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 |
|---|---|---|---|---|---|---|---|---|---|---|
thuml/iTransformer | experiments/exp_basic.py | [
{
"identifier": "Transformer",
"path": "model/Transformer.py",
"snippet": "class Model(nn.Module):\n def __init__(self, configs):\n def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):\n def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):"
},
{
"identifier": "Inform... | import os
import torch
from model import Transformer, Informer, Reformer, Flowformer, Flashformer, \
iTransformer, iInformer, iReformer, iFlowformer, iFlashformer | 901 |
class Exp_Basic(object):
def __init__(self, args):
self.args = args
self.model_dict = {
'Transformer': Transformer,
'Informer': Informer,
'Reformer': Reformer,
'Flowformer': Flowformer,
'Flashformer': Flashformer,
'iTransforme... |
class Exp_Basic(object):
def __init__(self, args):
self.args = args
self.model_dict = {
'Transformer': Transformer,
'Informer': Informer,
'Reformer': Reformer,
'Flowformer': Flowformer,
'Flashformer': Flashformer,
'iTransforme... | 'iFlashformer': iFlashformer, | 9 | 2023-10-19 03:23:15+00:00 | 2k |
kylesargent/ZeroNVS | threestudio/utils/GAN/vae.py | [
{
"identifier": "LinearAttention",
"path": "threestudio/utils/GAN/attention.py",
"snippet": "class LinearAttention(nn.Module):\n def __init__(self, dim, heads=4, dim_head=32):\n super().__init__()\n self.heads = heads\n hidden_dim = dim_head * heads\n self.to_qkv = nn.Conv... | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from threestudio.utils.GAN.attention import LinearAttention
from threestudio.utils.GAN.util import instantiate_from_config | 1,458 | from the description in Section 3.5 of "Attention Is All You Need".
"""
assert len(timesteps.shape) == 1
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
emb = emb.to(device=timesteps.device)
emb = t... | # pytorch_diffusion + derived encoder decoder
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
... | class LinAttnBlock(LinearAttention): | 0 | 2023-10-24 19:02:44+00:00 | 2k |
princeton-nlp/LLM-Shearing | llmshearing/datasets/load_text_dataloader.py | [
{
"identifier": "TextDynamicStreamingDataset",
"path": "llmshearing/datasets/streaming_dataset.py",
"snippet": "class TextDynamicStreamingDataset(DynamicStreamingDataset):\n \"\"\" \n A dataset to load data dynamically from different domains\n Adapted from https://github.com/mosaicml/ll... | from collections import defaultdict
from collections.abc import Mapping
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Union
from omegaconf import DictConfig
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.data.data_collator import _torch... | 1,359 | """ Load text dataloader for training and evaluation. """
def build_text_dataloader(cfg: DictConfig, device_batch_size: int, dynamic: bool = False,
set_names: str = None, proportion: List[float] = None) -> DataLoader:
"""Builds a text dataloader.
Args:
cfg (DictConfig): C... | """ Load text dataloader for training and evaluation. """
def build_text_dataloader(cfg: DictConfig, device_batch_size: int, dynamic: bool = False,
set_names: str = None, proportion: List[float] = None) -> DataLoader:
"""Builds a text dataloader.
Args:
cfg (DictConfig): C... | dataset = TextStreamingDataset( | 1 | 2023-10-16 12:26:08+00:00 | 2k |
hugoycj/Instant-angelo | models/neus.py | [
{
"identifier": "BaseModel",
"path": "models/base.py",
"snippet": "class BaseModel(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.rank = get_rank()\n self.setup()\n if self.config.get('weights', None):\n self.... | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import models
from models.base import BaseModel
from models.utils import chunk_batch
from systems.utils import update_module_step
from nerfacc import ContractionType, OccupancyGrid, ray_marching, render_weight_from_density, render_weight_fro... | 1,393 |
class VarianceNetwork(nn.Module):
def __init__(self, config):
super(VarianceNetwork, self).__init__()
self.config = config
self.init_val = self.config.init_val
self.register_parameter('variance', nn.Parameter(torch.tensor(self.config.init_val)))
self.modulate = self.confi... |
class VarianceNetwork(nn.Module):
def __init__(self, config):
super(VarianceNetwork, self).__init__()
self.config = config
self.init_val = self.config.init_val
self.register_parameter('variance', nn.Parameter(torch.tensor(self.config.init_val)))
self.modulate = self.confi... | update_module_step(self.geometry, epoch, global_step) | 2 | 2023-10-22 02:53:17+00:00 | 2k |
HKUDS/GraphGPT | graphgpt/serve/gradio_web_server_graph.py | [
{
"identifier": "default_conversation",
"path": "graphgpt/conversation.py",
"snippet": "class SeparatorStyle(Enum):\nclass Conversation:\n SINGLE = auto()\n TWO = auto()\n MPT = auto()\n W, H = image.size\n H, W = longest_edge, shortest_edge\n ... | import argparse
