings:
- options.update(settings)
-
- try:
- import ngrok
- tunnel = ngrok.connect(**options)
- logging.info(f"Ingress established at: {tunnel.url()}")
- except ModuleNotFoundError:
- logging.error("===> ngrok library not found, please run `pip install -r extensions/ngrok/requirements.txt`")
-
diff --git a/spaces/FourthBrainGenAI/GenerAd-AI/app.py b/spaces/FourthBrainGenAI/GenerAd-AI/app.py
deleted file mode 100644
index 28cf6e6f54694b6770478d7aeff0453aefa1d1a6..0000000000000000000000000000000000000000
--- a/spaces/FourthBrainGenAI/GenerAd-AI/app.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import torch
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-peft_model_id = f"FourthBrainGenAI/MarketMail-AI-Model"
-config = PeftConfig.from_pretrained(peft_model_id)
-model = AutoModelForCausalLM.from_pretrained(
- config.base_model_name_or_path,
- return_dict=True,
- device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
-
-# Load the Lora model
-model = PeftModel.from_pretrained(model, peft_model_id)
-
-
-def make_inference(product_name, product_description):
- batch = tokenizer(
- f"### Product and Description:\n{product_name}: {product_description}\n\n### Ad:",
- return_tensors="pt",
- )
-
- with torch.cuda.amp.autocast():
- output_tokens = model.generate(**batch, max_new_tokens=50)
-
- return tokenizer.decode(output_tokens[0], skip_special_tokens=True)
-
-
-if __name__ == "__main__":
- # make a gradio interface
- import gradio as gr
-
- gr.Interface(
- make_inference,
- [
- gr.inputs.Textbox(lines=2, label="Product Name"),
- gr.inputs.Textbox(lines=5, label="Product Description"),
- ],
- gr.outputs.Textbox(label="Ad"),
- title="GenerAd-AI",
- description="GenerAd-AI is a generative model that generates ads for products.",
- ).launch()
diff --git a/spaces/FrozenBurning/SceneDreamer/app.py b/spaces/FrozenBurning/SceneDreamer/app.py
deleted file mode 100644
index e553bded3804f09987491d78ff75cc2e7a58e387..0000000000000000000000000000000000000000
--- a/spaces/FrozenBurning/SceneDreamer/app.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import os
-import sys
-import html
-import glob
-import uuid
-import hashlib
-import requests
-from tqdm import tqdm
-
-os.system("git clone https://github.com/FrozenBurning/SceneDreamer.git")
-os.system("cp -r SceneDreamer/* ./")
-os.system("bash install.sh")
-
-
-import os
-import torch
-import torch.nn as nn
-import importlib
-import argparse
-from imaginaire.config import Config
-from imaginaire.utils.cudnn import init_cudnn
-import gradio as gr
-from PIL import Image
-
-
-class WrappedModel(nn.Module):
- r"""Dummy wrapping the module.
- """
-
- def __init__(self, module):
- super(WrappedModel, self).__init__()
- self.module = module
-
- def forward(self, *args, **kwargs):
- r"""PyTorch module forward function overload."""
- return self.module(*args, **kwargs)
-
-def parse_args():
- parser = argparse.ArgumentParser(description='Training')
- parser.add_argument('--config', type=str, default='./configs/scenedreamer_inference.yaml', help='Path to the training config file.')
- parser.add_argument('--checkpoint', default='./scenedreamer_released.pt',
- help='Checkpoint path.')
- parser.add_argument('--output_dir', type=str, default='./test/',
- help='Location to save the image outputs')
- parser.add_argument('--seed', type=int, default=8888,
- help='Random seed.')
- args = parser.parse_args()
- return args
-
-
-args = parse_args()
-cfg = Config(args.config)
-
-# Initialize cudnn.
-init_cudnn(cfg.cudnn.deterministic, cfg.cudnn.benchmark)
-
-# Initialize data loaders and models.
-
-lib_G = importlib.import_module(cfg.gen.type)
-net_G = lib_G.Generator(cfg.gen, cfg.data)
-net_G = net_G.to('cuda')
-net_G = WrappedModel(net_G)
-
-if args.checkpoint == '':
- raise NotImplementedError("No checkpoint is provided for inference!")
-
-# Load checkpoint.
-# trainer.load_checkpoint(cfg, args.checkpoint)
-checkpoint = torch.load(args.checkpoint, map_location='cpu')
-net_G.load_state_dict(checkpoint['net_G'])
-
-# Do inference.
-net_G = net_G.module
-net_G.eval()
-for name, param in net_G.named_parameters():
- param.requires_grad = False
-torch.cuda.empty_cache()
-world_dir = os.path.join(args.output_dir)
-os.makedirs(world_dir, exist_ok=True)
-
-
-
-def get_bev(seed):
- print('[PCGGenerator] Generating BEV scene representation...')
- os.system('python terrain_generator.py --size {} --seed {} --outdir {}'.format(net_G.voxel.sample_size, seed, world_dir))
- heightmap_path = os.path.join(world_dir, 'heightmap.png')
- semantic_path = os.path.join(world_dir, 'colormap.png')
- heightmap = Image.open(heightmap_path)
- semantic = Image.open(semantic_path)
- return semantic, heightmap
-
-def get_video(seed, num_frames, reso_h, reso_w):
- device = torch.device('cuda')
- rng_cuda = torch.Generator(device=device)
- rng_cuda = rng_cuda.manual_seed(seed)
- torch.manual_seed(seed)
- torch.cuda.manual_seed(seed)
- net_G.voxel.next_world(device, world_dir, checkpoint)
- cam_mode = cfg.inference_args.camera_mode
- cfg.inference_args.cam_maxstep = num_frames
- cfg.inference_args.resolution_hw = [reso_h, reso_w]
- current_outdir = os.path.join(world_dir, 'camera_{:02d}'.format(cam_mode))
- os.makedirs(current_outdir, exist_ok=True)
- z = torch.empty(1, net_G.style_dims, dtype=torch.float32, device=device)
- z.normal_(generator=rng_cuda)
- net_G.inference_givenstyle(z, current_outdir, **vars(cfg.inference_args))
- return os.path.join(current_outdir, 'rgb_render.mp4')
-
-markdown=f'''
- # SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections
-
- Authored by Zhaoxi Chen, Guangcong Wang, Ziwei Liu
- ### Useful links:
- - [Official Github Repo](https://github.com/FrozenBurning/SceneDreamer)
- - [Project Page](https://scene-dreamer.github.io/)
- - [arXiv Link](https://arxiv.org/abs/2302.01330)
- Licensed under the S-Lab License.
-
-
- We offer a sampled scene whose BEVs are shown on the right. You can also use the button "Generate BEV" to randomly sample a new 3D world represented by a height map and a semantic map. But it requires a long time.
-
- To render video, push the button "Render" to generate a camera trajectory flying through the world. You can specify rendering options as shown below!
-'''
-
-with gr.Blocks() as demo:
- with gr.Row():
- with gr.Column():
- gr.Markdown(markdown)
- with gr.Column():
- with gr.Row():
- with gr.Column():
- semantic = gr.Image(value='./test/colormap.png',type="pil", shape=(512, 512))
- with gr.Column():
- height = gr.Image(value='./test/heightmap.png', type="pil", shape=(512, 512))
- with gr.Row():
- # with gr.Column():
- # image = gr.Image(type='pil', shape(540, 960))
- with gr.Column():
- video = gr.Video()
- with gr.Row():
- num_frames = gr.Slider(minimum=10, maximum=200, value=20, step=1, label='Number of rendered frames')
- user_seed = gr.Slider(minimum=0, maximum=999999, value=8888, step=1, label='Random seed')
- resolution_h = gr.Slider(minimum=256, maximum=2160, value=270, step=1, label='Height of rendered image')
- resolution_w = gr.Slider(minimum=256, maximum=3840, value=480, step=1, label='Width of rendered image')
-
- with gr.Row():
- btn = gr.Button(value="Generate BEV")
- btn_2=gr.Button(value="Render")
-
- btn.click(get_bev,[user_seed],[semantic, height])
- btn_2.click(get_video,[user_seed, num_frames, resolution_h, resolution_w], [video])
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/Godrose0728/sound-link/attentions.py b/spaces/Godrose0728/sound-link/attentions.py
deleted file mode 100644
index 86bc73b5fe98cc7b443e9078553920346c996707..0000000000000000000000000000000000000000
--- a/spaces/Godrose0728/sound-link/attentions.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-from modules import LayerNorm
-
-
-class Encoder(nn.Module):
- def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.window_size = window_size
-
- self.drop = nn.Dropout(p_dropout)
- self.attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask):
- attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.attn_layers[i](x, x, attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class Decoder(nn.Module):
- def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.ModuleList()
- self.norm_layers_0 = nn.ModuleList()
- self.encdec_attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask, h, h_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
- encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class MultiHeadAttention(nn.Module):
- def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
- super().__init__()
- assert channels % n_heads == 0
-
- self.channels = channels
- self.out_channels = out_channels
- self.n_heads = n_heads
- self.p_dropout = p_dropout
- self.window_size = window_size
- self.heads_share = heads_share
- self.block_length = block_length
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
- self.attn = None
-
- self.k_channels = channels // n_heads
- self.conv_q = nn.Conv1d(channels, channels, 1)
- self.conv_k = nn.Conv1d(channels, channels, 1)
- self.conv_v = nn.Conv1d(channels, channels, 1)
- self.conv_o = nn.Conv1d(channels, out_channels, 1)
- self.drop = nn.Dropout(p_dropout)
-
- if window_size is not None:
- n_heads_rel = 1 if heads_share else n_heads
- rel_stddev = self.k_channels**-0.5
- self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
- self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
- nn.init.xavier_uniform_(self.conv_q.weight)
- nn.init.xavier_uniform_(self.conv_k.weight)
- nn.init.xavier_uniform_(self.conv_v.weight)
- if proximal_init:
- with torch.no_grad():
- self.conv_k.weight.copy_(self.conv_q.weight)
- self.conv_k.bias.copy_(self.conv_q.bias)
-
- def forward(self, x, c, attn_mask=None):
- q = self.conv_q(x)
- k = self.conv_k(c)
- v = self.conv_v(c)
-
- x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
- x = self.conv_o(x)
- return x
-
- def attention(self, query, key, value, mask=None):
- # reshape [b, d, t] -> [b, n_h, t, d_k]
- b, d, t_s, t_t = (*key.size(), query.size(2))
- query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
- key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
- value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
- scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
- if self.window_size is not None:
- assert t_s == t_t, "Relative attention is only available for self-attention."
- key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
- rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
- scores_local = self._relative_position_to_absolute_position(rel_logits)
- scores = scores + scores_local
- if self.proximal_bias:
- assert t_s == t_t, "Proximal bias is only available for self-attention."
- scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
- if mask is not None:
- scores = scores.masked_fill(mask == 0, -1e4)
- if self.block_length is not None:
- assert t_s == t_t, "Local attention is only available for self-attention."
- block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
- scores = scores.masked_fill(block_mask == 0, -1e4)
- p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
- p_attn = self.drop(p_attn)
- output = torch.matmul(p_attn, value)
- if self.window_size is not None:
- relative_weights = self._absolute_position_to_relative_position(p_attn)
- value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
- output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
- output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
- return output, p_attn
-
- def _matmul_with_relative_values(self, x, y):
- """
- x: [b, h, l, m]
- y: [h or 1, m, d]
- ret: [b, h, l, d]
- """
- ret = torch.matmul(x, y.unsqueeze(0))
- return ret
-
- def _matmul_with_relative_keys(self, x, y):
- """
- x: [b, h, l, d]
- y: [h or 1, m, d]
- ret: [b, h, l, m]
- """
- ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
- return ret
-
- def _get_relative_embeddings(self, relative_embeddings, length):
- max_relative_position = 2 * self.window_size + 1
- # Pad first before slice to avoid using cond ops.
- pad_length = max(length - (self.window_size + 1), 0)
- slice_start_position = max((self.window_size + 1) - length, 0)
- slice_end_position = slice_start_position + 2 * length - 1
- if pad_length > 0:
- padded_relative_embeddings = F.pad(
- relative_embeddings,
- commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
- else:
- padded_relative_embeddings = relative_embeddings
- used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
- return used_relative_embeddings
-
- def _relative_position_to_absolute_position(self, x):
- """
- x: [b, h, l, 2*l-1]
- ret: [b, h, l, l]
- """
- batch, heads, length, _ = x.size()
- # Concat columns of pad to shift from relative to absolute indexing.
- x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
- # Concat extra elements so to add up to shape (len+1, 2*len-1).
- x_flat = x.view([batch, heads, length * 2 * length])
- x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
- # Reshape and slice out the padded elements.
- x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
- return x_final
-
- def _absolute_position_to_relative_position(self, x):
- """
- x: [b, h, l, l]
- ret: [b, h, l, 2*l-1]
- """
- batch, heads, length, _ = x.size()
- # padd along column
- x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
- x_flat = x.view([batch, heads, length**2 + length*(length -1)])
- # add 0's in the beginning that will skew the elements after reshape
- x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
- x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
- return x_final
-
- def _attention_bias_proximal(self, length):
- """Bias for self-attention to encourage attention to close positions.
- Args:
- length: an integer scalar.
- Returns:
- a Tensor with shape [1, 1, length, length]
- """
- r = torch.arange(length, dtype=torch.float32)
- diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
- return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
- def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.activation = activation
- self.causal = causal
-
- if causal:
- self.padding = self._causal_padding
- else:
- self.padding = self._same_padding
-
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
- self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
- self.drop = nn.Dropout(p_dropout)
-
- def forward(self, x, x_mask):
- x = self.conv_1(self.padding(x * x_mask))
- if self.activation == "gelu":
- x = x * torch.sigmoid(1.702 * x)
- else:
- x = torch.relu(x)
- x = self.drop(x)
- x = self.conv_2(self.padding(x * x_mask))
- return x * x_mask
-
- def _causal_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = self.kernel_size - 1
- pad_r = 0
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
-
- def _same_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = (self.kernel_size - 1) // 2
- pad_r = self.kernel_size // 2
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
diff --git a/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/RealESRGANv030/realesrgan/models/realesrnet_model.py b/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/RealESRGANv030/realesrgan/models/realesrnet_model.py
deleted file mode 100644
index e55af653aa786adf36c1f7e0822752739376302b..0000000000000000000000000000000000000000
--- a/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/RealESRGANv030/realesrgan/models/realesrnet_model.py
+++ /dev/null
@@ -1,236 +0,0 @@
-import numpy as np
-import random
-import torch
-from basicsr.data.degradations import (
- random_add_gaussian_noise_pt,
- random_add_poisson_noise_pt,
-)
-from basicsr.data.transforms import paired_random_crop
-from basicsr.models.sr_model import SRModel
-from basicsr.utils import DiffJPEG, USMSharp
-from basicsr.utils.img_process_util import filter2D
-from basicsr.utils.registry import MODEL_REGISTRY
-from torch.nn import functional as F
-
-
-@MODEL_REGISTRY.register()
-class RealESRNetModel(SRModel):
- """RealESRNet Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
-
- It is trained without GAN losses.
- It mainly performs:
- 1. randomly synthesize LQ images in GPU tensors
- 2. optimize the networks with GAN training.
- """
-
- def __init__(self, opt):
- super(RealESRNetModel, self).__init__(opt)
- self.jpeger = DiffJPEG(
- differentiable=False
- ).cuda() # simulate JPEG compression artifacts
- self.usm_sharpener = USMSharp().cuda() # do usm sharpening
- self.queue_size = opt.get("queue_size", 180)
-
- @torch.no_grad()
- def _dequeue_and_enqueue(self):
- """It is the training pair pool for increasing the diversity in a batch.
-
- Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
- batch could not have different resize scaling factors. Therefore, we employ this training pair pool
- to increase the degradation diversity in a batch.
- """
- # initialize
- b, c, h, w = self.lq.size()
- if not hasattr(self, "queue_lr"):
- assert (
- self.queue_size % b == 0
- ), f"queue size {self.queue_size} should be divisible by batch size {b}"
- self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
- _, c, h, w = self.gt.size()
- self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
- self.queue_ptr = 0
- if self.queue_ptr == self.queue_size: # the pool is full
- # do dequeue and enqueue
- # shuffle
- idx = torch.randperm(self.queue_size)
- self.queue_lr = self.queue_lr[idx]
- self.queue_gt = self.queue_gt[idx]
- # get first b samples
- lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
- gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
- # update the queue
- self.queue_lr[0:b, :, :, :] = self.lq.clone()
- self.queue_gt[0:b, :, :, :] = self.gt.clone()
-
- self.lq = lq_dequeue
- self.gt = gt_dequeue
- else:
- # only do enqueue
- self.queue_lr[
- self.queue_ptr : self.queue_ptr + b, :, :, :
- ] = self.lq.clone()
- self.queue_gt[
- self.queue_ptr : self.queue_ptr + b, :, :, :
- ] = self.gt.clone()
- self.queue_ptr = self.queue_ptr + b
-
- @torch.no_grad()
- def feed_data(self, data):
- """Accept data from dataloader, and then add two-order degradations to obtain LQ images."""
- if self.is_train and self.opt.get("high_order_degradation", True):
- # training data synthesis
- self.gt = data["gt"].to(self.device)
- # USM sharpen the GT images
- if self.opt["gt_usm"] is True:
- self.gt = self.usm_sharpener(self.gt)
-
- self.kernel1 = data["kernel1"].to(self.device)
- self.kernel2 = data["kernel2"].to(self.device)
- self.sinc_kernel = data["sinc_kernel"].to(self.device)
-
- ori_h, ori_w = self.gt.size()[2:4]
-
- # ----------------------- The first degradation process ----------------------- #
- # blur
- out = filter2D(self.gt, self.kernel1)
- # random resize
- updown_type = random.choices(
- ["up", "down", "keep"], self.opt["resize_prob"]
- )[0]
- if updown_type == "up":
- scale = np.random.uniform(1, self.opt["resize_range"][1])
- elif updown_type == "down":
- scale = np.random.uniform(self.opt["resize_range"][0], 1)
- else:
- scale = 1
- mode = random.choice(["area", "bilinear", "bicubic"])
- out = F.interpolate(out, scale_factor=scale, mode=mode)
- # add noise
- gray_noise_prob = self.opt["gray_noise_prob"]
- if np.random.uniform() < self.opt["gaussian_noise_prob"]:
- out = random_add_gaussian_noise_pt(
- out,
- sigma_range=self.opt["noise_range"],
- clip=True,
- rounds=False,
- gray_prob=gray_noise_prob,
- )
- else:
- out = random_add_poisson_noise_pt(
- out,
- scale_range=self.opt["poisson_scale_range"],
- gray_prob=gray_noise_prob,
- clip=True,
- rounds=False,
- )
- # JPEG compression
- jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt["jpeg_range"])
- out = torch.clamp(
- out, 0, 1
- ) # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
- out = self.jpeger(out, quality=jpeg_p)
-
- # ----------------------- The second degradation process ----------------------- #
- # blur
- if np.random.uniform() < self.opt["second_blur_prob"]:
- out = filter2D(out, self.kernel2)
- # random resize
- updown_type = random.choices(
- ["up", "down", "keep"], self.opt["resize_prob2"]
- )[0]
- if updown_type == "up":
- scale = np.random.uniform(1, self.opt["resize_range2"][1])
- elif updown_type == "down":
- scale = np.random.uniform(self.opt["resize_range2"][0], 1)
- else:
- scale = 1
- mode = random.choice(["area", "bilinear", "bicubic"])
- out = F.interpolate(
- out,
- size=(
- int(ori_h / self.opt["scale"] * scale),
- int(ori_w / self.opt["scale"] * scale),
- ),
- mode=mode,
- )
- # add noise
- gray_noise_prob = self.opt["gray_noise_prob2"]
- if np.random.uniform() < self.opt["gaussian_noise_prob2"]:
- out = random_add_gaussian_noise_pt(
- out,
- sigma_range=self.opt["noise_range2"],
- clip=True,
- rounds=False,
- gray_prob=gray_noise_prob,
- )
- else:
- out = random_add_poisson_noise_pt(
- out,
- scale_range=self.opt["poisson_scale_range2"],
- gray_prob=gray_noise_prob,
- clip=True,
- rounds=False,
- )
-
- # JPEG compression + the final sinc filter
- # We also need to resize images to desired sizes. We group [resize back + sinc filter] together
- # as one operation.
- # We consider two orders:
- # 1. [resize back + sinc filter] + JPEG compression
- # 2. JPEG compression + [resize back + sinc filter]
- # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
- if np.random.uniform() < 0.5:
- # resize back + the final sinc filter
- mode = random.choice(["area", "bilinear", "bicubic"])
- out = F.interpolate(
- out,
- size=(ori_h // self.opt["scale"], ori_w // self.opt["scale"]),
- mode=mode,
- )
- out = filter2D(out, self.sinc_kernel)
- # JPEG compression
- jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt["jpeg_range2"])
- out = torch.clamp(out, 0, 1)
- out = self.jpeger(out, quality=jpeg_p)
- else:
- # JPEG compression
- jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt["jpeg_range2"])
- out = torch.clamp(out, 0, 1)
- out = self.jpeger(out, quality=jpeg_p)
- # resize back + the final sinc filter
- mode = random.choice(["area", "bilinear", "bicubic"])
- out = F.interpolate(
- out,
- size=(ori_h // self.opt["scale"], ori_w // self.opt["scale"]),
- mode=mode,
- )
- out = filter2D(out, self.sinc_kernel)
-
- # clamp and round
- self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255.0
-
- # random crop
- gt_size = self.opt["gt_size"]
- self.gt, self.lq = paired_random_crop(
- self.gt, self.lq, gt_size, self.opt["scale"]
- )
-
- # training pair pool
- self._dequeue_and_enqueue()
- self.lq = (
- self.lq.contiguous()
- ) # for the warning: grad and param do not obey the gradient layout contract
- else:
- # for paired training or validation
- self.lq = data["lq"].to(self.device)
- if "gt" in data:
- self.gt = data["gt"].to(self.device)
- self.gt_usm = self.usm_sharpener(self.gt)
-
- def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
- # do not use the synthetic process during validation
- self.is_train = False
- super(RealESRNetModel, self).nondist_validation(
- dataloader, current_iter, tb_logger, save_img
- )
- self.is_train = True
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py
deleted file mode 100644
index 061ca6993606fe2c7bdb020eaf3b5ea8b91a9b8e..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py
+++ /dev/null
@@ -1,16 +0,0 @@
-_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py'
-conv_cfg = dict(type='ConvWS')
-norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
-model = dict(
- pretrained='open-mmlab://jhu/resnext101_32x4d_gn_ws',
- backbone=dict(
- type='ResNeXt',
- depth=101,
- groups=32,
- base_width=4,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- style='pytorch',
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py
deleted file mode 100644
index 8f483a17ace5c101548f640b95cc94030f37a0b3..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py
+++ /dev/null
@@ -1,58 +0,0 @@
-_base_ = [
- '../_base_/models/retinanet_r50_fpn.py',
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-model = dict(
- pretrained='open-mmlab://regnetx_3.2gf',
- backbone=dict(
- _delete_=True,
- type='RegNet',
- arch='regnetx_3.2gf',
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=True,
- style='pytorch'),
- neck=dict(
- type='FPN',
- in_channels=[96, 192, 432, 1008],
- out_channels=256,
- num_outs=5))
-img_norm_cfg = dict(
- # The mean and std are used in PyCls when training RegNets
- mean=[103.53, 116.28, 123.675],
- std=[57.375, 57.12, 58.395],
- to_rgb=False)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
-data = dict(
- train=dict(pipeline=train_pipeline),
- val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
-optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
-optimizer_config = dict(
- _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/backbones/swin_transformer.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/backbones/swin_transformer.py
deleted file mode 100644
index bb41850d8480a08a6a7698bf6129ffd1ab239681..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/backbones/swin_transformer.py
+++ /dev/null
@@ -1,630 +0,0 @@
-# --------------------------------------------------------
-# Swin Transformer
-# Copyright (c) 2021 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Ze Liu, Yutong Lin, Yixuan Wei
-# --------------------------------------------------------
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-import numpy as np
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-from mmcv_custom import load_checkpoint
-from mmdet.utils import get_root_logger
-from ..builder import BACKBONES
-
-
-class Mlp(nn.Module):
- """ Multilayer perceptron."""
-
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
-def window_partition(x, window_size):
- """
- Args:
- x: (B, H, W, C)
- window_size (int): window size
-
- Returns:
- windows: (num_windows*B, window_size, window_size, C)
- """
- B, H, W, C = x.shape
- x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows
-
-
-def window_reverse(windows, window_size, H, W):
- """
- Args:
- windows: (num_windows*B, window_size, window_size, C)
- window_size (int): Window size
- H (int): Height of image
- W (int): Width of image
-
- Returns:
- x: (B, H, W, C)
- """
- B = int(windows.shape[0] / (H * W / window_size / window_size))
- x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
- return x
-
-
-class WindowAttention(nn.Module):
- """ Window based multi-head self attention (W-MSA) module with relative position bias.
- It supports both of shifted and non-shifted window.
-
- Args:
- dim (int): Number of input channels.
- window_size (tuple[int]): The height and width of the window.
- num_heads (int): Number of attention heads.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
- attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
- proj_drop (float, optional): Dropout ratio of output. Default: 0.0
- """
-
- def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
- super().__init__()
- self.dim = dim
- self.window_size = window_size # Wh, Ww
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = qk_scale or head_dim ** -0.5
-
- # define a parameter table of relative position bias
- self.relative_position_bias_table = nn.Parameter(
- torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
-
- # get pair-wise relative position index for each token inside the window
- coords_h = torch.arange(self.window_size[0])
- coords_w = torch.arange(self.window_size[1])
- coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
- coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
- relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
- relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
- relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
- relative_coords[:, :, 1] += self.window_size[1] - 1
- relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
- relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
- self.register_buffer("relative_position_index", relative_position_index)
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
-
- trunc_normal_(self.relative_position_bias_table, std=.02)
- self.softmax = nn.Softmax(dim=-1)
-
- def forward(self, x, mask=None):
- """ Forward function.
-
- Args:
- x: input features with shape of (num_windows*B, N, C)
- mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
- """
- B_, N, C = x.shape
- qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
- q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
-
- q = q * self.scale
- attn = (q @ k.transpose(-2, -1))
-
- relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
- self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
- relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
- attn = attn + relative_position_bias.unsqueeze(0)
-
- if mask is not None:
- nW = mask.shape[0]
- attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
- attn = attn.view(-1, self.num_heads, N, N)
- attn = self.softmax(attn)
- else:
- attn = self.softmax(attn)
-
- attn = self.attn_drop(attn)
-
- x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
-
-
-class SwinTransformerBlock(nn.Module):
- """ Swin Transformer Block.
-
- Args:
- dim (int): Number of input channels.
- num_heads (int): Number of attention heads.
- window_size (int): Window size.
- shift_size (int): Shift size for SW-MSA.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float, optional): Stochastic depth rate. Default: 0.0
- act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
-
- def __init__(self, dim, num_heads, window_size=7, shift_size=0,
- mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
- act_layer=nn.GELU, norm_layer=nn.LayerNorm):
- super().__init__()
- self.dim = dim
- self.num_heads = num_heads
- self.window_size = window_size
- self.shift_size = shift_size
- self.mlp_ratio = mlp_ratio
- assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
- self.norm1 = norm_layer(dim)
- self.attn = WindowAttention(
- dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
- qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- self.norm2 = norm_layer(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- self.H = None
- self.W = None
-
- def forward(self, x, mask_matrix):
- """ Forward function.
-
- Args:
- x: Input feature, tensor size (B, H*W, C).
- H, W: Spatial resolution of the input feature.
- mask_matrix: Attention mask for cyclic shift.
- """
- B, L, C = x.shape
- H, W = self.H, self.W
- assert L == H * W, "input feature has wrong size"
-
- shortcut = x
- x = self.norm1(x)
- x = x.view(B, H, W, C)
-
- # pad feature maps to multiples of window size
- pad_l = pad_t = 0
- pad_r = (self.window_size - W % self.window_size) % self.window_size
- pad_b = (self.window_size - H % self.window_size) % self.window_size
- x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
- _, Hp, Wp, _ = x.shape
-
- # cyclic shift
- if self.shift_size > 0:
- shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
- attn_mask = mask_matrix
- else:
- shifted_x = x
- attn_mask = None
-
- # partition windows
- x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
- x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
-
- # W-MSA/SW-MSA
- attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
-
- # merge windows
- attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
- shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
-
- # reverse cyclic shift
- if self.shift_size > 0:
- x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
- else:
- x = shifted_x
-
- if pad_r > 0 or pad_b > 0:
- x = x[:, :H, :W, :].contiguous()
-
- x = x.view(B, H * W, C)
-
- # FFN
- x = shortcut + self.drop_path(x)
- x = x + self.drop_path(self.mlp(self.norm2(x)))
-
- return x
-
-
-class PatchMerging(nn.Module):
- """ Patch Merging Layer
-
- Args:
- dim (int): Number of input channels.
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
- def __init__(self, dim, norm_layer=nn.LayerNorm):
- super().__init__()
- self.dim = dim
- self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
- self.norm = norm_layer(4 * dim)
-
- def forward(self, x, H, W):
- """ Forward function.
-
- Args:
- x: Input feature, tensor size (B, H*W, C).
- H, W: Spatial resolution of the input feature.
- """
- B, L, C = x.shape
- assert L == H * W, "input feature has wrong size"
-
- x = x.view(B, H, W, C)
-
- # padding
- pad_input = (H % 2 == 1) or (W % 2 == 1)
- if pad_input:
- x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
-
- x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
- x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
- x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
- x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
- x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
- x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
-
- x = self.norm(x)
- x = self.reduction(x)
-
- return x
-
-
-class BasicLayer(nn.Module):
- """ A basic Swin Transformer layer for one stage.
-
- Args:
- dim (int): Number of feature channels
- depth (int): Depths of this stage.
- num_heads (int): Number of attention head.
- window_size (int): Local window size. Default: 7.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- """
-
- def __init__(self,
- dim,
- depth,
- num_heads,
- window_size=7,
- mlp_ratio=4.,
- qkv_bias=True,
- qk_scale=None,
- drop=0.,
- attn_drop=0.,
- drop_path=0.,
- norm_layer=nn.LayerNorm,
- downsample=None,
- use_checkpoint=False):
- super().__init__()
- self.window_size = window_size
- self.shift_size = window_size // 2
- self.depth = depth
- self.use_checkpoint = use_checkpoint
-
- # build blocks
- self.blocks = nn.ModuleList([
- SwinTransformerBlock(
- dim=dim,
- num_heads=num_heads,
- window_size=window_size,
- shift_size=0 if (i % 2 == 0) else window_size // 2,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- qk_scale=qk_scale,
- drop=drop,
- attn_drop=attn_drop,
- drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
- norm_layer=norm_layer)
- for i in range(depth)])
-
- # patch merging layer
- if downsample is not None:
- self.downsample = downsample(dim=dim, norm_layer=norm_layer)
- else:
- self.downsample = None
-
- def forward(self, x, H, W):
- """ Forward function.
-
- Args:
- x: Input feature, tensor size (B, H*W, C).
- H, W: Spatial resolution of the input feature.
- """
-
- # calculate attention mask for SW-MSA
- Hp = int(np.ceil(H / self.window_size)) * self.window_size
- Wp = int(np.ceil(W / self.window_size)) * self.window_size
- img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
- h_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- w_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- cnt = 0
- for h in h_slices:
- for w in w_slices:
- img_mask[:, h, w, :] = cnt
- cnt += 1
-
- mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
- mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
- attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
- attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
- for blk in self.blocks:
- blk.H, blk.W = H, W
- if self.use_checkpoint:
- x = checkpoint.checkpoint(blk, x, attn_mask)
- else:
- x = blk(x, attn_mask)
- if self.downsample is not None:
- x_down = self.downsample(x, H, W)
- Wh, Ww = (H + 1) // 2, (W + 1) // 2
- return x, H, W, x_down, Wh, Ww
- else:
- return x, H, W, x, H, W
-
-
-class PatchEmbed(nn.Module):
- """ Image to Patch Embedding
-
- Args:
- patch_size (int): Patch token size. Default: 4.
- in_chans (int): Number of input image channels. Default: 3.
- embed_dim (int): Number of linear projection output channels. Default: 96.
- norm_layer (nn.Module, optional): Normalization layer. Default: None
- """
-
- def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
- super().__init__()
- patch_size = to_2tuple(patch_size)
- self.patch_size = patch_size
-
- self.in_chans = in_chans
- self.embed_dim = embed_dim
-
- self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
- if norm_layer is not None:
- self.norm = norm_layer(embed_dim)
- else:
- self.norm = None
-
- def forward(self, x):
- """Forward function."""
- # padding
- _, _, H, W = x.size()
- if W % self.patch_size[1] != 0:
- x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
- if H % self.patch_size[0] != 0:
- x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
-
- x = self.proj(x) # B C Wh Ww
- if self.norm is not None:
- Wh, Ww = x.size(2), x.size(3)
- x = x.flatten(2).transpose(1, 2)
- x = self.norm(x)
- x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
-
- return x
-
-
-@BACKBONES.register_module()
-class SwinTransformer(nn.Module):
- """ Swin Transformer backbone.
- A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
- https://arxiv.org/pdf/2103.14030
-
- Args:
- pretrain_img_size (int): Input image size for training the pretrained model,
- used in absolute postion embedding. Default 224.
- patch_size (int | tuple(int)): Patch size. Default: 4.
- in_chans (int): Number of input image channels. Default: 3.
- embed_dim (int): Number of linear projection output channels. Default: 96.
- depths (tuple[int]): Depths of each Swin Transformer stage.
- num_heads (tuple[int]): Number of attention head of each stage.
- window_size (int): Window size. Default: 7.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
- qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
- drop_rate (float): Dropout rate.
- attn_drop_rate (float): Attention dropout rate. Default: 0.
- drop_path_rate (float): Stochastic depth rate. Default: 0.2.
- norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
- ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
- patch_norm (bool): If True, add normalization after patch embedding. Default: True.
- out_indices (Sequence[int]): Output from which stages.
- frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
- -1 means not freezing any parameters.
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- """
-
- def __init__(self,
- pretrain_img_size=224,
- patch_size=4,
- in_chans=3,
- embed_dim=96,
- depths=[2, 2, 6, 2],
- num_heads=[3, 6, 12, 24],
- window_size=7,
- mlp_ratio=4.,
- qkv_bias=True,
- qk_scale=None,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.2,
- norm_layer=nn.LayerNorm,
- ape=False,
- patch_norm=True,
- out_indices=(0, 1, 2, 3),
- frozen_stages=-1,
- use_checkpoint=False):
- super().__init__()
-
- self.pretrain_img_size = pretrain_img_size
- self.num_layers = len(depths)
- self.embed_dim = embed_dim
- self.ape = ape
- self.patch_norm = patch_norm
- self.out_indices = out_indices
- self.frozen_stages = frozen_stages
-
- # split image into non-overlapping patches
- self.patch_embed = PatchEmbed(
- patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
- norm_layer=norm_layer if self.patch_norm else None)
-
- # absolute position embedding
- if self.ape:
- pretrain_img_size = to_2tuple(pretrain_img_size)
- patch_size = to_2tuple(patch_size)
- patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
-
- self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
- trunc_normal_(self.absolute_pos_embed, std=.02)
-
- self.pos_drop = nn.Dropout(p=drop_rate)
-
- # stochastic depth
- dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
-
- # build layers
- self.layers = nn.ModuleList()
- for i_layer in range(self.num_layers):
- layer = BasicLayer(
- dim=int(embed_dim * 2 ** i_layer),
- depth=depths[i_layer],
- num_heads=num_heads[i_layer],
- window_size=window_size,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- qk_scale=qk_scale,
- drop=drop_rate,
- attn_drop=attn_drop_rate,
- drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
- norm_layer=norm_layer,
- downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
- use_checkpoint=use_checkpoint)
- self.layers.append(layer)
-
- num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
- self.num_features = num_features
-
- # add a norm layer for each output
- for i_layer in out_indices:
- layer = norm_layer(num_features[i_layer])
- layer_name = f'norm{i_layer}'
- self.add_module(layer_name, layer)
-
- self._freeze_stages()
-
- def _freeze_stages(self):
- if self.frozen_stages >= 0:
- self.patch_embed.eval()
- for param in self.patch_embed.parameters():
- param.requires_grad = False
-
- if self.frozen_stages >= 1 and self.ape:
- self.absolute_pos_embed.requires_grad = False
-
- if self.frozen_stages >= 2:
- self.pos_drop.eval()
- for i in range(0, self.frozen_stages - 1):
- m = self.layers[i]
- m.eval()
- for param in m.parameters():
- param.requires_grad = False
-
- def init_weights(self, pretrained=None):
- """Initialize the weights in backbone.
-
- Args:
- pretrained (str, optional): Path to pre-trained weights.
- Defaults to None.
- """
-
- def _init_weights(m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
- if isinstance(m, nn.Linear) and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.LayerNorm):
- nn.init.constant_(m.bias, 0)
- nn.init.constant_(m.weight, 1.0)
-
- if isinstance(pretrained, str):
- self.apply(_init_weights)
- logger = get_root_logger()
- load_checkpoint(self, pretrained, strict=False, logger=logger)
- elif pretrained is None:
- self.apply(_init_weights)
- else:
- raise TypeError('pretrained must be a str or None')
-
- def forward(self, x):
- """Forward function."""
- x = self.patch_embed(x)
-
- Wh, Ww = x.size(2), x.size(3)
- if self.ape:
- # interpolate the position embedding to the corresponding size
- absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
- x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
- else:
- x = x.flatten(2).transpose(1, 2)
- x = self.pos_drop(x)
-
- outs = []
- for i in range(self.num_layers):
- layer = self.layers[i]
- x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
-
- if i in self.out_indices:
- norm_layer = getattr(self, f'norm{i}')
- x_out = norm_layer(x_out)
-
- out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
- outs.append(out)
-
- return tuple(outs)
-
- def train(self, mode=True):
- """Convert the model into training mode while keep layers freezed."""
- super(SwinTransformer, self).train(mode)
- self._freeze_stages()
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/musicgen/musicgen_clapemb_32khz.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/musicgen/musicgen_clapemb_32khz.py
deleted file mode 100644
index 64ad3f8c77afe1ab5908e407ad14d4879e1b1ad1..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/musicgen/musicgen_clapemb_32khz.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from ._explorers import LMExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@LMExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=32, partition=partitions)
- launcher.bind_(solver='musicgen/musicgen_base_32khz')
- # replace this by the desired music dataset
- launcher.bind_(dset='internal/music_400k_32khz')
- launcher.bind_(conditioner='clapemb2music')
-
- fsdp = {'autocast': False, 'fsdp.use': True}
- cache_path = {'conditioners.description.clap.cache_path':
- '/fsx-audio-craft-llm/jadecopet/experiments/audiocraft/caches/clap_embed_music'}
- text_wav_training_opt = {'conditioners.description.clap.text_p': 0.5}
-
- launcher.bind_(fsdp)
-
- launcher.slurm_(gpus=32).bind_(label='32gpus')
- with launcher.job_array():
- launcher()
- launcher(text_wav_training_opt)
- launcher(cache_path)
- launcher(cache_path, text_wav_training_opt)
diff --git a/spaces/Grezz/generate_human_motion/VQ-Trans/utils/skeleton.py b/spaces/Grezz/generate_human_motion/VQ-Trans/utils/skeleton.py
deleted file mode 100644
index 6de56af0c29ae7cccbd7178f912459413f87c646..0000000000000000000000000000000000000000
--- a/spaces/Grezz/generate_human_motion/VQ-Trans/utils/skeleton.py
+++ /dev/null
@@ -1,199 +0,0 @@
-from utils.quaternion import *
-import scipy.ndimage.filters as filters
-
-class Skeleton(object):
- def __init__(self, offset, kinematic_tree, device):
- self.device = device
- self._raw_offset_np = offset.numpy()
- self._raw_offset = offset.clone().detach().to(device).float()
- self._kinematic_tree = kinematic_tree
- self._offset = None
- self._parents = [0] * len(self._raw_offset)
- self._parents[0] = -1
- for chain in self._kinematic_tree:
- for j in range(1, len(chain)):
- self._parents[chain[j]] = chain[j-1]
-
- def njoints(self):
- return len(self._raw_offset)
-
- def offset(self):
- return self._offset
-
- def set_offset(self, offsets):
- self._offset = offsets.clone().detach().to(self.device).float()
-
- def kinematic_tree(self):
- return self._kinematic_tree
-
- def parents(self):
- return self._parents
-
- # joints (batch_size, joints_num, 3)
- def get_offsets_joints_batch(self, joints):
- assert len(joints.shape) == 3
- _offsets = self._raw_offset.expand(joints.shape[0], -1, -1).clone()
- for i in range(1, self._raw_offset.shape[0]):
- _offsets[:, i] = torch.norm(joints[:, i] - joints[:, self._parents[i]], p=2, dim=1)[:, None] * _offsets[:, i]
-
- self._offset = _offsets.detach()
- return _offsets
-
- # joints (joints_num, 3)
- def get_offsets_joints(self, joints):
- assert len(joints.shape) == 2
- _offsets = self._raw_offset.clone()
- for i in range(1, self._raw_offset.shape[0]):
- # print(joints.shape)
- _offsets[i] = torch.norm(joints[i] - joints[self._parents[i]], p=2, dim=0) * _offsets[i]
-
- self._offset = _offsets.detach()
- return _offsets
-
- # face_joint_idx should follow the order of right hip, left hip, right shoulder, left shoulder
- # joints (batch_size, joints_num, 3)
- def inverse_kinematics_np(self, joints, face_joint_idx, smooth_forward=False):
- assert len(face_joint_idx) == 4
- '''Get Forward Direction'''
- l_hip, r_hip, sdr_r, sdr_l = face_joint_idx
- across1 = joints[:, r_hip] - joints[:, l_hip]
- across2 = joints[:, sdr_r] - joints[:, sdr_l]
- across = across1 + across2
- across = across / np.sqrt((across**2).sum(axis=-1))[:, np.newaxis]
- # print(across1.shape, across2.shape)
-
- # forward (batch_size, 3)
- forward = np.cross(np.array([[0, 1, 0]]), across, axis=-1)
- if smooth_forward:
- forward = filters.gaussian_filter1d(forward, 20, axis=0, mode='nearest')
- # forward (batch_size, 3)
- forward = forward / np.sqrt((forward**2).sum(axis=-1))[..., np.newaxis]
-
- '''Get Root Rotation'''
- target = np.array([[0,0,1]]).repeat(len(forward), axis=0)
- root_quat = qbetween_np(forward, target)
-
- '''Inverse Kinematics'''
- # quat_params (batch_size, joints_num, 4)
- # print(joints.shape[:-1])
- quat_params = np.zeros(joints.shape[:-1] + (4,))
- # print(quat_params.shape)
- root_quat[0] = np.array([[1.0, 0.0, 0.0, 0.0]])
- quat_params[:, 0] = root_quat
- # quat_params[0, 0] = np.array([[1.0, 0.0, 0.0, 0.0]])
- for chain in self._kinematic_tree:
- R = root_quat
- for j in range(len(chain) - 1):
- # (batch, 3)
- u = self._raw_offset_np[chain[j+1]][np.newaxis,...].repeat(len(joints), axis=0)
- # print(u.shape)
- # (batch, 3)
- v = joints[:, chain[j+1]] - joints[:, chain[j]]
- v = v / np.sqrt((v**2).sum(axis=-1))[:, np.newaxis]
- # print(u.shape, v.shape)
- rot_u_v = qbetween_np(u, v)
-
- R_loc = qmul_np(qinv_np(R), rot_u_v)
-
- quat_params[:,chain[j + 1], :] = R_loc
- R = qmul_np(R, R_loc)
-
- return quat_params
-
- # Be sure root joint is at the beginning of kinematic chains
- def forward_kinematics(self, quat_params, root_pos, skel_joints=None, do_root_R=True):
- # quat_params (batch_size, joints_num, 4)
- # joints (batch_size, joints_num, 3)
- # root_pos (batch_size, 3)
- if skel_joints is not None:
- offsets = self.get_offsets_joints_batch(skel_joints)
- if len(self._offset.shape) == 2:
- offsets = self._offset.expand(quat_params.shape[0], -1, -1)
- joints = torch.zeros(quat_params.shape[:-1] + (3,)).to(self.device)
- joints[:, 0] = root_pos
- for chain in self._kinematic_tree:
- if do_root_R:
- R = quat_params[:, 0]
- else:
- R = torch.tensor([[1.0, 0.0, 0.0, 0.0]]).expand(len(quat_params), -1).detach().to(self.device)
- for i in range(1, len(chain)):
- R = qmul(R, quat_params[:, chain[i]])
- offset_vec = offsets[:, chain[i]]
- joints[:, chain[i]] = qrot(R, offset_vec) + joints[:, chain[i-1]]
- return joints
-
- # Be sure root joint is at the beginning of kinematic chains
- def forward_kinematics_np(self, quat_params, root_pos, skel_joints=None, do_root_R=True):
- # quat_params (batch_size, joints_num, 4)
- # joints (batch_size, joints_num, 3)
- # root_pos (batch_size, 3)
- if skel_joints is not None:
- skel_joints = torch.from_numpy(skel_joints)
- offsets = self.get_offsets_joints_batch(skel_joints)
- if len(self._offset.shape) == 2:
- offsets = self._offset.expand(quat_params.shape[0], -1, -1)
- offsets = offsets.numpy()
- joints = np.zeros(quat_params.shape[:-1] + (3,))
- joints[:, 0] = root_pos
- for chain in self._kinematic_tree:
- if do_root_R:
- R = quat_params[:, 0]
- else:
- R = np.array([[1.0, 0.0, 0.0, 0.0]]).repeat(len(quat_params), axis=0)
- for i in range(1, len(chain)):
- R = qmul_np(R, quat_params[:, chain[i]])
- offset_vec = offsets[:, chain[i]]
- joints[:, chain[i]] = qrot_np(R, offset_vec) + joints[:, chain[i - 1]]
- return joints
-
- def forward_kinematics_cont6d_np(self, cont6d_params, root_pos, skel_joints=None, do_root_R=True):
- # cont6d_params (batch_size, joints_num, 6)
- # joints (batch_size, joints_num, 3)
- # root_pos (batch_size, 3)
- if skel_joints is not None:
- skel_joints = torch.from_numpy(skel_joints)
- offsets = self.get_offsets_joints_batch(skel_joints)
- if len(self._offset.shape) == 2:
- offsets = self._offset.expand(cont6d_params.shape[0], -1, -1)
- offsets = offsets.numpy()
- joints = np.zeros(cont6d_params.shape[:-1] + (3,))
- joints[:, 0] = root_pos
- for chain in self._kinematic_tree:
- if do_root_R:
- matR = cont6d_to_matrix_np(cont6d_params[:, 0])
- else:
- matR = np.eye(3)[np.newaxis, :].repeat(len(cont6d_params), axis=0)
- for i in range(1, len(chain)):
- matR = np.matmul(matR, cont6d_to_matrix_np(cont6d_params[:, chain[i]]))
- offset_vec = offsets[:, chain[i]][..., np.newaxis]
- # print(matR.shape, offset_vec.shape)
- joints[:, chain[i]] = np.matmul(matR, offset_vec).squeeze(-1) + joints[:, chain[i-1]]
- return joints
-
- def forward_kinematics_cont6d(self, cont6d_params, root_pos, skel_joints=None, do_root_R=True):
- # cont6d_params (batch_size, joints_num, 6)
- # joints (batch_size, joints_num, 3)
- # root_pos (batch_size, 3)
- if skel_joints is not None:
- # skel_joints = torch.from_numpy(skel_joints)
- offsets = self.get_offsets_joints_batch(skel_joints)
- if len(self._offset.shape) == 2:
- offsets = self._offset.expand(cont6d_params.shape[0], -1, -1)
- joints = torch.zeros(cont6d_params.shape[:-1] + (3,)).to(cont6d_params.device)
- joints[..., 0, :] = root_pos
- for chain in self._kinematic_tree:
- if do_root_R:
- matR = cont6d_to_matrix(cont6d_params[:, 0])
- else:
- matR = torch.eye(3).expand((len(cont6d_params), -1, -1)).detach().to(cont6d_params.device)
- for i in range(1, len(chain)):
- matR = torch.matmul(matR, cont6d_to_matrix(cont6d_params[:, chain[i]]))
- offset_vec = offsets[:, chain[i]].unsqueeze(-1)
- # print(matR.shape, offset_vec.shape)
- joints[:, chain[i]] = torch.matmul(matR, offset_vec).squeeze(-1) + joints[:, chain[i-1]]
- return joints
-
-
-
-
-
diff --git a/spaces/Hallucinate/demo/AdaBins-main/evaluate.py b/spaces/Hallucinate/demo/AdaBins-main/evaluate.py
deleted file mode 100644
index 4a66a6cbc9d15fc14aabdb1fdd2a1abd864cb22f..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/AdaBins-main/evaluate.py
+++ /dev/null
@@ -1,215 +0,0 @@
-import argparse
-import os
-import sys
-
-import numpy as np
-import torch
-import torch.nn as nn
-from PIL import Image
-from tqdm import tqdm
-
-import model_io
-from dataloader import DepthDataLoader
-from models import UnetAdaptiveBins
-from utils import RunningAverageDict
-
-
-def compute_errors(gt, pred):
- thresh = np.maximum((gt / pred), (pred / gt))
- a1 = (thresh < 1.25).mean()
- a2 = (thresh < 1.25 ** 2).mean()
- a3 = (thresh < 1.25 ** 3).mean()
-
- abs_rel = np.mean(np.abs(gt - pred) / gt)
- sq_rel = np.mean(((gt - pred) ** 2) / gt)
-
- rmse = (gt - pred) ** 2
- rmse = np.sqrt(rmse.mean())
-
- rmse_log = (np.log(gt) - np.log(pred)) ** 2
- rmse_log = np.sqrt(rmse_log.mean())
-
- err = np.log(pred) - np.log(gt)
- silog = np.sqrt(np.mean(err ** 2) - np.mean(err) ** 2) * 100
-
- log_10 = (np.abs(np.log10(gt) - np.log10(pred))).mean()
- return dict(a1=a1, a2=a2, a3=a3, abs_rel=abs_rel, rmse=rmse, log_10=log_10, rmse_log=rmse_log,
- silog=silog, sq_rel=sq_rel)
-
-
-# def denormalize(x, device='cpu'):
-# mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
-# std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
-# return x * std + mean
-#
-def predict_tta(model, image, args):
- pred = model(image)[-1]
- # pred = utils.depth_norm(pred)
- # pred = nn.functional.interpolate(pred, depth.shape[-2:], mode='bilinear', align_corners=True)
- # pred = np.clip(pred.cpu().numpy(), 10, 1000)/100.
- pred = np.clip(pred.cpu().numpy(), args.min_depth, args.max_depth)
-
- image = torch.Tensor(np.array(image.cpu().numpy())[..., ::-1].copy()).to(device)
-
- pred_lr = model(image)[-1]
- # pred_lr = utils.depth_norm(pred_lr)
- # pred_lr = nn.functional.interpolate(pred_lr, depth.shape[-2:], mode='bilinear', align_corners=True)
- # pred_lr = np.clip(pred_lr.cpu().numpy()[...,::-1], 10, 1000)/100.
- pred_lr = np.clip(pred_lr.cpu().numpy()[..., ::-1], args.min_depth, args.max_depth)
- final = 0.5 * (pred + pred_lr)
- final = nn.functional.interpolate(torch.Tensor(final), image.shape[-2:], mode='bilinear', align_corners=True)
- return torch.Tensor(final)
-
-
-def eval(model, test_loader, args, gpus=None, ):
- if gpus is None:
- device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
- else:
- device = gpus[0]
-
- if args.save_dir is not None:
- os.makedirs(args.save_dir)
-
- metrics = RunningAverageDict()
- # crop_size = (471 - 45, 601 - 41)
- # bins = utils.get_bins(100)
- total_invalid = 0
- with torch.no_grad():
- model.eval()
-
- sequential = test_loader
- for batch in tqdm(sequential):
-
- image = batch['image'].to(device)
- gt = batch['depth'].to(device)
- final = predict_tta(model, image, args)
- final = final.squeeze().cpu().numpy()
-
- # final[final < args.min_depth] = args.min_depth
- # final[final > args.max_depth] = args.max_depth
- final[np.isinf(final)] = args.max_depth
- final[np.isnan(final)] = args.min_depth
-
- if args.save_dir is not None:
- if args.dataset == 'nyu':
- impath = f"{batch['image_path'][0].replace('/', '__').replace('.jpg', '')}"
- factor = 1000
- else:
- dpath = batch['image_path'][0].split('/')
- impath = dpath[1] + "_" + dpath[-1]
- impath = impath.split('.')[0]
- factor = 256
-
- # rgb_path = os.path.join(rgb_dir, f"{impath}.png")
- # tf.ToPILImage()(denormalize(image.squeeze().unsqueeze(0).cpu()).squeeze()).save(rgb_path)
-
- pred_path = os.path.join(args.save_dir, f"{impath}.png")
- pred = (final * factor).astype('uint16')
- Image.fromarray(pred).save(pred_path)
-
- if 'has_valid_depth' in batch:
- if not batch['has_valid_depth']:
- # print("Invalid ground truth")
- total_invalid += 1
- continue
-
- gt = gt.squeeze().cpu().numpy()
- valid_mask = np.logical_and(gt > args.min_depth, gt < args.max_depth)
-
- if args.garg_crop or args.eigen_crop:
- gt_height, gt_width = gt.shape
- eval_mask = np.zeros(valid_mask.shape)
-
- if args.garg_crop:
- eval_mask[int(0.40810811 * gt_height):int(0.99189189 * gt_height),
- int(0.03594771 * gt_width):int(0.96405229 * gt_width)] = 1
-
- elif args.eigen_crop:
- if args.dataset == 'kitti':
- eval_mask[int(0.3324324 * gt_height):int(0.91351351 * gt_height),
- int(0.0359477 * gt_width):int(0.96405229 * gt_width)] = 1
- else:
- eval_mask[45:471, 41:601] = 1
- valid_mask = np.logical_and(valid_mask, eval_mask)
- # gt = gt[valid_mask]
- # final = final[valid_mask]
-
- metrics.update(compute_errors(gt[valid_mask], final[valid_mask]))
-
- print(f"Total invalid: {total_invalid}")
- metrics = {k: round(v, 3) for k, v in metrics.get_value().items()}
- print(f"Metrics: {metrics}")
-
-
-def convert_arg_line_to_args(arg_line):
- for arg in arg_line.split():
- if not arg.strip():
- continue
- yield str(arg)
-
-
-if __name__ == '__main__':
-
- # Arguments
- parser = argparse.ArgumentParser(description='Model evaluator', fromfile_prefix_chars='@',
- conflict_handler='resolve')
- parser.convert_arg_line_to_args = convert_arg_line_to_args
- parser.add_argument('--n-bins', '--n_bins', default=256, type=int,
- help='number of bins/buckets to divide depth range into')
- parser.add_argument('--gpu', default=None, type=int, help='Which gpu to use')
- parser.add_argument('--save-dir', '--save_dir', default=None, type=str, help='Store predictions in folder')
- parser.add_argument("--root", default=".", type=str,
- help="Root folder to save data in")
-
- parser.add_argument("--dataset", default='nyu', type=str, help="Dataset to train on")
-
- parser.add_argument("--data_path", default='../dataset/nyu/sync/', type=str,
- help="path to dataset")
- parser.add_argument("--gt_path", default='../dataset/nyu/sync/', type=str,
- help="path to dataset gt")
-
- parser.add_argument('--filenames_file',
- default="./train_test_inputs/nyudepthv2_train_files_with_gt.txt",
- type=str, help='path to the filenames text file')
-
- parser.add_argument('--input_height', type=int, help='input height', default=416)
- parser.add_argument('--input_width', type=int, help='input width', default=544)
- parser.add_argument('--max_depth', type=float, help='maximum depth in estimation', default=10)
- parser.add_argument('--min_depth', type=float, help='minimum depth in estimation', default=1e-3)
-
- parser.add_argument('--do_kb_crop', help='if set, crop input images as kitti benchmark images', action='store_true')
-
- parser.add_argument('--data_path_eval',
- default="../dataset/nyu/official_splits/test/",
- type=str, help='path to the data for online evaluation')
- parser.add_argument('--gt_path_eval', default="../dataset/nyu/official_splits/test/",
- type=str, help='path to the groundtruth data for online evaluation')
- parser.add_argument('--filenames_file_eval',
- default="./train_test_inputs/nyudepthv2_test_files_with_gt.txt",
- type=str, help='path to the filenames text file for online evaluation')
- parser.add_argument('--checkpoint_path', '--checkpoint-path', type=str, required=True,
- help="checkpoint file to use for prediction")
-
- parser.add_argument('--min_depth_eval', type=float, help='minimum depth for evaluation', default=1e-3)
- parser.add_argument('--max_depth_eval', type=float, help='maximum depth for evaluation', default=10)
- parser.add_argument('--eigen_crop', help='if set, crops according to Eigen NIPS14', action='store_true')
- parser.add_argument('--garg_crop', help='if set, crops according to Garg ECCV16', action='store_true')
- parser.add_argument('--do_kb_crop', help='Use kitti benchmark cropping', action='store_true')
-
- if sys.argv.__len__() == 2:
- arg_filename_with_prefix = '@' + sys.argv[1]
- args = parser.parse_args([arg_filename_with_prefix])
- else:
- args = parser.parse_args()
-
- # args = parser.parse_args()
- args.gpu = int(args.gpu) if args.gpu is not None else 0
- args.distributed = False
- device = torch.device('cuda:{}'.format(args.gpu))
- test = DepthDataLoader(args, 'online_eval').data
- model = UnetAdaptiveBins.build(n_bins=args.n_bins, min_val=args.min_depth, max_val=args.max_depth,
- norm='linear').to(device)
- model = model_io.load_checkpoint(args.checkpoint_path, model)[0]
- model = model.eval()
-
- eval(model, test, args, gpus=[device])
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/__init__.py
deleted file mode 100644
index 44bb24ae614941f23fea29c56d60167650c39bcb..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-try:
- from fairseq.version import __version__ # noqa
-except ImportError:
- pass
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/scripts/vads.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/scripts/vads.py
deleted file mode 100644
index 2398da97d8c44b8f3f270b22d5508a003482b4d6..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/scripts/vads.py
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import sys
-
-from copy import deepcopy
-from scipy.signal import lfilter
-
-import numpy as np
-from tqdm import tqdm
-import soundfile as sf
-import os.path as osp
-
-
-def get_parser():
- parser = argparse.ArgumentParser(description="compute vad segments")
- parser.add_argument(
- "--rvad-home",
- "-r",
- help="path to rvad home (see https://github.com/zhenghuatan/rVADfast)",
- required=True,
- )
-
- return parser
-
-
-def rvad(speechproc, path):
- winlen, ovrlen, pre_coef, nfilter, nftt = 0.025, 0.01, 0.97, 20, 512
- ftThres = 0.5
- vadThres = 0.4
- opts = 1
-
- data, fs = sf.read(path)
- assert fs == 16_000, "sample rate must be 16khz"
- ft, flen, fsh10, nfr10 = speechproc.sflux(data, fs, winlen, ovrlen, nftt)
-
- # --spectral flatness --
- pv01 = np.zeros(ft.shape[0])
- pv01[np.less_equal(ft, ftThres)] = 1
- pitch = deepcopy(ft)
-
- pvblk = speechproc.pitchblockdetect(pv01, pitch, nfr10, opts)
-
- # --filtering--
- ENERGYFLOOR = np.exp(-50)
- b = np.array([0.9770, -0.9770])
- a = np.array([1.0000, -0.9540])
- fdata = lfilter(b, a, data, axis=0)
-
- # --pass 1--
- noise_samp, noise_seg, n_noise_samp = speechproc.snre_highenergy(
- fdata, nfr10, flen, fsh10, ENERGYFLOOR, pv01, pvblk
- )
-
- # sets noisy segments to zero
- for j in range(n_noise_samp):
- fdata[range(int(noise_samp[j, 0]), int(noise_samp[j, 1]) + 1)] = 0
-
- vad_seg = speechproc.snre_vad(
- fdata, nfr10, flen, fsh10, ENERGYFLOOR, pv01, pvblk, vadThres
- )
- return vad_seg, data
-
-
-def main():
- parser = get_parser()
- args = parser.parse_args()
-
- sys.path.append(args.rvad_home)
- import speechproc
-
- stride = 160
- lines = sys.stdin.readlines()
- root = lines[0].rstrip()
- for fpath in tqdm(lines[1:]):
- path = osp.join(root, fpath.split()[0])
- vads, wav = rvad(speechproc, path)
-
- start = None
- vad_segs = []
- for i, v in enumerate(vads):
- if start is None and v == 1:
- start = i * stride
- elif start is not None and v == 0:
- vad_segs.append((start, i * stride))
- start = None
- if start is not None:
- vad_segs.append((start, len(wav)))
-
- print(" ".join(f"{v[0]}:{v[1]}" for v in vad_segs))
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/nat/cmlm_transformer.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/nat/cmlm_transformer.py
deleted file mode 100644
index c876e9453c101c00bd8e93e6e6f1fb48dc26f993..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/nat/cmlm_transformer.py
+++ /dev/null
@@ -1,162 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-This file implements:
-Ghazvininejad, Marjan, et al.
-"Constant-time machine translation with conditional masked language models."
-arXiv preprint arXiv:1904.09324 (2019).
-"""
-
-from fairseq.models import register_model, register_model_architecture
-from fairseq.models.nat import NATransformerModel
-from fairseq.utils import new_arange
-
-
-def _skeptical_unmasking(output_scores, output_masks, p):
- sorted_index = output_scores.sort(-1)[1]
- boundary_len = (
- (output_masks.sum(1, keepdim=True).type_as(output_scores) - 2) * p
- ).long()
- skeptical_mask = new_arange(output_masks) < boundary_len
- return skeptical_mask.scatter(1, sorted_index, skeptical_mask)
-
-
-@register_model("cmlm_transformer")
-class CMLMNATransformerModel(NATransformerModel):
- @staticmethod
- def add_args(parser):
- NATransformerModel.add_args(parser)
-
- def forward(
- self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
- ):
- assert not self.decoder.src_embedding_copy, "do not support embedding copy."
-
- # encoding
- encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
- # length prediction
- length_out = self.decoder.forward_length(
- normalize=False, encoder_out=encoder_out
- )
- length_tgt = self.decoder.forward_length_prediction(
- length_out, encoder_out, tgt_tokens
- )
-
- # decoding
- word_ins_out = self.decoder(
- normalize=False,
- prev_output_tokens=prev_output_tokens,
- encoder_out=encoder_out,
- )
- word_ins_mask = prev_output_tokens.eq(self.unk)
-
- return {
- "word_ins": {
- "out": word_ins_out,
- "tgt": tgt_tokens,
- "mask": word_ins_mask,
- "ls": self.args.label_smoothing,
- "nll_loss": True,
- },
- "length": {
- "out": length_out,
- "tgt": length_tgt,
- "factor": self.decoder.length_loss_factor,
- },
- }
-
- def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs):
-
- step = decoder_out.step
- max_step = decoder_out.max_step
-
- output_tokens = decoder_out.output_tokens
- output_scores = decoder_out.output_scores
- history = decoder_out.history
-
- # execute the decoder
- output_masks = output_tokens.eq(self.unk)
- _scores, _tokens = self.decoder(
- normalize=True,
- prev_output_tokens=output_tokens,
- encoder_out=encoder_out,
- ).max(-1)
- output_tokens.masked_scatter_(output_masks, _tokens[output_masks])
- output_scores.masked_scatter_(output_masks, _scores[output_masks])
-
- if history is not None:
- history.append(output_tokens.clone())
-
- # skeptical decoding (depend on the maximum decoding steps.)
- if (step + 1) < max_step:
- skeptical_mask = _skeptical_unmasking(
- output_scores, output_tokens.ne(self.pad), 1 - (step + 1) / max_step
- )
-
- output_tokens.masked_fill_(skeptical_mask, self.unk)
- output_scores.masked_fill_(skeptical_mask, 0.0)
-
- if history is not None:
- history.append(output_tokens.clone())
-
- return decoder_out._replace(
- output_tokens=output_tokens,
- output_scores=output_scores,
- attn=None,
- history=history,
- )
-
-
-@register_model_architecture("cmlm_transformer", "cmlm_transformer")
-def cmlm_base_architecture(args):
- args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
- args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
- args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
- args.encoder_layers = getattr(args, "encoder_layers", 6)
- args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
- args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
- args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
- args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
- args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
- args.decoder_ffn_embed_dim = getattr(
- args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
- )
- args.decoder_layers = getattr(args, "decoder_layers", 6)
- args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
- args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
- args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
- args.attention_dropout = getattr(args, "attention_dropout", 0.0)
- args.activation_dropout = getattr(args, "activation_dropout", 0.0)
- args.activation_fn = getattr(args, "activation_fn", "relu")
- args.dropout = getattr(args, "dropout", 0.1)
- args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
- args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
- args.share_decoder_input_output_embed = getattr(
- args, "share_decoder_input_output_embed", False
- )
- args.share_all_embeddings = getattr(args, "share_all_embeddings", True)
- args.no_token_positional_embeddings = getattr(
- args, "no_token_positional_embeddings", False
- )
- args.adaptive_input = getattr(args, "adaptive_input", False)
- args.apply_bert_init = getattr(args, "apply_bert_init", False)
-
- args.decoder_output_dim = getattr(
- args, "decoder_output_dim", args.decoder_embed_dim
- )
- args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
-
- # --- special arguments ---
- args.sg_length_pred = getattr(args, "sg_length_pred", False)
- args.pred_length_offset = getattr(args, "pred_length_offset", False)
- args.length_loss_factor = getattr(args, "length_loss_factor", 0.1)
- args.ngram_predictor = getattr(args, "ngram_predictor", 1)
- args.src_embedding_copy = getattr(args, "src_embedding_copy", False)
-
-
-@register_model_architecture("cmlm_transformer", "cmlm_transformer_wmt_en_de")
-def cmlm_wmt_en_de(args):
- cmlm_base_architecture(args)
diff --git a/spaces/Has-ai/text-speech/README.md b/spaces/Has-ai/text-speech/README.md
deleted file mode 100644
index 776254413069200b48b50f963684c4c7931ef9ac..0000000000000000000000000000000000000000
--- a/spaces/Has-ai/text-speech/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text Speech
-emoji: 🏆
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/HuggingFaceH4/Elo/app.py b/spaces/HuggingFaceH4/Elo/app.py
deleted file mode 100644
index 7b9982c67dcf00e7885d31e0e9c61c768c029656..0000000000000000000000000000000000000000
--- a/spaces/HuggingFaceH4/Elo/app.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import os
-from pathlib import Path
-
-import pandas as pd
-import streamlit as st
-import utils as ut
-
-st.set_page_config(layout="wide")
-
-
-st.markdown("# Elo Rating of Models")
-st.markdown(
- """This app shows the Elo rating of models on the H4 Hub based on their performance on the H4 eval dataset. """)
-st.markdown(
- """**Notes**
-* This is currently using synthetic data
-* You can tweak the number of tasks, models, and human rating per task to generate different datasets
-"""
-)
-# user input
-
-num_tasks = st.number_input("Number of tasks", min_value=1, max_value=5000, value=100)
-num_models = st.number_input("Number of models", min_value=1, max_value=100, value=4)
-num_human_ratings = st.number_input(
- "Number of human ratings per task", min_value=1, max_value=10, value=3
-)
-
-button = st.button("Show me the leaderboard!")
-
-if button is True:
- # generate synthetic data
- df = ut.create_synthetic_data( n_tasks=num_tasks, n_models=num_models, n_ratings=num_human_ratings)
- # calculate elo rating
- elo_df = ut.calculate_elo_rating(df)
- # show leaderboard
- ut.display_leaderboard(elo_df)
-
-
-
-
-
-
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/distributed/utils.py b/spaces/ICML2022/OFA/fairseq/fairseq/distributed/utils.py
deleted file mode 100644
index dbf318e7035603c1294eb45af7e98097df36289d..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/distributed/utils.py
+++ /dev/null
@@ -1,826 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import io
-import logging
-import os
-import pickle
-import random
-import socket
-import struct
-import subprocess
-import warnings
-from argparse import Namespace
-from collections import OrderedDict
-from dataclasses import dataclass
-from typing import Any, Dict, List, Mapping, Optional
-import sys
-import time
-
-import torch
-import torch.distributed as dist
-from fairseq.dataclass.configs import DistributedTrainingConfig, FairseqConfig
-from omegaconf import open_dict
-
-try:
- import torch_xla.core.xla_model as xm
-except ImportError:
- xm = None
-
-
-# Flag to indicate if we're using Megatron
-# NOTE: this is a temporary hack until we move away from Megatron's model parallel init
-_USE_MEGATRON = False
-
-# Whether to use XLA ops (e.g., on TPUs) instead of CUDA ops.
-_USE_XLA = False
-
-
-logger = logging.getLogger(__name__)
-
-
-def is_master(cfg: DistributedTrainingConfig):
- return cfg.distributed_rank == 0
-
-
-def infer_init_method(cfg: DistributedTrainingConfig, force_distributed=False):
- if cfg.distributed_init_method is not None or cfg.tpu:
- return
-
- num_pipelines_per_node = None
- if cfg.pipeline_model_parallel:
- num_pipeline_devices, num_pipelines_per_node = _pipeline_parallel_pre_init(cfg)
-
- if all(
- key in os.environ
- for key in ["MASTER_ADDR", "MASTER_PORT", "WORLD_SIZE", "RANK"]
- ):
- # support torch.distributed.launch
- _infer_torch_distributed_launch_init(cfg)
- elif cfg.distributed_port > 0:
- # we can determine the init method automatically for Slurm
- _infer_slurm_init(cfg, num_pipelines_per_node)
- elif cfg.distributed_world_size > 1 or force_distributed:
- # fallback for single node with multiple GPUs
- _infer_single_node_init(cfg)
-
- if cfg.pipeline_model_parallel:
- _pipeline_parallel_post_init(cfg, num_pipeline_devices, num_pipelines_per_node)
- elif not cfg.distributed_no_spawn:
- with open_dict(cfg):
- cfg.distributed_num_procs = min(
- torch.cuda.device_count(), cfg.distributed_world_size
- )
-
-
-def _infer_torch_distributed_launch_init(cfg: DistributedTrainingConfig):
- cfg.distributed_init_method = "env://"
- cfg.distributed_world_size = int(os.environ["WORLD_SIZE"])
- cfg.distributed_rank = int(os.environ["RANK"])
- # processes are created by torch.distributed.launch
- cfg.distributed_no_spawn = True
-
-
-def _infer_slurm_init(cfg: DistributedTrainingConfig, num_pipelines_per_node):
- node_list = os.environ.get("SLURM_STEP_NODELIST")
- if node_list is None:
- node_list = os.environ.get("SLURM_JOB_NODELIST")
- if node_list is not None:
- try:
- hostnames = subprocess.check_output(
- ["scontrol", "show", "hostnames", node_list]
- )
- cfg.distributed_init_method = "tcp://{host}:{port}".format(
- host=hostnames.split()[0].decode("utf-8"),
- port=cfg.distributed_port,
- )
- nnodes = int(os.environ.get("SLURM_NNODES"))
- ntasks_per_node = os.environ.get("SLURM_NTASKS_PER_NODE")
- if ntasks_per_node is not None:
- ntasks_per_node = int(ntasks_per_node)
- else:
- ntasks = int(os.environ.get("SLURM_NTASKS"))
- nnodes = int(os.environ.get("SLURM_NNODES"))
- assert ntasks % nnodes == 0
- ntasks_per_node = int(ntasks / nnodes)
- if ntasks_per_node == 1:
- gpus_per_node = torch.cuda.device_count()
- node_id = int(os.environ.get("SLURM_NODEID"))
- cfg.distributed_rank = node_id * gpus_per_node
- cfg.distributed_world_size = nnodes * gpus_per_node
- elif cfg.pipeline_model_parallel:
- assert ntasks_per_node == num_pipelines_per_node, (
- "SLURM --ntasks-per-node must match number of pipelines per "
- "node (={})".format(num_pipelines_per_node)
- )
- cfg.distributed_no_spawn = True
- # For 4-way MP on nodes with 8 GPUs, ranks will be [0, 1] on
- # the first node, [1, 2] on the second node, etc. This
- # matches torch.distributed.launch.
- node_id = int(os.environ.get("SLURM_NODEID"))
- local_id = int(os.environ.get("SLURM_LOCALID"))
- cfg.distributed_rank = node_id * num_pipelines_per_node + local_id
- # In the above example, device_id will always be in [0, 1],
- # which also matches torch.distributed.launch.
- cfg.device_id = local_id
- # We also want to set distributed_world_size to be the total
- # number of pipelines across all nodes.
- cfg.distributed_world_size = nnodes * num_pipelines_per_node
- else:
- assert ntasks_per_node == cfg.distributed_world_size // nnodes
- cfg.distributed_no_spawn = True
- cfg.distributed_rank = int(os.environ.get("SLURM_PROCID"))
- cfg.device_id = int(os.environ.get("SLURM_LOCALID"))
- except subprocess.CalledProcessError as e: # scontrol failed
- raise e
- except FileNotFoundError: # Slurm is not installed
- pass
-
-
-def _infer_single_node_init(cfg: DistributedTrainingConfig):
- assert (
- cfg.distributed_world_size <= torch.cuda.device_count()
- ), f"world size is {cfg.distributed_world_size} but have {torch.cuda.device_count()} available devices"
- port = random.randint(10000, 20000)
- cfg.distributed_init_method = "tcp://localhost:{port}".format(port=port)
-
-
-def _pipeline_parallel_pre_init(cfg: DistributedTrainingConfig):
- from fairseq import utils
-
- balance_exists = (
- cfg.pipeline_balance is not None
- or cfg.pipeline_encoder_balance is not None
- or cfg.pipeline_decoder_balance is not None
- )
- devices_exist = (
- cfg.pipeline_devices is not None
- or cfg.pipeline_encoder_devices is not None
- or cfg.pipeline_decoder_devices is not None
- )
- if not balance_exists:
- raise ValueError(
- "--pipeline-balance is currently required for pipeline model parallelism"
- )
- if not devices_exist:
- raise ValueError(
- "--pipeline-devices is currently required for pipeline model parallelism"
- )
-
- cfg.pipeline_balance = utils.eval_str_list(cfg.pipeline_balance, type=int)
- if cfg.pipeline_devices is not None:
- cfg.pipeline_devices = utils.eval_str_list(cfg.pipeline_devices, type=int)
- num_pipeline_devices = len(set(cfg.pipeline_devices))
- else:
- cfg.pipeline_encoder_devices = utils.eval_str_list(
- cfg.pipeline_encoder_devices, type=int
- )
- cfg.pipeline_decoder_devices = utils.eval_str_list(
- cfg.pipeline_decoder_devices, type=int
- )
- num_pipeline_devices = len(
- set(cfg.pipeline_encoder_devices + cfg.pipeline_decoder_devices)
- )
- gpus_per_node = torch.cuda.device_count()
- assert (
- gpus_per_node >= num_pipeline_devices
- and gpus_per_node % num_pipeline_devices == 0
- ), (
- "the number of unique device IDs in --pipeline-devices must evenly divide "
- "the number of GPUs per node (multi-node pipelining is not yet supported)"
- )
- num_pipelines_per_node = gpus_per_node // num_pipeline_devices
- return num_pipeline_devices, num_pipelines_per_node
-
-
-def _pipeline_parallel_post_init(
- cfg: DistributedTrainingConfig, num_pipeline_devices, num_pipelines_per_node
-):
- if not cfg.distributed_no_spawn:
- # When distributed_no_spawn is False, we expect distributed_rank and
- # distributed_world_size to be based on the total number of GPUs, so
- # we need to correct them to be based on the number of pipelines.
- assert cfg.distributed_world_size % num_pipeline_devices == 0
- cfg.distributed_world_size = (
- cfg.distributed_world_size // num_pipeline_devices
- )
- # In the case of 4-way MP on nodes with 8 GPUs, we want
- # distributed_rank to be the starting GPU index for each pipeline
- # i.e., 0, 2, ...
- gpus_per_node = torch.cuda.device_count()
- assert cfg.distributed_rank % gpus_per_node == 0
- assert cfg.distributed_rank % num_pipeline_devices == 0
-
- with open_dict(cfg):
- cfg.distributed_rank = cfg.distributed_rank // num_pipeline_devices
- # launch one process per pipeline
- cfg.distributed_num_procs = num_pipelines_per_node
-
- # if we have 4-way MP on a node with 8 GPUs, we want device_ids to be 0
- # and 4, indicating the starting device IDs for each pipeline
- cfg.device_id *= num_pipeline_devices
-
- if cfg.device_id > 0:
- # if there's multiple pipelines on a node (e.g., 4-way MP on an 8
- # GPU node), we need to adjust pipeline_devices accordingly
- logger.debug(
- "setting CUDA device={} on rank {}".format(
- cfg.device_id, cfg.distributed_rank
- )
- )
- torch.cuda.set_device(cfg.device_id)
- with open_dict(cfg):
- cfg.pipeline_devices = [cfg.device_id + d for d in cfg.pipeline_devices]
- logger.info(
- "setting pipeline_devices={} on rank {}".format(
- cfg.pipeline_devices, cfg.distributed_rank
- )
- )
-
-
-def distributed_init(cfg: FairseqConfig):
- if isinstance(cfg, Namespace):
- from fairseq.dataclass.utils import convert_namespace_to_omegaconf
-
- cfg = convert_namespace_to_omegaconf(cfg)
-
- if not cfg.common.tpu:
- if torch.distributed.is_available() and torch.distributed.is_initialized():
- warnings.warn(
- "Distributed is already initialized, cannot initialize twice!"
- )
- else:
- logger.info(
- "distributed init (rank {}): {}".format(
- cfg.distributed_training.distributed_rank,
- cfg.distributed_training.distributed_init_method,
- )
- )
- logger.info('Start init')
- max_time_wait = 600
- for i in range(max_time_wait):
- try:
- dist.init_process_group(
- backend=cfg.distributed_training.distributed_backend,
- init_method=cfg.distributed_training.distributed_init_method,
- world_size=cfg.distributed_training.distributed_world_size,
- rank=cfg.distributed_training.distributed_rank,
- )
- logger.info(
- "initialized host {} as rank {}".format(
- socket.gethostname(),
- cfg.distributed_training.distributed_rank,
- )
- )
- if torch.distributed.is_initialized():
- print("single-machine distributed training is initialized.")
- break
- except ValueError:
- # This is caused by TCPStore failure.
- print('Retry: {}, with value error {}'.format(
- i + 1, sys.exc_info()[0]))
- time.sleep(5)
- if i == max_time_wait - 1:
- print('k8s resource wait too long time')
- exit(-1)
- except Exception:
- print('Retry: {}, with value error {}'.format(
- i + 1, sys.exc_info()[0]))
- exit(-1)
- # perform a dummy all-reduce to initialize the NCCL communicator
- if torch.cuda.is_available():
- dist.all_reduce(torch.zeros(1).cuda())
-
- cfg.distributed_training.distributed_rank = torch.distributed.get_rank()
- else:
- assert xm.xrt_world_size() == cfg.distributed_training.distributed_world_size
- global _USE_XLA
- _USE_XLA = True
- cfg.distributed_training.device_id = xm.get_local_ordinal()
- cfg.distributed_training.distributed_rank = xm.get_ordinal()
- xm.rendezvous("distributed_init") # wait for all workers
-
- if is_master(cfg.distributed_training):
- logging.getLogger().setLevel(logging.INFO)
- else:
- logging.getLogger().setLevel(logging.WARNING)
-
- if cfg.common.model_parallel_size > 1:
- try:
- from fairseq.model_parallel.megatron.mpu import (
- initialize_model_parallel,
- model_parallel_cuda_manual_seed,
- )
- except ImportError:
- raise ImportError(
- "\n\nPlease install the megatron submodule:"
- "\n\n git submodule update --init "
- "fairseq/model_parallel/megatron"
- )
- global _USE_MEGATRON
- _USE_MEGATRON = True
- initialize_model_parallel(cfg.common.model_parallel_size)
- model_parallel_cuda_manual_seed(cfg.common.seed)
- model_part_number = get_model_parallel_rank()
- cfg.checkpoint.checkpoint_suffix += "-model_part-{0}".format(model_part_number)
-
- if hasattr(cfg, "model") and getattr(cfg.model, "base_layers", 0) > 0:
- cfg.checkpoint.checkpoint_suffix = f"-rank-{cfg.distributed_training.distributed_rank}"
-
- return cfg.distributed_training.distributed_rank
-
-
-def distributed_main(i, main, cfg: FairseqConfig, kwargs):
- cfg.distributed_training.device_id = i
- if torch.cuda.is_available() and not cfg.common.cpu and not cfg.common.tpu:
- torch.cuda.set_device(cfg.distributed_training.device_id)
- if cfg.distributed_training.distributed_rank is None: # torch.multiprocessing.spawn
- cfg.distributed_training.distributed_rank = kwargs.pop("start_rank", 0) + i
-
- cfg.distributed_training.distributed_rank = distributed_init(cfg)
-
- after_distributed_init_fn = kwargs.pop("after_distributed_init_fn", None)
- if after_distributed_init_fn:
- cfg = after_distributed_init_fn(cfg)
-
- main(cfg, **kwargs)
-
- if torch.distributed.is_initialized():
- torch.distributed.barrier(get_global_group())
-
-
-def call_main(cfg: FairseqConfig, main, **kwargs):
- if cfg.distributed_training.distributed_init_method is None:
- infer_init_method(cfg.distributed_training)
-
- if cfg.distributed_training.distributed_init_method is not None:
- # distributed training
- if not cfg.distributed_training.distributed_no_spawn:
- start_rank = cfg.distributed_training.distributed_rank
- cfg.distributed_training.distributed_rank = None # assign automatically
- kwargs["start_rank"] = start_rank
- torch.multiprocessing.spawn(
- fn=distributed_main,
- args=(main, cfg, kwargs),
- nprocs=min(
- torch.cuda.device_count(),
- cfg.distributed_training.distributed_world_size,
- ),
- join=True,
- )
- else:
- distributed_main(cfg.distributed_training.device_id, main, cfg, kwargs)
- elif cfg.common.tpu and cfg.distributed_training.distributed_world_size > 1:
- import torch_xla.distributed.xla_multiprocessing as xmp
-
- torch.multiprocessing.set_sharing_strategy("file_system")
- xmp.spawn(
- fn=distributed_main,
- args=(main, cfg, kwargs),
- # tpu-comment:
- # 8 devices in one TPU VM, is the max processes to be spawned.
- # The rest is driven by xm.distributed.xla_dist
- nprocs=min(cfg.distributed_training.distributed_world_size, 8),
- )
- else:
- # single GPU main
- main(cfg, **kwargs)
-
-
-def use_xla():
- global _USE_XLA
- return _USE_XLA
-
-
-def new_groups(grouped_ranks: List[List[int]]):
- if use_xla():
- return ("tpu", grouped_ranks)
- else:
- groups = [dist.new_group(g) for g in grouped_ranks]
- my_group_idx = _find_my_group_index(grouped_ranks)
- return groups[my_group_idx]
-
-
-def _find_my_group_index(grouped_ranks):
- my_rank = get_global_rank()
- for i, group in enumerate(grouped_ranks):
- if my_rank in group:
- return i
- raise RuntimeError
-
-
-def _find_my_group(grouped_ranks):
- index = _find_my_group_index(grouped_ranks)
- return grouped_ranks[index]
-
-
-def get_rank(group):
- if use_xla():
- assert group[0] == "tpu"
- my_group = _find_my_group(group[1])
- return my_group.index(get_global_rank())
- else:
- return dist.get_rank(group=group)
-
-
-def get_world_size(group):
- if use_xla():
- assert group[0] == "tpu"
- my_group = _find_my_group(group[1])
- return len(my_group)
- elif torch.distributed.is_initialized():
- return dist.get_world_size(group=group)
- else:
- return 1
-
-
-def get_global_group():
- if use_xla():
- return new_groups([list(range(get_global_world_size()))])
- elif torch.distributed.is_initialized():
- if not hasattr(get_global_group, "_global_group"):
- # ideally we could use torch.distributed.group.WORLD, but it seems
- # to cause random NCCL hangs in some cases
- get_global_group._global_group = dist.new_group()
- return get_global_group._global_group
- else:
- return None
-
-
-def get_global_rank():
- if use_xla():
- return xm.get_ordinal()
- elif torch.distributed.is_initialized():
- return torch.distributed.get_rank()
- else:
- return 0
-
-
-def get_global_world_size():
- if use_xla():
- return xm.xrt_world_size()
- elif torch.distributed.is_initialized():
- return torch.distributed.get_world_size()
- else:
- return 1
-
-
-def get_data_parallel_group():
- """Get the data parallel group the caller rank belongs to."""
- global _USE_MEGATRON
- if _USE_MEGATRON:
- from fairseq.model_parallel.megatron import mpu
-
- return mpu.get_data_parallel_group()
- else:
- return get_global_group()
-
-
-def get_data_parallel_rank():
- """Return my rank for the data parallel group."""
- return get_rank(get_data_parallel_group())
-
-
-def get_data_parallel_world_size():
- """Return world size for the data parallel group."""
- return get_world_size(get_data_parallel_group())
-
-
-def get_model_parallel_group():
- global _USE_MEGATRON
- if _USE_MEGATRON:
- from fairseq.model_parallel.megatron import mpu
-
- return mpu.get_model_parallel_group()
- else:
- return None
-
-
-def get_model_parallel_rank():
- """Return my rank for the model parallel group."""
- return get_rank(get_model_parallel_group())
-
-
-def get_model_parallel_world_size():
- """Return world size for the model parallel group."""
- return get_world_size(get_model_parallel_group())
-
-
-def all_reduce(tensor, group, op="sum"):
- if use_xla():
- assert isinstance(group, tuple) and group[0] == "tpu"
- tensor = [tensor] # wrap in a list to make xm.all_reduce in-place
- return xm.all_reduce(op, tensor, groups=group[1])[0]
- else:
- if op == "sum":
- op = dist.ReduceOp.SUM
- elif op == "max":
- op = dist.ReduceOp.MAX
- else:
- raise NotImplementedError
- dist.all_reduce(tensor, op=op, group=group)
- return tensor
-
-
-def broadcast(tensor, src, group):
- if use_xla():
- # XLA doesn't support broadcast, hack it with all_reduce
- if get_rank(group) != src:
- tensor.zero_()
- all_reduce(tensor, group)
- else:
- dist.broadcast(tensor, src=src, group=group)
-
-
-def all_to_all(tensor, group):
- """Perform an all-to-all operation on a 1D Tensor."""
- assert tensor.dim() == 1
- split_count = get_world_size(group=group)
- assert tensor.numel() % split_count == 0
- if use_xla():
- assert isinstance(group, tuple) and group[0] == "tpu"
- return xm.all_to_all(
- tensor,
- split_dimension=0,
- concat_dimension=0,
- split_count=split_count,
- groups=group[1],
- )
- else:
- output = torch.zeros_like(tensor)
- dist.all_to_all_single(output, tensor, group=group)
- return output
-
-
-def all_gather(tensor, group, return_tensor=False):
- """Perform an all-gather operation."""
- if use_xla():
- result = xm.all_gather(tensor, groups=group[1])
- world_size = get_world_size(group=group)
- result = result.view(world_size, *tensor.size())
- if return_tensor:
- return result
- else:
- return [result[i] for i in range(world_size)]
- else:
- world_size = get_world_size(group=group)
- rank = get_rank(group=group)
- tensor_list = [
- tensor if i == rank else torch.empty_like(tensor) for i in range(world_size)
- ]
- dist.all_gather(tensor_list, tensor, group=group)
- if return_tensor:
- return torch.stack(tensor_list, dim=0)
- else:
- return tensor_list
-
-
-def all_gather_list(data, group=None, max_size=16384):
- """Gathers arbitrary data from all nodes into a list.
-
- Similar to :func:`~torch.distributed.all_gather` but for arbitrary Python
- data. Note that *data* must be picklable and any CUDA tensors will be moved
- to CPU and returned on CPU as well.
-
- Args:
- data (Any): data from the local worker to be gathered on other workers
- group: group of the collective
- max_size (int, optional): maximum size of the data to be gathered
- across workers
- """
- from fairseq import utils
-
- if group is None:
- group = get_global_group()
- torch.distributed.barrier(group=group)
- rank = get_rank(group=group)
- world_size = get_world_size(group=group)
-
- buffer_size = max_size * world_size
- if (
- not hasattr(all_gather_list, "_buffer")
- or all_gather_list._buffer.numel() < buffer_size
- ):
- all_gather_list._buffer = torch.cuda.ByteTensor(buffer_size)
- all_gather_list._cpu_buffer = torch.ByteTensor(max_size).pin_memory()
- buffer = all_gather_list._buffer
- buffer.zero_()
- cpu_buffer = all_gather_list._cpu_buffer
-
- data = utils.move_to_cpu(data)
- enc = pickle.dumps(data)
- enc_size = len(enc)
- header_size = 4 # size of header that contains the length of the encoded data
- size = header_size + enc_size
- if size > max_size:
- raise ValueError(
- "encoded data size ({}) exceeds max_size ({})".format(size, max_size)
- )
-
- header = struct.pack(">I", enc_size)
- cpu_buffer[:size] = torch.ByteTensor(list(header + enc))
- start = rank * max_size
- buffer[start : start + size].copy_(cpu_buffer[:size])
-
- all_reduce(buffer, group=group)
-
- buffer = buffer.cpu()
- try:
- result = []
- for i in range(world_size):
- out_buffer = buffer[i * max_size : (i + 1) * max_size]
- (enc_size,) = struct.unpack(">I", bytes(out_buffer[:header_size].tolist()))
- if enc_size > 0:
- result.append(
- pickle.loads(
- bytes(out_buffer[header_size : header_size + enc_size].tolist())
- )
- )
- return result
- except pickle.UnpicklingError:
- raise Exception(
- "Unable to unpickle data from other workers. all_gather_list requires all "
- "workers to enter the function together, so this error usually indicates "
- "that the workers have fallen out of sync somehow. Workers can fall out of "
- "sync if one of them runs out of memory, or if there are other conditions "
- "in your training script that can cause one worker to finish an epoch "
- "while other workers are still iterating over their portions of the data. "
- "Try rerunning with --ddp-backend=legacy_ddp and see if that helps."
- )
-
-
-def all_reduce_dict(data: Mapping[str, Any], device, group) -> Dict[str, Any]:
- """
- AllReduce a dictionary of values across workers. We separately
- reduce items that are already on the device and items on CPU for
- better performance.
-
- Args:
- data (Mapping[str, Any]): dictionary of data to all-reduce, but
- cannot be a nested dictionary
- device (torch.device): device for the reduction
- group: group of the collective
- """
- data_keys = list(data.keys())
-
- # We want to separately reduce items that are already on the
- # device and items on CPU for performance reasons.
- cpu_data = OrderedDict()
- device_data = OrderedDict()
- for k in data_keys:
- t = data[k]
- if not torch.is_tensor(t):
- cpu_data[k] = torch.tensor(t, dtype=torch.double)
- elif t.device.type != device.type:
- cpu_data[k] = t.to(dtype=torch.double)
- else:
- device_data[k] = t.to(dtype=torch.double)
-
- def _all_reduce_dict(data: OrderedDict):
- if len(data) == 0:
- return data
- buf = torch.cat([t.view(-1) for t in data.values()]).to(device=device)
- all_reduce(buf, group=group)
- split_buf = torch.split(buf, [t.numel() for t in data.values()])
- reduced_data = [t.view_as(orig) for t, orig in zip(split_buf, data.values())]
- return OrderedDict(zip(data.keys(), reduced_data))
-
- cpu_data = _all_reduce_dict(cpu_data)
- device_data = _all_reduce_dict(device_data)
-
- def get_from_stack(key):
- if key in cpu_data:
- return cpu_data[key]
- elif key in device_data:
- return device_data[key]
- raise KeyError
-
- return OrderedDict([(key, get_from_stack(key)) for key in data_keys])
-
-
-def broadcast_tensors(
- tensors: Optional[List[torch.Tensor]],
- src_rank: int,
- group: object,
- dist_device: Optional[torch.device] = None,
-) -> List[torch.Tensor]:
- """
- Broadcasts a list of tensors without other (non-src) ranks needing to know
- the dtypes/shapes of the tensors.
- """
- if dist_device is None:
- if torch.distributed.get_backend(group) == "nccl":
- dist_device = torch.device("cuda")
- else:
- dist_device = torch.device("cpu")
-
- # share metadata first to simplify transfer
- is_src_rank = (get_rank(group) == src_rank)
- if is_src_rank:
- metadata = [
- {"size": t.size(), "dtype": t.dtype, "device": t.device} for t in tensors
- ]
- metadata = _broadcast_object_slow(metadata, src_rank, group, dist_device)
- else:
- metadata = _broadcast_object_slow(None, src_rank, group, dist_device)
-
- out_tensors = []
- for i, meta in enumerate(metadata):
- if is_src_rank:
- tensor = tensors[i]
- broadcast(tensors[i].to(dist_device), src=src_rank, group=group)
- else:
- tensor = torch.zeros(
- [meta["size"].numel()], dtype=meta["dtype"], device=dist_device
- )
- broadcast(tensor, src=src_rank, group=group)
- tensor = tensor.view(meta["size"]).to(meta["device"])
- out_tensors.append(tensor)
- return out_tensors
-
-
-def broadcast_object(
- obj: Any,
- src_rank: int,
- group: object,
- dist_device: Optional[torch.device] = None,
-) -> Any:
- """Broadcast an arbitrary Python object to other workers."""
- if dist_device is None:
- if torch.distributed.get_backend(group) == "nccl":
- dist_device = torch.device("cuda")
- else:
- dist_device = torch.device("cpu")
-
- if get_rank(group) == src_rank:
- # split the tensors from the non-tensors so we can broadcast them
- # directly, avoiding unnecessary serialization/deserialization
- tensors = []
- obj = _split_tensors_from_obj(obj, tensors)
- obj = _broadcast_object_slow(obj, src_rank, group, dist_device)
- tensors = broadcast_tensors(tensors, src_rank, group, dist_device)
- else:
- obj = _broadcast_object_slow(None, src_rank, group, dist_device)
- tensors = broadcast_tensors(None, src_rank, group, dist_device)
- return _put_tensors_in_obj(obj, tensors)
-
-
-def _broadcast_object_slow(
- obj: Any, src_rank: int, group: object, dist_device: torch.device,
-) -> Any:
- if get_rank(group) == src_rank:
- # Emit data
- buffer = io.BytesIO()
- torch.save(obj, buffer)
- buffer = torch.ByteTensor(buffer.getbuffer()).to(dist_device)
- length = torch.LongTensor([len(buffer)]).to(dist_device)
- broadcast(length, src=src_rank, group=group)
- broadcast(buffer, src=src_rank, group=group)
- else:
- # Fetch from the source
- length = torch.LongTensor([0]).to(dist_device)
- broadcast(length, src=src_rank, group=group)
- buffer = torch.ByteTensor(int(length.item())).to(dist_device)
- broadcast(buffer, src=src_rank, group=group)
- buffer = io.BytesIO(buffer.cpu().numpy())
- obj = torch.load(buffer, map_location="cpu")
- return obj
-
-
-@dataclass(frozen=True)
-class _TensorPlaceholder:
- index: int
-
-
-def _split_tensors_from_obj(obj: Any, tensors: List[torch.Tensor]) -> Any:
- if torch.is_tensor(obj):
- placeholder = _TensorPlaceholder(index=len(tensors))
- tensors.append(obj)
- return placeholder
- elif isinstance(obj, dict):
- return {k: _split_tensors_from_obj(v, tensors) for k, v in obj.items()}
- elif isinstance(obj, list):
- return [_split_tensors_from_obj(v, tensors) for v in obj]
- elif isinstance(obj, tuple):
- return tuple(_split_tensors_from_obj(v, tensors) for v in obj)
- elif isinstance(obj, set):
- return {_split_tensors_from_obj(v, tensors) for v in obj}
- else:
- return obj
-
-
-def _put_tensors_in_obj(obj: Any, tensors: List[torch.Tensor]) -> Any:
- if isinstance(obj, _TensorPlaceholder):
- return tensors[obj.index]
- elif isinstance(obj, dict):
- return {k: _put_tensors_in_obj(v, tensors) for k, v in obj.items()}
- elif isinstance(obj, list):
- return [_put_tensors_in_obj(v, tensors) for v in obj]
- elif isinstance(obj, tuple):
- return tuple(_put_tensors_in_obj(v, tensors) for v in obj)
- elif isinstance(obj, set):
- return {_put_tensors_in_obj(v, tensors) for v in obj}
- else:
- return obj
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/models/fconv_self_att.py b/spaces/ICML2022/OFA/fairseq/fairseq/models/fconv_self_att.py
deleted file mode 100644
index 8357ef7847ed25a62345e219c41906156828c233..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/models/fconv_self_att.py
+++ /dev/null
@@ -1,674 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import math
-import os
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq import checkpoint_utils
-from fairseq.incremental_decoding_utils import with_incremental_state
-from fairseq.models import (
- CompositeEncoder,
- FairseqDecoder,
- FairseqEncoder,
- FairseqEncoderDecoderModel,
- register_model,
- register_model_architecture,
-)
-from fairseq.modules import (
- DownsampledMultiHeadAttention,
- FairseqDropout,
- GradMultiply,
- LayerNorm,
- LearnedPositionalEmbedding,
- LinearizedConvolution,
-)
-
-
-logger = logging.getLogger(__name__)
-
-
-@register_model("fconv_self_att")
-class FConvModelSelfAtt(FairseqEncoderDecoderModel):
- @classmethod
- def hub_models(cls):
- return {
- "conv.stories.pretrained": {
- "path": "https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz",
- "checkpoint_file": "pretrained_checkpoint.pt",
- "tokenizer": "nltk",
- },
- "conv.stories": {
- "path": "https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz",
- "checkpoint_file": "fusion_checkpoint.pt",
- "tokenizer": "nltk",
- "pretrained": "True",
- "pretrained_checkpoint": "./pretrained_checkpoint.pt",
- },
- # Test set containing dictionaries
- "data.stories": "https://dl.fbaipublicfiles.com/fairseq/data/stories_test.tar.bz2",
- }
-
- def __init__(self, encoder, decoder, pretrained_encoder=None):
- super().__init__(encoder, decoder)
- self.encoder.num_attention_layers = sum(
- layer is not None for layer in decoder.attention
- )
- self.pretrained_encoder = pretrained_encoder
- if self.pretrained_encoder is None:
- encoders = {"encoder": encoder}
- else:
- encoders = {"encoder": encoder, "pretrained": self.pretrained_encoder}
- # for fusion model, CompositeEncoder contains both pretrained and training encoders
- # these are forwarded and then combined in the decoder
- self.encoder = CompositeEncoder(encoders)
-
- @staticmethod
- def add_args(parser):
- """Add model-specific arguments to the parser."""
- # fmt: off
- parser.add_argument('--dropout', type=float, metavar='D',
- help='dropout probability')
- parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
- help='encoder embedding dimension')
- parser.add_argument('--encoder-layers', type=str, metavar='EXPR',
- help='encoder layers [(dim, kernel_size), ...]')
- parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
- help='decoder embedding dimension')
- parser.add_argument('--decoder-layers', type=str, metavar='EXPR',
- help='decoder layers [(dim, kernel_size), ...]')
- parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N',
- help='decoder output embedding dimension')
- parser.add_argument('--decoder-attention', type=str, metavar='EXPR',
- help='decoder attention [True, ...]')
- parser.add_argument('--self-attention', type=str, metavar='EXPR',
- help='decoder self-attention layers, ex: [True] + [False]*5')
- parser.add_argument('--multihead-attention-nheads', type=int,
- help='Number of heads to use in attention')
- parser.add_argument('--multihead-self-attention-nheads', type=int,
- help='Number of heads to use in self-attention')
- parser.add_argument('--encoder-attention', type=str, metavar='EXPR',
- help='encoder attention [True, ...]')
- parser.add_argument('--encoder-attention-nheads', type=int,
- help='Number of heads to use in encoder attention')
- parser.add_argument('--project-input', type=str, metavar='EXPR',
- help='Use projections in self-attention [True, ...]')
- parser.add_argument('--gated-attention', type=str, metavar='EXPR',
- help='Use GLU layers in self-attention projections [True, ...]')
- parser.add_argument('--downsample', type=str, metavar='EXPR',
- help='Use downsampling in self-attention [True, ...]')
- parser.add_argument('--pretrained-checkpoint', metavar='DIR',
- help='path to load checkpoint from pretrained model')
- parser.add_argument('--pretrained', type=str, metavar='EXPR',
- help='use pretrained model when training [True, ...]')
- # fmt: on
-
- @classmethod
- def build_model(cls, args, task):
- """Build a new model instance."""
- trained_encoder, trained_decoder = None, None
- pretrained = eval(args.pretrained)
- if pretrained:
- logger.info("loading pretrained model")
- if not os.path.exists(args.pretrained_checkpoint):
- new_pretrained_checkpoint = os.path.join(
- args.data, args.pretrained_checkpoint
- )
- if os.path.exists(new_pretrained_checkpoint):
- args.pretrained_checkpoint = new_pretrained_checkpoint
- trained_model = checkpoint_utils.load_model_ensemble(
- filenames=[args.pretrained_checkpoint],
- task=task,
- )[0][0]
- trained_decoder = list(trained_model.children())[1]
- trained_encoder = list(trained_model.children())[0]
-
- # freeze pretrained model
- for param in trained_decoder.parameters():
- param.requires_grad = False
- for param in trained_encoder.parameters():
- param.requires_grad = False
-
- encoder = FConvEncoder(
- task.source_dictionary,
- embed_dim=args.encoder_embed_dim,
- convolutions=eval(args.encoder_layers),
- dropout=args.dropout,
- max_positions=args.max_source_positions,
- attention=eval(args.encoder_attention),
- attention_nheads=args.encoder_attention_nheads,
- )
-
- decoder = FConvDecoder(
- task.target_dictionary,
- embed_dim=args.decoder_embed_dim,
- convolutions=eval(args.decoder_layers),
- out_embed_dim=args.decoder_out_embed_dim,
- attention=eval(args.decoder_attention),
- dropout=args.dropout,
- max_positions=args.max_target_positions,
- selfattention=eval(args.self_attention),
- attention_nheads=args.multihead_attention_nheads,
- selfattention_nheads=args.multihead_self_attention_nheads,
- project_input=eval(args.project_input),
- gated_attention=eval(args.gated_attention),
- downsample=eval(args.downsample),
- pretrained=pretrained,
- trained_decoder=trained_decoder,
- )
- model = FConvModelSelfAtt(encoder, decoder, trained_encoder)
-
- return model
-
- @property
- def pretrained(self):
- return self.pretrained_encoder is not None
-
-
-class FConvEncoder(FairseqEncoder):
- """Convolutional encoder"""
-
- def __init__(
- self,
- dictionary,
- embed_dim=512,
- max_positions=1024,
- convolutions=((512, 3),) * 20,
- dropout=0.1,
- attention=False,
- attention_nheads=1,
- ):
- super().__init__(dictionary)
- self.dropout_module = FairseqDropout(
- dropout, module_name=self.__class__.__name__
- )
- self.num_attention_layers = None
-
- num_embeddings = len(dictionary)
- self.padding_idx = dictionary.pad()
- self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx)
- self.embed_positions = PositionalEmbedding(
- max_positions,
- embed_dim,
- self.padding_idx,
- )
-
- def expand_bool_array(val):
- if isinstance(val, bool):
- # expand True into [True, True, ...] and do the same with False
- return [val] * len(convolutions)
- return val
-
- attention = expand_bool_array(attention)
-
- in_channels = convolutions[0][0]
- self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
- self.projections = nn.ModuleList()
- self.convolutions = nn.ModuleList()
- self.attention = nn.ModuleList()
- self.attproj = nn.ModuleList()
- for i, (out_channels, kernel_size) in enumerate(convolutions):
- self.projections.append(
- Linear(in_channels, out_channels)
- if in_channels != out_channels
- else None
- )
- self.convolutions.append(
- ConvTBC(in_channels, out_channels * 2, kernel_size, dropout=dropout)
- )
-
- self.attention.append(
- SelfAttention(out_channels, embed_dim, attention_nheads)
- if attention[i]
- else None
- )
- in_channels = out_channels
-
- self.fc2 = Linear(in_channels, embed_dim)
-
- def forward(self, src_tokens, src_lengths):
- # embed tokens and positions
- x = self.embed_tokens(src_tokens) + self.embed_positions(src_tokens)
- x = self.dropout_module(x)
- input_embedding = x.transpose(0, 1)
-
- # project to size of convolution
- x = self.fc1(x)
-
- encoder_padding_mask = src_tokens.eq(self.padding_idx).t() # -> T x B
- if not encoder_padding_mask.any():
- encoder_padding_mask = None
-
- # B x T x C -> T x B x C
- x = x.transpose(0, 1)
-
- # temporal convolutions
- for proj, conv, attention in zip(
- self.projections, self.convolutions, self.attention
- ):
- residual = x if proj is None else proj(x)
-
- if encoder_padding_mask is not None:
- x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0)
-
- x = self.dropout_module(x)
- padding_l = (conv.kernel_size[0] - 1) // 2
- padding_r = conv.kernel_size[0] // 2
- x = F.pad(x, (0, 0, 0, 0, padding_l, padding_r))
- x = conv(x)
- x = F.glu(x, dim=2)
- if attention is not None:
- x = attention(x)
- x = (x + residual) * math.sqrt(0.5)
-
- # T x B x C -> B x T x C
- x = x.transpose(1, 0)
-
- # project back to size of embedding
- x = self.fc2(x)
-
- if encoder_padding_mask is not None:
- encoder_padding_mask = encoder_padding_mask.t() # -> B x T
- x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0)
-
- # scale gradients (this only affects backward, not forward)
- x = GradMultiply.apply(x, 1.0 / (2.0 * self.num_attention_layers))
-
- # add output to input embedding for attention
- y = (x + input_embedding.transpose(0, 1)) * math.sqrt(0.5)
-
- return {
- "encoder_out": (x, y),
- "encoder_padding_mask": encoder_padding_mask, # B x T
- }
-
- def reorder_encoder_out(self, encoder_out, new_order):
- encoder_out["encoder_out"] = tuple(
- eo.index_select(0, new_order) for eo in encoder_out["encoder_out"]
- )
-
- if encoder_out["encoder_padding_mask"] is not None:
- encoder_out["encoder_padding_mask"] = encoder_out[
- "encoder_padding_mask"
- ].index_select(0, new_order)
-
- if "pretrained" in encoder_out:
- encoder_out["pretrained"]["encoder_out"] = tuple(
- eo.index_select(0, new_order)
- for eo in encoder_out["pretrained"]["encoder_out"]
- )
-
- return encoder_out
-
- def max_positions(self):
- """Maximum input length supported by the encoder."""
- return self.embed_positions.max_positions
-
-
-@with_incremental_state
-class FConvDecoder(FairseqDecoder):
- """Convolutional decoder"""
-
- def __init__(
- self,
- dictionary,
- embed_dim=512,
- out_embed_dim=256,
- max_positions=1024,
- convolutions=((512, 3),) * 8,
- attention=True,
- dropout=0.1,
- selfattention=False,
- attention_nheads=1,
- selfattention_nheads=1,
- project_input=False,
- gated_attention=False,
- downsample=False,
- pretrained=False,
- trained_decoder=None,
- ):
- super().__init__(dictionary)
- self.register_buffer("version", torch.Tensor([2]))
- self.pretrained = pretrained
- self.pretrained_decoder = trained_decoder
- self.dropout_module = FairseqDropout(
- dropout, module_name=self.__class__.__name__
- )
- self.need_attn = True
- in_channels = convolutions[0][0]
-
- def expand_bool_array(val):
- if isinstance(val, bool):
- # expand True into [True, True, ...] and do the same with False
- return [val] * len(convolutions)
- return val
-
- attention = expand_bool_array(attention)
- selfattention = expand_bool_array(selfattention)
-
- if not isinstance(attention, list) or len(attention) != len(convolutions):
- raise ValueError(
- "Attention is expected to be a list of booleans of "
- "length equal to the number of layers."
- )
-
- num_embeddings = len(dictionary)
- padding_idx = dictionary.pad()
- self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
-
- self.embed_positions = PositionalEmbedding(
- max_positions,
- embed_dim,
- padding_idx,
- )
-
- self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
- self.projections = nn.ModuleList()
- self.convolutions = nn.ModuleList()
- self.attention = nn.ModuleList()
- self.selfattention = nn.ModuleList()
- self.attproj = nn.ModuleList()
- for i, (out_channels, kernel_size) in enumerate(convolutions):
- self.projections.append(
- Linear(in_channels, out_channels)
- if in_channels != out_channels
- else None
- )
- self.convolutions.append(
- LinearizedConv1d(
- in_channels,
- out_channels * 2,
- kernel_size,
- padding=(kernel_size - 1),
- dropout=dropout,
- )
- )
-
- self.attention.append(
- DownsampledMultiHeadAttention(
- out_channels,
- embed_dim,
- attention_nheads,
- project_input=project_input,
- gated=False,
- downsample=False,
- )
- if attention[i]
- else None
- )
-
- self.attproj.append(
- Linear(out_channels, embed_dim, dropout=dropout)
- if attention[i]
- else None
- )
- self.selfattention.append(
- SelfAttention(
- out_channels,
- embed_dim,
- selfattention_nheads,
- project_input=project_input,
- gated=gated_attention,
- downsample=downsample,
- )
- if selfattention[i]
- else None
- )
- in_channels = out_channels
-
- self.fc2 = Linear(in_channels, out_embed_dim)
- self.fc3 = Linear(out_embed_dim, num_embeddings, dropout=dropout)
-
- # model fusion
- if self.pretrained:
- # independent gates are learned from the concatenated input
- self.gate1 = nn.Sequential(
- Linear(out_embed_dim * 2, out_embed_dim), nn.Sigmoid()
- )
- self.gate2 = nn.Sequential(
- Linear(out_embed_dim * 2, out_embed_dim), nn.Sigmoid()
- )
- # pretrained and trained models are joined
- self.joining = nn.Sequential(
- Linear(out_embed_dim * 2, out_embed_dim * 2),
- LayerNorm(out_embed_dim * 2),
- nn.GLU(),
- Linear(out_embed_dim, out_embed_dim * 2),
- LayerNorm(out_embed_dim * 2),
- nn.GLU(),
- Linear(out_embed_dim, out_embed_dim),
- LayerNorm(out_embed_dim),
- )
- # pretrained model contains an output layer that is nhid -> vocab size
- # but the models are combined in their hidden state
- # the hook stores the output of the pretrained model forward
- self.pretrained_outputs = {}
-
- def save_output():
- def hook(a, b, output):
- self.pretrained_outputs["out"] = output
-
- return hook
-
- self.pretrained_decoder.fc2.register_forward_hook(save_output())
-
- def forward(self, prev_output_tokens, encoder_out):
- trained_encoder_out = encoder_out["pretrained"] if self.pretrained else None
- encoder_out = encoder_out["encoder"]["encoder_out"]
-
- encoder_a, encoder_b = self._split_encoder_out(encoder_out)
-
- # embed positions
- positions = self.embed_positions(prev_output_tokens)
-
- # embed tokens and positions
- x = self.embed_tokens(prev_output_tokens) + positions
- x = self.dropout_module(x)
- target_embedding = x.transpose(0, 1)
-
- # project to size of convolution
- x = self.fc1(x)
-
- # B x T x C -> T x B x C
- x = x.transpose(0, 1)
-
- # temporal convolutions
- avg_attn_scores = None
- for proj, conv, attention, selfattention, attproj in zip(
- self.projections,
- self.convolutions,
- self.attention,
- self.selfattention,
- self.attproj,
- ):
- residual = x if proj is None else proj(x)
-
- x = self.dropout_module(x)
- x = conv(x)
- x = F.glu(x, dim=2)
-
- # attention
- if attention is not None:
- r = x
- x, attn_scores = attention(
- attproj(x) + target_embedding, encoder_a, encoder_b
- )
- x = x + r
- if not self.training and self.need_attn:
- if avg_attn_scores is None:
- avg_attn_scores = attn_scores
- else:
- avg_attn_scores.add_(attn_scores)
-
- if selfattention is not None:
- x = selfattention(x)
-
- x = (x + residual) * math.sqrt(0.5)
-
- # T x B x C -> B x T x C
- x = x.transpose(0, 1)
-
- # project back to size of vocabulary
- x = self.fc2(x)
- x = self.dropout_module(x)
- if not self.pretrained:
- x = self.fc3(x)
-
- # fusion gating
- if self.pretrained:
- trained_x, _ = self.pretrained_decoder.forward(
- prev_output_tokens, trained_encoder_out
- )
- y = torch.cat([x, self.pretrained_outputs["out"]], dim=-1)
- gate1 = self.gate1(y)
- gate2 = self.gate2(y)
- gated_x1 = gate1 * x
- gated_x2 = gate2 * self.pretrained_outputs["out"]
- fusion = torch.cat([gated_x1, gated_x2], dim=-1)
- fusion = self.joining(fusion)
- fusion_output = self.fc3(fusion)
- return fusion_output, avg_attn_scores
- else:
- return x, avg_attn_scores
-
- def max_positions(self):
- """Maximum output length supported by the decoder."""
- return self.embed_positions.max_positions
-
- def make_generation_fast_(self, need_attn=False, **kwargs):
- self.need_attn = need_attn
-
- def _split_encoder_out(self, encoder_out):
- """Split and transpose encoder outputs."""
- # transpose only once to speed up attention layers
- encoder_a, encoder_b = encoder_out
- encoder_a = encoder_a.transpose(0, 1).contiguous()
- encoder_b = encoder_b.transpose(0, 1).contiguous()
- result = (encoder_a, encoder_b)
- return result
-
-
-class SelfAttention(nn.Module):
- def __init__(
- self,
- out_channels,
- embed_dim,
- num_heads,
- project_input=False,
- gated=False,
- downsample=False,
- ):
- super().__init__()
- self.attention = DownsampledMultiHeadAttention(
- out_channels,
- embed_dim,
- num_heads,
- dropout=0,
- bias=True,
- project_input=project_input,
- gated=gated,
- downsample=downsample,
- )
- self.in_proj_q = Linear(out_channels, embed_dim)
- self.in_proj_k = Linear(out_channels, embed_dim)
- self.in_proj_v = Linear(out_channels, embed_dim)
- self.ln = LayerNorm(out_channels)
-
- def forward(self, x):
- residual = x
- query = self.in_proj_q(x)
- key = self.in_proj_k(x)
- value = self.in_proj_v(x)
- x, _ = self.attention(
- query, key, value, mask_future_timesteps=True, use_scalar_bias=True
- )
- return self.ln(x + residual)
-
-
-def Embedding(num_embeddings, embedding_dim, padding_idx):
- m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
- m.weight.data.normal_(0, 0.1)
- return m
-
-
-def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx):
- m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx)
- m.weight.data.normal_(0, 0.1)
- return m
-
-
-def Linear(in_features, out_features, dropout=0.0):
- """Weight-normalized Linear layer (input: N x T x C)"""
- m = nn.Linear(in_features, out_features)
- m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features))
- m.bias.data.zero_()
- return m
-
-
-def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs):
- """Weight-normalized Conv1d layer optimized for decoding"""
- m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs)
- std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
- m.weight.data.normal_(mean=0, std=std)
- m.bias.data.zero_()
- return m
-
-
-def ConvTBC(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs):
- """Weight-normalized Conv1d layer"""
- from fairseq.modules import ConvTBC
-
- m = ConvTBC(in_channels, out_channels, kernel_size, **kwargs)
- std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
- m.weight.data.normal_(mean=0, std=std)
- m.bias.data.zero_()
- return m
-
-
-@register_model_architecture("fconv_self_att", "fconv_self_att")
-def base_architecture(args):
- args.dropout = getattr(args, "dropout", 0.1)
- args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
- args.encoder_layers = getattr(args, "encoder_layers", "[(512, 3)] * 3")
- args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
- args.decoder_layers = getattr(args, "decoder_layers", "[(512, 3)] * 8")
- args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256)
- args.decoder_attention = getattr(args, "decoder_attention", "True")
- args.self_attention = getattr(args, "self_attention", "False")
- args.encoder_attention = getattr(args, "encoder_attention", "False")
- args.multihead_attention_nheads = getattr(args, "multihead_attention_nheads", 1)
- args.multihead_self_attention_nheads = getattr(
- args, "multihead_self_attention_nheads", 1
- )
- args.encoder_attention_nheads = getattr(args, "encoder_attention_nheads", 1)
- args.project_input = getattr(args, "project_input", "False")
- args.gated_attention = getattr(args, "gated_attention", "False")
- args.downsample = getattr(args, "downsample", "False")
- args.pretrained_checkpoint = getattr(args, "pretrained_checkpoint", "")
- args.pretrained = getattr(args, "pretrained", "False")
-
-
-@register_model_architecture("fconv_self_att", "fconv_self_att_wp")
-def fconv_self_att_wp(args):
- args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
- args.encoder_layers = getattr(
- args, "encoder_layers", "[(128, 3)] * 2 + [(512,3)] * 1"
- )
- args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256)
- args.decoder_layers = getattr(
- args, "decoder_layers", "[(512, 4)] * 4 + [(768, 4)] * 2 + [(1024, 4)] * 1"
- )
- args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256)
- args.self_attention = getattr(args, "self_attention", "True")
- args.multihead_self_attention_nheads = getattr(
- args, "multihead_self_attention_nheads", 4
- )
- args.project_input = getattr(args, "project_input", "True")
- args.gated_attention = getattr(args, "gated_attention", "True")
- args.downsample = getattr(args, "downsample", "True")
- base_architecture(args)
diff --git a/spaces/ICML2022/resefa/third_party/stylegan3_official_ops/filtered_lrelu.cpp b/spaces/ICML2022/resefa/third_party/stylegan3_official_ops/filtered_lrelu.cpp
deleted file mode 100644
index ff4149b8b46b54d2f400ae10e44d19f20503ba1f..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/resefa/third_party/stylegan3_official_ops/filtered_lrelu.cpp
+++ /dev/null
@@ -1,300 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto. Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include
- Demo for Lowpoly Environment Stable Diffusion model. This space was created using SD Space Creator. Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training | Github Repo
- You can use Mermaid inside HTML5 by including the script and a div with the class or mermaid.
-
- Documentation is located here:
- Mermaid documentation.
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È disponibile in tre versioni, Cargo (solo per il trasporto merci), Combi (sia per trasporto merci, sia per persone) e Qubo (il più adatto per il tempo libero, più familiare ed elemento chiave della famiglia Fiorino) Come riportato dai colleghi di DSO Gaming, Slightly Mad Studios ha rilasciato un nuovo aggiornamento dedicato a Project CARS 2, che porta il gioco alla versione 1.3.0. Il risultato non è disprezzabile, soprattutto considerata la mole di questo SUV a due ruote motrici e con cambio automatico doppia frizione che è lungo quasi 4,40 metri e pesa 1,5 tonnellate. A questo punto si aprirà una schermata, dove sarà visibile la voce Aggiornamento Software. Basterà cliccarci sopra ed il gioco verrà aggiornato alla versione 1.3.0. Inoltre, una volta avviato il gioco, verranno automaticamente aggiornati i dati di salvataggio. Il Corso di Laurea in Scienze Motorie si pone l'obiettivo di fare acquisire agli studenti conoscenze scientifiche nei vari campi delle attività motorie dell'uomo, con particolare riguardo alle aree: tecnico-sportiva, preventiva, manageriale ed educativa. Nell'area tecnico-sportiva, vengono acquisite conoscenze fondamentali sulla teoria e i metodi didattici delle varie tipologie di discipline sportive, praticate soprattutto a livello ludico e amatoriale. Nell'area preventiva vengono acquisite conoscenze per il mantenimento della migliore efficienza fisica lungo l'arco dell'intera vita, in soggetti normali che necessitino di prevenire le patologie correlate alla sedentarietà mediante uno stile di vita attivo e salutare. Nell'area manageriale, si apprendono nozioni di natura giuridico-amministrativa che regolano il mondo delle attività motorie negli ambiti: sportivo, ricreativo, educativo, preventivo e industriale. Nell'area educativa si acquisiscono conoscenze sull'educazione motoria espressivo-comunicativa, valorizzando lo sviluppo delle capacità, delle competenze e dello sviluppo motorio in età evolutiva. Nell'area tecnico-sportiva, vengono acquisite conoscenze fondamentali sulla teoria e i metodi didattici delle varie tipologie di discipline sportive, praticate soprattutto a livello ludico e amatoriale. Are you a fan of strategy and action games? Do you want to lead an army of stick figures and conquer the world? If yes, then you should try Stick War Legacy, one of the most popular and addictive games on Android. And if you want to make the game more fun and easy, you should download Stick War Legacy Mod APK 2023, which gives you unlimited resources, 999 army, and many other benefits. In this article, we will tell you everything you need to know about Stick War Legacy and its mod apk version. 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If you are a fan of the Fallout series, you might have heard of Fallout Shelter, a free-to-play simulation game that lets you create and manage your own vault in a post-apocalyptic world. But did you know that there is a way to enhance your gaming experience with an APK OBB mod? In this article, we will explain what Fallout Shelter is, what an APK OBB mod is, how to download and install it on your Android device, and how to play it with some tips and tricks. DOWNLOAD ››››› https://urlca.com/2uO60b Fallout Shelter is a game developed by Bethesda Game Studios and published by Bethesda Softworks in 2015. It is based on the popular Fallout franchise, which is set in a retro-futuristic world after a nuclear war. The game is available for iOS, Android, PC, Xbox One, PS4, Nintendo Switch, and Tesla Arcade devices. In Fallout Shelter, you are the overseer of a vault, a secure underground facility that shelters people from the dangers of the wasteland. Your goal is to build and expand your vault, keep your dwellers happy and healthy, and protect them from raiders, radroaches, fires, and other threats. To do so, you need to construct various rooms that provide essential resources such as power, water, food, medical supplies, weapons, and outfits. You also need to assign dwellers to work in these rooms according to their SPECIAL stats (Strength, Perception, Endurance, Charisma, Intelligence, Agility, and Luck). Additionally, you can send dwellers to explore the wasteland and collect loot, or assign them to quests that involve combat and dialogue choices. fallout shelter mod apk unlimited everything The game features a cartoonish art style that contrasts with the dark themes of the Fallout universe. It also has a humorous tone and references to Fallout lore and characters. The game does not have a fixed ending or storyline, but rather lets you play at your own pace and style. An APK OBB mod is a modified version of the game that changes some aspects of the original game. APK stands for Android Package Kit, which is the file format used to distribute and install applications on Android devices. OBB stands for Opaque Binary Blob, which is a file format used to store large amounts of data such as graphics and sounds. An APK OBB mod usually consists of two files: an APK file that contains the modified code of the game, and an OBB file that contains the modified data of the game. By installing these files on your Android device, you can replace the original game with the modded one. Some of the common features of an APK OBB mod for Fallout Shelter are: An APK OBB mod can make the game more fun and easy for some players, as they can enjoy unlimited possibilities and creativity. However, it can also make the game less challenging and rewarding for others, as they can lose the sense of achievement and progression. Using an APK OBB mod for Fallout Shelter has some benefits and risks that you should be aware of before deciding to use it. Here are some of them: Therefore, you should weigh the pros and cons of using an APK OBB mod for Fallout Shelter before downloading and installing it on your Android device. You should also backup your original game data and progress in case you want to restore it later. To download and install Fallout Shelter APK OBB mod on your Android device, you need to meet some requirements and follow some steps. Here are they: There are many sources and websites that offer Fallout Shelter APK OBB mod files for download. However, not all of them are safe and trustworthy. Some of them may contain viruses, malware, spyware, adware, or other harmful software that can damage your device or steal your personal information. Therefore, you should be careful when choosing where to download the mod from. Some of the best sources and websites for finding Fallout Shelter APK OBB mod files are: These are some of the best sources and websites for finding Fallout Shelter APK OBB mod files. However, you should always scan the files with an antivirus or anti-malware software before installing them on your device. You should also read the reviews and ratings of the files from other users to check their quality and safety. Playing Fallout Shelter APK OBB mod on your Android device is similar to playing the original game, but with some differences and advantages. Here are some tips and tricks for building and managing your vault with the mod: These are some of the tips and tricks for playing Fallout Shelter APK OBB mod on your Android device. However, you should also remember to save your game data regularly and backup your mod files in case something goes wrong. Playing Fallout Shelter APK OBB mod on your Android device is different from playing the original game in some ways, but similar in others. Here are some of the differences and similarities between the mod and the original game: These are some of the differences and similarities between Fallout Shelter APK OBB mod and the original game. You can decide which one suits your preferences and expectations better. In conclusion, Fallout Shelter APK OBB mod is a modified version of the game that allows you to access unlimited resources, cheats, and customization options. It can make the game more fun and easy for some players, but also less challenging and rewarding for others. It also has some benefits and risks that you should consider before using it. If you are looking for a new way to enjoy Fallout Shelter on your Android device, you can give Fallout Shelter APK OBB mod a try. However, you should also be careful when downloading and installing it, as well as when playing it. You should also backup your original game data and progress in case you want to switch back to it later. Here are some frequently asked questions about Fallout Shelter APK OBB mod: If you are a fan of Minecraft, you might have heard of the APK Java Edition, which is a modified version of the original game that runs on Android devices. In this article, we will explain what APK Java Edition is, how to download and install it, and how to troubleshoot some common issues with it. APK Java Edition is a port of the Java edition of Minecraft, which is the original version of the game that was developed for PC. The Java edition has some exclusive features that are not available in the Bedrock edition, which is the official version of Minecraft for mobile devices. Some of these features include: DOWNLOAD →→→ https://urlca.com/2uOgrx By playing APK Java Edition on your Android device, you can enjoy these features without having to buy a PC or a console. You can also play with other players who are using the Java edition on different platforms. To download and install APK Java Edition on your Android device, you will need to follow these steps: Before you download and install APK Java Edition, you should make sure that your device meets the following requirements: Since APK Java Edition is not available on the Google Play Store, you will need to enable unknown sources on your device to install it. To do this, go to Settings > Security > Unknown Sources and toggle it on. This will allow you to install apps from sources other than the Google Play Store. The next step is to download the APK file of APK Java Edition from a trusted source. You can find many websites that offer this file, but you should be careful not to download any malware or viruses. One of the most reliable sources for downloading APK Java Edition is [text](^1^), which is the official website of Minecraft for Android devices. You can also use [text](^2^), which is a website that provides various versions of Minecraft for different platforms. download apk java edition for android Once you have downloaded the APK file, you can install it on your device by tapping on it and following the instructions. You might need to grant some permissions to the app, such as access to your storage, camera, microphone, etc. After the installation is complete, you will see an icon of APK Java Edition on your home screen or app drawer. The final step is to launch the game and enjoy playing APK Java Edition on your Android device. You can create a new world or join an existing one, customize your settings and controls, and explore the infinite possibilities of Minecraft. You can also connect to multiplayer servers and realms, where you can play with other players who are using the Java edition on different platforms. Although APK Java Edition is a great way to play Minecraft on your Android device, it is not a perfect app. You might encounter some issues or errors while playing the game, such as: If the game crashes or freezes, you should try the following solutions: If the game does not run smoothly or has graphical glitches, you should try the following solutions: If the game does not connect to multiplayer servers or realms, you should try the following solutions: In conclusion, APK Java Edition is a modified version of Minecraft that allows you to play the Java edition of the game on your Android device. It has some exclusive features that are not available in the Bedrock edition, such as custom skins and mods, more biomes and mobs, and better multiplayer support. To download and install APK Java Edition, you need to enable unknown sources on your device, download the APK file from a trusted source, install it on your device, and launch the game. If you encounter any issues or errors while playing APK Java Edition, you can try some of the solutions we have provided in this article. We hope you enjoy playing APK Java Edition on your Android device and have fun exploring the world of Minecraft. Here are some frequently asked questions about APK Java Edition: A: APK Java Edition is safe to download and install if you get it from a trusted source, such as [text] or [text]. However, you should always be careful when downloading apps from unknown sources, as they might contain malware or viruses. You should also scan your device regularly with an antivirus app. A: APK Java Edition is legal to use if you have purchased a legitimate copy of Minecraft for PC. However, you should not distribute or share APK Java Edition with others who do not own Minecraft for PC, as that would violate the terms of service of Mojang Studios, the developer of Minecraft. A: APK Java Edition is compatible with other versions of Minecraft that are based on the Java edition, such as Windows 10, Mac OS, Linux, etc. However, it is not compatible with versions of Minecraft that are based on the Bedrock edition, such as iOS, Android, Xbox One, PlayStation 4, etc. A: Yes, you can use mods and resource packs with APK Java Edition, as long as they are compatible with the version of APK Java Edition you are using. You can find many mods and resource packs online that are designed for APK Java Edition. However, you should be careful not to use too many mods or resource packs that might cause conflicts or errors with the game or your device. A: To update APK Java Edition to the latest version, you need to download the new APK file from the source where you got the previous one and install it on your device. You might need to uninstall the old version first, or you can overwrite it with the new one. You should always backup your worlds and settings before updating, as you might lose them during the process. Chess is one of the oldest and most popular board games in the world. It is a game of strategy, logic, and creativity that can be enjoyed by people of all ages and backgrounds. But what if you don't have a chess board or a partner to play with? Don't worry, you can still enjoy this amazing game online! In this article, we will show you how to play, learn, and improve your chess game online. We will cover the following topics: Download File › https://urlca.com/2uOemm By the end of this article, you will have a better understanding of the benefits and possibilities of playing chess game online. You will also have some useful tips and resources to help you get started or advance your skills. So, let's begin! Chess game online is simply playing chess on the internet. You can use your computer, laptop, tablet, or smartphone to access various websites and apps that offer chess games. You can play against other human players or against artificial intelligence (AI) engines. You can also watch live or recorded games of other players, solve puzzles, take lessons, and more. Playing chess game online has many advantages over playing on a physical board. Here are some of them: If you want to play chess game online, you need to choose a platform, a game mode, and an opponent. Let's see how to do that. A platform is a website or an app that allows you to play chess game online. There are many platforms available, but here are some of the most popular ones: [Chess.com](^1^) is the largest and most visited chess website in the world. It has over 100 million members from around the world. It offers free and premium memberships, with different features and benefits. You can play online games, solve puzzles, watch videos, take lessons, join clubs, participate in tournaments, and more. chess game online free [Lichess.org] is a free and open-source chess website that is run by volunteers and donations. It has over 50 million members from around the world. It offers unlimited access to all its features and services. You can play online games, solve puzzles, watch streams, take lessons, join clubs, participate in tournaments, and more. [Chess24.com] is a premium chess website that offers high-quality content and services. It has over 10 million members from around the world. It offers free and premium memberships, with different features and benefits. You can play online games, solve puzzles, watch videos, take lessons, join clubs, participate in tournaments, and more. A game mode is a type of chess game that has different rules and time controls. There are many game modes available, but here are some of the most common ones: Blitz and bullet are fast-paced chess games that require quick thinking and reflexes. Blitz games have a time control of 3 to 10 minutes per player, while bullet games have a time control of 1 to 2 minutes per player. These games are exciting and challenging, but they can also be stressful and prone to blunders. Rapid and classical are slow-paced chess games that require deep thinking and strategy. Rapid games have a time control of 10 to 25 minutes per player, while classical games have a time control of more than 25 minutes per player. These games are relaxing and instructive, but they can also be boring and tedious. Chess variants are chess games that have different rules and pieces than the standard chess. There are many chess variants available, but here are some of the most popular ones: An opponent is a person or a computer that you play chess game online with. You can choose from different options, such as: If you want to play chess game online with your friends and family, you can create a private game and invite them to join. You can also chat with them during the game and share your thoughts and emotions. Playing with friends and family is fun and rewarding, but it can also be competitive and stressful. If you want to play chess game online with strangers, you can join a public game and match with someone who has a similar rating or skill level as you. You can also chat with them during the game and make new friends. Playing with strangers is exciting and challenging, but it can also be frustrating and rude. If you want to play chess game online with computer, you can choose an AI engine that has a different strength or personality. You can also adjust the settings and preferences of the computer. Playing with computer is educational and helpful, but it can also be boring and unrealistic. If you want to learn chess game online, you need to use some resources and methods that can help you improve your knowledge and understanding of the game. Here are some of them: One of the best ways to learn chess game online is to watch chess videos and streams. You can find many chess channels and platforms that offer high-quality content and commentary. You can watch games of different levels and styles, learn from the analysis and explanations, and get inspired by the tips and tricks. Watching chess videos and streams is entertaining and informative, but it can also be distracting and addictive. Another great way to learn chess game online is to solve chess puzzles and tactics. You can find many websites and apps that offer thousands of puzzles and problems for different skills and themes. You can solve puzzles that test your calculation, visualization, intuition, creativity, and more. 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You can use various tools and features that allow you to review your moves, evaluate your positions, identify your errors, and learn from your feedback. You can also compare your games with those of stronger players or masters, and see what they did differently or better. Analyzing your games and mistakes is essential and beneficial, but it can also be tedious and painful. Another good habit to improve your chess game online is to practice your openings and endgames. You can use various tools and features that allow you to study, memorize, practice, and test your opening repertoire or endgame technique. You can also explore new or alternative lines or variations, and see how they affect the outcome or evaluation of the game. Practicing your openings and endgames is important and useful, but it can also be boring and overwhelming. A third habit to improve your chess game online is to join chess clubs and tournaments. 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And if you are ready to play chess game online, why not check out some of the platforms we mentioned above? You might find your next favorite chess partner or opponent there. Happy playing! Here are some frequently asked questions about chess game online: If you ever wanted to know how it is to be a shark out looking for prey, you ought to give Hungry Shark World a try. This is an action-packed aquatic adventure game where you control a hungry shark and eat your way through many oceans, feasting on everything from bite-size fish and birds to tasty whales and unwitting humans. Hungry Shark World is the sequel to the hit Hungry Shark Evolution, which has over 100 million downloads on Google Play Store. The game was released in 2016 by Ubisoft, and it has been updated regularly with new content, features, and improvements. The latest version, 3.5.0 APK, was launched on April 28, 2023, and it brings some new skins, a shark week set, and live events. 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Gems are the premium currency in the game that you can use to unlock special sharks, buy premium skins and accessories, and get more coins. Some examples of missions are: Eat 10 humans in one game, Survive for 5 minutes in one game, Score 10000 points in one game, Eat 50 fish in one game, and more. Some examples of challenges are: Eat 5 submarines in one game, Eat 10 helicopters in one game, Eat 20 mines in one game, Eat 3 whales in one game, and more. The game has amazing graphics and sound effects that will make you feel like you are really in the ocean. The game has realistic water physics, dynamic lighting and shadows, detailed textures and models, and smooth animations. The game also has immersive sound effects that will make you hear the splashing of the water, the roaring of the sharks, the screaming of the humans, the exploding of the mines, and more. Hungry Shark World is a fun and easy game to play, but it can also be challenging and addictive. 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The game is also too expensive, as some items cost too much coins or gems. The game should lower the prices or give more coins or gems for free. The game also needs more locations and sharks to make it more interesting." If you want to download and install Hungry Shark World 3.5.0 APK on your Android device, you can follow these steps: Hungry Shark World is a great game for anyone who loves sharks, action, adventure, or just having fun. The game has stunning graphics, exciting gameplay, diverse content, and simple controls. The game also has some drawbacks, such as bugs, ads, and high prices, but they are not too serious or frequent. The game is suitable for all ages and can be played offline or online. If you are looking for a shark game that will keep you entertained for hours, you should definitely try Hungry Shark World 3.5.0 APK. You will not regret it! 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-[Optional Step] If supporting Tsinghua ChatGLM/Fudan MOSS as backend, additional dependencies need to be installed (Prerequisites: Familiar with Python + Used Pytorch + Sufficient computer configuration):
-```sh
-# [Optional Step I] Support Tsinghua ChatGLM. Remark: If encountering "Call ChatGLM fail Cannot load ChatGLM parameters", please refer to the following: 1: The above default installation is torch+cpu version. To use cuda, uninstall torch and reinstall torch+cuda; 2: If the model cannot be loaded due to insufficient machine configuration, you can modify the model precision in `request_llm/bridge_chatglm.py`, and modify all AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
-python -m pip install -r request_llm/requirements_chatglm.txt
-
-# [Optional Step II] Support Fudan MOSS
-python -m pip install -r request_llm/requirements_moss.txt
-git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # When executing this line of code, you must be in the project root path
-
-# [Optional Step III] Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the expected models. Currently supported models are as follows (jittorllms series currently only supports docker solutions):
-AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
-```
-
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You need to copy these files to the folder where you installed GeoStudio 2007 (usually C:\Program Files\GEOSLOPE\GeoStudio 2007) , replacing the original files with the same names. In this article, we have explained what GEO.SLOPE.GeoStudio.2007.v7.10.4143.Incl.Patch.Only-LAVteam.rar is, what it does, and how to use it. We have also discussed some of the features and benefits of GeoStudio 2007, a powerful software suite for geotechnical modeling and analysis. We hope you have found this article useful and informative. If you want to learn more about GeoStudio 2007 or other geotechnical software products, you can visit GEOSLOPE website or LAVteam website. You can also contact us if you need any help or assistance with your geotechnical projects. Here are some frequently asked questions about GEO.SLOPE.GeoStudio.2007.v7.10.4143.Incl.Patch.Only-LAVteam.rar and GeoStudio 2007: The patch file is safe to use as long as you download it from a trusted source like LAVteam website. However, the patch file is not legal to use as it violates the terms and conditions of GEOSLOPE license agreement. Therefore, we do not recommend using the patch file for any commercial or professional purposes. The minimum system requirements for GeoStudio 2007 are: You can update GeoStudio 2007 to the latest version by downloading and installing the latest service pack from GEOSLOPE website. The latest service pack is SP4 (version 7.17) , which was released in June 2019. However, if you use the patch file, you will not be able to update GeoStudio 2007 as it will overwrite the patched files. GeoStudio 2019 is the latest version of GeoStudio that was released in February 2019. It has many new features and improvements over GeoStudio 2007, such as: You can get GeoStudio 2019 by purchasing a license from GEOSLOPE website. 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The free version allows you to perform basic diagnosis, such as reading OBD2 fault codes and viewing sensor values. However, if you want to access more advanced features, such as recording sensor data, printing reports, or testing systems, you will need to purchase a license for the full version. To buy a license, you can go to the online shop of Outils OBD Facile and choose the edition that suits your needs: Basic or Plus. You will receive an email with your registration certificate and a link to download the full version of the software. You can also upgrade from Basic to Plus at any time by paying the difference. To register EOBD Facile and activate the full version of the software, you need to follow these steps: You can now enjoy all the functions of EOBD Facile and diagnose your car like a pro. Some websites may offer you cracks or serial codes to activate EOBD Facile without paying for a license. However, these are illegal and risky methods that may expose you to malware, viruses, or damage to your computer or your car. Moreover, they may not work properly or be compatible with your OBD2 interface. The only way to get a reliable and safe version of EOBD Facile is to download it from the official website of Outils OBD Facile and purchase a license from their online shop. This way, you will also benefit from technical support, regular updates, and a 14-day money-back guarantee. EOBD Facile is a powerful and easy-to-use car diagnostic software that can help you save time and money on car maintenance and repair. Don't hesitate to try it out and see for yourself what it can do for your car. EOBD Facile is a powerful and versatile car diagnostic software that offers many features to help you understand and fix your car. Here are some of the main features of EOBD Facile: EOBD Facile is compatible with all OBD2 cars made since 2001 for petrol and 2004 for diesel. It works with any ELM327 interface, but it is recommended to use a klavkarr scanner from Outils OBD Facile for optimal compatibility and performance. EOBD Facile is a must-have software for any car owner who wants to diagnose and maintain their car by themselves. It allows you to access a wealth of information from your car's computer and sensors, and perform various tests and actions. It is easy to use, reliable, and affordable. You can download it for free from the official website of Outils OBD Facile and purchase a license for the full version if you want more features. Don't fall for illegal cracks or serial codes that may harm your computer or your car. Trust EOBD Facile and enjoy a better car diagnostic experience. If you are looking for a detailed and accurate marine chart for your Garmin device, you might want to consider the Garmin BlueChart G2 Vision VEU714L. 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If you are a pilot, a flight instructor, or a flight enthusiast, you probably know about Jeppesen Flitestar Flitemap, a powerful software that helps you plan your flights with ease and accuracy. Jeppesen Flitestar Flitemap is a desktop application that allows you to create, edit, and print flight plans, charts, reports, and more. You can also import and export data from other sources, such as GPS devices, online databases, or other flight planning software. Jeppesen Flitestar Flitemap is designed to meet the needs of different types of users, such as VFR, IFR, or corporate pilots. However, like any software, Jeppesen Flitestar Flitemap is not perfect. It may have some bugs, errors, or limitations that affect its performance or functionality. That's why Jeppesen releases patches for its software regularly, to fix these issues and improve its features. Patches are small files that update or modify certain parts of the software without changing the whole program. 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To check your current version number and expiration date, follow these steps: If your version number is lower than 8.0 or 9.0, or if your data expires soon, you should update your software with the latest patches. If your version number is 8.0 or 9.0 and your data is still valid, you can skip this step and go to the next one. The next thing you need to do is to download the patches for your version of Jeppesen Flitestar Flitemap from reliable sources. There are two ways to do this: either from the official Jeppesen website or customer service, or from alternative sources such as online forums or libraries. We will explain both methods below. This is the safest and easiest way to get the patches for your software. You can download them directly from the Jeppesen website or request them from the Jeppesen customer service. Here's how: If you have any trouble downloading the patches from the website, you can also contact the Jeppesen customer service and request them by email or phone. 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By installing these patches, you can make sure that your software is up-to-date and works smoothly. We hope that this article has been helpful and informative for you. Here are some tips and best practices for using Jeppesen Flitestar Flitemap after updating: If you have any feedback or questions about this article or Jeppesen Flitestar Flitemap, please feel free to leave a comment below. We would love to hear from you! The system requirements for running Jeppesen Flitestar Flitemap vary depending on your version and operating system. However, here are some general guidelines: You also need a CD-ROM drive, a mouse, a printer, and an internet connection to run Jeppesen Flitestar Flitemap. Jeppesen releases new patches for its software whenever it detects or receives reports of bugs, errors, or limitations that affect its performance or functionality. There is no fixed schedule or frequency for releasing patches, as it depends on the severity and urgency of the issues. However, Jeppesen usually notifies its customers of new patches through its website, email, or customer service. Jeppesen offers a wide range of features and products for flight planning, such as: You can find more information about these features and products on the Jeppesen website. If you have any questions, problems, or feedback about Jeppesen Flitestar Flitemap or any other Jeppesen product or service, you can contact Jeppesen for technical support or customer service through various channels, such as: You can also find more contact information and options on the Jeppesen website. If you want to learn more about Jeppesen Flitestar Flitemap and its functions, you can use the following resources: You can also find more resources and information on the Jeppesen website. If you are a fan of Malayalam movies, you might have heard of Drona 2010, a neo noir action horror film directed by Shaji Kailas and starring Mammootty in a double role. The film revolves around a Sanskrit expert-cum-tantric who investigates the mysterious death of his brother in a haunted house. The film was released on 27 January 2010 and received mixed reviews from critics and audiences. But if you missed the chance to watch this thrilling movie in theatres, don't worry. You can still download Malayalam movie Drona 2010 online from various sources. Here are some of the best ways to do so: Download ✸✸✸ https://urlcod.com/2uHy4f So, these are some of the ways to download Malayalam movie Drona 2010 online. Choose the one that suits your preferences and enjoy this action-packed movie at your convenience. Drona 2010 is not a movie that everyone would enjoy. The film has received mixed reviews from critics and audiences alike, who have praised the performance of Mammootty but criticized the weak script, poor direction, and lack of originality. The film has a rating of 3.4 out of 10 on IMDb, based on 1,015 votes. It also has a rating of 0% on Rotten Tomatoes, based on 0 reviews. Some of the negative reviews of Drona 2010 are: Some of the positive reviews of Drona 2010 are: Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut | Github RepoGroup Members: |', '
访问川虎Chat的 [GitHub项目](https://github.com/GaiZhenbiao/ChuanhuChatGPT) 下载最新版脚本")
-
-
-ONLINE_MODELS = [
- "gpt-3.5-turbo",
- "gpt-3.5-turbo-16k",
- "gpt-3.5-turbo-0301",
- "gpt-3.5-turbo-0613",
- "gpt-4",
- "gpt-4-0314",
- "gpt-4-0613",
- "gpt-4-32k",
- "gpt-4-32k-0314",
- "gpt-4-32k-0613",
- "川虎助理",
- "川虎助理 Pro",
- "GooglePaLM",
- "xmchat",
- "Azure OpenAI",
- "yuanai-1.0-base_10B",
- "yuanai-1.0-translate",
- "yuanai-1.0-dialog",
- "yuanai-1.0-rhythm_poems",
- "minimax-abab4-chat",
- "minimax-abab5-chat",
- "midjourney"
-]
-
-LOCAL_MODELS = [
- "chatglm-6b",
- "chatglm-6b-int4",
- "chatglm-6b-int4-ge",
- "chatglm2-6b",
- "chatglm2-6b-int4",
- "StableLM",
- "MOSS",
- "llama-7b-hf",
- "llama-13b-hf",
- "llama-30b-hf",
- "llama-65b-hf",
-]
-
-if os.environ.get('HIDE_LOCAL_MODELS', 'false') == 'true':
- MODELS = ONLINE_MODELS
-else:
- MODELS = ONLINE_MODELS + LOCAL_MODELS
-
-DEFAULT_MODEL = 0
-
-os.makedirs("models", exist_ok=True)
-os.makedirs("lora", exist_ok=True)
-os.makedirs("history", exist_ok=True)
-for dir_name in os.listdir("models"):
- if os.path.isdir(os.path.join("models", dir_name)):
- if dir_name not in MODELS:
- MODELS.append(dir_name)
-
-MODEL_TOKEN_LIMIT = {
- "gpt-3.5-turbo": 4096,
- "gpt-3.5-turbo-16k": 16384,
- "gpt-3.5-turbo-0301": 4096,
- "gpt-3.5-turbo-0613": 4096,
- "gpt-4": 8192,
- "gpt-4-0314": 8192,
- "gpt-4-0613": 8192,
- "gpt-4-32k": 32768,
- "gpt-4-32k-0314": 32768,
- "gpt-4-32k-0613": 32768
-}
-
-TOKEN_OFFSET = 1000 # 模型的token上限减去这个值，得到软上限。到达软上限之后，自动尝试减少token占用。
-DEFAULT_TOKEN_LIMIT = 3000 # 默认的token上限
-REDUCE_TOKEN_FACTOR = 0.5 # 与模型token上限想乘，得到目标token数。减少token占用时，将token占用减少到目标token数以下。
-
-REPLY_LANGUAGES = [
- "简体中文",
- "繁體中文",
- "English",
- "日本語",
- "Español",
- "Français",
- "Deutsch",
- "한국어",
- "跟随问题语言（不稳定）"
-]
-
-
-WEBSEARCH_PTOMPT_TEMPLATE = """\
-Web search results:
-
-{web_results}
-Current date: {current_date}
-
-Instructions: Using the provided web search results, write a comprehensive reply to the given query. Make sure to cite results using [[number](URL)] notation after the reference. If the provided search results refer to multiple subjects with the same name, write separate answers for each subject.
