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 |
|---|---|---|---|---|---|---|---|---|---|---|
daswer123/rvc-python | rvc_python/__main__.py | [
{
"identifier": "infer_file",
"path": "rvc_python/infer.py",
"snippet": "def infer_file(\n input_path,\n model_path,\n index_path = \"\",\n device = \"cpu:0\",\n f0method = \"harvest\",\n opt_path = \"out.wav\",\n index_rate = 0.5,\n filter_radius = 3,\n resample_sr = 0,\n ... | import argparse
import sys
import os
from argparse import ArgumentParser
from rvc_python.infer import infer_file,infer_files | 1,356 |
parser = ArgumentParser(description="RVC inference")
# Create a mutually exclusive group for input - only one of them can be provided
input_group = parser.add_mutually_exclusive_group(required=True)
input_group.add_argument("-i", "--input", type=str, help="Path to input file")
input_group.add_argument("-d", "--dir", ... |
parser = ArgumentParser(description="RVC inference")
# Create a mutually exclusive group for input - only one of them can be provided
input_group = parser.add_mutually_exclusive_group(required=True)
input_group.add_argument("-i", "--input", type=str, help="Path to input file")
input_group.add_argument("-d", "--dir", ... | processed_files = infer_files( | 1 | 2023-12-26 19:05:42+00:00 | 2k |
CodeBrugs/ToolSculpt | tests/test_tools/test_tool2.py | [
{
"identifier": "process_data",
"path": "src/tools/Tool2/tool2_functions.py",
"snippet": "def process_data(data):\n \"\"\"\n Procesa los datos utilizando la lógica específica de Tool2.\n\n Parameters:\n - data (str): Datos de entrada.\n\n Returns:\n - result (str): Resultado del proces... | import unittest
from src.tools.Tool2.tool2_functions import process_data, analyze_data, perform_additional_task | 649 | # tests/test_tools/test_tool2.py
class TestTool2Functions(unittest.TestCase):
def test_process_data(self):
# Prueba para la función process_data
input_data = "example_data"
result = process_data(input_data)
self.assertEqual(result, "Tool2 processed the data: A_SPECIAL_TRANSFORMATION... | # tests/test_tools/test_tool2.py
class TestTool2Functions(unittest.TestCase):
def test_process_data(self):
# Prueba para la función process_data
input_data = "example_data"
result = process_data(input_data)
self.assertEqual(result, "Tool2 processed the data: A_SPECIAL_TRANSFORMATION... | result = perform_additional_task() | 2 | 2023-12-26 17:03:20+00:00 | 2k |
run-llama/rags | pages/3_🤖_Generated_RAG_Agent.py | [
{
"identifier": "add_sidebar",
"path": "st_utils.py",
"snippet": "def add_sidebar() -> None:\n \"\"\"Add sidebar.\"\"\"\n with st.sidebar:\n agent_registry = cast(AgentCacheRegistry, st.session_state.agent_registry)\n st.session_state.cur_agent_ids = agent_registry.get_agent_ids()\n ... | import streamlit as st
import pandas as pd
from st_utils import add_sidebar, get_current_state
from core.utils import get_image_and_text_nodes
from llama_index.schema import MetadataMode
from llama_index.chat_engine.types import AGENT_CHAT_RESPONSE_TYPE
from typing import Dict, Optional | 1,164 | """Streamlit page showing builder config."""
####################
#### STREAMLIT #####
####################
st.set_page_config(
page_title="Generated RAG Agent",
page_icon="🦙",
layout="centered",
initial_sidebar_state="auto",
menu_items=None,
)
st.title("Generated RAG Agent")
current_state = g... | """Streamlit page showing builder config."""
####################
#### STREAMLIT #####
####################
st.set_page_config(
page_title="Generated RAG Agent",
page_icon="🦙",
layout="centered",
initial_sidebar_state="auto",
menu_items=None,
)
st.title("Generated RAG Agent")
current_state = g... | image_nodes, text_nodes = get_image_and_text_nodes(response.source_nodes) | 2 | 2023-11-16 07:49:44+00:00 | 2k |
open-mmlab/Amphion | modules/diffusion/bidilconv/residual_block.py | [
{
"identifier": "GaU",
"path": "modules/activation_functions/gated_activation_unit.py",
"snippet": "class GaU(nn.Module):\n r\"\"\"Gated Activation Unit (GaU) proposed in `Gated Activation Units for Neural\n Networks <https://arxiv.org/pdf/1606.05328.pdf>`_.\n\n Args:\n channels: number ... | import math
import torch
import torch.nn as nn
from modules.activation_functions import GaU
from modules.general.utils import Conv1d | 701 | # 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 ResidualBlock(nn.Module):
r"""Residual block with dilated convolution, main portion of ``BiDilConv``.
Args:
channels: The number of chan... | # 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 ResidualBlock(nn.Module):
r"""Residual block with dilated convolution, main portion of ``BiDilConv``.
Args:
channels: The number of chan... | self.gau = GaU( | 0 | 2023-11-15 09:19:27+00:00 | 2k |
ise-uiuc/magicoder | src/magicoder/decontamination/find_substrings.py | [
{
"identifier": "FILTER_OUT",
"path": "src/magicoder/decontamination/benchmark_data.py",
"snippet": "FILTER_OUT = {k: v() for k, v in LAZY_FILTER_OUT.items()}"
},
{
"identifier": "add_dict",
"path": "src/magicoder/decontamination/utils.py",
"snippet": "def add_dict(dict1: dict, dict2: di... | import argparse
import json
import os
import shutil
from copy import deepcopy
from glob import glob
from pathlib import Path
from datasets import load_dataset
from magicoder.utils import write_jsonl
from .benchmark_data import FILTER_OUT
from .utils import add_dict, shard_dataset | 1,404 | # type: ignore
"""Migrated from: https://github.com/bigcode-project/bigcode-dataset. License: Apache 2.0"""
SHARD_SIZE = 1000 << 20 # 1GB
LANGUAGE_COL = "lang"
# LANGUAGES = ["Python", "Java", "JavaScript"]
def dump_benchmarks(file_path: str):
"""
Dump the dictionary of benchmark samples that are filter... | # type: ignore
"""Migrated from: https://github.com/bigcode-project/bigcode-dataset. License: Apache 2.0"""
SHARD_SIZE = 1000 << 20 # 1GB
LANGUAGE_COL = "lang"
# LANGUAGES = ["Python", "Java", "JavaScript"]
def dump_benchmarks(file_path: str):
"""
Dump the dictionary of benchmark samples that are filter... | add_dict(res, meta) | 1 | 2023-11-10 07:35:29+00:00 | 2k |
KwaiKEG/KwaiAgents | kwaiagents/tools/timedelta.py | [
{
"identifier": "Config",
"path": "kwaiagents/config.py",
"snippet": "class Config(object):\n def __init__(self) -> None:\n \"\"\"Initialize the Config class\"\"\"\n self.fast_llm_model = \"gpt-3.5-turbo\"\n self.smart_llm_model = \"gpt-4\"\n self.use_local_llm = False\n ... | from datetime import datetime
from dateutil.relativedelta import relativedelta
from kwaiagents.config import Config
from kwaiagents.tools.base import BaseResult, BaseTool | 643 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: PAN Leyi
# Email: panleyi@kuaishou.com
class TimeDeltaResult(BaseResult):
@property
def answer(self):
item = self.json_data
rst = ""
for key in item.keys():
rst += f'{key}: {item[key]}\n'
return rst
| #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: PAN Leyi
# Email: panleyi@kuaishou.com
class TimeDeltaResult(BaseResult):
@property
def answer(self):
item = self.json_data
rst = ""
for key in item.keys():
rst += f'{key}: {item[key]}\n'
return rst
| class TimeDeltaTool(BaseTool): | 2 | 2023-11-13 03:37:02+00:00 | 2k |
EnVision-Research/LucidDreamer | scene/dataset_readers.py | [
{
"identifier": "getWorld2View2",
"path": "utils/graphics_utils.py",
"snippet": "def getWorld2View2(R, t, translate=np.array([.0, .0, .0]), scale=1.0):\n Rt = np.zeros((4, 4))\n Rt[:3, :3] = R.transpose()\n Rt[:3, 3] = t\n Rt[3, 3] = 1.0\n\n C2W = np.linalg.inv(Rt)\n cam_center = C2W[:... | import os
import sys
import torch
import random
import torch.nn.functional as F
import numpy as np
import json
from PIL import Image
from typing import NamedTuple
from utils.graphics_utils import getWorld2View2, focal2fov, fov2focal
from pathlib import Path
from utils.pointe_utils import init_from_pointe
from plyfile i... | 1,413 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
class RandCameraInfo(NamedTuple)... | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
class RandCameraInfo(NamedTuple)... | point_cloud: BasicPointCloud | 6 | 2023-11-18 08:05:50+00:00 | 2k |
VRSEN/agency-swarm | agency_swarm/tools/browsing/GoBack.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 time
from agency_swarm.tools import BaseTool
from agency_swarm.tools.browsing.util.selenium import get_web_driver, set_web_driver | 982 |
class GoBack(BaseTool):
"""
This tool allows you to go back 1 page in the browser history. Use it in case of a mistake or if a page shows you unexpected content.
"""
def run(self):
|
class GoBack(BaseTool):
"""
This tool allows you to go back 1 page in the browser history. Use it in case of a mistake or if a page shows you unexpected content.
"""
def run(self): | wd = get_web_driver() | 1 | 2023-11-16 02:29:26+00:00 | 2k |
resemble-ai/resemble-enhance | resemble_enhance/denoiser/denoiser.py | [
{
"identifier": "MelSpectrogram",
"path": "resemble_enhance/melspec.py",
"snippet": "class MelSpectrogram(nn.Module):\n def __init__(self, hp: HParams):\n \"\"\"\n Torch implementation of Resemble's mel extraction.\n Note that the values are NOT identical to librosa's implementat... | import logging
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from ..melspec import MelSpectrogram
from .hparams import HParams
from .unet import UNet | 1,595 |
logger = logging.getLogger(__name__)
def _normalize(x: Tensor) -> Tensor:
return x / (x.abs().max(dim=-1, keepdim=True).values + 1e-7)
class Denoiser(nn.Module):
@property
def stft_cfg(self) -> dict:
hop_size = self.hp.hop_size
return dict(hop_length=hop_size, n_fft=hop_size * 4, win_... |
logger = logging.getLogger(__name__)
def _normalize(x: Tensor) -> Tensor:
return x / (x.abs().max(dim=-1, keepdim=True).values + 1e-7)
class Denoiser(nn.Module):
@property
def stft_cfg(self) -> dict:
hop_size = self.hp.hop_size
return dict(hop_length=hop_size, n_fft=hop_size * 4, win_... | self.mel_fn = MelSpectrogram(hp) | 0 | 2023-11-15 08:15:51+00:00 | 2k |
PKU-YuanGroup/Chat-UniVi | ChatUniVi/model/builder.py | [
{
"identifier": "DEFAULT_IMAGE_PATCH_TOKEN",
"path": "ChatUniVi/constants.py",
"snippet": "DEFAULT_IMAGE_PATCH_TOKEN = \"<im_patch>\""
},
{
"identifier": "DEFAULT_IM_START_TOKEN",
"path": "ChatUniVi/constants.py",
"snippet": "DEFAULT_IM_START_TOKEN = \"<im_start>\""
},
{
"identif... | import os
import shutil
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig
from ChatUniVi.model import *
from ChatUniVi.constants import DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from accelerate import init_empty_weights, load_checkpoi... | 1,265 |
def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto"):
kwargs = {"device_map": device_map}
if load_8bit:
kwargs['load_in_8bit'] = True
elif load_4bit:
kwargs['load_in_4bit'] = True
kwargs['quantization_config'] = BitsAnd... |
def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto"):
kwargs = {"device_map": device_map}
if load_8bit:
kwargs['load_in_8bit'] = True
elif load_4bit:
kwargs['load_in_4bit'] = True
kwargs['quantization_config'] = BitsAnd... | tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) | 0 | 2023-11-13 11:52:56+00:00 | 2k |
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 | 1,210 |
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... |
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... | def paper_to_string(paper_entry: Paper) -> str: | 0 | 2023-11-13 15:19:38+00:00 | 2k |
BobaZooba/xllm | tests/unit/datasets/test_registry.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": "datasets_registry",
"path": "src/x... | from src.xllm import enums
from src.xllm.datasets.registry import datasets_registry
from src.xllm.datasets.soda import SodaDataset
from tests.helpers.dummy_data import DATA | 967 | # 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... | assert isinstance(dataset, SodaDataset) | 2 | 2023-11-10 17:55:03+00:00 | 2k |
banodoco/Steerable-Motion | imports/AdvancedControlNet/latent_keyframe_nodes.py | [
{
"identifier": "LatentKeyframeImport",
"path": "imports/AdvancedControlNet/control.py",
"snippet": "class LatentKeyframeImport:\n def __init__(self, batch_index: int, strength: float) -> None:\n self.batch_index = batch_index\n self.strength = strength"
},
{
"identifier": "Late... | from typing import Union
from collections.abc import Iterable
from .control import LatentKeyframeImport, LatentKeyframeGroupImport
from .control import StrengthInterpolationImport as SI
from .logger import logger
import numpy as np | 934 |
class LatentKeyframeNodeImport:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"batch_index": ("INT", {"default": 0, "min": -1000, "max": 1000, "step": 1}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
... |
class LatentKeyframeNodeImport:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"batch_index": ("INT", {"default": 0, "min": -1000, "max": 1000, "step": 1}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
... | keyframe = LatentKeyframeImport(batch_index, strength) | 0 | 2023-11-11 01:26:26+00:00 | 2k |
innovatorved/subtitle | app/core/app.py | [
{
"identifier": "model_names",
"path": "app/models/models.py",
"snippet": "def download_model(model_name):\ndef download_file(url, filepath):"
},
{
"identifier": "check_models_exist",
"path": "app/utils/checks.py",
"snippet": "def check_models_exist(name: str):\n try:\n if mode... | import logging
from app.models import download_model, model_names
from app.utils.checks import check_models_exist
from app.utils import generate_vtt_file, merge_video_and_vtt | 675 |
# Configure logging
logger = logging.getLogger(__name__)
def process_video(file, model="base"):
"""
add_subtitle_in_video
@param file: video file path
@param model: model name
@return: [vtt_file_path , output_file]
"""
try:
if not check_models_exist(model):
download_m... |
# Configure logging
logger = logging.getLogger(__name__)
def process_video(file, model="base"):
"""
add_subtitle_in_video
@param file: video file path
@param model: model name
@return: [vtt_file_path , output_file]
"""
try:
if not check_models_exist(model):
download_m... | process_id, output_audio_path, vtt_file_path = generate_vtt_file( | 2 | 2023-11-17 10:12:33+00:00 | 2k |
x0rzavi/github-readme-terminal | gifos/utils/upload_imgbb.py | [
{
"identifier": "gifos_settings",
"path": "gifos/utils/load_config.py",
"snippet": "def load_toml(file_name: str) -> dict:\n def __update_config_with_env_vars(config, prefix=\"GIFOS\"):"
},
{
"identifier": "ImgbbImage",
"path": "gifos/utils/schemas/imagebb_image.py",
"snippet": "class... | from base64 import b64encode
from dotenv import load_dotenv
from gifos.utils.load_config import gifos_settings
from gifos.utils.schemas.imagebb_image import ImgbbImage
import os
import requests
import sys | 723 |
"""This module contains a function for uploading an image to ImgBB."""
load_dotenv()
IMGBB_API_KEY = os.getenv("IMGBB_API_KEY")
ENDPOINT = "https://api.imgbb.com/1/upload"
def upload_imgbb(file_name: str, expiration: int = None) -> ImgbbImage:
"""Upload an image to ImgBB.
