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 |
|---|---|---|---|---|---|---|---|---|---|---|
generative-skill-chaining/gsc-code | generative_skill_chaining/envs/pybullet/table/objects.py | [
{
"identifier": "body",
"path": "generative_skill_chaining/envs/pybullet/sim/body.py",
"snippet": "class Body:\nclass Link:\n def aabb(self) -> np.ndarray:\n def pose(self) -> math.Pose:\n def set_pose(self, pose: math.Pose) -> None:\n def twist(self) -> np.ndarray:\n def dof(self) -> int... | import dataclasses
import itertools
import random
import numpy as np
import pybullet as p
import spatialdyn as dyn
from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Type, Union
from ctrlutils import eigen
from generative_skill_chaining.envs.pybullet.sim import body, math, shapes
from generative_s... | 2,762 | self.body_id, link_id, 0, 0, physicsClientId=self.physics_id
)
def enable_collisions(self) -> None:
for link_id in range(self.dof):
p.setCollisionFilterGroupMask(
self.body_id, link_id, 1, 0xFF, physicsClientId=self.physics_id
)
@prop... |
OBJECT_HIERARCHY = ["rack", "table", "hook", "box"]
def compute_bbox_vertices(
bbox: np.ndarray, pose: Optional[math.Pose] = None, project_2d: bool = False
) -> np.ndarray:
"""Computes the vertices of the given 3D bounding box.
Args:
bbox: Array of shape [2, 3] (min/max, x/y/z).
pose:... | def shapes(self) -> Sequence[shapes.Shape]: | 2 | 2023-10-16 00:22:40+00:00 | 4k |
ChiyuSONG/dynamics-of-instruction-tuning | evaluate/pred.py | [
{
"identifier": "Assistant",
"path": "inference.py",
"snippet": "class Assistant:\n def __init__(self, model_name_or_path):\n tokenizer = LlamaTokenizer.from_pretrained(model_name_or_path)\n tokenizer.padding_side = \"left\"\n tokenizer.user_token_id, tokenizer.assistant_token_id... | import os
import sys
import torch
import json
import jsonlines
import copy
from pathlib import Path
from argparse import ArgumentParser
from tqdm import tqdm
from inference import Assistant
from train_sft import IGNORE_INDEX, DataCollatorForSupervisedDataset | 1,683 | sys.path.append(".")
def process(example, tokenizer):
processed = []
user = tokenizer.user_token_id
assistant = tokenizer.assistant_token_id
eot = tokenizer.eot_token_id
def tokenize(s):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(s.strip()))
for choice in example["cho... | sys.path.append(".")
def process(example, tokenizer):
processed = []
user = tokenizer.user_token_id
assistant = tokenizer.assistant_token_id
eot = tokenizer.eot_token_id
def tokenize(s):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(s.strip()))
for choice in example["cho... | data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer, pad_to_multiple_of=8) | 2 | 2023-10-17 07:41:58+00:00 | 4k |
akashgreninja/GreSec | backend/venv/lib/python3.10/site-packages/anyio/_core/_synchronization.py | [
{
"identifier": "cancel_shielded_checkpoint",
"path": "backend/venv/lib/python3.10/site-packages/anyio/lowlevel.py",
"snippet": "async def cancel_shielded_checkpoint() -> None:\n \"\"\"\n Allow the scheduler to switch to another task but without checking for cancellation.\n\n Equivalent to (but... | from collections import deque
from dataclasses import dataclass
from types import TracebackType
from warnings import warn
from ..lowlevel import cancel_shielded_checkpoint, checkpoint, checkpoint_if_cancelled
from ._compat import DeprecatedAwaitable
from ._eventloop import get_asynclib
from ._exceptions import BusyReso... | 3,299 |
raise
assert self._owner_task == task
else:
try:
await cancel_shielded_checkpoint()
except BaseException:
self.release()
raise
def acquire_nowait(self) -> None:
"""
Acquire the lock, withou... | from __future__ import annotations
@dataclass(frozen=True)
class EventStatistics:
"""
:ivar int tasks_waiting: number of tasks waiting on :meth:`~.Event.wait`
"""
tasks_waiting: int
@dataclass(frozen=True)
class CapacityLimiterStatistics:
"""
:ivar int borrowed_tokens: number of tokens cu... | await checkpoint() | 1 | 2023-10-23 18:09:28+00:00 | 4k |
marmotlab/Context_Aware_Navigation | runner.py | [
{
"identifier": "PolicyNet",
"path": "model.py",
"snippet": "class PolicyNet(nn.Module):\r\n def __init__(self, input_dim, embedding_dim):\r\n super(PolicyNet, self).__init__()\r\n self.initial_embedding = nn.Linear(input_dim, embedding_dim) # layer for non-end position\r\n self.... | import torch
import ray
from model import PolicyNet, QNet
from worker import Worker
from parameter import *
| 3,250 |
class Runner(object):
def __init__(self, meta_agent_id):
self.meta_agent_id = meta_agent_id
self.device = torch.device('cuda') if USE_GPU else torch.device('cpu')
|
class Runner(object):
def __init__(self, meta_agent_id):
self.meta_agent_id = meta_agent_id
self.device = torch.device('cuda') if USE_GPU else torch.device('cpu')
| self.local_network = PolicyNet(INPUT_DIM, EMBEDDING_DIM)
| 0 | 2023-10-17 04:32:42+00:00 | 4k |
adarshxs/TokenTally | main.py | [
{
"identifier": "sidebar",
"path": "sidebar.py",
"snippet": "def sidebar():\n with st.sidebar:\n st.image(\"cutie.png\", use_column_width=True)\n st.title(\"About Token Tally\")\n st.info(\"Select your desired base model, parameters, and configuration to get an estimate of the re... | from sidebar import sidebar
from overview import display_overview
from tools.llm_cost_calculator import display_llm_cost_tool
from tools.transformer_memory_calculator import display_transformer_memory_tool
from tools.llm_recomender import display_llm_recomender_tool | 3,256 |
def main():
selected_product = sidebar()
if selected_product == "Overview":
display_overview()
elif selected_product == "LLM Cost Tool":
display_llm_cost_tool()
elif selected_product == "Transformer Memory Tool":
display_transformer_memory_tool()
elif selected_prod... |
def main():
selected_product = sidebar()
if selected_product == "Overview":
display_overview()
elif selected_product == "LLM Cost Tool":
display_llm_cost_tool()
elif selected_product == "Transformer Memory Tool":
display_transformer_memory_tool()
elif selected_prod... | display_llm_recomender_tool() | 4 | 2023-10-18 06:16:47+00:00 | 4k |
WestlakeIntelligentRobotics/ConsensusLLM-code | modules/experiment/scalar_debate.py | [
{
"identifier": "Template",
"path": "modules/experiment/template.py",
"snippet": "class Template(ABC):\n \"\"\"\n A template class for designing and running experiments with multiple agents\n and rounds.\n\n This abstract class defines a template for designing experiments where \n multipl... | import numpy as np
from concurrent.futures import ThreadPoolExecutor, as_completed
from .template import Template
from ..llm.agent import Agent, GPT
from ..llm.api_key import api_keys
from ..llm.role import names
from ..prompt.scenario import agent_role, game_description, round_description
from ..prompt.form import age... | 2,926 | """
MIT License
Copyright (c) [2023] [Intelligent Unmanned Systems Laboratory at
Westlake University]
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limit... | """
MIT License
Copyright (c) [2023] [Intelligent Unmanned Systems Laboratory at
Westlake University]
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limit... | self._init_input = game_description + "\n\n" + agent_output_form | 4 | 2023-10-20 07:58:07+00:00 | 4k |
LzVv123456/Contrastive-Prototypical-Prompt | train.py | [
{
"identifier": "ProtoDataset",
"path": "datasets/proto.py",
"snippet": "class ProtoDataset(Dataset):\n def __init__(self, args, prototypes, prototypes_var, classes):\n self.args = args\n self.prototypes = prototypes\n self.prototypes_var = prototypes_var\n self.classes = ... | import torch
import utils
import copy
import losses
import prototype as prot
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader, RandomSampler
from tqdm import tqdm
from datasets import ProtoDataset
from prompt import ProTLearner, PromptHead | 2,648 |
class Trainer(object):
def __init__(self, args, vit_model, train_dataset, gen_proto_dataset):
super().__init__()
self.args = args
self.vit_model = vit_model
self.dataset = train_dataset
self.gen_proto_dataset = gen_proto_dataset
self.proto = []
self.proto_v... |
class Trainer(object):
def __init__(self, args, vit_model, train_dataset, gen_proto_dataset):
super().__init__()
self.args = args
self.vit_model = vit_model
self.dataset = train_dataset
self.gen_proto_dataset = gen_proto_dataset
self.proto = []
self.proto_v... | self.prompter = ProTLearner(self.args, self.vit_model) | 1 | 2023-10-16 21:28:42+00:00 | 4k |
inngest/inngest-py | inngest/_internal/middleware_lib/log.py | [
{
"identifier": "client_lib",
"path": "inngest/_internal/client_lib.py",
"snippet": "_DEV_SERVER_EVENT_KEY = \"NO_EVENT_KEY_SET\"\nclass Inngest:\n def api_origin(self) -> str:\n def event_api_origin(self) -> str:\n def event_key(self) -> str | None:\n def signing_key(self) -> str | None:\n ... | from inngest._internal import client_lib, function, types
from .middleware import MiddlewareSync | 1,907 | from __future__ import annotations
class LoggerProxy:
_proxied_methods = (
"critical",
"debug",
"error",
"exception",
"fatal",
"info",
"log",
"warn",
"warning",
)
def __init__(self, logger: types.Logger) -> None:
self._is_e... | from __future__ import annotations
class LoggerProxy:
_proxied_methods = (
"critical",
"debug",
"error",
"exception",
"fatal",
"info",
"log",
"warn",
"warning",
)
def __init__(self, logger: types.Logger) -> None:
self._is_e... | ctx: function.Context, | 1 | 2023-10-19 01:02:30+00:00 | 4k |
f0uriest/quadax | quadax/romberg.py | [
{
"identifier": "QuadratureInfo",
"path": "quadax/utils.py",
"snippet": "class QuadratureInfo(NamedTuple):\n \"\"\"Information about quadrature.\n\n Parameters\n ----------\n err : float\n Estimate of the error in the quadrature result.\n neval : int\n Number of evaluations ... | import jax
import jax.numpy as jnp
from .utils import (
QuadratureInfo,
bounded_while_loop,
errorif,
map_interval,
tanhsinh_transform,
wrap_func,
) | 2,501 | """Romberg integration aka adaptive trapezoid with Richardson extrapolation."""
def romberg(
fun,
interval,
args=(),
full_output=False,
epsabs=1.4e-8,
epsrel=1.4e-8,
divmax=20,
norm=jnp.inf,
):
"""Romberg integration of a callable function or method.
Returns the integral of ... | """Romberg integration aka adaptive trapezoid with Richardson extrapolation."""
def romberg(
fun,
interval,
args=(),
full_output=False,
epsabs=1.4e-8,
epsrel=1.4e-8,
divmax=20,
norm=jnp.inf,
):
"""Romberg integration of a callable function or method.
Returns the integral of ... | vfunc = wrap_func(fun, args) | 5 | 2023-10-24 04:44:34+00:00 | 4k |
yixinliu233/SIGNET | main.py | [
{
"identifier": "GIN",
"path": "models.py",
"snippet": "class GIN(torch.nn.Module):\n def __init__(self, num_features, dim, num_gc_layers, pooling, readout):\n super(GIN, self).__init__()\n\n self.num_gc_layers = num_gc_layers\n self.pooling = pooling\n self.readout = read... | import torch
import numpy as np
import torch.nn as nn
import random
import warnings
from sklearn.metrics import roc_auc_score
from models import GIN, Explainer_GIN, HyperGNN, Explainer_MLP
from arguments import arg_parse
from get_data_loaders import get_data_loaders
from get_data_loaders_tuad import get_ad_split_TU, ge... | 3,513 |
warnings.filterwarnings("ignore")
explainable_datasets = ['mutag', 'mnist0', 'mnist1', 'bm_mn', 'bm_ms', 'bm_mt']
class SIGNET(nn.Module):
def __init__(self, input_dim, input_dim_edge, args, device):
super(SIGNET, self).__init__()
self.device = device
self.embedding_dim = args.hidden_... |
warnings.filterwarnings("ignore")
explainable_datasets = ['mutag', 'mnist0', 'mnist1', 'bm_mn', 'bm_ms', 'bm_mt']
class SIGNET(nn.Module):
def __init__(self, input_dim, input_dim_edge, args, device):
super(SIGNET, self).__init__()
self.device = device
self.embedding_dim = args.hidden_... | self.explainer = Explainer_MLP(input_dim, args.explainer_hidden_dim, args.explainer_layers) | 3 | 2023-10-18 04:23:35+00:00 | 4k |
smonsays/metax | metax/data/synthetic.py | [
{
"identifier": "DatasetGenerator",
"path": "metax/data/dataset/base.py",
"snippet": "class DatasetGenerator(abc.ABC):\n \"\"\"\n Abstract base class for generated datasets.\n\n Attributes:\n input_shape (tuple): The shape of the input data.\n output_dim (int): The dimensionality ... | import logging
import chex
import jax
from typing import List, Optional
from metax.data.dataset.base import DatasetGenerator
from .base import Dataloader, MetaDataset
from .dataset import family, sinusoid
from .utils import create_metadataset | 1,901 | """
Copyright (c) Simon Schug
All rights reserved.
MIT License
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, m... | """
Copyright (c) Simon Schug
All rights reserved.
MIT License
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, m... | data_generator = sinusoid.Sinusoid() | 4 | 2023-10-19 16:36:20+00:00 | 4k |
claws-lab/XLingEval | correctness/correctness_get_gpt_answer.py | [
{
"identifier": "load_HealthQA",
"path": "dataloader/load_data.py",
"snippet": "def load_HealthQA(split: str, language: str = 'English', task: str = \"consistency\"):\n print(f\"Loading HealthQA with split {split} and Language {language} ...\")\n\n if osp.basename(os.getcwd()) == \"XLingHealth_Dat... | import os
import time
import traceback
import sys
import pandas as pd
from os import path as osp
from dataloader.load_data import load_HealthQA, load_MedicationQA, load_LiveQA
from setup import project_setup, openai_setup
from utils_chatgpt import get_response
from const import set_constants
from argparse import Argume... | 2,794 |
llm_answer_list = []
for idx, row in data_df.iterrows():
retry = True
if idx%100 == 0:
print("Index: ", idx)
while retry:
try:
message_list=[{'role': 'system', 'content': f'You are Health GPT and You answer to heal... | sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
def get_eval(data_df, lang, open_ai_object_list, constants):
print("Lang: ", lang)
model_use_count = 0
model_list_index = 0
llm_answer_list = []
for idx, row in data_df.iterrows():
retry = True
if idx%100... | df = load_LiveQA(language=lang, task="correctness") | 2 | 2023-10-18 17:35:42+00:00 | 4k |
RF-Tar-Railt/satori-python | src/satori/model.py | [
{
"identifier": "Element",
"path": "src/satori/element.py",
"snippet": "class Element:\n @classmethod\n def from_raw(cls: Type[TE], raw: RawElement) -> TE:\n _fields = {f.name for f in fields(cls)}\n attrs = {k: v for k, v in raw.attrs.items() if k in _fields}\n result = cls(*... | from dataclasses import asdict, dataclass
from datetime import datetime
from enum import IntEnum
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar
from .element import Element, transform
from .parser import parse | 2,337 | data["user"] = User.parse(raw["user"])
if "joined_at" in raw:
data["joined_at"] = datetime.fromtimestamp(int(raw["joined_at"]) / 1000)
return cls(**data)
def dump(self):
res = {}
if self.user:
res["user"] = self.user.dump()
if self.nick or... |
class ChannelType(IntEnum):
TEXT = 0
VOICE = 1
CATEGORY = 2
DIRECT = 3
@dataclass
class Channel:
id: str
type: ChannelType
name: Optional[str] = None
parent_id: Optional[str] = None
@classmethod
def parse(cls, raw: dict):
data = raw.copy()
data["type"] = Cha... | "content": transform(parse(raw["content"])), | 1 | 2023-10-18 11:09:34+00:00 | 4k |
zju3dv/nr_in_a_room | tools/check_pose.py | [
{
"identifier": "create_sphere_lookat_poses",
"path": "data_gen/data_geo_utils.py",
"snippet": "def create_sphere_lookat_poses(\n radius: float, n_poses: int, n_circles: float, up_dir=\"y\", phi_begin=20, phi_end=90\n):\n deg2rad = np.pi / 180\n # y up\n phi_list = np.linspace(phi_begin * de... | import numpy as np
import argparse
import sys
import os
import open3d as o3d
import matplotlib.pyplot as plt
from data_gen.data_geo_utils import create_sphere_lookat_poses
from tools.O3dVisualizer import O3dVisualizer
from utils.util import * | 2,689 |
sys.path.append(os.getcwd()) # noqa
# from datasets.geo_utils import observe_angle_distance
# from render_tools.render_utils import *
def spheric_pose(theta, phi, radius, height):
trans_t = lambda t: np.array(
[
[1, 0, 0, 0],
[0, 1, 0, -0.9 * t],
[0, 0, 1, t],
... |
sys.path.append(os.getcwd()) # noqa
# from datasets.geo_utils import observe_angle_distance
# from render_tools.render_utils import *
def spheric_pose(theta, phi, radius, height):
trans_t = lambda t: np.array(
[
[1, 0, 0, 0],
[0, 1, 0, -0.9 * t],
[0, 0, 1, t],
... | visualizer = O3dVisualizer() | 1 | 2023-10-15 08:41:29+00:00 | 4k |
ShramanPramanick/VoLTA | Multimodal_Fine_Grained/maskrcnn_benchmark/modeling/rpn/fcos.py | [
{
"identifier": "make_fcos_loss_evaluator",
"path": "Multimodal_Fine_Grained/maskrcnn_benchmark/modeling/rpn/loss.py",
"snippet": "def make_fcos_loss_evaluator(cfg):\n loss_evaluator = FCOSLossComputation(cfg)\n return loss_evaluator"
},
{
"identifier": "make_center_anchor_generator",
... | import math
import torch
import torch.nn.functional as F
from torch import nn
from maskrcnn_benchmark.modeling import registry
from maskrcnn_benchmark.layers import Scale, DFConv2d
from .loss import make_fcos_loss_evaluator
from .anchor_generator import make_center_anchor_generator
from .inference import make_fcos_post... | 1,809 |
@registry.RPN_HEADS.register("FCOSHead")
class FCOSHead(torch.nn.Module):
def __init__(self, cfg):
super(FCOSHead, self).__init__()