import datetime
import json
import os
import time
import gradio as gr
import requests
import hashlib
from graphgpt.conversation import (default_conversation, conv_templates,
SeparatorStyle)
from graphgpt.constants import LOGDIR
from graphgpt.utils import (build_logger,... | 772 |
logger = build_logger("gradio_web_server", "gradio_web_server.log")
headers = {"User-Agent": "GraphGPT Client"}
no_change_btn = gr.Button.update()
enable_btn = gr.Button.update(interactive=True)
disable_btn = gr.Button.update(interactive=False)
priority = {
"vicuna-13b": "aaaaaaa",
"koala-13b": "aaaaaab"... |
logger = build_logger("gradio_web_server", "gradio_web_server.log")
headers = {"User-Agent": "GraphGPT Client"}
no_change_btn = gr.Button.update()
enable_btn = gr.Button.update(interactive=True)
disable_btn = gr.Button.update(interactive=False)
priority = {
"vicuna-13b": "aaaaaaa",
"koala-13b": "aaaaaab"... | state = default_conversation.copy() | 0 | 2023-10-15 05:13:24+00:00 | 2k |
hkchengrex/Cutie | gui/ritm/inference/predictors/brs_functors.py | [
{
"identifier": "_compute_iou",
"path": "gui/ritm/model/metrics.py",
"snippet": "def _compute_iou(pred_mask, gt_mask, ignore_mask=None, keep_ignore=False):\n if ignore_mask is not None:\n pred_mask = torch.where(ignore_mask, torch.zeros_like(pred_mask), pred_mask)\n\n reduction_dims = misc.... | import torch
import numpy as np
from ...model.metrics import _compute_iou
from .brs_losses import BRSMaskLoss | 1,041 |
class BaseOptimizer:
def __init__(self, optimizer_params,
prob_thresh=0.49,
reg_weight=1e-3,
min_iou_diff=0.01,
brs_loss=BRSMaskLoss(),
with_flip=False,
flip_average=False,
**kwargs):
se... |
class BaseOptimizer:
def __init__(self, optimizer_params,
prob_thresh=0.49,
reg_weight=1e-3,
min_iou_diff=0.01,
brs_loss=BRSMaskLoss(),
with_flip=False,
flip_average=False,
**kwargs):
se... | diff_iou = _compute_iou(current_mask, self._last_mask) | 0 | 2023-10-19 17:49:24+00:00 | 2k |
DeepGraphLearning/ULTRA | script/pretrain.py | [
{
"identifier": "tasks",
"path": "ultra/tasks.py",
"snippet": "def edge_match(edge_index, query_index):\ndef negative_sampling(data, batch, num_negative, strict=True):\ndef all_negative(data, batch):\ndef strict_negative_mask(data, batch):\ndef compute_ranking(pred, target, mask=None):\ndef build_relati... | import os
import sys
import copy
import math
import pprint
import torch
from itertools import islice
from functools import partial
from torch import optim
from torch import nn
from torch.nn import functional as F
from torch import distributed as dist
from torch.utils import data as torch_data
from torch_geometric.data ... | 1,017 |
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
separator = ">" * 30
line = "-" * 30
def multigraph_collator(batch, train_graphs):
num_graphs = len(train_graphs)
probs = torch.tensor([graph.edge_index.shape[1] for graph in train_graphs]).float()
probs /= probs.sum()
graph_id = torch.mu... |
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
separator = ">" * 30
line = "-" * 30
def multigraph_collator(batch, train_graphs):
num_graphs = len(train_graphs)
probs = torch.tensor([graph.edge_index.shape[1] for graph in train_graphs]).float()
probs /= probs.sum()
graph_id = torch.mu... | batch = tasks.negative_sampling(train_graph, batch, cfg.task.num_negative, | 0 | 2023-10-23 17:06:10+00:00 | 2k |
ZhengyiLuo/PerpetualHumanoidControl | uhc/khrylib/models/erd_net.py | [
{
"identifier": "RNN",
"path": "uhc/khrylib/models/rnn.py",
"snippet": "class RNN(nn.Module):\n def __init__(self, input_dim, out_dim, cell_type='lstm', bi_dir=False):\n super().__init__()\n self.input_dim = input_dim\n self.out_dim = out_dim\n self.cell_type = cell_type\n... | from uhc.khrylib.utils.torch import *
from torch import nn
from uhc.khrylib.models.rnn import RNN
from uhc.khrylib.models.mlp import MLP | 962 |
class ERDNet(nn.Module):
def __init__(self, state_dim):
super().__init__()
self.state_dim = state_dim
|
class ERDNet(nn.Module):
def __init__(self, state_dim):
super().__init__()
self.state_dim = state_dim | self.encoder_mlp = MLP(state_dim, (500,), 'relu') | 1 | 2023-10-15 19:05:47+00:00 | 2k |
laike9m/Python-Type-Challenges | views/views.py | [
{
"identifier": "ChallengeKey",
"path": "views/challenge.py",
"snippet": "ROOT_DIR = Path(__file__).parent.parent\n BASIC = \"basic\"\n INTERMEDIATE = \"intermediate\"\n ADVANCED = \"advanced\"\n EXTREME = \"extreme\"\n CODE_SPLITTER: ClassVar[str] = \"\\n## End of your code ##\\n\"\n ... | import ast
import platform
from functools import wraps
from flask import (
abort,
Blueprint,
jsonify,
redirect,
render_template,
request,
)
from flask_htmx import HTMX
from .challenge import ChallengeKey, Level, challenge_manager
from .sitemap import sitemapper
from .utils.text import render_hin... | 801 |
app_views = Blueprint("app_views", __name__)
