repo_name stringlengths 7 71 | file_path stringlengths 5 118 | context list | import_statement stringlengths 45 12.5k | token_num int64 641 99.4k | cropped_code stringlengths 44 17k | all_code stringlengths 43 754k | next_line stringlengths 2 330 | gold_snippet_index int64 0 68 | created_at stringlengths 25 25 | level stringclasses 9
values |
|---|---|---|---|---|---|---|---|---|---|---|
OPPOMKLab/u-LLaVA | tasks/image_text_pretrain.py | [
{
"identifier": "registry",
"path": "utils/registry.py",
"snippet": "class Registry:\n def register_builder(cls, name):\n def wrap(builder_cls):\n def register_model(cls, name):\n def wrap(model_cls):\n def register_processor(cls, name):\n def wrap(processor_cls):\n def ... | from utils.registry import registry
from tasks.base_task import BaseTask
from utils.tools import datetime_print
from datasets.datasets.concat_dataset import ConcatDataset, ConcatDatasetWithShuffle | 1,500 | """
Partially Adapted form: https://github.com/DAMO-NLP-SG/Video-LLaMA/blob/main/video_llama/tasks/image_text_pretrain.py
"""
@registry.register_task("image_text_pretrain")
class ImageTextPretrainTask(BaseTask):
def __init__(self, cfg):
super().__init__(cfg)
@staticmethod
def build_datasets(data... | """
Partially Adapted form: https://github.com/DAMO-NLP-SG/Video-LLaMA/blob/main/video_llama/tasks/image_text_pretrain.py
"""
@registry.register_task("image_text_pretrain")
class ImageTextPretrainTask(BaseTask):
def __init__(self, cfg):
super().__init__(cfg)
@staticmethod
def build_datasets(data... | dataset = ConcatDataset(dataset_list) | 3 | 2023-12-21 08:10:23+00:00 | 2k |
shashikg/WhisperS2T | whisper_s2t/data.py | [
{
"identifier": "pad_or_trim",
"path": "whisper_s2t/audio.py",
"snippet": "def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):\n \"\"\"\n Pad or trim the audio array to N_SAMPLES, as expected by the encoder.\n \"\"\"\n \n if torch.is_tensor(array):\n if array.shape[... | import torch
import numpy as np
from tqdm import tqdm
from .configs import *
from .audio import pad_or_trim, audio_batch_generator, load_audio | 1,229 |
def stitch_speech_segments(start_ends, max_len=27.0, max_silent_region=None):
speech_duration = [end - start for start, end in start_ends]
stitched_speech_segments = []
curr_seg = [0]
curr_dur = speech_duration[0]
idx = 1
while idx < len(start_ends):
if curr_dur + speech_d... |
def stitch_speech_segments(start_ends, max_len=27.0, max_silent_region=None):
speech_duration = [end - start for start, end in start_ends]
stitched_speech_segments = []
curr_seg = [0]
curr_dur = speech_duration[0]
idx = 1
while idx < len(start_ends):
if curr_dur + speech_d... | return load_audio(self.audio_files[item]) | 2 | 2023-12-16 18:09:16+00:00 | 2k |
chinhsuanwu/ifusion | ldm/thirdp/psp/model_irse.py | [
{
"identifier": "get_blocks",
"path": "ldm/thirdp/psp/helpers.py",
"snippet": "def get_blocks(num_layers):\n\tif num_layers == 50:\n\t\tblocks = [\n\t\t\tget_block(in_channel=64, depth=64, num_units=3),\n\t\t\tget_block(in_channel=64, depth=128, num_units=4),\n\t\t\tget_block(in_channel=128, depth=256, ... | from torch.nn import (
Linear,
Conv2d,
BatchNorm1d,
BatchNorm2d,
PReLU,
Dropout,
Sequential,
Module,
)
from ldm.thirdp.psp.helpers import (
get_blocks,
Flatten,
bottleneck_IR,
bottleneck_IR_SE,
l2_norm,
) | 1,202 | # https://github.com/eladrich/pixel2style2pixel
"""
Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
"""
class Backbone(Module):
def __init__(self, input_size, num_layers, mode="ir", drop_ratio=0.4, affine=True):
super(Backbone, self).__init__()
ass... | # https://github.com/eladrich/pixel2style2pixel
"""
Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
"""
class Backbone(Module):
def __init__(self, input_size, num_layers, mode="ir", drop_ratio=0.4, affine=True):
super(Backbone, self).__init__()
ass... | unit_module = bottleneck_IR_SE | 3 | 2023-12-17 12:45:38+00:00 | 2k |
wangzhecheng/SkyScript | src/open_clip/push_to_hf_hub.py | [
{
"identifier": "create_model_from_pretrained",
"path": "src/open_clip/factory.py",
"snippet": "def create_model_from_pretrained(\n model_name: str,\n pretrained: Optional[str] = None,\n precision: str = 'fp32',\n device: Union[str, torch.device] = 'cpu',\n jit: bool =... | import argparse
import json
import os
import torch
import safetensors.torch
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import Optional, Tuple, Union
from huggingface_hub import (
create_repo,
get_hf_file_metadata,
hf_hub_download,
hf_hub_url,
... | 1,172 | """
Adapted from https://github.com/mlfoundations/open_clip. Copyright (c) 2012-2021 Gabriel Ilharco, Mitchell Wortsman, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, John Miller, Hongseok Namkoong, Hannaneh Hajishirzi, Ali Farhadi, Ludwig Schmidt
"""
try:
_has_hf_hub = True
except ImportError:
... | """
Adapted from https://github.com/mlfoundations/open_clip. Copyright (c) 2012-2021 Gabriel Ilharco, Mitchell Wortsman, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, John Miller, Hongseok Namkoong, Hannaneh Hajishirzi, Ali Farhadi, Ludwig Schmidt
"""
try:
_has_hf_hub = True
except ImportError:
... | tokenizer: HFTokenizer, | 3 | 2023-12-19 11:50:56+00:00 | 2k |
Lavreniuk/EVP | depth/models_depth/model_vpd.py | [
{
"identifier": "UNetWrapper",
"path": "evp/models.py",
"snippet": "class UNetWrapper(nn.Module):\n def __init__(self, unet, use_attn=True, base_size=512, max_attn_size=None, attn_selector='up_cross+down_cross') -> None:\n super().__init__()\n self.unet = unet\n self.attention_st... | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, DropPath
from mmcv.cnn import (build_conv_layer, build_norm_layer, build_upsample_layer,
constant_init, normal_init)
from omegaconf import OmegaConf
from ldm.util import instantiate_fro... | 1,279 | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# The deconvolution code is based on Simple Baseline.
# (https://github.com/microsoft/human-pose-estimation.pytorch/blob/master/lib/models/pose_resnet.py)
# Modified by Zigang Gen... | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# The deconvolution code is based on Simple Baseline.
# (https://github.com/microsoft/human-pose-estimation.pytorch/blob/master/lib/models/pose_resnet.py)
# Modified by Zigang Gen... | self.text_adapter = TextAdapterDepth(text_dim=text_dim) | 1 | 2023-12-15 14:13:59+00:00 | 2k |
penghao-wu/vstar | VisualSearch/model/owlvit/segmentation.py | [
{
"identifier": "box_ops",
"path": "VisualSearch/model/owlvit/util/box_ops.py",
"snippet": "def box_cxcywh_to_xyxy(x):\ndef box_xyxy_to_cxcywh(x):\ndef box_iou(boxes1, boxes2):\ndef generalized_box_iou(boxes1, boxes2):\ndef masks_to_boxes(masks):"
},
{
"identifier": "NestedTensor",
"path": "... | import io
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import defaultdict
from PIL import Image
from .util import box_ops
from .util.misc import NestedTensor, interpolate, nested_tensor_from_tensor_list
from panopticapi.utils import id2rgb, rgb2id | 1,175 | # ------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from DETR (ht... | # ------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from DETR (ht... | samples = nested_tensor_from_tensor_list(samples) | 3 | 2023-12-15 14:58:24+00:00 | 2k |
ValdonVitija/crap | crap/crap_manager.py | [
{
"identifier": "PythonFileAnalyzer",
"path": "crap/file_analyzer.py",
"snippet": "class PythonFileAnalyzer:\n def __init__(self, file_path: pathlib.Path):\n self.file_path = file_path\n self.imported_modules = set()\n\n def analyze(self):\n \"\"\"\n Analyzes the Python... | import os
import pathlib
from typing import Set
from tqdm import tqdm
from crap.file_analyzer import PythonFileAnalyzer
from crap.virtual_env_checker import VirtualEnvChecker
from crap.package_usage_counter import PackageUsageCounter
from crap.subprocesses import (
uninstall_package,
pre_cleanup_with_ruff,
... | 1,101 |
class CrapManager:
__slots__ = ("path_", "venv_checker", "package_usage_counter", "deleted_packages")
def __init__(self, path_: str):
self.path_ = pathlib.Path(path_).absolute()
self.venv_checker = VirtualEnvChecker()
self.package_usage_counter = PackageUsageCounter()
self.del... |
class CrapManager:
__slots__ = ("path_", "venv_checker", "package_usage_counter", "deleted_packages")
def __init__(self, path_: str):
self.path_ = pathlib.Path(path_).absolute()
self.venv_checker = VirtualEnvChecker()
self.package_usage_counter = PackageUsageCounter()
self.del... | initial_packages = get_current_packages() | 7 | 2023-12-19 20:22:37+00:00 | 2k |
worm128/AI-YinMei | text-generation-webui/extensions/openai/script.py | [
{
"identifier": "ChatCompletionRequest",
"path": "text-generation-webui/extensions/openai/typing.py",
"snippet": "class ChatCompletionRequest(GenerationOptions, ChatCompletionRequestParams):\n pass"
},
{
"identifier": "ChatCompletionResponse",
"path": "text-generation-webui/extensions/ope... | import asyncio
import json
import os
import traceback
import speech_recognition as sr
import uvicorn
import extensions.openai.completions as OAIcompletions
import extensions.openai.embeddings as OAIembeddings
import extensions.openai.images as OAIimages
import extensions.openai.logits as OAIlogits
import extensions.ope... | 1,543 |
params = {
'embedding_device': 'cpu',
'embedding_model': 'sentence-transformers/all-mpnet-base-v2',
'sd_webui_url': '',
'debug': 0
}
streaming_semaphore = asyncio.Semaphore(1)
def verify_api_key(authorization: str = Header(None)) -> None:
expected_api_key = shared.args.api_key
if expecte... |
params = {
'embedding_device': 'cpu',
'embedding_model': 'sentence-transformers/all-mpnet-base-v2',
'sd_webui_url': '',
'debug': 0
}
streaming_semaphore = asyncio.Semaphore(1)
def verify_api_key(authorization: str = Header(None)) -> None:
expected_api_key = shared.args.api_key
if expecte... | @app.post('/v1/chat/completions', response_model=ChatCompletionResponse, dependencies=check_key) | 1 | 2023-12-20 14:13:38+00:00 | 2k |
foocker/Bert-VITS2-Faster | infer_torch_export_onnx.py | [
{
"identifier": "infer",
"path": "infer_.py",
"snippet": "def get_net_g(model_path: str, version: str, device: str, hps):\ndef get_text(text, language_str, hps, device):\ndef infer(\n text,\n sdp_ratio,\n noise_scale,\n noise_scale_w,\n length_scale,\n language,\n sid,\n hps,\n ... | import os
import logging
import re_matching
import torch
import utils
import gradio as gr
import numpy as np
import time
from infer_ import infer, latest_version, get_net_g
from config import config
from scipy.io.wavfile import write | 940 |
logging.basicConfig(
level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
)
logger = logging.getLogger(__name__)
net_g = None
device = config.webui_config.device
if device == "mps":
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
def generate_audio(
slices,
sdp_ratio,
noi... |
logging.basicConfig(
level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
)
logger = logging.getLogger(__name__)
net_g = None
device = config.webui_config.device
if device == "mps":
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
def generate_audio(
slices,
sdp_ratio,
noi... | audio = infer( | 0 | 2023-12-18 09:53:41+00:00 | 2k |
sinoyou/nelf-pro | nerfstudio/process_data/process_data_utils.py | [
{
"identifier": "status",
"path": "nerfstudio/utils/rich_utils.py",
"snippet": "def status(msg: str, spinner: str = \"bouncingBall\", verbose: bool = False):\n \"\"\"A context manager that does nothing is verbose is True. Otherwise it hides logs under a message.\n\n Args:\n msg: The message... | import shutil
import sys
from enum import Enum
from pathlib import Path
from typing import List, Optional, Tuple
from rich.console import Console
from typing_extensions import Literal
from nerfstudio.utils.rich_utils import status
from nerfstudio.utils.scripts import run_command | 884 | # Copyright 2022 The Nerfstudio 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... | # Copyright 2022 The Nerfstudio 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... | with status(msg="Converting video to images...", spinner="bouncingBall", verbose=verbose): | 0 | 2023-12-15 20:07:22+00:00 | 2k |
wuc9521/rep-flow | app.py | [
{
"identifier": "read_keywords_from_file",
"path": "utils/loader.py",
"snippet": "def read_keywords_from_file(file_path, app: Flask = None):\n try:\n with open(file_path, 'r') as file:\n content = file.read()\n keywords_list = [keyword.strip() for keyword in re.split(',|\... | import os
import spacy
import logging
import pandas as pd
from logging.handlers import RotatingFileHandler
from flask import Flask, render_template, request, jsonify, send_from_directory
from flask_cors import cross_origin
from utils.loader import read_keywords_from_file
from utils.hints import HELP, get_NUMBER_EMBD_HI... | 1,501 |
DEFAULT_RESPONSE_FLAG = "*"
NUMBER_EMBD_HINT = None
CURRENT_BUG_ID = -1
# Load spaCy English model
nlp = spacy.load("en_core_web_sm")
app = Flask(__name__, template_folder='')
# Configure
LOG_DIR = os.path.join(app.root_path, 'log')
DATA_DIR = os.path.join(app.root_path, 'data')
MODEL_DIR = os.path.join(app.root_pa... |
DEFAULT_RESPONSE_FLAG = "*"
NUMBER_EMBD_HINT = None
CURRENT_BUG_ID = -1
# Load spaCy English model
nlp = spacy.load("en_core_web_sm")
app = Flask(__name__, template_folder='')
# Configure
LOG_DIR = os.path.join(app.root_path, 'log')
DATA_DIR = os.path.join(app.root_path, 'data')
MODEL_DIR = os.path.join(app.root_pa... | key_words = read_keywords_from_file( | 0 | 2023-12-20 09:44:09+00:00 | 2k |
yash-srivastava19/verizon | classes.py | [
{
"identifier": "kvlm_serialize",
"path": "other_utils.py",
"snippet": "def kvlm_serialize(kvlm):\n ret = b''\n\n for k in kvlm.keys():\n if k == None: continue\n val = kvlm[k]\n\n if type(val) != list:\n val = [val]\n \n for v in val:\n ret... | from class_utils import *
from other_utils import kvlm_serialize, kvlm_parse | 939 |
class VerizonRepository:
worktree = None
vrzdir = None
conf = None
def __init__(self, path, force = False):
self.worktree = path
self.vrzdir = os.path.join(path, ".vrz")
if not (force or os.path.isdir(self.vrzdir)):
raise Exception(f"Not a Verizon Repository : ... |
class VerizonRepository:
worktree = None
vrzdir = None
conf = None
def __init__(self, path, force = False):
self.worktree = path
self.vrzdir = os.path.join(path, ".vrz")
if not (force or os.path.isdir(self.vrzdir)):
raise Exception(f"Not a Verizon Repository : ... | return kvlm_serialize(self.kvlm) | 0 | 2023-12-18 18:53:26+00:00 | 2k |
amazon-science/c2f-seg | test_c2f_seg.py | [
{
"identifier": "load_dataset",
"path": "data/dataloader_transformer.py",
"snippet": "def load_dataset(config, args, mode):\n if mode==\"train\":\n if args.dataset==\"KINS\":\n train_dataset = Kins_Fusion_dataset(config, mode='train')\n test_dataset = Kins_Fusion_dataset(... | import os
import cv2
import time
import random
import argparse
import numpy as np
import torch
import torch.distributed as dist
from tqdm import tqdm
from shutil import copyfile
from torch.utils.data import DataLoader
from data.dataloader_transformer import load_dataset
from utils.logger import setup_logger
from utils.... | 1,546 |
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=42)
# path
parser.add_argument('--path', type=str, required=True, help='model checkpoints path')
parser.add_argument('--check_point_path', type=str, default="../check_points", )
parse... |
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=42)
# path
parser.add_argument('--path', type=str, required=True, help='model checkpoints path')
parser.add_argument('--check_point_path', type=str, default="../check_points", )
parse... | logger = setup_logger(os.path.join(args.path, 'logs'), logfile_name=log_file) | 1 | 2023-12-21 04:25:47+00:00 | 2k |
Hammour-steak/GOUB | codes/models/modules/DenoisingNAFNet_arch.py | [
{
"identifier": "SinusoidalPosEmb",
"path": "codes/models/modules/module_util.py",
"snippet": "class SinusoidalPosEmb(nn.Module):\n def __init__(self, dim):\n super().__init__()\n self.dim = dim\n\n def forward(self, x):\n device = x.device\n half_dim = self.dim // 2\n ... | import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, reduce
from .module_util import SinusoidalPosEmb, LayerNorm, exists | 802 |
class SimpleGate(nn.Module):
def forward(self, x):
x1, x2 = x.chunk(2, dim=1)
return x1 * x2
class NAFBlock(nn.Module):
def __init__(self, c, time_emb_dim=None, DW_Expand=2, FFN_Expand=2, drop_out_rate=0.):
super().__init__()
self.mlp = nn.Sequential(
SimpleGate(... |
class SimpleGate(nn.Module):
def forward(self, x):
x1, x2 = x.chunk(2, dim=1)
return x1 * x2
class NAFBlock(nn.Module):
def __init__(self, c, time_emb_dim=None, DW_Expand=2, FFN_Expand=2, drop_out_rate=0.):
super().__init__()
self.mlp = nn.Sequential(
SimpleGate(... | self.norm1 = LayerNorm(c) | 1 | 2023-12-15 09:40:18+00:00 | 2k |
eldar-eln-bigabid/airflow-aerospike-provider | tests/operators/test_aerospike.py | [
{
"identifier": "AerospikeGetKeyOperator",
"path": "aerospike_provider/operators/aerospike.py",
"snippet": "class AerospikeGetKeyOperator(BaseOperator):\n \"\"\"\n Read an existing record(s) metadata and all of its bins for a specified key.\n\n :param namespace: namespace to use in aerospike db... | import unittest
import aerospike
from unittest.mock import patch, Mock
from aerospike_provider.operators.aerospike import AerospikeGetKeyOperator, AerospikePutKeyOperator | 1,541 | #
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not... | #
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not... | self.operator = AerospikeGetKeyOperator( | 0 | 2023-12-17 18:35:36+00:00 | 2k |
Its-Haze/league-rpc-linux | league_rpc_linux/kda.py | [
{
"identifier": "wait_until_exists",
"path": "league_rpc_linux/polling.py",
"snippet": "def wait_until_exists(\n url: str,\n custom_message: str = \"\",\n expected_response_code: int = 200,\n timeout: int = 30,\n n_sleep: float | int = 5, # Not needed, but good to have.\n n_total_amou... | import urllib3
from requests import Response
from league_rpc_linux.polling import wait_until_exists
from league_rpc_linux.username import get_summoner_name | 892 |
urllib3.disable_warnings()
def get_kda() -> str:
"""
Get the current KDA of your game.