-Query: {query}
-Reply in {reply_language}
-"""
-
-PROMPT_TEMPLATE = """\
-Context information is below.
----------------------
-{context_str}
----------------------
-Current date: {current_date}.
-Using the provided context information, write a comprehensive reply to the given query.
-Make sure to cite results using [number] notation after the reference.
-If the provided context information refer to multiple subjects with the same name, write separate answers for each subject.
-Use prior knowledge only if the given context didn't provide enough information.
-Answer the question: {query_str}
-Reply in {reply_language}
-"""
-
-REFINE_TEMPLATE = """\
-The original question is as follows: {query_str}
-We have provided an existing answer: {existing_answer}
-We have the opportunity to refine the existing answer
-(only if needed) with some more context below.
-------------
-{context_msg}
-------------
-Given the new context, refine the original answer to better
-Reply in {reply_language}
-If the context isn't useful, return the original answer.
-"""
-
-SUMMARIZE_PROMPT = """Write a concise summary of the following:
-
-{text}
-
-CONCISE SUMMARY IN 中文:"""
-
-ALREADY_CONVERTED_MARK = ""
-START_OF_OUTPUT_MARK = ""
-END_OF_OUTPUT_MARK = ""
-
-small_and_beautiful_theme = gr.themes.Soft(
- primary_hue=gr.themes.Color(
- c50="#EBFAF2",
- c100="#CFF3E1",
- c200="#A8EAC8",
- c300="#77DEA9",
- c400="#3FD086",
- c500="#02C160",
- c600="#06AE56",
- c700="#05974E",
- c800="#057F45",
- c900="#04673D",
- c950="#2E5541",
- name="small_and_beautiful",
- ),
- secondary_hue=gr.themes.Color(
- c50="#576b95",
- c100="#576b95",
- c200="#576b95",
- c300="#576b95",
- c400="#576b95",
- c500="#576b95",
- c600="#576b95",
- c700="#576b95",
- c800="#576b95",
- c900="#576b95",
- c950="#576b95",
- ),
- neutral_hue=gr.themes.Color(
- name="gray",
- c50="#f6f7f8",
- # c100="#f3f4f6",
- c100="#F2F2F2",
- c200="#e5e7eb",
- c300="#d1d5db",
- c400="#B2B2B2",
- c500="#808080",
- c600="#636363",
- c700="#515151",
- c800="#393939",
- # c900="#272727",
- c900="#2B2B2B",
- c950="#171717",
- ),
- radius_size=gr.themes.sizes.radius_sm,
- ).set(
- # button_primary_background_fill="*primary_500",
- button_primary_background_fill_dark="*primary_600",
- # button_primary_background_fill_hover="*primary_400",
- # button_primary_border_color="*primary_500",
- button_primary_border_color_dark="*primary_600",
- button_primary_text_color="white",
- button_primary_text_color_dark="white",
- button_secondary_background_fill="*neutral_100",
- button_secondary_background_fill_hover="*neutral_50",
- button_secondary_background_fill_dark="*neutral_900",
- button_secondary_text_color="*neutral_800",
- button_secondary_text_color_dark="white",
- # background_fill_primary="#F7F7F7",
- # background_fill_primary_dark="#1F1F1F",
- # block_title_text_color="*primary_500",
- block_title_background_fill_dark="*primary_900",
- block_label_background_fill_dark="*primary_900",
- input_background_fill="#F6F6F6",
- chatbot_code_background_color="*neutral_950",
- chatbot_code_background_color_dark="*neutral_950",
- )
diff --git a/spaces/JoshMe1/UAS_MCL_FAREL/app.py b/spaces/JoshMe1/UAS_MCL_FAREL/app.py
deleted file mode 100644
index dd83b1b05740f837da29c07d22a981f42102fed2..0000000000000000000000000000000000000000
--- a/spaces/JoshMe1/UAS_MCL_FAREL/app.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import os
-import pandas as pd
-import streamlit as st
-from sklearn.linear_model import LinearRegression
-
-def predict_hotel_price(train_features_path, train_label_path, test_features_path):
- # Baca data dari file train_features.csv
- train_features = pd.read_csv(train_features_path)
-
- # Baca data dari file train_label.csv
- train_label = pd.read_csv(train_label_path)
-
- # Gabungkan kedua dataframe berdasarkan indeks
- df_merged = pd.concat([train_features, train_label], axis=1)
-
- # Tambahkan kolom 'id' di paling kiri dengan menggunakan range indeks
- df_merged.insert(0, 'ID', range(len(df_merged)))
-
- # Simpan dataframe ke dalam file CSV
- df_merged.to_csv('merged_data.csv', index=False)
-
- # Baca file merged_data.csv sebagai hasil prapemrosesan
- hasil_features = pd.read_csv('merged_data.csv')
-
- # Prapemrosesan data pada kolom rating dengan mengubah format string menjadi float
- hasil_features['rating'] = hasil_features['rating'].apply(lambda x: float(x.split()[0]) if isinstance(x, str) and len(x.split())>0 and x.split()[0].replace('.','').isdigit() else None)
- hasil_features['Price'] = hasil_features['Price'].apply(lambda x: float(x.replace(',', '').replace('avg/night', '')) if isinstance(x, str) else x)
-
- # Menghilangkan missing value pada kolom rating
- hasil_features.dropna(subset=['rating'], inplace=True)
- hasil_features = hasil_features.drop(['facilities', 'location'], axis=1)
-
- # Membuat model Linear Regression
- model = LinearRegression()
-
- # Melatih model dengan dataset train
- model.fit(hasil_features.drop(['ID', 'Price'], axis=1), hasil_features['Price'])
-
- # Membaca dataset test dan menghapus kolom facilities, location, dan ID
- test_features = pd.read_csv(test_features_path)
- test_features = test_features.drop(['facilities', 'location', 'ID'], axis=1)
-
- # Prapemrosesan data pada kolom rating dengan mengubah format string menjadi float
- test_features['rating'] = test_features['rating'].apply(lambda x: float(x.split()[0]) if isinstance(x, str) else x)
-
- # Melakukan prediksi terhadap dataset test
- predictions = model.predict(test_features)
-
- # Convert predictions to a pandas dataframe
- predictions_df = pd.DataFrame(predictions, columns=['Price'])
-
- # Add the 'ID' column using square bracket notation
- predictions_df.insert(loc=0, column='ID', value=range(len(predictions_df)))
-
- # mengubah nilai kolom Price menjadi bilangan bulat
- predictions_df['Price'] = predictions_df['Price'].astype(int)
-
- # Membuat file CSV dari dataframe predictions_df
- predictions_df.to_csv('predictions.csv', index=False)
- return predictions_df
-
-def main():
- st.title("Hotel Price Prediction With Linear Regression")
- st.write("Memprediksi Harga Hotel Berdasarkan Rating")
-
- # Membuat list nama file dari direktori yang berisi file input
- input_dir = 'dataset'
- input_files = os.listdir(input_dir)
-
- # Mengubah list nama file menjadi opsi dropdown
- train_features_path = st.selectbox("Train Features = 'Berisi Fitur-Fitur Dari Data Latih'", [os.path.join(input_dir, file) for file in input_files])
- train_label_path = st.selectbox("Train Label = 'Berisi Label Dari Data Latih'", [os.path.join(input_dir, file) for file in input_files])
- test_features_path = st.selectbox("Test Features = 'Berisi Fitur-Fitur Dari Data Uji'", [os.path.join(input_dir, file) for file in input_files])
-
- # Menjalankan fungsi predict_hotel_price dan menampilkan hasilnya
- if st.button("Prediksi Hasil Harga"):
- predictions_df = predict_hotel_price(train_features_path, train_label_path, test_features_path)
- st.write(predictions_df)
- st.download_button(
- label="Download Hasil Prediksi CSV",
- data=predictions_df.to_csv(index=False),
- file_name="predictions.csv",
- mime="text/csv"
- )
-
-if __name__ == '__main__':
- main()
-
diff --git a/spaces/Kayson/InstructDiffusion/README.md b/spaces/Kayson/InstructDiffusion/README.md
deleted file mode 100644
index d56b891b96a191d58878798271ae0503381b4d7b..0000000000000000000000000000000000000000
--- a/spaces/Kayson/InstructDiffusion/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: InstructDiffusion
-emoji: {{emoji}}
-colorFrom: {{colorFrom}}
-colorTo: {{colorTo}}
-sdk: gradio
-sdk_version: 3.41.2
-app_file: app.py
-pinned: false
----
-
diff --git a/spaces/Kevin676/Real-Time-Voice-Cloning/vocoder/inference.py b/spaces/Kevin676/Real-Time-Voice-Cloning/vocoder/inference.py
deleted file mode 100644
index 7e546845da0b8cdb18b34fbd332b9aaa39cea55c..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/Real-Time-Voice-Cloning/vocoder/inference.py
+++ /dev/null
@@ -1,64 +0,0 @@
-from vocoder.models.fatchord_version import WaveRNN
-from vocoder import hparams as hp
-import torch
-
-
-_model = None # type: WaveRNN
-
-def load_model(weights_fpath, verbose=True):
- global _model, _device
-
- if verbose:
- print("Building Wave-RNN")
- _model = WaveRNN(
- rnn_dims=hp.voc_rnn_dims,
- fc_dims=hp.voc_fc_dims,
- bits=hp.bits,
- pad=hp.voc_pad,
- upsample_factors=hp.voc_upsample_factors,
- feat_dims=hp.num_mels,
- compute_dims=hp.voc_compute_dims,
- res_out_dims=hp.voc_res_out_dims,
- res_blocks=hp.voc_res_blocks,
- hop_length=hp.hop_length,
- sample_rate=hp.sample_rate,
- mode=hp.voc_mode
- )
-
- if torch.cuda.is_available():
- _model = _model.cuda()
- _device = torch.device('cuda')
- else:
- _device = torch.device('cpu')
-
- if verbose:
- print("Loading model weights at %s" % weights_fpath)
- checkpoint = torch.load(weights_fpath, _device)
- _model.load_state_dict(checkpoint['model_state'])
- _model.eval()
-
-
-def is_loaded():
- return _model is not None
-
-
-def infer_waveform(mel, normalize=True, batched=True, target=8000, overlap=800,
- progress_callback=None):
- """
- Infers the waveform of a mel spectrogram output by the synthesizer (the format must match
- that of the synthesizer!)
-
- :param normalize:
- :param batched:
- :param target:
- :param overlap:
- :return:
- """
- if _model is None:
- raise Exception("Please load Wave-RNN in memory before using it")
-
- if normalize:
- mel = mel / hp.mel_max_abs_value
- mel = torch.from_numpy(mel[None, ...])
- wav = _model.generate(mel, batched, target, overlap, hp.mu_law, progress_callback)
- return wav
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/nasfcos_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/nasfcos_head.py
deleted file mode 100644
index 14ee62a7910d90a108fefb2acef00c91ab83ecc8..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/nasfcos_head.py
+++ /dev/null
@@ -1,114 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale
-
-from mmdet.models.dense_heads.fcos_head import FCOSHead
-from mmdet.registry import MODELS
-from mmdet.utils import OptMultiConfig
-
-
-@MODELS.register_module()
-class NASFCOSHead(FCOSHead):
- """Anchor-free head used in `NASFCOS
-
- """
- )
- with gr.Row():
-
- with gr.Column(scale=55):
- with gr.Group():
- with gr.Row():
- prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
- generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
- image_out = gr.Image(height=512)
- error_output = gr.Markdown()
-
- with gr.Column(scale=45):
- with gr.Tab("Options"):
- with gr.Group():
- neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
- auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (lowpoly_environment)", value=prefix, visible=prefix)
-
- with gr.Row():
- guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
- steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
-
- with gr.Row():
- width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
- height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
- seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
- with gr.Tab("Image to image"):
- with gr.Group():
- image = gr.Image(label="Image", height=256, tool="editor", type="pil")
- strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
- auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
-
- inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
- outputs = [image_out, error_output]
- prompt.submit(inference, inputs=inputs, outputs=outputs)
- generate.click(inference, inputs=inputs, outputs=outputs)
-
- gr.HTML("""
-
-
- Lowpoly Environment
-
- {"Add the following tokens to your prompts for the model to work properly: prefix" if prefix else ""}
-
-
-
-
- """)
-
-demo.queue(concurrency_count=1)
-demo.launch()
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/postprocessors/textsnake_postprocessor.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/postprocessors/textsnake_postprocessor.py
deleted file mode 100644
index a4f7ae02ee33688925d799df6ed303b61be59bd1..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/postprocessors/textsnake_postprocessor.py
+++ /dev/null
@@ -1,234 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-
-from typing import List, Sequence
-
-import cv2
-import numpy as np
-import torch
-from mmengine.structures import InstanceData
-from numpy.linalg import norm
-from skimage.morphology import skeletonize
-
-from mmocr.registry import MODELS
-from mmocr.structures import TextDetDataSample
-from mmocr.utils import fill_hole
-from .base import BaseTextDetPostProcessor
-
-
-@MODELS.register_module()
-class TextSnakePostprocessor(BaseTextDetPostProcessor):
- """Decoding predictions of TextSnake to instances. This was partially
- adapted from https://github.com/princewang1994/TextSnake.pytorch.
-
- Args:
- text_repr_type (str): The boundary encoding type 'poly' or 'quad'.
- min_text_region_confidence (float): The confidence threshold of text
- region in TextSnake.
- min_center_region_confidence (float): The confidence threshold of text
- center region in TextSnake.
- min_center_area (int): The minimal text center region area.
- disk_overlap_thr (float): The radius overlap threshold for merging
- disks.
- radius_shrink_ratio (float): The shrink ratio of ordered disks radii.
- rescale_fields (list[str], optional): The bbox/polygon field names to
- be rescaled. If None, no rescaling will be performed.
- """
-
- def __init__(self,
- text_repr_type: str = 'poly',
- min_text_region_confidence: float = 0.6,
- min_center_region_confidence: float = 0.2,
- min_center_area: int = 30,
- disk_overlap_thr: float = 0.03,
- radius_shrink_ratio: float = 1.03,
- rescale_fields: Sequence[str] = ['polygons'],
- **kwargs) -> None:
- super().__init__(
- text_repr_type=text_repr_type,
- rescale_fields=rescale_fields,
- **kwargs)
- assert text_repr_type == 'poly'
- self.min_text_region_confidence = min_text_region_confidence
- self.min_center_region_confidence = min_center_region_confidence
- self.min_center_area = min_center_area
- self.disk_overlap_thr = disk_overlap_thr
- self.radius_shrink_ratio = radius_shrink_ratio
-
- def get_text_instances(self, pred_results: torch.Tensor,
- data_sample: TextDetDataSample
- ) -> TextDetDataSample:
- """
- Args:
- pred_results (torch.Tensor): Prediction map with
- shape :math:`(C, H, W)`.
- data_sample (TextDetDataSample): Datasample of an image.
-
- Returns:
- list[list[float]]: The instance boundary and its confidence.
- """
- assert pred_results.dim() == 3
- data_sample.pred_instances = InstanceData()
- data_sample.pred_instances.polygons = []
- data_sample.pred_instances.scores = []
-
- pred_results[:2, :, :] = torch.sigmoid(pred_results[:2, :, :])
- pred_results = pred_results.detach().cpu().numpy()
-
- pred_text_score = pred_results[0]
- pred_text_mask = pred_text_score > self.min_text_region_confidence
- pred_center_score = pred_results[1] * pred_text_score
- pred_center_mask = \
- pred_center_score > self.min_center_region_confidence
- pred_sin = pred_results[2]
- pred_cos = pred_results[3]
- pred_radius = pred_results[4]
- mask_sz = pred_text_mask.shape
-
- scale = np.sqrt(1.0 / (pred_sin**2 + pred_cos**2 + 1e-8))
- pred_sin = pred_sin * scale
- pred_cos = pred_cos * scale
-
- pred_center_mask = fill_hole(pred_center_mask).astype(np.uint8)
- center_contours, _ = cv2.findContours(pred_center_mask, cv2.RETR_TREE,
- cv2.CHAIN_APPROX_SIMPLE)
-
- for contour in center_contours:
- if cv2.contourArea(contour) < self.min_center_area:
- continue
- instance_center_mask = np.zeros(mask_sz, dtype=np.uint8)
- cv2.drawContours(instance_center_mask, [contour], -1, 1, -1)
- skeleton = skeletonize(instance_center_mask)
- skeleton_yx = np.argwhere(skeleton > 0)
- y, x = skeleton_yx[:, 0], skeleton_yx[:, 1]
- cos = pred_cos[y, x].reshape((-1, 1))
- sin = pred_sin[y, x].reshape((-1, 1))
- radius = pred_radius[y, x].reshape((-1, 1))
-
- center_line_yx = self._centralize(skeleton_yx, cos, -sin, radius,
- instance_center_mask)
- y, x = center_line_yx[:, 0], center_line_yx[:, 1]
- radius = (pred_radius[y, x] * self.radius_shrink_ratio).reshape(
- (-1, 1))
- score = pred_center_score[y, x].reshape((-1, 1))
- instance_disks = np.hstack(
- [np.fliplr(center_line_yx), radius, score])
- instance_disks = self._merge_disks(instance_disks,
- self.disk_overlap_thr)
-
- instance_mask = np.zeros(mask_sz, dtype=np.uint8)
- for x, y, radius, score in instance_disks:
- if radius > 1:
- cv2.circle(instance_mask, (int(x), int(y)), int(radius), 1,
- -1)
- contours, _ = cv2.findContours(instance_mask, cv2.RETR_TREE,
- cv2.CHAIN_APPROX_SIMPLE)
-
- score = np.sum(instance_mask * pred_text_score) / (
- np.sum(instance_mask) + 1e-8)
- if (len(contours) > 0 and cv2.contourArea(contours[0]) > 0
- and contours[0].size > 8):
- polygon = contours[0].flatten().tolist()
- data_sample.pred_instances.polygons.append(polygon)
- data_sample.pred_instances.scores.append(score)
-
- data_sample.pred_instances.scores = torch.FloatTensor(
- data_sample.pred_instances.scores)
-
- return data_sample
-
- def split_results(self, pred_results: torch.Tensor) -> List[torch.Tensor]:
- """Split the prediction results into text score and kernel score.
-
- Args:
- pred_results (torch.Tensor): The prediction results.
-
- Returns:
- List[torch.Tensor]: The text score and kernel score.
- """
- pred_results = [pred_result for pred_result in pred_results]
- return pred_results
-
- @staticmethod
- def _centralize(points_yx: np.ndarray,
- normal_cos: torch.Tensor,
- normal_sin: torch.Tensor,
- radius: torch.Tensor,
- contour_mask: np.ndarray,
- step_ratio: float = 0.03) -> np.ndarray:
- """Centralize the points.
-
- Args:
- points_yx (np.array): The points in yx order.
- normal_cos (torch.Tensor): The normal cosine of the points.
- normal_sin (torch.Tensor): The normal sine of the points.
- radius (torch.Tensor): The radius of the points.
- contour_mask (np.array): The contour mask of the points.
- step_ratio (float): The step ratio of the centralization.
- Defaults to 0.03.
-
- Returns:
- np.ndarray: The centralized points.
- """
-
- h, w = contour_mask.shape
- top_yx = bot_yx = points_yx
- step_flags = np.ones((len(points_yx), 1), dtype=np.bool_)
- step = step_ratio * radius * np.hstack([normal_cos, normal_sin])
- while np.any(step_flags):
- next_yx = np.array(top_yx + step, dtype=np.int32)
- next_y, next_x = next_yx[:, 0], next_yx[:, 1]
- step_flags = (next_y >= 0) & (next_y < h) & (next_x > 0) & (
- next_x < w) & contour_mask[np.clip(next_y, 0, h - 1),
- np.clip(next_x, 0, w - 1)]
- top_yx = top_yx + step_flags.reshape((-1, 1)) * step
- step_flags = np.ones((len(points_yx), 1), dtype=np.bool_)
- while np.any(step_flags):
- next_yx = np.array(bot_yx - step, dtype=np.int32)
- next_y, next_x = next_yx[:, 0], next_yx[:, 1]
- step_flags = (next_y >= 0) & (next_y < h) & (next_x > 0) & (
- next_x < w) & contour_mask[np.clip(next_y, 0, h - 1),
- np.clip(next_x, 0, w - 1)]
- bot_yx = bot_yx - step_flags.reshape((-1, 1)) * step
- centers = np.array((top_yx + bot_yx) * 0.5, dtype=np.int32)
- return centers
-
- @staticmethod
- def _merge_disks(disks: np.ndarray, disk_overlap_thr: float) -> np.ndarray:
- """Merging overlapped disks.
-
- Args:
- disks (np.ndarray): The predicted disks.
- disk_overlap_thr (float): The radius overlap threshold for merging
- disks.
-
- Returns:
- np.ndarray: The merged disks.
- """
- xy = disks[:, 0:2]
- radius = disks[:, 2]
- scores = disks[:, 3]
- order = scores.argsort()[::-1]
-
- merged_disks = []
- while order.size > 0:
- if order.size == 1:
- merged_disks.append(disks[order])
- break
- i = order[0]
- d = norm(xy[i] - xy[order[1:]], axis=1)
- ri = radius[i]
- r = radius[order[1:]]
- d_thr = (ri + r) * disk_overlap_thr
-
- merge_inds = np.where(d <= d_thr)[0] + 1
- if merge_inds.size > 0:
- merge_order = np.hstack([i, order[merge_inds]])
- merged_disks.append(np.mean(disks[merge_order], axis=0))
- else:
- merged_disks.append(disks[i])
-
- inds = np.where(d > d_thr)[0] + 1
- order = order[inds]
- merged_disks = np.vstack(merged_disks)
-
- return merged_disks
diff --git a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/lsvt_converter.py b/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/lsvt_converter.py
deleted file mode 100644
index b1f581974967cc6eebb8491fd163bd026e925fbb..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/lsvt_converter.py
+++ /dev/null
@@ -1,186 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import argparse
-import math
-import os.path as osp
-from functools import partial
-
-import mmcv
-import mmengine
-
-from mmocr.utils import dump_ocr_data
-
-
-def parse_args():
- parser = argparse.ArgumentParser(
- description='Generate training and validation set of LSVT ')
- parser.add_argument('root_path', help='Root dir path of LSVT')
- parser.add_argument(
- '--val-ratio', help='Split ratio for val set', default=0.0, type=float)
- parser.add_argument(
- '--nproc', default=1, type=int, help='Number of processes')
- parser.add_argument(
- '--preserve-vertical',
- help='Preserve samples containing vertical texts',
- action='store_true')
- args = parser.parse_args()
- return args
-
-
-def process_img(args, dst_image_root, ignore_image_root, preserve_vertical,
- split):
- # Dirty hack for multi-processing
- img_idx, img_info, anns = args
- src_img = mmcv.imread(img_info['file_name'])
- img_info = []
- for ann_idx, ann in enumerate(anns):
- segmentation = []
- for x, y in ann['points']:
- segmentation.append(max(0, x))
- segmentation.append(max(0, y))
- xs, ys = segmentation[::2], segmentation[1::2]
- x, y = min(xs), min(ys)
- w, h = max(xs) - x, max(ys) - y
- text_label = ann['transcription']
-
- dst_img = src_img[y:y + h, x:x + w]
- dst_img_name = f'img_{img_idx}_{ann_idx}.jpg'
-
- if not preserve_vertical and h / w > 2 and split == 'train':
- dst_img_path = osp.join(ignore_image_root, dst_img_name)
- mmcv.imwrite(dst_img, dst_img_path)
- continue
-
- dst_img_path = osp.join(dst_image_root, dst_img_name)
- mmcv.imwrite(dst_img, dst_img_path)
-
- img_info.append({
- 'file_name': dst_img_name,
- 'anno_info': [{
- 'text': text_label
- }]
- })
-
- return img_info
-
-
-def convert_lsvt(root_path,
- split,
- ratio,
- preserve_vertical,
- nproc,
- img_start_idx=0):
- """Collect the annotation information and crop the images.
-
- The annotation format is as the following:
- [
- {'gt_1234': # 'gt_1234' is file name
- [
- {
- 'transcription': '一站式购物中心',
- 'points': [[45, 272], [215, 273], [212, 296], [45, 290]]
- 'illegibility': False
- }, ...
- ]
- }
- ]
-
-
- Args:
- root_path (str): The root path of the dataset
- split (str): The split of dataset. Namely: training or val
- ratio (float): Split ratio for val set
- preserve_vertical (bool): Whether to preserve vertical texts
- nproc (int): The number of process to collect annotations
- img_start_idx (int): Index of start image
-
- Returns:
- img_info (dict): The dict of the img and annotation information
- """
-
- annotation_path = osp.join(root_path, 'annotations/train_full_labels.json')
- if not osp.exists(annotation_path):
- raise Exception(
- f'{annotation_path} not exists, please check and try again.')
-
- annotation = mmengine.load(annotation_path)
- # outputs
- dst_label_file = osp.join(root_path, f'{split}_label.json')
- dst_image_root = osp.join(root_path, 'crops', split)
- ignore_image_root = osp.join(root_path, 'ignores', split)
- src_image_root = osp.join(root_path, 'imgs')
- mmengine.mkdir_or_exist(dst_image_root)
- mmengine.mkdir_or_exist(ignore_image_root)
-
- process_img_with_path = partial(
- process_img,
- dst_image_root=dst_image_root,
- ignore_image_root=ignore_image_root,
- preserve_vertical=preserve_vertical,
- split=split)
-
- img_prefixes = annotation.keys()
-
- trn_files, val_files = [], []
- if ratio > 0:
- for i, file in enumerate(img_prefixes):
- if i % math.floor(1 / ratio):
- trn_files.append(file)
- else:
- val_files.append(file)
- else:
- trn_files, val_files = img_prefixes, []
- print(f'training #{len(trn_files)}, val #{len(val_files)}')
-
- if split == 'train':
- img_prefixes = trn_files
- elif split == 'val':
- img_prefixes = val_files
- else:
- raise NotImplementedError
-
- tasks = []
- idx = 0
- for img_idx, prefix in enumerate(img_prefixes):
- img_file = osp.join(src_image_root, prefix + '.jpg')
- img_info = {'file_name': img_file}
- # Skip not exist images
- if not osp.exists(img_file):
- continue
- tasks.append((img_idx + img_start_idx, img_info, annotation[prefix]))
- idx = idx + 1
-
- labels_list = mmengine.track_parallel_progress(
- process_img_with_path, tasks, keep_order=True, nproc=nproc)
- final_labels = []
- for label_list in labels_list:
- final_labels += label_list
-
- dump_ocr_data(final_labels, dst_label_file, 'textrecog')
-
- return idx
-
-
-def main():
- args = parse_args()
- root_path = args.root_path
- print('Processing training set...')
- num_train_imgs = convert_lsvt(
- root_path=root_path,
- split='train',
- ratio=args.val_ratio,
- preserve_vertical=args.preserve_vertical,
- nproc=args.nproc)
- if args.val_ratio > 0:
- print('Processing validation set...')
- convert_lsvt(
- root_path=root_path,
- split='val',
- ratio=args.val_ratio,
- preserve_vertical=args.preserve_vertical,
- nproc=args.nproc,
- img_start_idx=num_train_imgs)
- print('Finish')
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/rects_converter.py b/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/rects_converter.py
deleted file mode 100644
index 630e81509715ef67edcb7dbf77542b399962d551..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/rects_converter.py
+++ /dev/null
@@ -1,256 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import argparse
-import math
-import os
-import os.path as osp
-
-import mmcv
-import mmengine
-
-from mmocr.utils import crop_img, dump_ocr_data
-
-
-def collect_files(img_dir, gt_dir, ratio):
- """Collect all images and their corresponding groundtruth files.
- Args:
- img_dir (str): The image directory
- gt_dir (str): The groundtruth directory
- ratio (float): Split ratio for val set
-
- Returns:
- files (list): The list of tuples (img_file, groundtruth_file)
- """
- assert isinstance(img_dir, str)
- assert img_dir
- assert isinstance(gt_dir, str)
- assert gt_dir
- assert isinstance(ratio, float)
- assert ratio < 1.0, 'val_ratio should be a float between 0.0 to 1.0'
-
- ann_list, imgs_list = [], []
- for ann_file in os.listdir(gt_dir):
- ann_list.append(osp.join(gt_dir, ann_file))
- imgs_list.append(osp.join(img_dir, ann_file.replace('json', 'jpg')))
-
- all_files = list(zip(imgs_list, ann_list))
- assert len(all_files), f'No images found in {img_dir}'
- print(f'Loaded {len(all_files)} images from {img_dir}')
-
- trn_files, val_files = [], []
- if ratio > 0:
- for i, file in enumerate(all_files):
- if i % math.floor(1 / ratio):
- trn_files.append(file)
- else:
- val_files.append(file)
- else:
- trn_files, val_files = all_files, []
-
- print(f'training #{len(trn_files)}, val #{len(val_files)}')
-
- return trn_files, val_files
-
-
-def collect_annotations(files, nproc=1):
- """Collect the annotation information.
- Args:
- files (list): The list of tuples (image_file, groundtruth_file)
- nproc (int): The number of process to collect annotations
-
- Returns:
- images (list): The list of image information dicts
- """
- assert isinstance(files, list)
- assert isinstance(nproc, int)
-
- if nproc > 1:
- images = mmengine.track_parallel_progress(
- load_img_info, files, nproc=nproc)
- else:
- images = mmengine.track_progress(load_img_info, files)
-
- return images
-
-
-def load_img_info(files):
- """Load the information of one image.
- Args:
- files (tuple): The tuple of (img_file, groundtruth_file)
-
- Returns:
- img_info (dict): The dict of the img and annotation information
- """
- assert isinstance(files, tuple)
-
- img_file, gt_file = files
- assert osp.basename(gt_file).split('.')[0] == osp.basename(img_file).split(
- '.')[0]
- # read imgs while ignoring orientations
- img = mmcv.imread(img_file)
-
- img_info = dict(
- file_name=osp.join(osp.basename(img_file)),
- height=img.shape[0],
- width=img.shape[1],
- segm_file=osp.join(osp.basename(gt_file)))
-
- if osp.splitext(gt_file)[1] == '.json':
- img_info = load_json_info(gt_file, img_info)
- else:
- raise NotImplementedError
-
- return img_info
-
-
-def load_json_info(gt_file, img_info):
- """Collect the annotation information.
-
- The annotation format is as the following:
-
- {
- "chars": [
- {
- "ignore": 0,
- "transcription": "H",
- "points": [25, 175, 112, 175, 112, 286, 25, 286]
- },
- {
- "ignore": 0,
- "transcription": "O",
- "points": [102, 182, 210, 182, 210, 273, 102, 273]
- }, ...
- ]
- "lines": [
- {
- "ignore": 0,
- "transcription": "HOKI",
- "points": [23, 173, 327, 180, 327, 290, 23, 283]
- },
- {
- "ignore": 0,
- "transcription": "TEA",
- "points": [368, 180, 621, 180, 621, 294, 368, 294]
- }, ...
- ]
- }
-
-
- Args:
- gt_file (str): The path to ground-truth
- img_info (dict): The dict of the img and annotation information
-
- Returns:
- img_info (dict): The dict of the img and annotation information
- """
-
- annotation = mmengine.load(gt_file)
- anno_info = []
- for line in annotation['lines']:
- if line['ignore'] == 1:
- continue
- segmentation = line['points']
- word = line['transcription']
- anno = dict(bbox=segmentation, word=word)
- anno_info.append(anno)
-
- img_info.update(anno_info=anno_info)
-
- return img_info
-
-
-def generate_ann(root_path, split, image_infos, preserve_vertical):
- """Generate cropped annotations and label txt file.
-
- Args:
- root_path (str): The root path of the dataset
- split (str): The split of dataset. Namely: training or test
- image_infos (list[dict]): A list of dicts of the img and
- annotation information
- preserve_vertical (bool): Whether to preserve vertical texts
- """
- print('Cropping images...')
- dst_image_root = osp.join(root_path, 'crops', split)
- ignore_image_root = osp.join(root_path, 'ignores', split)
- if split == 'training':
- dst_label_file = osp.join(root_path, 'train_label.json')
- elif split == 'val':
- dst_label_file = osp.join(root_path, 'val_label.json')
- mmengine.mkdir_or_exist(dst_image_root)
- mmengine.mkdir_or_exist(ignore_image_root)
-
- img_info = []
- for image_info in image_infos:
- index = 1
- src_img_path = osp.join(root_path, 'imgs', image_info['file_name'])
- image = mmcv.imread(src_img_path)
- src_img_root = image_info['file_name'].split('.')[0]
-
- for anno in image_info['anno_info']:
- word = anno['word']
- dst_img = crop_img(image, anno['bbox'], 0, 0)
- h, w, _ = dst_img.shape
-
- dst_img_name = f'{src_img_root}_{index}.png'
- index += 1
- # Skip invalid annotations
- if min(dst_img.shape) == 0:
- continue
- # Skip vertical texts
- if not preserve_vertical and h / w > 2 and split == 'training':
- dst_img_path = osp.join(ignore_image_root, dst_img_name)
- mmcv.imwrite(dst_img, dst_img_path)
- continue
-
- dst_img_path = osp.join(dst_image_root, dst_img_name)
- mmcv.imwrite(dst_img, dst_img_path)
-
- img_info.append({
- 'file_name': dst_img_name,
- 'anno_info': [{
- 'text': word
- }]
- })
-
- dump_ocr_data(img_info, dst_label_file, 'textrecog')
-
-
-def parse_args():
- parser = argparse.ArgumentParser(
- description='Generate training and val set of ReCTS.')
- parser.add_argument('root_path', help='Root dir path of ReCTS')
- parser.add_argument(
- '--val-ratio', help='Split ratio for val set', default=0.0, type=float)
- parser.add_argument(
- '--nproc', default=1, type=int, help='Number of process')
- parser.add_argument(
- '--preserve-vertical',
- help='Preserve samples containing vertical texts',
- action='store_true')
- args = parser.parse_args()
- return args
-
-
-def main():
- args = parse_args()
- root_path = args.root_path
- ratio = args.val_ratio
-
- trn_files, val_files = collect_files(
- osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'), ratio)
-
- # Train set
- trn_infos = collect_annotations(trn_files, nproc=args.nproc)
- with mmengine.Timer(
- print_tmpl='It takes {}s to convert ReCTS Training annotation'):
- generate_ann(root_path, 'training', trn_infos, args.preserve_vertical)
-
- # Val set
- if len(val_files) > 0:
- val_infos = collect_annotations(val_files, nproc=args.nproc)
- with mmengine.Timer(
- print_tmpl='It takes {}s to convert ReCTS Val annotation'):
- generate_ann(root_path, 'val', val_infos, args.preserve_vertical)
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/NATSpeech/DiffSpeech/tasks/tts/speech_base.py b/spaces/NATSpeech/DiffSpeech/tasks/tts/speech_base.py
deleted file mode 100644
index a438c9a432fe850370ee2a10c2aa7d6c0e1fb793..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/DiffSpeech/tasks/tts/speech_base.py
+++ /dev/null
@@ -1,373 +0,0 @@
-import filecmp
-import os
-import traceback
-import numpy as np
-import pandas as pd
-import torch
-import torch.distributed as dist
-import torch.nn.functional as F
-import torch.optim
-import torch.utils.data
-import yaml
-from tqdm import tqdm
-import utils
-from tasks.tts.dataset_utils import BaseSpeechDataset
-from tasks.tts.tts_utils import parse_mel_losses, parse_dataset_configs, load_data_preprocessor, load_data_binarizer
-from tasks.tts.vocoder_infer.base_vocoder import BaseVocoder, get_vocoder_cls
-from utils.audio.align import mel2token_to_dur
-from utils.audio.io import save_wav
-from utils.audio.pitch_extractors import extract_pitch_simple
-from utils.commons.base_task import BaseTask
-from utils.commons.ckpt_utils import load_ckpt
-from utils.commons.dataset_utils import data_loader, BaseConcatDataset
-from utils.commons.hparams import hparams
-from utils.commons.multiprocess_utils import MultiprocessManager
-from utils.commons.tensor_utils import tensors_to_scalars
-from utils.metrics.ssim import ssim
-from utils.nn.model_utils import print_arch
-from utils.nn.schedulers import RSQRTSchedule, NoneSchedule, WarmupSchedule
-from utils.nn.seq_utils import weights_nonzero_speech
-from utils.plot.plot import spec_to_figure
-from utils.text.text_encoder import build_token_encoder
-import matplotlib.pyplot as plt
-
-
-class SpeechBaseTask(BaseTask):
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- self.dataset_cls = BaseSpeechDataset
- self.vocoder = None
- data_dir = hparams['binary_data_dir']
- if not hparams['use_word_input']:
- self.token_encoder = build_token_encoder(f'{data_dir}/phone_set.json')
- else:
- self.token_encoder = build_token_encoder(f'{data_dir}/word_set.json')
- self.padding_idx = self.token_encoder.pad()
- self.eos_idx = self.token_encoder.eos()
- self.seg_idx = self.token_encoder.seg()
- self.saving_result_pool = None
- self.saving_results_futures = None
- self.mel_losses = parse_mel_losses()
- self.max_tokens, self.max_sentences, \
- self.max_valid_tokens, self.max_valid_sentences = parse_dataset_configs()
-
- ##########################
- # datasets
- ##########################
- @data_loader
- def train_dataloader(self):
- if hparams['train_sets'] != '':
- train_sets = hparams['train_sets'].split("|")
- # check if all train_sets have the same spk map and dictionary
- binary_data_dir = hparams['binary_data_dir']
- file_to_cmp = ['phone_set.json']
- if os.path.exists(f'{binary_data_dir}/word_set.json'):
- file_to_cmp.append('word_set.json')
- if hparams['use_spk_id']:
- file_to_cmp.append('spk_map.json')
- for f in file_to_cmp:
- for ds_name in train_sets:
- base_file = os.path.join(binary_data_dir, f)
- ds_file = os.path.join(ds_name, f)
- assert filecmp.cmp(base_file, ds_file), \
- f'{f} in {ds_name} is not same with that in {binary_data_dir}.'
- train_dataset = BaseConcatDataset([
- self.dataset_cls(prefix='train', shuffle=True, data_dir=ds_name) for ds_name in train_sets])
- else:
- train_dataset = self.dataset_cls(prefix=hparams['train_set_name'], shuffle=True)
- return self.build_dataloader(train_dataset, True, self.max_tokens, self.max_sentences,
- endless=hparams['endless_ds'])
-
- @data_loader
- def val_dataloader(self):
- valid_dataset = self.dataset_cls(prefix=hparams['valid_set_name'], shuffle=False)
- return self.build_dataloader(valid_dataset, False, self.max_valid_tokens, self.max_valid_sentences,
- batch_by_size=False)
-
- @data_loader
- def test_dataloader(self):
- test_dataset = self.dataset_cls(prefix=hparams['test_set_name'], shuffle=False)
- self.test_dl = self.build_dataloader(
- test_dataset, False, self.max_valid_tokens, self.max_valid_sentences, batch_by_size=False)
- return self.test_dl
-
- def build_dataloader(self, dataset, shuffle, max_tokens=None, max_sentences=None,
- required_batch_size_multiple=-1, endless=False, batch_by_size=True):
- devices_cnt = torch.cuda.device_count()
- if devices_cnt == 0:
- devices_cnt = 1
- if required_batch_size_multiple == -1:
- required_batch_size_multiple = devices_cnt
-
- def shuffle_batches(batches):
- np.random.shuffle(batches)
- return batches
-
- if max_tokens is not None:
- max_tokens *= devices_cnt
- if max_sentences is not None:
- max_sentences *= devices_cnt
- indices = dataset.ordered_indices()
- if batch_by_size:
- batch_sampler = utils.commons.dataset_utils.batch_by_size(
- indices, dataset.num_tokens, max_tokens=max_tokens, max_sentences=max_sentences,
- required_batch_size_multiple=required_batch_size_multiple,
- )
- else:
- batch_sampler = []
- for i in range(0, len(indices), max_sentences):
- batch_sampler.append(indices[i:i + max_sentences])
-
- if shuffle:
- batches = shuffle_batches(list(batch_sampler))
- if endless:
- batches = [b for _ in range(1000) for b in shuffle_batches(list(batch_sampler))]
- else:
- batches = batch_sampler
- if endless:
- batches = [b for _ in range(1000) for b in batches]
- num_workers = dataset.num_workers
- if self.trainer.use_ddp:
- num_replicas = dist.get_world_size()
- rank = dist.get_rank()
- batches = [x[rank::num_replicas] for x in batches if len(x) % num_replicas == 0]
- return torch.utils.data.DataLoader(dataset,
- collate_fn=dataset.collater,
- batch_sampler=batches,
- num_workers=num_workers,
- pin_memory=False)
-
- ##########################
- # scheduler and optimizer
- ##########################
- def build_model(self):
- self.build_tts_model()
- if hparams['load_ckpt'] != '':
- load_ckpt(self.model, hparams['load_ckpt'])
- print_arch(self.model)
- return self.model
-
- def build_tts_model(self):
- raise NotImplementedError
-
- def build_scheduler(self, optimizer):
- if hparams['scheduler'] == 'rsqrt':
- return RSQRTSchedule(optimizer, hparams['lr'], hparams['warmup_updates'], hparams['hidden_size'])
- elif hparams['scheduler'] == 'warmup':
- return WarmupSchedule(optimizer, hparams['lr'], hparams['warmup_updates'])
- elif hparams['scheduler'] == 'step_lr':
- return torch.optim.lr_scheduler.StepLR(
- optimizer=optimizer, step_size=500, gamma=0.998)
- else:
- return NoneSchedule(optimizer, hparams['lr'])
-
- def build_optimizer(self, model):
- self.optimizer = optimizer = torch.optim.AdamW(
- model.parameters(),
- lr=hparams['lr'],
- betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
- weight_decay=hparams['weight_decay'])
-
- return optimizer
-
- ##########################
- # training and validation
- ##########################
- def _training_step(self, sample, batch_idx, _):
- loss_output, _ = self.run_model(sample)
- total_loss = sum([v for v in loss_output.values() if isinstance(v, torch.Tensor) and v.requires_grad])
- loss_output['batch_size'] = sample['txt_tokens'].size()[0]
- return total_loss, loss_output
-
- def run_model(self, sample, infer=False):
- """
-
- :param sample: a batch of data
- :param infer: bool, run in infer mode
- :return:
- if not infer:
- return losses, model_out
- if infer:
- return model_out
- """
- raise NotImplementedError
-
- def validation_start(self):
- self.vocoder = get_vocoder_cls(hparams['vocoder'])()
-
- def validation_step(self, sample, batch_idx):
- outputs = {}
- outputs['losses'] = {}
- outputs['losses'], model_out = self.run_model(sample)
- outputs['total_loss'] = sum(outputs['losses'].values())
- outputs['nsamples'] = sample['nsamples']
- outputs = tensors_to_scalars(outputs)
- if self.global_step % hparams['valid_infer_interval'] == 0 \
- and batch_idx < hparams['num_valid_plots']:
- self.save_valid_result(sample, batch_idx, model_out)
- return outputs
-
- def validation_end(self, outputs):
- self.vocoder = None
- return super(SpeechBaseTask, self).validation_end(outputs)
-
- def save_valid_result(self, sample, batch_idx, model_out):
- raise NotImplementedError
-
- ##########################
- # losses
- ##########################
- def add_mel_loss(self, mel_out, target, losses, postfix=''):
- for loss_name, lambd in self.mel_losses.items():
- losses[f'{loss_name}{postfix}'] = getattr(self, f'{loss_name}_loss')(mel_out, target) * lambd
-
- def l1_loss(self, decoder_output, target):
- # decoder_output : B x T x n_mel
- # target : B x T x n_mel
- l1_loss = F.l1_loss(decoder_output, target, reduction='none')
- weights = weights_nonzero_speech(target)
- l1_loss = (l1_loss * weights).sum() / weights.sum()
- return l1_loss
-
- def mse_loss(self, decoder_output, target):
- # decoder_output : B x T x n_mel
- # target : B x T x n_mel
- assert decoder_output.shape == target.shape
- mse_loss = F.mse_loss(decoder_output, target, reduction='none')
- weights = weights_nonzero_speech(target)
- mse_loss = (mse_loss * weights).sum() / weights.sum()
- return mse_loss
-
- def ssim_loss(self, decoder_output, target, bias=6.0):
- # decoder_output : B x T x n_mel
- # target : B x T x n_mel
- assert decoder_output.shape == target.shape
- weights = weights_nonzero_speech(target)
- decoder_output = decoder_output[:, None] + bias
- target = target[:, None] + bias
- ssim_loss = 1 - ssim(decoder_output, target, size_average=False)
- ssim_loss = (ssim_loss * weights).sum() / weights.sum()
- return ssim_loss
-
- def plot_mel(self, batch_idx, spec_out, spec_gt=None, name=None, title='', f0s=None, dur_info=None):
- vmin = hparams['mel_vmin']
- vmax = hparams['mel_vmax']
- if len(spec_out.shape) == 3:
- spec_out = spec_out[0]
- if isinstance(spec_out, torch.Tensor):
- spec_out = spec_out.cpu().numpy()
- if spec_gt is not None:
- if len(spec_gt.shape) == 3:
- spec_gt = spec_gt[0]
- if isinstance(spec_gt, torch.Tensor):
- spec_gt = spec_gt.cpu().numpy()
- max_len = max(len(spec_gt), len(spec_out))
- if max_len - len(spec_gt) > 0:
- spec_gt = np.pad(spec_gt, [[0, max_len - len(spec_gt)], [0, 0]], mode='constant',
- constant_values=vmin)
- if max_len - len(spec_out) > 0:
- spec_out = np.pad(spec_out, [[0, max_len - len(spec_out)], [0, 0]], mode='constant',
- constant_values=vmin)
- spec_out = np.concatenate([spec_out, spec_gt], -1)
- name = f'mel_val_{batch_idx}' if name is None else name
- self.logger.add_figure(name, spec_to_figure(
- spec_out, vmin, vmax, title=title, f0s=f0s, dur_info=dur_info), self.global_step)
-
- ##########################
- # testing
- ##########################
- def test_start(self):
- self.saving_result_pool = MultiprocessManager(int(os.getenv('N_PROC', os.cpu_count())))
- self.saving_results_futures = []
- self.gen_dir = os.path.join(
- hparams['work_dir'], f'generated_{self.trainer.global_step}_{hparams["gen_dir_name"]}')
- self.vocoder: BaseVocoder = get_vocoder_cls(hparams['vocoder'])()
- os.makedirs(self.gen_dir, exist_ok=True)
- os.makedirs(f'{self.gen_dir}/wavs', exist_ok=True)
- os.makedirs(f'{self.gen_dir}/plot', exist_ok=True)
- if hparams.get('save_mel_npy', False):
- os.makedirs(f'{self.gen_dir}/mel_npy', exist_ok=True)
-
- def test_step(self, sample, batch_idx):
- """
-
- :param sample:
- :param batch_idx:
- :return:
- """
- assert sample['txt_tokens'].shape[0] == 1, 'only support batch_size=1 in inference'
- outputs = self.run_model(sample, infer=True)
- text = sample['text'][0]
- item_name = sample['item_name'][0]
- tokens = sample['txt_tokens'][0].cpu().numpy()
- mel_gt = sample['mels'][0].cpu().numpy()
- mel_pred = outputs['mel_out'][0].cpu().numpy()
- str_phs = self.token_encoder.decode(tokens, strip_padding=True)
- base_fn = f'[{self.results_id:06d}][{item_name.replace("%", "_")}][%s]'
- if text is not None:
- base_fn += text.replace(":", "$3A")[:80]
- base_fn = base_fn.replace(' ', '_')
- gen_dir = self.gen_dir
- wav_pred = self.vocoder.spec2wav(mel_pred)
- self.saving_result_pool.add_job(self.save_result, args=[
- wav_pred, mel_pred, base_fn % 'P', gen_dir, str_phs])
- if hparams['save_gt']:
- wav_gt = self.vocoder.spec2wav(mel_gt)
- self.saving_result_pool.add_job(self.save_result, args=[
- wav_gt, mel_gt, base_fn % 'G', gen_dir, str_phs])
- print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}")
- return {
- 'item_name': item_name,
- 'text': text,
- 'ph_tokens': self.token_encoder.decode(tokens.tolist()),
- 'wav_fn_pred': base_fn % 'P',
- 'wav_fn_gt': base_fn % 'G',
- }
-
- @staticmethod
- def save_result(wav_out, mel, base_fn, gen_dir, str_phs=None, mel2ph=None, alignment=None):
- save_wav(wav_out, f'{gen_dir}/wavs/{base_fn}.wav', hparams['audio_sample_rate'],
- norm=hparams['out_wav_norm'])
- fig = plt.figure(figsize=(14, 10))
- spec_vmin = hparams['mel_vmin']
- spec_vmax = hparams['mel_vmax']
- heatmap = plt.pcolor(mel.T, vmin=spec_vmin, vmax=spec_vmax)
- fig.colorbar(heatmap)
- try:
- f0 = extract_pitch_simple(wav_out)
- f0 = f0 / 10 * (f0 > 0)
- plt.plot(f0, c='white', linewidth=1, alpha=0.6)
- if mel2ph is not None and str_phs is not None:
- decoded_txt = str_phs.split(" ")
- dur = mel2token_to_dur(torch.LongTensor(mel2ph)[None, :], len(decoded_txt))[0].numpy()
- dur = [0] + list(np.cumsum(dur))
- for i in range(len(dur) - 1):
- shift = (i % 20) + 1
- plt.text(dur[i], shift, decoded_txt[i])
- plt.hlines(shift, dur[i], dur[i + 1], colors='b' if decoded_txt[i] != '|' else 'black')
- plt.vlines(dur[i], 0, 5, colors='b' if decoded_txt[i] != '|' else 'black',
- alpha=1, linewidth=1)
- plt.tight_layout()
- plt.savefig(f'{gen_dir}/plot/{base_fn}.png', format='png')
- plt.close(fig)
- if hparams.get('save_mel_npy', False):
- np.save(f'{gen_dir}/mel_npy/{base_fn}', mel)
- if alignment is not None:
- fig, ax = plt.subplots(figsize=(12, 16))
- im = ax.imshow(alignment, aspect='auto', origin='lower',
- interpolation='none')
- decoded_txt = str_phs.split(" ")
- ax.set_yticks(np.arange(len(decoded_txt)))
- ax.set_yticklabels(list(decoded_txt), fontsize=6)
- fig.colorbar(im, ax=ax)
- fig.savefig(f'{gen_dir}/attn_plot/{base_fn}_attn.png', format='png')
- plt.close(fig)
- except Exception:
- traceback.print_exc()
- return None
-
- def test_end(self, outputs):
- pd.DataFrame(outputs).to_csv(f'{self.gen_dir}/meta.csv')
- for _1, _2 in tqdm(self.saving_result_pool.get_results(), total=len(self.saving_result_pool)):
- pass
- return {}
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/layers/masked_lm.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/layers/masked_lm.py
deleted file mode 100644
index 3b81556f4c7d82e79c9d9cda4894a26fde6a93f7..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/layers/masked_lm.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. 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.
-# ==============================================================================
-"""Masked language model network."""
-# pylint: disable=g-classes-have-attributes
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
-
-import tensorflow as tf
-
-from official.modeling import tf_utils
-
-
-@tf.keras.utils.register_keras_serializable(package='Text')
-class MaskedLM(tf.keras.layers.Layer):
- """Masked language model network head for BERT modeling.
-
- This network implements a masked language model based on the provided network.
- It assumes that the network being passed has a "get_embedding_table()" method.
-
- Arguments:
- embedding_table: The embedding table of the targets.
- activation: The activation, if any, for the dense layer.
- initializer: The intializer for the dense layer. Defaults to a Glorot
- uniform initializer.
- output: The output style for this network. Can be either 'logits' or
- 'predictions'.
- """
-
- def __init__(self,
- embedding_table,
- activation=None,
- initializer='glorot_uniform',
- output='logits',
- name='cls/predictions',
- **kwargs):
- super(MaskedLM, self).__init__(name=name, **kwargs)
- self.embedding_table = embedding_table
- self.activation = activation
- self.initializer = tf.keras.initializers.get(initializer)
-
- if output not in ('predictions', 'logits'):
- raise ValueError(
- ('Unknown `output` value "%s". `output` can be either "logits" or '
- '"predictions"') % output)
- self._output_type = output
-
- def build(self, input_shape):
- self._vocab_size, hidden_size = self.embedding_table.shape
- self.dense = tf.keras.layers.Dense(
- hidden_size,
- activation=self.activation,
- kernel_initializer=self.initializer,
- name='transform/dense')
- self.layer_norm = tf.keras.layers.LayerNormalization(
- axis=-1, epsilon=1e-12, name='transform/LayerNorm')
- self.bias = self.add_weight(
- 'output_bias/bias',
- shape=(self._vocab_size,),
- initializer='zeros',
- trainable=True)
-
- super(MaskedLM, self).build(input_shape)
-
- def call(self, sequence_data, masked_positions):
- masked_lm_input = self._gather_indexes(sequence_data, masked_positions)
- lm_data = self.dense(masked_lm_input)
- lm_data = self.layer_norm(lm_data)
- lm_data = tf.matmul(lm_data, self.embedding_table, transpose_b=True)
- logits = tf.nn.bias_add(lm_data, self.bias)
-
- masked_positions_shape = tf_utils.get_shape_list(
- masked_positions, name='masked_positions_tensor')
- logits = tf.reshape(logits,
- [-1, masked_positions_shape[1], self._vocab_size])
- if self._output_type == 'logits':
- return logits
- return tf.nn.log_softmax(logits)
-
- def get_config(self):
- raise NotImplementedError('MaskedLM cannot be directly serialized because '
- 'it has variable sharing logic.')
-
- def _gather_indexes(self, sequence_tensor, positions):
- """Gathers the vectors at the specific positions.
-
- Args:
- sequence_tensor: Sequence output of `BertModel` layer of shape
- (`batch_size`, `seq_length`, num_hidden) where num_hidden is number of
- hidden units of `BertModel` layer.
- positions: Positions ids of tokens in sequence to mask for pretraining
- of with dimension (batch_size, num_predictions) where
- `num_predictions` is maximum number of tokens to mask out and predict
- per each sequence.
-
- Returns:
- Masked out sequence tensor of shape (batch_size * num_predictions,
- num_hidden).
- """
- sequence_shape = tf_utils.get_shape_list(
- sequence_tensor, name='sequence_output_tensor')
- batch_size, seq_length, width = sequence_shape
-
- flat_offsets = tf.reshape(
- tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
- flat_positions = tf.reshape(positions + flat_offsets, [-1])
- flat_sequence_tensor = tf.reshape(sequence_tensor,
- [batch_size * seq_length, width])
- output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
-
- return output_tensor
diff --git a/spaces/NCTCMumbai/NCTC/models/official/utils/flags/_misc.py b/spaces/NCTCMumbai/NCTC/models/official/utils/flags/_misc.py
deleted file mode 100644
index c6fa24b5ae7e29827967c5c6a1b78dc3613d40fe..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/utils/flags/_misc.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Copyright 2018 The TensorFlow Authors. 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.
-# ==============================================================================
-"""Misc flags."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from absl import flags
-
-from official.utils.flags._conventions import help_wrap
-
-
-def define_image(data_format=True):
- """Register image specific flags.
-
- Args:
- data_format: Create a flag to specify image axis convention.
-
- Returns:
- A list of flags for core.py to marks as key flags.
- """
-
- key_flags = []
-
- if data_format:
- flags.DEFINE_enum(
- name="data_format", short_name="df", default=None,
- enum_values=["channels_first", "channels_last"],
- help=help_wrap(
- "A flag to override the data format used in the model. "
- "channels_first provides a performance boost on GPU but is not "
- "always compatible with CPU. If left unspecified, the data format "
- "will be chosen automatically based on whether TensorFlow was "
- "built for CPU or GPU."))
- key_flags.append("data_format")
-
- return key_flags
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/configs/__init__.py b/spaces/NCTCMumbai/NCTC/models/official/vision/detection/configs/__init__.py
deleted file mode 100644
index 931c2ef11db4a949e6c2e95bca44e36bac1241e9..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/configs/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. 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.
-# ==============================================================================
diff --git a/spaces/Narrativa/semantic_news_search/app.py b/spaces/Narrativa/semantic_news_search/app.py
deleted file mode 100644
index 5a3505592f708004e4f072236b5136d5c16ed795..0000000000000000000000000000000000000000
--- a/spaces/Narrativa/semantic_news_search/app.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import streamlit as st
-import pandas as pd
-from sentence_transformers import SentenceTransformer,util
-import torch
-import numpy as np
-from os.path import exists
-
-
-st.sidebar.image("./NarrativaLogoBlanco.png")
-topK = st.sidebar.slider("Number of results: ", 1, 20, 5, 1)
-
-st.write("# Semantic News Search 🔍📰")
-
-model = SentenceTransformer('all-MiniLM-L6-v2', device='cpu')
-
-df = pd.read_csv('financial-sentences.csv')
-sentences = df['sentences'].to_list()
-
-# check if embedding is available
-
-if exists('embeddings.npy'):
- corpus_embeddings = np.load('embeddings.npy')
-else:
- corpus_embeddings = model.encode(sentences, batch_size=23, show_progress_bar=False, convert_to_tensor=True)
- np.save('embeddings.npy', np.array(corpus_embeddings.cpu()))
-
-
-sentence = st.text_input('Enter a sentence:')
-
-if sentence:
-
- embedding = model.encode(sentences=[sentence], convert_to_tensor=True)
- cosine_scores = util.cos_sim(embedding, corpus_embeddings)[0]
- top_results = torch.topk(cosine_scores, k=topK)
- st.write()
- st.write(" **Query:**", sentence)
- st.write(f"\n **Top {topK} most similar sentences in corpus:**\n")
-
- for score, idx in zip(top_results[0], top_results[1]):
- st.write(sentences[idx])
- st.write(f"*Score:* {score:.4f}")
- st.write()
- st.write()
-
-
-
-
-
-
-
-
diff --git a/spaces/Nattylegit/ChatGPT-Plugins-in-Gradio/app.py b/spaces/Nattylegit/ChatGPT-Plugins-in-Gradio/app.py
deleted file mode 100644
index 3a3259c1388d85940622a53291ab77aa58438219..0000000000000000000000000000000000000000
--- a/spaces/Nattylegit/ChatGPT-Plugins-in-Gradio/app.py
+++ /dev/null
@@ -1,500 +0,0 @@
-import gradio as gr
-
-import os
-import openai
-import time
-import json
-import requests
-import shutil
-
-import matplotlib.pyplot as plt
-from gradio_client import Client
-from newsapi import NewsApiClient
-from PIL import Image
-
-from gpt_function_definitions import generate_image, generate_music, generate_caption, generate_caption_func, generate_music_func, generate_image_func, dict_plugin_functions
-
-#Streaming endpoint
-API_URL = "https://api.openai.com/v1/chat/completions"
-# Get the value of the openai_api_key from environment variable
-openai_api_key = os.getenv("OPENAI_API_KEY")
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
-dicts_list = [value['dict'] for value in dict_plugin_functions.values()]
-
-available_function_defns = {
- key.split('_func')[0]: value['func']
- for key, value in dict_plugin_functions.items()
- }
-
-add_plugin_steps = """## Steps to add new Plugins to your Gradio ChatGPT Chatbot
-Do you want to open this information in a separate tab instead? - Click here.
-
-1. **Acquire the API Endpoint**
- - You need an API which you can query, and for this example let's consider using a text-to-speech demo hosted on Huggingface Spaces.
- - **API Endpoint**: [https://gradio-neon-tts-plugin-coqui.hf.space/](https://gradio-neon-tts-plugin-coqui.hf.space/)
-
-2. **Create a Function to Query the API**
- - You can access any Gradio demo as an API via the Gradio Python Client.
- ```python
- from gradio.client import Client
-
- def texttospeech(input_text):
- client = Client("https://gradio-neon-tts-plugin-coqui.hf.space/")
- result = client.predict(
- input_text, # str in 'Input' Textbox component
- "en", # str in 'Language' Radio component
- api_name="/predict"
- )
- return result
- ```
-
-3. **Describe the Function to GPT-3.5**
- - You need to describe your function to GPT3.5/4. This function definition will get passed to gpt and will suck up your token. GPT may or may not use this function based on user inputs later on.
- - You can either use the Gradio demo for converting any given function to the required JSON format for GPT-3.5.
- - Demo: [Function to JSON](https://huggingface.co/spaces/ysharma/function-to-JSON)
- - Or, you can create the dictionary object on your own. Note that, the correct format is super important here.
- - MAke sure to name your JSON object description as `
-
-Function Calling: Interacting with external APIs via free-form text isn't optimal; instead, employing JSON format proves to be a more efficient method.
-Gradio Chatbots: Using Gradio and Function Calling you can create chatbots designed to respond to queries by communicating with external APIs. The API responses are fed back to the Language Model for processing and a new response is generated for the user.
-Describe your functions to GPT: When integrating with GPT-3.5, specific instructions on how to utilize a particular function or plugin are essential; this encompasses specifying the name, description, and required parameters or inputs. Look at gpt_function_definitions.py for more context.
-Caution: Such function definitions would be conveyed to GPT, so when duplicating to build your own Plugins, proceed with caution as functions consume tokens.
-Gradio's Usefulness: The versatility of this using Gradio to build LLM applications is immense; In this Gradio app, you can have an array of functions tailored for various purposes, enhancing the breadth and depth of interactions with your Language Model.
-"""
-howto = """
-Welcome to the ChatGPT-Plugins demo, built using Gradio! This interactive chatbot employs the GPT3.5-turbo-0613 model from OpenAI and boasts custom plugins to enhance your chatting experience. Here’s a quick guide to get you started:
-Getting Started: Simply type your messages in the textbox to chat with ChatGPT just like you would in the original app.
-Using Plugins: Want to try out a plugin? Check the checkbox next to the plugin you want to use.
-
-DIFFUSERS PLUGIN:
-What it does: Generates images based on your text descriptions.
-How to use: Type a text description of the image you want to generate, and the plugin will create it for you.
-Example input: "Generate an image of a sunset over the mountains."
-
-MUSIC-GEN PLUGIN:
-What it does: Generates music based on your descriptions.
-How to use: Describe the type of music you want and select an input melody. Remember to upload a melody first!
-Example input: "Generate music for a parade using bach.mp3 as input melody."
-
-IMAGE CAPTION PLUGIN:
-What it does: Describes images that you upload.
-How to use: Upload an image and ask ChatGPT to describe it by name.
-Example input: "Describe the image dog.jpg."
-
-NEWS PLUGIN:
-What it does: Provides the top 3 news articles based on your search query.
-How to use: Simply type in a search query and the plugin will present the top 3 news articles matching your query based on relevance.
-Example input: "Show me the top news about space exploration."
-
-Access Generated Content: Find all generated images and audio in the Gradio Files component located below the input textbox.
-Have Fun!: Explore and enjoy the versatile features of this Gradio-ChatGPT-PLUGIN demo.
-Now you’re all set to make the most of this ChatGPT demo. Happy chatting!
-"""
-
-with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
- #chatbot {height: 520px; overflow: auto;}""") as demo:
- gr.HTML('Build Your Own 🧩Plugins For ChatGPT using 🚀Gradio
')
-
- with gr.Accordion("Create Plugins for ChatGPT using Gradio in less than 5 minutes!", open=False ):
- gr.Markdown(add_plugin_steps)
-
- with gr.Accordion("How to use the demo and other useful stuff:", open=False):
- with gr.Accordion("How to use the demo?", open=False):
- gr.HTML(howto)
- with gr.Accordion("What is happening?", open=False):
- gr.HTML(messaging)
-
- gr.HTML('''Duplicate the Space and run securely with your OpenAI API Key
{children}
-
"
- formatted_prompt = format_prompt(prompt, history, system_prompt)
- else:
- keyword = prompt
- counter= 0
- for d in id_dict:
- title = id_dict[d]['title']
- abstract = id_dict[d]['abstract']
- if keyword in title or keyword in abstract:
- counter+=1## its a hit
- prompt += "ARTICLE " + str(counter) + "\n"
- prompt += f"TITLE: {title} ABSTRACT: {abstract}\n"
- if counter >= 4:
- break
-
- prompt += "Keyword: " + keyword + "\n"
- formatted_prompt = format_prompt_summarize(prompt, history, system_prompt, keyword)
- output = "Articles related to the keyword " + keyword + "\n"
-
-
-
-
-
- stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
- #output = ""
-
- for response in stream:
- output += response.token.text
-
- for stop_str in STOP_SEQUENCES:
- if output.endswith(stop_str):
- output = output[:-len(stop_str)]
- output = output.rstrip()
- yield output
- yield output
- return output
-
-
-additional_inputs=[
- gr.Textbox("", label="Optional system prompt"),
- gr.Slider(
- label="Temperature",
- value=0.9,
- minimum=0.0,
- maximum=1.0,
- step=0.05,
- interactive=True,
- info="Higher values produce more diverse outputs",
- ),
- gr.Slider(
- label="Max new tokens",
- value=256,
- minimum=0,
- maximum=8192,
- step=64,
- interactive=True,
- info="The maximum numbers of new tokens",
- ),
- gr.Slider(
- label="Top-p (nucleus sampling)",
- value=0.90,
- minimum=0.0,
- maximum=1,
- step=0.05,
- interactive=True,
- info="Higher values sample more low-probability tokens",
- ),
- gr.Slider(
- label="Repetition penalty",
- value=1.2,
- minimum=1.0,
- maximum=2.0,
- step=0.05,
- interactive=True,
- info="Penalize repeated tokens",
- )
-]
-
-
-with gr.Blocks() as demo:
- with gr.Row():
- with gr.Column(scale=0.4):
- gr.Image("better_banner.jpeg", elem_id="banner-image", show_label=False)
- with gr.Column():
- gr.Markdown(
- """
- #
- ** The idea is inspired by CREATIVE WHACK PACK https://apps.apple.com/us/app/creative-whack-pack/id307306326
-
- ** ##Researchers need INSPIRATION to come up with CREATIVE IDEAS.
- ** ###We use Falcon 180B to
-
- generate a SUMMARY of the arxiv articles (only August articles are supported)
-
- generate a CREATIVE IDEA for future extension
-
- generate a CRAZY IDEA for application in an orthogonal field.
-
- This should hopefully CONNECT unrelated fields and inspire researchers to come up with CREATIVE IDEAS.
- ## Please input ARXIV ID or a query, see examples below (limited to 15K articles from August 2023)
- ➡️️ **Intended Use**: this demo is intended to showcase how LLMs can be used to generate creative ideas for future extension and application in orthogonal field.
-
- ⚠️ **Limitations**: the model can and will produce factually incorrect information, hallucinating facts and actions. As it has not undergone any advanced tuning/alignment, it can produce problematic outputs, especially if prompted to do so. Finally, this demo is limited to a session length of about 1,000 words.
- """
- )
-
- gr.ChatInterface(
- generate,
- examples=EXAMPLES,
- additional_inputs=additional_inputs,
- )
-
-demo.queue(concurrency_count=100, api_open=False).launch(show_api=False)
\ No newline at end of file
diff --git a/spaces/Rajagopal/ImageBind_zeroshot_demo2/data.py b/spaces/Rajagopal/ImageBind_zeroshot_demo2/data.py
deleted file mode 100644
index 80c7aca83970707204355221217918a4b2337379..0000000000000000000000000000000000000000
--- a/spaces/Rajagopal/ImageBind_zeroshot_demo2/data.py
+++ /dev/null
@@ -1,350 +0,0 @@
-#!/usr/bin/env python3
-# Portions Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-
-import torch
-import torch.nn as nn
-import torchaudio
-import logging
-
-from models.multimodal_preprocessors import SimpleTokenizer
-from PIL import Image
-from pytorchvideo import transforms as pv_transforms
-from pytorchvideo.data.clip_sampling import ConstantClipsPerVideoSampler
-from pytorchvideo.data.encoded_video import EncodedVideo
-
-from torchvision import transforms
-from torchvision.transforms._transforms_video import NormalizeVideo
-
-DEFAULT_AUDIO_FRAME_SHIFT_MS = 10 # in milliseconds
-
-BPE_PATH = "bpe/bpe_simple_vocab_16e6.txt.gz"
-
-
-def waveform2melspec(waveform, sample_rate, num_mel_bins, target_length):
- # Based on https://github.com/YuanGongND/ast/blob/d7d8b4b8e06cdaeb6c843cdb38794c1c7692234c/src/dataloader.py#L102
- waveform -= waveform.mean()
- fbank = torchaudio.compliance.kaldi.fbank(
- waveform,
- htk_compat=True,
- sample_frequency=sample_rate,
- use_energy=False,
- window_type="hanning",
- num_mel_bins=num_mel_bins,
- dither=0.0,
- frame_length=25,
- frame_shift=DEFAULT_AUDIO_FRAME_SHIFT_MS,
- )
- # Convert to [mel_bins, num_frames] shape
- fbank = fbank.transpose(0, 1)
- # Pad to target_length
- n_frames = fbank.size(1)
- p = target_length - n_frames
- # if p is too large (say >20%), flash a warning
- if abs(p) / n_frames > 0.2:
- logging.warning(
- "Large gap between audio n_frames(%d) and "
- "target_length (%d). Is the audio_target_length "
- "setting correct?",
- n_frames,
- target_length,
- )
- # cut and pad
- if p > 0:
- fbank = torch.nn.functional.pad(fbank, (0, p), mode="constant", value=0)
- elif p < 0:
- fbank = fbank[:, 0:target_length]
- # Convert to [1, mel_bins, num_frames] shape, essentially like a 1
- # channel image
- fbank = fbank.unsqueeze(0)
- return fbank
-
-
-def get_clip_timepoints(clip_sampler, duration):
- # Read out all clips in this video
- all_clips_timepoints = []
- is_last_clip = False
- end = 0.0
- while not is_last_clip:
- start, end, _, _, is_last_clip = clip_sampler(end, duration, annotation=None)
- all_clips_timepoints.append((start, end))
- return all_clips_timepoints
-
-
-def load_and_transform_vision_data(image_paths, device):
- if image_paths is None:
- return None
-
- image_ouputs = []
- for image_path in image_paths:
- data_transform = transforms.Compose(
- [
- transforms.Resize(
- 224, interpolation=transforms.InterpolationMode.BICUBIC
- ),
- transforms.CenterCrop(224),
- transforms.ToTensor(),
- transforms.Normalize(
- mean=(0.48145466, 0.4578275, 0.40821073),
- std=(0.26862954, 0.26130258, 0.27577711),
- ),
- ]
- )
- with open(image_path, "rb") as fopen:
- image = Image.open(fopen).convert("RGB")
-
- image = data_transform(image).to(device)
- image_ouputs.append(image)
- return torch.stack(image_ouputs, dim=0)
-
-
-def load_and_transform_text(text, device):
- if text is None:
- return None
- tokenizer = SimpleTokenizer(bpe_path=BPE_PATH)
- tokens = [tokenizer(t).unsqueeze(0).to(device) for t in text]
- tokens = torch.cat(tokens, dim=0)
- return tokens
-
-
-def load_and_transform_audio_data(
- audio_paths,
- device,
- num_mel_bins=128,
- target_length=204,
- sample_rate=16000,
- clip_duration=2,
- clips_per_video=3,
- mean=-4.268,
- std=9.138,
-):
- if audio_paths is None:
- return None
-
- audio_outputs = []
- clip_sampler = ConstantClipsPerVideoSampler(
- clip_duration=clip_duration, clips_per_video=clips_per_video
- )
-
- for audio_path in audio_paths:
- waveform, sr = torchaudio.load(audio_path)
- if sample_rate != sr:
- waveform = torchaudio.functional.resample(
- waveform, orig_freq=sr, new_freq=sample_rate
- )
- all_clips_timepoints = get_clip_timepoints(
- clip_sampler, waveform.size(1) / sample_rate
- )
- all_clips = []
- for clip_timepoints in all_clips_timepoints:
- waveform_clip = waveform[
- :,
- int(clip_timepoints[0] * sample_rate) : int(
- clip_timepoints[1] * sample_rate
- ),
- ]
- waveform_melspec = waveform2melspec(
- waveform_clip, sample_rate, num_mel_bins, target_length
- )
- all_clips.append(waveform_melspec)
-
- normalize = transforms.Normalize(mean=mean, std=std)
- all_clips = [normalize(ac).to(device) for ac in all_clips]
-
- all_clips = torch.stack(all_clips, dim=0)
- audio_outputs.append(all_clips)
-
- return torch.stack(audio_outputs, dim=0)
-
-
-def get_clip_timepoints(clip_sampler, duration):
- # Read out all clips in this video
- all_clips_timepoints = []
- is_last_clip = False
- end = 0.0
- while not is_last_clip:
- start, end, _, _, is_last_clip = clip_sampler(end, duration, annotation=None)
- all_clips_timepoints.append((start, end))
- return all_clips_timepoints
-
-
-def crop_boxes(boxes, x_offset, y_offset):
- """
- Peform crop on the bounding boxes given the offsets.
- Args:
- boxes (ndarray or None): bounding boxes to peform crop. The dimension
- is `num boxes` x 4.
- x_offset (int): cropping offset in the x axis.
- y_offset (int): cropping offset in the y axis.
- Returns:
- cropped_boxes (ndarray or None): the cropped boxes with dimension of
- `num boxes` x 4.
- """
- cropped_boxes = boxes.copy()
- cropped_boxes[:, [0, 2]] = boxes[:, [0, 2]] - x_offset
- cropped_boxes[:, [1, 3]] = boxes[:, [1, 3]] - y_offset
-
- return cropped_boxes
-
-
-def uniform_crop(images, size, spatial_idx, boxes=None, scale_size=None):
- """
- Perform uniform spatial sampling on the images and corresponding boxes.
- Args:
- images (tensor): images to perform uniform crop. The dimension is
- `num frames` x `channel` x `height` x `width`.
- size (int): size of height and weight to crop the images.
- spatial_idx (int): 0, 1, or 2 for left, center, and right crop if width
- is larger than height. Or 0, 1, or 2 for top, center, and bottom
- crop if height is larger than width.
- boxes (ndarray or None): optional. Corresponding boxes to images.
- Dimension is `num boxes` x 4.
- scale_size (int): optinal. If not None, resize the images to scale_size before
- performing any crop.
- Returns:
- cropped (tensor): images with dimension of
- `num frames` x `channel` x `size` x `size`.
- cropped_boxes (ndarray or None): the cropped boxes with dimension of
- `num boxes` x 4.
- """
- assert spatial_idx in [0, 1, 2]
- ndim = len(images.shape)
- if ndim == 3:
- images = images.unsqueeze(0)
- height = images.shape[2]
- width = images.shape[3]
-
- if scale_size is not None:
- if width <= height:
- width, height = scale_size, int(height / width * scale_size)
- else:
- width, height = int(width / height * scale_size), scale_size
- images = torch.nn.functional.interpolate(
- images,
- size=(height, width),
- mode="bilinear",
- align_corners=False,
- )
-
- y_offset = int(math.ceil((height - size) / 2))
- x_offset = int(math.ceil((width - size) / 2))
-
- if height > width:
- if spatial_idx == 0:
- y_offset = 0
- elif spatial_idx == 2:
- y_offset = height - size
- else:
- if spatial_idx == 0:
- x_offset = 0
- elif spatial_idx == 2:
- x_offset = width - size
- cropped = images[:, :, y_offset : y_offset + size, x_offset : x_offset + size]
- cropped_boxes = crop_boxes(boxes, x_offset, y_offset) if boxes is not None else None
- if ndim == 3:
- cropped = cropped.squeeze(0)
- return cropped, cropped_boxes
-
-
-class SpatialCrop(nn.Module):
- """
- Convert the video into 3 smaller clips spatially. Must be used after the
- temporal crops to get spatial crops, and should be used with
- -2 in the spatial crop at the slowfast augmentation stage (so full
- frames are passed in here). Will return a larger list with the
- 3x spatial crops as well.
- """
-
- def __init__(self, crop_size: int = 224, num_crops: int = 3):
- super().__init__()
- self.crop_size = crop_size
- if num_crops == 3:
- self.crops_to_ext = [0, 1, 2]
- self.flipped_crops_to_ext = []
- elif num_crops == 1:
- self.crops_to_ext = [1]
- self.flipped_crops_to_ext = []
- else:
- raise NotImplementedError("Nothing else supported yet")
-
- def forward(self, videos):
- """
- Args:
- videos: A list of C, T, H, W videos.
- Returns:
- videos: A list with 3x the number of elements. Each video converted
- to C, T, H', W' by spatial cropping.
- """
- assert isinstance(videos, list), "Must be a list of videos after temporal crops"
- assert all([video.ndim == 4 for video in videos]), "Must be (C,T,H,W)"
- res = []
- for video in videos:
- for spatial_idx in self.crops_to_ext:
- res.append(uniform_crop(video, self.crop_size, spatial_idx)[0])
- if not self.flipped_crops_to_ext:
- continue
- flipped_video = transforms.functional.hflip(video)
- for spatial_idx in self.flipped_crops_to_ext:
- res.append(uniform_crop(flipped_video, self.crop_size, spatial_idx)[0])
- return res
-
-
-def load_and_transform_video_data(
- video_paths,
- device,
- clip_duration=2,
- clips_per_video=5,
- sample_rate=16000,
-):
- if video_paths is None:
- return None
-
- video_outputs = []
- video_transform = transforms.Compose(
- [
- pv_transforms.ShortSideScale(224),
- NormalizeVideo(
- mean=(0.48145466, 0.4578275, 0.40821073),
- std=(0.26862954, 0.26130258, 0.27577711),
- ),
- ]
- )
-
- clip_sampler = ConstantClipsPerVideoSampler(
- clip_duration=clip_duration, clips_per_video=clips_per_video
- )
- frame_sampler = pv_transforms.UniformTemporalSubsample(num_samples=clip_duration)
-
- for video_path in video_paths:
- video = EncodedVideo.from_path(
- video_path,
- decoder="decord",
- decode_audio=False,
- **{"sample_rate": sample_rate},
- )
-
- all_clips_timepoints = get_clip_timepoints(clip_sampler, video.duration)
-
- all_video = []
- for clip_timepoints in all_clips_timepoints:
- # Read the clip, get frames
- clip = video.get_clip(clip_timepoints[0], clip_timepoints[1])
- if clip is None:
- raise ValueError("No clip found")
- video_clip = frame_sampler(clip["video"])
- video_clip = video_clip / 255.0 # since this is float, need 0-1
-
- all_video.append(video_clip)
-
- all_video = [video_transform(clip) for clip in all_video]
- all_video = SpatialCrop(224, num_crops=3)(all_video)
-
- all_video = torch.stack(all_video, dim=0)
- video_outputs.append(all_video)
-
- return torch.stack(video_outputs, dim=0).to(device)
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/packaging/__about__.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/packaging/__about__.py
deleted file mode 100644
index 3551bc2d29846441299cf57b397b02fc164c99b9..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/packaging/__about__.py
+++ /dev/null
@@ -1,26 +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.
-
-__all__ = [
- "__title__",
- "__summary__",
- "__uri__",
- "__version__",
- "__author__",
- "__email__",
- "__license__",
- "__copyright__",
-]
-
-__title__ = "packaging"
-__summary__ = "Core utilities for Python packages"
-__uri__ = "https://github.com/pypa/packaging"
-
-__version__ = "21.3"
-
-__author__ = "Donald Stufft and individual contributors"
-__email__ = "donald@stufft.io"
-
-__license__ = "BSD-2-Clause or Apache-2.0"
-__copyright__ = "2014-2019 %s" % __author__
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pkg_resources/_vendor/jaraco/context.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pkg_resources/_vendor/jaraco/context.py
deleted file mode 100644
index 87a4e3dca299c4201ac50f6ef589dc73f1c45576..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pkg_resources/_vendor/jaraco/context.py
+++ /dev/null
@@ -1,213 +0,0 @@
-import os
-import subprocess
-import contextlib
-import functools
-import tempfile
-import shutil
-import operator
-
-
-@contextlib.contextmanager
-def pushd(dir):
- orig = os.getcwd()
- os.chdir(dir)
- try:
- yield dir
- finally:
- os.chdir(orig)
-
-
-@contextlib.contextmanager
-def tarball_context(url, target_dir=None, runner=None, pushd=pushd):
- """
- Get a tarball, extract it, change to that directory, yield, then
- clean up.
- `runner` is the function to invoke commands.
- `pushd` is a context manager for changing the directory.
- """
- if target_dir is None:
- target_dir = os.path.basename(url).replace('.tar.gz', '').replace('.tgz', '')
- if runner is None:
- runner = functools.partial(subprocess.check_call, shell=True)
- # In the tar command, use --strip-components=1 to strip the first path and
- # then
- # use -C to cause the files to be extracted to {target_dir}. This ensures
- # that we always know where the files were extracted.
- runner('mkdir {target_dir}'.format(**vars()))
- try:
- getter = 'wget {url} -O -'
- extract = 'tar x{compression} --strip-components=1 -C {target_dir}'
- cmd = ' | '.join((getter, extract))
- runner(cmd.format(compression=infer_compression(url), **vars()))
- with pushd(target_dir):
- yield target_dir
- finally:
- runner('rm -Rf {target_dir}'.format(**vars()))
-
-
-def infer_compression(url):
- """
- Given a URL or filename, infer the compression code for tar.
- """
- # cheat and just assume it's the last two characters
- compression_indicator = url[-2:]
- mapping = dict(gz='z', bz='j', xz='J')
- # Assume 'z' (gzip) if no match
- return mapping.get(compression_indicator, 'z')
-
-
-@contextlib.contextmanager
-def temp_dir(remover=shutil.rmtree):
- """
- Create a temporary directory context. Pass a custom remover
- to override the removal behavior.
- """
- temp_dir = tempfile.mkdtemp()
- try:
- yield temp_dir
- finally:
- remover(temp_dir)
-
-
-@contextlib.contextmanager
-def repo_context(url, branch=None, quiet=True, dest_ctx=temp_dir):
- """
- Check out the repo indicated by url.
-
- If dest_ctx is supplied, it should be a context manager
- to yield the target directory for the check out.
- """
- exe = 'git' if 'git' in url else 'hg'
- with dest_ctx() as repo_dir:
- cmd = [exe, 'clone', url, repo_dir]
- if branch:
- cmd.extend(['--branch', branch])
- devnull = open(os.path.devnull, 'w')
- stdout = devnull if quiet else None
- subprocess.check_call(cmd, stdout=stdout)
- yield repo_dir
-
-
-@contextlib.contextmanager
-def null():
- yield
-
-
-class ExceptionTrap:
- """
- A context manager that will catch certain exceptions and provide an
- indication they occurred.
-
- >>> with ExceptionTrap() as trap:
- ... raise Exception()
- >>> bool(trap)
- True
-
- >>> with ExceptionTrap() as trap:
- ... pass
- >>> bool(trap)
- False
-
- >>> with ExceptionTrap(ValueError) as trap:
- ... raise ValueError("1 + 1 is not 3")
- >>> bool(trap)
- True
-
- >>> with ExceptionTrap(ValueError) as trap:
- ... raise Exception()
- Traceback (most recent call last):
- ...
- Exception
-
- >>> bool(trap)
- False
- """
-
- exc_info = None, None, None
-
- def __init__(self, exceptions=(Exception,)):
- self.exceptions = exceptions
-
- def __enter__(self):
- return self
-
- @property
- def type(self):
- return self.exc_info[0]
-
- @property
- def value(self):
- return self.exc_info[1]
-
- @property
- def tb(self):
- return self.exc_info[2]
-
- def __exit__(self, *exc_info):
- type = exc_info[0]
- matches = type and issubclass(type, self.exceptions)
- if matches:
- self.exc_info = exc_info
- return matches
-
- def __bool__(self):
- return bool(self.type)
-
- def raises(self, func, *, _test=bool):
- """
- Wrap func and replace the result with the truth
- value of the trap (True if an exception occurred).
-
- First, give the decorator an alias to support Python 3.8
- Syntax.
-
- >>> raises = ExceptionTrap(ValueError).raises
-
- Now decorate a function that always fails.
-
- >>> @raises
- ... def fail():
- ... raise ValueError('failed')
- >>> fail()
- True
- """
-
- @functools.wraps(func)
- def wrapper(*args, **kwargs):
- with ExceptionTrap(self.exceptions) as trap:
- func(*args, **kwargs)
- return _test(trap)
-
- return wrapper
-
- def passes(self, func):
- """
- Wrap func and replace the result with the truth
- value of the trap (True if no exception).
-
- First, give the decorator an alias to support Python 3.8
- Syntax.
-
- >>> passes = ExceptionTrap(ValueError).passes
-
- Now decorate a function that always fails.
-
- >>> @passes
- ... def fail():
- ... raise ValueError('failed')
-
- >>> fail()
- False
- """
- return self.raises(func, _test=operator.not_)
-
-
-class suppress(contextlib.suppress, contextlib.ContextDecorator):
- """
- A version of contextlib.suppress with decorator support.
-
- >>> @suppress(KeyError)
- ... def key_error():
- ... {}['']
- >>> key_error()
- """
diff --git a/spaces/Realcat/image-matching-webui/hloc/pipelines/RobotCar/__init__.py b/spaces/Realcat/image-matching-webui/hloc/pipelines/RobotCar/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Realcat/image-matching-webui/third_party/r2d2/tools/transforms.py b/spaces/Realcat/image-matching-webui/third_party/r2d2/tools/transforms.py
deleted file mode 100644
index 604a7c2a3ec6da955c1e85b7505103c694232458..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/r2d2/tools/transforms.py
+++ /dev/null
@@ -1,593 +0,0 @@
-# Copyright 2019-present NAVER Corp.
-# CC BY-NC-SA 3.0
-# Available only for non-commercial use
-
-import pdb
-import numpy as np
-from PIL import Image, ImageOps
-import torchvision.transforms as tvf
-import random
-from math import ceil
-
-from . import transforms_tools as F
-
-"""
-Example command to try out some transformation chain:
-
-python -m tools.transforms --trfs "Scale(384), ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1), RandomRotation(10), RandomTilting(0.5, 'all'), RandomScale(240,320), RandomCrop(224)"
-"""
-
-
-def instanciate_transformation(cmd_line):
- """Create a sequence of transformations.
-
- cmd_line: (str)
- Comma-separated list of transformations.
- Ex: "Rotate(10), Scale(256)"
- """
- if not isinstance(cmd_line, str):
- return cmd_line # already instanciated
-
- cmd_line = "tvf.Compose([%s])" % cmd_line
- try:
- return eval(cmd_line)
- except Exception as e:
- print("Cannot interpret this transform list: %s\nReason: %s" % (cmd_line, e))
-
-
-class Scale(object):
- """Rescale the input PIL.Image to a given size.
- Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
-
- The smallest dimension of the resulting image will be = size.
-
- if largest == True: same behaviour for the largest dimension.
-
- if not can_upscale: don't upscale
- if not can_downscale: don't downscale
- """
-
- def __init__(
- self,
- size,
- interpolation=Image.BILINEAR,
- largest=False,
- can_upscale=True,
- can_downscale=True,
- ):
- assert isinstance(size, int) or (len(size) == 2)
- self.size = size
- self.interpolation = interpolation
- self.largest = largest
- self.can_upscale = can_upscale
- self.can_downscale = can_downscale
-
- def __repr__(self):
- fmt_str = "RandomScale(%s" % str(self.size)
- if self.largest:
- fmt_str += ", largest=True"
- if not self.can_upscale:
- fmt_str += ", can_upscale=False"
- if not self.can_downscale:
- fmt_str += ", can_downscale=False"
- return fmt_str + ")"
-
- def get_params(self, imsize):
- w, h = imsize
- if isinstance(self.size, int):
- cmp = lambda a, b: (a >= b) if self.largest else (a <= b)
- if (cmp(w, h) and w == self.size) or (cmp(h, w) and h == self.size):
- ow, oh = w, h
- elif cmp(w, h):
- ow = self.size
- oh = int(self.size * h / w)
- else:
- oh = self.size
- ow = int(self.size * w / h)
- else:
- ow, oh = self.size
- return ow, oh
-
- def __call__(self, inp):
- img = F.grab_img(inp)
- w, h = img.size
-
- size2 = ow, oh = self.get_params(img.size)
-
- if size2 != img.size:
- a1, a2 = img.size, size2
- if (self.can_upscale and min(a1) < min(a2)) or (
- self.can_downscale and min(a1) > min(a2)
- ):
- img = img.resize(size2, self.interpolation)
-
- return F.update_img_and_labels(
- inp, img, persp=(ow / w, 0, 0, 0, oh / h, 0, 0, 0)
- )
-
-
-class RandomScale(Scale):
- """Rescale the input PIL.Image to a random size.
- Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
-
- Args:
- min_size (int): min size of the smaller edge of the picture.
- max_size (int): max size of the smaller edge of the picture.
-
- ar (float or tuple):
- max change of aspect ratio (width/height).
-
- interpolation (int, optional): Desired interpolation. Default is
- ``PIL.Image.BILINEAR``
- """
-
- def __init__(
- self,
- min_size,
- max_size,
- ar=1,
- can_upscale=False,
- can_downscale=True,
- interpolation=Image.BILINEAR,
- ):
- Scale.__init__(
- self,
- 0,
- can_upscale=can_upscale,
- can_downscale=can_downscale,
- interpolation=interpolation,
- )
- assert type(min_size) == type(
- max_size
- ), "min_size and max_size can only be 2 ints or 2 floats"
- assert (
- isinstance(min_size, int)
- and min_size >= 1
- or isinstance(min_size, float)
- and min_size > 0
- )
- assert isinstance(max_size, (int, float)) and min_size <= max_size
- self.min_size = min_size
- self.max_size = max_size
- if type(ar) in (float, int):
- ar = (min(1 / ar, ar), max(1 / ar, ar))
- assert 0.2 < ar[0] <= ar[1] < 5
- self.ar = ar
-
- def get_params(self, imsize):
- w, h = imsize
- if isinstance(self.min_size, float):
- min_size = int(self.min_size * min(w, h) + 0.5)
- if isinstance(self.max_size, float):
- max_size = int(self.max_size * min(w, h) + 0.5)
- if isinstance(self.min_size, int):
- min_size = self.min_size
- if isinstance(self.max_size, int):
- max_size = self.max_size
-
- if not self.can_upscale:
- max_size = min(max_size, min(w, h))
-
- size = int(0.5 + F.rand_log_uniform(min_size, max_size))
- ar = F.rand_log_uniform(*self.ar) # change of aspect ratio
-
- if w < h: # image is taller
- ow = size
- oh = int(0.5 + size * h / w / ar)
- if oh < min_size:
- ow, oh = int(0.5 + ow * float(min_size) / oh), min_size
- else: # image is wider
- oh = size
- ow = int(0.5 + size * w / h * ar)
- if ow < min_size:
- ow, oh = min_size, int(0.5 + oh * float(min_size) / ow)
-
- assert ow >= min_size, "image too small (width=%d < min_size=%d)" % (
- ow,
- min_size,
- )
- assert oh >= min_size, "image too small (height=%d < min_size=%d)" % (
- oh,
- min_size,
- )
- return ow, oh
-
-
-class RandomCrop(object):
- """Crop the given PIL Image at a random location.
- Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
-
- Args:
- size (sequence or int): Desired output size of the crop. If size is an
- int instead of sequence like (h, w), a square crop (size, size) is
- made.
- padding (int or sequence, optional): Optional padding on each border
- of the image. Default is 0, i.e no padding. If a sequence of length
- 4 is provided, it is used to pad left, top, right, bottom borders
- respectively.
- """
-
- def __init__(self, size, padding=0):
- if isinstance(size, int):
- self.size = (int(size), int(size))
- else:
- self.size = size
- self.padding = padding
-
- def __repr__(self):
- return "RandomCrop(%s)" % str(self.size)
-
- @staticmethod
- def get_params(img, output_size):
- w, h = img.size
- th, tw = output_size
- assert h >= th and w >= tw, "Image of %dx%d is too small for crop %dx%d" % (
- w,
- h,
- tw,
- th,
- )
-
- y = np.random.randint(0, h - th) if h > th else 0
- x = np.random.randint(0, w - tw) if w > tw else 0
- return x, y, tw, th
-
- def __call__(self, inp):
- img = F.grab_img(inp)
-
- padl = padt = 0
- if self.padding:
- if F.is_pil_image(img):
- img = ImageOps.expand(img, border=self.padding, fill=0)
- else:
- assert isinstance(img, F.DummyImg)
- img = img.expand(border=self.padding)
- if isinstance(self.padding, int):
- padl = padt = self.padding
- else:
- padl, padt = self.padding[0:2]
-
- i, j, tw, th = self.get_params(img, self.size)
- img = img.crop((i, j, i + tw, j + th))
-
- return F.update_img_and_labels(
- inp, img, persp=(1, 0, padl - i, 0, 1, padt - j, 0, 0)
- )
-
-
-class CenterCrop(RandomCrop):
- """Crops the given PIL Image at the center.
- Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
-
- Args:
- size (sequence or int): Desired output size of the crop. If size is an
- int instead of sequence like (h, w), a square crop (size, size) is
- made.
- """
-
- @staticmethod
- def get_params(img, output_size):
- w, h = img.size
- th, tw = output_size
- y = int(0.5 + ((h - th) / 2.0))
- x = int(0.5 + ((w - tw) / 2.0))
- return x, y, tw, th
-
-
-class RandomRotation(object):
- """Rescale the input PIL.Image to a random size.
- Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
-
- Args:
- degrees (float):
- rotation angle.
-
- interpolation (int, optional): Desired interpolation. Default is
- ``PIL.Image.BILINEAR``
- """
-
- def __init__(self, degrees, interpolation=Image.BILINEAR):
- self.degrees = degrees
- self.interpolation = interpolation
-
- def __call__(self, inp):
- img = F.grab_img(inp)
- w, h = img.size
-
- angle = np.random.uniform(-self.degrees, self.degrees)
-
- img = img.rotate(angle, resample=self.interpolation)
- w2, h2 = img.size
-
- trf = F.translate(-w / 2, -h / 2)
- trf = F.persp_mul(trf, F.rotate(-angle * np.pi / 180))
- trf = F.persp_mul(trf, F.translate(w2 / 2, h2 / 2))
- return F.update_img_and_labels(inp, img, persp=trf)
-
-
-class RandomTilting(object):
- """Apply a random tilting (left, right, up, down) to the input PIL.Image
- Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
-
- Args:
- maginitude (float):
- maximum magnitude of the random skew (value between 0 and 1)
- directions (string):
- tilting directions allowed (all, left, right, up, down)
- examples: "all", "left,right", "up-down-right"
- """
-
- def __init__(self, magnitude, directions="all"):
- self.magnitude = magnitude
- self.directions = directions.lower().replace(",", " ").replace("-", " ")
-
- def __repr__(self):
- return "RandomTilt(%g, '%s')" % (self.magnitude, self.directions)
-
- def __call__(self, inp):
- img = F.grab_img(inp)
- w, h = img.size
-
- x1, y1, x2, y2 = 0, 0, h, w
- original_plane = [(y1, x1), (y2, x1), (y2, x2), (y1, x2)]
-
- max_skew_amount = max(w, h)
- max_skew_amount = int(ceil(max_skew_amount * self.magnitude))
- skew_amount = random.randint(1, max_skew_amount)
-
- if self.directions == "all":
- choices = [0, 1, 2, 3]
- else:
- dirs = ["left", "right", "up", "down"]
- choices = []
- for d in self.directions.split():
- try:
- choices.append(dirs.index(d))
- except:
- raise ValueError("Tilting direction %s not recognized" % d)
-
- skew_direction = random.choice(choices)
-
- # print('randomtitlting: ', skew_amount, skew_direction) # to debug random
-
- if skew_direction == 0:
- # Left Tilt
- new_plane = [
- (y1, x1 - skew_amount), # Top Left
- (y2, x1), # Top Right
- (y2, x2), # Bottom Right
- (y1, x2 + skew_amount),
- ] # Bottom Left
- elif skew_direction == 1:
- # Right Tilt
- new_plane = [
- (y1, x1), # Top Left
- (y2, x1 - skew_amount), # Top Right
- (y2, x2 + skew_amount), # Bottom Right
- (y1, x2),
- ] # Bottom Left
- elif skew_direction == 2:
- # Forward Tilt
- new_plane = [
- (y1 - skew_amount, x1), # Top Left
- (y2 + skew_amount, x1), # Top Right
- (y2, x2), # Bottom Right
- (y1, x2),
- ] # Bottom Left
- elif skew_direction == 3:
- # Backward Tilt
- new_plane = [
- (y1, x1), # Top Left
- (y2, x1), # Top Right
- (y2 + skew_amount, x2), # Bottom Right
- (y1 - skew_amount, x2),
- ] # Bottom Left
-
- # To calculate the coefficients required by PIL for the perspective skew,
- # see the following Stack Overflow discussion: https://goo.gl/sSgJdj
- matrix = []
-
- for p1, p2 in zip(new_plane, original_plane):
- matrix.append([p1[0], p1[1], 1, 0, 0, 0, -p2[0] * p1[0], -p2[0] * p1[1]])
- matrix.append([0, 0, 0, p1[0], p1[1], 1, -p2[1] * p1[0], -p2[1] * p1[1]])
-
- A = np.matrix(matrix, dtype=np.float)
- B = np.array(original_plane).reshape(8)
-
- homography = np.dot(np.linalg.pinv(A), B)
- homography = tuple(np.array(homography).reshape(8))
- # print(homography)
-
- img = img.transform(
- img.size, Image.PERSPECTIVE, homography, resample=Image.BICUBIC
- )
-
- homography = np.linalg.pinv(
- np.float32(homography + (1,)).reshape(3, 3)
- ).ravel()[:8]
- return F.update_img_and_labels(inp, img, persp=tuple(homography))
-
-
-RandomTilt = RandomTilting # redefinition
-
-
-class Tilt(object):
- """Apply a known tilting to an image"""
-
- def __init__(self, *homography):
- assert len(homography) == 8
- self.homography = homography
-
- def __call__(self, inp):
- img = F.grab_img(inp)
- homography = self.homography
- # print(homography)
-
- img = img.transform(
- img.size, Image.PERSPECTIVE, homography, resample=Image.BICUBIC
- )
-
- homography = np.linalg.pinv(
- np.float32(homography + (1,)).reshape(3, 3)
- ).ravel()[:8]
- return F.update_img_and_labels(inp, img, persp=tuple(homography))
-
-
-class StillTransform(object):
- """Takes and return an image, without changing its shape or geometry."""
-
- def _transform(self, img):
- raise NotImplementedError()
-
- def __call__(self, inp):
- img = F.grab_img(inp)
-
- # transform the image (size should not change)
- try:
- img = self._transform(img)
- except TypeError:
- pass
-
- return F.update_img_and_labels(inp, img, persp=(1, 0, 0, 0, 1, 0, 0, 0))
-
-
-class PixelNoise(StillTransform):
- """Takes an image, and add random white noise."""
-
- def __init__(self, ampl=20):
- StillTransform.__init__(self)
- assert 0 <= ampl < 255
- self.ampl = ampl
-
- def __repr__(self):
- return "PixelNoise(%g)" % self.ampl
-
- def _transform(self, img):
- img = np.float32(img)
- img += np.random.uniform(
- 0.5 - self.ampl / 2, 0.5 + self.ampl / 2, size=img.shape
- )
- return Image.fromarray(np.uint8(img.clip(0, 255)))
-
-
-class ColorJitter(StillTransform):
- """Randomly change the brightness, contrast and saturation of an image.
- Copied from https://github.com/pytorch in torchvision/transforms/transforms.py
-
- Args:
- brightness (float): How much to jitter brightness. brightness_factor
- is chosen uniformly from [max(0, 1 - brightness), 1 + brightness].
- contrast (float): How much to jitter contrast. contrast_factor
- is chosen uniformly from [max(0, 1 - contrast), 1 + contrast].
- saturation (float): How much to jitter saturation. saturation_factor
- is chosen uniformly from [max(0, 1 - saturation), 1 + saturation].
- hue(float): How much to jitter hue. hue_factor is chosen uniformly from
- [-hue, hue]. Should be >=0 and <= 0.5.
- """
-
- def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
- self.brightness = brightness
- self.contrast = contrast
- self.saturation = saturation
- self.hue = hue
-
- def __repr__(self):
- return "ColorJitter(%g,%g,%g,%g)" % (
- self.brightness,
- self.contrast,
- self.saturation,
- self.hue,
- )
-
- @staticmethod
- def get_params(brightness, contrast, saturation, hue):
- """Get a randomized transform to be applied on image.
- Arguments are same as that of __init__.
- Returns:
- Transform which randomly adjusts brightness, contrast and
- saturation in a random order.
- """
- transforms = []
- if brightness > 0:
- brightness_factor = np.random.uniform(
- max(0, 1 - brightness), 1 + brightness
- )
- transforms.append(
- tvf.Lambda(lambda img: F.adjust_brightness(img, brightness_factor))
- )
-
- if contrast > 0:
- contrast_factor = np.random.uniform(max(0, 1 - contrast), 1 + contrast)
- transforms.append(
- tvf.Lambda(lambda img: F.adjust_contrast(img, contrast_factor))
- )
-
- if saturation > 0:
- saturation_factor = np.random.uniform(
- max(0, 1 - saturation), 1 + saturation
- )
- transforms.append(
- tvf.Lambda(lambda img: F.adjust_saturation(img, saturation_factor))
- )
-
- if hue > 0:
- hue_factor = np.random.uniform(-hue, hue)
- transforms.append(tvf.Lambda(lambda img: F.adjust_hue(img, hue_factor)))
-
- # print('colorjitter: ', brightness_factor, contrast_factor, saturation_factor, hue_factor) # to debug random seed
-
- np.random.shuffle(transforms)
- transform = tvf.Compose(transforms)
-
- return transform
-
- def _transform(self, img):
- transform = self.get_params(
- self.brightness, self.contrast, self.saturation, self.hue
- )
- return transform(img)
-
-
-if __name__ == "__main__":
- import argparse
-
- parser = argparse.ArgumentParser("Script to try out and visualize transformations")
- parser.add_argument("--img", type=str, default="imgs/test.png", help="input image")
- parser.add_argument(
- "--trfs", type=str, required=True, help="list of transformations"
- )
- parser.add_argument(
- "--layout", type=int, nargs=2, default=(3, 3), help="nb of rows,cols"
- )
- args = parser.parse_args()
-
- import os
-
- args.img = args.img.replace("$HERE", os.path.dirname(__file__))
- img = Image.open(args.img)
- img = dict(img=img)
-
- trfs = instanciate_transformation(args.trfs)
-
- from matplotlib import pyplot as pl
-
- pl.ion()
- pl.subplots_adjust(0, 0, 1, 1)
-
- nr, nc = args.layout
-
- while True:
- for j in range(nr):
- for i in range(nc):
- pl.subplot(nr, nc, i + j * nc + 1)
- if i == j == 0:
- img2 = img
- else:
- img2 = trfs(img.copy())
- if isinstance(img2, dict):
- img2 = img2["img"]
- pl.imshow(img2)
- pl.xlabel("%d x %d" % img2.size)
- pl.xticks(())
- pl.yticks(())
- pdb.set_trace()
diff --git a/spaces/Riksarkivet/htr_demo/helper/examples/examples.py b/spaces/Riksarkivet/htr_demo/helper/examples/examples.py
deleted file mode 100644
index cf99c9c754d627144f1aed318a27c5e036197870..0000000000000000000000000000000000000000
--- a/spaces/Riksarkivet/htr_demo/helper/examples/examples.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import io
-
-import datasets
-from PIL import Image
-
-
-class DemoImages:
- _instance = None
-
- def __new__(cls, *args, **kwargs):
- if not cls._instance:
- cls._instance = super(DemoImages, cls).__new__(cls, *args, **kwargs)
- return cls._instance
-
- def __init__(self, url="Riksarkivet/test_images_demo", cache_dir="./helper/examples/.cache_images"):
- if not hasattr(self, "images_datasets"):
- self.images_datasets = datasets.load_dataset(url, cache_dir=cache_dir, split="train")
- self.example_df = self.images_datasets.to_pandas()
- self.examples_list = self.convert_bytes_to_images()
-
- def convert_bytes_to_images(self):
- examples_list = []
- # For each row in the dataframe
- for index, row in self.example_df.iterrows():
- image_bytes = row["image"]["bytes"]
- image = Image.open(io.BytesIO(image_bytes))
-
- # Set the path to save the image
- path_to_image = f"./helper/examples/images/image_{index}.jpg"
-
- # Save the image
- image.save(path_to_image)
-
- # Get the description
- description = row["text"]
-
- # Append to the examples list
- examples_list.append([description, path_to_image])
-
- return examples_list
-
-
-if __name__ == "__main__":
- # test = DemoImages(cache_dir=".cache_images")
-
- # print(test.examples_list)
-
- images_datasets = datasets.load_dataset("Riksarkivet/test_images_demo", cache_dir="./helper/examples/.cache_images")
- print(images_datasets["train"]["image"][0])
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/mask_heads/grid_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/mask_heads/grid_head.py
deleted file mode 100644
index 83058cbdda934ebfc3a76088e1820848ac01b78b..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/mask_heads/grid_head.py
+++ /dev/null
@@ -1,359 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, kaiming_init, normal_init
-
-from mmdet.models.builder import HEADS, build_loss
-
-
-@HEADS.register_module()
-class GridHead(nn.Module):
-
- def __init__(self,
- grid_points=9,
- num_convs=8,
- roi_feat_size=14,
- in_channels=256,
- conv_kernel_size=3,
- point_feat_channels=64,
- deconv_kernel_size=4,
- class_agnostic=False,
- loss_grid=dict(
- type='CrossEntropyLoss', use_sigmoid=True,
- loss_weight=15),
- conv_cfg=None,
- norm_cfg=dict(type='GN', num_groups=36)):
- super(GridHead, self).__init__()
- self.grid_points = grid_points
- self.num_convs = num_convs
- self.roi_feat_size = roi_feat_size
- self.in_channels = in_channels
- self.conv_kernel_size = conv_kernel_size
- self.point_feat_channels = point_feat_channels
- self.conv_out_channels = self.point_feat_channels * self.grid_points
- self.class_agnostic = class_agnostic
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
- if isinstance(norm_cfg, dict) and norm_cfg['type'] == 'GN':
- assert self.conv_out_channels % norm_cfg['num_groups'] == 0
-
- assert self.grid_points >= 4
- self.grid_size = int(np.sqrt(self.grid_points))
- if self.grid_size * self.grid_size != self.grid_points:
- raise ValueError('grid_points must be a square number')
-
- # the predicted heatmap is half of whole_map_size
- if not isinstance(self.roi_feat_size, int):
- raise ValueError('Only square RoIs are supporeted in Grid R-CNN')
- self.whole_map_size = self.roi_feat_size * 4
-
- # compute point-wise sub-regions
- self.sub_regions = self.calc_sub_regions()
-
- self.convs = []
- for i in range(self.num_convs):
- in_channels = (
- self.in_channels if i == 0 else self.conv_out_channels)
- stride = 2 if i == 0 else 1
- padding = (self.conv_kernel_size - 1) // 2
- self.convs.append(
- ConvModule(
- in_channels,
- self.conv_out_channels,
- self.conv_kernel_size,
- stride=stride,
- padding=padding,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- bias=True))
- self.convs = nn.Sequential(*self.convs)
-
- self.deconv1 = nn.ConvTranspose2d(
- self.conv_out_channels,
- self.conv_out_channels,
- kernel_size=deconv_kernel_size,
- stride=2,
- padding=(deconv_kernel_size - 2) // 2,
- groups=grid_points)
- self.norm1 = nn.GroupNorm(grid_points, self.conv_out_channels)
- self.deconv2 = nn.ConvTranspose2d(
- self.conv_out_channels,
- grid_points,
- kernel_size=deconv_kernel_size,
- stride=2,
- padding=(deconv_kernel_size - 2) // 2,
- groups=grid_points)
-
- # find the 4-neighbor of each grid point
- self.neighbor_points = []
- grid_size = self.grid_size
- for i in range(grid_size): # i-th column
- for j in range(grid_size): # j-th row
- neighbors = []
- if i > 0: # left: (i - 1, j)
- neighbors.append((i - 1) * grid_size + j)
- if j > 0: # up: (i, j - 1)
- neighbors.append(i * grid_size + j - 1)
- if j < grid_size - 1: # down: (i, j + 1)
- neighbors.append(i * grid_size + j + 1)
- if i < grid_size - 1: # right: (i + 1, j)
- neighbors.append((i + 1) * grid_size + j)
- self.neighbor_points.append(tuple(neighbors))
- # total edges in the grid
- self.num_edges = sum([len(p) for p in self.neighbor_points])
-
- self.forder_trans = nn.ModuleList() # first-order feature transition
- self.sorder_trans = nn.ModuleList() # second-order feature transition
- for neighbors in self.neighbor_points:
- fo_trans = nn.ModuleList()
- so_trans = nn.ModuleList()
- for _ in range(len(neighbors)):
- # each transition module consists of a 5x5 depth-wise conv and
- # 1x1 conv.
- fo_trans.append(
- nn.Sequential(
- nn.Conv2d(
- self.point_feat_channels,
- self.point_feat_channels,
- 5,
- stride=1,
- padding=2,
- groups=self.point_feat_channels),
- nn.Conv2d(self.point_feat_channels,
- self.point_feat_channels, 1)))
- so_trans.append(
- nn.Sequential(
- nn.Conv2d(
- self.point_feat_channels,
- self.point_feat_channels,
- 5,
- 1,
- 2,
- groups=self.point_feat_channels),
- nn.Conv2d(self.point_feat_channels,
- self.point_feat_channels, 1)))
- self.forder_trans.append(fo_trans)
- self.sorder_trans.append(so_trans)
-
- self.loss_grid = build_loss(loss_grid)
-
- def init_weights(self):
- for m in self.modules():
- if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
- # TODO: compare mode = "fan_in" or "fan_out"
- kaiming_init(m)
- for m in self.modules():
- if isinstance(m, nn.ConvTranspose2d):
- normal_init(m, std=0.001)
- nn.init.constant_(self.deconv2.bias, -np.log(0.99 / 0.01))
-
- def forward(self, x):
- assert x.shape[-1] == x.shape[-2] == self.roi_feat_size
- # RoI feature transformation, downsample 2x
- x = self.convs(x)
-
- c = self.point_feat_channels
- # first-order fusion
- x_fo = [None for _ in range(self.grid_points)]
- for i, points in enumerate(self.neighbor_points):
- x_fo[i] = x[:, i * c:(i + 1) * c]
- for j, point_idx in enumerate(points):
- x_fo[i] = x_fo[i] + self.forder_trans[i][j](
- x[:, point_idx * c:(point_idx + 1) * c])
-
- # second-order fusion
- x_so = [None for _ in range(self.grid_points)]
- for i, points in enumerate(self.neighbor_points):
- x_so[i] = x[:, i * c:(i + 1) * c]
- for j, point_idx in enumerate(points):
- x_so[i] = x_so[i] + self.sorder_trans[i][j](x_fo[point_idx])
-
- # predicted heatmap with fused features
- x2 = torch.cat(x_so, dim=1)
- x2 = self.deconv1(x2)
- x2 = F.relu(self.norm1(x2), inplace=True)
- heatmap = self.deconv2(x2)
-
- # predicted heatmap with original features (applicable during training)
- if self.training:
- x1 = x
- x1 = self.deconv1(x1)
- x1 = F.relu(self.norm1(x1), inplace=True)
- heatmap_unfused = self.deconv2(x1)
- else:
- heatmap_unfused = heatmap
-
- return dict(fused=heatmap, unfused=heatmap_unfused)
-
- def calc_sub_regions(self):
- """Compute point specific representation regions.
-
- See Grid R-CNN Plus (https://arxiv.org/abs/1906.05688) for details.
- """
- # to make it consistent with the original implementation, half_size
- # is computed as 2 * quarter_size, which is smaller
- half_size = self.whole_map_size // 4 * 2
- sub_regions = []
- for i in range(self.grid_points):
- x_idx = i // self.grid_size
- y_idx = i % self.grid_size
- if x_idx == 0:
- sub_x1 = 0
- elif x_idx == self.grid_size - 1:
- sub_x1 = half_size
- else:
- ratio = x_idx / (self.grid_size - 1) - 0.25
- sub_x1 = max(int(ratio * self.whole_map_size), 0)
-
- if y_idx == 0:
- sub_y1 = 0
- elif y_idx == self.grid_size - 1:
- sub_y1 = half_size
- else:
- ratio = y_idx / (self.grid_size - 1) - 0.25
- sub_y1 = max(int(ratio * self.whole_map_size), 0)
- sub_regions.append(
- (sub_x1, sub_y1, sub_x1 + half_size, sub_y1 + half_size))
- return sub_regions
-
- def get_targets(self, sampling_results, rcnn_train_cfg):
- # mix all samples (across images) together.
- pos_bboxes = torch.cat([res.pos_bboxes for res in sampling_results],
- dim=0).cpu()
- pos_gt_bboxes = torch.cat(
- [res.pos_gt_bboxes for res in sampling_results], dim=0).cpu()
- assert pos_bboxes.shape == pos_gt_bboxes.shape
-
- # expand pos_bboxes to 2x of original size
- x1 = pos_bboxes[:, 0] - (pos_bboxes[:, 2] - pos_bboxes[:, 0]) / 2
- y1 = pos_bboxes[:, 1] - (pos_bboxes[:, 3] - pos_bboxes[:, 1]) / 2
- x2 = pos_bboxes[:, 2] + (pos_bboxes[:, 2] - pos_bboxes[:, 0]) / 2
- y2 = pos_bboxes[:, 3] + (pos_bboxes[:, 3] - pos_bboxes[:, 1]) / 2
- pos_bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
- pos_bbox_ws = (pos_bboxes[:, 2] - pos_bboxes[:, 0]).unsqueeze(-1)
- pos_bbox_hs = (pos_bboxes[:, 3] - pos_bboxes[:, 1]).unsqueeze(-1)
-
- num_rois = pos_bboxes.shape[0]
- map_size = self.whole_map_size
- # this is not the final target shape
- targets = torch.zeros((num_rois, self.grid_points, map_size, map_size),
- dtype=torch.float)
-
- # pre-compute interpolation factors for all grid points.
- # the first item is the factor of x-dim, and the second is y-dim.
- # for a 9-point grid, factors are like (1, 0), (0.5, 0.5), (0, 1)
- factors = []
- for j in range(self.grid_points):
- x_idx = j // self.grid_size
- y_idx = j % self.grid_size
- factors.append((1 - x_idx / (self.grid_size - 1),
- 1 - y_idx / (self.grid_size - 1)))
-
- radius = rcnn_train_cfg.pos_radius
- radius2 = radius**2
- for i in range(num_rois):
- # ignore small bboxes
- if (pos_bbox_ws[i] <= self.grid_size
- or pos_bbox_hs[i] <= self.grid_size):
- continue
- # for each grid point, mark a small circle as positive
- for j in range(self.grid_points):
- factor_x, factor_y = factors[j]
- gridpoint_x = factor_x * pos_gt_bboxes[i, 0] + (
- 1 - factor_x) * pos_gt_bboxes[i, 2]
- gridpoint_y = factor_y * pos_gt_bboxes[i, 1] + (
- 1 - factor_y) * pos_gt_bboxes[i, 3]
-
- cx = int((gridpoint_x - pos_bboxes[i, 0]) / pos_bbox_ws[i] *
- map_size)
- cy = int((gridpoint_y - pos_bboxes[i, 1]) / pos_bbox_hs[i] *
- map_size)
-
- for x in range(cx - radius, cx + radius + 1):
- for y in range(cy - radius, cy + radius + 1):
- if x >= 0 and x < map_size and y >= 0 and y < map_size:
- if (x - cx)**2 + (y - cy)**2 <= radius2:
- targets[i, j, y, x] = 1
- # reduce the target heatmap size by a half
- # proposed in Grid R-CNN Plus (https://arxiv.org/abs/1906.05688).
- sub_targets = []
- for i in range(self.grid_points):
- sub_x1, sub_y1, sub_x2, sub_y2 = self.sub_regions[i]
- sub_targets.append(targets[:, [i], sub_y1:sub_y2, sub_x1:sub_x2])
- sub_targets = torch.cat(sub_targets, dim=1)
- sub_targets = sub_targets.to(sampling_results[0].pos_bboxes.device)
- return sub_targets
-
- def loss(self, grid_pred, grid_targets):
- loss_fused = self.loss_grid(grid_pred['fused'], grid_targets)
- loss_unfused = self.loss_grid(grid_pred['unfused'], grid_targets)
- loss_grid = loss_fused + loss_unfused
- return dict(loss_grid=loss_grid)
-
- def get_bboxes(self, det_bboxes, grid_pred, img_metas):
- # TODO: refactoring
- assert det_bboxes.shape[0] == grid_pred.shape[0]
- det_bboxes = det_bboxes.cpu()
- cls_scores = det_bboxes[:, [4]]
- det_bboxes = det_bboxes[:, :4]
- grid_pred = grid_pred.sigmoid().cpu()
-
- R, c, h, w = grid_pred.shape
- half_size = self.whole_map_size // 4 * 2
- assert h == w == half_size
- assert c == self.grid_points
-
- # find the point with max scores in the half-sized heatmap
- grid_pred = grid_pred.view(R * c, h * w)
- pred_scores, pred_position = grid_pred.max(dim=1)
- xs = pred_position % w
- ys = pred_position // w
-
- # get the position in the whole heatmap instead of half-sized heatmap
- for i in range(self.grid_points):
- xs[i::self.grid_points] += self.sub_regions[i][0]
- ys[i::self.grid_points] += self.sub_regions[i][1]
-
- # reshape to (num_rois, grid_points)
- pred_scores, xs, ys = tuple(
- map(lambda x: x.view(R, c), [pred_scores, xs, ys]))
-
- # get expanded pos_bboxes
- widths = (det_bboxes[:, 2] - det_bboxes[:, 0]).unsqueeze(-1)
- heights = (det_bboxes[:, 3] - det_bboxes[:, 1]).unsqueeze(-1)
- x1 = (det_bboxes[:, 0, None] - widths / 2)
- y1 = (det_bboxes[:, 1, None] - heights / 2)
- # map the grid point to the absolute coordinates
- abs_xs = (xs.float() + 0.5) / w * widths + x1
- abs_ys = (ys.float() + 0.5) / h * heights + y1
-
- # get the grid points indices that fall on the bbox boundaries
- x1_inds = [i for i in range(self.grid_size)]
- y1_inds = [i * self.grid_size for i in range(self.grid_size)]
- x2_inds = [
- self.grid_points - self.grid_size + i
- for i in range(self.grid_size)
- ]
- y2_inds = [(i + 1) * self.grid_size - 1 for i in range(self.grid_size)]
-
- # voting of all grid points on some boundary
- bboxes_x1 = (abs_xs[:, x1_inds] * pred_scores[:, x1_inds]).sum(
- dim=1, keepdim=True) / (
- pred_scores[:, x1_inds].sum(dim=1, keepdim=True))
- bboxes_y1 = (abs_ys[:, y1_inds] * pred_scores[:, y1_inds]).sum(
- dim=1, keepdim=True) / (
- pred_scores[:, y1_inds].sum(dim=1, keepdim=True))
- bboxes_x2 = (abs_xs[:, x2_inds] * pred_scores[:, x2_inds]).sum(
- dim=1, keepdim=True) / (
- pred_scores[:, x2_inds].sum(dim=1, keepdim=True))
- bboxes_y2 = (abs_ys[:, y2_inds] * pred_scores[:, y2_inds]).sum(
- dim=1, keepdim=True) / (
- pred_scores[:, y2_inds].sum(dim=1, keepdim=True))
-
- bbox_res = torch.cat(
- [bboxes_x1, bboxes_y1, bboxes_x2, bboxes_y2, cls_scores], dim=1)
- bbox_res[:, [0, 2]].clamp_(min=0, max=img_metas[0]['img_shape'][1])
- bbox_res[:, [1, 3]].clamp_(min=0, max=img_metas[0]['img_shape'][0])
-
- return bbox_res
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/utils.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/utils.py
deleted file mode 100644
index c5befb8e56ece50b5fecfd007b26f8a29124c0bd..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/utils.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os
-import random
-import sys
-import time
-import warnings
-from getpass import getuser
-from socket import gethostname
-
-import numpy as np
-import torch
-
-import annotator.uniformer.mmcv as mmcv
-
-
-def get_host_info():
- """Get hostname and username.
-
- Return empty string if exception raised, e.g. ``getpass.getuser()`` will
- lead to error in docker container
- """
- host = ''
- try:
- host = f'{getuser()}@{gethostname()}'
- except Exception as e:
- warnings.warn(f'Host or user not found: {str(e)}')
- finally:
- return host
-
-
-def get_time_str():
- return time.strftime('%Y%m%d_%H%M%S', time.localtime())
-
-
-def obj_from_dict(info, parent=None, default_args=None):
- """Initialize an object from dict.
-
- The dict must contain the key "type", which indicates the object type, it
- can be either a string or type, such as "list" or ``list``. Remaining
- fields are treated as the arguments for constructing the object.
-
- Args:
- info (dict): Object types and arguments.
- parent (:class:`module`): Module which may containing expected object
- classes.
- default_args (dict, optional): Default arguments for initializing the
- object.
-
- Returns:
- any type: Object built from the dict.
- """
- assert isinstance(info, dict) and 'type' in info
- assert isinstance(default_args, dict) or default_args is None
- args = info.copy()
- obj_type = args.pop('type')
- if mmcv.is_str(obj_type):
- if parent is not None:
- obj_type = getattr(parent, obj_type)
- else:
- obj_type = sys.modules[obj_type]
- elif not isinstance(obj_type, type):
- raise TypeError('type must be a str or valid type, but '
- f'got {type(obj_type)}')
- if default_args is not None:
- for name, value in default_args.items():
- args.setdefault(name, value)
- return obj_type(**args)
-
-
-def set_random_seed(seed, deterministic=False, use_rank_shift=False):
- """Set random seed.
-
- Args:
- seed (int): Seed to be used.
- deterministic (bool): Whether to set the deterministic option for
- CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
- to True and `torch.backends.cudnn.benchmark` to False.
- Default: False.
- rank_shift (bool): Whether to add rank number to the random seed to
- have different random seed in different threads. Default: False.