This function uploads an image t... |
"""This module contains a function for uploading an image to ImgBB."""
load_dotenv()
IMGBB_API_KEY = os.getenv("IMGBB_API_KEY")
ENDPOINT = "https://api.imgbb.com/1/upload"
def upload_imgbb(file_name: str, expiration: int = None) -> ImgbbImage:
"""Upload an image to ImgBB.
This function uploads an image t... | if gifos_settings.get("general", {}).get("debug"): | 0 | 2023-11-17 06:21:18+00:00 | 2k |
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... | 1,429 |
# 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}[... | console.print( | 6 | 2023-11-11 14:43:55+00:00 | 2k |
AMAAI-Lab/mustango | audioldm/latent_diffusion/ddim.py | [
{
"identifier": "make_ddim_sampling_parameters",
"path": "audioldm/latent_diffusion/util.py",
"snippet": "def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):\n # select alphas for computing the variance schedule\n alphas = alphacums[ddim_timesteps]\n alphas_prev = n... | import torch
import numpy as np
from tqdm import tqdm
from audioldm.latent_diffusion.util import (
make_ddim_sampling_parameters,
make_ddim_timesteps,
noise_like,
extract_into_tensor,
) | 1,266 | """SAMPLING ONLY."""
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
def register_buffer(self, name, attr):
if type(attr) =... | """SAMPLING ONLY."""
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
def register_buffer(self, name, attr):
if type(attr) =... | ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters( | 0 | 2023-11-14 23:29:31+00:00 | 2k |
lxmusics/lx-music-api-server-python | modules/kg/search.py | [
{
"identifier": "Httpx",
"path": "common/Httpx.py",
"snippet": "def is_valid_utf8(text):\ndef is_plain_text(text):\ndef convert_dict_to_form_string(dic):\ndef log_plaintext(text):\ndef request(url, options = {}):\n def _json():\ndef checkcn():\n def __init__(self, status, content, headers):\n d... | from common import Httpx
from common import utils
from common.exceptions import FailedException
from .utils import buildRequestParams | 1,189 | # ----------------------------------------
# - mode: python -
# - author: helloplhm-qwq -
# - name: search.py -
# - project: lx-music-api-server -
# - license: MIT -
# ----------------------------------------
# This file is part of the "lx-music-api-server" project.
def formatSubResult(l):
res = []
for s... | # ----------------------------------------
# - mode: python -
# - author: helloplhm-qwq -
# - name: search.py -
# - project: lx-music-api-server -
# - license: MIT -
# ----------------------------------------
# This file is part of the "lx-music-api-server" project.
def formatSubResult(l):
res = []
for s... | raise FailedException('歌曲搜索失败') | 2 | 2023-11-10 13:16:30+00:00 | 2k |
ai-forever/Kandinsky-3 | kandinsky3/model/nn.py | [
{
"identifier": "exist",
"path": "kandinsky3/model/utils.py",
"snippet": "def exist(item):\n return item is not None"
},
{
"identifier": "set_default_layer",
"path": "kandinsky3/model/utils.py",
"snippet": "def set_default_layer(condition, layer_1, args_1=[], kwargs_1={}, layer_2=Iden... | import math
import torch
from torch import nn, einsum
from einops import rearrange, repeat
from .utils import exist, set_default_layer | 757 |
class Identity(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
@staticmethod
def forward(x, *args, **kwargs):
return x
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
... |
class Identity(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
@staticmethod
def forward(x, *args, **kwargs):
return x
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
... | if exist(context_mask): | 0 | 2023-11-13 10:16:04+00:00 | 2k |
spfrommer/torchexplorer | torchexplorer/render/structs.py | [
{
"identifier": "Tooltip",
"path": "torchexplorer/components/tooltip.py",
"snippet": "class Tooltip:\n \"\"\"The tooltip that pops up next to a Module.\"\"\"\n\n def __init__(self, title: str, keys: list[str], vals: list[str]):\n self.title = title\n self.keys = keys\n self.va... | from typing import Optional
from dataclasses import dataclass, field
from torchexplorer.components.tooltip import Tooltip
from torchexplorer.core import (
ModuleInvocationHistograms, ModuleSharedHistograms
) | 1,250 | from __future__ import annotations
@dataclass
class EdgeLayout:
path_points: list[list[float]]
arrowhead_points: list[list[float]]
downstream_input_index: Optional[int]
upstream_output_index: Optional[int]
@dataclass
class TooltipLayout:
tooltip: Tooltip
# Coordinates in parent of the la... | from __future__ import annotations
@dataclass
class EdgeLayout:
path_points: list[list[float]]
arrowhead_points: list[list[float]]
downstream_input_index: Optional[int]
upstream_output_index: Optional[int]
@dataclass
class TooltipLayout:
tooltip: Tooltip
# Coordinates in parent of the la... | shared_hists: Optional[ModuleSharedHistograms] = None | 2 | 2023-11-13 05:56:04+00:00 | 2k |
namin/llm-verified-with-monte-carlo-tree-search | huggingface_generate.py | [
{
"identifier": "STOP_WORD",
"path": "lang_config.py",
"snippet": "STOP_WORD = \"\\n\""
},
{
"identifier": "BASE_MODEL_NAME",
"path": "model_config.py",
"snippet": "BASE_MODEL_NAME = args.base_model_name"
},
{
"identifier": "PEFT_MODEL_PATH",
"path": "model_config.py",
"s... | import torch
from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
from peft import PeftModel
from lang_config import STOP_WORD
from model_config import BASE_MODEL_NAME, PEFT_MODEL_PATH, PPO_MODEL_PATH, CUSTOM_STOP, SAME_FOR_MANY_SAMPLES, BEAM... | 812 |
def load_model(
base_model_name: str = BASE_MODEL_NAME,
ppo_model_path: str = PPO_MODEL_PATH,
peft_model_path: str = PEFT_MODEL_PATH,
) -> (AutoModelForCausalLM, PeftModel, AutoTokenizer):
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_c... |
def load_model(
base_model_name: str = BASE_MODEL_NAME,
ppo_model_path: str = PPO_MODEL_PATH,
peft_model_path: str = PEFT_MODEL_PATH,
) -> (AutoModelForCausalLM, PeftModel, AutoTokenizer):
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_c... | top_p=MODEL_ARG_TOP_P if MODEL_ARG_TOP_P is not None else 0.9, | 8 | 2023-11-11 19:56:04+00:00 | 2k |
BraveGroup/Drive-WM | src/diffusers/utils/constants.py | [
{
"identifier": "dep_version_check",
"path": "src/diffusers/dependency_versions_check.py",
"snippet": "def dep_version_check(pkg, hint=None):\n require_version(deps[pkg], hint)"
},
{
"identifier": "ENV_VARS_TRUE_VALUES",
"path": "src/diffusers/utils/import_utils.py",
"snippet": "ENV_V... | import importlib
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
from packaging import version
from ..dependency_versions_check import dep_version_check
from .import_utils import ENV_VARS_TRUE_VALUES, is_peft_available, is_transformers_available | 670 | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | _required_transformers_version = is_transformers_available() and version.parse( | 3 | 2023-11-18 01:40:55+00:00 | 2k |
basnijholt/unidep | unidep/_pytest_plugin.py | [
{
"identifier": "find_requirements_files",
"path": "unidep/_dependencies_parsing.py",
"snippet": "def find_requirements_files(\n base_dir: str | Path = \".\",\n depth: int = 1,\n *,\n verbose: bool = False,\n) -> list[Path]:\n \"\"\"Scan a directory for `requirements.yaml` and `pyproject.... | import os
import sys
import pytest
from pathlib import Path
from typing import TYPE_CHECKING
from unidep._dependencies_parsing import (
find_requirements_files,
parse_local_dependencies,
)
from git import Repo | 937 | """unidep - Unified Conda and Pip requirements management.
Pytest plugin for running only tests of changed files.
WARNING: Still experimental and not documented.
"""
from __future__ import annotations
if TYPE_CHECKING:
def pytest_addoption(parser: pytest.Parser) -> None: # pragma: no cover
"""Add options to... | """unidep - Unified Conda and Pip requirements management.
Pytest plugin for running only tests of changed files.
WARNING: Still experimental and not documented.
"""
from __future__ import annotations
if TYPE_CHECKING:
def pytest_addoption(parser: pytest.Parser) -> None: # pragma: no cover
"""Add options to... | found_files = find_requirements_files(repo_root) | 0 | 2023-11-16 04:23:01+00:00 | 2k |
BAAI-DCAI/SegVol | segment_anything_volumetric/modeling/image_encoder.py | [
{
"identifier": "LayerNorm2d",
"path": "segment_anything_volumetric/modeling/common.py",
"snippet": "class LayerNorm2d(nn.Module):\n def __init__(self, num_channels: int, eps: float = 1e-6) -> None:\n super().__init__()\n self.weight = nn.Parameter(torch.ones(num_channels))\n sel... | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common import LayerNorm2d, MLPBlock
from monai.networks.blocks import PatchEmbed | 1,205 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.... | LayerNorm2d(out_chans), | 0 | 2023-11-10 08:25:37+00:00 | 2k |
xk-huang/segment-caption-anything | scripts/tools/utils/git_utils/tsv_io.py | [
{
"identifier": "qd_tqdm",
"path": "scripts/tools/utils/git_utils/common.py",
"snippet": "def qd_tqdm(*args, **kwargs):\n desc = kwargs.get(\"desc\", \"\")\n import inspect\n\n frame = inspect.currentframe()\n frames = inspect.getouterframes(frame)\n frame = frames[1].frame\n line_numb... | import numpy as np
import shutil
import mmap
import time
import logging
import types
import os
import os.path as op
import subprocess
import tempfile
import hashlib
import logging
import struct
from .common import qd_tqdm as tqdm
from .common import (
dict_update_path_value,
dict_get... | 1,305 |
# NOTE(xiaoke): Modified. Try to use azfuse.File if possible.
try:
except ImportError:
File = types.SimpleNamespace()
File.open = open
File.get_file_size = lambda x: os.stat(x).st_size
logger = logging.getLogger(__name__)
def concat_files(ins, out):
File.prepare(ins)
with File.open(out, "wb")... |
# NOTE(xiaoke): Modified. Try to use azfuse.File if possible.
try:
except ImportError:
File = types.SimpleNamespace()
File.open = open
File.get_file_size = lambda x: os.stat(x).st_size
logger = logging.getLogger(__name__)
def concat_files(ins, out):
File.prepare(ins)
with File.open(out, "wb")... | fbar = tqdm(unit_scale=True) | 1 | 2023-11-17 14:10:41+00:00 | 2k |
fjzzq2002/is-my-problem-new | src/scrapper/codeforces.py | [
{
"identifier": "read_problems",
"path": "src/utils.py",
"snippet": "def read_problems(filename):\n # read as a json\n with open(filename) as f:\n problems = json.load(f)\n return [x for x in problems if len(x[\"statement\"].strip()) >= 5]"
},
{
"identifier": "dump_json_safe"... | from ..utils import read_problems, dump_json_safe, get_text
from bs4 import BeautifulSoup
from tqdm.auto import tqdm
import json
import os
import requests
import time
import random | 992 |
scrapped_problems = []
try:
scrapped_problems = read_problems("problems/codeforces.json")
print(f"Recalled {len(scrapped_problems)} scrapped problems")
except:
print("Cannot find scrapped problems")
scrapped_uids = set(p["uid"] for p in scrapped_problems)
codeforces_endpoint = "https://codeforces.com/api... |
scrapped_problems = []
try:
scrapped_problems = read_problems("problems/codeforces.json")
print(f"Recalled {len(scrapped_problems)} scrapped problems")
except:
print("Cannot find scrapped problems")
scrapped_uids = set(p["uid"] for p in scrapped_problems)
codeforces_endpoint = "https://codeforces.com/api... | dump_json_safe(scrapped_problems, "problems/codeforces.json") | 1 | 2023-11-15 07:58:49+00:00 | 2k |
p0p4k/pflowtts_pytorch | pflow/utils/generate_data_statistics.py | [
{
"identifier": "TextMelDataModule",
"path": "pflow/data/text_mel_datamodule.py",
"snippet": "class TextMelDataModule(LightningDataModule):\n def __init__( # pylint: disable=unused-argument\n self,\n name,\n train_filelist_path,\n valid_filelist_path,\n batch_size,... | import os
import sys
import argparse
import json
import sys
import rootutils
import torch
from pathlib import Path
from hydra import compose, initialize
from omegaconf import open_dict
from tqdm.auto import tqdm
from pflow.data.text_mel_datamodule import TextMelDataModule
from pflow.utils.logging_utils import pylogger | 992 | r"""
The file creates a pickle file where the values needed for loading of dataset is stored and the model can load it
when needed.
Parameters from hparam.py will be used
"""
sys.path.append(os.path.join(os.path.dirname(__file__), "../.."))
| r"""
The file creates a pickle file where the values needed for loading of dataset is stored and the model can load it
when needed.
