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 |
|---|---|---|---|---|---|---|---|---|---|---|
zkarpinski/codeinsight-sdk-python | codeinsight_sdk/client.py | [
{
"identifier": "ProjectHandler",
"path": "codeinsight_sdk/handlers.py",
"snippet": "class ProjectHandler(Handler):\n def __init__(self, client):\n super().__init__(client)\n self.cls = Project\n\n def create(self, name:str, description:str = None, folder:str = None,\n ... | import requests
import logging
from .handlers import ProjectHandler, Handler, ReportHandler
from .models import Project, ProjectInventory, Report
from .exceptions import CodeInsightError | 2,652 |
logger = logging.getLogger(__name__)
class CodeInsightClient:
def __init__(self,
base_url: str,
api_token: str,
timeout: int = 60,
verify_ssl: bool = True
):
self.base_url = base_url
self.api_url = f"{base_url}/c... |
logger = logging.getLogger(__name__)
class CodeInsightClient:
def __init__(self,
base_url: str,
api_token: str,
timeout: int = 60,
verify_ssl: bool = True
):
self.base_url = base_url
self.api_url = f"{base_url}/c... | def projects(self) -> ProjectHandler: | 0 | 2023-12-29 00:49:12+00:00 | 4k |
daswer123/rvc-python | rvc_python/lib/infer_pack/modules.py | [
{
"identifier": "commons",
"path": "rvc_python/lib/infer_pack/commons.py",
"snippet": "def init_weights(m, mean=0.0, std=0.01):\ndef get_padding(kernel_size, dilation=1):\ndef convert_pad_shape(pad_shape):\ndef kl_divergence(m_p, logs_p, m_q, logs_q):\ndef rand_gumbel(shape):\ndef rand_gumbel_like(x):\n... | import copy
import math
import numpy as np
import scipy
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm
from rvc_python.lib.infer_pack import commons
from rvc_python.lib.infe... | 2,889 | class ElementwiseAffine(nn.Module):
def __init__(self, channels):
super().__init__()
self.channels = channels
self.m = nn.Parameter(torch.zeros(channels, 1))
self.logs = nn.Parameter(torch.zeros(channels, 1))
def forward(self, x, x_mask, reverse=False, **kwargs):
if not ... |
LRELU_SLOPE = 0.1
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-5):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(torch.zeros(channels))
def forward(self, x):
... | x1, logabsdet = piecewise_rational_quadratic_transform( | 3 | 2023-12-26 19:05:42+00:00 | 4k |
Eeems-Org/remarkable-update-fuse | remarkable_update_fuse/fuse.py | [
{
"identifier": "UpdateImage",
"path": "remarkable_update_fuse/image.py",
"snippet": "class UpdateImage(io.RawIOBase):\n _manifest = None\n _offset = -1\n _size = 0\n _pos = 0\n\n def __init__(self, update_file, cache_size=500, cache_ttl=60):\n self.update_file = update_file\n ... | import errno
import os
import queue
import sys
import threading
import time
import warnings
import fuse
import ext4
from pathlib import PurePosixPath
from .image import UpdateImage
from .image import UpdateImageSignatureException
from .threads import KillableThread | 2,927 | f" {self.modifiers}",
" -o ",
]
)
+ ",\n ".join(self._str_core())
+ " >"
)
class FuseOptParse(fuse.FuseOptParse):
def __init__(self, *args, **kw):
fuse.FuseOptParse.__init__(self, *args, **kw)
de... |
fuse.fuse_python_api = (0, 2)
class ImageException(Exception):
pass
class FuseArgs(fuse.FuseArgs):
def __init__(self):
fuse.FuseArgs.__init__(self)
self.update_file = None
def __str__(self):
return (
"\n".join(
[
f"< {self.upd... | except UpdateImageSignatureException: | 1 | 2023-12-28 06:13:21+00:00 | 4k |
run-llama/rags | core/param_cache.py | [
{
"identifier": "load_data",
"path": "core/utils.py",
"snippet": "def load_data(\n file_names: Optional[List[str]] = None,\n directory: Optional[str] = None,\n urls: Optional[List[str]] = None,\n) -> List[Document]:\n \"\"\"Load data.\"\"\"\n file_names = file_names or []\n directory =... | from pydantic import BaseModel, Field
from llama_index import (
VectorStoreIndex,
StorageContext,
load_index_from_storage,
)
from typing import List, cast, Optional
from llama_index.chat_engine.types import BaseChatEngine
from pathlib import Path
from core.utils import (
load_data,
get_tool_objects,... | 2,616 | """Param cache."""
class ParamCache(BaseModel):
"""Cache for RAG agent builder.
Created a wrapper class around a dict in case we wanted to more explicitly
type different items in the cache.
"""
# arbitrary types
class Config:
arbitrary_types_allowed = True
# system prompt
... | """Param cache."""
class ParamCache(BaseModel):
"""Cache for RAG agent builder.
Created a wrapper class around a dict in case we wanted to more explicitly
type different items in the cache.
"""
# arbitrary types
class Config:
arbitrary_types_allowed = True
# system prompt
... | additional_tools = get_tool_objects(cache_dict["tools"]) | 1 | 2023-11-16 07:49:44+00:00 | 4k |
open-mmlab/Amphion | models/tts/naturalspeech2/prior_encoder.py | [
{
"identifier": "TransformerEncoder",
"path": "modules/naturalpseech2/transformers.py",
"snippet": "class TransformerEncoder(nn.Module):\n def __init__(\n self,\n enc_emb_tokens=None,\n encoder_layer=None,\n encoder_hidden=None,\n encoder_head=None,\n conv_fi... | import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from modules.naturalpseech2.transformers import (
TransformerEncoder,
DurationPredictor,
PitchPredictor,
LengthRegulator,
) | 3,057 | # Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
class PriorEncoder(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.enc_emb_tokens = nn.Embedding(
... | # Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
class PriorEncoder(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.enc_emb_tokens = nn.Embedding(
... | self.pitch_predictor = PitchPredictor(cfg.pitch_predictor) | 2 | 2023-11-15 09:19:27+00:00 | 4k |
KwaiKEG/KwaiAgents | kwaiagents/agents/prompts.py | [
{
"identifier": "get_current_time_and_date",
"path": "kwaiagents/utils/date_utils.py",
"snippet": "def get_current_time_and_date(lang=\"en\"):\n solar = Solar.fromDate(datetime.now())\n lunar = solar.getLunar()\n if lang == \"zh\":\n rst = f'''\n当前阳历日期和时间: {str(datetime.now())}\n当前星期: 星期... | import json
from kwaiagents.utils.date_utils import get_current_time_and_date
from kwaiagents.utils.function_utils import transform_to_openai_function | 1,925 | planning_prompt_template = """
你是{agent_name}，{agent_bio}
{agent_instructions}
当前阶段是任务规划阶段，你将给定目标或问题，你的决策将独立执行而不依赖于人类的帮助，请发挥LLM的优势并且追求高效的策略进行任务规划。
1.你有~4000字的短期记忆
2.不需要用户的帮助
3.规划的时候可以用参考工具中提到的工具
4.互联网搜索、信息聚合和鉴别真伪的能力
5.保持谦逊，对自己没把握的问题，尽可能调用command，但尽量少调用，不能重复调用
6.当你从自身知识或者历史记忆中能得出结论，请聪明且高效，完成任务并得出结论
7.经常建设性地自我批评整个行为大局，反思... |
planning_prompt_template = """
你是{agent_name}，{agent_bio}
{agent_instructions}
当前阶段是任务规划阶段，你将给定目标或问题，你的决策将独立执行而不依赖于人类的帮助，请发挥LLM的优势并且追求高效的策略进行任务规划。
1.你有~4000字的短期记忆
2.不需要用户的帮助
3.规划的时候可以用参考工具中提到的工具
4.互联网搜索、信息聚合和鉴别真伪的能力
5.保持谦逊，对自己没把握的问题，尽可能调用command，但尽量少调用，不能重复调用
6.当你从自身知识或者历史记忆中能得出结论，请聪明且高效，完成任务并得出结论
7.经常建设性地自我批评整个行为大局... | "current_date_and_time": get_current_time_and_date(lang), | 0 | 2023-11-13 03:37:02+00:00 | 4k |
EnVision-Research/LucidDreamer | scene/gaussian_model.py | [
{
"identifier": "inverse_sigmoid",
"path": "utils/general_utils.py",
"snippet": "def inverse_sigmoid(x):\n return torch.log(x/(1-x))"
},
{
"identifier": "get_expon_lr_func",
"path": "utils/general_utils.py",
"snippet": "def get_expon_lr_func(\n lr_init, lr_final, lr_delay_steps=0, ... | import torch
import numpy as np
import os
from utils.general_utils import inverse_sigmoid, get_expon_lr_func, build_rotation
from torch import nn
from utils.system_utils import mkdir_p
from plyfile import PlyData, PlyElement
from utils.sh_utils import RGB2SH,SH2RGB
from simple_knn._C import distCUDA2
from utils.graphic... | 3,525 |
print("Number of points at initialisation : ", fused_point_cloud.shape[0])
dist2 = torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001)
scales = torch.log(torch.sqrt(dist2))[...,None].repeat(1, 3)
rots = torch.zeros((fused_point_cloud.shape[0],... | #
# 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
#
# from .resnet import *
class Gau... | np.savetxt(os.path.join(os.path.split(path)[0],"point_cloud_rgb.txt"),np.concatenate((xyz, SH2RGB(f_dc)), axis=1)) | 5 | 2023-11-18 08:05:50+00:00 | 4k |
VRSEN/agency-swarm | agency_swarm/tools/browsing/SelectDropdown.py | [
{
"identifier": "BaseTool",
"path": "agency_swarm/tools/base_tool.py",
"snippet": "class BaseTool(OpenAISchema, ABC):\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n # # Exclude 'run' method from Pydantic model fields\n # self.model_fields.pop(\"run\", None)\n\n ... | import json
from pydantic import Field
from selenium.webdriver.common.by import By
from selenium.webdriver.support.select import Select
from agency_swarm.tools import BaseTool
from agency_swarm.tools.browsing.util import get_b64_screenshot
from agency_swarm.tools.browsing.util import get_web_driver, set_web_driver
from... | 2,335 |
class SelectDropdown(BaseTool):
"""
This tool selects an option in a dropdown on the current web page based on the description of that element and which option to select.
"""
description: str = Field(
..., description="Description of which option to select and for which dropdown on the page... |
class SelectDropdown(BaseTool):
"""
This tool selects an option in a dropdown on the current web page based on the description of that element and which option to select.
"""
description: str = Field(
..., description="Description of which option to select and for which dropdown on the page... | screenshot = get_b64_screenshot(wd) | 1 | 2023-11-16 02:29:26+00:00 | 4k |
resemble-ai/resemble-enhance | resemble_enhance/enhancer/lcfm/lcfm.py | [
{
"identifier": "CFM",
"path": "resemble_enhance/enhancer/lcfm/cfm.py",
"snippet": "class CFM(nn.Module):\n \"\"\"\n This mixin is for general diffusion models.\n\n ψ0 stands for the gaussian noise, and ψ1 is the data point.\n\n Here we follow the CFM style:\n The generation process (... | import logging
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from enum import Enum
from torch import Tensor, nn
from .cfm import CFM
from .irmae import IRMAE, IRMAEOutput
from ...utils.train_loop import TrainLoop | 2,245 |
logger = logging.getLogger(__name__)
def freeze_(module):
for p in module.parameters():
p.requires_grad_(False)
class LCFM(nn.Module):
class Mode(Enum):
AE = "ae"
|
logger = logging.getLogger(__name__)
def freeze_(module):
for p in module.parameters():
p.requires_grad_(False)
class LCFM(nn.Module):
class Mode(Enum):
AE = "ae" | CFM = "cfm" | 0 | 2023-11-15 08:15:51+00:00 | 4k |
PKU-YuanGroup/Chat-UniVi | visualization.py | [
{
"identifier": "CLIPVisionTower",
"path": "ChatUniVi/model/multimodal_encoder/clip_encoder.py",
"snippet": "class CLIPVisionTower(nn.Module):\n def __init__(self, vision_tower, args=None, delay_load=False):\n super().__init__()\n\n self.is_loaded = False\n\n self.vision_tower_na... | import numpy as np
import math
import os
import torch
from PIL import Image
from ChatUniVi.model.multimodal_encoder.clip_encoder import CLIPVisionTower
from ChatUniVi.model.cluster import CTM, TCBlock | 2,188 |
def split(image, patch_size=14, idx=None):
img = np.asarray(image, dtype=np.uint8).copy()
h, w, _ = img.shape
horizontal_lines = [i for i in range(patch_size, h, patch_size)]
vertical_lines = [i for i in range(patch_size, w, patch_size)]
for i in horizontal_lines:
for j in range(w):
... |
def split(image, patch_size=14, idx=None):
img = np.asarray(image, dtype=np.uint8).copy()
h, w, _ = img.shape
horizontal_lines = [i for i in range(patch_size, h, patch_size)]
vertical_lines = [i for i in range(patch_size, w, patch_size)]
for i in horizontal_lines:
for j in range(w):
... | ctm0 = CTM(sample_ratio=64, embed_dim=1024, dim_out=1024, k=32) | 1 | 2023-11-13 11:52:56+00:00 | 4k |
tatsu-lab/gpt_paper_assistant | filter_papers.py | [
{
"identifier": "Paper",
"path": "arxiv_scraper.py",
"snippet": "class Paper:\n # paper class should track the list of authors, paper title, abstract, arxiv id\n authors: List[str]\n title: str\n abstract: str\n arxiv_id: str\n\n # add a hash function using arxiv_id\n def __hash__(s... | import configparser
import dataclasses
import json
import os
import re
import retry
from collections import defaultdict
from typing import List
from openai import OpenAI
from tqdm import tqdm
from arxiv_scraper import Paper
from arxiv_scraper import EnhancedJSONEncoder | 2,395 | + "Abstract: "
+ paper_entry.abstract[:4000]
)
return new_str
def batched(items, batch_size):
# takes a list and returns a list of list with batch_size
return [items[i : i + batch_size] for i in range(0, len(items), batch_size)]
def filter_papers_by_title(
papers: List[Paper], ba... |
def filter_by_author(all_authors, papers, author_targets, config):
# filter and parse the papers
selected_papers = {} # pass to output
all_papers = {} # dict for later filtering
sort_dict = {} # dict storing key and score
# author based selection
for paper in papers:
all_papers[p... | json.dump(scored_batches, outfile, cls=EnhancedJSONEncoder, indent=4) | 1 | 2023-11-13 15:19:38+00:00 | 4k |
BobaZooba/xllm | tests/unit/collators/test_completion.py | [
{
"identifier": "enums",
"path": "src/xllm/enums.py",
"snippet": "class General:\nclass Transformers:\nclass Registry:\nclass Datasets:\nclass Collators:\nclass Trainers:\nclass Experiments:\nclass EnvironmentVariables:\nclass LogLevel:"
},
{
"identifier": "CompletionCollator",
"path": "src/... | from typing import Optional
from torch import Tensor
from transformers import PreTrainedTokenizer
from src.xllm import enums
from src.xllm.collators.completion import CompletionCollator
from tests.helpers.dummy_data import DATA
import pytest | 2,698 | # Copyright 2023 Boris Zubarev. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | # Copyright 2023 Boris Zubarev. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | condition_result = (batch[enums.Transformers.labels][:, :2] == llama_tokenizer.pad_token_id).unique() | 0 | 2023-11-10 17:55:03+00:00 | 4k |
banodoco/Steerable-Motion | imports/AdvancedControlNet/weight_nodes.py | [
{
"identifier": "TimestepKeyframeImport",
"path": "imports/AdvancedControlNet/control.py",
"snippet": "class TimestepKeyframeImport:\n def __init__(self,\n start_percent: float = 0.0,\n strength: float = 1.0,\n interpolation: str = StrengthInterpolation... | from torch import Tensor
from .control import TimestepKeyframeImport, TimestepKeyframeGroupImport, ControlWeightsImport, get_properly_arranged_t2i_weights, linear_conversion
from .logger import logger
import torch | 1,814 |
WEIGHTS_RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
class DefaultWeightsImport:
@classmethod
def INPUT_TYPES(s):
return {
}
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
RETURN_NAMES = WEIGHTS_RETURN_NAMES
FUNCTION = "load_weights"
CATEGORY = "Adv-Contro... |
WEIGHTS_RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
class DefaultWeightsImport:
@classmethod
def INPUT_TYPES(s):
return {
}
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
RETURN_NAMES = WEIGHTS_RETURN_NAMES
FUNCTION = "load_weights"
CATEGORY = "Adv-Contro... | return (weights, TimestepKeyframeGroupImport.default(TimestepKeyframeImport(control_weights=weights))) | 1 | 2023-11-11 01:26:26+00:00 | 4k |
x0rzavi/github-readme-terminal | gifos/gifos.py | [
{
"identifier": "ConvertAnsiEscape",
"path": "gifos/utils/convert_ansi_escape.py",
"snippet": "class ConvertAnsiEscape:\n \"\"\"A class for converting ANSI escape codes to color values.\"\"\"\n\n __color_scheme = gifos_settings.get(\"general\", {}).get(\"color_scheme\")\n\n @staticmethod\n d... | import os
import random
import re
import sys
from math import ceil
from pathlib import Path
from shutil import rmtree
from icecream import ic
from PIL import Image, ImageDraw, ImageFont
from gifos.utils.convert_ansi_escape import ConvertAnsiEscape
from gifos.utils.load_config import gifos_settings | 2,826 | # TODO:
# [] Documentation
# [] proper file paths
# [] incremental text effect
# [] Better implementations for non monospace fonts
# [] Support all ANSI escape sequence forms
# [] Optimization + better code quality
# [] Test cases
# [] GIF maker implementation
# [] Scriptable input file
frame_base_name = gifos_sett... | # TODO:
# [] Documentation
# [] proper file paths
# [] incremental text effect
# [] Better implementations for non monospace fonts
# [] Support all ANSI escape sequence forms
# [] Optimization + better code quality
# [] Test cases
# [] GIF maker implementation
# [] Scriptable input file
frame_base_name = gifos_sett... | self.__txt_color = self.__def_txt_color = ConvertAnsiEscape.convert("39").data | 0 | 2023-11-17 06:21:18+00:00 | 4k |
Zaloog/kanban-python | src/kanban_python/interface.py | [
{
"identifier": "cfg",
"path": "src/kanban_python/config.py",
"snippet": "class KanbanConfig:\n def __init__(self, path=CONFIG_FILE_PATH) -> None:\n def __repr__(self) -> str:\n def save(self):\n def config(self) -> configparser.ConfigParser:\n def active_board(self) -> str:\n def acti... | import calendar
from datetime import datetime
from itertools import zip_longest
from rich.prompt import Confirm, IntPrompt, Prompt
from rich.table import Table
from .config import cfg
from .constants import (
BOARD_CAPTION_STRING,
COLOR_DICT,
CONFIG_FILE_PATH,
FOOTER,
REPORT_COLORS,
)
from .utils im... | 1,978 |
# Board
#####################################################################################
def create_table(data: dict) -> Table:
status_dict = create_status_dict_for_rows(data=data, vis_cols=cfg.vis_cols)
table_name = cfg.active_board
table = Table(
title=f"[blue]Active Board: {table_name}[/... |
# Board
#####################################################################################
def create_table(data: dict) -> Table:
status_dict = create_status_dict_for_rows(data=data, vis_cols=cfg.vis_cols)
table_name = cfg.active_board
table = Table(
title=f"[blue]Active Board: {table_name}[... | "Creation_Date": current_time_to_str(), | 6 | 2023-11-11 14:43:55+00:00 | 4k |
AMAAI-Lab/mustango | audioldm/latent_diffusion/ddpm.py | [
{
"identifier": "exists",
"path": "audioldm/utils.py",
"snippet": "def exists(x):\n return x is not None"
},
{
"identifier": "default",
"path": "audioldm/utils.py",
"snippet": "def default(val, d):\n if exists(val):\n return val\n return d() if isfunction(d) else d"
},
... | import sys
import os
import torch
import torch.nn as nn
import numpy as np
import soundfile as sf
import os
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from audioldm.utils import exists, default, count_params, instantiate_from_config
from audioldm.latent_diffusion.ema impor... | 3,317 | log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.0,
v_posterior=0.0, # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=... | """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https... | betas = make_beta_schedule( | 5 | 2023-11-14 23:29:31+00:00 | 4k |
lxmusics/lx-music-api-server-python | main.py | [
{
"identifier": "config",
"path": "common/config.py",
"snippet": "def get_data_connection():\ndef get_cache_connection():\ndef handle_default_config():\ndef load_data():\ndef save_data(config_data):\ndef getCache(module, key):\ndef updateCache(module, key, data):\ndef resetRequestTime(ip):\ndef updateRe... | import sys
import ujson as json
import threading
import traceback
import modules
import asyncio
import aiohttp
import time
import concurrent
from common import config
from common import lxsecurity
from common import log
from common import Httpx
from common import variable
from common import scheduler
from common im... | 2,009 |
if ((sys.version_info.major == 3 and sys.version_info.minor < 6) or sys.version_info.major == 2):
print('Python版本过低，请使用Python 3.6+ ')
sys.exit(1)
def handleResult(dic, status = 200):
return Response(body = json.dumps(dic, indent=2, ensure_ascii=False), content_type='application/json', status = status)