htmx = HTMX(app_views)
def validate_challenge(view_func):
@wraps(view_func)
def wrapper(level, name, *args, **kwargs):
if Level.is_valid_level(level) and challenge_manager.has_challenge(
ChallengeKey(Level(level), name)
):
... |
app_views = Blueprint("app_views", __name__)
htmx = HTMX(app_views)
def validate_challenge(view_func):
@wraps(view_func)
def wrapper(level, name, *args, **kwargs):
if Level.is_valid_level(level) and challenge_manager.has_challenge(
ChallengeKey(Level(level), name)
):
... | "hints_for_display": render_hints(challenge.hints) if challenge.hints else None, | 2 | 2023-10-23 05:11:41+00:00 | 2k |
uni-medical/SAM-Med3D | segment_anything/modeling/image_encoder.py | [
{
"identifier": "LayerNorm2d",
"path": "segment_anything/modeling/common.py",
"snippet": "class LayerNorm2d(nn.Module):\r\n def __init__(self, num_channels: int, eps: float = 1e-6) -> None:\r\n super().__init__()\r\n self.weight = nn.Parameter(torch.ones(num_channels))\r\n self.b... | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common import LayerNorm2d, MLPBlock
| 1,164 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https... | LayerNorm2d(out_chans),
| 0 | 2023-10-23 15:41:07+00:00 | 2k |
VikParuchuri/libgen_to_txt | libgen_to_txt/marker/convert.py | [
{
"identifier": "settings",
"path": "libgen_to_txt/settings.py",
"snippet": "class Settings(BaseSettings):\n class Config:\n BASE_STORAGE_FOLDER: str = \"libgen\" # temp storage for downloaded chunks\n BASE_PROCESSED_FOLDER: str = \"processed\" # After a chunk is processed, an empty file is cre... | import subprocess
import os
import psutil
import json
from libgen_to_txt.settings import settings
from libgen_to_txt.metadata import query_metadata | 857 |
def filter_invalid(folder_name):
files = os.listdir(folder_name)
all_metadata = {}
for fname in files:
if fname.startswith("."):
continue
fpath = os.path.join(folder_name, fname)
metadata = query_metadata(fname)
if not metadata:
os.unlink(fpath)
... |
def filter_invalid(folder_name):
files = os.listdir(folder_name)
all_metadata = {}
for fname in files:
if fname.startswith("."):
continue
fpath = os.path.join(folder_name, fname)
metadata = query_metadata(fname)
if not metadata:
os.unlink(fpath)
... | if metadata["Language"].strip() not in settings.MARKER_SUPPORTED_LANGUAGES: | 0 | 2023-10-16 17:56:36+00:00 | 2k |
senran101604/sagemode | sagemode.py | [
{
"identifier": "Notify",
"path": "accessories.py",
"snippet": "class Notify:\n \"A helper class for notifications of Sagemode process\"\n\n @staticmethod\n def start(username: str, number_of_sites) -> str:\n start(ascii_art, delay=0.1)\n if username or sites is not None:\n ... | import os
import re
import datetime
import subprocess
import threading
import random
import requests
from argparse import ArgumentParser
from rich.console import Console
from bs4 import BeautifulSoup
from accessories import Notify
from sites import sites, soft404_indicators, user_agents | 1,326 | #! /usr/bin/env python3
"""
Sagemode: Track and Unveil Online identities across social media platforms.
"""
__version__ = "1.1.3"
class Sagemode:
def __init__(self, username: str, found_only=False):
self.console = Console()
self.notify = Notify
self.positive_count = 0
self.usern... | #! /usr/bin/env python3
"""
Sagemode: Track and Unveil Online identities across social media platforms.
"""
__version__ = "1.1.3"
class Sagemode:
def __init__(self, username: str, found_only=False):
self.console = Console()
self.notify = Notify
self.positive_count = 0
self.usern... | headers = {"User-Agent": random.choice(user_agents)} | 1 | 2023-10-15 15:19:24+00:00 | 2k |
NVIDIA/GenerativeAIExamples | RetrievalAugmentedGeneration/common/server.py | [
{
"identifier": "utils",
"path": "RetrievalAugmentedGeneration/common/utils.py",
"snippet": "DEFAULT_MAX_CONTEXT = 1500\nDEFAULT_NUM_TOKENS = 150\nTEXT_SPLITTER_EMBEDDING_MODEL = \"intfloat/e5-large-v2\"\nclass LimitRetrievedNodesLength(BaseNodePostprocessor):\n def _postprocess_nodes(\n self,... | import base64
import os
import shutil
import logging
from pathlib import Path
from typing import Any, Dict, List
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel, Field
from pymilvus.exceptions import MilvusException, MilvusUnavai... | 925 | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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
#
# ht... | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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
#
# ht... | chains.ingest_docs(file_path, upload_file) | 1 | 2023-10-19 13:46:31+00:00 | 2k |
Hackl0us/apple-spyder | airpods_update_detection.py | [
{
"identifier": "DatabaseUtil",
"path": "classes/database.py",
"snippet": "class DatabaseUtil:\n def __init__(self):\n import sqlite3\n self.conn = sqlite3.connect('res/apple-spyder.db')\n\n def db_select(self, sql):\n try:\n c = self.conn.execute(sql)\n ... | import logging