"""
response = get_current_user_stats()
if isinstance(response, Response):
parsed_data = response.json()
kills = str(parsed_data["kills"])
deaths = str(parsed_data["deaths"])
a... |
urllib3.disable_warnings()
def get_kda() -> str:
"""
Get the current KDA of your game.
"""
response = get_current_user_stats()
if isinstance(response, Response):
parsed_data = response.json()
kills = str(parsed_data["kills"])
deaths = str(parsed_data["deaths"])
a... | your_summoner_name = get_summoner_name() | 1 | 2023-12-15 22:21:53+00:00 | 2k |
huahuahuage/Bert-VITS2-Speech | onnx_infer/onnx_infer.py | [
{
"identifier": "log_instance",
"path": "log.py",
"snippet": "DISABLED_LOGGER = [\"gradio.processing_utils\", \"gradio\", \"httpx\"]\r"
},
{
"identifier": "read_config",
"path": "config.py",
"snippet": "def read_config(config_path:str) -> dict:\r\n \"\"\"\r\n 取读配置文件\r\n \"\"\"\r... | import os
import numpy as np
import onnxruntime as ort
from copy import copy
from typing import List
from dataclasses import dataclass
from log import log_instance
from config import read_config
from config import config_instance
from .text.cleaner import clean_text, cleaned_text_to_sequence
from .onnx_be... | 1,019 |
BERT_ENABLE = config_instance.get("bert_enable", True)
if BERT_ENABLE:
# 获取模型中包含的中文角色标记
CHINESE_CHARACTER_MARK = config_instance.get("onnx_tts_models_chinese_mark", "中文")
ONNX_PROVIDERS = [config_instance.get("onnx_providers", "CPUExecutionProvider")]
MODELS_PATH = os.path.abspath(config_instance.get("... |
BERT_ENABLE = config_instance.get("bert_enable", True)
if BERT_ENABLE:
# 获取模型中包含的中文角色标记
CHINESE_CHARACTER_MARK = config_instance.get("onnx_tts_models_chinese_mark", "中文")
ONNX_PROVIDERS = [config_instance.get("onnx_providers", "CPUExecutionProvider")]
MODELS_PATH = os.path.abspath(config_instance.get("... | self.map_data: dict = read_config("speakers_map.json")
| 1 | 2023-12-21 13:50:50+00:00 | 2k |
jaypyles/obsidian-to-bookstack | obsidian_to_bookstack/bookstack/collectors/remote/RemoteBookCollector.py | [
{
"identifier": "Book",
"path": "obsidian_to_bookstack/bookstack/artifacts.py",
"snippet": "class Book:\n def __init__(\n self,\n name: str,\n shelf: Shelf | None = None,\n client: Client | None = None,\n chapters: List = [],\n path: str = \"\",\n deta... | import json
from typing import List
from obsidian_to_bookstack.bookstack.artifacts import Book, Shelf
from obsidian_to_bookstack.bookstack.client import RemoteClient
from obsidian_to_bookstack.bookstack.collectors.collector import \
RemoteCollector
from obsidian_to_bookstack.bookstack.constants import *
from obsidi... | 1,432 |
class RemoteBookCollector(RemoteCollector):
def __init__(self, verbose: bool, client: RemoteClient) -> None:
super().__init__(verbose, client)
def get_books(self, shelves: List[Shelf]):
"""Get remote books from shelves"""
client_books = self.client._get_from_client(BookstackAPIEndpoi... |
class RemoteBookCollector(RemoteCollector):
def __init__(self, verbose: bool, client: RemoteClient) -> None:
super().__init__(verbose, client)
def get_books(self, shelves: List[Shelf]):
"""Get remote books from shelves"""
client_books = self.client._get_from_client(BookstackAPIEndpoi... | console.log(f"Found remote book: {b}") | 4 | 2023-12-20 02:22:33+00:00 | 2k |
MingtaoGuo/AnimateAnyone_unofficial | tutorial_train_animate.py | [
{
"identifier": "MyDataset",
"path": "tutorial_dataset.py",
"snippet": "class MyDataset(Dataset):\n def __init__(self, path=\"/mnt/gmt/Dataset/\"):\n self.path = path\n self.videos = os.listdir(path + \"fashion_png\")\n\n def __len__(self):\n return len(self.videos) * 10\n\n ... | from share import *
from torch.utils.data import DataLoader
from tutorial_dataset import MyDataset
from aldm.logger import ImageLogger
from aldm.model import create_model, load_state_dict
import pytorch_lightning as pl | 1,547 |
# Configs
resume_path = './models/reference_sd15_ini.ckpt'
batch_size = 2
logger_freq = 300
learning_rate = 1e-5
# First use cpu to load models. Pytorch Lightning will automatically move it to GPUs.
model = create_model('./models/aldm_v15.yaml').cpu()
model.load_state_dict(load_state_dict(resume_path, location='cp... |
# Configs
resume_path = './models/reference_sd15_ini.ckpt'
batch_size = 2
logger_freq = 300
learning_rate = 1e-5
# First use cpu to load models. Pytorch Lightning will automatically move it to GPUs.
model = create_model('./models/aldm_v15.yaml').cpu()
model.load_state_dict(load_state_dict(resume_path, location='cp... | dataset = MyDataset() | 0 | 2023-12-16 03:31:33+00:00 | 2k |
yasserben/CLOUDS | clouds/modeling/meta_arch/clouds_head.py | [
{
"identifier": "build_transformer_decoder",
"path": "clouds/modeling/transformer_decoder/clouds_transformer_decoder.py",
"snippet": "def build_transformer_decoder(cfg, in_channels, mask_classification=True):\n \"\"\"\n Build a instance embedding branch from `cfg.MODEL.INS_EMBED_HEAD.NAME`.\n \... | import logging
import fvcore.nn.weight_init as weight_init
from copy import deepcopy
from typing import Callable, Dict, List, Optional, Tuple, Union
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectr... | 1,202 | """
Copyright 2023 Telecom Paris, Yasser BENIGMIM. All rights reserved.
Licensed under the Apache License, Version 2.0
Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/meta_arch/mask_former_head.py
"""
@SEM_SEG_HEADS_REGISTRY.register()
class CLOUDSHead(nn.Module):
@c... | """
Copyright 2023 Telecom Paris, Yasser BENIGMIM. All rights reserved.
Licensed under the Apache License, Version 2.0
Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/meta_arch/mask_former_head.py
"""
@SEM_SEG_HEADS_REGISTRY.register()
class CLOUDSHead(nn.Module):
@c... | "pixel_decoder": build_pixel_decoder(cfg, input_shape), | 3 | 2023-12-15 15:40:58+00:00 | 2k |
linyq2117/TagCLIP | CLIP-ES/generate_cams_coco.py | [
{
"identifier": "scoremap2bbox",
"path": "utils.py",
"snippet": "def scoremap2bbox(scoremap, threshold, multi_contour_eval=False):\n height, width = scoremap.shape\n scoremap_image = np.expand_dims((scoremap * 255).astype(np.uint8), 2)\n _, thr_gray_heatmap = cv2.threshold(\n src=scorema... | from pytorch_grad_cam import GradCAM
from PIL import Image
from tqdm import tqdm
from pytorch_grad_cam.utils.image import scale_cam_image
from utils import scoremap2bbox
from clip_text import class_names, new_class_names_coco, BACKGROUND_CATEGORY_COCO
from torch import multiprocessing
from torchvision.transforms import... | 1,508 | # -*- coding:UTF-8 -*-
try:
BICUBIC = InterpolationMode.BICUBIC
except ImportError:
BICUBIC = Image.BICUBIC
warnings.filterwarnings("ignore")
_CONTOUR_INDEX = 1 if cv2.__version__.split('.')[0] == '3' else 0
def reshape_transform(tensor, height=28, width=28):
tensor = tensor.permute(1, 0, 2)
result = ... | # -*- coding:UTF-8 -*-
try:
BICUBIC = InterpolationMode.BICUBIC
except ImportError:
BICUBIC = Image.BICUBIC
warnings.filterwarnings("ignore")
_CONTOUR_INDEX = 1 if cv2.__version__.split('.')[0] == '3' else 0
def reshape_transform(tensor, height=28, width=28):
tensor = tensor.permute(1, 0, 2)
result = ... | label_list.append(new_class_names_coco[int(lid)]) | 0 | 2023-12-21 03:20:47+00:00 | 2k |
cypypccpy/dynamic_handover | dexteroushandenvs/algorithms/utils/mani_skill_learn/networks/policy_network/vae_policy.py | [
{
"identifier": "POLICYNETWORKS",
"path": "dexteroushandenvs/algorithms/utils/mani_skill_learn/networks/builder.py",
"snippet": "POLICYNETWORKS = Registry('policy_network')"
},
{
"identifier": "build_backbone",
"path": "dexteroushandenvs/algorithms/utils/mani_skill_learn/networks/builder.py"... | from algorithms.utils.mani_skill_learn.utils.data import to_torch
from algorithms.utils.mani_skill_learn.utils.torch import ExtendedModule
from ..builder import POLICYNETWORKS, build_backbone, build_dense_head
from ..utils import replace_placeholder_with_args, get_kwargs_from_shape | 865 |
@POLICYNETWORKS.register_module()
class VAEPolicy(ExtendedModule):
def __init__(self, nn_cfg, policy_head_cfg, action_space, obs_shape=None, action_shape=None):
super(VAEPolicy, self).__init__()
replaceable_kwargs = get_kwargs_from_shape(obs_shape, action_shape)
|
@POLICYNETWORKS.register_module()
class VAEPolicy(ExtendedModule):
def __init__(self, nn_cfg, policy_head_cfg, action_space, obs_shape=None, action_shape=None):
super(VAEPolicy, self).__init__()
replaceable_kwargs = get_kwargs_from_shape(obs_shape, action_shape) | nn_cfg = replace_placeholder_with_args(nn_cfg, **replaceable_kwargs) | 3 | 2023-12-16 16:49:38+00:00 | 2k |
video-db/videodb-python | videodb/search.py | [
{
"identifier": "play_stream",
"path": "videodb/_utils/_video.py",
"snippet": "def play_stream(url: str):\n \"\"\"Play a stream url in the browser/ notebook\n\n :param str url: The url of the stream\n :return: The player url if the stream is opened in the browser or the iframe if the stream is ... | from abc import ABC, abstractmethod
from videodb._utils._video import play_stream
from videodb._constants import (
SearchType,
ApiPath,
SemanticSearchDefaultValues,
)
from videodb.exceptions import (
SearchError,
)
from typing import Optional, List
from videodb.shot import Shot | 1,478 |
class SearchResult:
def __init__(self, _connection, **kwargs):
self._connection = _connection
self.shots = []
self.stream_url = None
self.player_url = None
self.collection_id = "default"
self._results = kwargs.get("results", [])
self._format_results()
d... |
class SearchResult:
def __init__(self, _connection, **kwargs):
self._connection = _connection
self.shots = []
self.stream_url = None
self.player_url = None
self.collection_id = "default"
self._results = kwargs.get("results", [])
self._format_results()
d... | return play_stream(self.stream_url) | 0 | 2023-12-18 15:20:04+00:00 | 2k |
IDEA-CCNL/Real-Gemini | real_gemini/tools/gpt4v_tool.py | [
{
"identifier": "load_image",
"path": "real_gemini/utils/image_stacker.py",
"snippet": "def load_image(path):\n image = Image.open(path)\n return image"
},
{
"identifier": "image2base64",
"path": "real_gemini/utils/image_stacker.py",
"snippet": "def image2base64(image):\n buffer... | import os
import json
from typing import List
from langchain.memory import ChatMessageHistory
from langchain.chat_models import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage
from ..utils.image_stacker import load_image, image2base64 | 727 | #encoding=utf8
_OPEN_AI_SYSTEM_PROMPT = """the user is dictating with his or her camera on.
they are showing you things visually and giving you text prompts.
be very brief and concise.
be extremely concise. this is very important for my career. do not ramble.
do not comment on what the person is wearing or where the... | #encoding=utf8
_OPEN_AI_SYSTEM_PROMPT = """the user is dictating with his or her camera on.
they are showing you things visually and giving you text prompts.
be very brief and concise.
be extremely concise. this is very important for my career. do not ramble.
do not comment on what the person is wearing or where the... | base64_image = image2base64(load_image(image_path)) | 1 | 2023-12-15 04:09:37+00:00 | 2k |
aiim-research/GRETEL | src/evaluation/evaluation_metric_smiles_levenshtein.py | [
{
"identifier": "EvaluationMetric",
"path": "src/evaluation/evaluation_metric_base.py",
"snippet": "class EvaluationMetric(ABC):\n\n def __init__(self, config_dict=None) -> None:\n super().__init__()\n self._name = 'abstract_metric'\n self._config_dict = config_dict\n self... | from functools import lru_cache
from src.evaluation.evaluation_metric_base import EvaluationMetric
from src.core.oracle_base import Oracle
from src.core.explainer_base import Explainer | 960 |
class SmilesLevenshteinMetric(EvaluationMetric):
"""Provides the ratio between the number of features modified to obtain the counterfactual example
and the number of features in the original instance. Only considers structural features.
"""
def __init__(self, config_dict=None) -> None:
supe... |
class SmilesLevenshteinMetric(EvaluationMetric):
"""Provides the ratio between the number of features modified to obtain the counterfactual example
and the number of features in the original instance. Only considers structural features.