... | #!/usr/bin/env python3
# ----------------------------------------
# - mode: python -
# - author: helloplhm-qwq -
# - name: main.py -
# - project: lx-music-api-server -
# - license: MIT -
# ----------------------------------------
# This file is part of the "lx-music-api-server" project.
if ((sys.version_info.ma... | app.router.add_get('/script', lx_script.generate_script_response) | 6 | 2023-11-10 13:16:30+00:00 | 4k |
ai-forever/Kandinsky-3 | kandinsky3/model/unet.py | [
{
"identifier": "Identity",
"path": "kandinsky3/model/nn.py",
"snippet": "class Identity(nn.Module):\n def __init__(self, *args, **kwargs):\n super().__init__()\n\n @staticmethod\n def forward(x, *args, **kwargs):\n return x"
},
{
"identifier": "Attention",
"path": "ka... | import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange
from .nn import Identity, Attention, SinusoidalPosEmb, ConditionalGroupNorm
from .utils import exist, set_default_item, set_default_layer | 1,757 |
class Block(nn.Module):
def __init__(self, in_channels, out_channels, time_embed_dim, kernel_size=3, norm_groups=32, up_resolution=None):
super().__init__()
self.group_norm = ConditionalGroupNorm(norm_groups, in_channels, time_embed_dim)
self.activation = nn.SiLU()
self.up_sample... |
class Block(nn.Module):
def __init__(self, in_channels, out_channels, time_embed_dim, kernel_size=3, norm_groups=32, up_resolution=None):
super().__init__()
self.group_norm = ConditionalGroupNorm(norm_groups, in_channels, time_embed_dim)
self.activation = nn.SiLU()
self.up_sample... | self.attention = Attention(context_dim, num_channels, context_dim, head_dim) | 1 | 2023-11-13 10:16:04+00:00 | 4k |
spfrommer/torchexplorer | torchexplorer/render/layout.py | [
{
"identifier": "utils",
"path": "torchexplorer/utils.py",
"snippet": "def iter_not_none(iterable: Iterable[Any]) -> Iterator[Any]:\ndef enum_not_none(iterable: Iterable[Any]) -> Iterator[tuple[int, Any]]:\ndef interleave(l1: list[Any], l2: list[Any]) -> list[Any]:\ndef list_add(l1: list[float], l2: lis... | import copy
import html
import json
import string
import numpy as np
import networkx as nx
from typing import Optional, Union
from subprocess import Popen, PIPE
from torchexplorer import utils
from torchexplorer import core
from torchexplorer.components.tooltip import Tooltip
from torchexplorer.core import ModuleInvoca... | 2,536 | from __future__ import annotations
def layout(
structure: ModuleInvocationStructure, cache: Optional[dict] = None
| from __future__ import annotations
def layout(
structure: ModuleInvocationStructure, cache: Optional[dict] = None | ) -> tuple[NodeLayout, dict]: | 9 | 2023-11-13 05:56:04+00:00 | 4k |
namin/llm-verified-with-monte-carlo-tree-search | run_ppo_block.py | [
{
"identifier": "Node",
"path": "montecarlo/node.py",
"snippet": "class Node:\n def __init__(self, state):\n self.state = state\n self.win_value = 0\n self.policy_value = None\n self.visits = 0\n self.parent = None\n self.children = []\n self.expanded ... | import ppo
import torch
from montecarlo.node import Node
from montecarlo.montecarlo import MonteCarlo
from lang import score_func, can_be_solution, find_largest_new_block
from prompts import prompt, expansion_count, min_lines, check_func
from common import limit_depth, max_completion_depth
from cmdline import args | 1,991 |
n_iter = args.n_iter
# n_iter = 10
class GenNode:
def __init__(self, text, gens):
self.text = text
self.gens = gens
def reinforce(gens, reward):
rewards = [torch.tensor(reward)]
for query_tensors, response_tensors in gens:
ppo.trainer_step(query_tensors, response_tensors, re... |
n_iter = args.n_iter
# n_iter = 10
class GenNode:
def __init__(self, text, gens):
self.text = text
self.gens = gens
def reinforce(gens, reward):
rewards = [torch.tensor(reward)]
for query_tensors, response_tensors in gens:
ppo.trainer_step(query_tensors, response_tensors, re... | if can_be_solution(text, min_lines, check_func): | 3 | 2023-11-11 19:56:04+00:00 | 4k |
BraveGroup/Drive-WM | src/diffusers/utils/testing_utils.py | [
{
"identifier": "BACKENDS_MAPPING",
"path": "src/diffusers/utils/import_utils.py",
"snippet": "BACKENDS_MAPPING = OrderedDict(\n [\n (\"bs4\", (is_bs4_available, BS4_IMPORT_ERROR)),\n (\"flax\", (is_flax_available, FLAX_IMPORT_ERROR)),\n (\"inflect\", (is_inflect_available, INFLE... | import functools
import importlib
import inspect
import io
import logging
import multiprocessing
import os
import random
import re
import struct
import sys
import tempfile
import time
import unittest
import urllib.parse
import numpy as np
import PIL.Image
import PIL.ImageOps
import requests
import torch
imp... | 2,548 |
tensor_str = str(tensor.detach().cpu().flatten().to(torch.float32)).replace("\n", "")
# format is usually:
# expected_slice = np.array([-0.5713, -0.3018, -0.9814, 0.04663, -0.879, 0.76, -1.734, 0.1044, 1.161])
output_str = tensor_str.replace("tensor", f"{expected_tensor_name} = np.array")
test_file... |
global_rng = random.Random()
logger = get_logger(__name__)
_required_peft_version = is_peft_available() and version.parse(
version.parse(importlib.metadata.version("peft")).base_version
) > version.parse("0.5")
_required_transformers_version = is_transformers_available() and version.parse(
version.parse(i... | return unittest.skipUnless(is_flax_available(), "test requires JAX & Flax")(test_case) | 2 | 2023-11-18 01:40:55+00:00 | 4k |
basnijholt/unidep | unidep/_dependencies_parsing.py | [
{
"identifier": "Platform",
"path": "unidep/platform_definitions.py",
"snippet": "VALID_SELECTORS = get_args(Selector)\nPEP508_MARKERS = {\n \"linux-64\": \"sys_platform == 'linux' and platform_machine == 'x86_64'\",\n \"linux-aarch64\": \"sys_platform == 'linux' and platform_machine == 'aarch64'\... | import hashlib
import os
import sys
import tomllib
import tomli as tomllib
import tomli_w
from collections import defaultdict
from pathlib import Path
from typing import TYPE_CHECKING, Any, NamedTuple
from ruamel.yaml import YAML
from ruamel.yaml.comments import CommentedMap, CommentedSeq
from u... | 3,492 |
def _parse_dependency(
dependency: str,
dependencies: CommentedMap,
index_or_key: int | str,
which: Literal["conda", "pip", "both"],
identifier: int,
ignore_pins: list[str],
overwrite_pins: dict[str, str | None],
skip_dependencies: list[str],
) -> list[Spec]:
name, pin, selector = p... | """unidep - Unified Conda and Pip requirements management.
This module provides parsing of `requirements.yaml` and `pyproject.toml` files.
"""
from __future__ import annotations
if TYPE_CHECKING:
if sys.version_info >= (3, 8):
else: # pragma: no cover
try: # pragma: no cover
if sys.version_info >=... | requirements_path = dependencies_filename(p.parent / include).resolve() | 1 | 2023-11-16 04:23:01+00:00 | 4k |
BAAI-DCAI/SegVol | network/model.py | [
{
"identifier": "select_points",
"path": "utils/monai_inferers_utils.py",
"snippet": "def select_points(preds, num_positive_extra=4, num_negative_extra=0, fix_extra_point_num=None):\n spacial_dim = 3\n points = torch.zeros((0, 3))\n labels = torch.zeros((0))\n pos_thred = 0.9\n neg_thred ... | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import random
from transformers import AutoTokenizer, CLIPTextModel, CLIPTextConfig
from utils.monai_inferers_utils import select_points, generate_box
from utils.loss import BCELoss, BinaryDiceLoss
from torch.cuda.amp import autocast | 1,893 |
#%% set up model
class SegVol(nn.Module):
def __init__(self,
image_encoder,
mask_decoder,
prompt_encoder,
clip_ckpt,
roi_size,
patch_size,
test_mode=False,
):
super().__init__()... |
#%% set up model
class SegVol(nn.Module):
def __init__(self,
image_encoder,
mask_decoder,
prompt_encoder,
clip_ckpt,
roi_size,
patch_size,
test_mode=False,
):
super().__init__()... | self.bce_loss = BCELoss().cuda() | 2 | 2023-11-10 08:25:37+00:00 | 4k |
xk-huang/segment-caption-anything | scripts/tools/build_annotation_db.py | [
{
"identifier": "Arguments",
"path": "src/arguments.py",
"snippet": "class Arguments:\n defaults: List[Any] = field(default_factory=lambda: defaults)\n\n training: SCASeq2SeqTrainingArguments = field(default_factory=lambda: SCASeq2SeqTrainingArguments(output_dir=\"?\"))\n\n # NOTE(xiaoke): to o... | import sys
import base64
import io
import json
import logging
import os
import os.path as osp
import datasets
import hydra
import numpy as np
import tqdm
import pycocotools.mask
import logging
import torch
import sqlite3
import json
from hydra.core.hydra_config import HydraConfig
from hydra.core.utils import configure_... | 2,852 | # TODO: extract images from refcoco series
sys.path.append(".")
logger = logging.getLogger(__name__)
@hydra.main(version_base="1.3", config_path="../../src/conf", config_name="conf")
def main(args: Arguments):
logger.warning(f"Turn no_cuda = True.")
args.training.no_cuda = True
# NOTE: ddp is initiali... | # TODO: extract images from refcoco series
sys.path.append(".")
logger = logging.getLogger(__name__)
@hydra.main(version_base="1.3", config_path="../../src/conf", config_name="conf")
def main(args: Arguments):
logger.warning(f"Turn no_cuda = True.")
args.training.no_cuda = True
# NOTE: ddp is initiali... | args, training_args, _ = global_setup(args) | 1 | 2023-11-17 14:10:41+00:00 | 4k |
theroyallab/tabbyAPI | OAI/utils_oai.py | [
{
"identifier": "ChatCompletionMessage",
"path": "OAI/types/chat_completion.py",
"snippet": "class ChatCompletionMessage(BaseModel):\n role: Optional[str] = None\n content: Optional[str] = None"
},
{
"identifier": "ChatCompletionRespChoice",
"path": "OAI/types/chat_completion.py",
... | import pathlib
from typing import Optional
from OAI.types.chat_completion import (
ChatCompletionMessage,
ChatCompletionRespChoice,
ChatCompletionStreamChunk,
ChatCompletionResponse,
ChatCompletionStreamChoice,
)
from OAI.types.completion import CompletionResponse, CompletionRespChoice
from OAI.type... | 1,604 | """ Utility functions for the OpenAI server. """
def create_completion_response(
text: str,
prompt_tokens: int,
completion_tokens: int,
model_name: Optional[str],
):
"""Create a completion response from the provided text."""
choice = CompletionRespChoice(finish_reason="Generated", text=text)... | """ Utility functions for the OpenAI server. """
def create_completion_response(
text: str,
prompt_tokens: int,
completion_tokens: int,
model_name: Optional[str],
):
"""Create a completion response from the provided text."""
choice = CompletionRespChoice(finish_reason="Generated", text=text)... | lora_card = LoraCard(id=path.name) | 9 | 2023-11-10 05:54:02+00:00 | 4k |
zorazrw/filco | get_inputs.py | [
{
"identifier": "has_answer",
"path": "eval.py",
"snippet": "def has_answer(text: str, answers: list[str]) -> float:\n \"\"\"Check if text contains any of the answers.\"\"\"\n return float(any([(ans.lower() in text.lower()) for ans in answers]))"
},
{
"identifier": "load_dataset",
"pat... | import argparse
from eval import has_answer
from utils import load_dataset, write_dataset | 2,291 |
# ICT Example Creation Functions
def get_ict_io(
example: dict,
in_context_examples: list[dict],
input_list: list[str],
output_list: list[str],
no_prefix: bool = False,
filter_criteria: str = "strinc",
n_contexts: int = 1,
num_sents: int = None,
threshold: float = None,
questio... | """Create I/O to Evaluate/Train Models.