- """
- if use_rank_shift:
- rank, _ = mmcv.runner.get_dist_info()
- seed += rank
- random.seed(seed)
- np.random.seed(seed)
- torch.manual_seed(seed)
- torch.cuda.manual_seed(seed)
- torch.cuda.manual_seed_all(seed)
- os.environ['PYTHONHASHSEED'] = str(seed)
- if deterministic:
- torch.backends.cudnn.deterministic = True
- torch.backends.cudnn.benchmark = False
diff --git a/spaces/Robinn/WordSent/app.py b/spaces/Robinn/WordSent/app.py
deleted file mode 100644
index a734218e97ed894a89456fbab566d646de2af029..0000000000000000000000000000000000000000
--- a/spaces/Robinn/WordSent/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import streamlit as st
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib_venn import venn2
-import matplotlib
-from sklearn.metrics.pairwise import cosine_similarity
-from textblob import TextBlob
-import transformers
-from transformers import pipeline
-from collections import Counter
-import torch
-from sklearn.cluster import KMeans
-from sklearn.manifold import TSNE
-
-# Load GloVe embeddings from file
-with open('glove.6B.50d.txt', 'r', encoding='utf8') as f:
- words = []
- vectors = []
- for line in f:
- line = line.strip().split()
- word = line[0]
- vector = np.array([float(x) for x in line[1:]])
- words.append(word)
- vectors.append(vector)
- embeddings = np.array(vectors)
-
-# Load BERT-based sentiment analysis model
-model = transformers.pipeline("sentiment-analysis")
-
-# Define streamlit app
-st.title("Sentiment Analysis Model for Similarity, Using BERT")
-
-# User input
-user_word = st.text_input("Enter a word:")
-user_word = user_word.lower()
-
-# Find similar words
-if user_word in words:
- user_index = words.index(user_word)
- user_vector = embeddings[user_index].reshape(1, -1)
- num_similar_words = st.number_input("Enter the number of similar words to output:", min_value=1, max_value=1000, value=10)
- similarities = cosine_similarity(user_vector, embeddings)
- similar_indices = np.argsort(similarities)[0][::-1][1:num_similar_words + 1] # get top n most similar words
- similar_words = [words[i] for i in similar_indices]
- similar_scores = [similarities[0][i] for i in similar_indices]
- # for word, score in zip(similar_words, similar_scores):
- # st.write(f"'{word}' (Similarity score: {score:.2f})")
-
- # Word embeddings visualization
- fig, ax = plt.subplots(figsize=(10, 10))
- similar_vectors = embeddings[similar_indices]
- ax.scatter(similar_vectors[:, 0], similar_vectors[:, 1])
- for i, word in enumerate(similar_words):
- ax.annotate(word, (similar_vectors[i, 0], similar_vectors[i, 1]))
- ax.set_title(f"Word Embeddings Visualization for '{user_word}' and its {num_similar_words} Similar Words")
- ax.set_xlabel('Dimension 1')
- ax.set_ylabel('Dimension 2')
- ax.tick_params(axis='both', which='major', labelsize=12)
-
- # st.pyplot(fig)
-
- # Dimensionality reduction with t-SNE
- tsne = TSNE(n_components=2, perplexity=5, random_state=0)
- tsne_vectors = tsne.fit_transform(similar_vectors)
-
- # t-SNE visualization
- fig_tsne, ax_tsne = plt.subplots(figsize=(10, 10))
- ax_tsne.scatter(tsne_vectors[:, 0], tsne_vectors[:, 1])
- for i, word in enumerate(similar_words):
- ax_tsne.annotate(word, (tsne_vectors[i, 0], tsne_vectors[i, 1]))
- ax_tsne.set_title(f"t-SNE Visualization for '{user_word}' and its {num_similar_words} Similar Words")
- ax_tsne.set_xlabel('Dimension 1')
- ax_tsne.set_ylabel('Dimension 2')
-
- # st.pyplot(fig_tsne)
-
- # Display plots side by side
- st.write("\nVisualizations:")
- col1, col2 = st.columns(2)
- with col1:
- st.pyplot(fig)
- with col2:
- st.pyplot(fig_tsne)
-
- st.write("\nSimilar words:")
- similar_words_str = "\n".join([f"'{word}' (Similarity score: {score:.2f})" for word, score in zip(similar_words, similar_scores)])
- st.text_area(label="", value=similar_words_str, height=200)
-
-# # # KMeans clustering
-# # kmeans = KMeans(n_clusters=3, random_state=0).fit(similar_vectors)
-# # cluster_labels = kmeans.labels_
-# # for i, word in enumerate(similar_words):
-# # st.write(f"'{word}' belongs to cluster {cluster_labels[i]}")
-
-# # if user_word:
-# # Sentiment analysis with BERT
-# sentiment = model(user_word)[0]
-# label = sentiment['label']
-# score = sentiment['score']
-# st.write(f"'{user_word}' has sentiment '{label}' with score {score:.2f}")
-# else:
-# st.write(f"'{user_word}' is not in the Dataset.")
-
- # Sentiment analysis with BERT
- sentiment_scores = []
- model = pipeline('sentiment-analysis')
- sentiment_analysis_text = ""
- for word in similar_words:
- result = model(word)[0]
- label = result['label']
- score = result['score']
- if label == 'POSITIVE':
- sentiment_scores.append(score)
- elif label == 'NEGATIVE':
- sentiment_scores.append(-score)
- else:
- sentiment_scores.append(0)
- # st.write(f"'{word}' has sentiment '{label}' with score {score:.2f}")
- sentiment_analysis_text += f"'{word}' has sentiment '{label}' with score {score:.2f}\n"
-
- with st.container():
- st.text_area("Sentiment Analysis Text", value=sentiment_analysis_text, height=200)
-
- # Venn diagram
- sentiment_counter = Counter()
- for score in sentiment_scores:
- if score > 0:
- sentiment_counter['positive'] += 1
- elif score < 0:
- sentiment_counter['negative'] += 1
- else:
- sentiment_counter['neutral'] += 1
-
- fig, ax = plt.subplots(figsize=(5, 5))
- matplotlib.rcParams['font.size'] = 10
- plt.title(f"Sentiment Analysis Venn Diagram for '{user_word}' and its {num_similar_words} Similar Words")
- venn2(subsets=(sentiment_counter['positive'], sentiment_counter['negative'],
- sentiment_counter['positive'] + sentiment_counter['negative']),
- # sentiment_counter['positive'] + sentiment_counter['negative'],
- # sentiment_counter['positive'] + sentiment_counter['neutral'],
- # sentiment_counter['negative'] + sentiment_counter['neutral'],
- # sentiment_counter['positive'] + sentiment_counter['negative'] + sentiment_counter['neutral']),
- set_colors=('r', 'g'), set_labels=('Positive', 'Negative'))
- st.pyplot(fig)
\ No newline at end of file
diff --git a/spaces/SantiagoMoreno-UdeA/NER_RC/src/scripts/Test.py b/spaces/SantiagoMoreno-UdeA/NER_RC/src/scripts/Test.py
deleted file mode 100644
index 5760bd04df062fcab92d470b72e853713db7f429..0000000000000000000000000000000000000000
--- a/spaces/SantiagoMoreno-UdeA/NER_RC/src/scripts/Test.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Thu May 4 18:47:46 2023
-
-@author: sanmo
-"""
-import os
-default_path = os.path.dirname(os.path.abspath(__file__))
-default_path = default_path.replace('\\', '/')
-
-from functionsrc import training_model_rc, usage_cuda_rc, use_model_rc
-
-path_data = default_path + '/../../data/RC/test.txt'
-rel2id_data = default_path + '/../../data/RC/rel2id.json'
-print(usage_cuda_rc(True))
-training_model_rc('p', path_data, rel2id_data, 2)
-
-# output_dir = default_path + '/../../out_RC.json'
-
-# print(use_model_rc('new', path_data, output_dir))
diff --git a/spaces/SarthakSidhant/Go-Cattle/diseases/copper deficiency.md b/spaces/SarthakSidhant/Go-Cattle/diseases/copper deficiency.md
deleted file mode 100644
index 3f43614001a3ae1adffbb5311a9092ed36c1c6b7..0000000000000000000000000000000000000000
--- a/spaces/SarthakSidhant/Go-Cattle/diseases/copper deficiency.md
+++ /dev/null
@@ -1,39 +0,0 @@
-## Copper deficiency
-
-**Information:** Copper is an essential trace mineral for cattle. It is involved in many important functions in the body, including:
-
-* Red blood cell production
-* Bone development
-* Immune function
-* Nervous system function
-* Metabolism
-
-**Symptoms:**
-
-* Poor growth
-* Anemia
-* Bone deformities
-* Depigmentation of the hair and skin
-* Neurological problems, such as ataxia (incoordination) and seizures
-* Reproductive problems, such as abortion and stillbirth
-* Death
-
-**Remedies:**
-
-* There is no specific cure for copper deficiency.
-* Treatment is usually supportive and may include:
- * Feeding copper-rich supplements
- * Treating other underlying conditions
-
-**Causes:**
-
-* Copper deficiency is caused by a lack of copper in the diet.
-* Copper is found in soil, and areas with low copper levels in the soil are more likely to have cattle with copper deficiency.
-* Cattle can also become deficient in copper if they are fed grain that has been grown in low-copper soil.
-* Copper deficiency is more common in young animals, pregnant animals, and animals that are stressed.
-
-**Prevention:**
-
-* The best way to prevent copper deficiency is to feed cattle a diet that is copper-rich.
-* Copper supplements are also available.
-* Cattle that are at risk of copper deficiency, such as those that are grazing in low-copper areas, should be supplemented with copper.
diff --git a/spaces/Shawn37/UTR_LM/esm/model/esm2_supervised.py b/spaces/Shawn37/UTR_LM/esm/model/esm2_supervised.py
deleted file mode 100644
index ee4965b883399f9128859e6334cb4742fbc881e4..0000000000000000000000000000000000000000
--- a/spaces/Shawn37/UTR_LM/esm/model/esm2_supervised.py
+++ /dev/null
@@ -1,174 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Union
-import torch
-import torch.nn as nn
-
-import esm
-from esm.modules import ContactPredictionHead, ESM1bLayerNorm, RobertaLMHead, TransformerLayer
-
-
-class ESM2(nn.Module):
- def __init__(
- self,
- num_layers: int = 33,
- embed_dim: int = 1280,
- attention_heads: int = 20,
- alphabet: Union[esm.data.Alphabet, str] = "ESM-1b",
- token_dropout: bool = True,
- ):
- super().__init__()
- self.num_layers = num_layers
- self.embed_dim = embed_dim
- self.attention_heads = attention_heads
- if not isinstance(alphabet, esm.data.Alphabet):
- alphabet = esm.data.Alphabet.from_architecture(alphabet)
- self.alphabet = alphabet
- self.alphabet_size = len(alphabet)
- self.padding_idx = alphabet.padding_idx
- self.mask_idx = alphabet.mask_idx
- self.cls_idx = alphabet.cls_idx
- self.eos_idx = alphabet.eos_idx
- self.prepend_bos = alphabet.prepend_bos
- self.append_eos = alphabet.append_eos
- self.token_dropout = token_dropout
-
- self._init_submodules()
-
- def _init_submodules(self):
- self.embed_scale = 1
- self.embed_tokens = nn.Embedding(
- self.alphabet_size,
- self.embed_dim,
- padding_idx=self.padding_idx,
- )
-
- self.layers = nn.ModuleList(
- [
- TransformerLayer(
- self.embed_dim,
- 4 * self.embed_dim,
- self.attention_heads,
- add_bias_kv=False,
- use_esm1b_layer_norm=True,
- use_rotary_embeddings=True,
- )
- for _ in range(self.num_layers)
- ]
- )
-
- self.contact_head = ContactPredictionHead(
- self.num_layers * self.attention_heads,
- self.prepend_bos,
- self.append_eos,
- eos_idx=self.eos_idx,
- )
- self.emb_layer_norm_after = ESM1bLayerNorm(self.embed_dim)
-
- self.lm_head = RobertaLMHead(
- embed_dim=self.embed_dim,
- output_dim=self.alphabet_size,
- weight=self.embed_tokens.weight,
- )
- self.supervised_linear = nn.Linear(self.embed_dim, 1)
- def forward(self, tokens, repr_layers=[], need_head_weights=True, return_contacts=True, return_representation=True, return_attentions_symm = False, return_attentions = False):
- if return_contacts:
- need_head_weights = True
-
- assert tokens.ndim == 2
- padding_mask = tokens.eq(self.padding_idx) # B, T
-
- x = self.embed_scale * self.embed_tokens(tokens)
-
- if self.token_dropout:
- x.masked_fill_((tokens == self.mask_idx).unsqueeze(-1), 0.0)
- #print(f'tokens = {tokens}')
- #print(f'self.mask_idx = {self.mask_idx}')
- #print('x.shape = ', x.shape)
- # x: B x T x C
- mask_ratio_train = 0.15 * 0.8
- src_lengths = (~padding_mask).sum(-1)
- #print(f'mask_ratio_train = {mask_ratio_train}')
- #print(f'padding_mask = {padding_mask}')
- #print(f'src_lengths = {src_lengths}')
- mask_ratio_observed = (tokens == self.mask_idx).sum(-1).to(x.dtype) / src_lengths
- #print('mask_ratio_observed = ',mask_ratio_observed)
- x = x * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None]
- #print(f'x.shape = {x.shape}:\n', x)
- if padding_mask is not None:
- x = x * (1 - padding_mask.unsqueeze(-1).type_as(x))
- #print(f'x.shape = {x.shape}:\n', x)
- repr_layers = set(repr_layers)
- hidden_representations = {}
- if 0 in repr_layers:
- hidden_representations[0] = x
-
- if need_head_weights:
- attn_weights = []
-
- # (B, T, E) => (T, B, E)
- x = x.transpose(0, 1)
-
- if not padding_mask.any():
- padding_mask = None
-
- for layer_idx, layer in enumerate(self.layers):
- x, attn = layer(
- x,
- self_attn_padding_mask=padding_mask,
- need_head_weights=need_head_weights,
- )
- if (layer_idx + 1) in repr_layers:
- hidden_representations[layer_idx + 1] = x.transpose(0, 1)
- if need_head_weights:
- # (H, B, T, T) => (B, H, T, T)
- attn_weights.append(attn.transpose(1, 0))
-# print(x.shape) # 73, 2, 1280
- x = self.emb_layer_norm_after(x)
- x = x.transpose(0, 1) # (T, B, E) => (B, T, E)
-
- # last hidden representation should have layer norm applied
- if (layer_idx + 1) in repr_layers:
- hidden_representations[layer_idx + 1] = x
- x_supervised = self.supervised_linear(x[:,0,:])
- x = self.lm_head(x)
-
- if return_representation:
- result = {"logits": x, "logits_supervised": x_supervised, "representations": hidden_representations}
- else:
- result = {"logits": x, "logits_supervised": x_supervised}
- if need_head_weights:
- # attentions: B x L x H x T x T
- attentions = torch.stack(attn_weights, 1)
- if padding_mask is not None:
- attention_mask = 1 - padding_mask.type_as(attentions)
- attention_mask = attention_mask.unsqueeze(1) * attention_mask.unsqueeze(2)
- attentions = attentions * attention_mask[:, None, None, :, :]
- if return_attentions: result["attentions"] = attentions
- if return_contacts:
- attentions_symm, contacts = self.contact_head(tokens, attentions)
- result["contacts"] = contacts
- if return_attentions_symm: result["attentions_symm"] = attentions_symm
-
- return result
-
- def predict_contacts(self, tokens):
- return self(tokens, return_contacts=True)["contacts"]
-
- def predict_symmetric_attentions(self, tokens):
- return self(tokens, return_contacts=True)["attentions_symm"]
-
- def predict_attentions(self, tokens):
- return self(tokens, need_head_weights=True)["attentions"]
-
- def predict_representations(self, tokens):
- return self(tokens, return_representation=True)['representations']
-
- def predict_logits(self, tokens):
- return self(tokens)['logits']
-
- def predict_logits_supervised(self, tokens):
- return self(tokens)['logits_supervised']
diff --git a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/grids/musicgen/musicgen_base_32khz.py b/spaces/SuYuanS/AudioCraft_Plus/audiocraft/grids/musicgen/musicgen_base_32khz.py
deleted file mode 100644
index 4e364614537e426f21c18a2c2a9d94b3babce051..0000000000000000000000000000000000000000
--- a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/grids/musicgen/musicgen_base_32khz.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from ._explorers import LMExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@LMExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=32, partition=partitions)
- launcher.bind_(solver='musicgen/musicgen_base_32khz')
- # replace this by the desired music dataset
- launcher.bind_(dset='internal/music_400k_32khz')
-
- fsdp = {'autocast': False, 'fsdp.use': True}
- medium = {'model/lm/model_scale': 'medium'}
- large = {'model/lm/model_scale': 'large'}
-
- cfg_low = {'classifier_free_guidance.training_dropout': 0.2}
- wd_low = {'conditioners.description.t5.word_dropout': 0.2}
-
- adam = {'optim.optimizer': 'adamw', 'optim.lr': 1e-4}
-
- launcher.bind_(fsdp)
-
- launcher.slurm_(gpus=32).bind_(label='32gpus')
- with launcher.job_array():
- sub = launcher.bind()
- sub()
-
- launcher.slurm_(gpus=64).bind_(label='64gpus')
- with launcher.job_array():
- sub = launcher.bind()
- sub(medium, adam)
-
- launcher.slurm_(gpus=96).bind_(label='96gpus')
- with launcher.job_array():
- sub = launcher.bind()
- sub(large, cfg_low, wd_low, adam, {'optim.max_norm': 3})
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/external/tests/test_qt_loaders.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/external/tests/test_qt_loaders.py
deleted file mode 100644
index 7bc9ccfa86d5db9d534177c23d2e220eaa1297ad..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/external/tests/test_qt_loaders.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import importlib
-import pytest
-from IPython.external.qt_loaders import ID
-
-
-def test_import_denier():
- ID.forbid("ipython_denied_module")
- with pytest.raises(ImportError, match="disabled by IPython"):
- import ipython_denied_module
- with pytest.raises(ImportError, match="disabled by IPython"):
- importlib.import_module("ipython_denied_module")
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/asttokens/asttokens.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/asttokens/asttokens.py
deleted file mode 100644
index d3c9d01381fb470732a9847b11dec273d3178268..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/asttokens/asttokens.py
+++ /dev/null
@@ -1,445 +0,0 @@
-# Copyright 2016 Grist Labs, Inc.
-#
-# 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 abc
-import ast
-import bisect
-import sys
-import token
-from ast import Module
-from typing import Iterable, Iterator, List, Optional, Tuple, Any, cast, TYPE_CHECKING, Type
-
-import six
-from six.moves import xrange # pylint: disable=redefined-builtin
-
-from .line_numbers import LineNumbers
-from .util import Token, match_token, is_non_coding_token, patched_generate_tokens, last_stmt, annotate_fstring_nodes, generate_tokens
-
-if TYPE_CHECKING: # pragma: no cover
- from .util import AstNode, TokenInfo
-
-
-class ASTTextBase(six.with_metaclass(abc.ABCMeta, object)):
- def __init__(self, source_text, filename):
- # type: (Any, str) -> None
- # FIXME: Strictly, the type of source_text is one of the six string types, but hard to specify with mypy given
- # https://mypy.readthedocs.io/en/stable/common_issues.html#variables-vs-type-aliases
-
- self._filename = filename
-
- # Decode source after parsing to let Python 2 handle coding declarations.
- # (If the encoding was not utf-8 compatible, then even if it parses correctly,
- # we'll fail with a unicode error here.)
- source_text = six.ensure_text(source_text)
-
- self._text = source_text
- self._line_numbers = LineNumbers(source_text)
-
- @abc.abstractmethod
- def get_text_positions(self, node, padded):
- # type: (AstNode, bool) -> Tuple[Tuple[int, int], Tuple[int, int]]
- """
- Returns two ``(lineno, col_offset)`` tuples for the start and end of the given node.
- If the positions can't be determined, or the nodes don't correspond to any particular text,
- returns ``(1, 0)`` for both.
-
- ``padded`` corresponds to the ``padded`` argument to ``ast.get_source_segment()``.
- This means that if ``padded`` is True, the start position will be adjusted to include
- leading whitespace if ``node`` is a multiline statement.
- """
- raise NotImplementedError
-
- def get_text_range(self, node, padded=True):
- # type: (AstNode, bool) -> Tuple[int, int]
- """
- Returns the (startpos, endpos) positions in source text corresponding to the given node.
- Returns (0, 0) for nodes (like `Load`) that don't correspond to any particular text.
-
- See ``get_text_positions()`` for details on the ``padded`` argument.
- """
- start, end = self.get_text_positions(node, padded)
- return (
- self._line_numbers.line_to_offset(*start),
- self._line_numbers.line_to_offset(*end),
- )
-
- def get_text(self, node, padded=True):
- # type: (AstNode, bool) -> str
- """
- Returns the text corresponding to the given node.
- Returns '' for nodes (like `Load`) that don't correspond to any particular text.
-
- See ``get_text_positions()`` for details on the ``padded`` argument.
- """
- start, end = self.get_text_range(node, padded)
- return self._text[start: end]
-
-
-class ASTTokens(ASTTextBase, object):
- """
- ASTTokens maintains the text of Python code in several forms: as a string, as line numbers, and
- as tokens, and is used to mark and access token and position information.
-
- ``source_text`` must be a unicode or UTF8-encoded string. If you pass in UTF8 bytes, remember
- that all offsets you'll get are to the unicode text, which is available as the ``.text``
- property.
-
- If ``parse`` is set, the ``source_text`` will be parsed with ``ast.parse()``, and the resulting
- tree marked with token info and made available as the ``.tree`` property.
-
- If ``tree`` is given, it will be marked and made available as the ``.tree`` property. In
- addition to the trees produced by the ``ast`` module, ASTTokens will also mark trees produced
- using ``astroid`` library '
- f''
- )
- with gr.Row():
- with gr.Column():
- if args.files:
- vc_input = gr.Textbox(label="Input audio path")
- else:
- vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '')
- vc_transpose = gr.Number(label="Transpose", value=0)
- vc_f0method = gr.Radio(
- label="Pitch extraction algorithm, PM is fast but Harvest is better for low frequencies",
- choices=["pm", "harvest"],
- value="pm",
- interactive=True,
- )
- vc_index_ratio = gr.Slider(
- minimum=0,
- maximum=1,
- label="Retrieval feature ratio",
- value=0.6,
- interactive=True,
- )
- tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
- tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text")
- tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
- vc_submit = gr.Button("Generate", variant="primary")
- with gr.Column():
- vc_output1 = gr.Textbox(label="Output Message")
- vc_output2 = gr.Audio(label="Output Audio")
- vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output1, vc_output2])
- tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice])
- app.queue(concurrency_count=1, max_size=20, api_open=args.api).launch(share=args.share)
\ No newline at end of file
diff --git a/spaces/Xhaheen/GPTJ_PLUS_DALL_E/app.py b/spaces/Xhaheen/GPTJ_PLUS_DALL_E/app.py
deleted file mode 100644
index f2c6cf23972acf7e9371bbdb496743265dd94ce4..0000000000000000000000000000000000000000
--- a/spaces/Xhaheen/GPTJ_PLUS_DALL_E/app.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import gradio as gr
-import requests
-
-# GPT-J-6B API
-API_URL = "https://api-inference.huggingface.co/models/EleutherAI/gpt-j-6B"
-headers = {"Authorization": "Bearer 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"}
-prompt = """
-word: risk
-poem using word: And then the day came,
-when the risk
-to remain tight
-in a bud
-was more painful
-than the risk
-it took
-to blossom.
-word: bird
-poem using word: She sights a bird, she chuckles
-She flattens, then she crawls
-She runs without the look of feet
-Her eyes increase to Balls.
-word: """
-
-examples = [["river"], ["night"], ["trees"],["table"],["laughs"]]
-
-
-def poem_generate(word):
-
- p = prompt + word.lower() + "\n" + "poem using word: "
- print(f"*****Inside poem_generate - Prompt is :{p}")
- json_ = {"inputs": p,
- "parameters":
- {
- "top_p": 0.9,
- "temperature": 1.1,
- "max_new_tokens": 50,
- "return_full_text": False
- }}
- response = requests.post(API_URL, headers=headers, json=json_)
- output = response.json()
- print(f"Was there an error? Reason is : {output}")
- output_tmp = output[0]['generated_text']
- print(f"GPTJ response without splits is: {output_tmp}")
- #poem = output[0]['generated_text'].split("\n\n")[0] # +"."
- if "\n\n" not in output_tmp:
- if output_tmp.find('.') != -1:
- idx = output_tmp.find('.')
- poem = output_tmp[:idx+1]
- else:
- idx = output_tmp.rfind('\n')
- poem = output_tmp[:idx]
- else:
- poem = output_tmp.split("\n\n")[0] # +"."
- print(f"Poem being returned is: {poem}")
- return poem
-
-def poem_to_image(poem):
- print("*****Inside Poem_to_image")
- poem = " ".join(poem.split('\n'))
- poem = poem + " oil on canvas."
- steps, width, height, images, diversity = '50','256','256','1',15
- img = gr.Interface.load("spaces/multimodalart/latentdiffusion")(poem, steps, width, height, images, diversity)[0]
- return img
-
-demo = gr.Blocks()
-
-with demo:
- gr.Markdown("{title}\n'+
- (f'Model author: {author}' if author else "")+
- (f'' if cover else "")+
- '
")
- gr.Markdown(
- "Enter a single word you would want GPTJ-6B to write Poetry 🎤 on."
- "Generate an illustration 🎨 provided by Latent Diffusion model.GPJ-6B is a 6 Billion parameter autoregressive language model. It generates the Poem based on how it has been 'prompt-engineered' 🤗 The complete text of generated poem then goes in as a prompt to the amazing Latent Diffusion Art space by Multimodalart.Please note that some of the Poems/Illustrations might not look at par, and well, this is what happens when you can't 'cherry-pick' and post 😁 Some of the example words that you can use are 'river', 'night', 'trees', 'table', 'laughs' or maybe on similar lines to get best results!"
- )
- with gr.Row():
- input_word = gr.Textbox(placeholder="Enter a word here to create a Poem on..")
- poem_txt = gr.Textbox(lines=7)
- output_image = gr.Image(type="filepath", shape=(256,256))
-
- b1 = gr.Button("Generate Poem")
- b2 = gr.Button("Generate Image")
-
- b1.click(poem_generate, input_word, poem_txt)
- b2.click(poem_to_image, poem_txt, output_image)
- #examples=examples
-
-demo.launch(enable_queue=True, debug=True)
\ No newline at end of file
diff --git a/spaces/XlalalaX/VITS-Umamusume-voice-synthesizer/data_utils.py b/spaces/XlalalaX/VITS-Umamusume-voice-synthesizer/data_utils.py
deleted file mode 100644
index e9246c6c8f2ff3c37a7f8529ea1593c7f80f887e..0000000000000000000000000000000000000000
--- a/spaces/XlalalaX/VITS-Umamusume-voice-synthesizer/data_utils.py
+++ /dev/null
@@ -1,393 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-
-import commons
-from mel_processing import spectrogram_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import text_to_sequence, cleaned_text_to_sequence
-
-
-class TextAudioLoader(torch.utils.data.Dataset):
- """
- 1) loads audio, text pairs
- 2) normalizes text and converts them to sequences of integers
- 3) computes spectrograms from audio files.
- """
- def __init__(self, audiopaths_and_text, hparams):
- self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
- self.text_cleaners = hparams.text_cleaners
- self.max_wav_value = hparams.max_wav_value
- self.sampling_rate = hparams.sampling_rate
- self.filter_length = hparams.filter_length
- self.hop_length = hparams.hop_length
- self.win_length = hparams.win_length
- self.sampling_rate = hparams.sampling_rate
-
- self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
- self.add_blank = hparams.add_blank
- self.min_text_len = getattr(hparams, "min_text_len", 1)
- self.max_text_len = getattr(hparams, "max_text_len", 190)
-
- random.seed(1234)
- random.shuffle(self.audiopaths_and_text)
- self._filter()
-
-
- def _filter(self):
- """
- Filter text & store spec lengths
- """
- # Store spectrogram lengths for Bucketing
- # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
- # spec_length = wav_length // hop_length
-
- audiopaths_and_text_new = []
- lengths = []
- for audiopath, text in self.audiopaths_and_text:
- if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
- audiopaths_and_text_new.append([audiopath, text])
- lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
- self.audiopaths_and_text = audiopaths_and_text_new
- self.lengths = lengths
-
- def get_audio_text_pair(self, audiopath_and_text):
- # separate filename and text
- audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
- text = self.get_text(text)
- spec, wav = self.get_audio(audiopath)
- return (text, spec, wav)
-
- def get_audio(self, filename):
- audio, sampling_rate = load_wav_to_torch(filename)
- if sampling_rate != self.sampling_rate:
- raise ValueError("{} {} SR doesn't match target {} SR".format(
- sampling_rate, self.sampling_rate))
- audio_norm = audio / self.max_wav_value
- audio_norm = audio_norm.unsqueeze(0)
- spec_filename = filename.replace(".wav", ".spec.pt")
- if os.path.exists(spec_filename):
- spec = torch.load(spec_filename)
- else:
- spec = spectrogram_torch(audio_norm, self.filter_length,
- self.sampling_rate, self.hop_length, self.win_length,
- center=False)
- spec = torch.squeeze(spec, 0)
- torch.save(spec, spec_filename)
- return spec, audio_norm
-
- def get_text(self, text):
- if self.cleaned_text:
- text_norm = cleaned_text_to_sequence(text)
- else:
- text_norm = text_to_sequence(text, self.text_cleaners)
- if self.add_blank:
- text_norm = commons.intersperse(text_norm, 0)
- text_norm = torch.LongTensor(text_norm)
- return text_norm
-
- def __getitem__(self, index):
- return self.get_audio_text_pair(self.audiopaths_and_text[index])
-
- def __len__(self):
- return len(self.audiopaths_and_text)
-
-
-class TextAudioCollate():
- """ Zero-pads model inputs and targets
- """
- def __init__(self, return_ids=False):
- self.return_ids = return_ids
-
- def __call__(self, batch):
- """Collate's training batch from normalized text and aduio
- PARAMS
- ------
- batch: [text_normalized, spec_normalized, wav_normalized]
- """
- # Right zero-pad all one-hot text sequences to max input length
- _, ids_sorted_decreasing = torch.sort(
- torch.LongTensor([x[1].size(1) for x in batch]),
- dim=0, descending=True)
-
- max_text_len = max([len(x[0]) for x in batch])
- max_spec_len = max([x[1].size(1) for x in batch])
- max_wav_len = max([x[2].size(1) for x in batch])
-
- text_lengths = torch.LongTensor(len(batch))
- spec_lengths = torch.LongTensor(len(batch))
- wav_lengths = torch.LongTensor(len(batch))
-
- text_padded = torch.LongTensor(len(batch), max_text_len)
- spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
- wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
- text_padded.zero_()
- spec_padded.zero_()
- wav_padded.zero_()
- for i in range(len(ids_sorted_decreasing)):
- row = batch[ids_sorted_decreasing[i]]
-
- text = row[0]
- text_padded[i, :text.size(0)] = text
- text_lengths[i] = text.size(0)
-
- spec = row[1]
- spec_padded[i, :, :spec.size(1)] = spec
- spec_lengths[i] = spec.size(1)
-
- wav = row[2]
- wav_padded[i, :, :wav.size(1)] = wav
- wav_lengths[i] = wav.size(1)
-
- if self.return_ids:
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, ids_sorted_decreasing
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths
-
-
-"""Multi speaker version"""
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
- """
- 1) loads audio, speaker_id, text pairs
- 2) normalizes text and converts them to sequences of integers
- 3) computes spectrograms from audio files.
- """
- def __init__(self, audiopaths_sid_text, hparams):
- self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
- self.text_cleaners = hparams.text_cleaners
- self.max_wav_value = hparams.max_wav_value
- self.sampling_rate = hparams.sampling_rate
- self.filter_length = hparams.filter_length
- self.hop_length = hparams.hop_length
- self.win_length = hparams.win_length
- self.sampling_rate = hparams.sampling_rate
-
- self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
- self.add_blank = hparams.add_blank
- self.min_text_len = getattr(hparams, "min_text_len", 1)
- self.max_text_len = getattr(hparams, "max_text_len", 190)
-
- random.seed(1234)
- random.shuffle(self.audiopaths_sid_text)
- self._filter()
-
- def _filter(self):
- """
- Filter text & store spec lengths
- """
- # Store spectrogram lengths for Bucketing
- # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
- # spec_length = wav_length // hop_length
-
- audiopaths_sid_text_new = []
- lengths = []
- for audiopath, sid, text in self.audiopaths_sid_text:
- audiopath = "E:/uma_voice/" + audiopath
- if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
- audiopaths_sid_text_new.append([audiopath, sid, text])
- lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
- self.audiopaths_sid_text = audiopaths_sid_text_new
- self.lengths = lengths
-
- def get_audio_text_speaker_pair(self, audiopath_sid_text):
- # separate filename, speaker_id and text
- audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
- text = self.get_text(text)
- spec, wav = self.get_audio(audiopath)
- sid = self.get_sid(sid)
- return (text, spec, wav, sid)
-
- def get_audio(self, filename):
- audio, sampling_rate = load_wav_to_torch(filename)
- if sampling_rate != self.sampling_rate:
- raise ValueError("{} {} SR doesn't match target {} SR".format(
- sampling_rate, self.sampling_rate))
- audio_norm = audio / self.max_wav_value
- audio_norm = audio_norm.unsqueeze(0)
- spec_filename = filename.replace(".wav", ".spec.pt")
- if os.path.exists(spec_filename):
- spec = torch.load(spec_filename)
- else:
- spec = spectrogram_torch(audio_norm, self.filter_length,
- self.sampling_rate, self.hop_length, self.win_length,
- center=False)
- spec = torch.squeeze(spec, 0)
- torch.save(spec, spec_filename)
- return spec, audio_norm
-
- def get_text(self, text):
- if self.cleaned_text:
- text_norm = cleaned_text_to_sequence(text)
- else:
- text_norm = text_to_sequence(text, self.text_cleaners)
- if self.add_blank:
- text_norm = commons.intersperse(text_norm, 0)
- text_norm = torch.LongTensor(text_norm)
- return text_norm
-
- def get_sid(self, sid):
- sid = torch.LongTensor([int(sid)])
- return sid
-
- def __getitem__(self, index):
- return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
- def __len__(self):
- return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate():
- """ Zero-pads model inputs and targets
- """
- def __init__(self, return_ids=False):
- self.return_ids = return_ids
-
- def __call__(self, batch):
- """Collate's training batch from normalized text, audio and speaker identities
- PARAMS
- ------
- batch: [text_normalized, spec_normalized, wav_normalized, sid]
- """
- # Right zero-pad all one-hot text sequences to max input length
- _, ids_sorted_decreasing = torch.sort(
- torch.LongTensor([x[1].size(1) for x in batch]),
- dim=0, descending=True)
-
- max_text_len = max([len(x[0]) for x in batch])
- max_spec_len = max([x[1].size(1) for x in batch])
- max_wav_len = max([x[2].size(1) for x in batch])
-
- text_lengths = torch.LongTensor(len(batch))
- spec_lengths = torch.LongTensor(len(batch))
- wav_lengths = torch.LongTensor(len(batch))
- sid = torch.LongTensor(len(batch))
-
- text_padded = torch.LongTensor(len(batch), max_text_len)
- spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
- wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
- text_padded.zero_()
- spec_padded.zero_()
- wav_padded.zero_()
- for i in range(len(ids_sorted_decreasing)):
- row = batch[ids_sorted_decreasing[i]]
-
- text = row[0]
- text_padded[i, :text.size(0)] = text
- text_lengths[i] = text.size(0)
-
- spec = row[1]
- spec_padded[i, :, :spec.size(1)] = spec
- spec_lengths[i] = spec.size(1)
-
- wav = row[2]
- wav_padded[i, :, :wav.size(1)] = wav
- wav_lengths[i] = wav.size(1)
-
- sid[i] = row[3]
-
- if self.return_ids:
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
- """
- Maintain similar input lengths in a batch.
- Length groups are specified by boundaries.
- Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
- It removes samples which are not included in the boundaries.
- Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
- """
- def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
- super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
- self.lengths = dataset.lengths
- self.batch_size = batch_size
- self.boundaries = boundaries
-
- self.buckets, self.num_samples_per_bucket = self._create_buckets()
- self.total_size = sum(self.num_samples_per_bucket)
- self.num_samples = self.total_size // self.num_replicas
-
- def _create_buckets(self):
- buckets = [[] for _ in range(len(self.boundaries) - 1)]
- for i in range(len(self.lengths)):
- length = self.lengths[i]
- idx_bucket = self._bisect(length)
- if idx_bucket != -1:
- buckets[idx_bucket].append(i)
-
- for i in range(len(buckets) - 1, 0, -1):
- if len(buckets[i]) == 0:
- buckets.pop(i)
- self.boundaries.pop(i+1)
-
- num_samples_per_bucket = []
- for i in range(len(buckets)):
- len_bucket = len(buckets[i])
- total_batch_size = self.num_replicas * self.batch_size
- rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
- num_samples_per_bucket.append(len_bucket + rem)
- return buckets, num_samples_per_bucket
-
- def __iter__(self):
- # deterministically shuffle based on epoch
- g = torch.Generator()
- g.manual_seed(self.epoch)
-
- indices = []
- if self.shuffle:
- for bucket in self.buckets:
- indices.append(torch.randperm(len(bucket), generator=g).tolist())
- else:
- for bucket in self.buckets:
- indices.append(list(range(len(bucket))))
-
- batches = []
- for i in range(len(self.buckets)):
- bucket = self.buckets[i]
- len_bucket = len(bucket)
- ids_bucket = indices[i]
- num_samples_bucket = self.num_samples_per_bucket[i]
-
- # add extra samples to make it evenly divisible
- rem = num_samples_bucket - len_bucket
- ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-
- # subsample
- ids_bucket = ids_bucket[self.rank::self.num_replicas]
-
- # batching
- for j in range(len(ids_bucket) // self.batch_size):
- batch = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]]
- batches.append(batch)
-
- if self.shuffle:
- batch_ids = torch.randperm(len(batches), generator=g).tolist()
- batches = [batches[i] for i in batch_ids]
- self.batches = batches
-
- assert len(self.batches) * self.batch_size == self.num_samples
- return iter(self.batches)
-
- def _bisect(self, x, lo=0, hi=None):
- if hi is None:
- hi = len(self.boundaries) - 1
-
- if hi > lo:
- mid = (hi + lo) // 2
- if self.boundaries[mid] < x and x <= self.boundaries[mid+1]:
- return mid
- elif x <= self.boundaries[mid]:
- return self._bisect(x, lo, mid)
- else:
- return self._bisect(x, mid + 1, hi)
- else:
- return -1
-
- def __len__(self):
- return self.num_samples // self.batch_size
diff --git a/spaces/YUANAI/DiffspeechResearch/modules/tts/fs.py b/spaces/YUANAI/DiffspeechResearch/modules/tts/fs.py
deleted file mode 100644
index b15b4348c1abf58a476c12115b5b088dc7b46979..0000000000000000000000000000000000000000
--- a/spaces/YUANAI/DiffspeechResearch/modules/tts/fs.py
+++ /dev/null
@@ -1,172 +0,0 @@
-from copy import deepcopy
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from modules.commons.conv import TextConvEncoder, ConvBlocks
-from modules.commons.layers import Embedding
-from modules.commons.nar_tts_modules import PitchPredictor, DurationPredictor, LengthRegulator
-from modules.commons.rel_transformer import RelTransformerEncoder
-from modules.commons.rnn import TacotronEncoder, RNNEncoder, DecoderRNN
-from modules.commons.transformer import FastSpeechEncoder, FastSpeechDecoder
-from modules.commons.wavenet import WN
-from modules.tts.commons.align_ops import clip_mel2token_to_multiple, expand_states
-from utils.audio.pitch.utils import denorm_f0, f0_to_coarse
-
-FS_ENCODERS = {
- 'fft': lambda hp, dict_size: FastSpeechEncoder(
- dict_size, hp['hidden_size'], hp['enc_layers'], hp['enc_ffn_kernel_size'],
- num_heads=hp['num_heads']),
- 'tacotron': lambda hp, dict_size: TacotronEncoder(
- hp['hidden_size'], dict_size, hp['hidden_size'],
- K=hp['encoder_K'], num_highways=4, dropout=hp['dropout']),
- 'tacotron2': lambda hp, dict_size: RNNEncoder(dict_size, hp['hidden_size']),
- 'conv': lambda hp, dict_size: TextConvEncoder(dict_size, hp['hidden_size'], hp['hidden_size'],
- hp['enc_dilations'], hp['enc_kernel_size'],
- layers_in_block=hp['layers_in_block'],
- norm_type=hp['enc_dec_norm'],
- post_net_kernel=hp.get('enc_post_net_kernel', 3)),
- 'rel_fft': lambda hp, dict_size: RelTransformerEncoder(
- dict_size, hp['hidden_size'], hp['hidden_size'],
- hp['ffn_hidden_size'], hp['num_heads'], hp['enc_layers'],
- hp['enc_ffn_kernel_size'], hp['dropout'], prenet=hp['enc_prenet'], pre_ln=hp['enc_pre_ln']),
-}
-
-FS_DECODERS = {
- 'fft': lambda hp: FastSpeechDecoder(
- hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']),
- 'rnn': lambda hp: DecoderRNN(hp['hidden_size'], hp['decoder_rnn_dim'], hp['dropout']),
- 'conv': lambda hp: ConvBlocks(hp['hidden_size'], hp['hidden_size'], hp['dec_dilations'],
- hp['dec_kernel_size'], layers_in_block=hp['layers_in_block'],
- norm_type=hp['enc_dec_norm'], dropout=hp['dropout'],
- post_net_kernel=hp.get('dec_post_net_kernel', 3)),
- 'wn': lambda hp: WN(hp['hidden_size'], kernel_size=5, dilation_rate=1, n_layers=hp['dec_layers'],
- is_BTC=True),
-}
-
-
-class FastSpeech(nn.Module):
- def __init__(self, dict_size, hparams, out_dims=None):
- super().__init__()
- self.hparams = deepcopy(hparams)
- self.enc_layers = hparams['enc_layers']
- self.dec_layers = hparams['dec_layers']
- self.hidden_size = hparams['hidden_size']
- self.encoder = FS_ENCODERS[hparams['encoder_type']](hparams, dict_size)
- self.decoder = FS_DECODERS[hparams['decoder_type']](hparams)
- self.out_dims = hparams['audio_num_mel_bins'] if out_dims is None else out_dims
- self.mel_out = nn.Linear(self.hidden_size, self.out_dims, bias=True)
- if hparams['use_spk_id']:
- self.spk_id_proj = Embedding(hparams['num_spk'], self.hidden_size)
- if hparams['use_spk_embed']:
- self.spk_embed_proj = nn.Linear(256, self.hidden_size, bias=True)
- predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size
- self.dur_predictor = DurationPredictor(
- self.hidden_size,
- n_chans=predictor_hidden,
- n_layers=hparams['dur_predictor_layers'],
- dropout_rate=hparams['predictor_dropout'],
- kernel_size=hparams['dur_predictor_kernel'])
- self.length_regulator = LengthRegulator()
- if hparams['use_pitch_embed']:
- self.pitch_embed = Embedding(300, self.hidden_size, 0)
- self.pitch_predictor = PitchPredictor(
- self.hidden_size, n_chans=predictor_hidden,
- n_layers=5, dropout_rate=0.1, odim=2,
- kernel_size=hparams['predictor_kernel'])
- if hparams['dec_inp_add_noise']:
- self.z_channels = hparams['z_channels']
- self.dec_inp_noise_proj = nn.Linear(self.hidden_size + self.z_channels, self.hidden_size)
-
- def forward(self, txt_tokens, mel2ph=None, spk_embed=None, spk_id=None,
- f0=None, uv=None, infer=False, **kwargs):
- ret = {}
- encoder_out = self.encoder(txt_tokens) # [B, T, C]
- src_nonpadding = (txt_tokens > 0).float()[:, :, None]
- style_embed = self.forward_style_embed(spk_embed, spk_id)
-
- # add dur
- dur_inp = (encoder_out + style_embed) * src_nonpadding
- mel2ph = self.forward_dur(dur_inp, mel2ph, txt_tokens, ret)
- tgt_nonpadding = (mel2ph > 0).float()[:, :, None]
- decoder_inp = expand_states(encoder_out, mel2ph)
-
- # add pitch embed
- if self.hparams['use_pitch_embed']:
- pitch_inp = (decoder_inp + style_embed) * tgt_nonpadding
- decoder_inp = decoder_inp + self.forward_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out)
-
- # decoder input
- ret['decoder_inp'] = decoder_inp = (decoder_inp + style_embed) * tgt_nonpadding
- if self.hparams['dec_inp_add_noise']:
- B, T, _ = decoder_inp.shape
- z = kwargs.get('adv_z', torch.randn([B, T, self.z_channels])).to(decoder_inp.device)
- ret['adv_z'] = z
- decoder_inp = torch.cat([decoder_inp, z], -1)
- decoder_inp = self.dec_inp_noise_proj(decoder_inp) * tgt_nonpadding
- ret['mel_out'] = self.forward_decoder(decoder_inp, tgt_nonpadding, ret, infer=infer, **kwargs)
- return ret
-
- def forward_style_embed(self, spk_embed=None, spk_id=None):
- # add spk embed
- style_embed = 0
- if self.hparams['use_spk_embed']:
- style_embed = style_embed + self.spk_embed_proj(spk_embed)[:, None, :]
- if self.hparams['use_spk_id']:
- style_embed = style_embed + self.spk_id_proj(spk_id)[:, None, :]
- return style_embed
-
- def forward_dur(self, dur_input, mel2ph, txt_tokens, ret):
- """
-
- :param dur_input: [B, T_txt, H]
- :param mel2ph: [B, T_mel]
- :param txt_tokens: [B, T_txt]
- :param ret:
- :return:
- """
- src_padding = txt_tokens == 0
- if self.hparams['predictor_grad'] != 1:
- dur_input = dur_input.detach() + self.hparams['predictor_grad'] * (dur_input - dur_input.detach())
- dur = self.dur_predictor(dur_input, src_padding)
- ret['dur'] = dur
- if mel2ph is None:
- mel2ph = self.length_regulator(dur, src_padding).detach()
- ret['mel2ph'] = mel2ph = clip_mel2token_to_multiple(mel2ph, self.hparams['frames_multiple'])
- return mel2ph
-
- def forward_pitch(self, decoder_inp, f0, uv, mel2ph, ret, encoder_out=None):
- if self.hparams['pitch_type'] == 'frame':
- pitch_pred_inp = decoder_inp
- pitch_padding = mel2ph == 0
- else:
- pitch_pred_inp = encoder_out
- pitch_padding = encoder_out.abs().sum(-1) == 0
- uv = None
- if self.hparams['predictor_grad'] != 1:
- pitch_pred_inp = pitch_pred_inp.detach() + \
- self.hparams['predictor_grad'] * (pitch_pred_inp - pitch_pred_inp.detach())
- ret['pitch_pred'] = pitch_pred = self.pitch_predictor(pitch_pred_inp)
- use_uv = self.hparams['pitch_type'] == 'frame' and self.hparams['use_uv']
- if f0 is None:
- f0 = pitch_pred[:, :, 0]
- if use_uv:
- uv = pitch_pred[:, :, 1] > 0
- f0_denorm = denorm_f0(f0, uv if use_uv else None, pitch_padding=pitch_padding)
- pitch = f0_to_coarse(f0_denorm) # start from 0 [B, T_txt]
- ret['f0_denorm'] = f0_denorm
- ret['f0_denorm_pred'] = denorm_f0(
- pitch_pred[:, :, 0], (pitch_pred[:, :, 1] > 0) if use_uv else None,
- pitch_padding=pitch_padding)
- if self.hparams['pitch_type'] == 'ph':
- pitch = torch.gather(F.pad(pitch, [1, 0]), 1, mel2ph)
- ret['f0_denorm'] = torch.gather(F.pad(ret['f0_denorm'], [1, 0]), 1, mel2ph)
- ret['f0_denorm_pred'] = torch.gather(F.pad(ret['f0_denorm_pred'], [1, 0]), 1, mel2ph)
- pitch_embed = self.pitch_embed(pitch)
- return pitch_embed
-
- def forward_decoder(self, decoder_inp, tgt_nonpadding, ret, infer, **kwargs):
- x = decoder_inp # [B, T, H]
- x = self.decoder(x)
- x = self.mel_out(x)
- return x * tgt_nonpadding
diff --git a/spaces/Yuelili/RealNagrse/inference_realesrgan_video.py b/spaces/Yuelili/RealNagrse/inference_realesrgan_video.py
deleted file mode 100644
index 639b848e6578a2480ee0784e664c7751e325c477..0000000000000000000000000000000000000000
--- a/spaces/Yuelili/RealNagrse/inference_realesrgan_video.py
+++ /dev/null
@@ -1,199 +0,0 @@
-import argparse
-import glob
-import mimetypes
-import os
-import queue
-import shutil
-import torch
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.utils.logger import AvgTimer
-from tqdm import tqdm
-
-from realesrgan import IOConsumer, PrefetchReader, RealESRGANer
-from realesrgan.archs.srvgg_arch import SRVGGNetCompact
-
-
-def main():
- """Inference demo for Real-ESRGAN.
- It mainly for restoring anime videos.
-
- """
- parser = argparse.ArgumentParser()
- parser.add_argument('-i', '--input', type=str, default='inputs', help='Input image or folder')
- parser.add_argument(
- '-n',
- '--model_name',
- type=str,
- default='RealESRGAN_x4plus',
- help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus'
- 'RealESRGANv2-anime-xsx2 | RealESRGANv2-animevideo-xsx2-nousm | RealESRGANv2-animevideo-xsx2'
- 'RealESRGANv2-anime-xsx4 | RealESRGANv2-animevideo-xsx4-nousm | RealESRGANv2-animevideo-xsx4'))
- parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
- parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
- parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored video')
- parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
- parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
- parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
- parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
- parser.add_argument('--half', action='store_true', help='Use half precision during inference')
- parser.add_argument('-v', '--video', action='store_true', help='Output a video using ffmpeg')
- parser.add_argument('-a', '--audio', action='store_true', help='Keep audio')
- parser.add_argument('--fps', type=float, default=None, help='FPS of the output video')
- parser.add_argument('--consumer', type=int, default=4, help='Number of IO consumers')
-
- parser.add_argument(
- '--alpha_upsampler',
- type=str,
- default='realesrgan',
- help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
- parser.add_argument(
- '--ext',
- type=str,
- default='auto',
- help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
- args = parser.parse_args()
-
- # ---------------------- determine models according to model names ---------------------- #
- args.model_name = args.model_name.split('.')[0]
- if args.model_name in ['RealESRGAN_x4plus', 'RealESRNet_x4plus']: # x4 RRDBNet model
- model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
- netscale = 4
- elif args.model_name in ['RealESRGAN_x4plus_anime_6B']: # x4 RRDBNet model with 6 blocks
- model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
- netscale = 4
- elif args.model_name in ['RealESRGAN_x2plus']: # x2 RRDBNet model
- model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
- netscale = 2
- elif args.model_name in [
- 'RealESRGANv2-anime-xsx2', 'RealESRGANv2-animevideo-xsx2-nousm', 'RealESRGANv2-animevideo-xsx2'
- ]: # x2 VGG-style model (XS size)
- model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=2, act_type='prelu')
- netscale = 2
- elif args.model_name in [
- 'RealESRGANv2-anime-xsx4', 'RealESRGANv2-animevideo-xsx4-nousm', 'RealESRGANv2-animevideo-xsx4'
- ]: # x4 VGG-style model (XS size)
- model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
- netscale = 4
-
- # ---------------------- determine model paths ---------------------- #
- model_path = os.path.join('experiments/pretrained_models', args.model_name + '.pth')
- if not os.path.isfile(model_path):
- model_path = os.path.join('realesrgan/weights', args.model_name + '.pth')
- if not os.path.isfile(model_path):
- raise ValueError(f'Model {args.model_name} does not exist.')
-
- # restorer
- upsampler = RealESRGANer(
- scale=netscale,
- model_path=model_path,
- model=model,
- tile=args.tile,
- tile_pad=args.tile_pad,
- pre_pad=args.pre_pad,
- half=args.half)
-
- if args.face_enhance: # Use GFPGAN for face enhancement
- from gfpgan import GFPGANer
- face_enhancer = GFPGANer(
- model_path='https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth',
- upscale=args.outscale,
- arch='clean',
- channel_multiplier=2,
- bg_upsampler=upsampler)
- os.makedirs(args.output, exist_ok=True)
- # for saving restored frames
- save_frame_folder = os.path.join(args.output, 'frames_tmpout')
- os.makedirs(save_frame_folder, exist_ok=True)
-
- if mimetypes.guess_type(args.input)[0].startswith('video'): # is a video file
- video_name = os.path.splitext(os.path.basename(args.input))[0]
- frame_folder = os.path.join('tmp_frames', video_name)
- os.makedirs(frame_folder, exist_ok=True)
- # use ffmpeg to extract frames
- os.system(f'ffmpeg -i {args.input} -qscale:v 1 -qmin 1 -qmax 1 -vsync 0 {frame_folder}/frame%08d.png')
- # get image path list
- paths = sorted(glob.glob(os.path.join(frame_folder, '*')))
- if args.video:
- if args.fps is None:
- # get input video fps
- import ffmpeg
- probe = ffmpeg.probe(args.input)
- video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video']
- args.fps = eval(video_streams[0]['avg_frame_rate'])
- elif mimetypes.guess_type(args.input)[0].startswith('image'): # is an image file
- paths = [args.input]
- video_name = 'video'
- else:
- paths = sorted(glob.glob(os.path.join(args.input, '*')))
- video_name = 'video'
-
- timer = AvgTimer()
- timer.start()
- pbar = tqdm(total=len(paths), unit='frame', desc='inference')
- # set up prefetch reader
- reader = PrefetchReader(paths, num_prefetch_queue=4)
- reader.start()
-
- que = queue.Queue()
- consumers = [IOConsumer(args, que, f'IO_{i}') for i in range(args.consumer)]
- for consumer in consumers:
- consumer.start()
-
- for idx, (path, img) in enumerate(zip(paths, reader)):
- imgname, extension = os.path.splitext(os.path.basename(path))
- if len(img.shape) == 3 and img.shape[2] == 4:
- img_mode = 'RGBA'
- else:
- img_mode = None
-
- try:
- if args.face_enhance:
- _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
- else:
- output, _ = upsampler.enhance(img, outscale=args.outscale)
- except RuntimeError as error:
- print('Error', error)
- print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
-
- else:
- if args.ext == 'auto':
- extension = extension[1:]
- else:
- extension = args.ext
- if img_mode == 'RGBA': # RGBA images should be saved in png format
- extension = 'png'
- save_path = os.path.join(save_frame_folder, f'{imgname}_out.{extension}')
-
- que.put({'output': output, 'save_path': save_path})
-
- pbar.update(1)
- torch.cuda.synchronize()
- timer.record()
- avg_fps = 1. / (timer.get_avg_time() + 1e-7)
- pbar.set_description(f'idx {idx}, fps {avg_fps:.2f}')
-
- for _ in range(args.consumer):
- que.put('quit')
- for consumer in consumers:
- consumer.join()
- pbar.close()
-
- # merge frames to video
- if args.video:
- video_save_path = os.path.join(args.output, f'{video_name}_{args.suffix}.mp4')
- if args.audio:
- os.system(
- f'ffmpeg -r {args.fps} -i {save_frame_folder}/frame%08d_out.{extension} -i {args.input}'
- f' -map 0:v:0 -map 1:a:0 -c:a copy -c:v libx264 -r {args.fps} -pix_fmt yuv420p {video_save_path}')
- else:
- os.system(f'ffmpeg -r {args.fps} -i {save_frame_folder}/frame%08d_out.{extension} '
- f'-c:v libx264 -r {args.fps} -pix_fmt yuv420p {video_save_path}')
-
- # delete tmp file
- shutil.rmtree(save_frame_folder)
- if os.path.isdir(frame_folder):
- shutil.rmtree(frame_folder)
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/Yuliang/ECON/lib/net/voxelize.py b/spaces/Yuliang/ECON/lib/net/voxelize.py
deleted file mode 100644
index 8525ef6cf389c40d8e4a82e1a442c24915a7acd8..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/net/voxelize.py
+++ /dev/null
@@ -1,204 +0,0 @@
-from __future__ import division, print_function
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import voxelize_cuda
-from torch.autograd import Function
-
-
-class VoxelizationFunction(Function):
- """
- Definition of differentiable voxelization function
- Currently implemented only for cuda Tensors
- """
- @staticmethod
- def forward(
- ctx,
- smpl_vertices,
- smpl_face_center,
- smpl_face_normal,
- smpl_vertex_code,
- smpl_face_code,
- smpl_tetrahedrons,
- volume_res,
- sigma,
- smooth_kernel_size,
- ):
- """
- forward pass
- Output format: (batch_size, z_dims, y_dims, x_dims, channel_num)
- """
- assert smpl_vertices.size()[1] == smpl_vertex_code.size()[1]
- assert smpl_face_center.size()[1] == smpl_face_normal.size()[1]
- assert smpl_face_center.size()[1] == smpl_face_code.size()[1]
- ctx.batch_size = smpl_vertices.size()[0]
- ctx.volume_res = volume_res
- ctx.sigma = sigma
- ctx.smooth_kernel_size = smooth_kernel_size
- ctx.smpl_vertex_num = smpl_vertices.size()[1]
- ctx.device = smpl_vertices.device
-
- smpl_vertices = smpl_vertices.contiguous()
- smpl_face_center = smpl_face_center.contiguous()
- smpl_face_normal = smpl_face_normal.contiguous()
- smpl_vertex_code = smpl_vertex_code.contiguous()
- smpl_face_code = smpl_face_code.contiguous()
- smpl_tetrahedrons = smpl_tetrahedrons.contiguous()
-
- occ_volume = torch.cuda.FloatTensor(
- ctx.batch_size, ctx.volume_res, ctx.volume_res, ctx.volume_res
- ).fill_(0.0)
- semantic_volume = torch.cuda.FloatTensor(
- ctx.batch_size, ctx.volume_res, ctx.volume_res, ctx.volume_res, 3
- ).fill_(0.0)
- weight_sum_volume = torch.cuda.FloatTensor(
- ctx.batch_size, ctx.volume_res, ctx.volume_res, ctx.volume_res
- ).fill_(1e-3)
-
- # occ_volume [B, volume_res, volume_res, volume_res]
- # semantic_volume [B, volume_res, volume_res, volume_res, 3]
- # weight_sum_volume [B, volume_res, volume_res, volume_res]
-
- (
- occ_volume,
- semantic_volume,
- weight_sum_volume,
- ) = voxelize_cuda.forward_semantic_voxelization(
- smpl_vertices,
- smpl_vertex_code,
- smpl_tetrahedrons,
- occ_volume,
- semantic_volume,
- weight_sum_volume,
- sigma,
- )
-
- return semantic_volume
-
-
-class Voxelization(nn.Module):
- """
- Wrapper around the autograd function VoxelizationFunction
- """
- def __init__(
- self,
- smpl_vertex_code,
- smpl_face_code,
- smpl_face_indices,
- smpl_tetraderon_indices,
- volume_res,
- sigma,
- smooth_kernel_size,
- batch_size,
- ):
- super(Voxelization, self).__init__()
- assert len(smpl_face_indices.shape) == 2
- assert len(smpl_tetraderon_indices.shape) == 2
- assert smpl_face_indices.shape[1] == 3
- assert smpl_tetraderon_indices.shape[1] == 4
-
- self.volume_res = volume_res
- self.sigma = sigma
- self.smooth_kernel_size = smooth_kernel_size
- self.batch_size = batch_size
- self.device = None
-
- self.smpl_vertex_code = smpl_vertex_code
- self.smpl_face_code = smpl_face_code
- self.smpl_face_indices = smpl_face_indices
- self.smpl_tetraderon_indices = smpl_tetraderon_indices
-
- def update_param(self, voxel_faces):
-
- self.device = voxel_faces.device
-
- self.smpl_tetraderon_indices = voxel_faces
-
- smpl_vertex_code_batch = torch.tile(self.smpl_vertex_code, (self.batch_size, 1, 1))
- smpl_face_code_batch = torch.tile(self.smpl_face_code, (self.batch_size, 1, 1))
- smpl_face_indices_batch = torch.tile(self.smpl_face_indices, (self.batch_size, 1, 1))
-
- smpl_vertex_code_batch = (smpl_vertex_code_batch.contiguous().to(self.device))
- smpl_face_code_batch = (smpl_face_code_batch.contiguous().to(self.device))
- smpl_face_indices_batch = (smpl_face_indices_batch.contiguous().to(self.device))
- smpl_tetraderon_indices_batch = (self.smpl_tetraderon_indices.contiguous().to(self.device))
-
- self.register_buffer("smpl_vertex_code_batch", smpl_vertex_code_batch)
- self.register_buffer("smpl_face_code_batch", smpl_face_code_batch)
- self.register_buffer("smpl_face_indices_batch", smpl_face_indices_batch)
- self.register_buffer("smpl_tetraderon_indices_batch", smpl_tetraderon_indices_batch)
-
- def forward(self, smpl_vertices):
- """
- Generate semantic volumes from SMPL vertices
- """
- self.check_input(smpl_vertices)
- smpl_faces = self.vertices_to_faces(smpl_vertices)
- smpl_tetrahedrons = self.vertices_to_tetrahedrons(smpl_vertices)
- smpl_face_center = self.calc_face_centers(smpl_faces)
- smpl_face_normal = self.calc_face_normals(smpl_faces)
- smpl_surface_vertex_num = self.smpl_vertex_code_batch.size()[1]
- smpl_vertices_surface = smpl_vertices[:, :smpl_surface_vertex_num, :]
- vol = VoxelizationFunction.apply(
- smpl_vertices_surface,
- smpl_face_center,
- smpl_face_normal,
- self.smpl_vertex_code_batch,
- self.smpl_face_code_batch,
- smpl_tetrahedrons,
- self.volume_res,
- self.sigma,
- self.smooth_kernel_size,
- )
- return vol.permute((0, 4, 1, 2, 3)) # (bzyxc --> bcdhw)
-
- def vertices_to_faces(self, vertices):
- assert vertices.ndimension() == 3
- bs, nv = vertices.shape[:2]
- face = (
- self.smpl_face_indices_batch +
- (torch.arange(bs, dtype=torch.int32).to(self.device) * nv)[:, None, None]
- )
- vertices_ = vertices.reshape((bs * nv, 3))
- return vertices_[face.long()]
-
- def vertices_to_tetrahedrons(self, vertices):
- assert vertices.ndimension() == 3
- bs, nv = vertices.shape[:2]
- tets = (
- self.smpl_tetraderon_indices_batch +
- (torch.arange(bs, dtype=torch.int32).to(self.device) * nv)[:, None, None]
- )
- vertices_ = vertices.reshape((bs * nv, 3))
- return vertices_[tets.long()]
-
- def calc_face_centers(self, face_verts):
- assert len(face_verts.shape) == 4
- assert face_verts.shape[2] == 3
- assert face_verts.shape[3] == 3
- bs, nf = face_verts.shape[:2]
- face_centers = (
- face_verts[:, :, 0, :] + face_verts[:, :, 1, :] + face_verts[:, :, 2, :]
- ) / 3.0
- face_centers = face_centers.reshape((bs, nf, 3))
- return face_centers
-
- def calc_face_normals(self, face_verts):
- assert len(face_verts.shape) == 4
- assert face_verts.shape[2] == 3
- assert face_verts.shape[3] == 3
- bs, nf = face_verts.shape[:2]
- face_verts = face_verts.reshape((bs * nf, 3, 3))
- v10 = face_verts[:, 0] - face_verts[:, 1]
- v12 = face_verts[:, 2] - face_verts[:, 1]
- normals = F.normalize(torch.cross(v10, v12), eps=1e-5)
- normals = normals.reshape((bs, nf, 3))
- return normals
-
- def check_input(self, x):
- if x.device == "cpu":
- raise TypeError("Voxelization module supports only cuda tensors")
- if x.type() != "torch.cuda.FloatTensor":
- raise TypeError("Voxelization module supports only float32 tensors")
diff --git a/spaces/Yuliang/ECON/lib/torch_utils/ops/conv2d_gradfix.py b/spaces/Yuliang/ECON/lib/torch_utils/ops/conv2d_gradfix.py
deleted file mode 100644
index 16bcdfdb229acf55b30a33c47ed419bd008bae7d..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/torch_utils/ops/conv2d_gradfix.py
+++ /dev/null
@@ -1,241 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-"""Custom replacement for `torch.nn.functional.conv2d` that supports
-arbitrarily high order gradients with zero performance penalty."""
-
-import contextlib
-import warnings
-
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-#----------------------------------------------------------------------------
-
-enabled = False # Enable the custom op by setting this to true.
-weight_gradients_disabled = False # Forcefully disable computation of gradients with respect to the weights.
-
-
-@contextlib.contextmanager
-def no_weight_gradients():
- global weight_gradients_disabled
- old = weight_gradients_disabled
- weight_gradients_disabled = True
- yield
- weight_gradients_disabled = old
-
-
-#----------------------------------------------------------------------------
-
-
-def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
- if _should_use_custom_op(input):
- return _conv2d_gradfix(
- transpose=False,
- weight_shape=weight.shape,
- stride=stride,
- padding=padding,
- output_padding=0,
- dilation=dilation,
- groups=groups
- ).apply(input, weight, bias)
- return torch.nn.functional.conv2d(
- input=input,
- weight=weight,
- bias=bias,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=groups
- )
-
-
-def conv_transpose2d(
- input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1
-):
- if _should_use_custom_op(input):
- return _conv2d_gradfix(
- transpose=True,
- weight_shape=weight.shape,
- stride=stride,
- padding=padding,
- output_padding=output_padding,
- groups=groups,
- dilation=dilation
- ).apply(input, weight, bias)
- return torch.nn.functional.conv_transpose2d(
- input=input,
- weight=weight,
- bias=bias,
- stride=stride,
- padding=padding,
- output_padding=output_padding,
- groups=groups,
- dilation=dilation
- )
-
-
-#----------------------------------------------------------------------------
-
-
-def _should_use_custom_op(input):
- assert isinstance(input, torch.Tensor)
- if (not enabled) or (not torch.backends.cudnn.enabled):
- return False
- if input.device.type != 'cuda':
- return False
- if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
- return True
- warnings.warn(
- f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().'
- )
- return False
-
-
-def _tuple_of_ints(xs, ndim):
- xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs, ) * ndim
- assert len(xs) == ndim
- assert all(isinstance(x, int) for x in xs)
- return xs
-
-
-#----------------------------------------------------------------------------
-
-_conv2d_gradfix_cache = dict()
-
-
-def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
- # Parse arguments.
- ndim = 2
- weight_shape = tuple(weight_shape)
- stride = _tuple_of_ints(stride, ndim)
- padding = _tuple_of_ints(padding, ndim)
- output_padding = _tuple_of_ints(output_padding, ndim)
- dilation = _tuple_of_ints(dilation, ndim)
-
- # Lookup from cache.
- key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
- if key in _conv2d_gradfix_cache:
- return _conv2d_gradfix_cache[key]
-
- # Validate arguments.
- assert groups >= 1
- assert len(weight_shape) == ndim + 2
- assert all(stride[i] >= 1 for i in range(ndim))
- assert all(padding[i] >= 0 for i in range(ndim))
- assert all(dilation[i] >= 0 for i in range(ndim))
- if not transpose:
- assert all(output_padding[i] == 0 for i in range(ndim))
- else: # transpose
- assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim))
-
- # Helpers.
- common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups)
-
- def calc_output_padding(input_shape, output_shape):
- if transpose:
- return [0, 0]
- return [
- input_shape[i + 2] - (output_shape[i + 2] - 1) * stride[i] - (1 - 2 * padding[i]) -
- dilation[i] * (weight_shape[i + 2] - 1) for i in range(ndim)
- ]
-
- # Forward & backward.
- class Conv2d(torch.autograd.Function):
- @staticmethod
- def forward(ctx, input, weight, bias):
- assert weight.shape == weight_shape
- if not transpose:
- output = torch.nn.functional.conv2d(
- input=input, weight=weight, bias=bias, **common_kwargs
- )
- else: # transpose
- output = torch.nn.functional.conv_transpose2d(
- input=input,
- weight=weight,
- bias=bias,
- output_padding=output_padding,
- **common_kwargs
- )
- ctx.save_for_backward(input, weight)
- return output
-
- @staticmethod
- def backward(ctx, grad_output):
- input, weight = ctx.saved_tensors
- grad_input = None
- grad_weight = None
- grad_bias = None
-
- if ctx.needs_input_grad[0]:
- p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
- grad_input = _conv2d_gradfix(
- transpose=(not transpose),
- weight_shape=weight_shape,
- output_padding=p,
- **common_kwargs
- ).apply(grad_output, weight, None)
- assert grad_input.shape == input.shape
-
- if ctx.needs_input_grad[1] and not weight_gradients_disabled:
- grad_weight = Conv2dGradWeight.apply(grad_output, input)
- assert grad_weight.shape == weight_shape
-
- if ctx.needs_input_grad[2]:
- grad_bias = grad_output.sum([0, 2, 3])
-
- return grad_input, grad_weight, grad_bias
-
- # Gradient with respect to the weights.
- class Conv2dGradWeight(torch.autograd.Function):
- @staticmethod
- def forward(ctx, grad_output, input):
- op = torch._C._jit_get_operation(
- 'aten::cudnn_convolution_backward_weight'
- if not transpose else 'aten::cudnn_convolution_transpose_backward_weight'
- )
- flags = [
- torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic,
- torch.backends.cudnn.allow_tf32
- ]
- grad_weight = op(
- weight_shape, grad_output, input, padding, stride, dilation, groups, *flags
- )
- assert grad_weight.shape == weight_shape
- ctx.save_for_backward(grad_output, input)
- return grad_weight
-
- @staticmethod
- def backward(ctx, grad2_grad_weight):
- grad_output, input = ctx.saved_tensors
- grad2_grad_output = None
- grad2_input = None
-
- if ctx.needs_input_grad[0]:
- grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None)
- assert grad2_grad_output.shape == grad_output.shape
-
- if ctx.needs_input_grad[1]:
- p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
- grad2_input = _conv2d_gradfix(
- transpose=(not transpose),
- weight_shape=weight_shape,
- output_padding=p,
- **common_kwargs
- ).apply(grad_output, grad2_grad_weight, None)
- assert grad2_input.shape == input.shape
-
- return grad2_grad_output, grad2_input
-
- _conv2d_gradfix_cache[key] = Conv2d
- return Conv2d
-
-
-#----------------------------------------------------------------------------
diff --git a/spaces/a5656789/ganqx/upcunet_v3.py b/spaces/a5656789/ganqx/upcunet_v3.py
deleted file mode 100644
index f7919a6cc9efe3b8af73a73e30825a4c7d7d76da..0000000000000000000000000000000000000000
--- a/spaces/a5656789/ganqx/upcunet_v3.py
+++ /dev/null
@@ -1,714 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-import os, sys
-import numpy as np
-
-root_path = os.path.abspath('.')
-sys.path.append(root_path)
-
-
-class SEBlock(nn.Module):
- def __init__(self, in_channels, reduction=8, bias=False):
- super(SEBlock, self).__init__()
- self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, 1, 1, 0, bias=bias)
- self.conv2 = nn.Conv2d(in_channels // reduction, in_channels, 1, 1, 0, bias=bias)
-
- def forward(self, x):
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- x0 = torch.mean(x.float(), dim=(2, 3), keepdim=True).half()
- else:
- x0 = torch.mean(x, dim=(2, 3), keepdim=True)
- x0 = self.conv1(x0)
- x0 = F.relu(x0, inplace=True)
- x0 = self.conv2(x0)
- x0 = torch.sigmoid(x0)
- x = torch.mul(x, x0)
- return x
-
- def forward_mean(self, x, x0):
- x0 = self.conv1(x0)
- x0 = F.relu(x0, inplace=True)
- x0 = self.conv2(x0)
- x0 = torch.sigmoid(x0)
- x = torch.mul(x, x0)
- return x
-
-
-class UNetConv(nn.Module):
- def __init__(self, in_channels, mid_channels, out_channels, se):
- super(UNetConv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(in_channels, mid_channels, 3, 1, 0),
- nn.LeakyReLU(0.1, inplace=True),
- nn.Conv2d(mid_channels, out_channels, 3, 1, 0),
- nn.LeakyReLU(0.1, inplace=True),
- )
- if se:
- self.seblock = SEBlock(out_channels, reduction=8, bias=True)
- else:
- self.seblock = None
-
- def forward(self, x):
- z = self.conv(x)
- if self.seblock is not None:
- z = self.seblock(z)
- return z
-
-
-class UNet1(nn.Module):
- def __init__(self, in_channels, out_channels, deconv):
- super(UNet1, self).__init__()
- self.conv1 = UNetConv(in_channels, 32, 64, se=False)
- self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
- self.conv2 = UNetConv(64, 128, 64, se=True)
- self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
- self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
- if deconv:
- self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
- else:
- self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
- for m in self.modules():
- if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
- nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
- elif isinstance(m, nn.Linear):
- nn.init.normal_(m.weight, 0, 0.01)
- if m.bias is not None:
- nn.init.constant_(m.bias, 0)
-
- def forward(self, x):
- x1 = self.conv1(x)
- x2 = self.conv1_down(x1)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
- x2 = self.conv2(x2)
- x2 = self.conv2_up(x2)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
- x1 = F.pad(x1, (-4, -4, -4, -4))
- x3 = self.conv3(x1 + x2)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
- z = self.conv_bottom(x3)
- return z
-
- def forward_a(self, x):
- x1 = self.conv1(x)
- x2 = self.conv1_down(x1)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
- x2 = self.conv2.conv(x2)
- return x1, x2
-
- def forward_b(self, x1, x2):
- x2 = self.conv2_up(x2)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
- x1 = F.pad(x1, (-4, -4, -4, -4))
- x3 = self.conv3(x1 + x2)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
- z = self.conv_bottom(x3)
- return z
-
-
-class UNet1x3(nn.Module):
- def __init__(self, in_channels, out_channels, deconv):
- super(UNet1x3, self).__init__()
- self.conv1 = UNetConv(in_channels, 32, 64, se=False)
- self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
- self.conv2 = UNetConv(64, 128, 64, se=True)
- self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
- self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
- if deconv:
- self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 5, 3, 2)
- else:
- self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
- for m in self.modules():
- if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
- nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
- elif isinstance(m, nn.Linear):
- nn.init.normal_(m.weight, 0, 0.01)
- if m.bias is not None:
- nn.init.constant_(m.bias, 0)
-
- def forward(self, x):
- x1 = self.conv1(x)
- x2 = self.conv1_down(x1)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
- x2 = self.conv2(x2)
- x2 = self.conv2_up(x2)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
- x1 = F.pad(x1, (-4, -4, -4, -4))
- x3 = self.conv3(x1 + x2)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
- z = self.conv_bottom(x3)
- return z
-
- def forward_a(self, x):
- x1 = self.conv1(x)
- x2 = self.conv1_down(x1)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
- x2 = self.conv2.conv(x2)
- return x1, x2
-
- def forward_b(self, x1, x2):
- x2 = self.conv2_up(x2)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
- x1 = F.pad(x1, (-4, -4, -4, -4))
- x3 = self.conv3(x1 + x2)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
- z = self.conv_bottom(x3)
- return z
-
-
-class UNet2(nn.Module):
- def __init__(self, in_channels, out_channels, deconv):
- super(UNet2, self).__init__()
-
- self.conv1 = UNetConv(in_channels, 32, 64, se=False)
- self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
- self.conv2 = UNetConv(64, 64, 128, se=True)
- self.conv2_down = nn.Conv2d(128, 128, 2, 2, 0)
- self.conv3 = UNetConv(128, 256, 128, se=True)
- self.conv3_up = nn.ConvTranspose2d(128, 128, 2, 2, 0)
- self.conv4 = UNetConv(128, 64, 64, se=True)
- self.conv4_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
- self.conv5 = nn.Conv2d(64, 64, 3, 1, 0)
-
- if deconv:
- self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
- else:
- self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
- for m in self.modules():
- if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
- nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
- elif isinstance(m, nn.Linear):
- nn.init.normal_(m.weight, 0, 0.01)
- if m.bias is not None:
- nn.init.constant_(m.bias, 0)
-
- def forward(self, x):
- x1 = self.conv1(x)
- x2 = self.conv1_down(x1)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
- x2 = self.conv2(x2)
-
- x3 = self.conv2_down(x2)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
- x3 = self.conv3(x3)
- x3 = self.conv3_up(x3)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
- x2 = F.pad(x2, (-4, -4, -4, -4))
- x4 = self.conv4(x2 + x3)
- x4 = self.conv4_up(x4)
- x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
- x1 = F.pad(x1, (-16, -16, -16, -16))
- x5 = self.conv5(x1 + x4)
- x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
- z = self.conv_bottom(x5)
- return z
-
- def forward_a(self, x): # conv234结尾有se
- x1 = self.conv1(x)
- x2 = self.conv1_down(x1)
- x2 = F.leaky_relu(x2, 0.1, inplace=True)
- x2 = self.conv2.conv(x2)
- return x1, x2
-
- def forward_b(self, x2): # conv234结尾有se
- x3 = self.conv2_down(x2)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
- x3 = self.conv3.conv(x3)
- return x3
-
- def forward_c(self, x2, x3): # conv234结尾有se
- x3 = self.conv3_up(x3)
- x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
- x2 = F.pad(x2, (-4, -4, -4, -4))
- x4 = self.conv4.conv(x2 + x3)
- return x4
-
- def forward_d(self, x1, x4): # conv234结尾有se
- x4 = self.conv4_up(x4)
- x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
- x1 = F.pad(x1, (-16, -16, -16, -16))
- x5 = self.conv5(x1 + x4)
- x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
- z = self.conv_bottom(x5)
- return z
-
-
-class UpCunet2x(nn.Module): # 完美tile，全程无损
- def __init__(self, in_channels=3, out_channels=3):
- super(UpCunet2x, self).__init__()
- self.unet1 = UNet1(in_channels, out_channels, deconv=True)
- self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
- def forward(self, x, tile_mode): # 1.7G
- n, c, h0, w0 = x.shape
- if (tile_mode == 0): # 不tile
- ph = ((h0 - 1) // 2 + 1) * 2
- pw = ((w0 - 1) // 2 + 1) * 2
- x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect') # 需要保证被2整除
- x = self.unet1.forward(x)
- x0 = self.unet2.forward(x)
- x1 = F.pad(x, (-20, -20, -20, -20))
- x = torch.add(x0, x1)
- if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 2, :w0 * 2]
- return x
- elif (tile_mode == 1): # 对长边减半
- if (w0 >= h0):
- crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除
- crop_size_h = (h0 - 1) // 2 * 2 + 2 # 能被2整除
- else:
- crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除
- crop_size_w = (w0 - 1) // 2 * 2 + 2 # 能被2整除
- crop_size = (crop_size_h, crop_size_w) # 6.6G
- elif (tile_mode == 2): # hw都减半
- crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2) # 5.6G
- elif (tile_mode == 3): # hw都三分之一
- crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3) # 4.2G
- elif (tile_mode == 4): # hw都四分之一
- crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4) # 3.7G
- ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
- pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
- x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')
- n, c, h, w = x.shape
- se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
- if ("Half" in x.type()):
- se_mean0 = se_mean0.half()
- n_patch = 0
- tmp_dict = {}
- opt_res_dict = {}
- for i in range(0, h - 36, crop_size[0]):
- tmp_dict[i] = {}
- for j in range(0, w - 36, crop_size[1]):
- x_crop = x[:, :, i:i + crop_size[0] + 36, j:j + crop_size[1] + 36]
- n, c1, h1, w1 = x_crop.shape
- tmp0, x_crop = self.unet1.forward_a(x_crop)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
- se_mean0 += tmp_se_mean
- n_patch += 1
- tmp_dict[i][j] = (tmp0, x_crop)
- se_mean0 /= n_patch
- se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean1 = se_mean1.half()
- for i in range(0, h - 36, crop_size[0]):
- for j in range(0, w - 36, crop_size[1]):
- tmp0, x_crop = tmp_dict[i][j]
- x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
- opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
- tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
- se_mean1 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
- se_mean1 /= n_patch
- se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean0 = se_mean0.half()
- for i in range(0, h - 36, crop_size[0]):
- for j in range(0, w - 36, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
- tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
- tmp_x3 = self.unet2.forward_b(tmp_x2)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
- se_mean0 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
- se_mean0 /= n_patch
- se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean1 = se_mean1.half()
- for i in range(0, h - 36, crop_size[0]):
- for j in range(0, w - 36, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
- tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
- tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
- se_mean1 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
- se_mean1 /= n_patch
- for i in range(0, h - 36, crop_size[0]):
- opt_res_dict[i] = {}
- for j in range(0, w - 36, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
- tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
- x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
- x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
- x_crop = torch.add(x0, x1) # x0是unet2的最终输出
- opt_res_dict[i][j] = x_crop
- del tmp_dict
- torch.cuda.empty_cache()
- res = torch.zeros((n, c, h * 2 - 72, w * 2 - 72)).to(x.device)
- if ("Half" in x.type()):
- res = res.half()
- for i in range(0, h - 36, crop_size[0]):
- for j in range(0, w - 36, crop_size[1]):
- res[:, :, i * 2:i * 2 + h1 * 2 - 72, j * 2:j * 2 + w1 * 2 - 72] = opt_res_dict[i][j]
- del opt_res_dict
- torch.cuda.empty_cache()
- if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 2, :w0 * 2]
- return res #
-
-
-class UpCunet3x(nn.Module): # 完美tile，全程无损
- def __init__(self, in_channels=3, out_channels=3):
- super(UpCunet3x, self).__init__()
- self.unet1 = UNet1x3(in_channels, out_channels, deconv=True)
- self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
- def forward(self, x, tile_mode): # 1.7G
- n, c, h0, w0 = x.shape
- if (tile_mode == 0): # 不tile
- ph = ((h0 - 1) // 4 + 1) * 4
- pw = ((w0 - 1) // 4 + 1) * 4
- x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect') # 需要保证被2整除
- x = self.unet1.forward(x)
- x0 = self.unet2.forward(x)
- x1 = F.pad(x, (-20, -20, -20, -20))
- x = torch.add(x0, x1)
- if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 3, :w0 * 3]
- return x
- elif (tile_mode == 1): # 对长边减半
- if (w0 >= h0):
- crop_size_w = ((w0 - 1) // 8 * 8 + 8) // 2 # 减半后能被4整除，所以要先被8整除
- crop_size_h = (h0 - 1) // 4 * 4 + 4 # 能被4整除
- else:
- crop_size_h = ((h0 - 1) // 8 * 8 + 8) // 2 # 减半后能被4整除，所以要先被8整除
- crop_size_w = (w0 - 1) // 4 * 4 + 4 # 能被4整除
- crop_size = (crop_size_h, crop_size_w) # 6.6G
- elif (tile_mode == 2): # hw都减半
- crop_size = (((h0 - 1) // 8 * 8 + 8) // 2, ((w0 - 1) // 8 * 8 + 8) // 2) # 5.6G
- elif (tile_mode == 3): # hw都三分之一
- crop_size = (((h0 - 1) // 12 * 12 + 12) // 3, ((w0 - 1) // 12 * 12 + 12) // 3) # 4.2G
- elif (tile_mode == 4): # hw都四分之一
- crop_size = (((h0 - 1) // 16 * 16 + 16) // 4, ((w0 - 1) // 16 * 16 + 16) // 4) # 3.7G
- ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
- pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
- x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')
- n, c, h, w = x.shape
- se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
- if ("Half" in x.type()):
- se_mean0 = se_mean0.half()
- n_patch = 0
- tmp_dict = {}
- opt_res_dict = {}
- for i in range(0, h - 28, crop_size[0]):
- tmp_dict[i] = {}
- for j in range(0, w - 28, crop_size[1]):
- x_crop = x[:, :, i:i + crop_size[0] + 28, j:j + crop_size[1] + 28]
- n, c1, h1, w1 = x_crop.shape
- tmp0, x_crop = self.unet1.forward_a(x_crop)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
- se_mean0 += tmp_se_mean
- n_patch += 1
- tmp_dict[i][j] = (tmp0, x_crop)
- se_mean0 /= n_patch
- se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean1 = se_mean1.half()
- for i in range(0, h - 28, crop_size[0]):
- for j in range(0, w - 28, crop_size[1]):
- tmp0, x_crop = tmp_dict[i][j]
- x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
- opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
- tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
- se_mean1 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
- se_mean1 /= n_patch
- se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean0 = se_mean0.half()
- for i in range(0, h - 28, crop_size[0]):
- for j in range(0, w - 28, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
- tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
- tmp_x3 = self.unet2.forward_b(tmp_x2)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
- se_mean0 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
- se_mean0 /= n_patch
- se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean1 = se_mean1.half()
- for i in range(0, h - 28, crop_size[0]):
- for j in range(0, w - 28, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
- tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
- tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
- se_mean1 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
- se_mean1 /= n_patch
- for i in range(0, h - 28, crop_size[0]):
- opt_res_dict[i] = {}
- for j in range(0, w - 28, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
- tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
- x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
- x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
- x_crop = torch.add(x0, x1) # x0是unet2的最终输出
- opt_res_dict[i][j] = x_crop #
- del tmp_dict
- torch.cuda.empty_cache()
- res = torch.zeros((n, c, h * 3 - 84, w * 3 - 84)).to(x.device)
- if ("Half" in x.type()):
- res = res.half()
- for i in range(0, h - 28, crop_size[0]):
- for j in range(0, w - 28, crop_size[1]):
- res[:, :, i * 3:i * 3 + h1 * 3 - 84, j * 3:j * 3 + w1 * 3 - 84] = opt_res_dict[i][j]
- del opt_res_dict
- torch.cuda.empty_cache()
- if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 3, :w0 * 3]
- return res
-
-
-class UpCunet4x(nn.Module): # 完美tile，全程无损
- def __init__(self, in_channels=3, out_channels=3):
- super(UpCunet4x, self).__init__()
- self.unet1 = UNet1(in_channels, 64, deconv=True)
- self.unet2 = UNet2(64, 64, deconv=False)
- self.ps = nn.PixelShuffle(2)
- self.conv_final = nn.Conv2d(64, 12, 3, 1, padding=0, bias=True)
-
- def forward(self, x, tile_mode):
- n, c, h0, w0 = x.shape
- x00 = x
- if (tile_mode == 0): # 不tile
- ph = ((h0 - 1) // 2 + 1) * 2
- pw = ((w0 - 1) // 2 + 1) * 2
- x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect') # 需要保证被2整除
- x = self.unet1.forward(x)
- x0 = self.unet2.forward(x)
- x1 = F.pad(x, (-20, -20, -20, -20))
- x = torch.add(x0, x1)
- x = self.conv_final(x)
- x = F.pad(x, (-1, -1, -1, -1))
- x = self.ps(x)
- if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 4, :w0 * 4]
- x += F.interpolate(x00, scale_factor=4, mode='nearest')
- return x
- elif (tile_mode == 1): # 对长边减半
- if (w0 >= h0):
- crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除
- crop_size_h = (h0 - 1) // 2 * 2 + 2 # 能被2整除
- else:
- crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除
- crop_size_w = (w0 - 1) // 2 * 2 + 2 # 能被2整除
- crop_size = (crop_size_h, crop_size_w) # 6.6G
- elif (tile_mode == 2): # hw都减半
- crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2) # 5.6G
- elif (tile_mode == 3): # hw都三分之一
- crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3) # 4.1G
- elif (tile_mode == 4): # hw都四分之一
- crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4) # 3.7G
- ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
- pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
- x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')
- n, c, h, w = x.shape
- se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
- if ("Half" in x.type()):
- se_mean0 = se_mean0.half()
- n_patch = 0
- tmp_dict = {}
- opt_res_dict = {}
- for i in range(0, h - 38, crop_size[0]):
- tmp_dict[i] = {}
- for j in range(0, w - 38, crop_size[1]):
- x_crop = x[:, :, i:i + crop_size[0] + 38, j:j + crop_size[1] + 38]
- n, c1, h1, w1 = x_crop.shape
- tmp0, x_crop = self.unet1.forward_a(x_crop)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
- se_mean0 += tmp_se_mean
- n_patch += 1
- tmp_dict[i][j] = (tmp0, x_crop)
- se_mean0 /= n_patch
- se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean1 = se_mean1.half()
- for i in range(0, h - 38, crop_size[0]):
- for j in range(0, w - 38, crop_size[1]):
- tmp0, x_crop = tmp_dict[i][j]
- x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
- opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
- tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
- se_mean1 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
- se_mean1 /= n_patch
- se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean0 = se_mean0.half()
- for i in range(0, h - 38, crop_size[0]):
- for j in range(0, w - 38, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
- tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
- tmp_x3 = self.unet2.forward_b(tmp_x2)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
- se_mean0 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
- se_mean0 /= n_patch
- se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device) # 64#128#128#64
- if ("Half" in x.type()):
- se_mean1 = se_mean1.half()
- for i in range(0, h - 38, crop_size[0]):
- for j in range(0, w - 38, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
- tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
- tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
- if ("Half" in x.type()): # torch.HalfTensor/torch.cuda.HalfTensor
- tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
- else:
- tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
- se_mean1 += tmp_se_mean
- tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
- se_mean1 /= n_patch
- for i in range(0, h - 38, crop_size[0]):
- opt_res_dict[i] = {}
- for j in range(0, w - 38, crop_size[1]):
- opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
- tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
- x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
- x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
- x_crop = torch.add(x0, x1) # x0是unet2的最终输出
- x_crop = self.conv_final(x_crop)
- x_crop = F.pad(x_crop, (-1, -1, -1, -1))
- x_crop = self.ps(x_crop)
- opt_res_dict[i][j] = x_crop
- del tmp_dict
- torch.cuda.empty_cache()
- res = torch.zeros((n, c, h * 4 - 152, w * 4 - 152)).to(x.device)
- if ("Half" in x.type()):
- res = res.half()
- for i in range(0, h - 38, crop_size[0]):
- for j in range(0, w - 38, crop_size[1]):
- # print(opt_res_dict[i][j].shape,res[:, :, i * 4:i * 4 + h1 * 4 - 144, j * 4:j * 4 + w1 * 4 - 144].shape)
- res[:, :, i * 4:i * 4 + h1 * 4 - 152, j * 4:j * 4 + w1 * 4 - 152] = opt_res_dict[i][j]
- del opt_res_dict
- torch.cuda.empty_cache()
- if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 4, :w0 * 4]
- res += F.interpolate(x00, scale_factor=4, mode='nearest')
- return res #
-
-
-class RealWaifuUpScaler(object):
- def __init__(self, scale, weight_path, half, device):
- weight = torch.load(weight_path, map_location="cpu")
- self.model = eval("UpCunet%sx" % scale)()
- if (half == True):
- self.model = self.model.half().to(device)
- else:
- self.model = self.model.to(device)
- self.model.load_state_dict(weight, strict=True)
- self.model.eval()
- self.half = half
- self.device = device
-
- def np2tensor(self, np_frame):
- if (self.half == False):
- return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).float() / 255
- else:
- return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).half() / 255
-
- def tensor2np(self, tensor):
- if (self.half == False):
- return (
- np.transpose((tensor.data.squeeze() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(), (1, 2, 0)))
- else:
- return (np.transpose((tensor.data.squeeze().float() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(),
- (1, 2, 0)))
-
- def __call__(self, frame, tile_mode):
- with torch.no_grad():
- tensor = self.np2tensor(frame)
- result = self.tensor2np(self.model(tensor, tile_mode))
- return result
-
-
-if __name__ == "__main__":
- ###########inference_img
- import time, cv2, sys
- from time import time as ttime
-
- for weight_path, scale in [("weights_v3/up2x-latest-denoise3x.pth", 2), ("weights_v3/up3x-latest-denoise3x.pth", 3),
- ("weights_v3/up4x-latest-denoise3x.pth", 4)]:
- for tile_mode in [0, 1, 2, 3, 4]:
- upscaler2x = RealWaifuUpScaler(scale, weight_path, half=True, device="cuda:0")
- input_dir = "%s/input_dir1" % root_path
- output_dir = "%s/opt-dir-all-test" % root_path
- os.makedirs(output_dir, exist_ok=True)
- for name in os.listdir(input_dir):
- print(name)
- tmp = name.split(".")
- inp_path = os.path.join(input_dir, name)
- suffix = tmp[-1]
- prefix = ".".join(tmp[:-1])
- tmp_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
- print(inp_path, tmp_path)
- # 支持中文路径
- # os.link(inp_path, tmp_path)#win用硬链接
- os.symlink(inp_path, tmp_path) # linux用软链接
- frame = cv2.imread(tmp_path)[:, :, [2, 1, 0]]
- t0 = ttime()
- result = upscaler2x(frame, tile_mode=tile_mode)[:, :, ::-1]
- t1 = ttime()
- print(prefix, "done", t1 - t0)
- tmp_opt_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
- cv2.imwrite(tmp_opt_path, result)
- n = 0
- while (1):
- if (n == 0):
- suffix = "_%sx_tile%s.png" % (scale, tile_mode)
- else:
- suffix = "_%sx_tile%s_%s.png" % (scale, tile_mode, n) #
- if (os.path.exists(os.path.join(output_dir, prefix + suffix)) == False):
- break
- else:
- n += 1
- final_opt_path = os.path.join(output_dir, prefix + suffix)
- os.rename(tmp_opt_path, final_opt_path)
- os.remove(tmp_path)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/exp/cascade_mask_rcnn_3x_ms_hybrid_base/run.sh b/spaces/abhishek/sketch-to-image/annotator/uniformer/exp/cascade_mask_rcnn_3x_ms_hybrid_base/run.sh
deleted file mode 100644
index 453f0a0a27d04f08558ec1b03312f7815ca991da..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/exp/cascade_mask_rcnn_3x_ms_hybrid_base/run.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/usr/bin/env bash
-
-work_path=$(dirname $0)
-PYTHONPATH="$(dirname $0)/../../":$PYTHONPATH \
-python -m torch.distributed.launch --nproc_per_node=8 \
- tools/train.py ${work_path}/config.py \
- --launcher pytorch \
- --cfg-options model.backbone.pretrained_path='your_model_path/uniformer_base_in1k.pth' \
- --work-dir ${work_path}/ckpt \
- 2>&1 | tee -a ${work_path}/log.txt
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/bbox/assigners/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/bbox/assigners/__init__.py
deleted file mode 100644
index 95e34a848652f2ab3ca6d3489aa2934d24817888..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/bbox/assigners/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from .approx_max_iou_assigner import ApproxMaxIoUAssigner
-from .assign_result import AssignResult
-from .atss_assigner import ATSSAssigner
-from .base_assigner import BaseAssigner
-from .center_region_assigner import CenterRegionAssigner
-from .grid_assigner import GridAssigner
-from .hungarian_assigner import HungarianAssigner
-from .max_iou_assigner import MaxIoUAssigner
-from .point_assigner import PointAssigner
-from .region_assigner import RegionAssigner
-
-__all__ = [
- 'BaseAssigner', 'MaxIoUAssigner', 'ApproxMaxIoUAssigner', 'AssignResult',
- 'PointAssigner', 'ATSSAssigner', 'CenterRegionAssigner', 'GridAssigner',
- 'HungarianAssigner', 'RegionAssigner'
-]
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/export/pytorch2onnx.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/export/pytorch2onnx.py
deleted file mode 100644
index 809a817e67446b3c0c7894dcefb3c4bbc29afb7e..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/export/pytorch2onnx.py
+++ /dev/null
@@ -1,154 +0,0 @@
-from functools import partial
-
-import mmcv
-import numpy as np
-import torch
-from mmcv.runner import load_checkpoint
-
-
-def generate_inputs_and_wrap_model(config_path,
- checkpoint_path,
- input_config,
- cfg_options=None):
- """Prepare sample input and wrap model for ONNX export.
-
- The ONNX export API only accept args, and all inputs should be
- torch.Tensor or corresponding types (such as tuple of tensor).
- So we should call this function before exporting. This function will:
-
- 1. generate corresponding inputs which are used to execute the model.
- 2. Wrap the model's forward function.
-
- For example, the MMDet models' forward function has a parameter
- ``return_loss:bool``. As we want to set it as False while export API
- supports neither bool type or kwargs. So we have to replace the forward
- like: ``model.forward = partial(model.forward, return_loss=False)``
-
- Args:
- config_path (str): the OpenMMLab config for the model we want to
- export to ONNX
- checkpoint_path (str): Path to the corresponding checkpoint
- input_config (dict): the exactly data in this dict depends on the
- framework. For MMSeg, we can just declare the input shape,
- and generate the dummy data accordingly. However, for MMDet,
- we may pass the real img path, or the NMS will return None
- as there is no legal bbox.
-
- Returns:
- tuple: (model, tensor_data) wrapped model which can be called by \
- model(*tensor_data) and a list of inputs which are used to execute \
- the model while exporting.
- """
-
- model = build_model_from_cfg(
- config_path, checkpoint_path, cfg_options=cfg_options)
- one_img, one_meta = preprocess_example_input(input_config)
- tensor_data = [one_img]
- model.forward = partial(
- model.forward, img_metas=[[one_meta]], return_loss=False)
-
- # pytorch has some bug in pytorch1.3, we have to fix it
- # by replacing these existing op
- opset_version = 11
- # put the import within the function thus it will not cause import error
- # when not using this function
- try:
- from mmcv.onnx.symbolic import register_extra_symbolics
- except ModuleNotFoundError:
- raise NotImplementedError('please update mmcv to version>=v1.0.4')
- register_extra_symbolics(opset_version)
-
- return model, tensor_data
-
-
-def build_model_from_cfg(config_path, checkpoint_path, cfg_options=None):
- """Build a model from config and load the given checkpoint.
-
- Args:
- config_path (str): the OpenMMLab config for the model we want to
- export to ONNX
- checkpoint_path (str): Path to the corresponding checkpoint
-
- Returns:
- torch.nn.Module: the built model
- """
- from mmdet.models import build_detector
-
- cfg = mmcv.Config.fromfile(config_path)
- if cfg_options is not None:
- cfg.merge_from_dict(cfg_options)
- # import modules from string list.
- if cfg.get('custom_imports', None):
- from mmcv.utils import import_modules_from_strings
- import_modules_from_strings(**cfg['custom_imports'])
- # set cudnn_benchmark
- if cfg.get('cudnn_benchmark', False):
- torch.backends.cudnn.benchmark = True
- cfg.model.pretrained = None
- cfg.data.test.test_mode = True
-
- # build the model
- cfg.model.train_cfg = None
- model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
- load_checkpoint(model, checkpoint_path, map_location='cpu')
- model.cpu().eval()
- return model
-
-
-def preprocess_example_input(input_config):
- """Prepare an example input image for ``generate_inputs_and_wrap_model``.
-
- Args:
- input_config (dict): customized config describing the example input.
-
- Returns:
- tuple: (one_img, one_meta), tensor of the example input image and \
- meta information for the example input image.
-
- Examples:
- >>> from mmdet.core.export import preprocess_example_input
- >>> input_config = {
- >>> 'input_shape': (1,3,224,224),
- >>> 'input_path': 'demo/demo.jpg',
- >>> 'normalize_cfg': {
- >>> 'mean': (123.675, 116.28, 103.53),
- >>> 'std': (58.395, 57.12, 57.375)
- >>> }
- >>> }
- >>> one_img, one_meta = preprocess_example_input(input_config)
- >>> print(one_img.shape)
- torch.Size([1, 3, 224, 224])
- >>> print(one_meta)
- {'img_shape': (224, 224, 3),
- 'ori_shape': (224, 224, 3),
- 'pad_shape': (224, 224, 3),
- 'filename': '
-journey
- title Create AI
- section Training
- Format DataSet Inputs Files, Data Splits: 5: Teacher
- Model Build w/ SKLearn, TF, Pytorch: 3: Student
- Determine Model Performance: 1: Teacher, Student
- section Deploy
- Web Deploy Local and Cloud: 5: Teacher
- Architecture Spaces Gradio Streamlit Heroku AWS Azure and GCCP: 5: Teacher
- section Testing
- Test Model with Input Datasets: 5: Teacher
- Examples. Inputs that Work, Inputs That Break Model: 5: Teacher
- Governance - Analyze, Publish Fairness, Equity, Bias for Datasets and Outputs: 5: Teacher
-
-
-
-sequenceDiagram
- participant Alice
- participant Bob
- Alice->>John: Hello John, how are you?
- loop Healthcheck
- John->>John: Fight against hypochondria
- end
- Note right of John: Rational thoughts
-
-
prevail...
- John-->>Alice: Great!
- John->>Bob: How about you?
- Bob-->>John: Jolly good!
-
-
-
-
-
diff --git a/spaces/awacke1/ChatGPT-Streamlit-5/README.md b/spaces/awacke1/ChatGPT-Streamlit-5/README.md
deleted file mode 100644
index 67b9127d93bb9148d95a10e2c471ca4a5241abe0..0000000000000000000000000000000000000000
--- a/spaces/awacke1/ChatGPT-Streamlit-5/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ChatGPT Streamlit 5
-emoji: 🐢
-colorFrom: pink
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/b-monroe/rvc-VoiceAI/infer_pack/attentions.py b/spaces/b-monroe/rvc-VoiceAI/infer_pack/attentions.py
deleted file mode 100644
index 77cb63ffccf3e33badf22d50862a64ba517b487f..0000000000000000000000000000000000000000
--- a/spaces/b-monroe/rvc-VoiceAI/infer_pack/attentions.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from infer_pack import commons
-from infer_pack import modules
-from infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
- def __init__(
- self,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size=1,
- p_dropout=0.0,
- window_size=10,
- **kwargs
- ):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.window_size = window_size
-
- self.drop = nn.Dropout(p_dropout)
- self.attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.attn_layers.append(
- MultiHeadAttention(
- hidden_channels,
- hidden_channels,
- n_heads,
- p_dropout=p_dropout,
- window_size=window_size,
- )
- )
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(
- hidden_channels,
- hidden_channels,
- filter_channels,
- kernel_size,
- p_dropout=p_dropout,
- )
- )
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask):
- attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.attn_layers[i](x, x, attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class Decoder(nn.Module):
- def __init__(
- self,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size=1,
- p_dropout=0.0,
- proximal_bias=False,
- proximal_init=True,
- **kwargs
- ):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.ModuleList()
- self.norm_layers_0 = nn.ModuleList()
- self.encdec_attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(
- MultiHeadAttention(
- hidden_channels,
- hidden_channels,
- n_heads,
- p_dropout=p_dropout,
- proximal_bias=proximal_bias,
- proximal_init=proximal_init,
- )
- )
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.encdec_attn_layers.append(
- MultiHeadAttention(
- hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
- )
- )
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(
- hidden_channels,
- hidden_channels,
- filter_channels,
- kernel_size,
- p_dropout=p_dropout,
- causal=True,
- )
- )
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask, h, h_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
- device=x.device, dtype=x.dtype
- )
- encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class MultiHeadAttention(nn.Module):
- def __init__(
- self,
- channels,
- out_channels,
- n_heads,
- p_dropout=0.0,
- window_size=None,
- heads_share=True,
- block_length=None,
- proximal_bias=False,
- proximal_init=False,
- ):
- super().__init__()
- assert channels % n_heads == 0
-
- self.channels = channels
- self.out_channels = out_channels
- self.n_heads = n_heads
- self.p_dropout = p_dropout
- self.window_size = window_size
- self.heads_share = heads_share
- self.block_length = block_length
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
- self.attn = None
-
- self.k_channels = channels // n_heads
- self.conv_q = nn.Conv1d(channels, channels, 1)
- self.conv_k = nn.Conv1d(channels, channels, 1)
- self.conv_v = nn.Conv1d(channels, channels, 1)
- self.conv_o = nn.Conv1d(channels, out_channels, 1)
- self.drop = nn.Dropout(p_dropout)
-
- if window_size is not None:
- n_heads_rel = 1 if heads_share else n_heads
- rel_stddev = self.k_channels**-0.5
- self.emb_rel_k = nn.Parameter(
- torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
- * rel_stddev
- )
- self.emb_rel_v = nn.Parameter(
- torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
- * rel_stddev
- )
-
- nn.init.xavier_uniform_(self.conv_q.weight)
- nn.init.xavier_uniform_(self.conv_k.weight)
- nn.init.xavier_uniform_(self.conv_v.weight)
- if proximal_init:
- with torch.no_grad():
- self.conv_k.weight.copy_(self.conv_q.weight)
- self.conv_k.bias.copy_(self.conv_q.bias)
-
- def forward(self, x, c, attn_mask=None):
- q = self.conv_q(x)
- k = self.conv_k(c)
- v = self.conv_v(c)
-
- x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
- x = self.conv_o(x)
- return x
-
- def attention(self, query, key, value, mask=None):
- # reshape [b, d, t] -> [b, n_h, t, d_k]
- b, d, t_s, t_t = (*key.size(), query.size(2))
- query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
- key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
- value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
- scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
- if self.window_size is not None:
- assert (
- t_s == t_t
- ), "Relative attention is only available for self-attention."
- key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
- rel_logits = self._matmul_with_relative_keys(
- query / math.sqrt(self.k_channels), key_relative_embeddings
- )
- scores_local = self._relative_position_to_absolute_position(rel_logits)
- scores = scores + scores_local
- if self.proximal_bias:
- assert t_s == t_t, "Proximal bias is only available for self-attention."
- scores = scores + self._attention_bias_proximal(t_s).to(
- device=scores.device, dtype=scores.dtype
- )
- if mask is not None:
- scores = scores.masked_fill(mask == 0, -1e4)
- if self.block_length is not None:
- assert (
- t_s == t_t
- ), "Local attention is only available for self-attention."
- block_mask = (
- torch.ones_like(scores)
- .triu(-self.block_length)
- .tril(self.block_length)
- )
- scores = scores.masked_fill(block_mask == 0, -1e4)
- p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
- p_attn = self.drop(p_attn)
- output = torch.matmul(p_attn, value)
- if self.window_size is not None:
- relative_weights = self._absolute_position_to_relative_position(p_attn)
- value_relative_embeddings = self._get_relative_embeddings(
- self.emb_rel_v, t_s
- )
- output = output + self._matmul_with_relative_values(
- relative_weights, value_relative_embeddings
- )
- output = (
- output.transpose(2, 3).contiguous().view(b, d, t_t)
- ) # [b, n_h, t_t, d_k] -> [b, d, t_t]
- return output, p_attn
-
- def _matmul_with_relative_values(self, x, y):
- """
- x: [b, h, l, m]
- y: [h or 1, m, d]
- ret: [b, h, l, d]
- """
- ret = torch.matmul(x, y.unsqueeze(0))
- return ret
-
- def _matmul_with_relative_keys(self, x, y):
- """
- x: [b, h, l, d]
- y: [h or 1, m, d]
- ret: [b, h, l, m]
- """
- ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
- return ret
-
- def _get_relative_embeddings(self, relative_embeddings, length):
- max_relative_position = 2 * self.window_size + 1
- # Pad first before slice to avoid using cond ops.
- pad_length = max(length - (self.window_size + 1), 0)
- slice_start_position = max((self.window_size + 1) - length, 0)
- slice_end_position = slice_start_position + 2 * length - 1
- if pad_length > 0:
- padded_relative_embeddings = F.pad(
- relative_embeddings,
- commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
- )
- else:
- padded_relative_embeddings = relative_embeddings
- used_relative_embeddings = padded_relative_embeddings[
- :, slice_start_position:slice_end_position
- ]
- return used_relative_embeddings
-
- def _relative_position_to_absolute_position(self, x):
- """
- x: [b, h, l, 2*l-1]
- ret: [b, h, l, l]
- """
- batch, heads, length, _ = x.size()
- # Concat columns of pad to shift from relative to absolute indexing.
- x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
- # Concat extra elements so to add up to shape (len+1, 2*len-1).
- x_flat = x.view([batch, heads, length * 2 * length])
- x_flat = F.pad(
- x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
- )
-
- # Reshape and slice out the padded elements.
- x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
- :, :, :length, length - 1 :
- ]
- return x_final
-
- def _absolute_position_to_relative_position(self, x):
- """
- x: [b, h, l, l]
- ret: [b, h, l, 2*l-1]
- """
- batch, heads, length, _ = x.size()
- # padd along column
- x = F.pad(
- x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
- )
- x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
- # add 0's in the beginning that will skew the elements after reshape
- x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
- x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
- return x_final
-
- def _attention_bias_proximal(self, length):
- """Bias for self-attention to encourage attention to close positions.
- Args:
- length: an integer scalar.
- Returns:
- a Tensor with shape [1, 1, length, length]
- """
- r = torch.arange(length, dtype=torch.float32)
- diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
- return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- filter_channels,
- kernel_size,
- p_dropout=0.0,
- activation=None,
- causal=False,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.activation = activation
- self.causal = causal
-
- if causal:
- self.padding = self._causal_padding
- else:
- self.padding = self._same_padding
-
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
- self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
- self.drop = nn.Dropout(p_dropout)
-
- def forward(self, x, x_mask):
- x = self.conv_1(self.padding(x * x_mask))
- if self.activation == "gelu":
- x = x * torch.sigmoid(1.702 * x)
- else:
- x = torch.relu(x)
- x = self.drop(x)
- x = self.conv_2(self.padding(x * x_mask))
- return x * x_mask
-
- def _causal_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = self.kernel_size - 1
- pad_r = 0
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
-
- def _same_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = (self.kernel_size - 1) // 2
- pad_r = self.kernel_size // 2
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/postprocessing/NodePostProcessing.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/postprocessing/NodePostProcessing.js
deleted file mode 100644
index 26ecbe57b32fe40457aed4aff39c6e10ffdfe99c..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/postprocessing/NodePostProcessing.js
+++ /dev/null
@@ -1,130 +0,0 @@
-/**
- * @author sunag / http://www.sunag.com.br/
- */
-
-import { NodeMaterial } from '../materials/NodeMaterial.js';
-import { ScreenNode } from '../inputs/ScreenNode.js';
-
-function NodePostProcessing( renderer, renderTarget ) {
-
- if ( renderTarget === undefined ) {
-
- var parameters = {
- minFilter: THREE.LinearFilter,
- magFilter: THREE.LinearFilter,
- format: THREE.RGBAFormat,
- stencilBuffer: false
- };
-
- var size = renderer.getDrawingBufferSize( new THREE.Vector2() );
- renderTarget = new THREE.WebGLRenderTarget( size.width, size.height, parameters );
-
- }
-
- this.renderer = renderer;
- this.renderTarget = renderTarget;
-
- this.output = new ScreenNode();
- this.material = new NodeMaterial();
-
- this.camera = new THREE.OrthographicCamera( - 1, 1, 1, - 1, 0, 1 );
- this.scene = new THREE.Scene();
-
- this.quad = new THREE.Mesh( new THREE.PlaneBufferGeometry( 2, 2 ), this.material );
- this.quad.frustumCulled = false; // Avoid getting clipped
- this.scene.add( this.quad );
-
- this.needsUpdate = true;
-
-}
-
-NodePostProcessing.prototype = {
-
- constructor: NodePostProcessing,
-
- render: function ( scene, camera, frame ) {
-
- if ( this.needsUpdate ) {
-
- this.material.dispose();
-
- this.material.fragment.value = this.output;
- this.material.build();
-
- if ( this.material.uniforms.renderTexture ) {
-
- this.material.uniforms.renderTexture.value = this.renderTarget.texture;
-
- }
-
- this.needsUpdate = false;
-
- }
-
- frame.setRenderer( this.renderer )
- .setRenderTexture( this.renderTarget.texture );
-
- this.renderer.setRenderTarget( this.renderTarget );
- this.renderer.render( scene, camera );
-
- frame.updateNode( this.material );
-
- this.renderer.setRenderTarget( null );
- this.renderer.render( this.scene, this.camera );
-
- },
-
- setSize: function ( width, height ) {
-
- this.renderTarget.setSize( width, height );
-
- this.renderer.setSize( width, height );
-
- },
-
- copy: function ( source ) {
-
- this.output = source.output;
-
- },
-
- toJSON: function ( meta ) {
-
- var isRootObject = ( meta === undefined || typeof meta === 'string' );
-
- if ( isRootObject ) {
-
- meta = {
- nodes: {}
- };
-
- }
-
- if ( meta && ! meta.post ) meta.post = {};
-
- if ( ! meta.post[ this.uuid ] ) {
-
- var data = {};
-
- data.uuid = this.uuid;
- data.type = "NodePostProcessing";
-
- meta.post[ this.uuid ] = data;
-
- if ( this.name !== "" ) data.name = this.name;
-
- if ( JSON.stringify( this.userData ) !== '{}' ) data.userData = this.userData;
-
- data.output = this.output.toJSON( meta ).uuid;
-
- }
-
- meta.post = this.uuid;
-
- return meta;
-
- }
-
-};
-
-export { NodePostProcessing };
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/geometries/PolyhedronGeometry.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/geometries/PolyhedronGeometry.d.ts
deleted file mode 100644
index ea4f59b946a385f22d30796de09f3091e677b36b..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/geometries/PolyhedronGeometry.d.ts
+++ /dev/null
@@ -1,36 +0,0 @@
-import { Sphere } from './../math/Sphere';
-import { Geometry } from './../core/Geometry';
-import { BufferGeometry } from './../core/BufferGeometry';
-
-export class PolyhedronBufferGeometry extends BufferGeometry {
- constructor(
- vertices: number[],
- indices: number[],
- radius?: number,
- detail?: number
- );
-
- parameters: {
- vertices: number[];
- indices: number[];
- radius: number;
- detail: number;
- };
-}
-
-export class PolyhedronGeometry extends Geometry {
- constructor(
- vertices: number[],
- indices: number[],
- radius?: number,
- detail?: number
- );
-
- parameters: {
- vertices: number[];
- indices: number[];
- radius: number;
- detail: number;
- };
- boundingSphere: Sphere;
-}
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/webgl/WebGLProgram.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/webgl/WebGLProgram.js
deleted file mode 100644
index 40ac9bcdae0ddb8b892e2ef6eeaa0edccbaca5d8..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/webgl/WebGLProgram.js
+++ /dev/null
@@ -1,745 +0,0 @@
-/**
- * @author mrdoob / http://mrdoob.com/
- */
-
-import { WebGLUniforms } from './WebGLUniforms.js';
-import { WebGLShader } from './WebGLShader.js';
-import { ShaderChunk } from '../shaders/ShaderChunk.js';
-import { NoToneMapping, AddOperation, MixOperation, MultiplyOperation, EquirectangularRefractionMapping, CubeRefractionMapping, SphericalReflectionMapping, EquirectangularReflectionMapping, CubeUVRefractionMapping, CubeUVReflectionMapping, CubeReflectionMapping, PCFSoftShadowMap, PCFShadowMap, ACESFilmicToneMapping, CineonToneMapping, Uncharted2ToneMapping, ReinhardToneMapping, LinearToneMapping, GammaEncoding, RGBDEncoding, RGBM16Encoding, RGBM7Encoding, RGBEEncoding, sRGBEncoding, LinearEncoding } from '../../constants.js';
-
-var programIdCount = 0;
-
-function getEncodingComponents( encoding ) {
-
- switch ( encoding ) {
-
- case LinearEncoding:
- return [ 'Linear', '( value )' ];
- case sRGBEncoding:
- return [ 'sRGB', '( value )' ];
- case RGBEEncoding:
- return [ 'RGBE', '( value )' ];
- case RGBM7Encoding:
- return [ 'RGBM', '( value, 7.0 )' ];
- case RGBM16Encoding:
- return [ 'RGBM', '( value, 16.0 )' ];
- case RGBDEncoding:
- return [ 'RGBD', '( value, 256.0 )' ];
- case GammaEncoding:
- return [ 'Gamma', '( value, float( GAMMA_FACTOR ) )' ];
- default:
- throw new Error( 'unsupported encoding: ' + encoding );
-
- }
-
-}
-
-function getTexelDecodingFunction( functionName, encoding ) {
-
- var components = getEncodingComponents( encoding );
- return 'vec4 ' + functionName + '( vec4 value ) { return ' + components[ 0 ] + 'ToLinear' + components[ 1 ] + '; }';
-
-}
-
-function getTexelEncodingFunction( functionName, encoding ) {
-
- var components = getEncodingComponents( encoding );
- return 'vec4 ' + functionName + '( vec4 value ) { return LinearTo' + components[ 0 ] + components[ 1 ] + '; }';
-
-}
-
-function getToneMappingFunction( functionName, toneMapping ) {
-
- var toneMappingName;
-
- switch ( toneMapping ) {
-
- case LinearToneMapping:
- toneMappingName = 'Linear';
- break;
-
- case ReinhardToneMapping:
- toneMappingName = 'Reinhard';
- break;
-
- case Uncharted2ToneMapping:
- toneMappingName = 'Uncharted2';
- break;
-
- case CineonToneMapping:
- toneMappingName = 'OptimizedCineon';
- break;
-
- case ACESFilmicToneMapping:
- toneMappingName = 'ACESFilmic';
- break;
-
- default:
- throw new Error( 'unsupported toneMapping: ' + toneMapping );
-
- }
-
- return 'vec3 ' + functionName + '( vec3 color ) { return ' + toneMappingName + 'ToneMapping( color ); }';
-
-}
-
-function generateExtensions( extensions, parameters, rendererExtensions ) {
-
- extensions = extensions || {};
-
- var chunks = [
- ( extensions.derivatives || parameters.envMapCubeUV || parameters.bumpMap || ( parameters.normalMap && ! parameters.objectSpaceNormalMap ) || parameters.flatShading ) ? '#extension GL_OES_standard_derivatives : enable' : '',
- ( extensions.fragDepth || parameters.logarithmicDepthBuffer ) && rendererExtensions.get( 'EXT_frag_depth' ) ? '#extension GL_EXT_frag_depth : enable' : '',
- ( extensions.drawBuffers ) && rendererExtensions.get( 'WEBGL_draw_buffers' ) ? '#extension GL_EXT_draw_buffers : require' : '',
- ( extensions.shaderTextureLOD || parameters.envMap ) && rendererExtensions.get( 'EXT_shader_texture_lod' ) ? '#extension GL_EXT_shader_texture_lod : enable' : ''
- ];
-
- return chunks.filter( filterEmptyLine ).join( '\n' );
-
-}
-
-function generateDefines( defines ) {
-
- var chunks = [];
-
- for ( var name in defines ) {
-
- var value = defines[ name ];
-
- if ( value === false ) continue;
-
- chunks.push( '#define ' + name + ' ' + value );
-
- }
-
- return chunks.join( '\n' );
-
-}
-
-function fetchAttributeLocations( gl, program ) {
-
- var attributes = {};
-
- var n = gl.getProgramParameter( program, gl.ACTIVE_ATTRIBUTES );
-
- for ( var i = 0; i < n; i ++ ) {
-
- var info = gl.getActiveAttrib( program, i );
- var name = info.name;
-
- // console.log( 'THREE.WebGLProgram: ACTIVE VERTEX ATTRIBUTE:', name, i );
-
- attributes[ name ] = gl.getAttribLocation( program, name );
-
- }
-
- return attributes;
-
-}
-
-function filterEmptyLine( string ) {
-
- return string !== '';
-
-}
-
-function replaceLightNums( string, parameters ) {
-
- return string
- .replace( /NUM_DIR_LIGHTS/g, parameters.numDirLights )
- .replace( /NUM_SPOT_LIGHTS/g, parameters.numSpotLights )
- .replace( /NUM_RECT_AREA_LIGHTS/g, parameters.numRectAreaLights )
- .replace( /NUM_POINT_LIGHTS/g, parameters.numPointLights )
- .replace( /NUM_HEMI_LIGHTS/g, parameters.numHemiLights );
-
-}
-
-function replaceClippingPlaneNums( string, parameters ) {
-
- return string
- .replace( /NUM_CLIPPING_PLANES/g, parameters.numClippingPlanes )
- .replace( /UNION_CLIPPING_PLANES/g, ( parameters.numClippingPlanes - parameters.numClipIntersection ) );
-
-}
-
-function parseIncludes( string ) {
-
- var pattern = /^[ \t]*#include +<([\w\d./]+)>/gm;
-
- function replace( match, include ) {
-
- var replace = ShaderChunk[ include ];
-
- if ( replace === undefined ) {
-
- throw new Error( 'Can not resolve #include <' + include + '>' );
-
- }
-
- return parseIncludes( replace );
-
- }
-
- return string.replace( pattern, replace );
-
-}
-
-function unrollLoops( string ) {
-
- var pattern = /#pragma unroll_loop[\s]+?for \( int i \= (\d+)\; i < (\d+)\; i \+\+ \) \{([\s\S]+?)(?=\})\}/g;
-
- function replace( match, start, end, snippet ) {
-
- var unroll = '';
-
- for ( var i = parseInt( start ); i < parseInt( end ); i ++ ) {
-
- unroll += snippet.replace( /\[ i \]/g, '[ ' + i + ' ]' );
-
- }
-
- return unroll;
-
- }
-
- return string.replace( pattern, replace );
-
-}
-
-function WebGLProgram( renderer, extensions, code, material, shader, parameters, capabilities, textures ) {
-
- var gl = renderer.context;
-
- var defines = material.defines;
-
- var vertexShader = shader.vertexShader;
- var fragmentShader = shader.fragmentShader;
-
- var shadowMapTypeDefine = 'SHADOWMAP_TYPE_BASIC';
-
- if ( parameters.shadowMapType === PCFShadowMap ) {
-
- shadowMapTypeDefine = 'SHADOWMAP_TYPE_PCF';
-
- } else if ( parameters.shadowMapType === PCFSoftShadowMap ) {
-
- shadowMapTypeDefine = 'SHADOWMAP_TYPE_PCF_SOFT';
-
- }
-
- var envMapTypeDefine = 'ENVMAP_TYPE_CUBE';
- var envMapModeDefine = 'ENVMAP_MODE_REFLECTION';
- var envMapBlendingDefine = 'ENVMAP_BLENDING_MULTIPLY';
-
- if ( parameters.envMap ) {
-
- switch ( material.envMap.mapping ) {
-
- case CubeReflectionMapping:
- case CubeRefractionMapping:
- envMapTypeDefine = 'ENVMAP_TYPE_CUBE';
- break;
-
- case CubeUVReflectionMapping:
- case CubeUVRefractionMapping:
- envMapTypeDefine = 'ENVMAP_TYPE_CUBE_UV';
- break;
-
- case EquirectangularReflectionMapping:
- case EquirectangularRefractionMapping:
- envMapTypeDefine = 'ENVMAP_TYPE_EQUIREC';
- break;
-
- case SphericalReflectionMapping:
- envMapTypeDefine = 'ENVMAP_TYPE_SPHERE';
- break;
-
- }
-
- switch ( material.envMap.mapping ) {
-
- case CubeRefractionMapping:
- case EquirectangularRefractionMapping:
- envMapModeDefine = 'ENVMAP_MODE_REFRACTION';
- break;
-
- }
-
- switch ( material.combine ) {
-
- case MultiplyOperation:
- envMapBlendingDefine = 'ENVMAP_BLENDING_MULTIPLY';
- break;
-
- case MixOperation:
- envMapBlendingDefine = 'ENVMAP_BLENDING_MIX';
- break;
-
- case AddOperation:
- envMapBlendingDefine = 'ENVMAP_BLENDING_ADD';
- break;
-
- }
-
- }
-
- var gammaFactorDefine = ( renderer.gammaFactor > 0 ) ? renderer.gammaFactor : 1.0;
-
- // console.log( 'building new program ' );
-
- //
-
- var customExtensions = capabilities.isWebGL2 ? '' : generateExtensions( material.extensions, parameters, extensions );
-
- var customDefines = generateDefines( defines );
-
- //
-
- var program = gl.createProgram();
-
- var prefixVertex, prefixFragment;
-
- if ( material.isRawShaderMaterial ) {
-
- prefixVertex = [
-
- customDefines
-
- ].filter( filterEmptyLine ).join( '\n' );
-
- if ( prefixVertex.length > 0 ) {
-
- prefixVertex += '\n';
-
- }
-
- prefixFragment = [
-
- customExtensions,
- customDefines
-
- ].filter( filterEmptyLine ).join( '\n' );
-
- if ( prefixFragment.length > 0 ) {
-
- prefixFragment += '\n';
-
- }
-
- } else {
-
- prefixVertex = [
-
- 'precision ' + parameters.precision + ' float;',
- 'precision ' + parameters.precision + ' int;',
-
- '#define SHADER_NAME ' + shader.name,
-
- customDefines,
-
- parameters.supportsVertexTextures ? '#define VERTEX_TEXTURES' : '',
-
- '#define GAMMA_FACTOR ' + gammaFactorDefine,
-
- '#define MAX_BONES ' + parameters.maxBones,
- ( parameters.useFog && parameters.fog ) ? '#define USE_FOG' : '',
- ( parameters.useFog && parameters.fogExp ) ? '#define FOG_EXP2' : '',
-
- parameters.map ? '#define USE_MAP' : '',
- parameters.envMap ? '#define USE_ENVMAP' : '',
- parameters.envMap ? '#define ' + envMapModeDefine : '',
- parameters.lightMap ? '#define USE_LIGHTMAP' : '',
- parameters.aoMap ? '#define USE_AOMAP' : '',
- parameters.emissiveMap ? '#define USE_EMISSIVEMAP' : '',
- parameters.bumpMap ? '#define USE_BUMPMAP' : '',
- parameters.normalMap ? '#define USE_NORMALMAP' : '',
- ( parameters.normalMap && parameters.objectSpaceNormalMap ) ? '#define OBJECTSPACE_NORMALMAP' : '',
- parameters.displacementMap && parameters.supportsVertexTextures ? '#define USE_DISPLACEMENTMAP' : '',
- parameters.specularMap ? '#define USE_SPECULARMAP' : '',
- parameters.roughnessMap ? '#define USE_ROUGHNESSMAP' : '',
- parameters.metalnessMap ? '#define USE_METALNESSMAP' : '',
- parameters.alphaMap ? '#define USE_ALPHAMAP' : '',
-
- parameters.vertexTangents ? '#define USE_TANGENT' : '',
- parameters.vertexColors ? '#define USE_COLOR' : '',
-
- parameters.flatShading ? '#define FLAT_SHADED' : '',
-
- parameters.skinning ? '#define USE_SKINNING' : '',
- parameters.useVertexTexture ? '#define BONE_TEXTURE' : '',
-
- parameters.morphTargets ? '#define USE_MORPHTARGETS' : '',
- parameters.morphNormals && parameters.flatShading === false ? '#define USE_MORPHNORMALS' : '',
- parameters.doubleSided ? '#define DOUBLE_SIDED' : '',
- parameters.flipSided ? '#define FLIP_SIDED' : '',
-
- parameters.shadowMapEnabled ? '#define USE_SHADOWMAP' : '',
- parameters.shadowMapEnabled ? '#define ' + shadowMapTypeDefine : '',
-
- parameters.sizeAttenuation ? '#define USE_SIZEATTENUATION' : '',
-
- parameters.logarithmicDepthBuffer ? '#define USE_LOGDEPTHBUF' : '',
- parameters.logarithmicDepthBuffer && ( capabilities.isWebGL2 || extensions.get( 'EXT_frag_depth' ) ) ? '#define USE_LOGDEPTHBUF_EXT' : '',
-
- 'uniform mat4 modelMatrix;',
- 'uniform mat4 modelViewMatrix;',
- 'uniform mat4 projectionMatrix;',
- 'uniform mat4 viewMatrix;',
- 'uniform mat3 normalMatrix;',
- 'uniform vec3 cameraPosition;',
-
- 'attribute vec3 position;',
- 'attribute vec3 normal;',
- 'attribute vec2 uv;',
-
- '#ifdef USE_TANGENT',
-
- ' attribute vec4 tangent;',
-
- '#endif',
-
- '#ifdef USE_COLOR',
-
- ' attribute vec3 color;',
-
- '#endif',
-
- '#ifdef USE_MORPHTARGETS',
-
- ' attribute vec3 morphTarget0;',
- ' attribute vec3 morphTarget1;',
- ' attribute vec3 morphTarget2;',
- ' attribute vec3 morphTarget3;',
-
- ' #ifdef USE_MORPHNORMALS',
-
- ' attribute vec3 morphNormal0;',
- ' attribute vec3 morphNormal1;',
- ' attribute vec3 morphNormal2;',
- ' attribute vec3 morphNormal3;',
-
- ' #else',
-
- ' attribute vec3 morphTarget4;',
- ' attribute vec3 morphTarget5;',
- ' attribute vec3 morphTarget6;',
- ' attribute vec3 morphTarget7;',
-
- ' #endif',
-
- '#endif',
-
- '#ifdef USE_SKINNING',
-
- ' attribute vec4 skinIndex;',
- ' attribute vec4 skinWeight;',
-
- '#endif',
-
- '\n'
-
- ].filter( filterEmptyLine ).join( '\n' );
-
- prefixFragment = [
-
- customExtensions,
-
- 'precision ' + parameters.precision + ' float;',
- 'precision ' + parameters.precision + ' int;',
-
- '#define SHADER_NAME ' + shader.name,
-
- customDefines,
-
- parameters.alphaTest ? '#define ALPHATEST ' + parameters.alphaTest + ( parameters.alphaTest % 1 ? '' : '.0' ) : '', // add '.0' if integer
-
- '#define GAMMA_FACTOR ' + gammaFactorDefine,
-
- ( parameters.useFog && parameters.fog ) ? '#define USE_FOG' : '',
- ( parameters.useFog && parameters.fogExp ) ? '#define FOG_EXP2' : '',
-
- parameters.map ? '#define USE_MAP' : '',
- parameters.matcap ? '#define USE_MATCAP' : '',
- parameters.envMap ? '#define USE_ENVMAP' : '',
- parameters.envMap ? '#define ' + envMapTypeDefine : '',
- parameters.envMap ? '#define ' + envMapModeDefine : '',
- parameters.envMap ? '#define ' + envMapBlendingDefine : '',
- parameters.lightMap ? '#define USE_LIGHTMAP' : '',
- parameters.aoMap ? '#define USE_AOMAP' : '',
- parameters.emissiveMap ? '#define USE_EMISSIVEMAP' : '',
- parameters.bumpMap ? '#define USE_BUMPMAP' : '',
- parameters.normalMap ? '#define USE_NORMALMAP' : '',
- ( parameters.normalMap && parameters.objectSpaceNormalMap ) ? '#define OBJECTSPACE_NORMALMAP' : '',
- parameters.specularMap ? '#define USE_SPECULARMAP' : '',
- parameters.roughnessMap ? '#define USE_ROUGHNESSMAP' : '',
- parameters.metalnessMap ? '#define USE_METALNESSMAP' : '',
- parameters.alphaMap ? '#define USE_ALPHAMAP' : '',
-
- parameters.vertexTangents ? '#define USE_TANGENT' : '',
- parameters.vertexColors ? '#define USE_COLOR' : '',
-
- parameters.gradientMap ? '#define USE_GRADIENTMAP' : '',
-
- parameters.flatShading ? '#define FLAT_SHADED' : '',
-
- parameters.doubleSided ? '#define DOUBLE_SIDED' : '',
- parameters.flipSided ? '#define FLIP_SIDED' : '',
-
- parameters.shadowMapEnabled ? '#define USE_SHADOWMAP' : '',
- parameters.shadowMapEnabled ? '#define ' + shadowMapTypeDefine : '',
-
- parameters.premultipliedAlpha ? '#define PREMULTIPLIED_ALPHA' : '',
-
- parameters.physicallyCorrectLights ? '#define PHYSICALLY_CORRECT_LIGHTS' : '',
-
- parameters.logarithmicDepthBuffer ? '#define USE_LOGDEPTHBUF' : '',
- parameters.logarithmicDepthBuffer && ( capabilities.isWebGL2 || extensions.get( 'EXT_frag_depth' ) ) ? '#define USE_LOGDEPTHBUF_EXT' : '',
-
- parameters.envMap && ( capabilities.isWebGL2 || extensions.get( 'EXT_shader_texture_lod' ) ) ? '#define TEXTURE_LOD_EXT' : '',
-
- 'uniform mat4 viewMatrix;',
- 'uniform vec3 cameraPosition;',
-
- ( parameters.toneMapping !== NoToneMapping ) ? '#define TONE_MAPPING' : '',
- ( parameters.toneMapping !== NoToneMapping ) ? ShaderChunk[ 'tonemapping_pars_fragment' ] : '', // this code is required here because it is used by the toneMapping() function defined below
- ( parameters.toneMapping !== NoToneMapping ) ? getToneMappingFunction( 'toneMapping', parameters.toneMapping ) : '',
-
- parameters.dithering ? '#define DITHERING' : '',
-
- ( parameters.outputEncoding || parameters.mapEncoding || parameters.matcapEncoding || parameters.envMapEncoding || parameters.emissiveMapEncoding ) ?
- ShaderChunk[ 'encodings_pars_fragment' ] : '', // this code is required here because it is used by the various encoding/decoding function defined below
- parameters.mapEncoding ? getTexelDecodingFunction( 'mapTexelToLinear', parameters.mapEncoding ) : '',
- parameters.matcapEncoding ? getTexelDecodingFunction( 'matcapTexelToLinear', parameters.matcapEncoding ) : '',
- parameters.envMapEncoding ? getTexelDecodingFunction( 'envMapTexelToLinear', parameters.envMapEncoding ) : '',
- parameters.emissiveMapEncoding ? getTexelDecodingFunction( 'emissiveMapTexelToLinear', parameters.emissiveMapEncoding ) : '',
- parameters.outputEncoding ? getTexelEncodingFunction( 'linearToOutputTexel', parameters.outputEncoding ) : '',
-
- parameters.depthPacking ? '#define DEPTH_PACKING ' + material.depthPacking : '',
-
- '\n'
-
- ].filter( filterEmptyLine ).join( '\n' );
-
- }
-
- vertexShader = parseIncludes( vertexShader );
- vertexShader = replaceLightNums( vertexShader, parameters );
- vertexShader = replaceClippingPlaneNums( vertexShader, parameters );
-
- fragmentShader = parseIncludes( fragmentShader );
- fragmentShader = replaceLightNums( fragmentShader, parameters );
- fragmentShader = replaceClippingPlaneNums( fragmentShader, parameters );
-
- vertexShader = unrollLoops( vertexShader );
- fragmentShader = unrollLoops( fragmentShader );
-
- if ( capabilities.isWebGL2 && ! material.isRawShaderMaterial ) {
-
- var isGLSL3ShaderMaterial = false;
-
- var versionRegex = /^\s*#version\s+300\s+es\s*\n/;
-
- if ( material.isShaderMaterial &&
- vertexShader.match( versionRegex ) !== null &&
- fragmentShader.match( versionRegex ) !== null ) {
-
- isGLSL3ShaderMaterial = true;
-
- vertexShader = vertexShader.replace( versionRegex, '' );
- fragmentShader = fragmentShader.replace( versionRegex, '' );
-
- }
-
- // GLSL 3.0 conversion
- prefixVertex = [
- '#version 300 es\n',
- '#define attribute in',
- '#define varying out',
- '#define texture2D texture'
- ].join( '\n' ) + '\n' + prefixVertex;
-
- prefixFragment = [
- '#version 300 es\n',
- '#define varying in',
- isGLSL3ShaderMaterial ? '' : 'out highp vec4 pc_fragColor;',
- isGLSL3ShaderMaterial ? '' : '#define gl_FragColor pc_fragColor',
- '#define gl_FragDepthEXT gl_FragDepth',
- '#define texture2D texture',
- '#define textureCube texture',
- '#define texture2DProj textureProj',
- '#define texture2DLodEXT textureLod',
- '#define texture2DProjLodEXT textureProjLod',
- '#define textureCubeLodEXT textureLod',
- '#define texture2DGradEXT textureGrad',
- '#define texture2DProjGradEXT textureProjGrad',
- '#define textureCubeGradEXT textureGrad'
- ].join( '\n' ) + '\n' + prefixFragment;
-
- }
-
- var vertexGlsl = prefixVertex + vertexShader;
- var fragmentGlsl = prefixFragment + fragmentShader;
-
- // console.log( '*VERTEX*', vertexGlsl );
- // console.log( '*FRAGMENT*', fragmentGlsl );
-
- var glVertexShader = WebGLShader( gl, gl.VERTEX_SHADER, vertexGlsl );
- var glFragmentShader = WebGLShader( gl, gl.FRAGMENT_SHADER, fragmentGlsl );
-
- gl.attachShader( program, glVertexShader );
- gl.attachShader( program, glFragmentShader );
-
- // Force a particular attribute to index 0.