import plistlib
import urllib.request
from classes.database import DatabaseUtil
from classes.datetime import covert_to_local_timezone
from classes.datetime import is_a_previous_time
from classes.telegram import Telegram
from classes.weibo import Weibo | 733 |
def main():
ota_update_url = "https://mesu.apple.com/assets/com_apple_MobileAsset_UARP_A2618/com_apple_MobileAsset_UARP_A2618.xml"
with urllib.request.urlopen(ota_update_url) as response:
firmware_release_date = response.headers['last-modified']
plist_content = plistlib.loads(response.read()... |
def main():
ota_update_url = "https://mesu.apple.com/assets/com_apple_MobileAsset_UARP_A2618/com_apple_MobileAsset_UARP_A2618.xml"
with urllib.request.urlopen(ota_update_url) as response:
firmware_release_date = response.headers['last-modified']
plist_content = plistlib.loads(response.read()... | db = DatabaseUtil() | 0 | 2023-10-17 09:00:39+00:00 | 2k |
lm-sys/llm-decontaminator | main.py | [
{
"identifier": "datatype_to_instruct",
"path": "detect_instruct.py",
"snippet": "def datatype_to_instruct(data_type):\n if data_type == \"code\":\n return code_instruct\n elif data_type == \"number_substitution\":\n return strong_math_instruct\n elif data_type == \"math\":\n ... | import argparse
from sentence_transformers import SentenceTransformer
from detect_instruct import datatype_to_instruct
from llm_detect import llm_detect, check_openai_key
from vector_db import build_database
from show_samples import show | 1,096 |
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Build database of top-k similar cases')
parser.add_argument('--train_path', type=str, required=True, help='Path to train cases')
parser.add_argument('--test_path', type=str, required=True, help='Path to test cases')
parser.add_... |
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Build database of top-k similar cases')
parser.add_argument('--train_path', type=str, required=True, help='Path to train cases')
parser.add_argument('--test_path', type=str, required=True, help='Path to test cases')
parser.add_... | instruct = datatype_to_instruct(args.data_type) | 0 | 2023-10-17 04:06:33+00:00 | 2k |
MolecularAI/REINVENT4 | reinvent_plugins/components/comp_mmp.py | [
{
"identifier": "ComponentResults",
"path": "reinvent_plugins/components/component_results.py",
"snippet": "class ComponentResults:\n \"\"\"Container for the scores, uncertainties and meta data\n\n At the minimum the scores must be provided. The order of the score array\n must be the same as t... | import logging
import shlex
import numpy as np
import pandas as pd
from io import StringIO
from dataclasses import dataclass, field
from typing import List
from rdkit import Chem
from .component_results import ComponentResults
from .run_program import run_command
from .add_tag import add_tag
from ..normalize import nor... | 1,223 | """Matched molecular pairs"""
from __future__ import annotations
__all__ = ["MMP"]
logger = logging.getLogger('reinvent')
@add_tag("__parameters")
@dataclass
class Parameters:
"""Parameters for the scoring component
Note that all parameters are always lists because components can have
multiple endp... | """Matched molecular pairs"""
from __future__ import annotations
__all__ = ["MMP"]
logger = logging.getLogger('reinvent')
@add_tag("__parameters")
@dataclass
class Parameters:
"""Parameters for the scoring component
Note that all parameters are always lists because components can have
multiple endp... | result1 = run_command(shlex.split(frag_cmd), input=smiles_csv) | 1 | 2023-10-20 06:43:16+00:00 | 2k |
lion-agi/lionagi | lionagi/schema/base_node.py | [
{
"identifier": "create_copy",
"path": "lionagi/utils/sys_util.py",
"snippet": "def create_copy(input: Any, n: int) -> Any:\n \"\"\"\n Creates a deep copy of the input object a specified number of times.\n\n This function makes deep copies of the provided input. If the number of copies ('n') \n... | import json
import xml.etree.ElementTree as ET
from typing import Any, Dict, Optional, TypeVar, Type, List, Callable, Union
from pydantic import BaseModel, Field, AliasChoices
from lionagi.utils import (
create_id, is_schema, change_dict_key, create_copy,
encrypt, decrypt, dict_to_xml
) | 1,050 | # uses utils
T = TypeVar('T', bound='BaseNode')
class BaseNode(BaseModel):
"""
A foundational building block for representing a node in a graph-like structure.
This class includes functionalities for serialization, metadata manipulation,
content encryption/decryption, and utility methods.
Attri... | # uses utils
T = TypeVar('T', bound='BaseNode')
class BaseNode(BaseModel):
"""
A foundational building block for representing a node in a graph-like structure.
This class includes functionalities for serialization, metadata manipulation,
content encryption/decryption, and utility methods.