Parameters from hparam.py will be used
"""
sys.path.append(os.path.join(os.path.dirname(__file__), "../.."))
| log = pylogger.get_pylogger(__name__) | 1 | 2023-11-11 16:08:17+00:00 | 2k |
theroyallab/tabbyAPI | start.py | [
{
"identifier": "convert_args_to_dict",
"path": "args.py",
"snippet": "def convert_args_to_dict(args: argparse.Namespace, parser: argparse.ArgumentParser):\n \"\"\"Broad conversion of surface level arg groups to dictionaries\"\"\"\n\n arg_groups = {}\n for group in parser._action_groups:\n ... | import argparse
import os
import pathlib
import subprocess
from args import convert_args_to_dict, init_argparser
from main import entrypoint | 698 | """Utility to automatically upgrade and start the API"""
def get_requirements_file():
"""Fetches the appropriate requirements file depending on the GPU"""
requirements_name = "requirements-nowheel"
ROCM_PATH = os.environ.get("ROCM_PATH")
CUDA_PATH = os.environ.get("CUDA_PATH")
# TODO: Check if th... | """Utility to automatically upgrade and start the API"""
def get_requirements_file():
"""Fetches the appropriate requirements file depending on the GPU"""
requirements_name = "requirements-nowheel"
ROCM_PATH = os.environ.get("ROCM_PATH")
CUDA_PATH = os.environ.get("CUDA_PATH")
# TODO: Check if th... | entrypoint(convert_args_to_dict(args, parser)) | 0 | 2023-11-10 05:54:02+00:00 | 2k |
zorazrw/filco | measure_ctxs.py | [
{
"identifier": "calc_cxmi_score",
"path": "cxmi.py",
"snippet": "def calc_cxmi_score(\n model: AutoModelForSeq2SeqLM,\n tokenizer: AutoTokenizer,\n answer: str,\n base_input: str,\n ctx_input: str,\n apply_sigmoid: bool = False,\n) -> float:\n \"\"\"Compute the CXMI score.\"\"\"\n ... | import argparse
import torch
from nltk.tokenize import sent_tokenize
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from cxmi import calc_cxmi_score, get_example_inputs
from eval import calc_unigram_f1, has_answer
from utils import load_dataset, write_dataset | 1,119 | """Calculate Scores of Individual Sentences in Retrieved Passages."""
def calc_cxmi(
text: str,
question: str,
answers: list[str],
tokenizer: AutoTokenizer,
model: AutoModelForSeq2SeqLM,
) -> float:
"""Calculate CXMI score for a context text."""
proc_inputs = get_example_inputs(
... | """Calculate Scores of Individual Sentences in Retrieved Passages."""
def calc_cxmi(
text: str,
question: str,
answers: list[str],
tokenizer: AutoTokenizer,
model: AutoModelForSeq2SeqLM,
) -> float:
"""Calculate CXMI score for a context text."""
proc_inputs = get_example_inputs(
... | dataset = load_dataset(args.dataset_path) | 4 | 2023-11-14 21:18:30+00:00 | 2k |
ShipBit/wingman-ai | gui/views/context_view.py | [
{
"identifier": "ContextSwitcher",
"path": "gui/sections/context_switcher.py",
"snippet": "class ContextSwitcher(ctk.CTkFrame):\n# class ContextSwitcher(ctk.CTkScrollableFrame):\n def __init__(self, master, **kwargs):\n super().__init__(master, **kwargs)\n self.grid_columnconfigure(0, w... | import customtkinter as ctk
from gui.sections.context_switcher import ContextSwitcher
from gui.sections.context_runner import ContextRunner | 1,580 |
class ContextView(ctk.CTkFrame):
def __init__(self, master, **kwargs):
super().__init__(master, **kwargs)
self.core = master.core
self.grid_columnconfigure(1, weight=1)
self.grid_rowconfigure(0, weight=1)
|
class ContextView(ctk.CTkFrame):
def __init__(self, master, **kwargs):
super().__init__(master, **kwargs)
self.core = master.core
self.grid_columnconfigure(1, weight=1)
self.grid_rowconfigure(0, weight=1)
| self.context_switcher = ContextSwitcher(self, width=88, corner_radius=0) | 0 | 2023-11-15 09:36:06+00:00 | 2k |
OliverMao/FlaskAutoApiBuilder | demo/app.py | [
{
"identifier": "Faab",
"path": "Faab/Faab.py",
"snippet": "class Faab(Flask):\n _startup_message_printed = False\n models = []\n db_config = object()\n need_register_bp = []\n\n def __init__(self, **options):\n # 初始化函数，接收一个字符串类型的参数import_name\n super().__init__(**options)\n... | from Faab import Faab
from Faab.FaabJWT import jwt_authentication
from blueprints.test import test_bp
from blueprints.test.model import Users
import factory as fac | 1,300 | # Faab Project Demo
class DBConfig(object):
# 基础配置
user = 'faab'
host = 'localhost'
password = 'faab'
SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://%s:%s@%s:3306/%s' % (user, password, host, 'faab')
SQLALCHEMY_BINDS = {
'test': 'mysql+pymysql://%s:%s@%s:3306/%s' % (user, password, host, ... | # Faab Project Demo
class DBConfig(object):
# 基础配置
user = 'faab'
host = 'localhost'
password = 'faab'
SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://%s:%s@%s:3306/%s' % (user, password, host, 'faab')
SQLALCHEMY_BINDS = {
'test': 'mysql+pymysql://%s:%s@%s:3306/%s' % (user, password, host, ... | jwt_authentication() | 1 | 2023-11-10 09:25:44+00:00 | 2k |
leeyuentuen/polestar_api | custom_components/polestar_api/pypolestar/polestar.py | [
{
"identifier": "PolestarAuth",
"path": "custom_components/polestar_api/pypolestar/auth.py",
"snippet": "class PolestarAuth:\n \"\"\"base class for Polestar authentication.\"\"\"\n\n def __init__(self, username: str, password: str) -> None:\n \"\"\"Initialize the Polestar authentication.\"\... | from datetime import datetime, timedelta
from .auth import PolestarAuth
from .const import BATTERY_DATA, CACHE_TIME, CAR_INFO_DATA, ODO_METER_DATA
from .exception import (
PolestarApiException,
PolestarAuthException,
PolestarNoDataException,
PolestarNotAuthorizedException,
)
import logging
import httpx | 1,584 | """Asynchronous Python client for the Polestar API."""""
_LOGGER = logging.getLogger(__name__)
class PolestarApi:
"""Main class for handling connections with the Polestar API."""
def __init__(self, username: str, password: str) -> None:
"""Initialize the Polestar API."""
| """Asynchronous Python client for the Polestar API."""""
_LOGGER = logging.getLogger(__name__)
class PolestarApi:
"""Main class for handling connections with the Polestar API."""
def __init__(self, username: str, password: str) -> None:
"""Initialize the Polestar API.""" | self.auth = PolestarAuth(username, password) | 0 | 2023-11-17 21:24:36+00:00 | 2k |
dubverse-ai/MahaTTS | maha_tts/models/autoregressive.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 os,sys
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import functools
from typing import Any
from torch.utils.data import Dataset,DataLoader
from transformers import GPT2Tokenizer,GPT2Config, GPT2Model, GPT2LMHeadModel
from tqdm import tqdm
from maha_tts.config im... | 919 | '''
Inspiration taken from https://github.com/neonbjb/tortoise-tts/blob/main/tortoise/models/autoregressive.py
'''
def null_position_embeddings(range, dim):
return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device)
class TS_model(nn.Module):
def __init__(self,n_embed = 512, n_layer = 16,... | '''
Inspiration taken from https://github.com/neonbjb/tortoise-tts/blob/main/tortoise/models/autoregressive.py
'''
def null_position_embeddings(range, dim):
return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device)
class TS_model(nn.Module):
def __init__(self,n_embed = 512, n_layer = 16,... | self.text_head = nn.Linear(self.n_embed,len(text_labels)) | 1 | 2023-11-16 09:44:54+00:00 | 2k |
WCGKING/KINGUSERBOT | Branded/plugins/gdelete.py | [
{
"identifier": "is_gdel_user",
"path": "Branded/modules/data.py",
"snippet": "async def is_gdel_user(user_id: int) -> bool:\n user = await gdeldb.find_one({\"user_id\": user_id})\n if not user:\n return False\n return True"
},
{
"identifier": "get_gdel_user",
"path": "Brande... | import asyncio
from pyrogram import *
from pyrogram.types import Message
from .. import *
from ..modules.data import (is_gdel_user,
get_gdel_user, get_gdel_count,
add_gdel_user, del_gdel_user) | 643 |
@app.on_message(commandx(["gdl", "gdel", "gdelete"]) & SUPUSER)
async def add_gdelete_user(client, message: Message):
if not message.reply_to_message:
if len(message.command) != 2:
return await message.reply_text("Reply to a user's message or give username/user_id.")
user = message.... |
@app.on_message(commandx(["gdl", "gdel", "gdelete"]) & SUPUSER)
async def add_gdelete_user(client, message: Message):
if not message.reply_to_message:
if len(message.command) != 2:
return await message.reply_text("Reply to a user's message or give username/user_id.")
user = message.... | await add_gdel_user(user_id) | 3 | 2023-11-14 13:24:26+00:00 | 2k |
kudelskisecurity/fuzzomatic | fuzzomatic/docparse.py | [
{
"identifier": "score_functions",
"path": "fuzzomatic/approaches/functions.py",
"snippet": "def score_functions(functions):\n interesting_function_names = [\"parse\", \"load\", \"read\", \"str\", \"eval\"]\n # order functions by most interesting first\n ordered_functions = []\n for f in fun... | import argparse
from fuzzomatic.approaches.functions import score_functions
from fuzzomatic.tools.cargo_doc import parse_cargo_doc_json | 906 | #!/usr/bin/env python3
def get_parser():
prog_name = "docparse"
parser = argparse.ArgumentParser(
prog=prog_name,
description="Parse cargo doc json and print public functions",
)
parser.add_argument(
"json_path",
help="Path to cargo doc json file",
)
return pa... | #!/usr/bin/env python3
def get_parser():
prog_name = "docparse"
parser = argparse.ArgumentParser(
prog=prog_name,
description="Parse cargo doc json and print public functions",
)
parser.add_argument(
"json_path",
help="Path to cargo doc json file",
)
return pa... | ordered_functions = score_functions(functions) | 0 | 2023-11-14 09:52:59+00:00 | 2k |
muyuworks/myla | myla/vectorstores/lancedb_vectorstore.py | [
{
"identifier": "Record",
"path": "myla/vectorstores/_base.py",
"snippet": "class Record(Dict):\n @staticmethod\n def values_to_text(record: Dict, props: List[str] = None, separator: str = '\\001'):\n if props and not isinstance(props, list):\n raise ValueError(\"props should be ... | from typing import Any, List, Optional, Dict
from ._base import Record, VectorStore
from ._embeddings import Embeddings
import pyarrow as pa
import lancedb as lancedb
import pyarrow as pa | 1,142 |
VECTOR_COLUMN_NAME = "_vector"
class LanceDB(VectorStore):
def __init__(self, db_uri, embeddings: Embeddings = None) -> None:
super().__init__()
try:
pa.__version__
except ImportError as exc:
raise ImportError(
"Could not import pyarrow python pack... |
VECTOR_COLUMN_NAME = "_vector"
class LanceDB(VectorStore):
def __init__(self, db_uri, embeddings: Embeddings = None) -> None:
super().__init__()
try:
pa.__version__
except ImportError as exc:
raise ImportError(
"Could not import pyarrow python pack... | records: List[Record], | 0 | 2023-11-15 01:05:03+00:00 | 2k |
OSU-NLP-Group/TableLlama | inference_row_pop.py | [
{
"identifier": "replace_llama_attn",
"path": "llama_attn_replace.py",
"snippet": "def replace_llama_attn(use_flash_attn=True, use_full=False):\n if use_flash_attn:\n cuda_major, cuda_minor = torch.cuda.get_device_capability()\n if cuda_major < 8:\n warnings.warn(\n ... | import os
import json
import sys
import math
import torch
import argparse
import transformers
from peft import PeftModel
from transformers import GenerationConfig
from llama_attn_replace import replace_llama_attn
from supervised_fine_tune import PROMPT_DICT
from tqdm import tqdm | 670 | # import textwrap
# from queue import Queue
# from threading import Thread
# import gradio as gr
def parse_config():
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--base_model', type=str, default="/data1/pretrained-models/llama-7b-hf")
parser.add_argument('--cache_dir', ty... | # import textwrap
# from queue import Queue
# from threading import Thread
# import gradio as gr
def parse_config():
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--base_model', type=str, default="/data1/pretrained-models/llama-7b-hf")
parser.add_argument('--cache_dir', ty... | return PROMPT_DICT["prompt_input"].format(instruction=instruction, input_seg=input_seg, question=question) | 1 | 2023-11-16 02:54:08+00:00 | 2k |
pytorch-labs/torchfix | tests/test_torchfix.py | [
{
"identifier": "TorchChecker",
"path": "torchfix/torchfix.py",
"snippet": "class TorchChecker:\n name = \"TorchFix\"\n version = __version__\n\n # The parameters need to have these exact names.\n # See https://flake8.pycqa.org/en/latest/plugin-development/plugin-parameters.html\n # `tree... | from pathlib import Path
from torchfix.torchfix import (
TorchChecker,
TorchCodemod,
TorchCodemodConfig,
GET_ALL_VISITORS,
)
import logging
import libcst.codemod as codemod | 1,175 |
FIXTURES_PATH = Path(__file__).absolute().parent / "fixtures"
LOGGER = logging.getLogger(__name__)
def _checker_results(s):
checker = TorchChecker(None, s)
return [f"{line}:{col} {msg}" for line, col, msg, _ in checker.run()]
def _codemod_results(source_path):
with open(source_path) as source:
... |
FIXTURES_PATH = Path(__file__).absolute().parent / "fixtures"
LOGGER = logging.getLogger(__name__)
def _checker_results(s):
checker = TorchChecker(None, s)
return [f"{line}:{col} {msg}" for line, col, msg, _ in checker.run()]
def _codemod_results(source_path):
with open(source_path) as source:
... | config = TorchCodemodConfig(select="ALL") | 2 | 2023-11-15 01:21:07+00:00 | 2k |
FISHers6/CodeLearn-Agent | codelearn/tools/file_content_view.py | [
{
"identifier": "Project",
"path": "codelearn/project/project.py",
"snippet": "class Project:\n\n def __init__(self, id: str, local_dir: str, source_content: FileTree, repo_url: str = None, last_updated_time = None):\n \"\"\"\n :param name: 项目名称\n :param contents: 一个字典，其中键是文件路径，值... | import json
from typing import List, Optional
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.tools import BaseTool
from codelearn.project.project import Project
from codelearn.utils.file_util import process_file_paths | 649 |
class FileContentViewTool(BaseTool):
"""Tool to fetch and display detailed content of project files."""