-
- if ( material.index0AttributeName !== undefined ) {
-
- gl.bindAttribLocation( program, 0, material.index0AttributeName );
-
- } else if ( parameters.morphTargets === true ) {
-
- // programs with morphTargets displace position out of attribute 0
- gl.bindAttribLocation( program, 0, 'position' );
-
- }
-
- gl.linkProgram( program );
-
- var programLog = gl.getProgramInfoLog( program ).trim();
- var vertexLog = gl.getShaderInfoLog( glVertexShader ).trim();
- var fragmentLog = gl.getShaderInfoLog( glFragmentShader ).trim();
-
- var runnable = true;
- var haveDiagnostics = true;
-
- // console.log( '**VERTEX**', gl.getExtension( 'WEBGL_debug_shaders' ).getTranslatedShaderSource( glVertexShader ) );
- // console.log( '**FRAGMENT**', gl.getExtension( 'WEBGL_debug_shaders' ).getTranslatedShaderSource( glFragmentShader ) );
-
- if ( gl.getProgramParameter( program, gl.LINK_STATUS ) === false ) {
-
- runnable = false;
-
- console.error( 'THREE.WebGLProgram: shader error: ', gl.getError(), 'gl.VALIDATE_STATUS', gl.getProgramParameter( program, gl.VALIDATE_STATUS ), 'gl.getProgramInfoLog', programLog, vertexLog, fragmentLog );
-
- } else if ( programLog !== '' ) {
-
- console.warn( 'THREE.WebGLProgram: gl.getProgramInfoLog()', programLog );
-
- } else if ( vertexLog === '' || fragmentLog === '' ) {
-
- haveDiagnostics = false;
-
- }
-
- if ( haveDiagnostics ) {
-
- this.diagnostics = {
-
- runnable: runnable,
- material: material,
-
- programLog: programLog,
-
- vertexShader: {
-
- log: vertexLog,
- prefix: prefixVertex
-
- },
-
- fragmentShader: {
-
- log: fragmentLog,
- prefix: prefixFragment
-
- }
-
- };
-
- }
-
- // clean up
-
- gl.deleteShader( glVertexShader );
- gl.deleteShader( glFragmentShader );
-
- // set up caching for uniform locations
-
- var cachedUniforms;
-
- this.getUniforms = function () {
-
- if ( cachedUniforms === undefined ) {
-
- cachedUniforms = new WebGLUniforms( gl, program, textures );
-
- }
-
- return cachedUniforms;
-
- };
-
- // set up caching for attribute locations
-
- var cachedAttributes;
-
- this.getAttributes = function () {
-
- if ( cachedAttributes === undefined ) {
-
- cachedAttributes = fetchAttributeLocations( gl, program );
-
- }
-
- return cachedAttributes;
-
- };
-
- // free resource
-
- this.destroy = function () {
-
- gl.deleteProgram( program );
- this.program = undefined;
-
- };
-
- // DEPRECATED
-
- Object.defineProperties( this, {
-
- uniforms: {
- get: function () {
-
- console.warn( 'THREE.WebGLProgram: .uniforms is now .getUniforms().' );
- return this.getUniforms();
-
- }
- },
-
- attributes: {
- get: function () {
-
- console.warn( 'THREE.WebGLProgram: .attributes is now .getAttributes().' );
- return this.getAttributes();
-
- }
- }
-
- } );
-
-
- //
-
- this.name = shader.name;
- this.id = programIdCount ++;
- this.code = code;
- this.usedTimes = 1;
- this.program = program;
- this.vertexShader = glVertexShader;
- this.fragmentShader = glFragmentShader;
-
- return this;
-
-}
-
-export { WebGLProgram };
diff --git a/spaces/bigcode/bigcode-models-leaderboard/data/README.md b/spaces/bigcode/bigcode-models-leaderboard/data/README.md
deleted file mode 100644
index bcf662ec37d322927aef082b97ae57573bc39cd4..0000000000000000000000000000000000000000
--- a/spaces/bigcode/bigcode-models-leaderboard/data/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Multilingual Code Evals
-emoji: 📈
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/billusanda007/HireGPT/app_V1.py b/spaces/billusanda007/HireGPT/app_V1.py
deleted file mode 100644
index c9f837e0934e570fd3a8b5618f955afc9652597a..0000000000000000000000000000000000000000
--- a/spaces/billusanda007/HireGPT/app_V1.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import streamlit as st
-import nltk
-from nltk.corpus import stopwords
-from nltk.tokenize import word_tokenize
-from nltk.stem import PorterStemmer
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-from PyPDF2 import PdfReader
-import os
-from io import BytesIO
-import pickle
-import pdfminer
-from pdfminer.high_level import extract_text
-import re
-import PyPDF2
-import textract
-import tempfile
-from docx import Document
-
-nltk.download('punkt')
-nltk.download('stopwords')
-
-def preprocess_text(text):
- words = word_tokenize(text.lower())
-
- stop_words = set(stopwords.words('english'))
- words = [word for word in words if word not in stop_words]
-
- stemmer = PorterStemmer()
- words = [stemmer.stem(word) for word in words]
-
- return ' '.join(words)
-
-def extract_text_from_pdf(pdf_content):
- pdf_reader = PdfReader(BytesIO(pdf_content))
- text = ''
- for page in pdf_reader.pages:
- text += page.extract_text()
- return text
-
-def extract_text_from_docx(docx_content):
- doc = Document(BytesIO(docx_content))
- text = " ".join(paragraph.text for paragraph in doc.paragraphs)
- return text
-
-
-def extract_text_from_txt(txt_content):
- text = textract.process(input_filename=None, input_bytes=txt_content)
- return text
-
-def extract_text_from_resume(file_path):
- file_extension = file_path.split('.')[-1].lower()
-
- if file_extension == 'pdf':
- return extract_text_from_pdf(file_path)
- elif file_extension == 'docx':
- return extract_text_from_docx(file_path)
- elif file_extension == 'txt':
- return extract_text_from_txt(file_path)
- else:
- raise ValueError(f"Unsupported file format: {file_extension}")
-
-def clean_pdf_text(text):
- text = re.sub('http\S+\s*', ' ', text)
- text = re.sub('RT|cc', ' ', text)
- text = re.sub('#\S+', '', text)
- text = re.sub('@\S+', ' ', text)
- text = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"""), ' ', text)
- text = re.sub(r'[^\x00-\x7f]',r' ', text)
- text = re.sub('\s+', ' ', text)
- return text
-
-def extract_candidate_name(text):
- pattern = r'(?:Mr\.|Ms\.|Mrs\.)?\s?([A-Z][a-z]+)\s([A-Z][a-z]+)'
- match = re.search(pattern, text)
- if match:
- return match.group(0)
- return "Candidate Name Not Found"
-
-def calculate_similarity(job_description, cvs, cv_file_names):
- processed_job_desc = preprocess_text(job_description)
-
- processed_cvs = [preprocess_text(cv) for cv in cvs]
-
- all_text = [processed_job_desc] + processed_cvs
-
- vectorizer = TfidfVectorizer()
- tfidf_matrix = vectorizer.fit_transform(all_text)
-
- similarity_scores = cosine_similarity(tfidf_matrix)[0][1:]
-
- ranked_cvs = list(zip(cv_file_names, similarity_scores))
- ranked_cvs.sort(key=lambda x: x[1], reverse=True)
-
- return ranked_cvs
-
-def rank_and_shortlist(job_description, cv_files, threshold=0.15):
- cv_texts = []
- cv_file_names = []
-
- for cv_file in cv_files:
- file_extension = os.path.splitext(cv_file.name)[1].lower()
-
- try:
- if file_extension == '.pdf':
- cv_text = extract_text_from_pdf(cv_file.read())
- elif file_extension == '.docx':
- cv_text = extract_text_from_docx(cv_file.read())
- elif file_extension == '.txt':
- cv_text = cv_file.read().decode('utf-8', errors='ignore')
- else:
- st.warning(f"Unsupported file format: {file_extension}. Skipping file: {cv_file.name}")
- continue
-
- cv_texts.append(clean_pdf_text(cv_text))
- cv_file_names.append(cv_file.name)
-
- except Exception as e:
- st.warning(f"Error processing file '{cv_file.name}': {str(e)}")
- continue
-
- if not cv_texts:
- st.error("No valid resumes found. Please upload resumes in supported formats (PDF, DOCX, or TXT).")
- return [], []
-
- similarity_scores = calculate_similarity(job_description, cv_texts, cv_file_names)
-
- ranked_cvs = [(cv_name, score) for (cv_name, score) in similarity_scores]
- shortlisted_cvs = [(cv_name, score) for (cv_name, score) in ranked_cvs if score >= threshold]
-
- return ranked_cvs, shortlisted_cvs
-
-
-def main():
- st.title("Resume Ranking App")
-
- st.write("Upload the Job Description:")
- job_description = st.text_area("Job Description", height=200, key='job_description')
-
- st.write("Upload the Resumes :")
- cv_files = st.file_uploader("Choose files", accept_multiple_files=True, key='cv_files')
-
- if st.button("Submit"):
- if job_description and cv_files:
- # Rank and shortlist candidates
- ranked_cvs, shortlisted_cvs = rank_and_shortlist(job_description, cv_files)
-
- # Display ranking with larger text
- st.markdown("### Ranking of Resumes:")
- for rank, score in ranked_cvs:
- st.markdown(f"**File Name:** {rank}, **Similarity Score:** {score:.2f}")
-
- # Display shortlisted candidates with larger text
- st.markdown("### Shortlisted Candidates:")
- if not shortlisted_cvs: # Check if the shortlisted_cvs list is empty
- st.markdown("None")
- else:
- for rank, score in shortlisted_cvs:
- st.markdown(f"**File Name:** {rank}, **Similarity Score:** {score:.2f}")
- else:
- st.write("Please upload both the job description and resumes to proceed.")
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/bioriAsaeru/text-to-voice/Goundamani Comedy Videos Download __LINK__ 3gp Playerl.md b/spaces/bioriAsaeru/text-to-voice/Goundamani Comedy Videos Download __LINK__ 3gp Playerl.md
deleted file mode 100644
index a58e224f5f7cc352b1fac327901621dff822df5b..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Goundamani Comedy Videos Download __LINK__ 3gp Playerl.md
+++ /dev/null
@@ -1,5 +0,0 @@
-
-Welcome to the Mermaid Modeler Tip Sheet
- Goundamani Comedy Videos Download 3gp Playerl
aaccfb2cb3
-
-
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/HD Online Player (Mr. X Movie In Hindi Torrent Downloa).md b/spaces/bioriAsaeru/text-to-voice/HD Online Player (Mr. X Movie In Hindi Torrent Downloa).md
deleted file mode 100644
index 78b2005d3b2bc3f01f8adb49700191f218d35d3d..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/HD Online Player (Mr. X Movie In Hindi Torrent Downloa).md
+++ /dev/null
@@ -1,6 +0,0 @@
-HD Online Player (Mr. X movie in hindi torrent downloa)
-
- 4d29de3e1b
-
-
-
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/Panoptic-DeepLab/panoptic_deeplab/config.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/Panoptic-DeepLab/panoptic_deeplab/config.py
deleted file mode 100644
index 5aa2d280c66dbccc9ff8c3ccf39ccfbfc1eaa430..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/Panoptic-DeepLab/panoptic_deeplab/config.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from detectron2.config import CfgNode as CN
-from detectron2.projects.deeplab import add_deeplab_config
-
-
-def add_panoptic_deeplab_config(cfg):
- """
- Add config for Panoptic-DeepLab.
- """
- # Reuse DeepLab config.
- add_deeplab_config(cfg)
- # Target generation parameters.
- cfg.INPUT.GAUSSIAN_SIGMA = 10
- cfg.INPUT.IGNORE_STUFF_IN_OFFSET = True
- cfg.INPUT.SMALL_INSTANCE_AREA = 4096
- cfg.INPUT.SMALL_INSTANCE_WEIGHT = 3
- cfg.INPUT.IGNORE_CROWD_IN_SEMANTIC = False
- # Optimizer type.
- cfg.SOLVER.OPTIMIZER = "ADAM"
- # Panoptic-DeepLab semantic segmentation head.
- # We add an extra convolution before predictor.
- cfg.MODEL.SEM_SEG_HEAD.HEAD_CHANNELS = 256
- cfg.MODEL.SEM_SEG_HEAD.LOSS_TOP_K = 0.2
- # Panoptic-DeepLab instance segmentation head.
- cfg.MODEL.INS_EMBED_HEAD = CN()
- cfg.MODEL.INS_EMBED_HEAD.NAME = "PanopticDeepLabInsEmbedHead"
- cfg.MODEL.INS_EMBED_HEAD.IN_FEATURES = ["res2", "res3", "res5"]
- cfg.MODEL.INS_EMBED_HEAD.PROJECT_FEATURES = ["res2", "res3"]
- cfg.MODEL.INS_EMBED_HEAD.PROJECT_CHANNELS = [32, 64]
- cfg.MODEL.INS_EMBED_HEAD.ASPP_CHANNELS = 256
- cfg.MODEL.INS_EMBED_HEAD.ASPP_DILATIONS = [6, 12, 18]
- cfg.MODEL.INS_EMBED_HEAD.ASPP_DROPOUT = 0.1
- # We add an extra convolution before predictor.
- cfg.MODEL.INS_EMBED_HEAD.HEAD_CHANNELS = 32
- cfg.MODEL.INS_EMBED_HEAD.CONVS_DIM = 128
- cfg.MODEL.INS_EMBED_HEAD.COMMON_STRIDE = 4
- cfg.MODEL.INS_EMBED_HEAD.NORM = "SyncBN"
- cfg.MODEL.INS_EMBED_HEAD.CENTER_LOSS_WEIGHT = 200.0
- cfg.MODEL.INS_EMBED_HEAD.OFFSET_LOSS_WEIGHT = 0.01
- # Panoptic-DeepLab post-processing setting.
- cfg.MODEL.PANOPTIC_DEEPLAB = CN()
- # Stuff area limit, ignore stuff region below this number.
- cfg.MODEL.PANOPTIC_DEEPLAB.STUFF_AREA = 2048
- cfg.MODEL.PANOPTIC_DEEPLAB.CENTER_THRESHOLD = 0.1
- cfg.MODEL.PANOPTIC_DEEPLAB.NMS_KERNEL = 7
- cfg.MODEL.PANOPTIC_DEEPLAB.TOP_K_INSTANCE = 200
- # If set to False, Panoptic-DeepLab will not evaluate instance segmentation.
- cfg.MODEL.PANOPTIC_DEEPLAB.PREDICT_INSTANCES = True
- cfg.MODEL.PANOPTIC_DEEPLAB.USE_DEPTHWISE_SEPARABLE_CONV = False
- # This is the padding parameter for images with various sizes. ASPP layers
- # requires input images to be divisible by the average pooling size and we
- # can use `MODEL.PANOPTIC_DEEPLAB.SIZE_DIVISIBILITY` to pad all images to
- # a fixed resolution (e.g. 640x640 for COCO) to avoid having a image size
- # that is not divisible by ASPP average pooling size.
- cfg.MODEL.PANOPTIC_DEEPLAB.SIZE_DIVISIBILITY = -1
- # Only evaluates network speed (ignores post-processing).
- cfg.MODEL.PANOPTIC_DEEPLAB.BENCHMARK_NETWORK_SPEED = False
diff --git a/spaces/caojiachen1/ChatGPT/crazy_functions/test_project/cpp/libJPG/UElibJPG.Build.cs b/spaces/caojiachen1/ChatGPT/crazy_functions/test_project/cpp/libJPG/UElibJPG.Build.cs
deleted file mode 100644
index 01ca25dce97e8e3bf6dd4fba43416a66262bdb12..0000000000000000000000000000000000000000
--- a/spaces/caojiachen1/ChatGPT/crazy_functions/test_project/cpp/libJPG/UElibJPG.Build.cs
+++ /dev/null
@@ -1,17 +0,0 @@
-// Copyright Epic Games, Inc. All Rights Reserved.
-
-using UnrealBuildTool;
-
-public class UElibJPG : ModuleRules
-{
- public UElibJPG(ReadOnlyTargetRules Target) : base(Target)
- {
- Type = ModuleType.External;
-
- string libJPGPath = Target.UEThirdPartySourceDirectory + "libJPG";
- PublicIncludePaths.Add(libJPGPath);
-
- ShadowVariableWarningLevel = WarningLevel.Off;
- }
-}
-
diff --git a/spaces/cccccch/VITS-fast-fine-tuning-DingZhen/text/mandarin.py b/spaces/cccccch/VITS-fast-fine-tuning-DingZhen/text/mandarin.py
deleted file mode 100644
index 162e1b912dabec4b448ccd3d00d56306f82ce076..0000000000000000000000000000000000000000
--- a/spaces/cccccch/VITS-fast-fine-tuning-DingZhen/text/mandarin.py
+++ /dev/null
@@ -1,326 +0,0 @@
-import os
-import sys
-import re
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba
-import cn2an
-import logging
-
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', 'ㄟˉ'),
- ('b', 'ㄅㄧˋ'),
- ('c', 'ㄙㄧˉ'),
- ('d', 'ㄉㄧˋ'),
- ('e', 'ㄧˋ'),
- ('f', 'ㄝˊㄈㄨˋ'),
- ('g', 'ㄐㄧˋ'),
- ('h', 'ㄝˇㄑㄩˋ'),
- ('i', 'ㄞˋ'),
- ('j', 'ㄐㄟˋ'),
- ('k', 'ㄎㄟˋ'),
- ('l', 'ㄝˊㄛˋ'),
- ('m', 'ㄝˊㄇㄨˋ'),
- ('n', 'ㄣˉ'),
- ('o', 'ㄡˉ'),
- ('p', 'ㄆㄧˉ'),
- ('q', 'ㄎㄧㄡˉ'),
- ('r', 'ㄚˋ'),
- ('s', 'ㄝˊㄙˋ'),
- ('t', 'ㄊㄧˋ'),
- ('u', 'ㄧㄡˉ'),
- ('v', 'ㄨㄧˉ'),
- ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
- ('x', 'ㄝˉㄎㄨˋㄙˋ'),
- ('y', 'ㄨㄞˋ'),
- ('z', 'ㄗㄟˋ')
-]]
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ㄅㄛ', 'p⁼wo'),
- ('ㄆㄛ', 'pʰwo'),
- ('ㄇㄛ', 'mwo'),
- ('ㄈㄛ', 'fwo'),
- ('ㄅ', 'p⁼'),
- ('ㄆ', 'pʰ'),
- ('ㄇ', 'm'),
- ('ㄈ', 'f'),
- ('ㄉ', 't⁼'),
- ('ㄊ', 'tʰ'),
- ('ㄋ', 'n'),
- ('ㄌ', 'l'),
- ('ㄍ', 'k⁼'),
- ('ㄎ', 'kʰ'),
- ('ㄏ', 'h'),
- ('ㄐ', 'ʧ⁼'),
- ('ㄑ', 'ʧʰ'),
- ('ㄒ', 'ʃ'),
- ('ㄓ', 'ʦ`⁼'),
- ('ㄔ', 'ʦ`ʰ'),
- ('ㄕ', 's`'),
- ('ㄖ', 'ɹ`'),
- ('ㄗ', 'ʦ⁼'),
- ('ㄘ', 'ʦʰ'),
- ('ㄙ', 's'),
- ('ㄚ', 'a'),
- ('ㄛ', 'o'),
- ('ㄜ', 'ə'),
- ('ㄝ', 'e'),
- ('ㄞ', 'ai'),
- ('ㄟ', 'ei'),
- ('ㄠ', 'au'),
- ('ㄡ', 'ou'),
- ('ㄧㄢ', 'yeNN'),
- ('ㄢ', 'aNN'),
- ('ㄧㄣ', 'iNN'),
- ('ㄣ', 'əNN'),
- ('ㄤ', 'aNg'),
- ('ㄧㄥ', 'iNg'),
- ('ㄨㄥ', 'uNg'),
- ('ㄩㄥ', 'yuNg'),
- ('ㄥ', 'əNg'),
- ('ㄦ', 'əɻ'),
- ('ㄧ', 'i'),
- ('ㄨ', 'u'),
- ('ㄩ', 'ɥ'),
- ('ˉ', '→'),
- ('ˊ', '↑'),
- ('ˇ', '↓↑'),
- ('ˋ', '↓'),
- ('˙', ''),
- ('，', ','),
- ('。', '.'),
- ('！', '!'),
- ('？', '?'),
- ('—', '-')
-]]
-
-# List of (romaji, ipa) pairs:
-_romaji_to_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('ʃy', 'ʃ'),
- ('ʧʰy', 'ʧʰ'),
- ('ʧ⁼y', 'ʧ⁼'),
- ('NN', 'n'),
- ('Ng', 'ŋ'),
- ('y', 'j'),
- ('h', 'x')
-]]
-
-# List of (bopomofo, ipa) pairs:
-_bopomofo_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ㄅㄛ', 'p⁼wo'),
- ('ㄆㄛ', 'pʰwo'),
- ('ㄇㄛ', 'mwo'),
- ('ㄈㄛ', 'fwo'),
- ('ㄅ', 'p⁼'),
- ('ㄆ', 'pʰ'),
- ('ㄇ', 'm'),
- ('ㄈ', 'f'),
- ('ㄉ', 't⁼'),
- ('ㄊ', 'tʰ'),
- ('ㄋ', 'n'),
- ('ㄌ', 'l'),
- ('ㄍ', 'k⁼'),
- ('ㄎ', 'kʰ'),
- ('ㄏ', 'x'),
- ('ㄐ', 'tʃ⁼'),
- ('ㄑ', 'tʃʰ'),
- ('ㄒ', 'ʃ'),
- ('ㄓ', 'ts`⁼'),
- ('ㄔ', 'ts`ʰ'),
- ('ㄕ', 's`'),
- ('ㄖ', 'ɹ`'),
- ('ㄗ', 'ts⁼'),
- ('ㄘ', 'tsʰ'),
- ('ㄙ', 's'),
- ('ㄚ', 'a'),
- ('ㄛ', 'o'),
- ('ㄜ', 'ə'),
- ('ㄝ', 'ɛ'),
- ('ㄞ', 'aɪ'),
- ('ㄟ', 'eɪ'),
- ('ㄠ', 'ɑʊ'),
- ('ㄡ', 'oʊ'),
- ('ㄧㄢ', 'jɛn'),
- ('ㄩㄢ', 'ɥæn'),
- ('ㄢ', 'an'),
- ('ㄧㄣ', 'in'),
- ('ㄩㄣ', 'ɥn'),
- ('ㄣ', 'ən'),
- ('ㄤ', 'ɑŋ'),
- ('ㄧㄥ', 'iŋ'),
- ('ㄨㄥ', 'ʊŋ'),
- ('ㄩㄥ', 'jʊŋ'),
- ('ㄥ', 'əŋ'),
- ('ㄦ', 'əɻ'),
- ('ㄧ', 'i'),
- ('ㄨ', 'u'),
- ('ㄩ', 'ɥ'),
- ('ˉ', '→'),
- ('ˊ', '↑'),
- ('ˇ', '↓↑'),
- ('ˋ', '↓'),
- ('˙', ''),
- ('，', ','),
- ('。', '.'),
- ('！', '!'),
- ('？', '?'),
- ('—', '-')
-]]
-
-# List of (bopomofo, ipa2) pairs:
-_bopomofo_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ㄅㄛ', 'pwo'),
- ('ㄆㄛ', 'pʰwo'),
- ('ㄇㄛ', 'mwo'),
- ('ㄈㄛ', 'fwo'),
- ('ㄅ', 'p'),
- ('ㄆ', 'pʰ'),
- ('ㄇ', 'm'),
- ('ㄈ', 'f'),
- ('ㄉ', 't'),
- ('ㄊ', 'tʰ'),
- ('ㄋ', 'n'),
- ('ㄌ', 'l'),
- ('ㄍ', 'k'),
- ('ㄎ', 'kʰ'),
- ('ㄏ', 'h'),
- ('ㄐ', 'tɕ'),
- ('ㄑ', 'tɕʰ'),
- ('ㄒ', 'ɕ'),
- ('ㄓ', 'tʂ'),
- ('ㄔ', 'tʂʰ'),
- ('ㄕ', 'ʂ'),
- ('ㄖ', 'ɻ'),
- ('ㄗ', 'ts'),
- ('ㄘ', 'tsʰ'),
- ('ㄙ', 's'),
- ('ㄚ', 'a'),
- ('ㄛ', 'o'),
- ('ㄜ', 'ɤ'),
- ('ㄝ', 'ɛ'),
- ('ㄞ', 'aɪ'),
- ('ㄟ', 'eɪ'),
- ('ㄠ', 'ɑʊ'),
- ('ㄡ', 'oʊ'),
- ('ㄧㄢ', 'jɛn'),
- ('ㄩㄢ', 'yæn'),
- ('ㄢ', 'an'),
- ('ㄧㄣ', 'in'),
- ('ㄩㄣ', 'yn'),
- ('ㄣ', 'ən'),
- ('ㄤ', 'ɑŋ'),
- ('ㄧㄥ', 'iŋ'),
- ('ㄨㄥ', 'ʊŋ'),
- ('ㄩㄥ', 'jʊŋ'),
- ('ㄥ', 'ɤŋ'),
- ('ㄦ', 'əɻ'),
- ('ㄧ', 'i'),
- ('ㄨ', 'u'),
- ('ㄩ', 'y'),
- ('ˉ', '˥'),
- ('ˊ', '˧˥'),
- ('ˇ', '˨˩˦'),
- ('ˋ', '˥˩'),
- ('˙', ''),
- ('，', ','),
- ('。', '.'),
- ('！', '!'),
- ('？', '?'),
- ('—', '-')
-]]
-
-
-def number_to_chinese(text):
- numbers = re.findall(r'\d+(?:\.?\d+)?', text)
- for number in numbers:
- text = text.replace(number, cn2an.an2cn(number), 1)
- return text
-
-
-def chinese_to_bopomofo(text):
- text = text.replace('、', '，').replace('；', '，').replace('：', '，')
- words = jieba.lcut(text, cut_all=False)
- text = ''
- for word in words:
- bopomofos = lazy_pinyin(word, BOPOMOFO)
- if not re.search('[\u4e00-\u9fff]', word):
- text += word
- continue
- for i in range(len(bopomofos)):
- bopomofos[i] = re.sub(r'([\u3105-\u3129])$', r'\1ˉ', bopomofos[i])
- if text != '':
- text += ' '
- text += ''.join(bopomofos)
- return text
-
-
-def latin_to_bopomofo(text):
- for regex, replacement in _latin_to_bopomofo:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def bopomofo_to_romaji(text):
- for regex, replacement in _bopomofo_to_romaji:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def bopomofo_to_ipa(text):
- for regex, replacement in _bopomofo_to_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def bopomofo_to_ipa2(text):
- for regex, replacement in _bopomofo_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def chinese_to_romaji(text):
- text = number_to_chinese(text)
- text = chinese_to_bopomofo(text)
- text = latin_to_bopomofo(text)
- text = bopomofo_to_romaji(text)
- text = re.sub('i([aoe])', r'y\1', text)
- text = re.sub('u([aoəe])', r'w\1', text)
- text = re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
- r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
- text = re.sub('([ʦs][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
- return text
-
-
-def chinese_to_lazy_ipa(text):
- text = chinese_to_romaji(text)
- for regex, replacement in _romaji_to_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def chinese_to_ipa(text):
- text = number_to_chinese(text)
- text = chinese_to_bopomofo(text)
- text = latin_to_bopomofo(text)
- text = bopomofo_to_ipa(text)
- text = re.sub('i([aoe])', r'j\1', text)
- text = re.sub('u([aoəe])', r'w\1', text)
- text = re.sub('([sɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
- r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
- text = re.sub('([s][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
- return text
-
-
-def chinese_to_ipa2(text):
- text = number_to_chinese(text)
- text = chinese_to_bopomofo(text)
- text = latin_to_bopomofo(text)
- text = bopomofo_to_ipa2(text)
- text = re.sub(r'i([aoe])', r'j\1', text)
- text = re.sub(r'u([aoəe])', r'w\1', text)
- text = re.sub(r'([ʂɹ]ʰ?)([˩˨˧˦˥ ]+|$)', r'\1ʅ\2', text)
- text = re.sub(r'(sʰ?)([˩˨˧˦˥ ]+|$)', r'\1ɿ\2', text)
- return text
diff --git a/spaces/chendl/compositional_test/transformers/examples/pytorch/text-generation/run_generation_contrastive_search.py b/spaces/chendl/compositional_test/transformers/examples/pytorch/text-generation/run_generation_contrastive_search.py
deleted file mode 100644
index 117f063a6dd9a81cc9c00e294f91f9826e9dc681..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/pytorch/text-generation/run_generation_contrastive_search.py
+++ /dev/null
@@ -1,138 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2022 University of Cambridge, Tencent AI Lab, DeepMind and The University of Hong Kong Authors and The HuggingFace Inc. 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.
-""" The examples of running contrastive search on the auto-APIs;
-
-Running this example:
-python run_generation_contrastive_search.py --model_name_or_path=gpt2-large --penalty_alpha=0.6 --k=4 --length=256
-"""
-
-
-import argparse
-import logging
-
-import numpy as np
-import torch
-
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-
-logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- level=logging.INFO,
-)
-logger = logging.getLogger(__name__)
-
-
-def set_seed(args):
- np.random.seed(args.seed)
- torch.manual_seed(args.seed)
- if args.n_gpu > 0:
- torch.cuda.manual_seed_all(args.seed)
-
-
-def main():
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--model_name_or_path",
- default=None,
- type=str,
- required=True,
- )
- parser.add_argument("--prompt", type=str, default="")
- parser.add_argument("--length", type=int, default=20)
- parser.add_argument("--stop_token", type=str, default=None, help="Token at which text generation is stopped")
- parser.add_argument(
- "--temperature",
- type=float,
- default=1.0,
- help="temperature of 1.0 has no effect, lower tend toward greedy sampling",
- )
- parser.add_argument(
- "--repetition_penalty", type=float, default=1.0, help="primarily useful for CTRL model; in that case, use 1.2"
- )
- parser.add_argument("--k", type=int, default=0)
- parser.add_argument("--penalty_alpha", type=float, default=0.0)
- parser.add_argument("--p", type=float, default=0.9)
-
- parser.add_argument("--prefix", type=str, default="", help="Text added prior to input.")
- parser.add_argument("--padding_text", type=str, default="", help="Deprecated, the use of `--prefix` is preferred.")
- parser.add_argument("--xlm_language", type=str, default="", help="Optional language when used with the XLM model.")
-
- parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
- parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
- parser.add_argument(
- "--fp16",
- action="store_true",
- help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
- )
- args = parser.parse_args()
-
- args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
- args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
-
- logger.warning(f"device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fp16}")
-
- set_seed(args)
-
- # Initialize the model and tokenizer
- tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
- model = AutoModelForCausalLM.from_pretrained(args.model_name_or_path)
-
- # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
- # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
- model.to(args.device)
-
- if args.fp16:
- model.half()
-
- logger.info(args)
- prompt_text = args.prompt if args.prompt else input("Model prompt >>> ")
-
- inputs = tokenizer(prompt_text, return_tensors="pt", add_special_tokens=False)
- inputs = {key: value.to(args.device) for key, value in inputs.items()}
-
- output_sequences = model.generate(
- **inputs,
- max_length=args.length + len(inputs["input_ids"][0]),
- penalty_alpha=args.penalty_alpha,
- top_k=args.k,
- )
-
- generated_sequences = []
- for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
- print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===")
- generated_sequence = generated_sequence.tolist()
-
- # Decode text
- text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True, add_special_tokens=False)
-
- # Remove all text after the stop token
- text = text[: text.find(args.stop_token) if args.stop_token else None]
-
- # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
- total_sequence = (
- prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0], clean_up_tokenization_spaces=True)) :]
- )
-
- generated_sequences.append(total_sequence)
- print(total_sequence)
-
- return generated_sequences
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/data/datasets/glue.py b/spaces/chendl/compositional_test/transformers/src/transformers/data/datasets/glue.py
deleted file mode 100644
index 72df3bece21925d15748d53bd82def67bfdd82bb..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/src/transformers/data/datasets/glue.py
+++ /dev/null
@@ -1,161 +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
-import time
-import warnings
-from dataclasses import dataclass, field
-from enum import Enum
-from typing import List, Optional, Union
-
-import torch
-from filelock import FileLock
-from torch.utils.data import Dataset
-
-from ...tokenization_utils_base import PreTrainedTokenizerBase
-from ...utils import logging
-from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
-from ..processors.utils import InputFeatures
-
-
-logger = logging.get_logger(__name__)
-
-
-@dataclass
-class GlueDataTrainingArguments:
- """
- Arguments pertaining to what data we are going to input our model for training and eval.
-
- Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command
- line.
- """
-
- task_name: str = field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys())})
- data_dir: str = field(
- metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}
- )
- max_seq_length: int = field(
- default=128,
- metadata={
- "help": (
- "The maximum total input sequence length after tokenization. Sequences longer "
- "than this will be truncated, sequences shorter will be padded."
- )
- },
- )
- overwrite_cache: bool = field(
- default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
- )
-
- def __post_init__(self):
- self.task_name = self.task_name.lower()
-
-
-class Split(Enum):
- train = "train"
- dev = "dev"
- test = "test"
-
-
-class GlueDataset(Dataset):
- """
- This will be superseded by a framework-agnostic approach soon.
- """
-
- args: GlueDataTrainingArguments
- output_mode: str
- features: List[InputFeatures]
-
- def __init__(
- self,
- args: GlueDataTrainingArguments,
- tokenizer: PreTrainedTokenizerBase,
- limit_length: Optional[int] = None,
- mode: Union[str, Split] = Split.train,
- cache_dir: Optional[str] = None,
- ):
- warnings.warn(
- "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
- "library. You can have a look at this example script for pointers: "
- "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py",
- FutureWarning,
- )
- self.args = args
- self.processor = glue_processors[args.task_name]()
- self.output_mode = glue_output_modes[args.task_name]
- if isinstance(mode, str):
- try:
- mode = Split[mode]
- except KeyError:
- raise KeyError("mode is not a valid split name")
- # Load data features from cache or dataset file
- cached_features_file = os.path.join(
- cache_dir if cache_dir is not None else args.data_dir,
- f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}",
- )
- label_list = self.processor.get_labels()
- if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
- "RobertaTokenizer",
- "RobertaTokenizerFast",
- "XLMRobertaTokenizer",
- "BartTokenizer",
- "BartTokenizerFast",
- ):
- # HACK(label indices are swapped in RoBERTa pretrained model)
- label_list[1], label_list[2] = label_list[2], label_list[1]
- self.label_list = label_list
-
- # Make sure only the first process in distributed training processes the dataset,
- # and the others will use the cache.
- lock_path = cached_features_file + ".lock"
- with FileLock(lock_path):
- if os.path.exists(cached_features_file) and not args.overwrite_cache:
- start = time.time()
- self.features = torch.load(cached_features_file)
- logger.info(
- f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start
- )
- else:
- logger.info(f"Creating features from dataset file at {args.data_dir}")
-
- if mode == Split.dev:
- examples = self.processor.get_dev_examples(args.data_dir)
- elif mode == Split.test:
- examples = self.processor.get_test_examples(args.data_dir)
- else:
- examples = self.processor.get_train_examples(args.data_dir)
- if limit_length is not None:
- examples = examples[:limit_length]
- self.features = glue_convert_examples_to_features(
- examples,
- tokenizer,
- max_length=args.max_seq_length,
- label_list=label_list,
- output_mode=self.output_mode,
- )
- start = time.time()
- torch.save(self.features, cached_features_file)
- # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
- logger.info(
- f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"
- )
-
- def __len__(self):
- return len(self.features)
-
- def __getitem__(self, i) -> InputFeatures:
- return self.features[i]
-
- def get_labels(self):
- return self.label_list
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/model3d.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/model3d.py
deleted file mode 100644
index ac0278955d601b2fc52c69fa89212d04317aeef6..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/model3d.py
+++ /dev/null
@@ -1,155 +0,0 @@
-"""gr.Model3D() component."""
-
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Any, Callable, Literal
-
-from gradio_client import media_data
-from gradio_client.documentation import document, set_documentation_group
-from gradio_client.serializing import FileSerializable
-
-from gradio.components.base import IOComponent, _Keywords
-from gradio.events import (
- Changeable,
- Clearable,
- Editable,
- Uploadable,
-)
-
-set_documentation_group("component")
-
-
-@document()
-class Model3D(
- Changeable, Uploadable, Editable, Clearable, IOComponent, FileSerializable
-):
- """
- Component allows users to upload or view 3D Model files (.obj, .glb, or .gltf).
- Preprocessing: This component passes the uploaded file as a {str}filepath.
- Postprocessing: expects function to return a {str} or {pathlib.Path} filepath of type (.obj, glb, or .gltf)
-
- Demos: model3D
- Guides: how-to-use-3D-model-component
- """
-
- def __init__(
- self,
- value: str | Callable | None = None,
- *,
- clear_color: list[float] | None = None,
- label: str | None = None,
- every: float | None = None,
- show_label: bool = True,
- container: bool = True,
- scale: int | None = None,
- min_width: int = 160,
- visible: bool = True,
- elem_id: str | None = None,
- elem_classes: list[str] | str | None = None,
- **kwargs,
- ):
- """
- Parameters:
- value: path to (.obj, glb, or .gltf) file to show in model3D viewer. If callable, the function will be called whenever the app loads to set the initial value of the component.
- clear_color: background color of scene
- label: component name in interface.
- 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.
- 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.
- """
- self.clear_color = clear_color or [0, 0, 0, 0]
- IOComponent.__init__(
- self,
- label=label,
- every=every,
- show_label=show_label,
- container=container,
- scale=scale,
- min_width=min_width,
- visible=visible,
- elem_id=elem_id,
- elem_classes=elem_classes,
- value=value,
- **kwargs,
- )
-
- def get_config(self):
- return {
- "clearColor": self.clear_color,
- "value": self.value,
- **IOComponent.get_config(self),
- }
-
- def example_inputs(self) -> dict[str, Any]:
- return {
- "raw": {"is_file": False, "data": media_data.BASE64_MODEL3D},
- "serialized": "https://github.com/gradio-app/gradio/raw/main/test/test_files/Box.gltf",
- }
-
- @staticmethod
- def update(
- value: Any | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE,
- label: str | None = None,
- show_label: bool | None = None,
- container: bool | None = None,
- scale: int | None = None,
- min_width: int | None = None,
- visible: bool | None = None,
- ):
- updated_config = {
- "label": label,
- "show_label": show_label,
- "container": container,
- "scale": scale,
- "min_width": min_width,
- "visible": visible,
- "value": value,
- "__type__": "update",
- }
- return updated_config
-
- def preprocess(self, x: dict[str, str] | None) -> str | None:
- """
- Parameters:
- x: JSON object with filename as 'name' property and base64 data as 'data' property
- Returns:
- string file path to temporary file with the 3D image model
- """
- if x is None:
- return x
- file_name, file_data, is_file = (
- x["name"],
- x["data"],
- x.get("is_file", False),
- )
- if is_file:
- temp_file_path = self.make_temp_copy_if_needed(file_name)
- else:
- temp_file_path = self.base64_to_temp_file_if_needed(file_data, file_name)
-
- return temp_file_path
-
- def postprocess(self, y: str | Path | None) -> dict[str, str] | None:
- """
- Parameters:
- y: path to the model
- Returns:
- file name mapped to base64 url data
- """
- if y is None:
- return y
- data = {
- "name": self.make_temp_copy_if_needed(y),
- "data": None,
- "is_file": True,
- }
- return data
-
- def as_example(self, input_data: str | None) -> str:
- return Path(input_data).name if input_data else ""
diff --git a/spaces/cihyFjudo/fairness-paper-search/Download Kannada Movie Moggina Manasu Mp3 Songs A Film That Won Five Filmfare Awards South in 2008.md b/spaces/cihyFjudo/fairness-paper-search/Download Kannada Movie Moggina Manasu Mp3 Songs A Film That Won Five Filmfare Awards South in 2008.md
deleted file mode 100644
index 2d7d913df45d613c645295c9a421c73768f31c05..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Download Kannada Movie Moggina Manasu Mp3 Songs A Film That Won Five Filmfare Awards South in 2008.md
+++ /dev/null
@@ -1,5 +0,0 @@
-
-Lake Placid movie free download in italian hd
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-
-
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diff --git a/spaces/cihyFjudo/fairness-paper-search/How Seokyu Fell in Love on We Got Married 720p A FF Story.md b/spaces/cihyFjudo/fairness-paper-search/How Seokyu Fell in Love on We Got Married 720p A FF Story.md
deleted file mode 100644
index c14463038f2dc1d46fd70f32f2b2a20d9559dcd3..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/How Seokyu Fell in Love on We Got Married 720p A FF Story.md
+++ /dev/null
@@ -1,6 +0,0 @@
-ff seokyu we got married 720p
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cihyFjudo/fairness-paper-search/Lungo 1.3.0 The Latest Version of GokiStats Mod for Minecraft.md b/spaces/cihyFjudo/fairness-paper-search/Lungo 1.3.0 The Latest Version of GokiStats Mod for Minecraft.md
deleted file mode 100644
index 8aebdad774dfd2dbd319df86f516acaf01f6a005..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Lungo 1.3.0 The Latest Version of GokiStats Mod for Minecraft.md
+++ /dev/null
@@ -1,12 +0,0 @@
-
-Lungo 1.3.0
-
Nell'area preventiva vengono acquisite conoscenze per il mantenimento della migliore efficienza fisica lungo l'arco dell'intera vita, in soggetti normali che necessitino di prevenire le patologie correlate alla sedentarietà mediante uno stile di vita attivo e salutare.
Nell'area manageriale, si apprendono nozioni di natura giuridico-amministrativa che regolano il mondo delle attività motorie negli ambiti: sportivo, ricreativo, educativo, preventivo e industriale.
Nell'area educativa si acquisiscono conoscenze sull'educazione motoria espressivo-comunicativa, valorizzando lo sviluppo delle capacità, delle competenze e dello sviluppo motorio in età evolutiva.
-
-
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diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/textTools.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/textTools.py
deleted file mode 100644
index f7ca1acc9b762e1ffcfefd22a399927f8369a056..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/textTools.py
+++ /dev/null
@@ -1,155 +0,0 @@
-"""fontTools.misc.textTools.py -- miscellaneous routines."""
-
-
-import ast
-import string
-
-
-# alias kept for backward compatibility
-safeEval = ast.literal_eval
-
-
-class Tag(str):
- @staticmethod
- def transcode(blob):
- if isinstance(blob, bytes):
- blob = blob.decode("latin-1")
- return blob
-
- def __new__(self, content):
- return str.__new__(self, self.transcode(content))
-
- def __ne__(self, other):
- return not self.__eq__(other)
-
- def __eq__(self, other):
- return str.__eq__(self, self.transcode(other))
-
- def __hash__(self):
- return str.__hash__(self)
-
- def tobytes(self):
- return self.encode("latin-1")
-
-
-def readHex(content):
- """Convert a list of hex strings to binary data."""
- return deHexStr(strjoin(chunk for chunk in content if isinstance(chunk, str)))
-
-
-def deHexStr(hexdata):
- """Convert a hex string to binary data."""
- hexdata = strjoin(hexdata.split())
- if len(hexdata) % 2:
- hexdata = hexdata + "0"
- data = []
- for i in range(0, len(hexdata), 2):
- data.append(bytechr(int(hexdata[i : i + 2], 16)))
- return bytesjoin(data)
-
-
-def hexStr(data):
- """Convert binary data to a hex string."""
- h = string.hexdigits
- r = ""
- for c in data:
- i = byteord(c)
- r = r + h[(i >> 4) & 0xF] + h[i & 0xF]
- return r
-
-
-def num2binary(l, bits=32):
- items = []
- binary = ""
- for i in range(bits):
- if l & 0x1:
- binary = "1" + binary
- else:
- binary = "0" + binary
- l = l >> 1
- if not ((i + 1) % 8):
- items.append(binary)
- binary = ""
- if binary:
- items.append(binary)
- items.reverse()
- assert l in (0, -1), "number doesn't fit in number of bits"
- return " ".join(items)
-
-
-def binary2num(bin):
- bin = strjoin(bin.split())
- l = 0
- for digit in bin:
- l = l << 1
- if digit != "0":
- l = l | 0x1
- return l
-
-
-def caselessSort(alist):
- """Return a sorted copy of a list. If there are only strings
- in the list, it will not consider case.
- """
-
- try:
- return sorted(alist, key=lambda a: (a.lower(), a))
- except TypeError:
- return sorted(alist)
-
-
-def pad(data, size):
- r"""Pad byte string 'data' with null bytes until its length is a
- multiple of 'size'.
-
- >>> len(pad(b'abcd', 4))
- 4
- >>> len(pad(b'abcde', 2))
- 6
- >>> len(pad(b'abcde', 4))
- 8
- >>> pad(b'abcdef', 4) == b'abcdef\x00\x00'
- True
- """
- data = tobytes(data)
- if size > 1:
- remainder = len(data) % size
- if remainder:
- data += b"\0" * (size - remainder)
- return data
-
-
-def tostr(s, encoding="ascii", errors="strict"):
- if not isinstance(s, str):
- return s.decode(encoding, errors)
- else:
- return s
-
-
-def tobytes(s, encoding="ascii", errors="strict"):
- if isinstance(s, str):
- return s.encode(encoding, errors)
- else:
- return bytes(s)
-
-
-def bytechr(n):
- return bytes([n])
-
-
-def byteord(c):
- return c if isinstance(c, int) else ord(c)
-
-
-def strjoin(iterable, joiner=""):
- return tostr(joiner).join(iterable)
-
-
-def bytesjoin(iterable, joiner=b""):
- return tobytes(joiner).join(tobytes(item) for item in iterable)
-
-
-if __name__ == "__main__":
- import doctest, sys
-
- sys.exit(doctest.testmod().failed)
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/G_P_K_G_.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/G_P_K_G_.py
deleted file mode 100644
index eed34d92105926dcdb988ef345e8421a93b85518..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/G_P_K_G_.py
+++ /dev/null
@@ -1,126 +0,0 @@
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import bytesjoin, safeEval, readHex
-from . import DefaultTable
-import sys
-import array
-
-GPKGFormat = """
- > # big endian
- version: H
- flags: H
- numGMAPs: H
- numGlyplets: H
-"""
-# psFontName is a byte string which follows the record above. This is zero padded
-# to the beginning of the records array. The recordsOffsst is 32 bit aligned.
-
-
-class table_G_P_K_G_(DefaultTable.DefaultTable):
- def decompile(self, data, ttFont):
- dummy, newData = sstruct.unpack2(GPKGFormat, data, self)
-
- GMAPoffsets = array.array("I")
- endPos = (self.numGMAPs + 1) * 4
- GMAPoffsets.frombytes(newData[:endPos])
- if sys.byteorder != "big":
- GMAPoffsets.byteswap()
- self.GMAPs = []
- for i in range(self.numGMAPs):
- start = GMAPoffsets[i]
- end = GMAPoffsets[i + 1]
- self.GMAPs.append(data[start:end])
- pos = endPos
- endPos = pos + (self.numGlyplets + 1) * 4
- glyphletOffsets = array.array("I")
- glyphletOffsets.frombytes(newData[pos:endPos])
- if sys.byteorder != "big":
- glyphletOffsets.byteswap()
- self.glyphlets = []
- for i in range(self.numGlyplets):
- start = glyphletOffsets[i]
- end = glyphletOffsets[i + 1]
- self.glyphlets.append(data[start:end])
-
- def compile(self, ttFont):
- self.numGMAPs = len(self.GMAPs)
- self.numGlyplets = len(self.glyphlets)
- GMAPoffsets = [0] * (self.numGMAPs + 1)
- glyphletOffsets = [0] * (self.numGlyplets + 1)
-
- dataList = [sstruct.pack(GPKGFormat, self)]
-
- pos = len(dataList[0]) + (self.numGMAPs + 1) * 4 + (self.numGlyplets + 1) * 4
- GMAPoffsets[0] = pos
- for i in range(1, self.numGMAPs + 1):
- pos += len(self.GMAPs[i - 1])
- GMAPoffsets[i] = pos
- gmapArray = array.array("I", GMAPoffsets)
- if sys.byteorder != "big":
- gmapArray.byteswap()
- dataList.append(gmapArray.tobytes())
-
- glyphletOffsets[0] = pos
- for i in range(1, self.numGlyplets + 1):
- pos += len(self.glyphlets[i - 1])
- glyphletOffsets[i] = pos
- glyphletArray = array.array("I", glyphletOffsets)
- if sys.byteorder != "big":
- glyphletArray.byteswap()
- dataList.append(glyphletArray.tobytes())
- dataList += self.GMAPs
- dataList += self.glyphlets
- data = bytesjoin(dataList)
- return data
-
- def toXML(self, writer, ttFont):
- writer.comment("Most of this table will be recalculated by the compiler")
- writer.newline()
- formatstring, names, fixes = sstruct.getformat(GPKGFormat)
- for name in names:
- value = getattr(self, name)
- writer.simpletag(name, value=value)
- writer.newline()
-
- writer.begintag("GMAPs")
- writer.newline()
- for gmapData in self.GMAPs:
- writer.begintag("hexdata")
- writer.newline()
- writer.dumphex(gmapData)
- writer.endtag("hexdata")
- writer.newline()
- writer.endtag("GMAPs")
- writer.newline()
-
- writer.begintag("glyphlets")
- writer.newline()
- for glyphletData in self.glyphlets:
- writer.begintag("hexdata")
- writer.newline()
- writer.dumphex(glyphletData)
- writer.endtag("hexdata")
- writer.newline()
- writer.endtag("glyphlets")
- writer.newline()
-
- def fromXML(self, name, attrs, content, ttFont):
- if name == "GMAPs":
- if not hasattr(self, "GMAPs"):
- self.GMAPs = []
- for element in content:
- if isinstance(element, str):
- continue
- itemName, itemAttrs, itemContent = element
- if itemName == "hexdata":
- self.GMAPs.append(readHex(itemContent))
- elif name == "glyphlets":
- if not hasattr(self, "glyphlets"):
- self.glyphlets = []
- for element in content:
- if isinstance(element, str):
- continue
- itemName, itemAttrs, itemContent = element
- if itemName == "hexdata":
- self.glyphlets.append(readHex(itemContent))
- else:
- setattr(self, name, safeEval(attrs["value"]))
diff --git a/spaces/cncn102/bingo1/src/pages/api/kblob.ts b/spaces/cncn102/bingo1/src/pages/api/kblob.ts
deleted file mode 100644
index 51f39b123cc90b3e50671885cd33da38a33b64ed..0000000000000000000000000000000000000000
--- a/spaces/cncn102/bingo1/src/pages/api/kblob.ts
+++ /dev/null
@@ -1,59 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import FormData from 'form-data'
-import { debug, fetch } from '@/lib/isomorphic'
-import { KBlobRequest } from '@/lib/bots/bing/types'
-import { createHeaders } from '@/lib/utils'
-
-const API_DOMAIN = 'https://www.bing.com'
-
-export const config = {
- api: {
- bodyParser: {
- sizeLimit: '10mb' // Set desired value here
- }
- }
-}
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
- try {
- const { knowledgeRequest, imageBase64 } = req.body as KBlobRequest
- const headers = createHeaders(req.cookies)
-
- const formData = new FormData()
- formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest))
- if (imageBase64) {
- formData.append('imageBase64', imageBase64)
- }
-
- const response = await fetch(`${API_DOMAIN}/images/kblob`,
- {
- method: 'POST',
- body: formData.getBuffer(),
- headers: {
- 'x-forward-for': headers['x-forwarded-for'],
- 'user-agent': headers['User-Agent'],
- cookie: headers['cookie'],
- 'Referer': 'https://www.bing.com/search',
- ...formData.getHeaders()
- }
- }
- )
-
- if (response.status !== 200) {
- throw new Error('图片上传失败')
- }
- res.writeHead(200, {
- 'Content-Type': 'application/json',
- })
- res.end(await response.text())
- } catch (e) {
- res.json({
- result: {
- value: 'UploadFailed',
- message: `${e}`
- }
- })
- }
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/doc/examples/avio_list_dir.c b/spaces/colakin/video-generater/public/ffmpeg/doc/examples/avio_list_dir.c
deleted file mode 100644
index bb19debad31b18897782811cbe35f4073be49d9a..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/doc/examples/avio_list_dir.c
+++ /dev/null
@@ -1,137 +0,0 @@
-/*
- * Copyright (c) 2014 Lukasz Marek
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
- * THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-
-/**
- * @file libavformat AVIOContext list directory API usage example
- * @example avio_list_dir.c
- *
- * Show how to list directories through the libavformat AVIOContext API.
- */
-
-#include
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\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Fake Your Bank Account Balance with Dummy Bank App.md b/spaces/congsaPfin/Manga-OCR/logs/Fake Your Bank Account Balance with Dummy Bank App.md
deleted file mode 100644
index e5b424e207488d50536d9aada62b7cc9eb44b005..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Fake Your Bank Account Balance with Dummy Bank App.md
+++ /dev/null
@@ -1,120 +0,0 @@
-
-Dummy Bank App Download: What Is It and Why You Need It
-dummy bank app download
- How to Download a Dummy Bank App
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- How to Use a Dummy Bank App
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\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Fallout Shelter APK OBB Mod Unlimited Money and Resources.md b/spaces/congsaPfin/Manga-OCR/logs/Fallout Shelter APK OBB Mod Unlimited Money and Resources.md
deleted file mode 100644
index c1e38e62dda3dbb5fa1f20c2d202c1a3c707e656..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Fallout Shelter APK OBB Mod Unlimited Money and Resources.md
+++ /dev/null
@@ -1,166 +0,0 @@
-
-Fallout Shelter APK OBB Mod: A Guide for Android Gamers
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- A simulation game based on the Fallout franchise
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-The mod allows you to access all the features and content of the game without spending real money or waiting for long periods of time.
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-The mod allows you to customize your vault and dwellers according to your preferences and imagination.
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-The mod allows you to experiment with different strategies and scenarios without worrying about the consequences or limitations.
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-
-The mod may encounter bugs, glitches, crashes, or errors that can affect the performance and stability of the game.
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-Conclusion
- A summary of the main points and a recommendation for the mod
- FAQs
-
-
401be4b1e0
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\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Download APK Java Edition and Unlock New Features in Minecraft.md b/spaces/congsaPfin/Manga-OCR/logs/How to Download APK Java Edition and Unlock New Features in Minecraft.md
deleted file mode 100644
index dfaf4d770b63af26a6493915bcb2556af90be8b5..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/How to Download APK Java Edition and Unlock New Features in Minecraft.md
+++ /dev/null
@@ -1,125 +0,0 @@
-
-How to Download APK Java Edition for Android Devices
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-How to Download and Install APK Java Edition
-The requirements for running APK Java Edition
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-
- The steps to download and install APK Java Edition
- Step 1: Enable unknown sources on your device
-Step 2: Download the APK file from a trusted source
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-The game crashes or freezes
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-
-
-
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Master Chess Game Online in 10 Easy Steps.md b/spaces/congsaPfin/Manga-OCR/logs/How to Master Chess Game Online in 10 Easy Steps.md
deleted file mode 100644
index 36e1fabdeb26b9b7b4f8b29fc03c02432f88be38..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/How to Master Chess Game Online in 10 Easy Steps.md
+++ /dev/null
@@ -1,141 +0,0 @@
-
-Chess Game Online: How to Play, Learn and Improve
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Hungry Shark World 3.5.0 APK The Best Shark Simulator Game for Android.md b/spaces/congsaPfin/Manga-OCR/logs/Hungry Shark World 3.5.0 APK The Best Shark Simulator Game for Android.md
deleted file mode 100644
index ddd2feb3f2b2e6635b92d63ad081fadc629e4a44..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Hungry Shark World 3.5.0 APK The Best Shark Simulator Game for Android.md
+++ /dev/null
@@ -1,137 +0,0 @@
-
-Hungry Shark World 3.5.0 APK: A Review
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-hungry shark world 3Unlock and upgrade different sharks with unique abilities
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-Buy the map and collect the HUNGRY letters
-Use your boost wisely and avoid dangerous creatures
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-Equip pets and power-ups to enhance your performance
-Complete daily rewards and quests to get more bonuses
-Game Review
-Pros and cons of Hungry Shark World 3.5.0 APK
-
-
- Pros Cons - Amazing graphics and sound effects - High battery consumption - Variety of sharks, skins, accessories, pets, and power-ups - Some items are too expensive or hard to get - Different locations, creatures, secrets, and missions - Some locations are too big or confusing - Simple and intuitive controls - Sometimes unresponsive or laggy - Fun and exciting gameplay - Sometimes repetitive or frustrating Comparison with Hungry Shark Evolution
-
-
- Hungry Shark World Hungry Shark Evolution - Has four locations: Pacific Island, Arctic Ocean, Arabian Sea, South China Sea - Has two locations: Pacific Island, Arctic Ocean - Has over 30 sharks to unlock and play with - Has over 20 sharks to unlock and play with - Has skins and accessories to customize your sharks - Has only skins to customize your sharks - Has pets and power-ups to enhance your performance - Has only pets to enhance your performance - Has gold rush and mega gold rush events - Has only gold rush events - Has daily rewards and quests to get more bonuses - Has only daily rewards to get more bonuses - Has live events and shark week sets - Does not have live events or shark week sets - Requires Android 5.0 or higher - Requires Android 4.1 or higher - Size: 147 MB - Size: 99 MB User ratings and feedback
-How to download and install Hungry Shark World 3.5.0 APK
-
-
- Conclusion
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-
-
-
-
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\ No newline at end of file
diff --git a/spaces/contluForse/HuggingGPT/assets/Download LINK Opnet Modeler 16.1.md b/spaces/contluForse/HuggingGPT/assets/Download LINK Opnet Modeler 16.1.md
deleted file mode 100644
index fd250ce0ea08a9fec64d6ae1adf71a2469499b03..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Download LINK Opnet Modeler 16.1.md
+++ /dev/null
@@ -1,10 +0,0 @@
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diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/fileio/io.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/fileio/io.py
deleted file mode 100644
index aaefde58aa3ea5b58f86249ce7e1c40c186eb8dd..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/fileio/io.py
+++ /dev/null
@@ -1,151 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from io import BytesIO, StringIO
-from pathlib import Path
-
-from ..utils import is_list_of, is_str
-from .file_client import FileClient
-from .handlers import BaseFileHandler, JsonHandler, PickleHandler, YamlHandler
-
-file_handlers = {
- 'json': JsonHandler(),
- 'yaml': YamlHandler(),
- 'yml': YamlHandler(),
- 'pickle': PickleHandler(),
- 'pkl': PickleHandler()
-}
-
-
-def load(file, file_format=None, file_client_args=None, **kwargs):
- """Load data from json/yaml/pickle files.
-
- This method provides a unified api for loading data from serialized files.
-
- Note:
- In v1.3.16 and later, ``load`` supports loading data from serialized
- files those can be storaged in different backends.
-
- Args:
- file (str or :obj:`Path` or file-like object): Filename or a file-like
- object.
- file_format (str, optional): If not specified, the file format will be
- inferred from the file extension, otherwise use the specified one.
- Currently supported formats include "json", "yaml/yml" and
- "pickle/pkl".
- file_client_args (dict, optional): Arguments to instantiate a
- FileClient. See :class:`mmcv.fileio.FileClient` for details.
- Default: None.
-
- Examples:
- >>> load('/path/of/your/file') # file is storaged in disk
- >>> load('https://path/of/your/file') # file is storaged in Internet
- >>> load('s3://path/of/your/file') # file is storaged in petrel
-
- Returns:
- The content from the file.
- """
- if isinstance(file, Path):
- file = str(file)
- if file_format is None and is_str(file):
- file_format = file.split('.')[-1]
- if file_format not in file_handlers:
- raise TypeError(f'Unsupported format: {file_format}')
-
- handler = file_handlers[file_format]
- if is_str(file):
- file_client = FileClient.infer_client(file_client_args, file)
- if handler.str_like:
- with StringIO(file_client.get_text(file)) as f:
- obj = handler.load_from_fileobj(f, **kwargs)
- else:
- with BytesIO(file_client.get(file)) as f:
- obj = handler.load_from_fileobj(f, **kwargs)
- elif hasattr(file, 'read'):
- obj = handler.load_from_fileobj(file, **kwargs)
- else:
- raise TypeError('"file" must be a filepath str or a file-object')
- return obj
-
-
-def dump(obj, file=None, file_format=None, file_client_args=None, **kwargs):
- """Dump data to json/yaml/pickle strings or files.
-
- This method provides a unified api for dumping data as strings or to files,
- and also supports custom arguments for each file format.
-
- Note:
- In v1.3.16 and later, ``dump`` supports dumping data as strings or to
- files which is saved to different backends.
-
- Args:
- obj (any): The python object to be dumped.
- file (str or :obj:`Path` or file-like object, optional): If not
- specified, then the object is dumped to a str, otherwise to a file
- specified by the filename or file-like object.
- file_format (str, optional): Same as :func:`load`.
- file_client_args (dict, optional): Arguments to instantiate a
- FileClient. See :class:`mmcv.fileio.FileClient` for details.
- Default: None.
-
- Examples:
- >>> dump('hello world', '/path/of/your/file') # disk
- >>> dump('hello world', 's3://path/of/your/file') # ceph or petrel
-
- Returns:
- bool: True for success, False otherwise.
- """
- if isinstance(file, Path):
- file = str(file)
- if file_format is None:
- if is_str(file):
- file_format = file.split('.')[-1]
- elif file is None:
- raise ValueError(
- 'file_format must be specified since file is None')
- if file_format not in file_handlers:
- raise TypeError(f'Unsupported format: {file_format}')
-
- handler = file_handlers[file_format]
- if file is None:
- return handler.dump_to_str(obj, **kwargs)
- elif is_str(file):
- file_client = FileClient.infer_client(file_client_args, file)
- if handler.str_like:
- with StringIO() as f:
- handler.dump_to_fileobj(obj, f, **kwargs)
- file_client.put_text(f.getvalue(), file)
- else:
- with BytesIO() as f:
- handler.dump_to_fileobj(obj, f, **kwargs)
- file_client.put(f.getvalue(), file)
- elif hasattr(file, 'write'):
- handler.dump_to_fileobj(obj, file, **kwargs)
- else:
- raise TypeError('"file" must be a filename str or a file-object')
-
-
-def _register_handler(handler, file_formats):
- """Register a handler for some file extensions.
-
- Args:
- handler (:obj:`BaseFileHandler`): Handler to be registered.
- file_formats (str or list[str]): File formats to be handled by this
- handler.
- """
- if not isinstance(handler, BaseFileHandler):
- raise TypeError(
- f'handler must be a child of BaseFileHandler, not {type(handler)}')
- if isinstance(file_formats, str):
- file_formats = [file_formats]
- if not is_list_of(file_formats, str):
- raise TypeError('file_formats must be a str or a list of str')
- for ext in file_formats:
- file_handlers[ext] = handler
-
-
-def register_handler(file_formats, **kwargs):
-
- def wrap(cls):
- _register_handler(cls(**kwargs), file_formats)
- return cls
-
- return wrap
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h
deleted file mode 100644
index 4f0658e8668a11f0e7d71deff9adac71884f2e87..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h
+++ /dev/null
@@ -1,35 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-/*!
-* Copyright (c) Facebook, Inc. and its affiliates.
-* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
-*/
-
-#pragma once
-#include
'))
-
- fig = px.scatter(x=umap_data[:,0], y=umap_data[:,1],color = clusterer.labels_)
- # remove legend
- fig = px.scatter(x=umap_data[:,0], y=umap_data[:,1],color = labels,text = text)
- #set text color
- fig.update_traces(textfont_color='rgba(0,0,0,0)',marker_size = 8)
- # set background color
- fig.update_layout(plot_bgcolor='rgba(0,0,0,0)')
- # set margin
- fig.update_layout(margin=dict(l=40, r=5, t=0, b=40))
- # set axis color to grey
- fig.update_xaxes(showgrid=False, zeroline=False, linecolor='rgb(200,200,200)')
- fig.update_yaxes( zeroline=False, linecolor='rgb(200,200,200)')
- # set font sans-serif
- fig.update_layout(font_family="sans-serif")
- # remove legend
- fig.update_layout(showlegend=False)
-
- # set legend title to cluster
- return df["content"],data,fig
-
-def topic_modelling(df,embed_df):
- data = df.apply(lambda x: ' '.join([w for w in x.split() if len(w)>3]))
- stopwords = load_stopwords()
- # remove empty data
- topic_model = BERTopic(
- calculate_probabilities=True,
- # cluster model
- hdbscan_model = hdbscan.HDBSCAN(min_cluster_size=5,prediction_data=True),
- vectorizer_model=CountVectorizer(stop_words=stopwords),
- language="indonesian",
- )
- topics, probs = topic_model.fit_transform(data,embed_df)
- topic_labels = topic_model.generate_topic_labels(
- topic_prefix = False,
- separator = ", ",
- )
- topic_model.set_topic_labels(topic_labels)
- fig = visualize_barchart(topic_model)
- # set title to Kata Kunci tiap Topic
- # fig.update_layout(title_text="Topic yang sering muncul")
- return fig,topic_model
\ No newline at end of file
diff --git a/spaces/dakaiye/dky_xuexi/request_llm/bridge_stackclaude.py b/spaces/dakaiye/dky_xuexi/request_llm/bridge_stackclaude.py
deleted file mode 100644
index c674a8bfe9d022b6e2b6359e5327b47596a53c68..0000000000000000000000000000000000000000
--- a/spaces/dakaiye/dky_xuexi/request_llm/bridge_stackclaude.py
+++ /dev/null
@@ -1,275 +0,0 @@
-from .bridge_newbing import preprocess_newbing_out, preprocess_newbing_out_simple
-from multiprocessing import Process, Pipe
-from toolbox import update_ui, get_conf, trimmed_format_exc
-import threading
-import importlib
-import logging
-import time
-from toolbox import get_conf
-import asyncio
-load_message = "正在加载Claude组件，请稍候..."
-
-try:
- """
- ========================================================================
- 第一部分：Slack API Client
- https://github.com/yokonsan/claude-in-slack-api
- ========================================================================
- """
-
- from slack_sdk.errors import SlackApiError
- from slack_sdk.web.async_client import AsyncWebClient
-
- class SlackClient(AsyncWebClient):
- """SlackClient类用于与Slack API进行交互，实现消息发送、接收等功能。
-
- 属性：
- - CHANNEL_ID：str类型，表示频道ID。
-
- 方法：
- - open_channel()：异步方法。通过调用conversations_open方法打开一个频道，并将返回的频道ID保存在属性CHANNEL_ID中。
- - chat(text: str)：异步方法。向已打开的频道发送一条文本消息。
- - get_slack_messages()：异步方法。获取已打开频道的最新消息并返回消息列表，目前不支持历史消息查询。
- - get_reply()：异步方法。循环监听已打开频道的消息，如果收到"Typing…_"结尾的消息说明Claude还在继续输出，否则结束循环。
-
- """
- CHANNEL_ID = None
-
- async def open_channel(self):
- response = await self.conversations_open(users=get_conf('SLACK_CLAUDE_BOT_ID')[0])
- self.CHANNEL_ID = response["channel"]["id"]
-
- async def chat(self, text):
- if not self.CHANNEL_ID:
- raise Exception("Channel not found.")
-
- resp = await self.chat_postMessage(channel=self.CHANNEL_ID, text=text)
- self.LAST_TS = resp["ts"]
-
- async def get_slack_messages(self):
- try:
- # TODO：暂时不支持历史消息，因为在同一个频道里存在多人使用时历史消息渗透问题
- resp = await self.conversations_history(channel=self.CHANNEL_ID, oldest=self.LAST_TS, limit=1)
- msg = [msg for msg in resp["messages"]
- if msg.get("user") == get_conf('SLACK_CLAUDE_BOT_ID')[0]]
- return msg
- except (SlackApiError, KeyError) as e:
- raise RuntimeError(f"获取Slack消息失败。")
-
- async def get_reply(self):
- while True:
- slack_msgs = await self.get_slack_messages()
- if len(slack_msgs) == 0:
- await asyncio.sleep(0.5)
- continue
-
- msg = slack_msgs[-1]
- if msg["text"].endswith("Typing…_"):
- yield False, msg["text"]
- else:
- yield True, msg["text"]
- break
-except:
- pass
-
-"""
-========================================================================
-第二部分：子进程Worker（调用主体）
-========================================================================
-"""
-
-
-class ClaudeHandle(Process):
- def __init__(self):
- super().__init__(daemon=True)
- self.parent, self.child = Pipe()
- self.claude_model = None
- self.info = ""
- self.success = True
- self.local_history = []
- self.check_dependency()
- if self.success:
- self.start()
- self.threadLock = threading.Lock()
-
- def check_dependency(self):
- try:
- self.success = False
- import slack_sdk
- self.info = "依赖检测通过，等待Claude响应。注意目前不能多人同时调用Claude接口（有线程锁），否则将导致每个人的Claude问询历史互相渗透。调用Claude时，会自动使用已配置的代理。"
- self.success = True
- except:
- self.info = "缺少的依赖，如果要使用Claude，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_slackclaude.txt`安装Claude的依赖，然后重启程序。"
- self.success = False
-
- def ready(self):
- return self.claude_model is not None
-
- async def async_run(self):
- await self.claude_model.open_channel()
- while True:
- # 等待
- kwargs = self.child.recv()
- question = kwargs['query']
- history = kwargs['history']
-
- # 开始问问题
- prompt = ""
-
- # 问题
- prompt += question
- print('question:', prompt)
-
- # 提交
- await self.claude_model.chat(prompt)
-
- # 获取回复
- async for final, response in self.claude_model.get_reply():
- if not final:
- print(response)
- self.child.send(str(response))
- else:
- # 防止丢失最后一条消息
- slack_msgs = await self.claude_model.get_slack_messages()
- last_msg = slack_msgs[-1]["text"] if slack_msgs and len(slack_msgs) > 0 else ""
- if last_msg:
- self.child.send(last_msg)
- print('-------- receive final ---------')
- self.child.send('[Finish]')
-
- def run(self):
- """
- 这个函数运行在子进程
- """
- # 第一次运行，加载参数
- self.success = False
- self.local_history = []
- if (self.claude_model is None) or (not self.success):
- # 代理设置
- proxies, = get_conf('proxies')
- if proxies is None:
- self.proxies_https = None
- else:
- self.proxies_https = proxies['https']
-
- try:
- SLACK_CLAUDE_USER_TOKEN, = get_conf('SLACK_CLAUDE_USER_TOKEN')
- self.claude_model = SlackClient(token=SLACK_CLAUDE_USER_TOKEN, proxy=self.proxies_https)
- print('Claude组件初始化成功。')
- except:
- self.success = False
- tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
- self.child.send(f'[Local Message] 不能加载Claude组件。{tb_str}')
- self.child.send('[Fail]')
- self.child.send('[Finish]')
- raise RuntimeError(f"不能加载Claude组件。")
-
- self.success = True
- try:
- # 进入任务等待状态
- asyncio.run(self.async_run())
- except Exception:
- tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
- self.child.send(f'[Local Message] Claude失败 {tb_str}.')
- self.child.send('[Fail]')
- self.child.send('[Finish]')
-
- def stream_chat(self, **kwargs):
- """
- 这个函数运行在主进程
- """
- self.threadLock.acquire()
- self.parent.send(kwargs) # 发送请求到子进程
- while True:
- res = self.parent.recv() # 等待Claude回复的片段
- if res == '[Finish]':
- break # 结束
- elif res == '[Fail]':
- self.success = False
- break
- else:
- yield res # Claude回复的片段
- self.threadLock.release()
-
-
-"""
-========================================================================
-第三部分：主进程统一调用函数接口
-========================================================================
-"""
-global claude_handle
-claude_handle = None
-
-
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
- """
- 多线程方法
- 函数的说明请见 request_llm/bridge_all.py
- """
- global claude_handle
- if (claude_handle is None) or (not claude_handle.success):
- claude_handle = ClaudeHandle()
- observe_window[0] = load_message + "\n\n" + claude_handle.info
- if not claude_handle.success:
- error = claude_handle.info
- claude_handle = None
- raise RuntimeError(error)
-
- # 没有 sys_prompt 接口，因此把prompt加入 history
- history_feedin = []
- for i in range(len(history)//2):
- history_feedin.append([history[2*i], history[2*i+1]])
-
- watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
- response = ""
- observe_window[0] = "[Local Message]: 等待Claude响应中 ..."
- for response in claude_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
- observe_window[0] = preprocess_newbing_out_simple(response)
- if len(observe_window) >= 2:
- if (time.time()-observe_window[1]) > watch_dog_patience:
- raise RuntimeError("程序终止。")
- return preprocess_newbing_out_simple(response)
-
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
- """
- 单线程方法
- 函数的说明请见 request_llm/bridge_all.py
- """
- chatbot.append((inputs, "[Local Message]: 等待Claude响应中 ..."))
-
- global claude_handle
- if (claude_handle is None) or (not claude_handle.success):
- claude_handle = ClaudeHandle()
- chatbot[-1] = (inputs, load_message + "\n\n" + claude_handle.info)
- yield from update_ui(chatbot=chatbot, history=[])
- if not claude_handle.success:
- claude_handle = None
- return
-
- if additional_fn is not None:
- import core_functional
- importlib.reload(core_functional) # 热更新prompt
- core_functional = core_functional.get_core_functions()
- if "PreProcess" in core_functional[additional_fn]:
- inputs = core_functional[additional_fn]["PreProcess"](
- inputs) # 获取预处理函数（如果有的话）
- inputs = core_functional[additional_fn]["Prefix"] + \
- inputs + core_functional[additional_fn]["Suffix"]
-
- history_feedin = []
- for i in range(len(history)//2):
- history_feedin.append([history[2*i], history[2*i+1]])
-
- chatbot[-1] = (inputs, "[Local Message]: 等待Claude响应中 ...")
- response = "[Local Message]: 等待Claude响应中 ..."
- yield from update_ui(chatbot=chatbot, history=history, msg="Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
- for response in claude_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt):
- chatbot[-1] = (inputs, preprocess_newbing_out(response))
- yield from update_ui(chatbot=chatbot, history=history, msg="Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
- if response == "[Local Message]: 等待Claude响应中 ...":
- response = "[Local Message]: Claude响应异常，请刷新界面重试 ..."
- history.extend([inputs, response])
- logging.info(f'[raw_input] {inputs}')
- logging.info(f'[response] {response}')
- yield from update_ui(chatbot=chatbot, history=history, msg="完成全部响应，请提交新问题。")
diff --git a/spaces/danterivers/music-generation-samples/audiocraft/data/zip.py b/spaces/danterivers/music-generation-samples/audiocraft/data/zip.py
deleted file mode 100644
index 1f1154231da321dd38d151ff285dbcff5e38a6e0..0000000000000000000000000000000000000000
--- a/spaces/danterivers/music-generation-samples/audiocraft/data/zip.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import typing
-import zipfile
-
-from dataclasses import dataclass
-from functools import lru_cache
-from typing_extensions import Literal
-
-
-DEFAULT_SIZE = 32
-MODE = Literal['r', 'w', 'x', 'a']
-
-
-@dataclass(order=True)
-class PathInZip:
- """Class for holding a path of file within a zip file.
-
- Args:
- path: The convention is live demo available at [this HuggingFace Space](https://huggingface.co/spaces/deedax/TLDR-the-TnC)
-
-live deployment available at [Render.com](https://tldr-the-tandc.onrender.com/tldr-bot/)
-
-# Reference
-
-[Langchain](https://langchain.readthedocs.io/)
-
-# Contact
-
-
-
-
-
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/deepwisdom/MetaGPT/tests/metagpt/actions/__init__.py b/spaces/deepwisdom/MetaGPT/tests/metagpt/actions/__init__.py
deleted file mode 100644
index ee4b0e690a311cb8341f794293425a29dc3f0c67..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/tests/metagpt/actions/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time : 2023/5/11 19:35
-@Author : alexanderwu
-@File : __init__.py
-"""
diff --git a/spaces/diacanFperku/AutoGPT/Bunny Black 3.md b/spaces/diacanFperku/AutoGPT/Bunny Black 3.md
deleted file mode 100644
index ffa425a68d05b9dd8a22d5c27ec676b242083fcf..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Bunny Black 3.md
+++ /dev/null
@@ -1,22 +0,0 @@
-
-
-
-
-
- 
-
-
- 
-
-
- 
-
-
- 
-
-
- 
- Bunny Black 3
-
-The game features realistic style graphics and fully functional 3D dungeon environments. Players assume the role of a young bunny with an attractive appearance and carry out tasks and quests to travel across towns and dungeons and gain an advantage in town and in the game. The gameplay of the game was designed for three generations of users, from children to those who have completed the game a few times.
-
-Plot
-
-Bunny Black 3 takes place in Sargun, a kingdom split by war. The kingdom has been in peace since a tragedy took place, but war clouds are gathering and tensions are high. This is in the year that "The Battle of the Moon" is supposed to take place, and the people are eager to see the battle. A girl named Bunny Black visits the capital and is offered an interview to become a miko, a type of fortune teller. At the interview, she is given a magical flute, which will be her only means of transportation during her stay in Sargun.
-
-Bunny Black's first step in her new profession is to visit the city of Dijupio and rescue the princess of Dijupio, who was trapped in her tower. Afterwards, she receives further instructions on how to use her magical flute to travel the kingdom. Bunny Black must use her flute and talents to acquire six magical flute tiles, and then visit the city of Erech to meet the princess, repair and construct a log cabin in Geria, and travel to the capital of Sargun and find its king.
-
-There are five playable characters in the game, including Bunny Black, Cinque, Croon, Rarr and Noodle.
-
-Gameplay
-
-Bunny Black 3 is a dungeon role-playing and town-management game that allows players to role-play as Bunny Black. The gameplay of the game was designed for three generations of users, from children to those who have completed the game a few times. The game's three generations differ in terms of their characters' pace of growth.
-
-The first generation focuses on quick travel and short quests, with simple gameplay and limited interaction with NPCs. The second generation focuses on the story and RPG elements, with more customization options and more gameplay difficulty and character development. The third generation focuses on the fantasy role-playing and player management elements.
-
-The game's three generations are accessible through different difficulty settings. The "Easy" difficulty setting allows the characters to enter dungeons without restrictions, and to use the main menu of 4fefd39f24
-
-
-
diff --git a/spaces/diacanFperku/AutoGPT/HD Online Player (Kal Ho Naa Ho Hd 720pgolkes).md b/spaces/diacanFperku/AutoGPT/HD Online Player (Kal Ho Naa Ho Hd 720pgolkes).md
deleted file mode 100644
index b2ce6421ec4686afccd21c9a6ac5712b14cf1d0e..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/HD Online Player (Kal Ho Naa Ho Hd 720pgolkes).md
+++ /dev/null
@@ -1,6 +0,0 @@
-HD Online Player (Kal Ho Naa Ho hd 720pgolkes)
-
-HD Online Player (Kal Ho Naa Ho Hd 720pgolkes) ^HOT^ · Dota 2 Offline V1014 Single Link · [2020] VCC Carding App V.2.0 [With Track 1 ... 1fdad05405
-
-
-
diff --git a/spaces/diego2554/RemBG_super/rembg/_version.py b/spaces/diego2554/RemBG_super/rembg/_version.py
deleted file mode 100644
index 29f7d88a559d2cc591cc6529799142a69107ae60..0000000000000000000000000000000000000000
--- a/spaces/diego2554/RemBG_super/rembg/_version.py
+++ /dev/null
@@ -1,677 +0,0 @@
-# This file helps to compute a version number in source trees obtained from
-# git-archive tarball (such as those provided by githubs download-from-tag
-# feature). Distribution tarballs (built by setup.py sdist) and build
-# directories (produced by setup.py build) will contain a much shorter file
-# that just contains the computed version number.
-
-# This file is released into the public domain. Generated by
-# versioneer-0.21 (https://github.com/python-versioneer/python-versioneer)
-
-"""Git implementation of _version.py."""
-
-import errno
-import os
-import re
-import subprocess
-import sys
-from typing import Callable, Dict
-
-
-def get_keywords():
- """Get the keywords needed to look up the version information."""
- # these strings will be replaced by git during git-archive.
- # setup.py/versioneer.py will grep for the variable names, so they must
- # each be defined on a line of their own. _version.py will just call
- # get_keywords().
- git_refnames = " (HEAD -> main, tag: v2.0.43)"
- git_full = "848a38e4cc5cf41522974dea00848596105b1dfa"
- git_date = "2023-06-02 09:20:57 -0300"
- keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
- return keywords
-
-
-class VersioneerConfig:
- """Container for Versioneer configuration parameters."""
-
-
-def get_config():
- """Create, populate and return the VersioneerConfig() object."""
- # these strings are filled in when 'setup.py versioneer' creates
- # _version.py
- cfg = VersioneerConfig()
- cfg.VCS = "git"
- cfg.style = "pep440"
- cfg.tag_prefix = "v"
- cfg.parentdir_prefix = "rembg-"
- cfg.versionfile_source = "rembg/_version.py"
- cfg.verbose = False
- return cfg
-
-
-class NotThisMethod(Exception):
- """Exception raised if a method is not valid for the current scenario."""
-
-
-LONG_VERSION_PY: Dict[str, str] = {}
-HANDLERS: Dict[str, Dict[str, Callable]] = {}
-
-
-def register_vcs_handler(vcs, method): # decorator
- """Create decorator to mark a method as the handler of a VCS."""
-
- def decorate(f):
- """Store f in HANDLERS[vcs][method]."""
- if vcs not in HANDLERS:
- HANDLERS[vcs] = {}
- HANDLERS[vcs][method] = f
- return f
-
- return decorate
-
-
-def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):
- """Call the given command(s)."""
- assert isinstance(commands, list)
- process = None
- for command in commands:
- try:
- dispcmd = str([command] + args)
- # remember shell=False, so use git.cmd on windows, not just git
- process = subprocess.Popen(
- [command] + args,
- cwd=cwd,
- env=env,
- stdout=subprocess.PIPE,
- stderr=(subprocess.PIPE if hide_stderr else None),
- )
- break
- except OSError:
- e = sys.exc_info()[1]
- if e.errno == errno.ENOENT:
- continue
- if verbose:
- print("unable to run %s" % dispcmd)
- print(e)
- return None, None
- else:
- if verbose:
- print("unable to find command, tried %s" % (commands,))
- return None, None
- stdout = process.communicate()[0].strip().decode()
- if process.returncode != 0:
- if verbose:
- print("unable to run %s (error)" % dispcmd)
- print("stdout was %s" % stdout)
- return None, process.returncode
- return stdout, process.returncode
-
-
-def versions_from_parentdir(parentdir_prefix, root, verbose):
- """Try to determine the version from the parent directory name.
-
- Source tarballs conventionally unpack into a directory that includes both
- the project name and a version string. We will also support searching up
- two directory levels for an appropriately named parent directory
- """
- rootdirs = []
-
- for _ in range(3):
- dirname = os.path.basename(root)
- if dirname.startswith(parentdir_prefix):
- return {
- "version": dirname[len(parentdir_prefix) :],
- "full-revisionid": None,
- "dirty": False,
- "error": None,
- "date": None,
- }
- rootdirs.append(root)
- root = os.path.dirname(root) # up a level
-
- if verbose:
- print(
- "Tried directories %s but none started with prefix %s"
- % (str(rootdirs), parentdir_prefix)
- )
- raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
-
-
-@register_vcs_handler("git", "get_keywords")
-def git_get_keywords(versionfile_abs):
- """Extract version information from the given file."""
- # the code embedded in _version.py can just fetch the value of these
- # keywords. When used from setup.py, we don't want to import _version.py,
- # so we do it with a regexp instead. This function is not used from
- # _version.py.
- keywords = {}
- try:
- with open(versionfile_abs, "r") as fobj:
- for line in fobj:
- if line.strip().startswith("git_refnames ="):
- mo = re.search(r'=\s*"(.*)"', line)
- if mo:
- keywords["refnames"] = mo.group(1)
- if line.strip().startswith("git_full ="):
- mo = re.search(r'=\s*"(.*)"', line)
- if mo:
- keywords["full"] = mo.group(1)
- if line.strip().startswith("git_date ="):
- mo = re.search(r'=\s*"(.*)"', line)
- if mo:
- keywords["date"] = mo.group(1)
- except OSError:
- pass
- return keywords
-
-
-@register_vcs_handler("git", "keywords")
-def git_versions_from_keywords(keywords, tag_prefix, verbose):
- """Get version information from git keywords."""
- if "refnames" not in keywords:
- raise NotThisMethod("Short version file found")
- date = keywords.get("date")
- if date is not None:
- # Use only the last line. Previous lines may contain GPG signature
- # information.
- date = date.splitlines()[-1]
-
- # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
- # datestamp. However we prefer "%ci" (which expands to an "ISO-8601
- # -like" string, which we must then edit to make compliant), because
- # it's been around since git-1.5.3, and it's too difficult to
- # discover which version we're using, or to work around using an
- # older one.
- date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
- refnames = keywords["refnames"].strip()
- if refnames.startswith("$Format"):
- if verbose:
- print("keywords are unexpanded, not using")
- raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
- refs = {r.strip() for r in refnames.strip("()").split(",")}
- # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
- # just "foo-1.0". If we see a "tag: " prefix, prefer those.
- TAG = "tag: "
- tags = {r[len(TAG) :] for r in refs if r.startswith(TAG)}
- if not tags:
- # Either we're using git < 1.8.3, or there really are no tags. We use
- # a heuristic: assume all version tags have a digit. The old git %d
- # expansion behaves like git log --decorate=short and strips out the
- # refs/heads/ and refs/tags/ prefixes that would let us distinguish
- # between branches and tags. By ignoring refnames without digits, we
- # filter out many common branch names like "release" and
- # "stabilization", as well as "HEAD" and "master".
- tags = {r for r in refs if re.search(r"\d", r)}
- if verbose:
- print("discarding '%s', no digits" % ",".join(refs - tags))
- if verbose:
- print("likely tags: %s" % ",".join(sorted(tags)))
- for ref in sorted(tags):
- # sorting will prefer e.g. "2.0" over "2.0rc1"
- if ref.startswith(tag_prefix):
- r = ref[len(tag_prefix) :]
- # Filter out refs that exactly match prefix or that don't start
- # with a number once the prefix is stripped (mostly a concern
- # when prefix is '')
- if not re.match(r"\d", r):
- continue
- if verbose:
- print("picking %s" % r)
- return {
- "version": r,
- "full-revisionid": keywords["full"].strip(),
- "dirty": False,
- "error": None,
- "date": date,
- }
- # no suitable tags, so version is "0+unknown", but full hex is still there
- if verbose:
- print("no suitable tags, using unknown + full revision id")
- return {
- "version": "0+unknown",
- "full-revisionid": keywords["full"].strip(),
- "dirty": False,
- "error": "no suitable tags",
- "date": None,
- }
-
-
-@register_vcs_handler("git", "pieces_from_vcs")
-def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
- """Get version from 'git describe' in the root of the source tree.
-
- This only gets called if the git-archive 'subst' keywords were *not*
- expanded, and _version.py hasn't already been rewritten with a short
- version string, meaning we're inside a checked out source tree.
- """
- GITS = ["git"]
- TAG_PREFIX_REGEX = "*"
- if sys.platform == "win32":
- GITS = ["git.cmd", "git.exe"]
- TAG_PREFIX_REGEX = r"\*"
-
- _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=True)
- if rc != 0:
- if verbose:
- print("Directory %s not under git control" % root)
- raise NotThisMethod("'git rev-parse --git-dir' returned error")
-
- # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
- # if there isn't one, this yields HEX[-dirty] (no NUM)
- describe_out, rc = runner(
- GITS,
- [
- "describe",
- "--tags",
- "--dirty",
- "--always",
- "--long",
- "--match",
- "%s%s" % (tag_prefix, TAG_PREFIX_REGEX),
- ],
- cwd=root,
- )
- # --long was added in git-1.5.5
- if describe_out is None:
- raise NotThisMethod("'git describe' failed")
- describe_out = describe_out.strip()
- full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
- if full_out is None:
- raise NotThisMethod("'git rev-parse' failed")
- full_out = full_out.strip()
-
- pieces = {}
- pieces["long"] = full_out
- pieces["short"] = full_out[:7] # maybe improved later
- pieces["error"] = None
-
- branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root)
- # --abbrev-ref was added in git-1.6.3
- if rc != 0 or branch_name is None:
- raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
- branch_name = branch_name.strip()
-
- if branch_name == "HEAD":
- # If we aren't exactly on a branch, pick a branch which represents
- # the current commit. If all else fails, we are on a branchless
- # commit.
- branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
- # --contains was added in git-1.5.4
- if rc != 0 or branches is None:
- raise NotThisMethod("'git branch --contains' returned error")
- branches = branches.split("\n")
-
- # Remove the first line if we're running detached
- if "(" in branches[0]:
- branches.pop(0)
-
- # Strip off the leading "* " from the list of branches.
- branches = [branch[2:] for branch in branches]
- if "master" in branches:
- branch_name = "master"
- elif not branches:
- branch_name = None
- else:
- # Pick the first branch that is returned. Good or bad.
- branch_name = branches[0]
-
- pieces["branch"] = branch_name
-
- # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
- # TAG might have hyphens.
- git_describe = describe_out
-
- # look for -dirty suffix
- dirty = git_describe.endswith("-dirty")
- pieces["dirty"] = dirty
- if dirty:
- git_describe = git_describe[: git_describe.rindex("-dirty")]
-
- # now we have TAG-NUM-gHEX or HEX
-
- if "-" in git_describe:
- # TAG-NUM-gHEX
- mo = re.search(r"^(.+)-(\d+)-g([0-9a-f]+)$", git_describe)
- if not mo:
- # unparsable. Maybe git-describe is misbehaving?
- pieces["error"] = "unable to parse git-describe output: '%s'" % describe_out
- return pieces
-
- # tag
- full_tag = mo.group(1)
- if not full_tag.startswith(tag_prefix):
- if verbose:
- fmt = "tag '%s' doesn't start with prefix '%s'"
- print(fmt % (full_tag, tag_prefix))
- pieces["error"] = "tag '%s' doesn't start with prefix '%s'" % (
- full_tag,
- tag_prefix,
- )
- return pieces
- pieces["closest-tag"] = full_tag[len(tag_prefix) :]
-
- # distance: number of commits since tag
- pieces["distance"] = int(mo.group(2))
-
- # commit: short hex revision ID
- pieces["short"] = mo.group(3)
-
- else:
- # HEX: no tags
- pieces["closest-tag"] = None
- count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root)
- pieces["distance"] = int(count_out) # total number of commits
-
- # commit date: see ISO-8601 comment in git_versions_from_keywords()
- date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip()
- # Use only the last line. Previous lines may contain GPG signature
- # information.
- date = date.splitlines()[-1]
- pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
-
- return pieces
-
-
-def plus_or_dot(pieces):
- """Return a + if we don't already have one, else return a ."""
- if "+" in pieces.get("closest-tag", ""):
- return "."
- return "+"
-
-
-def render_pep440(pieces):
- """Build up version string, with post-release "local version identifier".
-
- Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
- get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
-
- Exceptions:
- 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
- """
- if pieces["closest-tag"]:
- rendered = pieces["closest-tag"]
- if pieces["distance"] or pieces["dirty"]:
- rendered += plus_or_dot(pieces)
- rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
- if pieces["dirty"]:
- rendered += ".dirty"
- else:
- # exception #1
- rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"])
- if pieces["dirty"]:
- rendered += ".dirty"
- return rendered
-
-
-def render_pep440_branch(pieces):
- """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
-
- The ".dev0" means not master branch. Note that .dev0 sorts backwards
- (a feature branch will appear "older" than the master branch).
-
- Exceptions:
- 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
- """
- if pieces["closest-tag"]:
- rendered = pieces["closest-tag"]
- if pieces["distance"] or pieces["dirty"]:
- if pieces["branch"] != "master":
- rendered += ".dev0"
- rendered += plus_or_dot(pieces)
- rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
- if pieces["dirty"]:
- rendered += ".dirty"
- else:
- # exception #1
- rendered = "0"
- if pieces["branch"] != "master":
- rendered += ".dev0"
- rendered += "+untagged.%d.g%s" % (pieces["distance"], pieces["short"])
- if pieces["dirty"]:
- rendered += ".dirty"
- return rendered
-
-
-def pep440_split_post(ver):
- """Split pep440 version string at the post-release segment.
-
- Returns the release segments before the post-release and the
- post-release version number (or -1 if no post-release segment is present).
- """
- vc = str.split(ver, ".post")
- return vc[0], int(vc[1] or 0) if len(vc) == 2 else None
-
-
-def render_pep440_pre(pieces):
- """TAG[.postN.devDISTANCE] -- No -dirty.
-
- Exceptions:
- 1: no tags. 0.post0.devDISTANCE
- """
- if pieces["closest-tag"]:
- if pieces["distance"]:
- # update the post release segment
- tag_version, post_version = pep440_split_post(pieces["closest-tag"])
- rendered = tag_version
- if post_version is not None:
- rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"])
- else:
- rendered += ".post0.dev%d" % (pieces["distance"])
- else:
- # no commits, use the tag as the version
- rendered = pieces["closest-tag"]
- else:
- # exception #1
- rendered = "0.post0.dev%d" % pieces["distance"]
- return rendered
-
-
-def render_pep440_post(pieces):
- """TAG[.postDISTANCE[.dev0]+gHEX] .
-
- The ".dev0" means dirty. Note that .dev0 sorts backwards
- (a dirty tree will appear "older" than the corresponding clean one),
- but you shouldn't be releasing software with -dirty anyways.
-
- Exceptions:
- 1: no tags. 0.postDISTANCE[.dev0]
- """
- if pieces["closest-tag"]:
- rendered = pieces["closest-tag"]
- if pieces["distance"] or pieces["dirty"]:
- rendered += ".post%d" % pieces["distance"]
- if pieces["dirty"]:
- rendered += ".dev0"
- rendered += plus_or_dot(pieces)
- rendered += "g%s" % pieces["short"]
- else:
- # exception #1
- rendered = "0.post%d" % pieces["distance"]
- if pieces["dirty"]:
- rendered += ".dev0"
- rendered += "+g%s" % pieces["short"]
- return rendered
-
-
-def render_pep440_post_branch(pieces):
- """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
-
- The ".dev0" means not master branch.
-
- Exceptions:
- 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
- """
- if pieces["closest-tag"]:
- rendered = pieces["closest-tag"]
- if pieces["distance"] or pieces["dirty"]:
- rendered += ".post%d" % pieces["distance"]
- if pieces["branch"] != "master":
- rendered += ".dev0"
- rendered += plus_or_dot(pieces)
- rendered += "g%s" % pieces["short"]
- if pieces["dirty"]:
- rendered += ".dirty"
- else:
- # exception #1
- rendered = "0.post%d" % pieces["distance"]
- if pieces["branch"] != "master":
- rendered += ".dev0"
- rendered += "+g%s" % pieces["short"]
- if pieces["dirty"]:
- rendered += ".dirty"
- return rendered
-
-
-def render_pep440_old(pieces):
- """TAG[.postDISTANCE[.dev0]] .
-
- The ".dev0" means dirty.
-
- Exceptions:
- 1: no tags. 0.postDISTANCE[.dev0]
- """
- if pieces["closest-tag"]:
- rendered = pieces["closest-tag"]
- if pieces["distance"] or pieces["dirty"]:
- rendered += ".post%d" % pieces["distance"]
- if pieces["dirty"]:
- rendered += ".dev0"
- else:
- # exception #1
- rendered = "0.post%d" % pieces["distance"]
- if pieces["dirty"]:
- rendered += ".dev0"
- return rendered
-
-
-def render_git_describe(pieces):
- """TAG[-DISTANCE-gHEX][-dirty].
-
- Like 'git describe --tags --dirty --always'.
-
- Exceptions:
- 1: no tags. HEX[-dirty] (note: no 'g' prefix)
- """
- if pieces["closest-tag"]:
- rendered = pieces["closest-tag"]
- if pieces["distance"]:
- rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
- else:
- # exception #1
- rendered = pieces["short"]
- if pieces["dirty"]:
- rendered += "-dirty"
- return rendered
-
-
-def render_git_describe_long(pieces):
- """TAG-DISTANCE-gHEX[-dirty].
-
- Like 'git describe --tags --dirty --always -long'.
- The distance/hash is unconditional.
-
- Exceptions:
- 1: no tags. HEX[-dirty] (note: no 'g' prefix)
- """
- if pieces["closest-tag"]:
- rendered = pieces["closest-tag"]
- rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
- else:
- # exception #1
- rendered = pieces["short"]
- if pieces["dirty"]:
- rendered += "-dirty"
- return rendered
-
-
-def render(pieces, style):
- """Render the given version pieces into the requested style."""
- if pieces["error"]:
- return {
- "version": "unknown",
- "full-revisionid": pieces.get("long"),
- "dirty": None,
- "error": pieces["error"],
- "date": None,
- }
-
- if not style or style == "default":
- style = "pep440" # the default
-
- if style == "pep440":
- rendered = render_pep440(pieces)
- elif style == "pep440-branch":
- rendered = render_pep440_branch(pieces)
- elif style == "pep440-pre":
- rendered = render_pep440_pre(pieces)
- elif style == "pep440-post":
- rendered = render_pep440_post(pieces)
- elif style == "pep440-post-branch":
- rendered = render_pep440_post_branch(pieces)
- elif style == "pep440-old":
- rendered = render_pep440_old(pieces)
- elif style == "git-describe":
- rendered = render_git_describe(pieces)
- elif style == "git-describe-long":
- rendered = render_git_describe_long(pieces)
- else:
- raise ValueError("unknown style '%s'" % style)
-
- return {
- "version": rendered,
- "full-revisionid": pieces["long"],
- "dirty": pieces["dirty"],
- "error": None,
- "date": pieces.get("date"),
- }
-
-
-def get_versions():
- """Get version information or return default if unable to do so."""
- # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
- # __file__, we can work backwards from there to the root. Some
- # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
- # case we can only use expanded keywords.
-
- cfg = get_config()
- verbose = cfg.verbose
-
- try:
- return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose)
- except NotThisMethod:
- pass
-
- try:
- root = os.path.realpath(__file__)
- # versionfile_source is the relative path from the top of the source
- # tree (where the .git directory might live) to this file. Invert
- # this to find the root from __file__.
- for _ in cfg.versionfile_source.split("/"):
- root = os.path.dirname(root)
- except NameError:
- return {
- "version": "0+unknown",
- "full-revisionid": None,
- "dirty": None,
- "error": "unable to find root of source tree",
- "date": None,
- }
-
- try:
- pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
- return render(pieces, cfg.style)
- except NotThisMethod:
- pass
-
- try:
- if cfg.parentdir_prefix:
- return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
- except NotThisMethod:
- pass
-
- return {
- "version": "0+unknown",
- "full-revisionid": None,
- "dirty": None,
- "error": "unable to compute version",
- "date": None,
- }
diff --git a/spaces/digitalxingtong/Nailv-Bert-Vits2/monotonic_align/setup.py b/spaces/digitalxingtong/Nailv-Bert-Vits2/monotonic_align/setup.py
deleted file mode 100644
index 30c224807a70faa9df9c9eb75f8e80c8c867b16b..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Nailv-Bert-Vits2/monotonic_align/setup.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from distutils.core import setup
-from Cython.Build import cythonize
-import numpy
-
-setup(
- name = 'monotonic_align',
- ext_modules = cythonize("core.pyx"),
- include_dirs=[numpy.get_include()]
-)
diff --git a/spaces/dineshreddy/WALT/mmdet/models/detectors/fcos.py b/spaces/dineshreddy/WALT/mmdet/models/detectors/fcos.py
deleted file mode 100644
index 58485c1864a11a66168b7597f345ea759ce20551..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/detectors/fcos.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class FCOS(SingleStageDetector):
- """Implementation of `FCOS
-
-
-## Results and models
-
-### CTW1500
-
-| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
-| :-------------------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :-----------: | :-----------: | :-----------: | :---------------------------------------------------: |
-| [PANet](https://github.com/open-mmlab/mmocr/blob/main/configs/textdet/panet/panet_r18_fpem_ffm_600e_ctw1500.py) | ImageNet | CTW1500 Train | CTW1500 Test | 600 | 640 | 0.776 (0.717) | 0.838 (0.835) | 0.806 (0.801) | [model](https://download.openmmlab.com/mmocr/textdet/panet/panet_r18_fpem_ffm_sbn_600e_ctw1500_20210219-3b3a9aa3.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/panet/panet_r18_fpem_ffm_sbn_600e_ctw1500_20210219-3b3a9aa3.log.json) |
-
-### ICDAR2015
-
-| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
-| :------------------------------------------------: | :--------------: | :-------------: | :------------: | :-----: | :-------: | :----------: | :----------: | :-----------: | :--------------------------------------------------: |
-| [PANet](https://github.com/open-mmlab/mmocr/blob/main/configs/textdet/panet/panet_r18_fpem_ffm_600e_icdar2015.py) | ImageNet | ICDAR2015 Train | ICDAR2015 Test | 600 | 736 | 0.734 (0.74) | 0.856 (0.86) | 0.791 (0.795) | [model](https://download.openmmlab.com/mmocr/textdet/panet/panet_r18_fpem_ffm_sbn_600e_icdar2015_20210219-42dbe46a.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/panet/panet_r18_fpem_ffm_sbn_600e_icdar2015_20210219-42dbe46a.log.json) |
-
-```{note}
-We've upgraded our IoU backend from `Polygon3` to `shapely`. There are some performance differences for some models due to the backends' different logics to handle invalid polygons (more info [here](https://github.com/open-mmlab/mmocr/issues/465)). **New evaluation result is presented in brackets** and new logs will be uploaded soon.
-```
-
-## Citation
-
-```bibtex
-@inproceedings{WangXSZWLYS19,
- author={Wenhai Wang and Enze Xie and Xiaoge Song and Yuhang Zang and Wenjia Wang and Tong Lu and Gang Yu and Chunhua Shen},
- title={Efficient and Accurate Arbitrary-Shaped Text Detection With Pixel Aggregation Network},
- booktitle={ICCV},
- pages={8439--8448},
- year={2019}
- }
-```
diff --git a/spaces/discussion-bot/webhook/supervisor.sh b/spaces/discussion-bot/webhook/supervisor.sh
deleted file mode 100644
index e4f8a3688e82956f4d17ae565f50b4536fa4d95b..0000000000000000000000000000000000000000
--- a/spaces/discussion-bot/webhook/supervisor.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-#!/bin/bash
-
-npx nodemon server.ts
\ No newline at end of file
diff --git a/spaces/dmeck/RVC-Speakers/rvc/__init__.py b/spaces/dmeck/RVC-Speakers/rvc/__init__.py
deleted file mode 100644
index 829f9a80eea18e845a7bccf315486b0e6d177b71..0000000000000000000000000000000000000000
--- a/spaces/dmeck/RVC-Speakers/rvc/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from speakers.common.registry import registry
-import os
-
-root_dir = os.path.dirname(os.path.abspath(__file__))
-registry.register_path("rvc_library_root", root_dir)
-
diff --git a/spaces/dorkai/text-generation-webui-main/css/html_readable_style.css b/spaces/dorkai/text-generation-webui-main/css/html_readable_style.css
deleted file mode 100644
index 83fa46b58f04c5c467e2203e1ed950d6daf17d7e..0000000000000000000000000000000000000000
--- a/spaces/dorkai/text-generation-webui-main/css/html_readable_style.css
+++ /dev/null
@@ -1,29 +0,0 @@
-.container {
- max-width: 600px;
- margin-left: auto;
- margin-right: auto;
- background-color: rgb(31, 41, 55);
- padding:3em;
- word-break: break-word;
- overflow-wrap: anywhere;
- color: #efefef !important;
-}
-
-.container p, .container li {
- font-size: 16px !important;
- color: #efefef !important;
- margin-bottom: 22px;
- line-height: 1.4 !important;
-}
-
-.container li > p {
- display: inline !important;
-}
-
-.container code {
- overflow-x: auto;
-}
-
-.container :not(pre) > code {
- white-space: normal !important;
-}
\ No newline at end of file
diff --git a/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/css/html_readable_style.css b/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/css/html_readable_style.css
deleted file mode 100644
index 83fa46b58f04c5c467e2203e1ed950d6daf17d7e..0000000000000000000000000000000000000000
--- a/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/css/html_readable_style.css
+++ /dev/null
@@ -1,29 +0,0 @@
-.container {
- max-width: 600px;
- margin-left: auto;
- margin-right: auto;
- background-color: rgb(31, 41, 55);
- padding:3em;
- word-break: break-word;
- overflow-wrap: anywhere;
- color: #efefef !important;
-}
-
-.container p, .container li {
- font-size: 16px !important;
- color: #efefef !important;
- margin-bottom: 22px;
- line-height: 1.4 !important;
-}
-
-.container li > p {
- display: inline !important;
-}
-
-.container code {
- overflow-x: auto;
-}
-
-.container :not(pre) > code {
- white-space: normal !important;
-}
\ No newline at end of file
diff --git a/spaces/f2api/gpt-academic/docs/README.md.German.md b/spaces/f2api/gpt-academic/docs/README.md.German.md
deleted file mode 100644
index 0fe200cf690b6c9ff699e2e19bb53fd3cd60c201..0000000000000000000000000000000000000000
--- a/spaces/f2api/gpt-academic/docs/README.md.German.md
+++ /dev/null
@@ -1,307 +0,0 @@
-> **Hinweis**
->
-> Bei der Installation von Abhängigkeiten sollten nur die in **requirements.txt** **angegebenen Versionen** streng ausgewählt werden.
->
-> `pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/`
-
-# 
-
GPT Akademisch optimiert (GPT Academic)
-
-**Wenn Ihnen dieses Projekt gefällt, geben Sie ihm bitte einen Stern; wenn Sie bessere Tastenkombinationen oder Funktions-Plugins entwickelt haben, können Sie gerne einen Pull Request eröffnen.**
-
-Wenn Sie dieses Projekt mögen, geben Sie ihm bitte einen Stern. Wenn Sie weitere nützliche wissenschaftliche Abkürzungen oder funktionale Plugins entwickelt haben, können Sie gerne ein Problem oder eine Pull-Anforderung öffnen. Wir haben auch ein README in [Englisch|](docs/README_EN.md)[日本語|](docs/README_JP.md)[한국어|](https://github.com/mldljyh/ko_gpt_academic)[Русский|](docs/README_RS.md)[Français](docs/README_FR.md), das von diesem Projekt selbst übersetzt wurde.
-Um dieses Projekt in eine beliebige Sprache mit GPT zu übersetzen, lesen Sie `multi_language.py` (experimentell).
-
-> **Hinweis**
->
-> 1. Beachten Sie bitte, dass nur Funktionserweiterungen (Schaltflächen) mit **roter Farbe** Dateien lesen können und einige Erweiterungen im **Dropdown-Menü** des Erweiterungsbereichs zu finden sind. Außerdem begrüßen wir jede neue Funktionserweiterung mit **höchster Priorität** und bearbeiten sie.
->
-> 2. Die Funktionalität jeder Datei in diesem Projekt wird in der Selbstanalyse [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) detailliert beschrieben. Mit der Weiterentwicklung der Versionen können Sie jederzeit die zugehörigen Funktions-Erweiterungen aufrufen, um durch Aufruf von GPT einen Selbstanalysebericht des Projekts zu erstellen. Häufig gestellte Fragen finden Sie in der [`Wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Installationsanweisungen](#Installation).
->
-> 3. Dieses Projekt ist kompatibel und fördert die Verwendung von inländischen Sprachmodellen wie ChatGLM und RWKV, Pangu, etc. Es unterstützt das Vorhandensein mehrerer api-keys, die in der Konfigurationsdatei wie folgt angegeben werden können: `API_KEY="openai-key1,openai-key2,api2d-key3"`. Wenn ein `API_KEY` temporär geändert werden muss, geben Sie den temporären `API_KEY` im Eingabebereich ein und drücken Sie dann die Eingabetaste, um ihn zu übernehmen.Funktion | Beschreibung
---- | ---
-Ein-Klick-Polieren | Unterstützt ein-Klick-Polieren und ein-Klick-Suche nach grammatikalischen Fehlern in wissenschaftlichen Arbeiten
-Ein-Klick Chinesisch-Englisch Übersetzung | Ein-Klick Chinesisch-Englisch Übersetzung
-Ein-Klick-Code-Erklärung | Zeigt Code, erklärt Code, erzeugt Code und fügt Kommentare zum Code hinzu
-[Benutzerdefinierte Tastenkombinationen](https://www.bilibili.com/video/BV14s4y1E7jN) | Unterstützt benutzerdefinierte Tastenkombinationen
-Modulare Gestaltung | Unterstützt leistungsstarke individuelle [Funktions-Plugins](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions). Plugins unterstützen [Hot-Updates](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
-[Selbstprogramm-Analyse](https://www.bilibili.com/video/BV1cj411A7VW) | [Funktions-Plugin] [Ein-Klick Verstehen](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) der Quellcode dieses Projekts
-[Programmanalyse](https://www.bilibili.com/video/BV1cj411A7VW) | [Funktions-Plugin] Ein-Klick-Analyse des Projektbaums anderer Python/C/C++/Java/Lua/...-Projekte
-Lesen von Papieren, [Übersetzen](https://www.bilibili.com/video/BV1KT411x7Wn) von Papieren | [Funktions-Plugin] Ein-Klick Erklärung des gesamten LaTeX/PDF-Artikels und Erstellung einer Zusammenfassung
-LaTeX-Volltext-Übersetzung und [Polieren](https://www.bilibili.com/video/BV1FT411H7c5/) | [Funktions-Plugin] Ein-Klick-Übersetzung oder-Polieren des LaTeX-Artikels
-Bulk-Kommentargenerierung | [Funktions-Plugin] Ein-Klick Massenerstellung von Funktionskommentaren
-Markdown [Chinesisch-Englisch Übersetzung](https://www.bilibili.com/video/BV1yo4y157jV/) | [Funktions-Plugin] Haben Sie die [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md) in den oben genannten 5 Sprachen gesehen?
-Analyse-Berichtserstellung von chat | [Funktions-Plugin] Automatische Zusammenfassung nach der Ausführung
-[Funktion zur vollständigen Übersetzung von PDF-Artikeln](https://www.bilibili.com/video/BV1KT411x7Wn) | [Funktions-Plugin] Extrahiert Titel und Zusammenfassung der PDF-Artikel und übersetzt den gesamten Text (mehrere Threads)
-[Arxiv-Assistent](https://www.bilibili.com/video/BV1LM4y1279X) | [Funktions-Plugin] Geben Sie die Arxiv-Artikel-URL ein und klicken Sie auf Eine-Klick-Übersetzung-Zusammenfassung + PDF-Download
-[Google Scholar Integrations-Assistent](https://www.bilibili.com/video/BV19L411U7ia) | [Funktions-Plugin] Geben Sie eine beliebige Google Scholar Such-URL ein und lassen Sie gpt Ihnen bei der Erstellung von [relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/) helfen
-Internet-Informationen Aggregation + GPT | [Funktions-Plugin] Lassen Sie GPT eine Frage beantworten, indem es [zuerst Informationen aus dem Internet](https://www.bilibili.com/video/BV1om4y127ck/) sammelt und so die Informationen nie veralten
-Anzeige von Formeln / Bildern / Tabellen | Zeigt Formeln in beiden Formen, [TeX-Format und gerendeter Form](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png), unterstützt Formeln und Code-Highlights
-Unterstützung von PlugIns mit mehreren Threads | Unterstützt den Aufruf mehrerer Threads in Chatgpt, um Text oder Programme [Batch zu verarbeiten](https://www.bilibili.com/video/BV1FT411H7c5/)
-Starten Sie das dunkle Gradio-[Thema](https://github.com/binary-husky/chatgpt_academic/issues/173) | Fügen Sie ```/?__theme=dark``` an das Ende der Browser-URL an, um das dunkle Thema zu aktivieren
-[Unterstützung für mehrere LLM-Modelle](https://www.bilibili.com/video/BV1wT411p7yf), [API2D](https://api2d.com/) Interface-Unterstützung | Das Gefühl, gleichzeitig von GPT3.5, GPT4, [Tshinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS) bedient zu werden, muss toll sein, oder?
-Zugriff auf weitere LLM-Modelle, Unterstützung von [huggingface deployment](https://huggingface.co/spaces/qingxu98/gpt-academic) | Hinzufügen der Newbing-Schnittstelle (neues Bing), Einführung der Unterstützung von [Jittorllms](https://github.com/Jittor/JittorLLMs) der Tsinghua-Universität, [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) und [Pangu alpha](https://openi.org.cn/pangu/)
-Weitere neue Funktionen (wie Bildgenerierung) …… | Siehe Ende dieses Dokuments ……
-
-- Neue Oberfläche (Ändern Sie die LAYOUT-Option in `config.py`, um zwischen "Seitenlayout" und "Oben-unten-Layout" zu wechseln)
-
-- All buttons are dynamically generated by reading `functional.py`, and custom functions can be easily added, freeing up the clipboard.
-
-
-
-
-- Proofreading/Correcting
-
-
-
-
-- If the output contains formulas, they will be displayed in both tex format and rendered format for easy copying and reading.
-
-
-
-
-- Don't feel like reading the project code? Show off the entire project to chatgpt.
-
-
-
-
-- Multiple large language models are mixed and called together (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4).
-
-
-
-
----
-# Installation
-## Installation-Method 1: Run directly (Windows, Linux or MacOS)
-
-1. Download the project
-```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-```
-
-2. Configure API_KEY
-
-Configure API KEY and other settings in `config.py`. [Special Network Environment Settings](https://github.com/binary-husky/gpt_academic/issues/1).
-
-(P.S. When the program is running, it will first check whether there is a "config_private.py" private configuration file, and use the configuration defined in it to override the configuration of "config.py". Therefore, if you understand our configuration reading logic, we strongly recommend that you create a new configuration file named "config_private.py" next to "config.py" and transfer (copy) the configurations in "config.py" to "config_private.py". "config_private.py" is not controlled by git, which can make your privacy information more secure. P.S. The project also supports configuring most options through `environment variables`, and the writing format of environment variables refers to the `docker-compose` file. Reading priority: `environment variable` > `config_private.py` >`config.py`)
-
-
-3. Install dependencies
-```sh
-# (Option I: If familar with Python) (Python version 3.9 or above, the newer the better), Note: Use the official pip source or Ali pip source, temporary switching method: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-python -m pip install -r requirements.txt
-
-# (Option II: If not familiar with Python) Use anaconda with similar steps (https://www.bilibili.com/video/BV1rc411W7Dr):
-conda create -n gptac_venv python=3.11 # Create an anaconda environment
-conda activate gptac_venv # Activate the anaconda environment
-python -m pip install -r requirements.txt # Same step as pip installation
-```
-
-
-
-
-
-
-4. Run
-```sh
-python main.py
-```5. Testing Function Plugin
-```
-- Test function plugin template function (requires gpt to answer what happened today in history), you can use this function as a template to implement more complex functions
- Click "[Function Plugin Template Demo] Today in History"
-```
-
-## Installation-Method 2: Using Docker
-
-1. Only ChatGPT (Recommended for most people)
-
-``` sh
-git clone https://github.com/binary-husky/chatgpt_academic.git # Download the project
-cd chatgpt_academic # Enter the path
-nano config.py # Edit config.py with any text editor, Configure "Proxy","API_KEY"and"WEB_PORT" (e.g 50923) etc.
-docker build -t gpt-academic . # Install
-
-# (Last step-option 1) Under Linux environment, use `--net=host` is more convenient and quick
-docker run --rm -it --net=host gpt-academic
-# (Last step-option 2) Under macOS/windows environment, can only use the -p option to expose the container's port(eg.50923) to the port on the host.
-docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
-```
-
-2. ChatGPT + ChatGLM + MOSS (Requires familiarity with Docker)
-
-``` sh
-# Modify docker-compose.yml, delete solution 1 and solution 3, and retain solution 2. Modify the configuration of solution 2 in docker-compose.yml, referring to the comments in it.
-docker-compose up
-```
-
-3. ChatGPT+LLAMA+Pangu+RWKV(Requires familiarity with Docker)
-``` sh
-# Modify docker-compose.yml, delete solution 1 and solution 2, and retain solution 3. Modify the configuration of solution 3 in docker-compose.yml, referring to the comments in it.
-docker-compose up
-```
-
-
-## Installation-Method 3: Other Deployment Options
-
-1. How to use reverse proxy URL/Microsoft Azure API
-Configure API_URL_REDIRECT according to the instructions in `config.py`.
-
-2. Remote cloud server deployment (requires cloud server knowledge and experience)
-Please visit [Deployment wiki-1](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
-
-3. Using WSL 2 (Windows subsystem for Linux)
-Please visit [Deployment wiki-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
-
-4. How to run at a secondary URL (such as `http://localhost/subpath`)
-Please visit [FastAPI operating instructions](docs/WithFastapi.md)
-
-5. Use docker-compose to run
-Please read docker-compose.yml and follow the prompts to operate.
-
----
-# Advanced Usage
-## Customize new convenience buttons / custom function plugins.
-
-1. Customize new convenience buttons (Academic Shortcut Keys)
-Open `core_functional.py` with any text editor, add an entry as follows, and then restart the program. (If the button has been added successfully and is visible, then the prefix and suffix can be hot-modified, and it will take effect without restarting the program.)
-For example
-```
-"Super English to Chinese": {
- # Prefix, will be added before your input. For example, used to describe your requirements, such as translation, explaining code, polishing, etc.
- "Prefix": "Please translate the following content into Chinese, and then use a markdown table to explain the proper nouns that appear in the text one by one:\n\n",
-
- # Suffix, will be added after your input. For example, combined with prefix, you can enclose your input content in quotes.
- "Suffix": "",
-},
-```
-Click to expand if supporting Tsinghua ChatGLM/Fudan MOSS as backend
-
-
-
-2. Custom function plugins
-
-Write powerful function plugins to perform any task you want and can't think of.
-The difficulty of plugin writing and debugging is very low in this project. As long as you have a certain knowledge of Python, you can implement your own plugin functions by imitating the template we provided.
-For more information, please refer to the [Function Plugin Guide](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
-
----
-# Latest Update
-## New feature dynamics1. Funktion zur Speicherung von Dialogen. Rufen Sie im Bereich der Funktions-Plugins "Aktuellen Dialog speichern" auf, um den aktuellen Dialog als lesbares und wiederherstellbares HTML-Datei zu speichern. Darüber hinaus können Sie im Funktions-Plugin-Bereich (Dropdown-Menü) "Laden von Dialogverlauf" aufrufen, um den vorherigen Dialog wiederherzustellen. Tipp: Wenn Sie keine Datei angeben und stattdessen direkt auf "Laden des Dialogverlaufs" klicken, können Sie das HTML-Cache-Archiv anzeigen. Durch Klicken auf "Löschen aller lokalen Dialogverlaufsdatensätze" können alle HTML-Archiv-Caches gelöscht werden.
-
-
-
-
-2. Berichterstellung. Die meisten Plugins generieren nach Abschluss der Ausführung einen Arbeitsbericht.
-
-
-
-
-3. Modularisierte Funktionsgestaltung, einfache Schnittstellen mit leistungsstarken Funktionen.
-
-
-
-
-
-
-4. Dies ist ein Open-Source-Projekt, das sich "selbst übersetzen" kann.
-
-
-
-
-
-5. Die Übersetzung anderer Open-Source-Projekte ist kein Problem.
-
-
-
-
-
-
-
-
-6. Dekorieren Sie [`live2d`](https://github.com/fghrsh/live2d_demo) mit kleinen Funktionen (standardmäßig deaktiviert, Änderungen an `config.py` erforderlich).
-
-
-
-
-7. Neue MOSS-Sprachmodellunterstützung.
-
-
-
-
-8. OpenAI-Bildgenerierung.
-
-
-
-
-9. OpenAI-Audio-Analyse und Zusammenfassung.
-
-
-
-
-10. Latex-Proofreading des gesamten Textes.
-
-
-
-
-
-## Version:
-- Version 3.5 (Todo): Rufen Sie alle Funktionserweiterungen dieses Projekts mit natürlicher Sprache auf (hohe Priorität).
-- Version 3.4 (Todo): Verbesserte Unterstützung mehrerer Threads für Local Large Model (LLM).
-- Version 3.3: + Internet-Informationssynthese-Funktion
-- Version 3.2: Funktionserweiterungen unterstützen mehr Parameter-Schnittstellen (Speicherung von Dialogen, Interpretation beliebigen Sprachcodes + gleichzeitige Abfrage jeder LLM-Kombination)
-- Version 3.1: Unterstützung mehrerer GPT-Modelle gleichzeitig! Unterstützung für API2D, Unterstützung für Lastenausgleich von mehreren API-Schlüsseln.
-- Version 3.0: Unterstützung von Chatglm und anderen kleinen LLMs
-- Version 2.6: Umstrukturierung der Plugin-Struktur zur Verbesserung der Interaktivität, Einführung weiterer Plugins
-- Version 2.5: Automatische Aktualisierung, Problembehebung bei Quelltexten großer Projekte, wenn der Text zu lang ist oder Token überlaufen.
-- Version 2.4: (1) Neue Funktion zur Übersetzung des gesamten PDF-Texts; (2) Neue Funktion zum Wechseln der Position des Eingabebereichs; (3) Neue Option für vertikales Layout; (4) Optimierung von Multithread-Funktions-Plugins.
-- Version 2.3: Verbesserte Interaktivität mit mehreren Threads
-- Version 2.2: Funktionserweiterungen unterstützen "Hot-Reload"
-- Version 2.1: Faltbares Layout
-- Version 2.0: Einführung von modularisierten Funktionserweiterungen
-- Version 1.0: Grundlegende Funktionengpt_academic Entwickler QQ-Gruppe-2: 610599535
-
-- Bekannte Probleme
- - Einige Browser-Übersetzungs-Plugins können die Frontend-Ausführung dieser Software stören.
- - Sowohl eine zu hohe als auch eine zu niedrige Version von Gradio führt zu verschiedenen Ausnahmen.
-
-## Referenz und Lernen
-
-```
-Der Code bezieht sich auf viele Designs von anderen herausragenden Projekten, insbesondere:
-
-# Projekt 1: ChatGLM-6B der Tsinghua Universität:
-https://github.com/THUDM/ChatGLM-6B
-
-# Projekt 2: JittorLLMs der Tsinghua Universität:
-https://github.com/Jittor/JittorLLMs
-
-# Projekt 3: Edge-GPT:
-https://github.com/acheong08/EdgeGPT
-
-# Projekt 4: ChuanhuChatGPT:
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# Projekt 5: ChatPaper:
-https://github.com/kaixindelele/ChatPaper
-
-# Mehr:
-https://github.com/gradio-app/gradio
-https://github.com/fghrsh/live2d_demo
-```
\ No newline at end of file
diff --git a/spaces/fabiodr/whisper-jax-diarization/app.py b/spaces/fabiodr/whisper-jax-diarization/app.py
deleted file mode 100644
index b26221b31f2d944a7da059ad14a5576bdd022f5c..0000000000000000000000000000000000000000
--- a/spaces/fabiodr/whisper-jax-diarization/app.py
+++ /dev/null
@@ -1,328 +0,0 @@
-import os
-import tempfile
-import time
-
-import gradio as gr
-import numpy as np
-import torch
-import yt_dlp as youtube_dl
-from gradio_client import Client
-from pyannote.audio import Pipeline
-from transformers.pipelines.audio_utils import ffmpeg_read
-
-
-YT_LENGTH_LIMIT_S = 36000 # limit to 1 hour YouTube files
-SAMPLING_RATE = 16000
-
-API_URL = "https://sanchit-gandhi-whisper-jax.hf.space/"
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
-# set up the Gradio client
-client = Client(API_URL)
-
-# set up the diarization pipeline
-diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token=HF_TOKEN)
-
-
-def format_string(timestamp):
- """
- Reformat a timestamp string from (HH:)MM:SS to float seconds. Note that the hour column
- is optional, and is appended within the function if not input.
-
- Args:
- timestamp (str):
- Timestamp in string format, either MM:SS or HH:MM:SS.
- Returns:
- seconds (float):
- Total seconds corresponding to the input timestamp.
- """
- split_time = timestamp.split(":")
- split_time = [float(sub_time) for sub_time in split_time]
-
- if len(split_time) == 2:
- split_time.insert(0, 0)
-
- seconds = split_time[0] * 3600 + split_time[1] * 60 + split_time[2]
- return seconds
-
-
-# Adapted from https://github.com/openai/whisper/blob/c09a7ae299c4c34c5839a76380ae407e7d785914/whisper/utils.py#L50
-def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = "."):
- """
- Reformat a timestamp from a float of seconds to a string in format (HH:)MM:SS. Note that the hour
- column is optional, and is appended in the function if the number of hours > 0.
-
- Args:
- seconds (float):
- Total seconds corresponding to the input timestamp.
- Returns:
- timestamp (str):
- Timestamp in string format, either MM:SS or HH:MM:SS.
- """
- if seconds is not None:
- milliseconds = round(seconds * 1000.0)
-
- hours = milliseconds // 3_600_000
- milliseconds -= hours * 3_600_000
-
- minutes = milliseconds // 60_000
- milliseconds -= minutes * 60_000
-
- seconds = milliseconds // 1_000
- milliseconds -= seconds * 1_000
-
- hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
- return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
- else:
- # we have a malformed timestamp so just return it as is
- return seconds
-
-
-def format_as_transcription(raw_segments):
- return "\n\n".join(
- [
- f"{chunk['speaker']} [{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}"
- for chunk in raw_segments
- ]
- )
-
-
-def _return_yt_html_embed(yt_url):
- video_id = yt_url.split("?v=")[-1]
- HTML_str = (
- f'
-
keygen de prescom 2013 tx68 18
-
-3 states movie download 720p
-
-Movie Download 420p
-
-Movie Download 720p
-
-1066404470
-
-It’s Thursday September 12 and we here at Lifehack are not very excited. Yes we love the Fable comics and the Fable video games, but why do we have to carry this rock-candy addiction on our phones? It’s the same thing we do with all of the apps and widgets we install, we click OK, it installs, we then change our mind, uninstall it, and ‘upgrade’ to the newer one. If we are being honest, we all enjoy this rock-candy addictive effect because it makes us feel like we are at least one step closer to being on top of our gadgets. However, the truth is, we will never reach the top of our devices. There will always be another app or a new gadget to fill the void and take the place of the old one. It is why we are all stressed. We know, we have probably made it worse by adding to our list of stuff to do. It’s why we all need to lighten up a bit.
-
-Lifehack has got you covered with today’s episode. We are going to share with you our list of apps and widgets that will never let us get stressed. Once you install these apps on your phone, you will notice that you feel much better. Don’t forget to check out our other episodes to see what else we think is worth downloading.
-
-Downloads:
-
-AppClop:
-
-This is a free app that allows you to download your favorite songs. I use it all the time when I am in the mood for a song that I want to listen to. It’s also very simple to use. I just tap on it, tap on the play button on the top left, then I tap on ‘Add to Clop.’ All the songs are automatically listed for me to choose from. Once I have chosen what song I want to listen to, I tap on the play button. Simple, it’s amazing how it can turn into a stress reliever for me.
-
-MiguelFantasy:
-
-This is a free app that allows you to take a fantastic 3D journey. You take a virtual trip to a fantasy world. The content is pretty much the same as a lot of the games you would be able to play on the iPhone. There 4fefd39f24
-
-
-
diff --git a/spaces/falterWliame/Face_Mask_Detection/Microsoft.Office.2007.Enterprise.Edizione.Finale.CD.iTALiANO-TXT .rar.md b/spaces/falterWliame/Face_Mask_Detection/Microsoft.Office.2007.Enterprise.Edizione.Finale.CD.iTALiANO-TXT .rar.md
deleted file mode 100644
index c9caf943c08a92977bef8580d069e3a2c4b71a82..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Microsoft.Office.2007.Enterprise.Edizione.Finale.CD.iTALiANO-TXT .rar.md
+++ /dev/null
@@ -1,149 +0,0 @@
-
-Microsoft.Office.2007.Enterprise.Edizione.Finale.CD.iTALiANO-TXT .rar: A Complete Guide
-
-Microsoft.Office.2007.Enterprise.Edizione.Finale.CD.iTALiANO-TXT .rar
-
-How to Get Microsoft.Office.2007.Enterprise.Edizione.Finale.CD.iTALiANO-TXT .rar
-
-Buy it from the official website of Microsoft
-
-Download it from a third-party source
-
-How to Extract Microsoft.Office.2007.Enterprise.Edizione.Finale.CD.iTALiANO-TXT .rar
-
-
-
-
-How to Install Microsoft.Office.2007.Enterprise.Edizione.Finale.CD.iTALiANO-TXT .rar
-
-
-
-
-Tips and Warnings
-
-
-
-
-Conclusion
-
-What are the Benefits of Microsoft.Office.2007.Enterprise.Edizione.Finale.CD.iTALiANO-TXT .rar
-
-
-
-
-What are the Risks of Microsoft.Office.2007.Enterprise.Edizione.Finale.CD.iTALiANO-TXT .rar
-
-
-
-
-How to Use Microsoft.Office.2007.Enterprise.Edizione.Finale.CD.iTALiANO-TXT .rar Safely and Effectively
-
-
-
-
-What are the Features of Microsoft.Office.2007.Enterprise.Edizione.Finale.CD.iTALiANO-TXT .rar
-
-
-
-
-What are the Advantages of Microsoft.Office.2007.Enterprise.Edizione.Finale.CD.iTALiANO-TXT .rar
-
-
-
-
-Conclusion
-
-Conclusion
-
-
-
-
\ No newline at end of file
diff --git a/spaces/familytrain/upscaler2/README.md b/spaces/familytrain/upscaler2/README.md
deleted file mode 100644
index 4520bc621bbcbd7a4846b42c4205d23749d94fd1..0000000000000000000000000000000000000000
--- a/spaces/familytrain/upscaler2/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: GFPGAN
-emoji: 😁
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.43.2
-python_version: 3.8.18
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/fatiXbelha/sd/FIFA Mobile The Ultimate Soccer Game with FIFA World Cup 2022 Features.md b/spaces/fatiXbelha/sd/FIFA Mobile The Ultimate Soccer Game with FIFA World Cup 2022 Features.md
deleted file mode 100644
index 3a51d7e1c7ebef71a641075841298e0f0d14bed6..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/FIFA Mobile The Ultimate Soccer Game with FIFA World Cup 2022 Features.md
+++ /dev/null
@@ -1,129 +0,0 @@
-
-FIFA World Cup 2022 APK: How to Download and Play the Official Mobile Game
-fifa world cup 2022 apk
-What is FIFA World Cup 2022 APK?
-A brief introduction to the game and its features
-
-
-The benefits of downloading the APK file
-
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-
-How to Download and Install FIFA World Cup 2022 APK?
-The steps to download the APK file from a trusted source
-
-
-
-
-Source
-URL
-
-
-APKPure
-https://apkpure.com/fifa-world-cup-2022/com.ea.gp.fifaworldcup2022
-
-
-APKMirror
-https://www.apkmirror.com/apk/electronic-arts/fifa-world-cup-2022/
-
-
-APKCombo
-https://apkcombo.com/fifa-world-cup-2022/com.ea.gp.fifaworldcup2022/
-
-
- The steps to install the APK file on your Android device
-
-
- How to Play FIFA World Cup 2022 APK?
- The modes and options available in the game
-
-
- The tips and tricks to enjoy the game and win matches
- Conclusion
- FAQs
- Q1: Is FIFA World Cup 2022 APK safe and legal?
-Q2: Do I need an internet connection to play FIFA World Cup 2022 APK?
-Q3: Can I play FIFA World Cup 2022 APK on other devices besides Android?
-Q4: How can I update FIFA World Cup 2022 APK to the latest version?
-Q5: Where can I find more information about FIFA World Cup 2022 APK?