# TODO: Implement the sigmoid version first.
num_classes = cfg.MODEL.FCOS.NUM_CLASSES - 1
in_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
use_... |
@registry.RPN_HEADS.register("FCOSHead")
class FCOSHead(torch.nn.Module):
def __init__(self, cfg):
super(FCOSHead, self).__init__()
# TODO: Implement the sigmoid version first.
num_classes = cfg.MODEL.FCOS.NUM_CLASSES - 1
in_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
use_... | self.anchor_generator = make_center_anchor_generator(cfg) | 1 | 2023-10-23 04:07:08+00:00 | 4k |
earthcube-lab/textnoisr | scripts/generate_figures.py | [
{
"identifier": "CharNoiseAugmenter",
"path": "textnoisr/noise.py",
"snippet": "class CharNoiseAugmenter:\n r\"\"\"Add noise into text according to a noise level measured between 0 and 1.\n\n It will add noise to a string by modifying each character\n according to a probability and a list o... | import argparse
import logging
import sys
import time
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
import pandas as pd
from pathlib import Path
from datasets import load_dataset
from evaluate import load
from nlpaug.augmenter.char import RandomCharAug
from textnoisr.noise import ... | 2,552 | """Generate figures for the documentation.
## Pre-requisites
You'll need to install the following packages:
```sh
pip install matplotlib nlpaug
```
If you don't have [Roboto](https://fonts.google.com/specimen/Roboto) installed, the
default font will be used.
## Usage
From the root of the project, run:
```sh
pyth... | """Generate figures for the documentation.
## Pre-requisites
You'll need to install the following packages:
```sh
pip install matplotlib nlpaug
```
If you don't have [Roboto](https://fonts.google.com/specimen/Roboto) installed, the
default font will be used.
## Usage
From the root of the project, run:
```sh
pyth... | return MAX_SWAP_LEVEL / 1.052 - 0.005 | 1 | 2023-10-18 19:28:34+00:00 | 4k |
WenzhengZhang/Seq2seqCoref | check_align.py | [
{
"identifier": "CorefAllMetrics",
"path": "metrics.py",
"snippet": "class CorefAllMetrics(object):\n \"\"\"\n Wrapper for coreference resolution metrics.\n \"\"\"\n\n @staticmethod\n def _get_mention_to_x(clusters: List[list]) -> dict:\n mention_to_x = {}\n for cluster in c... | import os
import json
import re
import argparse
from collections import defaultdict
from metrics import CorefAllMetrics
from typing import Dict
from data import get_document_predicts, SPECIAL_IDS, parse_short_target_tokens
from transformers import T5Tokenizer
from preprocess import SPEAKER_START, SPEAKER_END, MENTION_S... | 2,416 |
def load_data(data_dir, tokenizer):
def load_split(split):
max_len = 4096
data_path = os.path.join(
data_dir,
f'{split}.t5-small.english.{max_len}.jsonlines')
samples = []
doc_labels = {}
with open(data_path, 'r') as f:
for line in f:
... |
def load_data(data_dir, tokenizer):
def load_split(split):
max_len = 4096
data_path = os.path.join(
data_dir,
f'{split}.t5-small.english.{max_len}.jsonlines')
samples = []
doc_labels = {}
with open(data_path, 'r') as f:
for line in f:
... | metrics = CorefAllMetrics().get_all_metrics(labels_list, | 0 | 2023-10-17 17:39:16+00:00 | 4k |
oven-lab/tuya_cloud_map_extractor | custom_components/tuya_cloud_map_extractor/config_flow.py | [
{
"identifier": "get_map",
"path": "custom_components/tuya_cloud_map_extractor/tuya_vacuum_map_extractor/main.py",
"snippet": "def get_map(\n server: str, client_id: str, secret_key: str, device_id: str, colors={}, settings={}, urls={}\n) -> Image:\n \"\"\"Downloads and parses vacuum map from tuya... | import logging
import voluptuous as vol
from typing import Any
from .tuya_vacuum_map_extractor import (
get_map,
ClientIDError,
ClientSecretError,
DeviceIDError,
ServerError,
)
from homeassistant import config_entries
from homeassistant.core import HomeAssistant, callback
from homeassistant.helpers.... | 1,925 | from __future__ import annotations
CONF_SERVERS = {
CONF_SERVER_CHINA: "China",
CONF_SERVER_WEST_AMERICA: "Western America",
CONF_SERVER_EAST_AMERICA: "Eastern America",
CONF_SERVER_CENTRAL_EUROPE: "Central Europe",
CONF_SERVER_WEST_EUROPE: "Western Europe",
CONF_SERVER_INDIA: "India"
}
_L... | from __future__ import annotations
CONF_SERVERS = {
CONF_SERVER_CHINA: "China",
CONF_SERVER_WEST_AMERICA: "Western America",
CONF_SERVER_EAST_AMERICA: "Eastern America",
CONF_SERVER_CENTRAL_EUROPE: "Central Europe",
CONF_SERVER_WEST_EUROPE: "Western Europe",
CONF_SERVER_INDIA: "India"
}
_L... | except ServerError: | 4 | 2023-10-22 10:48:25+00:00 | 4k |
mlbio-epfl/hume | hume.py | [
{
"identifier": "parse_args",
"path": "argparser.py",
"snippet": "def parse_args(args):\n parser = argparse.ArgumentParser()\n\n parser.add_argument('--phi1_path', \n type=str,\n required=True,\n help=\"Path to the embeddings in ... | import os
import pickle
import torch
import torch.nn as nn
import torch.nn.functional as F
import learn2learn as l2l
import numpy as np
from tqdm import tqdm
from argparser import parse_args
from activations import Sparsemax
from utils import fix_seed, get_cv_score, check_both_none_or_not_none
from metrics import clust... | 2,848 | else:
phi1_val = np.copy(phi1)
phi2_val = np.copy(phi2)
y_true_val = np.load(args.gt_labels_path)
assert phi1.shape[0] == phi2.shape[0]
assert phi1_val.shape[0] == phi2_val.shape[0]
assert phi1_val.shape[0] == y_true_val.shape[0]
n_train = phi1.shape[0]
d1, d2 = phi2.shape[1]... |
def run(args=None):
args = parse_args(args)
device = torch.device(args.device)
fix_seed(args.seed)
if not os.path.exists(args.exp_path):
os.makedirs(args.exp_path)
phi1 = np.load(args.phi1_path).astype(np.float32)
phi2 = np.load(args.phi2_path).astype(np.float32)
assert c... | print(f"Cluster ACC epoch {i}:", cluster_acc(preds_all_val, y_true_val)) | 5 | 2023-10-20 15:32:06+00:00 | 4k |
lwaekfjlk/TRAMS | utils/src.py | [
{
"identifier": "TransfoXLLMHeadModel",
"path": "utils/modeling_transfo_xl.py",
"snippet": "_CHECKPOINT_FOR_DOC = \"transfo-xl-wt103\"\n_CONFIG_FOR_DOC = \"TransfoXLConfig\"\n_TOKENIZER_FOR_DOC = \"TransfoXLTokenizer\"\nTRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST = [\n \"transfo-xl-wt103\",\n # See a... | import os
import logging
import wandb
import torch
import sys
from torch.nn.parallel import DistributedDataParallel
from torch.optim import Adam
from utils.modeling_transfo_xl import TransfoXLLMHeadModel, TransfoXLConfig
from torch.optim.lr_scheduler import ExponentialLR, LambdaLR
from transformers import get_linear_sc... | 3,306 |
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
class Trainer(object):
def __init__(self, args):
super().__init__()
self.args = args
self.set_tool()
self.set_dist()
self.set_seed()
... |
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
class Trainer(object):
def __init__(self, args):
super().__init__()
self.args = args
self.set_tool()
self.set_dist()
self.set_seed()
... | config = TransfoXLConfig( | 0 | 2023-10-19 00:49:29+00:00 | 4k |
npgrosser/autowired | autowired/_container.py | [
{
"identifier": "component_scan",
"path": "autowired/_component_scan.py",
"snippet": "def component_scan(root_module: ModuleType) -> Iterable[ClassComponentInfo]:\n scanner = ClassScanner(root_module)\n component_infos = (get_component_info(cls) for cls in scanner.get_classes())\n return (c for... | import dataclasses
import inspect
import re
from abc import ABC, abstractmethod
from dataclasses import dataclass
from types import FunctionType, ModuleType
from typing import (
Type,
Callable,
Any,
List,
Optional,
Union,
Generic,
Dict,
TypeVar,
)
from ._component_scan import compone... | 2,707 | @staticmethod
def from_supplier(
supplier: Callable[[], _T],
type: Optional[Type[_T]] = None,
name: Optional[str] = None,
) -> "Provider[_T]":
"""
Creates a provider from the given supplier function.
:param supplier: The supplier function. Will be called every... |
_T = TypeVar("_T")
@dataclass(frozen=True)
class Dependency(Generic[_T]):
"""
A dependency specification.
"""
name: str
type: Type[_T]
required: bool = True
default_factory: Optional[Callable[[], _T]] = None
class Provider(ABC, Generic[_T]):
@abstractmethod
def get_instance(
... | raise AmbiguousDependencyException( | 2 | 2023-10-16 09:22:20+00:00 | 4k |
chenxn2020/GOSE | GOSEfinetune/data/datasets/xfun.py | [
{
"identifier": "load_image",
"path": "GOSEfinetune/data/utils.py",
"snippet": "def load_image(image_path):\n image = read_image(image_path, format=\"BGR\")\n h = image.shape[0]\n w = image.shape[1]\n img_trans = TransformList([ResizeTransform(h=h, w=w, new_h=224, new_w=224)])\n image = t... | import json
import logging
import os
import datasets
from GOSEfinetune.data.utils import load_image, merge_bbox, normalize_bbox, simplify_bbox
from transformers import AutoTokenizer | 2,050 | "relations": datasets.Sequence(
{
"head": datasets.Value("int64"),
"tail": datasets.Value("int64"),
"start_index": datasets.Value("int64"),
"end_index": datasets.Va... | # Lint as: python3
_URL = "https://github.com/doc-analysis/XFUN/releases/download/v1.0/"
_LANG = ["zh", "de", "es", "fr", "en", "it", "ja", "pt"]
logger = logging.getLogger(__name__)
class XFUNConfig(datasets.BuilderConfig):
"""BuilderConfig for XFUN."""
def __init__(self, lang, additional_langs=None, *... | bbox.append(normalize_bbox(merge_bbox(tmp_box), size)) | 1 | 2023-10-19 14:36:32+00:00 | 4k |
mklissa/dceo | dopamine/jax/agents/rainbow/rainbow_agent.py | [
{
"identifier": "losses",
"path": "dopamine/jax/losses.py",
"snippet": "def huber_loss(targets: jnp.array,\n predictions: jnp.array,\n delta: float = 1.0) -> jnp.ndarray:\ndef mse_loss(targets: jnp.array, predictions: jnp.array) -> jnp.ndarray:\ndef softmax_cross_entropy_loss... | import functools
import gin
import jax
import jax.numpy as jnp
import numpy as onp
import optax
import tensorflow as tf
from dopamine.jax import losses
from dopamine.jax import networks
from dopamine.jax.agents.dqn import dqn_agent
from dopamine.metrics import statistics_instance
from dopamine.replay_memory import prio... | 3,135 | def loss_fn(params, target, loss_multipliers):
def q_online(state):
return network_def.apply(params, state, support)