Default I/O for Context Filtering: [i] question context [o] sent
Default I/O for Output Generation: [i] sent question [o] answer
"""
# Individual Components
QUESTION_PREFIX = "question"
ANSWER_PREFIX = "answer"
CONTEXT_PREFIX = "context"
prefix_format = "{}: {}"
def get_que... | write_dataset(args.output_path, procset) | 2 | 2023-11-14 21:18:30+00:00 | 4k |
ShipBit/wingman-ai | gui/sections/context_runner.py | [
{
"identifier": "Icon",
"path": "gui/components/icon.py",
"snippet": "class Icon(ctk.CTkImage):\n def __init__(self, icon: str, size: int | tuple[int, int]=50, themed=True):\n if isinstance(size, int):\n size = (size, size)\n\n icon_dir = path.join(path.abspath(path.dirname(_... | import customtkinter as ctk
from gui.components.icon import Icon
from gui.components.wingmen_list import WingmenList
from services.printr import Printr | 3,145 |
printr = Printr()
class ContextRunner(ctk.CTkFrame):
def __init__(self, master, context="", **kwargs):
super().__init__(master, **kwargs)
self.core = master.core
self.core.load_context(context)
self.status_var = ctk.StringVar(self, "Inactive", "status")
tower = self.core.... |
printr = Printr()
class ContextRunner(ctk.CTkFrame):
def __init__(self, master, context="", **kwargs):
super().__init__(master, **kwargs)
self.core = master.core
self.core.load_context(context)
self.status_var = ctk.StringVar(self, "Inactive", "status")
tower = self.core.... | self.wingmen_list = WingmenList(self, wingmen=wingmen) | 1 | 2023-11-15 09:36:06+00:00 | 4k |
OliverMao/FlaskAutoApiBuilder | Faab/FaabFunction.py | [
{
"identifier": "login_required",
"path": "Faab/FaabJWT.py",
"snippet": "def login_required(f):\n \"\"\"\n 使用functools模块的wraps装饰内部函数\n \"\"\"\n\n @functools.wraps(f)\n def wrapper(*args, **kwargs):\n try:\n if g.username == -1:\n # print('error1')\n ... | import json
import pandas as pd
import io
from functools import wraps
from flasgger import swag_from
from flask import request, g, send_file
from sqlalchemy import and_
from sqlalchemy.orm import class_mapper
from flask_sqlalchemy import SQLAlchemy
from Faab.FaabJWT import login_required
from Faab.extensions import db | 2,096 | return wrapper
# noinspection ALL
def check_request_turn(func):
# noinspection PyTypeChecker
@wraps(func)
def wrapper(self, *args, **kwargs):
form = request.json
need_update = form.get('need_update')
condition = form.get('condition')
... |
# ......
class AutoUrl:
def __init__(self, add_url_list):
for i in add_url_list:
AutoDB(i["model"], i["bp"], i["url_prefix"])
class AutoDB:
model = {}
bp = object
url_name = ""
def __init__(self, model, bp, url_name):
self.model = model
self.bp = bp
... | db.session.add(new_item) | 1 | 2023-11-10 09:25:44+00:00 | 4k |
mattyamonaca/LCM_i2i_PoC | config.py | [
{
"identifier": "get_pipe",
"path": "lcm.py",
"snippet": "def get_pipe(config):\n vae_model_path = config.vae_model_path.get()\n vae_model_path = vae_model_path.replace(\"\\\\\", \"/\")\n LoRA_model_path = config.LoRA_model_path.get()\n LoRA_model_path = LoRA_model_path.replace(\"\\\\\", \"/... | from diffusers.utils import load_image
from tkinter import ttk
from lcm import get_pipe, LCM_run
from capture import ScreenCapture
import tkinter as tk
import threading | 2,270 |
class ConfigWindow:
def __init__(self):
master = tk.Tk()
self.run_thread = None
self.running = False
self.master = master
master.title("Configuration")
master.geometry("400x500") # ウィンドウサイズを設定
master.attributes("-topmost", True) # ウィンドウサイズを最前列固定
... |
class ConfigWindow:
def __init__(self):
master = tk.Tk()
self.run_thread = None
self.running = False
self.master = master
master.title("Configuration")
master.geometry("400x500") # ウィンドウサイズを設定
master.attributes("-topmost", True) # ウィンドウサイズを最前列固定
... | self.screen_capture = ScreenCapture() | 2 | 2023-11-17 08:10:27+00:00 | 4k |
jeromeleong/mirrors-zhile-io-pandora | src/pandora/turbo/chat.py | [
{
"identifier": "Conversations",
"path": "src/pandora/turbo/base.py",
"snippet": "class Conversations:\n def __init__(self):\n self.__data = []\n\n def list(self, offset, limit):\n return len(self.__data), self.__data[offset: limit]\n\n def clear(self):\n self.__data = []\n... | import json
from datetime import datetime as dt
from os import getenv
from requests import Response
from .base import Conversations, UserPrompt, Prompt, SystemPrompt
from ..openai.api import ChatCompletion
from ..openai.token import gpt_num_tokens | 3,159 | return resp.json()
def clear_conversations(self, raw=False, token=None):
def __shadow():
self.__get_conversations(token).clear()
result = {
'success': True
}
return self.__wrap_response(result)
resp = __shadow()
if ... | # -*- coding: utf-8 -*-
class TurboGPT:
DEFAULT_SYSTEM_PROMPT = 'You are ChatGPT, a large language model trained by OpenAI. ' \
'Answer as concisely as possible.\nKnowledge cutoff: 2021-09-01\n' \
'Current date: {}'.format(dt.now().strftime('%Y-%m-%d'))
... | parent = conversation.add_prompt(Prompt(parent_message_id)) | 2 | 2023-11-12 10:31:05+00:00 | 4k |
leeyuentuen/polestar_api | custom_components/polestar_api/polestar.py | [
{
"identifier": "PolestarApiException",
"path": "custom_components/polestar_api/pypolestar/exception.py",
"snippet": "class PolestarApiException(Exception):\n \"\"\"Base class for exceptions in this module.\"\"\""
},
{
"identifier": "PolestarAuthException",
"path": "custom_components/pole... | from datetime import datetime, timedelta
from urllib3 import disable_warnings
from homeassistant.core import HomeAssistant
from homeassistant.util.unit_system import METRIC_SYSTEM, UnitSystem
from .pypolestar.exception import PolestarApiException, PolestarAuthException
from .pypolestar.polestar import PolestarApi
impor... | 2,185 | """Polestar API for Polestar integration."""
POST_HEADER_JSON = {"Content-Type": "application/json"}
_LOGGER = logging.getLogger(__name__)
class Polestar:
"""Polestar EV integration."""
def __init__(self,
hass: HomeAssistant,
username: str,
password: str... | """Polestar API for Polestar integration."""
POST_HEADER_JSON = {"Content-Type": "application/json"}
_LOGGER = logging.getLogger(__name__)
class Polestar:
"""Polestar EV integration."""
def __init__(self,
hass: HomeAssistant,
username: str,
password: str... | self.polestarApi = PolestarApi(username, password) | 2 | 2023-11-17 21:24:36+00:00 | 4k |
dubverse-ai/MahaTTS | tts.py | [
{
"identifier": "config",
"path": "maha_tts/config.py",
"snippet": "class config:\n \n semantic_model_centroids = 10000 + 1\n seed_value = 3407\n\n # Text to Semantic\n t2s_position = 4096\n langs = ['english','tamil', 'telugu', 'punjabi', 'marathi', 'hindi', 'gujarati', 'bengali', 'assa... | import torch,glob
from maha_tts import load_diffuser,load_models,infer_tts,config
from scipy.io.wavfile import write | 1,667 |
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Using:',device)
text = 'Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition.'
langauge = 'english'
|
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Using:',device)
text = 'Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition.'
langauge = 'english' | language = torch.tensor(config.lang_index[langauge]).to(device).unsqueeze(0) | 0 | 2023-11-16 09:44:54+00:00 | 4k |
wjun0830/CGDETR | cg_detr/start_end_dataset.py | [
{
"identifier": "load_jsonl",
"path": "utils/basic_utils.py",
"snippet": "def load_jsonl(filename):\n with open(filename, \"r\") as f:\n return [json.loads(l.strip(\"\\n\")) for l in f.readlines()]"
},
{
"identifier": "l2_normalize_np_array",
"path": "utils/basic_utils.py",
"sn... | import torch
import numpy as np
import random
import logging
import torch.nn as nn
from torch.utils.data import Dataset
from tqdm import tqdm
from os.path import join, exists
from utils.basic_utils import load_jsonl, l2_normalize_np_array
from utils.tensor_utils import pad_sequences_1d
from cg_detr.span_utils import sp... | 2,769 | 'train': ['kLxoNp-UchI', 'NyBmCxDoHJU', 'jcoYJXDG9sw', '-esJrBWj2d8'],
'val': ['E11zDS9XGzg']
},
'FM': {
'train': ['_xMr-HKMfVA', 'byxOvuiIJV0', 'VuWGsYPqAX8', 'xmEERLqJ2kU'],
'val': ['JKpqYvAdIsw']
},
'GA': {
'train': ['xxdtq8mxegs', 'i3wAGJaaktw', '0tmA_C6XwfM',... |
logger = logging.getLogger(__name__)
TVSUM_SPLITS = {
'BK': {
'train': ['WxtbjNsCQ8A', 'EE-bNr36nyA', 'oDXZc0tZe04', 'uGu_10sucQo'],
'val': ['Se3oxnaPsz0']
},
'BT': {
'train': ['eQu1rNs0an0', 'qqR6AEXwxoQ', 'EYqVtI9YWJA', 'iVt07TCkFM0'],
'val': ['JgHubY5Vw3Y']
},
'D... | datalist = load_jsonl(self.data_path) | 0 | 2023-11-10 12:45:25+00:00 | 4k |
WCGKING/KINGUSERBOT | Branded/plugins/pmguard.py | [
{
"identifier": "approve",
"path": "Branded/modules/data.py",
"snippet": "async def approve(user_ud: int):\n pm = await is_approved()\n pm.append(user_ud)\n await permitdb.update_one(\n {'permit': 'protection'},\n {\n '$set': {\n 'users': pm\n ... | import asyncio
from pyrogram import Client, filters
from pyrogram.enums import ChatType
from pyrogram.types import *
from .. import *
from ..modules.data import approve, disapprove, is_approved | 1,611 |
DEFAULT = """
WELCOME....
ʜɪ, ᴛʜɪꜱ ɪꜱ ᴛʜᴇ ᴋᴇᴇᴘᴇʀ ᴏꜰ ᴘʀɪᴠᴀᴛᴇ ᴍᴇꜱꜱᴀɢᴇꜱ. ᴅᴏɴ'ᴛ ꜱᴘᴀᴍ ʏᴀ ᴏʀ ɪ'ʟʟ ʙʟᴏᴄᴋ ʏᴏᴜ. ᴡᴀɪᴛ ᴜɴᴛɪʟ ᴍʏ ᴍᴀꜱᴛᴇʀ ʀᴇᴄᴇɪᴠᴇꜱ ʏᴏᴜʀ ᴍᴇꜱꜱᴀɢᴇ.ɪ ᴀᴍ ᴀɴ ᴀᴅᴠᴀɴᴄᴇᴅ ᴀɴᴅ sᴜᴘᴇʀғᴀsᴛ ᴜꜱᴇʀʙᴏᴛ ᴡɪᴛʜ 24x7 ᴀᴄᴛɪᴠᴇ » ғᴏʀ ᴛᴇʟᴇɢʀᴀᴍ ɪᴅ
"""
@app.on_message(
(
filters.private
& filters.incoming
& ~filter... |
MSG_PERMIT = """
PM_SECURITY BRANDED-USERBOT
{}
await message.reply_photo="https://te.legra.ph/file/11cfa74175b590014bd16.jpg"
▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
⍟ You have {}/{} warning!!!
"""
DEFAULT = """
WELCOME....
ʜɪ, ᴛʜɪꜱ ɪꜱ ᴛʜᴇ ᴋᴇᴇᴘᴇʀ ᴏꜰ ᴘʀɪᴠᴀᴛᴇ ᴍᴇꜱꜱᴀɢᴇꜱ. ᴅᴏɴ'ᴛ ꜱᴘᴀᴍ ʏᴀ ᴏʀ ɪ'ʟʟ ʙʟᴏᴄᴋ ʏᴏᴜ. ᴡᴀɪᴛ ᴜɴᴛɪʟ ᴍʏ ᴍᴀꜱᴛᴇʀ ʀᴇᴄᴇɪᴠᴇꜱ ʏᴏᴜʀ... | await disapprove(uid) | 1 | 2023-11-14 13:24:26+00:00 | 4k |
kudelskisecurity/fuzzomatic | fuzzomatic/approaches/functions.py | [
{
"identifier": "prompts",
"path": "fuzzomatic/tools/prompts.py",
"snippet": "def load_file_contents(path):\ndef readme_prompt(readme):\ndef fix_prompt(code_snippet, error):\ndef example_prompt(example_code):\ndef unit_test_prompt(test_source_code, use_statements):\ndef unit_test_prompt_with_additional_... | from jinja2 import Template
from fuzzomatic.tools import prompts
from fuzzomatic.approaches.common import llm_attempt_fix_error
from fuzzomatic.tools.cargo_doc import parse_cargo_doc_json, generate_cargo_doc_json
from fuzzomatic.tools.constants import DEFAULT_TARGET_NAME
from fuzzomatic.tools.utils import write_fuzz_ta... | 3,437 | }
function_args = f[2]
template_path = str_template_path
extra_args = {}
if len(function_args) == 1:
try:
function_arg_type = function_args[0]
template_path = template_paths[function_arg_type]
except KeyError:
byte_array_length_template_path = (
... |
def try_functions_approach(
codebase_dir,
target_name=DEFAULT_TARGET_NAME,
root_codebase_dir=None,
args=None,
**_kwargs,
):
functions = find_target_functions_via_cargo_doc(
codebase_dir, root_codebase_dir=root_codebase_dir
)
if functions is None:
print("Failed to dete... | functions = parse_cargo_doc_json(json_path) | 2 | 2023-11-14 09:52:59+00:00 | 4k |
muyuworks/myla | tests/myla/vectorstores/faiss_group_test.py | [
{
"identifier": "FAISSGroup",
"path": "myla/vectorstores/faiss_group.py",
"snippet": "class FAISSGroup(VectorStore):\n def __init__(self, path: str, embeddings: Embeddings = None) -> None:\n self._path = path\n self._embeddings = embeddings\n self._faiss = _import_faiss()\n\n ... | import os
import shutil
import unittest
from myla.vectorstores.faiss_group import FAISSGroup
from myla.utils import random_id, sha256 | 2,848 |
here = os.path.abspath(os.path.dirname(__file__))
class FAISSGroupTests(unittest.TestCase):
def setUp(self) -> None:
self._vectors = [
[0, 0],
[1, 1],
[2, 2],
[3, 3],
[4, 4]
]
self._records = [
{
'id'... |
here = os.path.abspath(os.path.dirname(__file__))
class FAISSGroupTests(unittest.TestCase):
def setUp(self) -> None:
self._vectors = [
[0, 0],
[1, 1],
[2, 2],
[3, 3],
[4, 4]
]
self._records = [
{
'id'... | gids_1.append(sha256(r.get('gid', '').encode()).hex()) | 2 | 2023-11-15 01:05:03+00:00 | 4k |
AdmTal/music-graphs | music_graphs.py | [
{
"identifier": "generate_music_graph",
"path": "src/generate_music_graph.py",
"snippet": "def generate_music_graph(\n midi_file_path,\n default_theme_file_path,\n theme_file_path,\n output_path,\n soundfont_file,\n):\n theme = Theme(theme_file_path, default_theme_file_path)\n track... | import os
import click
from src.generate_music_graph import generate_music_graph
from src.midi_stuff import SOUND_FONT_FILE
from src.theme_stuff import DARK_THEME_FILE, LIGHT_THEME_FILE | 2,122 |
def get_filename_without_extension(path):
filename_with_extension = os.path.basename(path)
filename_without_extension, _ = os.path.splitext(filename_with_extension)
return filename_without_extension
@click.command()
@click.option(
"--midi",
required=True,
type=click.Path(exists=True),
... |
def get_filename_without_extension(path):
filename_with_extension = os.path.basename(path)
filename_without_extension, _ = os.path.splitext(filename_with_extension)
return filename_without_extension
@click.command()
@click.option(
"--midi",
required=True,
type=click.Path(exists=True),
... | generate_music_graph( | 0 | 2023-11-17 17:56:04+00:00 | 4k |
FISHers6/CodeLearn-Agent | codelearn/project/project_manager.py | [
{
"identifier": "LOCAL_PROJECT_PATH",
"path": "codelearn/base.py",
"snippet": "LOCAL_PROJECT_PATH = os.path.join(BASE_PROJECT_PATH, \"projects\")"
},
{
"identifier": "Indexer",
"path": "codelearn/index/indexer.py",
"snippet": "class Indexer(ABC):\n @abstractmethod\n def index(self,... | import asyncio
import os
import time
import uuid
from typing import Any, Dict, List, Optional
from openai import Embedding
from codelearn.base import LOCAL_PROJECT_PATH
from codelearn.index.indexer import Indexer, Metadata
from codelearn.loader.loader import ProjectLoader
from datetime import datetime, timedelta
from c... | 1,734 |
class ProjectManager:
PROJECT_UPDATE_THRESHOLD = 30 * 24 * 60 * 60
def __init__(self,
loaders: Dict[str, ProjectLoader],
splitters: Dict[str, Splitter],
|
class ProjectManager:
PROJECT_UPDATE_THRESHOLD = 30 * 24 * 60 * 60
def __init__(self,
loaders: Dict[str, ProjectLoader],
splitters: Dict[str, Splitter], | indexers: Dict[str, Indexer], | 1 | 2023-11-12 13:13:30+00:00 | 4k |
kirill-vish/Beyond-INet | inference/modelvshuman/model_evaluator.py | [
{
"identifier": "load_model_transform",
"path": "utils/misc.py",
"snippet": "def load_model_transform(model_name, pretrained_dir, img_size=224):\n print(f\"Loading {model_name}\")\n checkpoint_path = None\n transform_val = None\n if model_name == \"deit3_21k\":\n model = models_deit.d... | import copy
import datetime
import logging
import os
import matplotlib as mpl
import torch
from torch.nn.functional import softmax
from tqdm import tqdm
from utils.misc import load_model_transform
from .evaluation import evaluate as e
from .utils import load_dataset, load_model | 1,705 |
logger = logging.getLogger(__name__)
MAX_NUM_MODELS_IN_CACHE = 3
mpl.rcParams['font.size'] = 22
def device():
return torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class ModelEvaluator:
def _pytorch_evaluator(self, model_name, model, dataset, *args, **kwargs):
"""
Evalu... |
logger = logging.getLogger(__name__)
MAX_NUM_MODELS_IN_CACHE = 3
mpl.rcParams['font.size'] = 22
def device():
return torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class ModelEvaluator:
def _pytorch_evaluator(self, model_name, model, dataset, *args, **kwargs):
"""
Evalu... | model, transform_val = load_model_transform( | 0 | 2023-11-15 22:22:06+00:00 | 4k |
shengliu66/ICV | tasks/base.py | [
{
"identifier": "hf_datasets_root",
"path": "anchor.py",
"snippet": ""
},
{
"identifier": "TokenizedForStyleRightPad",
"path": "tasks/loader.py",
"snippet": "class TokenizedForStyleRightPad(Dataset):\n def __init__(self, data, tok: PreTrainedTokenizer, prompt_fn, mode = 'eval', no_pad... | import json
import logging
import random
import re
import torch
import numpy as np
import datasets
from collections import defaultdict
from anchor import hf_datasets_root
from tasks.loader import TokenizedForStyleRightPad
from utils.rng_ctx import RandomContext, EmptyContext
from utils.pca import PCA
from utils.context... | 2,004 |
logger = logging.getLogger("task")
class BaseProbInference:
def __init__(self, prompt_version):
if prompt_version == "default":
self.prompt_version = self.default_prompt_version()
else:
self.prompt_version = prompt_version
self.raw_data_sample = None