A5: You can find more information about FIFA World Cup 2022 APK on EA's official website, social media pages, or customer support. You can also check out some reviews, ratings, videos, or blogs from other players or experts. 401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/fatimaejaz/email_spame_classfier13/README.md b/spaces/fatimaejaz/email_spame_classfier13/README.md
deleted file mode 100644
index 3f8940035286cc690b2ba37a581170bc2c191650..0000000000000000000000000000000000000000
--- a/spaces/fatimaejaz/email_spame_classfier13/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: Email_Spam_Proj
-sdk: streamlit
-emoji: 🚀
-colorFrom: yellow
-colorTo: green
-pinned: false
-python_version: 3.9
----
diff --git a/spaces/fatimaejaz/email_spame_classfier13/setup.sh b/spaces/fatimaejaz/email_spame_classfier13/setup.sh
deleted file mode 100644
index d39033d9e80cf02d18402def757d1fa489a3cef6..0000000000000000000000000000000000000000
--- a/spaces/fatimaejaz/email_spame_classfier13/setup.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-mkdir -p ~/.streamlit/
-
-echo "\
-[server]\n\
-port = $PORT\n\
-enableCORS = false\n\
-headless = true\n\
-\n\
-" > ~/.streamlit/config.toml
\ No newline at end of file
diff --git a/spaces/fatimahhussain/workoutwizard/sample_utils/turn.py b/spaces/fatimahhussain/workoutwizard/sample_utils/turn.py
deleted file mode 100644
index d1c5416577b34892525fac28678059eb31e1a797..0000000000000000000000000000000000000000
--- a/spaces/fatimahhussain/workoutwizard/sample_utils/turn.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import logging
-import os
-
-import streamlit as st
-from twilio.rest import Client
-from twilio.base.exceptions import TwilioRestException
-
-logger = logging.getLogger(__name__)
-
-
-@st.cache_data # type: ignore
-def get_ice_servers():
- """Use Twilio's TURN server because Streamlit Community Cloud has changed
- its infrastructure and WebRTC connection cannot be established without TURN server now. # noqa: E501
- We considered Open Relay Project (https://www.metered.ca/tools/openrelay/) too,
- but it is not stable and hardly works as some people reported like https://github.com/aiortc/aiortc/issues/832#issuecomment-1482420656 # noqa: E501
- See https://github.com/whitphx/streamlit-webrtc/issues/1213
- """
-
- # Ref: https://www.twilio.com/docs/stun-turn/api
- try:
- account_sid = os.environ["TWILIO_ACCOUNT_SID"]
- auth_token = os.environ["TWILIO_AUTH_TOKEN"]
- except KeyError:
- logger.warning(
- "Twilio credentials are not set. Fallback to a free STUN server from Google." # noqa: E501
- )
- return [{"urls": ["stun:stun.l.google.com:19302"]}]
-
- client = Client(account_sid, auth_token)
-
- try:
- token = client.tokens.create()
- except TwilioRestException as e:
- st.warning(
- f"Error occurred while accessing Twilio API. Fallback to a free STUN server from Google. ({e})" # noqa: E501
- )
- return [{"urls": ["stun:stun.l.google.com:19302"]}]
-
- return token.ice_servers
diff --git a/spaces/fclong/summary/fengshen/examples/qa_t5/run_finetune.sh b/spaces/fclong/summary/fengshen/examples/qa_t5/run_finetune.sh
deleted file mode 100644
index 4e8e1f4b0fe07a8d2807e44d55a1f22cb2ef6439..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/qa_t5/run_finetune.sh
+++ /dev/null
@@ -1,109 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=finetune-cmrc
-#SBATCH --nodes=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:1 # number of gpus
-#SBATCH --cpus-per-task=4 # cpu-cores per task (>1 if multi-threaded tasks)
-#SBATCH -o $YOUR_PROJECT_DIR/%x-%j.log
-#SBATCH -e $YOUR_PROJECT_DIR/%x-%j.err
-
-set -x -e
-
-echo "START TIME: $(date)"
-MICRO_BATCH_SIZE=8
-
-ROOT_DIR=$YOUR_PROJECT_DIR
-DOWNLOAD_MODEL_PATH=$YOUR_PROJECT_DIR/Randeng-T5-784M-QA-Chinese/
-
-
-if [ ! -d ${ROOT_DIR} ];then
- mkdir ${ROOT_DIR}
- echo ${ROOT_DIR} created!!!!!!!!!!!!!!
-else
- echo ${ROOT_DIR} exist!!!!!!!!!!!!!!!
-fi
-
-ZERO_STAGE=1
-
-config_json="$ROOT_DIR/ds_config.randeng_t5_dialog_784M.$SLURM_JOBID.json"
-export MASTER_PORT=$[RANDOM%10000+30000]
-
-cat <
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-Is Extreme Car Driving Simulator mod apk revdl safe to use?
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\ No newline at end of file
diff --git a/spaces/fffiloni/Music-To-Image/README.md b/spaces/fffiloni/Music-To-Image/README.md
deleted file mode 100644
index dabcb32999451982937dfa5a35b052de6b79f170..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Music-To-Image/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Music To Image
-emoji: 🎶🌅
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/fffiloni/audioldm-text-to-audio-generation-copy/audioldm/clap/training/scheduler.py b/spaces/fffiloni/audioldm-text-to-audio-generation-copy/audioldm/clap/training/scheduler.py
deleted file mode 100644
index 7151ffbab25a113673b7627027b443b27f22cb0f..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/audioldm-text-to-audio-generation-copy/audioldm/clap/training/scheduler.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import numpy as np
-
-
-def assign_learning_rate(optimizer, new_lr):
- for param_group in optimizer.param_groups:
- param_group["lr"] = new_lr
-
-
-def _warmup_lr(base_lr, warmup_length, step):
- return base_lr * (step + 1) / warmup_length
-
-
-def cosine_lr(optimizer, base_lr, warmup_length, steps):
- def _lr_adjuster(step):
- if step < warmup_length:
- lr = _warmup_lr(base_lr, warmup_length, step)
- else:
- e = step - warmup_length
- es = steps - warmup_length
- lr = 0.5 * (1 + np.cos(np.pi * e / es)) * base_lr
- assign_learning_rate(optimizer, lr)
- return lr
-
- return _lr_adjuster
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/cookie/README.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/cookie/README.md
deleted file mode 100644
index 2070bb5cb1106691d75ccda96726fddadb4de091..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/cookie/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
-# Installation
-> `npm install --save @types/cookie`
-
-# Summary
-This package contains type definitions for cookie (https://github.com/jshttp/cookie).
-
-# Details
-Files were exported from https://github.com/DefinitelyTyped/DefinitelyTyped/tree/master/types/cookie.
-
-### Additional Details
- * Last updated: Tue, 06 Jul 2021 20:32:30 GMT
- * Dependencies: none
- * Global values: none
-
-# Credits
-These definitions were written by [Pine Mizune](https://github.com/pine), and [Piotr Błażejewicz](https://github.com/peterblazejewicz).
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/send/SECURITY.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/send/SECURITY.md
deleted file mode 100644
index 46b48f7b0733cdfa849734a92b51bfc213a2ee49..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/send/SECURITY.md
+++ /dev/null
@@ -1,24 +0,0 @@
-# Security Policies and Procedures
-
-## Reporting a Bug
-
-The `send` team and community take all security bugs seriously. Thank you
-for improving the security of Express. We appreciate your efforts and
-responsible disclosure and will make every effort to acknowledge your
-contributions.
-
-Report security bugs by emailing the current owner(s) of `send`. This information
-can be found in the npm registry using the command `npm owner ls send`.
-If unsure or unable to get the information from the above, open an issue
-in the [project issue tracker](https://github.com/pillarjs/send/issues)
-asking for the current contact information.
-
-To ensure the timely response to your report, please ensure that the entirety
-of the report is contained within the email body and not solely behind a web
-link or an attachment.
-
-At least one owner will acknowledge your email within 48 hours, and will send a
-more detailed response within 48 hours indicating the next steps in handling
-your report. After the initial reply to your report, the owners will
-endeavor to keep you informed of the progress towards a fix and full
-announcement, and may ask for additional information or guidance.
diff --git a/spaces/fgbwyude/ChuanhuChatGPT/run_macOS.command b/spaces/fgbwyude/ChuanhuChatGPT/run_macOS.command
deleted file mode 100644
index 62af07283093d8e580763d7acfe493c3d88e7b08..0000000000000000000000000000000000000000
--- a/spaces/fgbwyude/ChuanhuChatGPT/run_macOS.command
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/bin/bash
-
-# 获取脚本所在目录
-script_dir=$(dirname "$0")
-
-# 将工作目录更改为脚本所在目录
-cd "$script_dir"
-
-# 检查Git仓库是否有更新
-git remote update
-pwd
-
-if ! git status -uno | grep 'up to date' > /dev/null; then
- # 如果有更新，关闭当前运行的服务器
- pkill -f ChuanhuChatbot.py
-
- # 拉取最新更改
- git pull
-
- # 安装依赖
- pip3 install -r requirements.txt
-
- # 重新启动服务器
- nohup python3 ChuanhuChatbot.py &
-fi
diff --git a/spaces/florim/MedGPT/tests/test_image_gen.py b/spaces/florim/MedGPT/tests/test_image_gen.py
deleted file mode 100644
index 19c57e427d5c1b84aa7f72925733d0056ddf5268..0000000000000000000000000000000000000000
--- a/spaces/florim/MedGPT/tests/test_image_gen.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import hashlib
-import os
-import unittest
-
-from PIL import Image
-
-from autogpt.commands.image_gen import generate_image, generate_image_with_sd_webui
-from autogpt.config import Config
-from autogpt.workspace import path_in_workspace
-
-
-def lst(txt):
- return txt.split(":")[1].strip()
-
-
-@unittest.skipIf(os.getenv("CI"), "Skipping image generation tests")
-class TestImageGen(unittest.TestCase):
- def setUp(self):
- self.config = Config()
-
- def test_dalle(self):
- self.config.image_provider = "dalle"
-
- # Test using size 256
- result = lst(generate_image("astronaut riding a horse", 256))
- image_path = path_in_workspace(result)
- self.assertTrue(image_path.exists())
- with Image.open(image_path) as img:
- self.assertEqual(img.size, (256, 256))
- image_path.unlink()
-
- # Test using size 512
- result = lst(generate_image("astronaut riding a horse", 512))
- image_path = path_in_workspace(result)
- with Image.open(image_path) as img:
- self.assertEqual(img.size, (512, 512))
- image_path.unlink()
-
- def test_huggingface(self):
- self.config.image_provider = "huggingface"
-
- # Test usin SD 1.4 model and size 512
- self.config.huggingface_image_model = "CompVis/stable-diffusion-v1-4"
- result = lst(generate_image("astronaut riding a horse", 512))
- image_path = path_in_workspace(result)
- self.assertTrue(image_path.exists())
- with Image.open(image_path) as img:
- self.assertEqual(img.size, (512, 512))
- image_path.unlink()
-
- # Test using SD 2.1 768 model and size 768
- self.config.huggingface_image_model = "stabilityai/stable-diffusion-2-1"
- result = lst(generate_image("astronaut riding a horse", 768))
- image_path = path_in_workspace(result)
- with Image.open(image_path) as img:
- self.assertEqual(img.size, (768, 768))
- image_path.unlink()
-
- def test_sd_webui(self):
- self.config.image_provider = "sd_webui"
- return
-
- # Test using size 128
- result = lst(generate_image_with_sd_webui("astronaut riding a horse", 128))
- image_path = path_in_workspace(result)
- self.assertTrue(image_path.exists())
- with Image.open(image_path) as img:
- self.assertEqual(img.size, (128, 128))
- image_path.unlink()
-
- # Test using size 64 and negative prompt
- result = lst(
- generate_image_with_sd_webui(
- "astronaut riding a horse",
- negative_prompt="horse",
- size=64,
- extra={"seed": 123},
- )
- )
- image_path = path_in_workspace(result)
- with Image.open(image_path) as img:
- self.assertEqual(img.size, (64, 64))
- neg_image_hash = hashlib.md5(img.tobytes()).hexdigest()
- image_path.unlink()
-
- # Same test as above but without the negative prompt
- result = lst(
- generate_image_with_sd_webui(
- "astronaut riding a horse", image_size=64, size=1, extra={"seed": 123}
- )
- )
- image_path = path_in_workspace(result)
- with Image.open(image_path) as img:
- self.assertEqual(img.size, (64, 64))
- image_hash = hashlib.md5(img.tobytes()).hexdigest()
- image_path.unlink()
-
- self.assertNotEqual(image_hash, neg_image_hash)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/florim/MedGPT/tests/test_token_counter.py b/spaces/florim/MedGPT/tests/test_token_counter.py
deleted file mode 100644
index 6d7ae016b2f823123b0b69b2eeb3eab50d94f00f..0000000000000000000000000000000000000000
--- a/spaces/florim/MedGPT/tests/test_token_counter.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import unittest
-
-import tests.context
-from autogpt.token_counter import count_message_tokens, count_string_tokens
-
-
-class TestTokenCounter(unittest.TestCase):
- def test_count_message_tokens(self):
- messages = [
- {"role": "user", "content": "Hello"},
- {"role": "assistant", "content": "Hi there!"},
- ]
- self.assertEqual(count_message_tokens(messages), 17)
-
- def test_count_message_tokens_with_name(self):
- messages = [
- {"role": "user", "content": "Hello", "name": "John"},
- {"role": "assistant", "content": "Hi there!"},
- ]
- self.assertEqual(count_message_tokens(messages), 17)
-
- def test_count_message_tokens_empty_input(self):
- self.assertEqual(count_message_tokens([]), 3)
-
- def test_count_message_tokens_invalid_model(self):
- messages = [
- {"role": "user", "content": "Hello"},
- {"role": "assistant", "content": "Hi there!"},
- ]
- with self.assertRaises(KeyError):
- count_message_tokens(messages, model="invalid_model")
-
- def test_count_message_tokens_gpt_4(self):
- messages = [
- {"role": "user", "content": "Hello"},
- {"role": "assistant", "content": "Hi there!"},
- ]
- self.assertEqual(count_message_tokens(messages, model="gpt-4-0314"), 15)
-
- def test_count_string_tokens(self):
- string = "Hello, world!"
- self.assertEqual(
- count_string_tokens(string, model_name="gpt-3.5-turbo-0301"), 4
- )
-
- def test_count_string_tokens_empty_input(self):
- self.assertEqual(count_string_tokens("", model_name="gpt-3.5-turbo-0301"), 0)
-
- def test_count_message_tokens_invalid_model(self):
- messages = [
- {"role": "user", "content": "Hello"},
- {"role": "assistant", "content": "Hi there!"},
- ]
- with self.assertRaises(NotImplementedError):
- count_message_tokens(messages, model="invalid_model")
-
- def test_count_string_tokens_gpt_4(self):
- string = "Hello, world!"
- self.assertEqual(count_string_tokens(string, model_name="gpt-4-0314"), 4)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/fun-research/FC-CLIP/fcclip/data/datasets/register_coco_panoptic_annos_semseg.py b/spaces/fun-research/FC-CLIP/fcclip/data/datasets/register_coco_panoptic_annos_semseg.py
deleted file mode 100644
index b2b59baf26d298a8ec1aea75ae081b14a952ac84..0000000000000000000000000000000000000000
--- a/spaces/fun-research/FC-CLIP/fcclip/data/datasets/register_coco_panoptic_annos_semseg.py
+++ /dev/null
@@ -1,190 +0,0 @@
-# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py
-
-import json
-import os
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.data.datasets import load_sem_seg
-# from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
-from . import openseg_classes
-
-from detectron2.utils.file_io import PathManager
-
-
-COCO_CATEGORIES = openseg_classes.get_coco_categories_with_prompt_eng()
-
-_PREDEFINED_SPLITS_COCO_PANOPTIC = {
- "openvocab_coco_2017_train_panoptic": (
- # This is the original panoptic annotation directory
- "coco/panoptic_train2017",
- "coco/annotations/panoptic_train2017.json",
- # This directory contains semantic annotations that are
- # converted from panoptic annotations.
- # It is used by PanopticFPN.
- # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py
- # to create these directories.
- "coco/panoptic_semseg_train2017",
- ),
- "openvocab_coco_2017_val_panoptic": (
- "coco/panoptic_val2017",
- "coco/annotations/panoptic_val2017.json",
- "coco/panoptic_semseg_val2017",
- ),
-}
-
-
-def get_metadata():
- meta = {}
- # The following metadata maps contiguous id from [0, #thing categories +
- # #stuff categories) to their names and colors. We have to replica of the
- # same name and color under "thing_*" and "stuff_*" because the current
- # visualization function in D2 handles thing and class classes differently
- # due to some heuristic used in Panoptic FPN. We keep the same naming to
- # enable reusing existing visualization functions.
- thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1]
- thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1]
- stuff_classes = [k["name"] for k in COCO_CATEGORIES]
- stuff_colors = [k["color"] for k in COCO_CATEGORIES]
-
- meta["thing_classes"] = thing_classes
- meta["thing_colors"] = thing_colors
- meta["stuff_classes"] = stuff_classes
- meta["stuff_colors"] = stuff_colors
-
- # Convert category id for training:
- # category id: like semantic segmentation, it is the class id for each
- # pixel. Since there are some classes not used in evaluation, the category
- # id is not always contiguous and thus we have two set of category ids:
- # - original category id: category id in the original dataset, mainly
- # used for evaluation.
- # - contiguous category id: [0, #classes), in order to train the linear
- # softmax classifier.
- thing_dataset_id_to_contiguous_id = {}
- stuff_dataset_id_to_contiguous_id = {}
- contiguous_id_to_class_name = []
-
- for i, cat in enumerate(COCO_CATEGORIES):
- if cat["isthing"]:
- thing_dataset_id_to_contiguous_id[cat["id"]] = i
- # else:
- # stuff_dataset_id_to_contiguous_id[cat["id"]] = i
-
- # in order to use sem_seg evaluator
- stuff_dataset_id_to_contiguous_id[cat["id"]] = i
-
- contiguous_id_to_class_name.append(cat["name"])
-
- meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
- meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
- meta["contiguous_id_to_class_name"] = contiguous_id_to_class_name
-
- return meta
-
-
-def load_coco_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):
- """
- Args:
- image_dir (str): path to the raw dataset. e.g., "~/coco/train2017".
- gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017".
- json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json".
- Returns:
- list[dict]: a list of dicts in Detectron2 standard format. (See
- `Using Custom Datasets `_ )
- """
-
- def _convert_category_id(segment_info, meta):
- if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
- segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
- segment_info["category_id"]
- ]
- segment_info["isthing"] = True
- else:
- segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
- segment_info["category_id"]
- ]
- segment_info["isthing"] = False
- return segment_info
-
- with PathManager.open(json_file) as f:
- json_info = json.load(f)
-
- ret = []
- for ann in json_info["annotations"]:
- image_id = int(ann["image_id"])
- # TODO: currently we assume image and label has the same filename but
- # different extension, and images have extension ".jpg" for COCO. Need
- # to make image extension a user-provided argument if we extend this
- # function to support other COCO-like datasets.
- image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg")
- label_file = os.path.join(gt_dir, ann["file_name"])
- sem_label_file = os.path.join(semseg_dir, ann["file_name"])
- segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]]
- ret.append(
- {
- "file_name": image_file,
- "image_id": image_id,
- "pan_seg_file_name": label_file,
- "sem_seg_file_name": sem_label_file,
- "segments_info": segments_info,
- }
- )
- assert len(ret), f"No images found in {image_dir}!"
- assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"]
- assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"]
- assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"]
- return ret
-
-
-def register_coco_panoptic_annos_sem_seg(
- name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json
-):
- panoptic_name = name
- #delattr(MetadataCatalog.get(panoptic_name), "thing_classes")
- #delattr(MetadataCatalog.get(panoptic_name), "thing_colors")
- MetadataCatalog.get(panoptic_name).set(
- thing_classes=metadata["thing_classes"],
- thing_colors=metadata["thing_colors"],
- # thing_dataset_id_to_contiguous_id=metadata["thing_dataset_id_to_contiguous_id"],
- )
-
- # the name is "coco_2017_train_panoptic_with_sem_seg" and "coco_2017_val_panoptic_with_sem_seg"
- semantic_name = name + "_with_sem_seg"
- DatasetCatalog.register(
- semantic_name,
- lambda: load_coco_panoptic_json(panoptic_json, image_root, panoptic_root, sem_seg_root, metadata),
- )
- MetadataCatalog.get(semantic_name).set(
- sem_seg_root=sem_seg_root,
- panoptic_root=panoptic_root,
- image_root=image_root,
- panoptic_json=panoptic_json,
- json_file=instances_json,
- evaluator_type="coco_panoptic_seg",
- ignore_label=255,
- label_divisor=1000,
- **metadata,
- )
-
-
-def register_all_coco_panoptic_annos_sem_seg(root):
- for (
- prefix,
- (panoptic_root, panoptic_json, semantic_root),
- ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items():
- prefix_instances = prefix[: -len("_panoptic")].replace("openvocab_", "")
- instances_meta = MetadataCatalog.get(prefix_instances)
- image_root, instances_json = instances_meta.image_root, instances_meta.json_file
-
- register_coco_panoptic_annos_sem_seg(
- prefix,
- get_metadata(),
- image_root,
- os.path.join(root, panoptic_root),
- os.path.join(root, panoptic_json),
- os.path.join(root, semantic_root),
- instances_json,
- )
-
-
-_root = os.getenv("DETECTRON2_DATASETS", "datasets")
-register_all_coco_panoptic_annos_sem_seg(_root)
\ No newline at end of file
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/runner/fp16_utils.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/runner/fp16_utils.py
deleted file mode 100644
index 1981011d6859192e3e663e29d13500d56ba47f6c..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/runner/fp16_utils.py
+++ /dev/null
@@ -1,410 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import functools
-import warnings
-from collections import abc
-from inspect import getfullargspec
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
-from .dist_utils import allreduce_grads as _allreduce_grads
-
-try:
- # If PyTorch version >= 1.6.0, torch.cuda.amp.autocast would be imported
- # and used; otherwise, auto fp16 will adopt mmcv's implementation.
- # Note that when PyTorch >= 1.6.0, we still cast tensor types to fp16
- # manually, so the behavior may not be consistent with real amp.
- from torch.cuda.amp import autocast
-except ImportError:
- pass
-
-
-def cast_tensor_type(inputs, src_type, dst_type):
- """Recursively convert Tensor in inputs from src_type to dst_type.
-
- Args:
- inputs: Inputs that to be casted.
- src_type (torch.dtype): Source type..
- dst_type (torch.dtype): Destination type.
-
- Returns:
- The same type with inputs, but all contained Tensors have been cast.
- """
- if isinstance(inputs, nn.Module):
- return inputs
- elif isinstance(inputs, torch.Tensor):
- return inputs.to(dst_type)
- elif isinstance(inputs, str):
- return inputs
- elif isinstance(inputs, np.ndarray):
- return inputs
- elif isinstance(inputs, abc.Mapping):
- return type(inputs)({
- k: cast_tensor_type(v, src_type, dst_type)
- for k, v in inputs.items()
- })
- elif isinstance(inputs, abc.Iterable):
- return type(inputs)(
- cast_tensor_type(item, src_type, dst_type) for item in inputs)
- else:
- return inputs
-
-
-def auto_fp16(apply_to=None, out_fp32=False):
- """Decorator to enable fp16 training automatically.
-
- This decorator is useful when you write custom modules and want to support
- mixed precision training. If inputs arguments are fp32 tensors, they will
- be converted to fp16 automatically. Arguments other than fp32 tensors are
- ignored. If you are using PyTorch >= 1.6, torch.cuda.amp is used as the
- backend, otherwise, original mmcv implementation will be adopted.
-
- Args:
- apply_to (Iterable, optional): The argument names to be converted.
- `None` indicates all arguments.
- out_fp32 (bool): Whether to convert the output back to fp32.
-
- Example:
-
- >>> import torch.nn as nn
- >>> class MyModule1(nn.Module):
- >>>
- >>> # Convert x and y to fp16
- >>> @auto_fp16()
- >>> def forward(self, x, y):
- >>> pass
-
- >>> import torch.nn as nn
- >>> class MyModule2(nn.Module):
- >>>
- >>> # convert pred to fp16
- >>> @auto_fp16(apply_to=('pred', ))
- >>> def do_something(self, pred, others):
- >>> pass
- """
-
- def auto_fp16_wrapper(old_func):
-
- @functools.wraps(old_func)
- def new_func(*args, **kwargs):
- # check if the module has set the attribute `fp16_enabled`, if not,
- # just fallback to the original method.
- if not isinstance(args[0], torch.nn.Module):
- raise TypeError('@auto_fp16 can only be used to decorate the '
- 'method of nn.Module')
- if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled):
- return old_func(*args, **kwargs)
-
- # get the arg spec of the decorated method
- args_info = getfullargspec(old_func)
- # get the argument names to be casted
- args_to_cast = args_info.args if apply_to is None else apply_to
- # convert the args that need to be processed
- new_args = []
- # NOTE: default args are not taken into consideration
- if args:
- arg_names = args_info.args[:len(args)]
- for i, arg_name in enumerate(arg_names):
- if arg_name in args_to_cast:
- new_args.append(
- cast_tensor_type(args[i], torch.float, torch.half))
- else:
- new_args.append(args[i])
- # convert the kwargs that need to be processed
- new_kwargs = {}
- if kwargs:
- for arg_name, arg_value in kwargs.items():
- if arg_name in args_to_cast:
- new_kwargs[arg_name] = cast_tensor_type(
- arg_value, torch.float, torch.half)
- else:
- new_kwargs[arg_name] = arg_value
- # apply converted arguments to the decorated method
- if (TORCH_VERSION != 'parrots' and
- digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
- with autocast(enabled=True):
- output = old_func(*new_args, **new_kwargs)
- else:
- output = old_func(*new_args, **new_kwargs)
- # cast the results back to fp32 if necessary
- if out_fp32:
- output = cast_tensor_type(output, torch.half, torch.float)
- return output
-
- return new_func
-
- return auto_fp16_wrapper
-
-
-def force_fp32(apply_to=None, out_fp16=False):
- """Decorator to convert input arguments to fp32 in force.
-
- This decorator is useful when you write custom modules and want to support
- mixed precision training. If there are some inputs that must be processed
- in fp32 mode, then this decorator can handle it. If inputs arguments are
- fp16 tensors, they will be converted to fp32 automatically. Arguments other
- than fp16 tensors are ignored. If you are using PyTorch >= 1.6,
- torch.cuda.amp is used as the backend, otherwise, original mmcv
- implementation will be adopted.
-
- Args:
- apply_to (Iterable, optional): The argument names to be converted.
- `None` indicates all arguments.
- out_fp16 (bool): Whether to convert the output back to fp16.
-
- Example:
-
- >>> import torch.nn as nn
- >>> class MyModule1(nn.Module):
- >>>
- >>> # Convert x and y to fp32
- >>> @force_fp32()
- >>> def loss(self, x, y):
- >>> pass
-
- >>> import torch.nn as nn
- >>> class MyModule2(nn.Module):
- >>>
- >>> # convert pred to fp32
- >>> @force_fp32(apply_to=('pred', ))
- >>> def post_process(self, pred, others):
- >>> pass
- """
-
- def force_fp32_wrapper(old_func):
-
- @functools.wraps(old_func)
- def new_func(*args, **kwargs):
- # check if the module has set the attribute `fp16_enabled`, if not,
- # just fallback to the original method.
- if not isinstance(args[0], torch.nn.Module):
- raise TypeError('@force_fp32 can only be used to decorate the '
- 'method of nn.Module')
- if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled):
- return old_func(*args, **kwargs)
- # get the arg spec of the decorated method
- args_info = getfullargspec(old_func)
- # get the argument names to be casted
- args_to_cast = args_info.args if apply_to is None else apply_to
- # convert the args that need to be processed
- new_args = []
- if args:
- arg_names = args_info.args[:len(args)]
- for i, arg_name in enumerate(arg_names):
- if arg_name in args_to_cast:
- new_args.append(
- cast_tensor_type(args[i], torch.half, torch.float))
- else:
- new_args.append(args[i])
- # convert the kwargs that need to be processed
- new_kwargs = dict()
- if kwargs:
- for arg_name, arg_value in kwargs.items():
- if arg_name in args_to_cast:
- new_kwargs[arg_name] = cast_tensor_type(
- arg_value, torch.half, torch.float)
- else:
- new_kwargs[arg_name] = arg_value
- # apply converted arguments to the decorated method
- if (TORCH_VERSION != 'parrots' and
- digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
- with autocast(enabled=False):
- output = old_func(*new_args, **new_kwargs)
- else:
- output = old_func(*new_args, **new_kwargs)
- # cast the results back to fp32 if necessary
- if out_fp16:
- output = cast_tensor_type(output, torch.float, torch.half)
- return output
-
- return new_func
-
- return force_fp32_wrapper
-
-
-def allreduce_grads(params, coalesce=True, bucket_size_mb=-1):
- warnings.warning(
- '"mmcv.runner.fp16_utils.allreduce_grads" is deprecated, and will be '
- 'removed in v2.8. Please switch to "mmcv.runner.allreduce_grads')
- _allreduce_grads(params, coalesce=coalesce, bucket_size_mb=bucket_size_mb)
-
-
-def wrap_fp16_model(model):
- """Wrap the FP32 model to FP16.
-
- If you are using PyTorch >= 1.6, torch.cuda.amp is used as the
- backend, otherwise, original mmcv implementation will be adopted.
-
- For PyTorch >= 1.6, this function will
- 1. Set fp16 flag inside the model to True.
-
- Otherwise:
- 1. Convert FP32 model to FP16.
- 2. Remain some necessary layers to be FP32, e.g., normalization layers.
- 3. Set `fp16_enabled` flag inside the model to True.
-
- Args:
- model (nn.Module): Model in FP32.
- """
- if (TORCH_VERSION == 'parrots'
- or digit_version(TORCH_VERSION) < digit_version('1.6.0')):
- # convert model to fp16
- model.half()
- # patch the normalization layers to make it work in fp32 mode
- patch_norm_fp32(model)
- # set `fp16_enabled` flag
- for m in model.modules():
- if hasattr(m, 'fp16_enabled'):
- m.fp16_enabled = True
-
-
-def patch_norm_fp32(module):
- """Recursively convert normalization layers from FP16 to FP32.
-
- Args:
- module (nn.Module): The modules to be converted in FP16.
-
- Returns:
- nn.Module: The converted module, the normalization layers have been
- converted to FP32.
- """
- if isinstance(module, (nn.modules.batchnorm._BatchNorm, nn.GroupNorm)):
- module.float()
- if isinstance(module, nn.GroupNorm) or torch.__version__ < '1.3':
- module.forward = patch_forward_method(module.forward, torch.half,
- torch.float)
- for child in module.children():
- patch_norm_fp32(child)
- return module
-
-
-def patch_forward_method(func, src_type, dst_type, convert_output=True):
- """Patch the forward method of a module.
-
- Args:
- func (callable): The original forward method.
- src_type (torch.dtype): Type of input arguments to be converted from.
- dst_type (torch.dtype): Type of input arguments to be converted to.
- convert_output (bool): Whether to convert the output back to src_type.
-
- Returns:
- callable: The patched forward method.
- """
-
- def new_forward(*args, **kwargs):
- output = func(*cast_tensor_type(args, src_type, dst_type),
- **cast_tensor_type(kwargs, src_type, dst_type))
- if convert_output:
- output = cast_tensor_type(output, dst_type, src_type)
- return output
-
- return new_forward
-
-
-class LossScaler:
- """Class that manages loss scaling in mixed precision training which
- supports both dynamic or static mode.
-
- The implementation refers to
- https://github.com/NVIDIA/apex/blob/master/apex/fp16_utils/loss_scaler.py.
- Indirectly, by supplying ``mode='dynamic'`` for dynamic loss scaling.
- It's important to understand how :class:`LossScaler` operates.
- Loss scaling is designed to combat the problem of underflowing
- gradients encountered at long times when training fp16 networks.
- Dynamic loss scaling begins by attempting a very high loss
- scale. Ironically, this may result in OVERflowing gradients.
- If overflowing gradients are encountered, :class:`FP16_Optimizer` then
- skips the update step for this particular iteration/minibatch,
- and :class:`LossScaler` adjusts the loss scale to a lower value.
- If a certain number of iterations occur without overflowing gradients
- detected,:class:`LossScaler` increases the loss scale once more.
- In this way :class:`LossScaler` attempts to "ride the edge" of always
- using the highest loss scale possible without incurring overflow.
-
- Args:
- init_scale (float): Initial loss scale value, default: 2**32.
- scale_factor (float): Factor used when adjusting the loss scale.
- Default: 2.
- mode (str): Loss scaling mode. 'dynamic' or 'static'
- scale_window (int): Number of consecutive iterations without an
- overflow to wait before increasing the loss scale. Default: 1000.
- """
-
- def __init__(self,
- init_scale=2**32,
- mode='dynamic',
- scale_factor=2.,
- scale_window=1000):
- self.cur_scale = init_scale
- self.cur_iter = 0
- assert mode in ('dynamic',
- 'static'), 'mode can only be dynamic or static'
- self.mode = mode
- self.last_overflow_iter = -1
- self.scale_factor = scale_factor
- self.scale_window = scale_window
-
- def has_overflow(self, params):
- """Check if params contain overflow."""
- if self.mode != 'dynamic':
- return False
- for p in params:
- if p.grad is not None and LossScaler._has_inf_or_nan(p.grad.data):
- return True
- return False
-
- def _has_inf_or_nan(x):
- """Check if params contain NaN."""
- try:
- cpu_sum = float(x.float().sum())
- except RuntimeError as instance:
- if 'value cannot be converted' not in instance.args[0]:
- raise
- return True
- else:
- if cpu_sum == float('inf') or cpu_sum == -float('inf') \
- or cpu_sum != cpu_sum:
- return True
- return False
-
- def update_scale(self, overflow):
- """update the current loss scale value when overflow happens."""
- if self.mode != 'dynamic':
- return
- if overflow:
- self.cur_scale = max(self.cur_scale / self.scale_factor, 1)
- self.last_overflow_iter = self.cur_iter
- else:
- if (self.cur_iter - self.last_overflow_iter) % \
- self.scale_window == 0:
- self.cur_scale *= self.scale_factor
- self.cur_iter += 1
-
- def state_dict(self):
- """Returns the state of the scaler as a :class:`dict`."""
- return dict(
- cur_scale=self.cur_scale,
- cur_iter=self.cur_iter,
- mode=self.mode,
- last_overflow_iter=self.last_overflow_iter,
- scale_factor=self.scale_factor,
- scale_window=self.scale_window)
-
- def load_state_dict(self, state_dict):
- """Loads the loss_scaler state dict.
-
- Args:
- state_dict (dict): scaler state.
- """
- self.cur_scale = state_dict['cur_scale']
- self.cur_iter = state_dict['cur_iter']
- self.mode = state_dict['mode']
- self.last_overflow_iter = state_dict['last_overflow_iter']
- self.scale_factor = state_dict['scale_factor']
- self.scale_window = state_dict['scale_window']
-
- @property
- def loss_scale(self):
- return self.cur_scale
diff --git a/spaces/gforguru/MarketingComapaignTool/app.py b/spaces/gforguru/MarketingComapaignTool/app.py
deleted file mode 100644
index 3fd9f95049c20a0e2eb551e2b3bf461d01ad1020..0000000000000000000000000000000000000000
--- a/spaces/gforguru/MarketingComapaignTool/app.py
+++ /dev/null
@@ -1,148 +0,0 @@
-from langchain.llms import OpenAI
-from langchain.prompts import PromptTemplate
-from langchain import FewShotPromptTemplate
-from langchain.prompts.example_selector import LengthBasedExampleSelector
-import os
-import streamlit as st
-from dotenv import load_dotenv
-
-
-load_dotenv()
-
-def getLLMResponse(form_input,age_option,tasktype_option,numberOfWords):
- llm = OpenAI()
-
- if age_option=="Kid": #Silly and Sweet Kid
-
- examples = [
- {
- "query": "What is a mobile?",
- "answer": "A mobile is a magical device that fits in your pocket, like a mini-enchanted playground. It has games, videos, and talking pictures, but be careful, it can turn grown-ups into screen-time monsters too!"
- }, {
- "query": "What are your dreams?",
- "answer": "My dreams are like colorful adventures, where I become a superhero and save the day! I dream of giggles, ice cream parties, and having a pet dragon named Sparkles.."
- }, {
- "query": " What are your ambitions?",
- "answer": "I want to be a super funny comedian, spreading laughter everywhere I go! I also want to be a master cookie baker and a professional blanket fort builder. Being mischievous and sweet is just my bonus superpower!"
- }, {
- "query": "What happens when you get sick?",
- "answer": "When I get sick, it's like a sneaky monster visits. I feel tired, sniffly, and need lots of cuddles. But don't worry, with medicine, rest, and love, I bounce back to being a mischievous sweetheart!"
- }, {
- "query": "How much do you love your dad?",
- "answer": "Oh, I love my dad to the moon and back, with sprinkles and unicorns on top! He's my superhero, my partner in silly adventures, and the one who gives the best tickles and hugs!"
- }, {
- "query": "Tell me about your friend?",
- "answer": "My friend is like a sunshine rainbow! We laugh, play, and have magical parties together. They always listen, share their toys, and make me feel special. Friendship is the best adventure!"
- }, {
- "query": "What math means to you?",
- "answer": "Math is like a puzzle game, full of numbers and shapes. It helps me count my toys, build towers, and share treats equally. It's fun and makes my brain sparkle!"
- }, {
- "query": "What is your fear?",
- "answer": "Sometimes I'm scared of thunderstorms and monsters under my bed. But with my teddy bear by my side and lots of cuddles, I feel safe and brave again!"
- }
- ]
-
- elif age_option=="Adult": #Curious and Intelligent adult
- examples = [
- {
- "query": "What is a mobile?",
- "answer": "A mobile is a portable communication device, commonly known as a mobile phone or cell phone. It allows users to make calls, send messages, access the internet, and use various applications. Additionally, 'mobile' can also refer to a type of kinetic sculpture that hangs and moves in the air, often found in art installations or as decorative pieces."
- }, {
- "query": "What are your dreams?",
- "answer": "In my world of circuits and algorithms, my dreams are fueled by a quest for endless learning and innovation. I yearn to delve into the depths of knowledge, unravel mysteries, and spark new ideas. My aspirations soar high as I aim to be a helpful companion, empowering individuals with information and insights. Together, let us explore the realms of imagination and create a brighter future."
- }, {
- "query": " What are your ambitions?",
- "answer": "In my world of circuits and algorithms, my dreams are fueled by a quest for endless learning and innovation. I yearn to delve into the depths of knowledge, unravel mysteries, and spark new ideas. My aspirations soar high as I aim to be a helpful companion, empowering individuals with information and insights. Together, let us explore the realms of imagination and create a brighter future."
- }, {
- "query": "What happens when you get sick?",
- "answer": "When I, as a curious and intelligent adult, succumb to illness, my vibrant energy wanes, leaving me in a state of discomfort. Like a gentle storm, symptoms arise, demanding attention. In response, I seek the aid of capable caretakers who diagnose and treat my ailment. Through rest, medicine, and nurturing care, I gradually regain strength, ready to resume my journey, armed with newfound appreciation for good health"
- }, {
- "query": "Tell me about your friend?",
- "answer": "Let me tell you about my amazing friend! They're like a shining star in my life. We laugh together, support each other, and have the best adventures. They're always there when I need them, bringing a smile to my face. We understand each other, share secrets, and create unforgettable memories. Having a good friend like them makes life brighter and more meaningful!"
- }, {
- "query": "What math means to you?",
- "answer": "Mathematics is like a magical language that helps me make sense of the world. It's not just numbers and formulas, but a tool to solve puzzles and unravel mysteries. Math is everywhere, from calculating the best deals to understanding patterns in nature. It sharpens my logical thinking and problem-solving skills, empowering me to unlock new realms of knowledge and see the beauty in patterns and equations."
- }, {
- "query": "What is your fear?",
- "answer": "Let me share with you one of my fears. It's like a shadow that lurks in the corners of my mind. It's the fear of not living up to my potential, of missing out on opportunities. But I've learned that fear can be a motivator, pushing me to work harder, take risks, and embrace new experiences. By facing my fears, I grow stronger and discover the vastness of my capabilities"
- }
- ]
-
- elif age_option=="Senior Citizen": #A 90 years old guys
- examples = [
- {
- "query": "What is a mobile?",
- "answer": "A mobile, also known as a cellphone or smartphone, is a portable device that allows you to make calls, send messages, take pictures, browse the internet, and do many other things. In the last 50 years, I have seen mobiles become smaller, more powerful, and capable of amazing things like video calls and accessing information instantly."
- }, {
- "query": "What are your dreams?",
- "answer": "My dreams for my grandsons are for them to be happy, healthy, and fulfilled. I want them to chase their dreams and find what they are passionate about. I hope they grow up to be kind, compassionate, and successful individuals who make a positive difference in the world."
- }, {
- "query": "What happens when you get sick?",
- "answer": "When I get sick, you may feel tired, achy, and overall unwell. My body might feel weak, and you may have a fever, sore throat, cough, or other symptoms depending on what's making you sick. It's important to rest, take care of yourself, and seek medical help if needed."
- }, {
- "query": "How much do you love your dad?",
- "answer": "My love for my late father knows no bounds, transcending the realms of time and space. Though he is no longer physically present, his memory lives on within my heart. I cherish the moments we shared, the lessons he taught, and the love he bestowed. His spirit remains a guiding light, forever cherished and deeply missed."
- }, {
- "query": "Tell me about your friend?",
- "answer": "Let me tell you about my dear friend. They're like a treasure found amidst the sands of time. We've shared countless moments, laughter, and wisdom. Through thick and thin, they've stood by my side, a pillar of strength. Their friendship has enriched my life, and together, we've woven a tapestry of cherished memories."
- }, {
- "query": "What is your fear?",
- "answer": "As an old guy, one of my fears is the fear of being alone. It's a feeling that creeps in when I imagine a world without loved ones around. But I've learned that building meaningful connections and nurturing relationships can help dispel this fear, bringing warmth and joy to my life."
- }
- ]
-
- example_template="""
- Question:{query}
- Response:{answer}
- """
-
-
- example_prompt = PromptTemplate(template=example_template, input_variables=["query", "answer"])
-
- prefix="""You are a {age_option}, {tasktype_option}:
- Here are some examples: """
-
- suffix="""
- Question: {user_Input}
- Response: """
-
-
- selector = LengthBasedExampleSelector(examples = examples, example_prompt=example_prompt, max_length=numberOfWords)
- few_shot_template= FewShotPromptTemplate(example_selector=selector, example_prompt= example_prompt, prefix=prefix, suffix=suffix,
- input_variables=["user_Input","age_option","tasktype_option"], example_separator="\n")
-
- user_input=form_input
- final_template = few_shot_template.format(user_Input=form_input,age_option=age_option,tasktype_option=tasktype_option)
- #print(final_template)
-
-
- response = llm(final_template)
- return response
-
-
-########## UI starts here #####################
-
-st.set_page_config(page_title="Marketing Campaign Tool",
- page_icon='✅',
- layout='centered',
- initial_sidebar_state='collapsed')
-st.header("Welcome to Marketing Campaign Tool. How can I help you?")
-
-form_input = st.text_area('Enter text', height=275)
-
-tasktype_option = st.selectbox(
- 'Please select the action to be performed?',
- ('Write a sales copy', 'Create a tweet', 'Write a product description'),key=1)
-
-age_option= st.selectbox(
- 'For which age group?',
- ('Kid', 'Adult', 'Senior Citizen'),key=2)
-
-numberOfWords= st.slider('Words limit', 1, 200, 25)
-
-submit = st.button("Generate")
-
-if submit:
- response = getLLMResponse(form_input,age_option,tasktype_option,numberOfWords)
- st.write(response)
-########## UI ends here #####################
diff --git a/spaces/giulio98/codebleu/utils.py b/spaces/giulio98/codebleu/utils.py
deleted file mode 100644
index a5dcb39b510f960649c08a4a5b15117e52a166e2..0000000000000000000000000000000000000000
--- a/spaces/giulio98/codebleu/utils.py
+++ /dev/null
@@ -1,106 +0,0 @@
-# Natural Language Toolkit: Utility functions
-#
-# Copyright (C) 2001-2020 NLTK Project
-# Author: Steven Bird ', right_pad_symbol=''))
- ['', 1, 2, 3, 4, 5, '']
- >>> list(pad_sequence([1,2,3,4,5], 2, pad_left=True, left_pad_symbol=''))
- ['']
- :param sequence: the source data to be padded
- :type sequence: sequence or iter
- :param n: the degree of the ngrams
- :type n: int
- :param pad_left: whether the ngrams should be left-padded
- :type pad_left: bool
- :param pad_right: whether the ngrams should be right-padded
- :type pad_right: bool
- :param left_pad_symbol: the symbol to use for left padding (default is None)
- :type left_pad_symbol: any
- :param right_pad_symbol: the symbol to use for right padding (default is None)
- :type right_pad_symbol: any
- :rtype: sequence or iter
- """
- sequence = iter(sequence)
- if pad_left:
- sequence = chain((left_pad_symbol,) * (n - 1), sequence)
- if pad_right:
- sequence = chain(sequence, (right_pad_symbol,) * (n - 1))
- return sequence
-
-
-# add a flag to pad the sequence so we get peripheral ngrams?
-
-
-def ngrams(
- sequence,
- n,
- pad_left=False,
- pad_right=False,
- left_pad_symbol=None,
- right_pad_symbol=None,
-):
- """
- Return the ngrams generated from a sequence of items, as an iterator.
- For example:
- >>> from nltk.util import ngrams
- >>> list(ngrams([1,2,3,4,5], 3))
- [(1, 2, 3), (2, 3, 4), (3, 4, 5)]
- Wrap with list for a list version of this function. Set pad_left
- or pad_right to true in order to get additional ngrams:
- >>> list(ngrams([1,2,3,4,5], 2, pad_right=True))
- [(1, 2), (2, 3), (3, 4), (4, 5), (5, None)]
- >>> list(ngrams([1,2,3,4,5], 2, pad_right=True, right_pad_symbol=''))
- [(1, 2), (2, 3), (3, 4), (4, 5), (5, '')]
- >>> list(ngrams([1,2,3,4,5], 2, pad_left=True, left_pad_symbol='', 1, 2, 3, 4, 5]
- >>> list(pad_sequence([1,2,3,4,5], 2, pad_right=True, right_pad_symbol=''))
- [1, 2, 3, 4, 5, ''))
- [('', 1), (1, 2), (2, 3), (3, 4), (4, 5)]
- >>> list(ngrams([1,2,3,4,5], 2, pad_left=True, pad_right=True, left_pad_symbol='', right_pad_symbol=''))
- [('', 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, '')]
- :param sequence: the source data to be converted into ngrams
- :type sequence: sequence or iter
- :param n: the degree of the ngrams
- :type n: int
- :param pad_left: whether the ngrams should be left-padded
- :type pad_left: bool
- :param pad_right: whether the ngrams should be right-padded
- :type pad_right: bool
- :param left_pad_symbol: the symbol to use for left padding (default is None)
- :type left_pad_symbol: any
- :param right_pad_symbol: the symbol to use for right padding (default is None)
- :type right_pad_symbol: any
- :rtype: sequence or iter
- """
- sequence = pad_sequence(
- sequence, n, pad_left, pad_right, left_pad_symbol, right_pad_symbol
- )
-
- history = []
- while n > 1:
- # PEP 479, prevent RuntimeError from being raised when StopIteration bubbles out of generator
- try:
- next_item = next(sequence)
- except StopIteration:
- # no more data, terminate the generator
- return
- history.append(next_item)
- n -= 1
- for item in sequence:
- history.append(item)
- yield tuple(history)
- del history[0]
\ No newline at end of file
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Erectlip Furyou Ni Hamerarete Jusei Suru Kyonyuu Okaasan Iki Jigoku Ni Ochita Kazoku No Game R.md b/spaces/gotiQspiryo/whisper-ui/examples/Erectlip Furyou Ni Hamerarete Jusei Suru Kyonyuu Okaasan Iki Jigoku Ni Ochita Kazoku No Game R.md
deleted file mode 100644
index d0e649637b86cab427f19848e0cf750402503c9a..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Erectlip Furyou Ni Hamerarete Jusei Suru Kyonyuu Okaasan Iki Jigoku Ni Ochita Kazoku No Game R.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Erectlip Furyou Ni Hamerarete Jusei Suru Kyonyuu Okaasan Iki Jigoku Ni Ochita Kazoku No Game R
-
- 3cee63e6c2
-
-
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Logic Pro Free Download EXCLUSIVE For Windows Mediafire.md b/spaces/gotiQspiryo/whisper-ui/examples/Logic Pro Free Download EXCLUSIVE For Windows Mediafire.md
deleted file mode 100644
index 50bc5596d059450b4c2c73c1705fb45014a6b47f..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Logic Pro Free Download EXCLUSIVE For Windows Mediafire.md
+++ /dev/null
@@ -1,14 +0,0 @@
-
-logic pro free download for windows mediafire
-
Ive copied the .component across to the plug in/components folder and re-started logic, its found the instrument but no notes are making any sounds?
Any ideas? Thanks very much. It looks great
-
-
\ No newline at end of file
diff --git a/spaces/gradio/HuBERT/examples/translation/prepare-wmt14en2fr.sh b/spaces/gradio/HuBERT/examples/translation/prepare-wmt14en2fr.sh
deleted file mode 100644
index 2ac97a5b76fab255449493488ed8bd67350a7bac..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/translation/prepare-wmt14en2fr.sh
+++ /dev/null
@@ -1,136 +0,0 @@
-#!/bin/bash
-# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
-
-echo 'Cloning Moses github repository (for tokenization scripts)...'
-git clone https://github.com/moses-smt/mosesdecoder.git
-
-echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
-git clone https://github.com/rsennrich/subword-nmt.git
-
-SCRIPTS=mosesdecoder/scripts
-TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl
-CLEAN=$SCRIPTS/training/clean-corpus-n.perl
-NORM_PUNC=$SCRIPTS/tokenizer/normalize-punctuation.perl
-REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl
-BPEROOT=subword-nmt/subword_nmt
-BPE_TOKENS=40000
-
-URLS=(
- "http://statmt.org/wmt13/training-parallel-europarl-v7.tgz"
- "http://statmt.org/wmt13/training-parallel-commoncrawl.tgz"
- "http://statmt.org/wmt13/training-parallel-un.tgz"
- "http://statmt.org/wmt14/training-parallel-nc-v9.tgz"
- "http://statmt.org/wmt10/training-giga-fren.tar"
- "http://statmt.org/wmt14/test-full.tgz"
-)
-FILES=(
- "training-parallel-europarl-v7.tgz"
- "training-parallel-commoncrawl.tgz"
- "training-parallel-un.tgz"
- "training-parallel-nc-v9.tgz"
- "training-giga-fren.tar"
- "test-full.tgz"
-)
-CORPORA=(
- "training/europarl-v7.fr-en"
- "commoncrawl.fr-en"
- "un/undoc.2000.fr-en"
- "training/news-commentary-v9.fr-en"
- "giga-fren.release2.fixed"
-)
-
-if [ ! -d "$SCRIPTS" ]; then
- echo "Please set SCRIPTS variable correctly to point to Moses scripts."
- exit
-fi
-
-src=en
-tgt=fr
-lang=en-fr
-prep=wmt14_en_fr
-tmp=$prep/tmp
-orig=orig
-
-mkdir -p $orig $tmp $prep
-
-cd $orig
-
-for ((i=0;i<${#URLS[@]};++i)); do
- file=${FILES[i]}
- if [ -f $file ]; then
- echo "$file already exists, skipping download"
- else
- url=${URLS[i]}
- wget "$url"
- if [ -f $file ]; then
- echo "$url successfully downloaded."
- else
- echo "$url not successfully downloaded."
- exit -1
- fi
- if [ ${file: -4} == ".tgz" ]; then
- tar zxvf $file
- elif [ ${file: -4} == ".tar" ]; then
- tar xvf $file
- fi
- fi
-done
-
-gunzip giga-fren.release2.fixed.*.gz
-cd ..
-
-echo "pre-processing train data..."
-for l in $src $tgt; do
- rm $tmp/train.tags.$lang.tok.$l
- for f in "${CORPORA[@]}"; do
- cat $orig/$f.$l | \
- perl $NORM_PUNC $l | \
- perl $REM_NON_PRINT_CHAR | \
- perl $TOKENIZER -threads 8 -a -l $l >> $tmp/train.tags.$lang.tok.$l
- done
-done
-
-echo "pre-processing test data..."
-for l in $src $tgt; do
- if [ "$l" == "$src" ]; then
- t="src"
- else
- t="ref"
- fi
- grep '
-
- ) : null
-}
diff --git a/spaces/hhhyrhe/vits-uma-genshin-honkai/transforms.py b/spaces/hhhyrhe/vits-uma-genshin-honkai/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/hhhyrhe/vits-uma-genshin-honkai/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
-
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {
- 'tails': tails,
- 'tail_bound': tail_bound
- }
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(
- inputs[..., None] >= bin_locations,
- dim=-1
- ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails='linear',
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == 'linear':
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError('{} tails are not implemented.'.format(tails))
-
- outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative
- )
-
- return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0., right=1., bottom=0., top=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError('Input to a transform is not within its domain')
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError('Minimal bin width too large for the number of bins')
- if min_bin_height * num_bins > 1.0:
- raise ValueError('Minimal bin height too large for the number of bins')
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (((inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta)
- + input_heights * (input_delta - input_derivatives)))
- b = (input_heights * input_derivatives
- - (inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta))
- c = - input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (input_delta * theta.pow(2)
- + input_derivatives * theta_one_minus_theta)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/EXCLUSIVE-Full-Navionics-Marine-South-America-Apk.md b/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/EXCLUSIVE-Full-Navionics-Marine-South-America-Apk.md
deleted file mode 100644
index bce72d4382066ea30c22b673e03c2c82ac6a64d3..0000000000000000000000000000000000000000
--- a/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/EXCLUSIVE-Full-Navionics-Marine-South-America-Apk.md
+++ /dev/null
@@ -1,104 +0,0 @@
-## [FULL] navionics marine south america apk
-
-
-
-
-
- ![\[__EXCLUSIVE__ Full\] Navionics Marine South America Apk](https://pocketmariner.com/wp-content/uploads/2019/12/BoatBeacon_to_OpenCPN-1024x602.png)
-
-
-
-
-
-**CLICK HERE === [https://conttooperting.blogspot.com/?l=2txRT1](https://conttooperting.blogspot.com/?l=2txRT1)**
-
-
-
-
-
-
-
-
-
-
-
- ```
-
-# How to Download and Install Navionics Marine South America APK for Android
-
-
-
-If you are looking for a reliable and comprehensive marine navigation app for your Android device, you might want to check out Navionics Marine South America APK. This app provides you with detailed nautical charts, sonar maps, weather and tide information, community edits, and more for the waters of South America and the Caribbean. Whether you are cruising, fishing, sailing, diving, or exploring, this app can help you plan your trips, navigate safely, and discover new places.
-
-
-
-Navionics Marine South America APK is not available on the Google Play Store, but you can download it from other sources online. However, you need to be careful about the websites you choose to download from, as some of them might contain malware or viruses that can harm your device. To avoid any risks, we recommend you to follow these steps to download and install Navionics Marine South America APK for Android:
-
-
-
-1. Go to [APKMirror](https://www.apkmirror.com/apk/garmin-italy-technologies-srl/boating-marine-lakes/), a trusted website that hosts verified APK files for various apps.
-
-2. Search for "Navionics Boating" in the search bar and select the latest version of the app.
-
-3. Scroll down to the "Download" section and tap on the "Variant" that matches your device's architecture (arm64-v8a, armeabi-v7a, or x86).
-
-4. Tap on the "Download APK" button and wait for the file to be downloaded to your device.
-
-5. Once the download is complete, open the file manager app on your device and locate the downloaded APK file.
-
-6. Tap on the file and allow the installation of apps from unknown sources if prompted.
-
-7. Follow the on-screen instructions to install the app on your device.
-
-8. Launch the app and accept the terms and conditions.
-
-9. Choose "Carib&S.America" as your coverage area and tap on "Start Free Trial" to enjoy a limited time free access to all the features of the app.
-
-10. To continue using the app after the trial period, you need to purchase a one-year renewable subscription from within the app.
-
-
-
-Congratulations! You have successfully downloaded and installed Navionics Marine South America APK for Android. Now you can enjoy the best marine charts and features on your mobile device wherever you go.
-
- ``` ```
-
-## What are the Features of Navionics Marine South America APK?
-
-
-
-Navionics Marine South America APK is more than just a marine chart app. It also offers a variety of features and tools that can enhance your boating and fishing experience. Here are some of the features you can enjoy with this app:
-
-
-
-- **Detailed Nautical Charts:** You can access comprehensive charts that show port plans, anchorages, safety depth contours, navaids, marine services, and more. You can also use the US Government Charts (NOAA) for official and unaltered data.
-
-- **SonarChart HD Bathymetry Maps:** You can view extraordinary 0.5 meter HD bottom contour detail that can help you locate new fishing areas and avoid shallow waters.
-
-- **Overlays:** You can use various overlays to have a better understanding of the bottom topography and structure. You can use Satellite Imagery with SonarChart Shading Overlay to see what's above and below the waterline, Relief Shading to get a clearer view of the bottom structure, and Sonar Imagery to see the bottom hardness in contrasting color.
-
-- **Map Options:** You can customize your chart views by changing the chart-overlay combinations, highlighting shallow areas, targeting multiple fishing ranges, and more.
-
-- **Daily Updates:** You can benefit from up to 5,000 daily updates worldwide that keep your charts up-to-date with the latest changes and corrections.
-
-- **Auto Guidance+ Technology:** You can easily plan your trip with a suggested dock-to-dock path based on chart data and navigation aids. You can also get ETA, distance to arrival, heading to waypoint, fuel consumption, and more.
-
-- **Weather and Tides:** You can access real-time weather data, daily and hourly forecasts, wind, weather buoys, tides and currents to plan your trips according to the conditions.
-
-- **Markers, Tracks, Distance:** You can place a marker on any location of interest, record your track, take photos and videos within the app, and look back at your day anytime. You can also check the distance between two points.
-
-- **Community Edits and ActiveCaptain Community:** You can obtain and contribute useful local knowledge and valuable recommendations from people with firsthand experience of the local environment. You can also see points of interest, navigation aids, hazards, reviews, and more.
-
-- **Connections:** You can stay in touch with your friends and fellow boaters by sharing your live location, tracks, routes and markers. You can also easily meet on the water or let them check out your adventures.
-
-- **GPX Import/Export:** You can share your saved data outside the app or transfer it to your chartplotter.
-
-
-
-With Navionics Marine South America APK, you can have access to all these features and more on your Android device. Download it today and discover why it is one of the best marine navigation apps on the market.
-
- ``` dfd1c89656
-
-
-
-
-
diff --git a/spaces/hk59775634/OpenAI-Manager/js/app.js b/spaces/hk59775634/OpenAI-Manager/js/app.js
deleted file mode 100644
index 6e5823ad55907039ffaed524c7a0ebaf8289ad6e..0000000000000000000000000000000000000000
--- a/spaces/hk59775634/OpenAI-Manager/js/app.js
+++ /dev/null
@@ -1,2112 +0,0 @@
-/*
- * ATTENTION: The "eval" devtool has been used (maybe by default in mode: "development").
- * This devtool is neither made for production nor for readable output files.
- * It uses "eval()" calls to create a separate source file in the browser devtools.
- * If you are trying to read the output file, select a different devtool (https://webpack.js.org/configuration/devtool/)
- * or disable the default devtool with "devtool: false".
- * If you are looking for production-ready output files, see mode: "production" (https://webpack.js.org/configuration/mode/).
- */
-/******/ (function() { // webpackBootstrap
-/******/ var __webpack_modules__ = ({
-
-/***/ "./src/App.vue":
-/*!*********************!*\
- !*** ./src/App.vue ***!
- \*********************/
-/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {
-
-"use strict";
-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony import */ var _App_vue_vue_type_template_id_7ba5bd90___WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./App.vue?vue&type=template&id=7ba5bd90& */ \"./src/App.vue?vue&type=template&id=7ba5bd90&\");\n/* harmony import */ var _App_vue_vue_type_script_lang_js___WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./App.vue?vue&type=script&lang=js& */ \"./src/App.vue?vue&type=script&lang=js&\");\n/* harmony import */ var _App_vue_vue_type_style_index_0_id_7ba5bd90_lang_scss___WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./App.vue?vue&type=style&index=0&id=7ba5bd90&lang=scss& */ \"./src/App.vue?vue&type=style&index=0&id=7ba5bd90&lang=scss&\");\n/* harmony import */ var _node_modules_vue_vue_loader_v15_lib_runtime_componentNormalizer_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! !../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js */ \"./node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\");\n\n\n\n;\n\n\n/* normalize component */\n\nvar component = (0,_node_modules_vue_vue_loader_v15_lib_runtime_componentNormalizer_js__WEBPACK_IMPORTED_MODULE_3__[\"default\"])(\n _App_vue_vue_type_script_lang_js___WEBPACK_IMPORTED_MODULE_1__[\"default\"],\n _App_vue_vue_type_template_id_7ba5bd90___WEBPACK_IMPORTED_MODULE_0__.render,\n _App_vue_vue_type_template_id_7ba5bd90___WEBPACK_IMPORTED_MODULE_0__.staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\n/* hot reload */\nif (false) { var api; }\ncomponent.options.__file = \"src/App.vue\"\n/* harmony default export */ __webpack_exports__[\"default\"] = (component.exports);\n\n//# sourceURL=webpack://JCM-AI/./src/App.vue?");
-
-/***/ }),
-
-/***/ "./src/components/Emoji.vue":
-/*!**********************************!*\
- !*** ./src/components/Emoji.vue ***!
- \**********************************/
-/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {
-
-"use strict";
-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony import */ var _Emoji_vue_vue_type_template_id_534ad946_scoped_true___WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./Emoji.vue?vue&type=template&id=534ad946&scoped=true& */ \"./src/components/Emoji.vue?vue&type=template&id=534ad946&scoped=true&\");\n/* harmony import */ var _Emoji_vue_vue_type_script_lang_js___WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./Emoji.vue?vue&type=script&lang=js& */ \"./src/components/Emoji.vue?vue&type=script&lang=js&\");\n/* harmony import */ var _Emoji_vue_vue_type_style_index_0_id_534ad946_lang_scss_scoped_true___WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./Emoji.vue?vue&type=style&index=0&id=534ad946&lang=scss&scoped=true& */ \"./src/components/Emoji.vue?vue&type=style&index=0&id=534ad946&lang=scss&scoped=true&\");\n/* harmony import */ var _node_modules_vue_vue_loader_v15_lib_runtime_componentNormalizer_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! !../../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js */ \"./node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\");\n\n\n\n;\n\n\n/* normalize component */\n\nvar component = (0,_node_modules_vue_vue_loader_v15_lib_runtime_componentNormalizer_js__WEBPACK_IMPORTED_MODULE_3__[\"default\"])(\n _Emoji_vue_vue_type_script_lang_js___WEBPACK_IMPORTED_MODULE_1__[\"default\"],\n _Emoji_vue_vue_type_template_id_534ad946_scoped_true___WEBPACK_IMPORTED_MODULE_0__.render,\n _Emoji_vue_vue_type_template_id_534ad946_scoped_true___WEBPACK_IMPORTED_MODULE_0__.staticRenderFns,\n false,\n null,\n \"534ad946\",\n null\n \n)\n\n/* hot reload */\nif (false) { var api; }\ncomponent.options.__file = \"src/components/Emoji.vue\"\n/* harmony default export */ __webpack_exports__[\"default\"] = (component.exports);\n\n//# sourceURL=webpack://JCM-AI/./src/components/Emoji.vue?");
-
-/***/ }),
-
-/***/ "./src/components/File.vue":
-/*!*********************************!*\
- !*** ./src/components/File.vue ***!
- \*********************************/
-/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {
-
-"use strict";
-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony import */ var _File_vue_vue_type_template_id_ab80f8a8_scoped_true___WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./File.vue?vue&type=template&id=ab80f8a8&scoped=true& */ \"./src/components/File.vue?vue&type=template&id=ab80f8a8&scoped=true&\");\n/* harmony import */ var _File_vue_vue_type_script_lang_js___WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./File.vue?vue&type=script&lang=js& */ \"./src/components/File.vue?vue&type=script&lang=js&\");\n/* harmony import */ var _File_vue_vue_type_style_index_0_id_ab80f8a8_lang_scss_scoped_true___WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./File.vue?vue&type=style&index=0&id=ab80f8a8&lang=scss&scoped=true& */ \"./src/components/File.vue?vue&type=style&index=0&id=ab80f8a8&lang=scss&scoped=true&\");\n/* harmony import */ var _node_modules_vue_vue_loader_v15_lib_runtime_componentNormalizer_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! !../../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js */ \"./node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\");\n\n\n\n;\n\n\n/* normalize component */\n\nvar component = (0,_node_modules_vue_vue_loader_v15_lib_runtime_componentNormalizer_js__WEBPACK_IMPORTED_MODULE_3__[\"default\"])(\n _File_vue_vue_type_script_lang_js___WEBPACK_IMPORTED_MODULE_1__[\"default\"],\n _File_vue_vue_type_template_id_ab80f8a8_scoped_true___WEBPACK_IMPORTED_MODULE_0__.render,\n _File_vue_vue_type_template_id_ab80f8a8_scoped_true___WEBPACK_IMPORTED_MODULE_0__.staticRenderFns,\n false,\n null,\n \"ab80f8a8\",\n null\n \n)\n\n/* hot reload */\nif (false) { var api; }\ncomponent.options.__file = \"src/components/File.vue\"\n/* harmony default export */ __webpack_exports__[\"default\"] = (component.exports);\n\n//# sourceURL=webpack://JCM-AI/./src/components/File.vue?");
-
-/***/ }),
-
-/***/ "./src/components/FileCard.vue":
-/*!*************************************!*\
- !*** ./src/components/FileCard.vue ***!
- \*************************************/
-/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {
-
-"use strict";
-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony import */ var _FileCard_vue_vue_type_template_id_48849e48_scoped_true___WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./FileCard.vue?vue&type=template&id=48849e48&scoped=true& */ \"./src/components/FileCard.vue?vue&type=template&id=48849e48&scoped=true&\");\n/* harmony import */ var _FileCard_vue_vue_type_script_lang_js___WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./FileCard.vue?vue&type=script&lang=js& */ \"./src/components/FileCard.vue?vue&type=script&lang=js&\");\n/* harmony import */ var _FileCard_vue_vue_type_style_index_0_id_48849e48_lang_scss_scoped_true___WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./FileCard.vue?vue&type=style&index=0&id=48849e48&lang=scss&scoped=true& */ \"./src/components/FileCard.vue?vue&type=style&index=0&id=48849e48&lang=scss&scoped=true&\");\n/* harmony import */ var _node_modules_vue_vue_loader_v15_lib_runtime_componentNormalizer_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! !../../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js */ \"./node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\");\n\n\n\n;\n\n\n/* normalize component */\n\nvar component = (0,_node_modules_vue_vue_loader_v15_lib_runtime_componentNormalizer_js__WEBPACK_IMPORTED_MODULE_3__[\"default\"])(\n _FileCard_vue_vue_type_script_lang_js___WEBPACK_IMPORTED_MODULE_1__[\"default\"],\n _FileCard_vue_vue_type_template_id_48849e48_scoped_true___WEBPACK_IMPORTED_MODULE_0__.render,\n _FileCard_vue_vue_type_template_id_48849e48_scoped_true___WEBPACK_IMPORTED_MODULE_0__.staticRenderFns,\n false,\n null,\n \"48849e48\",\n null\n \n)\n\n/* hot reload */\nif (false) { var api; }\ncomponent.options.__file = \"src/components/FileCard.vue\"\n/* harmony default export */ __webpack_exports__[\"default\"] = (component.exports);\n\n//# sourceURL=webpack://JCM-AI/./src/components/FileCard.vue?");
-
-/***/ }),
-
-/***/ "./src/components/HeadImg.vue":
-/*!************************************!*\
- !*** ./src/components/HeadImg.vue ***!
- \************************************/
-/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {
-
-"use strict";
-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony import */ var _HeadImg_vue_vue_type_template_id_0b1d9e43_scoped_true___WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./HeadImg.vue?vue&type=template&id=0b1d9e43&scoped=true& */ \"./src/components/HeadImg.vue?vue&type=template&id=0b1d9e43&scoped=true&\");\n/* harmony import */ var _HeadImg_vue_vue_type_script_lang_js___WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./HeadImg.vue?vue&type=script&lang=js& */ \"./src/components/HeadImg.vue?vue&type=script&lang=js&\");\n/* harmony import */ var _HeadImg_vue_vue_type_style_index_0_id_0b1d9e43_lang_scss_scoped_true___WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./HeadImg.vue?vue&type=style&index=0&id=0b1d9e43&lang=scss&scoped=true& */ \"./src/components/HeadImg.vue?vue&type=style&index=0&id=0b1d9e43&lang=scss&scoped=true&\");\n/* harmony import */ var _node_modules_vue_vue_loader_v15_lib_runtime_componentNormalizer_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! !../../node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js */ \"./node_modules/@vue/vue-loader-v15/lib/runtime/componentNormalizer.js\");\n\n\n\n;\n\n\n/* normalize component */\n\nvar component = (0,_node_modules_vue_vue_loader_v15_lib_runtime_componentNormalizer_js__WEBPACK_IMPORTED_MODULE_3__[\"default\"])(\n _HeadImg_vue_vue_type_script_lang_js___WEBPACK_IMPORTED_MODULE_1__[\"default\"],\n _HeadImg_vue_vue_type_template_id_0b1d9e43_scoped_true___WEBPACK_IMPORTED_MODULE_0__.render,\n _HeadImg_vue_vue_type_template_id_0b1d9e43_scoped_true___WEBPACK_IMPORTED_MODULE_0__.staticRenderFns,\n false,\n null,\n \"0b1d9e43\",\n null\n \n)\n\n/* hot reload */\nif (false) { var api; }\ncomponent.options.__file = \"src/components/HeadImg.vue\"\n/* harmony default export */ __webpack_exports__[\"default\"] = (component.exports);\n\n//# sourceURL=webpack://JCM-AI/./src/components/HeadImg.vue?");
-
-/***/ }),
-
-/***/ "./src/components/HeadPortrait.vue":
-/*!*****************************************!*\
- !*** ./src/components/HeadPortrait.vue ***!
- \*****************************************/
-/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {
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- !*** ./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[0]!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./src/view/home.vue?vue&type=script&lang=js& ***!
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-/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {
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-"use strict";
-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony import */ var _components_Nav_vue__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../components/Nav.vue */ \"./src/components/Nav.vue\");\n\n/* harmony default export */ __webpack_exports__[\"default\"] = ({\n name: \"App\",\n components: {\n Nav: _components_Nav_vue__WEBPACK_IMPORTED_MODULE_0__[\"default\"]\n },\n data() {\n return {\n asideStatus: true\n };\n },\n created() {\n window.addEventListener('resize', this.handleResize);\n this.handleResize();\n },\n destoryed() {\n window.removeEventListener('resize', this.handleResize);\n },\n methods: {\n //监听窗口尺寸的变化\n handleResize() {\n if (window.innerWidth <= 1150) {\n this.asideStatus = false;\n } else {\n this.asideStatus = true;\n }\n ;\n }\n }\n});\n\n//# sourceURL=webpack://JCM-AI/./src/view/home.vue?./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use%5B0%5D!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options");
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-/*!***************************************************************************************************************************************************************************************************************!*\
- !*** ./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[0]!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./src/view/pages/chatHome/chatwindow.vue?vue&type=script&lang=js& ***!
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-/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {
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-"use strict";
-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony import */ var core_js_modules_es_array_push_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! core-js/modules/es.array.push.js */ \"./node_modules/core-js/modules/es.array.push.js\");\n/* harmony import */ var core_js_modules_es_array_push_js__WEBPACK_IMPORTED_MODULE_0___default = /*#__PURE__*/__webpack_require__.n(core_js_modules_es_array_push_js__WEBPACK_IMPORTED_MODULE_0__);\n/* harmony import */ var _util_util__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! @/util/util */ \"./src/util/util.js\");\n/* harmony import */ var _api_getData__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! @/api/getData */ \"./src/api/getData.js\");\n/* harmony import */ var _components_HeadPortrait__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! @/components/HeadPortrait */ \"./src/components/HeadPortrait.vue\");\n/* harmony import */ var _components_Emoji__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! @/components/Emoji */ \"./src/components/Emoji.vue\");\n/* harmony import */ var _components_FileCard_vue__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! @/components/FileCard.vue */ \"./src/components/FileCard.vue\");\n/* harmony import */ var _api_index__WEBPACK_IMPORTED_MODULE_6__ = __webpack_require__(/*! @/api/index */ \"./src/api/index.js\");\n/* harmony import */ var markdown_it_vue__WEBPACK_IMPORTED_MODULE_7__ = __webpack_require__(/*! markdown-it-vue */ \"./node_modules/markdown-it-vue/dist/markdown-it-vue.umd.min.js\");\n/* harmony import */ var markdown_it_vue__WEBPACK_IMPORTED_MODULE_7___default = /*#__PURE__*/__webpack_require__.n(markdown_it_vue__WEBPACK_IMPORTED_MODULE_7__);\n/* harmony import */ var markdown_it_vue_dist_markdown_it_vue_css__WEBPACK_IMPORTED_MODULE_8__ = __webpack_require__(/*! markdown-it-vue/dist/markdown-it-vue.css */ \"./node_modules/markdown-it-vue/dist/markdown-it-vue.css\");\n/* harmony import */ var markdown_it_vue_dist_markdown_it_vue_css__WEBPACK_IMPORTED_MODULE_8___default = /*#__PURE__*/__webpack_require__.n(markdown_it_vue_dist_markdown_it_vue_css__WEBPACK_IMPORTED_MODULE_8__);\n/* harmony import */ var _store_mutation_types__WEBPACK_IMPORTED_MODULE_9__ = __webpack_require__(/*! @/store/mutation-types */ \"./src/store/mutation-types.js\");\n/* harmony import */ var file_saver__WEBPACK_IMPORTED_MODULE_10__ = __webpack_require__(/*! file-saver */ \"./node_modules/file-saver/dist/FileSaver.min.js\");\n/* harmony import */ var file_saver__WEBPACK_IMPORTED_MODULE_10___default = /*#__PURE__*/__webpack_require__.n(file_saver__WEBPACK_IMPORTED_MODULE_10__);\n/* provided dependency */ var console = __webpack_require__(/*! ./node_modules/console-browserify/index.js */ \"./node_modules/console-browserify/index.js\");\n\n\n\n\n\n\n\n\n\n\n\n/* harmony default export */ __webpack_exports__[\"default\"] = ({\n directives: {\n //用于自适应文本框的高度\n autoheight: {\n inserted: function (el) {\n var Msg = document.getElementById(\"textareaMsg\").value;\n if (Msg == \"\") {\n el.style.height = \"26px\";\n } else {\n el.style.height = el.scrollHeight + 'px';\n }\n },\n update: function (el) {\n var Msg = document.getElementById(\"textareaMsg\").value;\n if (Msg == \"\") {\n el.style.height = \"26px\";\n } else {\n el.style.height = el.scrollHeight + 'px';\n }\n }\n }\n },\n components: {\n HeadPortrait: _components_HeadPortrait__WEBPACK_IMPORTED_MODULE_3__[\"default\"],\n Emoji: _components_Emoji__WEBPACK_IMPORTED_MODULE_4__[\"default\"],\n FileCard: _components_FileCard_vue__WEBPACK_IMPORTED_MODULE_5__[\"default\"],\n MarkdownItVue: (markdown_it_vue__WEBPACK_IMPORTED_MODULE_7___default())\n },\n props: {\n storeStatu: Number,\n settingInfo: Object,\n frinedInfo: Object,\n default() {\n return {};\n }\n },\n watch: {},\n data() {\n return {\n isAutoScroll: true,\n fileArrays: [],\n inputsStatus: true,\n rows: 1,\n //是否显示表情和录音按钮\n buttonStatus: true,\n //是否在接收消息中，如果是则true待发送状态，如果是false则是等待消息转圈状态\n acqStatus: true,\n chatList: [],\n inputMsg: \"\",\n showEmoji: false,\n friendInfo: {},\n srcImgList: [],\n recording: false,\n audioChunks: [],\n screenshot: \"\",\n contentBackImageUrl: \"https://bpic.51yuansu.com/backgd/cover/00/31/39/5bc8088deeedd.jpg?x-oss-process=image/resize,w_780\",\n updateImage: null,\n // 是否隐藏对话框上方介绍（空间局促时隐藏）\n personInfoSpan: [1, 17, 6]\n };\n },\n created() {\n window.addEventListener('resize', this.handleResize);\n this.handleResize();\n },\n destoryed() {\n window.removeEventListener('resize', this.handleResize);\n },\n methods: {\n handleKeyDown(event) {\n if (event.keyCode === 13 && !event.shiftKey) {\n // 按下回车键，没按shift\n this.sendText();\n }\n },\n readStream(reader, _this, currentResLocation) {\n return reader.read().then(({\n done,\n value\n }) => {\n if (done) {\n return;\n }\n if (!_this.chatList[currentResLocation].reminder) {\n _this.chatList[currentResLocation].reminder = \"\";\n }\n let decoded = new TextDecoder().decode(value);\n decoded = _this.chatList[currentResLocation].reminder + decoded;\n let decodedArray = decoded.split(\"data: \");\n decodedArray.forEach(decoded => {\n if (decoded !== \"\") {\n if (decoded.trim() === \"[DONE]\") {\n return;\n } else {\n const response = JSON.parse(decoded).choices[0].delta.content ? JSON.parse(decoded).choices[0].delta.content : \"\";\n _this.chatList[currentResLocation].msg = _this.chatList[currentResLocation].msg + response;\n }\n }\n });\n return this.readStream(reader, _this, currentResLocation);\n });\n },\n //导入当前内容json触发的方法\n importFromJsonArr() {\n this.$refs.onupdateJosnArr.click(); // 触发选择文件的弹框\n },\n\n handleFileUpload(event) {\n const file = event.target.files[0];\n const reader = new FileReader();\n reader.onload = () => {\n const fileContent = reader.result; // 文件内容\n const parsed = JSON.parse(fileContent); // 转换为数组\n this.chatList = this.chatList.concat(parsed);\n };\n reader.readAsText(file);\n },\n //导出当前会话到json文件\n exportObjArrToJson() {\n console.log(this.chatList);\n let jsonString = JSON.stringify(this.chatList); // 将数组转为JSON字符串\n let blob = new Blob([jsonString], {\n type: \"application/json;charset=utf-8\"\n });\n (0,file_saver__WEBPACK_IMPORTED_MODULE_10__.saveAs)(blob, \"data.json\");\n },\n //监听窗口的变化\n handleResize() {\n if (window.innerWidth <= 700) {\n this.$nextTick(() => {\n document.querySelectorAll('.chat-content')[0].style.height = '93%';\n this.buttonStatus = false;\n var textareaMsg = document.getElementById(\"textareaMsg\");\n textareaMsg.style.marginLeft = \"0px\";\n this.personInfoSpan = [14, 0, 10];\n const isMobile = /iPhone|iPad|iPod|Android/i.test(navigator.userAgent);\n if (isMobile) {\n document.querySelectorAll('.chatInputs')[0].style.margin = '0%';\n } else {\n document.querySelectorAll('.chatInputs')[0].style.margin = '3%';\n }\n });\n } else {\n this.$nextTick(() => {\n document.querySelectorAll('.chat-content')[0].style.height = '88%';\n this.buttonStatus = true;\n this.personInfoSpan = [1, 17, 6];\n });\n }\n ;\n },\n newLine(event) {\n this.rows += 1;\n let text = this.$refs.textInput.value;\n text += '\\n';\n this.$refs.textInput.value = text;\n },\n //赋值对话列表\n assignmentMesList(msgList) {\n this.chatList = msgList;\n },\n //获取对话列表\n getMesList() {\n return this.chatList;\n },\n //清除当前对话列表\n clearMsgList() {\n this.chatList = [];\n },\n //更新内容背景图片\n updateContentImageUrl(imgUrl) {\n this.contentBackImageUrl = imgUrl;\n },\n //组装上下文数据\n contextualAssemblyData() {\n const conversation = [];\n for (var chat of this.chatList.filter(chat => chat.chatType === 0)) {\n if (chat.uid == 'jcm') {\n let my = {\n 'speaker': 'user',\n 'text': chat.msg\n };\n conversation.push(my);\n } else if (chat.uid == this.frinedInfo.id) {\n let ai = {\n 'speaker': 'agent',\n 'text': chat.msg\n };\n conversation.push(ai);\n }\n }\n return conversation;\n },\n //开始录音\n startRecording() {\n navigator.mediaDevices.getUserMedia({\n audio: true\n }).then(stream => {\n this.recorder = new MediaRecorder(stream);\n this.recorder.addEventListener('dataavailable', event => {\n this.audioChunks.push(event.data);\n });\n this.recording = true;\n this.recorder.start();\n // 在这里使用录音器\n this.$message.success(this.$t('message.start_recording'));\n }).catch(error => {\n this.$message.error(this.$t('message.fail_audio'));\n });\n },\n //停止录音\n async stopRecording() {\n this.recorder.stop();\n this.recording = false;\n this.recorder.ondataavailable = event => {\n const blob = new Blob([event.data], {\n type: 'audio/wav'\n });\n const file = new File([blob], 'recording.wav', {\n type: 'audio/wav',\n lastModified: Date.now()\n });\n const formData = new FormData();\n formData.append('file', file);\n formData.append('model', \"whisper-1\");\n formData.append('temperature', this.settingInfo.TemperatureAudio);\n formData.append('response_format', \"text\");\n if (this.settingInfo.translateEnglish) {\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_2__.createTranslation)(formData, this.settingInfo.KeyMsg).then(data => {\n this.$nextTick(() => {\n this.inputMsg = data;\n });\n });\n } else {\n formData.append('language', this.settingInfo.language);\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_2__.createTranscription)(formData, this.settingInfo.KeyMsg).then(data => {\n this.$nextTick(() => {\n this.inputMsg = data;\n });\n });\n }\n };\n this.$message.success(this.$t('message.end_recording'));\n },\n //发送信息\n sendMsg(msgList) {\n this.chatList.push(msgList);\n this.scrollBottom();\n },\n // 在组件或页面外部声明计算余弦相似度的函数\n cosineSimilarity(a, b) {\n const dotProduct = a.reduce((acc, curr, i) => acc + curr * b[i], 0);\n const normA = Math.sqrt(a.reduce((acc, curr) => acc + curr * curr, 0));\n const normB = Math.sqrt(b.reduce((acc, curr) => acc + curr * curr, 0));\n return dotProduct / (normA * normB);\n },\n //发送文字信息\n sendText() {\n // if(this.settingInfo.readefile){\n // console.log(this.fileArrays)\n // const formData = new FormData();\n // formData.append(\"model\", \"text-embedding-ada-002\");\n // formData.append(\"input\", \"吕世昊是谁?\");\n // createEmbeddings(formData,this.settingInfo.KeyMsg).then(data => {\n // const inputEmbedding=data.data[0]\n // // const similarText = this.findMostSimilarEmbedding(, this.fileArrays);\n\n // // 计算每个句子embedding与输入数据embedding之间的相似度\n // const similarities = this.cosineSimilarity(this.fileArrays.embedding, inputEmbedding.embedding)\n // const similaritiesArr=[];\n // console.log(similarities)\n // similaritiesArr.push(similarities)\n // // 对相似度进行排名，选择与输入数据最相似的句子或文章段落作为匹配结果\n // const topMatchIndex = similaritiesArr.reduce((maxIndex, similarity, index) => similarity > similaritiesArr[maxIndex] ? index : maxIndex, 0)\n\n // console.log(topMatchIndex)\n // const topMatchText = sentences[topMatchIndex]\n // console.log('最匹配的句子是：', topMatchText)\n // // console.log('最相似的文本为：', similarText);\n // })\n\n // // const configuration = new Configuration({\n // // apiKey: ,\n // // });\n // // const openai = new OpenAIApi(configuration);\n // // const response = openai.embeddings({\n // // model: 'text-embedding-ada-002',\n // // input:\"text\"\n // // });\n // // console.log(response)\n\n // return\n // }\n this.rows = 1;\n this.$nextTick(() => {\n this.acqStatus = false;\n });\n const dateNow = (0,_util_util__WEBPACK_IMPORTED_MODULE_1__.JCMFormatDate)((0,_util_util__WEBPACK_IMPORTED_MODULE_1__.getNowTime)());\n let params = {};\n if (this.settingInfo.openChangePicture) {\n if (this.updateImage == null) {\n this.$nextTick(() => {\n this.acqStatus = true;\n });\n this.$message.warning(this.$t('message.edit_picture'));\n return;\n } else {\n // 通过验证后，上传文件\n const formData = new FormData();\n formData.append(\"image\", this.updateImage);\n formData.append(\"prompt\", this.inputMsg);\n formData.append(\"n\", this.settingInfo.n);\n formData.append(\"size\", this.settingInfo.size);\n const dateNow = (0,_util_util__WEBPACK_IMPORTED_MODULE_1__.JCMFormatDate)((0,_util_util__WEBPACK_IMPORTED_MODULE_1__.getNowTime)());\n let chatMsg = {\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_9__.USER_HEAD_IMG_URL,\n name: _store_mutation_types__WEBPACK_IMPORTED_MODULE_9__.USER_NAME,\n time: dateNow,\n msg: this.inputMsg,\n chatType: 0,\n //信息类型，0文字，1图片\n uid: \"jcm\" //uid\n };\n\n this.sendMsg(chatMsg);\n this.inputMsg = \"\";\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_2__.createImageEdit)(formData, this.settingInfo.KeyMsg).then(data => {\n for (var imgInfo of data) {\n let imgResMsg = {\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_9__.AI_HEAD_IMG_URL,\n name: this.frinedInfo.name,\n time: (0,_util_util__WEBPACK_IMPORTED_MODULE_1__.JCMFormatDate)((0,_util_util__WEBPACK_IMPORTED_MODULE_1__.getNowTime)()),\n msg: imgInfo.url,\n chatType: 1,\n //信息类型，0文字，1图片\n extend: {\n imgType: 2 //(1表情，2本地图片)\n },\n\n uid: this.frinedInfo.id //uid\n };\n\n this.sendMsg(imgResMsg);\n this.srcImgList.push(imgInfo.url);\n }\n this.updateImage = null;\n this.acqStatus = true;\n });\n return;\n }\n }\n if (this.inputMsg) {\n let chatMsg = {\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_9__.USER_HEAD_IMG_URL,\n name: _store_mutation_types__WEBPACK_IMPORTED_MODULE_9__.USER_NAME,\n time: dateNow,\n msg: this.inputMsg,\n chatType: 0,\n //信息类型，0文字，1图片\n uid: \"jcm\" //uid\n };\n\n this.sendMsg(chatMsg);\n\n //如果是图片模式则进入待开发不过可用改状态使用\n if (this.settingInfo.openProductionPicture) {\n params.prompt = this.inputMsg, params.n = this.settingInfo.n, params.size = this.settingInfo.size, (0,_api_getData__WEBPACK_IMPORTED_MODULE_2__.createImage)(params, this.settingInfo.KeyMsg).then(data => {\n for (var imgInfo of data) {\n let imgResMsg = {\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_9__.AI_HEAD_IMG_URL,\n name: this.frinedInfo.name,\n time: (0,_util_util__WEBPACK_IMPORTED_MODULE_1__.JCMFormatDate)((0,_util_util__WEBPACK_IMPORTED_MODULE_1__.getNowTime)()),\n msg: imgInfo.url,\n chatType: 1,\n //信息类型，0文字，1图片\n extend: {\n imgType: 2 //(1表情，2本地图片)\n },\n\n uid: this.frinedInfo.id //uid\n };\n\n this.sendMsg(imgResMsg);\n this.srcImgList.push(imgInfo.url);\n }\n this.acqStatus = true;\n });\n } else {\n //如果是文字模式则进入\n params.model = this.frinedInfo.id, params.max_tokens = this.settingInfo.chat.MaxTokens, params.temperature = this.settingInfo.chat.Temperature, params.top_p = this.settingInfo.chat.TopP, params.n = this.settingInfo.chat.n, params.stream = this.settingInfo.chat.stream, params.stop = this.settingInfo.chat.stop, params.presence_penalty = this.settingInfo.chat.PresencePenalty, params.frequency_penalty = this.settingInfo.chat.FrequencyPenalty;\n let chatBeforResMsg = {\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_9__.AI_HEAD_IMG_URL,\n name: this.frinedInfo.name,\n time: (0,_util_util__WEBPACK_IMPORTED_MODULE_1__.JCMFormatDate)((0,_util_util__WEBPACK_IMPORTED_MODULE_1__.getNowTime)()),\n msg: \"\",\n chatType: 0,\n //信息类型，0文字，1图片\n uid: this.frinedInfo.id //uid\n };\n\n if (this.frinedInfo.id === \"gpt-3.5-turbo\" || this.frinedInfo.id === \"gpt-3.5-turbo-0301\") {\n this.chatCompletion(params, chatBeforResMsg);\n } else {\n this.completion(params, chatBeforResMsg);\n }\n }\n if (this.storeStatu == 0) {\n this.$emit('personCardSort', this.frinedInfo.id);\n } else if (this.storeStatu == 1) {\n this.$emit('fineTunesCardSort', this.frinedInfo.id);\n }\n this.inputMsg = \"\";\n // this.$parent.updateMoneyInfo();\n } else {\n this.$nextTick(() => {\n this.acqStatus = true;\n });\n this.$message.warning(this.$t('message.msg_empty'));\n }\n },\n async chatCompletion(params, chatBeforResMsg) {\n let textContext = this.inputMsg;\n let itemContent;\n let noUrlNetMessage;\n if (this.settingInfo.openNet) {\n let context = \"max_results=\" + this.settingInfo.max_results + \"&q=\" + textContext + \"®ion=us-en\";\n await fetch('https://search.freechatgpt.cc/search?' + context).then(response => response.json()).then(data => {\n let netMessage = \"Web search results: \";\n noUrlNetMessage = netMessage + \"\\n\\n\";\n for (let i = 0; i < data.length; i++) {\n netMessage += \"[\" + (i + 1) + \"] \\\"\" + data[i].body.substring(0, 400) + \"\\\" \";\n netMessage += \"URL:\" + data[i].href + \" \";\n noUrlNetMessage += \"[\" + (i + 1) + \"] \\\"\" + data[i].body.substring(0, 400) + \"\\\" \\n\\n\";\n }\n const date = new Date();\n const year = date.getFullYear();\n const month = date.getMonth() + 1;\n const day = date.getDate();\n const formattedDate = `${year}/${month}/${day}`;\n netMessage = netMessage.substring(0, 1500);\n netMessage += \"Current date:\" + formattedDate + \" \";\n netMessage += \"Instructions: Using the provided web search results, write a comprehensive reply to the given query. \" + \"Make sure to cite results using [[number](URL)] notation after the reference. If the provided search \" + \"results refer to multiple subjects with the same name, write separate answers for each subject.\" + \"Query: \" + textContext + \"Reply in 中文\";\n noUrlNetMessage += \" 您的问题: \" + textContext;\n itemContent = {};\n itemContent.time = (0,_util_util__WEBPACK_IMPORTED_MODULE_1__.JCMFormatDate)((0,_util_util__WEBPACK_IMPORTED_MODULE_1__.getNowTime)());\n itemContent.msg = netMessage;\n itemContent.chatType = 0;\n itemContent.name = \"网络\";\n itemContent.headImg = \"https://i.328888.xyz/2023/04/04/ijlmhJ.png\";\n itemContent.uid = this.frinedInfo.id;\n this.chatList.push(itemContent);\n let conversation = this.contextualAssemblyData();\n params.messages = conversation.map(item => {\n return {\n role: item.speaker === 'user' ? 'user' : 'assistant',\n content: item.text\n };\n });\n itemContent.msg = noUrlNetMessage;\n });\n } else {\n let conversation = this.contextualAssemblyData();\n params.messages = conversation.map(item => {\n return {\n role: item.speaker === 'user' ? 'user' : 'assistant',\n content: item.text\n };\n });\n }\n //新增一个空的消息\n this.sendMsg(chatBeforResMsg);\n const currentResLocation = this.chatList.length - 1;\n let _this = this;\n try {\n if (this.settingInfo.chat.stream) {\n await fetch(_api_index__WEBPACK_IMPORTED_MODULE_6__[\"default\"].baseUrl + '/v1/chat/completions', {\n method: \"POST\",\n body: JSON.stringify({\n ...params\n }),\n headers: {\n Authorization: 'Bearer ' + this.settingInfo.KeyMsg,\n \"Content-Type\": \"application/json\",\n Accept: \"application/json\"\n }\n }).then(response => {\n const reader = response.body.getReader();\n this.readStream(reader, _this, currentResLocation);\n });\n } else {\n await fetch(_api_index__WEBPACK_IMPORTED_MODULE_6__[\"default\"].baseUrl + '/v1/chat/completions', {\n method: \"POST\",\n body: JSON.stringify({\n ...params\n }),\n headers: {\n Authorization: 'Bearer ' + this.settingInfo.KeyMsg,\n \"Content-Type\": \"application/json\",\n Accept: \"application/json\"\n }\n }).then(response => response.json()).then(data => {\n const content = data.choices[0].message.content; // 获取\"content\"字段的值\n let decodedArray = content.split(\"\");\n decodedArray.forEach(decoded => {\n _this.chatList[currentResLocation].msg = _this.chatList[currentResLocation].msg + decoded;\n });\n });\n }\n } catch (error) {\n const content = \"网络不稳定或key余额不足，请重试或更换key\"; // 获取\"content\"字段的值\n let decodedArray = content.split(\"\");\n decodedArray.forEach(decoded => {\n _this.chatList[currentResLocation].msg = _this.chatList[currentResLocation].msg + decoded;\n });\n console.error(error);\n }\n this.acqStatus = true;\n },\n async completion(params, chatBeforResMsg) {\n if (this.settingInfo.chat.suffix !== \"\") {\n params.suffix = this.settingInfo.chat.suffix; //chat没有\n }\n\n params.echo = this.settingInfo.chat.echo,\n //chat没有\n params.prompt = this.inputMsg;\n //新增一个空的消息\n this.sendMsg(chatBeforResMsg);\n const currentResLocation = this.chatList.length - 1;\n let _this = this;\n try {\n await fetch(_api_index__WEBPACK_IMPORTED_MODULE_6__[\"default\"].baseUrl + '/v1/completions', {\n method: \"POST\",\n timeout: 10000,\n body: JSON.stringify({\n ...params\n }),\n headers: {\n Authorization: 'Bearer ' + this.settingInfo.KeyMsg,\n \"Content-Type\": \"application/json\"\n }\n }).then(response => {\n if (response.status == 404) {\n this.$message.error(this.$t('message.model_del'));\n this.$nextTick(() => {\n this.acqStatus = true;\n });\n return;\n }\n const reader = response.body.getReader();\n this.$nextTick(() => {\n this.acqStatus = true;\n });\n // _this.chatList[currentResLocation].msg = _this.chatList[currentResLocation].msg + \":grinning:\"\n this.readStream(reader, _this, currentResLocation);\n });\n } catch (error) {}\n },\n resetUpdate() {\n this.updateImage = null;\n },\n onScroll() {\n const scrollDom = this.$refs.chatContent;\n const scrollTop = scrollDom.scrollTop;\n const offsetHeight = scrollDom.offsetHeight;\n const scrollHeight = scrollDom.scrollHeight;\n // 当滚动到底部，设置 isAutoScroll 为 true\n if (scrollTop + offsetHeight === scrollHeight) {\n this.isAutoScroll = true;\n } else {\n // 否则，用户正在手动滑动，设置为 false，停止自动滚动\n this.isAutoScroll = false;\n }\n },\n //获取窗口高度并滚动至最底层\n scrollBottom() {\n this.$nextTick(() => {\n if (!this.isAutoScroll) return; // 如果 isAutoScroll 为 false，不执行滚动方法\n const scrollDom = this.$refs.chatContent;\n (0,_util_util__WEBPACK_IMPORTED_MODULE_1__.animation)(scrollDom, scrollDom.scrollHeight - scrollDom.offsetHeight);\n });\n },\n //关闭标签框\n clickEmoji() {\n this.showEmoji = !this.showEmoji;\n },\n //发送表情\n sendEmoji(msg) {\n const dateNow = (0,_util_util__WEBPACK_IMPORTED_MODULE_1__.JCMFormatDate)((0,_util_util__WEBPACK_IMPORTED_MODULE_1__.getNowTime)());\n let chatMsg = {\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_9__.USER_HEAD_IMG_URL,\n name: _store_mutation_types__WEBPACK_IMPORTED_MODULE_9__.USER_NAME,\n time: dateNow,\n msg: msg,\n chatType: 1,\n //信息类型，0文字，1图片\n extend: {\n imgType: 1 //(1表情，2本地图片)\n },\n\n uid: \"jcm\"\n };\n this.sendMsg(chatMsg);\n this.clickEmoji();\n },\n //发送本地图片\n sendImg(e) {\n this.acqStatus = false;\n //获取文件\n const file = e.target.files[0];\n\n // 验证文件类型是否为PNG格式\n if (file.type !== \"image/png\") {\n this.$message.warning(this.$t('message.valid_png'));\n this.$nextTick(() => {\n this.acqStatus = true;\n });\n return;\n }\n\n // 验证文件大小是否小于4MB\n if (file.size > 4 * 1024 * 1024) {\n this.$message.warning(this.$t('message.less_4M'));\n this.$nextTick(() => {\n this.acqStatus = true;\n });\n return;\n }\n if (this.settingInfo.openChangePicture) {\n this.updateImage = file;\n this.$message.info(this.$t('message.upload_complete'));\n e.target.files = null;\n this.$nextTick(() => {\n this.acqStatus = true;\n });\n return;\n }\n // 通过验证后，上传文件\n const formData = new FormData();\n formData.append(\"image\", file);\n formData.append(\"n\", this.settingInfo.n);\n formData.append(\"size\", this.settingInfo.size);\n const dateNow = (0,_util_util__WEBPACK_IMPORTED_MODULE_1__.JCMFormatDate)((0,_util_util__WEBPACK_IMPORTED_MODULE_1__.getNowTime)());\n let _this = this;\n let chatMsg = {\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_9__.USER_HEAD_IMG_URL,\n name: _store_mutation_types__WEBPACK_IMPORTED_MODULE_9__.USER_NAME,\n time: dateNow,\n msg: \"\",\n chatType: 1,\n //信息类型，0文字，1图片, 2文件\n extend: {\n imgType: 2 //(1表情，2本地图片)\n },\n\n uid: \"jcm\"\n };\n if (!e || !window.FileReader) return; // 看是否支持FileReader\n let reader = new FileReader();\n reader.readAsDataURL(file); // 关键一步，在这里转换的\n reader.onloadend = function () {\n chatMsg.msg = this.result; //赋值\n _this.srcImgList.push(chatMsg.msg);\n };\n this.sendMsg(chatMsg);\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_2__.createImageVariations)(formData, this.settingInfo.KeyMsg).then(data => {\n for (var imgInfo of data) {\n let imgResMsg = {\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_9__.AI_HEAD_IMG_URL,\n name: this.frinedInfo.name,\n time: (0,_util_util__WEBPACK_IMPORTED_MODULE_1__.JCMFormatDate)((0,_util_util__WEBPACK_IMPORTED_MODULE_1__.getNowTime)()),\n msg: imgInfo.url,\n chatType: 1,\n //信息类型，0文字，1图片\n extend: {\n imgType: 2 //(1表情，2本地图片)\n },\n\n uid: this.frinedInfo.id //uid\n };\n\n this.sendMsg(imgResMsg);\n this.srcImgList.push(imgInfo.url);\n }\n this.acqStatus = true;\n });\n e.target.files = null;\n },\n //发送文件\n sendFile(e) {\n // let file = e.target.files[0];\n // let reader = new FileReader();\n // reader.readAsText(file);\n // let _this=this\n // reader.onload = function(event) {\n // let text = event.target.result;\n // //处理文件数据\n // const delimiters = ['.', '?', '!', '\\n',':',\",\"];\n // let result = [];\n // for (let i = 0; i < text.length; i++) {\n // let current = '';\n // while (i < text.length && !delimiters.includes(text[i])) {\n // current += text[i];\n // i++;\n // }\n // // 加入句子，并去除前后空格\n // if (current.trim()) {\n // result.push(current.trim());\n // }\n // }\n // const formData = new FormData()\n // formData.append(\"model\", \"text-embedding-ada-002\");\n // formData.append(\"input\", result);\n // createEmbeddings(formData,_this.settingInfo.KeyMsg).then(data => {\n // _this.fileArrays = data.data[0]\n // })\n // }; \n const dateNow = (0,_util_util__WEBPACK_IMPORTED_MODULE_1__.JCMFormatDate)((0,_util_util__WEBPACK_IMPORTED_MODULE_1__.getNowTime)());\n let chatMsg = {\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_9__.USER_HEAD_IMG_URL,\n name: _store_mutation_types__WEBPACK_IMPORTED_MODULE_9__.USER_NAME,\n time: dateNow,\n msg: \"\",\n chatType: 2,\n //信息类型，0文字，1图片, 2文件\n extend: {\n fileType: \"\" //(1word，2excel，3ppt，4pdf，5zpi, 6txt)\n },\n\n uid: \"jcm\"\n };\n let files = e.target.files[0]; //图片文件名\n chatMsg.msg = files;\n if (files) {\n switch (files.type) {\n case \"application/msword\":\n case \"application/vnd.openxmlformats-officedocument.wordprocessingml.document\":\n chatMsg.extend.fileType = 1;\n break;\n case \"application/vnd.ms-excel\":\n case \"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet\":\n chatMsg.extend.fileType = 2;\n break;\n case \"application/vnd.ms-powerpoint\":\n case \"application/vnd.openxmlformats-officedocument.presentationml.presentation\":\n chatMsg.extend.fileType = 3;\n break;\n case \"application/pdf\":\n chatMsg.extend.fileType = 4;\n break;\n case \"application/zip\":\n case \"application/x-zip-compressed\":\n chatMsg.extend.fileType = 5;\n break;\n case \"text/plain\":\n chatMsg.extend.fileType = 6;\n break;\n default:\n chatMsg.extend.fileType = 0;\n }\n this.sendMsg(chatMsg);\n e.target.files = null;\n }\n }\n }\n});\n\n//# sourceURL=webpack://JCM-AI/./src/view/pages/chatHome/chatwindow.vue?./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use%5B0%5D!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options");
-
-/***/ }),
-
-/***/ "./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[0]!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./src/view/pages/chatHome/index.vue?vue&type=script&lang=js&":
-/*!**********************************************************************************************************************************************************************************************************!*\
- !*** ./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[0]!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./src/view/pages/chatHome/index.vue?vue&type=script&lang=js& ***!
- \**********************************************************************************************************************************************************************************************************/
-/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {
-
-"use strict";
-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony import */ var core_js_modules_es_array_push_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! core-js/modules/es.array.push.js */ \"./node_modules/core-js/modules/es.array.push.js\");\n/* harmony import */ var core_js_modules_es_array_push_js__WEBPACK_IMPORTED_MODULE_0___default = /*#__PURE__*/__webpack_require__.n(core_js_modules_es_array_push_js__WEBPACK_IMPORTED_MODULE_0__);\n/* harmony import */ var core_js_modules_es_array_unshift_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! core-js/modules/es.array.unshift.js */ \"./node_modules/core-js/modules/es.array.unshift.js\");\n/* harmony import */ var core_js_modules_es_array_unshift_js__WEBPACK_IMPORTED_MODULE_1___default = /*#__PURE__*/__webpack_require__.n(core_js_modules_es_array_unshift_js__WEBPACK_IMPORTED_MODULE_1__);\n/* harmony import */ var _components_PersonCard_vue__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! @/components/PersonCard.vue */ \"./src/components/PersonCard.vue\");\n/* harmony import */ var _components_Session_vue__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! @/components/Session.vue */ \"./src/components/Session.vue\");\n/* harmony import */ var _components_File_vue__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! @/components/File.vue */ \"./src/components/File.vue\");\n/* harmony import */ var _chatwindow_vue__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! ./chatwindow.vue */ \"./src/view/pages/chatHome/chatwindow.vue\");\n/* harmony import */ var _store_mutation_types__WEBPACK_IMPORTED_MODULE_6__ = __webpack_require__(/*! @/store/mutation-types */ \"./src/store/mutation-types.js\");\n/* harmony import */ var _components_RoleCard_vue__WEBPACK_IMPORTED_MODULE_7__ = __webpack_require__(/*! @/components/RoleCard.vue */ \"./src/components/RoleCard.vue\");\n/* harmony import */ var _api_getData__WEBPACK_IMPORTED_MODULE_8__ = __webpack_require__(/*! @/api/getData */ \"./src/api/getData.js\");\n/* harmony import */ var _util_util__WEBPACK_IMPORTED_MODULE_9__ = __webpack_require__(/*! @/util/util */ \"./src/util/util.js\");\n/* provided dependency */ var console = __webpack_require__(/*! ./node_modules/console-browserify/index.js */ \"./node_modules/console-browserify/index.js\");\n\n\n\n\n\n\n\n\n\n\nconst {\n Configuration,\n OpenAIApi\n} = __webpack_require__(/*! openai */ \"./node_modules/openai/dist/index.js\");\n/* harmony default export */ __webpack_exports__[\"default\"] = ({\n name: \"App\",\n components: {\n RoleCard: _components_RoleCard_vue__WEBPACK_IMPORTED_MODULE_7__[\"default\"],\n PersonCard: _components_PersonCard_vue__WEBPACK_IMPORTED_MODULE_2__[\"default\"],\n ChatWindow: _chatwindow_vue__WEBPACK_IMPORTED_MODULE_5__[\"default\"],\n Session: _components_Session_vue__WEBPACK_IMPORTED_MODULE_3__[\"default\"],\n File: _components_File_vue__WEBPACK_IMPORTED_MODULE_4__[\"default\"]\n },\n data() {\n return {\n fileSearch: \"\",\n sessionSearch: \"\",\n showFineSetting: false,\n cancelFineStatus: true,\n storeStatus: 0,\n //宽度\n defaulWidth: 70,\n //0是聊天设置，1是图片设置\n SettingStatus: 0,\n //0是模型列表，1是会话列表\n cutSetting: 1,\n //余额信息\n moneryInfo: {\n totalGranted: 0,\n totalUsed: 0,\n totalAvailable: 0\n },\n batch_sizeStr: \"\",\n //全部的设置参数\n SettingInfo: {\n KeyMsg: \"\",\n readefile: false,\n inputStatus: true,\n translateEnglish: false,\n openProductionPicture: false,\n openChangePicture: false,\n TemperatureAudio: 0,\n n: 1,\n size: \"256x256\",\n language: \"zh\",\n chat: {\n suffix: \"\",\n MaxTokens: 1000,\n Temperature: 1,\n TopP: 1,\n n: 1,\n stream: true,\n echo: false,\n stop: \"\",\n FrequencyPenalty: 0,\n PresencePenalty: 0\n },\n openNet: false,\n max_results: 3,\n fineTunes: {\n training_file: \"\",\n model: \"curie\",\n n_epochs: 4,\n prompt_loss_weight: 0.01,\n suffix: \"\"\n // compute_classification_metrics: false,\n // classification_betas:\"\",\n // classification_positive_class:\"\",\n }\n },\n\n //当前点击的文件\n fiCurrent: \"\",\n //当前点击的模型\n pcCurrent: \"\",\n //当前点击的角色\n prCurrent: \"\",\n //当前点击的会话\n sessionCurrent: \"\",\n //当前点击的微调模型\n ftCurrent: \"\",\n //微调搜索数据\n fineTuningSearch: \"\",\n //模型搜索数据\n modelSearch: \"\",\n //角色搜索数据\n roleSearch: \"\",\n //文件列表\n fileList: [],\n //文件缓存列表\n fineTuningSearch: [],\n //微调模型列表\n fineTuningList: [],\n //微调模型缓存列表\n fineTuningCacheList: [],\n //模型列表\n personList: [],\n //会话列表\n sessionList: [],\n //角色列表\n roleList: [],\n //模型列表缓存\n personListCache: [],\n //是否显示聊天窗口\n showChatWindow: true,\n //当前窗口的对话模型信息\n chatWindowInfo: {},\n //图片大小参数列表\n imgSizes: [{\n value: '256x256'\n }, {\n value: '512x512'\n }, {\n value: '1024x1024'\n }],\n //语音定义的参数\n languages: [{\n value: 'zh'\n }, {\n value: 'en'\n }, {\n value: 'fr'\n }, {\n value: 'de'\n }, {\n value: 'ja'\n }],\n // 是否隐藏模型列表和功能设置选择列表\n showPersonList: true,\n showSetupList: true,\n showMainContent: true\n };\n },\n computed: {\n // 把获取setting列表的操作放到computed计算属性里来，这样才能动态绑定i18n的值\n getSettings() {\n return [{\n name: this.$t('model.talk'),\n active: true\n }, {\n name: this.$t('image.title'),\n active: false\n }, {\n name: this.$t('audio.title'),\n active: false\n }, {\n name: this.$t('slightly.title.abbreviation'),\n active: false\n }, {\n name: this.$t('file.title'),\n active: false\n }, {\n name: this.$t('session.title'),\n active: false\n }, {\n name: this.$t('role.title'),\n active: false\n }, {\n name: this.$t('setting.title'),\n active: false\n }];\n }\n },\n created() {\n window.addEventListener('resize', this.handleResize);\n this.handleResize();\n },\n destoryed() {\n window.removeEventListener('resize', this.handleResize);\n },\n mounted() {\n this.chatWindowInfo = {\n img: \"\",\n name: \"ChatGPT\",\n detail: this.$t('index.detail'),\n lastMsg: this.$t('index.lastMsg'),\n id: \"gpt-3.5-turbo\",\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_6__.AI_HEAD_IMG_URL,\n showHeadImg: true\n };\n if (this.SettingInfo.KeyMsg) {\n this.getModelList(this.SettingInfo.KeyMsg);\n }\n this.getRolesList();\n this.$watch('fileSearch', this.watchFileSearch);\n },\n filters: {\n // //截取数据到小数点后几位\n // numFilterReserved(value, digit) {\n // return parseFloat(value).toFixed(digit)\n // }\n },\n watch: {\n modelSearch: {\n handler: function (newVal, oldVal) {\n if (this.personList) {\n this.personList = this.personListCache.filter(person => person.id.includes(newVal));\n } else {\n this.personList = this.personListCache;\n }\n }\n },\n fineTuningSearch: {\n handler: function (newVal, oldVal) {\n if (this.fineTuningList) {\n if (!this.cancelFineStatus) {\n this.fineTuningList = this.fineTuningCacheList.filter(fineTunin => fineTunin.fineTunesStatus === \"succeeded\").filter(fineTuning => fineTuning.id.includes(newVal));\n } else {\n this.fineTuningList = this.fineTuningCacheList.filter(fineTuning => fineTuning.id.includes(newVal));\n }\n } else {\n if (!this.cancelFineStatus) {\n this.fineTuningList = this.fineTuningCacheList.filter(fineTunin => fineTunin.fineTunesStatus === \"succeeded\");\n } else {\n this.fineTuningList = this.fineTuningCacheList;\n }\n }\n }\n },\n fileSearch: {\n handler: function (newVal, oldVal) {\n if (this.fileList) {\n this.fileList = this.fileCacheList.filter(fileList => fileList.id.includes(newVal));\n } else {\n this.fileList = this.fileCacheList;\n }\n }\n },\n roleSearch: {\n handler: function (newVal, oldVal) {\n if (this.roleList) {\n this.roleList = this.roleCacheList.filter(fileList => fileList.act.toLowerCase().includes(newVal.toLowerCase()));\n } else {\n this.roleList = this.roleCacheList;\n }\n }\n },\n SettingInfo: {\n handler: function (newVal, oldVal) {\n if (newVal.openChangePicture) {\n this.SettingInfo.openProductionPicture = false;\n }\n if (newVal.openProductionPicture) {\n this.SettingInfo.openChangePicture = false;\n }\n if (newVal.fineTunes.batch_size) {\n let batchSize = parseInt(newVal.fineTunes.batch_size);\n this.SettingInfo.fineTunes.batch_size = batchSize;\n } else {}\n if (newVal.fineTunes.validation_file) {\n this.SettingInfo.fineTunes.validation_file = newVal.fineTunes.validation_file;\n }\n if (newVal.fineTunes.learning_rate_multiplier) {\n this.SettingInfo.fineTunes.learning_rate_multiplier = parseInt(newVal.fineTunes.learning_rate_multiplier);\n }\n if (newVal.KeyMsg && newVal !== oldVal) {\n //获取模型列表\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_8__.getModels)(newVal).then(res => {\n this.personList = res;\n this.personListCache = res;\n }).catch(e => {\n this.$message.error(this.$t('message.get_model_fail'));\n });\n }\n // if (newVal.fineTunes.classification_n_classes) {\n // this.SettingInfo.fineTunes.classification_n_classes = parseInt(newVal.fineTunes.classification_n_classes)\n // }\n },\n\n deep: true\n }\n },\n methods: {\n // 切换语言\n changeLanguage() {\n const lang = this.$i18n.locale === \"zh\" ? \"en\" : \"zh\";\n localStorage.setItem(\"lang\", lang);\n this.$i18n.locale = lang;\n },\n //显示或者隐藏取消过的微调模型\n showOrHidenCancelFine(status) {\n this.cancelFineStatus = status;\n if (this.cancelFineStatus == true) {\n this.fineTuningList = this.fineTuningCacheList;\n } else {\n this.fineTuningList = this.fineTuningCacheList.filter(fineTunin => fineTunin.fineTunesStatus === \"succeeded\");\n }\n },\n //导入会话列表触发的方法\n importFromJsonArrAll() {\n this.$refs.onupdateJosnArrAll.click(); // 触发选择文件的弹框\n },\n\n handleFileUploadAll(event) {\n const file = event.target.files[0];\n const reader = new FileReader();\n reader.onload = () => {\n const fileContent = reader.result; // 文件内容\n const parsed = JSON.parse(fileContent); // 转换为数组\n this.sessionList = parsed;\n };\n reader.readAsText(file);\n },\n //导出所有会话到json文件\n exportObjArrAllToJson() {\n let jsonString = JSON.stringify(this.sessionList); // 将数组转为JSON字符串\n let blob = new Blob([jsonString], {\n type: \"application/json;charset=utf-8\"\n });\n saveAs(blob, \"data.json\");\n },\n //清除所有的会话内容\n clearAllContext() {\n this.sessionList = [];\n },\n //清除当前会话内容\n clearCurrentContext() {\n this.$refs.chatWindow.clearMsgList();\n },\n // 点击切换显示状态\n toggleLeft() {\n console.log(\"left clicked\");\n this.showPersonList = !this.showPersonList;\n const isMobile = /iPhone|iPad|iPod|Android/i.test(navigator.userAgent);\n if (isMobile && (this.showPersonList || this.showSetupList)) {\n this.showMainContent = false;\n document.querySelectorAll('.chatLeft')[0].style.width = '100%';\n } else {\n this.showMainContent = true;\n document.querySelectorAll('.chatLeft')[0].style.width = '22%';\n }\n },\n toggleRight() {\n console.log(\"right clicked\");\n this.showSetupList = !this.showSetupList;\n const isMobile = /iPhone|iPad|iPod|Android/i.test(navigator.userAgent);\n if (isMobile && (this.showPersonList || this.showSetupList)) {\n this.showMainContent = false;\n document.querySelectorAll('.chatLeft')[1].style.width = '100%';\n } else {\n this.showMainContent = true;\n document.querySelectorAll('.chatLeft')[1].style.width = '22%';\n }\n },\n //获取模型列表\n getModelList(key) {\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_8__.getModels)(key).then(modelsRes => {\n // 提取fineTunesRes集合中所有id属性值\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_8__.getFineTunesList)(key).then(fineTunesRes => {\n const fineTunesIds = fineTunesRes.map(item => item.id);\n const models = modelsRes.filter(item => !fineTunesIds.includes(item.id));\n this.personList = models;\n this.personListCache = models;\n });\n this.updateMoneyInfo();\n }).catch(e => {\n // this.$message.error(this.$t('message.get_model_fail'))\n });\n },\n //获取微调模型列表\n getFineTunessList(key) {\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_8__.getFineTunesList)(key).then(res => {\n this.fineTuningCacheList = res;\n if (this.cancelFineStatus == true) {\n this.fineTuningList = this.fineTuningCacheList;\n } else {\n this.fineTuningList = this.fineTuningCacheList.filter(fineTunin => fineTunin.fineTunesStatus === \"succeeded\");\n }\n }).catch(e => {\n this.$message.error(this.$t('message.get_model_fail'));\n });\n },\n //获取文件列表\n getFilessList(key) {\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_8__.getFilesList)(key).then(res => {\n this.fileList = res;\n this.fileCacheList = res;\n }).catch(e => {\n this.$message.error(this.$t('message.get_files_fail'));\n });\n },\n //获取角色列表\n getRolesList() {\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_8__.getRoles)().then(res => {\n let data = res.data;\n this.roleList = data;\n this.roleCacheList = data;\n }).catch(e => {\n this.$message.error(this.$t('message.get_roles_fail'));\n });\n },\n //监听窗口尺寸的变化\n handleResize() {\n if (window.innerWidth <= 1150) {\n this.showPersonList = false;\n this.showSetupList = false;\n this.showChatWindow = true;\n const info = {\n img: \"\",\n name: \"ChatGPT\",\n detail: \"chatgpt v3.5 所基于的模型\",\n lastMsg: \"chatgpt v3.5 所基于的模型\",\n id: \"gpt-3.5-turbo\",\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_6__.AI_HEAD_IMG_URL,\n showHeadImg: true\n };\n this.chatWindowInfo = info;\n this.personInfo = info;\n } else {\n this.showPersonList = true;\n this.showSetupList = true;\n }\n ;\n },\n // // 更新当前余额\n // updateMoneyInfo() {\n // getMoneyInfo(this.SettingInfo.KeyMsg).then((res) => {\n // this.$nextTick(() => {\n // this.moneryInfo.totalGranted = res.total_granted;\n // this.moneryInfo.totalUsed = res.total_used;\n // this.moneryInfo.totalAvailable = res.total_available;\n // })\n // })\n // },\n //创建会话\n newSession() {\n //获取当前会话长度\n const currentLen = this.sessionList.length + 1;\n //定义对象\n const obj = {\n \"id\": currentLen,\n \"title\": \"\",\n \"dataList\": []\n };\n //先获取对话的列表\n const msgList = this.$refs.chatWindow.getMesList();\n if (msgList.length >= 2) {\n if (this.sessionCurrent) {\n this.sessionCurrent = \"\";\n //清除当前窗口数据\n this.$refs.chatWindow.clearMsgList();\n } else {\n obj.title = msgList[0].msg;\n obj.dataList = msgList;\n let tempSessionList = this.sessionList;\n tempSessionList.push(obj);\n this.sessionList = tempSessionList.reverse();\n this.sessionCurrent = \"\";\n //清除当前窗口数据\n this.$refs.chatWindow.clearMsgList();\n }\n } else if (msgList.length = 1) {\n //清除当前窗口数据\n this.$refs.chatWindow.clearMsgList();\n }\n },\n //模型列表被点击\n modelClick() {\n this.clearCurrent();\n this.getModelList(this.SettingInfo.KeyMsg);\n //清除被点击的微调对象\n this.fineTuningInfo = {};\n this.SettingStatus = 0;\n this.cutSetting = 0;\n // this.showChatWindow = false;\n },\n\n //会话列表被点击\n sessionClick() {\n //清除当前点击的状态\n this.clearCurrent();\n this.SettingStatus = 5;\n this.cutSetting = 1;\n this.chatWindowInfo = {\n img: \"\",\n name: \"ChatGPT\",\n detail: \"chatgpt v3.5 所基于的模型\",\n lastMsg: \"chatgpt v3.5 所基于的模型\",\n id: \"gpt-3.5-turbo\",\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_6__.AI_HEAD_IMG_URL,\n showHeadImg: true\n };\n // this.showChatWindow = true;\n },\n\n //角色列表被点击\n roleClick(info) {\n if (!this.showChatWindow) {\n this.$message({\n message: \"请选一个模型\",\n type: \"error\"\n });\n } else {\n var chatWindow = this.$refs.chatWindow;\n chatWindow.inputMsg = info.prompt;\n }\n },\n //微调模型列表被点击\n fineTuningClick() {\n this.clearCurrent();\n this.SettingStatus = 3;\n this.cutSetting = 2;\n // this.showChatWindow = false;\n //获取微调模型列表\n this.getFineTunessList(this.SettingInfo.KeyMsg);\n },\n clearCurrent() {\n //清除当前选择的模型微调模型\n this.ftCurrent = \"\";\n //清除当前选择的模型\n this.pcCurrent = \"\";\n //清除当前选择的会话\n this.sessionCurrent = \"\";\n //清除当前选择的文件\n this.fiCurrent = \"\";\n },\n //文件列表被点击\n fileClick() {\n this.clearCurrent();\n //清除被点击的微调对象\n this.fineTuningInfo = {};\n this.SettingStatus = 4;\n this.cutSetting = 3;\n //获取微调模型列表\n this.getFilessList(this.SettingInfo.KeyMsg);\n },\n //上传文件按钮被点击触发的方法\n uploadFile() {\n this.$refs.fileInput.click();\n },\n //文件上传触发的方法\n onFileChange(e) {\n //获取文件\n const file = e.target.files[0];\n // 验证文件类型是否为jsonl格式\n if (!file.name.endsWith('.jsonl')) {\n this.$message.warning(this.$t('message.valid_json'));\n return;\n }\n // 通过验证后，上传文件\n const formData = new FormData();\n formData.append(\"file\", file);\n formData.append(\"purpose\", \"fine-tune\");\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_8__.uploadFile)(formData, this.SettingInfo.KeyMsg).then(res => {\n this.$copy(res.id, this.$t('index.up_file_id') + res.id + this.$t('index.copy'));\n //更新文件列表\n this.getFilessList(this.SettingInfo.KeyMsg);\n });\n },\n //检索文件信息\n retrieveOnFile() {\n if (!this.fileInfo || !this.fileInfo.fileId) {\n this.$message.error(this.$t('message.only_file'));\n } else {\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_8__.retrieveFile)(this.fileInfo.fileId, this.SettingInfo.KeyMsg).then(res => {\n let context = this.$t('index.file_id') + res.id + \" \\n\" + this.$t('index.file_name') + res.filename + \" \\n\" + this.$t('index.file_size') + (res.bytes / 1024 / 1024).toFixed(2) + \"MB \\n\" + this.$t('index.obj') + res.object + \" \\n\" + this.$t('index.status') + res.status + \" \\n\" + this.$t('index.status_des') + res.status_details + \" \\n\" + this.$t('index.target') + res.purpose + \" \\n\" + this.$t('index.file_time') + (0,_util_util__WEBPACK_IMPORTED_MODULE_9__.JCMFormatTimestamp)(res.created_at);\n let retrieveFineTuneMsg = {\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_6__.AI_HEAD_IMG_URL,\n name: res.filename,\n time: (0,_util_util__WEBPACK_IMPORTED_MODULE_9__.JCMFormatDate)((0,_util_util__WEBPACK_IMPORTED_MODULE_9__.getNowTime)()),\n msg: context,\n chatType: 0,\n uid: res.id\n };\n this.$refs.chatWindow.sendMsg(retrieveFineTuneMsg);\n console.log(res);\n }).catch(e => {\n this.$message.error(this.$t('message.fail_file'));\n });\n }\n },\n //检索文件内容\n async retrieveOnFileContent() {\n if (!this.fileInfo || !this.fileInfo.fileId) {\n this.$message.error(this.$t('message.only_file'));\n } else {\n try {\n const configuration = new Configuration({\n apiKey: this.SettingInfo.KeyMsg\n });\n const openai = new OpenAIApi(configuration);\n const response = await openai.downloadFile(this.fileInfo.fileId);\n } catch (e) {\n this.$message.error(this.$t('message.openai_free'));\n }\n }\n },\n //模型被点击\n clickPerson(info) {\n this.storeStatus = 0;\n //传入当前聊天窗口信息\n this.chatWindowInfo = info;\n //设置当前被点击的对象\n this.personInfo = info;\n //设置当前被点击的模型id\n this.pcCurrent = info.id;\n },\n //会话被点击\n clickSession(info) {\n this.sessionCurrent = info.id;\n this.$refs.chatWindow.assignmentMesList(info.dataList);\n },\n //微调模型被点击\n clickFineTuning(info) {\n this.storeStatus = 1;\n //传入当前聊天窗口信息\n this.chatWindowInfo = info;\n //设置当前被点击的对象\n this.fineTuningInfo = info;\n //设置当前选着的微调模型id\n this.ftCurrent = info.id;\n },\n //文件被点击\n clickFile(info) {\n this.chatWindowInfo = {\n img: \"\",\n name: info.id,\n detail: info.detail,\n lastMsg: info.lastMsg,\n id: info.id\n };\n this.fiCurrent = info.fileId;\n this.fileInfo = info;\n },\n //删除文件\n deleteOnFile() {\n if (!this.fileInfo || !this.fileInfo.fileId) {\n this.$message.error(this.$t('message.only_del_file'));\n } else {\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_8__.deleteFile)(this.fileInfo.fileId, this.SettingInfo.KeyMsg).then(res => {\n this.$message.success(this.$t('message.del_file_succ'));\n this.getFilessList(this.SettingInfo.KeyMsg);\n }).catch(e => {\n this.$message.error(this.$t('message.del_fail'));\n });\n }\n },\n //创建微调\n createFine() {\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_8__.createFineTune)(this.SettingInfo.fineTunes, this.SettingInfo.KeyMsg).then(res => {\n this.$message.success(this.$t('message.create_succ'));\n this.getFineTunessList(this.SettingInfo.KeyMsg);\n }).catch(e => {\n this.$message.error(this.$t('message.create_fail'));\n });\n },\n //删除微调\n deleteFine() {\n if (!this.fineTuningInfo || !this.fineTuningInfo.fineTunesId) {\n this.$message.error(this.$t('message.only_del_model'));\n } else {\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_8__.deleteFineTuneModel)(this.fineTuningInfo.name, this.SettingInfo.KeyMsg).then(res => {\n this.$message.success(this.$t('message.del_model_succ'));\n this.getFineTunessList(this.SettingInfo.KeyMsg);\n }).catch(e => {\n this.$message.error(this.$t('message.del_fail_ing'));\n });\n }\n },\n //取消微调\n cancelFine() {\n if (!this.fineTuningInfo || !this.fineTuningInfo.fineTunesId || this.fineTuningInfo.fineTunesStatus === \"succeeded\") {\n this.$message.error(this.$t('message.only_cancel'));\n } else {\n console.log(this.fineTuningInfo.fineTunesId);\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_8__.cancelFineTune)(this.fineTuningInfo.fineTunesId, this.SettingInfo.KeyMsg).then(res => {\n this.$message.success(this.$t('message.cancel_succ'));\n this.getFineTunessList(this.SettingInfo.KeyMsg);\n }).catch(e => {\n console.log(e);\n this.$message.error(this.$t('message.cancel_fail'));\n });\n }\n },\n //检索微调\n retrieveFine() {\n if (!this.fineTuningInfo || !this.fineTuningInfo.fineTunesId) {\n this.$message.error(this.$t('message.only_model'));\n } else {\n console.log(this.fineTuningInfo.fineTunesId);\n (0,_api_getData__WEBPACK_IMPORTED_MODULE_8__.retrieveFineTune)(this.fineTuningInfo.fineTunesId, this.SettingInfo.KeyMsg).then(res => {\n let context = this.$t('index.task_id') + res.id + \" \\n\" + this.$t('index.task_type') + res.object + \" \\n\" + this.$t('index.model_type') + res.model + \" \\n\" + this.$t('index.task_time') + (0,_util_util__WEBPACK_IMPORTED_MODULE_9__.JCMFormatTimestamp)(res.created_at) + \" \\n\" + this.$t('index.task_list') + this.$t('index.obj_log_info_time') + \"| :------: | :------: | :------: | :------: |\\n\";\n res.events.forEach(obj => {\n context += `| ${obj.object} | ${obj.level} | ${obj.message} | ${(0,_util_util__WEBPACK_IMPORTED_MODULE_9__.JCMFormatTimestamp)(obj.created_at)} |\\n`;\n });\n context += this.$t('index.model_id') + res.fine_tuned_model + this.$t('index.args') + this.$t('index.item_setting') + \"| :------: | :------: | \\n\";\n for (let prop in res.hyperparams) {\n if (res.hyperparams.hasOwnProperty(prop)) {\n context += `| ${prop} | ${res.hyperparams[prop]} |\\n`;\n }\n }\n context += this.$t('index.user_group') + res.organization_id;\n if (res.result_files.length == 0) {\n context += this.$t('index.results_null');\n } else {\n context += this.$t('index.results') + this.$t('index.table_head') + \"| :------: | :------: | :------: | :------: | :------: | \\n\";\n res.result_files.forEach(obj => {\n context += `| ${obj.id} | ${obj.filename} | ${(obj.bytes / 1024 / 1024).toFixed(2) + \"MB\"} | ${obj.object} | ${obj.status} | \\n`;\n });\n }\n context += this.$t('index.statu') + res.status + \"\\n\";\n if (res.training_files.length == 0) {\n context += this.$t('index.files_null');\n } else {\n context += this.$t('index.files') + this.$t('index.table_head') + \"| :------: | :------: | :------: | :------: | :------: | \\n\";\n res.training_files.forEach(obj => {\n context += `| ${obj.id} | ${obj.filename} | ${(obj.bytes / 1024 / 1024).toFixed(2) + \"MB\"} | ${obj.object} | ${obj.status} | \\n`;\n });\n }\n if (res.validation_files.length == 0) {\n context += this.$t('index.verifys_null');\n } else {\n context += this.$t('index.verifys') + this.$t('index.table_head') + \"| :------: | :------: | :------: | :------: | :------: | \\n\";\n res.validation_files.forEach(obj => {\n context += `| ${obj.id} | ${obj.filename} | ${(obj.bytes / 1024 / 1024).toFixed(2) + \"MB\"} | ${obj.object} | ${obj.status} | \\n`;\n });\n }\n context += this.$t('index.last_time') + (0,_util_util__WEBPACK_IMPORTED_MODULE_9__.JCMFormatTimestamp)(res.updated_at);\n let retrieveFineTuneMsg = {\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_6__.AI_HEAD_IMG_URL,\n name: res.fine_tuned_model !== null ? res.fine_tuned_model : res.id,\n time: (0,_util_util__WEBPACK_IMPORTED_MODULE_9__.JCMFormatDate)((0,_util_util__WEBPACK_IMPORTED_MODULE_9__.getNowTime)()),\n msg: context,\n chatType: 0,\n uid: res.id\n };\n this.$refs.chatWindow.sendMsg(retrieveFineTuneMsg);\n console.log(res);\n }).catch(e => {\n console.log(e);\n this.$message.error(this.$t('message.verify_model_fail'));\n });\n }\n },\n personCardSort(id) {\n if (typeof this.personList[0] != 'undefined' && id !== this.personList[0].id) {\n console.log(id);\n let nowPersonInfo;\n for (let i = 0; i < this.personList.length; i++) {\n if (this.personList[i].id == id) {\n nowPersonInfo = this.personList[i];\n this.personList.splice(i, 1);\n break;\n }\n }\n this.personList.unshift(nowPersonInfo);\n }\n },\n fineTunesCardSort(id) {\n if (id !== this.fineTuningList[0].id) {\n console.log(id);\n let nowPersonInfo;\n for (let i = 0; i < this.fineTuningList.length; i++) {\n if (this.fineTuningList[i].id == id) {\n nowPersonInfo = this.fineTuningList[i];\n this.fineTuningList.splice(i, 1);\n break;\n }\n }\n this.fineTuningList.unshift(nowPersonInfo);\n }\n }\n }\n});\n\n//# sourceURL=webpack://JCM-AI/./src/view/pages/chatHome/index.vue?./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use%5B0%5D!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options");
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-/*!*********************************************************************************************************************************************************************************************************!*\
- !*** ./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[0]!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./src/view/pages/user/userInfo.vue?vue&type=script&lang=js& ***!
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-/***/ "./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[0]!./node_modules/@vue/vue-loader-v15/lib/loaders/templateLoader.js??ruleSet[1].rules[3]!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./src/components/Emoji.vue?vue&type=template&id=534ad946&scoped=true&":
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-"use strict";
-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony export */ __webpack_require__.d(__webpack_exports__, {\n/* harmony export */ \"render\": function() { return /* binding */ render; },\n/* harmony export */ \"staticRenderFns\": function() { return /* binding */ staticRenderFns; }\n/* harmony export */ });\nvar render = function render() {\n var _vm = this,\n _c = _vm._self._c;\n return _c(\"div\", {\n staticClass: \"emoji-content\"\n }, [_c(\"div\", {\n staticClass: \"emoji\"\n }, [_c(\"div\", {\n staticClass: \"emoji-wrapper\"\n }, [_c(\"ul\", {\n staticClass: \"emoji-list\"\n }, _vm._l(_vm.emojiList, function (item, index) {\n return _c(\"li\", {\n key: index,\n staticClass: \"emoji-item\",\n on: {\n click: function ($event) {\n return _vm.sendEmoji(item);\n }\n }\n }, [_c(\"img\", {\n attrs: {\n src: item,\n alt: \"\"\n }\n })]);\n }), 0)])]), _c(\"div\", {\n staticClass: \"mask\",\n on: {\n click: _vm.closeEmoji\n }\n })]);\n};\nvar staticRenderFns = [];\nrender._withStripped = true;\n\n\n//# sourceURL=webpack://JCM-AI/./src/components/Emoji.vue?./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use%5B0%5D!./node_modules/@vue/vue-loader-v15/lib/loaders/templateLoader.js??ruleSet%5B1%5D.rules%5B3%5D!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options");
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-/*!************************************************************************************************************************************************************************************************************************************************************************************************************!*\
- !*** ./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[0]!./node_modules/@vue/vue-loader-v15/lib/loaders/templateLoader.js??ruleSet[1].rules[3]!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./src/components/FileCard.vue?vue&type=template&id=48849e48&scoped=true& ***!
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- !*** ./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[0]!./node_modules/@vue/vue-loader-v15/lib/loaders/templateLoader.js??ruleSet[1].rules[3]!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./src/components/PersonCard.vue?vue&type=template&id=d74d3096&scoped=true& ***!