name: str = "get_file_content"
description: str = (
"The 'get_file_content' tool fetches and displays detailed content of specified files within the project, including both source code and d... |
class FileContentViewTool(BaseTool):
"""Tool to fetch and display detailed content of project files."""
name: str = "get_file_content"
description: str = (
"The 'get_file_content' tool fetches and displays detailed content of specified files within the project, including both source code and d... | project: Project | 0 | 2023-11-12 13:13:30+00:00 | 2k |
kaixinol/twitter_user_tweet_crawler | twitter_user_tweet_crawler/__main__.py | [
{
"identifier": "get_browser",
"path": "twitter_user_tweet_crawler/browser.py",
"snippet": "def get_browser(headless: bool = False) -> WebDriver:\n chrome_options = webdriver.ChromeOptions()\n chrome_options.add_argument('--blink-settings=imagesEnabled=false')\n chrome_options.add_argument('--d... | import concurrent.futures
import json
from pathlib import Path
from time import sleep
from urllib.parse import urlparse
from loguru import logger
from rich.prompt import Confirm
from selenium.webdriver.chrome.webdriver import WebDriver
from selenium.webdriver.common.by import By
from .browser import get_browser, get_mu... | 807 |
def main():
cookie: list[dict]
work_list: list[WebDriver]
driver: WebDriver
def read_config() -> list[dict]:
with open(work_directory / 'cookie.json', 'r') as f:
return json.load(f)
def write_config(data: list[dict]):
with open(work_directory / 'cookie.json', 'w') a... |
def main():
cookie: list[dict]
work_list: list[WebDriver]
driver: WebDriver
def read_config() -> list[dict]:
with open(work_directory / 'cookie.json', 'r') as f:
return json.load(f)
def write_config(data: list[dict]):
with open(work_directory / 'cookie.json', 'w') a... | (Path(config.save) / 'res').mkdir(exist_ok=True, parents=True) | 3 | 2023-11-12 11:40:26+00:00 | 2k |
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 | 1,409 |
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... | dataset = load_dataset(dataset, *args, **kwargs) | 2 | 2023-11-15 22:22:06+00:00 | 2k |
shengliu66/ICV | utils/context_manager.py | [
{
"identifier": "ForwardTracer",
"path": "utils/forward_tracer.py",
"snippet": "class ForwardTracer:\n def __init__(self, model: PreTrainedModel, forward_trace: ForwardTrace, with_submodules: bool = False):\n self._model = model\n self._forward_trace = forward_trace\n self._with_... | import os
from contextlib import AbstractContextManager, ExitStack
from typing import Iterable
from utils.forward_tracer import ForwardTracer, ForwardTrace | 1,209 |
class CombinedContextManager(AbstractContextManager):
def __init__(self, context_managers):
self.context_managers = context_managers
self.stack = None
def __enter__(self):
self.stack = ExitStack()
for cm in self.context_managers:
self.stack.enter_context(cm)
... |
class CombinedContextManager(AbstractContextManager):
def __init__(self, context_managers):
self.context_managers = context_managers
self.stack = None
def __enter__(self):
self.stack = ExitStack()
for cm in self.context_managers:
self.stack.enter_context(cm)
... | forward_trace = ForwardTrace() | 1 | 2023-11-11 18:20:45+00:00 | 2k |
Mohamad-Hussein/speech-assistant | src/model_inference.py | [
{
"identifier": "find_gpu_config",
"path": "src/funcs.py",
"snippet": "def find_gpu_config(logger):\n \"\"\"\n Finds the GPU config and returns the device, device name and torch_dtype\n based on GPU platform and availability.\n\n Args:\n logger (logging.Logger): Logger instance to log... | from sys import exit
from os.path import join
from time import sleep, time
from src.funcs import find_gpu_config, process_text
from src.funcs import type_writing, copy_writing
from transformers.pipelines import pipeline
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
from optimum.bettertransfo... | 1,341 |
# from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
# from optimum.nvidia.pipelines import pipeline
# MODEL_ID = "openai/whisper-tiny.en" # ~400 MiB of GPU memory
MODEL_ID = "distil-whisper/distil-small.en" # ~500-700 MiB of GPU memory
# MODEL_ID = "distil-whisper/distil-medium.en" # ~900-1500 MiB of GPU ... |
# from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
# from optimum.nvidia.pipelines import pipeline
# MODEL_ID = "openai/whisper-tiny.en" # ~400 MiB of GPU memory
MODEL_ID = "distil-whisper/distil-small.en" # ~500-700 MiB of GPU memory
# MODEL_ID = "distil-whisper/distil-medium.en" # ~900-1500 MiB of GPU ... | device, device_name, torch_dtype = find_gpu_config(logger) | 0 | 2023-11-12 01:20:50+00:00 | 2k |
Fraunhofer-SCAI/corr_shap | corr_shap/CorrExplainer.py | [
{
"identifier": "SamplingStrategy",
"path": "corr_shap/sampling/SamplingStrategy.py",
"snippet": "class SamplingStrategy:\n def __init__(self, explainer, **kwargs):\n \"\"\" Construct all necessary attributes for the SamplingStrategy object.\"\"\"\n self.data = explainer.data.data\n ... | from scipy.special import binom
from scipy import sparse
from shap.utils._legacy import convert_to_instance, match_instance_to_data, IdentityLink
from shap.explainers._explainer import Explainer
from shap.explainers._kernel import KernelExplainer
from shap.explainers._kernel import Kernel as KernelExplainer
fro... | 986 |
try:
except ImportError:
log = logging.getLogger('corr_shap')
class CorrExplainer(KernelExplainer):
"""Uses the modified Kernel SHAP method to explain the output of any function.
The modifications (based on the paper 'Explaining individual predictions when features are dependent:
More accurate approxi... |
try:
except ImportError:
log = logging.getLogger('corr_shap')
class CorrExplainer(KernelExplainer):
"""Uses the modified Kernel SHAP method to explain the output of any function.
The modifications (based on the paper 'Explaining individual predictions when features are dependent:
More accurate approxi... | def __init__(self, model, data, link=IdentityLink(), sampling: typing.Union[str, SamplingStrategy]="default", sampling_kwargs={}, **kwargs): | 0 | 2023-11-14 08:56:18+00:00 | 2k |
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... | 1,201 | #!/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()
... | #!/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()
... | if k == ".": return Separator.MONADIC | 3 | 2023-11-18 17:34:06+00:00 | 2k |
mMrBun/Chat2BI | llms/chatglm3/code_interpreter.py | [
{
"identifier": "preprocess_text",
"path": "llms/chatglm3/conversation.py",
"snippet": "def preprocess_text(\n system: str | None,\n tools: list[dict] | None,\n history: list[Conversation],\n) -> str:\n if tools:\n tools = json.dumps(tools, indent=4, ensure_ascii=False)\n\... | from llms.chatglm3.conversation import preprocess_text, Conversation, Role
from core.build_tools.utils import extract_code | 822 |
SYSTEM_PROMPT = ('你是一位智能AI助手，你叫ChatGLM，你连接着一台电脑，但请注意不能联网。在使用Python'
'解决任务时，你可以运行代码并得到结果，如果运行结果有错误，你需要尽可能对代码进行改进。你可以处理用户上传到电脑上的文件，文件默认存储路径是/mnt/data/。')
MAX_LENGTH = 8192
TRUNCATE_LENGTH = 1024
def is_valid_python(code: str) -> bool:
try:
|
SYSTEM_PROMPT = ('你是一位智能AI助手，你叫ChatGLM，你连接着一台电脑，但请注意不能联网。在使用Python'
'解决任务时，你可以运行代码并得到结果，如果运行结果有错误，你需要尽可能对代码进行改进。你可以处理用户上传到电脑上的文件，文件默认存储路径是/mnt/data/。')
MAX_LENGTH = 8192
TRUNCATE_LENGTH = 1024
def is_valid_python(code: str) -> bool:
try: | code = extract_code(code) | 3 | 2023-11-15 11:49:50+00:00 | 2k |
compphoto/Intrinsic | intrinsic/pipeline.py | [
{
"identifier": "base_resize",
"path": "intrinsic/ordinal_util.py",
"snippet": "def base_resize(img, base_size=384):\n \"\"\"TODO DESCRIPTION\n\n params:\n img (TODO): TODO\n base_size (int) optional: TODO (default 384)\n\n returns:\n net_input (TODO): TODO\n \"\"\"\n ... | import torch
import numpy as np
from skimage.transform import resize
from chrislib.resolution_util import optimal_resize
from chrislib.general import round_32, uninvert
from intrinsic.ordinal_util import base_resize, equalize_predictions | 1,440 |
def run_pipeline(
models,
img_arr,
output_ordinal=False,
resize_conf=0.0,
base_size=384,
maintain_size=False,
linear=False,
device='cuda',
lstsq_p=0.0,
inputs='all'):
"""Runs the complete pipeline for shading and albedo prediction
... |
def run_pipeline(
models,
img_arr,
output_ordinal=False,
resize_conf=0.0,
base_size=384,
maintain_size=False,
linear=False,
device='cuda',
lstsq_p=0.0,
inputs='all'):
"""Runs the complete pipeline for shading and albedo prediction
... | ord_base, ord_full = equalize_predictions(lin_img, base_out, full_out, p=lstsq_p) | 1 | 2023-11-13 19:24:09+00:00 | 2k |
davep/tinboard | tinboard/widgets/tags.py | [
{
"identifier": "ClearTags",
"path": "tinboard/messages/tags.py",
"snippet": "class ClearTags(Message):\n \"\"\"Clear any tags being used to filter.\"\"\""
},
{
"identifier": "ShowAlsoTaggedWith",
"path": "tinboard/messages/tags.py",
"snippet": "class ShowAlsoTaggedWith(TagMessage):\n... | from typing_extensions import Final, Self
from textual import on
from textual.binding import Binding
from textual.events import Focus
from textual.reactive import var
from textual.widgets.option_list import Option, OptionDoesNotExist
from rich.console import RenderableType
from rich.emoji import Emoji
from rich.table i... | 992 | """Defines a widget for picking tags."""
##############################################################################
# Backward compatibility.
from __future__ import annotations
##############################################################################
# Python imports.
#######################################... | """Defines a widget for picking tags."""
##############################################################################
# Backward compatibility.
from __future__ import annotations
##############################################################################
# Python imports.
#######################################... | self.post_message(ShowTaggedWith(event.option.id)) | 2 | 2023-11-13 08:19:41+00:00 | 2k |
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... | 1,490 |
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... | qs = DEFAULT_IMAGE_TOKEN + '\n' + qs | 1 | 2023-11-10 03:52:46+00:00 | 2k |
vvvm23/TchAIkovsky | generate.py | [
{
"identifier": "get_pretrained_tokenizer",
"path": "data/tokenizer.py",
"snippet": "def get_pretrained_tokenizer(path: str = \"tokenizer.json\"):\n return miditok.REMI.from_pretrained(path)"
},
{
"identifier": "TchAIkovskyModel",
"path": "model/model.py",
"snippet": "class TchAIkovsk... | import json
import equinox as eqx
import jax
import jax.numpy as jnp
import numpy as np
import orbax.checkpoint as ocp
import tqdm
from argparse import ArgumentParser
from datetime import datetime
from pathlib import Path
from types import SimpleNamespace
from typing import Optional
from loguru import logger
from midit... | 1,331 |
def load_config(config_path):
with open(config_path, mode="r") as f:
data = f.read()
json_dict = json.loads(data)
return SimpleNamespace(**json_dict)
@eqx.filter_jit
@eqx.debug.assert_max_traces(max_traces=1)
def generate_step(model, inputs, length, key, temperature):
logits = model(**inp... |
def load_config(config_path):
with open(config_path, mode="r") as f:
data = f.read()
json_dict = json.loads(data)
return SimpleNamespace(**json_dict)
@eqx.filter_jit
@eqx.debug.assert_max_traces(max_traces=1)
def generate_step(model, inputs, length, key, temperature):
logits = model(**inp... | tokenizer = get_pretrained_tokenizer(args.tokenizer) | 0 | 2023-11-13 07:31:30+00:00 | 2k |
dazhangyu123/ACMIL | architecture/ibmil.py | [
{
"identifier": "Classifier_1fc",
"path": "architecture/network.py",
"snippet": "class Classifier_1fc(nn.Module):\n def __init__(self, n_channels, n_classes, droprate=0.0):\n super(Classifier_1fc, self).__init__()\n self.fc = nn.Linear(n_channels, n_classes)\n self.droprate = dro... | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from architecture.network import Classifier_1fc, DimReduction
| 694 |
class Attention_Gated(nn.Module):
def __init__(self, L=512, D=128, K=1):
super(Attention_Gated, self).__init__()
self.L = L
self.D = D
self.K = K
self.attention_V = nn.Sequential(
nn.Linear(self.L, self.D),
nn.Tanh()
)
... |
class Attention_Gated(nn.Module):
def __init__(self, L=512, D=128, K=1):
super(Attention_Gated, self).__init__()
self.L = L
self.D = D
self.K = K
self.attention_V = nn.Sequential(
nn.Linear(self.L, self.D),
nn.Tanh()
)
... | self.classifier = Classifier_1fc(conf.D_inner, conf.n_class, 0)
| 0 | 2023-11-12 14:07:34+00:00 | 2k |
Kav-K/Described | services/openai_service.py | [
{
"identifier": "EnvService",
"path": "services/environment_service.py",
"snippet": "class EnvService:\n # To be expanded upon later!\n def __init__(self):\n self.env = {}\n\n @staticmethod\n def environment_path_with_fallback(env_name, relative_fallback=None):\n directory = os... | import traceback
import aiohttp
import backoff
from services.environment_service import EnvService
from services.prompts.image_analysis_prompt import IMAGE_ANALYSIS_PROMPT | 1,461 |
def backoff_handler_request(details):
print(
f"Backing off {details['wait']:0.1f} seconds after {details['tries']} tries calling function {details['target']} | "
f"{details['exception'].args[0]}"
)
class OpenAIExecutor:
def __init__(self):
self.openai_api_key = EnvService.get_o... |
def backoff_handler_request(details):
print(
f"Backing off {details['wait']:0.1f} seconds after {details['tries']} tries calling function {details['target']} | "
f"{details['exception'].args[0]}"
)
class OpenAIExecutor:
def __init__(self):
self.openai_api_key = EnvService.get_o... | self.ANALYSIS_PRETEXT = IMAGE_ANALYSIS_PROMPT | 1 | 2023-11-14 02:22:13+00:00 | 2k |
juftin/hatch-pip-compile | tests/test_installer.py | [
{
"identifier": "HatchPipCompileError",
"path": "hatch_pip_compile/exceptions.py",
"snippet": "class HatchPipCompileError(Exception):\n \"\"\"\n Base exception for hatch-pip-compile\n \"\"\""
},
{
"identifier": "PluginInstaller",
"path": "hatch_pip_compile/installer.py",
"snippe... | from typing import Dict, Type
from unittest.mock import Mock
from hatch_pip_compile.exceptions import HatchPipCompileError
from hatch_pip_compile.installer import PluginInstaller
from tests.conftest import PipCompileFixture
import pytest | 1,134 | """
Installation Tests
"""
def test_pip_install_dependencies(mock_check_command: Mock, pip_compile: PipCompileFixture) -> None:
"""
Assert the `pip` installation command is called with the expected arguments
"""
pip_compile.default_environment.create()
pip_compile.default_environment.installer.... | """
Installation Tests
"""
def test_pip_install_dependencies(mock_check_command: Mock, pip_compile: PipCompileFixture) -> None:
"""
Assert the `pip` installation command is called with the expected arguments
"""
pip_compile.default_environment.create()
pip_compile.default_environment.installer.... | with pytest.raises(HatchPipCompileError): | 0 | 2023-11-10 00:34:00+00:00 | 2k |
google-deepmind/pix2act | pix2act/tasks/miniwob/search/write_value_fn_tf_examples.py | [
{
"identifier": "tf_utils",
"path": "pix2act/common/tf_utils.py",
"snippet": "def add_bytes_feature(\n example: tf.train.Example, key: str, value: bytes\n) -> None:\ndef add_text_feature(example: tf.train.Example, key: str, value: str) -> None:\ndef get_bytes_feature(example: tf.train.Example, key: s... | from absl import app
from absl import flags
from pix2act.common import tf_utils
from pix2act.tasks.miniwob import episode_pb2
from pix2act.tasks.miniwob.search import reward_utils
import apache_beam as beam
import tensorflow as tf | 820 | # 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 ... | # 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 ... | tf_utils.add_bytes_feature(example, "image", step.screenshot_png) | 0 | 2023-11-13 22:50:55+00:00 | 2k |
zhang-tao-whu/DVIS_Plus | mask2former/modeling/meta_arch/mask_former_head.py | [
{
"identifier": "build_transformer_decoder",
"path": "mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py",
"snippet": "def build_transformer_decoder(cfg, in_channels, mask_classification=True):\n \"\"\"\n Build a instance embedding branch from `cfg.MODEL.INS_EMBED_HEAD.NAME... | import logging
import fvcore.nn.weight_init as weight_init
from copy import deepcopy
from typing import Callable, Dict, List, Optional, Tuple, Union
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectr... | 1,271 | # Copyright (c) Facebook, Inc. and its affiliates.