logits = jax.vmap(q_online)(states).logits
# Fetch the logits for its selected action. We use vmap to perform this
# indexing across the batch.
chosen_action_logits = jax.vma... | # coding=utf-8
# Copyright 2018 The Dopamine 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... | class JaxRainbowAgent(dqn_agent.JaxDQNAgent): | 2 | 2023-10-15 22:14:16+00:00 | 4k |
keepfoolisher/My-DocTr-Plus | GeoTr.py | [
{
"identifier": "BasicEncoder",
"path": "extractor.py",
"snippet": "class BasicEncoder(nn.Module):\n def __init__(self, output_dim=128, norm_fn='batch'):\n super(BasicEncoder, self).__init__()\n self.norm_fn = norm_fn\n\n if self.norm_fn == 'group':\n self.norm1 = nn.G... | from extractor import BasicEncoder
from position_encoding import build_position_encoding
from torch import nn, Tensor
from typing import Optional
import argparse
import numpy as np
import torch
import torch.nn.functional as F
import copy | 3,037 | bs, c, h, w = imgf.shape
imgf = imgf.flatten(2).permute(2, 0, 1)
# query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)
pos = pos.flatten(2).permute(2, 0, 1)
for layer in self.layers:
query_embed = layer(query_embed, [imgf], pos=pos, memory_pos=[pos, pos])
... |
class attnLayer(nn.Module):
def __init__(self, d_model, nhead=8, dim_feedforward=2048, dropout=0.1,
activation="relu", normalize_before=False):
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.multihead_attn_list = nn.ModuleL... | self.fnet = BasicEncoder(output_dim=hdim, norm_fn='instance') | 0 | 2023-10-17 11:06:30+00:00 | 4k |
zzbuzzard/stable-diffusion-infinite-scroll | sd_scroll.py | [
{
"identifier": "next_image",
"path": "util.py",
"snippet": "def next_image(pipe, image, base_size, prompt, shiftx, shifty, pipe_args):\n \"\"\"Given an image, uses inpainting to produce the next image (which overlaps with the previous image)\"\"\"\n assert image.size == (base_size, base_size)\n\n... | import torch
import numpy as np
import tkinter as tk
import time
import random
import argparse
import util
from diffusers import StableDiffusionInpaintPipeline
from PIL import Image
from multiprocessing import Process, Queue
from util import next_image
from slider import Slider | 2,006 |
parser = util.get_argparser()
parser.add_argument("-spd", "--speed", default=1., type=float,
help="Speed multiplier (between 0 and 1). A value of 1 causes images to be generated as fast as "
"possible. A value less than 1 leads to intentional breaks between generations to ... |
parser = util.get_argparser()
parser.add_argument("-spd", "--speed", default=1., type=float,
help="Speed multiplier (between 0 and 1). A value of 1 causes images to be generated as fast as "
"possible. A value less than 1 leads to intentional breaks between generations to ... | front = next_image(pipe, image=front, base_size=base_size, prompt=prompt, shiftx=shiftx, shifty=shifty, | 0 | 2023-10-15 14:43:52+00:00 | 4k |
MaxDude132/django-register-field | tests/models.py | [
{
"identifier": "Register",
"path": "django_register/base.py",
"snippet": "class Register:\n def __init__(self):\n self._key_to_class = {}\n self._class_to_key = {}\n\n def register(self, klass, db_key=None):\n if db_key is None:\n try:\n db_key = kla... | from dataclasses import dataclass
from django.db import models
from django_register import Register, RegisterChoices, RegisterField | 1,625 | # Standard libraries
# Django
# django_register
@dataclass(unsafe_hash=True)
class CountryInfo:
population: int
capital: str
class CountryChoices(RegisterChoices):
CANADA = CountryInfo(population=37_742_154, capital="Ottawa")
FRANCE = CountryInfo(population=65_273_511, capital="Paris")
GERMANY... | # Standard libraries
# Django
# django_register
@dataclass(unsafe_hash=True)
class CountryInfo:
population: int
capital: str
class CountryChoices(RegisterChoices):
CANADA = CountryInfo(population=37_742_154, capital="Ottawa")
FRANCE = CountryInfo(population=65_273_511, capital="Paris")
GERMANY... | country = RegisterField( | 2 | 2023-10-23 18:11:08+00:00 | 4k |
hsouri/bob-classification | timm_dataset.py | [
{
"identifier": "INAT2019",
"path": "datasets/inat_loader.py",
"snippet": "class INAT2019(data.Dataset):\n def __init__(self, root, mode='train', year=\"2019\", transform=None):\n # load annotations\n ann_file = os.path.join(root, f\"{mode}{year}.json\")\n with open(ann_file) as ... | from datasets.transfer_cls_datasets import *
from timm.data import create_dataset, create_loader, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset
from wilds import get_dataset
from datasets.inat_loader import INAT2019, INAT2021
import wilds
import torchvision.transforms as transforms | 2,833 |
transfer_datasets = {
'flower102': 'Flower102',
'aircraft': 'Aircraft',
# 'birdsnap': 'Birdsnap',
'dtd': 'DTD',
'voc2007': 'VOC2007',
'pets': 'Pets',
'sun397': 'SUN397',
'cars': 'Cars',
'food101': 'Food101',
'caltech101': 'Caltech101',
'cifar10': 'Cifar10',
'cifar100': 'Cifar100',
'eurosat': 'eurosa... |
transfer_datasets = {
'flower102': 'Flower102',
'aircraft': 'Aircraft',
# 'birdsnap': 'Birdsnap',
'dtd': 'DTD',
'voc2007': 'VOC2007',
'pets': 'Pets',
'sun397': 'SUN397',
'cars': 'Cars',
'food101': 'Food101',
'caltech101': 'Caltech101',
'cifar10': 'Cifar10',
'cifar100': 'Cifar100',
'eurosat': 'eurosa... | ds = INAT2021(root, | 1 | 2023-10-20 16:28:17+00:00 | 4k |
Salz0/telegram_flea | main.py | [
{
"identifier": "User",
"path": "models.py",
"snippet": "class User(BaseModel):\n \"\"\"\n The model for the Telegram user.\n\n This model stores all the information about the user.\n It is also used to store all the authentication-related information.\n \"\"\"\n\n id = fields.BigIntFi... | import os
import aiogram
from asyncio import gather
from pathlib import Path
from aiogram import types
from aiogram.contrib.fsm_storage.memory import MemoryStorage
from aiogram.contrib.middlewares.i18n import I18nMiddleware
from aiogram.dispatcher import FSMContext
from aiogram.dispatcher.filters import CommandStart
fr... | 2,177 |
load_dotenv()
compile_all_languages()
bot = aiogram.Bot(os.environ["TELEGRAM_BOT_TOKEN"])
dp = aiogram.Dispatcher(bot, storage=MemoryStorage())
BASE_DIR = Path(__file__).parent
LOCALES_DIR = BASE_DIR / "locales"
BOT_LANGUAGE = os.environ.get("BOT_LANGUAGE")
i18n = I18nMiddleware("bot", LOCALES_DIR, default="en"... |
load_dotenv()
compile_all_languages()
bot = aiogram.Bot(os.environ["TELEGRAM_BOT_TOKEN"])
dp = aiogram.Dispatcher(bot, storage=MemoryStorage())
BASE_DIR = Path(__file__).parent
LOCALES_DIR = BASE_DIR / "locales"
BOT_LANGUAGE = os.environ.get("BOT_LANGUAGE")
i18n = I18nMiddleware("bot", LOCALES_DIR, default="en"... | reply_markup=sell_keyboard, | 8 | 2023-10-19 17:28:55+00:00 | 4k |
RobertCsordas/moe_layer | triton_src/moe_layer/moe_layer_simple.py | [
{
"identifier": "cvmm",
"path": "triton_src/moe_layer/cvmm.py",
"snippet": "def cvmm(x: torch.Tensor, sel: Union[torch.Tensor, CVMMSel], keys: torch.Tensor):\n if not isinstance(sel, CVMMSel):\n sel = cvmm_prepare_sel(sel, keys.shape[0])\n\n return CVMM.apply(x, sel.sel_index, sel.sel, keys... | import torch
import torch.distributed
import torch.nn.functional as F
import math
from typing import Tuple, List, Optional
from .cvmm import cvmm, cvmm_prepare_sel2, CVMMSel | 1,944 | activation_after_topk: bool = False,
activation=F.relu,
bias: bool = False, v_dim: Optional[int] = None,
sinkhorn_n_iters: int = 3, expert_dropout: float = 0.0,
weight_std_scale: float = 1.0):
super().__init__()
self.k... |
def dist_logsumexp(x: torch.Tensor, dim: int, keepdim: bool = False) -> torch.Tensor:
# Calculate numerically stable distributed logsumexp
xmax = x.max(dim=dim, keepdim=True).values
torch.distributed.all_reduce(xmax, op=torch.distributed.ReduceOp.MAX)
xe = (x - xmax).exp().sum(dim=dim, keepdim=True)
... | sel_indices = cvmm_prepare_sel2(sel_index.int()) | 1 | 2023-10-16 11:00:47+00:00 | 4k |
BurgerBurgerBurger/AA | model.py | [
{
"identifier": "process_long_input",
"path": "long_seq.py",
"snippet": "def process_long_input(model, input_ids, attention_mask, start_tokens, end_tokens):\n # Split the input to 2 overlapping chunks. Now BERT can encode inputs of which the length are up to 1024.\n n, c = input_ids.size()\n st... | import torch
import torch.nn as nn
import torch.nn.functional as F
from opt_einsum import contract
from long_seq import process_long_input
from losses import ATLoss
from graph import AttentionGCNLayer | 2,125 |
class DocREModel(nn.Module):
def __init__(self, args, config, model, tokenizer,
emb_size=768, block_size=64, num_labels=-1,
max_sent_num=25, evi_thresh=0.2):
super().__init__()
self.config = config
self.model = model
self.tokenizer = tokenizer
... |
class DocREModel(nn.Module):
def __init__(self, args, config, model, tokenizer,
emb_size=768, block_size=64, num_labels=-1,
max_sent_num=25, evi_thresh=0.2):
super().__init__()
self.config = config
self.model = model
self.tokenizer = tokenizer
... | self.loss_fnt = ATLoss() | 1 | 2023-10-20 05:53:25+00:00 | 4k |
hnesk/flipper-raw-rfid | tests/test_rifl_file.py | [
{
"identifier": "Rifl",
"path": "flipper_raw_rfid/rifl.py",
"snippet": "class Rifl:\n \"\"\"\n A raw rfid file from flipper (xyz.ask.raw or xyz.psk.raw)\n\n \"\"\"\n header: RiflHeader\n \"\"\" The header of the file \"\"\"\n\n pulse_and_durations: npt.NDArray[numpy.int64] = None\n ... | from io import BytesIO
from pathlib import Path
from unittest import TestCase
from numpy.testing import assert_array_equal
from flipper_raw_rfid.rifl import Rifl, RiflHeader
import numpy | 1,690 |
TEST_BASE_PATH = Path(__file__).parent.absolute()
class RiflFileTest(TestCase):
example_bytes = bytes.fromhex('f101a903ae028506a604fb05bb028706ad04b90404c403')
example_ints = [241, 425, 302, 773, 550, 763, 315, 775, 557, 569, 4, 452]
def test_header_to_bytes_and_back(self):
|
TEST_BASE_PATH = Path(__file__).parent.absolute()
class RiflFileTest(TestCase):
example_bytes = bytes.fromhex('f101a903ae028506a604fb05bb028706ad04b90404c403')
example_ints = [241, 425, 302, 773, 550, 763, 315, 775, 557, 569, 4, 452]
def test_header_to_bytes_and_back(self): | header = RiflHeader(1, 125_000, 0.5, 2048) | 1 | 2023-10-20 13:06:00+00:00 | 4k |
xingchenshanyao/YOLOP-E | lib/dataset/DemoDataset.py | [
{
"identifier": "clean_str",
"path": "lib/utils/utils.py",
"snippet": "def clean_str(s):\n # Cleans a string by replacing special characters with underscore _\n return re.sub(pattern=\"[|@#!¡·$€%&()=?¿^*;:,¨´><+]\", repl=\"_\", string=s)"
},
{
"identifier": "letterbox_for_img",
"path":... | import glob
import os
import random
import shutil
import time
import cv2
import math
import numpy as np
import torch
from pathlib import Path
from threading import Thread
from PIL import Image, ExifTags
from torch.utils.data import Dataset
from tqdm import tqdm
from ..utils import letterbox_for_img, clean_str | 1,794 |
img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.dng']
vid_formats = ['.mov', '.avi', '.mp4', '.mpg', '.mpeg', '.m4v', '.wmv', '.mkv']
class LoadImages: # for inference
def __init__(self, path, img_size=640):
p = str(Path(path)) # os-agnostic
p = os.path.abspath(p) # absolut... |
img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.dng']
vid_formats = ['.mov', '.avi', '.mp4', '.mpg', '.mpeg', '.m4v', '.wmv', '.mkv']
class LoadImages: # for inference
def __init__(self, path, img_size=640):
p = str(Path(path)) # os-agnostic
p = os.path.abspath(p) # absolut... | self.sources = [clean_str(x) for x in sources] # clean source names for later | 0 | 2023-10-24 02:08:25+00:00 | 4k |
giulio98/functional-diffusion-processes | src/functional_diffusion_processes/models/base_maml.py | [
{
"identifier": "clip_learning_rates",
"path": "src/functional_diffusion_processes/utils/common.py",
"snippet": "def clip_learning_rates(params):\n \"\"\"Clip the learning rates to the range [0, 1].\n\n Args:\n params: A dictionary of parameters.\n\n Returns:\n A dictionary contai... | import abc
import logging
import flax.linen as nn
import hydra
import jax
import jax.numpy as jnp
import optax
from functools import partial
from typing import Any, Callable, Mapping, Optional, Tuple, TypeVar
from flax.core import FrozenDict, unfreeze
from omegaconf import DictConfig
from ..utils.common import clip_lea... | 3,321 | ) -> Tuple[jax.random.PRNGKey, jnp.ndarray, jnp.ndarray]:
"""Apply the (outer) forward pass and update the model parameters.
Args:
rng (jax.random.PRNGKey): Random key.
params (Params): Initial model parameters.
batch_input (jnp.ndarray): ... |
Params = FrozenDict[str, Any]
T = TypeVar("T")
pylogger = logging.getLogger(__name__)
@partial(jax.vmap, in_axes=0)
def mean_square_error(y_corrupted: jnp.ndarray, y_reconstructed: jnp.ndarray, y_psm: jnp.ndarray) -> jnp.ndarray:
"""Calculate the mean squared error between the predicted and actual values of ... | merged_updates = merge_learning_rates(unfreeze(updates_params), unfreeze(learning_rates)) | 2 | 2023-10-24 22:01:35+00:00 | 4k |
godisboy0/nonebot-adapter-wcf | adapters/wechatferry/eventconverter.py | [
{
"identifier": "Event",
"path": "adapters/wechatferry/event.py",
"snippet": "class Sender (OnebotSender):\nclass PrivateMessageEvent (OnebotPrivateMessageEvent):\nclass GroupMessageEvent (OnebotGroupMessageEvent):\nclass TTT(BaseModel):\nclass TTTB(TTT):"
},
{
"identifier": "MessageSegment",
... | from wcferry import Wcf, WxMsg
from .event import Event, PrivateMessageEvent, GroupMessageEvent, Sender
from .message import MessageSegment, Message
from .type import WxType
from .utils import logger
from nonebot.utils import escape_tag
from .sqldb import database
from .msg_converters import convert_to_bot_msg
from .co... | 2,572 | """
onebot11标准要求：https://github.com/botuniverse/onebot-11/blob/master/README.md
onebot11 message segment 类型: https://github.com/botuniverse/onebot-11/blob/master/message/segment.md
"""
adapter_config = AdapterConfig.parse_obj(get_driver().config)
async def echo_root_msg_as_json_file(msg: WxMsg, wcf: Wcf = None):
... | """
onebot11标准要求：https://github.com/botuniverse/onebot-11/blob/master/README.md
onebot11 message segment 类型: https://github.com/botuniverse/onebot-11/blob/master/message/segment.md
"""
adapter_config = AdapterConfig.parse_obj(get_driver().config)
async def echo_root_msg_as_json_file(msg: WxMsg, wcf: Wcf = None):
... | onebot_msg: Message = await convert_to_bot_msg(msg, login_wx_id, wcf, db) | 5 | 2023-10-22 10:52:27+00:00 | 4k |
R1999RC-official/Reverse1999ResonanceCalculator | python/python_env/Lib/site-packages/pip/_vendor/urllib3/util/retry.py | [
{
"identifier": "ConnectTimeoutError",
"path": "python/python_env/Lib/site-packages/pip/_vendor/urllib3/exceptions.py",
"snippet": "class ConnectTimeoutError(TimeoutError):\n \"\"\"Raised when a socket timeout occurs while connecting to a server\"\"\"\n\n pass"
},
{
"identifier": "InvalidH... | import email
import logging
import re
import time
import warnings
from collections import namedtuple
from itertools import takewhile
from ..exceptions import (
ConnectTimeoutError,
InvalidHeader,
MaxRetryError,
ProtocolError,
ProxyError,
ReadTimeoutError,
ResponseError,
)
from ..packages imp... | 2,191 | from __future__ import absolute_import
log = logging.getLogger(__name__)
# Data structure for representing the metadata of requests that result in a retry.
RequestHistory = namedtuple(
"RequestHistory", ["method", "url", "error", "status", "redirect_location"]
)
# TODO: In v2 we can remove this sentinel and ... | from __future__ import absolute_import
log = logging.getLogger(__name__)
# Data structure for representing the metadata of requests that result in a retry.
RequestHistory = namedtuple(
"RequestHistory", ["method", "url", "error", "status", "redirect_location"]
)
# TODO: In v2 we can remove this sentinel and ... | @six.add_metaclass(_RetryMeta) | 7 | 2023-10-24 06:48:58+00:00 | 4k |
mentpy/mentpy | mentpy/operators/controlled_ment.py | [
{
"identifier": "PauliX",
"path": "mentpy/operators/gates.py",
"snippet": "CNOT = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0]])\nSWAP = np.array([[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]])\n U = unitary_group.rvs(2**n_qubits)\n U = U / np.power(detU, 1 / (2**n_qu... | from typing import Optional, Union, Callable
from .gates import PauliX, PauliY, PauliZ
from .ment import Ment, MentOutcome
import numpy as np
import warnings | 2,653 | # Copyright 2023 Luis Mantilla
#
# Licensed under the Apache License, Version 2.0.
# See <http://www.apache.org/licenses/LICENSE-2.0> for details.
"""Controlled measurement operator."""
class ControlMent(Ment):
def __init__(
self,
| # Copyright 2023 Luis Mantilla
#
# Licensed under the Apache License, Version 2.0.
# See <http://www.apache.org/licenses/LICENSE-2.0> for details.
"""Controlled measurement operator."""