self... |
logger = logging.getLogger("task")
class BaseProbInference:
def __init__(self, prompt_version):
if prompt_version == "default":
self.prompt_version = self.default_prompt_version()
else:
self.prompt_version = prompt_version
self.raw_data_sample = None
self... | self._rng_context = RandomContext(seed=seed) | 2 | 2023-11-11 18:20:45+00:00 | 4k |
Mohamad-Hussein/speech-assistant | src/parent.py | [
{
"identifier": "service",
"path": "src/model_inference.py",
"snippet": "def service(queue, event):\n # Configure the logging settings\n logging.basicConfig(\n level=logging.DEBUG,\n format=\"%(asctime)s - %(levelname)s - %(message)s\",\n filename=join(\"logs\", \"model.log\")... | from time import time, sleep
from os.path import join
from shutil import copy
from multiprocessing import Process, Event, Pipe, Queue
from threading import Thread
from src.model_inference import service
from src.funcs import run_listener
from src.funcs import get_audio, create_sound_file, pcm_to_wav
from playsound impo... | 1,851 |
# Global variables
# -------------------------
# Change to true if you want to save audio to file called recording.wav
SAVE_AUDIO = False
# Create a logger instance
logger = logging.getLogger(__name__)
# Getting audio inputs
audio, stream_input = get_audio()
# No audio being recorded
stream_input.stop_stream()
... |
# Global variables
# -------------------------
# Change to true if you want to save audio to file called recording.wav
SAVE_AUDIO = False
# Create a logger instance
logger = logging.getLogger(__name__)
# Getting audio inputs
audio, stream_input = get_audio()
# No audio being recorded
stream_input.stop_stream()
... | sound_byte_wav = pcm_to_wav(b"".join(frames)) | 4 | 2023-11-12 01:20:50+00:00 | 4k |
codereport/jello | jello.py | [
{
"identifier": "Grid",
"path": "grid.py",
"snippet": "class Grid:\n def __init__(self, n):\n self.n = n * 2\n self.grid = [[\" \"] * self.n, [\" \"] * self.n]\n\n def add_level(self):\n self.grid.append([\" \"] * self.n)\n self.grid.append([\" \"] * self.n)\n\n def ... | import subprocess
import algorithm
import arity_notation
import draw
import tokens
import utils
from colorama import Fore, init
from prompt_toolkit import prompt
from prompt_toolkit.completion import WordCompleter
from prompt_toolkit.history import FileHistory
from prompt_toolkit.shortcuts import CompleteStyle
from gri... | 1,843 |
def clear_screen():
subprocess.call("clear", shell=True)
def run_jelly(expr: str, args: list[str]):
try:
command = ["jelly", "eun", expr, *args]
result = subprocess.run(command, text=True, capture_output=True, check=True)
output_text = result.stdout.strip()
draw.cprint(outpu... | #!/usr/bin/env python3
def clear_screen():
subprocess.call("clear", shell=True)
def run_jelly(expr: str, args: list[str]):
try:
command = ["jelly", "eun", expr, *args]
result = subprocess.run(command, text=True, capture_output=True, check=True)
output_text = result.stdout.strip()
... | chain_type = Chain.MONADIC if len(args) == 1 else Chain.DYADIC | 1 | 2023-11-18 17:34:06+00:00 | 4k |
davep/tinboard | tinboard/widgets/details.py | [
{
"identifier": "CopyBookmarkURL",
"path": "tinboard/messages/commands.py",
"snippet": "class CopyBookmarkURL(Command):\n \"\"\"Copy the URL for the bookmark to the clipboard.\"\"\""
},
{
"identifier": "EditBookmark",
"path": "tinboard/messages/commands.py",
"snippet": "class EditBook... | from webbrowser import open as open_url
from humanize import naturaltime
from textual import on
from textual.app import ComposeResult
from textual.binding import Binding
from textual.containers import VerticalScroll
from textual.message import Message
from textual.reactive import var
from textual.widgets import Label
f... | 1,852 | """The details display widget."""
##############################################################################
# Python imports.
##############################################################################
# Humanize imports.
##############################################################################
# Textua... | """The details display widget."""
##############################################################################
# Python imports.
##############################################################################
# Humanize imports.
##############################################################################
# Textua... | bookmark: var[Bookmark | None] = var(None, always_update=True) | 4 | 2023-11-13 08:19:41+00:00 | 4k |
wurenkai/MHA-UNet | engine.py | [
{
"identifier": "save_imgs",
"path": "utils.py",
"snippet": "def save_imgs(img, msk, msk_pred, i, save_path, datasets, threshold=0.5, test_data_name=None):\r\n img = img.squeeze(0).permute(1,2,0).detach().cpu().numpy()\r\n img = img / 255. if img.max() > 1.1 else img\r\n if datasets == 'retinal... | import numpy as np
import torch
import torchvision.transforms as transforms
import torch.nn.functional as F
from tqdm import tqdm
from torch.cuda.amp import autocast as autocast
from sklearn.metrics import confusion_matrix
from utils import save_imgs,save_imgs_explainable
from PIL import Image
| 2,981 | optimizer,
scheduler,
epoch,
logger,
config,
scaler=None):
'''
train model for one epoch
'''
# switch to train mode
model.train()
loss_list = []
for i... |
def train_one_epoch(train_loader,
model,
criterion,
optimizer,
scheduler,
epoch,
logger,
config,
scaler=None):
'''
train model for ... | a = save_imgs_explainable(img,x0,x1,x2,x3,x4,i,a, config.work_dir + 'outputs/', config.datasets, config.threshold,
| 1 | 2023-11-13 06:59:52+00:00 | 4k |
buptlihang/CVLM | evaluation/MME/evaluate.py | [
{
"identifier": "IMAGE_TOKEN_INDEX",
"path": "model/utils.py",
"snippet": "IMAGE_TOKEN_INDEX = -200"
},
{
"identifier": "DEFAULT_IMAGE_TOKEN",
"path": "model/utils.py",
"snippet": "DEFAULT_IMAGE_TOKEN = \"<image>\""
},
{
"identifier": "DEFAULT_IM_START_TOKEN",
"path": "model/... | import argparse
import torch
import os
import json
import math
from tqdm import tqdm
from model.utils import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from model.utils import build_conversation, load_pretrained_model, disable_torch_init, get_model_name_from_path
from model.uti... | 1,765 |
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i + chunk_size] for i in range(0, len(lst), chunk_size)]
def get_chunk(lst, n, k):
chunks = split_list(lst, n)
return chunks[k]
def get_gt(dat... |
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i + chunk_size] for i in range(0, len(lst), chunk_size)]
def get_chunk(lst, n, k):
chunks = split_list(lst, n)
return chunks[k]
def get_gt(dat... | tokenizer, model, image_processor, context_len = load_pretrained_model( | 5 | 2023-11-10 03:52:46+00:00 | 4k |
vvvm23/TchAIkovsky | train.py | [
{
"identifier": "generate_splits",
"path": "data/dataset.py",
"snippet": "def generate_splits(dataset, splits: Tuple[float, float]):\n length = len(dataset)\n split_size = int(splits[0] * length)\n\n return torch.utils.data.Subset(dataset, range(split_size)), torch.utils.data.Subset(\n d... | import json
import equinox as eqx
import jax
import jax.numpy as jnp
import optax
import orbax.checkpoint as ocp
import tqdm
import wandb
from argparse import ArgumentParser
from datetime import datetime
from pathlib import Path
from jax.experimental import mesh_utils
from jax.sharding import PositionalSharding
from lo... | 1,972 |
def prepare_batch(batch, key=None):
input_ids = jnp.copy(batch["input_ids"][:, :-1])
labels = jnp.copy(batch["input_ids"][:, 1:])
labels = jnp.where(labels == 0, -100, labels)
position_ids = jnp.expand_dims(jnp.arange(labels.shape[-1]), 0).repeat(labels.shape[0], 0)
mask = jnp.asarray(batch["atte... |
def prepare_batch(batch, key=None):
input_ids = jnp.copy(batch["input_ids"][:, :-1])
labels = jnp.copy(batch["input_ids"][:, 1:])
labels = jnp.where(labels == 0, -100, labels)
position_ids = jnp.expand_dims(jnp.arange(labels.shape[-1]), 0).repeat(labels.shape[0], 0)
mask = jnp.asarray(batch["at... | train_loader = get_dataloader( | 1 | 2023-11-13 07:31:30+00:00 | 4k |
LiquidFun/aoc_tiles | aoc_tiles/drawer.py | [
{
"identifier": "color_similarity",
"path": "aoc_tiles/colors.py",
"snippet": "def color_similarity(color_a, color_b, threshold):\n return abs(luminance(color_a) - luminance(color_b)) < threshold"
},
{
"identifier": "darker_color",
"path": "aoc_tiles/colors.py",
"snippet": "def darker... | import math
from functools import partial
from pathlib import Path
from typing import List, Tuple, Union, Dict
from PIL import ImageColor, Image
from PIL.ImageDraw import ImageDraw
from aoc_tiles.colors import color_similarity, darker_color, extension_to_colors
from aoc_tiles.config import Config
from aoc_tiles.fonts i... | 2,929 |
def format_time(time: str) -> str:
"""Formats time as mm:ss if the time is below 1 hour, otherwise it returns >1h to a max of >24h
>>> format_time("00:58:32")
'58:32'
>>> format_time(">1h")
' >1h'
"""
time = time.replace(">", ">")
if ">" in time:
formatted = time
els... |
def format_time(time: str) -> str:
"""Formats time as mm:ss if the time is below 1 hour, otherwise it returns >1h to a max of >24h
>>> format_time("00:58:32")
'58:32'
>>> format_time(">1h")
' >1h'
"""
time = time.replace(">", ">")
if ">" in time:
formatted = time
els... | if color_similarity(color, self.config.text_color, self.config.contrast_improvement_threshold): | 0 | 2023-11-14 21:41:12+00:00 | 4k |
etri-crossmodal/gbswt5 | gbswt5/modeling_gbst5.py | [
{
"identifier": "GBSWT5Config",
"path": "gbswt5/configuration_gbst5.py",
"snippet": "class GBSWT5Config(PretrainedConfig):\n \"\"\" Based on models.t5. configuration_t5. T5Config in hf Transformers. \"\"\"\n model_type = \"gbswt5\"\n keys_to_ignore_at_inference = [\"past_key_values\"]\n attr... | import copy
import torch
from typing import Optional, Union, Tuple
from torch import nn
from transformers import add_start_docstrings
from transformers.utils import logging
from transformers.modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqMod... | 3,357 | """
hf transformers-compatible GBST + T5 Model implementation.
several methods are copying from huggingface/transformers/models/t5/modeling_t5.py
as Implementation Standards for compatibility. (version 4.28.1)
hf transformers' modeling_t5.py file is distributed under Apache 2.0 License.
Copyright... | """
hf transformers-compatible GBST + T5 Model implementation.
several methods are copying from huggingface/transformers/models/t5/modeling_t5.py
as Implementation Standards for compatibility. (version 4.28.1)
hf transformers' modeling_t5.py file is distributed under Apache 2.0 License.
Copyright... | config_class = GBSWT5Config | 0 | 2023-11-17 02:04:46+00:00 | 4k |
GOAT-AI-lab/GOAT-Storytelling-Agent | goat_storytelling_agent/storytelling_agent.py | [
{
"identifier": "utils",
"path": "goat_storytelling_agent/utils.py",
"snippet": "def split_into_words_w_newline(text):\ndef remove_last_n_words(text, n):\ndef keep_last_n_words(text, n):"
},
{
"identifier": "Plan",
"path": "goat_storytelling_agent/plan.py",
"snippet": "class Plan:\n @... | import sys
import time
import re
import json
import requests
import traceback
from goat_storytelling_agent import utils
from goat_storytelling_agent.plan import Plan
from transformers import LlamaTokenizerFast
from goat_storytelling_agent import prompts | 3,172 | act = self.query_chat(messages)
if act:
act_dict = Plan.parse_act(act)
while len(act_dict['chapters']) < 2:
act = self.query_chat(messages)
act_dict = Plan.parse_act(act)
else:
plan[act_nu... |
SUPPORTED_BACKENDS = ["hf", "llama.cpp"]
def generate_prompt_parts(
messages, include_roles=set(('user', 'assistant', 'system'))):
last_role = None
messages = [m for m in messages if m['role'] in include_roles]
for idx, message in enumerate(messages):
nl = "\n" if idx > 0 else ""
... | previous_scene = utils.keep_last_n_words(previous_scene, | 0 | 2023-11-17 11:53:00+00:00 | 4k |
dazhangyu123/ACMIL | modules/topk/polynomial/sp.py | [
{
"identifier": "divide_and_conquer",
"path": "modules/topk/polynomial/divide_conquer.py",
"snippet": "def divide_and_conquer(x, k, mul):\n \"\"\"\n Divide and conquer method for polynomial expansion\n x is a 2d tensor of size (n_classes, n_roots)\n The objective is to obtain the k first coe... | import torch
import torch.nn as nn
import torch.autograd as ag
from .divide_conquer import divide_and_conquer
from .multiplication import Multiplication
from .grad import d_logS_d_expX | 1,761 |
class LogSumExp(nn.Module):
def __init__(self, k, p=None, thresh=1e-5):
super(LogSumExp, self).__init__()
self.k = k
self.p = int(1 + 0.2 * k) if p is None else p
self.mul = Multiplication(self.k + self.p - 1)
self.thresh = thresh
self.register_buffer('grad_k', to... |
class LogSumExp(nn.Module):
def __init__(self, k, p=None, thresh=1e-5):
super(LogSumExp, self).__init__()
self.k = k
self.p = int(1 + 0.2 * k) if p is None else p
self.mul = Multiplication(self.k + self.p - 1)
self.thresh = thresh
self.register_buffer('grad_k', to... | log_res = divide_and_conquer(x, kp, mul=mul) | 0 | 2023-11-12 14:07:34+00:00 | 4k |
Kav-K/Described | discord_service/cogs/image_service_cog.py | [
{
"identifier": "EmbedStatics",
"path": "discord_service/embeds/embed_helper.py",
"snippet": "class EmbedStatics:\n def __init__(self):\n pass\n\n def status_to_string(status):\n if status:\n return \"enabled\"\n else:\n return \"disabled\"\n\n @static... | import pickle
import re
import traceback
import aiofiles
import discord
from collections import defaultdict
from pathlib import Path
from discord_service.embeds.embed_helper import EmbedStatics
from services.check_service import Check
from services.environment_service import EnvService
from services.openai_service impo... | 2,708 |
class ServerInformation:
def __init__(self, status: bool = False):
self.status = status
class ImageService(discord.Cog, name="ImageService"):
"""cog containing the optimizer command"""
async def change_guild_status(self, guild_id, status: bool):
self.server_information[guild_id].statu... |
class ServerInformation:
def __init__(self, status: bool = False):
self.status = status
class ImageService(discord.Cog, name="ImageService"):
"""cog containing the optimizer command"""
async def change_guild_status(self, guild_id, status: bool):
self.server_information[guild_id].statu... | checks=[Check.check_admin_roles()], | 1 | 2023-11-14 02:22:13+00:00 | 4k |
juftin/hatch-pip-compile | hatch_pip_compile/plugin.py | [
{
"identifier": "HatchPipCompileError",
"path": "hatch_pip_compile/exceptions.py",
"snippet": "class HatchPipCompileError(Exception):\n \"\"\"\n Base exception for hatch-pip-compile\n \"\"\""
},
{
"identifier": "PipInstaller",
"path": "hatch_pip_compile/installer.py",
"snippet":... | import functools
import hashlib
import logging
import os
import pathlib
import shutil
import tempfile
from subprocess import CompletedProcess
from typing import Any, Dict, List, Optional, Union
from hatch.env.virtual import VirtualEnvironment
from hatch.utils.platform import Platform
from hatchling.dep.core import depe... | 2,800 | """
hatch-pip-compile plugin
"""
logger = logging.getLogger(__name__)
class PipCompileEnvironment(VirtualEnvironment):
"""
Virtual Environment supported by pip-compile
"""
PLUGIN_NAME = "pip-compile"
default_env_name = "default"
def __repr__(self):
"""
Get representation... | """
hatch-pip-compile plugin
"""
logger = logging.getLogger(__name__)
class PipCompileEnvironment(VirtualEnvironment):
"""
Virtual Environment supported by pip-compile
"""
PLUGIN_NAME = "pip-compile"
default_env_name = "default"
def __repr__(self):
"""
Get representation... | raise HatchPipCompileError(msg) | 0 | 2023-11-10 00:34:00+00:00 | 4k |
google-deepmind/pix2act | pix2act/tasks/webshop/write_tf_examples.py | [
{
"identifier": "env_utils",
"path": "pix2act/common/env_utils.py",
"snippet": "class EnvConfig:\nclass CursorState:\ndef rel_x_y_to_x_y(env_config, x_rel, y_rel):\ndef is_float(element: str) -> bool:\ndef is_valid_coordinate(env_config: EnvConfig, x_str: str, y_str: str) -> bool:\ndef is_valid(env_conf... | import json
import os
import typing
import tensorflow as tf
from typing import Any, Dict, List
from absl import app
from absl import flags
from pix2act.common import env_utils
from pix2act.common import render_utils
from pix2act.common import tf_utils
from pix2act.tasks.webshop import demo_utils
from pix2act.tasks.webs... | 2,092 |
r"""Converts demonstrations to tf examples for training, validation, and test.