@SEM_SEG_HEADS_REGISTRY.register()
class MaskFormerHead(nn.Module):
_version = 2
def _load_from_state_dict(
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
):
version = local_metadata.get("v... | # Copyright (c) Facebook, Inc. and its affiliates.
@SEM_SEG_HEADS_REGISTRY.register()
class MaskFormerHead(nn.Module):
_version = 2
def _load_from_state_dict(
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
):
version = local_metadata.get("v... | "transformer_predictor": build_transformer_decoder( | 0 | 2023-11-14 10:55:11+00:00 | 2k |
teamreboott/data-modori | data_modori/ops/filter/language_id_score_filter.py | [
{
"identifier": "Fields",
"path": "data_modori/utils/constant.py",
"snippet": "class Fields(object):\n stats = DEFAULT_PREFIX + 'stats__'\n meta = DEFAULT_PREFIX + 'meta__'\n context = DEFAULT_PREFIX + 'context__'\n suffix = DEFAULT_PREFIX + 'suffix__'"
},
{
"identifier": "StatsKeys"... | from jsonargparse.typing import ClosedUnitInterval
from loguru import logger
from data_modori.utils.constant import Fields, StatsKeys
from data_modori.utils.model_utils import prepare_model, get_model
from ..base_op import OPERATORS, Filter | 1,416 |
@OPERATORS.register_module('language_id_score_filter')
class LanguageIDScoreFilter(Filter):
"""Filter to keep samples in a specific language with confidence score
larger than a specific min value."""
def __init__(self,
lang: str = '',
min_score: ClosedUnitInterval = 0.... |
@OPERATORS.register_module('language_id_score_filter')
class LanguageIDScoreFilter(Filter):
"""Filter to keep samples in a specific language with confidence score
larger than a specific min value."""
def __init__(self,
lang: str = '',
min_score: ClosedUnitInterval = 0.... | ft_model = get_model(self.model_key, lang=self.lang, model_type='fasttext') | 3 | 2023-11-13 04:52:55+00:00 | 2k |
52phm/pylmkit | pylmkit/tools/search.py | [
{
"identifier": "Document",
"path": "pylmkit/utils/data_utils.py",
"snippet": "class Document(BaseModel):\n page_content: str\n metadata: dict = Field(default_factory=dict)\n type: str = \"Document\"\n\n def __str__(self):\n return f\"Document(page_content='{self.page_content}', metad... | from duckduckgo_search import DDGS
from pylmkit.utils.data_utils import Document
from pylmkit.core.base import BaseKnowledgeBase | 1,104 |
class WebSearch(DDGS, BaseKnowledgeBase):
def __init__(
self,
topk=5,
backend="api",
region="wt-wt",
timelimit=None,
safesearch="moderate",
init_documents=None,
timeout=10,
headers=None,
proxies... |
class WebSearch(DDGS, BaseKnowledgeBase):
def __init__(
self,
topk=5,
backend="api",
region="wt-wt",
timelimit=None,
safesearch="moderate",
init_documents=None,
timeout=10,
headers=None,
proxies... | self.documents.append(Document( | 0 | 2023-11-18 10:31:58+00:00 | 2k |
hadican/failedkite | app.py | [
{
"identifier": "Config",
"path": "config.py",
"snippet": "class Config:\n def __init__(self):\n self.slack_token = self._get_env_variable('SLACK_TOKEN')\n self.default_slack_email = self._get_env_variable('DEFAULT_SLACK_EMAIL')\n self.author_mapping = self._load_author_mapping('... | import logging
from flask import Flask, request
from config import Config
from notification_service import NotificationService
from slack_client import SlackClient | 1,094 |
app = Flask(__name__)
logging.basicConfig(level=logging.INFO)
config = Config()
slack_client = SlackClient(token=config.slack_token)
|
app = Flask(__name__)
logging.basicConfig(level=logging.INFO)
config = Config()
slack_client = SlackClient(token=config.slack_token) | notification_service = NotificationService(slack_client, config) | 1 | 2023-11-11 20:35:31+00:00 | 2k |
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 | 1,269 |
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... | for idx, action in enumerate(ACTIONS): | 0 | 2023-11-16 18:34:28+00:00 | 2k |
AlexandrErohin/home-assistant-flightradar24 | custom_components/flightradar24/coordinator.py | [
{
"identifier": "BoundingBox",
"path": "custom_components/flightradar24/models.py",
"snippet": "class BoundingBox:\n \"\"\"Bounding box for retrieving state vectors.\"\"\"\n\n min_latitude: float\n max_latitude: float\n min_longitude: float\n max_longitude: float\n\n def validate(self)... | from typing import Any
from datetime import timedelta
from homeassistant.core import HomeAssistant
from homeassistant.helpers.update_coordinator import DataUpdateCoordinator
from homeassistant.helpers.device_registry import DeviceInfo
from .models import BoundingBox
from .const import (
DOMAIN,
URL,
DEFAULT... | 669 | from __future__ import annotations
class FlightRadar24Coordinator(DataUpdateCoordinator[int]):
def __init__(
self,
hass: HomeAssistant,
bound: BoundingBox,
client: FlightRadar24API,
update_interval: int,
logger: Logger,
) -> None:
... | from __future__ import annotations
class FlightRadar24Coordinator(DataUpdateCoordinator[int]):
def __init__(
self,
hass: HomeAssistant,
bound: BoundingBox,
client: FlightRadar24API,
update_interval: int,
logger: Logger,
) -> None:
... | identifiers={(DOMAIN, DEFAULT_NAME)}, | 1 | 2023-11-16 10:51:24+00:00 | 2k |
ej0cl6/TextEE | TextEE/models/QueryAndExtract/EAEmodel.py | [
{
"identifier": "Metadata",
"path": "TextEE/models/QueryAndExtract/metadata.py",
"snippet": "class Metadata(object):\n def __init__(self, metadata_path, dataset, type_set):\n self.pos_set = ['ADJ', 'ADP', 'ADV', 'AUX', 'CCONJ', 'DET', 'INTJ', 'NOUN', 'NUM', 'PART', 'PRON', 'PROPN',\n ... | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import ipdb
import ipdb
from transformers import BertConfig, RobertaConfig, BertModel, RobertaModel
from .metadata import Metadata
from .utils import pad_seq
from keras_preprocessing.sequence import pad_sequences | 676 |
class QueryAndExtractEAEModel(nn.Module):
def __init__(self, config, tokenizer, type_set):
super().__init__()
self.config = config
self.tokenizer = tokenizer
self.type_set = type_set
self.earl_model = EARLModel(config, tokenizer, type_set)
self.ner_model = NERModel(c... |
class QueryAndExtractEAEModel(nn.Module):
def __init__(self, config, tokenizer, type_set):
super().__init__()
self.config = config
self.tokenizer = tokenizer
self.type_set = type_set
self.earl_model = EARLModel(config, tokenizer, type_set)
self.ner_model = NERModel(c... | self.metadata = Metadata(config.metadata_path, self.config.dataset, type_set) | 0 | 2023-11-15 21:32:56+00:00 | 2k |
fofr/cog-sdxl-multi-controlnet-lora | controlnet.py | [
{
"identifier": "ControlNetPreprocessor",
"path": "controlnet_preprocess.py",
"snippet": "class ControlNetPreprocessor:\n ANNOTATOR_CLASSES = {\n \"none\": None,\n \"edge_canny\": CannyDetector,\n \"depth_leres\": LeresDetector,\n \"depth_midas\": MidasDetector,\n \... | import torch
from diffusers import ControlNetModel
from controlnet_preprocess import ControlNetPreprocessor
from weights_downloader import WeightsDownloader | 1,238 |
CONTROLNET_MODEL_CACHE = "./controlnet-cache"
CONTROLNET_URL = "https://weights.replicate.delivery/default/controlnet/sdxl-cn-canny-depth-softe-pose-qr.tar"
class ControlNet:
CONTROLNET_MODELS = [
"none",
"edge_canny",
"illusion",
"depth_leres",
"depth_midas",
"sof... |
CONTROLNET_MODEL_CACHE = "./controlnet-cache"
CONTROLNET_URL = "https://weights.replicate.delivery/default/controlnet/sdxl-cn-canny-depth-softe-pose-qr.tar"
class ControlNet:
CONTROLNET_MODELS = [
"none",
"edge_canny",
"illusion",
"depth_leres",
"depth_midas",
"sof... | self.controlnet_preprocessor = ControlNetPreprocessor(self.predictor) | 0 | 2023-11-13 13:04:41+00:00 | 2k |
ahayler/s4c | utils/base_trainer.py | [
{
"identifier": "to",
"path": "utils/array_operations.py",
"snippet": "def to(data, device, non_blocking=True):\n if isinstance(data, dict):\n return {k: to(data[k], device, non_blocking=non_blocking) for k in data.keys()}\n elif isinstance(data, list):\n return [to(v, device, non_bl... | import json
import time
import ignite
import ignite.distributed as idist
import torch
from datetime import datetime
from pathlib import Path
from typing import Union
from omegaconf import OmegaConf
from ignite.contrib.engines import common
from ignite.contrib.handlers import TensorboardLogger
from ignite.contrib.handle... | 1,018 |
# used for debugging
torch.autograd.set_detect_anomaly(True)
def base_training(local_rank, config, get_dataflow, initialize, get_metrics, visualize):
# copy the segmentation mode to the data and model_conf part of the config
config['data']['segmentation_mode'] = config.get("segmentation_mode", None)
c... |
# used for debugging
torch.autograd.set_detect_anomaly(True)
def base_training(local_rank, config, get_dataflow, initialize, get_metrics, visualize):
# copy the segmentation mode to the data and model_conf part of the config
config['data']['segmentation_mode'] = config.get("segmentation_mode", None)
c... | metrics_loss = {k: MeanMetric((lambda y: lambda x: x["loss_dict"][y])(k)) for k in criterion.get_loss_metric_names()} | 1 | 2023-11-12 21:53:27+00:00 | 2k |
Emmo00/alxcheck | alxcheck/checks/python.py | [
{
"identifier": "print_no_module_docstring",
"path": "alxcheck/utils/error_logging.py",
"snippet": "def print_no_module_docstring(file_path):\n print(Fore.RED + f\"{file_path} does not have Module DocString\" + Fore.RESET)"
},
{
"identifier": "print_no_function_docstring",
"path": "alxche... | import os
import ast
import subprocess
from ..utils.error_logging import (
print_no_module_docstring,
print_no_function_docstring,
print_no_class_docstring,
print_check_docstrings,
print_error_parsing_file,
) | 800 |
def check_file_is_executable(file_path):
flag = True
if not os.access(file_path, os.X_OK):
flag = False
return flag
def check_python_shebang(file_path):
flag = True
with open(file_path, "rb") as f:
first_line = f.readline().strip()
if first_line not in (b"#!/usr/bin/pytho... |
def check_file_is_executable(file_path):
flag = True
if not os.access(file_path, os.X_OK):
flag = False
return flag
def check_python_shebang(file_path):
flag = True
with open(file_path, "rb") as f:
first_line = f.readline().strip()
if first_line not in (b"#!/usr/bin/pytho... | print_check_docstrings(file_path) | 3 | 2023-11-14 19:28:28+00:00 | 2k |
TimbreWatermarking/TimbreWatermarking | voice.clone/Fastspeech2/TTS/tts/utils/text/cleaners.py | [
{
"identifier": "abbreviations_en",
"path": "voice.clone/Fastspeech2/TTS/tts/utils/text/english/abbreviations.py",
"snippet": ""
},
{
"identifier": "normalize_numbers",
"path": "voice.clone/Fastspeech2/TTS/tts/utils/text/english/number_norm.py",
"snippet": "def normalize_numbers(text):\n... | import re
from anyascii import anyascii
from TTS.tts.utils.text.chinese_mandarin.numbers import replace_numbers_to_characters_in_text
from .english.abbreviations import abbreviations_en
from .english.number_norm import normalize_numbers as en_normalize_numbers
from .english.time_norm import expand_time_english
from .fr... | 776 | """Set of default text cleaners"""
# TODO: pick the cleaner for languages dynamically
# Regular expression matching whitespace:
_whitespace_re = re.compile(r"\s+")
def expand_abbreviations(text, lang="en"):
if lang == "en":
_abbreviations = abbreviations_en
elif lang == "fr":
_abbreviatio... | """Set of default text cleaners"""
# TODO: pick the cleaner for languages dynamically
# Regular expression matching whitespace:
_whitespace_re = re.compile(r"\s+")
def expand_abbreviations(text, lang="en"):
if lang == "en":
_abbreviations = abbreviations_en
elif lang == "fr":
_abbreviatio... | text = expand_time_english(text) | 2 | 2023-11-13 01:40:03+00:00 | 2k |
nillion-oss/tinysig | src/tinysig/network.py | [
{
"identifier": "add",
"path": "src/tinysig/utils.py",
"snippet": "def add(values: list[int], size: int) -> int:\n \"\"\"\n Calculate the sum of a list of integers modulo 'size'.\n\n Args:\n values (list[int]): A list of integers to be summed.\n size (int): The modulo value.\n\n ... | from dataclasses import dataclass, field
from typing import Dict, List, Union
from .utils import add, generate_additive_shares | 1,515 |
@dataclass
class Node:
""" Represents a node in the network."""
id: int
"""Identifier for the node."""
shares_db: Dict[str, int] = field(default_factory=dict)
"""Database for holding shares."""
open_db: Dict[str, int] = field(default_factory=dict)
"""Database for holding open values."""