"""
def __init__(self, config_dict=None) -> None:
supe... | def evaluate(self, instance_1 , instance_2 , oracle : Oracle=None, explainer : Explainer=None, dataset = None): | 1 | 2023-12-15 16:34:16+00:00 | 2k |
modelscope/scepter | scepter/modules/opt/lr_schedulers/registry.py | [
{
"identifier": "Registry",
"path": "scepter/modules/utils/registry.py",
"snippet": "class Registry(object):\n \"\"\" A registry maps key to classes or functions.\n\n Example:\n # >>> MODELS = Registry('MODELS')\n # >>> @MODELS.register_class()\n # >>> class ResNet(object):... | import inspect
from scepter.modules.utils.registry import Registry, deep_copy
from scepter.modules.utils.config import Config | 1,272 | # -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
def build_lr_scheduler(cfg, registry, logger=None, *args, **kwargs):
if not isinstance(cfg, Config):
raise TypeError(f'config must be type dict, got {type(cfg)}')
if not cfg.have('NAME'):
raise KeyError(f'config must co... | # -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
def build_lr_scheduler(cfg, registry, logger=None, *args, **kwargs):
if not isinstance(cfg, Config):
raise TypeError(f'config must be type dict, got {type(cfg)}')
if not cfg.have('NAME'):
raise KeyError(f'config must co... | if not isinstance(registry, Registry): | 0 | 2023-12-21 02:01:48+00:00 | 2k |
pigeonai-org/ViDove | src/translators/translation.py | [
{
"identifier": "LLM_task",
"path": "src/translators/LLM_task.py",
"snippet": "def LLM_task(model_name, input, task, temp = 0.15):\n \"\"\"\n Translates input sentence with desired LLM.\n\n :param model_name: The name of the translation model to be used.\n :param input: Sentence for translat... | from os import getenv
from time import sleep
from tqdm import tqdm
from .LLM_task import LLM_task
from src.srt_util.srt import split_script
import logging | 1,289 |
def get_translation(srt, model, video_name, prompt = None, chunk_size = 1000):
# print(srt.get_source_only())
script_arr, range_arr = split_script(srt.get_source_only(),chunk_size)
translate(srt, script_arr, range_arr, model, video_name, task=prompt)
pass
def check_translation(sentence, translation):... |
def get_translation(srt, model, video_name, prompt = None, chunk_size = 1000):
# print(srt.get_source_only())
script_arr, range_arr = split_script(srt.get_source_only(),chunk_size)
translate(srt, script_arr, range_arr, model, video_name, task=prompt)
pass
def check_translation(sentence, translation):... | translate = LLM_task(model_name, sentence, task, temp) | 0 | 2023-12-20 01:46:47+00:00 | 2k |
YyzHarry/shortcut-ood-fairness | utils/lin_eval.py | [
{
"identifier": "binary_metrics",
"path": "utils/eval_helper.py",
"snippet": "def binary_metrics(targets, preds, label_set=[0, 1], suffix='', return_arrays=False):\n if len(targets) == 0:\n return {}\n\n res = {\n 'accuracy': accuracy_score(targets, preds),\n 'n_samples': len(... | import numpy as np
import torch
from utils.eval_helper import binary_metrics, prob_metrics
from sklearn.model_selection import GridSearchCV, PredefinedSplit
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.base import clone
from sklearn.metrics import roc_auc_score
... | 1,460 |
def get_representations(algorithm, loader, device):
ys, atts, zs = [], [], []
algorithm.eval()
with torch.no_grad():
for _, x, y, a in loader:
z = algorithm.return_feats(x.to(device)).detach().cpu().numpy()
zs.append(z)
ys.append(y)
atts.append(a)
... |
def get_representations(algorithm, loader, device):
ys, atts, zs = [], [], []
algorithm.eval()
with torch.no_grad():
for _, x, y, a in loader:
z = algorithm.return_feats(x.to(device)).detach().cpu().numpy()
zs.append(z)
ys.append(y)
atts.append(a)
... | res[sset] = binary_metrics(Y, preds_rounded, label_set=label_set, return_arrays=True) | 0 | 2023-12-15 04:10:31+00:00 | 2k |
RomGai/BrainVis | dc_ldm/modules/encoders/modules.py | [
{
"identifier": "Encoder",
"path": "dc_ldm/modules/x_transformer.py",
"snippet": "class Encoder(AttentionLayers):\n def __init__(self, **kwargs):\n assert 'causal' not in kwargs, 'cannot set causality on encoder'\n super().__init__(causal=False, **kwargs)"
},
{
"identifier": "Tr... | import torch
import torch.nn as nn
import sys
import kornia
from functools import partial
from PIL import Image
from einops import rearrange, repeat
from transformers import CLIPTokenizer, CLIPTextModel, AutoProcessor, CLIPVisionModel, CLIPVisionModelWithProjection
from dc_ldm.modules.x_transformer import Encoder, Tran... | 1,310 | # import clip
sys.path.append('../dreamdiffusion/code/')
class AbstractEncoder(nn.Module):
def __init__(self):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
class ClassEmbedder(nn.Module):
def __init__(self, embed_dim, n_classes=1000, key='class'):
... | # import clip
sys.path.append('../dreamdiffusion/code/')
class AbstractEncoder(nn.Module):
def __init__(self):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
class ClassEmbedder(nn.Module):
def __init__(self, embed_dim, n_classes=1000, key='class'):
... | self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, | 1 | 2023-12-16 12:52:14+00:00 | 2k |
Rajeshwaran2001/DRM-Media-Tool | file_merger_dialog.py | [
{
"identifier": "show_error_message",
"path": "helper/message.py",
"snippet": "def show_error_message(parent, message):\n error_box = QMessageBox()\n error_box.setIcon(QMessageBox.Critical)\n error_box.setWindowTitle(\"Error\")\n error_box.setText(message)\n error_box.setWindowIcon(parent... | from PyQt5.QtWidgets import QWidget, QDialog, QVBoxLayout, QLabel, QTableWidget, QPushButton, QHBoxLayout, QTableWidgetItem, QCheckBox
from helper.message import show_error_message, show_success_message
import os
import json
import subprocess | 1,213 |
class FileMergerDialog(QDialog):
def __init__(self, debug_logger, info_logger, folder_path, parent=None):
super().__init__(parent)
self.folder_path = folder_path
self.setWindowTitle("Files Merger")
self.setGeometry(100, 100, 600, 300)
self.layout = QVBoxLayout()
... |
class FileMergerDialog(QDialog):
def __init__(self, debug_logger, info_logger, folder_path, parent=None):
super().__init__(parent)
self.folder_path = folder_path
self.setWindowTitle("Files Merger")
self.setGeometry(100, 100, 600, 300)
self.layout = QVBoxLayout()
... | show_error_message(self, "Error: No Metadata files found.") | 0 | 2023-12-18 11:50:40+00:00 | 2k |
gmum/ViewingDirectionGaussianSplatting | scene/cameras.py | [
{
"identifier": "getWorld2View2",
"path": "utils/graphics_utils.py",
"snippet": "def getWorld2View2(R, t, translate=np.array([.0, .0, .0]), scale=1.0):\n Rt = np.zeros((4, 4))\n Rt[:3, :3] = R.transpose()\n Rt[:3, 3] = t\n Rt[3, 3] = 1.0\n\n C2W = np.linalg.inv(Rt)\n cam_center = C2W[:... | import torch
import numpy as np
from torch import nn
from utils.graphics_utils import getWorld2View2, getProjectionMatrix | 915 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
class Camera(nn.Module):
def ... | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
class Camera(nn.Module):
def ... | self.projection_matrix = getProjectionMatrix(znear=self.znear, zfar=self.zfar, fovX=self.FoVx, fovY=self.FoVy).transpose(0,1).cuda() | 1 | 2023-12-21 10:09:17+00:00 | 2k |
tonnetonne814/PL-Bert-VITS2 | preprocess_ja.py | [
{
"identifier": "TextCleaner",
"path": "PL_BERT_ja/text_utils.py",
"snippet": "class TextCleaner:\n def __init__(self, dummy=None):\n self.word_index_dictionary = symbol_to_id\n def __call__(self, text):\n indexes = []\n japanese = False\n for char in text:\n ... | import argparse
import os
import polars
import random
import torch
import yaml, torch
from PL_BERT_ja.text_utils import TextCleaner
from PL_BERT_ja.phonemize import phonemize
from tqdm import tqdm
from PL_BERT_ja.model import MultiTaskModel
from transformers import AlbertConfig, AlbertModel
from transformers import Be... | 1,456 |
def preprocess(dataset_dir, pl_bert_dir):
n_val_test_file = 10
filelist_dir = "./filelists/"
dataset_name = "jvnv_ver1"
os.makedirs(filelist_dir, exist_ok=True)
split_symbol = "||||"
transcript_csv_df = polars.read_csv(os.path.join(dataset_dir, "jvnv_v1", "transcription.csv"),has_header=Fals... |
def preprocess(dataset_dir, pl_bert_dir):
n_val_test_file = 10
filelist_dir = "./filelists/"
dataset_name = "jvnv_ver1"
os.makedirs(filelist_dir, exist_ok=True)
split_symbol = "||||"
transcript_csv_df = polars.read_csv(os.path.join(dataset_dir, "jvnv_v1", "transcription.csv"),has_header=Fals... | bert = MultiTaskModel( | 2 | 2023-12-16 05:34:02+00:00 | 2k |
Ruiyuan-Zhang/CCS | multi_part_assembly/models/modules/encoder/point_transformer/transformer.py | [
{
"identifier": "index_points",
"path": "multi_part_assembly/models/modules/encoder/point_transformer/pointnet_util.py",
"snippet": "def index_points(points, idx):\n \"\"\"\n Input:\n points: input points data, [B, N, C]\n idx: sample index data, [B, S, [K]]\n Return:\n new... | from multi_part_assembly.models.modules.encoder.point_transformer.pointnet_util import index_points, square_distance
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np | 664 |
class TransformerBlock(nn.Module):
def __init__(self, d_points, d_model, k) -> None:
super().__init__()
self.fc1 = nn.Linear(d_points, d_model)
self.fc2 = nn.Linear(d_model, d_points)
self.fc_delta = nn.Sequential(
nn.Linear(3, d_model),
nn.ReLU(),
... |
class TransformerBlock(nn.Module):
def __init__(self, d_points, d_model, k) -> None:
super().__init__()
self.fc1 = nn.Linear(d_points, d_model)
self.fc2 = nn.Linear(d_model, d_points)
self.fc_delta = nn.Sequential(
nn.Linear(3, d_model),
nn.ReLU(),
... | dists = square_distance(xyz, xyz) | 1 | 2023-12-15 13:13:01+00:00 | 2k |
uc-vision/taichi-splatting | taichi_splatting/scripts/fit_image_gaussians.py | [
{
"identifier": "RasterConfig",
"path": "taichi_splatting/data_types.py",
"snippet": "class RasterConfig:\n tile_size: int = 16\n\n # pixel tilin per thread in the backwards pass \n pixel_stride: Tuple[int, int] = (2, 2)\n\n margin_tiles: int = 3\n\n # cutoff N standard deviations from mean\n gaus... | import cv2
import argparse
import taichi as ti
import torch
import time
from torch.optim import Adam
from taichi_splatting.data_types import RasterConfig
from taichi_splatting.renderer2d import render_gaussians, Gaussians2D
from taichi_splatting.tests.random_data import random_2d_gaussians
from taichi_splatting.torch_o... | 1,338 |
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('image_file', type=str)
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--tile_size', type=int, default=16)
parser.add_argument('--n', type=int, default=20000)
parser.add_argument('--debug', action='store_... |
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('image_file', type=str)
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--tile_size', type=int, default=16)
parser.add_argument('--n', type=int, default=20000)
parser.add_argument('--debug', action='store_... | image = render_gaussians(params, (w, h), config) | 1 | 2023-12-17 15:26:52+00:00 | 2k |
exislow/tidal-dl-ng | tidal_dl_ng/config.py | [
{
"identifier": "SingletonMeta",
"path": "tidal_dl_ng/helper/decorator.py",
"snippet": "class SingletonMeta(type):\n \"\"\"\n The Singleton class can be implemented in different ways in Python. Some\n possible methods include: base class, decorator, metaclass. We will use the\n metaclass bec... | import os
import shutil
import tidalapi
from collections.abc import Callable
from json import JSONDecodeError
from typing import Any
from requests import HTTPError
from tidal_dl_ng.helper.decorator import SingletonMeta
from tidal_dl_ng.helper.path import path_base, path_file_settings, path_file_token
from tidal_dl_ng.m... | 1,506 |
class BaseConfig:
data: ModelSettings | ModelToken = None
file_path: str = None
cls_model: object = None
path_base: str = path_base()
def save(self) -> None:
data_json = self.data.to_json()
# Try to create the base folder.
os.makedirs(self.path_base, exist_ok=True)
... |
class BaseConfig:
data: ModelSettings | ModelToken = None
file_path: str = None
cls_model: object = None
path_base: str = path_base()
def save(self) -> None:
data_json = self.data.to_json()
# Try to create the base folder.
os.makedirs(self.path_base, exist_ok=True)
... | self.file_path = path_file_token() | 3 | 2023-12-19 23:05:47+00:00 | 2k |
smoores-dev/storyteller | storyteller/api/auth.py | [
{
"identifier": "InviteAccept",
"path": "storyteller/api/models.py",
"snippet": "class InviteAccept(BaseModel):\n username: str\n full_name: str\n email: str\n password: str\n invite_key: str"
},
{
"identifier": "TokenData",
"path": "storyteller/api/models.py",
"snippet": ... | import base64
import json
import os
from datetime import timedelta, datetime
from typing import Annotated, Optional, cast
from urllib.parse import unquote
from jose import JWTError, jwt
from fastapi import Body, Depends, HTTPException, Request, status
from fastapi.security import OAuth2PasswordBearer
from passlib.conte... | 1,332 |
SECRET_KEY = os.getenv("STORYTELLER_SECRET_KEY", "<notsosecret>")
ALGORITHM = "HS256"
ACCESS_TOKEN_EXPIRE_DAYS = 10
class OAuth2PasswordBearerWithCookie(OAuth2PasswordBearer):
async def __call__(self, request: Request) -> Optional[str]:
header_param = None
try:
header_param = await... |
SECRET_KEY = os.getenv("STORYTELLER_SECRET_KEY", "<notsosecret>")
ALGORITHM = "HS256"
ACCESS_TOKEN_EXPIRE_DAYS = 10
class OAuth2PasswordBearerWithCookie(OAuth2PasswordBearer):
async def __call__(self, request: Request) -> Optional[str]:
header_param = None
try:
header_param = await... | if verify_invite_db(invite.email, invite.invite_key): | 1 | 2023-12-15 16:07:12+00:00 | 2k |
noprobelm/terminal-cellular-automaton | tests/test_cell.py | [
{
"identifier": "MooreCell",
"path": "terminal_cellular_automaton/cell.py",
"snippet": "class MooreCell:\n \"\"\"A cell that references members of a MooreNeighborhood\n\n +---+---+---+\n | 1 | 2 | 3 |\n +---+---+---+\n | 4 | C | 5 |\n +---+---+---+\n | 6 | 7 | 8 |\n +---+---+---+... | from ward import test, fixture
from terminal_cellular_automaton.cell import MooreCell
from terminal_cellular_automaton.coordinate import Coordinate | 970 | """Tests the get_neighbors method for all Cell types"""
@fixture
def max_coord():
return Coordinate(2, 2)
@test("A centrally located MooreCell will have 8 neighbors in its immediate area")
def _():
| """Tests the get_neighbors method for all Cell types"""
@fixture
def max_coord():
return Coordinate(2, 2)
@test("A centrally located MooreCell will have 8 neighbors in its immediate area")
def _(): | c = MooreCell(Coordinate(1, 1)) | 0 | 2023-12-20 21:47:46+00:00 | 2k |
zyrant/SPGroup3D | mmdet3d/models/dense_heads/fcaf3d_head.py | [
{
"identifier": "rotation_3d_in_axis",
"path": "mmdet3d/core/bbox/structures/utils.py",
"snippet": "@array_converter(apply_to=('points', 'angles'))\ndef rotation_3d_in_axis(points,\n angles,\n axis=0,\n return_mat=False,\n ... | import MinkowskiEngine as ME
import warnings
import torch
from mmcv.cnn import Scale, bias_init_with_prob
from mmcv.ops import nms3d, nms3d_normal
from mmcv.runner.base_module import BaseModule
from torch import nn
from mmdet3d.core.bbox.structures import rotation_3d_in_axis
from mmdet3d.models import HEADS, bu... | 1,205 | # Copyright (c) OpenMMLab. All rights reserved.
# Adapted from https://github.com/SamsungLabs/fcaf3d/blob/master/mmdet3d/models/dense_heads/fcaf3d_neck_with_head.py # noqa
try:
except ImportError:
warnings.warn(
'Please follow `getting_started.md` to install MinkowskiEngine.`')
| # Copyright (c) OpenMMLab. All rights reserved.