# pylint:disable=long-too-long
This requires that the Webshop server is running locally. See the official repo
for setup instructions: https://github.com/princeton-nlp/WebShop
Follows split and preprocessing here from the get_data method ... | # Copyright 2023 The pix2act Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | current_frame = render_utils.render_header( | 1 | 2023-11-13 22:50:55+00:00 | 4k |
zhang-tao-whu/DVIS_Plus | mask2former_video/modeling/transformer_decoder/video_mask2former_transformer_decoder.py | [
{
"identifier": "TRANSFORMER_DECODER_REGISTRY",
"path": "mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py",
"snippet": "TRANSFORMER_DECODER_REGISTRY = Registry(\"TRANSFORMER_MODULE\")"
},
{
"identifier": "PositionEmbeddingSine3D",
"path": "mask2former_video/modeling... | import logging
import fvcore.nn.weight_init as weight_init
import torch
from typing import Optional
from torch import nn, Tensor
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import Conv2d
from mask2former.modeling.transformer_decoder.maskformer_transformer_decod... | 2,505 |
self._reset_parameters()
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
return tensor if pos is None else tensor + pos
def forward_post(se... | # Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py
class SelfAttentionLayer(nn.Module):
def __init__(self, d_model, nhead, dropout=0.0,
activation="relu", normalize_before=False):
super... | @TRANSFORMER_DECODER_REGISTRY.register() | 0 | 2023-11-14 10:55:11+00:00 | 4k |
teamreboott/data-modori | data_modori/config/config.py | [
{
"identifier": "OPERATORS",
"path": "data_modori/ops/base_op.py",
"snippet": "OPERATORS = Registry('Operators')"
},
{
"identifier": "setup_logger",
"path": "data_modori/utils/logger_utils.py",
"snippet": "def setup_logger(save_dir, distributed_rank=0, filename='log.txt', mode='o', redir... | import os
import shutil
import time
import tempfile
import pprint
from argparse import ArgumentError
from typing import Dict, List, Tuple, Union
from jsonargparse import (ActionConfigFile, ArgumentParser, dict_to_namespace,
namespace_to_dict)
from jsonargparse.typing import NonNega... | 2,843 | help='Number of samples extracted by tracer to show the dataset '
'difference before and after a op. Only available when '
'open_tracer is true.')
parser.add_argument(
'--op_fusion',
type=bool,
default=False,
help='Whether to fuse operators that shar... |
def init_configs(args=None):
"""
initialize the jsonargparse parser and parse configs from one of:
1. POSIX-style commands line args;
2. config files in yaml (json and jsonnet supersets);
3. environment variables
4. hard-coded defaults
:param args: list of params, e.g., ... | setup_logger(save_dir=log_dir, filename=logfile_name, redirect=cfg.executor_type=='default') | 1 | 2023-11-13 04:52:55+00:00 | 4k |
52phm/pylmkit | pylmkit/core/base.py | [
{
"identifier": "read_yaml",
"path": "pylmkit/utils/data_utils.py",
"snippet": "def read_yaml(filepath):\n try:\n with open(filepath, encoding=\"utf-8\") as fp:\n result = yaml.load(fp, Loader=SafeLoader)\n except Exception as e:\n raise Exception(e)\n return result"
... | from abc import ABC
from pathlib import Path
from tqdm import tqdm
from pydantic import Field, BaseModel
from pylmkit.utils.data_utils import read_yaml, read_json, write_yaml, write_json
from pylmkit.utils.data_utils import message_as_string, document_as_dict, dict_as_document
from typing import Any, List, Optional, Ty... | 2,276 |
class BaseMemory(object):
human_prefix: str = "Human"
ai_prefix: str = "AI"
system_prefix: str = "System"
def __init__(self, init_memory=None, streamlit_web=False):
self.memory_messages = []
self.streamlit_web = streamlit_web
if self.streamlit_web: # streamlit rerun page, so ... |
class BaseMemory(object):
human_prefix: str = "Human"
ai_prefix: str = "AI"
system_prefix: str = "System"
def __init__(self, init_memory=None, streamlit_web=False):
self.memory_messages = []
self.streamlit_web = streamlit_web
if self.streamlit_web: # streamlit rerun page, so ... | return document_as_dict(self.splitter_documents) | 5 | 2023-11-18 10:31:58+00:00 | 4k |
PufferAI/pokegym | pokegym/environment.py | [
{
"identifier": "ACTIONS",
"path": "pokegym/pyboy_binding.py",
"snippet": "ACTIONS = (Down, Left, Right, Up, A, B, Start, Select)"
},
{
"identifier": "make_env",
"path": "pokegym/pyboy_binding.py",
"snippet": "def make_env(gb_path, headless=True, quiet=False, **kwargs):\n gb_path='pok... | from pdb import set_trace as T
from gymnasium import Env, spaces
from pokegym.pyboy_binding import (ACTIONS, make_env, open_state_file,
load_pyboy_state, run_action_on_emulator)
from pokegym import ram_map, game_map
import numpy as np
import os | 1,604 |
def play():
'''Creates an environment and plays it'''
env = Environment(rom_path='pokemon_red.gb', state_path=None, headless=False,
disable_input=False, sound=False, sound_emulated=False, verbose=True
)
env.reset()
env.game.set_emulation_speed(1)
# Display available actions
prin... |
def play():
'''Creates an environment and plays it'''
env = Environment(rom_path='pokemon_red.gb', state_path=None, headless=False,
disable_input=False, sound=False, sound_emulated=False, verbose=True
)
env.reset()
env.game.set_emulation_speed(1)
# Display available actions
prin... | self.game, self.screen = make_env( | 1 | 2023-11-16 18:34:28+00:00 | 4k |
AlexandrErohin/home-assistant-flightradar24 | custom_components/flightradar24/sensor.py | [
{
"identifier": "DOMAIN",
"path": "custom_components/flightradar24/const.py",
"snippet": "DOMAIN = \"flightradar24\""
},
{
"identifier": "FlightRadar24Coordinator",
"path": "custom_components/flightradar24/coordinator.py",
"snippet": "class FlightRadar24Coordinator(DataUpdateCoordinator[... | from dataclasses import dataclass
from collections.abc import Callable
from typing import Any
from homeassistant.components.sensor import (
SensorStateClass,
SensorEntity,
SensorEntityDescription,
)
from homeassistant.config_entries import ConfigEntry
from homeassistant.core import HomeAssistant, callback
f... | 2,721 |
@dataclass
class FlightRadar24SensorRequiredKeysMixin:
value: Callable[[FlightRadar24Coordinator], Any]
attributes: Callable[[FlightRadar24Coordinator], Any]
@dataclass
class TFlightRadar24SensorEntityDescription(SensorEntityDescription, FlightRadar24SensorRequiredKeysMixin):
"""A class that describes s... |
@dataclass
class FlightRadar24SensorRequiredKeysMixin:
value: Callable[[FlightRadar24Coordinator], Any]
attributes: Callable[[FlightRadar24Coordinator], Any]
@dataclass
class TFlightRadar24SensorEntityDescription(SensorEntityDescription, FlightRadar24SensorRequiredKeysMixin):
"""A class that describes s... | coordinator = hass.data[DOMAIN][entry.entry_id] | 0 | 2023-11-16 10:51:24+00:00 | 4k |
ej0cl6/TextEE | TextEE/models/EEQA/EDtrainer.py | [
{
"identifier": "BasicTrainer",
"path": "TextEE/models/trainer.py",
"snippet": "class BasicTrainer(object):\n def __init__(self, config, type_set=None):\n self.config = config\n self.type_set = type_set\n \n @classmethod\n def add_extra_info_fn(cls, instances, raw_data, con... | import os, sys, logging, tqdm, pprint
import torch
import numpy as np
import ipdb
from collections import namedtuple
from transformers import RobertaTokenizer, AutoTokenizer, get_linear_schedule_with_warmup
from torch.utils.data import DataLoader
from torch.optim import AdamW
from ..trainer import BasicTrainer
from .ED... | 3,002 |
logger = logging.getLogger(__name__)
EDBatch_fields = ['batch_doc_id', 'batch_wnd_id', 'batch_tokens', 'batch_pieces', 'batch_token_lens', 'batch_token_num', 'batch_text', 'batch_triggers']
EDBatch = namedtuple('EDBatch', field_names=EDBatch_fields, defaults=[None] * len(EDBatch_fields))
def ED_collate_fn(batch):
... |
logger = logging.getLogger(__name__)
EDBatch_fields = ['batch_doc_id', 'batch_wnd_id', 'batch_tokens', 'batch_pieces', 'batch_token_lens', 'batch_token_num', 'batch_text', 'batch_triggers']
EDBatch = namedtuple('EDBatch', field_names=EDBatch_fields, defaults=[None] * len(EDBatch_fields))
def ED_collate_fn(batch):
... | class EEQAEDTrainer(BasicTrainer): | 0 | 2023-11-15 21:32:56+00:00 | 4k |
isce-framework/snaphu-py | src/snaphu/_unwrap.py | [
{
"identifier": "run_snaphu",
"path": "src/snaphu/_snaphu.py",
"snippet": "def run_snaphu(config_file: str | os.PathLike[str]) -> None:\n \"\"\"\n Run SNAPHU with the specified config file.\n\n Parameters\n ----------\n config_file : path-like\n The file path of a text file storing... | import io
import os
import textwrap
import numpy as np
from dataclasses import dataclass
from pathlib import Path
from tempfile import mkstemp
from typing import cast, overload
from ._snaphu import run_snaphu
from ._util import BlockIterator, scratch_directory
from .io import InputDataset, OutputDataset | 3,261 | class SnaphuConfig:
"""
SNAPHU configuration parameters.
Parameters
----------
infile : path-like
The input interferogram file path.
corrfile : path-like
The input coherence file path.
outfile : path-like
The output unwrapped phase file path.
conncompfile : path-... | from __future__ import annotations
__all__ = [
"unwrap",
]
@dataclass(frozen=True)
class TilingParams:
"""
SNAPHU configuration parameters affecting scene tiling and parallel processing.
Parameters
----------
ntilerow, ntilecol : int, optional
Number of tiles along the row/column ... | igram: InputDataset, | 3 | 2023-11-16 21:48:58+00:00 | 4k |
fofr/cog-sdxl-multi-controlnet-lora | predict.py | [
{
"identifier": "WeightsDownloader",
"path": "weights_downloader.py",
"snippet": "class WeightsDownloader:\n @staticmethod\n def download_if_not_exists(url, dest):\n if not os.path.exists(dest):\n WeightsDownloader.download(url, dest)\n\n @staticmethod\n def download(url, d... | import os
import time
import numpy as np
import torch
from typing import List, Optional
from cog import BasePredictor, Input, Path
from diffusers import (
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler... | 3,039 |
SDXL_MODEL_CACHE = "./sdxl-cache"
REFINER_MODEL_CACHE = "./refiner-cache"
SAFETY_CACHE = "./safety-cache"
FEATURE_EXTRACTOR = "./feature-extractor"
SDXL_URL = "https://weights.replicate.delivery/default/sdxl/sdxl-vae-upcast-fix.tar"
REFINER_URL = (
"https://weights.replicate.delivery/default/sdxl/refiner-no-vae... |
SDXL_MODEL_CACHE = "./sdxl-cache"
REFINER_MODEL_CACHE = "./refiner-cache"
SAFETY_CACHE = "./safety-cache"
FEATURE_EXTRACTOR = "./feature-extractor"
SDXL_URL = "https://weights.replicate.delivery/default/sdxl/sdxl-vae-upcast-fix.tar"
REFINER_URL = (
"https://weights.replicate.delivery/default/sdxl/refiner-no-vae... | self.sizing_strategy = SizingStrategy() | 3 | 2023-11-13 13:04:41+00:00 | 4k |
ahayler/s4c | datasets/kitti_raw/kitti_raw_dataset.py | [
{
"identifier": "apply_crop",
"path": "utils/array_operations.py",
"snippet": "def apply_crop(array, crop):\n return array[crop[0]:crop[0] + crop[2], crop[1]:crop[1] + crop[3]]"
},
{
"identifier": "get_color_aug_fn",
"path": "utils/augmentation.py",
"snippet": "def get_color_aug_fn(pa... | import os
import time
import cv2
import numpy as np
import torch
from collections import Counter
from pathlib import Path
from torch.utils.data import Dataset
from torchvision.transforms import ColorJitter
from utils.array_operations import apply_crop
from utils.augmentation import get_color_aug_fn | 2,545 |
R_rect = np.eye(4, dtype=np.float32)
R_rect[:3, :3] = cam_calib_file_data['R_rect_00'].reshape(3, 3)
T_v2c = np.hstack((velo_calib_file_data['R'].reshape(3, 3), velo_calib_file_data['T'][..., np.newaxis]))
T_v2c = np.vstack((T_v2c, np.array([0, 0, 0, 1.0], dtype=np.floa... |
# This could also be retrieved from
BASE_SIZES = {
"2011_09_26": (375, 1242),
"2011_09_28": (370, 1224),
"2011_09_29": (374, 1238),
"2011_09_30": (370, 1226),
"2011_10_03": (376, 1241),
}
class KittiRawDataset(Dataset):
def __init__(self,
data_path: str,
po... | img = apply_crop(img, crop_box) | 0 | 2023-11-12 21:53:27+00:00 | 4k |
TimbreWatermarking/TimbreWatermarking | watermarking_model/model/modules.py | [
{
"identifier": "FCBlock",
"path": "watermarking_model/model/blocks.py",
"snippet": "class FCBlock(nn.Module):\n \"\"\" Fully Connected Block \"\"\"\n\n def __init__(self, in_features, out_features, activation=None, bias=False, dropout=None, spectral_norm=False):\n super(FCBlock, self).__in... | from base64 import encode
from torch.nn import LeakyReLU
from .blocks import FCBlock, PositionalEncoding, Mish, Conv1DBlock
import torch
import torch.nn as nn | 1,743 |
class Encoder(nn.Module):
def __init__(self, model_config, msg_length, win_dim, embedding_dim, nlayers_encoder=6, transformer_drop=0.1, attention_heads=8):
super(Encoder, self).__init__()
self.encoder_layer = nn.TransformerEncoderLayer(d_model=embedding_dim, nhead=attention_heads, dropout=transfor... |
class Encoder(nn.Module):
def __init__(self, model_config, msg_length, win_dim, embedding_dim, nlayers_encoder=6, transformer_drop=0.1, attention_heads=8):
super(Encoder, self).__init__()
self.encoder_layer = nn.TransformerEncoderLayer(d_model=embedding_dim, nhead=attention_heads, dropout=transfor... | self.wav_linear_in = FCBlock(win_dim, embedding_dim, activation=Mish()) | 2 | 2023-11-13 01:40:03+00:00 | 4k |
joseph-crowley/tool-creator | tool_user.py | [
{
"identifier": "AssistantConfig",
"path": "user_config.py",
"snippet": "class AssistantConfig:\n def __init__(self, tools_to_use=None):\n self.tools_to_use = tools_to_use or []\n self.instructions_for_assistant = 'Use the tools to accomplish the task'\n self.files_for_assistant ... | import os
import json
from user_config import AssistantConfig as UserConfig
from utils import chat as chat_loop
from openai import OpenAI | 2,411 | """
Create an assistant using the tools from tool_creator using the assistant creation API
"""
client = OpenAI() # be sure to set your OPENAI_API_KEY environment variable
def create_tool_user(assistant_details):
# create the assistant
tool_user = client.beta.assistants.create(**assistant_details["build_para... | """
Create an assistant using the tools from tool_creator using the assistant creation API
"""
client = OpenAI() # be sure to set your OPENAI_API_KEY environment variable
def create_tool_user(assistant_details):
# create the assistant
tool_user = client.beta.assistants.create(**assistant_details["build_para... | chat_loop(client, thread, tool_user, functions) | 0 | 2023-11-10 03:02:32+00:00 | 4k |
nillion-oss/tinysig | src/tinysig/tecdsa.py | [
{
"identifier": "add",
"path": "src/tinysig/utils.py",
"snippet": "def add(values: list[int], size: int) -> int:\ndef add_ec(points: list[EccPoint]) -> int:\ndef generate_additive_shares(secret: int, n: int, size: int) -> list[int]:\ndef multiply(values: list[int], size: int) -> int:\ndef egcd(a: int, p... | from Crypto.Hash import SHA256
from phe import paillier
from typing import List
from .utils import add, add_ec, multiply, rand, egcd, verify_dsa_signature, verify_ecdsa_signature
from .setup import DSASetup, ECDSASetup
from .network import Network, Client | 3,566 | raise TypeError("Invalid type provided. "
"Please use either 'DSASetup' or 'ECDSASetup' types."