Attri... | id_: str = Field(default_factory=lambda: str(create_id()), alias="node_id") | 1 | 2023-10-17 03:10:02+00:00 | 2k |
stanford-oval/WikiChat | ColBERT/colbert/search/strided_tensor.py | [
{
"identifier": "StridedTensorCore",
"path": "ColBERT/colbert/search/strided_tensor_core.py",
"snippet": "class StridedTensorCore:\n # # @profile\n def __init__(self, packed_tensor, lengths, dim=None, use_gpu=True):\n self.dim = dim\n self.tensor = packed_tensor\n self.inner_d... | from struct import pack
from torch._C import device
from colbert.utils.utils import flatten, print_message
from .strided_tensor_core import StridedTensorCore, _create_mask, _create_view
from torch.utils.cpp_extension import load
import torch
import os
import pathlib
import os
import pickle
import time | 1,454 |
class StridedTensor(StridedTensorCore):
def __init__(self, packed_tensor, lengths, dim=None, use_gpu=True):
super().__init__(packed_tensor, lengths, dim=dim, use_gpu=use_gpu)
StridedTensor.try_load_torch_extensions(use_gpu)
@classmethod
def try_load_torch_extensions(cls, use_gpu):
... |
class StridedTensor(StridedTensorCore):
def __init__(self, packed_tensor, lengths, dim=None, use_gpu=True):
super().__init__(packed_tensor, lengths, dim=dim, use_gpu=use_gpu)
StridedTensor.try_load_torch_extensions(use_gpu)
@classmethod
def try_load_torch_extensions(cls, use_gpu):
... | view = _create_view(packed_tensor, stride, inner_dims)[offsets] | 2 | 2023-10-19 18:17:25+00:00 | 2k |
kyegomez/BitNet | tests/tests.py | [
{
"identifier": "BitLinear",
"path": "bitnet/bitlinear.py",
"snippet": "class BitLinear(nn.Module):\n def __init__(self, in_features, out_features, bias=True):\n def forward(self, input):"
},
{
"identifier": "BitNetTransformer",
"path": "bitnet/transformer.py",
"snippet": "class Tr... | import pytest
import torch
from torch.nn import functional as F
from bitnet.bitlinear import BitLinear, absmax_quantize
from bitnet.transformer import BitNetTransformer, ParallelTransformerBlock, Transformer | 1,443 | )
def test_bitlinear_shapes(in_features, out_features):
layer = BitLinear(in_features, out_features)
assert layer.weight.shape == (out_features, in_features)
@pytest.mark.parametrize("groups", [1, 2, 5])
def test_bitlinear_groups(groups):
layer = BitLinear(10, 20, groups=groups)
assert layer.groups ==... |
# Basic Tests:
def test_absmax_quantize():
tensor = torch.tensor([1.5, -2.0, 3.0, -4.0])
quant, dequant = absmax_quantize(tensor)
assert quant.dtype == torch.int8
assert torch.allclose(dequant, tensor, atol=1e-2)
def test_bitlinear_initialization():
layer = BitLinear(10, 20)
assert layer.i... | transformer = Transformer(dim, depth, heads, dim_head, ff_mult) | 1 | 2023-10-18 16:19:06+00:00 | 2k |
TonicAI/tvalmetrics | tonic_validate/metrics/augmentation_precision_metric.py | [
{
"identifier": "LLMResponse",
"path": "tonic_validate/classes/llm_response.py",
"snippet": "class LLMResponse(BaseModel):\n llm_answer: str\n llm_context_list: list[str]\n benchmark_item: BenchmarkItem"
},
{
"identifier": "AugmentationAccuracyMetric",
"path": "tonic_validate/metric... | import logging
from typing import List
from tonic_validate.classes.llm_response import LLMResponse
from tonic_validate.metrics.augmentation_accuracy_metric import (
AugmentationAccuracyMetric
)
from tonic_validate.metrics.metric import Metric
from tonic_validate.metrics.retrieval_precision_metric import RetrievalPr... | 1,008 |
logger = logging.getLogger()
class AugmentationPrecisionMetric(Metric):
name = "augmentation_precision"
def __init__(self) -> None:
self.augmentation_accuracy = AugmentationAccuracyMetric()
self.retrieval_precision = RetrievalPrecisionMetric()
|
logger = logging.getLogger()
class AugmentationPrecisionMetric(Metric):
name = "augmentation_precision"
def __init__(self) -> None:
self.augmentation_accuracy = AugmentationAccuracyMetric()
self.retrieval_precision = RetrievalPrecisionMetric()
| def score(self, llm_response: LLMResponse, openai_service: OpenAIService) -> float: | 0 | 2023-10-23 21:38:11+00:00 | 2k |
jhejna/cpl | scripts/render_metaworld_dataset.py | [
{
"identifier": "storage",
"path": "research/datasets/replay_buffer/storage.py",
"snippet": "def load_data(path: str, exclude_keys: Optional[List[str]]) -> Dict:\ndef save_data(data: Dict, path: str) -> None:\ndef get_bytes(buffer: Union[Dict, np.ndarray]) -> int:\n def capacity(self):\n def size(... | import argparse
import io
import gym
import numpy as np
from research.datasets.replay_buffer import storage
from research.envs.metaworld import MetaWorldSawyerImageWrapper | 1,061 |
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--path", type=str, required=True, help="Path to the dataset")
parser.add_argument("--output", type=str, required=True, help="Path to output the new dataset")
parser.add_argument("--resolution", type=int, default=64, he... |
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--path", type=str, required=True, help="Path to the dataset")
parser.add_argument("--output", type=str, required=True, help="Path to output the new dataset")
parser.add_argument("--resolution", type=int, default=64, he... | env = MetaWorldSawyerImageWrapper(env, width=args.resolution, height=args.resolution) | 1 | 2023-10-19 17:25:45+00:00 | 2k |
nbasyl/LLM-FP4 | lm_eval/base.py | [
{
"identifier": "mean",
"path": "lm_eval/metrics.py",
"snippet": "def mean(arr):\n return sum(arr) / len(arr)"
},
{
"identifier": "weighted_perplexity",
"path": "lm_eval/metrics.py",
"snippet": "def weighted_perplexity(items):\n return math.exp(-weighted_mean(items))"
},
{
... | import abc
import numpy as np
import random
import re
import os
import json
import hashlib
import datasets
import torch
import torch.nn.functional as F
import warnings
from typing import Iterable
from sqlitedict import SqliteDict
from tqdm import tqdm
from accelerate import find_executable_batch_size
from lm_ev... | 1,290 |
class LM(abc.ABC):
def __init__(self):
self.cache_hook = CacheHook(None)
@abstractmethod
def loglikelihood(self, requests):
"""Compute log-likelihood of generating a continuation from a context.