# Adapted from https://github.com/SamsungLabs/fcaf3d/blob/master/mmdet3d/models/dense_heads/fcaf3d_neck_with_head.py # noqa
try:
except ImportError:
warnings.warn(
'Please follow `getting_started.md` to install MinkowskiEngine.`')
| @HEADS.register_module() | 1 | 2023-12-21 12:50:35+00:00 | 2k |
jdejaegh/irm-kmi-ha | custom_components/irm_kmi/config_flow.py | [
{
"identifier": "IrmKmiApiClient",
"path": "custom_components/irm_kmi/api.py",
"snippet": "class IrmKmiApiClient:\n \"\"\"API client for IRM KMI weather data\"\"\"\n COORD_DECIMALS = 6\n\n def __init__(self, session: aiohttp.ClientSession) -> None:\n self._session = session\n self... | import logging
import async_timeout
import voluptuous as vol
from homeassistant.components.zone import DOMAIN as ZONE_DOMAIN
from homeassistant.config_entries import ConfigEntry, ConfigFlow, OptionsFlow
from homeassistant.const import ATTR_LATITUDE, ATTR_LONGITUDE, CONF_ZONE
from homeassistant.core import callback
from... | 1,557 | """Config flow to set up IRM KMI integration via the UI."""
_LOGGER = logging.getLogger(__name__)
class IrmKmiConfigFlow(ConfigFlow, domain=DOMAIN):
VERSION = CONFIG_FLOW_VERSION
@staticmethod
@callback
def async_get_options_flow(config_entry: ConfigEntry) -> OptionsFlow:
"""Create the opt... | """Config flow to set up IRM KMI integration via the UI."""
_LOGGER = logging.getLogger(__name__)
class IrmKmiConfigFlow(ConfigFlow, domain=DOMAIN):
VERSION = CONFIG_FLOW_VERSION
@staticmethod
@callback
def async_get_options_flow(config_entry: ConfigEntry) -> OptionsFlow:
"""Create the opt... | CONF_STYLE: user_input[CONF_STYLE], | 2 | 2023-12-17 16:35:01+00:00 | 2k |
v3ucn/Bert-vits2-V2.2 | oldVersion/V210/text/japanese_bert.py | [
{
"identifier": "config",
"path": "config.py",
"snippet": "class Resample_config:\nclass Preprocess_text_config:\nclass Bert_gen_config:\nclass Emo_gen_config:\nclass Train_ms_config:\nclass Webui_config:\nclass Server_config:\nclass Translate_config:\nclass Config:\n def __init__(self, in_dir: str, ... | import sys
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer
from config import config
from .japanese import text2sep_kata | 976 |
LOCAL_PATH = "./bert/deberta-v2-large-japanese-char-wwm"
tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH)
models = dict()
|
LOCAL_PATH = "./bert/deberta-v2-large-japanese-char-wwm"
tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH)
models = dict()
| def get_bert_feature(text, word2ph, device=config.bert_gen_config.device): | 0 | 2023-12-18 04:54:46+00:00 | 2k |
NOrangeeroli/SecondPose | model/pcd_cross/modules/transformer/pe_transformer.py | [
{
"identifier": "build_dropout_layer",
"path": "model/pcd_cross/modules/layers/factory.py",
"snippet": "def build_dropout_layer(p: Optional[float], **kwargs) -> nn.Module:\n r\"\"\"Factory function for dropout layer.\"\"\"\n if p is None or p == 0:\n return nn.Identity()\n else:\n ... | import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from ..layers import build_dropout_layer
from .output_layer import AttentionOutput | 1,236 | r"""Vanilla Transformer without positional embeddings.
The shape of input tensor should be (B, N, C). Implemented with `nn.Linear` and `nn.LayerNorm` (with affine).
"""
class PEMultiHeadAttention(nn.Module):
def __init__(self, d_model, num_heads, dropout=None):
super(PEMultiHeadAttention, self).__init_... | r"""Vanilla Transformer without positional embeddings.
The shape of input tensor should be (B, N, C). Implemented with `nn.Linear` and `nn.LayerNorm` (with affine).
"""
class PEMultiHeadAttention(nn.Module):
def __init__(self, d_model, num_heads, dropout=None):
super(PEMultiHeadAttention, self).__init_... | self.output = AttentionOutput(d_model, dropout=dropout, activation_fn=activation_fn) | 1 | 2023-12-16 16:58:33+00:00 | 2k |
KatantDev/YMdantic | ymdantic/models/artists/artist.py | [
{
"identifier": "DeprecatedMixin",
"path": "ymdantic/mixins.py",
"snippet": "class DeprecatedMixin:\n \"\"\"Миксин, удаляющий устаревшие поля из модели.\"\"\"\n\n @model_validator(mode=\"before\")\n def remove_deprecated(cls, obj: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"\n Удал... | from typing import List, Optional, Dict, Any, Literal
from pydantic import model_validator, HttpUrl
from ymdantic.mixins import DeprecatedMixin
from ymdantic.models.base import YMBaseModel
from ymdantic.models.cover import Cover | 899 |
class Artist(YMBaseModel, DeprecatedMixin):
"""Pydantic модель, представляющая информацию об артисте."""
id: int
# Уникальный идентификатор артиста.
name: str
# Имя артиста.
various: bool
# Флаг, указывающий, является ли артист группой.
composer: bool
# Флаг, указывающий, являет... |
class Artist(YMBaseModel, DeprecatedMixin):
"""Pydantic модель, представляющая информацию об артисте."""
id: int
# Уникальный идентификатор артиста.
name: str
# Имя артиста.
various: bool
# Флаг, указывающий, является ли артист группой.
composer: bool
# Флаг, указывающий, являет... | cover: Optional[Cover] = None | 2 | 2023-12-21 21:24:10+00:00 | 2k |
MichealCodez/awesome-project-ideas | projects/artisans/backend/authentication/views.py | [
{
"identifier": "RegisterUserSerializer",
"path": "projects/artisans/backend/authentication/serializers.py",
"snippet": "class RegisterUserSerializer(serializers.ModelSerializer):\n class Meta:\n model = User # We defined the model to be the User model(default django User model).\n fiel... | from rest_framework.views import APIView
from .serializers import RegisterUserSerializer, ResetPasswordSerializer
from rest_framework.response import Response
from rest_framework.exceptions import AuthenticationFailed
from django.contrib.auth.models import User
from datetime import datetime, timedelta
import jwt | 1,071 |
# This is the view logic for registering a user.
# We defined the class and it inherits from the APIView class.
class RegisterUserView(APIView):
def post(self, request): # We defined a post method that takes in a request from a user.
# We defined a serializer variable that takes in the RegisterUserSeriali... |
# This is the view logic for registering a user.
# We defined the class and it inherits from the APIView class.
class RegisterUserView(APIView):
def post(self, request): # We defined a post method that takes in a request from a user.
# We defined a serializer variable that takes in the RegisterUserSeriali... | serializer = ResetPasswordSerializer(data=request.data) | 1 | 2023-12-17 17:21:10+00:00 | 2k |
liuhuang31/hifigan-sr | inference.py | [
{
"identifier": "AttrDict",
"path": "env.py",
"snippet": "class AttrDict(dict):\n def __init__(self, *args, **kwargs):\n super(AttrDict, self).__init__(*args, **kwargs)\n self.__dict__ = self"
},
{
"identifier": "mel_spectrogram",
"path": "meldataset.py",
"snippet": "def... | import glob
import os
import librosa
import argparse
import json
import torch
from scipy.io.wavfile import write
from env import AttrDict
from meldataset import mel_spectrogram, MAX_WAV_VALUE, load_wav
from models import Generator | 1,598 | from __future__ import absolute_import, division, print_function, unicode_literals
h = None
device = None
def load_checkpoint(filepath, device):
assert os.path.isfile(filepath)
print("Loading '{}'".format(filepath))
checkpoint_dict = torch.load(filepath, map_location=device)
print("Complete.")
r... | from __future__ import absolute_import, division, print_function, unicode_literals
h = None
device = None
def load_checkpoint(filepath, device):
assert os.path.isfile(filepath)
print("Loading '{}'".format(filepath))
checkpoint_dict = torch.load(filepath, map_location=device)
print("Complete.")
r... | generator = Generator(h).to(device) | 4 | 2023-12-16 01:21:00+00:00 | 2k |
edsu/marctable | test_marctable.py | [
{
"identifier": "MARC",
"path": "marctable/marc.py",
"snippet": "class MARC:\n def __init__(self) -> None:\n self.fields: List[Field] = []\n\n @cache\n def get_field(self, tag: str) -> Field:\n for field in self.fields:\n if field.tag == tag:\n return fie... | import json
import pathlib
import pandas
from io import StringIO
from marctable.marc import MARC, SchemaFieldError, SchemaSubfieldError, crawl
from marctable.utils import _mapping, dataframe_iter, to_csv, to_dataframe, to_parquet
from pytest import raises | 1,565 |
marc = MARC.from_avram()
def test_crawl() -> None:
# crawl the first 10 field definitions from the loc site (to save time)
outfile = StringIO()
crawl(10, quiet=True, outfile=outfile)
outfile.seek(0)
# ensure the Avram JSON parses and looks ok
schema = json.load(outfile)
assert schema
... |
marc = MARC.from_avram()
def test_crawl() -> None:
# crawl the first 10 field definitions from the loc site (to save time)
outfile = StringIO()
crawl(10, quiet=True, outfile=outfile)
outfile.seek(0)
# ensure the Avram JSON parses and looks ok
schema = json.load(outfile)
assert schema
... | with raises(SchemaFieldError, match="abc is not a defined field tag in Avram"): | 1 | 2023-12-21 21:14:29+00:00 | 2k |
WangWenhao0716/ViT4ICD | Stage_23/dg/models_gem_waveblock_balance_cos/resnet_ibn.py | [
{
"identifier": "resnet50_ibn_a",
"path": "Stage_23/dg/models_gem_waveblock_balance_cos/resnet_ibn_a.py",
"snippet": "def resnet50_ibn_a(pretrained=False, **kwargs):\n \"\"\"Constructs a ResNet-50 model.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"... | from torch import nn
from torch.nn import functional as F
from torch.nn import init
from .resnet_ibn_a import resnet50_ibn_a, resnet101_ibn_a
from .gem import GeneralizedMeanPoolingP
from .metric import build_metric
import torchvision
import torch
import random | 827 | from __future__ import absolute_import
__all__ = ['ResNetIBN', 'resnet_ibn50a', 'resnet_ibn101a']
class Waveblock(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
if self.training:
h, w = x.size()[-2:]
rh = round(0.3 * h)
s... | from __future__ import absolute_import
__all__ = ['ResNetIBN', 'resnet_ibn50a', 'resnet_ibn101a']
class Waveblock(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
if self.training:
h, w = x.size()[-2:]
rh = round(0.3 * h)
s... | '50a': resnet50_ibn_a, | 0 | 2023-12-17 11:32:48+00:00 | 2k |
Noubissie237/myShop | myShop/shop/views.py | [
{
"identifier": "commandeAnonyme",
"path": "myShop/shop/utiles.py",
"snippet": "def commandeAnonyme(request, data):\n print(\"utilisateur non authentifie\")\n\n print('cookies', request.COOKIES)\n \n name = data['form']['name']\n print('data', data)\n print('name', name)\n username ... | from django.shortcuts import render
from .models import *
from django.http import JsonResponse
from datetime import datetime
from .utiles import commandeAnonyme, data_cookie, panier_cookie
import json | 1,373 |
def shop(request, *args, **kwargs):
""" vue principale """
produits = Produit.objects.all()
data = data_cookie(request)
nombre_article = data['nombre_article']
context = {
'produits':produits,
'nombre_article': nombre_article
}
return render(request, 'shop/index.html', co... |
def shop(request, *args, **kwargs):
""" vue principale """
produits = Produit.objects.all()
data = data_cookie(request)
nombre_article = data['nombre_article']
context = {
'produits':produits,
'nombre_article': nombre_article
}
return render(request, 'shop/index.html', co... | client, commande = commandeAnonyme(request, data) | 0 | 2023-12-15 08:06:59+00:00 | 2k |
alibaba/u2mot | yolox/models/yolo_fpn.py | [
{
"identifier": "Darknet",
"path": "yolox/models/darknet.py",
"snippet": "class Darknet(nn.Module):\n # number of blocks from dark2 to dark5.\n depth2blocks = {21: [1, 2, 2, 1], 53: [2, 8, 8, 4]}\n\n def __init__(\n self,\n depth,\n in_channels=3,\n stem_out_channels... | import torch
import torch.nn as nn
from .darknet import Darknet
from .network_blocks import BaseConv | 1,525 | #!/usr/bin/env python3
# -*- encoding:utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
# Copyright (c) Alibaba, Inc. and its affiliates.
class YOLOFPN(nn.Module):
"""
YOLOFPN module. Darknet 53 is the default backbone of this model.
"""
def __init__(
self,
depth=... | #!/usr/bin/env python3
# -*- encoding:utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
# Copyright (c) Alibaba, Inc. and its affiliates.
class YOLOFPN(nn.Module):
"""
YOLOFPN module. Darknet 53 is the default backbone of this model.
"""
def __init__(
self,
depth=... | return BaseConv(_in, _out, ks, stride=1, act="lrelu") | 1 | 2023-12-18 10:04:40+00:00 | 2k |
liuhuang31/HiFTNet-sr | models.py | [
{
"identifier": "init_weights",
"path": "utils.py",
"snippet": "def init_weights(m, mean=0.0, std=0.01):\n classname = m.__class__.__name__\n if classname.find(\"Conv\") != -1:\n m.weight.data.normal_(mean, std)"
},
{
"identifier": "get_padding",
"path": "utils.py",
"snippet... | import torch
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from utils import init_weights, get_padding
from stft import TorchSTFT | 645 |
LRELU_SLOPE = 0.1
class ResBlock1(torch.nn.Module):
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, self).__init__()
self.h = h
self.convs1 = nn.ModuleList([
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
... |
LRELU_SLOPE = 0.1
class ResBlock1(torch.nn.Module):
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, self).__init__()
self.h = h
self.convs1 = nn.ModuleList([
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
... | self.convs1.apply(init_weights) | 0 | 2023-12-16 03:53:55+00:00 | 2k |
m-abr/FCPCodebase | behaviors/custom/Step/Step.py | [
{
"identifier": "Base_Agent",
"path": "agent/Base_Agent.py",
"snippet": "class Base_Agent():\n all_agents = []\n\n def __init__(self, host:str, agent_port:int, monitor_port:int, unum:int, robot_type:int, team_name:str, enable_log:bool=True,\n enable_draw:bool=True, apply_play_mode... | from agent.Base_Agent import Base_Agent
from behaviors.custom.Step.Step_Generator import Step_Generator
import numpy as np | 1,450 |
class Step():
def __init__(self, base_agent : Base_Agent) -> None:
self.world = base_agent.world
self.ik = base_agent.inv_kinematics
self.description = "Step (Skill-Set-Primitive)"
self.auto_head = True
nao_specs = self.ik.NAO_SPECS
self.leg_length = nao_specs[1] +... |
class Step():
def __init__(self, base_agent : Base_Agent) -> None:
self.world = base_agent.world
self.ik = base_agent.inv_kinematics
self.description = "Step (Skill-Set-Primitive)"
self.auto_head = True
nao_specs = self.ik.NAO_SPECS
self.leg_length = nao_specs[1] +... | self.step_generator = Step_Generator(feet_y_dev, sample_time, max_ankle_z) | 1 | 2023-12-16 23:40:23+00:00 | 2k |
koenhendriks/ha-button-plus | custom_components/button_plus/buttonplushub.py | [
{
"identifier": "LocalApiClient",
"path": "custom_components/button_plus/button_plus_api/local_api_client.py",
"snippet": "class LocalApiClient:\n \"\"\" Client to talk to Button+ local devices \"\"\"\n\n def __init__(self, ip_address, session) -> None:\n self._base = f\"http://{ip_address}... | import logging
from homeassistant.config_entries import ConfigEntry
from homeassistant.helpers import device_registry as dr
from .button_plus_api.local_api_client import LocalApiClient
from .button_plus_api.model import DeviceConfiguration
from homeassistant.core import HomeAssistant
from .const import DOMAIN, MANUFACT... | 1,506 | """Button+ connects several devices."""
from __future__ import annotations
_LOGGER: logging.Logger = logging.getLogger(__package__)
class ButtonPlusHub:
"""hub for Button+."""
def __init__(self, hass: HomeAssistant, config: DeviceConfiguration, entry: ConfigEntry) -> None:
_LOGGER.debug(f"New hub... | """Button+ connects several devices."""
from __future__ import annotations
_LOGGER: logging.Logger = logging.getLogger(__package__)
class ButtonPlusHub:
"""hub for Button+."""
def __init__(self, hass: HomeAssistant, config: DeviceConfiguration, entry: ConfigEntry) -> None:
_LOGGER.debug(f"New hub... | identifiers={(DOMAIN, self.config.info.device_id)}, | 2 | 2023-12-18 15:14:21+00:00 | 2k |
RosettaCommons/AF2_peptide_hallucination | run.py | [
{
"identifier": "select_positions",
"path": "util/util.py",
"snippet": "def select_positions(n_mutations, boundcomplex, select_positions, select_position_params):\n '''\n Select mutable positions in the binder based on a specific method.\n Returns a dictionary of binder with associated array in... | import os
import sys
import numpy as np
import hydra
import copy
from submodules.oligomer_hallucination.oligomer_hallucination import Protomers, Oligomer
from submodules.oligomer_hallucination.oligomer_hallucination import AA_FREQ
from submodules.oligomer_hallucination.modules.af2_net import setup_models, predict_struc... | 1,507 |
class BoundComplex(Protomers, Oligomer):
'''
Class for keeping track of binder sequence and complex predictions
during binder hallucination.