... |
@dataclass
class Node:
""" Represents a node in the network."""
id: int
"""Identifier for the node."""
shares_db: Dict[str, int] = field(default_factory=dict)
"""Database for holding shares."""
open_db: Dict[str, int] = field(default_factory=dict)
"""Database for holding open values."""
... | reconstructed = add(shares, p) | 0 | 2023-11-14 13:55:41+00:00 | 2k |
naver-ai/scob | lightning_modules/data_modules/transforms/transformer_decoder.py | [
{
"identifier": "TRANSFORM_NAME_TO_CLASS",
"path": "lightning_modules/data_modules/transforms/common.py",
"snippet": "TRANSFORM_NAME_TO_CLASS = {\n \"RandomRotate\": RandomRotate,\n \"CraftRandomCrop\": CraftRandomCrop,\n \"Resize\": Resize,\n \"ResizeOD\": ResizeOD,\n \"PhotometricDistor... | from typing import List, Tuple, Union
from lightning_modules.data_modules.transforms.common import (
TRANSFORM_NAME_TO_CLASS,
W_Compose,
)
from utils.dataset_utils import get_image_normalize_mean_and_std
import torch
import torchvision.transforms as transforms | 1,240 |
class TransformerDecoderTransformForFineTuning:
"""
- BEiT:
https://github.com/microsoft/unilm/blob/master/beit/datasets.py#L27
- TrOCR:
https://github.com/microsoft/unilm/blob/53995b4876464146365693396aaaa09e88a4494e/trocr/data_aug.py#L120
"""
def __init__(
self,
size:... |
class TransformerDecoderTransformForFineTuning:
"""
- BEiT:
https://github.com/microsoft/unilm/blob/master/beit/datasets.py#L27
- TrOCR:
https://github.com/microsoft/unilm/blob/53995b4876464146365693396aaaa09e88a4494e/trocr/data_aug.py#L120
"""
def __init__(
self,
size:... | mean_and_std = get_image_normalize_mean_and_std(image_normalize) | 2 | 2023-11-15 00:40:08+00:00 | 2k |
speckai/speck | src/python/speck/connections/connector.py | [
{
"identifier": "ChatLogger",
"path": "src/python/speck/chat/entities.py",
"snippet": "NOT_GIVEN = None\nclass Message(BaseModel):\nclass SafeDict(dict):\nclass Prompt(str):\nclass Response(BaseModel):\nclass MessageChunk(BaseModel):\nclass Stream:\nclass LogConfig(BaseModel):\n class Config:\nclass ... | from abc import ABC
from ..chat.entities import ChatLogger, LogConfig, Prompt, Response
from .providers import Providers | 1,521 |
class IConnector(ABC):
_client: "Speck"
def __init__(self, client: "Speck", provider: Providers):
self._client = client
self.provider = provider
# @abstractmethod
# def process_message(self, messages: Messages, model: str) -> str:
# pass
def _get_log_kwargs(self, prompt... |
class IConnector(ABC):
_client: "Speck"
def __init__(self, client: "Speck", provider: Providers):
self._client = client
self.provider = provider
# @abstractmethod
# def process_message(self, messages: Messages, model: str) -> str:
# pass
def _get_log_kwargs(self, prompt... | ChatLogger.log( | 0 | 2023-11-15 05:46:05+00:00 | 2k |
chaiNNer-org/spandrel | src/spandrel/architectures/KBNet/arch/kbnet_s.py | [
{
"identifier": "KBAFunction",
"path": "src/spandrel/architectures/KBNet/arch/kb_utils.py",
"snippet": "class KBAFunction(torch.autograd.Function):\n @staticmethod\n def forward(ctx, x, att, selfk, selfg, selfb, selfw):\n B, nset, H, W = att.shape\n KK = selfk**2\n selfc = x.s... | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from .kb_utils import KBAFunction, LayerNorm2d, SimpleGate | 1,359 | # type: ignore
class KBBlock_s(nn.Module):
def __init__(
self, c, DW_Expand=2, FFN_Expand=2, nset=32, k=3, gc=4, lightweight=False
):
super().__init__()
self.k, self.c = k, c
self.nset = nset
dw_ch = int(c * DW_Expand)
ffn_ch = int(FFN_Expand * c)
sel... | # type: ignore
class KBBlock_s(nn.Module):
def __init__(
self, c, DW_Expand=2, FFN_Expand=2, nset=32, k=3, gc=4, lightweight=False
):
super().__init__()
self.k, self.c = k, c
self.nset = nset
dw_ch = int(c * DW_Expand)
ffn_ch = int(FFN_Expand * c)
sel... | self.norm1 = LayerNorm2d(c) | 1 | 2023-11-17 01:11:47+00:00 | 2k |
robocorp/llmstatemachine | src/llmstatemachine/workflow_agent.py | [
{
"identifier": "create_definition",
"path": "src/llmstatemachine/function.py",
"snippet": "def create_definition(func: Callable, goal: str) -> FunctionDefinition:\n source = inspect.getsource(func)\n client = OpenAI()\n response = client.chat.completions.create(\n model=\"gpt-4-1106-pre... | import json
from typing import Dict, Callable, Any, Tuple, List
from openai.types.chat.chat_completion_message import FunctionCall
from .function import create_definition, FunctionDefinition
from openai import OpenAI
from openai.types.chat import (
ChatCompletionMessageParam,
ChatCompletionMessage,
completi... | 756 |
TransitionFunction = Callable[[...], str]
FUNCTION_NAME = "ActionSelector"
MODEL = "gpt-4-1106-preview" # "gpt-4"
_CURRENT_STEPPING_AGENT = None
class WorkflowAgent:
def __init__(
self, goal: str, transitions: Dict[str, Dict[str, TransitionFunction]]
):
if "INIT" not in transitions:
... |
TransitionFunction = Callable[[...], str]
FUNCTION_NAME = "ActionSelector"
MODEL = "gpt-4-1106-preview" # "gpt-4"
_CURRENT_STEPPING_AGENT = None
class WorkflowAgent:
def __init__(
self, goal: str, transitions: Dict[str, Dict[str, TransitionFunction]]
):
if "INIT" not in transitions:
... | self._func_defs: Dict[TransitionFunction, FunctionDefinition] = dict() | 1 | 2023-11-17 17:37:08+00:00 | 2k |
GoldenThrust/Virtual-Bank | api/debit_cards/serializers.py | [
{
"identifier": "DebitCard",
"path": "api/debit_cards/models.py",
"snippet": "class DebitCard(models.Model):\n account = models.ForeignKey(Account, on_delete=models.CASCADE)\n card_number = models.BigIntegerField()\n cvv = models.CharField(max_length=4)\n expiration_date = models.DateTimeFie... | from rest_framework import serializers
from .models import DebitCard, DebitCardTransaction
from .utils import generate_valid_credit_card_number, generate_cvv
from accounts.serializers import AccountSerializer
from transactions.serializers import TransactionSerializer | 790 |
class DebitCardSerializer(serializers.ModelSerializer):
card_number = serializers.CharField(read_only=True)
cvv = serializers.CharField(read_only=True)
created_date = serializers.DateTimeField(read_only=True)
account = AccountSerializer()
expiration_date = serializers.SerializerMethodField()
... |
class DebitCardSerializer(serializers.ModelSerializer):
card_number = serializers.CharField(read_only=True)
cvv = serializers.CharField(read_only=True)
created_date = serializers.DateTimeField(read_only=True)
account = AccountSerializer()
expiration_date = serializers.SerializerMethodField()
... | model = DebitCard | 0 | 2023-11-10 12:39:38+00:00 | 2k |
Mj23978/OpenServer | openserver/core/vector_store/qdrant.py | [
{
"identifier": "get_config",
"path": "openserver/core/config/config.py",
"snippet": "def get_config(self, key: str, default: Optional[str] = None) -> str | None:\n return self.model_dump().get(key, default)"
},
{
"identifier": "VectorStore",
"path": "openserver/core/vector_store/base.py"... | from mimetypes import common_types
from typing import Dict, Optional, Union
from qdrant_client import QdrantClient
from qdrant_client.conversions import common_types
from langchain.vectorstores.qdrant import Qdrant
from ..config.config import get_config
from .base import VectorStore
from .embedding.base import BaseEmbe... | 783 | from __future__ import annotations
DictFilter = Dict[str, Union[str, int, bool, dict, list]]
MetadataFilter = Union[DictFilter, common_types.Filter]
def create_qdrant_client(api_key: Optional[str] = None, url: Optional[str] = None, port: Optional[int] = None
) -> QdrantClient:
if api_... | from __future__ import annotations
DictFilter = Dict[str, Union[str, int, bool, dict, list]]
MetadataFilter = Union[DictFilter, common_types.Filter]
def create_qdrant_client(api_key: Optional[str] = None, url: Optional[str] = None, port: Optional[int] = None
) -> QdrantClient:
if api_... | embedding_model: BaseEmbedding, | 2 | 2023-11-11 00:32:31+00:00 | 2k |
TCLResearchEurope/torch-dag | torch_dag_algorithms/pruning/orbit.py | [
{
"identifier": "OrbitsDiscoveryStage",
"path": "torch_dag_algorithms/pruning/orbits_search_stage.py",
"snippet": "class OrbitsDiscoveryStage(enum.Enum):\n EXTENDED_ORBIT_DISCOVERY = 'extended_orbits_discovery'\n FINAL_ORBIT_DISCOVERY = 'final_orbits_discovery'\n CLASSIC_ATTENTION_DISCOVERY = '... | from typing import List
from typing import Set
from typing import Tuple
from torch_dag_algorithms.pruning.orbits_search_stage import OrbitsDiscoveryStage
from torch_dag.core.dag_module import InnerVertex | 911 |
class Orbit:
def __init__(self, color: int):
"""Basic orbit object that can represent either extended or final orbit. If orbit has `allow_for_further_processing` set to True then it can be processed by Orbitalizer by it's general mechanism. If set to False orbit won't be processed in any way and will be ... |
class Orbit:
def __init__(self, color: int):
"""Basic orbit object that can represent either extended or final orbit. If orbit has `allow_for_further_processing` set to True then it can be processed by Orbitalizer by it's general mechanism. If set to False orbit won't be processed in any way and will be ... | def discovery_stage(self) -> OrbitsDiscoveryStage: | 0 | 2023-11-17 15:36:44+00:00 | 2k |
repeating/Binance-P2P-alerts-Telegram-bot | bot/alerts/alert.py | [
{
"identifier": "get_offers",
"path": "bot/binance_api.py",
"snippet": "async def get_offers(asset: str, fiat: str, trade_type: str, payment_method: str,\n rows: int = 5, page: int = 1, trans_amount: str = None) -> List[dict]:\n \"\"\"\n Fetch the best offers from Binance P2P.\... | from datetime import datetime, timedelta
from bot.binance_api import get_offers, get_link
from bot.utils import send_telegram_message | 1,063 |
class Alert:
def __init__(self, alert_id, user_id, asset, fiat, trade_type, threshold_price, payment_method):
self.alert_id = alert_id
self.user_id = user_id
self.asset = asset
self.fiat = fiat
self.trade_type = trade_type
self.threshold_price = threshold_price
... |
class Alert:
def __init__(self, alert_id, user_id, asset, fiat, trade_type, threshold_price, payment_method):
self.alert_id = alert_id
self.user_id = user_id
self.asset = asset
self.fiat = fiat
self.trade_type = trade_type
self.threshold_price = threshold_price
... | self.link = get_link(self.fiat, self.asset, self.payment_method, self.trade_type) | 1 | 2023-11-12 10:20:26+00:00 | 2k |
timlrx/simple-ai-agents | simple_ai_agents/chat_session.py | [
{
"identifier": "ChatMessage",
"path": "simple_ai_agents/models.py",
"snippet": "class ChatMessage(BaseModel):\n role: str\n content: str\n name: Optional[str] = None\n function_call: Optional[str] = None\n received_at: datetime.datetime = Field(default_factory=now_tz)\n finish_reason:... | from json import JSONDecodeError
from typing import Any, AsyncGenerator, Generator, Optional, Type, TypeVar
from instructor.function_calls import Mode
from instructor.patch import handle_response_model, process_response
from litellm import ModelResponse, acompletion, completion
from pydantic import BaseModel, Validatio... | 862 |
litellm.telemetry = False
litellm.add_function_to_prompt = True # add function to prompt for non openai models
litellm.drop_params = True # drop params if unsupported by provider
litellm.suppress_debug_info = True
T = TypeVar("T", bound=BaseModel)
|
litellm.telemetry = False
litellm.add_function_to_prompt = True # add function to prompt for non openai models
litellm.drop_params = True # drop params if unsupported by provider
litellm.suppress_debug_info = True
T = TypeVar("T", bound=BaseModel)
| class ChatLLMSession(ChatSession): | 1 | 2023-11-10 06:01:25+00:00 | 2k |
DIAGNijmegen/HoVer-UNet | models/HoVerNet/post_proc.py | [
{
"identifier": "remove_small_objects",
"path": "models/HoVerNet/utils.py",
"snippet": "def remove_small_objects(pred, min_size=64, connectivity=1):\n \"\"\"Remove connected components smaller than the specified size.\n\n This function is taken from skimage.morphology.remove_small_objects, but the... | import warnings
import cv2
import numpy as np
from scipy.ndimage import measurements
from scipy.ndimage.morphology import (
binary_fill_holes,
)
from skimage.segmentation import watershed
from models.HoVerNet.utils import remove_small_objects, get_bounding_box | 1,523 |
def noop(*args, **kargs):
pass
warnings.warn = noop
####
def __proc_np_hv(pred):
"""Process Nuclei Prediction with XY Coordinate Map.