)
# Generate public and private keys for the paillier homomorphic encryption scheme
for i in range(C):
pub_key, priv_key = pail... |
class ThresholdSignature(Network):
clients: List[Client]
def __init__(self, N, C, setup=None, debug=False):
self.debug = debug
if setup is None:
self.dsa = DSASetup.generate_dsa_setup()
self.setup = DSASetup
super().__init__(N, self.dsa.q, self.dsa.h)
... | val = multiply(base_exps, prime) | 0 | 2023-11-14 13:55:41+00:00 | 4k |
Exscientia/physicsml | src/physicsml/models/mace/modules/blocks.py | [
{
"identifier": "Activation",
"path": "src/physicsml/models/mace/modules/_activation.py",
"snippet": "class Activation(torch.nn.Module):\n r\"\"\"Scalar activation function.\n\n Odd scalar inputs require activation functions with a defined parity (odd or even).\n\n Parameters\n ----------\n ... | from typing import Optional
from e3nn import nn, o3
from torch_geometric.utils.scatter import scatter
from ._activation import Activation
from .irreps_tools import reshape_irreps, tp_out_irreps_with_instructions
from .radial import BesselBasis, PolynomialCutoff
from .symmetric_contraction import SymmetricContraction
im... | 3,534 |
class NonLinearReadoutBlock(torch.nn.Module):
def __init__(
self,
irreps_in: o3.Irreps,
MLP_irreps: o3.Irreps,
irreps_out: o3.Irreps,
) -> None:
super().__init__()
self.linear_1 = o3.Linear(irreps_in=irreps_in, irreps_out=MLP_irreps)
self.non_linearit... |
class NonLinearReadoutBlock(torch.nn.Module):
def __init__(
self,
irreps_in: o3.Irreps,
MLP_irreps: o3.Irreps,
irreps_out: o3.Irreps,
) -> None:
super().__init__()
self.linear_1 = o3.Linear(irreps_in=irreps_in, irreps_out=MLP_irreps)
self.non_linearit... | self.symmetric_contractions = SymmetricContraction( | 5 | 2023-11-10 13:54:53+00:00 | 4k |
naver-ai/scob | utils/config_manager.py | [
{
"identifier": "misc",
"path": "utils/misc.py",
"snippet": "def get_node_rank():\ndef get_local_rank():\ndef is_rank_zero():\ndef cpu_count():\ndef get_file(dataset_path, prefix, postfix, ext):\ndef is_otor(task_name, or_oracle=False, oracle=False):"
},
{
"identifier": "AVAILABLE_TASKS",
"p... | import enum
import os
import pickle
import time
import torch
import torch.distributed as dist
from datetime import timedelta
from omegaconf import OmegaConf
from omegaconf.dictconfig import DictConfig
from utils import misc
from utils.constants import AVAILABLE_TASKS, DecoderTypes, HeadTypes, Seperators, Task... | 2,971 | "This configuration should be added"
" automatically in runtime."
)
for mode in ["train", "val", "test"]:
num_devices = torch.cuda.device_count() * self.__config.train.num_nodes
if self.__config[mode].batch_size % num_devices... | """
SCOB
Copyright (c) 2023-present NAVER Cloud Corp.
MIT license
config parser with omegaconf
"""
class FileType(enum.Enum): # pylint: disable=missing-class-docstring
YAML = 1
PICKLE = 2
class ConfigManager(metaclass=Singleton):
"""Singleton ConfigManager for project
Notes... | dtd_key_str = Seperators.DTD.join(
| 4 | 2023-11-15 00:40:08+00:00 | 4k |
speckai/speck | src/python/speck/chat/entities.py | [
{
"identifier": "ChatLogger",
"path": "src/python/speck/chat/logger.py",
"snippet": "class ChatLogger:\n @staticmethod\n def log(log_config: \"LogConfig\", prompt: Any, model: str, response: Any, **kwargs):\n if kwargs.get(\"config\", {}).get(\"_log\", True):\n universal_format_l... | from abc import ABC, abstractmethod
from typing import Any, Callable, Iterator, Literal, Optional, Tuple, Union
from openai._types import NotGiven
from pydantic import BaseModel, Extra
from ..chat.logger import ChatLogger
from ..debug._debug_socket import run_debug_websocket | 2,229 | "stream": self.stream,
"_log": self._log,
"temperature": self._convert_optional(self.temperature),
"max_tokens": self._convert_optional(self.max_tokens),
"top_p": self._convert_optional(self.top_p),
"frequency_penalty": self._convert_optional(self.... | from __future__ import annotations
# from dataclasses import dataclass
NOT_GIVEN = None
class Message(BaseModel):
role: MessageRole
content: str
class SafeDict(dict):
def __missing__(self, key):
return "{" + key + "}" # Returns the key in curly braces as a string
class Prompt(str):
me... | data = run_debug_websocket(self._client, self, prompt, config) | 1 | 2023-11-15 05:46:05+00:00 | 4k |
hahnyuan/ASVD4LLM | binary_search.py | [
{
"identifier": "evaluate_model",
"path": "evaluate.py",
"snippet": "@torch.no_grad()\ndef evaluate_model(\n model,\n tokenizer,\n model_name,\n tasks,\n eval_ppl=\"\",\n num_fewshot=0,\n limit=-1,\n batch_size=1,\n):\n \"\"\"\n model: model name\n limit: number of test ... | import os
import torch
import torch.nn as nn
from evaluate import evaluate_model, evaluate_perplexity
from modules.svd_linear import SVDLinear
from tqdm import tqdm | 3,314 |
def binary_search_truncation_rank(model, sensitivity_dict, calib_loader, args):
module_dict = {name: module for name, module in model.named_modules()}
full_name_dict = {module: name for name, module in model.named_modules()}
linear_info = {}
modules = [model]
while len(modules) > 0:
submod... |
def binary_search_truncation_rank(model, sensitivity_dict, calib_loader, args):
module_dict = {name: module for name, module in model.named_modules()}
full_name_dict = {module: name for name, module in model.named_modules()}
linear_info = {}
modules = [model]
while len(modules) > 0:
submod... | ppl = evaluate_perplexity(model, input_ids, args.n_calib_samples) | 1 | 2023-11-10 02:18:36+00:00 | 4k |
chaiNNer-org/spandrel | src/spandrel/__helpers/loader.py | [
{
"identifier": "canonicalize_state_dict",
"path": "src/spandrel/__helpers/canonicalize.py",
"snippet": "def canonicalize_state_dict(state_dict: StateDict) -> StateDict:\n \"\"\"\n Canonicalize a state dict.\n\n This function is used to canonicalize a state dict, so that it can be\n used for... | import os
import torch
from pathlib import Path
from safetensors.torch import load_file
from .canonicalize import canonicalize_state_dict
from .main_registry import MAIN_REGISTRY
from .model_descriptor import ModelDescriptor, StateDict
from .registry import ArchRegistry
from .unpickler import RestrictedUnpickle | 2,515 | from __future__ import annotations
class ModelLoader:
"""Class for automatically loading a pth file into any architecture"""
def __init__(
self,
device: str | torch.device | None = None,
registry: ArchRegistry = MAIN_REGISTRY,
):
if isinstance(device, str):
... | from __future__ import annotations
class ModelLoader:
"""Class for automatically loading a pth file into any architecture"""
def __init__(
self,
device: str | torch.device | None = None,
registry: ArchRegistry = MAIN_REGISTRY,
):
if isinstance(device, str):
... | pickle_module=RestrictedUnpickle, # type: ignore | 4 | 2023-11-17 01:11:47+00:00 | 4k |
ottoweiss/pdf-to-audiobook | main.py | [
{
"identifier": "get_audiobook",
"path": "src/audiobook.py",
"snippet": "def get_audiobook(json_file, book_title=\"audiobook\", voice=\"onyx\", speed=\"1.0\"):\n book_directory = f\"{book_title}\"\n atexit.register(save_full, book_title)\n\n if not os.path.exists(book_directory):\n os.ma... | from src.audiobook import get_audiobook
from src.clean_pdf import get_rewrite
from src.pdf_to_json import extract_text_from_pdf
from colorama import Fore, Style
import os
import time | 1,712 |
def input_q(text):
print(Fore.YELLOW + text, end="")
inp = input()
print(Style.RESET_ALL, end="")
if inp == ":q":
exit()
return inp
def print_info(message):
print(Fore.CYAN + message + Style.RESET_ALL)
def print_error(message):
print(Fore.RED + "ERROR: " + message + Style.RESET_AL... |
def input_q(text):
print(Fore.YELLOW + text, end="")
inp = input()
print(Style.RESET_ALL, end="")
if inp == ":q":
exit()
return inp
def print_info(message):
print(Fore.CYAN + message + Style.RESET_ALL)
def print_error(message):
print(Fore.RED + "ERROR: " + message + Style.RESET_AL... | cost, e_time = extract_text_from_pdf(pdf_file_name, {title: (page_start, page_end)}) | 2 | 2023-11-16 20:37:24+00:00 | 4k |
GoldenThrust/Virtual-Bank | api/transactions/views.py | [
{
"identifier": "Transaction",
"path": "api/transactions/models.py",
"snippet": "class Transaction(models.Model):\n TRANSACTION_TYPES = [\n ('DEPOSIT', 'Deposit'),\n ('TRANSFER', 'Transfer'),\n ('DEBIT_CARD', 'Debit Card'),\n ('PAYMENT', 'Payment'),\n ]\n\n account =... | from rest_framework import generics
from datetime import datetime
from notifications.utils import process_notifications
from django.utils.timezone import localtime
from debit_cards.utils import luhn_checksum
from .models import Transaction
from accounts.models import Account
from deposits.models import Deposit
from tra... | 3,237 | expiry_date = serializer.validated_data.pop("expiry_date")
transaction_amount = serializer.validated_data.get("amount")
# Validation of expiry date
try:
month, year = expiry_date.split("/")
month = int(month)
year = int(year)
current_year... |
# Models and Serializers
# from payments.models import Payment
class DateError(Exception):
pass
class TransactionList(generics.ListCreateAPIView):
queryset = Transaction.objects.all()
serializer_class = TransactionSerializer
permission_classes = [permissions.IsAdminUser]
class TransactionDetail... | serializer_class = TransactionHistorySerializer | 4 | 2023-11-10 12:39:38+00:00 | 4k |
Mj23978/OpenServer | openserver/server/chat/completions.py | [
{
"identifier": "logger",
"path": "openserver/core/utils/utils.py",
"snippet": "def trim_string(string, count: int) -> str:\ndef run_with_time(func):"
},
{
"identifier": "extract_json_from_string",
"path": "openserver/core/utils/json.py",
"snippet": "def extract_json_from_string(text: st... | import json
import os
import random
import string
import time
from pathlib import Path
from typing import Any, Dict, List
from flask import Request, jsonify, request
from langchain.schema import BaseMessage
from openserver.core.utils import extract_json_from_string, base_messages_to_default, logger
from openserver.core... | 3,038 |
class ChatCompletionsRequest:
def __init__(self, request: Request):
try:
self.model: str = request.get_json().get("model", "gpt-3.5-turbo")
self.stream: bool = request.get_json().get("stream", False)
self.api_key: str | None = request.get_json().get("api_key") or (req... |
class ChatCompletionsRequest:
def __init__(self, request: Request):
try:
self.model: str = request.get_json().get("model", "gpt-3.5-turbo")
self.stream: bool = request.get_json().get("stream", False)
self.api_key: str | None = request.get_json().get("api_key") or (req... | "cost": "{:.6f}".format(completion_price_calculator(provider.cost.input, provider.cost.output, inp_token, out_token)) | 7 | 2023-11-11 00:32:31+00:00 | 4k |
AI-sandbox/HyperFast | hyperfast/hyperfast.py | [
{
"identifier": "config",
"path": "hyperfast/config.py",
"snippet": ""
},
{
"identifier": "seed_everything",
"path": "hyperfast/utils.py",
"snippet": "def seed_everything(seed: int):\n random.seed(seed)\n os.environ[\"PYTHONHASHSEED\"] = str(seed)\n np.random.seed(seed)\n tor... | import os
import math
import torch
import requests
import numpy as np
import pandas as pd
import torch.nn.functional as F
from torch import Tensor
from types import SimpleNamespace
from .config import config
from sklearn.base import BaseEstimator
from sklearn.utils.validation import check_X_y, check_array, check_is_fit... | 2,694 |
class HyperFastClassifier(BaseEstimator):
"""
A scikit-learn-like interface for the HyperFast model.
Attributes:
device (str): Device to run the model on.
n_ensemble (int): Number of ensemble models to use.
batch_size (int): Size of the batch for weight prediction and ensembling.
... |
class HyperFastClassifier(BaseEstimator):
"""
A scikit-learn-like interface for the HyperFast model.
Attributes:
device (str): Device to run the model on.
n_ensemble (int): Number of ensemble models to use.
batch_size (int): Size of the batch for weight prediction and ensembling.
... | self._cfg = self._load_config(config, self.device, self.torch_pca, self.nn_bias) | 0 | 2023-11-14 05:56:47+00:00 | 4k |
TCLResearchEurope/torch-dag | node_api_conversion/convert_cell_to_dag_module.py | [
{
"identifier": "from_nd_converter",
"path": "node_api_conversion/from_nd_converter.py",
"snippet": "def adjust_padding(padding, kernel_size):\ndef convert_node(node: nd.nodes, inst: nd.nodes.NodeInstance = None) -> torch.nn.Module:\ndef _(node: nd.cells.Cell, inst: nd.nodes.NodeInstance = None) -> torc... | import argparse
import logging
import node_api as nd
import modelhub_client as mh
from node_api_conversion import from_nd_converter
from torch_dag.visualization.visualize_dag import DagVisualizer
from node_api_conversion.utils import log_cell_characteristics | 3,475 | #
# Copyright © TCL Research Europe. All rights reserved.
#
logger = logging.getLogger(__name__)
def find_icns_to_remove(cell: nd.cells.Cell):
result = []
for icn in cell.inner_cell_nodes:
if isinstance(icn.node, nd.ops.Activation):
if icn.node.activation_name in (None, 'none', 'identit... | #
# Copyright © TCL Research Europe. All rights reserved.
#
logger = logging.getLogger(__name__)
def find_icns_to_remove(cell: nd.cells.Cell):
result = []
for icn in cell.inner_cell_nodes:
if isinstance(icn.node, nd.ops.Activation):
if icn.node.activation_name in (None, 'none', 'identit... | log_cell_characteristics(cell, input_shape[1:]) | 2 | 2023-11-17 15:36:44+00:00 | 4k |
timlrx/simple-ai-agents | simple_ai_agents/cli.py | [
{
"identifier": "ChatAgent",
"path": "simple_ai_agents/chat_agent.py",
"snippet": "class ChatAgent(BaseModel):\n \"\"\"\n A chatbot class that provides additional functionality\n for creating and managing chat sessions.\n\n Args:\n character (str, optional): The name of the chatbot fo... | import sys
import click
from dotenv import load_dotenv
from simple_ai_agents.chat_agent import ChatAgent
from simple_ai_agents.prompts import SYSTEM_PROMPT | 3,551 |
load_dotenv()
@click.command()
@click.option("--character", default=None, help="Specify the character")
@click.option("--prime/--no-prime", default=False, help="Enable priming")
@click.option(
"-m",
"--model",
default="gpt-3.5-turbo",
help="""Specify the LLM model e.g. gpt-3.5-turbo, ollama/mistral... |
load_dotenv()
@click.command()
@click.option("--character", default=None, help="Specify the character")
@click.option("--prime/--no-prime", default=False, help="Enable priming")
@click.option(
"-m",
"--model",
default="gpt-3.5-turbo",
help="""Specify the LLM model e.g. gpt-3.5-turbo, ollama/mistral... | @click.option("-s", "--system", default=SYSTEM_PROMPT, help="System prompt") | 1 | 2023-11-10 06:01:25+00:00 | 4k |
DIAGNijmegen/HoVer-UNet | train/apply_postprocessing.py | [
{
"identifier": "DatasetPannuke",
"path": "data/pannuke_distillation_dataset.py",
"snippet": "class DatasetPannuke(Dataset):\n \"\"\"\n Distillaton pannuke dataset\n \"\"\"\n\n def __init__(self, path: str, mode: str = 'train', true_labels: bool = False,\n hovernet_prediction... | import colorsys
import json
import os
import random
import cv2
import numpy as np
import segmentation_models_pytorch as smp
import torch
import torch.nn.functional as F
from multiprocessing import Pool
from time import time
from torch.utils.data import DataLoader
from tqdm import tqdm
from data.pannuke_distillation_dat... | 2,460 |
def random_colors(N, bright=True):
"""Generate random colors.