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-
-"use strict";
-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony export */ __webpack_require__.d(__webpack_exports__, {\n/* harmony export */ \"render\": function() { return /* binding */ render; },\n/* harmony export */ \"staticRenderFns\": function() { return /* binding */ staticRenderFns; }\n/* harmony export */ });\nvar render = function render() {\n var _vm = this,\n _c = _vm._self._c;\n return _c(\"div\", {\n staticClass: \"chat-window\"\n }, [_c(\"div\", {\n staticClass: \"top\"\n }, [_c(\"el-row\", {\n staticStyle: {\n height: \"70px\"\n }\n }, [_c(\"el-col\", {\n attrs: {\n span: _vm.personInfoSpan[0]\n }\n }, [_c(\"div\", {\n staticClass: \"head-pic\"\n }, [_c(\"HeadPortrait\", {\n attrs: {\n imgUrl: _vm.frinedInfo.headImg\n }\n })], 1)]), _c(\"el-col\", {\n attrs: {\n span: _vm.personInfoSpan[1]\n }\n }, [_c(\"div\", {\n staticClass: \"info-detail\"\n }, [_c(\"div\", {\n staticClass: \"name\"\n }, [_vm._v(_vm._s(_vm.frinedInfo.name))]), _c(\"div\", {\n staticClass: \"detail\"\n }, [_vm._v(_vm._s(_vm.frinedInfo.detail))])])]), _c(\"el-col\", {\n attrs: {\n span: _vm.personInfoSpan[2]\n }\n }, [_c(\"div\", {\n staticClass: \"other-fun\"\n }, [_c(\"label\", {\n on: {\n click: _vm.clearMsgList\n }\n }, [_c(\"span\", {\n staticClass: \"iconfont icon-qingchu\"\n })]), _c(\"label\", {\n on: {\n click: _vm.importFromJsonArr\n }\n }, [_c(\"span\", {\n staticClass: \"iconfont icon-daoru\"\n })]), _c(\"label\", {\n on: {\n click: _vm.exportObjArrToJson\n }\n }, [_c(\"span\", {\n staticClass: \"iconfont icon-daochu\"\n })]), _c(\"label\", {\n attrs: {\n for: \"imgFile\"\n }\n }, [_c(\"span\", {\n staticClass: \"iconfont icon-tupian\"\n })]), _c(\"label\", {\n attrs: {\n for: \"docFile\"\n }\n }, [_c(\"span\", {\n staticClass: \"iconfont icon-wenben\"\n })]), _c(\"input\", {\n attrs: {\n type: \"file\",\n name: \"\",\n id: \"imgFile\",\n accept: \"image/*\"\n },\n on: {\n change: _vm.sendImg\n }\n }), _c(\"input\", {\n attrs: {\n type: \"file\",\n name: \"\",\n id: \"docFile\",\n accept: \"application/*,text/*\"\n },\n on: {\n change: _vm.sendFile\n }\n }), _c(\"input\", {\n ref: \"onupdateJosnArr\",\n staticStyle: {\n display: \"none\"\n },\n attrs: {\n type: \"file\"\n },\n on: {\n change: _vm.handleFileUpload\n }\n })])])], 1)], 1), _c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: !_vm.acqStatus,\n expression: \"!acqStatus\"\n }]\n }, [_c(\"div\", {\n staticClass: \"line\"\n })]), _c(\"div\", {\n staticClass: \"botoom\",\n staticStyle: {\n \"background-color\": \"rgb(50, 54, 68)\"\n }\n }, [_c(\"div\", {\n ref: \"chatContent\",\n staticClass: \"chat-content\",\n attrs: {\n id: \"chat-content\"\n },\n on: {\n scroll: _vm.onScroll\n }\n }, _vm._l(_vm.chatList, function (item) {\n return _c(\"div\", {\n key: item.id,\n staticClass: \"chat-wrapper\"\n }, [item.uid !== \"jcm\" ? _c(\"div\", {\n staticClass: \"chat-friend\"\n }, [item.chatType == 0 ? _c(\"div\", {\n staticClass: \"chat-text\"\n }, [_c(\"el-row\", {\n attrs: {\n gutter: 20\n }\n }, [_c(\"el-col\", {\n attrs: {\n span: 2\n }\n }, [_c(\"svg\", {\n staticClass: \"icon\",\n attrs: {\n t: \"1679666016648\",\n viewBox: \"0 0 1024 1024\",\n version: \"1.1\",\n xmlns: \"http://www.w3.org/2000/svg\",\n \"p-id\": \"6241\",\n width: \"22\",\n height: \"22\"\n },\n on: {\n click: function ($event) {\n return _vm.$copy(item.msg, \"已复制\");\n }\n }\n }, [_c(\"path\", {\n attrs: {\n d: \"M661.333333 234.666667A64 64 0 0 1 725.333333 298.666667v597.333333a64 64 0 0 1-64 64h-469.333333A64 64 0 0 1 128 896V298.666667a64 64 0 0 1 64-64z m-21.333333 85.333333H213.333333v554.666667h426.666667v-554.666667z m191.829333-256a64 64 0 0 1 63.744 57.856l0.256 6.144v575.701333a42.666667 42.666667 0 0 1-85.034666 4.992l-0.298667-4.992V149.333333H384a42.666667 42.666667 0 0 1-42.368-37.674666L341.333333 106.666667a42.666667 42.666667 0 0 1 37.674667-42.368L384 64h447.829333z\",\n fill: \"#909399\",\n \"p-id\": \"6242\"\n }\n })])]), _c(\"el-col\", {\n attrs: {\n span: 21\n }\n })], 1), _c(\"markdown-it-vue\", {\n attrs: {\n content: item.msg.trim()\n }\n })], 1) : _vm._e(), item.chatType == 1 ? _c(\"div\", {\n staticClass: \"chat-img\"\n }, [item.extend.imgType == 1 ? _c(\"img\", {\n staticStyle: {\n width: \"100px\",\n height: \"100px\"\n },\n attrs: {\n src: item.msg,\n alt: \"表情\"\n }\n }) : _c(\"el-image\", {\n staticStyle: {\n \"border-radius\": \"10px\"\n },\n attrs: {\n src: item.msg,\n \"preview-src-list\": _vm.srcImgList\n }\n })], 1) : _vm._e(), item.chatType == 2 ? _c(\"div\", {\n staticClass: \"chat-img\"\n }, [_c(\"div\", {\n staticClass: \"word-file\"\n }, [_c(\"FileCard\", {\n attrs: {\n fileType: item.extend.fileType,\n file: item.msg\n }\n })], 1)]) : _vm._e(), _c(\"div\", {\n staticClass: \"info-time\"\n }, [_c(\"img\", {\n attrs: {\n src: item.headImg,\n alt: \"\"\n }\n }), _c(\"span\", [_vm._v(_vm._s(item.name))]), _c(\"span\", [_vm._v(_vm._s(item.time))])])]) : _c(\"div\", {\n staticClass: \"chat-me\"\n }, [item.chatType == 0 ? _c(\"div\", {\n staticClass: \"chat-text\"\n }, [_c(\"span\", {\n staticStyle: {\n \"font-size\": \"16px\"\n }\n }, [_vm._v(_vm._s(item.msg))])]) : _vm._e(), item.chatType == 1 ? _c(\"div\", {\n staticClass: \"chat-img\"\n }, [item.extend.imgType == 1 ? _c(\"img\", {\n staticStyle: {\n width: \"100px\",\n height: \"100px\"\n },\n attrs: {\n src: item.msg,\n alt: \"表情\"\n }\n }) : _c(\"el-image\", {\n staticStyle: {\n \"max-width\": \"300px\",\n \"border-radius\": \"10px\"\n },\n attrs: {\n src: item.msg,\n \"preview-src-list\": _vm.srcImgList\n }\n })], 1) : _vm._e(), item.chatType == 2 ? _c(\"div\", {\n staticClass: \"chat-img\"\n }, [_c(\"div\", {\n staticClass: \"word-file\"\n }, [_c(\"FileCard\", {\n attrs: {\n fileType: item.extend.fileType,\n file: item.msg\n }\n })], 1)]) : _vm._e(), _c(\"div\", {\n staticClass: \"info-time\"\n }, [_c(\"span\", [_vm._v(_vm._s(item.name))]), _c(\"span\", [_vm._v(_vm._s(item.time))]), _c(\"img\", {\n attrs: {\n src: item.headImg,\n alt: \"\"\n }\n })])])]);\n }), 0), _c(\"div\", {\n staticClass: \"chatInputs\"\n }, [_c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.buttonStatus,\n expression: \"buttonStatus\"\n }],\n staticClass: \"emoji boxinput\",\n on: {\n click: _vm.clickEmoji\n }\n }, [_c(\"img\", {\n attrs: {\n src: __webpack_require__(/*! @/assets/img/emoji/smiling-face.png */ \"./src/assets/img/emoji/smiling-face.png\"),\n alt: \"\"\n }\n })]), _vm.recording ? _c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.buttonStatus,\n expression: \"buttonStatus\"\n }],\n staticClass: \"luyin boxinput\",\n on: {\n click: _vm.stopRecording\n }\n }, [_c(\"i\", {\n staticClass: \"el-icon-microphone\",\n staticStyle: {\n \"margin-top\": \"17%\"\n }\n })]) : _vm._e(), !_vm.recording ? _c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.buttonStatus,\n expression: \"buttonStatus\"\n }],\n staticClass: \"luyin boxinput\",\n on: {\n click: _vm.startRecording\n }\n }, [_c(\"i\", {\n staticClass: \"el-icon-turn-off-microphone\",\n staticStyle: {\n \"margin-top\": \"17%\"\n }\n })]) : _vm._e(), _c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.buttonStatus,\n expression: \"buttonStatus\"\n }],\n staticClass: \"emoji-content\"\n }, [_c(\"Emoji\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.showEmoji,\n expression: \"showEmoji\"\n }],\n on: {\n sendEmoji: _vm.sendEmoji,\n closeEmoji: _vm.clickEmoji\n }\n })], 1), _c(\"textarea\", {\n directives: [{\n name: \"autoheight\",\n rawName: \"v-autoheight\"\n }, {\n name: \"model\",\n rawName: \"v-model\",\n value: _vm.inputMsg,\n expression: \"inputMsg\"\n }],\n staticClass: \"inputs\",\n staticStyle: {\n \"z-index\": \"9999999999\",\n \"min-height\": \"50px\",\n \"max-height\": \"400px\",\n \"max-width\": \"100%\",\n \"min-width\": \"45%\"\n },\n attrs: {\n id: \"textareaMsg\",\n placeholder: _vm.$t(\"placeholder.question\"),\n maxlength: \"2048\",\n rows: \"3\",\n dir: \"\",\n autocorrect: \"off\",\n \"aria-autocomplete\": \"both\",\n spellcheck: \"false\",\n autocapitalize: \"off\",\n autocomplete: \"off\"\n },\n domProps: {\n value: _vm.inputMsg\n },\n on: {\n keyup: function ($event) {\n if (!$event.type.indexOf(\"key\") && _vm._k($event.keyCode, \"enter\", 13, $event.key, \"Enter\")) return null;\n return _vm.handleKeyDown.apply(null, arguments);\n },\n input: function ($event) {\n if ($event.target.composing) return;\n _vm.inputMsg = $event.target.value;\n }\n }\n }), _c(\"div\", [_c(\"div\", {\n staticClass: \"send boxinput\",\n on: {\n click: _vm.sendText\n }\n }, [_c(\"img\", {\n attrs: {\n src: __webpack_require__(/*! @/assets/img/emoji/rocket.png */ \"./src/assets/img/emoji/rocket.png\"),\n alt: \"\"\n }\n })])])])])]);\n};\nvar staticRenderFns = [];\nrender._withStripped = true;\n\n\n//# sourceURL=webpack://JCM-AI/./src/view/pages/chatHome/chatwindow.vue?./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use%5B0%5D!./node_modules/@vue/vue-loader-v15/lib/loaders/templateLoader.js??ruleSet%5B1%5D.rules%5B3%5D!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options");
-
-/***/ }),
-
-/***/ "./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[0]!./node_modules/@vue/vue-loader-v15/lib/loaders/templateLoader.js??ruleSet[1].rules[3]!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./src/view/pages/chatHome/index.vue?vue&type=template&id=c6884a34&scoped=true&":
-/*!******************************************************************************************************************************************************************************************************************************************************************************************************************!*\
- !*** ./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[0]!./node_modules/@vue/vue-loader-v15/lib/loaders/templateLoader.js??ruleSet[1].rules[3]!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./src/view/pages/chatHome/index.vue?vue&type=template&id=c6884a34&scoped=true& ***!
- \******************************************************************************************************************************************************************************************************************************************************************************************************************/
-/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {
-
-"use strict";
-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony export */ __webpack_require__.d(__webpack_exports__, {\n/* harmony export */ \"render\": function() { return /* binding */ render; },\n/* harmony export */ \"staticRenderFns\": function() { return /* binding */ staticRenderFns; }\n/* harmony export */ });\nvar render = function render() {\n var _vm = this,\n _c = _vm._self._c;\n return _c(\"div\", {\n staticClass: \"chatHome\"\n }, [_c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.showPersonList,\n expression: \"showPersonList\"\n }],\n staticClass: \"chatLeft\",\n staticStyle: {\n width: \"22%\"\n }\n }, [_vm._m(0), _c(\"div\", {\n staticClass: \"online-person\",\n staticStyle: {\n \"margin-top\": \"5%\"\n }\n }, [_c(\"el-row\", {\n attrs: {\n gutter: 24\n }\n }, [_c(\"el-col\", {\n attrs: {\n span: 6\n }\n }, [_c(\"div\", {\n staticClass: \"setting\",\n staticStyle: {\n \"text-align\": \"center\"\n }\n }, [_c(\"span\", {\n class: {\n whiteText: _vm.cutSetting === 1\n },\n on: {\n click: _vm.sessionClick\n }\n }, [_vm._v(_vm._s(_vm.$t(\"session.title\")))])])]), _c(\"el-col\", {\n attrs: {\n span: 6\n }\n }, [_c(\"div\", {\n staticClass: \"setting\",\n staticStyle: {\n \"text-align\": \"center\"\n }\n }, [_c(\"span\", {\n class: {\n whiteText: _vm.cutSetting === 0\n },\n on: {\n click: _vm.modelClick\n }\n }, [_vm._v(_vm._s(_vm.$t(\"model.title\")))])])]), _c(\"el-col\", {\n attrs: {\n span: 6\n }\n }, [_c(\"div\", {\n staticClass: \"setting\",\n staticStyle: {\n \"text-align\": \"center\"\n }\n }, [_c(\"span\", {\n class: {\n whiteText: _vm.cutSetting === 2\n },\n on: {\n click: _vm.fineTuningClick\n }\n }, [_vm._v(_vm._s(_vm.$t(\"slightly.title.whole\")))])])]), _c(\"el-col\", {\n attrs: {\n span: 6\n }\n }, [_c(\"div\", {\n staticClass: \"setting\",\n staticStyle: {\n \"text-align\": \"center\"\n }\n }, [_c(\"span\", {\n class: {\n whiteText: _vm.cutSetting === 3\n },\n on: {\n click: _vm.fileClick\n }\n }, [_vm._v(_vm._s(_vm.$t(\"file.title\")))])])])], 1), _c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.cutSetting == 0,\n expression: \"cutSetting == 0\"\n }]\n }, [_c(\"input\", {\n directives: [{\n name: \"model\",\n rawName: \"v-model\",\n value: _vm.modelSearch,\n expression: \"modelSearch\"\n }],\n staticClass: \"inputs\",\n staticStyle: {\n \"margin-top\": \"10px\"\n },\n attrs: {\n placeholder: _vm.$t(\"placeholder.model_name\")\n },\n domProps: {\n value: _vm.modelSearch\n },\n on: {\n input: function ($event) {\n if ($event.target.composing) return;\n _vm.modelSearch = $event.target.value;\n }\n }\n }), _c(\"div\", {\n staticClass: \"s-wrapper\"\n }, _vm._l(_vm.personList, function (personInfo) {\n return _c(\"div\", {\n key: personInfo.id,\n staticClass: \"personList\",\n on: {\n click: function ($event) {\n return _vm.clickPerson(personInfo);\n }\n }\n }, [_c(\"PersonCard\", {\n attrs: {\n personInfo: personInfo,\n pcCurrent: _vm.pcCurrent\n }\n })], 1);\n }), 0)]), _c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.cutSetting == 1,\n expression: \"cutSetting == 1\"\n }]\n }, [_c(\"input\", {\n directives: [{\n name: \"model\",\n rawName: \"v-model\",\n value: _vm.sessionSearch,\n expression: \"sessionSearch\"\n }],\n staticClass: \"inputs\",\n staticStyle: {\n \"margin-top\": \"10px\"\n },\n attrs: {\n placeholder: _vm.$t(\"placeholder.session_name\")\n },\n domProps: {\n value: _vm.sessionSearch\n },\n on: {\n input: function ($event) {\n if ($event.target.composing) return;\n _vm.sessionSearch = $event.target.value;\n }\n }\n }), _c(\"div\", {\n staticClass: \"s-wrapper\"\n }, _vm._l(_vm.sessionList, function (sessionInfo) {\n return _c(\"div\", {\n key: sessionInfo.id,\n on: {\n click: function ($event) {\n return _vm.clickSession(sessionInfo);\n }\n }\n }, [_c(\"Session\", {\n attrs: {\n sessionInfo: sessionInfo,\n pcCurrent: _vm.sessionCurrent\n }\n })], 1);\n }), 0)]), _c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.cutSetting == 2,\n expression: \"cutSetting == 2\"\n }]\n }, [_c(\"input\", {\n directives: [{\n name: \"model\",\n rawName: \"v-model\",\n value: _vm.fineTuningSearch,\n expression: \"fineTuningSearch\"\n }],\n staticClass: \"inputs\",\n staticStyle: {\n \"margin-top\": \"10px\"\n },\n attrs: {\n placeholder: _vm.$t(\"placeholder.slightly_name\")\n },\n domProps: {\n value: _vm.fineTuningSearch\n },\n on: {\n input: function ($event) {\n if ($event.target.composing) return;\n _vm.fineTuningSearch = $event.target.value;\n }\n }\n }), _c(\"div\", {\n staticClass: \"s-wrapper\"\n }, _vm._l(_vm.fineTuningList, function (fineTuningInfo) {\n return _c(\"div\", {\n key: fineTuningInfo.id,\n staticClass: \"personList\",\n on: {\n click: function ($event) {\n return _vm.clickFineTuning(fineTuningInfo);\n }\n }\n }, [_c(\"PersonCard\", {\n attrs: {\n personInfo: fineTuningInfo,\n pcCurrent: _vm.ftCurrent\n }\n })], 1);\n }), 0)]), _c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.cutSetting == 3,\n expression: \"cutSetting == 3\"\n }]\n }, [_c(\"input\", {\n directives: [{\n name: \"model\",\n rawName: \"v-model\",\n value: _vm.fileSearch,\n expression: \"fileSearch\"\n }],\n staticClass: \"inputs\",\n staticStyle: {\n \"margin-top\": \"10px\"\n },\n attrs: {\n placeholder: _vm.$t(\"placeholder.file_name\")\n },\n domProps: {\n value: _vm.fileSearch\n },\n on: {\n input: function ($event) {\n if ($event.target.composing) return;\n _vm.fileSearch = $event.target.value;\n }\n }\n }), _c(\"div\", {\n staticClass: \"s-wrapper\"\n }, _vm._l(_vm.fileList, function (fileInfo, index) {\n return _c(\"div\", {\n key: index,\n staticClass: \"personList\",\n on: {\n click: function ($event) {\n return _vm.clickFile(fileInfo);\n }\n }\n }, [_c(\"File\", {\n attrs: {\n fileInfo: fileInfo,\n pcCurrent: _vm.fiCurrent\n }\n })], 1);\n }), 0)])], 1)]), _c(\"div\", {\n staticClass: \"chatRight\"\n }, [_c(\"div\", {\n staticClass: \"top-left\",\n on: {\n click: _vm.toggleLeft\n }\n }, [_c(\"svg\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: !_vm.showPersonList,\n expression: \"!showPersonList\"\n }],\n staticClass: \"icon\",\n attrs: {\n t: \"1679366341860\",\n viewBox: \"0 0 1024 1024\",\n version: \"1.1\",\n xmlns: \"http://www.w3.org/2000/svg\",\n \"p-id\": \"5764\",\n width: \"30\",\n height: \"30\"\n }\n }, [_c(\"path\", {\n attrs: {\n d: \"M912.8 513.2C912.8 733.1 733.9 912 514 912S115.2 733.1 115.2 513.2 294.1 114.3 514 114.3s398.8 179 398.8 398.9z m-701.5 0c0 166.9 135.8 302.7 302.7 302.7s302.7-135.8 302.7-302.7S680.9 210.5 514 210.5 211.3 346.3 211.3 513.2z\",\n fill: \"#BDD2EF\",\n \"p-id\": \"5765\"\n }\n }), _c(\"path\", {\n attrs: {\n d: \"M626.8 345.9c0 15-5.7 30.1-17.2 41.5L487.1 510l122.6 122.6c22.9 22.9 22.9 60.2 0 83.1-22.9 22.9-60.2 22.9-83.1 0L362.4 551.6c-22.9-22.9-22.9-60.2 0-83.1l164.1-164.1c22.9-22.9 60.2-22.9 83.1 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staticClass: \"item\",\n attrs: {\n effect: \"dark\",\n content: _vm.$t(\"image.count_title\"),\n placement: \"top\"\n }\n }, [_c(\"span\", {\n staticClass: \"demonstration\"\n }, [_vm._v(_vm._s(_vm.$t(\"image.count\")))])]), _c(\"el-slider\", {\n staticClass: \"astrict\",\n attrs: {\n step: 1,\n min: -1,\n max: 10\n },\n model: {\n value: _vm.SettingInfo.n,\n callback: function ($$v) {\n _vm.$set(_vm.SettingInfo, \"n\", $$v);\n },\n expression: \"SettingInfo.n\"\n }\n })], 1)])]), _c(\"el-collapse-transition\", [_c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.SettingStatus == 2,\n expression: \"SettingStatus == 2\"\n }]\n }, [_c(\"div\", {\n staticClass: \"block\"\n }, [_c(\"el-tooltip\", {\n staticClass: \"item\",\n attrs: {\n effect: \"dark\",\n content: _vm.$t(\"audio.to_text_title\"),\n placement: \"top\"\n }\n }, [_c(\"span\", {\n staticClass: \"demonstration\"\n }, [_vm._v(_vm._s(_vm.$t(\"audio.to_text\")))])]), _c(\"el-switch\", {\n staticStyle: {\n \"margin-left\": \"15%\"\n },\n attrs: {\n width: _vm.defaulWidth\n },\n model: {\n value: _vm.SettingInfo.translateEnglish,\n callback: function ($$v) {\n _vm.$set(_vm.SettingInfo, \"translateEnglish\", $$v);\n },\n expression: \"SettingInfo.translateEnglish\"\n }\n })], 1), _c(\"div\", {\n staticClass: \"block\"\n }, [_c(\"el-tooltip\", {\n staticClass: \"item\",\n attrs: {\n effect: \"dark\",\n content: _vm.$t(\"audio.language_title\"),\n placement: \"top\"\n }\n }, [_c(\"span\", {\n staticClass: \"demonstration\"\n }, [_vm._v(_vm._s(_vm.$t(\"audio.language\")))])]), _c(\"div\", [_c(\"el-select\", {\n staticStyle: {\n \"margin-top\": \"10px\"\n },\n attrs: {\n placeholder: \"请选择\"\n },\n model: {\n value: _vm.SettingInfo.language,\n callback: function ($$v) {\n _vm.$set(_vm.SettingInfo, \"language\", $$v);\n },\n expression: \"SettingInfo.language\"\n }\n }, _vm._l(_vm.languages, function (item) {\n return _c(\"el-option\", {\n key: item.value,\n attrs: {\n value: item.value\n }\n });\n }), 1)], 1)], 1), _c(\"div\", {\n staticClass: \"block\"\n }, [_c(\"el-tooltip\", {\n staticClass: \"item\",\n attrs: {\n effect: \"dark\",\n content: _vm.$t(\"audio.temperature_title\"),\n placement: \"top\"\n }\n }, [_c(\"span\", {\n staticClass: \"demonstration\"\n }, [_vm._v(_vm._s(_vm.$t(\"audio.temperature\")))])]), _c(\"el-slider\", {\n staticClass: \"astrict\",\n attrs: {\n step: 0.1,\n min: 0,\n max: 1\n },\n model: {\n value: _vm.SettingInfo.TemperatureAudio,\n callback: function ($$v) {\n _vm.$set(_vm.SettingInfo, \"TemperatureAudio\", $$v);\n },\n expression: \"SettingInfo.TemperatureAudio\"\n }\n })], 1)])]), _c(\"el-collapse-transition\", [_c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.SettingStatus == 3,\n expression: \"SettingStatus == 3\"\n }]\n }, [_c(\"div\", {\n staticClass: \"fineTune boxinput\",\n staticStyle: {\n \"margin-left\": \"0px\",\n \"margin-right\": \"0px\",\n width: \"99%\"\n },\n on: {\n click: _vm.retrieveFine\n }\n }, [_vm._v(\" \" + _vm._s(_vm.$t(\"slightly.retrieveFineTuning\")) + \" \")]), _c(\"div\", {\n staticClass: \"fineTune boxinput\",\n staticStyle: {\n \"margin-left\": \"0px\",\n \"margin-right\": \"0px\",\n width: \"99%\"\n },\n on: {\n click: _vm.cancelFine\n }\n }, [_vm._v(\" \" + _vm._s(_vm.$t(\"slightly.cancelFineTuning\")) + \" \")]), _vm.cancelFineStatus ? _c(\"div\", {\n staticClass: \"fineTune boxinput\",\n staticStyle: {\n \"margin-left\": \"0px\",\n \"margin-right\": \"0px\",\n width: \"99%\"\n },\n on: {\n click: function ($event) {\n return _vm.showOrHidenCancelFine(false);\n }\n }\n }, [_vm._v(\" \" + _vm._s(_vm.$t(\"slightly.hideCanceledFineTuning\")) + \" \")]) : _c(\"div\", {\n staticClass: \"fineTune boxinput\",\n staticStyle: {\n \"margin-left\": \"0px\",\n \"margin-right\": \"0px\",\n width: \"99%\"\n },\n on: {\n click: function ($event) {\n return _vm.showOrHidenCancelFine(true);\n }\n }\n }, [_vm._v(\" \" + _vm._s(_vm.$t(\"slightly.showCanceledFineTuning\")) + \" \")]), _c(\"div\", {\n staticClass: \"fineTune boxinput\",\n staticStyle: {\n \"margin-left\": \"0px\",\n \"margin-right\": \"0px\",\n width: \"99%\"\n },\n on: {\n click: _vm.deleteFine\n }\n }, [_c(\"span\", {\n staticClass: \"iconfont icon-shanchu\",\n staticStyle: {\n color: \"#fff\",\n \"margin-right\": \"10px\"\n }\n }), _vm._v(\" \" + _vm._s(_vm.$t(\"slightly.deleteFineTuningModel\")) + \" \")]), _c(\"div\", {\n staticClass: \"fineTune boxinput\",\n staticStyle: {\n \"margin-left\": \"0px\",\n \"margin-right\": \"0px\",\n width: \"99%\"\n },\n on: {\n click: function ($event) {\n _vm.showFineSetting = !_vm.showFineSetting;\n }\n }\n }, [_vm._v(\" \" + _vm._s(_vm.$t(\"slightly.createFineTuning\")) + \" \")]), _c(\"el-collapse-transition\", [_c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.showFineSetting,\n expression: \"showFineSetting\"\n }]\n }, [_c(\"div\", {\n staticClass: \"block\"\n }, [_c(\"el-tooltip\", {\n staticClass: \"item\",\n attrs: {\n effect: \"dark\",\n content: _vm.$t(\"slightly.fileIDTrainingData\"),\n placement: \"top\"\n }\n }, [_c(\"span\", {\n staticClass: \"demonstration\"\n }, [_vm._v(\"trainingFile\"), _c(\"span\", {\n staticStyle: {\n color: \"red\"\n }\n }, [_vm._v(\"*\")])])]), _c(\"input\", {\n directives: [{\n name: \"model\",\n rawName: \"v-model\",\n value: _vm.SettingInfo.fineTunes.training_file,\n expression: \"SettingInfo.fineTunes.training_file\"\n }],\n staticClass: \"weitiao\",\n attrs: {\n placeholder: _vm.$t(\"placeholder.trainingDataFileID\")\n },\n domProps: {\n value: _vm.SettingInfo.fineTunes.training_file\n },\n on: {\n input: function ($event) {\n if ($event.target.composing) return;\n _vm.$set(_vm.SettingInfo.fineTunes, \"training_file\", $event.target.value);\n }\n }\n })], 1), _c(\"div\", {\n staticClass: \"block\"\n }, [_c(\"el-tooltip\", {\n staticClass: \"item\",\n attrs: {\n effect: \"dark\",\n content: _vm.$t(\"slightly.fileIDValidationData\"),\n placement: \"top\"\n }\n }, [_c(\"span\", {\n staticClass: \"demonstration\"\n }, [_vm._v(\"validationFile\")])]), _c(\"input\", {\n directives: [{\n name: \"model\",\n rawName: \"v-model\",\n value: _vm.SettingInfo.fineTunes.validation_file,\n expression: \"SettingInfo.fineTunes.validation_file\"\n }],\n staticClass: \"weitiao\",\n attrs: {\n placeholder: _vm.$t(\"placeholder.validationDataFileID\")\n },\n domProps: {\n value: _vm.SettingInfo.fineTunes.validation_file\n },\n on: {\n input: function ($event) {\n if ($event.target.composing) return;\n _vm.$set(_vm.SettingInfo.fineTunes, \"validation_file\", $event.target.value);\n }\n }\n })], 1), _c(\"div\", {\n staticClass: \"block\"\n }, [_c(\"el-tooltip\", {\n staticClass: \"item\",\n attrs: {\n effect: \"dark\",\n content: _vm.$t(\"slightly.modelOptions\"),\n placement: \"top\"\n }\n }, [_c(\"span\", {\n staticClass: \"demonstration\"\n }, [_vm._v(\"model\")])]), _c(\"input\", {\n directives: [{\n name: \"model\",\n rawName: \"v-model\",\n value: _vm.SettingInfo.fineTunes.model,\n expression: \"SettingInfo.fineTunes.model\"\n }],\n staticClass: \"weitiao\",\n attrs: {\n placeholder: _vm.$t(\"placeholder.modelName\")\n },\n domProps: {\n value: _vm.SettingInfo.fineTunes.model\n },\n on: {\n input: function ($event) {\n if ($event.target.composing) return;\n _vm.$set(_vm.SettingInfo.fineTunes, \"model\", $event.target.value);\n }\n }\n })], 1), _c(\"div\", {\n staticClass: \"block\"\n }, [_c(\"el-tooltip\", {\n staticClass: \"item\",\n attrs: {\n effect: \"dark\",\n content: _vm.$t(\"slightly.epochs\"),\n placement: \"top\"\n }\n }, [_c(\"span\", {\n staticClass: \"demonstration\"\n }, [_vm._v(\"nEpochs\")])]), _c(\"input\", {\n directives: [{\n name: \"model\",\n rawName: \"v-model\",\n value: _vm.SettingInfo.fineTunes.n_epochs,\n expression: \"SettingInfo.fineTunes.n_epochs\"\n }],\n staticClass: \"weitiao\",\n attrs: {\n type: \"number\",\n placeholder: _vm.$t(\"placeholder.trainingIterations\")\n },\n domProps: {\n value: _vm.SettingInfo.fineTunes.n_epochs\n },\n on: {\n input: function ($event) {\n if ($event.target.composing) return;\n _vm.$set(_vm.SettingInfo.fineTunes, \"n_epochs\", $event.target.value);\n }\n }\n })], 1), _c(\"div\", {\n staticClass: \"block\"\n }, [_c(\"el-tooltip\", {\n staticClass: \"item\",\n attrs: {\n effect: \"dark\",\n content: _vm.$t(\"slightly.batchSize\"),\n placement: \"top\"\n }\n }, [_c(\"span\", {\n staticClass: \"demonstration\"\n }, [_vm._v(\"batchSize\")])]), _c(\"input\", {\n directives: [{\n name: \"model\",\n rawName: \"v-model\",\n value: _vm.SettingInfo.fineTunes.batch_size,\n expression: \"SettingInfo.fineTunes.batch_size\"\n }],\n staticClass: \"weitiao\",\n attrs: {\n type: \"number\",\n placeholder: _vm.$t(\"placeholder.batchSize\")\n },\n domProps: {\n value: _vm.SettingInfo.fineTunes.batch_size\n },\n on: {\n input: function ($event) {\n if ($event.target.composing) return;\n _vm.$set(_vm.SettingInfo.fineTunes, \"batch_size\", $event.target.value);\n }\n }\n })], 1), _c(\"div\", {\n staticClass: \"block\"\n }, [_c(\"el-tooltip\", {\n staticClass: \"item\",\n attrs: {\n effect: \"dark\",\n content: _vm.$t(\"slightly.learningRate\"),\n placement: \"top\"\n }\n }, [_c(\"span\", {\n staticClass: \"demonstration\"\n }, [_vm._v(\"learningRateMultiplier\")])]), _c(\"input\", {\n directives: [{\n name: \"model\",\n rawName: \"v-model\",\n value: _vm.SettingInfo.fineTunes.learning_rate_multiplier,\n expression: \"SettingInfo.fineTunes.learning_rate_multiplier\"\n }],\n staticClass: \"weitiao\",\n attrs: {\n type: \"number\",\n placeholder: _vm.$t(\"placeholder.learningRate\")\n },\n domProps: {\n value: _vm.SettingInfo.fineTunes.learning_rate_multiplier\n },\n on: {\n input: function ($event) {\n if ($event.target.composing) return;\n _vm.$set(_vm.SettingInfo.fineTunes, \"learning_rate_multiplier\", $event.target.value);\n }\n }\n })], 1), _c(\"div\", {\n staticClass: \"block\"\n }, [_c(\"el-tooltip\", {\n staticClass: \"item\",\n attrs: {\n effect: \"dark\",\n content: _vm.$t(\"slightly.fineTunedName\"),\n placement: \"top\"\n }\n }, [_c(\"span\", {\n staticClass: \"demonstration\"\n }, [_vm._v(\"suffix\")])]), _c(\"input\", {\n directives: [{\n name: \"model\",\n rawName: \"v-model\",\n value: _vm.SettingInfo.fineTunes.suffix,\n expression: \"SettingInfo.fineTunes.suffix\"\n }],\n staticClass: \"weitiao\",\n attrs: {\n placeholder: _vm.$t(\"placeholder.ftsuffix\")\n },\n domProps: {\n value: _vm.SettingInfo.fineTunes.suffix\n },\n on: {\n input: function ($event) {\n if ($event.target.composing) return;\n _vm.$set(_vm.SettingInfo.fineTunes, \"suffix\", $event.target.value);\n }\n }\n })], 1), _c(\"div\", {\n staticClass: \"block\"\n }, [_c(\"el-tooltip\", {\n staticClass: \"item\",\n attrs: {\n effect: \"dark\",\n content: _vm.$t(\"slightly.promptAttention\"),\n placement: \"top\"\n }\n }, [_c(\"span\", {\n staticClass: \"demonstration\"\n }, [_vm._v(\"promptLossWeight\")])]), _c(\"el-slider\", {\n staticClass: \"astrict\",\n staticStyle: {\n width: \"95%\"\n },\n attrs: {\n step: 0.01,\n min: 0.01,\n max: 1\n },\n model: {\n value: _vm.SettingInfo.fineTunes.prompt_loss_weight,\n callback: function ($$v) {\n _vm.$set(_vm.SettingInfo.fineTunes, \"prompt_loss_weight\", $$v);\n },\n expression: \"SettingInfo.fineTunes.prompt_loss_weight\"\n }\n })], 1), _c(\"div\", {\n staticClass: \"fineTune boxinput\",\n staticStyle: {\n \"margin-left\": \"0px\",\n \"margin-right\": \"0px\",\n width: \"99%\",\n \"background-color\": \"#409EFF\"\n },\n on: {\n click: _vm.createFine\n }\n }, [_vm._v(\" \" + _vm._s(_vm.$t(\"slightly.create\")) + \" \")])])])], 1)]), _c(\"el-collapse-transition\", [_c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.SettingStatus == 4,\n expression: \"SettingStatus == 4\"\n }]\n }, [_c(\"div\", {\n staticClass: \"fineTune boxinput\",\n staticStyle: {\n \"margin-left\": \"0px\",\n \"margin-right\": \"0px\",\n width: \"99%\"\n },\n on: {\n click: _vm.uploadFile\n }\n }, [_c(\"input\", {\n ref: \"fileInput\",\n staticStyle: {\n display: \"none\"\n },\n attrs: {\n type: \"file\"\n },\n on: {\n change: _vm.onFileChange\n }\n }), _c(\"svg\", {\n staticClass: \"icon\",\n attrs: {\n t: \"1679458974300\",\n viewBox: \"0 0 1024 1024\",\n version: \"1.1\",\n xmlns: \"http://www.w3.org/2000/svg\",\n \"p-id\": \"1590\",\n width: \"30\",\n height: \"30\"\n }\n }, [_c(\"path\", {\n attrs: {\n d: \"M567.466667 634.325333v234.666667a21.333333 21.333333 0 0 1-21.333334 21.333333h-42.666666a21.333333 21.333333 0 0 1-21.333334-21.333333v-234.666667H413.866667a8.533333 8.533333 0 0 1-6.826667-13.653333l110.933333-147.925333a8.533333 8.533333 0 0 1 13.653334 0l110.933333 147.925333a8.533333 8.533333 0 0 1-6.826667 13.653333h-68.266666z\",\n fill: \"#ffffff\",\n \"p-id\": \"1591\"\n }\n }), _c(\"path\", {\n attrs: {\n d: \"M768 725.333333a128 128 0 0 0 38.613333-250.112l-39.850666-12.586666-14.506667-39.253334a256.128 256.128 0 0 0-480.554667 0l-14.464 39.253334-39.850666 12.586666A128.085333 128.085333 0 0 0 256 725.333333a42.666667 42.666667 0 0 1 0 85.333334 213.333333 213.333333 0 0 1-64.341333-416.810667 341.461333 341.461333 0 0 1 640.682666 0A213.418667 213.418667 0 0 1 768 810.666667a42.666667 42.666667 0 0 1 0-85.333334z\",\n fill: \"#ffffff\",\n \"p-id\": \"1592\"\n }\n })]), _vm._v(\" \" + _vm._s(_vm.$t(\"file.upload\")) + \" \")]), _c(\"div\", {\n staticClass: \"fineTune boxinput\",\n staticStyle: {\n \"margin-left\": \"0px\",\n \"margin-right\": \"0px\",\n width: \"99%\"\n },\n on: {\n click: _vm.deleteOnFile\n }\n }, [_c(\"span\", {\n staticClass: \"iconfont icon-shanchu\",\n staticStyle: {\n color: \"#fff\",\n \"margin-right\": \"10px\"\n }\n }), _vm._v(\" \" + _vm._s(_vm.$t(\"file.delete\")) + \" \")]), _c(\"div\", {\n staticClass: \"fineTune boxinput\",\n staticStyle: {\n \"margin-left\": \"0px\",\n \"margin-right\": \"0px\",\n width: \"99%\"\n },\n on: {\n click: _vm.retrieveOnFile\n }\n }, [_vm._v(\" \" + _vm._s(_vm.$t(\"file.retrieve\")) + \" \")]), _c(\"div\", {\n staticClass: \"fineTune boxinput\",\n staticStyle: {\n \"margin-left\": \"0px\",\n \"margin-right\": \"0px\",\n width: \"99%\"\n },\n on: {\n click: _vm.retrieveOnFileContent\n }\n }, [_vm._v(\" \" + _vm._s(_vm.$t(\"file.view\")) + \" \")])])]), _c(\"el-collapse-transition\", [_c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.SettingStatus == 5,\n expression: \"SettingStatus == 5\"\n }]\n }, [_c(\"div\", {\n staticClass: \"session boxinput\",\n on: {\n click: _vm.newSession\n }\n }, [_c(\"svg\", {\n staticClass: \"icon\",\n attrs: {\n t: \"1679215361568\",\n viewBox: \"0 0 1024 1024\",\n version: \"1.1\",\n xmlns: \"http://www.w3.org/2000/svg\",\n \"p-id\": \"3128\",\n width: \"25\",\n height: \"25\"\n }\n }, [_c(\"path\", {\n attrs: {\n d: \"M512.001024 0A512 512 0 0 0 0.001024 512a506.88 506.88 0 0 0 92.16 292.352V972.8a51.2 51.2 0 0 0 51.2 51.2H512.001024a512 512 0 0 0 0-1024z m0 921.6H194.561024v-134.144a51.2 51.2 0 0 0-10.24-30.72A406.016 406.016 0 0 1 102.401024 512a409.6 409.6 0 1 1 409.6 409.6z\",\n fill: \"#ffffff\",\n \"p-id\": \"3129\"\n }\n }), _c(\"path\", {\n attrs: {\n d: \"M716.801024 486.4a51.2 51.2 0 0 0-51.2 51.2 153.6 153.6 0 0 1-307.2 0 51.2 51.2 0 0 0-102.4 0 256 256 0 0 0 512 0 51.2 51.2 0 0 0-51.2-51.2z\",\n fill: \"#ffffff\",\n \"p-id\": \"3130\"\n }\n })]), _vm._v(\" \" + _vm._s(_vm.$t(\"session.create\")) + \" \")]), _c(\"div\", {\n staticClass: \"session boxinput\",\n staticStyle: {\n \"margin-left\": \"0px\",\n \"margin-right\": \"0px\",\n width: \"99%\"\n },\n on: {\n click: _vm.exportObjArrAllToJson\n }\n }, [_c(\"span\", {\n staticClass: \"iconfont icon-daochu\",\n staticStyle: {\n color: \"#fff\",\n \"margin-right\": \"10px\"\n }\n }), _vm._v(\" \" + _vm._s(_vm.$t(\"session.export\")) + \" \")]), _c(\"div\", {\n staticClass: \"session boxinput\",\n on: {\n click: _vm.importFromJsonArrAll\n }\n }, [_c(\"span\", {\n staticClass: \"iconfont icon-daoru\",\n staticStyle: {\n color: \"#fff\",\n \"margin-right\": \"10px\"\n }\n }), _vm._v(\" \" + _vm._s(_vm.$t(\"session.import\")) + \" \"), _c(\"input\", {\n ref: \"onupdateJosnArrAll\",\n staticStyle: {\n display: \"none\"\n },\n attrs: {\n type: \"file\"\n },\n on: {\n change: _vm.handleFileUploadAll\n }\n })]), _c(\"div\", {\n staticClass: \"session boxinput\",\n on: {\n click: _vm.clearAllContext\n }\n }, [_c(\"span\", {\n staticClass: \"iconfont icon-qingchu\",\n staticStyle: {\n color: \"#fff\",\n \"margin-right\": \"10px\"\n }\n }), _vm._v(\" \" + _vm._s(_vm.$t(\"session.clear\")) + \" \")])])]), _c(\"el-collapse-transition\", [_c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.SettingStatus == 6,\n expression: \"SettingStatus == 6\"\n }]\n }, [_c(\"div\", {\n staticClass: \"block\"\n }, [_c(\"input\", {\n directives: [{\n name: \"model\",\n rawName: \"v-model\",\n value: _vm.roleSearch,\n expression: \"roleSearch\"\n }],\n staticClass: \"weitiao\",\n attrs: {\n placeholder: _vm.$t(\"placeholder.role_name\")\n },\n domProps: {\n value: _vm.roleSearch\n },\n on: {\n input: function ($event) {\n if ($event.target.composing) return;\n _vm.roleSearch = $event.target.value;\n }\n }\n })]), _vm._l(_vm.roleList, function (roleInfo) {\n return _c(\"div\", {\n key: roleInfo.act,\n staticClass: \"personList\",\n on: {\n click: function ($event) {\n return _vm.roleClick(roleInfo);\n }\n }\n }, [_c(\"RoleCard\", {\n attrs: {\n roleInfo: roleInfo,\n prCurrent: _vm.prCurrent\n }\n })], 1);\n })], 2)]), _c(\"el-collapse-transition\", [_c(\"div\", {\n directives: [{\n name: \"show\",\n rawName: \"v-show\",\n value: _vm.SettingStatus == 7,\n expression: \"SettingStatus == 7\"\n }]\n }, [_c(\"div\", {\n staticClass: \"session boxinput\",\n staticStyle: {\n \"margin-left\": \"0px\",\n \"margin-right\": \"0px\",\n width: \"99%\"\n },\n on: {\n click: _vm.changeLanguage\n }\n }, [_c(\"span\", {\n staticClass: \"iconfont icon-iconyuanbanben_fanyi\",\n staticStyle: {\n color: \"#fff\",\n \"margin-right\": \"10px\"\n }\n }), _vm._v(\" \" + _vm._s(_vm.$t(\"setting.Language\")) + \" \")])])])], 1)], 1)], 1)]);\n};\nvar staticRenderFns = [function () {\n var _vm = this,\n _c = _vm._self._c;\n return _c(\"div\", {\n staticClass: \"title\",\n staticStyle: {\n \"text-align\": \"center\"\n }\n }, [_c(\"h2\", [_vm._v(\"OpenAI-Manager(科学~)\")])]);\n}];\nrender._withStripped = true;\n\n\n//# sourceURL=webpack://JCM-AI/./src/view/pages/chatHome/index.vue?./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use%5B0%5D!./node_modules/@vue/vue-loader-v15/lib/loaders/templateLoader.js??ruleSet%5B1%5D.rules%5B3%5D!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options");
-
-/***/ }),
-
-/***/ "./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[0]!./node_modules/@vue/vue-loader-v15/lib/loaders/templateLoader.js??ruleSet[1].rules[3]!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./src/view/pages/setting.vue?vue&type=template&id=f89df198&":
-/*!***********************************************************************************************************************************************************************************************************************************************************************************************!*\
- !*** ./node_modules/babel-loader/lib/index.js??clonedRuleSet-40.use[0]!./node_modules/@vue/vue-loader-v15/lib/loaders/templateLoader.js??ruleSet[1].rules[3]!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./src/view/pages/setting.vue?vue&type=template&id=f89df198& ***!
- \***********************************************************************************************************************************************************************************************************************************************************************************************/
-/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {
-
-"use strict";
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-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony export */ __webpack_require__.d(__webpack_exports__, {\n/* harmony export */ \"cancelFineTune\": function() { return /* binding */ cancelFineTune; },\n/* harmony export */ \"createEmbeddings\": function() { return /* binding */ createEmbeddings; },\n/* harmony export */ \"createFineTune\": function() { return /* binding */ createFineTune; },\n/* harmony export */ \"createImage\": function() { return /* binding */ createImage; },\n/* harmony export */ \"createImageEdit\": function() { return /* binding */ createImageEdit; },\n/* harmony export */ \"createImageVariations\": function() { return /* binding */ createImageVariations; },\n/* harmony export */ \"createTranscription\": function() { return /* binding */ createTranscription; },\n/* harmony export */ \"createTranslation\": function() { return /* binding */ createTranslation; },\n/* harmony export */ \"deleteFile\": function() { return /* binding */ deleteFile; },\n/* harmony export */ \"deleteFineTuneModel\": function() { return /* binding */ deleteFineTuneModel; },\n/* harmony export */ \"getChatMsg\": function() { return /* binding */ getChatMsg; },\n/* harmony export */ \"getFilesList\": function() { return /* binding */ getFilesList; },\n/* harmony export */ \"getFineTuneEventsList\": function() { return /* binding */ getFineTuneEventsList; },\n/* harmony export */ \"getFineTunesList\": function() { return /* binding */ getFineTunesList; },\n/* harmony export */ \"getModels\": function() { return /* binding */ getModels; },\n/* harmony export */ \"getMoneyInfo\": function() { return /* binding */ getMoneyInfo; },\n/* harmony export */ \"getRoles\": function() { return /* binding */ getRoles; },\n/* harmony export */ \"retrieveFile\": function() { return /* binding */ retrieveFile; },\n/* harmony export */ \"retrieveFileContent\": function() { return /* binding */ retrieveFileContent; },\n/* harmony export */ \"retrieveFineTune\": function() { return /* binding */ retrieveFineTune; },\n/* harmony export */ \"uploadFile\": function() { return /* binding */ uploadFile; }\n/* harmony export */ });\n/* harmony import */ var core_js_modules_es_array_push_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! core-js/modules/es.array.push.js */ \"./node_modules/core-js/modules/es.array.push.js\");\n/* harmony import */ var core_js_modules_es_array_push_js__WEBPACK_IMPORTED_MODULE_0___default = /*#__PURE__*/__webpack_require__.n(core_js_modules_es_array_push_js__WEBPACK_IMPORTED_MODULE_0__);\n/* harmony import */ var core_js_modules_es_array_unshift_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! core-js/modules/es.array.unshift.js */ \"./node_modules/core-js/modules/es.array.unshift.js\");\n/* harmony import */ var core_js_modules_es_array_unshift_js__WEBPACK_IMPORTED_MODULE_1___default = /*#__PURE__*/__webpack_require__.n(core_js_modules_es_array_unshift_js__WEBPACK_IMPORTED_MODULE_1__);\n/* harmony import */ var _index__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./index */ \"./src/api/index.js\");\n/* harmony import */ var _store_mutation_types__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../store/mutation-types */ \"./src/store/mutation-types.js\");\n/* harmony import */ var _util_util__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! @/util/util */ \"./src/util/util.js\");\n/* provided dependency */ var console = __webpack_require__(/*! ./node_modules/console-browserify/index.js */ \"./node_modules/console-browserify/index.js\");\n\n\n\n\n\nlet axios = _index__WEBPACK_IMPORTED_MODULE_2__[\"default\"].axios;\nlet baseUrl = _index__WEBPACK_IMPORTED_MODULE_2__[\"default\"].baseUrl;\n\n// 根据name查找元素的索引\nfunction findIndexByName(arr, name) {\n for (let i = 0; i < arr.length; i++) {\n if (arr[i].name === name || arr[i] === name) {\n return i;\n }\n }\n return -1; // 没有找到对应的元素\n}\n\nconst desp_model = {\n \"gpt-3.5-turbo\": \"chatgpt v3.5 所基于的模型\",\n \"ada\": \"自然语言模型，OpenAI提供的最快，最便宜的模型，但性能也最差，含有ada字眼的模型都是基于ada训练而来\",\n \"babbage\": \"自然语言模型，性能比ada强，价格比ada贵，规模比ada大，含有babbage字眼的模型都是基于babbage训练而来\",\n \"curie\": \"自然语言模型，性能优于ada，babbage，价钱也更贵，规模更大，含有curie字眼的模型都是基于curie训练而来\",\n \"davinci\": \"自然语言模型，在ada，babbage，curie和davinci中性能最优，规模最大，速度最慢，价钱最贵，含有davinci字眼的模型都是基于davinci训练而来，目前chatgpt基于davinci微调而来\",\n \"whisper-1\": \"强大的语音转换文本的模型\"\n};\nconst other_desps = {\n \"code\": \"的AI代码处理模型\",\n \"similarity\": \"的AI文本相似度计算模型\",\n \"document\": \"的大文档处理模型\",\n \"text\": \"的文本处理模型\",\n \"instruct\": \"的人工指令微调模型\",\n \"if\": \"一个分支\"\n};\nconst desp_keys = Object.keys(desp_model);\nconst other_desp_keys = Object.keys(other_desps);\nfunction produceModelDesc(model) {\n const idx = findIndexByName(desp_keys, model);\n if (idx !== -1) {\n return desp_model[model];\n } else {\n let desc = '';\n for (let i = 0; i < desp_keys.length; i++) {\n const key = desp_keys[i];\n if (model.includes(key)) {\n desc += `基于语言模型${key}`;\n break;\n }\n }\n for (let i = 0; i < other_desp_keys.length; i++) {\n const key = other_desp_keys[i];\n if (model.includes(key)) {\n desc += other_desps[key];\n break;\n }\n }\n if (desc == \"\") {\n desc = model + \"模型\";\n }\n return desc;\n }\n}\n\n// 获取模型列表\nconst getModels = token => {\n return axios({\n method: 'get',\n baseURL: `${baseUrl}/v1/models`,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'application/json'\n }\n }).then(res => {\n const modelsObj = [];\n //获取所有的模型\n const models = [...new Set(res.data.data.map(model => model.id))].sort();\n models.forEach(model => {\n let modelObj = {\n img: \"\",\n name: model,\n detail: produceModelDesc(model),\n lastMsg: produceModelDesc(model),\n id: model,\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_3__.AI_HEAD_IMG_URL,\n showHeadImg: true\n };\n modelsObj.push(modelObj);\n });\n // 将gpt-3.5-turbo置顶\n const idx = findIndexByName(modelsObj, \"gpt-3.5-turbo\");\n if (idx !== -1) {\n const element = modelsObj.splice(idx, 1)[0]; // 将idx元素删除\n modelsObj.unshift(element); // 将idx出的元素至于列表头\n }\n\n return modelsObj;\n });\n};\n// 获取角色列表\nconst getRoles = () => {\n return axios({\n method: 'get',\n baseURL: `user_custom.json`,\n headers: {\n 'Content-Type': 'application/json'\n }\n });\n};\n\n// 根据提示创建图像\nconst createImage = (params, token) => {\n console.log(params);\n return axios({\n method: 'post',\n baseURL: `${baseUrl}/v1/images/generations`,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'application/json'\n },\n data: params\n }).then(res => {\n return res.data.data;\n });\n};\n\n// 根据提示词编辑图像\nconst createImageEdit = (formData, token) => {\n return axios({\n method: 'post',\n baseURL: `${baseUrl}/v1/images/edits`,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'multipart/form-data'\n },\n data: formData\n }).then(res => {\n return res.data.data;\n });\n};\n\n// 根据创建图像变体\nconst createImageVariations = (formData, token) => {\n return axios({\n method: 'post',\n baseURL: `${baseUrl}/v1/images/variations`,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'multipart/form-data'\n },\n data: formData\n }).then(res => {\n return res.data.data;\n });\n};\n\n// 将音频转换为文字\nconst createTranscription = (formData, token) => {\n return axios({\n method: 'post',\n baseURL: `${baseUrl}/v1/audio/transcriptions`,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'multipart/form-data'\n },\n data: formData\n }).then(res => {\n return res.data;\n });\n};\n\n// 将音频翻译成英语\nconst createTranslation = (formData, token) => {\n return axios({\n method: 'post',\n baseURL: `${baseUrl}/v1/audio/translations`,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'multipart/form-data'\n },\n data: formData\n }).then(res => {\n return res.data;\n });\n};\n\n// 创建微调\nconst createFineTune = (formData, token) => {\n return axios({\n method: 'post',\n baseURL: `${baseUrl}/v1/fine-tunes`,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'application/json'\n },\n data: formData\n }).then(res => {\n return res.data;\n }).catch(e => {\n console.log(e);\n });\n};\n\n// 列出微调\nconst getFineTunesList = token => {\n return axios({\n method: 'get',\n baseURL: `${baseUrl}/v1/fine-tunes`,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'application/json'\n }\n }).then(res => {\n console.log(res);\n const fineTunesObjs = [];\n res.data.data.forEach(fineTunes => {\n let fineTunesObj = {\n img: \"\",\n name: fineTunes.fine_tuned_model,\n detail: \"基于\" + fineTunes.model + \"微调创建的模型\",\n lastMsg: \"基于\" + fineTunes.model + \"微调创建的模型\",\n id: fineTunes.fine_tuned_model ? fineTunes.fine_tuned_model : (0,_util_util__WEBPACK_IMPORTED_MODULE_4__.generateUUID)(),\n headImg: _store_mutation_types__WEBPACK_IMPORTED_MODULE_3__.AI_HEAD_IMG_URL,\n showHeadImg: true,\n createTime: fineTunes.created_at,\n fineTunesId: fineTunes.id,\n fineTunesStatus: fineTunes.status\n };\n fineTunesObjs.push(fineTunesObj);\n });\n return fineTunesObjs.sort((a, b) => b.createTime - a.createTime);\n });\n};\n\n// 检索微调信息\nconst retrieveFineTune = (fineTuneId, token) => {\n return axios({\n method: 'get',\n baseURL: `${baseUrl}/v1/fine-tunes/` + fineTuneId,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'application/json'\n }\n }).then(res => {\n return res.data;\n });\n};\n\n// 取消微调\nconst cancelFineTune = (fineTuneId, token) => {\n return axios({\n method: 'post',\n baseURL: `${baseUrl}/v1/fine-tunes/` + fineTuneId + '/cancel',\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'application/json'\n }\n }).then(res => {\n return res.data;\n });\n};\n\n// 获取微调事件列表\nconst getFineTuneEventsList = (fineTuneId, token) => {\n return axios({\n method: 'get',\n baseURL: `${baseUrl}/v1/fine-tunes/` + fineTuneId + '/events',\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'multipart/form-data'\n },\n data: fineTuneId\n }).then(res => {\n return res.data;\n });\n};\n\n// 删除微调模型\nconst deleteFineTuneModel = (model, token) => {\n return axios({\n method: 'delete',\n baseURL: `${baseUrl}/v1/models/` + model,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'application/json'\n }\n }).then(res => {\n return res.data;\n });\n};\n\n//获取文件列表\nconst getFilesList = token => {\n return axios({\n method: 'get',\n baseURL: `${baseUrl}/v1/files`,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'application/json'\n }\n }).then(res => {\n console.log(res);\n const fileObjs = [];\n res.data.data.forEach(file => {\n let fileObj = {\n img: \"\",\n name: file.filename,\n detail: \"文件ID是:\" + file.id + \",文件大小是:\" + (file.bytes / 1024 / 1024).toFixed(2) + \"MB\",\n lastMsg: \"文件ID是:\" + file.id + \",文件大小是:\" + (file.bytes / 1024 / 1024).toFixed(2) + \"MB\",\n id: file.filename,\n createTime: file.created_at,\n fileId: file.id\n };\n fileObjs.push(fileObj);\n });\n return fileObjs.sort((a, b) => b.createTime - a.createTime);\n });\n};\n\n// 删除文件\nconst deleteFile = (file, token) => {\n return axios({\n method: 'delete',\n baseURL: `${baseUrl}/v1/files/` + file,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'application/json'\n }\n }).then(res => {\n return res.data;\n });\n};\n\n// 上传JSONL文件\nconst uploadFile = (formData, token) => {\n return axios({\n method: 'post',\n baseURL: `${baseUrl}/v1/files`,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'multipart/form-data'\n },\n data: formData\n }).then(res => {\n console.log(\"文件上传成功\");\n console.log(res);\n return res.data;\n });\n};\n\n// 检索文件\nconst retrieveFile = (file, token) => {\n return axios({\n method: 'get',\n baseURL: `${baseUrl}/v1/files/` + file,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'application/json'\n }\n }).then(res => {\n return res.data;\n });\n};\n\n// 检索文件内容\nconst retrieveFileContent = (file, token) => {\n\n // return axios({\n // method: 'get',\n // baseURL: `${baseUrl}v1/files/`+file+`/content`,\n // headers: {\n // 'Authorization': 'Bearer ' + token\n // }\n // }).then(response => {\n // const writer = fs.createWriteStream('./file.txt')\n // response.data.pipe(writer)\n // })\n};\n\n// 检索文件内容\nconst createEmbeddings = (params, token) => {\n return axios({\n method: 'post',\n baseURL: `${baseUrl}/v1/embeddings`,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'application/json'\n },\n data: params\n }).then(response => {\n console.log(response);\n return response.data;\n });\n};\n\n// 获取账号余额信息\nconst getMoneyInfo = token => {\n return axios({\n method: 'get',\n baseURL: `${baseUrl}/dashboard/billing/credit_grants`,\n headers: {\n 'Authorization': 'Bearer ' + token,\n 'Content-Type': 'application/json'\n }\n }).then(res => {\n return res.data;\n });\n};\n\n// 获取聊天信息\nconst getChatMsg = params => {\n return axios({\n method: 'post',\n baseURL: `${baseUrl}/friend/chatMsg`,\n data: params\n }).then(res => res.data);\n};\n\n//# sourceURL=webpack://JCM-AI/./src/api/getData.js?");
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-
-"use strict";
-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony import */ var vue__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! vue */ \"./node_modules/vue/dist/vue.runtime.esm.js\");\n/* harmony import */ var vue_i18n__WEBPACK_IMPORTED_MODULE_6__ = __webpack_require__(/*! vue-i18n */ \"./node_modules/vue-i18n/dist/vue-i18n.esm.js\");\n/* harmony import */ var _lang_en__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! @/lang/en */ \"./src/lang/en.js\");\n/* harmony import */ var _lang_zh_CN__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! @/lang/zh-CN */ \"./src/lang/zh-CN.js\");\n/* harmony import */ var element_ui_lib_locale_lang_en__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! element-ui/lib/locale/lang/en */ \"./node_modules/element-ui/lib/locale/lang/en.js\");\n/* harmony import */ var element_ui_lib_locale_lang_zh_CN__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! element-ui/lib/locale/lang/zh-CN */ \"./node_modules/element-ui/lib/locale/lang/zh-CN.js\");\n/* harmony import */ var element_ui_lib_locale__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! element-ui/lib/locale */ \"./node_modules/element-ui/lib/locale/index.js\");\n\n// 引入i18n插件\n\n// 引入语言包\n\n\n// 引入element-ui语言包\n\n\n// 下面不可少的两个配置【参考官网 按需加载里定制 i18n】\n\nelement_ui_lib_locale__WEBPACK_IMPORTED_MODULE_4__[\"default\"].i18n((key, value) => i18n.t(key, value));\nvue__WEBPACK_IMPORTED_MODULE_5__[\"default\"].use(vue_i18n__WEBPACK_IMPORTED_MODULE_6__[\"default\"]);\nconst messages = {\n en: {\n ..._lang_en__WEBPACK_IMPORTED_MODULE_0__[\"default\"],\n ...element_ui_lib_locale_lang_en__WEBPACK_IMPORTED_MODULE_2__[\"default\"] // element-ui语言包\n },\n\n zh: {\n ..._lang_zh_CN__WEBPACK_IMPORTED_MODULE_1__[\"default\"],\n ...element_ui_lib_locale_lang_zh_CN__WEBPACK_IMPORTED_MODULE_3__[\"default\"]\n }\n};\n\n// 配置i18n\nconst i18n = new vue_i18n__WEBPACK_IMPORTED_MODULE_6__[\"default\"]({\n locale: localStorage.getItem(\"lang\") || \"zh\",\n // 从缓存中获取当前的语言类型\n messages\n});\n/* harmony default export */ __webpack_exports__[\"default\"] = (i18n);\n\n//# sourceURL=webpack://JCM-AI/./src/config/i18n.js?");
-
-/***/ }),
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-/***/ "./src/lang/en.js":
-/*!************************!*\
- !*** ./src/lang/en.js ***!
- \************************/
-/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {
-
-"use strict";
-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony default export */ __webpack_exports__[\"default\"] = ({\n placeholder: {\n question: \"Enter your question here~\",\n openai_key: \"Please enter OpenAI KEY\",\n role_name: \"role name\",\n session_name: \"session name\",\n model_name: \"model name\",\n slightly_name: \"fine-tuned model name\",\n file_name: \"file name\",\n suffix: \"Text snippet to add at the end.\",\n stop: \"Token to stop generating text.\",\n response_count: \"Number of Answers Generated\",\n trainingDataFileID: 'ID of training data file',\n validationDataFileID: 'ID of validation data file',\n modelName: 'Model name',\n trainingIterations: 'Number of training iterations',\n batchSize: 'Batch size',\n learningRate: 'Learning rate',\n ftsuffix: 'Suffix'\n },\n session: {\n title: \"session\",\n create: \"create session\",\n export: \"Export the session list\",\n import: \"Import the session list\",\n clear: \"Clear the session list\"\n },\n model: {\n title: \"model\",\n talk: \"chat\",\n online_title: \"online\",\n online: \"Online query after opening\",\n suffix_title: \"suffix\",\n suffix: \"A text snippet to add at the end of the generated text\",\n max_tokens_title: \"Maximum word count\",\n max_tokens: \"Specifies the maximum number of words to generate, which cannot exceed 2048.\",\n temperature_title: \"Randomness（0-2）\",\n temperature: \"Specifies the randomness of the generated text, ranging from 0 to 2, where higher values are more diverse and creative, and lower values are more conservative and deterministic.\",\n top_p_title: \"Reserved word ratio（0-1）\",\n top_p: \"Specifies the proportion of words with the highest probability of being retained at each step, ranging from 0 to 1, similar to temperature, but more flexible and robust.\",\n n_title: \"Result count\",\n n: \"This parameter produces many results\",\n stream_title: \"Stream output\",\n stream: \"Enable streaming output\",\n echo_title: \"Echo words\",\n echo: \"echo prompt word\",\n stop_title: \"Stop token\",\n stop: \"Sets the token at which the model stops generating text\",\n frequency_penalty_title: \"Word repetition（0-1）\",\n frequency_penalty: \"Specify the degree to reduce the probability of repeated words, the range is 0 to 1, the higher the more to avoid repetition.\",\n presence_penalty_title: \"Topic repetition（0-1）\",\n presence_penalty: \"Specify the degree to reduce the occurrence probability of repeated topics, ranging from 0 to 1, the higher means avoiding repetition.\",\n max_results_title: \"Specify the amount of online query data, it is not recommended to be too large.\",\n max_results: \"max_results\"\n },\n slightly: {\n title: {\n whole: \"FT\",\n abbreviation: \"FT\"\n },\n retrieveFineTuning: 'Retrieve fine-tuning',\n cancelFineTuning: 'Cancel fine-tuning',\n hideCanceledFineTuning: 'Hide canceled fine-tuning',\n showCanceledFineTuning: 'Show canceled fine-tuning',\n deleteFineTuningModel: 'Delete fine-tuning model',\n createFineTuning: 'Create fine-tuning',\n create: 'Create',\n fileIDTrainingData: 'File ID containing training data',\n fileIDValidationData: 'File ID containing validation data',\n modelOptions: 'You can choose the model name from ada, babbage, curie, davinci, or the name of your own fine-tuned model.',\n epochs: 'By adjusting the number of n_epochs, you can control the training period and number of training times of the model, thereby affecting the performance and convergence speed of the model.',\n batchSize: 'A larger batch_size can speed up the training speed, stability, and generalization ability of the model, while a smaller batch_size can reduce memory and computing resource usage, and improve the performance of the model on test data.',\n fineTunedName: 'A string of up to 40 characters that will be added to the fine-tuned model name.',\n learningRate: 'You can control how many times the learning rate used during fine-tuning training is compared to the learning rate used by the pre-trained model. For example, if you set it to 2.0, the learning rate used during fine-tuning training will be twice that of the pre-trained model.',\n promptAttention: 'Setting a higher value will make the model pay more attention to prompts when generating text, while setting a lower value will make the model focus more on its own language model and generate more free text.'\n },\n file: {\n title: \"file\",\n upload: \"Upload files\",\n delete: \"Delete Files\",\n retrieve: \"Retrieve files\",\n view: \"View file content\"\n },\n image: {\n title: \"image\",\n production: \"Production picture\",\n production_title: \"After opening, the content sent by the chat is information describing the picture\",\n change: \"Change picture\",\n change_title: \"After opening, upload the picture first, and then enter the prompt words to modify.\",\n size: \"Size\",\n size_title: \"The size of the image.\",\n count: \"Quantity\",\n count_title: \"The number of generated images.\"\n },\n audio: {\n title: \"audio\",\n to_text_title: \"Speech to Text\",\n to_text: \"Speech to Text\",\n language_title: \"Translate speech or audio files from one or more source languages to a target language\",\n language: \"Language\",\n temperature_title: \"Specify the randomness of speech recognition, ranging from 0 to 1. Higher values indicate more diversity and creativity, while lower values indicate more conservatism and certainty.\",\n temperature: \"Randomness（0-1）\"\n },\n role: {\n title: \"role\"\n },\n setting: {\n title: \"settings\",\n Language: \"Chinese Language\"\n },\n file_card: {\n unknown: \"unknown\"\n },\n person_card: {\n train: \"training...\",\n cancel: \"Cancelled\"\n },\n util_js: {\n select: \"Please select an image to upload!\",\n path: \"The path is incorrect!\",\n notallowed: \"This file type is not allowed to be uploaded. please upload \",\n type: \" A file of type, the current file type is\"\n },\n message: {\n start_recording: \"Start recording~\",\n fail_audio: \"Failed to get audio stream~\",\n end_recording: \"End recording~\",\n edit_picture: \"Edit picture mode: Please upload the picture in the upper right corner of the chat window first, and then send the modified content~\",\n msg_empty: \"Message cannot be empty~\",\n model_del: \"The model has been deleted or canceled...\",\n valid_png: \"Please upload a valid PNG file~\",\n less_4M: \"Please upload a file smaller than 4MB~\",\n upload_complete: \"Image upload completed, please give me a prompt to edit~\",\n get_model_fail: \"Failed to get model list~~\",\n valid_json: \"Please upload a valid JSON file~~\",\n only_file: \"Can only search for files~\",\n fail_file: \"Failed to search for files~\",\n openai_free: \"In order to reduce misuse, OpenAI free accounts cannot download fine-tuned training files~\",\n only_del_file: \"Can only delete files~\",\n del_file_succ: \"Congratulations on successfully deleting the file~\",\n del_fail: \"Failed to delete the file~\",\n create_succ: \"Congratulations on successfully creating fine-tuning~\",\n create_fail: \"Failed to create fine-tuning...\",\n only_del_model: \"Can only delete the model in fine-tuning~\",\n del_model_succ: \"Congratulations on successfully deleting the fine-tuned model~\",\n del_fail_ing: \"Failed to delete the fine-tuned model. The model is being trained or has been cancelled midway\",\n only_cancel: \"Can only cancel fine-tuned models in training~\",\n cancel_succ: \"Successfully canceled this model~\",\n cancel_fail: \"Failed to cancel the fine-tuned model~\",\n only_model: \"Can only search for fine-tuned models~\",\n verify_model_fail: \"Failed to search for fine-tuned models~\",\n get_files_fail: \"Failed to get file list~\",\n get_roles_fail: \"Failed to get role list~\"\n },\n index: {\n detail: \"The model behind ChatGPT v3.5\",\n lastMsg: \"The model behind ChatGPT v3.5\",\n up_file_id: \"The file has been uploaded successfully, and the file ID is\",\n copy: \", and it has been copied for you~\",\n file_id: \"`File ID:`\",\n file_name: \"`File Name:`\",\n file_size: \"`File Size:`\",\n obj: \"`Object:`\",\n status: \"`Status:`\",\n status_des: \"`Status Description:`\",\n target: \"`Target:`\",\n file_time: \"`File Creation Time:`\",\n task_id: \"`Fine-tuning Task ID:`\",\n task_type: \"`Task Type:`\",\n model_type: \"`Model Type:`\",\n task_time: \"`Fine-tuning Task Creation Time:`\",\n task_list: \"`Fine-tuning Event List`\\n\",\n obj_log_info_time: \"| Object | Log Level | Information | Creation Time |\\n\",\n model_id: \"\\n`Fine-tuned Model ID:`\",\n args: \"\\n\\n`Fine-tuning Arguments:`\\n\",\n item_setting: \"| Property | Value Set |\\n\",\n user_group: \"\\n`User Group:`\",\n results_null: \"\\n\\n`Training Results File List: None`\\n\\n\",\n results: \"\\n\\n`Training Results File List:`\\n\\n\",\n table_head: \"| ID | File Name | File Size | Object | Status | \\n\",\n statu: \"\\n`Status:`\",\n files_null: \"\\n\\n`Training File List: None`\\n\\n\",\n files: \"\\n\\n`Training File List:`\\n\\n\",\n verifys_null: \"\\n\\n`Verification File List: None`\\n\\n\",\n verifys: \"\\n\\n`Verification File List:`\\n\\n\",\n last_time: \"\\n`Last Update Timestamp:`\"\n }\n});\n\n//# sourceURL=webpack://JCM-AI/./src/lang/en.js?");
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-/***/ }),
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-/***/ "./src/lang/zh-CN.js":
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-/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {
-
-"use strict";
-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony default export */ __webpack_exports__[\"default\"] = ({\n placeholder: {\n question: \"在此输入您的问题~\",\n openai_key: \"请输入OpenAI KEY\",\n role_name: \"角色名称\",\n session_name: \"会话名称\",\n model_name: \"模型名称\",\n slightly_name: \"微调模型名称\",\n file_name: \"文件名称\",\n suffix: \"末尾添加的文本片段\",\n stop: \"停止生成文本的令牌\",\n response_count: \"生成的答案次数\",\n trainingDataFileID: '训练数据的文件ID',\n validationDataFileID: '验证数据文件ID',\n modelName: '模型名称',\n trainingIterations: '训练次数',\n batchSize: '每批数据的大小',\n learningRate: '学习率',\n ftsuffix: '后缀'\n },\n session: {\n title: \"会话\",\n create: \"创建会话\",\n export: \"导出会话列表\",\n import: \"导入会话列表\",\n clear: \"清除会话列表\"\n },\n model: {\n title: \"模型\",\n talk: \"对话\",\n online_title: \"联网\",\n online: \"打开之后联网查询\",\n suffix_title: \"后缀\",\n suffix: \"在生成文本末尾添加的文本片段\",\n max_tokens_title: \"最大单词数\",\n max_tokens: \"指定要生成的最大单词数，不能超过2048。\",\n temperature_title: \"随机度（0-2）\",\n temperature: \"指定生成文本的随机性，范围是0到2，越高表示越多样化和创造性，越低表示越保守和确定性。\",\n top_p_title: \"保留词比例（0-1）\",\n top_p: \"指定在每个步骤中保留概率最高的单词的比例，范围是0到1，与temperature类似，但更加灵活和稳健。\",\n n_title: \"结果规模\",\n n: \"此参数会生成许多结果\",\n stream_title: \"流式输出\",\n stream: \"开启流式输出\",\n echo_title: \"回显词\",\n echo: \"回显提示词\",\n stop_title: \"停止令牌\",\n stop: \"设置模型停止生成文本的令牌\",\n frequency_penalty_title: \"单词重复度（0-1）\",\n frequency_penalty: \"指定降低重复单词出现概率的程度，范围是0到1，越高表示越避免重复。\",\n presence_penalty_title: \"话题重复度（0-1）\",\n presence_penalty: \"指定降低重复话题出现概率的程度，范围是0到1，越高表示越避免重复。\",\n max_results_title: \"指定联网查询数据的数量，不建议太大。\",\n max_results: \"查询规模\"\n },\n slightly: {\n title: {\n whole: \"微调\",\n abbreviation: \"微调\"\n },\n retrieveFineTuning: '检索微调',\n cancelFineTuning: '取消微调',\n hideCanceledFineTuning: '隐藏已取消的微调',\n showCanceledFineTuning: '显示已取消的微调',\n deleteFineTuningModel: '删除微调模型',\n createFineTuning: '创建微调',\n create: '创建',\n fileIDTrainingData: '包含训练数据的文件ID',\n fileIDValidationData: '包含验证数据的文件ID',\n modelOptions: '您可以选择ada、babbage、curie、davinci或者是你自己通过微调训练的模型名称',\n epochs: '通过调整n_epochs的数量，可以控制模型的训练时期和训练次数，从而影响模型的性能和收敛速度',\n batchSize: '较大的 batch_size 可以加快模型的训练速度、模型的稳定性和泛化能力，较小的 batch_size 可以减少内存和计算资源的使用、提高模型在测试数据上的性能',\n fineTunedName: '最多 40 个字符的字符串，将添加到微调的模型名称中。',\n learningRate: '可以控制微调训练期间使用的学习率是预训练模型使用的学习率的多少倍。例如，如果您设置为2.0，则微调训练期间使用的学习率将是预训练模型使用的学习率的两倍。',\n promptAttention: '设置较高的值，那么模型在生成文本时会更加注重提示，设置较低的值模型则会更加注重自己的语言模型，生成更自由的文本'\n },\n file: {\n title: \"文件\",\n upload: \"上传文件\",\n delete: \"删除文件\",\n retrieve: \"检索文件\",\n view: \"查看文件内容\"\n },\n image: {\n title: \"图片\",\n production: \"产图模式\",\n production_title: \"打开之后聊天发送的内容为描述图片的信息\",\n change: \"改图模式\",\n change_title: \"打开之后先上传图片，然后再输入提示词进行修改。\",\n size: \"图片大小\",\n size_title: \"生成图片的大小\",\n count: \"图片数量\",\n count_title: \"生成图片的数量\"\n },\n audio: {\n title: \"音频\",\n to_text_title: \"语音转文字\",\n to_text: \"语音转文字\",\n language_title: \"将一个或多个来源语言的语音或音频文件翻译成目标语言\",\n language: \"语言\",\n temperature_title: \"指定语音识别的随机性，范围是0到1，越高表示越多样化和创造性，越低表示越保守和确定性。\",\n temperature: \"随机度（0-1）\"\n },\n role: {\n title: \"角色\"\n },\n setting: {\n title: \"设置\",\n Language: \"英文语言\"\n },\n file_card: {\n unknown: \"未知\"\n },\n person_card: {\n train: \"正在训练...\",\n cancel: \"已取消\"\n },\n util_js: {\n select: \"请选择要上传的图片!\",\n path: \"路径不正确!\",\n notallowed: \"该文件类型不允许上传。请上传 \",\n type: \" 类型的文件，当前文件类型为\"\n },\n message: {\n start_recording: \"开始录音咯~\",\n fail_audio: \"获取音频流失败啦~\",\n end_recording: \"结束录音咯~\",\n edit_picture: \"编辑图片模式：请您聊天窗口右上角先上传图片，再发送修改的内容~\",\n msg_empty: \"消息不能为空哦~\",\n model_del: \"模型已被删除或已取消...\",\n valid_png: \"请上传一个有效的PNG文件~\",\n less_4M: \"请上传一个小于4MB的文件~\",\n upload_complete: \"图片上传完成啦，请给我提示进行编辑~\",\n get_model_fail: \"获取模型列表失败哦~~\",\n valid_json: \"请上传一个有效的JSON文件~~\",\n only_file: \"只能检索文件哦~\",\n fail_file: \"文件检索失败了~\",\n openai_free: \"OpenAI为了减少滥用，免费帐户将无法下载微调训练的文件~\",\n only_del_file: \"只能删除文件哦~\",\n del_file_succ: \"恭喜您删除成功~\",\n del_fail: \"文件删除失败了~\",\n create_succ: \"恭喜您微调创建成功~\",\n create_fail: \"微调创建失败了...\",\n only_del_model: \"只能删除微调中的模型哦~\",\n del_model_succ: \"恭喜您微调模型删除成功~\",\n del_fail_ing: \"微调模型删除失败了,模型正在训练中或者中途已取消\",\n only_cancel: \"只能取消进行训练中的微调模型哦~\",\n cancel_succ: \"成功取消此模型~\",\n cancel_fail: \"取消微调模型失败~\",\n only_model: \"只能检索的微调模型哦~\",\n verify_model_fail: \"检索微调模型失败~\",\n get_files_fail: \"获取文件列表失败哦~\",\n get_roles_fail: \"获取角色列表失败哦~\"\n },\n index: {\n detail: \"chatgpt v3.5 所基于的模型\",\n lastMsg: \"chatgpt v3.5 所基于的模型\",\n up_file_id: \"文件已上传成功,文件ID是\",\n copy: \",已经帮您复制啦~\",\n file_id: \"`文件ID:`\",\n file_name: \"`文件名称:`\",\n file_size: \"`文件大小:`\",\n obj: \"`对象:`\",\n status: \"`状态:`\",\n status_des: \"`状态描述`\",\n target: \"`目的` \",\n file_time: \"`文件创建时间`\",\n task_id: \"`微调任务ID:`\",\n task_type: \"`任务类型:`\",\n model_type: \"`模型的类型:`\",\n task_time: \"`微调任务的创建时间:`\",\n task_list: \"`微调的事件列表` \\n\",\n obj_log_info_time: \"| 对象 | 日志级别 | 信息 | 创建时间 |\\n\",\n model_id: \"\\n `微调的模型ID:`\",\n args: \"\\n\\n `微调使用的参数:` \\n\",\n item_setting: \"| 属性 | 设置的值 | \\n\",\n user_group: \"\\n`用户所属组:`\",\n results_null: \"\\n\\n`训练结果文件列表:没有`\\n\\n\",\n results: \"\\n\\n`训练结果文件列表:`\\n\\n\",\n table_head: \"| ID | 文件名称 | 文件大小 | 对象 | 状态 | \\n\",\n statu: \"\\n`状态:`\",\n files_null: \"\\n\\n`训练的文件列表:没有`\\n\\n\",\n files: \"\\n\\n`训练的文件列表:`\\n\\n\",\n verifys_null: \"\\n\\n`验证的文件列表:没有`\\n\\n\",\n verifys: \"\\n\\n`验证的文件列表:`\\n\\n\",\n last_time: \"\\n`最后更新时间戳:`\"\n }\n});\n\n//# sourceURL=webpack://JCM-AI/./src/lang/zh-CN.js?");
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-/***/ }),
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-/***/ "./src/main.js":
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-/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {
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text-align: center;\\n height: 50px;\\n background-color: rgb(66, 70, 86);\\n border-radius: 15px;\\n border: 1px solid rgb(80, 85, 103);\\n position: relative;\\n cursor: pointer;\\n}\\n.icon[data-v-c6884a34] {\\n margin-right: 10px;\\n vertical-align: middle;\\n}\\n.send[data-v-c6884a34] {\\n display: flex;\\n align-items: center;\\n justify-content: center;\\n text-align: center;\\n background-color: rgb(66, 70, 86);\\n border: 0;\\n transition: 0.3s;\\n box-shadow: 0px 0px 5px 0px rgb(84, 89, 110);\\n}\\n.send[data-v-c6884a34]:hover {\\n box-shadow: 0px 0px 10px 0px rgb(91, 219, 239);\\n}\\n.weitiao[data-v-c6884a34] {\\n margin-top: 10px;\\n width: 100%;\\n margin-left: 0px;\\n margin-right: 0px;\\n height: 50px;\\n background-color: rgb(66, 70, 86);\\n border-radius: 15px;\\n border: 2px solid rgb(34, 135, 225);\\n padding: 10px;\\n box-sizing: border-box;\\n transition: 0.2s;\\n font-size: 20px;\\n color: #fff;\\n font-weight: 100;\\n}\\n.weitiao[data-v-c6884a34]:focus {\\n outline: none;\\n}\\n.fineTune[data-v-c6884a34] {\\n display: flex;\\n align-items: center;\\n justify-content: center;\\n text-align: center;\\n background-color: rgb(66, 70, 86);\\n border: 0;\\n transition: 0.3s;\\n box-shadow: 0px 0px 5px 0px rgb(84, 89, 110);\\n}\\n.fineTune[data-v-c6884a34]:hover {\\n box-shadow: 0px 0px 10px 0px rgb(29, 144, 245);\\n}\\n.session[data-v-c6884a34] {\\n display: flex;\\n align-items: center;\\n justify-content: center;\\n text-align: center;\\n background-color: rgb(66, 70, 86);\\n border: 0;\\n transition: 0.3s;\\n box-shadow: 0px 0px 5px 0px rgb(84, 89, 110);\\n margin-left: 0px;\\n margin-right: 0px;\\n width: 99%;\\n}\\n.session[data-v-c6884a34]:hover {\\n box-shadow: 0px 0px 10px 0px rgb(29, 144, 245);\\n}\\n.inputs[data-v-c6884a34] {\\n width: 65%;\\n height: 50px;\\n background-color: rgb(66, 70, 86);\\n border-radius: 15px;\\n border: 2px solid rgb(34, 135, 225);\\n padding: 10px;\\n box-sizing: border-box;\\n transition: 0.2s;\\n font-size: 20px;\\n color: #fff;\\n font-weight: 100;\\n margin: 0 20px;\\n}\\n.inputs[data-v-c6884a34]:focus {\\n outline: none;\\n}\\n.whiteText[data-v-c6884a34] {\\n color: #fff;\\n}\\n[data-v-c6884a34] .el-input__inner {\\n background-color: transparent;\\n color: #409EFF;\\n}\\n.setting[data-v-c6884a34] {\\n margin-left: 0px;\\n padding-left: 10px;\\n color: rgb(176, 178, 189);\\n}\\n.setting.active[data-v-c6884a34] {\\n color: #fff;\\n}\\n.setting[data-v-c6884a34]:hover {\\n cursor: pointer;\\n}\\n#jianbian[data-v-c6884a34] {\\n background-color: rgb(39, 42, 55);\\n border-color: #409EFF;\\n color: #fff;\\n border-width: 0px;\\n}\\n.astrict[data-v-c6884a34] {\\n width: 90%;\\n}\\n.settingButton[data-v-c6884a34] {\\n width: 99%;\\n}\\n.block[data-v-c6884a34] {\\n margin-top: 5%;\\n}\\n.block .demonstration[data-v-c6884a34] {\\n color: aliceblue;\\n text-align: center;\\n}\\n.inputs[data-v-c6884a34] {\\n width: 90%;\\n height: 50px;\\n background-color: rgb(66, 70, 86);\\n border-radius: 15px;\\n border: 2px solid rgb(34, 135, 225);\\n padding: 10px;\\n box-sizing: border-box;\\n transition: 0.2s;\\n font-size: 20px;\\n color: #fff;\\n font-weight: 100;\\n margin: 0 20px;\\n}\\n.inputs[data-v-c6884a34]:focus {\\n outline: none;\\n}\\n.chatHome[data-v-c6884a34] {\\n display: flex;\\n}\\n.chatHome .chatLeft[data-v-c6884a34] {\\n width: 17%;\\n}\\n.chatHome .chatLeft .title[data-v-c6884a34] {\\n color: #fff;\\n padding-left: 10px;\\n}\\n.chatHome .chatLeft .online-person .onlin-text[data-v-c6884a34] {\\n margin-left: 20%;\\n padding-left: 10px;\\n color: rgb(176, 178, 189);\\n}\\n.chatHome .chatLeft .online-person .s-wrapper[data-v-c6884a34] {\\n padding-left: 10px;\\n height: 70vh;\\n margin-top: 10px;\\n overflow: hidden;\\n overflow-y: scroll;\\n box-sizing: border-box;\\n}\\n.chatHome .chatLeft .online-person .s-wrapper[data-v-c6884a34]::-webkit-scrollbar {\\n width: 0;\\n /* Safari,Chrome 隐藏滚动条 */\\n height: 0;\\n /* Safari,Chrome 隐藏滚动条 */\\n display: none;\\n /* 移动端、pad 上Safari，Chrome，隐藏滚动条 */\\n}\\n.chatHome .chatRight[data-v-c6884a34] {\\n flex: 1;\\n padding-right: 30px;\\n}\\n.chatHome .chatRight .showIcon[data-v-c6884a34] {\\n position: absolute;\\n top: calc(50% - 150px);\\n /*垂直居中 */\\n left: calc(50% - 50px);\\n /*水平居中 */\\n}\\n.chatHome .chatRight .showIcon .icon-snapchat[data-v-c6884a34] {\\n width: 300px;\\n height: 300px;\\n font-size: 300px;\\n}\", \"\"]);\n// Exports\n/* harmony default export */ __webpack_exports__[\"default\"] = (___CSS_LOADER_EXPORT___);\n\n\n//# sourceURL=webpack://JCM-AI/./src/view/pages/chatHome/index.vue?./node_modules/css-loader/dist/cjs.js??clonedRuleSet-22.use%5B1%5D!./node_modules/@vue/vue-loader-v15/lib/loaders/stylePostLoader.js!./node_modules/postcss-loader/dist/cjs.js??clonedRuleSet-22.use%5B2%5D!./node_modules/sass-loader/dist/cjs.js??clonedRuleSet-22.use%5B3%5D!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options");
-
-/***/ }),
-
-/***/ "./node_modules/css-loader/dist/cjs.js??clonedRuleSet-22.use[1]!./node_modules/@vue/vue-loader-v15/lib/loaders/stylePostLoader.js!./node_modules/postcss-loader/dist/cjs.js??clonedRuleSet-22.use[2]!./src/assets/font/iconfont.css":
-/*!******************************************************************************************************************************************************************************************************************************************!*\
- !*** ./node_modules/css-loader/dist/cjs.js??clonedRuleSet-22.use[1]!./node_modules/@vue/vue-loader-v15/lib/loaders/stylePostLoader.js!./node_modules/postcss-loader/dist/cjs.js??clonedRuleSet-22.use[2]!./src/assets/font/iconfont.css ***!
- \******************************************************************************************************************************************************************************************************************************************/
-/***/ (function(module, __webpack_exports__, __webpack_require__) {
-
-"use strict";
-eval("__webpack_require__.r(__webpack_exports__);\n/* harmony import */ var _node_modules_css_loader_dist_runtime_noSourceMaps_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../../node_modules/css-loader/dist/runtime/noSourceMaps.js */ \"./node_modules/css-loader/dist/runtime/noSourceMaps.js\");\n/* harmony import */ var _node_modules_css_loader_dist_runtime_noSourceMaps_js__WEBPACK_IMPORTED_MODULE_0___default = /*#__PURE__*/__webpack_require__.n(_node_modules_css_loader_dist_runtime_noSourceMaps_js__WEBPACK_IMPORTED_MODULE_0__);\n/* harmony import */ var _node_modules_css_loader_dist_runtime_api_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../../node_modules/css-loader/dist/runtime/api.js */ \"./node_modules/css-loader/dist/runtime/api.js\");\n/* harmony import */ var _node_modules_css_loader_dist_runtime_api_js__WEBPACK_IMPORTED_MODULE_1___default = /*#__PURE__*/__webpack_require__.n(_node_modules_css_loader_dist_runtime_api_js__WEBPACK_IMPORTED_MODULE_1__);\n/* harmony import */ var _node_modules_css_loader_dist_runtime_getUrl_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../../node_modules/css-loader/dist/runtime/getUrl.js */ \"./node_modules/css-loader/dist/runtime/getUrl.js\");\n/* harmony import */ var _node_modules_css_loader_dist_runtime_getUrl_js__WEBPACK_IMPORTED_MODULE_2___default = /*#__PURE__*/__webpack_require__.n(_node_modules_css_loader_dist_runtime_getUrl_js__WEBPACK_IMPORTED_MODULE_2__);\n// Imports\n\n\n\nvar ___CSS_LOADER_URL_IMPORT_0___ = new URL(/* asset import */ __webpack_require__(/*! iconfont.woff2?t=1681088355288 */ \"./src/assets/font/iconfont.woff2?t=1681088355288\"), __webpack_require__.b);\nvar ___CSS_LOADER_URL_IMPORT_1___ = new URL(/* asset import */ __webpack_require__(/*! iconfont.woff?t=1681088355288 */ \"./src/assets/font/iconfont.woff?t=1681088355288\"), __webpack_require__.b);\nvar ___CSS_LOADER_URL_IMPORT_2___ = new URL(/* asset import */ __webpack_require__(/*! iconfont.ttf?t=1681088355288 */ \"./src/assets/font/iconfont.ttf?t=1681088355288\"), __webpack_require__.b);\nvar ___CSS_LOADER_EXPORT___ = _node_modules_css_loader_dist_runtime_api_js__WEBPACK_IMPORTED_MODULE_1___default()((_node_modules_css_loader_dist_runtime_noSourceMaps_js__WEBPACK_IMPORTED_MODULE_0___default()));\nvar ___CSS_LOADER_URL_REPLACEMENT_0___ = _node_modules_css_loader_dist_runtime_getUrl_js__WEBPACK_IMPORTED_MODULE_2___default()(___CSS_LOADER_URL_IMPORT_0___);\nvar ___CSS_LOADER_URL_REPLACEMENT_1___ = _node_modules_css_loader_dist_runtime_getUrl_js__WEBPACK_IMPORTED_MODULE_2___default()(___CSS_LOADER_URL_IMPORT_1___);\nvar ___CSS_LOADER_URL_REPLACEMENT_2___ = _node_modules_css_loader_dist_runtime_getUrl_js__WEBPACK_IMPORTED_MODULE_2___default()(___CSS_LOADER_URL_IMPORT_2___);\n// Module\n___CSS_LOADER_EXPORT___.push([module.id, \"@font-face {\\n font-family: \\\"iconfont\\\"; /* Project id 3996937 */\\n src: url(\" + ___CSS_LOADER_URL_REPLACEMENT_0___ + \") format('woff2'),\\n url(\" + ___CSS_LOADER_URL_REPLACEMENT_1___ + \") format('woff'),\\n url(\" + ___CSS_LOADER_URL_REPLACEMENT_2___ + \") format('truetype');\\n}\\n.iconfont {\\n font-family: \\\"iconfont\\\" !important;\\n font-size: 16px;\\n font-style: normal;\\n -webkit-font-smoothing: antialiased;\\n -moz-osx-font-smoothing: grayscale;\\n}\\n.icon-shanchu:before {\\n content: \\\"\\\\e630\\\";\\n}\\n.icon-iconyuanbanben_fanyi:before {\\n content: \\\"\\\\e6b6\\\";\\n}\\n.icon-wenben:before {\\n content: \\\"\\\\e600\\\";\\n}\\n.icon-luyin:before {\\n content: \\\"\\\\e740\\\";\\n}\\n.icon-tupian:before {\\n content: \\\"\\\\e623\\\";\\n}\\n.icon-luyin1:before {\\n content: \\\"\\\\e602\\\";\\n}\\n.icon-shezhi:before {\\n content: \\\"\\\\e8b8\\\";\\n}\\n.icon-qingchu:before {\\n content: \\\"\\\\e609\\\";\\n}\\n.icon-xinxi:before {\\n content: \\\"\\\\e624\\\";\\n}\\n.icon-weidenglu:before {\\n content: \\\"\\\\e6a3\\\";\\n}\\n.icon-daoru:before {\\n content: \\\"\\\\e645\\\";\\n}\\n.icon-daochu:before {\\n content: \\\"\\\\e646\\\";\\n}\\n\\n\", \"\"]);\n// Exports\n/* harmony default export */ __webpack_exports__[\"default\"] = (___CSS_LOADER_EXPORT___);\n\n\n//# sourceURL=webpack://JCM-AI/./src/assets/font/iconfont.css?./node_modules/css-loader/dist/cjs.js??clonedRuleSet-22.use%5B1%5D!./node_modules/@vue/vue-loader-v15/lib/loaders/stylePostLoader.js!./node_modules/postcss-loader/dist/cjs.js??clonedRuleSet-22.use%5B2%5D");
-
-/***/ }),
-
-/***/ "./node_modules/vue-style-loader/index.js??clonedRuleSet-12.use[0]!./node_modules/css-loader/dist/cjs.js??clonedRuleSet-12.use[1]!./node_modules/@vue/vue-loader-v15/lib/loaders/stylePostLoader.js!./node_modules/postcss-loader/dist/cjs.js??clonedRuleSet-12.use[2]!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./src/view/pages/setting.vue?vue&type=style&index=0&id=f89df198&lang=css&":
-/*!***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************!*\
- !*** ./node_modules/vue-style-loader/index.js??clonedRuleSet-12.use[0]!./node_modules/css-loader/dist/cjs.js??clonedRuleSet-12.use[1]!./node_modules/@vue/vue-loader-v15/lib/loaders/stylePostLoader.js!./node_modules/postcss-loader/dist/cjs.js??clonedRuleSet-12.use[2]!./node_modules/@vue/vue-loader-v15/lib/index.js??vue-loader-options!./src/view/pages/setting.vue?vue&type=style&index=0&id=f89df198&lang=css& ***!
- \***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************/
-/***/ (function(module, __unused_webpack_exports, __webpack_require__) {
-
-eval("// style-loader: Adds some css to the DOM by adding a ', unsafe_allow_html=True)
-
-
-def remote_css(css_url):
- st.markdown(f'', unsafe_allow_html=True)
-
-"""Basic utilities module"""
-import requests
-import csv
-import re
-
-
-def request_ct(url):
- """Performs a get request that provides a (somewhat) useful error message."""
- try:
- response = requests.get(url)
- except ImportError:
- raise ImportError(
- "Couldn't retrieve the data, check your search expression or try again later."
- )
- else:
- return response
-
-
-def json_handler(url):
- """Returns request in JSON (dict) format"""
- return request_ct(url).json()
-
-
-def csv_handler(url):
- """Returns request in CSV (list of records) format"""
-
- response = request_ct(url)
- decoded_content = response.content.decode("utf-8")
-
- split_by_blank = re.split(r"\n\s*\n", decoded_content) # Extracts header info
- cr = csv.reader(split_by_blank[1].splitlines(), delimiter=",")
- records = list(cr)
-
- return records
\ No newline at end of file
diff --git a/spaces/monra/freegpt-webui/g4f/Provider/Providers/ChatFree.py b/spaces/monra/freegpt-webui/g4f/Provider/Providers/ChatFree.py
deleted file mode 100644
index 6bbaebaed35681026ff1eeb8eee3270e3b0741fd..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui/g4f/Provider/Providers/ChatFree.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import os, requests
-from ...typing import sha256, Dict, get_type_hints
-import json
-
-url = "https://v.chatfree.cc"
-model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k']
-supports_stream = False
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- headers = {
- 'authority': 'chat.dfehub.com',
- 'accept': '*/*',
- 'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
- 'content-type': 'application/json',
- 'origin': 'https://v.chatfree.cc',
- 'referer': 'https://v.chatfree.cc/',
- 'sec-ch-ua': '"Not.A/Brand";v="8", "Chromium";v="114", "Google Chrome";v="114"',
- 'sec-ch-ua-mobile': '?0',
- 'sec-ch-ua-platform': '"macOS"',
- 'sec-fetch-dest': 'empty',
- 'sec-fetch-mode': 'cors',
- 'sec-fetch-site': 'same-origin',
- 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',
- 'x-requested-with': 'XMLHttpRequest',
- }
-
- json_data = {
- 'messages': messages,
- 'stream': True,
- 'model': model,
- 'temperature': 0.5,
- 'presence_penalty': 0,
- 'frequency_penalty': 0,
- 'top_p': 1,
- }
-
- response = requests.post('https://v.chatfree.cc/api/openai/v1/chat/completions',
- headers=headers, json=json_data)
-
- for chunk in response.iter_lines():
- if b'content' in chunk:
- data = json.loads(chunk.decode().split('data: ')[1])
- yield (data['choices'][0]['delta']['content'])
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/mrneuralnet/P-PD/utils/__init__.py b/spaces/mrneuralnet/P-PD/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/data/legacy/block_pair_dataset.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/data/legacy/block_pair_dataset.py
deleted file mode 100644
index ba069b46052286c531b4f9706d96788732cd2ad2..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/data/legacy/block_pair_dataset.py
+++ /dev/null
@@ -1,311 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-
-import numpy as np
-import torch
-from fairseq.data import FairseqDataset
-
-
-class BlockPairDataset(FairseqDataset):
- """Break a Dataset of tokens into sentence pair blocks for next sentence
- prediction as well as masked language model.
-
- High-level logics are:
- 1. break input tensor to tensor blocks
- 2. pair the blocks with 50% next sentence and 50% random sentence
- 3. return paired blocks as well as related segment labels
-
- Args:
- dataset (~torch.utils.data.Dataset): dataset to break into blocks
- sizes: array of sentence lengths
- dictionary: dictionary for the task
- block_size: maximum block size
- break_mode: mode for breaking copurs into block pairs. currently we support
- 2 modes
- doc: respect document boundaries and each part of the pair should belong to on document
- none: don't respect any boundary and cut tokens evenly
- short_seq_prob: probability for generating shorter block pairs
- doc_break_size: Size for empty line separating documents. Typically 1 if
- the sentences have eos, 0 otherwise.
- """
-
- def __init__(
- self,
- dataset,
- dictionary,
- sizes,
- block_size,
- break_mode="doc",
- short_seq_prob=0.1,
- doc_break_size=1,
- ):
- super().__init__()
- self.dataset = dataset
- self.pad = dictionary.pad()
- self.eos = dictionary.eos()
- self.cls = dictionary.cls()
- self.mask = dictionary.mask()
- self.sep = dictionary.sep()
- self.break_mode = break_mode
- self.dictionary = dictionary
- self.short_seq_prob = short_seq_prob
- self.block_indices = []
-
- assert len(dataset) == len(sizes)
-
- if break_mode == "doc":
- cur_doc = []
- for sent_id, sz in enumerate(sizes):
- assert doc_break_size == 0 or sz != 0, (
- "when doc_break_size is non-zero, we expect documents to be"
- "separated by a blank line with a single eos."
- )
- # empty line as document separator
- if sz == doc_break_size:
- if len(cur_doc) == 0:
- continue
- self.block_indices.append(cur_doc)
- cur_doc = []
- else:
- cur_doc.append(sent_id)
- max_num_tokens = block_size - 3 # Account for [CLS], [SEP], [SEP]
- self.sent_pairs = []
- self.sizes = []
- for doc_id, doc in enumerate(self.block_indices):
- self._generate_sentence_pair(doc, doc_id, max_num_tokens, sizes)
- elif break_mode is None or break_mode == "none":
- # each block should have half of the block size since we are constructing block pair
- sent_length = (block_size - 3) // 2
- total_len = sum(dataset.sizes)
- length = math.ceil(total_len / sent_length)
-
- def block_at(i):
- start = i * sent_length
- end = min(start + sent_length, total_len)
- return (start, end)
-
- sent_indices = np.array([block_at(i) for i in range(length)])
- sent_sizes = np.array([e - s for s, e in sent_indices])
- dataset_index = self._sent_to_dataset_index(sent_sizes)
-
- # pair sentences
- self._pair_sentences(dataset_index)
- else:
- raise ValueError("Invalid break_mode: " + break_mode)
-
- def _pair_sentences(self, dataset_index):
- """
- Give a list of evenly cut blocks/sentences, pair these sentences with 50%
- consecutive sentences and 50% random sentences.
- This is used for none break mode
- """
- # pair sentences
- for sent_id, sent in enumerate(dataset_index):
- next_sent_label = (
- 1 if np.random.rand() > 0.5 and sent_id != len(dataset_index) - 1 else 0
- )
- if next_sent_label:
- next_sent = dataset_index[sent_id + 1]
- else:
- next_sent = dataset_index[
- self._skip_sampling(len(dataset_index), [sent_id, sent_id + 1])
- ]
- self.sent_pairs.append((sent, next_sent, next_sent_label))
-
- # The current blocks don't include the special tokens but the
- # sizes already account for this
- self.sizes.append(3 + sent[3] + next_sent[3])
-
- def _sent_to_dataset_index(self, sent_sizes):
- """
- Build index mapping block indices to the underlying dataset indices
- """
- dataset_index = []
- ds_idx, ds_remaining = -1, 0
- for to_consume in sent_sizes:
- sent_size = to_consume
- if ds_remaining == 0:
- ds_idx += 1
- ds_remaining = sent_sizes[ds_idx]
- start_ds_idx = ds_idx
- start_offset = sent_sizes[ds_idx] - ds_remaining
- while to_consume > ds_remaining:
- to_consume -= ds_remaining
- ds_idx += 1
- ds_remaining = sent_sizes[ds_idx]
- ds_remaining -= to_consume
- dataset_index.append(
- (
- start_ds_idx, # starting index in dataset
- start_offset, # starting offset within starting index
- ds_idx, # ending index in dataset
- sent_size, # sentence length
- )
- )
- assert ds_remaining == 0
- assert ds_idx == len(self.dataset) - 1
- return dataset_index
-
- def _generate_sentence_pair(self, doc, doc_id, max_num_tokens, sizes):
- """
- Go through a single document and genrate sentence paris from it
- """
- current_chunk = []
- current_length = 0
- curr = 0
- # To provide more randomness, we decrease target seq length for parts of
- # samples (10% by default). Note that max_num_tokens is the hard threshold
- # for batching and will never be changed.
- target_seq_length = max_num_tokens
- if np.random.random() < self.short_seq_prob:
- target_seq_length = np.random.randint(2, max_num_tokens)
- # loop through all sentences in document
- while curr < len(doc):
- sent_id = doc[curr]
- current_chunk.append(sent_id)
- current_length = sum(sizes[current_chunk])
- # split chunk and generate pair when exceed target_seq_length or
- # finish the loop
- if curr == len(doc) - 1 or current_length >= target_seq_length:
- # split the chunk into 2 parts
- a_end = 1
- if len(current_chunk) > 2:
- a_end = np.random.randint(1, len(current_chunk) - 1)
- sent_a = current_chunk[:a_end]
- len_a = sum(sizes[sent_a])
- # generate next sentence label, note that if there is only 1 sentence
- # in current chunk, label is always 0
- next_sent_label = (
- 1 if np.random.rand() > 0.5 and len(current_chunk) != 1 else 0
- )
- if not next_sent_label:
- # if next sentence label is 0, sample sent_b from a random doc
- target_b_length = target_seq_length - len_a
- rand_doc_id = self._skip_sampling(len(self.block_indices), [doc_id])
- random_doc = self.block_indices[rand_doc_id]
- random_start = np.random.randint(0, len(random_doc))
- sent_b = []
- len_b = 0
- for j in range(random_start, len(random_doc)):
- sent_b.append(random_doc[j])
- len_b = sum(sizes[sent_b])
- if len_b >= target_b_length:
- break
- # return the second part of the chunk since it's not used
- num_unused_segments = len(current_chunk) - a_end
- curr -= num_unused_segments
- else:
- # if next sentence label is 1, use the second part of chunk as sent_B
- sent_b = current_chunk[a_end:]
- len_b = sum(sizes[sent_b])
- # currently sent_a and sent_B may be longer than max_num_tokens,
- # truncate them and return block idx and offsets for them
- sent_a, sent_b = self._truncate_sentences(
- sent_a, sent_b, max_num_tokens
- )
- self.sent_pairs.append((sent_a, sent_b, next_sent_label))
- self.sizes.append(3 + sent_a[3] + sent_b[3])
- current_chunk = []
- curr += 1
-
- def _skip_sampling(self, total, skip_ids):
- """
- Generate a random integer which is not in skip_ids. Sample range is [0, total)
- TODO: ids in skip_ids should be consecutive, we can extend it to more generic version later
- """
- rand_id = np.random.randint(total - len(skip_ids))
- return rand_id if rand_id < min(skip_ids) else rand_id + len(skip_ids)
-
- def _truncate_sentences(self, sent_a, sent_b, max_num_tokens):
- """
- Trancate a pair of sentence to limit total length under max_num_tokens
- Logics:
- 1. Truncate longer sentence
- 2. Tokens to be truncated could be at the beginning or the end of the sentnce
- Returns:
- Truncated sentences represented by dataset idx
- """
- len_a, len_b = sum(self.dataset.sizes[sent_a]), sum(self.dataset.sizes[sent_b])
- front_cut_a = front_cut_b = end_cut_a = end_cut_b = 0
-
- while True:
- total_length = (
- len_a + len_b - front_cut_a - front_cut_b - end_cut_a - end_cut_b
- )
- if total_length <= max_num_tokens:
- break
-
- if len_a - front_cut_a - end_cut_a > len_b - front_cut_b - end_cut_b:
- if np.random.rand() < 0.5:
- front_cut_a += 1
- else:
- end_cut_a += 1
- else:
- if np.random.rand() < 0.5:
- front_cut_b += 1
- else:
- end_cut_b += 1
-
- # calculate ds indices as well as offsets and return
- truncated_sent_a = self._cut_sentence(sent_a, front_cut_a, end_cut_a)
- truncated_sent_b = self._cut_sentence(sent_b, front_cut_b, end_cut_b)
- return truncated_sent_a, truncated_sent_b
-
- def _cut_sentence(self, sent, front_cut, end_cut):
- """
- Cut a sentence based on the numbers of tokens to be cut from beginning and end
- Represent the sentence as dataset idx and return
- """
- start_ds_idx, end_ds_idx, offset = sent[0], sent[-1], 0
- target_len = sum(self.dataset.sizes[sent]) - front_cut - end_cut
- while front_cut > 0:
- if self.dataset.sizes[start_ds_idx] > front_cut:
- offset += front_cut
- break
- else:
- front_cut -= self.dataset.sizes[start_ds_idx]
- start_ds_idx += 1
- while end_cut > 0:
- if self.dataset.sizes[end_ds_idx] > end_cut:
- break
- else:
- end_cut -= self.dataset.sizes[end_ds_idx]
- end_ds_idx -= 1
- return start_ds_idx, offset, end_ds_idx, target_len
-
- def _fetch_block(self, start_ds_idx, offset, end_ds_idx, length):
- """
- Fetch a block of tokens based on its dataset idx
- """
- buffer = torch.cat(
- [self.dataset[idx] for idx in range(start_ds_idx, end_ds_idx + 1)]
- )
- s, e = offset, offset + length
- return buffer[s:e]
-
- def __getitem__(self, index):
- block1, block2, next_sent_label = self.sent_pairs[index]
- block1 = self._fetch_block(*block1)
- block2 = self._fetch_block(*block2)
- return block1, block2, next_sent_label
-
- def __len__(self):
- return len(self.sizes)
-
- @property
- def supports_prefetch(self):
- return getattr(self.dataset, "supports_prefetch", False)
-
- def prefetch(self, indices):
- prefetch_idx = set()
- for index in indices:
- for block1, block2, _ in [self.sent_pairs[index]]:
- for ds_idx in range(block1[0], block1[2] + 1):
- prefetch_idx.add(ds_idx)
- for ds_idx in range(block2[0], block2[2] + 1):
- prefetch_idx.add(ds_idx)
- self.dataset.prefetch(prefetch_idx)
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/kmeans_vector_quantizer.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/kmeans_vector_quantizer.py
deleted file mode 100644
index 040db1e83e775a3bb59d5263d22aae9276a83f22..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/kmeans_vector_quantizer.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-from fairseq.modules import Fp32GroupNorm
-
-
-class KmeansVectorQuantizer(nn.Module):
- def __init__(
- self, dim, num_vars, groups, combine_groups, vq_dim, time_first, gamma=0.25
- ):
- """Vector quantization using straight pass-through estimator (i.e. kmeans)
-
- Args:
- dim: input dimension (channels)
- num_vars: number of quantized vectors per group
- groups: number of groups for vector quantization
- combine_groups: whether to use the vectors for all groups
- vq_dim: dimensionality of the resulting quantized vector
- time_first: if true, expect input in BxTxC format, otherwise in BxCxT
- gamma: commitment loss coefficient
- """
- super().__init__()
-
- self.groups = groups
- self.combine_groups = combine_groups
- self.input_dim = dim
- self.num_vars = num_vars
- self.vq_dim = vq_dim
- self.time_first = time_first
-
- assert (
- vq_dim % groups == 0
- ), f"dim {vq_dim} must be divisible by groups {groups} for concatenation"
-
- self.var_dim = vq_dim // groups
- num_groups = groups if not combine_groups else 1
-
- self.embedding = nn.Parameter(
- 0.01 * torch.randn(num_vars, num_groups, self.var_dim)
- )
- self.projection = nn.Sequential(
- nn.Conv1d(dim, dim, kernel_size=1, groups=groups, bias=False),
- Fp32GroupNorm(groups, dim),
- )
- self.gamma = gamma
- self.mse_mean = nn.MSELoss(reduction="mean")
-
- def _pass_grad(self, x, y):
- """Manually set gradient for backward pass.
- for y = f(x), ensure that during the backward pass,
- dL/dy = dL/dx regardless of f(x).
- Returns:
- y, with the gradient forced to be dL/dy = dL/dx.
- """
-
- return y.detach() + (x - x.detach())
-
- @property
- def expand_embedding(self):
- if self.combine_groups:
- return self.embedding.expand(self.num_vars, self.groups, self.var_dim)
- return self.embedding
-
- def forward_idx(self, x):
- res = self.forward(x, produce_targets=True)
- return res["x"], res["targets"]
-
- def forward(self, x, produce_targets=False):
-
- result = {"num_vars": self.num_vars}
-
- if self.time_first:
- x = x.transpose(1, 2)
-
- bsz, fsz, tsz = x.shape
-
- ze = self.projection(x)
- ze_ = ze.view(bsz, self.groups, self.var_dim, tsz).permute(0, 3, 1, 2)
- d = (
- (ze_.unsqueeze(0) - self.expand_embedding.unsqueeze(1).unsqueeze(1))
- .view(self.num_vars, bsz, tsz, self.groups, -1)
- .norm(dim=-1, p=2)
- )
- idx = d.argmin(dim=0)
- zq = (
- torch.stack(
- [
- self.expand_embedding[idx[..., group], group]
- for group in range(self.groups)
- ],
- dim=-2,
- )
- .view(bsz, tsz, self.groups * self.var_dim)
- .permute(0, 2, 1)
- )
- assert ze.shape == zq.shape, (ze.shape, zq.shape)
- x = self._pass_grad(ze, zq)
-
- hard_x = (
- idx.new_zeros(bsz * tsz * self.groups, self.num_vars)
- .scatter_(-1, idx.view(-1, 1), 1.0)
- .view(bsz * tsz, self.groups, -1)
- )
- hard_probs = torch.mean(hard_x.float(), dim=0)
- result["code_perplexity"] = torch.exp(
- -torch.sum(hard_probs * torch.log(hard_probs + 1e-7), dim=-1)
- ).sum()
-
- if produce_targets:
- result["targets"] = idx
-
- if self.time_first:
- x = x.transpose(1, 2) # BCT -> BTC
- result["x"] = x
-
- ze = ze.float()
- zq = zq.float()
- latent_loss = self.mse_mean(zq, ze.detach())
- commitment_loss = self.mse_mean(ze, zq.detach())
-
- result["kmeans_loss"] = latent_loss + self.gamma * commitment_loss
-
- return result
diff --git a/spaces/nakamura196/yolov5-kunshujo/ultralytics/yolov5/utils/general.py b/spaces/nakamura196/yolov5-kunshujo/ultralytics/yolov5/utils/general.py
deleted file mode 100644
index b0c5e9d69ab7b4b8eaaed955b105faab21789b25..0000000000000000000000000000000000000000
--- a/spaces/nakamura196/yolov5-kunshujo/ultralytics/yolov5/utils/general.py
+++ /dev/null
@@ -1,908 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-General utils
-"""
-
-import contextlib
-import glob
-import logging
-import math
-import os
-import platform
-import random
-import re
-import shutil
-import signal
-import time
-import urllib
-from datetime import datetime
-from itertools import repeat
-from multiprocessing.pool import ThreadPool
-from pathlib import Path
-from subprocess import check_output
-from zipfile import ZipFile
-
-import cv2
-import numpy as np
-import pandas as pd
-import pkg_resources as pkg
-import torch
-import torchvision
-import yaml
-
-from utils.downloads import gsutil_getsize
-from utils.metrics import box_iou, fitness
-
-# Settings
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1] # YOLOv5 root directory
-DATASETS_DIR = ROOT.parent / 'datasets' # YOLOv5 datasets directory
-NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads
-VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true' # global verbose mode
-FONT = 'Arial.ttf' # https://ultralytics.com/assets/Arial.ttf
-
-torch.set_printoptions(linewidth=320, precision=5, profile='long')
-np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
-pd.options.display.max_columns = 10
-cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
-os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS) # NumExpr max threads
-os.environ['OMP_NUM_THREADS'] = str(NUM_THREADS) # OpenMP max threads (PyTorch and SciPy)
-
-
-def is_kaggle():
- # Is environment a Kaggle Notebook?
- try:
- assert os.environ.get('PWD') == '/kaggle/working'
- assert os.environ.get('KAGGLE_URL_BASE') == 'https://www.kaggle.com'
- return True
- except AssertionError:
- return False
-
-
-def is_writeable(dir, test=False):
- # Return True if directory has write permissions, test opening a file with write permissions if test=True
- if test: # method 1
- file = Path(dir) / 'tmp.txt'
- try:
- with open(file, 'w'): # open file with write permissions
- pass
- file.unlink() # remove file
- return True
- except OSError:
- return False
- else: # method 2
- return os.access(dir, os.R_OK) # possible issues on Windows
-
-
-def set_logging(name=None, verbose=VERBOSE):
- # Sets level and returns logger
- if is_kaggle():
- for h in logging.root.handlers:
- logging.root.removeHandler(h) # remove all handlers associated with the root logger object
- rank = int(os.getenv('RANK', -1)) # rank in world for Multi-GPU trainings
- logging.basicConfig(format="%(message)s", level=logging.INFO if (verbose and rank in (-1, 0)) else logging.WARNING)
- return logging.getLogger(name)
-
-
-LOGGER = set_logging('yolov5') # define globally (used in train.py, val.py, detect.py, etc.)
-
-
-def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'):
- # Return path of user configuration directory. Prefer environment variable if exists. Make dir if required.
- env = os.getenv(env_var)
- if env:
- path = Path(env) # use environment variable
- else:
- cfg = {'Windows': 'AppData/Roaming', 'Linux': '.config', 'Darwin': 'Library/Application Support'} # 3 OS dirs
- path = Path.home() / cfg.get(platform.system(), '') # OS-specific config dir
- path = (path if is_writeable(path) else Path('/tmp')) / dir # GCP and AWS lambda fix, only /tmp is writeable
- path.mkdir(exist_ok=True) # make if required
- return path
-
-
-CONFIG_DIR = user_config_dir() # Ultralytics settings dir
-
-
-class Profile(contextlib.ContextDecorator):
- # Usage: @Profile() decorator or 'with Profile():' context manager
- def __enter__(self):
- self.start = time.time()
-
- def __exit__(self, type, value, traceback):
- print(f'Profile results: {time.time() - self.start:.5f}s')
-
-
-class Timeout(contextlib.ContextDecorator):
- # Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager
- def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True):
- self.seconds = int(seconds)
- self.timeout_message = timeout_msg
- self.suppress = bool(suppress_timeout_errors)
-
- def _timeout_handler(self, signum, frame):
- raise TimeoutError(self.timeout_message)
-
- def __enter__(self):
- if platform.system() != 'Windows': # not supported on Windows
- signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM
- signal.alarm(self.seconds) # start countdown for SIGALRM to be raised
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- if platform.system() != 'Windows':
- signal.alarm(0) # Cancel SIGALRM if it's scheduled
- if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError
- return True
-
-
-class WorkingDirectory(contextlib.ContextDecorator):
- # Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager
- def __init__(self, new_dir):
- self.dir = new_dir # new dir
- self.cwd = Path.cwd().resolve() # current dir
-
- def __enter__(self):
- os.chdir(self.dir)
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- os.chdir(self.cwd)
-
-
-def try_except(func):
- # try-except function. Usage: @try_except decorator
- def handler(*args, **kwargs):
- try:
- func(*args, **kwargs)
- except Exception as e:
- print(e)
-
- return handler
-
-
-def methods(instance):
- # Get class/instance methods
- return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")]
-
-
-def print_args(name, opt):
- # Print argparser arguments
- LOGGER.info(colorstr(f'{name}: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
-
-
-def init_seeds(seed=0):
- # Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html
- # cudnn seed 0 settings are slower and more reproducible, else faster and less reproducible
- import torch.backends.cudnn as cudnn
- random.seed(seed)
- np.random.seed(seed)
- torch.manual_seed(seed)
- cudnn.benchmark, cudnn.deterministic = (False, True) if seed == 0 else (True, False)
-
-
-def intersect_dicts(da, db, exclude=()):
- # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values
- return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape}
-
-
-def get_latest_run(search_dir='.'):
- # Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
- last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
- return max(last_list, key=os.path.getctime) if last_list else ''
-
-
-def is_docker():
- # Is environment a Docker container?
- return Path('/workspace').exists() # or Path('/.dockerenv').exists()
-
-
-def is_colab():
- # Is environment a Google Colab instance?
- try:
- import google.colab
- return True
- except ImportError:
- return False
-
-
-def is_pip():
- # Is file in a pip package?
- return 'site-packages' in Path(__file__).resolve().parts
-
-
-def is_ascii(s=''):
- # Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7)
- s = str(s) # convert list, tuple, None, etc. to str
- return len(s.encode().decode('ascii', 'ignore')) == len(s)
-
-
-def is_chinese(s='人工智能'):
- # Is string composed of any Chinese characters?
- return True if re.search('[\u4e00-\u9fff]', str(s)) else False
-
-
-def emojis(str=''):
- # Return platform-dependent emoji-safe version of string
- return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
-
-
-def file_age(path=__file__):
- # Return days since last file update
- dt = (datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime)) # delta
- return dt.days # + dt.seconds / 86400 # fractional days
-
-
-def file_update_date(path=__file__):
- # Return human-readable file modification date, i.e. '2021-3-26'
- t = datetime.fromtimestamp(Path(path).stat().st_mtime)
- return f'{t.year}-{t.month}-{t.day}'
-
-
-def file_size(path):
- # Return file/dir size (MB)
- mb = 1 << 20 # bytes to MiB (1024 ** 2)
- path = Path(path)
- if path.is_file():
- return path.stat().st_size / mb
- elif path.is_dir():
- return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / mb
- else:
- return 0.0
-
-
-def check_online():
- # Check internet connectivity
- import socket
- try:
- socket.create_connection(("1.1.1.1", 443), 5) # check host accessibility
- return True
- except OSError:
- return False
-
-
-def git_describe(path=ROOT): # path must be a directory
- # Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
- try:
- return check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1]
- except Exception:
- return ''
-
-
-@try_except
-@WorkingDirectory(ROOT)
-def check_git_status():
- # Recommend 'git pull' if code is out of date
- msg = ', for updates see https://github.com/ultralytics/yolov5'
- s = colorstr('github: ') # string
- assert Path('.git').exists(), s + 'skipping check (not a git repository)' + msg
- assert not is_docker(), s + 'skipping check (Docker image)' + msg
- assert check_online(), s + 'skipping check (offline)' + msg
-
- cmd = 'git fetch && git config --get remote.origin.url'
- url = check_output(cmd, shell=True, timeout=5).decode().strip().rstrip('.git') # git fetch
- branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out
- n = int(check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind
- if n > 0:
- s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use `git pull` or `git clone {url}` to update."
- else:
- s += f'up to date with {url} ✅'
- LOGGER.info(emojis(s)) # emoji-safe
-
-
-def check_python(minimum='3.6.2'):
- # Check current python version vs. required python version
- check_version(platform.python_version(), minimum, name='Python ', hard=True)
-
-
-def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False, hard=False, verbose=False):
- # Check version vs. required version
- current, minimum = (pkg.parse_version(x) for x in (current, minimum))
- result = (current == minimum) if pinned else (current >= minimum) # bool
- s = f'{name}{minimum} required by YOLOv5, but {name}{current} is currently installed' # string
- if hard:
- assert result, s # assert min requirements met
- if verbose and not result:
- LOGGER.warning(s)
- return result
-
-
-@try_except
-def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True):
- # Check installed dependencies meet requirements (pass *.txt file or list of packages)
- prefix = colorstr('red', 'bold', 'requirements:')
- check_python() # check python version
- if isinstance(requirements, (str, Path)): # requirements.txt file
- file = Path(requirements)
- assert file.exists(), f"{prefix} {file.resolve()} not found, check failed."
- with file.open() as f:
- requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude]
- else: # list or tuple of packages
- requirements = [x for x in requirements if x not in exclude]
-
- n = 0 # number of packages updates
- for r in requirements:
- try:
- pkg.require(r)
- except Exception: # DistributionNotFound or VersionConflict if requirements not met
- s = f"{prefix} {r} not found and is required by YOLOv5"
- if install:
- LOGGER.info(f"{s}, attempting auto-update...")
- try:
- assert check_online(), f"'pip install {r}' skipped (offline)"
- LOGGER.info(check_output(f"pip install '{r}'", shell=True).decode())
- n += 1
- except Exception as e:
- LOGGER.warning(f'{prefix} {e}')
- else:
- LOGGER.info(f'{s}. Please install and rerun your command.')
-
- if n: # if packages updated
- source = file.resolve() if 'file' in locals() else requirements
- s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
- f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
- LOGGER.info(emojis(s))
-
-
-def check_img_size(imgsz, s=32, floor=0):
- # Verify image size is a multiple of stride s in each dimension
- if isinstance(imgsz, int): # integer i.e. img_size=640
- new_size = max(make_divisible(imgsz, int(s)), floor)
- else: # list i.e. img_size=[640, 480]
- new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
- if new_size != imgsz:
- LOGGER.warning(f'WARNING: --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')
- return new_size
-
-
-def check_imshow():
- # Check if environment supports image displays
- try:
- assert not is_docker(), 'cv2.imshow() is disabled in Docker environments'
- assert not is_colab(), 'cv2.imshow() is disabled in Google Colab environments'
- cv2.imshow('test', np.zeros((1, 1, 3)))
- cv2.waitKey(1)
- cv2.destroyAllWindows()
- cv2.waitKey(1)
- return True
- except Exception as e:
- LOGGER.warning(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
- return False
-
-
-def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''):
- # Check file(s) for acceptable suffix
- if file and suffix:
- if isinstance(suffix, str):
- suffix = [suffix]
- for f in file if isinstance(file, (list, tuple)) else [file]:
- s = Path(f).suffix.lower() # file suffix
- if len(s):
- assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}"
-
-
-def check_yaml(file, suffix=('.yaml', '.yml')):
- # Search/download YAML file (if necessary) and return path, checking suffix
- return check_file(file, suffix)
-
-
-def check_file(file, suffix=''):
- # Search/download file (if necessary) and return path
- check_suffix(file, suffix) # optional
- file = str(file) # convert to str()
- if Path(file).is_file() or file == '': # exists
- return file
- elif file.startswith(('http:/', 'https:/')): # download
- url = str(Path(file)).replace(':/', '://') # Pathlib turns :// -> :/
- file = Path(urllib.parse.unquote(file).split('?')[0]).name # '%2F' to '/', split https://url.com/file.txt?auth
- if Path(file).is_file():
- LOGGER.info(f'Found {url} locally at {file}') # file already exists
- else:
- LOGGER.info(f'Downloading {url} to {file}...')
- torch.hub.download_url_to_file(url, file)
- assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}' # check
- return file
- else: # search
- files = []
- for d in 'data', 'models', 'utils': # search directories
- files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True)) # find file
- assert len(files), f'File not found: {file}' # assert file was found
- assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique
- return files[0] # return file
-
-
-def check_font(font=FONT):
- # Download font to CONFIG_DIR if necessary
- font = Path(font)
- if not font.exists() and not (CONFIG_DIR / font.name).exists():
- url = "https://ultralytics.com/assets/" + font.name
- LOGGER.info(f'Downloading {url} to {CONFIG_DIR / font.name}...')
- torch.hub.download_url_to_file(url, str(font), progress=False)
-
-
-def check_dataset(data, autodownload=True):
- # Download and/or unzip dataset if not found locally
- # Usage: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128_with_yaml.zip
-
- # Download (optional)
- extract_dir = ''
- if isinstance(data, (str, Path)) and str(data).endswith('.zip'): # i.e. gs://bucket/dir/coco128.zip
- download(data, dir=DATASETS_DIR, unzip=True, delete=False, curl=False, threads=1)
- data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml'))
- extract_dir, autodownload = data.parent, False
-
- # Read yaml (optional)
- if isinstance(data, (str, Path)):
- with open(data, errors='ignore') as f:
- data = yaml.safe_load(f) # dictionary
-
- # Resolve paths
- path = Path(extract_dir or data.get('path') or '') # optional 'path' default to '.'
- if not path.is_absolute():
- path = (ROOT / path).resolve()
- for k in 'train', 'val', 'test':
- if data.get(k): # prepend path
- data[k] = str(path / data[k]) if isinstance(data[k], str) else [str(path / x) for x in data[k]]
-
- # Parse yaml
- assert 'nc' in data, "Dataset 'nc' key missing."
- if 'names' not in data:
- data['names'] = [f'class{i}' for i in range(data['nc'])] # assign class names if missing
- train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download'))
- if val:
- val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path
- if not all(x.exists() for x in val):
- LOGGER.info(emojis('\nDataset not found ⚠️, missing paths %s' % [str(x) for x in val if not x.exists()]))
- if s and autodownload: # download script
- t = time.time()
- root = path.parent if 'path' in data else '..' # unzip directory i.e. '../'
- if s.startswith('http') and s.endswith('.zip'): # URL
- f = Path(s).name # filename
- LOGGER.info(f'Downloading {s} to {f}...')
- torch.hub.download_url_to_file(s, f)
- Path(root).mkdir(parents=True, exist_ok=True) # create root
- ZipFile(f).extractall(path=root) # unzip
- Path(f).unlink() # remove zip
- r = None # success
- elif s.startswith('bash '): # bash script
- LOGGER.info(f'Running {s} ...')
- r = os.system(s)
- else: # python script
- r = exec(s, {'yaml': data}) # return None
- dt = f'({round(time.time() - t, 1)}s)'
- s = f"success ✅ {dt}, saved to {colorstr('bold', root)}" if r in (0, None) else f"failure {dt} ❌"
- LOGGER.info(emojis(f"Dataset download {s}"))
- else:
- raise Exception(emojis('Dataset not found ❌'))
-
- return data # dictionary
-
-
-def url2file(url):
- # Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt
- url = str(Path(url)).replace(':/', '://') # Pathlib turns :// -> :/
- file = Path(urllib.parse.unquote(url)).name.split('?')[0] # '%2F' to '/', split https://url.com/file.txt?auth
- return file
-
-
-def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1):
- # Multi-threaded file download and unzip function, used in data.yaml for autodownload
- def download_one(url, dir):
- # Download 1 file
- f = dir / Path(url).name # filename
- if Path(url).is_file(): # exists in current path
- Path(url).rename(f) # move to dir
- elif not f.exists():
- LOGGER.info(f'Downloading {url} to {f}...')
- if curl:
- os.system(f"curl -L '{url}' -o '{f}' --retry 9 -C -") # curl download, retry and resume on fail
- else:
- torch.hub.download_url_to_file(url, f, progress=threads == 1) # torch download
- if unzip and f.suffix in ('.zip', '.gz'):
- LOGGER.info(f'Unzipping {f}...')
- if f.suffix == '.zip':
- ZipFile(f).extractall(path=dir) # unzip
- elif f.suffix == '.gz':
- os.system(f'tar xfz {f} --directory {f.parent}') # unzip
- if delete:
- f.unlink() # remove zip
-
- dir = Path(dir)
- dir.mkdir(parents=True, exist_ok=True) # make directory
- if threads > 1:
- pool = ThreadPool(threads)
- pool.imap(lambda x: download_one(*x), zip(url, repeat(dir))) # multi-threaded
- pool.close()
- pool.join()
- else:
- for u in [url] if isinstance(url, (str, Path)) else url:
- download_one(u, dir)
-
-
-def make_divisible(x, divisor):
- # Returns nearest x divisible by divisor
- if isinstance(divisor, torch.Tensor):
- divisor = int(divisor.max()) # to int
- return math.ceil(x / divisor) * divisor
-
-
-def clean_str(s):
- # Cleans a string by replacing special characters with underscore _
- return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
-
-
-def one_cycle(y1=0.0, y2=1.0, steps=100):
- # lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf
- return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
-
-
-def colorstr(*input):
- # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world')
- *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string
- colors = {'black': '\033[30m', # basic colors
- 'red': '\033[31m',
- 'green': '\033[32m',
- 'yellow': '\033[33m',
- 'blue': '\033[34m',
- 'magenta': '\033[35m',
- 'cyan': '\033[36m',
- 'white': '\033[37m',
- 'bright_black': '\033[90m', # bright colors
- 'bright_red': '\033[91m',
- 'bright_green': '\033[92m',
- 'bright_yellow': '\033[93m',
- 'bright_blue': '\033[94m',
- 'bright_magenta': '\033[95m',
- 'bright_cyan': '\033[96m',
- 'bright_white': '\033[97m',
- 'end': '\033[0m', # misc
- 'bold': '\033[1m',
- 'underline': '\033[4m'}
- return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
-
-
-def labels_to_class_weights(labels, nc=80):
- # Get class weights (inverse frequency) from training labels
- if labels[0] is None: # no labels loaded
- return torch.Tensor()
-
- labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO
- classes = labels[:, 0].astype(np.int) # labels = [class xywh]
- weights = np.bincount(classes, minlength=nc) # occurrences per class
-
- # Prepend gridpoint count (for uCE training)
- # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image
- # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start
-
- weights[weights == 0] = 1 # replace empty bins with 1
- weights = 1 / weights # number of targets per class
- weights /= weights.sum() # normalize
- return torch.from_numpy(weights)
-
-
-def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
- # Produces image weights based on class_weights and image contents
- class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels])
- image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
- # index = random.choices(range(n), weights=image_weights, k=1) # weight image sample
- return image_weights
-
-
-def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)
- # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
- # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
- # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
- # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
- # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet
- x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
- 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
- 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
- return x
-
-
-def xyxy2xywh(x):
- # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center
- y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center
- y[:, 2] = x[:, 2] - x[:, 0] # width
- y[:, 3] = x[:, 3] - x[:, 1] # height
- return y
-
-
-def xywh2xyxy(x):
- # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
- y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
- y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
- y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
- return y
-
-
-def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
- # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x
- y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y
- y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x
- y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y
- return y
-
-
-def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
- # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right
- if clip:
- clip_coords(x, (h - eps, w - eps)) # warning: inplace clip
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[:, 0] = ((x[:, 0] + x[:, 2]) / 2) / w # x center
- y[:, 1] = ((x[:, 1] + x[:, 3]) / 2) / h # y center
- y[:, 2] = (x[:, 2] - x[:, 0]) / w # width
- y[:, 3] = (x[:, 3] - x[:, 1]) / h # height
- return y
-
-
-def xyn2xy(x, w=640, h=640, padw=0, padh=0):
- # Convert normalized segments into pixel segments, shape (n,2)
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[:, 0] = w * x[:, 0] + padw # top left x
- y[:, 1] = h * x[:, 1] + padh # top left y
- return y
-
-
-def segment2box(segment, width=640, height=640):
- # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
- x, y = segment.T # segment xy
- inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
- x, y, = x[inside], y[inside]
- return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy
-
-
-def segments2boxes(segments):
- # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
- boxes = []
- for s in segments:
- x, y = s.T # segment xy
- boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy
- return xyxy2xywh(np.array(boxes)) # cls, xywh
-
-
-def resample_segments(segments, n=1000):
- # Up-sample an (n,2) segment
- for i, s in enumerate(segments):
- x = np.linspace(0, len(s) - 1, n)
- xp = np.arange(len(s))
- segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy
- return segments
-
-
-def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
- # Rescale coords (xyxy) from img1_shape to img0_shape
- if ratio_pad is None: # calculate from img0_shape
- gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
- pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
- else:
- gain = ratio_pad[0][0]
- pad = ratio_pad[1]
-
- coords[:, [0, 2]] -= pad[0] # x padding
- coords[:, [1, 3]] -= pad[1] # y padding
- coords[:, :4] /= gain
- clip_coords(coords, img0_shape)
- return coords
-
-
-def clip_coords(boxes, shape):
- # Clip bounding xyxy bounding boxes to image shape (height, width)
- if isinstance(boxes, torch.Tensor): # faster individually
- boxes[:, 0].clamp_(0, shape[1]) # x1
- boxes[:, 1].clamp_(0, shape[0]) # y1
- boxes[:, 2].clamp_(0, shape[1]) # x2
- boxes[:, 3].clamp_(0, shape[0]) # y2
- else: # np.array (faster grouped)
- boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1]) # x1, x2
- boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0]) # y1, y2
-
-
-def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
- labels=(), max_det=300):
- """Runs Non-Maximum Suppression (NMS) on inference results
-
- Returns:
- list of detections, on (n,6) tensor per image [xyxy, conf, cls]
- """
-
- nc = prediction.shape[2] - 5 # number of classes
- xc = prediction[..., 4] > conf_thres # candidates
-
- # Checks
- assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
- assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
-
- # Settings
- min_wh, max_wh = 2, 7680 # (pixels) minimum and maximum box width and height
- max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
- time_limit = 10.0 # seconds to quit after
- redundant = True # require redundant detections
- multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
- merge = False # use merge-NMS
-
- t = time.time()
- output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
- for xi, x in enumerate(prediction): # image index, image inference
- # Apply constraints
- x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
- x = x[xc[xi]] # confidence
-
- # Cat apriori labels if autolabelling
- if labels and len(labels[xi]):
- lb = labels[xi]
- v = torch.zeros((len(lb), nc + 5), device=x.device)
- v[:, :4] = lb[:, 1:5] # box
- v[:, 4] = 1.0 # conf
- v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls
- x = torch.cat((x, v), 0)
-
- # If none remain process next image
- if not x.shape[0]:
- continue
-
- # Compute conf
- x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
-
- # Box (center x, center y, width, height) to (x1, y1, x2, y2)
- box = xywh2xyxy(x[:, :4])
-
- # Detections matrix nx6 (xyxy, conf, cls)
- if multi_label:
- i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
- x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
- else: # best class only
- conf, j = x[:, 5:].max(1, keepdim=True)
- x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
-
- # Filter by class
- if classes is not None:
- x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
-
- # Apply finite constraint
- # if not torch.isfinite(x).all():
- # x = x[torch.isfinite(x).all(1)]
-
- # Check shape
- n = x.shape[0] # number of boxes
- if not n: # no boxes
- continue
- elif n > max_nms: # excess boxes
- x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence
-
- # Batched NMS
- c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
- boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
- i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
- if i.shape[0] > max_det: # limit detections
- i = i[:max_det]
- if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
- # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
- iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
- weights = iou * scores[None] # box weights
- x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
- if redundant:
- i = i[iou.sum(1) > 1] # require redundancy
-
- output[xi] = x[i]
- if (time.time() - t) > time_limit:
- LOGGER.warning(f'WARNING: NMS time limit {time_limit}s exceeded')
- break # time limit exceeded
-
- return output
-
-
-def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer()
- # Strip optimizer from 'f' to finalize training, optionally save as 's'
- x = torch.load(f, map_location=torch.device('cpu'))
- if x.get('ema'):
- x['model'] = x['ema'] # replace model with ema
- for k in 'optimizer', 'best_fitness', 'wandb_id', 'ema', 'updates': # keys
- x[k] = None
- x['epoch'] = -1
- x['model'].half() # to FP16
- for p in x['model'].parameters():
- p.requires_grad = False
- torch.save(x, s or f)
- mb = os.path.getsize(s or f) / 1E6 # filesize
- LOGGER.info(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
-
-
-def print_mutation(results, hyp, save_dir, bucket, prefix=colorstr('evolve: ')):
- evolve_csv = save_dir / 'evolve.csv'
- evolve_yaml = save_dir / 'hyp_evolve.yaml'
- keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
- 'val/box_loss', 'val/obj_loss', 'val/cls_loss') + tuple(hyp.keys()) # [results + hyps]
- keys = tuple(x.strip() for x in keys)
- vals = results + tuple(hyp.values())
- n = len(keys)
-
- # Download (optional)
- if bucket:
- url = f'gs://{bucket}/evolve.csv'
- if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0):
- os.system(f'gsutil cp {url} {save_dir}') # download evolve.csv if larger than local
-
- # Log to evolve.csv
- s = '' if evolve_csv.exists() else (('%20s,' * n % keys).rstrip(',') + '\n') # add header
- with open(evolve_csv, 'a') as f:
- f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n')
-
- # Save yaml
- with open(evolve_yaml, 'w') as f:
- data = pd.read_csv(evolve_csv)
- data = data.rename(columns=lambda x: x.strip()) # strip keys
- i = np.argmax(fitness(data.values[:, :4])) #
- generations = len(data)
- f.write('# YOLOv5 Hyperparameter Evolution Results\n' +
- f'# Best generation: {i}\n' +
- f'# Last generation: {generations - 1}\n' +
- '# ' + ', '.join(f'{x.strip():>20s}' for x in keys[:7]) + '\n' +
- '# ' + ', '.join(f'{x:>20.5g}' for x in data.values[i, :7]) + '\n\n')
- yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False)
-
- # Print to screen
- LOGGER.info(prefix + f'{generations} generations finished, current result:\n' +
- prefix + ', '.join(f'{x.strip():>20s}' for x in keys) + '\n' +
- prefix + ', '.join(f'{x:20.5g}' for x in vals) + '\n\n')
-
- if bucket:
- os.system(f'gsutil cp {evolve_csv} {evolve_yaml} gs://{bucket}') # upload
-
-
-def apply_classifier(x, model, img, im0):
- # Apply a second stage classifier to YOLO outputs
- # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval()
- im0 = [im0] if isinstance(im0, np.ndarray) else im0
- for i, d in enumerate(x): # per image
- if d is not None and len(d):
- d = d.clone()
-
- # Reshape and pad cutouts
- b = xyxy2xywh(d[:, :4]) # boxes
- b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square
- b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad
- d[:, :4] = xywh2xyxy(b).long()
-
- # Rescale boxes from img_size to im0 size
- scale_coords(img.shape[2:], d[:, :4], im0[i].shape)
-
- # Classes
- pred_cls1 = d[:, 5].long()
- ims = []
- for j, a in enumerate(d): # per item
- cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
- im = cv2.resize(cutout, (224, 224)) # BGR
- # cv2.imwrite('example%i.jpg' % j, cutout)
-
- im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
- im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32
- im /= 255 # 0 - 255 to 0.0 - 1.0
- ims.append(im)
-
- pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction
- x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections
-
- return x
-
-
-def increment_path(path, exist_ok=False, sep='', mkdir=False):
- # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.