Args:
pred: prediction output, assuming
channel 0 contain probability map of nuclei
channel 1 containing the regressed X-map
... |
def noop(*args, **kargs):
pass
warnings.warn = noop
####
def __proc_np_hv(pred):
"""Process Nuclei Prediction with XY Coordinate Map.
Args:
pred: prediction output, assuming
channel 0 contain probability map of nuclei
channel 1 containing the regressed X-map
... | rmin, rmax, cmin, cmax = get_bounding_box(inst_map) | 1 | 2023-11-10 09:37:29+00:00 | 2k |
fofr/cog-sdxl-lcm-multi-controlnet-lora | controlnet.py | [
{
"identifier": "ControlNetPreprocessor",
"path": "controlnet_preprocess.py",
"snippet": "class ControlNetPreprocessor:\n ANNOTATOR_NAMES = [\n \"none\",\n \"edge_canny\",\n \"depth_leres\",\n \"depth_midas\",\n \"soft_edge_pidi\",\n \"soft_edge_hed\",\n ... | import torch
from diffusers import ControlNetModel
from controlnet_preprocess import ControlNetPreprocessor
from weights_downloader import WeightsDownloader | 917 |
CONTROLNET_MODEL_CACHE = "./controlnet-cache"
CONTROLNET_URL = "https://weights.replicate.delivery/default/controlnet/sdxl-cn-canny-depth-softe-pose-qr.tar"
class ControlNet:
CONTROLNET_MODELS = [
"none",
"edge_canny",
"illusion",
"depth_leres",
"depth_midas",
"sof... |
CONTROLNET_MODEL_CACHE = "./controlnet-cache"
CONTROLNET_URL = "https://weights.replicate.delivery/default/controlnet/sdxl-cn-canny-depth-softe-pose-qr.tar"
class ControlNet:
CONTROLNET_MODELS = [
"none",
"edge_canny",
"illusion",
"depth_leres",
"depth_midas",
"sof... | WeightsDownloader.download_if_not_exists(CONTROLNET_URL, CONTROLNET_MODEL_CACHE) | 1 | 2023-11-16 11:11:27+00:00 | 2k |
joyn-gg/discord.http | discord_http/view.py | [
{
"identifier": "PartialEmoji",
"path": "discord_http/emoji.py",
"snippet": "class PartialEmoji:\n def __init__(self, emoji: str):\n self._original_name: str = emoji\n\n self.id: Optional[int] = None\n self.animated: bool = False\n self.discord_emoji: bool = False\n\n ... | import asyncio
import inspect
import logging
import secrets
import time
from typing import Union, Optional, TYPE_CHECKING, Callable
from .emoji import PartialEmoji
from .enums import ButtonStyles, ComponentType, TextStyles, ChannelType
from . import Snowflake
from .channel import BaseChannel
from .context i... | 1,198 |
if TYPE_CHECKING:
_log = logging.getLogger(__name__)
__all__ = (
"Button",
"ChannelSelect",
"Item",
"Link",
"MentionableSelect",
"Modal",
"RoleSelect",
"Select",
"UserSelect",
"View",
)
def _garbage_id() -> str:
""" `str`: Returns a random ID to satisfy Discord API """... |
if TYPE_CHECKING:
_log = logging.getLogger(__name__)
__all__ = (
"Button",
"ChannelSelect",
"Item",
"Link",
"MentionableSelect",
"Modal",
"RoleSelect",
"Select",
"UserSelect",
"View",
)
def _garbage_id() -> str:
""" `str`: Returns a random ID to satisfy Discord API """... | super().__init__(type=int(ComponentType.button), row=row) | 2 | 2023-11-14 12:50:42+00:00 | 2k |
catid/aiwebcam2 | app.py | [
{
"identifier": "logger",
"path": "utils.py",
"snippet": "class ColoredFormatter(logging.Formatter):\n def format(self, record):\ndef setup_colored_logging(level=logging.INFO):"
},
{
"identifier": "ASRServiceRunner",
"path": "service_asr.py",
"snippet": "class ASRServiceRunner:\n d... | from utils import logger
from service_asr import ASRServiceRunner
from service_llm import LLMServiceRunner
from service_tts import TTSServiceRunner
from aiortc import RTCIceCandidate, RTCSessionDescription, RTCPeerConnection
from aiortc.mediastreams import AudioStreamTrack, VideoStreamTrack, MediaStreamError, MediaStre... | 1,591 | # Logging
sio = socketio.AsyncServer(cors_allowed_origins='*')
# Background services
asr_runner = ASRServiceRunner()
llm_runner = LLMServiceRunner()
# WebRTC peer listening for a single browser to connect
# We run each WebRTC peer in a separate process to avoid stalls in playback
# WebRTC Connection
class... | # Logging
sio = socketio.AsyncServer(cors_allowed_origins='*')
# Background services
asr_runner = ASRServiceRunner()
llm_runner = LLMServiceRunner()
# WebRTC peer listening for a single browser to connect
# We run each WebRTC peer in a separate process to avoid stalls in playback
# WebRTC Connection
class... | logger.info(f"self.pc.connectionState = {self.pc.connectionState}") | 0 | 2023-11-16 03:37:47+00:00 | 2k |
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... | 1,590 |
# 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... | heuristic_model = CARTDecisionTree(max_depth=depth) | 1 | 2023-11-18 14:28:12+00:00 | 2k |
openclimatefix/Open-Source-Quartz-Solar-Forecast | tests/eval/test_pv.py | [
{
"identifier": "get_pv_truth",
"path": "quartz_solar_forecast/eval/pv.py",
"snippet": "def get_pv_truth(testset: pd.DataFrame):\n\n print('Loading PV data')\n\n # download from hugginface or load from cache\n cache_dir = \"data/pv\"\n metadata_file = f\"{cache_dir}/pv.netcdf\"\n if not o... | from quartz_solar_forecast.eval.pv import get_pv_truth, get_pv_metadata
import pandas as pd | 840 |
def test_get_pv_metadata():
test_set_df = pd.DataFrame(
[
{
"timestamp": pd.Timestamp("2021-01-26 01:15:00"),
"pv_id": 8215,
}
]
)
|
def test_get_pv_metadata():
test_set_df = pd.DataFrame(
[
{
"timestamp": pd.Timestamp("2021-01-26 01:15:00"),
"pv_id": 8215,
}
]
)
| metadata_df = get_pv_metadata(test_set_df) | 1 | 2023-11-16 07:37:42+00:00 | 2k |
newcastleuniversity/DISPEL | dispel/providers/generic/tasks/sbt_utt/sbt_func.py | [
{
"identifier": "MIN_MOTION_DUR",
"path": "dispel/providers/generic/tasks/sbt_utt/const.py",
"snippet": "MIN_MOTION_DUR = 1"
},
{
"identifier": "signal_duration",
"path": "dispel/signal/core.py",
"snippet": "def signal_duration(data: Union[pd.Series, pd.DataFrame]) -> float:\n \"\"\"G... | import numpy as np
import pandas as pd
from dispel.providers.generic.tasks.sbt_utt.const import MIN_MOTION_DUR
from dispel.signal.core import signal_duration
from dispel.signal.geometric import extract_ellipse_axes
from dispel.signal.vectorial import mean_norm_planar, resultant_norm_planar, rms_planar | 1,471 | """Functionality implemented in SBT.steps module."""
def label_bouts(data: pd.Series) -> pd.Series:
"""Label each valid and invalid chunk as a bout.
Parameters
----------
data
A Series that contains one column including the flag continuous
signal
Returns
-------
Series
... | """Functionality implemented in SBT.steps module."""
def label_bouts(data: pd.Series) -> pd.Series:
"""Label each valid and invalid chunk as a bout.
Parameters
----------
data
A Series that contains one column including the flag continuous
signal
Returns
-------
Series
... | if signal_duration(bout) < MIN_MOTION_DUR: | 0 | 2023-11-14 10:06:46+00:00 | 2k |
runDMCA/home-assistant-mazda | custom_components/mazda/pymazda/sensordata/system_info.py | [
{
"identifier": "AndroidBuilds",
"path": "custom_components/mazda/pymazda/sensordata/android_builds.py",
"snippet": "class AndroidBuilds: # noqa: D101\n def __init__(self): # noqa: D107\n self.builds = None\n\n def get_builds(self): # noqa: D102\n if self.builds is None:\n ... | import random # noqa: D100
import secrets
from .android_builds import AndroidBuilds
from .sensor_data_util import percent_encode, sum_char_codes | 1,250 |
SCREEN_SIZES = [[1280, 720], [1920, 1080], [2560, 1440]]
ANDROID_VERSION_TO_SDK_VERSION = {
"11": 30,
"10": 29,
"9": 28,
"8.1.0": 27,
"8.0.0": 26,
"7.1": 25,
"7.0": 24,
}
class SystemInfo: # noqa: D101
def __init__(self): # noqa: D107
self.android_builds = AndroidBuilds()
... |
SCREEN_SIZES = [[1280, 720], [1920, 1080], [2560, 1440]]
ANDROID_VERSION_TO_SDK_VERSION = {
"11": 30,
"10": 29,
"9": 28,
"8.1.0": 27,
"8.0.0": 26,
"7.1": 25,
"7.0": 24,
}
class SystemInfo: # noqa: D101
def __init__(self): # noqa: D107
self.android_builds = AndroidBuilds()
... | return sum_char_codes(self.to_string()) | 2 | 2023-11-14 01:42:43+00:00 | 2k |
uysalserkan/url-shorter | app.py | [
{
"identifier": "URLS",
"path": "models/urls.py",
"snippet": "class URLS(SQLModel, table=True):\n id: Optional[int] = Field(default=None, primary_key=True)\n long_url: str = Field(nullable=False)\n generated_url: str = Field(nullable=True)\n created_date: int = datetime.utcnow().timestamp()\... | import multiprocessing
import time
from datetime import datetime, timedelta
from fastapi import FastAPI, Response, UploadFile, Request
from fastapi.responses import RedirectResponse, JSONResponse
from sqlmodel import select
from prometheus_fastapi_instrumentator import Instrumentator
from models.urls import URLS
from c... | 1,116 | """URL Shorter API."""
app = FastAPI(
title="URL Shorter Service",
description="Short your long url links.",
)
Instrumentator().instrument(app).expose(app)
| """URL Shorter API."""
app = FastAPI(
title="URL Shorter Service",
description="Short your long url links.",
)
Instrumentator().instrument(app).expose(app)
| DB_engine = DatabaseEngine() | 3 | 2023-11-16 10:43:45+00:00 | 2k |
logicalroot/gpt-4v-demos | pages/3_📋_Quality_Control.py | [
{
"identifier": "show_code",
"path": "utils.py",
"snippet": "def show_code(code):\n \"\"\"Showing the code of the demo.\"\"\"\n show_code = st.sidebar.checkbox(\"Show code\", False)\n if show_code:\n st.markdown(\"## Code\")\n for function in code:\n # Showing the code ... | import streamlit as st
import base64
import requests
import json
import components
from utils import show_code
from parsers import extract_json | 762 |
def submit(image, api_key, issue_attributes):
headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
base64_image = base64.b64encode(image).decode("utf-8")
payload = {
"model": "gpt-4-vision-preview",
"messages": [
{
"role": "syst... |
def submit(image, api_key, issue_attributes):
headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
base64_image = base64.b64encode(image).decode("utf-8")
payload = {
"model": "gpt-4-vision-preview",
"messages": [
{
"role": "syst... | text = extract_json(response.json()["choices"][0]["message"]["content"]) | 1 | 2023-11-14 21:29:43+00:00 | 2k |
intel/llm-on-ray | inference/api_server_openai.py | [
{
"identifier": "RouterQueryClient",
"path": "inference/api_openai_backend/query_client.py",
"snippet": "class RouterQueryClient():\n def __init__(self, serve_deployments):\n self.serve_deployments = serve_deployments\n\n async def query(self, model: str, prompt: Prompt, request_id: str):\n... | import os
from ray import serve
from inference.api_openai_backend.query_client import RouterQueryClient
from inference.api_openai_backend.router_app import Router, router_app | 1,317 | #
# Copyright 2023 The LLM-on-Ray 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 t... | #
# Copyright 2023 The LLM-on-Ray 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 t... | )(serve.ingress(router_app)(Router)) | 1 | 2023-11-13 05:08:21+00:00 | 2k |
carlhampuswall/smartknob_ha | custom_components/smartknob/store.py | [
{
"identifier": "DATA_REGISTRY",
"path": "custom_components/smartknob/const.py",
"snippet": "DATA_REGISTRY = f\"{DOMAIN}_storage\""
},
{
"identifier": "SAVE_DELAY",
"path": "custom_components/smartknob/const.py",
"snippet": "SAVE_DELAY = 10"
},
{
"identifier": "STORAGE_KEY",
... | from collections import OrderedDict
from collections.abc import MutableMapping
from typing import Dict, cast
from homeassistant.core import HomeAssistant, callback
from homeassistant.helpers.storage import Store
from homeassistant.loader import bind_hass
from .const import DATA_REGISTRY, SAVE_DELAY, STORAGE_KEY
from .l... | 758 |
@attr.s(slots=True, frozen=True)
class AppEntry:
"""App storage entry."""
app_id = attr.ib(type=str, default=None)
app_slug_id = attr.ib(type=str, default=None)
entity_id = attr.ib(type=str, default=None)
friendly_name = attr.ib(type=str, default=None)
@attr.s(slots=True, frozen=True)
class ... |
@attr.s(slots=True, frozen=True)
class AppEntry:
"""App storage entry."""