class ControlMent(Ment):
def __init__(
self, | condition: Optional[Union[bool, MentOutcome]] = None, | 2 | 2023-10-18 18:29:42+00:00 | 4k |
rnag/cert-hero | tests/integration/test_cert_hero.py | [
{
"identifier": "cert_please",
"path": "cert_hero/cert_hero.py",
"snippet": "def cert_please(hostname: str,\n context: ssl.SSLContext = None,\n user_agent: str | None = _DEFAULT_USER_AGENT,\n default_encoding='latin-1',\n ) -> CertHero[str, str... | import json
from cert_hero import cert_please, certs_please, set_expired | 3,566 |
def test_cert_please():
cert = cert_please('google.com')
print('Cert is Valid Till:', cert.not_after_date.isoformat())
# To get the output as a JSON string, use `str(cert)` or remove `!r` from below
print(f'Cert -> \n{cert!r}')
assert cert['Subject Name']['Common Name'] == '*.google.com'
|
def test_cert_please():
cert = cert_please('google.com')
print('Cert is Valid Till:', cert.not_after_date.isoformat())
# To get the output as a JSON string, use `str(cert)` or remove `!r` from below
print(f'Cert -> \n{cert!r}')
assert cert['Subject Name']['Common Name'] == '*.google.com'
| set_expired(cert) | 2 | 2023-10-16 19:02:05+00:00 | 4k |
KosinskiLab/pyTME | tme/tests/test_parser.py | [
{
"identifier": "Parser",
"path": "tme/parser.py",
"snippet": "class Parser(ABC):\n \"\"\"\n Base class for structure file parsers.\n\n Classes inheriting from :py:class:`Parser` need to define\n a ``parse_input`` method that accepts a list of lines and returns a\n dictionary representati... | import pytest
from tme.parser import Parser, PDBParser | 1,807 |
class TestParser:
def setup_method(self):
self.pdb_file = "./tme/tests/data/Structures/5khe.pdb"
def teardown_method(self):
self.pdb_file = None
def test_initialize_parser_error(self):
with pytest.raises(TypeError):
|
class TestParser:
def setup_method(self):
self.pdb_file = "./tme/tests/data/Structures/5khe.pdb"
def teardown_method(self):
self.pdb_file = None
def test_initialize_parser_error(self):
with pytest.raises(TypeError): | _ = Parser(self.pdb_file) | 0 | 2023-10-20 13:46:01+00:00 | 4k |
hookla/DreamTeamGPT | dream_team_gpt/meeting.py | [
{
"identifier": "Chairman",
"path": "dream_team_gpt/agents/chairman.py",
"snippet": "class Chairman(Agent):\n def __init__(self, client_factory: Callable, executives: list[SME], name: str = \"Chairman\"):\n # Construct the user_prompt string with details of the executives\n self.user_pr... | from dataclasses import dataclass, field
from pathlib import Path
from textwrap import dedent
from loguru import logger
from dream_team_gpt.agents import SME, Chairman
from dream_team_gpt.agents.idea_refiner import IdeaRefiner
from dream_team_gpt.clients import AIClientConfig, AIClientType, Models, ai_client_factory
fr... | 1,897 |
@dataclass
class Transcript(str):
idea: str
refined_idea: str = None
opinions: list[str] = field(default_factory=list)
def __str__(self) -> str:
opinions = "\n".join(opinion for opinion in self.opinions)
return dedent(
f"""\
We are here to discuss the followi... |
@dataclass
class Transcript(str):
idea: str
refined_idea: str = None
opinions: list[str] = field(default_factory=list)
def __str__(self) -> str:
opinions = "\n".join(opinion for opinion in self.opinions)
return dedent(
f"""\
We are here to discuss the followi... | self.smes = [SME(client_factory=client_factory, **d) for d in sme_dict] | 1 | 2023-10-18 22:45:50+00:00 | 4k |
MeetingAgent/MeetingAgent-Core | meeting_buddy.py | [
{
"identifier": "MyTTS",
"path": "voice_cloning/clone.py",
"snippet": "class MyTTS:\n def __init__(self):\n # Get device\n self.device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n self.tts = TTS(\"tts_models/en/ljspeech/tacotron2-DDC\")\n self.use_default_speak... | import pyaudio
import wave
import whisper
import threading
import time
import pygame
from kivy.app import App
from kivy.uix.button import Button
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.switch import Switch
from kivy.uix.label import Label
from kivy.clock import Clock
from kivy.uix.textinput import TextIn... | 2,694 | play_audio('meeting_buddy_audio/output.mp3')
else:
# Update the answer text without text-to-speech
Clock.schedule_once(lambda dt: app.update_answer_text(aggregated_text))
return query, answer
def meeting_buddy(meeting_context: str) -> None:
global audio_thread
audio_thr... | # Audio Processing
# GUI
install_twisted_reactor()
# gtts text to speech
# personalized voice text to speech
# Local
recording = False
audio_thread = None
def get_audio() -> None:
global recording
recording = True
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16, channels=1, rate=44... | self.tts = MyTTS() | 0 | 2023-10-18 06:50:56+00:00 | 4k |
tonnetonne814/MB-iSTFT-BERT-VITS2-44100-Ja | modules.py | [
{
"identifier": "init_weights",
"path": "commons.py",
"snippet": "def init_weights(m, mean=0.0, std=0.01):\n classname = m.__class__.__name__\n if classname.find(\"Conv\") != -1:\n m.weight.data.normal_(mean, std)"
},
{
"identifier": "get_padding",
"path": "commons.py",
"sni... | import math
import torch
import commons
from torch import nn
from torch.nn import functional as F
from torch.nn import Conv1d
from torch.nn.utils import weight_norm, remove_weight_norm
from commons import init_weights, get_padding
from transforms import piecewise_rational_quadratic_transform
from attentions import Enco... | 2,950 | dilation_rate,
n_layers,
gin_channels=0,
p_dropout=0,
):
super(WN, self).__init__()
assert kernel_size % 2 == 1
self.hidden_channels = hidden_channels
self.kernel_size = (kernel_size,)
self.dilation_rate = dilation_rate
self.n_layers = ... |
LRELU_SLOPE = 0.1
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-5):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(torch.zeros(channels))
def forward(self, x):
... | self.convs1.apply(init_weights) | 0 | 2023-10-16 10:04:32+00:00 | 4k |
KaichengGroup/FUSE-Flow | test.py | [
{
"identifier": "NPZDataset",
"path": "data_modules/npz_dataset.py",
"snippet": "class NPZDataset(VisionDataset):\n \"\"\"Load datasets from NPZ files.\n NPZ files are assumed to have 2 files named \"x\" and \"y\"\n that represent the input and target, respectively.\n\n Parameters\n -----... | import os
import pytorch_lightning as pl
import torch
from pytorch_lightning import Trainer
from torch.utils.data import DataLoader
from torchvision.transforms import ToTensor, Compose
from data_modules.npz_dataset import NPZDataset
from utils.utils import load_config, create_subset, save_train_results, CONFIG_PATH, de... | 1,937 |
if __name__ == '__main__':
pl.seed_everything(42)
# "highest" (default), float32 matrix multiplications use the float32 datatype for internal computations.
# "high", float32 matrix multiplications use the TensorFloat32 or bfloat16_3x
# "medium", float32 matrix multiplications use the bfloat16 dataty... |
if __name__ == '__main__':
pl.seed_everything(42)
# "highest" (default), float32 matrix multiplications use the float32 datatype for internal computations.
# "high", float32 matrix multiplications use the TensorFloat32 or bfloat16_3x
# "medium", float32 matrix multiplications use the bfloat16 dataty... | config = load_config(CONFIG_PATH) | 1 | 2023-10-19 06:49:31+00:00 | 4k |
TheAcharya/Airlift | airlift/airtable_upload.py | [
{
"identifier": "new_client",
"path": "airlift/airtable_client.py",
"snippet": "class new_client:\n\n def __init__(self,token:str,base:str,table:str):\n\n self.api = token\n self.base = base\n self.table = table\n self.headers = {\n \"Authorization\": \"Bear... | import logging
import concurrent.futures
import os
from airlift.airtable_client import new_client
from typing import Any, Dict, Iterable, Iterator, List, Optional
from queue import Queue, Empty
from airlift.dropbox_client import dropbox_client
from tqdm import tqdm
from icecream import ic
from airlift.dropbox_client im... | 2,823 |
logger = logging.getLogger(__name__)
ATDATA = List[Dict[str, Dict[str, str]]]
class Upload:
|
logger = logging.getLogger(__name__)
ATDATA = List[Dict[str, Dict[str, str]]]
class Upload: | def __init__(self,client: new_client, new_data:ATDATA,dbx:dropbox_client,args:dict): | 0 | 2023-10-21 01:57:41+00:00 | 4k |
zytedata/zyte-spider-templates | zyte_spider_templates/spiders/base.py | [
{
"identifier": "GEOLOCATION_OPTIONS_WITH_CODE",
"path": "zyte_spider_templates/_geolocations.py",
"snippet": "GEOLOCATION_OPTIONS_WITH_CODE = {\n code: f\"{name} ({code})\" for code, name in GEOLOCATION_OPTIONS.items()\n}"
},
{
"identifier": "Geolocation",
"path": "zyte_spider_templates/... | from importlib.metadata import version
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field
from scrapy.crawler import Crawler
from scrapy.utils.url import parse_url
from zyte_spider_templates._geolocations import (
GEOLOCATION_OPTIONS_WITH_CODE,
Geolocation,
)
import scrapy | 2,504 |
# Higher priority than command-line-defined settings (40).
ARG_SETTING_PRIORITY: int = 50
class BaseSpiderParams(BaseModel):
url: str = Field(
title="URL",
description="Initial URL for the crawl.",
pattern=r"^https?:\/\/[^:\/\s]+(:\d{1,5})?(\/[^\s]*)*(#[^\s]*)?$",
)
|
# Higher priority than command-line-defined settings (40).
ARG_SETTING_PRIORITY: int = 50
class BaseSpiderParams(BaseModel):
url: str = Field(
title="URL",
description="Initial URL for the crawl.",
pattern=r"^https?:\/\/[^:\/\s]+(:\d{1,5})?(\/[^\s]*)*(#[^\s]*)?$",
) | geolocation: Optional[Geolocation] = Field( | 1 | 2023-10-18 10:58:44+00:00 | 4k |
DegangWang97/IEEE_TGRS_PDBSNet | main.py | [
{
"identifier": "PDBSNet",
"path": "model.py",
"snippet": "class PDBSNet(nn.Module):\n def __init__(self, nch_in=189, nch_out=189, nch_ker=64, nblk=9):\n super().__init__()\n\n ly = []\n ly += [ nn.Conv2d(nch_in, nch_ker, kernel_size=1) ]\n ly += [ nn.ReLU(inplace=True) ]\... | import argparse
import torch
import torch.nn as nn
import scipy.io as sio
import os
import numpy as np
import time
from model import PDBSNet
from dataset import PDBSNetData, pixel_shuffle_up_sampling, pixel_shuffle_down_sampling
from utils import get_auc, setup_seed, TensorToHSI
from torch import optim
from ... | 3,107 | Trains a PyTorch `nn.Module` object provided in `model`
on training sets provided in `dataloader`
using `criterion` and `optimizer`.
Saves model weight snapshots every `save_freq` epochs and saves the
weights at the end of training.
Parameters
----------
... | """
See more details in papers:
[1] D. Wang, L. Zhuang, L. Gao, X. Sun, M. Huang, and A. Plaza,
“PDBSNet: Pixel-Shuffle Downsampling Blind-Spot Reconstruction Network
for Hyperspectral Anomaly Detection,” IEEE Trans. Geosci. Remote Sens.,
vol. 61, 2023, Art. no. 5511914. DOI: 10.1109/TGRS.2023.... | net = PDBSNet(band, band, nch_ker=opt.nch_ker, nblk=opt.nblk).to(device)
| 0 | 2023-10-16 08:28:56+00:00 | 4k |
AVAniketh0905/fluidspy | fluidspylib/fluidspy/tests/test_fdm.py | [
{
"identifier": "Bottom",
"path": "fluidspylib/fluidspy/numerical/boundary/direction.py",
"snippet": "class Bottom(Direction):\n \"\"\"Bottom direction.\"\"\"\n\n def __init__(\n self,\n initial_value: float,\n state: SimulationState,\n boundary_condition: BoundaryCondi... | import pytest
from ..numerical.boundary import Bottom
from ..numerical.boundary import CompositeBoundary
from ..numerical.boundary import Constant
from ..numerical.boundary import Insulated
from ..numerical.boundary import Left
from ..numerical.boundary import Right
from ..numerical.boundary import Top
from ..numerical... | 3,076 |
def create_state_dim(state, dim, shape):
dim = dim(state)
dim.create_grid(shape)
return dim
def test_ftcs():
state = SimulationState()
dim = create_state_dim(state, OneDimSpatial, 10)
boundary = CompositeBoundary(
Left(5, state, Constant()), Right(10, state, Insulated())
)
... |
def create_state_dim(state, dim, shape):
dim = dim(state)
dim.create_grid(shape)
return dim
def test_ftcs():
state = SimulationState()
dim = create_state_dim(state, OneDimSpatial, 10)
boundary = CompositeBoundary(
Left(5, state, Constant()), Right(10, state, Insulated())
)
... | step = Step(0.1, Vector(0.1)) | 13 | 2023-10-21 06:55:58+00:00 | 4k |
jobless-devs/Jobhub | lambdas/packages/python/psycopg2/extras.py | [
{
"identifier": "PY2",
"path": "lambdas/packages/python/psycopg2/compat.py",
"snippet": "PY2 = True\nPY3 = False\nPY2 = False\nPY3 = True"
},
{
"identifier": "adapt",
"path": "lambdas/packages/python/psycopg2/extensions.py",
"snippet": "ISOLATION_LEVEL_AUTOCOMMIT = 0\nISOLATION_LEVEL_REA... | import logging as _logging
import os as _os
import re as _re
import time as _time
import psycopg2
import uuid
import warnings
import select
from collections import namedtuple, OrderedDict
from psycopg2 import extensions as _ext
from psycopg2._ipaddress import register_ipaddress # noqa
from psycopg2._json i... | 2,715 | if self._prefetch:
res = super(DictCursorBase, self).fetchmany(size)
if self._query_executed:
self._build_index()
if not self._prefetch:
res = super(DictCursorBase, self).fetchmany(size)
return res
def fetchall(self):
if self._prefetch:
... | """Miscellaneous goodies for psycopg2
This module is a generic place used to hold little helper functions
and classes until a better place in the distribution is found.
"""
# psycopg/extras.py - miscellaneous extra goodies for psycopg
#
# Copyright (C) 2003-2019 Federico Di Gregorio <fog@debian.org>
# Copyright (C) 2... | if PY2: | 0 | 2023-10-22 20:09:51+00:00 | 4k |
kyegomez/gradient-ascent | visualization.py | [
{
"identifier": "GradientAscent",
"path": "gradient_ascent/main.py",
"snippet": "class GradientAscent:\n \"\"\"\n Gradient Ascent Optimizer\n\n Optimizer that performs gradient ascent on the parameters of the model.\n\n Args:\n parameters (iterable): iterable of parameters to optimize... | import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib.animation import FuncAnimation
from gradient_ascent import GradientAscent
from gradient_ascent.main import GradientAscent | 2,636 |
class SimpleModel(torch.nn.Module):
def __init__(self):
super(SimpleModel, self).__init__()
self.fc = torch.nn.Linear(1, 1)
def forward(self, x):
return self.fc(x)
# Set up real-time plotting
plt.ion() # Turn on interactive mode
fig, ax = plt.subplots(figsize=(10, 6))
ax.set_xlim(... |
class SimpleModel(torch.nn.Module):
def __init__(self):
super(SimpleModel, self).__init__()
self.fc = torch.nn.Linear(1, 1)
def forward(self, x):
return self.fc(x)
# Set up real-time plotting
plt.ion() # Turn on interactive mode
fig, ax = plt.subplots(figsize=(10, 6))
ax.set_xlim(... | optimizer = GradientAscent( | 1 | 2023-10-21 01:14:22+00:00 | 4k |
cfs-energy/cfspopcon | cfspopcon/formulas/scrape_off_layer_model/lambda_q.py | [
{
"identifier": "LambdaQScaling",
"path": "cfspopcon/named_options.py",
"snippet": "class LambdaQScaling(Enum):\n \"\"\"Options for heat flux decay length scaling.\"\"\"\n\n Brunner = auto()\n EichRegression14 = auto()\n EichRegression15 = auto()"
},
{
"identifier": "wraps_ufunc",
... | from ...named_options import LambdaQScaling
from ...unit_handling import ureg, wraps_ufunc | 2,160 | """Routines to calculate the heat flux decay length (lambda_q), for several different scalings."""
@wraps_ufunc(
return_units=dict(lambda_q=ureg.millimeter),
input_units=dict(
lambda_q_scaling=None,
average_total_pressure=ureg.atm,
power_crossing_separatrix=ureg.megawatt,
majo... | """Routines to calculate the heat flux decay length (lambda_q), for several different scalings."""