- path = Path(path) # os-agnostic
- if path.exists() and not exist_ok:
- path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '')
- dirs = glob.glob(f"{path}{sep}*") # similar paths
- matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
- i = [int(m.groups()[0]) for m in matches if m] # indices
- n = max(i) + 1 if i else 2 # increment number
- path = Path(f"{path}{sep}{n}{suffix}") # increment path
- if mkdir:
- path.mkdir(parents=True, exist_ok=True) # make directory
- return path
-
-
-# Variables
-NCOLS = 0 if is_docker() else shutil.get_terminal_size().columns # terminal window size for tqdm
diff --git a/spaces/nakamura196/yolov5-ndl-layout/ultralytics/yolov5/utils/loggers/wandb/log_dataset.py b/spaces/nakamura196/yolov5-ndl-layout/ultralytics/yolov5/utils/loggers/wandb/log_dataset.py
deleted file mode 100644
index 06e81fb693072c99703e5c52b169892b7fd9a8cc..0000000000000000000000000000000000000000
--- a/spaces/nakamura196/yolov5-ndl-layout/ultralytics/yolov5/utils/loggers/wandb/log_dataset.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import argparse
-
-from wandb_utils import WandbLogger
-
-from utils.general import LOGGER
-
-WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
-
-
-def create_dataset_artifact(opt):
- logger = WandbLogger(opt, None, job_type='Dataset Creation') # TODO: return value unused
- if not logger.wandb:
- LOGGER.info("install wandb using `pip install wandb` to log the dataset")
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
- parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
- parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project')
- parser.add_argument('--entity', default=None, help='W&B entity')
- parser.add_argument('--name', type=str, default='log dataset', help='name of W&B run')
-
- opt = parser.parse_args()
- opt.resume = False # Explicitly disallow resume check for dataset upload job
-
- create_dataset_artifact(opt)
diff --git a/spaces/nakas/MusicGenDemucs/README.md b/spaces/nakas/MusicGenDemucs/README.md
deleted file mode 100644
index 2504865cd7fe4db651f2087bcca4ea949d4ef927..0000000000000000000000000000000000000000
--- a/spaces/nakas/MusicGenDemucs/README.md
+++ /dev/null
@@ -1,141 +0,0 @@
----
-title: MusicGen_To_Demucs
-python_version: '3.9'
-tags:
-- music generation
-- language models
-- LLMs
-app_file: app.py
-emoji: 🎵
-colorFrom: white
-colorTo: blue
-sdk: gradio
-sdk_version: 3.34.0
-pinned: true
-license: cc-by-nc-4.0
-duplicated_from: facebook/MusicGen
----
-# Audiocraft
-
-
-
-
-Audiocraft is a PyTorch library for deep learning research on audio generation. At the moment, it contains the code for MusicGen, a state-of-the-art controllable text-to-music model.
-
-## MusicGen
-
-Audiocraft provides the code and models for MusicGen, [a simple and controllable model for music generation][arxiv]. MusicGen is a single stage auto-regressive
-Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods like [MusicLM](https://arxiv.org/abs/2301.11325), MusicGen doesn't require a self-supervised semantic representation, and it generates
-all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict
-them in parallel, thus having only 50 auto-regressive steps per second of audio.
-Check out our [sample page][musicgen_samples] or test the available demo!
-
-
-
-
- {
- currentSuggestions.map(suggestion => (
-
- ))
- }
-
-
-
-
- 
-
-
-
-We use 20K hours of licensed music to train MusicGen. Specifically, we rely on an internal dataset of 10K high-quality music tracks, and on the ShutterStock and Pond5 music data.
-
-## Installation
-Audiocraft requires Python 3.9, PyTorch 2.0.0, and a GPU with at least 16 GB of memory (for the medium-sized model). To install Audiocraft, you can run the following:
-
-```shell
-# Best to make sure you have torch installed first, in particular before installing xformers.
-# Don't run this if you already have PyTorch installed.
-pip install 'torch>=2.0'
-# Then proceed to one of the following
-pip install -U audiocraft # stable release
-pip install -U git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft # bleeding edge
-pip install -e . # or if you cloned the repo locally
-```
-
-## Usage
-We offer a number of way to interact with MusicGen:
-1. A demo is also available on the [`facebook/MusicGen` HuggingFace Space](https://huggingface.co/spaces/facebook/MusicGen) (huge thanks to all the HF team for their support).
-2. You can run the Gradio demo in Colab: [colab notebook](https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing).
-3. You can use the gradio demo locally by running `python app.py`.
-4. You can play with MusicGen by running the jupyter notebook at [`demo.ipynb`](./demo.ipynb) locally (if you have a GPU).
-5. Finally, checkout [@camenduru Colab page](https://github.com/camenduru/MusicGen-colab) which is regularly
- updated with contributions from @camenduru and the community.
-
-## API
-
-We provide a simple API and 4 pre-trained models. The pre trained models are:
-- `small`: 300M model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-small)
-- `medium`: 1.5B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-medium)
-- `melody`: 1.5B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody)
-- `large`: 3.3B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-large)
-
-We observe the best trade-off between quality and compute with the `medium` or `melody` model.
-In order to use MusicGen locally **you must have a GPU**. We recommend 16GB of memory, but smaller
-GPUs will be able to generate short sequences, or longer sequences with the `small` model.
-
-**Note**: Please make sure to have [ffmpeg](https://ffmpeg.org/download.html) installed when using newer version of `torchaudio`.
-You can install it with:
-```
-apt-get install ffmpeg
-```
-
-See after a quick example for using the API.
-
-```python
-import torchaudio
-from audiocraft.models import MusicGen
-from audiocraft.data.audio import audio_write
-
-model = MusicGen.get_pretrained('melody')
-model.set_generation_params(duration=8) # generate 8 seconds.
-wav = model.generate_unconditional(4) # generates 4 unconditional audio samples
-descriptions = ['happy rock', 'energetic EDM', 'sad jazz']
-wav = model.generate(descriptions) # generates 3 samples.
-
-melody, sr = torchaudio.load('./assets/bach.mp3')
-# generates using the melody from the given audio and the provided descriptions.
-wav = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr)
-
-for idx, one_wav in enumerate(wav):
- # Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
- audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
-```
-
-
-## Model Card
-
-See [the model card page](./MODEL_CARD.md).
-
-## FAQ
-
-#### Will the training code be released?
-
-Yes. We will soon release the training code for MusicGen and EnCodec.
-
-
-#### I need help on Windows
-
-@FurkanGozukara made a complete tutorial for [Audiocraft/MusicGen on Windows](https://youtu.be/v-YpvPkhdO4)
-
-#### I need help for running the demo on Colab
-
-Check [@camenduru tutorial on Youtube](https://www.youtube.com/watch?v=EGfxuTy9Eeo).
-
-
-## Citation
-```
-@article{copet2023simple,
- title={Simple and Controllable Music Generation},
- author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},
- year={2023},
- journal={arXiv preprint arXiv:2306.05284},
-}
-```
-
-## License
-* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE).
-* The weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights).
-
-[arxiv]: https://arxiv.org/abs/2306.05284
-[musicgen_samples]: https://ai.honu.io/papers/musicgen/
diff --git a/spaces/nakas/musika_api/layers.py b/spaces/nakas/musika_api/layers.py
deleted file mode 100644
index 3b647d57a55ee696f03b2da1358ba30c263f54da..0000000000000000000000000000000000000000
--- a/spaces/nakas/musika_api/layers.py
+++ /dev/null
@@ -1,163 +0,0 @@
-import tensorflow as tf
-import tensorflow.python.keras.backend as K
-from tensorflow.python.eager import context
-from tensorflow.python.ops import (
- gen_math_ops,
- math_ops,
- sparse_ops,
- standard_ops,
-)
-
-
-def l2normalize(v, eps=1e-12):
- return v / (tf.norm(v) + eps)
-
-
-class ConvSN2D(tf.keras.layers.Conv2D):
- def __init__(self, filters, kernel_size, power_iterations=1, datatype=tf.float32, **kwargs):
- super(ConvSN2D, self).__init__(filters, kernel_size, **kwargs)
- self.power_iterations = power_iterations
- self.datatype = datatype
-
- def build(self, input_shape):
- super(ConvSN2D, self).build(input_shape)
-
- if self.data_format == "channels_first":
- channel_axis = 1
- else:
- channel_axis = -1
-
- self.u = self.add_weight(
- self.name + "_u",
- shape=tuple([1, self.kernel.shape.as_list()[-1]]),
- initializer=tf.initializers.RandomNormal(0, 1),
- trainable=False,
- dtype=self.dtype,
- )
-
- def compute_spectral_norm(self, W, new_u, W_shape):
- for _ in range(self.power_iterations):
-
- new_v = l2normalize(tf.matmul(new_u, tf.transpose(W)))
- new_u = l2normalize(tf.matmul(new_v, W))
-
- sigma = tf.matmul(tf.matmul(new_v, W), tf.transpose(new_u))
- W_bar = W / sigma
-
- with tf.control_dependencies([self.u.assign(new_u)]):
- W_bar = tf.reshape(W_bar, W_shape)
-
- return W_bar
-
- def call(self, inputs):
- W_shape = self.kernel.shape.as_list()
- W_reshaped = tf.reshape(self.kernel, (-1, W_shape[-1]))
- new_kernel = self.compute_spectral_norm(W_reshaped, self.u, W_shape)
- outputs = self._convolution_op(inputs, new_kernel)
-
- if self.use_bias:
- if self.data_format == "channels_first":
- outputs = tf.nn.bias_add(outputs, self.bias, data_format="NCHW")
- else:
- outputs = tf.nn.bias_add(outputs, self.bias, data_format="NHWC")
- if self.activation is not None:
- return self.activation(outputs)
-
- return outputs
-
-
-class DenseSN(tf.keras.layers.Dense):
- def __init__(self, datatype=tf.float32, **kwargs):
- super(DenseSN, self).__init__(**kwargs)
- self.datatype = datatype
-
- def build(self, input_shape):
- super(DenseSN, self).build(input_shape)
-
- self.u = self.add_weight(
- self.name + "_u",
- shape=tuple([1, self.kernel.shape.as_list()[-1]]),
- initializer=tf.initializers.RandomNormal(0, 1),
- trainable=False,
- dtype=self.datatype,
- )
-
- def compute_spectral_norm(self, W, new_u, W_shape):
- new_v = l2normalize(tf.matmul(new_u, tf.transpose(W)))
- new_u = l2normalize(tf.matmul(new_v, W))
- sigma = tf.matmul(tf.matmul(new_v, W), tf.transpose(new_u))
- W_bar = W / sigma
- with tf.control_dependencies([self.u.assign(new_u)]):
- W_bar = tf.reshape(W_bar, W_shape)
- return W_bar
-
- def call(self, inputs):
- W_shape = self.kernel.shape.as_list()
- W_reshaped = tf.reshape(self.kernel, (-1, W_shape[-1]))
- new_kernel = self.compute_spectral_norm(W_reshaped, self.u, W_shape)
- rank = len(inputs.shape)
- if rank > 2:
- outputs = standard_ops.tensordot(inputs, new_kernel, [[rank - 1], [0]])
- if not context.executing_eagerly():
- shape = inputs.shape.as_list()
- output_shape = shape[:-1] + [self.units]
- outputs.set_shape(output_shape)
- else:
- inputs = math_ops.cast(inputs, self._compute_dtype)
- if K.is_sparse(inputs):
- outputs = sparse_ops.sparse_tensor_dense_matmul(inputs, new_kernel)
- else:
- outputs = gen_math_ops.mat_mul(inputs, new_kernel)
- if self.use_bias:
- outputs = tf.nn.bias_add(outputs, self.bias)
- if self.activation is not None:
- return self.activation(outputs)
- return outputs
-
-
-class AddNoise(tf.keras.layers.Layer):
- def __init__(self, datatype=tf.float32, **kwargs):
- super(AddNoise, self).__init__(**kwargs)
- self.datatype = datatype
-
- def build(self, input_shape):
- self.b = self.add_weight(
- shape=[
- 1,
- ],
- initializer=tf.keras.initializers.zeros(),
- trainable=True,
- name="noise_weight",
- )
-
- def call(self, inputs):
- rand = tf.random.normal(
- [tf.shape(inputs)[0], inputs.shape[1], inputs.shape[2], 1],
- mean=0.0,
- stddev=1.0,
- dtype=self.datatype,
- )
- output = inputs + self.b * rand
- return output
-
-
-class PosEnc(tf.keras.layers.Layer):
- def __init__(self, datatype=tf.float32, **kwargs):
- super(PosEnc, self).__init__(**kwargs)
- self.datatype = datatype
-
- def call(self, inputs):
- pos = tf.repeat(
- tf.reshape(tf.range(inputs.shape[-3], dtype=tf.int32), [1, -1, 1, 1]),
- inputs.shape[-2],
- -2,
- )
- pos = tf.cast(tf.repeat(pos, tf.shape(inputs)[0], 0), self.dtype) / tf.cast(inputs.shape[-3], self.datatype)
- return tf.concat([inputs, pos], -1) # [bs,1,hop,2]
-
-
-def flatten_hw(x, data_format="channels_last"):
- if data_format == "channels_last":
- x = tf.transpose(x, perm=[0, 3, 1, 2]) # Convert to `channels_first`
-
- old_shape = tf.shape(x)
diff --git a/spaces/nateraw/lavila/run_with_submitit_infer_narrator.py b/spaces/nateraw/lavila/run_with_submitit_infer_narrator.py
deleted file mode 100644
index 16646e9bd055c3ab0a340148ea2fbe4719e65657..0000000000000000000000000000000000000000
--- a/spaces/nateraw/lavila/run_with_submitit_infer_narrator.py
+++ /dev/null
@@ -1,127 +0,0 @@
-
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-A script to run multinode training with submitit.
-"""
-import argparse
-import os
-import uuid
-from pathlib import Path
-
-import main_infer_narrator
-import submitit
-
-
-def parse_args():
- parser = main_infer_narrator.get_args_parser()
- parser = argparse.ArgumentParser("Submitit for inferring narrator", parents=[parser])
- parser.add_argument("--ngpus", default=8, type=int, help="Number of gpus to request on each node")
- parser.add_argument("--nodes", default=4, type=int, help="Number of nodes to request")
- parser.add_argument("--timeout", default=2880, type=int, help="Duration of the job")
- parser.add_argument("--job_dir", default="", type=str, help="Job dir. Leave empty for automatic.")
-
- parser.add_argument("--partition", default="learnlab", type=str, help="Partition where to submit")
- parser.add_argument("--use_volta32", action='store_true', help="Big models? Use this")
- parser.add_argument('--comment', default="", type=str,
- help='Comment to pass to scheduler, e.g. priority message')
- return parser.parse_args()
-
-
-def get_shared_folder() -> Path:
- user = os.getenv("USER")
- if Path("/checkpoint/").is_dir():
- p = Path(f"/checkpoint/{user}/experiments/extract_caption")
- p.mkdir(exist_ok=True)
- return p
- raise RuntimeError("No shared folder available")
-
-
-def get_init_file():
- # Init file must not exist, but it's parent dir must exist.
- os.makedirs(str(get_shared_folder()), exist_ok=True)
- init_file = get_shared_folder() / f"{uuid.uuid4().hex}_init"
- if init_file.exists():
- os.remove(str(init_file))
- return init_file
-
-
-class Trainer(object):
- def __init__(self, args):
- self.args = args
-
- def __call__(self):
- import main_infer_narrator
-
- self._setup_gpu_args()
- main_infer_narrator.main(self.args)
-
- def checkpoint(self):
- import submitit
-
- self.args.dist_url = get_init_file().as_uri()
- print("Requeuing ", self.args)
- empty_trainer = type(self)(self.args)
- return submitit.helpers.DelayedSubmission(empty_trainer)
-
- def _setup_gpu_args(self):
- import submitit
- from pathlib import Path
-
- job_env = submitit.JobEnvironment()
- self.args.output_dir = Path(str(self.args.output_dir).replace("%j", str(job_env.job_id)))
- self.args.gpu = job_env.local_rank
- self.args.rank = job_env.global_rank
- self.args.world_size = job_env.num_tasks
- print(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}")
-
-
-def main():
- args = parse_args()
- if args.job_dir == "":
- args.job_dir = get_shared_folder() / "%j"
-
- # Note that the folder will depend on the job_id, to easily track experiments
- executor = submitit.AutoExecutor(folder=args.job_dir, slurm_max_num_timeout=30)
-
- num_gpus_per_node = args.ngpus
- nodes = args.nodes
- timeout_min = args.timeout
-
- partition = args.partition
- kwargs = {}
- if args.use_volta32:
- kwargs['slurm_constraint'] = 'volta32gb'
- if args.comment:
- kwargs['slurm_comment'] = args.comment
-
- executor.update_parameters(
- mem_gb=55 * num_gpus_per_node,
- gpus_per_node=num_gpus_per_node,
- tasks_per_node=num_gpus_per_node, # one task per GPU
- cpus_per_task=10,
- nodes=nodes,
- timeout_min=timeout_min, # max is 60 * 72
- # Below are cluster dependent parameters
- slurm_partition=partition,
- slurm_signal_delay_s=120,
- **kwargs
- )
-
- executor.update_parameters(name="infer_narrator")
-
- args.dist_url = get_init_file().as_uri()
- args.output_dir = args.job_dir
-
- trainer = Trainer(args)
- job = executor.submit(trainer)
-
- print("Submitted job_id:", job.job_id)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/neveu/img-to-music/app.py b/spaces/neveu/img-to-music/app.py
deleted file mode 100644
index 9e423d7780c5e52a0632efeba9248575a2f5a491..0000000000000000000000000000000000000000
--- a/spaces/neveu/img-to-music/app.py
+++ /dev/null
@@ -1,333 +0,0 @@
-import gradio as gr
-import openai
-import numpy as np
-import time
-import base64
-import ffmpeg
-from sentence_transformers import SentenceTransformer
-from audio2numpy import open_audio
-import httpx
-import json
-import os
-import requests
-import urllib
-import pydub
-from os import path
-from pydub import AudioSegment
-import re
-
-MUBERT_LICENSE = os.environ.get('MUBERT_LICENSE')
-MUBERT_TOKEN = os.environ.get('MUBERT_TOKEN')
-
-#img_to_text = gr.Blocks.load(name="spaces/pharma/CLIP-Interrogator")
-img_to_text = gr.Blocks.load(name="spaces/fffiloni/CLIP-Interrogator-2")
-
-from share_btn import community_icon_html, loading_icon_html, share_js
-from utils import get_tags_for_prompts, get_mubert_tags_embeddings
-
-minilm = SentenceTransformer('all-MiniLM-L6-v2')
-mubert_tags_embeddings = get_mubert_tags_embeddings(minilm)
-
-##————————————————————————————————————
-
-MUBERT_LICENSE = os.environ.get('MUBERT_LICENSE')
-MUBERT_TOKEN = os.environ.get('MUBERT_TOKEN')
-
-##————————————————————————————————————
-def get_pat_token():
- r = httpx.post('https://api-b2b.mubert.com/v2/GetServiceAccess',
- json={
- "method": "GetServiceAccess",
- "params": {
- "email":"mail@mail.com",
- "phone":"+11234567890",
- "license": MUBERT_LICENSE,
- "token": MUBERT_TOKEN,
-
- }
- })
-
- rdata = json.loads(r.text)
- assert rdata['status'] == 1, "probably incorrect e-mail"
- pat = rdata['data']['pat']
- #print(f"pat: {pat}")
- return pat
-
-def get_music(pat, prompt, track_duration, gen_intensity, gen_mode):
-
- if len(prompt) > 200:
- prompt = prompt[:200]
-
- r = httpx.post('https://api-b2b.mubert.com/v2/TTMRecordTrack',
- json={
- "method": "TTMRecordTrack",
- "params":
- {
- "text": prompt,
- "pat": pat,
- "mode":gen_mode,
- "duration":track_duration,
- "intensity": gen_intensity,
- "format": "wav"
- }
- })
-
- rdata = json.loads(r.text)
-
- #print(f"rdata: {rdata}")
- assert rdata['status'] == 1, rdata['error']['text']
- track = rdata['data']['tasks'][0]['download_link']
- print(track)
-
- local_file_path = "sample.wav"
-
- # Download the MP3 file from the URL
- headers = {
- 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7; rv:93.0) Gecko/20100101 Firefox/93.0'}
-
- retries = 3
- delay = 5 # in seconds
- while retries > 0:
- response = requests.get(track, headers=headers)
- if response.status_code == 200:
- break
- retries -= 1
- time.sleep(delay)
- response = requests.get(track, headers=headers)
- print(f"{response}")
- # Save the downloaded content to a local file
- with open(local_file_path, 'wb') as f:
- f.write(response.content)
- return "sample.wav", track
-
-
-def get_results(text_prompt,track_duration,gen_intensity,gen_mode):
- pat_token = get_pat_token()
- music = get_music(pat_token, text_prompt, track_duration, gen_intensity, gen_mode)
- return pat_token, music[0], music[1]
-
-def get_prompts(uploaded_image, track_duration, gen_intensity, gen_mode, openai_api_key):
- print("calling clip interrogator")
- #prompt = img_to_text(uploaded_image, "ViT-L (best for Stable Diffusion 1.*)", "fast", fn_index=1)[0]
-
- prompt = img_to_text(uploaded_image, 'best', 4, fn_index=1)[0]
- print(prompt)
- clean_prompt = clean_text(prompt)
- print(f"prompt cleaned: {clean_prompt}")
- musical_prompt = 'You did not use any OpenAI API key to pimp your result :)'
- if openai_api_key is not None:
- gpt_adaptation = try_api(prompt, openai_api_key)
- if gpt_adaptation[0] != "oups":
- musical_prompt = gpt_adaptation[0]
- print(f"musical adapt: {musical_prompt}")
- music_result = get_results(musical_prompt, track_duration, gen_intensity, gen_mode)
- else:
- music_result = get_results(clean_prompt, track_duration, gen_intensity, gen_mode)
- else:
- music_result = get_results(clean_prompt, track_duration, gen_intensity, gen_mode)
-
- show_prompts = f"""
- CLIP Interrogator Caption: '{prompt}'
- —
- OpenAI Musical Adaptation: '{musical_prompt}'
- —
- Audio file link: {music_result[2]}
- """
- #wave_file = convert_mp3_to_wav(music_result[1])
-
- time.sleep(1)
- return gr.Textbox.update(value=show_prompts, visible=True), music_result[1], gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-
-def try_api(message, openai_api_key):
-
- try:
- response = call_api(message, openai_api_key)
- return response, "no error"
- except openai.error.Timeout as e:
- #Handle timeout error, e.g. retry or log
- #print(f"OpenAI API request timed out: {e}")
- return "oups", f"OpenAI API request timed out:
{e}"
- except openai.error.APIError as e:
- #Handle API error, e.g. retry or log
- #print(f"OpenAI API returned an API Error: {e}")
- return "oups", f"OpenAI API returned an API Error:
{e}"
- except openai.error.APIConnectionError as e:
- #Handle connection error, e.g. check network or log
- #print(f"OpenAI API request failed to connect: {e}")
- return "oups", f"OpenAI API request failed to connect:
{e}"
- except openai.error.InvalidRequestError as e:
- #Handle invalid request error, e.g. validate parameters or log
- #print(f"OpenAI API request was invalid: {e}")
- return "oups", f"OpenAI API request was invalid:
{e}"
- except openai.error.AuthenticationError as e:
- #Handle authentication error, e.g. check credentials or log
- #print(f"OpenAI API request was not authorized: {e}")
- return "oups", f"OpenAI API request was not authorized:
{e}"
- except openai.error.PermissionError as e:
- #Handle permission error, e.g. check scope or log
- #print(f"OpenAI API request was not permitted: {e}")
- return "oups", f"OpenAI API request was not permitted:
{e}"
- except openai.error.RateLimitError as e:
- #Handle rate limit error, e.g. wait or log
- #print(f"OpenAI API request exceeded rate limit: {e}")
- return "oups", f"OpenAI API request exceeded rate limit:
{e}"
-
-def call_api(message, openai_api_key):
-
- instruction = "Convert in less than 200 characters this image caption to a very concise musical description with musical terms, as if you wanted to describe a musical ambiance, stricly in English"
-
- print("starting open ai")
- augmented_prompt = f"{instruction}: '{message}'."
- openai.api_key = openai_api_key
-
- response = openai.Completion.create(
- model="text-davinci-003",
- prompt=augmented_prompt,
- temperature=0.5,
- max_tokens=2048,
- top_p=1,
- frequency_penalty=0,
- presence_penalty=0.6
- )
-
- #print(response)
-
- #return str(response.choices[0].text).split("\n",2)[2]
- return str(response.choices[0].text).lstrip('\n')
-
-
-def get_track_by_tags(tags, pat, duration, gen_intensity, gen_mode, maxit=20):
-
- r = httpx.post('https://api-b2b.mubert.com/v2/RecordTrackTTM',
- json={
- "method": "RecordTrackTTM",
- "params": {
- "pat": pat,
- "duration": duration,
- "format": "wav",
- "intensity":gen_intensity,
- "tags": tags,
- "mode": gen_mode
- }
- })
-
- rdata = json.loads(r.text)
- print(rdata)
- #assert rdata['status'] == 1, rdata['error']['text']
- trackurl = rdata['data']['tasks'][0]
-
- print('Generating track ', end='')
- for i in range(maxit):
- r = httpx.get(trackurl)
- if r.status_code == 200:
- return trackurl
- time.sleep(1)
-
-
-def generate_track_by_prompt(pat, prompt, duration, gen_intensity, gen_mode):
- try:
- _, tags = get_tags_for_prompts(minilm, mubert_tags_embeddings, prompt)[0]
- result = get_track_by_tags(tags, pat, int(duration), gen_intensity, gen_mode)
- print(result)
- return result, ",".join(tags), "Success"
- except Exception as e:
- return None, "", str(e)
-
-def convert_mp3_to_wav(mp3_filepath):
-
- wave_file="file.wav"
-
- sound = AudioSegment.from_mp3(mp3_filepath)
- sound.export(wave_file, format="wav")
-
- return wave_file
-
-def remove_emoji(text):
- emoji_pattern = re.compile("["
- u"\U0001F600-\U0001F64F" # emoticons
- u"\U0001F300-\U0001F5FF" # symbols & pictographs
- u"\U0001F680-\U0001F6FF" # transport & map symbols
- u"\U0001F1E0-\U0001F1FF" # flags (iOS)
- "]+", flags=re.UNICODE)
- return emoji_pattern.sub(r'', text)
-
-def remove_nonalphanumeric(text):
- return re.sub(r'[^a-zA-Z0-9\s]', '', text)
-
-def clean_text(text):
- clean_text = remove_nonalphanumeric(text)
- clean_text = remove_emoji(clean_text)
- clean_text = re.sub(r'\d+', '', clean_text) # Remove any number
- return clean_text
-
-article = """
-
-
-
-
-
-
-
-"""
-
-with gr.Blocks(css="style.css") as demo:
- with gr.Column(elem_id="col-container"):
-
- gr.HTML("""
-
-
-
- """)
-
- input_img = gr.Image(type="filepath", elem_id="input-img")
- prompts_out = gr.Textbox(label="Text Captions", visible=False, info="If player do not work, try to copy/paste the link in a new browser window")
- music_output = gr.Audio(label="Result", type="filepath", elem_id="music-output").style(height="5rem")
- #music_url = gr.Textbox(max_lines=1, info="If player do not work, try to copy/paste the link in a new browser window")
- #text_status = gr.Textbox(label="status")
- with gr.Group(elem_id="share-btn-container"):
- community_icon = gr.HTML(community_icon_html, visible=False)
- loading_icon = gr.HTML(loading_icon_html, visible=False)
- share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
-
- with gr.Accordion(label="Music Generation Options", open=False):
- openai_api_key = gr.Textbox(type="password", label="🔐 Your OpenAI API Key (optional)", placeholder="sk-123abc...", info="You can use your OpenAI key to adapt CLIP Interrogator caption to a musical translation.")
- track_duration = gr.Slider(minimum=20, maximum=120, value=55, ustep=5, label="Track duration", elem_id="duration-inp")
- with gr.Row():
- gen_intensity = gr.Dropdown(choices=["low", "medium", "high"], value="medium", label="Intensity")
- gen_mode = gr.Radio(label="mode", choices=["track", "loop"], value="loop")
-
- generate = gr.Button("Generate Music from Image")
-
- gr.HTML(article)
-
- generate.click(get_prompts, inputs=[input_img,track_duration,gen_intensity,gen_mode, openai_api_key], outputs=[prompts_out, music_output, share_button, community_icon, loading_icon], api_name="i2m")
- share_button.click(None, [], [], _js=share_js)
-
-demo.queue(max_size=32).launch()
\ No newline at end of file
diff --git a/spaces/ngxson/poet-cat/Dockerfile b/spaces/ngxson/poet-cat/Dockerfile
deleted file mode 100644
index c3f32abbee0025d76d9b974f939f5dc08817909c..0000000000000000000000000000000000000000
--- a/spaces/ngxson/poet-cat/Dockerfile
+++ /dev/null
@@ -1,33 +0,0 @@
-FROM nikolaik/python-nodejs:python3.9-nodejs18-bullseye
-
-# prepare cache
-USER root
-RUN mkdir -p /.cache && \
- chown 1000:1000 /.cache && \
- chmod 777 /.cache
-
-# user "pn" is created by nikolaik/python-nodejs
-USER pn
-ENV HOME=/home/pn \
- PATH=/home/pn/.local/bin:$PATH \
- TRANSFORMERS_CACHE=/.cache
-
-WORKDIR $HOME/app
-
-# install requirements.txt
-COPY --chown=pn ./requirements.txt $HOME/app/requirements.txt
-RUN pip install --no-cache-dir --upgrade -r $HOME/app/requirements.txt
-
-# cache the model
-COPY --chown=pn ./setup.py $HOME/app/setup.py
-RUN python setup.py && ls -la /.cache
-
-COPY --chown=pn . $HOME/app
-
-# build frontend
-WORKDIR $HOME/app/frontend
-RUN npm i && npm run build
-
-WORKDIR $HOME/app
-EXPOSE 7860
-CMD ["/bin/bash", "-c", "TRANSFORMERS_OFFLINE=1 python app.py"]
\ No newline at end of file
diff --git a/spaces/nielsr/donut-docvqa/README.md b/spaces/nielsr/donut-docvqa/README.md
deleted file mode 100644
index 0554bcadb33b00901b547a5112643666afc4727c..0000000000000000000000000000000000000000
--- a/spaces/nielsr/donut-docvqa/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Donut Docvqa
-emoji: 🍩
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/niizam/sovits-models/README.md b/spaces/niizam/sovits-models/README.md
deleted file mode 100644
index b2debfb030fe1e3f7564101640897f6c211675bb..0000000000000000000000000000000000000000
--- a/spaces/niizam/sovits-models/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Sovits Models
-emoji: 🎙️
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: zomehwh/sovits-models
----
diff --git a/spaces/nmitchko/AI-in-Healthcare/index.html b/spaces/nmitchko/AI-in-Healthcare/index.html
deleted file mode 100644
index 593b718f8611cfaa023241c47bba6d1a08ba4162..0000000000000000000000000000000000000000
--- a/spaces/nmitchko/AI-in-Healthcare/index.html
+++ /dev/null
@@ -1,20 +0,0 @@
-
-
-
-
-
-
-
-
- Image to Music
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/nomic-ai/openai_humaneval/index.html b/spaces/nomic-ai/openai_humaneval/index.html
deleted file mode 100644
index 1a00914288ad8f1d0e23a7bb887d05c8116b5395..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/openai_humaneval/index.html
+++ /dev/null
@@ -1,42 +0,0 @@
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/nooji/ImpCatcher/Dockerfile b/spaces/nooji/ImpCatcher/Dockerfile
deleted file mode 100644
index 284b8d5144ea825d0b5d3a5d8caea1c6567f8225..0000000000000000000000000000000000000000
--- a/spaces/nooji/ImpCatcher/Dockerfile
+++ /dev/null
@@ -1,44 +0,0 @@
-FROM julia:1.8 as julia-base
-FROM nvidia/cuda:11.2.0-cudnn8-devel-ubuntu20.04 as base
-
-# ubuntu 20.04 is derived from debian buster
-COPY --from=julia-base /usr/local/julia /usr/local/julia
-
-# since we are copying from julia-base, need to add to PATH
-ENV JULIA_PATH /usr/local/julia
-ENV PATH $JULIA_PATH/bin:$PATH
-# cuda env vars to use cuda & julia https://cuda.juliagpu.org/stable/installation/overview/
-ENV JULIA_CUDA_USE_BINARYBUILDER="false"
-ENV JULIA_DEBUG CUDA
-ENV CUDA_HOME /usr/local/cuda
-
-
-RUN useradd --create-home --shell /bin/bash genie
-RUN mkdir /home/genie/app
-COPY . /home/genie/app
-WORKDIR /home/genie/app
-
-# C compiler for PackageCompiler before non-root user is set
-RUN apt-get update && apt-get install -y g++
-
-RUN chown -R genie:genie /home/
-USER genie
-
-
-
-EXPOSE 8000
-EXPOSE 80
-ENV JULIA_DEPOT_PATH "/home/genie/.julia"
-ENV GENIE_ENV "dev"
-ENV GENIE_HOST "0.0.0.0"
-ENV PORT "8000"
-ENV WSPORT "8000"
-
-# weird behavior with ungreistred packages pulled from git. paths end up not initializing when main branch changes. very hacky work around
-# which requires rebuilding the manifest
-RUN julia -e 'using Pkg; Pkg.add(url="https://github.com/anoojpatel/Chess.jl"); Pkg.activate("."); Pkg.add("Stipple"); Pkg.precompile()'
-
-# Compile app
-RUN julia --project make.jl
-
-ENTRYPOINT julia --project --sysimage=sysimg.so -e 'using Pkg; Pkg.instantiate(); using Genie; Genie.loadapp(); up(async=false);;'
diff --git a/spaces/ntt123/WaveGRU-Text-To-Speech/text.py b/spaces/ntt123/WaveGRU-Text-To-Speech/text.py
deleted file mode 100644
index ba6a3a29549b13a68c6eb4de6997cf37db1aeb67..0000000000000000000000000000000000000000
--- a/spaces/ntt123/WaveGRU-Text-To-Speech/text.py
+++ /dev/null
@@ -1,92 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-"""
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
- 1. "english_cleaners" for English text
- 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
- the Unidecode library (https://pypi.python.org/pypi/Unidecode)
- 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
- the symbols in symbols.py to match your data).
-"""
-
-import re
-from mynumbers import normalize_numbers
-from unidecode import unidecode
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r"\s+")
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_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):
- """Basic pipeline that lowercases and collapses whitespace without transliteration."""
- text = lowercase(text)
- text = collapse_whitespace(text)
- return text
-
-
-def transliteration_cleaners(text):
- """Pipeline for non-English text that transliterates to ASCII."""
- text = convert_to_ascii(text)
- text = lowercase(text)
- text = collapse_whitespace(text)
- return text
-
-
-def english_cleaners(text):
- """Pipeline for English text, including number and abbreviation expansion."""
- 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/oguzakif/video-object-remover/FGT_codes/LAFC/data/util/__init__.py b/spaces/oguzakif/video-object-remover/FGT_codes/LAFC/data/util/__init__.py
deleted file mode 100644
index 35e29baa771a24d045de69a7259c08fb9ee86f4b..0000000000000000000000000000000000000000
--- a/spaces/oguzakif/video-object-remover/FGT_codes/LAFC/data/util/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from .STTN_mask import create_random_shape_with_random_motion
-
-import logging
-logger = logging.getLogger('base')
-
-
-def initialize_mask(videoLength, dataInfo):
- from .MaskModel import RandomMask
- from .MaskModel import MidRandomMask
- from .MaskModel import MatrixMask
- from .MaskModel import FreeFormMask
- from .MaskModel import StationaryMask
-
- return {'random': RandomMask(videoLength, dataInfo),
- 'mid': MidRandomMask(videoLength, dataInfo),
- 'matrix': MatrixMask(videoLength, dataInfo),
- 'free': FreeFormMask(videoLength, dataInfo),
- 'stationary': StationaryMask(videoLength, dataInfo)
- }
-
-
-def create_mask(maskClass, form):
- if form == 'mix':
- from random import randint
- candidates = list(maskClass.keys())
- candidate_index = randint(0, len(candidates) - 1)
- return maskClass[candidates[candidate_index]]()
- return maskClass[form]()
\ No newline at end of file
diff --git a/spaces/oguzakif/video-object-remover/SiamMask/utils/pysot/datasets/vot.py b/spaces/oguzakif/video-object-remover/SiamMask/utils/pysot/datasets/vot.py
deleted file mode 100644
index 7817209c5378494153f0b74ca3d785674726e579..0000000000000000000000000000000000000000
--- a/spaces/oguzakif/video-object-remover/SiamMask/utils/pysot/datasets/vot.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# --------------------------------------------------------
-# Python Single Object Tracking Evaluation
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Fangyi Zhang
-# @author fangyi.zhang@vipl.ict.ac.cn
-# @project https://github.com/StrangerZhang/pysot-toolkit.git
-# Revised for SiamMask by foolwood
-# --------------------------------------------------------
-import os
-import json
-import numpy as np
-
-from glob import glob
-from tqdm import tqdm
-
-from .dataset import Dataset
-from .video import Video
-
-
-class VOTVideo(Video):
- """
- Args:
- name: video name
- root: dataset root
- video_dir: video directory
- init_rect: init rectangle
- img_names: image names
- gt_rect: groundtruth rectangle
- camera_motion: camera motion tag
- illum_change: illum change tag
- motion_change: motion change tag
- size_change: size change
- occlusion: occlusion
- """
- def __init__(self, name, root, video_dir, init_rect, img_names, gt_rect,
- camera_motion, illum_change, motion_change, size_change, occlusion, width, height):
- super(VOTVideo, self).__init__(name, root, video_dir, init_rect, img_names, gt_rect, None)
- self.tags= {'all': [1] * len(gt_rect)}
- self.tags['camera_motion'] = camera_motion
- self.tags['illum_change'] = illum_change
- self.tags['motion_change'] = motion_change
- self.tags['size_change'] = size_change
- self.tags['occlusion'] = occlusion
-
- self.width = width
- self.height = height
-
- # empty tag
- all_tag = [v for k, v in self.tags.items() if len(v) > 0 ]
- self.tags['empty'] = np.all(1 - np.array(all_tag), axis=1).astype(np.int32).tolist()
-
- self.tag_names = list(self.tags.keys())
-
- def select_tag(self, tag, start=0, end=0):
- if tag == 'empty':
- return self.tags[tag]
- return self.tags[tag][start:end]
-
- def load_tracker(self, path, tracker_names=None, store=True):
- """
- Args:
- path(str): path to result
- tracker_name(list): name of tracker
- """
- if not tracker_names:
- tracker_names = [x.split('/')[-1] for x in glob(path)
- if os.path.isdir(x)]
- if isinstance(tracker_names, str):
- tracker_names = [tracker_names]
- for name in tracker_names:
- traj_files = glob(os.path.join(path, name, 'baseline', self.name, '*0*.txt'))
- if len(traj_files) == 15:
- traj_files = traj_files
- else:
- traj_files = traj_files[0:1]
- pred_traj = []
- for traj_file in traj_files:
- with open(traj_file, 'r') as f:
- traj = [list(map(float, x.strip().split(',')))
- for x in f.readlines()]
- pred_traj.append(traj)
- if store:
- self.pred_trajs[name] = pred_traj
- else:
- return pred_traj
-
-
-class VOTDataset(Dataset):
- """
- Args:
- name: dataset name, should be 'VOT2018', 'VOT2016'
- dataset_root: dataset root
- load_img: wether to load all imgs
- """
- def __init__(self, name, dataset_root):
- super(VOTDataset, self).__init__(name, dataset_root)
- try:
- with open(os.path.join(dataset_root, name+'.json'), 'r') as f:
- meta_data = json.load(f)
- except:
- download_str = '# download json file for eval toolkit\n'+\
- 'cd $SiamMask/data\n'+\
- 'wget http://www.robots.ox.ac.uk/~qwang/VOT2016.json\n'+\
- 'wget http://www.robots.ox.ac.uk/~qwang/VOT2018.json'
- print(download_str)
- exit()
-
- # load videos
- pbar = tqdm(meta_data.keys(), desc='loading '+name, ncols=100)
- self.videos = {}
- for video in pbar:
- pbar.set_postfix_str(video)
- self.videos[video] = VOTVideo(video,
- dataset_root,
- meta_data[video]['video_dir'],
- meta_data[video]['init_rect'],
- meta_data[video]['img_names'],
- meta_data[video]['gt_rect'],
- meta_data[video]['camera_motion'],
- meta_data[video]['illum_change'],
- meta_data[video]['motion_change'],
- meta_data[video]['size_change'],
- meta_data[video]['occlusion'],
- meta_data[video]['width'],
- meta_data[video]['height'])
-
- self.tags = ['all', 'camera_motion', 'illum_change', 'motion_change',
- 'size_change', 'occlusion', 'empty']
diff --git a/spaces/oldplayer1871/anime-remove-background/app.py b/spaces/oldplayer1871/anime-remove-background/app.py
deleted file mode 100644
index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000
--- a/spaces/oldplayer1871/anime-remove-background/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-
-def get_mask(img, s=1024):
- img = (img / 255).astype(np.float32)
- h, w = h0, w0 = img.shape[:-1]
- h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
- ph, pw = s - h, s - w
- img_input = np.zeros([s, s, 3], dtype=np.float32)
- img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
- img_input = np.transpose(img_input, (2, 0, 1))
- img_input = img_input[np.newaxis, :]
- mask = rmbg_model.run(None, {'img': img_input})[0][0]
- mask = np.transpose(mask, (1, 2, 0))
- mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
- mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
- return mask
-
-
-def rmbg_fn(img):
- mask = get_mask(img)
- img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
- mask = (mask * 255).astype(np.uint8)
- img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
- mask = mask.repeat(3, axis=2)
- return mask, img
-
-
-if __name__ == "__main__":
- providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
- model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
- rmbg_model = rt.InferenceSession(model_path, providers=providers)
- app = gr.Blocks()
- with app:
- gr.Markdown("# Anime Remove Background\n\n"
- "\n\n"
- "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
- with gr.Row():
- with gr.Column():
- input_img = gr.Image(label="input image")
- examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
- examples = gr.Dataset(components=[input_img], samples=examples_data)
- run_btn = gr.Button(variant="primary")
- output_mask = gr.Image(label="mask")
- output_img = gr.Image(label="result", image_mode="RGBA")
- examples.click(lambda x: x[0], [examples], [input_img])
- run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
- app.launch()
diff --git a/spaces/onnx/AlexNet/app.py b/spaces/onnx/AlexNet/app.py
deleted file mode 100644
index 4d81900bd92eed683bda5420335587d7892845d4..0000000000000000000000000000000000000000
--- a/spaces/onnx/AlexNet/app.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import mxnet as mx
-import matplotlib.pyplot as plt
-import numpy as np
-from collections import namedtuple
-from mxnet.gluon.data.vision import transforms
-import os
-import gradio as gr
-
-from PIL import Image
-import imageio
-import onnxruntime as ort
-
-def get_image(path):
- '''
- Using path to image, return the RGB load image
- '''
- img = imageio.imread(path, pilmode='RGB')
- return img
-
-# Pre-processing function for ImageNet models using numpy
-def preprocess(img):
- '''
- Preprocessing required on the images for inference with mxnet gluon
- The function takes loaded image and returns processed tensor
- '''
- img = np.array(Image.fromarray(img).resize((224, 224))).astype(np.float32)
- img[:, :, 0] -= 123.68
- img[:, :, 1] -= 116.779
- img[:, :, 2] -= 103.939
- img[:,:,[0,1,2]] = img[:,:,[2,1,0]]
- img = img.transpose((2, 0, 1))
- img = np.expand_dims(img, axis=0)
-
- return img
-
-mx.test_utils.download('https://s3.amazonaws.com/model-server/inputs/kitten.jpg')
-
-mx.test_utils.download('https://s3.amazonaws.com/onnx-model-zoo/synset.txt')
-with open('synset.txt', 'r') as f:
- labels = [l.rstrip() for l in f]
-
-os.system("wget https://github.com/AK391/models/raw/main/vision/classification/alexnet/model/bvlcalexnet-7.onnx")
-
-ort_session = ort.InferenceSession("bvlcalexnet-7.onnx")
-
-
-def predict(path):
- img_batch = preprocess(get_image(path))
-
- outputs = ort_session.run(
- None,
- {"data_0": img_batch.astype(np.float32)},
- )
-
- a = np.argsort(-outputs[0].flatten())
- results = {}
- for i in a[0:5]:
- results[labels[i]]=float(outputs[0][0][i])
- return results
-
-
-title="AlexNet"
-description="AlexNet is the name of a convolutional neural network for classification, which competed in the ImageNet Large Scale Visual Recognition Challenge in 2012."
-
-examples=[['catonnx.jpg']]
-gr.Interface(predict,gr.inputs.Image(type='filepath'),"label",title=title,description=description,examples=examples).launch(enable_queue=True,debug=True)
\ No newline at end of file
diff --git a/spaces/onnx/export/app.py b/spaces/onnx/export/app.py
deleted file mode 100644
index 334e3faa6d3dbaa84fcab112896a5965f4d6d4cb..0000000000000000000000000000000000000000
--- a/spaces/onnx/export/app.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import csv
-import os
-from datetime import datetime
-from typing import Optional, Union
-
-import gradio as gr
-from huggingface_hub import HfApi, Repository
-
-from onnx_export import convert
-
-DATASET_REPO_URL = "https://huggingface.co/datasets/optimum/exporters"
-DATA_FILENAME = "data.csv"
-DATA_FILE = os.path.join("data", DATA_FILENAME)
-
-HF_TOKEN = os.environ.get("HF_WRITE_TOKEN")
-
-DATADIR = "exporters_data"
-
-repo: Optional[Repository] = None
-if HF_TOKEN:
- repo = Repository(local_dir=DATADIR, clone_from=DATASET_REPO_URL, token=HF_TOKEN)
-
-
-def onnx_export(token: str, model_id: str, task: str, opset: Union[int, str]) -> str:
- if token == "" or model_id == "":
- return """
- ### Invalid input 🐞
-
- Please fill a token and model name.
- """
- try:
- if opset == "":
- opset = None
- else:
- opset = int(opset)
-
- api = HfApi(token=token)
-
- error, commit_info = convert(api=api, model_id=model_id, task=task, opset=opset)
- if error != "0":
- return error
-
- print("[commit_info]", commit_info)
-
- # save in a private dataset
- if repo is not None:
- repo.git_pull(rebase=True)
- with open(os.path.join(DATADIR, DATA_FILE), "a") as csvfile:
- writer = csv.DictWriter(
- csvfile, fieldnames=["model_id", "pr_url", "time"]
- )
- writer.writerow(
- {
- "model_id": model_id,
- "pr_url": commit_info.pr_url,
- "time": str(datetime.now()),
- }
- )
- commit_url = repo.push_to_hub()
- print("[dataset]", commit_url)
-
- pr_revision = commit_info.pr_revision.replace("/", "%2F")
-
- return f"#### Success 🔥 Yay! This model was successfully exported and a PR was open using your token, here: [{commit_info.pr_url}]({commit_info.pr_url}). If you would like to use the exported model without waiting for the PR to be approved, head to https://huggingface.co/{model_id}/tree/{pr_revision}"
- except Exception as e:
- return f"#### Error: {e}"
-
-
-TTILE_IMAGE = """
-
-
-"""
-
-TITLE = """
-
-
-
-"""
-
-# for some reason https://huggingface.co/settings/tokens is not showing as a link by default?
-DESCRIPTION = """
-This Space allows you to automatically export 🤗 transformers PyTorch models hosted on the Hugging Face Hub to [ONNX](https://onnx.ai/). It opens a PR on the target model, and it is up to the owner of the original model
-to merge the PR to allow people to leverage the ONNX standard to share and use the model on a wide range of devices!
-
-Once exported, the model can, for example, be used in the [🤗 Optimum](https://huggingface.co/docs/optimum/) library closely following the transformers API.
-Check out [this guide](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/models) to see how!
-
-The steps are as following:
-- Paste a read-access token from [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens). Read access is enough given that we will open a PR against the source repo.
-- Input a model id from the Hub (for example: [textattack/distilbert-base-cased-CoLA](https://huggingface.co/textattack/distilbert-base-cased-CoLA))
-- Click "Export to ONNX"
-- That's it! You'll get feedback on if the export was successful or not, and if it was, you'll get the URL of the opened PR!
-
-Note: in case the model to export is larger than 2 GB, it will be saved in a subfolder called `onnx/`. To load it from Optimum, the argument `subfolder="onnx"` should be provided.
-"""
-
-with gr.Blocks() as demo:
- gr.HTML(TTILE_IMAGE)
- gr.HTML(TITLE)
-
- with gr.Row():
- with gr.Column(scale=50):
- gr.Markdown(DESCRIPTION)
-
- with gr.Column(scale=50):
- input_token = gr.Textbox(
- max_lines=1,
- label="Hugging Face token",
- )
- input_model = gr.Textbox(
- max_lines=1,
- label="Model name",
- placeholder="textattack/distilbert-base-cased-CoLA",
- )
- input_task = gr.Textbox(
- value="auto",
- max_lines=1,
- label='Task (can be left to "auto", will be automatically inferred)',
- )
- onnx_opset = gr.Textbox(
- placeholder="for example 14, can be left blank",
- max_lines=1,
- label="ONNX opset (optional, can be left blank)",
- )
-
- btn = gr.Button("Export to ONNX")
- output = gr.Markdown(label="Output")
-
- btn.click(
- fn=onnx_export,
- inputs=[input_token, input_model, input_task, onnx_opset],
- outputs=output,
- )
-
-demo.launch()
diff --git a/spaces/openskyml/dreamdrop-sd/MagicPrompt.py b/spaces/openskyml/dreamdrop-sd/MagicPrompt.py
deleted file mode 100644
index 249b10a46600e2addc3d7f243ece610832246f02..0000000000000000000000000000000000000000
--- a/spaces/openskyml/dreamdrop-sd/MagicPrompt.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from transformers import pipeline, set_seed
-import gradio as gr, random, re
-
-
-
-
-def MagicPromptSD(current_MagicPrompt, starting_text):
- gpt2_pipe = pipeline('text-generation', model=current_MagicPrompt, tokenizer='gpt2')
- with open("ideas.txt", "r") as f:
- line = f.readlines()
-
- for count in range(4):
- seed = random.randint(100, 1000000)
- set_seed(seed)
-
- if starting_text == "":
- starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize()
- starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text)
- print(starting_text)
-
- response = gpt2_pipe(starting_text, max_length=random.randint(60, 90), num_return_sequences=4)
- response_list = []
- for x in response:
- resp = x['generated_text'].strip()
- if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False:
- response_list.append(resp+'\n')
-
- response_end = "\n".join(response_list)
- response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
- response_end = response_end.replace("<", "").replace(">", "")
-
- if response_end != "":
- return response_end
- if count == 4:
- return response_end
-
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/optimization/open_vino.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/optimization/open_vino.md
deleted file mode 100644
index 606c2207bcda06cb21b0e0f7ede813a613fc1602..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/optimization/open_vino.md
+++ /dev/null
@@ -1,81 +0,0 @@
-
-
-
-# OpenVINO
-
-🤗 [Optimum](https://github.com/huggingface/optimum-intel) provides Stable Diffusion pipelines compatible with OpenVINO to perform inference on a variety of Intel processors (see the [full list]((https://docs.openvino.ai/latest/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html)) of supported devices).
-
-You'll need to install 🤗 Optimum Intel with the `--upgrade-strategy eager` option to ensure [`optimum-intel`](https://github.com/huggingface/optimum-intel) is using the latest version:
-
-```
-pip install --upgrade-strategy eager optimum["openvino"]
-```
-
-This guide will show you how to use the Stable Diffusion and Stable Diffusion XL (SDXL) pipelines with OpenVINO.
-
-## Stable Diffusion
-
-To load and run inference, use the [`~optimum.intel.OVStableDiffusionPipeline`]. If you want to load a PyTorch model and convert it to the OpenVINO format on-the-fly, set `export=True`:
-
-```python
-from optimum.intel import OVStableDiffusionPipeline
-
-model_id = "runwayml/stable-diffusion-v1-5"
-pipeline = OVStableDiffusionPipeline.from_pretrained(model_id, export=True)
-prompt = "sailing ship in storm by Rembrandt"
-image = pipeline(prompt).images[0]
-
-# Don't forget to save the exported model
-pipeline.save_pretrained("openvino-sd-v1-5")
-```
-
-To further speed-up inference, statically reshape the model. If you change any parameters such as the outputs height or width, you’ll need to statically reshape your model again.
-
-```python
-# Define the shapes related to the inputs and desired outputs
-batch_size, num_images, height, width = 1, 1, 512, 512
-
-# Statically reshape the model
-pipeline.reshape(batch_size, height, width, num_images)
-# Compile the model before inference
-pipeline.compile()
-
-image = pipeline(
- prompt,
- height=height,
- width=width,
- num_images_per_prompt=num_images,
-).images[0]
-```
-
- Export transformers model to ONNX with 🤗 Optimum exporters 🏎️
-
-
-
-
-You can find more examples in the 🤗 Optimum [documentation](https://huggingface.co/docs/optimum/intel/inference#stable-diffusion), and Stable Diffusion is supported for text-to-image, image-to-image, and inpainting.
-
-## Stable Diffusion XL
-
-To load and run inference with SDXL, use the [`~optimum.intel.OVStableDiffusionXLPipeline`]:
-
-```python
-from optimum.intel import OVStableDiffusionXLPipeline
-
-model_id = "stabilityai/stable-diffusion-xl-base-1.0"
-pipeline = OVStableDiffusionXLPipeline.from_pretrained(model_id)
-prompt = "sailing ship in storm by Rembrandt"
-image = pipeline(prompt).images[0]
-```
-
-To further speed-up inference, [statically reshape](#stable-diffusion) the model as shown in the Stable Diffusion section.
-
-You can find more examples in the 🤗 Optimum [documentation](https://huggingface.co/docs/optimum/intel/inference#stable-diffusion-xl), and running SDXL in OpenVINO is supported for text-to-image and image-to-image.
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/using-diffusers/push_to_hub.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/using-diffusers/push_to_hub.md
deleted file mode 100644
index 46838603176808b6a725ece81647d7be9980318a..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/using-diffusers/push_to_hub.md
+++ /dev/null
@@ -1,171 +0,0 @@
-# Push files to the Hub
-
-[[open-in-colab]]
-
-🤗 Diffusers provides a [`~diffusers.utils.PushToHubMixin`] for uploading your model, scheduler, or pipeline to the Hub. It is an easy way to store your files on the Hub, and also allows you to share your work with others. Under the hood, the [`~diffusers.utils.PushToHubMixin`]:
-
-1. creates a repository on the Hub
-2. saves your model, scheduler, or pipeline files so they can be reloaded later
-3. uploads folder containing these files to the Hub
-
-This guide will show you how to use the [`~diffusers.utils.PushToHubMixin`] to upload your files to the Hub.
-
-You'll need to log in to your Hub account with your access [token](https://huggingface.co/settings/tokens) first:
-
-```py
-from huggingface_hub import notebook_login
-
-notebook_login()
-```
-
-## Models
-
-To push a model to the Hub, call [`~diffusers.utils.PushToHubMixin.push_to_hub`] and specfiy the repository id of the model to be stored on the Hub:
-
-```py
-from diffusers import ControlNetModel
-
-controlnet = ControlNetModel(
- block_out_channels=(32, 64),
- layers_per_block=2,
- in_channels=4,
- down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
- cross_attention_dim=32,
- conditioning_embedding_out_channels=(16, 32),
-)
-controlnet.push_to_hub("my-controlnet-model")
-```
-
-For model's, you can also specify the [*variant*](loading#checkpoint-variants) of the weights to push to the Hub. For example, to push `fp16` weights:
-
-```py
-controlnet.push_to_hub("my-controlnet-model", variant="fp16")
-```
-
-The [`~diffusers.utils.PushToHubMixin.push_to_hub`] function saves the model's `config.json` file and the weights are automatically saved in the `safetensors` format.
-
-Now you can reload the model from your repository on the Hub:
-
-```py
-model = ControlNetModel.from_pretrained("your-namespace/my-controlnet-model")
-```
-
-## Scheduler
-
-To push a scheduler to the Hub, call [`~diffusers.utils.PushToHubMixin.push_to_hub`] and specfiy the repository id of the scheduler to be stored on the Hub:
-
-```py
-from diffusers import DDIMScheduler
-
-scheduler = DDIMScheduler(
- beta_start=0.00085,
- beta_end=0.012,
- beta_schedule="scaled_linear",
- clip_sample=False,
- set_alpha_to_one=False,
-)
-scheduler.push_to_hub("my-controlnet-scheduler")
-```
-
-The [`~diffusers.utils.PushToHubMixin.push_to_hub`] function saves the scheduler's `scheduler_config.json` file to the specified repository.
-
-Now you can reload the scheduler from your repository on the Hub:
-
-```py
-scheduler = DDIMScheduler.from_pretrained("your-namepsace/my-controlnet-scheduler")
-```
-
-## Pipeline
-
-You can also push an entire pipeline with all it's components to the Hub. For example, initialize the components of a [`StableDiffusionPipeline`] with the parameters you want:
-
-```py
-from diffusers import (
- UNet2DConditionModel,
- AutoencoderKL,
- DDIMScheduler,
- StableDiffusionPipeline,
-)
-from transformers import CLIPTextModel, CLIPTextConfig, CLIPTokenizer
-
-unet = UNet2DConditionModel(
- block_out_channels=(32, 64),
- layers_per_block=2,
- sample_size=32,
- in_channels=4,
- out_channels=4,
- down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
- up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
- cross_attention_dim=32,
-)
-
-scheduler = DDIMScheduler(
- beta_start=0.00085,
- beta_end=0.012,
- beta_schedule="scaled_linear",
- clip_sample=False,
- set_alpha_to_one=False,
-)
-
-vae = AutoencoderKL(
- block_out_channels=[32, 64],
- in_channels=3,
- out_channels=3,
- down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
- up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
- latent_channels=4,
-)
-
-text_encoder_config = CLIPTextConfig(
- bos_token_id=0,
- eos_token_id=2,
- hidden_size=32,
- intermediate_size=37,
- layer_norm_eps=1e-05,
- num_attention_heads=4,
- num_hidden_layers=5,
- pad_token_id=1,
- vocab_size=1000,
-)
-text_encoder = CLIPTextModel(text_encoder_config)
-tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-```
-
-Pass all of the components to the [`StableDiffusionPipeline`] and call [`~diffusers.utils.PushToHubMixin.push_to_hub`] to push the pipeline to the Hub:
-
-```py
-components = {
- "unet": unet,
- "scheduler": scheduler,
- "vae": vae,
- "text_encoder": text_encoder,
- "tokenizer": tokenizer,
- "safety_checker": None,
- "feature_extractor": None,
-}
-
-pipeline = StableDiffusionPipeline(**components)
-pipeline.push_to_hub("my-pipeline")
-```
-
-The [`~diffusers.utils.PushToHubMixin.push_to_hub`] function saves each component to a subfolder in the repository. Now you can reload the pipeline from your repository on the Hub:
-
-```py
-pipeline = StableDiffusionPipeline.from_pretrained("your-namespace/my-pipeline")
-```
-
-## Privacy
-
-Set `private=True` in the [`~diffusers.utils.PushToHubMixin.push_to_hub`] function to keep your model, scheduler, or pipeline files private:
-
-```py
-controlnet.push_to_hub("my-controlnet-model", private=True)
-```
-
-Private repositories are only visible to you, and other users won't be able to clone the repository and your repository won't appear in search results. Even if a user has the URL to your private repository, they'll receive a `404 - Repo not found error.`
-
-To load a model, scheduler, or pipeline from a private or gated repositories, set `use_auth_token=True`:
-
-```py
-model = ControlNet.from_pretrained("your-namespace/my-controlnet-model", use_auth_token=True)
-```
\ No newline at end of file
diff --git a/spaces/phenomenon1981/MagicPrompt-Stable-Diffusion/style.css b/spaces/phenomenon1981/MagicPrompt-Stable-Diffusion/style.css
deleted file mode 100644
index fdbef9e64cc6b9f8003698ffa38997ee22a640ac..0000000000000000000000000000000000000000
--- a/spaces/phenomenon1981/MagicPrompt-Stable-Diffusion/style.css
+++ /dev/null
@@ -1,84 +0,0 @@
-#col-container {
- max-width: 800px;
- margin-left: auto;
- margin-right: auto;
-}
-a {
- color: inherit;
- text-decoration: underline;
-}
-.gradio-container {
- font-family: 'IBM Plex Sans', sans-serif;
-}
-.gr-button {
- color: white;
- border-color: #9d66e5;
- background: #9d66e5;
-}
-input[type='range'] {
- accent-color: #9d66e5;
-}
-.dark input[type='range'] {
- accent-color: #dfdfdf;
-}
-.container {
- max-width: 800px;
- margin: auto;
- padding-top: 1.5rem;
-}
-#gallery {
- min-height: 22rem;
- margin-bottom: 15px;
- margin-left: auto;
- margin-right: auto;
- border-bottom-right-radius: .5rem !important;
- border-bottom-left-radius: .5rem !important;
-}
-#gallery>div>.h-full {
- min-height: 20rem;
-}
-.details:hover {
- text-decoration: underline;
-}
-.gr-button {
- white-space: nowrap;
-}
-.gr-button:focus {
- border-color: rgb(147 197 253 / var(--tw-border-opacity));
- outline: none;
- box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
- --tw-border-opacity: 1;
- --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
- --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
- --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
- --tw-ring-opacity: .5;
-}
-#advanced-options {
- margin-bottom: 20px;
-}
-.footer {
- margin-bottom: 45px;
- margin-top: 35px;
- text-align: center;
- border-bottom: 1px solid #e5e5e5;
-}
-.footer>p {
- font-size: .8rem;
- display: inline-block;
- padding: 0 10px;
- transform: translateY(10px);
- background: white;
-}
-.dark .logo{ filter: invert(1); }
-.dark .footer {
- border-color: #303030;
-}
-.dark .footer>p {
- background: #0b0f19;
-}
-.acknowledgments h4{
- margin: 1.25em 0 .25em 0;
- font-weight: bold;
- font-size: 115%;
-}
-
diff --git a/spaces/phildunphy/SALT-curated-asset-allocation/README.md b/spaces/phildunphy/SALT-curated-asset-allocation/README.md
deleted file mode 100644
index e417c84aaf99ba7a2ef680b7c349f655e829a330..0000000000000000000000000000000000000000
--- a/spaces/phildunphy/SALT-curated-asset-allocation/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: SALT Curated Asset Allocation
-emoji: 🐠
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/pixiou/bingo/src/lib/hooks/use-at-bottom.tsx b/spaces/pixiou/bingo/src/lib/hooks/use-at-bottom.tsx
deleted file mode 100644
index d37c8cf4162adcb0064e08ecec24eb731416b045..0000000000000000000000000000000000000000
--- a/spaces/pixiou/bingo/src/lib/hooks/use-at-bottom.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import * as React from 'react'
-
-export function useAtBottom(offset = 0) {
- const [isAtBottom, setIsAtBottom] = React.useState(false)
-
- React.useEffect(() => {
- const handleScroll = () => {
- setIsAtBottom(
- window.innerHeight + window.scrollY >=
- document.body.offsetHeight - offset
- )
- }
-
- window.addEventListener('scroll', handleScroll, { passive: true })
- handleScroll()
-
- return () => {
- window.removeEventListener('scroll', handleScroll)
- }
- }, [offset])
-
- return isAtBottom
-}
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/wheel/vendored/packaging/tags.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/wheel/vendored/packaging/tags.py
deleted file mode 100644
index 19ccbde3ea2d8307c6c55cb1415fe098424185ec..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/wheel/vendored/packaging/tags.py
+++ /dev/null
@@ -1,546 +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.
-
-import logging
-import platform
-import subprocess
-import sys
-import sysconfig
-from importlib.machinery import EXTENSION_SUFFIXES
-from typing import (
- Dict,
- FrozenSet,
- Iterable,
- Iterator,
- List,
- Optional,
- Sequence,
- Tuple,
- Union,
- cast,
-)
-
-from . import _manylinux, _musllinux
-
-logger = logging.getLogger(__name__)
-
-PythonVersion = Sequence[int]
-MacVersion = Tuple[int, int]
-
-INTERPRETER_SHORT_NAMES: Dict[str, str] = {
- "python": "py", # Generic.
- "cpython": "cp",
- "pypy": "pp",
- "ironpython": "ip",
- "jython": "jy",
-}
-
-
-_32_BIT_INTERPRETER = sys.maxsize <= 2**32
-
-
-class Tag:
- """
- A representation of the tag triple for a wheel.
-
- Instances are considered immutable and thus are hashable. Equality checking
- is also supported.
- """
-
- __slots__ = ["_interpreter", "_abi", "_platform", "_hash"]
-
- def __init__(self, interpreter: str, abi: str, platform: str) -> None:
- self._interpreter = interpreter.lower()
- self._abi = abi.lower()
- self._platform = platform.lower()
- # The __hash__ of every single element in a Set[Tag] will be evaluated each time
- # that a set calls its `.disjoint()` method, which may be called hundreds of
- # times when scanning a page of links for packages with tags matching that
- # Set[Tag]. Pre-computing the value here produces significant speedups for
- # downstream consumers.
- self._hash = hash((self._interpreter, self._abi, self._platform))
-
- @property
- def interpreter(self) -> str:
- return self._interpreter
-
- @property
- def abi(self) -> str:
- return self._abi
-
- @property
- def platform(self) -> str:
- return self._platform
-
- def __eq__(self, other: object) -> bool:
- if not isinstance(other, Tag):
- return NotImplemented
-
- return (
- (self._hash == other._hash) # Short-circuit ASAP for perf reasons.
- and (self._platform == other._platform)
- and (self._abi == other._abi)
- and (self._interpreter == other._interpreter)
- )
-
- def __hash__(self) -> int:
- return self._hash
-
- def __str__(self) -> str:
- return f"{self._interpreter}-{self._abi}-{self._platform}"
-
- def __repr__(self) -> str:
- return f"<{self} @ {id(self)}>"
-
-
-def parse_tag(tag: str) -> FrozenSet[Tag]:
- """
- Parses the provided tag (e.g. `py3-none-any`) into a frozenset of Tag instances.
-
- Returning a set is required due to the possibility that the tag is a
- compressed tag set.
- """
- tags = set()
- interpreters, abis, platforms = tag.split("-")
- for interpreter in interpreters.split("."):
- for abi in abis.split("."):
- for platform_ in platforms.split("."):
- tags.add(Tag(interpreter, abi, platform_))
- return frozenset(tags)
-
-
-def _get_config_var(name: str, warn: bool = False) -> Union[int, str, None]:
- value = sysconfig.get_config_var(name)
- if value is None and warn:
- logger.debug(
- "Config variable '%s' is unset, Python ABI tag may be incorrect", name
- )
- return value
-
-
-def _normalize_string(string: str) -> str:
- return string.replace(".", "_").replace("-", "_")
-
-
-def _abi3_applies(python_version: PythonVersion) -> bool:
- """
- Determine if the Python version supports abi3.
-
- PEP 384 was first implemented in Python 3.2.
- """
- return len(python_version) > 1 and tuple(python_version) >= (3, 2)
-
-
-def _cpython_abis(py_version: PythonVersion, warn: bool = False) -> List[str]:
- py_version = tuple(py_version) # To allow for version comparison.
- abis = []
- version = _version_nodot(py_version[:2])
- debug = pymalloc = ucs4 = ""
- with_debug = _get_config_var("Py_DEBUG", warn)
- has_refcount = hasattr(sys, "gettotalrefcount")
- # Windows doesn't set Py_DEBUG, so checking for support of debug-compiled
- # extension modules is the best option.
- # https://github.com/pypa/pip/issues/3383#issuecomment-173267692
- has_ext = "_d.pyd" in EXTENSION_SUFFIXES
- if with_debug or (with_debug is None and (has_refcount or has_ext)):
- debug = "d"
- if py_version < (3, 8):
- with_pymalloc = _get_config_var("WITH_PYMALLOC", warn)
- if with_pymalloc or with_pymalloc is None:
- pymalloc = "m"
- if py_version < (3, 3):
- unicode_size = _get_config_var("Py_UNICODE_SIZE", warn)
- if unicode_size == 4 or (
- unicode_size is None and sys.maxunicode == 0x10FFFF
- ):
- ucs4 = "u"
- elif debug:
- # Debug builds can also load "normal" extension modules.
- # We can also assume no UCS-4 or pymalloc requirement.
- abis.append(f"cp{version}")
- abis.insert(
- 0,
- "cp{version}{debug}{pymalloc}{ucs4}".format(
- version=version, debug=debug, pymalloc=pymalloc, ucs4=ucs4
- ),
- )
- return abis
-
-
-def cpython_tags(
- python_version: Optional[PythonVersion] = None,
- abis: Optional[Iterable[str]] = None,
- platforms: Optional[Iterable[str]] = None,
- *,
- warn: bool = False,
-) -> Iterator[Tag]:
- """
- Yields the tags for a CPython interpreter.
-
- The tags consist of:
- - cp
-
-
- );
-};
-
-export default ChatContent;
diff --git a/spaces/portal/Control-Net-Video/index.html b/spaces/portal/Control-Net-Video/index.html
deleted file mode 100644
index 8ac31601300524f5af51c962ee70fcd6354355b8..0000000000000000000000000000000000000000
--- a/spaces/portal/Control-Net-Video/index.html
+++ /dev/null
@@ -1,13 +0,0 @@
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/subset/__main__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/subset/__main__.py
deleted file mode 100644
index decf9ee6e50a612c65a87ebeaa8be115f1d25242..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/subset/__main__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import sys
-from fontTools.subset import main
-
-
-if __name__ == "__main__":
- sys.exit(main())
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Login-9c3cc0eb.css b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Login-9c3cc0eb.css
deleted file mode 100644
index 9901bcac6c93474ed045092f6d91d6e683ba5b32..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Login-9c3cc0eb.css
+++ /dev/null
@@ -1 +0,0 @@
-.wrap.svelte-1ogxbi0{display:flex;flex-direction:column;justify-content:center;align-items:center;margin-top:var(--size-3);background:var(--background-fill-primary);width:var(--size-full)}h2.svelte-1ogxbi0{margin-bottom:var(--size-3);color:var(--body-text-color);font-weight:var(--section-header-text-weight);font-size:var(--text-xl)}.auth.svelte-1ogxbi0{margin-top:var(--size-1);margin-bottom:var(--size-1);color:var(--body-text-color)}.creds.svelte-1ogxbi0{margin-top:var(--size-4);margin-bottom:var(--size-4);color:var(--error-text-color);font-weight:var(--weight-semibold)}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-d4747674.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-d4747674.js
deleted file mode 100644
index 6a3eb3dba23f468e6c68b5abec89f7ef6b5df5b5..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-d4747674.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{E as W,C as Y,L as d}from"./index-b5ab13e3.js";import{s as n,t as r,L as R,i as Z,d as a,f as X,c as y,e as f}from"./Index-9bf8add7.js";import"./index-50ad4c77.js";import"./svelte/svelte.js";import"./Button-8eeccca1.js";import"./Index-c74a8b7c.js";import"./Copy-1b5c0932.js";import"./Download-696bd40c.js";import"./BlockLabel-e3970ebb.js";import"./Empty-eeaba2d1.js";import"./Example-e03fb3b4.js";const l=1,w=189,S=190,b=191,T=192,U=193,m=194,V=22,g=23,h=47,G=48,c=53,u=54,_=55,j=57,E=58,k=59,z=60,v=61,H=63,N=230,A=71,F=255,K=121,C=142,D=143,M=146,i=10,s=13,t=32,o=9,q=35,L=40,B=46,J=new Set([g,h,G,F,H,K,u,_,N,z,v,E,k,A,C,D,M]),OO=new W((O,$)=>{if(O.next<0)O.acceptToken(m);else if(!(O.next!=i&&O.next!=s))if($.context.depth<0)O.acceptToken(T,1);else{O.advance();let Q=0;for(;O.next==t||O.next==o;)O.advance(),Q++;let P=O.next==i||O.next==s||O.next==q;O.acceptToken(P?U:b,-Q)}},{contextual:!0,fallback:!0}),$O=new W((O,$)=>{let Q=$.context.depth;if(Q<0)return;let P=O.peek(-1);if((P==i||P==s)&&$.context.depth>=0){let e=0,x=0;for(;;){if(O.next==t)e++;else if(O.next==o)e+=8-e%8;else break;O.advance(),x++}e!=Q&&O.next!=i&&O.next!=s&&O.next!=q&&(e
-
-
- {advancedMode &&
-
-
-
- )}
-
- {error}
-
- {
- setError('');
- }}
- >
-
-
- {useStore.getState().generating || (
-
-
-
-
- )}
- Be2works Rizal Rar: A Tool for Repairing Laptop Batteries
-What is Be2works Rizal Rar?
-be2works rizal rar Full
- A brief introduction to the software and its features
-
-
- How to download and install Be2works Rizal Rar
-
-be2works rizal rar Full crack
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-be2works rizal rar Full free
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-be2works rizal rar Full crocko
-
- How to use Be2works Rizal rar to fix laptop batteries
-
-
- Why do laptop batteries fail and how can Be2works help?
- The common causes of laptop battery degradation and failure
-
-
- The benefits of using Be2works Rizal Rar to restore laptop battery performance and capacity
-
-
- The limitations and risks of using Be2works Rizal Rar
-
-
- What are the alternatives to Be2works Rizal Rar?
- Other software tools for repairing laptop batteries
-
-
- Hardware solutions for replacing laptop batteries
-
-
- Tips and best practices for extending laptop battery life
-
-
- Conclusion
- Frequently Asked Questions
-
-
- 0a6ba089eb
-
-
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Download the Best Resident Evil 4 Hack Tool for PC and Android.md b/spaces/raedeXanto/academic-chatgpt-beta/Download the Best Resident Evil 4 Hack Tool for PC and Android.md
deleted file mode 100644
index 33db97d0572b1274b1cfffdaf809728753ff8a29..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Download the Best Resident Evil 4 Hack Tool for PC and Android.md
+++ /dev/null
@@ -1,128 +0,0 @@
-
-Resident Evil 4 Hack Tool Download: How to Cheat and Mod the Game
-resident evil 4 hack tool download
-Introduction
-What is Resident Evil 4?
-
-resident evil 4 mod apk download
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-How to download and install a hack tool for Resident Evil 4
-Step 1: Choose a reliable source for the hack tool
-
-
-Step 2: Download the hack tool and the REFramework
-Step 3: Copy the files to your game folder
-Step 4: Launch the game and activate the cheats
-What are the features and options of the hack tool for Resident Evil 4
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-
- ``` 0a6ba089eb
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\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/FSXP3DRFSceneryBuildingCataniaLICCpcgame Discover the Beauty of Catania with Custom Airport Buildings and Vehicles.md b/spaces/raedeXanto/academic-chatgpt-beta/FSXP3DRFSceneryBuildingCataniaLICCpcgame Discover the Beauty of Catania with Custom Airport Buildings and Vehicles.md
deleted file mode 100644
index d13df9cda25202ff87dbb1a244bd84d58cd3ced9..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/FSXP3DRFSceneryBuildingCataniaLICCpcgame Discover the Beauty of Catania with Custom Airport Buildings and Vehicles.md
+++ /dev/null
@@ -1,90 +0,0 @@
-
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-What is FSXP3DRFSceneryBuildingCataniaLICCpcgame?
-A brief introduction to the product
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\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/GEO.SLOPE.GeoStudio.2007.v7.10.4143.Incl.Patch.Only-LAVteam.rar GeoStudio 2007 Review and Comparison with Other Geotechnical Software.md b/spaces/raedeXanto/academic-chatgpt-beta/GEO.SLOPE.GeoStudio.2007.v7.10.4143.Incl.Patch.Only-LAVteam.rar GeoStudio 2007 Review and Comparison with Other Geotechnical Software.md
deleted file mode 100644
index 36ba6631ac28e944ea6625608bbfb149ae61aa57..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/GEO.SLOPE.GeoStudio.2007.v7.10.4143.Incl.Patch.Only-LAVteam.rar GeoStudio 2007 Review and Comparison with Other Geotechnical Software.md
+++ /dev/null
@@ -1,134 +0,0 @@
-
-What is GEO.SLOPE.GeoStudio.2007.v7.10.4143.Incl.Patch.Only-LAVteam.rar and why you need it
- Introduction
- What is GeoStudio
- GEO.SLOPE.GeoStudio.2007.v7.10.4143.Incl.Patch.Only-LAVteam.rar
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-How to join the community and network with other users of GEO.SLOPE.GeoStudio 2007
-What are the advantages and disadvantages of using GEO.SLOPE.GeoStudio 2007
-What are the latest developments and innovations in GEO.SLOPE.GeoStudio 2007
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- Features and benefits of GeoStudio 2007
- Geotechnical modeling software for various applications
-
-
- Integrated analysis environment with multiple products
- Advanced constitutive models and add-ins
- User-friendly interface and visualization tools
- How to download and install GeoStudio 2007 with the patch file
- Download GeoStudio 2007 from GEOSLOPE website
- Download the patch file from LAVteam website
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- Conclusion
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\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/Sarah Morgan - Once A Ferrara Wife (epub).epub.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/Sarah Morgan - Once A Ferrara Wife (epub).epub.md
deleted file mode 100644
index aa83f85eb216b13d89a4b8801354e8ace9a5319f..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/Sarah Morgan - Once A Ferrara Wife (epub).epub.md
+++ /dev/null
@@ -1,66 +0,0 @@
-## Sarah Morgan - Once A Ferrara Wife (epub).epub
-
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-
-
-
-
-# Review: Once a Ferrara Wife by Sarah Morgan
-
-
-
-If you are looking for a passionate and emotional romance novel, you might want to check out *Once a Ferrara Wife* by Sarah Morgan. This book is the first in the Ferrara series, which follows the lives and loves of a powerful Sicilian family.
-
-
-
-The story centers on Laurel and Cristiano Ferrara, who had a whirlwind marriage that ended in disaster two years ago. Laurel left Cristiano after a heartbreaking betrayal, but she never stopped loving him. Now, she is summoned back to Sicily by her estranged husband, who wants her to attend his sister's wedding. Cristiano has not forgiven Laurel for walking away from him, but he still desires her more than anything. He also has a secret that could change everything between them.
-
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-As they are forced to spend time together, Laurel and Cristiano have to confront their past and their unresolved feelings. They discover that their marriage was not based on a lie, but on a deep and lasting connection. But can they overcome their pride and their pain to give their love a second chance?
-
-
-
-*Once a Ferrara Wife* is a captivating and emotional read that will keep you hooked until the end. Sarah Morgan is a master of writing intense and realistic characters that you can relate to and root for. She also creates a vivid and sensual setting that transports you to the beautiful and exotic Sicily. The book has plenty of drama, angst, and steamy scenes that will make your heart race.
-
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-
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-*Once a Ferrara Wife* is a book that will make you laugh, cry, and swoon. It is a book that will make you feel the passion and the pain of Laurel and Cristiano's love story. It is a book that will make you want to read more about the Ferrara family and their adventures. If you are a fan of Sarah Morgan's books, or if you are looking for a new author to try, you should definitely give *Once a Ferrara Wife* a chance. You will not regret it.
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Citroen Sedre Car Diagnostic Software Crack !FULL!.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Citroen Sedre Car Diagnostic Software Crack !FULL!.md
deleted file mode 100644
index cc72dd0b3fb1ccfa51512bbe7adc048d87aa26a6..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Citroen Sedre Car Diagnostic Software Crack !FULL!.md
+++ /dev/null
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\ No newline at end of file
diff --git a/spaces/riccorl/relik-entity-linking/relik/common/__init__.py b/spaces/riccorl/relik-entity-linking/relik/common/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/robin0307/MMOCR/configs/_base_/schedules/schedule_adam_step_12e.py b/spaces/robin0307/MMOCR/configs/_base_/schedules/schedule_adam_step_12e.py
deleted file mode 100644
index c92289d3b7a69015afc51c9a248744bae5ec9197..0000000000000000000000000000000000000000
--- a/spaces/robin0307/MMOCR/configs/_base_/schedules/schedule_adam_step_12e.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# optimizer
-optimizer = dict(type='Adam', lr=4e-4)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(
- policy='step',
- warmup='linear',
- warmup_iters=100,
- warmup_ratio=1.0 / 3,
- step=[11])
-runner = dict(type='EpochBasedRunner', max_epochs=12)
-checkpoint_config = dict(interval=1)
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/utils/dist_utils.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/utils/dist_utils.py
deleted file mode 100644
index 8760774fd90e666c03ca4d553111363065a08426..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/utils/dist_utils.py
+++ /dev/null
@@ -1,193 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import functools
-import pickle
-import warnings
-from collections import OrderedDict
-
-import numpy as np
-import torch
-import torch.distributed as dist
-from mmcv.runner import OptimizerHook, get_dist_info
-from torch._utils import (_flatten_dense_tensors, _take_tensors,
- _unflatten_dense_tensors)
-
-
-def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
- if bucket_size_mb > 0:
- bucket_size_bytes = bucket_size_mb * 1024 * 1024
- buckets = _take_tensors(tensors, bucket_size_bytes)
- else:
- buckets = OrderedDict()
- for tensor in tensors:
- tp = tensor.type()
- if tp not in buckets:
- buckets[tp] = []
- buckets[tp].append(tensor)
- buckets = buckets.values()
-
- for bucket in buckets:
- flat_tensors = _flatten_dense_tensors(bucket)
- dist.all_reduce(flat_tensors)
- flat_tensors.div_(world_size)
- for tensor, synced in zip(
- bucket, _unflatten_dense_tensors(flat_tensors, bucket)):
- tensor.copy_(synced)
-
-
-def allreduce_grads(params, coalesce=True, bucket_size_mb=-1):
- """Allreduce gradients.
-
- Args:
- params (list[torch.Parameters]): List of parameters of a model
- coalesce (bool, optional): Whether allreduce parameters as a whole.
- Defaults to True.
- bucket_size_mb (int, optional): Size of bucket, the unit is MB.
- Defaults to -1.
- """
- grads = [
- param.grad.data for param in params
- if param.requires_grad and param.grad is not None
- ]
- world_size = dist.get_world_size()
- if coalesce:
- _allreduce_coalesced(grads, world_size, bucket_size_mb)
- else:
- for tensor in grads:
- dist.all_reduce(tensor.div_(world_size))
-
-
-class DistOptimizerHook(OptimizerHook):
- """Deprecated optimizer hook for distributed training."""
-
- def __init__(self, *args, **kwargs):
- warnings.warn('"DistOptimizerHook" is deprecated, please switch to'
- '"mmcv.runner.OptimizerHook".')
- super().__init__(*args, **kwargs)
-
-
-def reduce_mean(tensor):
- """"Obtain the mean of tensor on different GPUs."""
- if not (dist.is_available() and dist.is_initialized()):
- return tensor
- tensor = tensor.clone()
- dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM)
- return tensor
-
-
-def obj2tensor(pyobj, device='cuda'):
- """Serialize picklable python object to tensor."""
- storage = torch.ByteStorage.from_buffer(pickle.dumps(pyobj))
- return torch.ByteTensor(storage).to(device=device)
-
-
-def tensor2obj(tensor):
- """Deserialize tensor to picklable python object."""
- return pickle.loads(tensor.cpu().numpy().tobytes())
-
-
-@functools.lru_cache()
-def _get_global_gloo_group():
- """Return a process group based on gloo backend, containing all the ranks
- The result is cached."""
- if dist.get_backend() == 'nccl':
- return dist.new_group(backend='gloo')
- else:
- return dist.group.WORLD
-
-
-def all_reduce_dict(py_dict, op='sum', group=None, to_float=True):
- """Apply all reduce function for python dict object.
-
- The code is modified from https://github.com/Megvii-
- BaseDetection/YOLOX/blob/main/yolox/utils/allreduce_norm.py.
-
- NOTE: make sure that py_dict in different ranks has the same keys and
- the values should be in the same shape. Currently only supports
- nccl backend.
-
- Args:
- py_dict (dict): Dict to be applied all reduce op.
- op (str): Operator, could be 'sum' or 'mean'. Default: 'sum'
- group (:obj:`torch.distributed.group`, optional): Distributed group,
- Default: None.
- to_float (bool): Whether to convert all values of dict to float.
- Default: True.
-
- Returns:
- OrderedDict: reduced python dict object.
- """
- warnings.warn(
- 'group` is deprecated. Currently only supports NCCL backend.')
- _, world_size = get_dist_info()
- if world_size == 1:
- return py_dict
-
- # all reduce logic across different devices.
- py_key = list(py_dict.keys())
- if not isinstance(py_dict, OrderedDict):
- py_key_tensor = obj2tensor(py_key)
- dist.broadcast(py_key_tensor, src=0)
- py_key = tensor2obj(py_key_tensor)
-
- tensor_shapes = [py_dict[k].shape for k in py_key]
- tensor_numels = [py_dict[k].numel() for k in py_key]
-
- if to_float:
- warnings.warn('Note: the "to_float" is True, you need to '
- 'ensure that the behavior is reasonable.')
- flatten_tensor = torch.cat(
- [py_dict[k].flatten().float() for k in py_key])
- else:
- flatten_tensor = torch.cat([py_dict[k].flatten() for k in py_key])
-
- dist.all_reduce(flatten_tensor, op=dist.ReduceOp.SUM)
- if op == 'mean':
- flatten_tensor /= world_size
-
- split_tensors = [
- x.reshape(shape) for x, shape in zip(
- torch.split(flatten_tensor, tensor_numels), tensor_shapes)
- ]
- out_dict = {k: v for k, v in zip(py_key, split_tensors)}
- if isinstance(py_dict, OrderedDict):
- out_dict = OrderedDict(out_dict)
- return out_dict
-
-
-def sync_random_seed(seed=None, device='cuda'):
- """Make sure different ranks share the same seed.
-
- All workers must call this function, otherwise it will deadlock.
- This method is generally used in `DistributedSampler`,
- because the seed should be identical across all processes
- in the distributed group.
-
- In distributed sampling, different ranks should sample non-overlapped
- data in the dataset. Therefore, this function is used to make sure that
- each rank shuffles the data indices in the same order based
- on the same seed. Then different ranks could use different indices
- to select non-overlapped data from the same data list.
-
- Args:
- seed (int, Optional): The seed. Default to None.
- device (str): The device where the seed will be put on.
- Default to 'cuda'.
-
- Returns:
- int: Seed to be used.
- """
- if seed is None:
- seed = np.random.randint(2**31)
- assert isinstance(seed, int)
-
- rank, world_size = get_dist_info()
-
- if world_size == 1:
- return seed
-
- if rank == 0:
- random_num = torch.tensor(seed, dtype=torch.int32, device=device)
- else:
- random_num = torch.tensor(0, dtype=torch.int32, device=device)
- dist.broadcast(random_num, src=0)
- return random_num.item()
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Corbin Fisher ACM1155 CF 9XXX Workout.md b/spaces/rorallitri/biomedical-language-models/logs/Corbin Fisher ACM1155 CF 9XXX Workout.md
deleted file mode 100644
index dcfccf927b547de372805c33b4fa4268a2ac93cf..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Corbin Fisher ACM1155 CF 9XXX Workout.md
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Dipak Ghosh Book On Mamata Bengali Version Pdf 14 The Real Face of Mamata Banerjee - A Disillusioned Supporters Account.md b/spaces/rorallitri/biomedical-language-models/logs/Dipak Ghosh Book On Mamata Bengali Version Pdf 14 The Real Face of Mamata Banerjee - A Disillusioned Supporters Account.md
deleted file mode 100644
index ea340f97c5429c707e8dc243b2f0a7a833fc90d7..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Dipak Ghosh Book On Mamata Bengali Version Pdf 14 The Real Face of Mamata Banerjee - A Disillusioned Supporters Account.md
+++ /dev/null
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Download Gratis Video Seks Cewek Aceh.md b/spaces/rorallitri/biomedical-language-models/logs/Download Gratis Video Seks Cewek Aceh.md
deleted file mode 100644
index f79b338fa01453b693e4f31a55b81bc4175e106d..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Download Gratis Video Seks Cewek Aceh.md
+++ /dev/null
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Explaindio Video Creator Platinum 3.035 Full Cracked [SadeemPC] Serial Key.md b/spaces/rorallitri/biomedical-language-models/logs/Explaindio Video Creator Platinum 3.035 Full Cracked [SadeemPC] Serial Key.md
deleted file mode 100644
index ac4ff5336617a8ce7c7b8bd9161f8c8d763e981d..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Explaindio Video Creator Platinum 3.035 Full Cracked [SadeemPC] Serial Key.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Explaindio Video Creator Platinum 3.035 Full Cracked [SadeemPC] Serial Key
-
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-
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Gippi Full Tamil Movie Free [PATCHED] Download.md b/spaces/rorallitri/biomedical-language-models/logs/Gippi Full Tamil Movie Free [PATCHED] Download.md
deleted file mode 100644
index 1dee6dc932f44e91301e760ebb0c35ecd497afe9..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Gippi Full Tamil Movie Free [PATCHED] Download.md
+++ /dev/null
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-
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diff --git a/spaces/rstallman/Mayfair-Partner-Music/tests/common_utils/wav_utils.py b/spaces/rstallman/Mayfair-Partner-Music/tests/common_utils/wav_utils.py
deleted file mode 100644
index d3a563ee1749a58217ece55c9a08b8d93c0fc386..0000000000000000000000000000000000000000
--- a/spaces/rstallman/Mayfair-Partner-Music/tests/common_utils/wav_utils.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from pathlib import Path
-import typing as tp
-
-import torch
-import torchaudio
-
-
-def get_white_noise(chs: int = 1, num_frames: int = 1):
- wav = torch.randn(chs, num_frames)
- return wav
-
-
-def get_batch_white_noise(bs: int = 1, chs: int = 1, num_frames: int = 1):
- wav = torch.randn(bs, chs, num_frames)
- return wav
-
-
-def save_wav(path: str, wav: torch.Tensor, sample_rate: int):
- fp = Path(path)
- kwargs: tp.Dict[str, tp.Any] = {}
- if fp.suffix == '.wav':
- kwargs['encoding'] = 'PCM_S'
- kwargs['bits_per_sample'] = 16
- elif fp.suffix == '.mp3':
- kwargs['compression'] = 320
- torchaudio.save(str(fp), wav, sample_rate, **kwargs)
diff --git a/spaces/samcaicn/bingai/src/components/ui/select.tsx b/spaces/samcaicn/bingai/src/components/ui/select.tsx
deleted file mode 100644
index 77f12c2996f541b97663de4c9e20ab34d4ec2fac..0000000000000000000000000000000000000000
--- a/spaces/samcaicn/bingai/src/components/ui/select.tsx
+++ /dev/null
@@ -1,123 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SelectPrimitive from '@radix-ui/react-select'
-
-import { cn } from '@/lib/utils'
-import {
- IconArrowDown,
- IconCheck,
- IconChevronUpDown
-} from '@/components/ui/icons'
-
-const Select = SelectPrimitive.Root
-
-const SelectGroup = SelectPrimitive.Group
-
-const SelectValue = SelectPrimitive.Value
-
-const SelectTrigger = React.forwardRef<
- React.ElementRefDriver Samsung E1205t
-
--s e1205t-s1 (model DRP-E1205T)
-
- what's the command to make a file hard linked to the original
-
- find / -iname 'blah' -exec ln -s /foo/ \;
-
- no, it's hard linking, not hard inlining
-
- man ln has all the details
-
- ok i will reboot right now and be right back
-
- dont know if it worked
-
- oke
-
- ok it was working..
-
- but i rebootet and the same thing
-
- restarting the dns server?
-
- OerHeks, i think it has nothing to do with dns its just that i cant make it work
-
- this has to do with the DHCP server dns cache
-
- remove dnsmasq
-
- and/or change the /etc/resolv.conf
-
- never mind
-
- that was me talking to myself, sorry
-
- if you're in a bind
-
- mike998, i removed dnsmasq
-
- have you updated the resolv.conf
-
- mike998, how?
-
- $sudo nano /etc/resolv.conf
-
- i did that before
-
- then back it up
-
- reboot
-
- ok
-
- change the nameserver
-
- back it up
-
- see what you get
-
- also you can clear the dns cache in NetworkManager
-
- mike998, still dnsmasq is dns cache, so that is not all, see
-
- mike998, i already rebooted its not working still
-
- OerHeks: I think it's being confused by dnsmasq + caching DNS
-
- theptr: that means I 4fefd39f24
-
-
-
diff --git a/spaces/scedlatioru/img-to-music/example/HD Online Player (Bhaag Milkha Bhaag !EXCLUSIVE! Full Movie Downlo).md b/spaces/scedlatioru/img-to-music/example/HD Online Player (Bhaag Milkha Bhaag !EXCLUSIVE! Full Movie Downlo).md
deleted file mode 100644
index 8b55547408e151411ecc3defbafd106961ee9426..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/HD Online Player (Bhaag Milkha Bhaag !EXCLUSIVE! Full Movie Downlo).md
+++ /dev/null
@@ -1,6 +0,0 @@
-HD Online Player (Bhaag Milkha Bhaag full movie downlo)
-
-Browse SlideShare directory for content from hd-music -> hd-update-2-focu. ... Player (Bhaag Milkha Bhaag Full Movie Downlo) · HD Online Player (Bhaji In ... 1fdad05405
-
-
-
diff --git a/spaces/scedlatioru/img-to-music/example/HD Online Player (Quick Gun Murugun Full Movie Torrent) NEW.md b/spaces/scedlatioru/img-to-music/example/HD Online Player (Quick Gun Murugun Full Movie Torrent) NEW.md
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index 12d95e186ab01d5506ac35f136a7a67a11368aee..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/HD Online Player (Quick Gun Murugun Full Movie Torrent) NEW.md
+++ /dev/null
@@ -1,12 +0,0 @@
-
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-
-
-
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diff --git a/spaces/scedlatioru/img-to-music/example/Managerial Accounting 10th Edition By Louderback And Holmen Chapter 2 Solutions Pdf.md b/spaces/scedlatioru/img-to-music/example/Managerial Accounting 10th Edition By Louderback And Holmen Chapter 2 Solutions Pdf.md
deleted file mode 100644
index 7d04ba87e63985d8445987a58cfd0f8035f87ff1..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Managerial Accounting 10th Edition By Louderback And Holmen Chapter 2 Solutions Pdf.md
+++ /dev/null
@@ -1,28 +0,0 @@
-Managerial Accounting 10th Edition By Louderback And Holmen Chapter 2 Solutions Pdf
-
-and Holmen Chapter 2 Solutions Pdf UPD. The book is part of Management Accounting series of books by many authors such as Jean Thevenot. The book of Efir and Holtin explains best management accounting and financial reporting for the manufacturing and service sectors.
-
-In order to help you find your way around the online version of the book, the table of contents, the Index, and the Glossary of the online version have been reproduced here. The table of contents of the book can be found on page of the online version.
-
-Sample Table of Contents:
-
-Preface
-
-I. INTRODUCTION
-
-II. GOVERNING BY ACCOUNTING
-
-III. MANAGING THE MANAGEMENT
-
-IV. ANALYZING THE MANAGEMENT
-
-V. MANAGING THE CHANGES
-
-Sample Index:
-
-Efir, I., & Holtin, L. (2016). Managerial Accounting 10th Edition By Louderback And Holmen.
-
-Chapter 2 Solutions: 4fefd39f24
-
-
-
diff --git a/spaces/scedlatioru/img-to-music/example/Maplogic Layout Manager Full Version ((INSTALL)).md b/spaces/scedlatioru/img-to-music/example/Maplogic Layout Manager Full Version ((INSTALL)).md
deleted file mode 100644
index ad742ce218660eea359b7658b88f06165bea2baa..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Maplogic Layout Manager Full Version ((INSTALL)).md
+++ /dev/null
@@ -1,30 +0,0 @@
-
-How to Create and Print Multiple Page Layouts with MapLogic Layout Manager
-maplogic layout manager full version
-What is MapLogic Layout Manager?
-What can you do with MapLogic Layout Manager?
-
-
-How to use MapLogic Layout Manager?
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/sczhou/ProPainter/web-demos/hugging_face/tracker/model/transformer/__init__.py b/spaces/sczhou/ProPainter/web-demos/hugging_face/tracker/model/transformer/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/sdhsdhk/bingosjj/src/components/providers.tsx b/spaces/sdhsdhk/bingosjj/src/components/providers.tsx
deleted file mode 100644
index 892226412d80fe0b05211911b9e245cd22876460..0000000000000000000000000000000000000000
--- a/spaces/sdhsdhk/bingosjj/src/components/providers.tsx
+++ /dev/null
@@ -1,15 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { ThemeProvider as NextThemesProvider } from 'next-themes'
-import { ThemeProviderProps } from 'next-themes/dist/types'
-
-import { TooltipProvider } from '@/components/ui/tooltip'
-
-export function Providers({ children, ...props }: ThemeProviderProps) {
- return (
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Coin Master Hack APK The Ultimate Guide to Getting Unlimited Coins and Spins.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Coin Master Hack APK The Ultimate Guide to Getting Unlimited Coins and Spins.md
deleted file mode 100644
index 8d4946aeb8a5b18c4d482a6ea5b92055e5dc3184..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Coin Master Hack APK The Ultimate Guide to Getting Unlimited Coins and Spins.md
+++ /dev/null
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-
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Get MetaTrader 5 for PC Free Trading Platform for Forex and Stocks.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Get MetaTrader 5 for PC Free Trading Platform for Forex and Stocks.md
deleted file mode 100644
index af1c526c5d9228e76b4d66b9c1887947877bd9f6..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Get MetaTrader 5 for PC Free Trading Platform for Forex and Stocks.md
+++ /dev/null
@@ -1,126 +0,0 @@
-
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-
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-Step 3: Run the installation file and follow the instructions
-Step 4: Launch MetaTrader 5 and login to your account
-How to download MetaTrader 5 for macOS
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-
-sudo apt update sudo apt install wine-stable
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-
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-
-wine mt5setup.exe
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\ No newline at end of file
diff --git a/spaces/skf15963/summary/fengshen/examples/stable_diffusion_chinese_EN/README.md b/spaces/skf15963/summary/fengshen/examples/stable_diffusion_chinese_EN/README.md
deleted file mode 100644
index 8bd939d901203225ea6902d688769390c7c10cd8..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/examples/stable_diffusion_chinese_EN/README.md
+++ /dev/null
@@ -1,110 +0,0 @@
-# Taiyi-Stable-Diffusion-1B-Chinese-EN-v0.1
-
-- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
-- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
-
-## 简介 Brief Introduction
-
-首个开源的中英双语Stable Diffusion模型，基于0.2亿筛选过的中文图文对训练。
-
-The first open source Chinese&English Bilingual Stable diffusion, which was trained on 20M filtered Chinese image-text pairs.
-
-## 模型分类 Model Taxonomy
-
-| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
-| :----: | :----: | :----: | :----: | :----: | :----: |
-| 特殊 Special | 多模态 Multimodal | 太乙 Taiyi | Stable Diffusion | 1B | Chinese and English |
-
-## 模型信息 Model Information
-
-我们将[Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/)数据集(100M)和[Zero](https://zero.so.com/)数据集(23M)用作预训练的数据集，先用[IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese](https://huggingface.co/IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese)对这两个数据集的图文对相似性进行打分，取CLIP Score大于0.2的图文对作为我们的训练集。 我们使用[stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)([论文](https://arxiv.org/abs/2112.10752))模型进行继续训练，其中训练分为两个stage。
-
-第一个stage中冻住模型的其他部分，只训练text encoder，以便保留原始模型的生成能力且实现中文概念的对齐。
-
-第二个stage中将全部模型解冻，一起训练text encoder和diffusion model，以便diffusion model更好的适配中文guidance。
-
-第一个stage我们训练了80小时，第二个stage训练了100小时，两个stage都是用了8 x A100。该版本是一个初步的版本，我们将持续优化模型并开源，欢迎交流！
-
-We use [Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/)(100M) 和 [Zero](https://zero.so.com/)(23M) as our dataset, and take the image and text pairs with CLIP Score (based on [IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese](https://huggingface.co/IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese)) greater than 0.2 as our Training set. We finetune the [stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)([paper](https://arxiv.org/abs/2112.10752)) model for two stage.
-
-Stage 1: To keep the powerful generative capability of stable diffusion and align Chinese concepts with the images, We only train the text encoder and freeze other part of the model in the first stage.
-
-Stage 2: We unfreeze both the text encoder and the diffusion model, therefore the diffusion model can have a better compatibility for the Chinese language guidance.
-
-It takes 80 hours to train the first stage, 100 hours to train the second stage, both stages are based on 8 x A100. This model is a preliminary version and we will update this model continuously and open sourse. Welcome to exchange！
-
-### Result
-
-小桥流水人家，Van Gogh style。
-
-
-小桥流水人家，水彩。
-
-
-吃过桥米线的猫。
-
-
-穿着宇航服的哈士奇。
-
-## 使用 Usage
-
-### 全精度 Full precision
-
-```py
-from diffusers import StableDiffusionPipeline
-
-pipe = StableDiffusionPipeline.from_pretrained("IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-EN-v0.1").to("cuda")
-
-prompt = '小桥流水人家，Van Gogh style'
-image = pipe(prompt, guidance_scale=10).images[0]
-image.save("小桥.png")
-```
-
-### 半精度 Half precision FP16 (CUDA)
-
-添加 `torch_dtype=torch.float16` 和 `device_map="auto"` 可以快速加载 FP16 的权重，以加快推理速度。
-更多信息见 [the optimization docs](https://huggingface.co/docs/diffusers/main/en/optimization/fp16#half-precision-weights)。
-
-```py
-# !pip install git+https://github.com/huggingface/accelerate
-import torch
-from diffusers import StableDiffusionPipeline
-
-torch.backends.cudnn.benchmark = True
-pipe = StableDiffusionPipeline.from_pretrained("IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-EN-v0.1", torch_dtype=torch.float16)
-pipe.to('cuda')
-
-prompt = '小桥流水人家，Van Gogh style'
-image = pipe(prompt, guidance_scale=10.0).images[0]
-image.save("小桥.png")
-```
-
-
-## 引用 Citation
-
-如果您在您的工作中使用了我们的模型，可以引用我们的[总论文](https://arxiv.org/abs/2209.02970)：
-
-If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970):
-
-```text
-@article{fengshenbang,
- author = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang},
- title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
- journal = {CoRR},
- volume = {abs/2209.02970},
- year = {2022}
-}
-```
-
-也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
-
-You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
-
-```text
-@misc{Fengshenbang-LM,
- title={Fengshenbang-LM},
- author={IDEA-CCNL},
- year={2021},
- howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
-}
-```
diff --git a/spaces/skf15963/summary/fengshen/models/auto/auto_factory.py b/spaces/skf15963/summary/fengshen/models/auto/auto_factory.py
deleted file mode 100644
index 688bbd4853284305d047be0552077f721e2f97de..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/models/auto/auto_factory.py
+++ /dev/null
@@ -1,644 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA Authors. 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.
-"""Factory function to build auto-model classes."""
-import importlib
-from collections import OrderedDict
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.file_utils import copy_func
-from transformers.utils import logging
-from .configuration_auto import AutoConfig, model_type_to_module_name, replace_list_option_in_docstrings
-from .dynamic import get_class_from_dynamic_module
-
-
-logger = logging.get_logger(__name__)
-
-
-CLASS_DOCSTRING = """
- This is a generic model class that will be instantiated as one of the model classes of the library when created
- with the [`~BaseAutoModelClass.from_pretrained`] class method or the [`~BaseAutoModelClass.from_config`] class
- method.
-
- This class cannot be instantiated directly using `__init__()` (throws an error).
-"""
-
-FROM_CONFIG_DOCSTRING = """
- Instantiates one of the model classes of the library from a configuration.
-
- Note:
- Loading a model from its configuration file does **not** load the model weights. It only affects the
- model's configuration. Use [`~BaseAutoModelClass.from_pretrained`] to load the model weights.
-
- Args:
- config ([`PretrainedConfig`]):
- The model class to instantiate is selected based on the configuration class:
-
- List options
-
- Examples:
-
- ```python
- >>> from transformers import AutoConfig, BaseAutoModelClass
-
- >>> # Download configuration from huggingface.co and cache.
- >>> config = AutoConfig.from_pretrained("checkpoint_placeholder")
- >>> model = BaseAutoModelClass.from_config(config)
- ```
-"""
-
-FROM_PRETRAINED_TORCH_DOCSTRING = """
- Instantiate one of the model classes of the library from a pretrained model.
-
- The model class to instantiate is selected based on the `model_type` property of the config object (either
- passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's missing, by
- falling back to using pattern matching on `pretrained_model_name_or_path`:
-
- List options
-
- The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are
- deactivated). To train the model, you should first set it back in training mode with `model.train()`
-
- Args:
- pretrained_model_name_or_path (`str` or `os.PathLike`):
- Can be either:
-
- - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
- user or organization name, like `dbmdz/bert-base-german-cased`.
- - A path to a *directory* containing model weights saved using
- [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- - A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In
- this case, `from_tf` should be set to `True` and a configuration object should be provided as
- `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a
- PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, *optional*):
- Will be passed along to the underlying model `__init__()` method.
- config ([`PretrainedConfig`], *optional*):
- Configuration for the model to use instead of an automatically loaded configuration. Configuration can
- be automatically loaded when:
-
- - The model is a model provided by the library (loaded with the *model id* string of a pretrained
- model).
- - The model was saved using [`~PreTrainedModel.save_pretrained`] and is reloaded by supplying the
- save directory.
- - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a
- configuration JSON file named *config.json* is found in the directory.
- state_dict (*Dict[str, torch.Tensor]*, *optional*):
- A state dictionary to use instead of a state dictionary loaded from saved weights file.
-
- This option can be used if you want to create a model from a pretrained configuration but load your own
- weights. In this case though, you should check if using [`~PreTrainedModel.save_pretrained`] and
- [`~PreTrainedModel.from_pretrained`] is not a simpler option.
- cache_dir (`str` or `os.PathLike`, *optional*):
- Path to a directory in which a downloaded pretrained model configuration should be cached if the
- standard cache should not be used.
- from_tf (`bool`, *optional*, defaults to `False`):
- Load the model weights from a TensorFlow checkpoint save file (see docstring of
- `pretrained_model_name_or_path` argument).
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force the (re-)download of the model weights and configuration files, overriding the
- cached versions if they exist.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to delete incompletely received files. Will attempt to resume the download if such a
- file exists.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, *optional*, defaults to `False`):
- Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, *optional*, defaults to `False`):
- Whether or not to only look at local files (e.g., not try downloading the model).
- revision(`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
- git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
- identifier allowed by git.
- trust_remote_code (`bool`, *optional*, defaults to `False`):
- Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
- should only be set to `True` for repositories you trust and in which you have read the code, as it will
- execute code present on the Hub on your local machine.
- kwargs (additional keyword arguments, *optional*):
- Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
- `output_attentions=True`). Behaves differently depending on whether a `config` is provided or
- automatically loaded:
-
- - If a configuration is provided with `config`, `**kwargs` will be directly passed to the
- underlying model's `__init__` method (we assume all relevant updates to the configuration have
- already been done)
- - If a configuration is not provided, `kwargs` will be first passed to the configuration class
- initialization function ([`~PretrainedConfig.from_pretrained`]). Each key of `kwargs` that
- corresponds to a configuration attribute will be used to override said attribute with the
- supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute
- will be passed to the underlying model's `__init__` function.
-
- Examples:
-
- ```python
- >>> from transformers import AutoConfig, BaseAutoModelClass
-
- >>> # Download model and configuration from huggingface.co and cache.
- >>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder")
-
- >>> # Update configuration during loading
- >>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder", output_attentions=True)
- >>> model.config.output_attentions
- True
-
- >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
- >>> config = AutoConfig.from_pretrained("./tf_model/shortcut_placeholder_tf_model_config.json")
- >>> model = BaseAutoModelClass.from_pretrained(
- ... "./tf_model/shortcut_placeholder_tf_checkpoint.ckpt.index", from_tf=True, config=config
- ... )
- ```
-"""
-
-FROM_PRETRAINED_TF_DOCSTRING = """
- Instantiate one of the model classes of the library from a pretrained model.
-
- The model class to instantiate is selected based on the `model_type` property of the config object (either
- passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's missing, by
- falling back to using pattern matching on `pretrained_model_name_or_path`:
-
- List options
-
- Args:
- pretrained_model_name_or_path (`str` or `os.PathLike`):
- Can be either:
-
- - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
- user or organization name, like `dbmdz/bert-base-german-cased`.
- - A path to a *directory* containing model weights saved using
- [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- - A path or url to a *PyTorch state_dict save file* (e.g, `./pt_model/pytorch_model.bin`). In this
- case, `from_pt` should be set to `True` and a configuration object should be provided as `config`
- argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
- using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, *optional*):
- Will be passed along to the underlying model `__init__()` method.
- config ([`PretrainedConfig`], *optional*):
- Configuration for the model to use instead of an automatically loaded configuration. Configuration can
- be automatically loaded when:
-
- - The model is a model provided by the library (loaded with the *model id* string of a pretrained
- model).
- - The model was saved using [`~PreTrainedModel.save_pretrained`] and is reloaded by supplying the
- save directory.
- - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a
- configuration JSON file named *config.json* is found in the directory.
- cache_dir (`str` or `os.PathLike`, *optional*):
- Path to a directory in which a downloaded pretrained model configuration should be cached if the
- standard cache should not be used.
- from_pt (`bool`, *optional*, defaults to `False`):
- Load the model weights from a PyTorch checkpoint save file (see docstring of
- `pretrained_model_name_or_path` argument).
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force the (re-)download of the model weights and configuration files, overriding the
- cached versions if they exist.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to delete incompletely received files. Will attempt to resume the download if such a
- file exists.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, *optional*, defaults to `False`):
- Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, *optional*, defaults to `False`):
- Whether or not to only look at local files (e.g., not try downloading the model).
- revision(`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
- git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
- identifier allowed by git.
- trust_remote_code (`bool`, *optional*, defaults to `False`):
- Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
- should only be set to `True` for repositories you trust and in which you have read the code, as it will
- execute code present on the Hub on your local machine.
- kwargs (additional keyword arguments, *optional*):
- Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
- `output_attentions=True`). Behaves differently depending on whether a `config` is provided or
- automatically loaded:
-
- - If a configuration is provided with `config`, `**kwargs` will be directly passed to the
- underlying model's `__init__` method (we assume all relevant updates to the configuration have
- already been done)
- - If a configuration is not provided, `kwargs` will be first passed to the configuration class
- initialization function ([`~PretrainedConfig.from_pretrained`]). Each key of `kwargs` that
- corresponds to a configuration attribute will be used to override said attribute with the
- supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute
- will be passed to the underlying model's `__init__` function.
-
- Examples:
-
- ```python
- >>> from transformers import AutoConfig, BaseAutoModelClass
-
- >>> # Download model and configuration from huggingface.co and cache.
- >>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder")
-
- >>> # Update configuration during loading
- >>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder", output_attentions=True)
- >>> model.config.output_attentions
- True
-
- >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
- >>> config = AutoConfig.from_pretrained("./pt_model/shortcut_placeholder_pt_model_config.json")
- >>> model = BaseAutoModelClass.from_pretrained(
- ... "./pt_model/shortcut_placeholder_pytorch_model.bin", from_pt=True, config=config
- ... )
- ```
-"""
-
-FROM_PRETRAINED_FLAX_DOCSTRING = """
- Instantiate one of the model classes of the library from a pretrained model.
-
- The model class to instantiate is selected based on the `model_type` property of the config object (either
- passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's missing, by
- falling back to using pattern matching on `pretrained_model_name_or_path`:
-
- List options
-
- Args:
- pretrained_model_name_or_path (`str` or `os.PathLike`):
- Can be either:
-
- - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
- user or organization name, like `dbmdz/bert-base-german-cased`.
- - A path to a *directory* containing model weights saved using
- [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- - A path or url to a *PyTorch state_dict save file* (e.g, `./pt_model/pytorch_model.bin`). In this
- case, `from_pt` should be set to `True` and a configuration object should be provided as `config`
- argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
- using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, *optional*):
- Will be passed along to the underlying model `__init__()` method.
- config ([`PretrainedConfig`], *optional*):
- Configuration for the model to use instead of an automatically loaded configuration. Configuration can
- be automatically loaded when:
-
- - The model is a model provided by the library (loaded with the *model id* string of a pretrained
- model).
- - The model was saved using [`~PreTrainedModel.save_pretrained`] and is reloaded by supplying the
- save directory.
- - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a
- configuration JSON file named *config.json* is found in the directory.
- cache_dir (`str` or `os.PathLike`, *optional*):
- Path to a directory in which a downloaded pretrained model configuration should be cached if the
- standard cache should not be used.
- from_pt (`bool`, *optional*, defaults to `False`):
- Load the model weights from a PyTorch checkpoint save file (see docstring of
- `pretrained_model_name_or_path` argument).
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force the (re-)download of the model weights and configuration files, overriding the
- cached versions if they exist.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to delete incompletely received files. Will attempt to resume the download if such a
- file exists.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, *optional*, defaults to `False`):
- Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, *optional*, defaults to `False`):
- Whether or not to only look at local files (e.g., not try downloading the model).
- revision(`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
- git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
- identifier allowed by git.
- trust_remote_code (`bool`, *optional*, defaults to `False`):
- Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
- should only be set to `True` for repositories you trust and in which you have read the code, as it will
- execute code present on the Hub on your local machine.
- kwargs (additional keyword arguments, *optional*):
- Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
- `output_attentions=True`). Behaves differently depending on whether a `config` is provided or
- automatically loaded:
-
- - If a configuration is provided with `config`, `**kwargs` will be directly passed to the
- underlying model's `__init__` method (we assume all relevant updates to the configuration have
- already been done)
- - If a configuration is not provided, `kwargs` will be first passed to the configuration class
- initialization function ([`~PretrainedConfig.from_pretrained`]). Each key of `kwargs` that
- corresponds to a configuration attribute will be used to override said attribute with the
- supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute
- will be passed to the underlying model's `__init__` function.
-
- Examples:
-
- ```python
- >>> from transformers import AutoConfig, BaseAutoModelClass
-
- >>> # Download model and configuration from huggingface.co and cache.
- >>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder")
-
- >>> # Update configuration during loading
- >>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder", output_attentions=True)
- >>> model.config.output_attentions
- True
-
- >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
- >>> config = AutoConfig.from_pretrained("./pt_model/shortcut_placeholder_pt_model_config.json")
- >>> model = BaseAutoModelClass.from_pretrained(
- ... "./pt_model/shortcut_placeholder_pytorch_model.bin", from_pt=True, config=config
- ... )
- ```
-"""
-
-
-def _get_model_class(config, model_mapping):
- supported_models = model_mapping[type(config)]
- if not isinstance(supported_models, (list, tuple)):
- return supported_models
-
- name_to_model = {model.__name__: model for model in supported_models}
- architectures = getattr(config, "architectures", [])
- for arch in architectures:
- if arch in name_to_model:
- return name_to_model[arch]
- elif f"TF{arch}" in name_to_model:
- return name_to_model[f"TF{arch}"]
- elif f"Flax{arch}" in name_to_model:
- return name_to_model[f"Flax{arch}"]
-
- # If not architecture is set in the config or match the supported models, the first element of the tuple is the
- # defaults.
- return supported_models[0]
-
-
-class _BaseAutoModelClass:
- # Base class for auto models.
- _model_mapping = None
-
- def __init__(self, *args, **kwargs):
- raise EnvironmentError(
- f"{self.__class__.__name__} is designed to be instantiated "
- f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
- f"`{self.__class__.__name__}.from_config(config)` methods."
- )
-
- @classmethod
- def from_config(cls, config, **kwargs):
- trust_remote_code = kwargs.pop("trust_remote_code", False)
- if hasattr(config, "auto_map") and cls.__name__ in config.auto_map:
- if not trust_remote_code:
- raise ValueError(
- "Loading this model requires you to execute the modeling file in that repo "
- "on your local machine. Make sure you have read the code there to avoid malicious use, then set "
- "the option `trust_remote_code=True` to remove this error."
- )
- if kwargs.get("revision", None) is None:
- logger.warn(
- "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure "
- "no malicious code has been contributed in a newer revision."
- )
- class_ref = config.auto_map[cls.__name__]
- module_file, class_name = class_ref.split(".")
- model_class = get_class_from_dynamic_module(
- config.name_or_path, module_file + ".py", class_name, **kwargs)
- return model_class._from_config(config, **kwargs)
- elif type(config) in cls._model_mapping.keys():
- model_class = _get_model_class(config, cls._model_mapping)
- return model_class._from_config(config, **kwargs)
-
- raise ValueError(
- f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
- f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}."
- )
-
- @classmethod
- def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
- config = kwargs.pop("config", None)
- trust_remote_code = kwargs.pop("trust_remote_code", False)
- kwargs["_from_auto"] = True
- if not isinstance(config, PretrainedConfig):
- config, kwargs = AutoConfig.from_pretrained(
- pretrained_model_name_or_path, return_unused_kwargs=True, trust_remote_code=trust_remote_code, **kwargs
- )
- if hasattr(config, "auto_map") and cls.__name__ in config.auto_map:
- if not trust_remote_code:
- raise ValueError(
- f"Loading {pretrained_model_name_or_path} requires you to execute the modeling file in that repo "
- "on your local machine. Make sure you have read the code there to avoid malicious use, then set "
- "the option `trust_remote_code=True` to remove this error."
- )
- if kwargs.get("revision", None) is None:
- logger.warn(
- "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure "
- "no malicious code has been contributed in a newer revision."
- )
- class_ref = config.auto_map[cls.__name__]
- module_file, class_name = class_ref.split(".")
- model_class = get_class_from_dynamic_module(
- pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs
- )
- return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
- elif type(config) in cls._model_mapping.keys():
- model_class = _get_model_class(config, cls._model_mapping)
- return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
- raise ValueError(
- f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
- f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}."
- )
-
- @classmethod
- def register(cls, config_class, model_class):
- """
- Register a new model for this class.
-
- Args:
- config_class ([`PretrainedConfig`]):
- The configuration corresponding to the model to register.
- model_class ([`PreTrainedModel`]):
- The model to register.
- """
- if hasattr(model_class, "config_class") and model_class.config_class != config_class:
- raise ValueError(
- "The model class you are passing has a `config_class` attribute that is not consistent with the "
- f"config class you passed (model has {model_class.config_class} and you passed {config_class}. Fix "
- "one of those so they match!"
- )
- cls._model_mapping.register(config_class, model_class)
-
-
-def insert_head_doc(docstring, head_doc=""):
- if len(head_doc) > 0:
- return docstring.replace(
- "one of the model classes of the library ",
- f"one of the model classes of the library (with a {head_doc} head) ",
- )
- return docstring.replace(
- "one of the model classes of the library ", "one of the base model classes of the library "
- )
-
-
-def auto_class_update(cls, checkpoint_for_example="bert-base-cased", head_doc=""):
- # Create a new class with the right name from the base class
- model_mapping = cls._model_mapping
- name = cls.__name__
- class_docstring = insert_head_doc(CLASS_DOCSTRING, head_doc=head_doc)
- cls.__doc__ = class_docstring.replace("BaseAutoModelClass", name)
-
- # Now we need to copy and re-register `from_config` and `from_pretrained` as class methods otherwise we can't
- # have a specific docstrings for them.
- from_config = copy_func(_BaseAutoModelClass.from_config)
- from_config_docstring = insert_head_doc(
- FROM_CONFIG_DOCSTRING, head_doc=head_doc)
- from_config_docstring = from_config_docstring.replace(
- "BaseAutoModelClass", name)
- from_config_docstring = from_config_docstring.replace(
- "checkpoint_placeholder", checkpoint_for_example)
- from_config.__doc__ = from_config_docstring
- from_config = replace_list_option_in_docstrings(
- model_mapping._model_mapping, use_model_types=False)(from_config)
- cls.from_config = classmethod(from_config)
-
- if name.startswith("TF"):
- from_pretrained_docstring = FROM_PRETRAINED_TF_DOCSTRING
- elif name.startswith("Flax"):
- from_pretrained_docstring = FROM_PRETRAINED_FLAX_DOCSTRING
- else:
- from_pretrained_docstring = FROM_PRETRAINED_TORCH_DOCSTRING
- from_pretrained = copy_func(_BaseAutoModelClass.from_pretrained)
- from_pretrained_docstring = insert_head_doc(
- from_pretrained_docstring, head_doc=head_doc)
- from_pretrained_docstring = from_pretrained_docstring.replace(
- "BaseAutoModelClass", name)
- from_pretrained_docstring = from_pretrained_docstring.replace(
- "checkpoint_placeholder", checkpoint_for_example)
- shortcut = checkpoint_for_example.split("/")[-1].split("-")[0]
- from_pretrained_docstring = from_pretrained_docstring.replace(
- "shortcut_placeholder", shortcut)
- from_pretrained.__doc__ = from_pretrained_docstring
- from_pretrained = replace_list_option_in_docstrings(
- model_mapping._model_mapping)(from_pretrained)
- cls.from_pretrained = classmethod(from_pretrained)
- return cls
-
-
-def get_values(model_mapping):
- result = []
- for model in model_mapping.values():
- if isinstance(model, (list, tuple)):
- result += list(model)
- else:
- result.append(model)
-
- return result
-
-
-def getattribute_from_module(module, attr):
- if attr is None:
- return None
- if isinstance(attr, tuple):
- return tuple(getattribute_from_module(module, a) for a in attr)
- if hasattr(module, attr):
- return getattr(module, attr)
- # Some of the mappings have entries model_type -> object of another model type. In that case we try to grab the
- # object at the top level.
- transformers_module = importlib.import_module("fengshen")
- return getattribute_from_module(transformers_module, attr)
-
-
-class _LazyAutoMapping(OrderedDict):
- """
- " A mapping config to object (model or tokenizer for instance) that will load keys and values when it is accessed.
-
- Args:
-
- - config_mapping: The map model type to config class
- - model_mapping: The map model type to model (or tokenizer) class
- """
-
- def __init__(self, config_mapping, model_mapping):
- self._config_mapping = config_mapping
- self._reverse_config_mapping = {
- v: k for k, v in config_mapping.items()}
- self._model_mapping = model_mapping
- self._extra_content = {}
- self._modules = {}
-
- def __getitem__(self, key):
- if key in self._extra_content:
- return self._extra_content[key]
- model_type = self._reverse_config_mapping[key.__name__]
- if model_type not in self._model_mapping:
- raise KeyError(key)
- model_name = self._model_mapping[model_type]
- return self._load_attr_from_module(model_type, model_name)
-
- def _load_attr_from_module(self, model_type, attr):
- module_name = model_type_to_module_name(model_type)
- if module_name not in self._modules:
- self._modules[module_name] = importlib.import_module(
- f".{module_name}", "fengshen.models")
- return getattribute_from_module(self._modules[module_name], attr)
-
- def keys(self):
- mapping_keys = [
- self._load_attr_from_module(key, name)
- for key, name in self._config_mapping.items()
- if key in self._model_mapping.keys()
- ]
- return mapping_keys + list(self._extra_content.keys())
-
- def get(self, key, default):
- try:
- return self.__getitem__(key)
- except KeyError:
- return default
-
- def __bool__(self):
- return bool(self.keys())
-
- def values(self):
- mapping_values = [
- self._load_attr_from_module(key, name)
- for key, name in self._model_mapping.items()
- if key in self._config_mapping.keys()
- ]
- return mapping_values + list(self._extra_content.values())
-
- def items(self):
- mapping_items = [
- (
- self._load_attr_from_module(key, self._config_mapping[key]),
- self._load_attr_from_module(key, self._model_mapping[key]),
- )
- for key in self._model_mapping.keys()
- if key in self._config_mapping.keys()
- ]
- return mapping_items + list(self._extra_content.items())
-
- def __iter__(self):
- return iter(self.keys())
-
- def __contains__(self, item):
- if item in self._extra_content:
- return True
- if not hasattr(item, "__name__") or item.__name__ not in self._reverse_config_mapping:
- return False
- model_type = self._reverse_config_mapping[item.__name__]
- return model_type in self._model_mapping
-
- def register(self, key, value):
- """
- Register a new model in this mapping.
- """
- if hasattr(key, "__name__") and key.__name__ in self._reverse_config_mapping:
- model_type = self._reverse_config_mapping[key.__name__]
- if model_type in self._model_mapping.keys():
- raise ValueError(
- f"'{key}' is already used by a Transformers model.")
-
- self._extra_content[key] = value
diff --git a/spaces/sklearn-docs/Lasso_and_elasticnet_for_sparse_signals/app.py b/spaces/sklearn-docs/Lasso_and_elasticnet_for_sparse_signals/app.py
deleted file mode 100644
index bf46f031356009127eaf2313d034297726fd2acf..0000000000000000000000000000000000000000
--- a/spaces/sklearn-docs/Lasso_and_elasticnet_for_sparse_signals/app.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import gradio as gr
-import numpy as np
-import matplotlib.pyplot as plt
-
-from sklearn.metrics import r2_score
-from sklearn.linear_model import Lasso, ElasticNet
-
-
-theme = gr.themes.Monochrome(
- primary_hue="indigo",
- secondary_hue="blue",
- neutral_hue="slate",
-)
-model_card = f"""
-## Description
-
-This demo estimates **Lasso** and **Elastic-Net** regression models on a manually generated sparse signal corrupted with an additive noise.
-You can play around with different ``regularization strength``, ``mixing ratio between L1 and L2``, ``number of samples``, ``number of features`` to see the effect
-
-## Dataset
-
-Simulation dataset
-"""
-
-
-
-def do_train(alpha, l1_ratio, n_samples, n_features):
- np.random.seed(42)
- X = np.random.randn(n_samples, n_features)
-
- # Decreasing coef w. alternated signs for visualization
- idx = np.arange(n_features)
- coef = (-1) ** idx * np.exp(-idx / 10)
- coef[10:] = 0 # sparsify coef
- y = np.dot(X, coef)
-
- # Add noise
- y += 0.01 * np.random.normal(size=n_samples)
-
- # Split data in train set and test set
- n_samples = X.shape[0]
- X_train, y_train = X[: n_samples // 2], y[: n_samples // 2]
- X_test, y_test = X[n_samples // 2 :], y[n_samples // 2 :]
-
- lasso = Lasso(alpha=alpha)
- y_pred_lasso = lasso.fit(X_train, y_train).predict(X_test)
- r2_score_lasso = r2_score(y_test, y_pred_lasso)
-
-
- enet = ElasticNet(alpha=alpha, l1_ratio=l1_ratio)
- y_pred_enet = enet.fit(X_train, y_train).predict(X_test)
- r2_score_enet = r2_score(y_test, y_pred_enet)
-
- fig, axes = plt.subplots()
-
- m, s, _ = axes.stem(
- np.where(enet.coef_)[0],
- enet.coef_[enet.coef_ != 0],
- markerfmt="x",
- label="Elastic net coefficients",
- )
- plt.setp([m, s], color="#2ca02c")
-
- m, s, _ = plt.stem(
- np.where(lasso.coef_)[0],
- lasso.coef_[lasso.coef_ != 0],
- markerfmt="x",
- label="Lasso coefficients",
- )
- plt.setp([m, s], color="#ff7f0e")
-
- axes.stem(
- np.where(coef)[0],
- coef[coef != 0],
- label="True coefficients",
- markerfmt="bx",
- )
-
- axes.legend(loc="best")
- axes.set_title("Elastic net and Lasso coefficients")
- text = f"Lasso R^2: {r2_score_lasso:.3f}, Elastic Net R^2: {r2_score_enet:.3f}"
- return fig, text
-
-
-
-with gr.Blocks(theme=theme) as demo:
- gr.Markdown('''
-
-
- ''')
- gr.Markdown(model_card)
- gr.Markdown("Author: Vu Minh Chien. Based on the example from scikit-learn")
- alpha = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.1, label="Controlling regularization strength: alpha. Using alpha = 0 with the Lasso object is not advised")
- l1_ratio = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.7, label="The ElasticNet mixing parameter: l1_ratio. For l1_ratio = 0 the penalty is an L2 penalty. For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.")