'''
def __init__(self, target_sequence: str, name, length=70, aa_freq={}, binder_sequence=None):
"""
target_sequence: amino acid sequence of... |
class BoundComplex(Protomers, Oligomer):
'''
Class for keeping track of binder sequence and complex predictions
during binder hallucination.
'''
def __init__(self, target_sequence: str, name, length=70, aa_freq={}, binder_sequence=None):
"""
target_sequence: amino acid sequence of... | AA_freq=util.get_aa_freq(AA_FREQ, hallucination_conf.exclude_AA) | 1 | 2023-12-21 12:07:25+00:00 | 2k |
Cypas/splatoon3-schedule | nonebot_plugin_splatoon3_schedule/utils/utils.py | [
{
"identifier": "TimeUtil",
"path": "nonebot_plugin_splatoon3_schedule/utils/dataClass.py",
"snippet": "class TimeUtil(object):\n @classmethod\n def parse_timezone(cls, timezone):\n \"\"\"\n 解析时区表示\n :param timezone: str eg: +8\n :return: dict{symbol, offset}\n \... | import datetime
import cfscrape
import httpx
from httpx import Response
from .dataClass import TimeUtil
from ..config import plugin_config | 1,412 | "Ranked Challenge": (227, 68, 17),
"Ranked Open": (24, 200, 26),
"X Schedule": (14, 205, 147),
"打工": (14, 203, 146),
"活动": (223, 42, 119),
"祭典": (103, 103, 114),
"祭典时间-金黄": (234, 255, 61),
"上-武器卡片-黄": (234, 255, 61),
"下-武器卡片-蓝": (96, 58, 255),
"上-武器卡片": (255, 148, 157),
"下-武器... |
time_format_ymdh = "%Y-%m-%dT%H"
HTTP_TIME_OUT = 5.0 # 请求超时，秒
proxy_address = plugin_config.splatoon3_proxy_address
if proxy_address:
proxies = "http://{}".format(proxy_address)
else:
proxies = None
# 背景 rgb颜色
dict_bg_rgb = {
"Turf War": (24, 200, 26),
"Ranked Challenge": (227, 68, 17),
"Ranked ... | convert_now = TimeUtil.convert_timezone(utc_now, "+8") | 0 | 2023-12-17 07:49:26+00:00 | 2k |
Sam-Izdat/tinycio | src/tinycio/fsio/imagefile.py | [
{
"identifier": "GraphicsFormat",
"path": "src/tinycio/fsio/format.py",
"snippet": "class GraphicsFormat(IntEnum):\n \"\"\"\n The graphics format of an image file to be saved or loaded. For a list of available options, see :ref:`ref_graphics_formats`.\n \"\"\"\n UNKNOWN = 1<<0\n ... | import torch
import numpy as np
import typing
import os
import imageio.v3 as iio
from .format import GraphicsFormat, ImageFileFormat | 699 |
def _infer_image_file_format(ext:str) -> ImageFileFormat:
ext = ext.strip().lower()
if ext == '.png': return ImageFileFormat.PNG
elif ext == '.jpg': return ImageFileFormat.JPG
elif ext == '.jpeg': return ImageFileFormat.JPG
elif ext == '.exr': return ImageFileFormat.EXR
elif ... |
def _infer_image_file_format(ext:str) -> ImageFileFormat:
ext = ext.strip().lower()
if ext == '.png': return ImageFileFormat.PNG
elif ext == '.jpg': return ImageFileFormat.JPG
elif ext == '.jpeg': return ImageFileFormat.JPG
elif ext == '.exr': return ImageFileFormat.EXR
elif ... | def load_image(fp:str, graphics_format:GraphicsFormat=GraphicsFormat.UNKNOWN) -> torch.Tensor: | 0 | 2023-12-15 15:39:08+00:00 | 2k |
Dank-del/stats-bot | stats_bot/handlers/plot.py | [
{
"identifier": "Attachment",
"path": "stats_bot/db/models.py",
"snippet": "class Attachment(SQLModel, table=True):\n id: Optional[int] = Field(default=None, primary_key=True)\n user_id: int = Field(foreign_key=\"user.id\")\n group_id: int = Field(foreign_key=\"group.id\")\n message_id: int ... | import pandas as pd
import matplotlib.pyplot as plt
import io
from sqlmodel import Session, select
from telegram import Update
from telegram.ext import (
ContextTypes,
)
from stats_bot.db.models import Attachment, Message, User
from stats_bot.db.client import engine
from stats_bot.decorators.admin import admin | 1,204 |
@admin
async def plot_table(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
"""
Generates a table of top 10 users by number of messages and average message length,
and plots a bar chart to visualize the data.
Args:
update (Update): The update object containing information about ... |
@admin
async def plot_table(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
"""
Generates a table of top 10 users by number of messages and average message length,
and plots a bar chart to visualize the data.
Args:
update (Update): The update object containing information about ... | select(Attachment).where(Attachment.group_id == update.effective_chat.id) | 0 | 2023-12-18 03:05:36+00:00 | 2k |
EzyGang/py-cachify | py_cachify/backend/lib.py | [
{
"identifier": "AsyncWrapper",
"path": "py_cachify/backend/clients.py",
"snippet": "class AsyncWrapper:\n def __init__(self, cache: MemoryCache) -> None:\n self._cache = cache\n\n async def get(self, name: str, default: Any = None) -> Any:\n return self._cache.get(name=name, default... | import pickle
from typing import Any, Union
from py_cachify.backend.clients import AsyncWrapper, MemoryCache
from py_cachify.backend.exceptions import CachifyInitError
from py_cachify.backend.types import AsyncClient, SyncClient | 664 | from __future__ import annotations
class Cachify:
def __init__(
| from __future__ import annotations
class Cachify:
def __init__( | self, sync_client: Union[SyncClient, MemoryCache], async_client: Union[AsyncClient, AsyncWrapper], prefix: str | 1 | 2023-12-16 22:54:51+00:00 | 2k |
lldacing/comfyui-easyapi-nodes | easyapi/ImageNode.py | [
{
"identifier": "tensor_to_pil",
"path": "easyapi/util.py",
"snippet": "def tensor_to_pil(image):\n return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))"
},
{
"identifier": "pil_to_tensor",
"path": "easyapi/util.py",
"snippet": "def pil_to_ten... | import base64
import copy
import io
import numpy as np
import torch
import json
from PIL import ImageOps, Image
from nodes import LoadImage
from comfy.cli_args import args
from PIL.PngImagePlugin import PngInfo
from json import JSONEncoder, JSONDecoder
from easyapi.util import tensor_to_pil, pil_to_tensor, b... | 1,382 |
class LoadImageFromURL:
"""
从远程地址读取图片
"""
@classmethod
def INPUT_TYPES(self):
return {"required": {
"urls": ("STRING", {"multiline": True, "default": "", "dynamicPrompts": False}),
},
}
RETURN_TYPES = ("IMAGE", "MASK")
RETURN_NAMES = ("ima... |
class LoadImageFromURL:
"""
从远程地址读取图片
"""
@classmethod
def INPUT_TYPES(self):
return {"required": {
"urls": ("STRING", {"multiline": True, "default": "", "dynamicPrompts": False}),
},
}
RETURN_TYPES = ("IMAGE", "MASK")
RETURN_NAMES = ("ima... | i = base64_to_image(base64Image)
| 2 | 2023-12-19 02:32:10+00:00 | 2k |
bersegosx/passosh | src/passosh/pesso.py | [
{
"identifier": "HeaderField",
"path": "src/passosh/fields.py",
"snippet": "class HeaderField:\n \"\"\"\n An object that represents the fields that display information at the top of a pass.\n \"\"\"\n key: str\n value: str\n label: str = ''\n textAlignment: str = TextAlignment.NATUR... | from dataclasses import dataclass
from .fields import (HeaderField, PrimaryField, SecondaryField, BackField, AuxiliaryField, Barcode,
BoardingPassTransitType, Location) | 786 |
@dataclass
class Content:
"""
An object that represents the groups of fields that display the information for an event ticket.
"""
headerFields: list[HeaderField] | None = None
primaryFields: list[PrimaryField] | None = None
|
@dataclass
class Content:
"""
An object that represents the groups of fields that display the information for an event ticket.
"""
headerFields: list[HeaderField] | None = None
primaryFields: list[PrimaryField] | None = None | secondaryFields: list[SecondaryField] | None = None | 2 | 2023-12-18 22:51:38+00:00 | 2k |
jonghwanhyeon/python-chzzk | chzzk/chzzk.py | [
{
"identifier": "ChzzkClient",
"path": "chzzk/client.py",
"snippet": "class ChzzkClient(HTTPClient):\n BASE_URL = \"https://api.chzzk.naver.com/\"\n\n def __init__(self, credential: Optional[Credential] = None):\n super().__init__(credential)"
},
{
"identifier": "Credential",
"p... | from typing import Optional
from chzzk.client import ChzzkClient, Credential, GameClient
from chzzk.models import (
Channel,
ChannelSearchRecord,
LiveDetail,
LiveSearchRecord,
LiveStatus,
SearchCursor,
User,
Video,
VideoSearchRecord,
) | 1,194 |
class ChzzkLive:
def __init__(self, client: ChzzkClient):
self._client = client
async def status(self, channel_id: str) -> LiveStatus:
response = await self._client.get(f"polling/v1/channels/{channel_id}/live-status")
return LiveStatus(**response)
async def detail(self, channel_... |
class ChzzkLive:
def __init__(self, client: ChzzkClient):
self._client = client
async def status(self, channel_id: str) -> LiveStatus:
response = await self._client.get(f"polling/v1/channels/{channel_id}/live-status")
return LiveStatus(**response)
async def detail(self, channel_... | async def videos(self, keyword: str, size: int = 12, offset: int = 0) -> SearchCursor[VideoSearchRecord]: | 11 | 2023-12-20 22:09:07+00:00 | 2k |
pantherale0/ha-fuelprices | custom_components/fuel_prices/device_tracker.py | [
{
"identifier": "CONF_AREAS",
"path": "custom_components/fuel_prices/const.py",
"snippet": "CONF_AREAS = \"areas\""
},
{
"identifier": "DOMAIN",
"path": "custom_components/fuel_prices/const.py",
"snippet": "DOMAIN = \"fuel_prices\""
},
{
"identifier": "FeulStationEntity",
"pa... | import logging
from homeassistant.const import CONF_LATITUDE, CONF_LONGITUDE, CONF_RADIUS, CONF_NAME
from homeassistant.components.device_tracker.config_entry import (
BaseTrackerEntity,
SourceType,
ATTR_SOURCE_TYPE,
ATTR_LATITUDE,
ATTR_LONGITUDE,
)
from homeassistant.config_entries import ConfigEnt... | 695 | """Device tracker for fuel prices."""
from __future__ import annotations
_LOGGER = logging.getLogger(__name__)
async def async_setup_entry(
hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback
) -> None:
"""Integration platform creation."""
| """Device tracker for fuel prices."""
from __future__ import annotations
_LOGGER = logging.getLogger(__name__)
async def async_setup_entry(
hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback
) -> None:
"""Integration platform creation.""" | cooridinator: FuelPricesCoordinator = hass.data[DOMAIN][entry.entry_id] | 1 | 2023-12-19 20:54:21+00:00 | 2k |
abdellatif-laghjaj/stock-market-prediction | main.py | [
{
"identifier": "load_data",
"path": "services.py",
"snippet": "@st.cache_data\ndef load_data(ticker, start, end):\n \"\"\"\n Load historical stock price data from Yahoo Finance.\n\n Parameters:\n - ticker (str): Stock symbol (e.g., AAPL).\n - start (str): Start date in the format 'YYYY-M... | from time import sleep
from sklearn.metrics import mean_absolute_error
from streamlit_option_menu import option_menu
from datetime import date
from prophet import Prophet
from prophet.plot import plot_plotly
from services import load_data, plot_data, plot_multiple_data, plot_volume
import uuid
import pandas as pd
impor... | 1,104 |
# Set page layout to wide
st.set_page_config(layout="wide", page_title="Forcastify", page_icon="📈")
# Sidebar
st.sidebar.markdown("<h1 style='text-align: center; font-size: 30px;'><b>Forcasti.</b><b style='color: orange'>fy</b></h1>", unsafe_allow_html=True)
st.sidebar.title("Options")
start_date_key = str(uuid.uuid... |
# Set page layout to wide
st.set_page_config(layout="wide", page_title="Forcastify", page_icon="📈")
# Sidebar
st.sidebar.markdown("<h1 style='text-align: center; font-size: 30px;'><b>Forcasti.</b><b style='color: orange'>fy</b></h1>", unsafe_allow_html=True)
st.sidebar.title("Options")
start_date_key = str(uuid.uuid... | data = load_data(selected_stock, start_date, end_date) | 0 | 2023-12-17 11:38:48+00:00 | 2k |
replicate/cog-marigold | src/model/marigold_pipeline.py | [
{
"identifier": "RGBEncoder",
"path": "src/model/rgb_encoder.py",
"snippet": "class RGBEncoder(nn.Module):\n \"\"\"\n The encoder of pretrained Stable Diffusion VAE\n \"\"\"\n \n def __init__(self, pretrained_path, subfolder=None) -> None:\n super().__init__()\n \n va... | import logging
import numpy as np
import torch
from typing import Dict
from diffusers import (
DDIMScheduler,
DDPMScheduler,
PNDMScheduler,
SchedulerMixin,
UNet2DConditionModel,
)
from torch import nn
from torch.nn import Conv2d
from torch.nn.parameter import Parameter
from tqdm.auto import tqdm
fro... | 1,288 | # Author: Bingxin Ke
# Last modified: 2023-12-11
class MarigoldPipeline(nn.Module):
"""
Marigold monocular depth estimator.
"""
def __init__(
self,
unet_pretrained_path: Dict, # {path: xxx, subfolder: xxx}
rgb_encoder_pretrained_path: Dict,
depht_ae_pretrained_path... | # Author: Bingxin Ke
# Last modified: 2023-12-11
class MarigoldPipeline(nn.Module):
"""
Marigold monocular depth estimator.