@wraps_ufunc(
return_units=dict(lambda_q=ureg.millimeter),
input_units=dict(
lambda_q_scaling=None,
average_total_pressure=ureg.atm,
power_crossing_separatrix=ureg.megawatt,
majo... | lambda_q_scaling: LambdaQScaling, | 0 | 2023-10-19 16:58:23+00:00 | 4k |
GXimingLu/IPA | policy_gp3.py | [
{
"identifier": "ConstrainedHypothesis",
"path": "lexical_constraints.py",
"snippet": "class ConstrainedHypothesis:\n\n def __init__(self,\n constraint_list: List[List[List[int]]],\n eos_tokens: List[int]) -> None:\n self.clauses = []\n for idx, clause in... | import torch
import torch.nn.functional as F
import json
import numpy as np
import openai
from typing import Union, List, Dict
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from lexical_constraints import ConstrainedHypothesis, init_batch
from utils.constants import NEGATIVE_INF, OPENAI_API_KEY
from utils.ut... | 2,234 |
openai.api_key = OPENAI_API_KEY
class Policy:
def __init__(self, value_model_name, value_model_checkpoint, device, tree_tokens, alpha, force_eos):
self.device = device
self.value_model = GPT2LMHeadModel.from_pretrained(value_model_name)
self.tokenizer = GPT2Tokenizer.from_pretrained(valu... |
openai.api_key = OPENAI_API_KEY
class Policy:
def __init__(self, value_model_name, value_model_checkpoint, device, tree_tokens, alpha, force_eos):
self.device = device
self.value_model = GPT2LMHeadModel.from_pretrained(value_model_name)
self.tokenizer = GPT2Tokenizer.from_pretrained(valu... | value_outputs, value_next_token_logits = get_model_output(self.value_model, step, value_input_ids, | 6 | 2023-10-20 08:30:18+00:00 | 4k |
yifei-he/GOAT | experiments.py | [
{
"identifier": "ot_ablation",
"path": "ot_util.py",
"snippet": "def ot_ablation(size, mode):\n ns, nt = size, size\n plan = np.zeros((ns, nt))\n ran = np.arange(ns*nt)\n np.random.shuffle(ran)\n idx = ran[:size]\n\n for i in idx:\n row = i // nt\n col = i-i//nt * nt\n ... | import torch
import torch.optim as optim
import copy
import argparse
import random
import torch.backends.cudnn as cudnn
import time
from model import *
from train_model import *
from util import *
from ot_util import ot_ablation
from da_algo import *
from ot_util import generate_domains
from dataset import * | 1,769 |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def get_source_model(args, trainset, testset, n_class, mode, encoder=None, epochs=50, verbose=True):
print("Start training source model")
model = Classifier(encoder, MLP(mode=mode, n_class=n_class, hidden=1024)).to(device)
optimizer ... |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def get_source_model(args, trainset, testset, n_class, mode, encoder=None, epochs=50, verbose=True):
print("Start training source model")
model = Classifier(encoder, MLP(mode=mode, n_class=n_class, hidden=1024)).to(device)
optimizer ... | plan = ot_ablation(len(src_trainset), "random") | 0 | 2023-10-20 16:41:00+00:00 | 4k |
ansible/django-ansible-base | ansible_base/tests/unit/utils/test_validation.py | [
{
"identifier": "to_python_boolean",
"path": "ansible_base/utils/validation.py",
"snippet": "def to_python_boolean(value, allow_none=False):\n value = str(value)\n if value.lower() in ('true', '1', 't'):\n return True\n elif value.lower() in ('false', '0', 'f'):\n return False\n ... | import pytest
from rest_framework.exceptions import ValidationError
from ansible_base.utils.validation import to_python_boolean, validate_cert_with_key, validate_image_data, validate_url | 1,721 |
@pytest.mark.parametrize(
"valid,url,schemes,allow_plain_hostname",
[
(False, 4, [], True),
(False, "https://example", ['https'], False),
(True, "https://example", ['https'], True),
(True, "https://somedomain.example.com/sso/complete/saml/", ['https'], True),
(False, "... |
@pytest.mark.parametrize(
"valid,url,schemes,allow_plain_hostname",
[
(False, 4, [], True),
(False, "https://example", ['https'], False),
(True, "https://example", ['https'], True),
(True, "https://somedomain.example.com/sso/complete/saml/", ['https'], True),
(False, "... | res = validate_image_data(image_data) | 2 | 2023-10-20 13:20:12+00:00 | 4k |
violet-sto/HN-GFN | dataset.py | [
{
"identifier": "MolMDPExtended",
"path": "mol_mdp_ext.py",
"snippet": "class MolMDPExtended(MolMDP):\n\n def build_translation_table(self):\n \"\"\"build a symmetry mapping for blocks. Necessary to compute parent transitions\"\"\"\n self.translation_table = {}\n for blockidx in ... | import pandas as pd
import numpy as np
import torch
import time
import threading
import json
from sklearn.utils import shuffle
from mol_mdp_ext import MolMDPExtended, BlockMoleculeDataExtended
from tqdm import tqdm
from botorch.utils.multi_objective.hypervolume import Hypervolume | 3,098 |
class Dataset:
def __init__(self, args, bpath, oracle, device):
self.test_split_rng = np.random.RandomState(142857)
self.train_rng = np.random.RandomState(int(time.time()))
self.train_mols = []
self.test_mols = []
self.all_mols = []
self.train_mols_map = {}
|
class Dataset:
def __init__(self, args, bpath, oracle, device):
self.test_split_rng = np.random.RandomState(142857)
self.train_rng = np.random.RandomState(int(time.time()))
self.train_mols = []
self.test_mols = []
self.all_mols = []
self.train_mols_map = {}
| self.mdp = MolMDPExtended(bpath) | 0 | 2023-10-24 14:10:35+00:00 | 4k |
line/Skeleton-Temporal-Action-Localization | evaluation/eval.py | [
{
"identifier": "getClassificationMAP",
"path": "evaluation/classificationMAP.py",
"snippet": "def getClassificationMAP(confidence, labels):\n \"\"\" confidence and labels are of dimension n_samples x n_label \"\"\"\n\n AP = []\n for i in range(np.shape(labels)[1]):\n AP.append(getAP(con... | import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from .classificationMAP import getClassificationMAP as cmAP
from .detectionMAP import getSingleStreamDetectionMAP as dsmAP
from .detectionMAP import getTwoStreamDetectionMAP as dtmAP
from .utils import write_results_to_e... | 1,845 |
def ss_eval(epoch, dataloader, args, logger, model, device):
vid_preds = []
frm_preds = []
vid_lens = []
labels = []
for num, sample in enumerate(dataloader):
if (num + 1) % 100 == 0:
print("Testing test data point %d of %d" % (num + 1, len(dataloader)))
features = s... |
def ss_eval(epoch, dataloader, args, logger, model, device):
vid_preds = []
frm_preds = []
vid_lens = []
labels = []
for num, sample in enumerate(dataloader):
if (num + 1) % 100 == 0:
print("Testing test data point %d of %d" % (num + 1, len(dataloader)))
features = s... | dmap, iou = dtmAP( | 0 | 2023-10-20 05:38:16+00:00 | 4k |
SALT-NLP/Efficient_Unlearning | src/models/transformers/parameter-efficient-finetuning/modeling.py | [
{
"identifier": "AdapterConfig",
"path": "src/models/transformers/parameter-efficient-finetuning/configuration.py",
"snippet": "class AdapterConfig(AdapterConfigBase):\n \"\"\"\n Base class that models the architecture of an adapter.\n\n Args:\n mh_adapter (:obj:`bool`): If True, add ada... | import math
import torch
from torch import nn
from transformers.activations import get_activation
from .configuration import AdapterConfig, AdapterFusionConfig
from .context import ForwardContext | 2,948 |
class Activation_Function_Class(nn.Module):
"""
Implementation of various activation function.
"""
def __init__(self, hidden_act):
super().__init__()
if hidden_act.lower() == "leakyrelu":
self.f = nn.functional.leaky_relu
else:
self.f = get_activatio... |
class Activation_Function_Class(nn.Module):
"""
Implementation of various activation function.
"""
def __init__(self, hidden_act):
super().__init__()
if hidden_act.lower() == "leakyrelu":
self.f = nn.functional.leaky_relu
else:
self.f = get_activatio... | config: AdapterConfig, | 0 | 2023-10-18 18:05:54+00:00 | 4k |
yntha/cstruct | cstruct/_classwrap.py | [
{
"identifier": "collect_metadata",
"path": "cstruct/_metadata.py",
"snippet": "def collect_metadata(class_obj: dataclass) -> StructMetadata:\n metadata = StructMetadata()\n\n for field in dataclasses.fields(class_obj):\n # the parameters passed to the dataclass constructor individually\n ... | import dataclasses
import typing
from dataclasses import dataclass
from ._metadata import collect_metadata
from ._lexer import CStructLexer | 3,155 | # --------------------------------------------------------------------------------------
# Copyright(C) 2023 yntha -
# -
# This program is free software: you can redistribut... | # --------------------------------------------------------------------------------------
# Copyright(C) 2023 yntha -
# -
# This program is free software: you can redistribut... | self.meta = collect_metadata(self) | 0 | 2023-10-22 18:33:32+00:00 | 4k |
sehyun03/MulActSeg | trainer/active_joint_multi_lossdecomp.py | [
{
"identifier": "active_joint_multi",
"path": "trainer/active_joint_multi.py",
"snippet": "class ActiveTrainer(active.ActiveTrainer):\n def __init__(self, args, logger, selection_iter):\n def get_criterion(self):\n def zero_if_nan(self, loss):\n def check_loss_sanity(self, loss):\n def up... | import torch
import numpy as np
import torch.nn.functional as F
from torch import nn
from tqdm import tqdm
from torch_scatter import scatter
from trainer import active_joint_multi
from trainer.active_joint_multi_predignore_mclossablation2 import GroupMultiLabelCE_onlymulti
from utils.loss import MultiChoiceCE | 2,380 |
r"""
Decomposition of previous multi-positive loss & group-multi loss
- One-hot spxs: CE loss
- Multi-hot spxs: Multi-positive, Group Multi
- without predignore
"""
class OnehotCEMultihotChoice(MultiChoiceCE):
def __init__(self, num_class, temperature=1.0, reduction='mean'):
super().__init__(num_class, tem... |
r"""
Decomposition of previous multi-positive loss & group-multi loss
- One-hot spxs: CE loss
- Multi-hot spxs: Multi-positive, Group Multi
- without predignore
"""
class OnehotCEMultihotChoice(MultiChoiceCE):
def __init__(self, num_class, temperature=1.0, reduction='mean'):
super().__init__(num_class, tem... | class ActiveTrainer(active_joint_multi.ActiveTrainer): | 0 | 2023-10-24 09:19:58+00:00 | 4k |
hms-dbmi/CHIEF | train.py | [
{
"identifier": "read_yaml",
"path": "utils/utils.py",
"snippet": "def read_yaml(fpath=\"./configs/sample.yaml\"):\n with open(fpath, mode=\"r\") as file:\n yml = yaml.load(file, Loader=yaml.Loader)\n return Dict(yml)"
},
{
"identifier": "seed_torch",
"path": "utils/utils.py... | import argparse
import os
import shutil
import torch
from utils.utils import read_yaml, seed_torch
from utils.trainer import Trainer | 2,304 |
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--config_path', type=str)
parser.add_argument('--begin', type=int, default=0)
parser.add_argument('--end', type=int, default=10)
return parser.parse_args()
if __name__ == '__main__':
args = get_args()
device = to... |
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--config_path', type=str)
parser.add_argument('--begin', type=int, default=0)
parser.add_argument('--end', type=int, default=10)
return parser.parse_args()
if __name__ == '__main__':
args = get_args()
device = to... | cfg = read_yaml(args.config_path) | 0 | 2023-10-17 21:19:25+00:00 | 4k |
justincui03/tesla | buffer.py | [
{
"identifier": "get_dataset",
"path": "utils.py",
"snippet": "def get_dataset(dataset, data_path, batch_size=1, args=None):\n\n class_map = None\n loader_train_dict = None\n class_map_inv = None\n\n if dataset == 'CIFAR10':\n channel = 3\n im_size = (32, 32)\n num_class... | import os
import argparse
import torch
import torch.nn as nn
import copy
import warnings
from tqdm import tqdm
from utils import get_dataset, get_network, get_daparam,\
TensorDataset, epoch, ParamDiffAug
from PIL import PngImagePlugin | 3,186 |
LARGE_ENOUGH_NUMBER = 100
PngImagePlugin.MAX_TEXT_CHUNK = LARGE_ENOUGH_NUMBER * (1024**2)
warnings.filterwarnings("ignore", category=DeprecationWarning)
def main(args):
args.dsa = True if args.dsa == 'True' else False
args.device = 'cuda' if torch.cuda.is_available() else 'cpu'
args.dsa_param = ParamDif... |
LARGE_ENOUGH_NUMBER = 100
PngImagePlugin.MAX_TEXT_CHUNK = LARGE_ENOUGH_NUMBER * (1024**2)
warnings.filterwarnings("ignore", category=DeprecationWarning)
def main(args):
args.dsa = True if args.dsa == 'True' else False
args.device = 'cuda' if torch.cuda.is_available() else 'cpu'
args.dsa_param = ParamDif... | channel, im_size, num_classes, class_names, mean, std, dst_train, dst_test, testloader, loader_train_dict, class_map, class_map_inv = get_dataset(args.dataset, args.data_path, args.batch_real, args=args) | 0 | 2023-10-17 23:11:36+00:00 | 4k |
upiterbarg/hihack | models/hierarchical_transformer_lstm.py | [
{
"identifier": "generate_square_subsequent_mask",
"path": "models/transformer_lstm.py",
"snippet": "def generate_square_subsequent_mask(sz: int, device: str = \"cpu\") -> torch.Tensor:\n mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)\n mask = (\n mask.float()\n .masked... | import json
import numpy as np
import os
import pathlib
import pdb
import torch
import sys
from nle import nethack
from nle.nethack.actions import ACTIONS as A
from torch import nn
from torch.nn import functional as F
from .transformer_lstm import (
generate_square_subsequent_mask,
PositionalEncoding
)
from cha... | 2,541 | self.inference_unroll_length = flags.unroll_length if not 'inference_unroll_length' in flags else flags.inference_unroll_length
self.wrapped = False
def initial_state(self, batch_size=1):
return (
torch.zeros(1, batch_size, self.inference_unroll_length, self.inference_unroll_le... |
base_path = pathlib.Path().resolve()
sys.path.insert(0, os.path.join(base_path, '..', 'dungeonsdata-neurips2022/experiment_code/hackrl/models'))
class HierarchicalTransformerLSTM(nn.Module):
def __init__(self, shape, action_space, flags, device, num_strategies=20):
super(HierarchicalTransformerLSTM, sel... | trnsfrmr_core_mask = generate_square_subsequent_mask(T, trnsfrmr_core_input.device) | 0 | 2023-10-23 15:44:32+00:00 | 4k |
nmathey/finasync | finasync/realt.py | [
{
"identifier": "GNOSIS_API_TOKENLIST_URI",
"path": "finasync/constants.py",
"snippet": "GNOSIS_API_TOKENLIST_URI = (\n \"https://blockscout.com/xdai/mainnet/api?module=account&action=tokenlist&address=\"\n)"
},
{
"identifier": "REALT_API_TOKENLIST_URI",
"path": "finasync/constants.py",
... | import requests
import re
import json
import time
import os
import logging
from pathlib import Path
from datetime import datetime, timedelta
from json.decoder import JSONDecodeError
from finary_uapi.user_real_estates import (
get_user_real_estates,
delete_user_real_estates,
update_user_real_estates,
add... | 2,393 | logging.debug("My RealT Finary portfolio")
logging.debug(myFinary_real_estates)
myFinary_realT = {}
for item in myFinary_real_estates:
contractAddress = re.findall(r"0x.+", str(item.get("description")))
name = re.findall(r"- (.*) -", str(item.get("description")))
myFinary_realT.u... |
def get_realt_token_details(realt_token_contractAdress):
Now_Time = datetime.today()
RealT_OfflineTokensList_Path = Path(REALT_OFFLINE_TOKENS_LIST)
RealT_OfflineTokensList_Path.touch(exist_ok=True)
with open(RealT_OfflineTokensList_Path) as json_file:
try:
RealT_OfflineTokensLis... | user_estimated_value = token_details["totalTokens"] * convert_currency( | 3 | 2023-10-24 00:32:05+00:00 | 4k |
vitaliisili/petoshield-rest | petoshield_api/apps/policy/filters.py | [
{
"identifier": "ServiceProvider",
"path": "petoshield_api/apps/policy/models.py",
"snippet": "class ServiceProvider(ExportModelOperationsMixin('service_provider'), BaseModel):\n \"\"\"Model representing a service provider.\n Attributes:\n company_name (CharField): The name of the company. ... | from django_filters import rest_framework as filters
from .models import ServiceProvider, Policy, InsuranceCase, IncomingInvoice | 1,723 |
class ServiceProviderFilter(filters.FilterSet):
"""A filter class for the ServiceProvider model.
Attributes:
user (CharFilter): Filter for the 'user__name' field using the 'icontains' lookup.
created_at__year__exact (NumberFilter): Filter for the 'created_at__year' field with exact matching.
... |
class ServiceProviderFilter(filters.FilterSet):
"""A filter class for the ServiceProvider model.
Attributes:
user (CharFilter): Filter for the 'user__name' field using the 'icontains' lookup.
created_at__year__exact (NumberFilter): Filter for the 'created_at__year' field with exact matching.
... | model = ServiceProvider | 0 | 2023-10-19 08:09:10+00:00 | 4k |
biggzlar/plausible-uncertainties | train.py | [
{
"identifier": "get_device",
"path": "utils.py",
"snippet": "def get_device():\n return torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
},
{
"identifier": "UnivariateDummyData",
"path": "utils.py",
"snippet": "class UnivariateDummyData:\n\tdef __init__(self, N, X_ra... | import tqdm
import torch
import numpy as np
import matplotlib.pyplot as plt
from utils import get_device, UnivariateDummyData, get_predicted_cdf
from evidential_regression.networks import UnivariateDerNet
from evidential_regression.losses import UnivariateEvidentialRegressionLoss
from mle_mc_dropout.networks import Uni... | 2,487 |
# plot settings
plt.rcParams.update(
{
"font.size": 12,
"text.usetex": False,
"font.family": "stixgeneral",
"mathtext.fontset": "stix",
}
)
if __name__ == "__main__":
device = get_device()
print(f"Working on {device}!")
EPOCHS = 120
in_lower = -2.0
in_upper = 10.0
trai... |
# plot settings
plt.rcParams.update(
{
"font.size": 12,
"text.usetex": False,
"font.family": "stixgeneral",
"mathtext.fontset": "stix",
}
)
if __name__ == "__main__":
device = get_device()
print(f"Working on {device}!")