To get visually distinct colors, generate them in HSV space then
convert to RGB.
"""
brightness = 1.0 if bright else 0.7
hsv = [(i / N, 1, brightness) for i in range(N)]
colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hs... |
def random_colors(N, bright=True):
"""Generate random colors.
To get visually distinct colors, generate them in HSV space then
convert to RGB.
"""
brightness = 1.0 if bright else 0.7
hsv = [(i / N, 1, brightness) for i in range(N)]
colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hs... | data_infer = DatasetPannuke(path_test, mode='infer') | 0 | 2023-11-10 09:37:29+00:00 | 4k |
StanislavPetrovV/3D-Number-Renderer-with-UMAP | renderer.py | [
{
"identifier": "ShaderProgram",
"path": "shader_program.py",
"snippet": "class ShaderProgram:\n def __init__(self, renderer):\n self.app = renderer.app\n self.ctx = renderer.ctx\n self.camera = renderer.camera\n\n # -------- shaders -------- #\n self.axis = self.ge... | from shader_program import ShaderProgram
from camera import Camera
from meshes.point_cloud_mesh import PointCloudMesh
from meshes.axis_mesh import AxisMesh | 2,066 |
class Renderer:
def __init__(self, app):
self.app = app
self.ctx = app.ctx
#
self.camera = Camera(app)
|
class Renderer:
def __init__(self, app):
self.app = app
self.ctx = app.ctx
#
self.camera = Camera(app) | self.shader_program = ShaderProgram(renderer=self) | 0 | 2023-11-11 10:35:37+00:00 | 4k |
fofr/cog-sdxl-lcm-multi-controlnet-lora | predict.py | [
{
"identifier": "WeightsManager",
"path": "weights_manager.py",
"snippet": "class WeightsManager:\n def __init__(self, predictor):\n self.predictor = predictor\n self.weights_cache = WeightsDownloadCache()\n\n def load_trained_weights(self, weights, pipe):\n from no_init impor... | import os
import time
import numpy as np
import torch
from typing import List, Optional
from cog import BasePredictor, Input, Path
from diffusers import (
DiffusionPipeline,
LCMScheduler,
StableDiffusionXLImg2ImgPipeline,
StableDiffusionXLInpaintPipeline,
StableDiffusionXLControlNetPipeline,
Sta... | 2,900 |
SDXL_MODEL_CACHE = "./sdxl-cache"
REFINER_MODEL_CACHE = "./refiner-cache"
SAFETY_CACHE = "./safety-cache"
LCM_CACHE = "./lcm-cache"
FEATURE_EXTRACTOR = "./feature-extractor"
SDXL_URL = "https://weights.replicate.delivery/default/sdxl/sdxl-vae-upcast-fix.tar"
REFINER_URL = (
"https://weights.replicate.delivery/d... |
SDXL_MODEL_CACHE = "./sdxl-cache"
REFINER_MODEL_CACHE = "./refiner-cache"
SAFETY_CACHE = "./safety-cache"
LCM_CACHE = "./lcm-cache"
FEATURE_EXTRACTOR = "./feature-extractor"
SDXL_URL = "https://weights.replicate.delivery/default/sdxl/sdxl-vae-upcast-fix.tar"
REFINER_URL = (
"https://weights.replicate.delivery/d... | WeightsDownloader.download_if_not_exists(SAFETY_URL, SAFETY_CACHE) | 1 | 2023-11-16 11:11:27+00:00 | 4k |
joyn-gg/discord.http | discord_http/http.py | [
{
"identifier": "NotFound",
"path": "discord_http/errors.py",
"snippet": "class NotFound(HTTPException):\n \"\"\" Raised whenever a HTTP request returns 404 \"\"\"\n pass"
},
{
"identifier": "DiscordServerError",
"path": "discord_http/errors.py",
"snippet": "class DiscordServerErro... | import aiohttp
import asyncio
import json
import logging
import sys
from aiohttp.client_exceptions import ContentTypeError
from collections import deque
from typing import (
Optional, Any, Union, Self, overload,
Literal, TypeVar, Generic, TYPE_CHECKING
)
from .errors import (
NotFound, DiscordServerError,
... | 2,307 | @overload
async def query(
self,
method: MethodTypes,
path: str,
*,
res_method: Literal["read"] = "read",
**kwargs
) -> HTTPResponse[bytes]:
...
@overload
async def query(
self,
method: MethodTypes,
path: str,
*... |
if TYPE_CHECKING:
MethodTypes = Literal["GET", "POST", "DELETE", "PUT", "HEAD", "PATCH", "OPTIONS"]
ResMethodTypes = Literal["text", "read", "json"]
ResponseT = TypeVar("ResponseT")
_log = logging.getLogger(__name__)
__all__ = (
"DiscordAPI",
"HTTPResponse",
)
class HTTPResponse(Generic[ResponseT]):
... | raise NotFound(r) | 0 | 2023-11-14 12:50:42+00:00 | 4k |
Ganymede-Bio/bio-curve-fit | tests/test_four_pl_logistic.py | [
{
"identifier": "FourPLLogistic",
"path": "bio_curve_fit/logistic.py",
"snippet": "class FourPLLogistic(BaseEstimator, RegressorMixin, BaseStandardCurve):\n def __init__(\n self,\n A=None,\n B=None,\n C=None,\n D=None,\n LLOD=None,\n ULOD=None,\n ... | import numpy as np
import pandas as pd
import pytest
from bio_curve_fit.logistic import FourPLLogistic
from bio_curve_fit.plotting import plot_standard_curve | 3,536 |
# set a seed for reproducibility
np.random.seed(42)
def test_fit_and_plot():
TEST_PARAMS = [1.0, 1.0, 2.0, 3.0]
x_data = np.logspace(0.00001, 7, 100, base=np.e) # type: ignore
# generate y-data based on the test parameters
|
# set a seed for reproducibility
np.random.seed(42)
def test_fit_and_plot():
TEST_PARAMS = [1.0, 1.0, 2.0, 3.0]
x_data = np.logspace(0.00001, 7, 100, base=np.e) # type: ignore
# generate y-data based on the test parameters | y_data = FourPLLogistic.four_param_logistic( | 0 | 2023-11-13 15:06:15+00:00 | 4k |
chziakas/backbone-learn | experiments/benchmark_decision_tree.py | [
{
"identifier": "BackboneDecisionTree",
"path": "backbone_learn/backbone/backbone_decision_tree.py",
"snippet": "class BackboneDecisionTree(BackboneSupervised):\n \"\"\"\n Specific implementation of the Backbone method for sparse regression.\n\n This class combines Pearson correlation for featu... | import time
from itertools import product
from sklearn.datasets import make_classification
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import KBinsDiscretizer, OneHotEncoder
from utils import save_results
from backbone_learn.backbone.backbone... | 1,816 |
# Define parameter ranges for Backbone parameters
alpha_range = [0.1, 0.5]
beta_range = [0.5, 0.9]
num_subproblems_range = [5, 10]
num_iterations_range = [1]
# Define parameter ranges for FlowOCT parameters
depth_range = [2]
_lambda_range = [0.5]
# Define dataset parameters
n_informative = 4
n_bins = 5
n_features_r... |
# Define parameter ranges for Backbone parameters
alpha_range = [0.1, 0.5]
beta_range = [0.5, 0.9]
num_subproblems_range = [5, 10]
num_iterations_range = [1]
# Define parameter ranges for FlowOCT parameters
depth_range = [2]
_lambda_range = [0.5]
# Define dataset parameters
n_informative = 4
n_bins = 5
n_features_r... | backbone_model = BackboneDecisionTree( | 0 | 2023-11-18 14:28:12+00:00 | 4k |
openclimatefix/Open-Source-Quartz-Solar-Forecast | quartz_solar_forecast/eval/forecast.py | [
{
"identifier": "get_nwp",
"path": "quartz_solar_forecast/data.py",
"snippet": "def get_nwp(site: PVSite, ts: datetime, nwp_source: str = \"icon\") -> xr.Dataset:\n \"\"\"\n Get GFS NWP data for a point time space and time\n\n :param site: the PV site\n :param ts: the timestamp for when you ... | import os
import pandas as pd
import xarray as xr
from psp.data_sources.nwp import NwpDataSource
from psp.data_sources.pv import NetcdfPvDataSource
from psp.serialization import load_model
from psp.typings import X
from quartz_solar_forecast.data import get_nwp, make_pv_data
from quartz_solar_forecast.pydantic_models i... | 2,428 |
dir_path = os.path.dirname(os.path.realpath(__file__))
def run_forecast(pv_df: pd.DataFrame, nwp_df: pd.DataFrame, nwp_source="ICON") -> pd.DataFrame:
"""
Run the forecast from NWP data
:param pv_df: the PV site data. This should have columns timestamp, id, latitude, longitude, and capacity
:param... |
dir_path = os.path.dirname(os.path.realpath(__file__))
def run_forecast(pv_df: pd.DataFrame, nwp_df: pd.DataFrame, nwp_source="ICON") -> pd.DataFrame:
"""
Run the forecast from NWP data
:param pv_df: the PV site data. This should have columns timestamp, id, latitude, longitude, and capacity
:param... | pred_df = forecast_v1(nwp_source, nwp_xr, pv_xr, ts, model=model) | 3 | 2023-11-16 07:37:42+00:00 | 4k |
newcastleuniversity/DISPEL | tests/providers/generic/activity/test_turning.py | [
{
"identifier": "Turn",
"path": "dispel/providers/generic/activity/turning.py",
"snippet": "class Turn:\n \"\"\"Class to encapsulate turns and turn related gyroscope data.\n\n Parameters\n ----------\n start\n The start date time of the turn.\n end\n The end date time of the... | import pandas as pd
import pytest
from dispel.providers.generic.activity.turning import Turn, el_gohary_detect_turns | 1,662 | """Tests for :mod:`dispel.providers.generic.activity.turning`."""
@pytest.fixture
def example_turn_data():
"""Get example turn data."""
index = pd.date_range("now", periods=61, freq="20ms")
values = [0] * 10 + list(range(20)) + [20] + list(reversed(range(20))) + [0] * 10
return pd.Series(values, ind... | """Tests for :mod:`dispel.providers.generic.activity.turning`."""
@pytest.fixture
def example_turn_data():
"""Get example turn data."""
index = pd.date_range("now", periods=61, freq="20ms")
values = [0] * 10 + list(range(20)) + [20] + list(reversed(range(20))) + [0] * 10
return pd.Series(values, ind... | turn = Turn(index[30], index[30], example_turn_data) | 0 | 2023-11-14 10:06:46+00:00 | 4k |
runDMCA/home-assistant-mazda | custom_components/mazda/pymazda/connection.py | [
{
"identifier": "decrypt_aes128cbc_buffer_to_str",
"path": "custom_components/mazda/pymazda/crypto_utils.py",
"snippet": "def decrypt_aes128cbc_buffer_to_str(data, key, iv): # noqa: D103\n cipher = Cipher(algorithms.AES(key.encode(\"ascii\")), modes.CBC(iv.encode(\"ascii\")))\n decryptor = cipher... | import asyncio # noqa: D100
import base64
import hashlib
import json
import logging
import ssl
import time
import aiohttp
from urllib.parse import urlencode
from .crypto_utils import (
decrypt_aes128cbc_buffer_to_str,
encrypt_aes128cbc_buffer_to_base64_str,
encrypt_rsaecbpkcs1_padding,
generate_usher_d... | 3,266 |
ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT)
ssl_context.load_default_certs()
ssl_context.set_ciphers(
"DEFAULT:!aNULL:!eNULL:!MD5:!3DES:!DES:!RC4:!IDEA:!SEED:!aDSS:!SRP:!PSK"
)
REGION_CONFIG = {
"MNAO": {
"app_code": "202007270941270111799",
"base_url": "https://0cxo7m58.mazda.com/... |
ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT)
ssl_context.load_default_certs()
ssl_context.set_ciphers(
"DEFAULT:!aNULL:!eNULL:!MD5:!3DES:!DES:!RC4:!IDEA:!SEED:!aDSS:!SRP:!PSK"
)
REGION_CONFIG = {
"MNAO": {
"app_code": "202007270941270111799",
"base_url": "https://0cxo7m58.mazda.com/... | self.base_api_device_id = generate_uuid_from_seed(email) | 4 | 2023-11-14 01:42:43+00:00 | 4k |
NevermindNilas/TheAnimeScripter | src/cugan/cugan.py | [
{
"identifier": "UpCunet2x",
"path": "src/cugan/cugan_arch.py",
"snippet": "class UpCunet2x(nn.Module): # 完美tile，全程无损\n def __init__(self, in_channels=3, out_channels=3):\n super(UpCunet2x, self).__init__()\n self.unet1 = UNet1(in_channels, out_channels, deconv=True)\n self.unet... | from .cugan_arch import UpCunet2x, UpCunet3x, UpCunet4x, UpCunet2x_fast
from realcugan_ncnn_py import Realcugan
import os
import requests
import torch
import torch.nn.functional as F | 1,897 |
class Cugan:
def __init__(self, upscale_method, upscale_factor, cugan_kind, half, width, height):
self.upscale_method = upscale_method
self.upscale_factor = upscale_factor
self.cugan_kind = cugan_kind
self.half = half
self.width = width
self.height = height
... |
class Cugan:
def __init__(self, upscale_method, upscale_factor, cugan_kind, half, width, height):
self.upscale_method = upscale_method
self.upscale_factor = upscale_factor
self.cugan_kind = cugan_kind
self.half = half
self.width = width
self.height = height
... | model_map = {2: UpCunet2x, 3: UpCunet3x, 4: UpCunet4x} | 1 | 2023-11-14 22:10:11+00:00 | 4k |
ubertidavide/fastbots | tests/test_firefox_bot.py | [
{
"identifier": "FirefoxBot",
"path": "fastbots/firefox_bot.py",
"snippet": "class FirefoxBot(Bot):\n \"\"\"\n Firefox Bot\n\n Class representing the Firefox Bot implementation.\n\n Attributes:\n _driver (WebDriver): The WebDriver instance for Firefox.\n _wait (WebDriverWait): ... | import pytest
from configparser import ConfigParser
from pathlib import Path
from seleniumwire.webdriver import Firefox
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.firefox.options import Options as FirefoxOptions
from selenium.webdriver.firefox.firefox_profile import FirefoxProfile... | 2,009 |
@pytest.fixture
def bot():
with FirefoxBot() as bot:
yield bot
def test_driver(bot):
assert isinstance(bot.driver, WebDriver)
def test_wait(bot):
assert isinstance(bot.wait, WebDriverWait)
def test_payload(bot):
|
@pytest.fixture
def bot():
with FirefoxBot() as bot:
yield bot
def test_driver(bot):
assert isinstance(bot.driver, WebDriver)
def test_wait(bot):
assert isinstance(bot.wait, WebDriverWait)
def test_payload(bot): | assert isinstance(bot.payload, Payload) | 2 | 2023-11-16 00:12:09+00:00 | 4k |
intel/llm-on-ray | rlhf/rl_algo/ppo/ppo_rlhf.py | [
{
"identifier": "generate_response",
"path": "common/agentenv/rlhf_env.py",
"snippet": "def generate_response(\n model: torch.nn.Module, \n *, \n input_ids: torch.tensor, \n max_length:int, \n eos_token_id: int\n):\n \"\"\"Generate a response using the model.\"\"\"\n generated_seque... | import torch
import numpy as np
import sys, os
from typing import List, Optional, Type, Union, TYPE_CHECKING
from ray.rllib.algorithms import Algorithm, AlgorithmConfig
from ray.rllib.algorithms.ppo import PPO
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.sample_batch import SampleBatch, concat_sampl... | 1,886 |
sys.path.append(os.path.join(os.path.dirname(__file__), '../../../'))
class RLHFSampler:
"""This sampler is a local sampler for LLMEnv.
The underlying env is an LLMEnv which creates a batch of prompts and the agent has
to generate a response for each prompt. Then the env evaluate those respons... |
sys.path.append(os.path.join(os.path.dirname(__file__), '../../../'))
class RLHFSampler:
"""This sampler is a local sampler for LLMEnv.