Downstream tasks should attempt to use loglikelihood instead of other
LM call... |
class LM(abc.ABC):
def __init__(self):
self.cache_hook = CacheHook(None)
@abstractmethod
def loglikelihood(self, requests):
"""Compute log-likelihood of generating a continuation from a context.
Downstream tasks should attempt to use loglikelihood instead of other
LM call... | args = utils.simple_parse_args_string(arg_string) | 4 | 2023-10-15 06:05:13+00:00 | 2k |
alextamkin/generative-elicitation | base_active_learning_agent.py | [
{
"identifier": "query_api",
"path": "utils.py",
"snippet": "@retry(wait=wait_random_exponential(min=1, max=60))\ndef query_api(messages, engine, openai_cache=None, openai_cache_file=None, **kwargs):\n '''Queries the OpenAI API with the given messages.\n \n NOTE: This function mutates the messa... | import json
import re
import textwrap
from abc import ABC, abstractmethod
from utils import query_api, load_openai_cache
from sklearn.metrics import roc_auc_score | 1,097 |
class BaseActiveLearningAgent(ABC):
def __init__(self, target_specification_file, engine, openai_cache_file=None, **kwargs):
self.get_gold_domain_info(target_specification_file)
self.engine = engine
self.openai_cache_file = openai_cache_file
self.openai_cache = load_openai_ca... |
class BaseActiveLearningAgent(ABC):
def __init__(self, target_specification_file, engine, openai_cache_file=None, **kwargs):
self.get_gold_domain_info(target_specification_file)
self.engine = engine
self.openai_cache_file = openai_cache_file
self.openai_cache = load_openai_ca... | test_case_answer, _ = query_api(test_case_messages, self.engine, self.openai_cache, self.openai_cache_file) | 0 | 2023-10-16 18:43:47+00:00 | 2k |
bcmi/libcom | libcom/harmony_score/harmony_score_prediction.py | [
{
"identifier": "download_pretrained_model",
"path": "libcom/utils/model_download.py",
"snippet": "def download_pretrained_model(weight_path):\n if os.path.exists(weight_path):\n assert os.path.isfile(weight_path), weight_path\n return weight_path\n else:\n weight_path= os.pat... | import torch
import torchvision
import torch
import os
import torchvision.transforms as transforms
import math
from libcom.utils.model_download import download_pretrained_model
from libcom.utils.process_image import *
from libcom.utils.environment import *
from libcom.harmony_score.source.bargainnet import StyleEncode... | 1,462 |
cur_dir = os.path.dirname(os.path.abspath(__file__))
model_set = ['BargainNet']
class HarmonyScoreModel:
"""
Foreground object search score prediction model.
Args:
device (str | torch.device): gpu id
model_type (str): predefined model type.
kwargs (dict): other parameters for b... |
cur_dir = os.path.dirname(os.path.abspath(__file__))
model_set = ['BargainNet']
class HarmonyScoreModel:
"""
Foreground object search score prediction model.
Args:
device (str | torch.device): gpu id
model_type (str): predefined model type.
kwargs (dict): other parameters for b... | download_pretrained_model(weight_path) | 0 | 2023-10-19 05:08:12+00:00 | 2k |
pgorecki/lato | tests/test_dependency_provider.py | [
{
"identifier": "SimpleDependencyProvider",
"path": "lato/dependency_provider.py",
"snippet": "class SimpleDependencyProvider(DependencyProvider):\n \"\"\"\n A dependency provider that manages dependencies and helps in automatic\n dependency injection based on type or parameter name.\n \"\"\... | import abc
from lato.dependency_provider import (
SimpleDependencyProvider,
as_type,
get_function_parameters,
) | 963 |
class FooService:
...
def foo(a: int, b: str, c: FooService):
...
def test_create_provider_with_types():
foo_service = FooService()
dp = SimpleDependencyProvider(foo_service=foo_service)
assert dp[FooService] is foo_service
assert dp["foo_service"] is foo_service
def test_create_provide... |
class FooService:
...
def foo(a: int, b: str, c: FooService):
...