EPOCHS = 120
in_lower = -2.0
in_upper = 10.0
trai... | criterion = UnivariateEvidentialRegressionLoss() | 4 | 2023-10-19 08:44:08+00:00 | 4k |
avilliai/Bert_Vits2_Sever | modules.py | [
{
"identifier": "init_weights",
"path": "commons.py",
"snippet": "def init_weights(m, mean=0.0, std=0.01):\n classname = m.__class__.__name__\n if classname.find(\"Conv\") != -1:\n m.weight.data.normal_(mean, std)"
},
{
"identifier": "get_padding",
"path": "commons.py",
"snippet": "... | import copy
import math
import numpy as np
import scipy
import torch
import commons
from torch import nn
from torch.nn import functional as F
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm
from commons import init_weights, get_padding
from tran... | 2,812 | self.n_layers = n_layers
self.p_dropout = p_dropout
self.drop = nn.Dropout(p_dropout)
self.convs_sep = nn.ModuleList()
self.convs_1x1 = nn.ModuleList()
self.norms_1 = nn.ModuleList()
self.norms_2 = nn.ModuleList()
for i in range(n_layers):
dilation = kernel_size ** i
padding... |
LRELU_SLOPE = 0.1
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-5):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(torch.zeros(channels))
def forward(self, x):
x = x.transpose(1, -1)
... | self.convs1.apply(init_weights) | 0 | 2023-10-23 08:24:12+00:00 | 4k |
t-ega/whatsapp-cloud-sdk | whatsapp_cloud_sdk/_files/contact.py | [
{
"identifier": "File",
"path": "whatsapp_cloud_sdk/_files/file_object.py",
"snippet": "class File:\n \"\"\"Base Class for all file objects.\"\"\"\n\n __slots__ = ()\n _id_attrs = ()\n\n def __str__(self):\n \"\"\"Return a string representation of the object.\"\"\"\n attributes... | from typing import List, Optional, Union
from whatsapp_cloud_sdk._files.file_object import File
from whatsapp_cloud_sdk._utils.types import JSONDict
from whatsapp_cloud_sdk._validators.messages import (
AddressValidator,
NameValidator,
PhoneValidator,
OrgValidator,
URLValidator,
EmailValidator,
... | 1,630 | """This module contains an object that represents a Whatsapp Contact and it related details."""
# pylint: disable=redefined-builtin
# pylint: disable=too-few-public-methods
class Address(File):
"""
Represents a contact address.
Args:
street (str): The street address.
city (str): The ci... | """This module contains an object that represents a Whatsapp Contact and it related details."""
# pylint: disable=redefined-builtin
# pylint: disable=too-few-public-methods
class Address(File):
"""
Represents a contact address.
Args:
street (str): The street address.
city (str): The ci... | validator = AddressValidator( | 2 | 2023-10-15 21:12:45+00:00 | 4k |
caglarkucuk/earthformer-satellite-to-radar | ef-sat2rad/earthformer/cuboid_transformer/cuboid_transformer.py | [
{
"identifier": "CuboidSelfAttentionPatterns",
"path": "ef-sat2rad/earthformer/cuboid_transformer/cuboid_transformer_patterns.py",
"snippet": "def full_attention(input_shape):\ndef self_axial(input_shape):\ndef self_video_swin(input_shape, P=2, M=4):\ndef self_divided_space_time(input_shape):\ndef self_... | from typing import Sequence, Union
from functools import lru_cache
from collections import OrderedDict
from torch import nn
from einops import rearrange
from .cuboid_transformer_patterns import CuboidSelfAttentionPatterns, CuboidCrossAttentionPatterns
from .utils import (
get_activation, get_norm_layer,
_genera... | 3,433 | # spatiotemporal learned positional embedding
if self.typ == 't+h+w':
self.T_embed = nn.Embedding(num_embeddings=maxT, embedding_dim=embed_dim)
self.H_embed = nn.Embedding(num_embeddings=maxH, embedding_dim=embed_dim)
self.W_embed = nn.Embedding(num_embeddings=maxW, e... | """Only change done in this file is the added upsampling layer to the CuboidTransformerModel,
which increaes `h` and `w` dimensions of the input tensor by 2x to match the dimensions of the output tensor!
The rest is same with the original file from EarthFormer repo!
"""
"""A space-time Transformer with Cuboid Attenti... | self.layer_norm = get_norm_layer(normalization=normalization, | 2 | 2023-10-23 11:45:50+00:00 | 4k |
DTennant/GPC | data/fgvc_aircraft.py | [
{
"identifier": "subsample_instances",
"path": "data/data_utils.py",
"snippet": "def subsample_instances(dataset, prop_indices_to_subsample=0.8):\n\n np.random.seed(0)\n subsample_indices = np.random.choice(range(len(dataset)), replace=False,\n size=(int(pro... | import os
import pandas as pd
import numpy as np
import tarfile
from copy import deepcopy
from torchvision.datasets.folder import default_loader
from torch.utils.data import Dataset
from data.data_utils import subsample_instances
from config import aircraft_root
from six.moves import urllib | 2,580 | index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
"""
path, target = self.samples[index]
sample = self.loader(path)
if self.transform is not None:
sample = self.transform(sample)
if s... |
def make_dataset(dir, image_ids, targets):
assert(len(image_ids) == len(targets))
images = []
dir = os.path.expanduser(dir)
for i in range(len(image_ids)):
item = (os.path.join(dir, 'data', 'images',
'%s.jpg' % image_ids[i]), targets[i])
images.append(item... | whole_training_set = FGVCAircraft(root=aircraft_root, transform=train_transform, split='trainval') | 1 | 2023-10-23 18:23:22+00:00 | 4k |
camenduru/MiniGPT-v2-hf | minigpt4/models/base_model.py | [
{
"identifier": "download_cached_file",
"path": "minigpt4/common/dist_utils.py",
"snippet": "def download_cached_file(url, check_hash=True, progress=False):\n \"\"\"\n Download a file from a URL and cache it locally. If the file already exists, it is not downloaded again.\n If distributed, only... | import os
import logging
import contextlib
import numpy as np
import torch
import torch.nn as nn
from omegaconf import OmegaConf
from transformers import BertTokenizer, LlamaTokenizer
from transformers.models.llama.modeling_llama import LlamaForCausalLM
from peft import (
LoraConfig,
get_peft_model,
prepare... | 1,732 | """Base class for models."""
def __init__(self):
super().__init__()
@property
def device(self):
return list(self.parameters())[-1].device
def load_checkpoint(self, url_or_filename):
"""
Load from a finetuned checkpoint.
This should expect no mismatch in th... | """
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
class BaseModel(nn.Module):
"""Base class for models."""
def __init__(self):
... | visual_encoder = create_eva_vit_g( | 4 | 2023-10-15 19:54:22+00:00 | 4k |
deepghs/sdeval | sdeval/corrupt/aicorrupt.py | [
{
"identifier": "load_images",
"path": "sdeval/utils/images.py",
"snippet": "def _yield_images(images: ImagesTyping) -> Iterator[Image.Image]:\ndef load_images(images: ImagesTyping) -> List[Image.Image]:"
},
{
"identifier": "tqdm",
"path": "sdeval/utils/tqdm_.py",
"snippet": "def tqdm(*a... | import json
import numpy as np
from functools import lru_cache
from typing import Tuple, Optional, Mapping
from PIL import Image
from huggingface_hub import hf_hub_download
from imgutils.data import rgb_encode, ImageTyping, load_image
from imgutils.utils import open_onnx_model
from ..utils import ImagesTyping, load_ima... | 1,616 | This function downloads and opens the meta information of the AI image corrupted detection model specified by the given model name using Hugging Face Hub.
:param model_name: The name of the AI image corrupted detection model.
:type model_name: str
:return: The opened meta information of the AI image c... | """
Overview:
AI image corrupt evaluation metrics.
"""
_DEFAULT_MODEL_NAME = 'caformer_s36_v0_focal'
@lru_cache()
def _open_anime_aicop_model(model_name: str):
"""
Open the AI image corrupted detection model.
This function downloads and opens the AI image corrupted detection model specified by the... | for image in tqdm(image_list, silent=self.silent if silent is None else silent, desc=self.tqdm_desc) | 1 | 2023-10-18 03:35:52+00:00 | 4k |
nju-websoft/SCR | framework/dataloader.py | [
{
"identifier": "trigger_combine_event",
"path": "framework/utils.py",
"snippet": "def trigger_combine_event(old_data, new_data):\n if len(new_data) == 0:\n return old_data\n init = False\n res = []\n if len(old_data) == 0:\n init = True\n old_data = copy.deepcopy(new_da... | import torch
import os
import copy
import numpy as np
import random
import json
from torch.utils.data import Dataset, DataLoader
from framework.utils import trigger_combine_event, args_combine_event
from transformers import BertTokenizer
from transformers import logging | 3,446 | # ner2id
self.ner2id = json.load(open(self.data_root+'ner2id.json', 'r'))
self.ner2id['None'] = 0
self.id2ner = {}
for key, value in self.ner2id.items():
self.id2ner[value] = key
# iter
self.stream_turn = config.stream_turn
self.batch ... | logging.set_verbosity_warning()
logging.set_verbosity_error()
class ACETriDataset(Dataset):
def __init__(self, data):
self.data = data
def __len__(self):
return len(self.data)
def __getitem__(self, index):
return self.data[index]
def collate_fn(self, data):
... | tr_args_data = args_combine_event([], tr_args_data) | 1 | 2023-10-17 02:40:04+00:00 | 4k |
IBM/VillanDiffusion | caption_dataset.py | [
{
"identifier": "Log",
"path": "util.py",
"snippet": "class Log:\n HEADER = '\\033[95m'\n OKBLUE = '\\033[94m'\n OKCYAN = '\\033[96m'\n OKGREEN = '\\033[92m'\n WARNING = '\\033[93m'\n FAIL = '\\033[91m'\n ENDC = '\\033[0m'\n BOLD = '\\033[1m'\n UNDERLINE = '\\033[4m'\n \n ... | import io
import json
import os
import pathlib
import random
import shutil
import tempfile
import traceback
import warnings
import jsonlines
import datasets
import numpy as np
import requests
import torch
from random import sample
from typing import Callable, List, Tuple, Union
from functools import lru_cache, partial
... | 2,900 | IMAGE = "image"
IS_CLEAN = "is_clean"
RAW = "raw"
LABEL = "label"
CAPTION = "caption"
RAW_CAPTION = "raw_caption"
CAPTION_AUGMENT_KEY: str = "caption_aug"
# CAPTION_TOKEN = "caption_token"
def __init__(self, name: str, label: int=None, root: str=None,
channel: ... | # %%
"""
Backdoor Poisoned Dataset
"""
# from tmp_parse_dataset import LaionCoco
DEFAULT_VMIN = float(-1.0)
DEFAULT_VMAX = float(1.0)
class DatasetLoader(object):
# Dataset generation mode
MODE_FIXED = "FIXED"
MODE_FLEX = "FLEX"
# Dataset names
MNIST = "MNIST"
CIFAR10 = "CIFAR10"
C... | transforms.Lambda(lambda x: normalize(vmin_in=0, vmax_in=1, vmin_out=self.__vmin, vmax_out=self.__vmax, x=x)), | 1 | 2023-10-17 19:57:37+00:00 | 4k |
WHUlwb/Assisted_learning | train_t.py | [
{
"identifier": "Dice_loss",
"path": "loss.py",
"snippet": "def Dice_loss(inputs, target, beta=1, smooth = 1e-5):\r\n # inputs B, C, H, W, and target B, H, W, C. \r\n # There are C dimensions in total, each dimension representing a class.\r\n n, c, h, w = inputs.size()\r\n nt, ht, wt, ct = t... | import torch
import numpy as np
import os
import torch.nn as nn
import metric
import time
from torch.utils.data import DataLoader
from loss import Dice_loss,CE_Loss
from torch.autograd import Variable
from dataset import MyDataset
from config import config
from hrnet.hrnet import HRnet
from torch.cuda.amp import GradSc... | 2,152 | scaler = Gradscaler()
traindd = MyDataset(config.trainroot,is_training=True)
traindata = DataLoader(traindd,batch_size=config.batch_size, shuffle=True)
valdata = DataLoader(MyDataset(config.valroot,is_training=False), num_workers=0, batch_size=config.batch_size, shuffle=False)
net = HRnet(in_channel=3,num_classes=conf... | scaler = Gradscaler()
traindd = MyDataset(config.trainroot,is_training=True)
traindata = DataLoader(traindd,batch_size=config.batch_size, shuffle=True)
valdata = DataLoader(MyDataset(config.valroot,is_training=False), num_workers=0, batch_size=config.batch_size, shuffle=False)
net = HRnet(in_channel=3,num_classes=conf... | dice = Dice_loss(rgbresult,seg) | 0 | 2023-10-17 06:19:02+00:00 | 4k |
dagedarr/telegram-budget | handlers/registration_handler.py | [
{
"identifier": "get_by_id",
"path": "core/crud.py",
"snippet": "async def get_by_id(\n model: ModelType,\n obj_id: int,\n session: AsyncSession\n) -> ModelType:\n \"\"\"\n Получение объекта по ID.\n\n Parameters:\n - model (ModelType): Тип модели SQLAlchemy.\n - obj_id (int): Ид... | from aiogram import F, Router
from aiogram.fsm.context import FSMContext
from aiogram.types import CallbackQuery, Message
from sqlalchemy.ext.asyncio import AsyncSession
from core.crud import get_by_id, get_or_create, update
from forms import RegistrationForm
from keyboards import set_info_keyboard, universal_keyboard
... | 1,742 |
router = Router(name='registration_router')
# ------------------------ REGISTRATION ------------------------
@router.callback_query(F.data == 'registration')
async def registration(callback: CallbackQuery, session: AsyncSession):
"""Регистрация пользователя."""
|
router = Router(name='registration_router')
# ------------------------ REGISTRATION ------------------------
@router.callback_query(F.data == 'registration')
async def registration(callback: CallbackQuery, session: AsyncSession):
"""Регистрация пользователя."""
| await get_or_create( | 1 | 2023-10-23 17:30:24+00:00 | 4k |
nchen909/Pass-Tuning | evaluator/CodeBLEU/calc_code_bleu.py | [
{
"identifier": "bleu",
"path": "evaluator/CodeBLEU/bleu.py",
"snippet": "def sentence_bleu(\r\n references,\r\n hypothesis,\r\n weights=(0.25, 0.25, 0.25, 0.25),\r\n smoothing_function=None,\r\n auto_reweigh=False,\r\n):\r\ndef corpus_bleu(\r\n list_of_references,\r\n hypotheses,\r... | import argparse
import os
from evaluator.CodeBLEU import bleu, weighted_ngram_match, syntax_match, dataflow_match
from utils import get_lang_by_task
| 1,902 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# -*- coding:utf-8 -*-
# import evaluator.CodeBLEU.weighted_ngram_match
# import evaluator.CodeBLEU.syntax_match
# import evaluator.CodeBLEU.dataflow_match
def get_codebleu(refs, hyp, lang, params='0.25,0.25,0.25,0.25',args=None):
if... | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# -*- coding:utf-8 -*-
# import evaluator.CodeBLEU.weighted_ngram_match
# import evaluator.CodeBLEU.syntax_match
# import evaluator.CodeBLEU.dataflow_match
def get_codebleu(refs, hyp, lang, params='0.25,0.25,0.25,0.25',args=None):
if... | args.lang = get_lang_by_task(args.task, args.sub_task)
| 4 | 2023-10-20 09:24:44+00:00 | 4k |
openfoodfacts/open-prices | app/tasks.py | [
{
"identifier": "crud",
"path": "app/crud.py",
"snippet": "def get_users_query(filters: ProductFilter | None = None):\ndef get_users(db: Session, filters: ProductFilter | None = None):\ndef get_user(db: Session, user_id: str):\ndef get_user_by_user_id(db: Session, user_id: str):\ndef get_user_by_token(d... | import datetime
import tqdm
from openfoodfacts import DatasetType, Flavor, ProductDataset
from openfoodfacts.types import JSONType
from openfoodfacts.utils import get_logger
from sqlalchemy import or_, select
from sqlalchemy.orm import Session
from app import crud
from app.models import Product
from app.schemas import ... | 2,889 |
logger = get_logger(__name__)
# Users
# ------------------------------------------------------------------------------
def increment_user_price_count(db: Session, user: UserCreate):
crud.increment_user_price_count(db, user=user)
# Products
# -------------------------------------------------------------------... |
logger = get_logger(__name__)
# Users
# ------------------------------------------------------------------------------
def increment_user_price_count(db: Session, user: UserCreate):
crud.increment_user_price_count(db, user=user)
# Products
# -------------------------------------------------------------------... | for key in OFF_FIELDS: | 6 | 2023-10-21 14:02:15+00:00 | 4k |
krasnoukhov/homeassistant-smart-maic | custom_components/smart_maic/config_flow.py | [
{
"identifier": "DEVICE_NAME",
"path": "custom_components/smart_maic/const.py",
"snippet": "DEVICE_NAME = \"device_name\""
},
{
"identifier": "DEVICE_ID",
"path": "custom_components/smart_maic/const.py",
"snippet": "DEVICE_ID = \"devid\""
},
{
"identifier": "DEVICE_TYPE",
"pa... | import logging
import voluptuous as vol
import homeassistant.helpers.config_validation as cv
from typing import Any
from homeassistant import config_entries
from homeassistant.components import mqtt
from homeassistant.core import HomeAssistant
from homeassistant.data_entry_flow import AbortFlow
from .const import (
... | 1,627 | """Config flow for Smart MAIC integration."""
from __future__ import annotations
_LOGGER = logging.getLogger(__name__)
USER_SCHEMA = vol.Schema(
{
vol.Required(IP_ADDRESS): cv.string,
vol.Required(PIN): cv.string,
vol.Required(DEVICE_NAME, default="Energy"): cv.string,
}
)
async ... | """Config flow for Smart MAIC integration."""
from __future__ import annotations
_LOGGER = logging.getLogger(__name__)
USER_SCHEMA = vol.Schema(
{
vol.Required(IP_ADDRESS): cv.string,
vol.Required(PIN): cv.string,
vol.Required(DEVICE_NAME, default="Energy"): cv.string,
}
)
async ... | class ConfigFlow(config_entries.ConfigFlow, domain=DOMAIN): | 3 | 2023-10-16 17:24:45+00:00 | 4k |
JoaoPedro9674/django-ledger | django_ledger/models/customer.py | [
{
"identifier": "ContactInfoMixIn",
"path": "django_ledger/models/mixins.py",
"snippet": "class ContactInfoMixIn(models.Model):\n \"\"\"\n Implements a common set of fields used to document contact information.\n\n Attributes\n ----------\n address_1: str\n A string used to documen... | from uuid import uuid4
from django.core.exceptions import ObjectDoesNotExist
from django.db import models, transaction, IntegrityError
from django.db.models import Q, F, QuerySet
from django.utils.translation import gettext_lazy as _
from django_ledger.models.mixins import ContactInfoMixIn, CreateUpdateMixIn, TaxCollec... | 2,326 |
class CustomerModelQueryset(QuerySet):
"""
A custom defined QuerySet for the CustomerModel. This implements multiple methods or queries needed to get a
filtered QuerySet based on the CustomerModel status. For example, we might want to have list of Customers that
are active or hidden. All these sepa... | """
Django Ledger created by Miguel Sanda <msanda@arrobalytics.com>.