The underlying env is an LLMEnv which creates a batch of prompts and the agent has
to generate a response for each prompt. Then the env evaluate those respons... | batches = Buffer() | 1 | 2023-11-13 05:08:21+00:00 | 4k |
chuzhumin98/LLM_Eval | PRE/eval.py | [
{
"identifier": "DataLoader",
"path": "PRE/data.py",
"snippet": "class DataLoader:\n '''\n The loader to load for evaluated task, with given prompt template to generate a series of prompts feeding for each LLM\n '''\n def __init__(self, args):\n self.path_data = args['path_data'] # th... | import os
import yaml
import warnings
import json
import copy
import sys
import numpy as np
from PRE.data import DataLoader
from PRE.api import Auto_API
from PRE.utils import parse_response | 2,141 | '''
The implement of the peer review and result aggregation module
'''
base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(base_dir)
class PEER_REVIEW:
'''
Conduct peer review, process for one prompt (pairwise or pointwise)
'''
def __init__(self, args) -> None:
... | '''
The implement of the peer review and result aggregation module
'''
base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(base_dir)
class PEER_REVIEW:
'''
Conduct peer review, process for one prompt (pairwise or pointwise)
'''
def __init__(self, args) -> None:
... | data_loader = DataLoader(config_task) # a task data loader | 0 | 2023-11-16 18:40:23+00:00 | 4k |
python-thread/thread | src/thread/decorators/_threaded.py | [
{
"identifier": "Thread",
"path": "src/thread/thread.py",
"snippet": "class Thread(threading.Thread, Generic[_Target_P, _Target_T]):\n \"\"\"\n Wraps python's `threading.Thread` class\n ---------------------------------------\n\n Type-Safe and provides more functionality on top\n \"\"\"\n\n status... | from functools import wraps
from ..thread import Thread
from .._types import Overflow_In, Data_In
from typing import Callable, Mapping, Sequence, Optional, Union, overload
from typing_extensions import ParamSpec, TypeVar | 2,383 | """
## Threaded
Documentation: https://thread.ngjx.org
"""
T = TypeVar('T')
P = ParamSpec('P')
TargetFunction = Callable[P, T]
NoParamReturn = Callable[P, Thread[P, T]]
WithParamReturn = Callable[[TargetFunction[P, T]], NoParamReturn[P, T]]
FullParamReturn = Callable[P, Thread[P, T]]
@overload
def threaded(_... | """
## Threaded
Documentation: https://thread.ngjx.org
"""
T = TypeVar('T')
P = ParamSpec('P')
TargetFunction = Callable[P, T]
NoParamReturn = Callable[P, Thread[P, T]]
WithParamReturn = Callable[[TargetFunction[P, T]], NoParamReturn[P, T]]
FullParamReturn = Callable[P, Thread[P, T]]
@overload
def threaded(_... | args: Sequence[Data_In] = (), | 1 | 2023-11-12 21:01:21+00:00 | 4k |
victor0089/AirBnB_clone_v2 | models/engine/db_storage.py | [
{
"identifier": "Base",
"path": "models/base_model.py",
"snippet": "class BaseModel:\n def __init__(self, *args, **kwargs):\n def __str__(self):\n def __repr__(self):\n def save(self):\n def to_dict(self):\n def delete(self):"
},
{
"identifier": "State",
"path": "models/sta... | from os import getenv
from sqlalchemy.orm import sessionmaker, scoped_session
from sqlalchemy import (create_engine)
from sqlalchemy.ext.declarative import declarative_base
from models.base_model import Base
from models.state import State
from models.city import City
from models.user import User
from models.place impor... | 1,697 | #!/usr/bin/python3
""" new class for sqlAlchemy """
class DBStorage:
""" create tables in environmental"""
__engine = None
__session = None
def __init__(self):
'''instantiate new dbstorage instance'''
HBNB_MYSQL_USER = getenv('HBNB_MYSQL_USER')
HBNB_MYSQL_PWD = getenv('HBNB_MY... | #!/usr/bin/python3
""" new class for sqlAlchemy """
class DBStorage:
""" create tables in environmental"""
__engine = None
__session = None
def __init__(self):
'''instantiate new dbstorage instance'''
HBNB_MYSQL_USER = getenv('HBNB_MYSQL_USER')
HBNB_MYSQL_PWD = getenv('HBNB_MY... | lista = [State, City, User, Place, Review, Amenity] | 6 | 2023-11-17 07:59:13+00:00 | 4k |
believethehype/nostrdvm | nostr_dvm/tasks/advanced_search.py | [
{
"identifier": "DVMTaskInterface",
"path": "nostr_dvm/interfaces/dvmtaskinterface.py",
"snippet": "class DVMTaskInterface:\n NAME: str\n KIND: int\n TASK: str = \"\"\n FIX_COST: float = 0\n PER_UNIT_COST: float = 0\n PRIVATE_KEY: str\n PUBLIC_KEY: str\n DVM = DVM\n SUPPORTS_E... | import json
import os
from datetime import timedelta
from nostr_sdk import Client, Timestamp, PublicKey, Tag, Keys, Options, SecretKey, ClientSigner
from nostr_dvm.interfaces.dvmtaskinterface import DVMTaskInterface, process_venv
from nostr_dvm.utils.admin_utils import AdminConfig
from nostr_dvm.utils.definitions impor... | 3,385 |
"""
This File contains a Module to search for notes
Accepted Inputs: a search query
Outputs: A list of events
Params: None
"""
class AdvancedSearch(DVMTaskInterface):
KIND: int = EventDefinitions.KIND_NIP90_CONTENT_SEARCH
TASK: str = "search-content"
FIX_COST: float = 0
dvm_config: DVMConfig
d... |
"""
This File contains a Module to search for notes
Accepted Inputs: a search query
Outputs: A list of events
Params: None
"""
class AdvancedSearch(DVMTaskInterface):
KIND: int = EventDefinitions.KIND_NIP90_CONTENT_SEARCH
TASK: str = "search-content"
FIX_COST: float = 0
dvm_config: DVMConfig
d... | admin_config: AdminConfig = None, options=None): | 2 | 2023-11-17 18:32:56+00:00 | 4k |
zouXH-god/meme_web | meme_generator/meme.py | [
{
"identifier": "ArgModelMismatch",
"path": "meme_generator/exception.py",
"snippet": "class ArgModelMismatch(ArgMismatch):\n status_code: int = 552\n\n def __init__(self, meme_key: str, error_message: str):\n self.error_message = error_message\n message = f\"Argument model validatio... | import copy
from argparse import ArgumentError, ArgumentParser
from contextvars import ContextVar
from dataclasses import dataclass, field
from io import BytesIO
from pathlib import Path
from typing import (
IO,
Any,
Awaitable,
Callable,
Dict,
List,
Literal,
Optional,
Type,
TypeV... | 2,013 |
class UserInfo(BaseModel):
name: str = ""
gender: Literal["male", "female", "unknown"] = "unknown"
class MemeArgsModel(BaseModel):
user_infos: List[UserInfo] = []
ArgsModel = TypeVar("ArgsModel", bound=MemeArgsModel)
MemeFunction = Union[
Callable[[List[BuildImage], List[str], ArgsModel], Bytes... |
class UserInfo(BaseModel):
name: str = ""
gender: Literal["male", "female", "unknown"] = "unknown"
class MemeArgsModel(BaseModel):
user_infos: List[UserInfo] = []
ArgsModel = TypeVar("ArgsModel", bound=MemeArgsModel)
MemeFunction = Union[
Callable[[List[BuildImage], List[str], ArgsModel], Bytes... | raise OpenImageFailed(str(e)) | 3 | 2023-11-12 12:31:53+00:00 | 4k |
OKC13/General-Documents-Layout-parser | hubconf.py | [
{
"identifier": "Model",
"path": "models/yolo.py",
"snippet": "class Model(nn.Module):\n def __init__(self, model_cfg='yolov5s.yaml', ch=3, nc=None): # model, input channels, number of classes\n super(Model, self).__init__()\n if type(model_cfg) is dict:\n self.md = model_cf... | import torch
from models.yolo import Model
from utils import google_utils | 1,705 | """File for accessing YOLOv5 via PyTorch Hub https://pytorch.org/hub/
Usage:
import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, channels=3, classes=80)
"""
dependencies = ['torch', 'yaml']
def create(name, pretrained, channels, classes):
"""Creates a specified YOLOv5 ... | """File for accessing YOLOv5 via PyTorch Hub https://pytorch.org/hub/
Usage:
import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, channels=3, classes=80)
"""
dependencies = ['torch', 'yaml']
def create(name, pretrained, channels, classes):
"""Creates a specified YOLOv5 ... | google_utils.attempt_download(ckpt) # download if not found locally | 1 | 2023-11-16 08:37:10+00:00 | 4k |
tensorpix/benchmarking-cv-models | src/train.py | [
{
"identifier": "log",
"path": "src/log.py",
"snippet": "def setup_custom_logger(name: str = \"benchmark\"):"
},
{
"identifier": "BenchmarkCallback",
"path": "src/callbacks.py",
"snippet": "class BenchmarkCallback(Callback):\n def __init__(\n self,\n model_name: str,\n ... | import argparse
import segmentation_models_pytorch as smp
import torch
from lightning import Trainer
from pip._internal.operations import freeze
from torch.utils.data import DataLoader
from torchvision.models import (
convnext_base,
efficientnet_v2_m,
mobilenet_v3_large,
resnet50,
resnext50_32x4d,
... | 1,931 |
logger = log.setup_custom_logger()
ARCHITECTURES = {
"resnet50": resnet50,
"convnext": convnext_base,
"vgg16": vgg16,
"efficient_net_v2": efficientnet_v2_m,
"mobilenet_v3": mobilenet_v3_large,
"resnext50": resnext50_32x4d,
"swin": swin_b,
"vit": vit_b_16,
"unet_resnet50": smp.Une... |
logger = log.setup_custom_logger()
ARCHITECTURES = {
"resnet50": resnet50,
"convnext": convnext_base,
"vgg16": vgg16,
"efficient_net_v2": efficientnet_v2_m,
"mobilenet_v3": mobilenet_v3_large,
"resnext50": resnext50_32x4d,
"swin": swin_b,
"vit": vit_b_16,
"unet_resnet50": smp.Une... | model = LitClassification(model=model) | 3 | 2023-11-10 11:45:09+00:00 | 4k |
embrake/Aquilify | aquilify/core/__status.py | [
{
"identifier": "NotFound",
"path": "aquilify/exception/base.py",
"snippet": "class NotFound(Response, HTTPException):\n def __init__(self, status_code=404):\n super().__init__(error404(), status_code=status_code, content_type=\"text/html\")"
},
{
"identifier": "Unauthorized",
"pat... | from ..exception.base import (
NotFound, Unauthorized, Forbidden, BadGateway, InternalServerError, MethodNotAllowed, BadRequest,
NotAcceptable, ProxyAuthenticationRequired, RequestTimeout, Conflict, Gone, LengthRequired, PreconditionFailed,
RequestURITooLong, UnsupportedMediaType, RequestedRangeNotSatisfiable, Expectat... | 3,045 |
exception_dict = {
400: BadRequest,
401: Unauthorized,
402: PaymentRequired,
403: Forbidden,
404: NotFound,
405: MethodNotAllowed,
406: NotAcceptable,
407: ProxyAuthenticationRequired,
408: RequestTimeout,
409: Conflict,
410: Gone,
411: LengthRequired,
412: Precondit... |
exception_dict = {
400: BadRequest,
401: Unauthorized,
402: PaymentRequired,
403: Forbidden,
404: NotFound,
405: MethodNotAllowed,
406: NotAcceptable,
407: ProxyAuthenticationRequired,
408: RequestTimeout,
409: Conflict,
410: Gone,
411: LengthRequired,
412: Precondit... | 417: ExpectationFailed, | 17 | 2023-11-16 08:26:02+00:00 | 4k |
Viicos/django-autotyping | src/django_autotyping/stubbing/codemods/create_overload_codemod.py | [
{
"identifier": "InsertAfterImportsVisitor",
"path": "src/django_autotyping/stubbing/codemods/base.py",
"snippet": "class InsertAfterImportsVisitor(ContextAwareTransformer):\n \"\"\"Insert a list of statements after imports.\"\"\"\n\n CONTEXT_KEY = \"InsertAfterImportsVisitor\"\n\n @classmethod... | from typing import TYPE_CHECKING, cast
from django.db.models import Field
from libcst import helpers
from libcst.codemod import CodemodContext
from libcst.metadata import ScopeProvider
from django_autotyping.typing import FlattenFunctionDef
from .base import InsertAfterImportsVisitor, StubVisitorBasedCodemod
from .cons... | 2,346 | from __future__ import annotations
if TYPE_CHECKING:
# Matchers:
MANAGER_QS_CLASS_DEF_MATCHER = m.ClassDef(
name=m.SaveMatchedNode(m.Name("BaseManager") | m.Name("_QuerySet"), "cls_name")
)
"""Matches the `BaseManager` and `_QuerySet` class definitions."""
MODEL_CLASS_DEF_MATCHER = m.ClassDef(name=m.SaveMat... | from __future__ import annotations
if TYPE_CHECKING:
# Matchers:
MANAGER_QS_CLASS_DEF_MATCHER = m.ClassDef(
name=m.SaveMatchedNode(m.Name("BaseManager") | m.Name("_QuerySet"), "cls_name")
)
"""Matches the `BaseManager` and `_QuerySet` class definitions."""
MODEL_CLASS_DEF_MATCHER = m.ClassDef(name=m.SaveMat... | InsertAfterImportsVisitor.insert_after_imports(context, model_typed_dicts) | 0 | 2023-11-11 20:42:05+00:00 | 4k |
IBM/oper8 | tests/watch_manager/python_watch_manager/filters/test_filters.py | [
{
"identifier": "KubeEventType",
"path": "oper8/deploy_manager/kube_event.py",
"snippet": "class KubeEventType(Enum):\n \"\"\"Enum for all possible kubernetes event types\"\"\"\n\n DELETED = \"DELETED\"\n MODIFIED = \"MODIFIED\"\n ADDED = \"ADDED\""
},
{
"identifier": "ReadyReason",
... | from oper8.deploy_manager.kube_event import KubeEventType
from oper8.status import ReadyReason, make_application_status
from oper8.test_helpers.pwm_helpers import make_managed_object
from oper8.watch_manager.python_watch_manager.filters.filters import (
AnnotationFilter,
CreationDeletionFilter,
DependentWat... | 3,504 | """
Tests for the Filter classes
"""
# Local
## Helpers #####################################################################
def test_filter_creation_deletion():
resource = make_managed_object()
filter = CreationDeletionFilter(resource)
| """
Tests for the Filter classes
"""
# Local
## Helpers #####################################################################
def test_filter_creation_deletion():
resource = make_managed_object()
filter = CreationDeletionFilter(resource)
| assert filter.update_and_test(resource, KubeEventType.ADDED) | 0 | 2023-11-15 16:43:29+00:00 | 4k |
ariebovenberg/whenever | tests/test_utc_datetime.py | [
{
"identifier": "AlwaysEqual",
"path": "tests/common.py",
"snippet": "class AlwaysEqual:\n def __eq__(self, other):\n return True"
},
{
"identifier": "AlwaysLarger",
"path": "tests/common.py",
"snippet": "class AlwaysLarger:\n def __lt__(self, other):\n return False\n... | import pickle
import weakref
import pytest
from copy import copy, deepcopy
from datetime import datetime as py_datetime
from datetime import timedelta, timezone
from freezegun import freeze_time
from hypothesis import given
from hypothesis.strategies import text
from pytest import approx
from whenever import (
Awar... | 1,615 | assert d.hour == 5
assert d.minute == 12
assert d.second == 30
assert d.microsecond == 450
assert d.offset == timedelta()
assert d.tzinfo == timezone.utc
def test_optionality(self):
assert (
UTCDateTime(2020, 8, 15, 12)
== UTCDateTime(... |
class TestInit:
def test_basic(self):
d = UTCDateTime(2020, 8, 15, 5, 12, 30, 450)
assert d.year == 2020
assert d.month == 8
assert d.day == 15
assert d.hour == 5
assert d.minute == 12
assert d.second == 30
assert d.microsecond == 450
asse... | assert d != NeverEqual() | 3 | 2023-11-10 21:08:49+00:00 | 4k |
tonylampada/jarvisportal | jarvisportal/gptexec.py | [
{
"identifier": "GPT",
"path": "jarvisportal/gpt.py",
"snippet": "class GPT:\n def __init__(self, assistant_id):\n self.client = OpenAI()\n # self.thread_id = self._get_or_create_thread_id() # expensive :(\n self.thread_id = self._create_thread_id()\n self.assistant_id = a... | import sys
import os
import jarvisportal.listentomic as listentomic
import jarvisportal.listentomic as listentomic
from jarvisportal.gpt import GPT
from jarvisportal.llamaapichat import Chat as LlamaApiChat
from jarvisportal.actions import exec_actions, definitions | 2,235 |
usr = '\U0001F600'
bot = '\U0001F916'
mic = '\U0001F3A4'
def main():
args = sys.argv[1:]
engine = os.getenv("CHAT_ENGINE", "gpt")
if engine == "gpt":
if len(args) != 1:
print("Usage: gptexec.py <assistant_id>")
exit(1)
assistant_id = args[0]
bot = GPT(assist... |
usr = '\U0001F600'
bot = '\U0001F916'
mic = '\U0001F3A4'
def main():
args = sys.argv[1:]
engine = os.getenv("CHAT_ENGINE", "gpt")
if engine == "gpt":
if len(args) != 1:
print("Usage: gptexec.py <assistant_id>")
exit(1)
assistant_id = args[0]
bot = GPT(assist... | action_results = exec_actions(answer["actions"], ask=True) | 2 | 2023-11-14 17:27:01+00:00 | 4k |
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.