"""
def __init__(
self,
unet_pretrained_path: Dict, # {path: xxx, subfolder: xxx}
rgb_encoder_pretrained_path: Dict,
depht_ae_pretrained_path... | self.depth_ae = StackedDepthAE( | 1 | 2023-12-15 07:19:14+00:00 | 2k |
tungeverest/python-k8s-base | src/app.py | [
{
"identifier": "process_time_log_middleware",
"path": "core/middlewares/https/process_time.py",
"snippet": "async def process_time_log_middleware(request: Request, call_next):\n \"\"\"\n This middleware will log all requests and their processing time.\n E.g. log: HOST:PORT - GET /ping 200 OK ... | import logging
from os import getenv
from core.middlewares.https.process_time import process_time_log_middleware
from core.middlewares.https.rate_limit import RateLimitCoreMiddleware
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.middleware.trustedhost import TrustedHostMidd... | 881 |
logger = logging.getLogger(__name__)
def create_app():
settings = get_settings()
app = FastAPI(
title=f"{settings.PROJECT_NAME}",
version=settings.APP_VERSION,
debug=settings.DEBUG,
description=f"""
FastAPI Framework + K8s \n
- PROJECT NAME: {settings.PROJE... |
logger = logging.getLogger(__name__)
def create_app():
settings = get_settings()
app = FastAPI(
title=f"{settings.PROJECT_NAME}",
version=settings.APP_VERSION,
debug=settings.DEBUG,
description=f"""
FastAPI Framework + K8s \n
- PROJECT NAME: {settings.PROJE... | app.include_router(api_router, prefix=settings.API_VERSION_PREFIX) | 1 | 2023-12-20 03:40:34+00:00 | 2k |
CoolPointerException/Amigo | gui/llama_index_init.py | [
{
"identifier": "validate",
"path": "gui/input_validator.py",
"snippet": "def validate(gui, properties):\n for prop in properties:\n match prop:\n case Properties.PROJECT_NAME:\n project_name = gui.projects_tab.project_name_entry.get()\n if not project_... | from tkinter import messagebox
from llama_index import ServiceContext, set_global_service_context, OpenAIEmbedding
from llama_index.embeddings import AzureOpenAIEmbedding, GeminiEmbedding
from llama_index.llms import Gemini, OpenAI, AzureOpenAI
from gui.input_validator import validate, Properties | 1,186 |
def init_llama_index(self, api_type):
if self.isLlamaInitialized:
return
llm = None
embed_model = None
if api_type == "azure":
is_valid = validate(self, [
|
def init_llama_index(self, api_type):
if self.isLlamaInitialized:
return
llm = None
embed_model = None
if api_type == "azure":
is_valid = validate(self, [ | Properties.API_BASE, | 1 | 2023-12-15 14:06:38+00:00 | 2k |
redvulpecula/DRILL-Concurrent-Python-1 | main.py | [
{
"identifier": "VideoStream",
"path": "video_streaming.py",
"snippet": "class VideoStream:\n def __init__(self, url, frames):\n self.frames = frames\n self.url = url\n self.process = Process(target=self.capture, args=(self.frames, self.url))\n self.process.start()\n\n ... | import time
import torch
from multiprocessing import Process, Manager
from ultralytics import YOLO
from video_streaming import VideoStream, calculate_fps, display_and_save_frame, check_rtsp_url, read_url_from_file
from imgAlgSelect import YOLOProcessor | 1,420 |
class ConcurrencyManager:
def __init__(self, url):
self.device = 'cuda' if torch.backends.cuda.is_built() else 'mps' if torch.backends.mps.is_available() else 'cpu'
self.yolo_model = YOLO("yolov8m.pt")
self.manager = Manager()
self.url = url
self.frames = self.manager.Queue(... |
class ConcurrencyManager:
def __init__(self, url):
self.device = 'cuda' if torch.backends.cuda.is_built() else 'mps' if torch.backends.mps.is_available() else 'cpu'
self.yolo_model = YOLO("yolov8m.pt")
self.manager = Manager()
self.url = url
self.frames = self.manager.Queue(... | url = read_url_from_file() | 4 | 2023-12-18 02:58:03+00:00 | 2k |
LyubomirT/discord-lle | main.py | [
{
"identifier": "Colorizer",
"path": "colorizer.py",
"snippet": "class Colorizer:\n def __init__(self, color):\n self.color = color\n self.colors = {\n \"red\": \"\\033[31m\",\n \"green\": \"\\033[32m\",\n \"yellow\": \"\\033[33m\",\n \"blue\"... | from dotenv import load_dotenv
from discord.ext import commands
from discord.commands import Option
from discord.ui import Button, View, Select, Modal
from colorizer import Colorizer
from datetime import datetime
from verify_dir import verify_dir
import os
import requests
import json
import discord
import configparser
... | 1,460 |
load_dotenv()
token = os.getenv("BOT_TOKEN")
bot = commands.Bot(command_prefix="!", intents=discord.Intents.all())
log_dir = "_logs_"
dm_config = {
"enabled": True,
"download_images": True,
"download_videos": True,
"download_audio": True,
}
server_config = {
"enabled": True,
"download_image... |
load_dotenv()
token = os.getenv("BOT_TOKEN")
bot = commands.Bot(command_prefix="!", intents=discord.Intents.all())
log_dir = "_logs_"
dm_config = {
"enabled": True,
"download_images": True,
"download_videos": True,
"download_audio": True,
}
server_config = {
"enabled": True,
"download_image... | verify_dir(log_dir) | 1 | 2023-12-18 16:08:05+00:00 | 2k |
KR1470R/plagiator-py | utils/plagiator.py | [
{
"identifier": "exists",
"path": "utils/exists.py",
"snippet": "def exists(obj, *keys):\n format_keys = \"\".join(\n list(map(\n lambda key: f\"['{key}']\",\n keys\n ))\n )\n try:\n return eval(f\"obj{format_keys}\")\n except Exception:\n return None"
},
{
"identifier"... | import json
import logging
import requests
from .exists import exists
from configs.edupirdie import API_URI, HEADERS
from random_user_agent.user_agent import UserAgent
from random_user_agent.params import SoftwareName, OperatingSystem | 844 |
class Plagiator:
def __init__(self):
self.session = requests.Session()
adapter = requests.adapters.HTTPAdapter(pool_connections=10000, pool_maxsize=10000)
self.session.mount("https://", adapter)
software_names = [software_name.value for software_name in SoftwareName]
operating_systems = [operatin... |
class Plagiator:
def __init__(self):
self.session = requests.Session()
adapter = requests.adapters.HTTPAdapter(pool_connections=10000, pool_maxsize=10000)
self.session.mount("https://", adapter)
software_names = [software_name.value for software_name in SoftwareName]
operating_systems = [operatin... | **HEADERS, | 2 | 2023-12-21 17:29:18+00:00 | 2k |
fmhy/bot | cogs/rss.py | [
{
"identifier": "rss_chan_ids",
"path": "cogs/_config.py",
"snippet": "TOKEN = os.getenv(\"TOKEN\", None)\nGUILD_ID = os.getenv(\"GUILD_ID\", None)\nOWNERS = os.getenv(\"OWNERS\").split(\",\")\nRSS_CHANNELS = os.getenv(\"RSS_CHANNEL_IDS\", None)\nFEEDS = os.getenv(\"RSS_FEED_URLS\", None)\nDB = os.geten... | from typing import TYPE_CHECKING
from discord.ext import commands, tasks
from cogs._config import rss_chan_ids
from cogs._helpers import fetch_feed
from main import Bot
from discord.channel import TextChannel | 985 |
if TYPE_CHECKING:
class RSSFeeds(commands.Cog):
"""RSSFeeds commands"""
def __init__(self, bot: Bot):
self.bot = bot
@commands.Cog.listener()
async def on_ready(self):
self.send_rss.start()
async def cog_before_invoke(self, ctx):
"""Triggers typing indicator on Discor... |
if TYPE_CHECKING:
class RSSFeeds(commands.Cog):
"""RSSFeeds commands"""
def __init__(self, bot: Bot):
self.bot = bot
@commands.Cog.listener()
async def on_ready(self):
self.send_rss.start()
async def cog_before_invoke(self, ctx):
"""Triggers typing indicator on Discor... | for msg in fetch_feed(): | 1 | 2023-12-19 10:27:04+00:00 | 2k |
cvlab-yonsei/RankMixup | calibrate/evaluation/segment_evaluator.py | [
{
"identifier": "DatasetEvaluator",
"path": "calibrate/evaluation/evaluator.py",
"snippet": "class DatasetEvaluator(metaclass=ABCMeta):\n \"\"\"\n Base class for a dataset evaluator\n \"\"\"\n @abstractmethod\n def reset(self):\n \"\"\"\n Preparation for a new round of evalu... | import logging
import numpy as np
import pandas as pd
import wandb
from terminaltables import AsciiTable
from typing import List, Optional
from .evaluator import DatasetEvaluator
from calibrate.utils.constants import EPS | 973 |
logger = logging.getLogger(__name__)
def intersect_and_union(pred_label, label, num_classes, ignore_index):
mask = (label != ignore_index)
pred_label = pred_label[mask]
label = label[mask]
intersect = pred_label[pred_label == label]
area_intersect, _ = np.histogram(
intersect, bins=np.a... |
logger = logging.getLogger(__name__)
def intersect_and_union(pred_label, label, num_classes, ignore_index):
mask = (label != ignore_index)
pred_label = pred_label[mask]
label = label[mask]
intersect = pred_label[pred_label == label]
area_intersect, _ = np.histogram(
intersect, bins=np.a... | iou = batch_area_inter[1:].sum() / (batch_area_union[1:].sum() + EPS) | 1 | 2023-12-17 13:53:18+00:00 | 2k |
CaptainCook4D/downloader | download_gopro_data.py | [
{
"identifier": "prepare_gopro_2d_output_directory",
"path": "util.py",
"snippet": "def prepare_gopro_2d_output_directory(args, output_dir: Path):\n\toutput_dir.mkdir(parents=True, exist_ok=True)\n\t\n\tdata_directory = output_dir / Constants.CAPTAIN_COOK_4D\n\tdata_directory.mkdir(parents=True, exist_o... | import argparse
import json
from pathlib import Path
from util import prepare_gopro_2d_output_directory, Constants, download_data | 1,527 |
def process_download_gopro_data(download_args):
# ---- Parse Download Links Json ----
with open("metadata/download_links.json", "r") as f:
download_links = json.load(f)
output_dir = Path(download_args.output_dir)
|
def process_download_gopro_data(download_args):
# ---- Parse Download Links Json ----
with open("metadata/download_links.json", "r") as f:
download_links = json.load(f)
output_dir = Path(download_args.output_dir) | data_directory = prepare_gopro_2d_output_directory(download_args, output_dir) | 0 | 2023-12-16 00:27:29+00:00 | 2k |
mjavadpur/Sadtalker_LongVideos | src/audio2pose_models/audio2pose.py | [
{
"identifier": "CVAE",
"path": "src/audio2pose_models/cvae.py",
"snippet": "class CVAE(nn.Module):\n def __init__(self, cfg):\n super().__init__()\n encoder_layer_sizes = cfg.MODEL.CVAE.ENCODER_LAYER_SIZES\n decoder_layer_sizes = cfg.MODEL.CVAE.DECODER_LAYER_SIZES\n laten... | import torch
from torch import nn
from src.audio2pose_models.cvae import CVAE
from src.audio2pose_models.discriminator import PoseSequenceDiscriminator
from src.audio2pose_models.audio_encoder import AudioEncoder | 1,566 |
class Audio2Pose(nn.Module):
def __init__(self, cfg, wav2lip_checkpoint, device='cuda'):
super().__init__()
self.cfg = cfg
self.seq_len = cfg.MODEL.CVAE.SEQ_LEN
self.latent_dim = cfg.MODEL.CVAE.LATENT_SIZE
self.device = device
self.audio_encoder = AudioEncoder(wav2l... |
class Audio2Pose(nn.Module):
def __init__(self, cfg, wav2lip_checkpoint, device='cuda'):
super().__init__()
self.cfg = cfg
self.seq_len = cfg.MODEL.CVAE.SEQ_LEN
self.latent_dim = cfg.MODEL.CVAE.LATENT_SIZE
self.device = device
self.audio_encoder = AudioEncoder(wav2l... | self.netD_motion = PoseSequenceDiscriminator(cfg) | 1 | 2023-12-19 11:01:35+00:00 | 2k |
Angryrou/udao | udao/data/tests/iterators/dummy_udao_iterator.py | [
{
"identifier": "TabularContainer",
"path": "udao/data/containers/tabular_container.py",
"snippet": "class TabularContainer(BaseContainer):\n \"\"\"Container for tabular data, stored in DataFrame format.\"\"\"\n\n data: pd.DataFrame\n\n def get(self, key: str) -> np.ndarray:\n return sel... | from typing import Sequence, Tuple
from ....data.containers.tabular_container import TabularContainer
from ....data.iterators.base_iterator import UdaoIterator
from ....utils.interfaces import (
UdaoEmbedInput,
UdaoEmbedItemShape,
UdaoInput,
UdaoItemShape,
)
import torch as th | 1,099 |
class DummyUdaoIterator(UdaoIterator[UdaoInput, UdaoItemShape]):
def __init__(
self,
keys: Sequence[str],
tabular_features: TabularContainer,
objectives: TabularContainer,
) -> None:
super().__init__(keys, tabular_features=tabular_features, objectives=objectives)
... |
class DummyUdaoIterator(UdaoIterator[UdaoInput, UdaoItemShape]):
def __init__(
self,
keys: Sequence[str],
tabular_features: TabularContainer,
objectives: TabularContainer,
) -> None:
super().__init__(keys, tabular_features=tabular_features, objectives=objectives)
... | class DummyUdaoEmbedIterator(UdaoIterator[UdaoEmbedInput, UdaoEmbedItemShape]): | 3 | 2023-12-20 09:10:42+00:00 | 2k |
SnailForce/SIM-Net | data/mask_dataset.py | [
{
"identifier": "BaseDataset",
"path": "data/base_dataset.py",
"snippet": "class BaseDataset(data.Dataset, ABC):\n \"\"\"This class is an abstract base class (ABC) for datasets.\n\n To create a subclass, you need to implement the following four functions:\n -- <__init__>: i... | import os,yaml
import torch.nn.functional as F
import random
import numpy as np
import collections
import torch
from data.base_dataset import BaseDataset, get_transform
from data.image_folder import make_dataset_by_name
from PIL import Image,ImageFilter | 1,213 |
class MaskDataset(BaseDataset):
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.root_dir = os.path.join(... |
class MaskDataset(BaseDataset):
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.root_dir = os.path.join(... | self.transform = get_transform(self.opt) | 1 | 2023-12-16 12:49:10+00:00 | 2k |
adarshsankarrs/PhotoshopApp | app.py | [
{
"identifier": "MultiApp",
"path": "multiapp.py",
"snippet": "class MultiApp:\n \"\"\"Framework for combining multiple streamlit applications.\n \"\"\"\n def __init__(self):\n self.apps = []\n\n def add_app(self, title, func):\n \"\"\"Adds a new application.\n\n \"\"\"\... | import streamlit as st
import numpy as np
import pandas as pd
import cv2
from PIL import Image, ImageOps
from multiapp import MultiApp
from apps import home,sketch,inpaint,stadap,textonimg,Edge_Cont,Face_detect,Crop,filters,abtus,Feature_detect | 928 | app = MultiApp()
# option = st.selectbox(
# 'Select from the options',
# ('Home', 'Filters', 'Doc scanner','add text'), key = 1)
# if(option=='Filters'):
# opt = st.selectbox(
# 'Select from the options',
# ('sepia', 'Filter1', 'filter2','filter3'), key = 2)
# Add all your application here
ap... | app = MultiApp()