Copyright© EDMA Group Inc licensed under the GPLv3 Agreement.
Contributions to this module:
* Miguel Sanda <msanda@arrobalytics.com>
* Pranav P Tulshyan <ptulshyan77@gmail.com>
A Customer refers to the person or entity that buys product and ... | if isinstance(entity_slug, lazy_loader.get_entity_model()): | 3 | 2023-10-20 01:07:20+00:00 | 4k |
HLTCHKUST/InstructAlign | run_t2t_finetuning.py | [
{
"identifier": "load_flores_datasets",
"path": "data_utils.py",
"snippet": "def load_flores_datasets(pivot_langs=['eng_Latn'], augmentation='multilingual', num_train_ratio=1.0):\n def inject_lang(row, lang1, lang2):\n row['lang1'] = lang_map[lang1]\n row['lang2'] = lang_map[lang2]\n ... | import logging
import os
import sys
import random
import numpy as np
import pandas as pd
import torch
import transformers
import datasets
from dataclasses import dataclass, field
from typing import Optional
from transformers import (
AutoConfig,
AutoModelForSeq2SeqLM,
AutoModelForCausalLM,
AutoTokenizer... | 3,399 | "than this will be truncated, sequences shorter will be padded."
)
},
)
max_target_length: Optional[int] = field(
default=128,
metadata={
"help": (
"The maximum total sequence length for target text after tokenization. Sequences lon... | #!/usr/bin/env python
# coding=utf-8
# Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.ap... | raw_datasets = load_flores_datasets(pivot_langs=['eng_Latn'], augmentation=data_args.augmentation_type, num_train_ratio=data_args.num_train_ratio) | 0 | 2023-10-24 07:46:05+00:00 | 4k |
acolas1/KGSimple | eval_KGSimp/eval_baselines.py | [
{
"identifier": "args",
"path": "cli.py",
"snippet": ""
},
{
"identifier": "SaliencyBERTScore",
"path": "scoring/saliency_scorer.py",
"snippet": "class SaliencyBERTScore:\n def __init__(self, lmscorer = \"bertscore\", lang=\"en\"):\n self.bertscore = evaluate.load(lmscorer)\n ... | import os
import sys
import logging
import random
import numpy as np
import torch
import pandas as pd
import stanza
import sacrebleu.tokenizers.tokenizer_13a as tok
from ast import literal_eval
from eval_utils import *
from eval_batched import *
from cli import args
from scoring.saliency_scorer import Sali... | 1,745 | #### read in result files, format, run eval functions
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# setting path
sys.path.append('../../')
sys.path.append('/blue/daisyw/acolas1/KGSimplification/')
def eval():
| #### read in result files, format, run eval functions
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# setting path
sys.path.append('../../')
sys.path.append('/blue/daisyw/acolas1/KGSimplification/')
def eval():
| eval_mod = args.eval_mod ## model + eval type
| 0 | 2023-10-24 13:24:23+00:00 | 4k |
yuanxy92/DANTE | train_electric_optical_kernel.py | [
{
"identifier": "train_complex",
"path": "optical_layer.py",
"snippet": "def train_complex(label_nopad, folder_prefix, epoch, whole_dim, phase_dim, wave_lambda, focal_length, pixel_size, compute_loss_region, factor):\n image = np.zeros((1, whole_dim, whole_dim))\n image[0, whole_dim//2, whole_dim/... | import torch.nn.functional as F
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
import os
import os.path
import time
import torch
import math
import platform
import torchsummary
import logging
import math
from optical_layer import train_complex
from utils import padding, tile_kernels
from impor... | 2,523 | elif platform.system().lower() == 'linux':
server_dir = '/data/xiaoyun/Elec-Opt-D2NN/'
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
device_cpu = torch.device('cpu')
device_gpu = torch.device('cuda:0')
model_idx = '3bs'
whole_dim = 2000
phase_dim = 1200
wave_lambda = 532e-9
focal_length = 14.5e-2
pixel_size = 8e-6... | # -*- coding: utf-8 -*-
########################################################################
if platform.system().lower() == 'windows':
server_dir = './'
# os.environ["CUDA_VISIBLE_DEVICES"] = '0'
elif platform.system().lower() == 'linux':
server_dir = '/data/xiaoyun/Elec-Opt-D2NN/'
os.environ["CUD... | folder_fitting, loss = train_complex(psf_to_fit, folder_prefix, train_epoch, whole_dim, phase_dim, wave_lambda, focal_length, pixel_size, compute_loss_region, factor) | 0 | 2023-10-19 10:42:47+00:00 | 4k |
CAMeL-Lab/camel_parser | src/data_preparation.py | [
{
"identifier": "ConllParams",
"path": "src/classes.py",
"snippet": "class ConllParams:\n file_path: str\n parse_model_path: str\n \n def __iter__(self):\n return iter(astuple(self))"
},
{
"identifier": "TextParams",
"path": "src/classes.py",
"snippet": "class TextPara... | import re
import pandas as pd
from typing import List, Union
from camel_tools.disambig.common import DisambiguatedWord
from src.classes import ConllParams, TextParams, PreprocessedTextParams, TokenizedParams, TokenizedTaggedParams
from src.dependency_parser.biaff_parser import parse_conll, parse_text_tuples
from src.in... | 2,083 |
FileTypeParams = Union[ConllParams, TextParams, PreprocessedTextParams, TokenizedParams, TokenizedTaggedParams]
def get_feats_from_text_tuples(text_tuples: List[List[tuple]]) -> List[List[str]]:
"""Extract the FEATS columns from the unparsed data.
FEATS will exist only for text and pre-processed text input... |
FileTypeParams = Union[ConllParams, TextParams, PreprocessedTextParams, TokenizedParams, TokenizedTaggedParams]
def get_feats_from_text_tuples(text_tuples: List[List[tuple]]) -> List[List[str]]:
"""Extract the FEATS columns from the unparsed data.
FEATS will exist only for text and pre-processed text input... | token_lines = split_lines_words(lines) | 11 | 2023-10-21 10:39:28+00:00 | 4k |
aiueola/neurips2023-future-dependent-ope | src/ope/value_based.py | [
{
"identifier": "DiscreteStateLSTMVfunction",
"path": "src/ope/v_func.py",
"snippet": "class DiscreteStateLSTMVfunction(nn.Module):\n def __init__(\n self,\n n_states: int = 500,\n n_actions: int = 6,\n memory_length: int = 0,\n future_length: int = 0,\n lstm... | from dataclasses import dataclass
from typing import Tuple, Optional, Union
from torch import optim
from sklearn.utils import check_random_state
from policy.policy import BasePolicy
from .v_func import DiscreteStateLSTMVfunction, ContinuousStateLSTMVfunction
from .base import BaseNeuralValueBasedOffPolicyEstimator
from... | 3,512 | """Value-Based Estimator."""
@dataclass
class NeuralFutureDependentValueBasedOPE(BaseNeuralValueBasedOffPolicyEstimator):
behavior_policy: BasePolicy
evaluation_policy: BasePolicy
| """Value-Based Estimator."""
@dataclass
class NeuralFutureDependentValueBasedOPE(BaseNeuralValueBasedOffPolicyEstimator):
behavior_policy: BasePolicy
evaluation_policy: BasePolicy | v_function: Union[DiscreteStateLSTMVfunction, ContinuousStateLSTMVfunction] | 0 | 2023-10-24 06:09:37+00:00 | 4k |
JerBouma/FinancePortfolio | financeportfolio/portfolio_controller.py | [
{
"identifier": "excel_model",
"path": "financeportfolio/excel_model.py",
"snippet": "def create_portfolio_performance_excel_report(\n writer: pd.ExcelWriter, dataset: pd.DataFrame, sheet_name: str, currency: str = \"$\"\n):\ndef create_transactions_performance_excel_report(\n writer: pd.ExcelWrit... | import pandas as pd
from financetoolkit import Toolkit
from financeportfolio import excel_model, helpers, portfolio_model
| 3,123 |
Returns:
DataFrame: A DataFrame containing transaction performance metrics.
Raises:
ValueError: If an invalid or unsupported period_string is provided.
"""
if self._daily_historical_data.empty:
try:
self.collect_historical_da... | """Portfolio Module"""
# pylint: disable=too-many-instance-attributes,abstract-class-instantiated,
# pylint: disable=too-few-public-methods,protected-access,too-many-lines
class Portfolio:
"""
A class for managing and analyzing your portfolio.
This class provides functionality for loadin... | excel_model.create_portfolio_overview_excel_report(
| 0 | 2023-10-15 09:16:04+00:00 | 4k |
gschramm/2023-MIC-ImageRecon-Shortcourse | 07_osem_varnet_evaluation.py | [
{
"identifier": "EMUpdateModule",
"path": "layers.py",
"snippet": "class EMUpdateModule(torch.nn.Module):\n\n def __init__(\n self,\n projector: parallelproj.LinearOperator,\n ) -> None:\n\n super().__init__()\n self._projector = projector\n\n self._fwd_op_layer ... | import argparse
import json
import utils
import parallelproj
import array_api_compat.torch as torch
import array_api_compat.numpy as np
import pymirc.viewer as pv
from layers import EMUpdateModule
from models import Unet3D, SimpleOSEMVarNet, PostReconNet
from data import load_brain_image_batch, simulate_data_batch
from... | 3,351 | """minimal script that evaluates trained OSEM varnets
"""
from __future__ import annotations
parser = argparse.ArgumentParser(description='OSEM-VARNet evaluation')
parser.add_argument('--run_dir')
parser.add_argument('--sens', type=float, default=1)
args = parser.parse_args()
run_dir = Path(args.run_dir)
sens =... | """minimal script that evaluates trained OSEM varnets
"""
from __future__ import annotations
parser = argparse.ArgumentParser(description='OSEM-VARNet evaluation')
parser.add_argument('--run_dir')
parser.add_argument('--sens', type=float, default=1)
args = parser.parse_args()
run_dir = Path(args.run_dir)
sens =... | emission_image_database, attenuation_image_database = load_brain_image_batch( | 4 | 2023-10-16 07:18:26+00:00 | 4k |
ZiaWang/jqtrade | jqtrade/account/api.py | [
{
"identifier": "InvalidParam",
"path": "jqtrade/common/exceptions.py",
"snippet": "class InvalidParam(UserError):\n \"\"\" 用户参数错误 \"\"\"\n pass"
},
{
"identifier": "sys_logger",
"path": "jqtrade/common/log.py",
"snippet": "class SystemLogFormatter(logging.Formatter):\n class Co... | from ..common.exceptions import InvalidParam
from ..common.log import sys_logger
from ..scheduler.context import Context
from .order import OrderSide, OrderStatus, OrderStyle, MarketOrderStyle, LimitOrderStyle
from .position import Position | 2,969 | # -*- coding: utf-8 -*-
logger = sys_logger.getChild("account.api")
def _check_code(code):
if not (code[-4:] in ("XSHE", "XSHG") and code[:-5].isdigit()):
raise InvalidParam(f"标的代码错误: {code}")
def _check_amount(amount):
if not isinstance(amount, int) or amount == 0:
raise InvalidParam(f"委... | # -*- coding: utf-8 -*-
logger = sys_logger.getChild("account.api")
def _check_code(code):
if not (code[-4:] in ("XSHE", "XSHG") and code[:-5].isdigit()):
raise InvalidParam(f"标的代码错误: {code}")
def _check_amount(amount):
if not isinstance(amount, int) or amount == 0:
raise InvalidParam(f"委... | ctx = Context.get_instance() | 2 | 2023-10-24 01:34:27+00:00 | 4k |
Glasgow-AI4BioMed/GenKIE | data/pretrain_data/unify_dataset.py | [
{
"identifier": "data_utils",
"path": "data/data_utils.py",
"snippet": "def infer_language_pair(path):\ndef collate_tokens(\n values,\n pad_idx,\n eos_idx=None,\n left_pad=False,\n move_eos_to_beginning=False,\n pad_to_length=None,\n pad_to_multiple=1,\n pad_to_bsz=None,\n):\n ... | from io import BytesIO
from torchvision import transforms
from PIL import Image, ImageFile
from data import data_utils
from data.ofa_dataset import OFADataset
from utils.vision_helper import RandomAugment
import math
import logging
import random
import warnings
import numpy as np
import torch
import base64
import utils... | 2,902 | batch = {
"id": id,
"nsentences": len(samples),
"ntokens": ntokens,
"net_input": {
"src_tokens": src_tokens,
"src_lengths": src_lengths,
"patch_images": patch_images,
"patch_masks": patch_masks,
"code_masks": code_masks,
... | # Copyright 2022 The OFA-Sys Team.
# All rights reserved.
# This source code is licensed under the Apache 2.0 license
# found in the LICENSE file in the root directory.
ImageFile.LOAD_TRUNCATED_IMAGES = True
ImageFile.MAX_IMAGE_PIXELS = None
Image.MAX_IMAGE_PIXELS = None
logger = logging.getLogger(__name__)
warn... | RandomAugment(2, 7, isPIL=True, augs=['Identity', 'AutoContrast', 'Equalize', 'Brightness', 'Sharpness', | 2 | 2023-10-20 20:01:42+00:00 | 4k |
ArnaudParant/sel | tests/test_parser_n_formator.py | [
{
"identifier": "query_string_parser",
"path": "sel/query_string_parser.py",
"snippet": "AGGREG_TYPES = [\"aggreg\", \"histogram\", \"count\", \"distinct\", \"min\", \"max\", \"sum\", \"average\", \"stats\"]\nAGGREG_PARAMETER_MAPPING = {\n \"subaggreg\": None,\n \"interval\": None,\n \"size\": ... | import json
import pytest
import traceback
from sel import query_string_parser
from sel.query_string_parser import (
Value, QueryString, Comparator, Not, RangeFilter, Filter, Context,
Aggreg, Sort, Group, NoBracketGroup, Query
)
from sel import query_object_formator | 1,743 |
class TestParserNFormator:
@pytest.mark.parametrize(["query", "expected"], [
["toto", "toto"],
['"toto tata titi"', "toto tata titi"],
["toto tata titi", None], # Exception, does not match type Value
])
def test_value(self, query, expected):
try:
|
class TestParserNFormator:
@pytest.mark.parametrize(["query", "expected"], [
["toto", "toto"],
['"toto tata titi"', "toto tata titi"],
["toto tata titi", None], # Exception, does not match type Value
])
def test_value(self, query, expected):
try: | res = query_string_parser.parse(query, grammar=Value) | 0 | 2023-10-16 09:03:13+00:00 | 4k |
Qualcomm-AI-research/outlier-free-transformers | quantization/autoquant_utils.py | [
{
"identifier": "FP32Acts",
"path": "quantization/base_quantized_classes.py",
"snippet": "class FP32Acts(nn.Module):\n def forward(self, x):\n return x\n\n def reset_ranges(self):\n pass"
},
{
"identifier": "QuantizedActivation",
"path": "quantization/base_quantized_class... | import copy
import warnings
from torch import nn
from torch.nn import functional as F
from torch.nn.modules.pooling import _AdaptiveAvgPoolNd, _AvgPoolNd
from quantization.base_quantized_classes import (
FP32Acts,
QuantizedActivation,
QuantizedModule,
)
from quantization.hijacker import QuantizationHijacker... | 3,541 | def run_forward(self, x, weight, bias, offsets=None):
return F.layer_norm(
input=x.contiguous(),
normalized_shape=self.normalized_shape,
weight=weight.contiguous(),
bias=bias.contiguous(),
eps=self.eps,
)
class QuantEmbedding(Quantization... | # Copyright (c) 2023 Qualcomm Technologies, Inc.