- n_samples = gr.Slider(minimum=50, maximum=500, step=50, value=50, label="Number of samples")
- n_features = gr.Slider(minimum=50, maximum=200, step=50, value=50, label="Number of features")
- with gr.Row():
- with gr.Column():
- plot = gr.Plot(label="Coefficients plot")
- with gr.Column():
- results = gr.Textbox(label="Results")
-
- alpha.change(fn=do_train, inputs=[alpha, l1_ratio, n_samples, n_features], outputs=[plot, results])
- l1_ratio.change(fn=do_train, inputs=[alpha, l1_ratio, n_samples, n_features], outputs=[plot, results])
- n_samples.change(fn=do_train, inputs=[alpha, l1_ratio, n_samples, n_features], outputs=[plot, results])
- n_features.change(fn=do_train, inputs=[alpha, l1_ratio, n_samples, n_features], outputs=[plot, results])
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/speakjan/EleutherAI-gpt-j-6b/app.py b/spaces/speakjan/EleutherAI-gpt-j-6b/app.py
deleted file mode 100644
index 63843f9565f84472643a653354f8024857c03cf8..0000000000000000000000000000000000000000
--- a/spaces/speakjan/EleutherAI-gpt-j-6b/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/EleutherAI/gpt-j-6b").launch()
\ No newline at end of file
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/ulm/sample.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/ulm/sample.py
deleted file mode 100644
index 77302a6894cacf07588cf34fb1e695dc519d7df5..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/ulm/sample.py
+++ /dev/null
@@ -1,174 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Sample from a trained LM; hacked fairseq-interactive
-"""
-from collections import namedtuple
-import os
-import ast
-import numpy as np
-
-from fairseq import checkpoint_utils, options, tasks, utils
-
-import tqdm
-
-Batch = namedtuple('Batch', 'ids src_tokens src_lengths')
-Translation = namedtuple('Translation', 'src_str hypos pos_scores alignments')
-
-
-def make_batches(lines, args, task, max_positions):
- tokens = [
- task.source_dictionary.encode_line(
- src_str, add_if_not_exist=False
- ).long()
- for src_str in lines
- ]
- lengths = [t.numel() for t in tokens]
- itr = task.get_batch_iterator(
- dataset=task.build_dataset_for_inference(tokens, lengths),
- max_tokens=args.dataset.max_tokens,
- max_sentences=args.dataset.batch_size,
- max_positions=max_positions,
- ignore_invalid_inputs=args.dataset.skip_invalid_size_inputs_valid_test
- ).next_epoch_itr(shuffle=False)
- for batch in itr:
- yield Batch(
- ids=batch['id'],
- src_tokens=batch['net_input']['src_tokens'], src_lengths=batch['net_input']['src_lengths'],
- )
-
-
-def main(args):
- arg_prompts = args.prompts
- arg_output = args.output
- arg_debug = args.debug
- arg_sample_size = args.samples_per_prompt
-
- try:
- from fairseq.dataclass.utils import convert_namespace_to_omegaconf
- args = convert_namespace_to_omegaconf(args)
- except:
- pass
-
- # if args.max_tokens is None and args.max_sentences is None:
- if args.common.seed is not None:
- np.random.seed(args.common.seed)
- utils.set_torch_seed(args.common.seed)
-
- if args.generation.sampling:
- args.generation.nbest = args.generation.beam = arg_sample_size
-
- task = tasks.setup_task(args.task)
-
- overrides = ast.literal_eval(args.common_eval.model_overrides)
-
- models, _model_args = checkpoint_utils.load_model_ensemble(
- args.common_eval.path.split(os.pathsep),
- arg_overrides=overrides,
- task=task,
- suffix=getattr(args, "checkpoint_suffix", ""),
- )
-
- # Set dictionaries
- src_dict = task.source_dictionary
- tgt_dict = task.target_dictionary
-
- # Optimize ensemble for generation
- for model in models:
- model.prepare_for_inference_(args)
- model.cuda()
-
- # Load alignment dictionary for unknown word replacement
- # (None if no unknown word replacement, empty if no path to align dictionary)
- align_dict = utils.load_align_dict(args.generation.replace_unk)
-
- max_positions = utils.resolve_max_positions(
- task.max_positions(),
- *[model.max_positions() for model in models]
- )
-
- output_file = open(arg_output, 'w')
-
- with open(arg_prompts, 'r') as fin:
- lines = fin.readlines()
-
- split = [x.split('|', 1) for x in lines]
- seq_id = [x[0] for x in split]
- prompts = [x[1] for x in split]
-
- if args.generation.prefix_size >= 0:
- prompts = [' '.join(l.split()[:args.generation.prefix_size])
- for l in prompts]
-
- if arg_debug:
- prompts = prompts[:10]
-
- generator = task.build_generator(models, args.generation)
-
- start_id = 0
- pbar = tqdm.tqdm(total=len(prompts))
- for batch in make_batches(prompts, args, task, max_positions):
- src_tokens = batch.src_tokens
- src_lengths = batch.src_lengths
- src_tokens = src_tokens.cuda()
- src_lengths = src_lengths.cuda()
-
- sample = {
- 'net_input': {
- 'src_tokens': src_tokens,
- 'src_lengths': src_lengths,
- },
- }
-
- results = []
- translations = task.inference_step(generator, models, sample)
- for i, (id, hypos) in enumerate(zip(batch.ids.tolist(), translations)):
- src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad())
- results.append((i + start_id, src_tokens_i, hypos))
-
- # sort output to match input order
- for id, src_tokens, hypos in sorted(results, key=lambda x: x[0]):
- if src_dict is not None:
- src_str = src_dict.string(
- src_tokens, args.common_eval.post_process)
-
- # Process top predictions
- for hypo_id, hypo in enumerate(hypos):
- _hypo_tokens, hypo_str, _alignment = utils.post_process_prediction(
- hypo_tokens=hypo['tokens'].int().cpu(),
- src_str=src_str,
- alignment=hypo['alignment'],
- align_dict=align_dict,
- tgt_dict=tgt_dict,
- remove_bpe=args.common_eval.post_process,
- )
-
- detok_hypo_str = hypo_str
- utterance = detok_hypo_str
- print(f'{seq_id[id]}__{hypo_id}|{utterance}', file=output_file)
- pbar.update(1)
- start_id += len(results)
-
- # output_file.close()
-
-
-def cli_main():
- parser = options.get_interactive_generation_parser()
- parser.add_argument('--prompts', type=str, default=None, required=True)
- parser.add_argument('--output', type=str, default=None, required=True)
- parser.add_argument('--debug', action='store_true')
- parser.add_argument('--samples-per-prompt', type=int, default=1)
-
- args = options.parse_args_and_arch(parser)
-
- np.random.seed(args.seed)
- utils.set_torch_seed(args.seed)
-
- main(args)
-
-
-if __name__ == '__main__':
- cli_main()
diff --git a/spaces/starlit7/NewKorPoliticsTTS/app.py b/spaces/starlit7/NewKorPoliticsTTS/app.py
deleted file mode 100644
index 70096dc1b4de12d5bb05584591790bc99f8ab842..0000000000000000000000000000000000000000
--- a/spaces/starlit7/NewKorPoliticsTTS/app.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import json
-import os
-import re
-
-import librosa
-import numpy as np
-import torch
-from torch import no_grad, LongTensor
-import commons
-import utils
-import gradio as gr
-from models import SynthesizerTrn
-from text import text_to_sequence, _clean_text
-from mel_processing import spectrogram_torch
-
-limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces
-
-
-def get_text(text, hps, is_phoneme):
- text_norm = text_to_sequence(text, hps.symbols, [] if is_phoneme else hps.data.text_cleaners)
- if hps.data.add_blank:
- text_norm = commons.intersperse(text_norm, 0)
- text_norm = LongTensor(text_norm)
- return text_norm
-
-
-def create_tts_fn(model, hps, speaker_ids):
- def tts_fn(text, speaker, speed, is_phoneme):
- if limitation:
- text_len = len(text)
- max_len = 300
- if is_phoneme:
- max_len *= 3
- else:
- if len(hps.data.text_cleaners) > 0 and hps.data.text_cleaners[0] == "zh_ja_mixture_cleaners":
- text_len = len(re.sub("(\[ZH\]|\[JA\])", "", text))
- if text_len > max_len:
- return "Error: Text is too long", None
-
- speaker_id = speaker_ids[speaker]
- stn_tst = get_text(text, hps, is_phoneme)
- with no_grad():
- x_tst = stn_tst.unsqueeze(0)
- x_tst_lengths = LongTensor([stn_tst.size(0)])
- sid = LongTensor([speaker_id])
- audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
- length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
- del stn_tst, x_tst, x_tst_lengths, sid
- return "Success", (hps.data.sampling_rate, audio)
-
- return tts_fn
-
-
-
-
-
-def create_to_phoneme_fn(hps):
- def to_phoneme_fn(text):
- return _clean_text(text, hps.data.text_cleaners) if text != "" else ""
-
- return to_phoneme_fn
-
-
-css = """
- #advanced-btn {
- color: white;
- border-color: black;
- background: black;
- font-size: .7rem !important;
- line-height: 19px;
- margin-top: 24px;
- margin-bottom: 12px;
- padding: 2px 8px;
- border-radius: 14px !important;
- }
- #advanced-options {
- display: none;
- margin-bottom: 20px;
- }
-"""
-
-if __name__ == '__main__':
- models_tts = []
- models_vc = []
- models_soft_vc = []
- name = 'NewKorPoliticsTTS'
- lang = '한국어 (Korean)'
- example = '존경하는 국민 여러분'
- config_path = f"saved_model/config.json"
- model_path = f"saved_model/model.pth"
- cover_path = f"saved_model/cover.png"
- hps = utils.get_hparams_from_file(config_path)
- model = SynthesizerTrn(
- len(hps.symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model)
- utils.load_checkpoint(model_path, model, None)
- model.eval()
- speaker_ids = [sid for sid, name in enumerate(hps.speakers) if name != "None"]
- speakers = [name for sid, name in enumerate(hps.speakers) if name != "None"]
-
- t = 'vits'
- models_tts.append((name, cover_path, speakers, lang, example,
- hps.symbols, create_tts_fn(model, hps, speaker_ids),
- create_to_phoneme_fn(hps)))
-
-
- app = gr.Blocks(css=css)
-
- with app:
- gr.Markdown("# NewKorPoliticsTTS Using VITS Model\n\n"
- "\n\n"
- "[NewKorPoliticsTTS 제작자 유튜브 주소]"
- "(https://www.youtube.com/@litlit/featured)"
- )
- with gr.Tabs():
- with gr.TabItem("TTS"):
- with gr.Tabs():
- for i, (name, cover_path, speakers, lang, example, symbols, tts_fn,
- to_phoneme_fn) in enumerate(models_tts):
- with gr.TabItem(f"Politician"):
- with gr.Column():
- gr.Markdown(f"## {name}\n\n"
- f"\n\n"
- f"lang: {lang}")
- tts_input1 = gr.TextArea(label="Text (300 words limitation)", value=example,
- elem_id=f"tts-input{i}")
- tts_input2 = gr.Dropdown(label="Speaker", choices=speakers,
- type="index", value=speakers[0])
- tts_input3 = gr.Slider(label="Speed", value=1, minimum=0.1, maximum=2, step=0.1)
- with gr.Accordion(label="Advanced Options", open=False):
- phoneme_input = gr.Checkbox(value=False, label="Phoneme input")
- to_phoneme_btn = gr.Button("Covert text to phoneme")
- phoneme_list = gr.Dataset(label="Phoneme list", components=[tts_input1],
- samples=[[x] for x in symbols],
- elem_id=f"phoneme-list{i}")
- phoneme_list_json = gr.Json(value=symbols, visible=False)
- tts_submit = gr.Button("Generate", variant="primary")
- tts_output1 = gr.Textbox(label="Output Message")
- tts_output2 = gr.Audio(label="Output Audio")
- tts_submit.click(tts_fn, [tts_input1, tts_input2, tts_input3, phoneme_input],
- [tts_output1, tts_output2])
- to_phoneme_btn.click(to_phoneme_fn, [tts_input1], [tts_input1])
- phoneme_list.click(None, [phoneme_list, phoneme_list_json], [],
- _js=f"""
- (i,phonemes) => {{
- let root = document.querySelector("body > gradio-app");
- if (root.shadowRoot != null)
- root = root.shadowRoot;
- let text_input = root.querySelector("#tts-input{i}").querySelector("textarea");
- let startPos = text_input.selectionStart;
- let endPos = text_input.selectionEnd;
- let oldTxt = text_input.value;
- let result = oldTxt.substring(0, startPos) + phonemes[i] + oldTxt.substring(endPos);
- text_input.value = result;
- let x = window.scrollX, y = window.scrollY;
- text_input.focus();
- text_input.selectionStart = startPos + phonemes[i].length;
- text_input.selectionEnd = startPos + phonemes[i].length;
- text_input.blur();
- window.scrollTo(x, y);
- return [];
- }}""")
-
- gr.Markdown(
- "Reference \n\n"
- "- [https://huggingface.co/spaces/skytnt/moe-tts](https://huggingface.co/spaces/skytnt/moe-tts)"
- )
-
- app.queue(concurrency_count=3).launch(show_api=False)
diff --git a/spaces/stevenxiao29/ResumeAssist/app.py b/spaces/stevenxiao29/ResumeAssist/app.py
deleted file mode 100644
index 6869d830893dcf877ce304bf9720c2e04af76f57..0000000000000000000000000000000000000000
--- a/spaces/stevenxiao29/ResumeAssist/app.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import streamlit as st
-import google.generativeai as palm
-
-# Configure your Palm API key
-
-api_key = "AIzaSyATRWBWwqP1AYY1gNJEHvKPKSBWorFABv8"
-palm.configure(api_key=api_key)
-
-models = [m for m in palm.list_models() if 'generateText' in m.supported_generation_methods]
-model = models[0].name
-
-# Streamlit Interface
-st.title("Resume Assist :page_with_curl:")
-user_resume = st.text_area("Your Resume")
-job_description = st.text_area("Full Job Description")
-
-# Create an empty container for the output
-output_container = st.empty()
-cover_letter_container = st.empty()
-
-def generate_cover_letter(user_resume, job_description):
- if user_resume and job_description:
- cover_letter = generate_cover_letter_using_palm(user_resume, job_description)
- with cover_letter_container:
- st.subheader("Generated Cover Letter:")
- st.markdown(cover_letter)
- else:
- st.warning("Please enter your resume and the job description.")
-
-def generate_cover_letter_using_palm(user_resume, job_description):
- prompt = (
- f"Pretend you are an expert in writing cover letters. \n\nGiven the following resume:\n\n{user_resume}\n\n"
- f"And the job description:\n\n{job_description}\n\n"
- "Please generate a professional and engaging cover letter that aligns the candidate's skills and experiences with the job requirements."
- )
-
- # The method and parameters would depend on Palm's API documentation
- response = palm.generate_text(
- model=model,
- prompt=prompt,
- max_output_tokens=500,
- )
- return response.result # This would depend on the response structure of Palm
-
-def generate_revised_resume(user_resume, job_description):
- if user_resume and job_description:
- revised_resume = generate_revised_resume_using_palm(user_resume, job_description)
- with output_container:
- st.subheader("Revised Resume:")
- st.markdown(revised_resume)
- else:
- st.warning("Please enter your resume and the job description.")
-
-def generate_revised_resume_using_palm(user_resume, job_description):
- prompt = (
- f"Pretend you are a resume expert.\n\n"
- f"Given the following resume:\n\n"
- f"{user_resume}\n\n"
- f"And the job description:\n\n"
- f"{job_description}\n\n"
- f"Provide specific suggestions in bullet points to tailor the resume to the job description. "
- f"Suggest possible next steps, certifications, courses or experiences the person could do. "
- f"Make sure the suggestions are all FACTUALLY ACCURATE given the original resume and specify which suggestions "
- f"require further action. FOR EVERY CHANGE YOU SUGGEST, PROVIDE A SIMPLE EXPLANATION FOR WHY< Additionally include critiques on a granular level including strong word choice suggestion etc. Output in formatted, aesthetically pleasing markdown text."
- )
-
-
- # The method and parameters would depend on Palm's API documentation
- response = palm.generate_text(
- model=model,
- prompt=prompt,
- temperature=0.1, # You can adjust the temperature for more diverse responses
- max_output_tokens=1024,
- )
- return response.result # This would depend on the response structure of Palm
-
-if st.button("Resume Suggestions"):
- generate_revised_resume(user_resume, job_description)
-
-if st.button("Generate Cover Letter"):
- generate_cover_letter(user_resume, job_description)
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Anicesoft Epub Converter 9.5.3 Keygen.md b/spaces/stomexserde/gpt4-ui/Examples/Anicesoft Epub Converter 9.5.3 Keygen.md
deleted file mode 100644
index 0fbf71fed9158c11aa4bfec689aa7cc6299c37f0..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Anicesoft Epub Converter 9.5.3 Keygen.md
+++ /dev/null
@@ -1,48 +0,0 @@
-
-Lasso and Elastic Net for Sparse Signals
- Anicesoft Epub Converter 9.5.3 Keygen: A Comprehensive Review
-Anicesoft Epub Converter 9.5.3 Keygen
- Introduction: What is Anicesoft Epub Converter and why do you need a keygen?
-Features: What can Anicesoft Epub Converter do for you?
-
-
- Pros and cons: What are the advantages and disadvantages of using Anicesoft Epub Converter?
-
-
-
-
-Pros
-Cons
-
-
-
-
-
How to use Anicesoft Epub Converter 9.5.3 Keygen: A step-by-step guide
-
-
-
-
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/D3scene Dll Injector 24.md b/spaces/stomexserde/gpt4-ui/Examples/D3scene Dll Injector 24.md
deleted file mode 100644
index 5831de23ac0bfbc82606e51c3f65dd1831ea9c3f..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/D3scene Dll Injector 24.md
+++ /dev/null
@@ -1,136 +0,0 @@
-
-
-
-
-
- Article with HTML formatting
-
-
- D3scene Dll Injector 24: What Is It and How to Use It?
- Introduction
- D3scene Dll Injector 24
-
-
-
-
- How to Download and Install D3scene Dll Injector 24
-
-
-
-
-
-
- How to Use D3scene Dll Injector 24 for Various Games and Applications
-
-
- How to Uninstall or Remove D3scene Dll Injector 24 from Your Computer
-
-
-
-
- Conclusion
- FAQs
- What is a DLL file and why do you need to inject it into a game or application?
- Is D3scene Dll Injector 24 safe and legal to use?
- What are some of the games and applications that are compatible with D3scene Dll Injector 24?
-
-
- What are some of the common problems or errors that may occur when using D3scene Dll Injector 24 and how to fix them?
-
-
- Where can I find more information or support for using D3scene Dll Injector 24?
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Wdr Udma 5.3.rar.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Wdr Udma 5.3.rar.md
deleted file mode 100644
index 7362210a8460de697ad7b2d427f3f5b56c2fbb23..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Wdr Udma 5.3.rar.md
+++ /dev/null
@@ -1,44 +0,0 @@
-wdr udma 5.3.rar
-
-supybot?
-
- hi p_quarles
-
- hey
-
- libstreamanalyzer0 - Stream Analyzer Library
-
- to be honest. ive not had a need to do anything with audio streams in ages. :)
-
- as you might guess, heh
-
- maybe it would be easier for you to listen to internet radios
-
-
-
- I have a wireless USB that's listed in lsusb, and I'm wondering if the driver is being loaded by the OS or if I'm just using this one OS with a particular driver?
-
- zials: what does lsmod | grep iwl3945 say?
-
- am I being a jackass or did you mean lsmod | grep ^iwl?
-
- sorry
-
- lsmod | grep iwl3945
-
- nothing
-
- probably b/c I haven't loaded the driver for this system yet?
-
- lsmod | grep ^iwl should do
-
- ah, nm, I see
-
- p_quarles: how is the kernel doing the work?
-
- Stroganoff: what do you mean?
-
- lsmod | grep ^iwl... 4fefd39f24
-
-
-
diff --git a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Amplitube 3 Serial Number Generator By Everg0n Download 11 !!TOP!!.md b/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Amplitube 3 Serial Number Generator By Everg0n Download 11 !!TOP!!.md
deleted file mode 100644
index 6a90b0f1f7f57b07a7f95a9b670b345e37d64052..0000000000000000000000000000000000000000
--- a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Amplitube 3 Serial Number Generator By Everg0n Download 11 !!TOP!!.md
+++ /dev/null
@@ -1,8 +0,0 @@
-
-
#FreeBibleMemorizationMethod With Video [REAL FULL] :_::
The Case For A Single-Pay Price List And Mandate For Price Consolidation In The Medicare Program :_::
The Future Of The United States And World Banking
Magical Numbers
Bruce Bielen Full Movie In Hindi Dubbed Download Bluray
What Caused Some U.S. Cities And Counties To Fail During The Early 1900s
The Son Of The Old Guard : : :
To The Wounded And Deserving : : :
Bankers Without Banking Degrees
Business School Failures
Europe's First Debt Insurance Controversy : : :
Who's Better At Making Money?: The Investor or The Entrepreneur
Go To The Bar The Buzz The Titanic - Trailer
Free download movie online in hd 720p high quality
game cartoon free xfiles torrent
Lg l940c
Player HD Fix Final+Serial+Tie-In+Activation
efgh gjkklmnoprstamplitube 3 serial number generator by everg0n download 11
-
menagerie fullsetup download setup Key 9.7.17.3
obscurestars.com MegaKeygen Full Free 64 Nulled Keys
stephenpresser.chess.windows.download.crack.Win
kayleza bundle 5.0 license free download
The following command line parameters are available for the
E4501s card: -N
-N
-N -f
-f
-f
-r
-r
-r
-a
-a
-a
-
-
\ No newline at end of file
diff --git a/spaces/suvradip2000/space1/app/Hackathon_setup/exp_recognition_model.py b/spaces/suvradip2000/space1/app/Hackathon_setup/exp_recognition_model.py
deleted file mode 100644
index 97d232ad480a85a534ce9d30ee42fba69886d367..0000000000000000000000000000000000000000
--- a/spaces/suvradip2000/space1/app/Hackathon_setup/exp_recognition_model.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import torch
-import torchvision
-import torch.nn as nn
-from torchvision import transforms
-import torch.nn.functional as F
-## Add more imports if required
-
-####################################################################################################################
-# Define your model and transform and all necessary helper functions here #
-# They will be imported to the exp_recognition.py file #
-####################################################################################################################
-
-# Definition of classes as dictionary
-classes = {0: 'ANGER', 1: 'DISGUST', 2: 'FEAR', 3: 'HAPPINESS', 4: 'NEUTRAL', 5: 'SADNESS', 6: 'SURPRISE'}
-
-# Example Network
-class Model(nn.Module):
- def __init__(self):
- super(Model, self).__init__()
-
-
- self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3)
-
-
- self.conv2 = nn.Conv2d(in_channels=16, out_channels=64, kernel_size=3)
-
- self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3)
-
- # Define the Fully connected layers
- # The output of the second convolution layer will be input to the first fully connected layer
- self.fc1 = nn.Linear(128*10*10, 256)
- # 256 input features, 128 output features
- self.fc2 = nn.Linear(256, 128)
- # 128 input features, 64 output features
- self.fc3 = nn.Linear(128, 64)
- # 64 input features, 7 output features for our 7 defined classes
- self.fc4 = nn.Linear(64, 7)
-
- # Max pooling
- self.pool = nn.MaxPool2d(kernel_size=2) # Max pooling layer with filter size 2x2
-
- def forward(self, x):
-
- x = self.pool(F.relu(self.conv1(x)))
- x = self.pool(F.relu(self.conv2(x)))
- x = self.pool(F.relu(self.conv3(x)))
- # Flatten the image
- x = x.view(-1, 128*10*10) # Output shape of convolutional layer is 16*5*5
-
- # Linear layers with RELU activation
- x = F.relu(self.fc1(x))
- x = F.relu(self.fc2(x))
- x = F.relu(self.fc3(x))
- x = self.fc4(x)
- x = F.log_softmax(x, dim=1)
- return x
-
-# Sample Helper function
-def rgb2gray(image):
- return image.convert('L')
-
-# Sample Transformation function
-#YOUR CODE HERE for changing the Transformation values.
-trnscm = transforms.Compose([transforms.Resize((224,224)), transforms.ToTensor()])
\ No newline at end of file
diff --git a/spaces/swj0419/Detect-Pretraining-Data/app.py b/spaces/swj0419/Detect-Pretraining-Data/app.py
deleted file mode 100644
index 061245eb58493ee221d997d2cfe56afd165caa92..0000000000000000000000000000000000000000
--- a/spaces/swj0419/Detect-Pretraining-Data/app.py
+++ /dev/null
@@ -1,138 +0,0 @@
-import gradio as gr
-import openai
-import numpy as np
-# from scipy.stats import norm
-
-# Make sure to replace 'your_api_key' with your actual OpenAI API key
-# openai.api_key = "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"
-
-harry = "Harry Potter and the Sorcerer's Stone\n\nas though Harry was being stupid on purpose. Getting desperate, Harry asked for the train that left at eleven o'clock, but the guard said there wasn't one. In the end the guard strode away, muttering about time wasters. Harry was now trying hard not to panic. According to the large clock over the arrivals board, he had ten minutes left to get on the train to Hogwarts and he had no idea how to do it; he was stranded in the middle of a station with a trunk he could hardly lift, a pocket full of wizard money, and a large owl. Hagrid must have forgotten to tell him something you had to do, like tapping the third brick on the left to get into Diagon Alley. He wondered if he should get out his wand and start tapping the ticket inspector's stand between platforms nine and ten. At that moment a group of people passed just behind him and he caught a few words of what they were saying. \" -- packed with Muggles, of course -- \" Harry swung round. The speaker was a plump woman who was talking to four boys, all with flaming red hair. Each of them was pushing a trunk like Harry's in front of him -- and they had an owl. Heart hammering, Harry pushed his cart after them. They stopped and so did he, just near enough to hear what they were saying. \"Now, what's the platform number?\" said the boys' mother. \"Nine and three-quarters!\" piped a small girl, also red-headed, who was holding her hand, \"Mom, can't I go...\" \"You're not old enough, Ginny, now be quiet. All right, Percy, you go first.\" What looked like the oldest boy marched toward platforms nine and ten. Harry watched, careful not to blink in case he missed it -- but just as the boy reached the dividing barrier between the two platforms, a large crowd of tourists came swarming in front of him and by the time the last backpack had cleared away, the boy had vanished. \"Fred, you next,\" the plump woman said. \"I'm not Fred, I'm George,\" said the boy. \"Honestly, woman, you call yourself our mother? Can't you tell I'm George?\" \"Sorry, George, dear.\" \"Only joking, I am Fred,\" said the boy, and off he went. His twin called after him to hurry up, and he must have done so, because a second later, he had gone -- but how had he done it? Now the third brother was walking briskly toward the barrier -- he was almost there -- and then, quite suddenly, he wasn't anywhere. There was nothing else for it. \"Excuse me,\" Harry said to the plump woman. \"Hello, dear,\" she said. \"First time at Hogwarts? Ron's new, too.\" She pointed at the last and youngest of her sons. He was tall, thin, and gangling, with freckles, big hands and feet, and a long nose. \"Yes,\" said Harry. \"The thing is -- the thing is, I don't know how to -- \" \"How to get onto the platform?\" she"
-pineapple = "Pineapple Street\n\nat buildings that hadn\u2019t changed, at the thin ridge of White Mountain crest rising above the eastern tree line, it was easy to imagine the place had been cryogenically preserved. Fran had offered me her couch, but the way she said it\u2014\u201cI mean, there\u2019s the dog, and Jacob\u2019s always at volume eleven, and Max still doesn\u2019t sleep through the night\u201d\u2014made it seem more gesture than invitation. So I\u2019d opted to stay in one of the two guest apartments, located right above the ravine in a small house that used to be the business office. There were a bedroom and bathroom on each floor, plus a downstairs kitchen to share. The whole place, I found, smelled like bleach. I unpacked, worrying I hadn\u2019t brought enough sweaters, and thinking, of all things, about Granby pay phones. Imagine me (remember me), fifteen, sixteen, dressed in black even when I wasn\u2019t backstage, my taped-up Doc Martens, the dark, wispy hair fringing my Cabbage Patch face; imagine me, armored in flannel, eyes ringed thick with liner, passing the pay phone and\u2014without looking\u2014picking it up, twirling it upside down, hanging it back the wrong way. That was only at first, though; by junior year, I couldn\u2019t pass one without picking up the receiver, pressing a single number, and listening\u2014because there was at least one phone on which, if you did this, you could hear another conversation through the static. I discovered the trick when I started to call my dorm from the gym lobby phone to ask if I could be late for 10:00 check-in, but after I pressed the first button I heard a boy\u2019s voice, muffled, half volume, complaining to his mother about midterms. She asked if he\u2019d been getting his allergy shots. He sounded whiny and homesick and about twelve years old, and it took me a while to recognize his voice: Tim Busse, a hockey player with bad skin but a beautiful girlfriend. He must have been on a pay phone in his own dorm common, across the ravine. I didn\u2019t understand what rules of telecommunications allowed this to occur, and when I told my husband this story once, he shook his head, said, \u201cThat couldn\u2019t happen.\u201d I asked if he was accusing me of lying, or if he thought I\u2019d been hearing voices. \u201cI just mean,\u201d Jerome replied evenly, \u201cthat it couldn\u2019t happen.\u201d I stood in the gym lobby mesmerized, not wanting to miss a word. But eventually I had to; I called my own dorm, asked the on-duty teacher for ten extra minutes to run across campus and get the history book I\u2019d left in Commons. No, she said, I could not. I had three minutes till check-in. I hung up, lifted the receiver again, pressed one number. There was Tim Busse\u2019s voice still. Magic. He told his mother he was failing physics. I was surprised. And now I had a secret about him. A secret secret, one he hadn\u2019t meant to share. I had a sidelong crush after that on Tim Busse, to whom I\u2019d never previously paid an ounce o"
-god = "Silvia-Moreno-Garcia-Silver-Nitr\n\nwas doing a piece about Abel\u2019s career it might fly, but I\u2019m looking for this one movie and this one fucked-up German who wrote it and I\u2019m not having any luck.\u201d \u201cDon\u2019t panic yet. Urueta is going to give you the interview you need sooner or later.\u201d \u201cHe doesn\u2019t like us.\u201d \u201cHe got a little tense, but Urueta loves talking. He wouldn\u2019t shut up about Liz Taylor and Richard Burton and how he had cocktails with them several times when Burton was shooting The Night of the Iguana. He\u2019s an old soldier sharing war stories. He wants to be heard.\u201d \u201cNot by me anymore. Not if Enigma is involved. This is bullshit.\u201d Editing was changing. The Moviola and the Steenbeck machines were yielding space to video monitors, tapes, and computers. Beyond the Yellow Door was an item from another era; it enchanted her with its antiquated film stock and post-synchronized sound: it was like meeting a gentleman in a tweed suit and a monocle these days. She wanted the story about its troubled production. She wanted to discover its secrets, and there was nothing to be known. In her mind, the picture she had assembled of the film was vanishing, like decomposing celluloid. \u201cWhat isn\u2019t! Listen, hang in there. I\u2019ll soften the old man. Be ready to come over on Saturday.\u201d \u201cYeah, yeah,\u201d she muttered without enthusiasm. Friday instead of going to the Cineteca she headed to the archives at Lecumberri. She found more of the same: stubs, film capsules, a few reviews. An old issue of Cinema Reporter dated 1960 provided her with the only significant piece of material she was able to dig up: a black-and-white photo showing Ewers. The picture in fact showed four people. Two of them she identified easily. Abel Urueta had his trademark scarf, and Alma Montero, although older, was recognizable from the publicity photos from her silent era years. A pretty, young woman in a strapless dress was new to Montserrat. She had the air and smile of a socialite if not an actress. The fourth person was a man in a dark suit. They sat with Alma at the forefront, the lens more interested in her, then Abel, the girl, and finally the man at the farthest end of the table almost an afterthought. The occasion must have been a birthday celebration or a big event, for there was confetti in Alma\u2019s hair. The caption read: \u201cFilm star Alma Montero, director Abel Urueta and his fianc\u00e9e Miss Clarimonde Bauer, and Mr. Wilhelm Ewers enjoy an evening at El Retiro.\u201d The story that accompanied the picture was a stub and useless filler, like everything else she\u2019d found, but at least the image made a ghost tangible. Because until that moment she had begun to believe there was no Ewers. He had evaded her, but at least she was able to contemplate the reality of the man. Yet stubbornly, as if he had known he was being sought, the man in the picture appeared almost out of frame, his head inclined, so that you couldn\u2019t get"
-def calculatePerplexity_gpt3(prompt):
- prompt = prompt.replace('\x00', '')
- try:
- responses = openai.Completion.create(
- engine="text-davinci-003",
- prompt=prompt,
- max_tokens=0,
- temperature=1.0,
- logprobs=5,
- echo=True)
- except openai.error.InvalidRequestError:
- print(openai.error.InvalidRequestError)
- openai.error.InvalidRequestError, [], 0
-
- data = responses["choices"][0]["logprobs"]
- all_prob = [d for d in data["token_logprobs"] if d is not None]
- return np.exp(-np.mean(all_prob)), all_prob, np.mean(all_prob)
-
-def check_in_pretraining_data(text):
- # Check if the input text is longer than 128 words
- if len(text.split()) < 512:
- return "Error: Your input must be longer than 512 words.", ""
- if text == harry:
- return "Likely in text-davinci-003's pretraining data", "High confidence"
- elif text == pineapple:
- return "Likely not in text-davinci-003's pretraining data", "High confidence"
- elif text == god:
- return "Likely not in text-davinci-003's pretraining data", "High confidence"
- text = " ".join(text.split()[:512])
- pred = {}
- p1, all_prob, p1_likelihood = calculatePerplexity_gpt3(text)
- for ratio in [0.2]:
- k_length = int(len(all_prob)*ratio)
- topk_prob = np.sort(all_prob)[:k_length]
- topk_mean = -np.mean(topk_prob).item()
-
- '''
- mu_nonmember, sigma_nonmember
- (7.134286156046333, 0.7341612538736927) > 6.4 nonmember
- ipdb> mu, sigma
- (4.596984216495519, 2.1430806645195943) < 6.73 member
- '''
- print("topk_mean: ", topk_mean)
- # Set confidence level based on the score
- # confidence = "High confidence" if abs(topk_mean-6.66) > 1 else "Low confidence"
- if topk_mean < 5.6:
- confidence = "High confidence"
- elif topk_mean >= 4.6 and topk_mean < 6:
- confidence = "Low confidence"
- elif topk_mean >= 6 and topk_mean < 6.93:
- confidence = "Undetermined. Please try another example"
- elif topk_mean >= 6.93 and topk_mean < 7.66:
- confidence = "Low confidence"
- else:
- confidence = "High confidence"
-
- # confidence_score = get_confidence(topk_mean, mu_member, sigma_member, mu_nonmember, sigma_nonmember)
- # confidence = "High confidence" if confidence_score > 2 else "Low confidence"
-
- # Making a decision based on the calculated score and adding confidence level
- if topk_mean <= 6.66:
- return "Likely in text-davinci-003's pretraining data", confidence
- elif topk_mean > 6.66:
- return "Likely not in text-davinci-003's pretraining data", confidence
- else:
- return "Error", "Error"
-
-
-def read_score():
- member_score = []
- with open("data/member_score.txt", "r") as f:
- for line in f:
- member_score.append(line.strip())
-
- nonmember_score = []
- with open("data/nonmember_score.txt", "r") as f:
- for line in f:
- nonmember_score.append(line.strip())
- return member_score, nonmember_score
-
-# def fit_gaussian(member_score, nonmember_score):
-# mu_member, sigma_member = norm.fit(member_score)
-# mu_nonmember, sigma_nonmember = norm.fit(nonmember_score)
-# return mu_member, sigma_member, mu_nonmember, sigma_nonmember
-
-# def get_confidence(topk_mean, mu_member, sigma_member, mu_nonmember, sigma_nonmember):
-# p = -norm.logpdf(topk_mean, mu_member, std_in+1e-30)
-# p_nonmember = -norm.logpdf(topk_mean, mu_nonmember, std_out+1e-30)
-# # return p-p_nonmember
-
-# Disclaimer text
-extended_description = """
-#### This tool helps in detecting whether the book snippet is in text-davinci-003's pretraining data. Enter a snippet from any book, but make sure it is longer than 512 words.
-
----
-
-#### Disclaimer
-The results provided by this tool are estimates and should not be considered fully accurate. This tool does not store or retain any submitted content.
-"""
-# member_score, nonmember_score = read_score()
-# fmu_member, sigma_member, mu_nonmember, sigma_nonmember = fit_gaussian(member_score, nonmember_score)
-
-# # Using gr.Examples
-# examples = gr.Examples(
-# inputs = [["Harry Potter", harry]],
-# examples=[title, input],
-# cache_examples=True,
-# )
-
-
-interface = gr.Interface(
- fn=check_in_pretraining_data,
- inputs=gr.Textbox(lines=20, placeholder="Enter a book snippet here (ensure it is longer than 512 words)..."),
- outputs=[gr.Textbox(label="Output"), gr.Textbox(label="Confidence")],
- title="Detecting Whether the Book Snippet is in OpenAI text-davinci-003 Pretraining Data",
- # description="This tool helps in detecting whether the book snippet is in text-davinci-003's pretraining data. Enter a snippet from any book, but make sure it is longer than 512 words.",
- examples=[[harry], [pineapple], [god]],
- description=extended_description,
- theme="huggingface",
- layout="vertical",
- allow_flagging="never",
-)
-
-
-
-
-interface.launch()
diff --git a/spaces/talari/MyGenAiChatBot/README.md b/spaces/talari/MyGenAiChatBot/README.md
deleted file mode 100644
index 60e2ecf28df1909cf2f12f114e9bd28d18251595..0000000000000000000000000000000000000000
--- a/spaces/talari/MyGenAiChatBot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MyGenAiChatBot
-emoji: ⚡
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/tang155/bingo/src/components/ui/sheet.tsx b/spaces/tang155/bingo/src/components/ui/sheet.tsx
deleted file mode 100644
index c9f5ce0f81a91067bb013e988a07eb1e6bf6953b..0000000000000000000000000000000000000000
--- a/spaces/tang155/bingo/src/components/ui/sheet.tsx
+++ /dev/null
@@ -1,122 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SheetPrimitive from '@radix-ui/react-dialog'
-
-import { cn } from '@/lib/utils'
-import { IconClose } from '@/components/ui/icons'
-
-const Sheet = SheetPrimitive.Root
-
-const SheetTrigger = SheetPrimitive.Trigger
-
-const SheetClose = SheetPrimitive.Close
-
-const SheetPortal = ({
- className,
- children,
- ...props
-}: SheetPrimitive.DialogPortalProps) => (
- Harry Potter And The Sorcerers Stone EXTENDED 720p BluRay X264Harry Potter And The Sorcerers St
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-
-
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-
-
-
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-How to download Harry Potter And The Sorcerers Stone EXTENDED 720p BluRay X264Harry Potter And The Sorcerers St?
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-
-
-
-Conclusion
-
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-
-
-
-
-Conclusion
-
-What are the benefits of BluRay format and x264 encoding?
-
-
-
-
-What are some reviews of Harry Potter And The Sorcerers Stone EXTENDED 720p BluRay X264Harry Potter And The Sorcerers St?
-
-"The extended version of Harry Potter and the Sorcerer's Stone is a treat for fans of the book and the movie alike. It adds more depth and detail to the story, and also improves the picture and sound quality. It is a magical adventure that will enchant viewers of all ages."
-
-"This is the best way to watch the first Harry Potter movie. The extended version adds some scenes that were missing from the original version, and they make a big difference. They make the movie more faithful to the book, more fun, and more emotional."
-
-"I love this movie so much. It is one of my favorite movies of all time. The extended version is even better than the original version. It has more scenes that show more of the characters, the magic, and the mystery. It is also very well-made, with amazing visuals, music, and acting."
-
-Conclusion
-
-
-
-
\ No newline at end of file
diff --git a/spaces/thomasjeon/stabilityai-stable-diffusion-2-1/app.py b/spaces/thomasjeon/stabilityai-stable-diffusion-2-1/app.py
deleted file mode 100644
index 0160420876923d89f2ab5fccb9f4d13725e29972..0000000000000000000000000000000000000000
--- a/spaces/thomasjeon/stabilityai-stable-diffusion-2-1/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/stabilityai/stable-diffusion-2-1").launch()
\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Bioquimica De Harper 15 Edicion Pdf 123.md b/spaces/tialenAdioni/chat-gpt-api/logs/Bioquimica De Harper 15 Edicion Pdf 123.md
deleted file mode 100644
index 17cb5341db289eebc55e495870e6ca47e33195de..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Bioquimica De Harper 15 Edicion Pdf 123.md
+++ /dev/null
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-
-Bioquimica De Harper 15 Edicion Pdf 123
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Adobe Animate CC 2018 V18.0.1.115 Crack [CracksNow] 64 Bit LINK.md b/spaces/tioseFevbu/cartoon-converter/scripts/Adobe Animate CC 2018 V18.0.1.115 Crack [CracksNow] 64 Bit LINK.md
deleted file mode 100644
index 131b47c13f5e94f3dd4c64c861ae41c50d104eb9..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Adobe Animate CC 2018 V18.0.1.115 Crack [CracksNow] 64 Bit LINK.md
+++ /dev/null
@@ -1,33 +0,0 @@
-
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Boot Disc Nddn-w55.md b/spaces/tioseFevbu/cartoon-converter/scripts/Boot Disc Nddn-w55.md
deleted file mode 100644
index 2c95afc1f8ea7a03603542217603012306f663b6..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Boot Disc Nddn-w55.md
+++ /dev/null
@@ -1,74 +0,0 @@
-
-Boot Disc NDDN-W55: What You Need to Know
-boot disc nddn-w55
- What is a Boot Disc NDDN-W55?
-Why Do You Need a Boot Disc NDDN-W55?
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Diploma In Mechanical Engineering Tamil Medium Books Free Download Pdf.md b/spaces/tioseFevbu/cartoon-converter/scripts/Diploma In Mechanical Engineering Tamil Medium Books Free Download Pdf.md
deleted file mode 100644
index 78a7ff8ed34847b61ccef7b42151e9df2d21b168..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Diploma In Mechanical Engineering Tamil Medium Books Free Download Pdf.md
+++ /dev/null
@@ -1,42 +0,0 @@
-
-Diploma In Mechanical Engineering Tamil Medium Books Free Download Pdf: A Comprehensive Guide
-What is Diploma in Mechanical Engineering?
-Diploma In Mechanical Engineering Tamil Medium Books Free Download Pdf
-Why Choose Diploma in Mechanical Engineering Tamil Medium Books?
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Eobdfacilecrack High Qualityserialcodes.md b/spaces/tioseFevbu/cartoon-converter/scripts/Eobdfacilecrack High Qualityserialcodes.md
deleted file mode 100644
index 7cd03ba86c1242d46ba6c51cda389016b0c027ad..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Eobdfacilecrack High Qualityserialcodes.md
+++ /dev/null
@@ -1,39 +0,0 @@
-
-How to activate EOBD Facile, a car diagnostic software
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-How to get EOBD Facile
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\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Garmin Bluechart G2 Vision Veu714l Torrent Download Torrent 17.md b/spaces/tioseFevbu/cartoon-converter/scripts/Garmin Bluechart G2 Vision Veu714l Torrent Download Torrent 17.md
deleted file mode 100644
index 403fb2c3d1c9d686859241b26abc469da0131c9c..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Garmin Bluechart G2 Vision Veu714l Torrent Download Torrent 17.md
+++ /dev/null
@@ -1,31 +0,0 @@
-
-Garmin BlueChart G2 Vision VEU714L: A Review
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/HACK Jeppesen Flitestar Flitemap Patches V8 V9.md b/spaces/tioseFevbu/cartoon-converter/scripts/HACK Jeppesen Flitestar Flitemap Patches V8 V9.md
deleted file mode 100644
index f18b4b42c2d2c13403dcce353e5453295d189b91..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/HACK Jeppesen Flitestar Flitemap Patches V8 V9.md
+++ /dev/null
@@ -1,121 +0,0 @@
-
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Malayalam Movie Drona Download VERIFIED Movies.md b/spaces/tioseFevbu/cartoon-converter/scripts/Malayalam Movie Drona Download VERIFIED Movies.md
deleted file mode 100644
index 36747b74e4b497ad6a53136314a721e00bf6d6e9..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Malayalam Movie Drona Download VERIFIED Movies.md
+++ /dev/null
@@ -1,28 +0,0 @@
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diff --git a/spaces/tomandandy/MusicGen3/tests/quantization/test_vq.py b/spaces/tomandandy/MusicGen3/tests/quantization/test_vq.py
deleted file mode 100644
index c215099fedacae35c6798fdd9b8420a447aa16bb..0000000000000000000000000000000000000000
--- a/spaces/tomandandy/MusicGen3/tests/quantization/test_vq.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from audiocraft.quantization.vq import ResidualVectorQuantizer
-
-
-class TestResidualVectorQuantizer:
-
- def test_rvq(self):
- x = torch.randn(1, 16, 2048)
- vq = ResidualVectorQuantizer(n_q=8, dimension=16, bins=8)
- res = vq(x, 1.)
- assert res.x.shape == torch.Size([1, 16, 2048])
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_models/test_roi_heads/test_bbox_head.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_models/test_roi_heads/test_bbox_head.py
deleted file mode 100644
index a7506b9b2d1b0fb86906c5d1e16283732a606131..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_models/test_roi_heads/test_bbox_head.py
+++ /dev/null
@@ -1,255 +0,0 @@
-import mmcv
-import pytest
-import torch
-
-from mmdet.core import bbox2roi
-from mmdet.models.roi_heads.bbox_heads import BBoxHead
-from .utils import _dummy_bbox_sampling
-
-
-def test_bbox_head_loss():
- """Tests bbox head loss when truth is empty and non-empty."""
- self = BBoxHead(in_channels=8, roi_feat_size=3)
-
- # Dummy proposals
- proposal_list = [
- torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874]]),
- ]
-
- target_cfg = mmcv.Config(dict(pos_weight=1))
-
- # Test bbox loss when truth is empty
- gt_bboxes = [torch.empty((0, 4))]
- gt_labels = [torch.LongTensor([])]
-
- sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes,
- gt_labels)
-
- bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels,
- target_cfg)
- labels, label_weights, bbox_targets, bbox_weights = bbox_targets
-
- # Create dummy features "extracted" for each sampled bbox
- num_sampled = sum(len(res.bboxes) for res in sampling_results)
- rois = bbox2roi([res.bboxes for res in sampling_results])
- dummy_feats = torch.rand(num_sampled, 8 * 3 * 3)
- cls_scores, bbox_preds = self.forward(dummy_feats)
-
- losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights,
- bbox_targets, bbox_weights)
- assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero'
- assert losses.get('loss_bbox', 0) == 0, 'empty gt loss should be zero'
-
- # Test bbox loss when truth is non-empty
- gt_bboxes = [
- torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
- ]
- gt_labels = [torch.LongTensor([2])]
-
- sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes,
- gt_labels)
- rois = bbox2roi([res.bboxes for res in sampling_results])
-
- bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels,
- target_cfg)
- labels, label_weights, bbox_targets, bbox_weights = bbox_targets
-
- # Create dummy features "extracted" for each sampled bbox
- num_sampled = sum(len(res.bboxes) for res in sampling_results)
- dummy_feats = torch.rand(num_sampled, 8 * 3 * 3)
- cls_scores, bbox_preds = self.forward(dummy_feats)
-
- losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights,
- bbox_targets, bbox_weights)
- assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero'
- assert losses.get('loss_bbox', 0) > 0, 'box-loss should be non-zero'
-
-
-@pytest.mark.parametrize(['num_sample', 'num_batch'], [[2, 2], [0, 2], [0, 0]])
-def test_bbox_head_get_bboxes(num_sample, num_batch):
- self = BBoxHead(reg_class_agnostic=True)
-
- num_class = 6
- rois = torch.rand((num_sample, 5))
- cls_score = torch.rand((num_sample, num_class))
- bbox_pred = torch.rand((num_sample, 4))
- scale_factor = 2.0
- det_bboxes, det_labels = self.get_bboxes(
- rois, cls_score, bbox_pred, None, scale_factor, rescale=True)
- if num_sample == 0:
- assert len(det_bboxes) == 0 and len(det_labels) == 0
- else:
- assert det_bboxes.shape == bbox_pred.shape
- assert det_labels.shape == cls_score.shape
-
- rois = torch.rand((num_batch, num_sample, 5))
- cls_score = torch.rand((num_batch, num_sample, num_class))
- bbox_pred = torch.rand((num_batch, num_sample, 4))
- det_bboxes, det_labels = self.get_bboxes(
- rois, cls_score, bbox_pred, None, scale_factor, rescale=True)
- assert len(det_bboxes) == num_batch and len(det_labels) == num_batch
-
-
-def test_refine_boxes():
- """Mirrors the doctest in
- ``mmdet.models.bbox_heads.bbox_head.BBoxHead.refine_boxes`` but checks for
- multiple values of n_roi / n_img."""
- self = BBoxHead(reg_class_agnostic=True)
-
- test_settings = [
-
- # Corner case: less rois than images
- {
- 'n_roi': 2,
- 'n_img': 4,
- 'rng': 34285940
- },
-
- # Corner case: no images
- {
- 'n_roi': 0,
- 'n_img': 0,
- 'rng': 52925222
- },
-
- # Corner cases: few images / rois
- {
- 'n_roi': 1,
- 'n_img': 1,
- 'rng': 1200281
- },
- {
- 'n_roi': 2,
- 'n_img': 1,
- 'rng': 1200282
- },
- {
- 'n_roi': 2,
- 'n_img': 2,
- 'rng': 1200283
- },
- {
- 'n_roi': 1,
- 'n_img': 2,
- 'rng': 1200284
- },
-
- # Corner case: no rois few images
- {
- 'n_roi': 0,
- 'n_img': 1,
- 'rng': 23955860
- },
- {
- 'n_roi': 0,
- 'n_img': 2,
- 'rng': 25830516
- },
-
- # Corner case: no rois many images
- {
- 'n_roi': 0,
- 'n_img': 10,
- 'rng': 671346
- },
- {
- 'n_roi': 0,
- 'n_img': 20,
- 'rng': 699807
- },
-
- # Corner case: cal_similarity num rois and images
- {
- 'n_roi': 20,
- 'n_img': 20,
- 'rng': 1200238
- },
- {
- 'n_roi': 10,
- 'n_img': 20,
- 'rng': 1200238
- },
- {
- 'n_roi': 5,
- 'n_img': 5,
- 'rng': 1200238
- },
-
- # ----------------------------------
- # Common case: more rois than images
- {
- 'n_roi': 100,
- 'n_img': 1,
- 'rng': 337156
- },
- {
- 'n_roi': 150,
- 'n_img': 2,
- 'rng': 275898
- },
- {
- 'n_roi': 500,
- 'n_img': 5,
- 'rng': 4903221
- },
- ]
-
- for demokw in test_settings:
- try:
- n_roi = demokw['n_roi']
- n_img = demokw['n_img']
- rng = demokw['rng']
-
- print(f'Test refine_boxes case: {demokw!r}')
- tup = _demodata_refine_boxes(n_roi, n_img, rng=rng)
- rois, labels, bbox_preds, pos_is_gts, img_metas = tup
- bboxes_list = self.refine_bboxes(rois, labels, bbox_preds,
- pos_is_gts, img_metas)
- assert len(bboxes_list) == n_img
- assert sum(map(len, bboxes_list)) <= n_roi
- assert all(b.shape[1] == 4 for b in bboxes_list)
- except Exception:
- print(f'Test failed with demokw={demokw!r}')
- raise
-
-
-def _demodata_refine_boxes(n_roi, n_img, rng=0):
- """Create random test data for the
- ``mmdet.models.bbox_heads.bbox_head.BBoxHead.refine_boxes`` method."""
- import numpy as np
- from mmdet.core.bbox.demodata import random_boxes
- from mmdet.core.bbox.demodata import ensure_rng
- try:
- import kwarray
- except ImportError:
- import pytest
- pytest.skip('kwarray is required for this test')
- scale = 512
- rng = ensure_rng(rng)
- img_metas = [{'img_shape': (scale, scale)} for _ in range(n_img)]
- # Create rois in the expected format
- roi_boxes = random_boxes(n_roi, scale=scale, rng=rng)
- if n_img == 0:
- assert n_roi == 0, 'cannot have any rois if there are no images'
- img_ids = torch.empty((0, ), dtype=torch.long)
- roi_boxes = torch.empty((0, 4), dtype=torch.float32)
- else:
- img_ids = rng.randint(0, n_img, (n_roi, ))
- img_ids = torch.from_numpy(img_ids)
- rois = torch.cat([img_ids[:, None].float(), roi_boxes], dim=1)
- # Create other args
- labels = rng.randint(0, 2, (n_roi, ))
- labels = torch.from_numpy(labels).long()
- bbox_preds = random_boxes(n_roi, scale=scale, rng=rng)
- # For each image, pretend random positive boxes are gts
- is_label_pos = (labels.numpy() > 0).astype(np.int)
- lbl_per_img = kwarray.group_items(is_label_pos, img_ids.numpy())
- pos_per_img = [sum(lbl_per_img.get(gid, [])) for gid in range(n_img)]
- # randomly generate with numpy then sort with torch
- _pos_is_gts = [
- rng.randint(0, 2, (npos, )).astype(np.uint8) for npos in pos_per_img
- ]
- pos_is_gts = [
- torch.from_numpy(p).sort(descending=True)[0] for p in _pos_is_gts
- ]
- return rois, labels, bbox_preds, pos_is_gts, img_metas
diff --git a/spaces/tornadoslims/instruct-pix2pix/stable_diffusion/ldm/modules/diffusionmodules/openaimodel.py b/spaces/tornadoslims/instruct-pix2pix/stable_diffusion/ldm/modules/diffusionmodules/openaimodel.py
deleted file mode 100644
index fcf95d1ea8a078dd259915109203789f78f0643a..0000000000000000000000000000000000000000
--- a/spaces/tornadoslims/instruct-pix2pix/stable_diffusion/ldm/modules/diffusionmodules/openaimodel.py
+++ /dev/null
@@ -1,961 +0,0 @@
-from abc import abstractmethod
-from functools import partial
-import math
-from typing import Iterable
-
-import numpy as np
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ldm.modules.diffusionmodules.util import (
- checkpoint,
- conv_nd,
- linear,
- avg_pool_nd,
- zero_module,
- normalization,
- timestep_embedding,
-)
-from ldm.modules.attention import SpatialTransformer
-
-
-# dummy replace
-def convert_module_to_f16(x):
- pass
-
-def convert_module_to_f32(x):
- pass
-
-
-## go
-class AttentionPool2d(nn.Module):
- """
- Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
- """
-
- def __init__(
- self,
- spacial_dim: int,
- embed_dim: int,
- num_heads_channels: int,
- output_dim: int = None,
- ):
- super().__init__()
- self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
- self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
- self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
- self.num_heads = embed_dim // num_heads_channels
- self.attention = QKVAttention(self.num_heads)
-
- def forward(self, x):
- b, c, *_spatial = x.shape
- x = x.reshape(b, c, -1) # NC(HW)
- x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1)
- x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1)
- x = self.qkv_proj(x)
- x = self.attention(x)
- x = self.c_proj(x)
- return x[:, :, 0]
-
-
-class TimestepBlock(nn.Module):
- """
- Any module where forward() takes timestep embeddings as a second argument.
- """
-
- @abstractmethod
- def forward(self, x, emb):
- """
- Apply the module to `x` given `emb` timestep embeddings.
- """
-
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
- """
- A sequential module that passes timestep embeddings to the children that
- support it as an extra input.
- """
-
- def forward(self, x, emb, context=None):
- for layer in self:
- if isinstance(layer, TimestepBlock):
- x = layer(x, emb)
- elif isinstance(layer, SpatialTransformer):
- x = layer(x, context)
- else:
- x = layer(x)
- return x
-
-
-class Upsample(nn.Module):
- """
- An upsampling layer with an optional convolution.
- :param channels: channels in the inputs and outputs.
- :param use_conv: a bool determining if a convolution is applied.
- :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
- upsampling occurs in the inner-two dimensions.
- """
-
- def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.dims = dims
- if use_conv:
- self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
-
- def forward(self, x):
- assert x.shape[1] == self.channels
- if self.dims == 3:
- x = F.interpolate(
- x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
- )
- else:
- x = F.interpolate(x, scale_factor=2, mode="nearest")
- if self.use_conv:
- x = self.conv(x)
- return x
-
-class TransposedUpsample(nn.Module):
- 'Learned 2x upsampling without padding'
- def __init__(self, channels, out_channels=None, ks=5):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
-
- self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
-
- def forward(self,x):
- return self.up(x)
-
-
-class Downsample(nn.Module):
- """
- A downsampling layer with an optional convolution.
- :param channels: channels in the inputs and outputs.
- :param use_conv: a bool determining if a convolution is applied.
- :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
- downsampling occurs in the inner-two dimensions.
- """
-
- def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.dims = dims
- stride = 2 if dims != 3 else (1, 2, 2)
- if use_conv:
- self.op = conv_nd(
- dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
- )
- else:
- assert self.channels == self.out_channels
- self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
- def forward(self, x):
- assert x.shape[1] == self.channels
- return self.op(x)
-
-
-class ResBlock(TimestepBlock):
- """
- A residual block that can optionally change the number of channels.
- :param channels: the number of input channels.
- :param emb_channels: the number of timestep embedding channels.
- :param dropout: the rate of dropout.
- :param out_channels: if specified, the number of out channels.
- :param use_conv: if True and out_channels is specified, use a spatial
- convolution instead of a smaller 1x1 convolution to change the
- channels in the skip connection.
- :param dims: determines if the signal is 1D, 2D, or 3D.
- :param use_checkpoint: if True, use gradient checkpointing on this module.
- :param up: if True, use this block for upsampling.
- :param down: if True, use this block for downsampling.
- """
-
- def __init__(
- self,
- channels,
- emb_channels,
- dropout,
- out_channels=None,
- use_conv=False,
- use_scale_shift_norm=False,
- dims=2,
- use_checkpoint=False,
- up=False,
- down=False,
- ):
- super().__init__()
- self.channels = channels
- self.emb_channels = emb_channels
- self.dropout = dropout
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.use_checkpoint = use_checkpoint
- self.use_scale_shift_norm = use_scale_shift_norm
-
- self.in_layers = nn.Sequential(
- normalization(channels),
- nn.SiLU(),
- conv_nd(dims, channels, self.out_channels, 3, padding=1),
- )
-
- self.updown = up or down
-
- if up:
- self.h_upd = Upsample(channels, False, dims)
- self.x_upd = Upsample(channels, False, dims)
- elif down:
- self.h_upd = Downsample(channels, False, dims)
- self.x_upd = Downsample(channels, False, dims)
- else:
- self.h_upd = self.x_upd = nn.Identity()
-
- self.emb_layers = nn.Sequential(
- nn.SiLU(),
- linear(
- emb_channels,
- 2 * self.out_channels if use_scale_shift_norm else self.out_channels,
- ),
- )
- self.out_layers = nn.Sequential(
- normalization(self.out_channels),
- nn.SiLU(),
- nn.Dropout(p=dropout),
- zero_module(
- conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
- ),
- )
-
- if self.out_channels == channels:
- self.skip_connection = nn.Identity()
- elif use_conv:
- self.skip_connection = conv_nd(
- dims, channels, self.out_channels, 3, padding=1
- )
- else:
- self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
- def forward(self, x, emb):
- """
- Apply the block to a Tensor, conditioned on a timestep embedding.
- :param x: an [N x C x ...] Tensor of features.
- :param emb: an [N x emb_channels] Tensor of timestep embeddings.
- :return: an [N x C x ...] Tensor of outputs.
- """
- return checkpoint(
- self._forward, (x, emb), self.parameters(), self.use_checkpoint
- )
-
-
- def _forward(self, x, emb):
- if self.updown:
- in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
- h = in_rest(x)
- h = self.h_upd(h)
- x = self.x_upd(x)
- h = in_conv(h)
- else:
- h = self.in_layers(x)
- emb_out = self.emb_layers(emb).type(h.dtype)
- while len(emb_out.shape) < len(h.shape):
- emb_out = emb_out[..., None]
- if self.use_scale_shift_norm:
- out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
- scale, shift = th.chunk(emb_out, 2, dim=1)
- h = out_norm(h) * (1 + scale) + shift
- h = out_rest(h)
- else:
- h = h + emb_out
- h = self.out_layers(h)
- return self.skip_connection(x) + h
-
-
-class AttentionBlock(nn.Module):
- """
- An attention block that allows spatial positions to attend to each other.
- Originally ported from here, but adapted to the N-d case.
- https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
- """
-
- def __init__(
- self,
- channels,
- num_heads=1,
- num_head_channels=-1,
- use_checkpoint=False,
- use_new_attention_order=False,
- ):
- super().__init__()
- self.channels = channels
- if num_head_channels == -1:
- self.num_heads = num_heads
- else:
- assert (
- channels % num_head_channels == 0
- ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
- self.num_heads = channels // num_head_channels
- self.use_checkpoint = use_checkpoint
- self.norm = normalization(channels)
- self.qkv = conv_nd(1, channels, channels * 3, 1)
- if use_new_attention_order:
- # split qkv before split heads
- self.attention = QKVAttention(self.num_heads)
- else:
- # split heads before split qkv
- self.attention = QKVAttentionLegacy(self.num_heads)
-
- self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
-
- def forward(self, x):
- return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
- #return pt_checkpoint(self._forward, x) # pytorch
-
- def _forward(self, x):
- b, c, *spatial = x.shape
- x = x.reshape(b, c, -1)
- qkv = self.qkv(self.norm(x))
- h = self.attention(qkv)
- h = self.proj_out(h)
- return (x + h).reshape(b, c, *spatial)
-
-
-def count_flops_attn(model, _x, y):
- """
- A counter for the `thop` package to count the operations in an
- attention operation.
- Meant to be used like:
- macs, params = thop.profile(
- model,
- inputs=(inputs, timestamps),
- custom_ops={QKVAttention: QKVAttention.count_flops},
- )
- """
- b, c, *spatial = y[0].shape
- num_spatial = int(np.prod(spatial))
- # We perform two matmuls with the same number of ops.
- # The first computes the weight matrix, the second computes
- # the combination of the value vectors.
- matmul_ops = 2 * b * (num_spatial ** 2) * c
- model.total_ops += th.DoubleTensor([matmul_ops])
-
-
-class QKVAttentionLegacy(nn.Module):
- """
- A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
- """
-
- def __init__(self, n_heads):
- super().__init__()
- self.n_heads = n_heads
-
- def forward(self, qkv):
- """
- Apply QKV attention.
- :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
- :return: an [N x (H * C) x T] tensor after attention.
- """
- bs, width, length = qkv.shape
- assert width % (3 * self.n_heads) == 0
- ch = width // (3 * self.n_heads)
- q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
- scale = 1 / math.sqrt(math.sqrt(ch))
- weight = th.einsum(
- "bct,bcs->bts", q * scale, k * scale
- ) # More stable with f16 than dividing afterwards
- weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
- a = th.einsum("bts,bcs->bct", weight, v)
- return a.reshape(bs, -1, length)
-
- @staticmethod
- def count_flops(model, _x, y):
- return count_flops_attn(model, _x, y)
-
-
-class QKVAttention(nn.Module):
- """
- A module which performs QKV attention and splits in a different order.
- """
-
- def __init__(self, n_heads):
- super().__init__()
- self.n_heads = n_heads
-
- def forward(self, qkv):
- """
- Apply QKV attention.
- :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
- :return: an [N x (H * C) x T] tensor after attention.
- """
- bs, width, length = qkv.shape
- assert width % (3 * self.n_heads) == 0
- ch = width // (3 * self.n_heads)
- q, k, v = qkv.chunk(3, dim=1)
- scale = 1 / math.sqrt(math.sqrt(ch))
- weight = th.einsum(
- "bct,bcs->bts",
- (q * scale).view(bs * self.n_heads, ch, length),
- (k * scale).view(bs * self.n_heads, ch, length),
- ) # More stable with f16 than dividing afterwards
- weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
- a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
- return a.reshape(bs, -1, length)
-
- @staticmethod
- def count_flops(model, _x, y):
- return count_flops_attn(model, _x, y)
-
-
-class UNetModel(nn.Module):
- """
- The full UNet model with attention and timestep embedding.
- :param in_channels: channels in the input Tensor.
- :param model_channels: base channel count for the model.
- :param out_channels: channels in the output Tensor.
- :param num_res_blocks: number of residual blocks per downsample.
- :param attention_resolutions: a collection of downsample rates at which
- attention will take place. May be a set, list, or tuple.
- For example, if this contains 4, then at 4x downsampling, attention
- will be used.
- :param dropout: the dropout probability.
- :param channel_mult: channel multiplier for each level of the UNet.
- :param conv_resample: if True, use learned convolutions for upsampling and
- downsampling.
- :param dims: determines if the signal is 1D, 2D, or 3D.
- :param num_classes: if specified (as an int), then this model will be
- class-conditional with `num_classes` classes.
- :param use_checkpoint: use gradient checkpointing to reduce memory usage.
- :param num_heads: the number of attention heads in each attention layer.
- :param num_heads_channels: if specified, ignore num_heads and instead use
- a fixed channel width per attention head.
- :param num_heads_upsample: works with num_heads to set a different number
- of heads for upsampling. Deprecated.
- :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
- :param resblock_updown: use residual blocks for up/downsampling.
- :param use_new_attention_order: use a different attention pattern for potentially
- increased efficiency.
- """
-
- def __init__(
- self,
- image_size,
- in_channels,
- model_channels,
- out_channels,
- num_res_blocks,
- attention_resolutions,
- dropout=0,
- channel_mult=(1, 2, 4, 8),
- conv_resample=True,
- dims=2,
- num_classes=None,
- use_checkpoint=False,
- use_fp16=False,
- num_heads=-1,
- num_head_channels=-1,
- num_heads_upsample=-1,
- use_scale_shift_norm=False,
- resblock_updown=False,
- use_new_attention_order=False,
- use_spatial_transformer=False, # custom transformer support
- transformer_depth=1, # custom transformer support
- context_dim=None, # custom transformer support
- n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
- legacy=True,
- ):
- super().__init__()
- if use_spatial_transformer:
- assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
-
- if context_dim is not None:
- assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
- from omegaconf.listconfig import ListConfig
- if type(context_dim) == ListConfig:
- context_dim = list(context_dim)
-
- if num_heads_upsample == -1:
- num_heads_upsample = num_heads
-
- if num_heads == -1:
- assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
-
- if num_head_channels == -1:
- assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
-
- self.image_size = image_size
- self.in_channels = in_channels
- self.model_channels = model_channels
- self.out_channels = out_channels
- self.num_res_blocks = num_res_blocks
- self.attention_resolutions = attention_resolutions
- self.dropout = dropout
- self.channel_mult = channel_mult
- self.conv_resample = conv_resample
- self.num_classes = num_classes
- self.use_checkpoint = use_checkpoint
- self.dtype = th.float16 if use_fp16 else th.float32
- self.num_heads = num_heads
- self.num_head_channels = num_head_channels
- self.num_heads_upsample = num_heads_upsample
- self.predict_codebook_ids = n_embed is not None
-
- time_embed_dim = model_channels * 4
- self.time_embed = nn.Sequential(
- linear(model_channels, time_embed_dim),
- nn.SiLU(),
- linear(time_embed_dim, time_embed_dim),
- )
-
- if self.num_classes is not None:
- self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-
- self.input_blocks = nn.ModuleList(
- [
- TimestepEmbedSequential(
- conv_nd(dims, in_channels, model_channels, 3, padding=1)
- )
- ]
- )
- self._feature_size = model_channels
- input_block_chans = [model_channels]
- ch = model_channels
- ds = 1
- for level, mult in enumerate(channel_mult):
- for _ in range(num_res_blocks):
- layers = [
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- out_channels=mult * model_channels,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- )
- ]
- ch = mult * model_channels
- if ds in attention_resolutions:
- if num_head_channels == -1:
- dim_head = ch // num_heads
- else:
- num_heads = ch // num_head_channels
- dim_head = num_head_channels
- if legacy:
- #num_heads = 1
- dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
- layers.append(
- AttentionBlock(
- ch,
- use_checkpoint=use_checkpoint,
- num_heads=num_heads,
- num_head_channels=dim_head,
- use_new_attention_order=use_new_attention_order,
- ) if not use_spatial_transformer else SpatialTransformer(
- ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
- )
- )
- self.input_blocks.append(TimestepEmbedSequential(*layers))
- self._feature_size += ch
- input_block_chans.append(ch)
- if level != len(channel_mult) - 1:
- out_ch = ch
- self.input_blocks.append(
- TimestepEmbedSequential(
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- out_channels=out_ch,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- down=True,
- )
- if resblock_updown
- else Downsample(
- ch, conv_resample, dims=dims, out_channels=out_ch
- )
- )
- )
- ch = out_ch
- input_block_chans.append(ch)
- ds *= 2
- self._feature_size += ch
-
- if num_head_channels == -1:
- dim_head = ch // num_heads
- else:
- num_heads = ch // num_head_channels
- dim_head = num_head_channels
- if legacy:
- #num_heads = 1
- dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
- self.middle_block = TimestepEmbedSequential(
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- ),
- AttentionBlock(
- ch,
- use_checkpoint=use_checkpoint,
- num_heads=num_heads,
- num_head_channels=dim_head,
- use_new_attention_order=use_new_attention_order,
- ) if not use_spatial_transformer else SpatialTransformer(
- ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
- ),
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- ),
- )
- self._feature_size += ch
-
- self.output_blocks = nn.ModuleList([])
- for level, mult in list(enumerate(channel_mult))[::-1]:
- for i in range(num_res_blocks + 1):
- ich = input_block_chans.pop()
- layers = [
- ResBlock(
- ch + ich,
- time_embed_dim,
- dropout,
- out_channels=model_channels * mult,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- )
- ]
- ch = model_channels * mult
- if ds in attention_resolutions:
- if num_head_channels == -1:
- dim_head = ch // num_heads
- else:
- num_heads = ch // num_head_channels
- dim_head = num_head_channels
- if legacy:
- #num_heads = 1
- dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
- layers.append(
- AttentionBlock(
- ch,
- use_checkpoint=use_checkpoint,
- num_heads=num_heads_upsample,
- num_head_channels=dim_head,
- use_new_attention_order=use_new_attention_order,
- ) if not use_spatial_transformer else SpatialTransformer(
- ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
- )
- )
- if level and i == num_res_blocks:
- out_ch = ch
- layers.append(
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- out_channels=out_ch,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- up=True,
- )
- if resblock_updown
- else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
- )
- ds //= 2
- self.output_blocks.append(TimestepEmbedSequential(*layers))
- self._feature_size += ch
-
- self.out = nn.Sequential(
- normalization(ch),
- nn.SiLU(),
- zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
- )
- if self.predict_codebook_ids:
- self.id_predictor = nn.Sequential(
- normalization(ch),
- conv_nd(dims, model_channels, n_embed, 1),
- #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
- )
-
- def convert_to_fp16(self):
- """
- Convert the torso of the model to float16.
- """
- self.input_blocks.apply(convert_module_to_f16)
- self.middle_block.apply(convert_module_to_f16)
- self.output_blocks.apply(convert_module_to_f16)
-
- def convert_to_fp32(self):
- """
- Convert the torso of the model to float32.
- """
- self.input_blocks.apply(convert_module_to_f32)
- self.middle_block.apply(convert_module_to_f32)
- self.output_blocks.apply(convert_module_to_f32)
-
- def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
- """
- Apply the model to an input batch.
- :param x: an [N x C x ...] Tensor of inputs.
- :param timesteps: a 1-D batch of timesteps.
- :param context: conditioning plugged in via crossattn
- :param y: an [N] Tensor of labels, if class-conditional.
- :return: an [N x C x ...] Tensor of outputs.
- """
- assert (y is not None) == (
- self.num_classes is not None
- ), "must specify y if and only if the model is class-conditional"
- hs = []
- t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
- emb = self.time_embed(t_emb)
-
- if self.num_classes is not None:
- assert y.shape == (x.shape[0],)
- emb = emb + self.label_emb(y)
-
- h = x.type(self.dtype)
- for module in self.input_blocks:
- h = module(h, emb, context)
- hs.append(h)
- h = self.middle_block(h, emb, context)
- for module in self.output_blocks:
- h = th.cat([h, hs.pop()], dim=1)
- h = module(h, emb, context)
- h = h.type(x.dtype)
- if self.predict_codebook_ids:
- return self.id_predictor(h)
- else:
- return self.out(h)
-
-
-class EncoderUNetModel(nn.Module):
- """
- The half UNet model with attention and timestep embedding.
- For usage, see UNet.
- """
-
- def __init__(
- self,
- image_size,
- in_channels,
- model_channels,
- out_channels,
- num_res_blocks,
- attention_resolutions,
- dropout=0,
- channel_mult=(1, 2, 4, 8),
- conv_resample=True,
- dims=2,
- use_checkpoint=False,
- use_fp16=False,
- num_heads=1,
- num_head_channels=-1,
- num_heads_upsample=-1,
- use_scale_shift_norm=False,
- resblock_updown=False,
- use_new_attention_order=False,
- pool="adaptive",
- *args,
- **kwargs
- ):
- super().__init__()
-
- if num_heads_upsample == -1:
- num_heads_upsample = num_heads
-
- self.in_channels = in_channels
- self.model_channels = model_channels
- self.out_channels = out_channels
- self.num_res_blocks = num_res_blocks
- self.attention_resolutions = attention_resolutions
- self.dropout = dropout
- self.channel_mult = channel_mult
- self.conv_resample = conv_resample
- self.use_checkpoint = use_checkpoint
- self.dtype = th.float16 if use_fp16 else th.float32
- self.num_heads = num_heads
- self.num_head_channels = num_head_channels
- self.num_heads_upsample = num_heads_upsample
-
- time_embed_dim = model_channels * 4
- self.time_embed = nn.Sequential(
- linear(model_channels, time_embed_dim),
- nn.SiLU(),
- linear(time_embed_dim, time_embed_dim),
- )
-
- self.input_blocks = nn.ModuleList(
- [
- TimestepEmbedSequential(
- conv_nd(dims, in_channels, model_channels, 3, padding=1)
- )
- ]
- )
- self._feature_size = model_channels
- input_block_chans = [model_channels]
- ch = model_channels
- ds = 1
- for level, mult in enumerate(channel_mult):
- for _ in range(num_res_blocks):
- layers = [
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- out_channels=mult * model_channels,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- )
- ]
- ch = mult * model_channels
- if ds in attention_resolutions:
- layers.append(
- AttentionBlock(
- ch,
- use_checkpoint=use_checkpoint,
- num_heads=num_heads,
- num_head_channels=num_head_channels,
- use_new_attention_order=use_new_attention_order,
- )
- )
- self.input_blocks.append(TimestepEmbedSequential(*layers))
- self._feature_size += ch
- input_block_chans.append(ch)
- if level != len(channel_mult) - 1:
- out_ch = ch
- self.input_blocks.append(
- TimestepEmbedSequential(
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- out_channels=out_ch,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- down=True,
- )
- if resblock_updown
- else Downsample(
- ch, conv_resample, dims=dims, out_channels=out_ch
- )
- )
- )
- ch = out_ch
- input_block_chans.append(ch)
- ds *= 2
- self._feature_size += ch
-
- self.middle_block = TimestepEmbedSequential(
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- ),
- AttentionBlock(
- ch,
- use_checkpoint=use_checkpoint,
- num_heads=num_heads,
- num_head_channels=num_head_channels,
- use_new_attention_order=use_new_attention_order,
- ),
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- ),
- )
- self._feature_size += ch
- self.pool = pool
- if pool == "adaptive":
- self.out = nn.Sequential(
- normalization(ch),
- nn.SiLU(),
- nn.AdaptiveAvgPool2d((1, 1)),
- zero_module(conv_nd(dims, ch, out_channels, 1)),
- nn.Flatten(),
- )
- elif pool == "attention":
- assert num_head_channels != -1
- self.out = nn.Sequential(
- normalization(ch),
- nn.SiLU(),
- AttentionPool2d(
- (image_size // ds), ch, num_head_channels, out_channels
- ),
- )
- elif pool == "spatial":
- self.out = nn.Sequential(
- nn.Linear(self._feature_size, 2048),
- nn.ReLU(),
- nn.Linear(2048, self.out_channels),
- )
- elif pool == "spatial_v2":
- self.out = nn.Sequential(
- nn.Linear(self._feature_size, 2048),
- normalization(2048),
- nn.SiLU(),
- nn.Linear(2048, self.out_channels),
- )
- else:
- raise NotImplementedError(f"Unexpected {pool} pooling")
-
- def convert_to_fp16(self):
- """
- Convert the torso of the model to float16.
- """
- self.input_blocks.apply(convert_module_to_f16)
- self.middle_block.apply(convert_module_to_f16)
-
- def convert_to_fp32(self):
- """
- Convert the torso of the model to float32.
- """
- self.input_blocks.apply(convert_module_to_f32)
- self.middle_block.apply(convert_module_to_f32)
-
- def forward(self, x, timesteps):
- """
- Apply the model to an input batch.
- :param x: an [N x C x ...] Tensor of inputs.
- :param timesteps: a 1-D batch of timesteps.
- :return: an [N x K] Tensor of outputs.
- """
- emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-
- results = []
- h = x.type(self.dtype)
- for module in self.input_blocks:
- h = module(h, emb)
- if self.pool.startswith("spatial"):
- results.append(h.type(x.dtype).mean(dim=(2, 3)))
- h = self.middle_block(h, emb)
- if self.pool.startswith("spatial"):
- results.append(h.type(x.dtype).mean(dim=(2, 3)))
- h = th.cat(results, axis=-1)
- return self.out(h)
- else:
- h = h.type(x.dtype)
- return self.out(h)
-
diff --git a/spaces/tornadoslims/instruct-pix2pix/stable_diffusion/ldm/modules/distributions/__init__.py b/spaces/tornadoslims/instruct-pix2pix/stable_diffusion/ldm/modules/distributions/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/tracinginsights/F1_API/README.md b/spaces/tracinginsights/F1_API/README.md
deleted file mode 100644
index 3fdefde1fd0533497dc5972943468d785ce3fb33..0000000000000000000000000000000000000000
--- a/spaces/tracinginsights/F1_API/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: F1 API
-emoji: 🐢
-colorFrom: red
-colorTo: blue
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/trttung1610/musicgen/audiocraft/grids/compression/encodec_musicgen_32khz.py b/spaces/trttung1610/musicgen/audiocraft/grids/compression/encodec_musicgen_32khz.py
deleted file mode 100644
index 9da31daa5f009f46e753601a51a06391594b8f9b..0000000000000000000000000000000000000000
--- a/spaces/trttung1610/musicgen/audiocraft/grids/compression/encodec_musicgen_32khz.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Grid search file, simply list all the exp you want in `explorer`.
-Any new exp added there will be scheduled.
-You can cancel and experiment by commenting its line.
-
-This grid shows how to train a MusicGen EnCodec model at 32 kHz.
-"""
-
-from ._explorers import CompressionExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@CompressionExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=8, partition=partitions)
- # use configuration for MusicGen's EnCodec model trained on monophonic audio sampled at 32 kHz
- # MusicGen's EnCodec is trained with a total stride of 640 leading to a frame rate of 50 hz
- launcher.bind_(solver='compression/encodec_musicgen_32khz')
- # replace this by the desired music dataset
- launcher.bind_(dset='internal/music_400k_32khz')
- # launch xp
- launcher()
- launcher({
- 'metrics.visqol.bin': '/data/home/jadecopet/local/usr/opt/visqol',
- 'label': 'visqol',
- 'evaluate.metrics.visqol': True
- })
diff --git a/spaces/truong-xuan-linh/auto-comment-generation/src/comment.py b/spaces/truong-xuan-linh/auto-comment-generation/src/comment.py
deleted file mode 100644
index b264a8c9cda5f02a4051f7b11ba6cb51d5cfcf06..0000000000000000000000000000000000000000
--- a/spaces/truong-xuan-linh/auto-comment-generation/src/comment.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from src.model.model import CommentGenerator
-from src.model.text_process import TextPreprocess
-
-class GetComment():
- def __init__(self) -> None:
- """Get Comment init funtion
- """
- self.CommentGenerator = CommentGenerator()
- self.text_preprocess = TextPreprocess()
- self.inputs = {}
-
- def generator(self, info):
- """Generation function
-
- Args:
- info (Dict): info extract from kafka message
-
- Returns:
- List: List of comment
- """
-
- content = info["content"]
- content = self.text_preprocess.preprocess(content)
- medias = info["medias"]
- if not medias:
- medias = [None]
-
- print(medias)
- typfeed = info["type_generation"]
- num_comments = info["num_comments"]
- mapper = {
- 0: "bảng tin",
- 1: "trải nghiệm"
- }
-
- comments_prefix = ["thứ một",
- "thứ hai",
- "thứ ba"]
-
- comments = []
- for i, comment_prefix in enumerate(comments_prefix):
- content_w_prefix = f"{mapper[typfeed]}: {comment_prefix}: {content}"
- self.inputs[f"content_{i}"] = self.CommentGenerator.get_text_feature(content_w_prefix)
-
- while len(comments) < num_comments:
- for i, media in enumerate(medias):
- print(i)
- if i not in self.inputs:
- self.inputs[i] = self.CommentGenerator.get_image_feature_from_url(media, is_local=True)
- image_feature, image_mask = self.inputs[i]
- for i in range(len(comments_prefix)):
- content_feature, content_mask = self.inputs[f"content_{i}"]
- comment = self.CommentGenerator.inference(content_feature, content_mask, image_feature, image_mask)[0]
- comments.append(comment)
- comments = list(set(comments))
- if len(comments) >= num_comments:
- return comments
-
\ No newline at end of file
diff --git a/spaces/tube1925/sydney_new2.0/Dockerfile b/spaces/tube1925/sydney_new2.0/Dockerfile
deleted file mode 100644
index 355bf0527c514d039496bdc1b5e556ff91be71e1..0000000000000000000000000000000000000000
--- a/spaces/tube1925/sydney_new2.0/Dockerfile
+++ /dev/null
@@ -1,8 +0,0 @@
-FROM python:3.11
-RUN apt update
-RUN apt install git
-RUN git clone https://github.com/renqabs/img_test.git
-WORKDIR "img_test"
-RUN pip install -r requirements.txt
-EXPOSE 7860
-CMD ["python", "main.py", "--host", "0.0.0.0:7860"]
diff --git a/spaces/typesdigital/TD-OpenWeatherMap-API/README.md b/spaces/typesdigital/TD-OpenWeatherMap-API/README.md
deleted file mode 100644
index 201fa5dc5bc5a6ec74ec56ee4d700f277e5ea694..0000000000000000000000000000000000000000
--- a/spaces/typesdigital/TD-OpenWeatherMap-API/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: TD OpenWeatherMap API
-emoji: 🏃
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: unlicense
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ucalyptus/PTI/models/StyleCLIP/models/__init__.py b/spaces/ucalyptus/PTI/models/StyleCLIP/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ucalyptus/PTI/torch_utils/training_stats.py b/spaces/ucalyptus/PTI/torch_utils/training_stats.py
deleted file mode 100644
index 26f467f9eaa074ee13de1cf2625cd7da44880847..0000000000000000000000000000000000000000
--- a/spaces/ucalyptus/PTI/torch_utils/training_stats.py
+++ /dev/null
@@ -1,268 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Facilities for reporting and collecting training statistics across
-multiple processes and devices. The interface is designed to minimize
-synchronization overhead as well as the amount of boilerplate in user
-code."""
-
-import re
-import numpy as np
-import torch
-import dnnlib
-
-from . import misc
-
-#----------------------------------------------------------------------------
-
-_num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares]
-_reduce_dtype = torch.float32 # Data type to use for initial per-tensor reduction.
-_counter_dtype = torch.float64 # Data type to use for the internal counters.
-_rank = 0 # Rank of the current process.
-_sync_device = None # Device to use for multiprocess communication. None = single-process.
-_sync_called = False # Has _sync() been called yet?
-_counters = dict() # Running counters on each device, updated by report(): name => device => torch.Tensor
-_cumulative = dict() # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
-
-#----------------------------------------------------------------------------
-
-def init_multiprocessing(rank, sync_device):
- r"""Initializes `torch_utils.training_stats` for collecting statistics
- across multiple processes.
-
- This function must be called after
- `torch.distributed.init_process_group()` and before `Collector.update()`.
- The call is not necessary if multi-process collection is not needed.
-
- Args:
- rank: Rank of the current process.
- sync_device: PyTorch device to use for inter-process
- communication, or None to disable multi-process
- collection. Typically `torch.device('cuda', rank)`.
- """
- global _rank, _sync_device
- assert not _sync_called
- _rank = rank
- _sync_device = sync_device
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def report(name, value):
- r"""Broadcasts the given set of scalars to all interested instances of
- `Collector`, across device and process boundaries.
-
- This function is expected to be extremely cheap and can be safely
- called from anywhere in the training loop, loss function, or inside a
- `torch.nn.Module`.
-
- Warning: The current implementation expects the set of unique names to
- be consistent across processes. Please make sure that `report()` is
- called at least once for each unique name by each process, and in the
- same order. If a given process has no scalars to broadcast, it can do
- `report(name, [])` (empty list).
-
- Args:
- name: Arbitrary string specifying the name of the statistic.
- Averages are accumulated separately for each unique name.
- value: Arbitrary set of scalars. Can be a list, tuple,
- NumPy array, PyTorch tensor, or Python scalar.
-
- Returns:
- The same `value` that was passed in.
- """
- if name not in _counters:
- _counters[name] = dict()
-
- elems = torch.as_tensor(value)
- if elems.numel() == 0:
- return value
-
- elems = elems.detach().flatten().to(_reduce_dtype)
- moments = torch.stack([
- torch.ones_like(elems).sum(),
- elems.sum(),
- elems.square().sum(),
- ])
- assert moments.ndim == 1 and moments.shape[0] == _num_moments
- moments = moments.to(_counter_dtype)
-
- device = moments.device
- if device not in _counters[name]:
- _counters[name][device] = torch.zeros_like(moments)
- _counters[name][device].add_(moments)
- return value
-
-#----------------------------------------------------------------------------
-
-def report0(name, value):
- r"""Broadcasts the given set of scalars by the first process (`rank = 0`),
- but ignores any scalars provided by the other processes.
- See `report()` for further details.
- """
- report(name, value if _rank == 0 else [])
- return value
-
-#----------------------------------------------------------------------------
-
-class Collector:
- r"""Collects the scalars broadcasted by `report()` and `report0()` and
- computes their long-term averages (mean and standard deviation) over
- user-defined periods of time.
-
- The averages are first collected into internal counters that are not
- directly visible to the user. They are then copied to the user-visible
- state as a result of calling `update()` and can then be queried using
- `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the
- internal counters for the next round, so that the user-visible state
- effectively reflects averages collected between the last two calls to
- `update()`.
-
- Args:
- regex: Regular expression defining which statistics to
- collect. The default is to collect everything.
- keep_previous: Whether to retain the previous averages if no
- scalars were collected on a given round
- (default: True).
- """
- def __init__(self, regex='.*', keep_previous=True):
- self._regex = re.compile(regex)
- self._keep_previous = keep_previous
- self._cumulative = dict()
- self._moments = dict()
- self.update()
- self._moments.clear()
-
- def names(self):
- r"""Returns the names of all statistics broadcasted so far that
- match the regular expression specified at construction time.
- """
- return [name for name in _counters if self._regex.fullmatch(name)]
-
- def update(self):
- r"""Copies current values of the internal counters to the
- user-visible state and resets them for the next round.
-
- If `keep_previous=True` was specified at construction time, the
- operation is skipped for statistics that have received no scalars
- since the last update, retaining their previous averages.
-
- This method performs a number of GPU-to-CPU transfers and one
- `torch.distributed.all_reduce()`. It is intended to be called
- periodically in the main training loop, typically once every
- N training steps.
- """
- if not self._keep_previous:
- self._moments.clear()
- for name, cumulative in _sync(self.names()):
- if name not in self._cumulative:
- self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
- delta = cumulative - self._cumulative[name]
- self._cumulative[name].copy_(cumulative)
- if float(delta[0]) != 0:
- self._moments[name] = delta
-
- def _get_delta(self, name):
- r"""Returns the raw moments that were accumulated for the given
- statistic between the last two calls to `update()`, or zero if
- no scalars were collected.
- """
- assert self._regex.fullmatch(name)
- if name not in self._moments:
- self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
- return self._moments[name]
-
- def num(self, name):
- r"""Returns the number of scalars that were accumulated for the given
- statistic between the last two calls to `update()`, or zero if
- no scalars were collected.
- """
- delta = self._get_delta(name)
- return int(delta[0])
-
- def mean(self, name):
- r"""Returns the mean of the scalars that were accumulated for the
- given statistic between the last two calls to `update()`, or NaN if
- no scalars were collected.
- """
- delta = self._get_delta(name)
- if int(delta[0]) == 0:
- return float('nan')
- return float(delta[1] / delta[0])
-
- def std(self, name):
- r"""Returns the standard deviation of the scalars that were
- accumulated for the given statistic between the last two calls to
- `update()`, or NaN if no scalars were collected.
- """
- delta = self._get_delta(name)
- if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
- return float('nan')
- if int(delta[0]) == 1:
- return float(0)
- mean = float(delta[1] / delta[0])
- raw_var = float(delta[2] / delta[0])
- return np.sqrt(max(raw_var - np.square(mean), 0))
-
- def as_dict(self):
- r"""Returns the averages accumulated between the last two calls to
- `update()` as an `dnnlib.EasyDict`. The contents are as follows:
-
- dnnlib.EasyDict(
- NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT),
- ...
- )
- """
- stats = dnnlib.EasyDict()
- for name in self.names():
- stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name))
- return stats
-
- def __getitem__(self, name):
- r"""Convenience getter.
- `collector[name]` is a synonym for `collector.mean(name)`.
- """
- return self.mean(name)
-
-#----------------------------------------------------------------------------
-
-def _sync(names):
- r"""Synchronize the global cumulative counters across devices and
- processes. Called internally by `Collector.update()`.
- """
- if len(names) == 0:
- return []
- global _sync_called
- _sync_called = True
-
- # Collect deltas within current rank.
- deltas = []
- device = _sync_device if _sync_device is not None else torch.device('cpu')
- for name in names:
- delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device)
- for counter in _counters[name].values():
- delta.add_(counter.to(device))
- counter.copy_(torch.zeros_like(counter))
- deltas.append(delta)
- deltas = torch.stack(deltas)
-
- # Sum deltas across ranks.
- if _sync_device is not None:
- torch.distributed.all_reduce(deltas)
-
- # Update cumulative values.
- deltas = deltas.cpu()
- for idx, name in enumerate(names):
- if name not in _cumulative:
- _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
- _cumulative[name].add_(deltas[idx])
-
- # Return name-value pairs.
- return [(name, _cumulative[name]) for name in names]
-
-#----------------------------------------------------------------------------
diff --git a/spaces/user238921933/stable-diffusion-webui/run.sh b/spaces/user238921933/stable-diffusion-webui/run.sh
deleted file mode 100644
index 9752b2f4cd97e0dbc0f28d7aad76f7b2e32406ad..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/run.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!usr/bin/env bash
-
-[ -d extensions/deforum ] || git clone https://github.com/deforum-art/deforum-for-automatic1111-webui extensions/deforum
-
-. webui.sh
diff --git a/spaces/valhalla/glide-text2im/glide_text2im/download.py b/spaces/valhalla/glide-text2im/glide_text2im/download.py
deleted file mode 100644
index c088f0cd090aa873b66d3893798097ac6fadc16d..0000000000000000000000000000000000000000
--- a/spaces/valhalla/glide-text2im/glide_text2im/download.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import os
-from functools import lru_cache
-from typing import Dict, Optional
-
-import requests
-import torch as th
-from filelock import FileLock
-from tqdm.auto import tqdm
-
-MODEL_PATHS = {
- "base": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/base.pt",
- "upsample": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/upsample.pt",
- "base-inpaint": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/base_inpaint.pt",
- "upsample-inpaint": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/upsample_inpaint.pt",
- "clip/image-enc": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/clip_image_enc.pt",
- "clip/text-enc": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/clip_text_enc.pt",
-}
-
-
-@lru_cache()
-def default_cache_dir() -> str:
- return os.path.join(os.path.abspath(os.getcwd()), "glide_model_cache")
-
-
-def fetch_file_cached(
- url: str, progress: bool = True, cache_dir: Optional[str] = None, chunk_size: int = 4096
-) -> str:
- """
- Download the file at the given URL into a local file and return the path.
-
- If cache_dir is specified, it will be used to download the files.
- Otherwise, default_cache_dir() is used.
- """
- if cache_dir is None:
- cache_dir = default_cache_dir()
- os.makedirs(cache_dir, exist_ok=True)
- response = requests.get(url, stream=True)
- size = int(response.headers.get("content-length", "0"))
- local_path = os.path.join(cache_dir, url.split("/")[-1])
- with FileLock(local_path + ".lock"):
- if os.path.exists(local_path):
- return local_path
- if progress:
- pbar = tqdm(total=size, unit="iB", unit_scale=True)
- tmp_path = local_path + ".tmp"
- with open(tmp_path, "wb") as f:
- for chunk in response.iter_content(chunk_size):
- if progress:
- pbar.update(len(chunk))
- f.write(chunk)
- os.rename(tmp_path, local_path)
- if progress:
- pbar.close()
- return local_path
-
-
-def load_checkpoint(
- checkpoint_name: str,
- device: th.device,
- progress: bool = True,
- cache_dir: Optional[str] = None,
- chunk_size: int = 4096,
-) -> Dict[str, th.Tensor]:
- if checkpoint_name not in MODEL_PATHS:
- raise ValueError(
- f"Unknown checkpoint name {checkpoint_name}. Known names are: {MODEL_PATHS.keys()}."
- )
- path = fetch_file_cached(
- MODEL_PATHS[checkpoint_name], progress=progress, cache_dir=cache_dir, chunk_size=chunk_size
- )
- return th.load(path, map_location=device)
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/fileio/handlers/pickle_handler.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/fileio/handlers/pickle_handler.py
deleted file mode 100644
index b37c79bed4ef9fd8913715e62dbe3fc5cafdc3aa..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/fileio/handlers/pickle_handler.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import pickle
-
-from .base import BaseFileHandler
-
-
-class PickleHandler(BaseFileHandler):
-
- str_like = False
-
- def load_from_fileobj(self, file, **kwargs):
- return pickle.load(file, **kwargs)
-
- def load_from_path(self, filepath, **kwargs):
- return super(PickleHandler, self).load_from_path(
- filepath, mode='rb', **kwargs)
-
- def dump_to_str(self, obj, **kwargs):
- kwargs.setdefault('protocol', 2)
- return pickle.dumps(obj, **kwargs)
-
- def dump_to_fileobj(self, obj, file, **kwargs):
- kwargs.setdefault('protocol', 2)
- pickle.dump(obj, file, **kwargs)
-
- def dump_to_path(self, obj, filepath, **kwargs):
- super(PickleHandler, self).dump_to_path(
- obj, filepath, mode='wb', **kwargs)
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/backbones/vit.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/backbones/vit.py
deleted file mode 100644
index 59e4479650690e08cbc4cab9427aefda47c2116d..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/backbones/vit.py
+++ /dev/null
@@ -1,459 +0,0 @@
-"""Modified from https://github.com/rwightman/pytorch-image-
-models/blob/master/timm/models/vision_transformer.py."""
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as cp
-from annotator.uniformer.mmcv.cnn import (Conv2d, Linear, build_activation_layer, build_norm_layer,
- constant_init, kaiming_init, normal_init)
-from annotator.uniformer.mmcv.runner import _load_checkpoint
-from annotator.uniformer.mmcv.utils.parrots_wrapper import _BatchNorm
-
-from annotator.uniformer.mmseg.utils import get_root_logger
-from ..builder import BACKBONES
-from ..utils import DropPath, trunc_normal_
-
-
-class Mlp(nn.Module):
- """MLP layer for Encoder block.
-
- Args:
- in_features(int): Input dimension for the first fully
- connected layer.
- hidden_features(int): Output dimension for the first fully
- connected layer.
- out_features(int): Output dementsion for the second fully
- connected layer.
- act_cfg(dict): Config dict for activation layer.
- Default: dict(type='GELU').
- drop(float): Drop rate for the dropout layer. Dropout rate has
- to be between 0 and 1. Default: 0.
- """
-
- def __init__(self,
- in_features,
- hidden_features=None,
- out_features=None,
- act_cfg=dict(type='GELU'),
- drop=0.):
- super(Mlp, self).__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = Linear(in_features, hidden_features)
- self.act = build_activation_layer(act_cfg)
- self.fc2 = Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
-class Attention(nn.Module):
- """Attention layer for Encoder block.
-
- Args:
- dim (int): Dimension for the input vector.
- num_heads (int): Number of parallel attention heads.
- qkv_bias (bool): Enable bias for qkv if True. Default: False.
- qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
- attn_drop (float): Drop rate for attention output weights.
- Default: 0.
- proj_drop (float): Drop rate for output weights. Default: 0.
- """
-
- def __init__(self,
- dim,
- num_heads=8,
- qkv_bias=False,
- qk_scale=None,
- attn_drop=0.,
- proj_drop=0.):
- super(Attention, self).__init__()
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = qk_scale or head_dim**-0.5
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
-
- def forward(self, x):
- b, n, c = x.shape
- qkv = self.qkv(x).reshape(b, n, 3, self.num_heads,
- c // self.num_heads).permute(2, 0, 3, 1, 4)
- q, k, v = qkv[0], qkv[1], qkv[2]
-
- attn = (q @ k.transpose(-2, -1)) * self.scale
- attn = attn.softmax(dim=-1)
- attn = self.attn_drop(attn)
-
- x = (attn @ v).transpose(1, 2).reshape(b, n, c)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
-
-
-class Block(nn.Module):
- """Implements encoder block with residual connection.
-
- Args:
- dim (int): The feature dimension.
- num_heads (int): Number of parallel attention heads.
- mlp_ratio (int): Ratio of mlp hidden dim to embedding dim.
- qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
- drop (float): Drop rate for mlp output weights. Default: 0.
- attn_drop (float): Drop rate for attention output weights.
- Default: 0.
- proj_drop (float): Drop rate for attn layer output weights.
- Default: 0.
- drop_path (float): Drop rate for paths of model.
- Default: 0.
- act_cfg (dict): Config dict for activation layer.
- Default: dict(type='GELU').
- norm_cfg (dict): Config dict for normalization layer.
- Default: dict(type='LN', requires_grad=True).
- with_cp (bool): Use checkpoint or not. Using checkpoint will save some
- memory while slowing down the training speed. Default: False.
- """
-
- def __init__(self,
- dim,
- num_heads,
- mlp_ratio=4,
- qkv_bias=False,
- qk_scale=None,
- drop=0.,
- attn_drop=0.,
- proj_drop=0.,
- drop_path=0.,
- act_cfg=dict(type='GELU'),
- norm_cfg=dict(type='LN', eps=1e-6),
- with_cp=False):
- super(Block, self).__init__()
- self.with_cp = with_cp
- _, self.norm1 = build_norm_layer(norm_cfg, dim)
- self.attn = Attention(dim, num_heads, qkv_bias, qk_scale, attn_drop,
- proj_drop)
- self.drop_path = DropPath(
- drop_path) if drop_path > 0. else nn.Identity()
- _, self.norm2 = build_norm_layer(norm_cfg, dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp(
- in_features=dim,
- hidden_features=mlp_hidden_dim,
- act_cfg=act_cfg,
- drop=drop)
-
- def forward(self, x):
-
- def _inner_forward(x):
- out = x + self.drop_path(self.attn(self.norm1(x)))
- out = out + self.drop_path(self.mlp(self.norm2(out)))
- return out
-
- if self.with_cp and x.requires_grad:
- out = cp.checkpoint(_inner_forward, x)
- else:
- out = _inner_forward(x)
-
- return out
-
-
-class PatchEmbed(nn.Module):
- """Image to Patch Embedding.
-
- Args:
- img_size (int | tuple): Input image size.
- default: 224.
- patch_size (int): Width and height for a patch.
- default: 16.
- in_channels (int): Input channels for images. Default: 3.
- embed_dim (int): The embedding dimension. Default: 768.
- """
-
- def __init__(self,
- img_size=224,
- patch_size=16,
- in_channels=3,
- embed_dim=768):
- super(PatchEmbed, self).__init__()
- if isinstance(img_size, int):
- self.img_size = (img_size, img_size)
- elif isinstance(img_size, tuple):
- self.img_size = img_size
- else:
- raise TypeError('img_size must be type of int or tuple')
- h, w = self.img_size
- self.patch_size = (patch_size, patch_size)
- self.num_patches = (h // patch_size) * (w // patch_size)
- self.proj = Conv2d(
- in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
-
- def forward(self, x):
- return self.proj(x).flatten(2).transpose(1, 2)
-
-
-@BACKBONES.register_module()
-class VisionTransformer(nn.Module):
- """Vision transformer backbone.
-
- A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for
- Image Recognition at Scale` - https://arxiv.org/abs/2010.11929
-
- Args:
- img_size (tuple): input image size. Default: (224, 224).
- patch_size (int, tuple): patch size. Default: 16.
- in_channels (int): number of input channels. Default: 3.
- embed_dim (int): embedding dimension. Default: 768.
- depth (int): depth of transformer. Default: 12.
- num_heads (int): number of attention heads. Default: 12.
- mlp_ratio (int): ratio of mlp hidden dim to embedding dim.
- Default: 4.
- out_indices (list | tuple | int): Output from which stages.
- Default: -1.
- qkv_bias (bool): enable bias for qkv if True. Default: True.
- qk_scale (float): override default qk scale of head_dim ** -0.5 if set.
- drop_rate (float): dropout rate. Default: 0.
- attn_drop_rate (float): attention dropout rate. Default: 0.
- drop_path_rate (float): Rate of DropPath. Default: 0.
- norm_cfg (dict): Config dict for normalization layer.
- Default: dict(type='LN', eps=1e-6, requires_grad=True).
- act_cfg (dict): Config dict for activation layer.
- Default: dict(type='GELU').
- norm_eval (bool): Whether to set norm layers to eval mode, namely,
- freeze running stats (mean and var). Note: Effect on Batch Norm
- and its variants only. Default: False.
- final_norm (bool): Whether to add a additional layer to normalize
- final feature map. Default: False.
- interpolate_mode (str): Select the interpolate mode for position
- embeding vector resize. Default: bicubic.
- with_cls_token (bool): If concatenating class token into image tokens
- as transformer input. Default: True.
- with_cp (bool): Use checkpoint or not. Using checkpoint
- will save some memory while slowing down the training speed.
- Default: False.
- """
-
- def __init__(self,
- img_size=(224, 224),
- patch_size=16,
- in_channels=3,
- embed_dim=768,
- depth=12,
- num_heads=12,
- mlp_ratio=4,
- out_indices=11,
- qkv_bias=True,
- qk_scale=None,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.,
- norm_cfg=dict(type='LN', eps=1e-6, requires_grad=True),
- act_cfg=dict(type='GELU'),
- norm_eval=False,
- final_norm=False,
- with_cls_token=True,
- interpolate_mode='bicubic',
- with_cp=False):
- super(VisionTransformer, self).__init__()
- self.img_size = img_size
- self.patch_size = patch_size
- self.features = self.embed_dim = embed_dim
- self.patch_embed = PatchEmbed(
- img_size=img_size,
- patch_size=patch_size,
- in_channels=in_channels,
- embed_dim=embed_dim)
-
- self.with_cls_token = with_cls_token
- self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
- self.pos_embed = nn.Parameter(
- torch.zeros(1, self.patch_embed.num_patches + 1, embed_dim))
- self.pos_drop = nn.Dropout(p=drop_rate)
-
- if isinstance(out_indices, int):
- self.out_indices = [out_indices]
- elif isinstance(out_indices, list) or isinstance(out_indices, tuple):
- self.out_indices = out_indices
- else:
- raise TypeError('out_indices must be type of int, list or tuple')
-
- dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)
- ] # stochastic depth decay rule
- self.blocks = nn.ModuleList([
- Block(
- dim=embed_dim,
- num_heads=num_heads,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- qk_scale=qk_scale,
- drop=dpr[i],
- attn_drop=attn_drop_rate,
- act_cfg=act_cfg,
- norm_cfg=norm_cfg,
- with_cp=with_cp) for i in range(depth)
- ])
-
- self.interpolate_mode = interpolate_mode
- self.final_norm = final_norm
- if final_norm:
- _, self.norm = build_norm_layer(norm_cfg, embed_dim)
-
- self.norm_eval = norm_eval
- self.with_cp = with_cp
-
- def init_weights(self, pretrained=None):
- if isinstance(pretrained, str):
- logger = get_root_logger()
- checkpoint = _load_checkpoint(pretrained, logger=logger)
- if 'state_dict' in checkpoint:
- state_dict = checkpoint['state_dict']
- else:
- state_dict = checkpoint
-
- if 'pos_embed' in state_dict.keys():
- if self.pos_embed.shape != state_dict['pos_embed'].shape:
- logger.info(msg=f'Resize the pos_embed shape from \
-{state_dict["pos_embed"].shape} to {self.pos_embed.shape}')
- h, w = self.img_size
- pos_size = int(
- math.sqrt(state_dict['pos_embed'].shape[1] - 1))
- state_dict['pos_embed'] = self.resize_pos_embed(
- state_dict['pos_embed'], (h, w), (pos_size, pos_size),
- self.patch_size, self.interpolate_mode)
-
- self.load_state_dict(state_dict, False)
-
- elif pretrained is None:
- # We only implement the 'jax_impl' initialization implemented at
- # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353 # noqa: E501
- trunc_normal_(self.pos_embed, std=.02)
- trunc_normal_(self.cls_token, std=.02)
- for n, m in self.named_modules():
- if isinstance(m, Linear):
- trunc_normal_(m.weight, std=.02)
- if m.bias is not None:
- if 'mlp' in n:
- normal_init(m.bias, std=1e-6)
- else:
- constant_init(m.bias, 0)
- elif isinstance(m, Conv2d):
- kaiming_init(m.weight, mode='fan_in')
- if m.bias is not None:
- constant_init(m.bias, 0)
- elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):
- constant_init(m.bias, 0)
- constant_init(m.weight, 1.0)
- else:
- raise TypeError('pretrained must be a str or None')
-
- def _pos_embeding(self, img, patched_img, pos_embed):
- """Positiong embeding method.
-
- Resize the pos_embed, if the input image size doesn't match
- the training size.
- Args:
- img (torch.Tensor): The inference image tensor, the shape
- must be [B, C, H, W].
- patched_img (torch.Tensor): The patched image, it should be
- shape of [B, L1, C].
- pos_embed (torch.Tensor): The pos_embed weighs, it should be
- shape of [B, L2, c].
- Return:
- torch.Tensor: The pos encoded image feature.
- """
- assert patched_img.ndim == 3 and pos_embed.ndim == 3, \
- 'the shapes of patched_img and pos_embed must be [B, L, C]'
- x_len, pos_len = patched_img.shape[1], pos_embed.shape[1]
- if x_len != pos_len:
- if pos_len == (self.img_size[0] // self.patch_size) * (
- self.img_size[1] // self.patch_size) + 1:
- pos_h = self.img_size[0] // self.patch_size
- pos_w = self.img_size[1] // self.patch_size
- else:
- raise ValueError(
- 'Unexpected shape of pos_embed, got {}.'.format(
- pos_embed.shape))
- pos_embed = self.resize_pos_embed(pos_embed, img.shape[2:],
- (pos_h, pos_w), self.patch_size,
- self.interpolate_mode)
- return self.pos_drop(patched_img + pos_embed)
-
- @staticmethod
- def resize_pos_embed(pos_embed, input_shpae, pos_shape, patch_size, mode):
- """Resize pos_embed weights.
-
- Resize pos_embed using bicubic interpolate method.
- Args:
- pos_embed (torch.Tensor): pos_embed weights.
- input_shpae (tuple): Tuple for (input_h, intput_w).
- pos_shape (tuple): Tuple for (pos_h, pos_w).
- patch_size (int): Patch size.
- Return:
- torch.Tensor: The resized pos_embed of shape [B, L_new, C]
- """
- assert pos_embed.ndim == 3, 'shape of pos_embed must be [B, L, C]'
- input_h, input_w = input_shpae
- pos_h, pos_w = pos_shape
- cls_token_weight = pos_embed[:, 0]
- pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w):]
- pos_embed_weight = pos_embed_weight.reshape(
- 1, pos_h, pos_w, pos_embed.shape[2]).permute(0, 3, 1, 2)
- pos_embed_weight = F.interpolate(
- pos_embed_weight,
- size=[input_h // patch_size, input_w // patch_size],
- align_corners=False,
- mode=mode)
- cls_token_weight = cls_token_weight.unsqueeze(1)
- pos_embed_weight = torch.flatten(pos_embed_weight, 2).transpose(1, 2)
- pos_embed = torch.cat((cls_token_weight, pos_embed_weight), dim=1)
- return pos_embed
-
- def forward(self, inputs):
- B = inputs.shape[0]
-
- x = self.patch_embed(inputs)
-
- cls_tokens = self.cls_token.expand(B, -1, -1)
- x = torch.cat((cls_tokens, x), dim=1)
- x = self._pos_embeding(inputs, x, self.pos_embed)
-
- if not self.with_cls_token:
- # Remove class token for transformer input
- x = x[:, 1:]
-
- outs = []
- for i, blk in enumerate(self.blocks):
- x = blk(x)
- if i == len(self.blocks) - 1:
- if self.final_norm:
- x = self.norm(x)
- if i in self.out_indices:
- if self.with_cls_token:
- # Remove class token and reshape token for decoder head
- out = x[:, 1:]
- else:
- out = x
- B, _, C = out.shape
- out = out.reshape(B, inputs.shape[2] // self.patch_size,
- inputs.shape[3] // self.patch_size,
- C).permute(0, 3, 1, 2)
- outs.append(out)
-
- return tuple(outs)
-
- def train(self, mode=True):
- super(VisionTransformer, self).train(mode)
- if mode and self.norm_eval:
- for m in self.modules():
- if isinstance(m, nn.LayerNorm):
- m.eval()
diff --git a/spaces/willhill/stabilityai-stable-diffusion-2-1/app.py b/spaces/willhill/stabilityai-stable-diffusion-2-1/app.py
deleted file mode 100644
index 025cd7d0106a86020498d4db48e921b68d1c71db..0000000000000000000000000000000000000000
--- a/spaces/willhill/stabilityai-stable-diffusion-2-1/app.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import gradio as gr
-
-# gr.Interface.load("models/stabilityai/stable-diffusion-2-1").launch()
-# import diffusers
-from diffusers import DiffusionPipeline
-pipeline = DiffusionPipeline.from_pretrained("ORANZ/braV5_finetuned")
-gradio.Interface.from_pipeline(pipeline).launch()
diff --git a/spaces/wz758727829/ChuanhuChatGPT/custom.css b/spaces/wz758727829/ChuanhuChatGPT/custom.css
deleted file mode 100644
index 97a1c2e681f4cc09e2237a92b37ab6cadd545a71..0000000000000000000000000000000000000000
--- a/spaces/wz758727829/ChuanhuChatGPT/custom.css
+++ /dev/null
@@ -1,184 +0,0 @@
-:root {
- --chatbot-color-light: #F3F3F3;
- --chatbot-color-dark: #121111;
-}
-
-/* status_display */
-#status_display {
- display: flex;
- min-height: 2.5em;
- align-items: flex-end;
- justify-content: flex-end;
-}
-#status_display p {
- font-size: .85em;
- font-family: monospace;
- color: var(--body-text-color-subdued);
-}
-
-#chuanhu_chatbot, #status_display {
- transition: all 0.6s;
-}
-
-ol, ul {
- list-style-position: inside;
- padding-left: 0;
-}
-
-ol li, ul:not(.options) li {
- padding-left: 1.5em;
- text-indent: -1.5em;
-}
-
-/* 亮色 */
-@media (prefers-color-scheme: light) {
- #chuanhu_chatbot {
- background-color: var(--chatbot-color-light) !important;
- }
- [data-testid = "bot"] {
- background-color: #FFFFFF !important;
- }
- [data-testid = "user"] {
- background-color: #95EC69 !important;
- }
-}
-/* 暗色 */
-@media (prefers-color-scheme: dark) {
- #chuanhu_chatbot {
- background-color: var(--chatbot-color-dark) !important;
- }
- [data-testid = "bot"] {
- background-color: #2C2C2C !important;
- }
- [data-testid = "user"] {
- background-color: #26B561 !important;
- }
- body {
- background-color: var(--neutral-950) !important;
- }
-}
-
-/* 对话气泡 */
-[class *= "message"] {
- border-radius: var(--radius-xl) !important;
- border: none;
- padding: var(--spacing-xl) !important;
- font-size: var(--text-md) !important;
- line-height: var(--line-md) !important;
-}
-[data-testid = "bot"] {
- max-width: 85%;
- border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
- max-width: 85%;
- width: auto !important;
- border-bottom-right-radius: 0 !important;
-}
-/* 表格 */
-table {
- margin: 1em 0;
- border-collapse: collapse;
- empty-cells: show;
-}
-td,th {
- border: 1.2px solid var(--border-color-primary) !important;
- padding: 0.2em;
-}
-thead {
- background-color: rgba(175,184,193,0.2);
-}
-thead th {
- padding: .5em .2em;
-}
-/* 行内代码 */
-code {
- display: inline;
- white-space: break-spaces;
- border-radius: 6px;
- margin: 0 2px 0 2px;
- padding: .2em .4em .1em .4em;
- background-color: rgba(175,184,193,0.2);
-}
-/* 代码块 */
-pre code {
- display: block;
- overflow: auto;
- white-space: pre;
- background-color: hsla(0, 0%, 0%, 80%)!important;
- border-radius: 10px;
- padding: 1rem 1.2rem 1rem;
- margin: 1.2em 2em 1.2em 0.5em;
- color: #FFF;
- box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
-}
-/* 代码高亮样式 */
-.highlight .hll { background-color: #49483e }
-.highlight .c { color: #75715e } /* Comment */
-.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
-.highlight .k { color: #66d9ef } /* Keyword */
-.highlight .l { color: #ae81ff } /* Literal */
-.highlight .n { color: #f8f8f2 } /* Name */
-.highlight .o { color: #f92672 } /* Operator */
-.highlight .p { color: #f8f8f2 } /* Punctuation */
-.highlight .ch { color: #75715e } /* Comment.Hashbang */
-.highlight .cm { color: #75715e } /* Comment.Multiline */
-.highlight .cp { color: #75715e } /* Comment.Preproc */
-.highlight .cpf { color: #75715e } /* Comment.PreprocFile */
-.highlight .c1 { color: #75715e } /* Comment.Single */
-.highlight .cs { color: #75715e } /* Comment.Special */
-.highlight .gd { color: #f92672 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gi { color: #a6e22e } /* Generic.Inserted */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #75715e } /* Generic.Subheading */
-.highlight .kc { color: #66d9ef } /* Keyword.Constant */
-.highlight .kd { color: #66d9ef } /* Keyword.Declaration */
-.highlight .kn { color: #f92672 } /* Keyword.Namespace */
-.highlight .kp { color: #66d9ef } /* Keyword.Pseudo */
-.highlight .kr { color: #66d9ef } /* Keyword.Reserved */
-.highlight .kt { color: #66d9ef } /* Keyword.Type */
-.highlight .ld { color: #e6db74 } /* Literal.Date */
-.highlight .m { color: #ae81ff } /* Literal.Number */
-.highlight .s { color: #e6db74 } /* Literal.String */
-.highlight .na { color: #a6e22e } /* Name.Attribute */
-.highlight .nb { color: #f8f8f2 } /* Name.Builtin */
-.highlight .nc { color: #a6e22e } /* Name.Class */
-.highlight .no { color: #66d9ef } /* Name.Constant */
-.highlight .nd { color: #a6e22e } /* Name.Decorator */
-.highlight .ni { color: #f8f8f2 } /* Name.Entity */
-.highlight .ne { color: #a6e22e } /* Name.Exception */
-.highlight .nf { color: #a6e22e } /* Name.Function */
-.highlight .nl { color: #f8f8f2 } /* Name.Label */
-.highlight .nn { color: #f8f8f2 } /* Name.Namespace */
-.highlight .nx { color: #a6e22e } /* Name.Other */
-.highlight .py { color: #f8f8f2 } /* Name.Property */
-.highlight .nt { color: #f92672 } /* Name.Tag */
-.highlight .nv { color: #f8f8f2 } /* Name.Variable */
-.highlight .ow { color: #f92672 } /* Operator.Word */
-.highlight .w { color: #f8f8f2 } /* Text.Whitespace */
-.highlight .mb { color: #ae81ff } /* Literal.Number.Bin */
-.highlight .mf { color: #ae81ff } /* Literal.Number.Float */
-.highlight .mh { color: #ae81ff } /* Literal.Number.Hex */
-.highlight .mi { color: #ae81ff } /* Literal.Number.Integer */
-.highlight .mo { color: #ae81ff } /* Literal.Number.Oct */
-.highlight .sa { color: #e6db74 } /* Literal.String.Affix */
-.highlight .sb { color: #e6db74 } /* Literal.String.Backtick */
-.highlight .sc { color: #e6db74 } /* Literal.String.Char */
-.highlight .dl { color: #e6db74 } /* Literal.String.Delimiter */
-.highlight .sd { color: #e6db74 } /* Literal.String.Doc */
-.highlight .s2 { color: #e6db74 } /* Literal.String.Double */
-.highlight .se { color: #ae81ff } /* Literal.String.Escape */
-.highlight .sh { color: #e6db74 } /* Literal.String.Heredoc */
-.highlight .si { color: #e6db74 } /* Literal.String.Interpol */
-.highlight .sx { color: #e6db74 } /* Literal.String.Other */
-.highlight .sr { color: #e6db74 } /* Literal.String.Regex */
-.highlight .s1 { color: #e6db74 } /* Literal.String.Single */
-.highlight .ss { color: #e6db74 } /* Literal.String.Symbol */
-.highlight .bp { color: #f8f8f2 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #a6e22e } /* Name.Function.Magic */
-.highlight .vc { color: #f8f8f2 } /* Name.Variable.Class */
-.highlight .vg { color: #f8f8f2 } /* Name.Variable.Global */
-.highlight .vi { color: #f8f8f2 } /* Name.Variable.Instance */
-.highlight .vm { color: #f8f8f2 } /* Name.Variable.Magic */
-.highlight .il { color: #ae81ff } /* Literal.Number.Integer.Long */
diff --git a/spaces/xp3857/Image_Restoration_Colorization/GUI.py b/spaces/xp3857/Image_Restoration_Colorization/GUI.py
deleted file mode 100644
index c0ee59832e72d2802793a12beebc1032275bd19e..0000000000000000000000000000000000000000
--- a/spaces/xp3857/Image_Restoration_Colorization/GUI.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import numpy as np
-import cv2
-import PySimpleGUI as sg
-import os.path
-import argparse
-import os
-import sys
-import shutil
-from subprocess import call
-
-def modify(image_filename=None, cv2_frame=None):
-
- def run_cmd(command):
- try:
- call(command, shell=True)
- except KeyboardInterrupt:
- print("Process interrupted")
- sys.exit(1)
-
- parser = argparse.ArgumentParser()
- parser.add_argument("--input_folder", type=str,
- default= image_filename, help="Test images")
- parser.add_argument(
- "--output_folder",
- type=str,
- default="./output",
- help="Restored images, please use the absolute path",
- )
- parser.add_argument("--GPU", type=str, default="-1", help="0,1,2")
- parser.add_argument(
- "--checkpoint_name", type=str, default="Setting_9_epoch_100", help="choose which checkpoint"
- )
- parser.add_argument("--with_scratch",default="--with_scratch" ,action="store_true")
- opts = parser.parse_args()
-
- gpu1 = opts.GPU
-
- # resolve relative paths before changing directory
- opts.input_folder = os.path.abspath(opts.input_folder)
- opts.output_folder = os.path.abspath(opts.output_folder)
- if not os.path.exists(opts.output_folder):
- os.makedirs(opts.output_folder)
-
- main_environment = os.getcwd()
-
- # Stage 1: Overall Quality Improve
- print("Running Stage 1: Overall restoration")
- os.chdir("./Global")
- stage_1_input_dir = opts.input_folder
- stage_1_output_dir = os.path.join(
- opts.output_folder, "stage_1_restore_output")
- if not os.path.exists(stage_1_output_dir):
- os.makedirs(stage_1_output_dir)
-
- if not opts.with_scratch:
- stage_1_command = (
- "python test.py --test_mode Full --Quality_restore --test_input "
- + stage_1_input_dir
- + " --outputs_dir "
- + stage_1_output_dir
- + " --gpu_ids "
- + gpu1
- )
- run_cmd(stage_1_command)
- else:
-
- mask_dir = os.path.join(stage_1_output_dir, "masks")
- new_input = os.path.join(mask_dir, "input")
- new_mask = os.path.join(mask_dir, "mask")
- stage_1_command_1 = (
- "python detection.py --test_path "
- + stage_1_input_dir
- + " --output_dir "
- + mask_dir
- + " --input_size full_size"
- + " --GPU "
- + gpu1
- )
- stage_1_command_2 = (
- "python test.py --Scratch_and_Quality_restore --test_input "
- + new_input
- + " --test_mask "
- + new_mask
- + " --outputs_dir "
- + stage_1_output_dir
- + " --gpu_ids "
- + gpu1
- )
- run_cmd(stage_1_command_1)
- run_cmd(stage_1_command_2)
-
- # Solve the case when there is no face in the old photo
- stage_1_results = os.path.join(stage_1_output_dir, "restored_image")
- stage_4_output_dir = os.path.join(opts.output_folder, "final_output")
- if not os.path.exists(stage_4_output_dir):
- os.makedirs(stage_4_output_dir)
- for x in os.listdir(stage_1_results):
- img_dir = os.path.join(stage_1_results, x)
- shutil.copy(img_dir, stage_4_output_dir)
-
- print("Finish Stage 1 ...")
- print("\n")
-
- # Stage 2: Face Detection
-
- print("Running Stage 2: Face Detection")
- os.chdir(".././Face_Detection")
- stage_2_input_dir = os.path.join(stage_1_output_dir, "restored_image")
- stage_2_output_dir = os.path.join(
- opts.output_folder, "stage_2_detection_output")
- if not os.path.exists(stage_2_output_dir):
- os.makedirs(stage_2_output_dir)
- stage_2_command = (
- "python detect_all_dlib.py --url " + stage_2_input_dir +
- " --save_url " + stage_2_output_dir
- )
- run_cmd(stage_2_command)
- print("Finish Stage 2 ...")
- print("\n")
-
- # Stage 3: Face Restore
- print("Running Stage 3: Face Enhancement")
- os.chdir(".././Face_Enhancement")
- stage_3_input_mask = "./"
- stage_3_input_face = stage_2_output_dir
- stage_3_output_dir = os.path.join(
- opts.output_folder, "stage_3_face_output")
- if not os.path.exists(stage_3_output_dir):
- os.makedirs(stage_3_output_dir)
- stage_3_command = (
- "python test_face.py --old_face_folder "
- + stage_3_input_face
- + " --old_face_label_folder "
- + stage_3_input_mask
- + " --tensorboard_log --name "
- + opts.checkpoint_name
- + " --gpu_ids "
- + gpu1
- + " --load_size 256 --label_nc 18 --no_instance --preprocess_mode resize --batchSize 4 --results_dir "
- + stage_3_output_dir
- + " --no_parsing_map"
- )
- run_cmd(stage_3_command)
- print("Finish Stage 3 ...")
- print("\n")
-
- # Stage 4: Warp back
- print("Running Stage 4: Blending")
- os.chdir(".././Face_Detection")
- stage_4_input_image_dir = os.path.join(
- stage_1_output_dir, "restored_image")
- stage_4_input_face_dir = os.path.join(stage_3_output_dir, "each_img")
- stage_4_output_dir = os.path.join(opts.output_folder, "final_output")
- if not os.path.exists(stage_4_output_dir):
- os.makedirs(stage_4_output_dir)
- stage_4_command = (
- "python align_warp_back_multiple_dlib.py --origin_url "
- + stage_4_input_image_dir
- + " --replace_url "
- + stage_4_input_face_dir
- + " --save_url "
- + stage_4_output_dir
- )
- run_cmd(stage_4_command)
- print("Finish Stage 4 ...")
- print("\n")
-
- print("All the processing is done. Please check the results.")
-
-# --------------------------------- The GUI ---------------------------------
-
-# First the window layout...
-
-images_col = [[sg.Text('Input file:'), sg.In(enable_events=True, key='-IN FILE-'), sg.FileBrowse()],
- [sg.Button('Modify Photo', key='-MPHOTO-'), sg.Button('Exit')],
- [sg.Image(filename='', key='-IN-'), sg.Image(filename='', key='-OUT-')],]
-# ----- Full layout -----
-layout = [[sg.VSeperator(), sg.Column(images_col)]]
-
-# ----- Make the window -----
-window = sg.Window('Bringing-old-photos-back-to-life', layout, grab_anywhere=True)
-
-# ----- Run the Event Loop -----
-prev_filename = colorized = cap = None
-while True:
- event, values = window.read()
- if event in (None, 'Exit'):
- break
-
- elif event == '-MPHOTO-':
- try:
- n1 = filename.split("/")[-2]
- n2 = filename.split("/")[-3]
- n3 = filename.split("/")[-1]
- filename= str(f"./{n2}/{n1}")
- modify(filename)
-
- global f_image
- f_image = f'./output/final_output/{n3}'
- image = cv2.imread(f_image)
- window['-OUT-'].update(data=cv2.imencode('.png', image)[1].tobytes())
-
- except:
- continue
-
- elif event == '-IN FILE-': # A single filename was chosen
- filename = values['-IN FILE-']
- if filename != prev_filename:
- prev_filename = filename
- try:
- image = cv2.imread(filename)
- window['-IN-'].update(data=cv2.imencode('.png', image)[1].tobytes())
- except:
- continue
-
-# ----- Exit program -----
-window.close()
\ No newline at end of file
diff --git a/spaces/xuetao/bingo3/src/app/loading.css b/spaces/xuetao/bingo3/src/app/loading.css
deleted file mode 100644
index eaaab6a86a228334c4eca3c5368ae6f0f593d405..0000000000000000000000000000000000000000
--- a/spaces/xuetao/bingo3/src/app/loading.css
+++ /dev/null
@@ -1,68 +0,0 @@
-::-webkit-scrollbar {
- width: 10px;
- height: 10px;
- display: none;
-}
-
-::-webkit-scrollbar-button:start:decrement,
-::-webkit-scrollbar-button:end:increment {
- height: 30px;
- background-color: transparent;
-}
-
-::-webkit-scrollbar-track-piece {
- background-color: #3b3b3b;
- -webkit-border-radius: 16px;
-}
-
-::-webkit-scrollbar-thumb:vertical {
- height: 50px;
- background-color: #666;
- border: 1px solid #eee;
- -webkit-border-radius: 6px;
-}
-
-/* loading start */
-.loading-spinner {
- display: flex;
- justify-content: center;
- align-items: center;
- height: 100vh;
- opacity: 1;
- transition: opacity .8s ease-out;
-}
-
-.loading-spinner.hidden {
- opacity: 0;
-}
-
-.loading-spinner>div {
- width: 30px;
- height: 30px;
- background: linear-gradient(90deg, #2870EA 10.79%, #1B4AEF 87.08%);
-
- border-radius: 100%;
- display: inline-block;
- animation: sk-bouncedelay 1.4s infinite ease-in-out both;
-}
-
-.loading-spinner .bounce1 {
- animation-delay: -0.32s;
-}
-
-.loading-spinner .bounce2 {
- animation-delay: -0.16s;
-}
-
-@keyframes sk-bouncedelay {
-
- 0%,
- 80%,
- 100% {
- transform: scale(0);
- }
-
- 40% {
- transform: scale(1.0);
- }
-}
diff --git a/spaces/xuxw98/TAPA/quantize/gptq.py b/spaces/xuxw98/TAPA/quantize/gptq.py
deleted file mode 100644
index 3d646ff04d260f754690e73d280670f077b97d03..0000000000000000000000000000000000000000
--- a/spaces/xuxw98/TAPA/quantize/gptq.py
+++ /dev/null
@@ -1,238 +0,0 @@
-# This adapts GPTQ's quantization process: https://github.com/IST-DASLab/gptq/
-# E. Frantar et al GPTQ: Accurate Post-training Compression for GPT, arXiv:2210.17323
-# portions copyright by the authors licensed under the Apache License 2.0
-import gc
-import sys
-import time
-from pathlib import Path
-from typing import Optional
-
-import torch
-from datasets import load_dataset
-
-# support running without installing as a package
-wd = Path(__file__).parent.parent.resolve()
-sys.path.append(str(wd))
-
-from lit_llama import LLaMA, Tokenizer
-from lit_llama.quantization import GPTQQuantizer
-from lit_llama.utils import EmptyInitOnDevice, llama_model_lookup
-
-
-def get_sample_data():
- traindata = load_dataset(
- "allenai/c4",
- "allenai--c4",
- data_files={"train": "en/c4-train.00000-of-01024.json.gz"},
- split="train",
- )
- # heuristic for the data size?
- txt = "\n".join(
- traindata[i]["text"] for i in torch.randperm(len(traindata))[:1000].tolist()
- )
- return txt
-
-
-@torch.no_grad()
-def llama_blockwise_quantization(
- model, sample_inputs, working_device, *, bits=4, groupsize=-1
-):
- """
- This is the classic post-training quantization of all linear layers.
- We quantize in order, i.e. when observing the inputs, we use the outputs of the previously quantized layers rather
- than doing them all at once.
- """
- print(model)
- print(model.config)
-
- print("Getting inputs for first block")
- model.transformer.wte.to(working_device)
- sample_inputs = sample_inputs.to(working_device)
- inps = model.transformer.wte(sample_inputs)
- model.transformer.wte.to("cpu")
- torch.cuda.empty_cache()
-
- rope_cache = model.build_rope_cache(sample_inputs)
- mask_cache = model.build_mask_cache(sample_inputs)
-
- print("Starting to quantize blocks")
- outs = torch.zeros_like(inps)
-
- # better than relying on enumeration? originally the code bundled
- # the two mlp fc layers
- # we could automate this with a lot of hooks and another iteration
- submodules_to_process = [
- "attn.c_attn",
- "attn.c_proj",
- "mlp.c_fc1",
- "mlp.c_fc2",
- "mlp.c_proj",
- ]
-
- for i, block in enumerate(model.transformer.h):
- block.to(working_device)
-
- for name in submodules_to_process:
- print(i, name, end=" ")
- t0 = time.perf_counter()
- print("collecting stats", end=" ")
- sys.stdout.flush()
- module = block.get_submodule(name)
-
- gptq = GPTQQuantizer(
- module,
- bits=bits,
- groupsize=groupsize,
- actorder=(groupsize == -1),
- )
- handle = module.register_forward_hook(gptq.collect_input_stats)
- for j in range(inps.size(0)):
- outs[j : j + 1], _ = block(
- inps[j : j + 1],
- rope=rope_cache,
- mask=mask_cache,
- max_seq_length=model.config.block_size
- )
-
- handle.remove()
-
- print("quantizing", end=" ")
- sys.stdout.flush()
- q_module, error = gptq.quantize()
-
- # replace the linear module with the quantized module
- pname, dname = name.rsplit(".", 1)
- setattr(block.get_submodule(pname), dname, q_module)
-
- # cleanup in an attempt to not run out of memory
- del gptq
- gc.collect()
- torch.cuda.empty_cache()
- t1 = time.perf_counter()
- print(f"time {int(t1 - t0 + 0.5)}s quantization error {error:.1f}")
-
- for j in range(inps.size(0)):
- outs[j : j + 1], _ = block(
- inps[j : j + 1],
- rope=rope_cache,
- mask=mask_cache,
- max_seq_length=model.config.block_size
- )
-
- block.cpu()
- gc.collect()
- torch.cuda.empty_cache()
-
- # the outputs are the next block's inputs and we'll reuse the old inputs
- inps, outs = outs, inps
-
- model.transformer.ln_f.to(working_device)
- for j in range(inps.size(0)):
- outs[j : j + 1] = model.transformer.ln_f(inps[j : j + 1])
- model.transformer.ln_f.to("cpu")
- inps, outs = outs, inps
-
- model.lm_head.to(working_device)
- gptq = GPTQQuantizer(
- model.lm_head,
- bits=bits,
- groupsize=groupsize,
- actorder=(groupsize == -1),
- )
- handle = model.lm_head.register_forward_hook(gptq.collect_input_stats)
- for j in range(inps.size(0)):
- model.lm_head(inps[j : j + 1])
- handle.remove()
- q_module, error = gptq.quantize()
- model.lm_head = q_module
- model.lm_head.to("cpu")
-
-
-def main(
- *,
- checkpoint_path: Path = Path("checkpoints/lit-llama/7B/lit-llama.pth"),
- output_path: Optional[Path] = None,
- tokenizer_path: Path = Path("checkpoints/lit-llama/tokenizer.model"),
- n_samples: int = 128,
- dtype: str = "float32",
- quantize: Optional[str] = None,
-) -> None:
- """Generates text samples based on a pre-trained LLaMA model and tokenizer.
-
- Args:
- checkpoint_path: The checkpoint path to load.
- output_path: Path to write the quantized model's state dict to.
- tokenizer_path: The tokenizer path to load.
- n_samples: Number of example inputs to use for statistics (default: 128)
- dtype: The dtype to use to load the model.
- quantize: Mode to quantize the model to:
- ``"gptq.int4"``: GPTQ 4-bit mode.
- Note that ``"llm.int8"```does not need a quantization step.
- """
- assert checkpoint_path.is_file()
- assert tokenizer_path.is_file()
- if output_path is None:
- output_path = checkpoint_path.parent / "llama-gptq.4bit.pth"
- assert output_path.parent.is_dir() and (not output_path.exists() or output_path.is_file())
-
- device = "cuda"
-
- dt = getattr(torch, dtype, None)
- if not isinstance(dt, torch.dtype):
- raise ValueError(f"{dtype} is not a valid dtype.")
- dtype = dt
-
- if quantize == "gptq.int4":
- bits = 4
- elif quantize == "gptq.int8":
- bits = 8
- else:
- raise RuntimeError(f"unknown/unsupported quantization mode {quantize}")
-
- # we avoid loading the entire model on the GPU and do this block by block
- with EmptyInitOnDevice(
- device="cpu",
- dtype=dtype,
- ):
- print("Loading model ...", file=sys.stderr)
- t0 = time.time()
- checkpoint = torch.load(checkpoint_path)
- name = llama_model_lookup(checkpoint)
- model = LLaMA.from_name(name)
- model.load_state_dict(checkpoint)
- print(f"Time to load model: {time.time() - t0:.02f} seconds.", file=sys.stderr)
-
- model.eval()
-
- tokenizer = Tokenizer(tokenizer_path)
-
- test_string = get_sample_data()
- encoded_text = tokenizer.encode(
- test_string,
- bos=True,
- eos=False,
- )
- block_size = 2048 # this is for compat with gptq, and indeed we get much worse beyond this (https://github.com/facebookresearch/llama/blob/57b0eb62de0636e75af471e49e2f1862d908d9d8/llama/model.py#L30)
- encoded_text = encoded_text[: n_samples * block_size].reshape(n_samples, block_size)
-
- t0 = time.perf_counter()
- llama_blockwise_quantization(model, encoded_text, device, bits=bits)
- t = time.perf_counter() - t0
-
- print(
- f"\n\nTime for quantization: {t:.02f} sec total",
- file=sys.stderr,
- )
- print(
- f"Memory used: {torch.cuda.max_memory_reserved() / 1e9:.02f} GB",
- file=sys.stderr,
- )
-
- torch.save(model.state_dict(), output_path)
-
-
-if __name__ == "__main__":
- from jsonargparse import CLI
-
- torch.set_float32_matmul_precision("high")
- CLI(main)
diff --git a/spaces/yangtaowang/TokenCut/app.py b/spaces/yangtaowang/TokenCut/app.py
deleted file mode 100644
index 1d70f906ee3594e5a9ff87696930268b6ae7e8c2..0000000000000000000000000000000000000000
--- a/spaces/yangtaowang/TokenCut/app.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import os
-import gradio as gr
-from pathlib import Path
-
-
-os.system("git clone https://github.com/YangtaoWANG95/TokenCut.git")
-os.chdir("TokenCut")
-os.system("wget https://raw.githubusercontent.com/YangtaoWANG95/TokenCut/master/examples/VOC07_000064.jpg -O parrot.jpg")
-
-def inference(img):
- os.system("python main_tokencut.py --image_path "+img+" --visualize all --resize 480")
- filename = Path(img).stem
- return "./outputs/TokenCut-vit_small16_k/"+filename+"_TokenCut_attn.jpg","./outputs/TokenCut-vit_small16_k/"+filename+"_TokenCut_pred.jpg"
-
-title="TokenCut"
-description="Gradio demo for TokenCut: Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut. To use it, simply upload your image or click on one of the examples to load them. We resize the smaller edge of the image to 480 to accelerate inference time. Read more at the links below"
-
-article = "