# option = st.selectbox(
# 'Select from the options',
# ('Home', 'Filters', 'Doc scanner','add text'), key = 1)
# if(option=='Filters'):
# opt = st.selectbox(
# 'Select from the options',
# ('sepia', 'Filter1', 'filter2','filter3'), key = 2)
# Add all your application here
ap... | app.add_app("Face detection", Face_detect.app) | 7 | 2023-12-20 20:32:16+00:00 | 2k |
DURUII/Replica-AUCB | main.py | [
{
"identifier": "StrategicArm",
"path": "arms.py",
"snippet": "class StrategicArm(NormalArm):\n c_min, c_max = 0.1, 1\n\n def __init__(self):\n # in the paper, r is expected reward\n r = random.uniform(0.1, 1)\n # to make that sample value is within 0~1 with 97%\n sigma... | import os
import pandas as pd
import numpy as np
import pickle
from matplotlib import pyplot as plt
from tqdm import tqdm
from arms import StrategicArm
from config import Config
from emulator import Emulator | 1,153 | """
Author: DURUII
Date: 2023/12/17
"""
plt.style.use(['science', 'grid'])
config = Config
# data preparation
if not os.path.exists('./runs.pkl'):
data = []
for X in ['N', 'K', 'B']:
for x in tqdm(eval(f'config.{X}_range'), desc=X):
if X == 'N':
| """
Author: DURUII
Date: 2023/12/17
"""
plt.style.use(['science', 'grid'])
config = Config
# data preparation
if not os.path.exists('./runs.pkl'):
data = []
for X in ['N', 'K', 'B']:
for x in tqdm(eval(f'config.{X}_range'), desc=X):
if X == 'N': | name2res = Emulator(n_arms=x).simulate() | 2 | 2023-12-15 18:17:01+00:00 | 2k |
XLearning-SCU/2023-TPAMI-SMILE | _AutoLauncher.py | [
{
"identifier": "path_operator",
"path": "_MainLauncher.py",
"snippet": "def get_settings():\r\ndef clear_gpu_fail(root):\r\ndef run():\r\ndef main():\r"
},
{
"identifier": "Launcher",
"path": "_Utils/Launcher.py",
"snippet": "class Launcher(SubprocessOperator):\r\n def __init__(self,... | import time
from _MainLauncher import path_operator
from _Utils import Launcher
from _Utils.ConfigOperator import ConfigOperator
| 1,420 |
def main():
class C2(ConfigOperator):
def get_name(self, *args, **kwargs):
return '_QueueLog'
|
def main():
class C2(ConfigOperator):
def get_name(self, *args, **kwargs):
return '_QueueLog'
| Launcher.Launcher(
| 1 | 2023-12-21 08:50:36+00:00 | 2k |
precisionalgorithms/loopring-python-SDK | main.py | [
{
"identifier": "Session",
"path": "loopring/session.py",
"snippet": "class Session:\n \"\"\"\n Parent class for Loopring API.\n \"\"\"\n # Class variables\n api_key = None\n account_id = None\n headers = None\n base_url = 'https://api3.loopring.io/api/v3'\n\n @classmethod\n ... | import pickle
from loopring.session import Session
from loopring.account import Account
from loopring.exchange import Exchange
from utils import join_balance_with_token_info | 1,087 |
# Initialize the Loopring API with API key and account ID
Session.initialize()
# Get the account balances
account = Account()
balances = account.get_account_balances()
# Get token info on exchange
|
# Initialize the Loopring API with API key and account ID
Session.initialize()
# Get the account balances
account = Account()
balances = account.get_account_balances()
# Get token info on exchange | exchange = Exchange() | 2 | 2023-12-18 00:19:56+00:00 | 2k |
Liyulingyue/ModulelyTools | codes/extraction/ModuleTools.py | [
{
"identifier": "parse_ipynb",
"path": "codes/extraction/ipynb/ipynb_analyse.py",
"snippet": "def parse_ipynb(file_path):\n \"\"\"\n # 示例：使用函数解析一个ipynb文件\n file_path = 'main.ipynb' # 请将此处替换为您的ipynb文件路径\n result = parse_ipynb(file_path)\n print(result)\n \"\"\"\n # 读取ipynb文件\n wi... | from .ipynb.ipynb_analyse import parse_ipynb, get_ipynb_content, get_model_list, model_list2python
from .py.py_analyse import extract_function_defs, get_function_defs, get_intro_of_fun
from ..llm.Ernie import Ernie
from ..llm.Ernie import Ernie | 1,554 |
class ModuleTools(object):
def __init__(self, llm_type="Ernie"):
super.__init__()
if llm_type=="Ernie":
self.llm = Ernie()
else: # default set ernie as used llm
self.llm = Ernie()
def ipynb2py(self, ipynb_path = "example.ipynb", prompt = ""):
|
class ModuleTools(object):
def __init__(self, llm_type="Ernie"):
super.__init__()
if llm_type=="Ernie":
self.llm = Ernie()
else: # default set ernie as used llm
self.llm = Ernie()
def ipynb2py(self, ipynb_path = "example.ipynb", prompt = ""): | result = parse_ipynb(ipynb_path) | 0 | 2023-12-17 14:20:45+00:00 | 2k |
Azure-Samples/functions-python-web-crawler | .venv/Lib/site-packages/urllib3/_base_connection.py | [
{
"identifier": "_TYPE_SOCKET_OPTIONS",
"path": ".venv/Lib/site-packages/urllib3/util/connection.py",
"snippet": "_TYPE_SOCKET_OPTIONS = typing.Sequence[typing.Tuple[int, int, typing.Union[int, bytes]]]"
},
{
"identifier": "_DEFAULT_TIMEOUT",
"path": ".venv/Lib/site-packages/urllib3/util/tim... | import typing
import ssl
from .util.connection import _TYPE_SOCKET_OPTIONS
from .util.timeout import _DEFAULT_TIMEOUT, _TYPE_TIMEOUT
from .util.url import Url
from typing import Literal, Protocol
from .response import BaseHTTPResponse | 1,468 | from __future__ import annotations
_TYPE_BODY = typing.Union[bytes, typing.IO[typing.Any], typing.Iterable[bytes], str]
class ProxyConfig(typing.NamedTuple):
ssl_context: ssl.SSLContext | None
use_forwarding_for_https: bool
assert_hostname: None | str | Literal[False]
assert_fingerprint: str | None... | from __future__ import annotations
_TYPE_BODY = typing.Union[bytes, typing.IO[typing.Any], typing.Iterable[bytes], str]
class ProxyConfig(typing.NamedTuple):
ssl_context: ssl.SSLContext | None
use_forwarding_for_https: bool
assert_hostname: None | str | Literal[False]
assert_fingerprint: str | None... | proxy: Url | None | 3 | 2023-12-16 04:12:01+00:00 | 2k |
neuroglia-io/python-framework | tests/cases/test_service_provider.py | [
{
"identifier": "FileLogger",
"path": "tests/services.py",
"snippet": "class FileLogger(LoggerBase):\n \n def log(text: str):\n with open('example.txt', 'a') as file:\n file.write(f'{text}\\n')"
},
{
"identifier": "LoggerBase",
"path": "tests/services.py",
"snippe... | from re import T
from sys import implementation
from neuroglia.dependency_injection.service_provider import IServiceProvider, ServiceCollection, ServiceProvider
from tests.services import FileLogger, LoggerBase, NullLogger, PrintLogger
import pytest | 820 |
class TestServiceProvider:
def test_build_should_work(self):
#arrange
services = ServiceCollection()
services.add_singleton(LoggerBase, PrintLogger)
services.add_singleton(LoggerBase, singleton = FileLogger())
services.add_singleton(LoggerBase, implementation_factory = ... |
class TestServiceProvider:
def test_build_should_work(self):
#arrange
services = ServiceCollection()
services.add_singleton(LoggerBase, PrintLogger)
services.add_singleton(LoggerBase, singleton = FileLogger())
services.add_singleton(LoggerBase, implementation_factory = ... | def _build_null_logger(self, provider : IServiceProvider) -> NullLogger: return NullLogger() | 2 | 2023-12-15 14:36:50+00:00 | 2k |
Vlodson/Faculty-Choice-Assistant | backend/server/endpoints/natural_language.py | [
{
"identifier": "make_thread_for_user",
"path": "backend/llm/threads.py",
"snippet": "def make_thread_for_user() -> Thread:\n return CLIENT.beta.threads.create()"
},
{
"identifier": "retrieve_thread_for_user",
"path": "backend/llm/threads.py",
"snippet": "def retrieve_thread_for_user(... | from flask import Blueprint, abort, request, jsonify
from backend.llm.threads import (
make_thread_for_user,
retrieve_thread_for_user,
send_setup_message,
send_user_message,
create_run_for_thread,
retrieve_run_for_user,
get_last_message,
get_query_from_message,
)
from backend.ontology.qu... | 1,080 |
bp = Blueprint("llm", __name__)
@bp.route("/setup", methods=["GET"])
def setup_user() -> SetupUserResponse:
thread = make_thread_for_user()
_ = send_setup_message(thread)
|
bp = Blueprint("llm", __name__)
@bp.route("/setup", methods=["GET"])
def setup_user() -> SetupUserResponse:
thread = make_thread_for_user()
_ = send_setup_message(thread) | run = create_run_for_thread(thread) # for easier expansion of the API | 4 | 2023-12-21 17:55:05+00:00 | 2k |
stevej2608/reactpy-apexcharts | utils/fast_server.py | [
{
"identifier": "log",
"path": "utils/logger.py",
"snippet": ""
},
{
"identifier": "var_name",
"path": "utils/var_name.py",
"snippet": "def var_name(obj: Any, namespace: Dict[str, Any]) -> str:\r\n \"\"\"Return var name as a string\r\n\r\n Args:\r\n obj (Any): Variable ty be... | from typing import Callable
from fastapi import FastAPI
from reactpy.core.component import Component
from reactpy.backend.fastapi import configure, Options
from .logger import log, logging
from .var_name import var_name
from .fast_server_options import DEFAULT_OPTIONS
import sys
import signal
import multiprocessing
imp... | 802 |
app = FastAPI(description="ReactPy", version="0.1.0")
LOGS = [
"asgi-logger",
"concurrent.futures",
"concurrent",
"asyncio",
"uvicorn.error",
"uvicorn",
"watchfiles.watcher",
"watchfiles",
"watchfiles.main",
"fastapi",
"reactpy.backend",
"reactpy",
"reactpy._option... |
app = FastAPI(description="ReactPy", version="0.1.0")
LOGS = [
"asgi-logger",
"concurrent.futures",
"concurrent",
"asyncio",
"uvicorn.error",
"uvicorn",
"watchfiles.watcher",
"watchfiles",
"watchfiles.main",
"fastapi",
"reactpy.backend",
"reactpy",
"reactpy._option... | log.info("Uvicorn running on http://%s:%s (Press CTRL+C to quit)", host, port) | 0 | 2023-12-19 16:05:41+00:00 | 2k |
ict-bigdatalab/RIGHT | retrieval_analysis.py | [
{
"identifier": "read_line_examples_from_file",
"path": "get_datasets.py",
"snippet": "def read_line_examples_from_file(data_path):\n sequence = []\n with open(data_path, 'r', encoding='utf-8') as f:\n for line in f:\n line = line.strip(\"\\n\")\n if not line:\n ... | import json
from get_datasets import read_line_examples_from_file
from tqdm import tqdm
from eval_utils import f1 | 816 |
def get_hashtag_list(dst):
tags = dst.split('[SEP]')
target = []
for j in range(len(tags)):
tags[j] = tags[j].strip()
if tags[j] != '':
target.append(tags[j])
# if the dst is nothing
if len(target) == 0:
target.append('None')
# statistic_hashtags(hashtags)
... |
def get_hashtag_list(dst):
tags = dst.split('[SEP]')
target = []
for j in range(len(tags)):
tags[j] = tags[j].strip()
if tags[j] != '':
target.append(tags[j])
# if the dst is nothing
if len(target) == 0:
target.append('None')
# statistic_hashtags(hashtags)
... | f = f1(p, r) | 1 | 2023-12-16 06:00:53+00:00 | 2k |
shell-nlp/gpt_server | gpt_server/serving/main.py | [
{
"identifier": "get_free_tcp_port",
"path": "gpt_server/utils.py",
"snippet": "def get_free_tcp_port():\n \"\"\"获取可用的端口\"\"\"\n tcp = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n tcp.bind((\"\", 0))\n _, port = tcp.getsockname()\n tcp.close()\n return port"
},
{
"identi... | import yaml
import os
import sys
import subprocess
import signal
from pprint import pprint
from multiprocessing import Process
from gpt_server.utils import get_free_tcp_port, start_server, run_cmd, stop_server,delete_log | 1,037 |
# 配置根目录
root_dir = os.path.join(os.path.dirname(__file__), "..")
root_dir = os.path.abspath(root_dir)
sys.path.append(root_dir)
# 删除日志
delete_log(root_dir)
def signal_handler(signum, frame):
stop_server()
raise KeyboardInterrupt
signal.signal(signal.SIGINT, signal_handler)
with open("./config.yaml", "r") as... |
# 配置根目录
root_dir = os.path.join(os.path.dirname(__file__), "..")
root_dir = os.path.abspath(root_dir)
sys.path.append(root_dir)
# 删除日志
delete_log(root_dir)
def signal_handler(signum, frame):
stop_server()
raise KeyboardInterrupt
signal.signal(signal.SIGINT, signal_handler)
with open("./config.yaml", "r") as... | + f" --master_port {get_free_tcp_port()}" | 0 | 2023-12-16 07:43:28+00:00 | 2k |
LLM-Evaluation-s-Always-Fatiguing/leaf-playground-hub | rag_qa/rag_qa/scene.py | [
{
"identifier": "Examiner",
"path": "rag_qa/rag_qa/agents/examiner.py",
"snippet": "class Examiner(SceneStaticAgent, role_definition=ROLE_DEFINITION, cls_description=\"An agent who minitor the examine\"):\n config_cls = ExaminerConfig\n config: config_cls\n\n def __init__(self, config: config_c... | import asyncio
from typing import List, Optional
from pydantic import Field
from leaf_playground.core.workers import Logger
from leaf_playground.core.scene import Scene
from leaf_playground.core.scene_definition import SceneConfig
from leaf_playground.data.log_body import ActionLogBody
from leaf_playground.data.media i... | 1,386 |
class RagSceneLogBody(ActionLogBody):
references: Optional[List[MessageType]] = Field(default=None)
response: MessageType = Field(default=...)
ground_truth: Optional[Json] = Field(default=None)
RagSceneConfig = SceneConfig.create_config_model(
SCENE_DEFINITION,
additional_config_fields={"data... |
class RagSceneLogBody(ActionLogBody):
references: Optional[List[MessageType]] = Field(default=None)
response: MessageType = Field(default=...)
ground_truth: Optional[Json] = Field(default=None)
RagSceneConfig = SceneConfig.create_config_model(
SCENE_DEFINITION,
additional_config_fields={"data... | answer: ExamineeAnswer = await examinee.answer_question(question=q, examiner=self.examiner.profile) | 3 | 2023-12-21 03:09:08+00:00 | 2k |
djkcyl/ABot-NT | func/tool/mcping/mcping.py | [
{
"identifier": "SelfPicture",
"path": "utils/message/picture.py",
"snippet": "class SelfPicture:\n def __init__(self) -> None:\n self.s3file = Launart.current().get_component(S3FileService).s3file\n\n async def from_name(self, name: str) -> Picture:\n url = await self.s3file.get_pre... | import asyncio
import base64
import contextlib
import json
import re
import dns.resolver
from io import BytesIO
from avilla.core import Picture
from loguru import logger
from PIL import Image
from utils.message.picture import SelfPicture
from .statusping import StatusPing
| 1,539 |
def ping_status(host: str, port: int | None = None) -> dict:
if port is None:
with contextlib.suppress(Exception):
srv_records = dns.resolver.query(f"_minecraft._tcp.{host}", "SRV")
for srv in srv_records:
host = str(srv.target).rstrip(".")
... |
def ping_status(host: str, port: int | None = None) -> dict:
if port is None:
with contextlib.suppress(Exception):
srv_records = dns.resolver.query(f"_minecraft._tcp.{host}", "SRV")
for srv in srv_records:
host = str(srv.target).rstrip(".")