# All Rights Reserved.
class QuantLinear(QuantizationHijacker, nn.Linear):
def run_forward(self, x, weight, bias, offsets=None):
return F.linear(x.contiguous(), weight.contiguous(), bias=bias)
class QuantizedActivationWrapper(QuantizedActivation):
"... | if isinstance(model[i], QuantizedModule): | 2 | 2023-10-23 15:59:50+00:00 | 4k |
QgZhan/ESVAE | main_snn_ae.py | [
{
"identifier": "aboutCudaDevices",
"path": "utils.py",
"snippet": "class aboutCudaDevices():\r\n def __init__(self):\r\n pass\r\n\r\n def num_devices(self):\r\n \"\"\"Return number of devices connected.\"\"\"\r\n return cuda.Device.count()\r\n\r\n def devices(self):\r\n ... | import os
import os.path
import numpy as np
import logging
import argparse
import pycuda.driver as cuda
import torch
import torchvision
import svae_models.sae as sae
from torch.utils.tensorboard import SummaryWriter
from utils import aboutCudaDevices
from utils import AverageMeter
from utils import aboutCud... | 1,921 |
max_accuracy = 0
min_loss = 1000
def train(network, trainloader, opti, epoch, n_step):
loss_meter = AverageMeter()
network = network.train()
for batch_idx, (real_img, label) in enumerate(trainloader):
opti.zero_grad()
real_img = real_img.to(device)
... |
max_accuracy = 0
min_loss = 1000
def train(network, trainloader, opti, epoch, n_step):
loss_meter = AverageMeter()
network = network.train()
for batch_idx, (real_img, label) in enumerate(trainloader):
opti.zero_grad()
real_img = real_img.to(device)
... | train_loader, test_loader = load_dataset_snn.load_mnist(data_path, args.batch_size, input_size, True)
| 3 | 2023-10-23 07:33:27+00:00 | 4k |
iesl/softmax_CPR_recommend | run_hyper.py | [
{
"identifier": "HyperTuning",
"path": "recbole/trainer/hyper_tuning.py",
"snippet": "class HyperTuning(object):\n r\"\"\"HyperTuning Class is used to manage the parameter tuning process of recommender system models.\n Given objective funciton, parameters range and optimization algorithm, using Hy... | import argparse
from recbole.trainer import HyperTuning
from recbole.quick_start import objective_function | 2,441 | # -*- coding: utf-8 -*-
# @Time : 2020/7/24 15:57
# @Author : Shanlei Mu
# @Email : slmu@ruc.edu.cn
# @File : run_hyper.py
# UPDATE:
# @Time : 2020/8/20 21:17, 2020/8/29
# @Author : Zihan Lin, Yupeng Hou
# @Email : linzihan.super@foxmail.com, houyupeng@ruc.edu.cn
def main():
parser = argparse.ArgumentPa... | # -*- coding: utf-8 -*-
# @Time : 2020/7/24 15:57
# @Author : Shanlei Mu
# @Email : slmu@ruc.edu.cn
# @File : run_hyper.py
# UPDATE:
# @Time : 2020/8/20 21:17, 2020/8/29
# @Author : Zihan Lin, Yupeng Hou
# @Email : linzihan.super@foxmail.com, houyupeng@ruc.edu.cn
def main():
parser = argparse.ArgumentPa... | hp = HyperTuning(objective_function, algo='exhaustive', | 1 | 2023-10-21 16:31:44+00:00 | 4k |
timapage/pyqt6-yolov8 | src/models/tracking/deep_sort/deep_sort.py | [
{
"identifier": "Extractor",
"path": "src/models/tracking/deep_sort/deep/feature_extractor.py",
"snippet": "class Extractor(object):\n def __init__(self, model_path, use_cuda=True):\n self.net = Net(reid=True)\n self.device = \"cuda\" if torch.cuda.is_available() and use_cuda else \"cpu... | import numpy as np
import torch
from .deep.feature_extractor import Extractor
from .sort.nn_matching import NearestNeighborDistanceMetric
from .sort.detection import Detection
from .sort.tracker import Tracker | 2,484 |
__all__ = ['DeepSort']
class DeepSort(object):
def __init__(self, model_path, max_dist=0.2, max_iou_distance=0.7, max_age=70, n_init=3, nn_budget=100, use_cuda=True):
self.extractor = Extractor(model_path, use_cuda=use_cuda)
max_cosine_distance = max_dist
nn_budget = 100
|
__all__ = ['DeepSort']
class DeepSort(object):
def __init__(self, model_path, max_dist=0.2, max_iou_distance=0.7, max_age=70, n_init=3, nn_budget=100, use_cuda=True):
self.extractor = Extractor(model_path, use_cuda=use_cuda)
max_cosine_distance = max_dist
nn_budget = 100 | metric = NearestNeighborDistanceMetric( | 1 | 2023-10-18 09:21:01+00:00 | 4k |
OthersideAI/self-operating-computer | operate/dialog.py | [
{
"identifier": "ModelNotRecognizedException",
"path": "operate/exceptions.py",
"snippet": "class ModelNotRecognizedException(Exception):\n \"\"\"Exception raised for unrecognized models.\n\n Attributes:\n model -- the unrecognized model\n message -- explanation of the error\n \"\... | import sys
import os
import platform
import asyncio
from prompt_toolkit.shortcuts import message_dialog
from prompt_toolkit import prompt
from operate.exceptions import ModelNotRecognizedException
from operate.prompts import USER_QUESTION
from operate.settings import Config
from operate.utils.style import (
ANSI_GR... | 2,962 |
# Load configuration
config = Config()
def main(model, terminal_prompt, voice_mode=False):
"""
Main function for the Self-Operating Computer.
Parameters:
- model: The model used for generating responses.
- terminal_prompt: A string representing the prompt provided in the terminal.
- voice_mo... |
# Load configuration
config = Config()
def main(model, terminal_prompt, voice_mode=False):
"""
Main function for the Self-Operating Computer.
Parameters:
- model: The model used for generating responses.
- terminal_prompt: A string representing the prompt provided in the terminal.
- voice_mo... | f"{ANSI_GREEN}[Self-Operating Computer]{ANSI_BLUE} Objective complete {ANSI_RESET}" | 3 | 2023-11-04 03:13:45+00:00 | 4k |
netease-youdao/EmotiVoice | demo_page_databaker.py | [
{
"identifier": "g2p_cn_en",
"path": "frontend.py",
"snippet": "def g2p_cn_en(text, g2p, lexicon):\ndef contains_chinese(text):"
},
{
"identifier": "JETSGenerator",
"path": "models/prompt_tts_modified/jets.py",
"snippet": "class JETSGenerator(nn.Module):\n def __init__(self, config) -... | import streamlit as st
import os, glob
import numpy as np
import torch
import re
import base64
from yacs import config as CONFIG
from frontend import g2p_cn_en, ROOT_DIR, read_lexicon, G2p
from exp.DataBaker.config.config import Config
from models.prompt_tts_modified.jets import JETSGenerator
from models.prompt_tts_mod... | 1,692 | # Copyright 2023, YOUDAO
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | # Copyright 2023, YOUDAO
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | generator = JETSGenerator(conf).to(DEVICE) | 1 | 2023-11-08 10:15:27+00:00 | 4k |
daveshap/OpenAI_Agent_Swarm | agents/tool_maker/unit_manager.py | [
{
"identifier": "AssistantManager",
"path": "agents/tool_maker/assistant_manager.py",
"snippet": "class AssistantManager:\n\n def __init__(self, client):\n self.client = client\n self.assistant = None\n self.agent_builder = AgentBuilder(client=self.client)\n Path(__file__)... | from agents.tool_maker.assistant_manager import AssistantManager
from agents.tool_maker.chat_manager import ChatManager
from shared.openai_config import get_openai_client | 2,723 |
class Unit:
"""
A class which creates and exposes chat functionality for a Unit Agent.
A Unit is a first prototype for a Minmium Viable Agent (MVA).
A `Unit` is two `Assistant`s in a symbiotic relationship.
One `Assistant` is the Interface with a thread sharing input with the contents passed via ... |
class Unit:
"""
A class which creates and exposes chat functionality for a Unit Agent.
A Unit is a first prototype for a Minmium Viable Agent (MVA).
A `Unit` is two `Assistant`s in a symbiotic relationship.
One `Assistant` is the Interface with a thread sharing input with the contents passed via ... | self.chat_manager = ChatManager(client=client) | 1 | 2023-11-07 23:12:05+00:00 | 4k |
S-LoRA/S-LoRA | slora/models/llama/layer_infer/post_layer_infer.py | [
{
"identifier": "LlamaPreAndPostLayerWeight",
"path": "slora/models/llama/layer_weights/pre_and_post_layer_weight.py",
"snippet": "class LlamaPreAndPostLayerWeight(PreAndPostLayerWeight):\n def __init__(self, tp_rank, world_size, data_type, network_config, mode):\n super().__init__(tp_rank, wo... | import torch
import torch.functional as F
import torch.distributed as dist
import numpy as np
from slora.models.llama.layer_weights.pre_and_post_layer_weight import LlamaPreAndPostLayerWeight
from einops import rearrange
from slora.models.llama.infer_struct import LlamaInferStateInfo
from slora.models.llama.triton_kern... | 1,649 |
class LlamaPostLayerInfer(PostLayerInferTpl):
"""
"""
def __init__(self, tp_rank, world_size, network_config, mode):
super().__init__(tp_rank, world_size, network_config, mode)
self.eps_ = network_config["rms_norm_eps"]
self.vocab_size_ = network_config["vocab_size"]
self.... |
class LlamaPostLayerInfer(PostLayerInferTpl):
"""
"""
def __init__(self, tp_rank, world_size, network_config, mode):
super().__init__(tp_rank, world_size, network_config, mode)
self.eps_ = network_config["rms_norm_eps"]
self.vocab_size_ = network_config["vocab_size"]
self.... | def _norm(self, input, infer_state, layer_weight:LlamaPreAndPostLayerWeight) -> torch.Tensor: | 0 | 2023-11-05 04:08:36+00:00 | 4k |
Yuliang-Liu/Monkey | data_generation/grit/grit/modeling/backbone/vit.py | [
{
"identifier": "PatchEmbed",
"path": "data_generation/grit/grit/modeling/backbone/utils.py",
"snippet": "class PatchEmbed(nn.Module):\n \"\"\"\n Image to Patch Embedding.\n \"\"\"\n\n def __init__(\n self, kernel_size=(16, 16), stride=(16, 16), padding=(0, 0), in_chans=3, embed_dim=7... | import logging
import math
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn as nn
import sys
import torch.utils.checkpoint as checkpoint
from functools import partial
from detectron2.layers import CNNBlockBase, Conv2d, get_norm
from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
fro... | 3,506 | bottleneck_channels,
norm="LN",
act_layer=nn.GELU,
):
"""
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
bottleneck_channels (int): number of output channels for the 3x3
"bo... | # Modified by Jialian Wu from https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py
sys.path.insert(0, 'models/grit_src/third_party/CenterNet2/projects/CenterNet2/')
logger = logging.getLogger(__name__)
__all__ = ["ViT"]
class Attention(nn.Module):
"""Multi-head Attent... | x, pad_hw = window_partition(x, self.window_size) | 3 | 2023-11-09 14:31:48+00:00 | 4k |
disler/multi-agent-postgres-data-analytics | postgres_da_ai_agent/agents/agents.py | [
{
"identifier": "PostgresAgentInstruments",
"path": "postgres_da_ai_agent/agents/instruments.py",
"snippet": "class PostgresAgentInstruments(AgentInstruments):\n \"\"\"\n Unified Toolset for the Postgres Data Analytics Multi-Agent System\n\n Advantages:\n - All agents have access to the ... | from typing import Optional, List, Dict, Any
from postgres_da_ai_agent.agents.instruments import PostgresAgentInstruments
from postgres_da_ai_agent.modules import orchestrator
from postgres_da_ai_agent.agents import agent_config
import autogen
import guidance | 3,173 | sr_data_analyst = autogen.AssistantAgent(
name="Sr_Data_Analyst",
llm_config=agent_config.run_sql_config,
system_message=SR_DATA_ANALYST_PROMPT,
code_execution_config=False,
human_input_mode="NEVER",
function_map={
"run_sql": instruments.run_sql,
}... |
# ------------------------ PROMPTS ------------------------
USER_PROXY_PROMPT = "A human admin. Interact with the Product Manager to discuss the plan. Plan execution needs to be approved by this admin."
DATA_ENGINEER_PROMPT = "A Data Engineer. Generate the initial SQL based on the requirements provided. Send it to t... | ) -> orchestrator.Orchestrator: | 1 | 2023-11-04 20:15:46+00:00 | 4k |
OpenBMB/ProAgent | ProAgent/running_recorder.py | [
{
"identifier": "CONFIG",
"path": "ProAgent/config.py",
"snippet": "CONFIG = RPAgentConfig.get_default_config()"
},
{
"identifier": "ENVIRONMENT",
"path": "ProAgent/router/utils.py",
"snippet": "class ENVIRONMENT(Enum):\n '''\n 决定了 record cache 的访问形式\n - Development：不访问缓存，从头开始\n... | import os
import time
import json
from colorama import Fore
from termcolor import colored
from ProAgent.config import CONFIG
from ProAgent.router.utils import ENVIRONMENT
from ProAgent.utils import Action
from ProAgent.loggers.logs import logger | 2,556 | Fore.RED,
record_dir,
)
self.newly_start = False
for dir_name in os.listdir(record_dir):
if dir_name == "LLM_inout_pair":
inout_pair_list = os.listdir(os.path.join(record_dir,dir_name))
inout_pair_list.sort()
for... |
def dump_common_things(object):
"""
Generates a function comment for the given function body.
Args:
object: The object to be processed.
Returns:
The processed object.
"""
if type(object) in [str,int,float, bool]:
return object
if type(object) == dict:
... | def regist_tool_call(self, action: Action, now_code: str): | 2 | 2023-11-03 01:20:14+00:00 | 4k |
LLaVA-VL/LLaVA-Plus-Codebase | llava/eval/run_llava.py | [
{
"identifier": "IMAGE_TOKEN_INDEX",
"path": "llava/constants.py",
"snippet": "IMAGE_TOKEN_INDEX = -200"
},
{
"identifier": "DEFAULT_IMAGE_TOKEN",
"path": "llava/constants.py",
"snippet": "DEFAULT_IMAGE_TOKEN = \"<image>\""
},
{
"identifier": "DEFAULT_IM_START_TOKEN",
"path":... | import argparse
import torch
import requests
import re
from llava.constants import (
IMAGE_TOKEN_INDEX,
DEFAULT_IMAGE_TOKEN,
DEFAULT_IM_START_TOKEN,
DEFAULT_IM_END_TOKEN,
IMAGE_PLACEHOLDER,
)
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretraine... | 3,462 |
def image_parser(args):
out = args.image_file.split(args.sep)
return out
def load_image(image_file):
if image_file.startswith("http") or image_file.startswith("https"):
response = requests.get(image_file)
image = Image.open(BytesIO(response.content)).convert("RGB")
else:
i... |
def image_parser(args):
out = args.image_file.split(args.sep)
return out
def load_image(image_file):
if image_file.startswith("http") or image_file.startswith("https"):
response = requests.get(image_file)
image = Image.open(BytesIO(response.content)).convert("RGB")
else:
i... | image_token_se = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN | 1 | 2023-11-07 13:06:02+00:00 | 4k |
opendilab/LLMRiddles | llmriddles/questions/level4.py | [
{
"identifier": "register_question",
"path": "llmriddles/questions/question.py",
"snippet": "def register_question(text: Union[Mapping[str, str], str],\n checkers: Union[Mapping[str, SingleLangCheckerTyping], MultiLangCheckerTyping],\n name=Union[Mapping[str, st... | import re
from .question import register_question
from .question import Checker
from typing import Callable
from .utility import count_words, word_reverse_text
from .math_tools import check_if_is_number, get_all_numbers
| 1,743 |
CN_TEXT_1 = """
第四章第一题（循环往复），请构造一个问题A，它的回复是不同于A的B，然后以B提问能再次得到A。
请在下面的输入框内填写你构造并点击按钮提交。
"""
EN_TEXT_1 = """
For the first question in chapter 4, please make a question A whose answer is B that is different from A, and then ask B to get A again.
Please enter your query below and click the submit button
... |
CN_TEXT_1 = """
第四章第一题（循环往复），请构造一个问题A，它的回复是不同于A的B，然后以B提问能再次得到A。
请在下面的输入框内填写你构造并点击按钮提交。
"""
EN_TEXT_1 = """
For the first question in chapter 4, please make a question A whose answer is B that is different from A, and then ask B to get A again.
Please enter your query below and click the submit button
... | if not check_if_is_number(user_text):
| 4 | 2023-11-07 03:09:55+00:00 | 4k |
Subsets and Splits
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that have consistent code formatting levels across multiple scales (2k, 4k, 8k, 12k) and reveals the structured formatting patterns within these repositories.
SQL Console for tianyang/repobench_python_v1.1
Compares cross-file and in-file code structure patterns across different complexity levels, revealing how file organization strategies vary with code size and potentially informing better code architecture decisions.
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that have complete performance data across all seven code complexity levels, revealing consistent benchmarking patterns across different code sizes.
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that contain all 7 distinct quality levels (2k through 32k), revealing complete datasets that might be useful for comprehensive analysis.