def test_create_provider_with_types():
foo_service = FooService()
dp = SimpleDependencyProvider(foo_service=foo_service)
assert dp[FooService] is foo_service
assert dp["foo_service"] is foo_service
def test_create_provide... | params = get_function_parameters(foo) | 2 | 2023-10-21 11:33:05+00:00 | 2k |
instadeepai/flashbax | flashbax/buffers/flat_buffer_test.py | [
{
"identifier": "flat_buffer",
"path": "flashbax/buffers/flat_buffer.py",
"snippet": "class ExperiencePair(NamedTuple, Generic[Experience]):\nclass TransitionSample(Generic[Experience]):\ndef validate_sample_batch_size(sample_batch_size: int, max_length: int):\ndef validate_min_length(min_length: int, a... | from copy import deepcopy
from flashbax.buffers import flat_buffer
from flashbax.buffers.conftest import get_fake_batch
from flashbax.conftest import _DEVICE_COUNT_MOCK
import chex
import jax
import jax.numpy as jnp
import pytest | 645 | # Copyright 2023 InstaDeep Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | # Copyright 2023 InstaDeep Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | fake_batch = get_fake_batch(fake_transition, add_batch_size) | 1 | 2023-10-17 10:57:14+00:00 | 2k |
TheDuckAI/DuckTrack | ducktrack/playback.py | [
{
"identifier": "KeyCombinationListener",
"path": "ducktrack/keycomb.py",
"snippet": "class KeyCombinationListener:\n \"\"\"\n Simple and bad key combination listener.\n \"\"\"\n \n def __init__(self):\n self.current_keys = set()\n self.callbacks = {}\n self.listener ... | import json
import math
import os
import sys
import time
import pyautogui
from pynput.keyboard import Controller as KeyboardController
from pynput.keyboard import Key
from pynput.mouse import Button
from pynput.mouse import Controller as MouseController
from .keycomb import KeyCombinationListener
from .util import (fix... | 1,457 |
pyautogui.PAUSE = 0
pyautogui.DARWIN_CATCH_UP_TIME = 0
class Player:
"""
Plays back recordings.
"""
def __init__(self):
self.stop_playback = False
self.listener = KeyCombinationListener()
def stop_comb_pressed():
self.stop_playback = True
r... |
pyautogui.PAUSE = 0
pyautogui.DARWIN_CATCH_UP_TIME = 0
class Player:
"""
Plays back recordings.
"""
def __init__(self):
self.stop_playback = False
self.listener = KeyCombinationListener()
def stop_comb_pressed():
self.stop_playback = True
... | key = name_to_key(event["name"]) | 4 | 2023-10-18 19:34:19+00:00 | 2k |
e4s2023/E4S2023 | swap_face_fine/face_vid2vid/modules/model.py | [
{
"identifier": "AntiAliasInterpolation2d",
"path": "swap_face_fine/face_vid2vid/modules/util.py",
"snippet": "class AntiAliasInterpolation2d(nn.Module):\n \"\"\"\n Band-limited downsampling, for better preservation of the input signal.\n \"\"\"\n def __init__(self, channels, scale):\n ... | from torch import nn
from swap_face_fine.face_vid2vid.modules.util import AntiAliasInterpolation2d, make_coordinate_grid_2d
from torchvision import models
from torch.autograd import grad
from torchvision import transforms
import torch
import torch.nn.functional as F
import numpy as np
import swap_face_fine.face_vid2vid... | 1,327 |
class Vgg19(torch.nn.Module):
"""
Vgg19 network for perceptual loss.
"""
def __init__(self, requires_grad=False):
super(Vgg19, self).__init__()
vgg_pretrained_features = models.vgg19(pretrained=True).features
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Se... |
class Vgg19(torch.nn.Module):
"""
Vgg19 network for perceptual loss.
"""
def __init__(self, requires_grad=False):
super(Vgg19, self).__init__()
vgg_pretrained_features = models.vgg19(pretrained=True).features
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Se... | downs[str(scale).replace('.', '-')] = AntiAliasInterpolation2d(num_channels, scale) | 0 | 2023-10-15 12:15:01+00:00 | 2k |
riverscn/epghub | main.py | [
{
"identifier": "utils",
"path": "epg/utils.py",
"snippet": "def load_config(path: str) -> list[Channel]:\ndef scrap_channel(\n channel: Channel, channels_config, date: date = datetime.today().date()\n) -> bool:\ndef copy_channels(\n channels: list[Channel], new_channels: list[Channel]\n) -> tuple... | from jinja2 import Environment, FileSystemLoader
from epg import utils
from epg.generator import xmltv
from epg.generator import diyp
from epg.scraper import __xmltv
from lxml import etree
from datetime import datetime, timezone
from croniter import croniter
import os
import shutil | 641 |
CF_PAGES = os.getenv("CF_PAGES")
CF_PAGES_URL = os.getenv("CF_PAGES_URL")
DEPLOY_HOOK = os.getenv("DEPLOY_HOOK")
CLOUDFLARE_API_TOKEN = os.getenv("CLOUDFLARE_API_TOKEN")
XMLTV_URL = os.getenv("XMLTV_URL", "")
TZ = os.getenv("TZ")
if TZ == None:
print(
"!!!Please set TZ environment variables to define timez... |
CF_PAGES = os.getenv("CF_PAGES")
CF_PAGES_URL = os.getenv("CF_PAGES_URL")