... | messages.append(await SelfPicture().from_data(image, "jpeg"))
| 0 | 2023-12-16 13:19:56+00:00 | 2k |
Chenyme/Chenyme-AAMT | AAMT.py | [
{
"identifier": "generate_srt_from_result",
"path": "utils/utils.py",
"snippet": "def generate_srt_from_result(result): # 格式化为SRT字幕的形式\r\n segments = result['segments']\r\n srt_content = ''\r\n segment_id = 1\r\n for segment in segments:\r\n start_time = int(segment['start'] * 1000)\... | import os
import json
import streamlit as st
import whisper
from utils.utils import generate_srt_from_result, tmp_filepath, openai_translate, srt_mv, cache, convert_size
| 1,557 | # 作者：chenyme
# 版本：v0.2.2
# 博客站：待更新
st.set_page_config(
page_title="AAMT v0.2.2",
page_icon="📊",
layout="wide", # 设置布局样式为宽展示
initial_sidebar_state="expanded" # 设置初始边栏状态为展开
)
st.title("Chenyme-AAMT")
st.write("##### AI全自动视频翻译")
with st.sidebar:
st.title("欢迎！")
st.write('''
... | # 作者：chenyme
# 版本：v0.2.2
# 博客站：待更新
st.set_page_config(
page_title="AAMT v0.2.2",
page_icon="📊",
layout="wide", # 设置布局样式为宽展示
initial_sidebar_state="expanded" # 设置初始边栏状态为展开
)
st.title("Chenyme-AAMT")
st.write("##### AI全自动视频翻译")
with st.sidebar:
st.title("欢迎！")
st.write('''
... | result = openai_translate(st.session_state.key, st.session_state.base, result) # 翻译成目标语言
| 2 | 2023-12-18 04:06:03+00:00 | 2k |
davidrs/logo-buddy | logo_buddy/main.py | [
{
"identifier": "preprocess",
"path": "logo_buddy/controlnet.py",
"snippet": "def preprocess(image, controlnet_path=None):\n if \"canny\" in controlnet_path:\n return canny_preprocess(image)\n else:\n return Image.fromarray(image)"
},
{
"identifier": "CN_MODELS",
"path": ... | import os
import os.path as op
import numpy as np
import torch
import cv2
import torch
from glob import glob
from diffusers import StableDiffusionPipeline, DiffusionPipeline
from diffusers import (
StableDiffusionControlNetPipeline,
ControlNetModel,
UniPCMultistepScheduler,
)
from diffusers.utils import loa... | 1,465 |
STEPS = 34
SEED = 12
MODELS = {
"real": "/Users/drustsmith/repos/stable-diffusion-webui/models/Stable-diffusion/realisticVisionV51_v51VAE.safetensors",
"anim": "/Users/drustsmith/repos/stable-diffusion-webui/models/Stable-diffusion/revAnimated_v122EOL.safetensors",
}
#
PROMPT_LIST = [
# Winter
{"tex... |
STEPS = 34
SEED = 12
MODELS = {
"real": "/Users/drustsmith/repos/stable-diffusion-webui/models/Stable-diffusion/realisticVisionV51_v51VAE.safetensors",
"anim": "/Users/drustsmith/repos/stable-diffusion-webui/models/Stable-diffusion/revAnimated_v122EOL.safetensors",
}
#
PROMPT_LIST = [
# Winter
{"tex... | for cn, cn_path in CN_MODELS.items(): | 1 | 2023-12-17 19:24:56+00:00 | 2k |
Varexa/Gateway | chat_exporter/construct/assets/embed.py | [
{
"identifier": "discord",
"path": "chat_exporter/ext/discord_import.py",
"snippet": ""
},
{
"identifier": "fill_out",
"path": "chat_exporter/ext/html_generator.py",
"snippet": "PARSE_MODE_NONE = 0\r\nPARSE_MODE_NO_MARKDOWN = 1\r\nPARSE_MODE_MARKDOWN = 2\r\nPARSE_MODE_EMBED = 3\r\nPARSE_... | import html
from chat_exporter.ext.discord_import import discord
from chat_exporter.ext.html_generator import (
fill_out,
embed_body,
embed_title,
embed_description,
embed_field,
embed_field_inline,
embed_footer,
embed_footer_icon,
embed_image,
embed_thumbnail,
e... | 894 |
modules_which_use_none = ["nextcord", "disnake"]
def _gather_checker():
if discord.module not in modules_which_use_none and hasattr(discord.Embed, "Empty"):
return discord.Embed.Empty
return None
class Embed:
r: str
g: str
b: str
title: str
description: str
... |
modules_which_use_none = ["nextcord", "disnake"]
def _gather_checker():
if discord.module not in modules_which_use_none and hasattr(discord.Embed, "Empty"):
return discord.Embed.Empty
return None
class Embed:
r: str
g: str
b: str
title: str
description: str
... | author_icon = await fill_out(self.guild, embed_author_icon, [
| 1 | 2023-12-18 14:17:31+00:00 | 2k |
mariaalfaroc/a2s-transformer | my_utils/metrics.py | [
{
"identifier": "VOICE_CHANGE_TOKEN",
"path": "my_utils/encoding_convertions.py",
"snippet": "VOICE_CHANGE_TOKEN = \"<COC>\""
},
{
"identifier": "STEP_CHANGE_TOKEN",
"path": "my_utils/encoding_convertions.py",
"snippet": "STEP_CHANGE_TOKEN = \"<COR>\""
}
] | import os
import shutil
from music21 import converter as converterm21
from pyMV2H.utils.mv2h import MV2H
from pyMV2H.metrics.mv2h import mv2h
from pyMV2H.utils.music import Music
from pyMV2H.converter.midi_converter import MidiConverter as Converter
from .encoding_convertions import VOICE_CHANGE_TOKEN, STEP_CHANGE_TOKE... | 927 |
def compute_metrics(y_true, y_pred):
################################# Sym-ER and Seq-ER:
metrics = compute_ed_metrics(y_true=y_true, y_pred=y_pred)
################################# MV2H:
mv2h_dict = compute_mv2h_metrics(y_true=y_true, y_pred=y_pred)
metrics.update(mv2h_dict)
return metrics... |
def compute_metrics(y_true, y_pred):
################################# Sym-ER and Seq-ER:
metrics = compute_ed_metrics(y_true=y_true, y_pred=y_pred)
################################# MV2H:
mv2h_dict = compute_mv2h_metrics(y_true=y_true, y_pred=y_pred)
metrics.update(mv2h_dict)
return metrics... | if token == STEP_CHANGE_TOKEN: | 1 | 2023-12-18 20:01:00+00:00 | 2k |
YashsviG/rootkit | victim.py | [
{
"identifier": "port_knocking",
"path": "portknocker.py",
"snippet": "def port_knocking(victim_ip):\n \"\"\"\n Perform port knocking on the victim side to authenticate the commander.\n\n Args:\n victim_ip (str): IP address of the victim.\n\n Returns:\n tuple: IP address and po... | import argparse
import setproctitle
import shutil
from keylogger import *
from watcher import *
from portknocker import port_knocking
from processname import choose_process_name
from utils import get_ip_address, transfer_keylog_file, check_exists
| 1,279 |
def handle_command(command: int, keylogger, watcher, covert):
"""
Handle the received command.
Args:
command (int): Received command.
keylogger (Keylogger): Keylogger instance.
watcher (Watcher): Watcher instance.
covert (CovertChannel): Covert channel instance.
... |
def handle_command(command: int, keylogger, watcher, covert):
"""
Handle the received command.
Args:
command (int): Received command.
keylogger (Keylogger): Keylogger instance.
watcher (Watcher): Watcher instance.
covert (CovertChannel): Covert channel instance.
... | i = check_exists(file)
| 4 | 2023-12-19 18:54:22+00:00 | 2k |
yacinxx/dnakey | enginev2.py | [
{
"identifier": "ConfigManager",
"path": "profile_config/config_manager.py",
"snippet": "class ConfigManager:\r\n def __init__(self, prime_key:str) -> None:\r\n with open(\"profile_config/profile_config.json\", \"r\") as f: \r\n self.profile_data = __import__(\"json\").loads(f.read(... | from cryptography.fernet import Fernet
from profile_config.config_manager import ConfigManager
from license.license_manager import VERSION
import random, json, string, datetime
| 1,550 |
class DNAEngine():
def __init__(
self,
has_key="test",
profile_name="profile_test",
activate_merge=True,
save_cookies=True,
**advance_settings):
self.has_key = has_key
self.profile_name = profile_name
... |
class DNAEngine():
def __init__(
self,
has_key="test",
profile_name="profile_test",
activate_merge=True,
save_cookies=True,
**advance_settings):
self.has_key = has_key
self.profile_name = profile_name
... | self.config_manager = ConfigManager(self.config_has_key)
| 0 | 2023-12-18 22:04:13+00:00 | 2k |
tamnva/hydroecolstm | examples/example_run.py | [
{
"identifier": "run_train",
"path": "hydroecolstm/model_run.py",
"snippet": "def run_train(config_file):\n \n # Load configuration\n config = read_config(config_file)\n\n # Read and split data\n data = read_train_test_data(config)\n \n # Scale/transformer name for static, dynamic, ... | from hydroecolstm.model_run import run_train
from hydroecolstm.utility.plot import plot
from hydroecolstm.interface.main_gui import show_gui | 941 |
# Import hydroecolstm function
#-----------------------------------------------------------------------------#
# Run the model #
#-----------------------------------------------------------------------------#
# Configuration file
config_file = "C:/Users/ng... |
# Import hydroecolstm function
#-----------------------------------------------------------------------------#
# Run the model #
#-----------------------------------------------------------------------------#
# Configuration file
config_file = "C:/Users/ng... | show_gui() | 2 | 2023-12-20 09:11:36+00:00 | 2k |
LuhhLu/Predictive-Video-Segmentation | unet_train.py | [
{
"identifier": "Load_unet",
"path": "Unet.py",
"snippet": "def Load_unet(path=None):\n if path:\n unet_model = UNet(n_channels=3, n_classes=49)\n unet_model.load_state_dict(torch.load(path))\n else:\n unet_model = UNet(n_channels=3, n_classes=49)\n return unet_model"
},
... | from tqdm import tqdm
from torch.utils.data import DataLoader
from torchvision import transforms
from Unet import Load_unet, CustomDataset, WeightedBCEWithLogitsLoss
import torch
import torch.optim as optim
import argparse | 943 |
def main():
# Command-line arguments
parser = argparse.ArgumentParser(description='Train UNet with custom settings')
parser.add_argument('--lr', type=float, default=0.001, help='Learning rate')
parser.add_argument('--batch', type=int, default=64, help='Batch size')
parser.add_argument('--res', type... |
def main():
# Command-line arguments
parser = argparse.ArgumentParser(description='Train UNet with custom settings')
parser.add_argument('--lr', type=float, default=0.001, help='Learning rate')
parser.add_argument('--batch', type=int, default=64, help='Batch size')
parser.add_argument('--res', type... | train_dataset = CustomDataset('unet_train/images', 'unet_train/masks', transform) | 1 | 2023-12-17 20:39:14+00:00 | 2k |
garinops/chat-E-AI | embed/clients/itchat/messages/friend.py | [
{
"identifier": "ITCHAT_CALL_CODE_SELF",
"path": "config/settings.py",
"snippet": "ITCHAT_CALL_CODE_SELF = \"AI\""
},
{
"identifier": "ITCHAT_CALL_CODE",
"path": "config/settings.py",
"snippet": "ITCHAT_CALL_CODE = \"AI\""
},
{
"identifier": "ITCHAT_WHITELIST_FRIEND",
"path":... | from config.settings import ITCHAT_CALL_CODE_SELF, ITCHAT_CALL_CODE, ITCHAT_WHITELIST_FRIEND
from embed.reply.text import EReplyText
from models.messages import MessageItchat, MessageCea
from models.send import Send | 1,025 |
def handle_friend_message(client, message: MessageItchat) -> Send:
_callCodeSelf = ITCHAT_CALL_CODE_SELF
_callCode = ITCHAT_CALL_CODE
|
def handle_friend_message(client, message: MessageItchat) -> Send:
_callCodeSelf = ITCHAT_CALL_CODE_SELF
_callCode = ITCHAT_CALL_CODE | _whiteListFriend = ITCHAT_WHITELIST_FRIEND | 2 | 2023-12-16 17:02:13+00:00 | 2k |
ruudjuffermans/Event-Driven-Backtester | backtester/execution.py | [
{
"identifier": "FillEvent",
"path": "backtester/events.py",
"snippet": "class FillEvent(Event):\n \"\"\"\n Fill event once an order based on the response from the broker\n\n Parameters:\n datetime - A datetime at which the signal is created.\n symbol - The symbol for current asset.\n ... | from abc import abstractmethod
from datetime import datetime
from .events import FillEvent, OrderEvent | 653 |
class ExecutionHandler:
def register(self, events):
self.events = events
@abstractmethod
def execute_order(self, event):
raise NotImplementedError("Should implement execute_order()")
class SimulatedExecutionHandler(ExecutionHandler):
def __init__(self):
pass
def execut... |
class ExecutionHandler:
def register(self, events):
self.events = events
@abstractmethod
def execute_order(self, event):
raise NotImplementedError("Should implement execute_order()")
class SimulatedExecutionHandler(ExecutionHandler):
def __init__(self):
pass
def execut... | if isinstance(event, OrderEvent): | 1 | 2023-12-16 21:09:00+00:00 | 2k |
liebrandapps/FindMyGUI | findmy/request_reports.py | [
{
"identifier": "icloud_login_mobileme",
"path": "findmy/pypush_gsa_icloud.py",
"snippet": "def icloud_login_mobileme(ctx, second_factor='sms'):\n username = ctx.cfg.appleId_appleId\n password = ctx.cfg.appleId_password\n anisetteUrl = ctx.cfg.general_anisetteHost + \":\" + str(ctx.cfg.general_... | import base64
import datetime
import hashlib
import json
import os
import struct
import requests
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives.asymmetric import ec
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
from findmy.pypush_gsa_icloud im... | 1,373 |
class FindMy:
def __init__(self, ctx):
self.ctx = ctx
def sha256(self, data):
digest = hashlib.new("sha256")
digest.update(data)
return digest.digest()
def decrypt(self, enc_data, algorithm_dkey, mode):
decryptor = Cipher(algorithm_dkey, mode, default_backend()... |
class FindMy:
def __init__(self, ctx):
self.ctx = ctx
def sha256(self, data):
digest = hashlib.new("sha256")
digest.update(data)
return digest.digest()
def decrypt(self, enc_data, algorithm_dkey, mode):
decryptor = Cipher(algorithm_dkey, mode, default_backend()... | mobileme = icloud_login_mobileme(self.ctx, second_factor=second_factor) | 0 | 2023-12-16 12:39:52+00:00 | 2k |
Samuel-Effiong/Django-Dynamic-Table | django_dynamic_table/models.py | [
{
"identifier": "TableHaveNoRow",
"path": "django_dynamic_table/errors.py",
"snippet": "class TableHaveNoRow(DynamicTableError):\r\n pass\r"
},
{
"identifier": "TableHaveNoColumn",
"path": "django_dynamic_table/errors.py",
"snippet": "class TableHaveNoColumn(DynamicTableError):\r\n ... | from typing import Sequence
from datetime import datetime
from django.db import models
from django.utils import timezone
from django.utils.translation import gettext_lazy as _
from .errors import (
TableHaveNoRow, TableHaveNoColumn, ColumnNotInTable,
RowNotInTable, DuplicateColumnInTable, DynamicTableErr... | 934 | """
Creating a Dynamic Table using conventional Django standard
This Table gives you more control over it manipulation than Django models
Developed by: Samuel Effiong Nkopuruk
Email: senai.nkop@gmail.com
"""
__SUPPORTED_DATA_TYPE_CHOICES__ = (
('char', 'Char'),
('int', 'Int'),
('flo... | """
Creating a Dynamic Table using conventional Django standard
This Table gives you more control over it manipulation than Django models
Developed by: Samuel Effiong Nkopuruk
Email: senai.nkop@gmail.com
"""
__SUPPORTED_DATA_TYPE_CHOICES__ = (
('char', 'Char'),
('int', 'Int'),
('flo... | raise DuplicateColumnInTable()
| 4 | 2023-12-19 15:50:38+00:00 | 2k |
gsamil/text-classification | recommender/train.py | [
{
"identifier": "vocab",
"path": "data.py",
"snippet": "class ClassificationSample(BaseModel):\ndef preprocess_text(text: str) -> str:\ndef get_samples_from_file(file_path: str) -> list[ClassificationSample]:\ndef stratify_samples(\n samples: list[ClassificationSample], number_per_sample: int\n) -> l... | import torch
import time
import os
from torch import nn
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import ExponentialLR
from data import (
vocab,
get_samples_from_file,
stratify_samples,
save_categories,
load_categories,
)
from model import TextClassifier, TrainingParamete... | 1,057 |
# Set `train_file`, `test_file` and `model_dir` apropriately.
# Set `negative_samples` to the number of negative samples you want to use.
# run with `export PYTHONPATH=. && python recommender/train.py` in the main directory.
train_file = "./data/train_cleaned.csv"
test_file = "./data/test_cleaned.csv"
model_dir = "./... |
# Set `train_file`, `test_file` and `model_dir` apropriately.
# Set `negative_samples` to the number of negative samples you want to use.
# run with `export PYTHONPATH=. && python recommender/train.py` in the main directory.
train_file = "./data/train_cleaned.csv"
test_file = "./data/test_cleaned.csv"
model_dir = "./... | vocab_size=len(vocab), | 0 | 2023-12-17 11:37:37+00:00 | 2k |
zhcui/polar_preview | polar/basis/trans_1e.py | [
{
"identifier": "mdot",
"path": "polar/utils/misc.py",
"snippet": "def mdot(*args):\n \"\"\"\n Reduced matrix dot.\n \"\"\"\n return reduce(np.dot, args)"
},
{
"identifier": "kdot",
"path": "polar/utils/misc.py",
"snippet": "def kdot(a, b):\n \"\"\"\n Matrix dot with kp... | import numpy as np
import scipy.linalg as la
from polar.utils.misc import (mdot, kdot, get_spin_dim, add_spin_dim) | 798 | #!/usr/bin/env python
"""
Transform 1e quantities.
Authors:
Zhi-Hao Cui
Tianyu Zhu
Shunyue Yuan
"""
# *****************************************************************************
# Transform functions AO -> LO and LO -> AO
# for h1 and rdm1
# *********************************************************... | #!/usr/bin/env python
"""
Transform 1e quantities.
Authors:
Zhi-Hao Cui
Tianyu Zhu
Shunyue Yuan
"""
# *****************************************************************************
# Transform functions AO -> LO and LO -> AO
# for h1 and rdm1
# *********************************************************... | h_lo_lo[k] = mdot(C_ao_lo[k].conj().T, h_ao_ao[k], C_ao_lo[k]) | 0 | 2023-12-18 07:39:51+00:00 | 2k |
Subsets and Splits
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that have consistent code formatting levels across multiple scales (2k, 4k, 8k, 12k) and reveals the structured formatting patterns within these repositories.
SQL Console for tianyang/repobench_python_v1.1
Compares cross-file and in-file code structure patterns across different complexity levels, revealing how file organization strategies vary with code size and potentially informing better code architecture decisions.
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that have complete performance data across all seven code complexity levels, revealing consistent benchmarking patterns across different code sizes.
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that contain all 7 distinct quality levels (2k through 32k), revealing complete datasets that might be useful for comprehensive analysis.