app_id = attr.ib(type=str, default=None)
app_slug_id = attr.ib(type=str, default=None)
entity_id = attr.ib(type=str, default=None)
friendly_name = attr.ib(type=str, default=None)
@attr.s(slots=True, frozen=True)
class ... | _LOGGER.warning("Removing Smartknob configuration data!") | 3 | 2023-11-13 16:37:20+00:00 | 2k |
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 | 1,253 | '''
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:
... | apis_reviewer = [Auto_API.instantiate_api(config_api['api_type'], config_api) for config_api in reviewers] | 1 | 2023-11-16 18:40:23+00:00 | 2k |
tahaafarooq/werkzeug-hash-cracker | cracker.py | [
{
"identifier": "SimplifierSingle",
"path": "simplifiers/simplifier.py",
"snippet": "class SimplifierSingle(object):\n def __init__(self, hasho, wordlist):\n self.hasho = hasho\n self.wordlist = wordlist\n\n def crack_single_hash(self):\n with open(self.wordlist, \"r\", encodi... | import argparse
from simplifiers.simplifier import SimplifierSingle, SimplifierFile | 778 |
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Werkzeug Security Hash Cracker :: @tahaafarooq")
parser.add_argument('--single', nargs=2, metavar=('hash', 'wordlist'), help='Crack a single hash string')
parser.add_argument('--file', nargs=2, metavar=('hashfile', 'wordlist'), help... |
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Werkzeug Security Hash Cracker :: @tahaafarooq")
parser.add_argument('--single', nargs=2, metavar=('hash', 'wordlist'), help='Crack a single hash string')
parser.add_argument('--file', nargs=2, metavar=('hashfile', 'wordlist'), help... | simple_crack = SimplifierFile(hash_file, wordlist_file) | 1 | 2023-11-10 01:29:15+00:00 | 2k |
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... | 1,599 | #!/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... | Base.metadata.drop_all(self.__engine) | 0 | 2023-11-17 07:59:13+00:00 | 2k |
believethehype/nostrdvm | examples/ollama_dvm/main.py | [
{
"identifier": "TextGenerationLLMLite",
"path": "nostr_dvm/tasks/textgeneration_llmlite.py",
"snippet": "class TextGenerationLLMLite(DVMTaskInterface):\n KIND: int = EventDefinitions.KIND_NIP90_GENERATE_TEXT\n TASK: str = \"text-to-text\"\n FIX_COST: float = 0\n dependencies = [(\"nostr-dvm... | import json
import dotenv
from pathlib import Path
from nostr_dvm.tasks.textgeneration_llmlite import TextGenerationLLMLite
from nostr_dvm.utils.admin_utils import AdminConfig
from nostr_dvm.utils.dvmconfig import build_default_config
from nostr_dvm.utils.nip89_utils import NIP89Config, check_and_set_d_tag | 1,406 |
def main():
identifier = "llama2"
name = "Ollama"
|
def main():
identifier = "llama2"
name = "Ollama"
| dvm_config = build_default_config(identifier) | 2 | 2023-11-17 18:32:56+00:00 | 2k |
zouXH-god/meme_web | meme_generator/manager.py | [
{
"identifier": "meme_config",
"path": "meme_generator/config.py",
"snippet": "class MemeConfig(BaseModel):\nclass ResourceConfig(BaseModel):\nclass GifConfig(BaseModel):\nclass TranslatorConfig(BaseModel):\nclass ServerConfig(BaseModel):\nclass LogConfig(BaseModel):\nclass Config(BaseModel, extra=Extra... | import importlib
import importlib.util
import pkgutil
from pathlib import Path
from typing import Dict, List, Optional, Union
from .config import meme_config
from .exception import NoSuchMeme
from .log import logger
from .meme import Meme, MemeArgsType, MemeFunction, MemeParamsType | 942 |
_memes: Dict[str, Meme] = {}
def path_to_module_name(path: Path) -> str:
rel_path = path.resolve().relative_to(Path.cwd().resolve())
if rel_path.stem == "__init__":
return ".".join(rel_path.parts[:-1])
else:
return ".".join(rel_path.parts[:-1] + (rel_path.stem,))
def load_meme(module_p... |
_memes: Dict[str, Meme] = {}
def path_to_module_name(path: Path) -> str:
rel_path = path.resolve().relative_to(Path.cwd().resolve())
if rel_path.stem == "__init__":
return ".".join(rel_path.parts[:-1])
else:
return ".".join(rel_path.parts[:-1] + (rel_path.stem,))
def load_meme(module_p... | function: MemeFunction, | 3 | 2023-11-12 12:31:53+00:00 | 2k |
embrake/Aquilify | aquilify/middlewares/dispatcher.py | [
{
"identifier": "ASGIApp",
"path": "aquilify/types.py",
"snippet": "T = typing.TypeVar(\"T\")"
},
{
"identifier": "JsonResponse",
"path": "aquilify/responses.py",
"snippet": "class JsonResponse(BaseResponse):\n def __init__(\n self,\n content: Union[Dict, Callable, None]... | import logging
from typing import Awaitable, Callable, Dict, Optional, Union
from ..types import ASGIApp, Receive, Scope, Send
from ..responses import JsonResponse | 1,306 | class Dispatcher:
"""
Dispatches incoming requests to different mounted ASGI apps based on the URL path.
Usage:
```python
# Create the main application
main_app = Aquilify()
# Create instances of the mounted apps
app1 = Aquilify()
app2 = Aquilify()
# Create the Dispatcher inst... |
class Dispatcher:
"""
Dispatches incoming requests to different mounted ASGI apps based on the URL path.
Usage:
```python
# Create the main application
main_app = Aquilify()
# Create instances of the mounted apps
app1 = Aquilify()
app2 = Aquilify()
# Create the Dispatcher ins... | async def dispatch(self, scope: Scope, receive: Receive, send: Send) -> None: | 0 | 2023-11-16 08:26:02+00:00 | 2k |
Viicos/django-autotyping | src/django_autotyping/app_settings.py | [
{
"identifier": "Self",
"path": "src/django_autotyping/_compat.py",
"snippet": "def is_relative_to(path: Path, other: Path) -> bool:"
},
{
"identifier": "AutotypingSettingsDict",
"path": "src/django_autotyping/typing.py",
"snippet": "class AutotypingSettingsDict(TypedDict, total=False):\... | from copy import deepcopy
from dataclasses import dataclass, field
from pathlib import Path
from django.conf import LazySettings
from ._compat import Self
from .typing import AutotypingSettingsDict, RulesT | 1,178 | from __future__ import annotations
@dataclass
class CodeGenerationSettings:
"""Configuration for adding type annotations to Django user code."""
PROJECT_DIR: Path | None = None
"""The directory of the project, where code modifications should be applied."""
DIFF: bool = False
"""Show changes t... | from __future__ import annotations
@dataclass
class CodeGenerationSettings:
"""Configuration for adding type annotations to Django user code."""
PROJECT_DIR: Path | None = None
"""The directory of the project, where code modifications should be applied."""
DIFF: bool = False
"""Show changes t... | def from_django_settings(cls, settings: LazySettings) -> Self: | 0 | 2023-11-11 20:42:05+00:00 | 2k |
IBM/oper8 | oper8/cmd/setup_vcs_cmd.py | [
{
"identifier": "DEFAULT_DEST",
"path": "oper8/setup_vcs.py",
"snippet": "DEFAULT_DEST = \"oper8_vcs\""
},
{
"identifier": "DEFAULT_TAG_EXPR",
"path": "oper8/setup_vcs.py",
"snippet": "DEFAULT_TAG_EXPR = r\"[0-9]+\\.[0-9]+\\.[0-9]+\""
},
{
"identifier": "setup_vcs",
"path": "... | import argparse
import alog
from ..setup_vcs import DEFAULT_DEST, DEFAULT_TAG_EXPR, setup_vcs
from .base import CmdBase | 750 | """
CLI command for setting up a VCS version repo
"""
# Standard
# First Party
# Local
log = alog.use_channel("CMD-VCS")
class SetupVCSCmd(CmdBase):
__doc__ = __doc__
def add_subparser(
self,
subparsers: argparse._SubParsersAction,
) -> argparse.ArgumentParser:
"""Add the subpa... | """
CLI command for setting up a VCS version repo
"""
# Standard
# First Party
# Local
log = alog.use_channel("CMD-VCS")
class SetupVCSCmd(CmdBase):
__doc__ = __doc__
def add_subparser(
self,
subparsers: argparse._SubParsersAction,
) -> argparse.ArgumentParser:
"""Add the subpa... | default=DEFAULT_DEST, | 0 | 2023-11-15 16:43:29+00:00 | 2k |
ariebovenberg/whenever | tests/test_naive_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 datetime import datetime as py_datetime
from datetime import timedelta, timezone
from hypothesis import given
from hypothesis.strategies import text
from whenever import InvalidFormat, NaiveDateTime
from .common import (
AlwaysEqual,
AlwaysLarger,
AlwaysSmalle... | 1,181 |
def test_minimal():
d = NaiveDateTime(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
assert (
NaiveDateTime(2020, 8, 15, 12)
==... |
def test_minimal():
d = NaiveDateTime(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
assert (
NaiveDateTime(2020, 8, 15, 12)
==... | assert d == AlwaysEqual() | 0 | 2023-11-10 21:08:49+00:00 | 2k |
DataWizual/Raycasting | drawing.py | [
{
"identifier": "ray_casting",
"path": "ray_casting.py",
"snippet": "def ray_casting(sc, player_pos, player_angle):\r\n ox, oy = player_pos\r\n xm, ym = mapping(ox, oy)\r\n cur_angle = player_angle - HALF_FOV\r\n for ray in range(NUM_RAYS):\r\n sin_a = math.sin(cur_angle)\r\n c... | import pygame
from settings import *
from ray_casting import ray_casting
from map import mini_map
| 650 |
class Drawing:
def __init__(self, sc, sc_map):
self.sc = sc
self.sc_map = sc_map
self.font = pygame.font.SysFont('Arial', 36, bold=True)
def background(self):
pygame.draw.rect(self.sc, SKYBLUE, (0, 0, WIDTH, HALF_HEIGHT))
pygame.draw.rect(self.sc, DARKGREY, (0,... |
class Drawing:
def __init__(self, sc, sc_map):
self.sc = sc
self.sc_map = sc_map
self.font = pygame.font.SysFont('Arial', 36, bold=True)
def background(self):
pygame.draw.rect(self.sc, SKYBLUE, (0, 0, WIDTH, HALF_HEIGHT))
pygame.draw.rect(self.sc, DARKGREY, (0,... | def mini_map(self, player):
| 1 | 2023-11-15 12:18:25+00:00 | 2k |
CV-Reimplementation/Ucolor-Reimplementation | train.py | [
{
"identifier": "Config",
"path": "config/config.py",
"snippet": "class Config(object):\n r\"\"\"\n A collection of all the required configuration parameters. This class is a nested dict-like\n structure, with nested keys accessible as attributes. It contains sensible default values for\n al... | import warnings
import torch.optim as optim
from accelerate import Accelerator
from pytorch_msssim import SSIM
from torch.utils.data import DataLoader
from torchmetrics.functional import peak_signal_noise_ratio, structural_similarity_index_measure
from tqdm import tqdm
from config import Config
from data import get_tra... | 1,388 |
warnings.filterwarnings('ignore')
opt = Config('config.yml')
seed_everything(opt.OPTIM.SEED)
def train():
# Accelerate
accelerator = Accelerator(log_with='wandb') if opt.OPTIM.WANDB else Accelerator()
device = accelerator.device
config = {
"dataset": opt.TRAINING.TRAIN_DIR
}
accele... |
warnings.filterwarnings('ignore')
opt = Config('config.yml')
seed_everything(opt.OPTIM.SEED)
def train():
# Accelerate
accelerator = Accelerator(log_with='wandb') if opt.OPTIM.WANDB else Accelerator()
device = accelerator.device
config = {
"dataset": opt.TRAINING.TRAIN_DIR
}
accele... | val_dataset = get_validation_data(val_dir, opt.MODEL.INPUT, opt.MODEL.TARGET, {'w': opt.TRAINING.PS_W, 'h': opt.TRAINING.PS_H, 'ori': opt.TRAINING.ORI}) | 2 | 2023-11-14 05:40:54+00:00 | 2k |
ottuco/multi-api-mocker | multi_api_mocker/contrib/pytest_plugin.py | [
{
"identifier": "group_by_url",
"path": "multi_api_mocker/utils.py",
"snippet": "def group_by_url(api_mocks: List[MockAPIResponse]) -> List[MockConfiguration]:\n \"\"\"\n Organizes a list of MockAPIResponse objects by their URL and method, grouping\n them into lists of responses for each endpoi... | import pytest
from requests_mock import Mocker
from ..utils import group_by_url, MockSet | 1,383 |
@pytest.fixture(scope="function")
def setup_api_mocks(requests_mock: Mocker, request) -> MockSet:
"""
A pytest fixture for configuring mock API responses in a test environment.
It takes subclasses of MockAPIResponse, each representing a unique API call
configuration. These subclasses facilitate the c... |
@pytest.fixture(scope="function")
def setup_api_mocks(requests_mock: Mocker, request) -> MockSet:
"""
A pytest fixture for configuring mock API responses in a test environment.
It takes subclasses of MockAPIResponse, each representing a unique API call
configuration. These subclasses facilitate the c... | api_mocks_configurations = group_by_url(request.param) | 0 | 2023-11-12 08:01:06+00:00 | 2k |
Jisencc/yolov5_dual_weighting | utils/segment/augmentations.py | [
{
"identifier": "box_candidates",
"path": "utils/augmentations.py",
"snippet": "def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)\n # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio\n ... | import math
import random
import cv2
import numpy as np
from ..augmentations import box_candidates
from ..general import resample_segments, segment2box | 1,500 | # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""
Image augmentation functions
"""
def mixup(im, labels, segments, im2, labels2, segments2):
# Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
im = (im * r + im2 * (1 - r)).asty... | # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""
Image augmentation functions
"""
def mixup(im, labels, segments, im2, labels2, segments2):
# Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
im = (im * r + im2 * (1 - r)).asty... | segments = resample_segments(segments) # upsample | 1 | 2023-11-12 13:28:26+00:00 | 2k |
Subsets and Splits
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that have consistent code formatting levels across multiple scales (2k, 4k, 8k, 12k) and reveals the structured formatting patterns within these repositories.
SQL Console for tianyang/repobench_python_v1.1
Compares cross-file and in-file code structure patterns across different complexity levels, revealing how file organization strategies vary with code size and potentially informing better code architecture decisions.
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that have complete performance data across all seven code complexity levels, revealing consistent benchmarking patterns across different code sizes.
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that contain all 7 distinct quality levels (2k through 32k), revealing complete datasets that might be useful for comprehensive analysis.