DEPLOY_HOOK = os.getenv("DEPLOY_HOOK")
CLOUDFLARE_API_TOKEN = os.getenv("CLOUDFLARE_API_TOKEN")
XMLTV_URL = os.getenv("XMLTV_URL", "")
TZ = os.getenv("TZ")
if TZ == None:
print(
"!!!Please set TZ environment variables to define timez... | xml_channels = __xmltv.get_channels(XMLTV_URL, dtd) | 3 | 2023-10-20 04:35:12+00:00 | 2k |
lancopku/label-words-are-anchors | icl/util_classes/context_solver.py | [
{
"identifier": "format_s_dict",
"path": "icl/utils/data_wrapper.py",
"snippet": "def sst2_wrap_data(demonstrations, input_sample, label_dict):\ndef trec_wrap_data(demonstrations, input_sample, label_dict):\ndef emo_wrap_data(demonstrations, input_sample, label_dict):\ndef agnews_wrap_data(demonstration... | import warnings
import torch
from copy import deepcopy
from ..utils.data_wrapper import format_s_dict
from ..utils.other import TensorStrFinder | 1,276 |
class ContextSolver:
def __init__(self, task_name, tokenizer=None):
assert task_name in ['sst2', 'trec', 'agnews', 'emo']
self.task_name = task_name
self.tokenizer = tokenizer
self.format_s = format_s_dict[task_name]
self.parse_format_s()
def parse_format_s(self):
... |
class ContextSolver:
def __init__(self, task_name, tokenizer=None):
assert task_name in ['sst2', 'trec', 'agnews', 'emo']
self.task_name = task_name
self.tokenizer = tokenizer
self.format_s = format_s_dict[task_name]
self.parse_format_s()
def parse_format_s(self):
... | tensor_str_finder = TensorStrFinder(tokenizer=tokenizer) | 1 | 2023-10-17 11:40:03+00:00 | 2k |
Aggify/aggify | tests/test_q.py | [
{
"identifier": "Aggify",
"path": "aggify/aggify.py",
"snippet": "def last_out_stage_check(method: AggifyType) -> AggifyType:\n def decorator(*args, **kwargs):\n def __init__(self, base_model: Type[Document]):\n def __iter__(self):\n def project(self, **kwargs: QueryParams) -> \"Aggify\":\n ... | import pytest
from aggify import Q, F, Aggify
from aggify.exceptions import InvalidOperator
from tests.test_aggify import BaseModel | 1,583 |
class TestQ:
# Test OR operator with multiple conditions
def test_or_operator_with_multiple_conditions(self):
q1 = Q(name="John")
q2 = Q(name="Alice")
q_combined = q1 | q2
assert dict(q_combined) == {
"$match": {"$or": [dict(q1)["$match"], dict(q2)["$match"]]}
... |
class TestQ:
# Test OR operator with multiple conditions
def test_or_operator_with_multiple_conditions(self):
q1 = Q(name="John")
q2 = Q(name="Alice")
q_combined = q1 | q2
assert dict(q_combined) == {
"$match": {"$or": [dict(q1)["$match"], dict(q2)["$match"]]}
... | with pytest.raises(InvalidOperator): | 1 | 2023-10-22 07:53:28+00:00 | 2k |
sotopia-lab/sotopia | tests/envs/test_get_bio.py | [
{
"identifier": "AgentProfile",
"path": "sotopia/database/persistent_profile.py",
"snippet": "class AgentProfile(JsonModel):\n first_name: str = Field(index=True)\n last_name: str = Field(index=True)\n age: int = Field(index=True, default_factory=lambda: 0)\n occupation: str = Field(index=Tr... | from typing import Any
from sotopia.database.persistent_profile import (
AgentProfile,
RelationshipType,
)
from sotopia.envs.parallel import get_bio, render_text_for_agent
import pytest | 763 |
@pytest.fixture
def _get_john_profile() -> AgentProfile:
return AgentProfile(
first_name="John",
last_name="Doe",
personality_and_values="I am a big five",
public_info="I am a public info",
secret="I am a secret",
)
def test_get_bio(_get_john_profile: Any) -> None:
... |
@pytest.fixture
def _get_john_profile() -> AgentProfile:
return AgentProfile(
first_name="John",
last_name="Doe",
personality_and_values="I am a big five",
public_info="I am a public info",
secret="I am a secret",
)
def test_get_bio(_get_john_profile: Any) -> None:
... | RelationshipType.stranger, | 1 | 2023-10-23 19:47:26+00:00 | 2k |
Zai-Kun/reverse-engineered-chatgpt | re_gpt/async_chatgpt.py | [
{
"identifier": "BackendError",
"path": "re_gpt/errors.py",
"snippet": "class BackendError(Exception):\n def __init__(self, error_code):\n self.error_code = error_code\n self.message = (\n f\"An error occurred on the backend. Error code: {self.error_code}\"\n )\n ... | import asyncio
import ctypes
import inspect
import json
import uuid
from typing import AsyncGenerator, Callable, Optional
from curl_cffi.requests import AsyncSession
from .errors import (
BackendError,
InvalidSessionToken,
RetryError,
TokenNotProvided,
UnexpectedResponseError,
InvalidModelName,
... | 1,300 |
# Constants
USER_AGENT = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36"
CHATGPT_API = "https://chat.openai.com/backend-api/{}"
BACKUP_ARKOSE_TOKEN_GENERATOR = "https://arkose-token-generator.zaieem.repl.co/token"
MODELS = {
"gpt-4": {"slug": "gpt-... |
# Constants
USER_AGENT = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36"
CHATGPT_API = "https://chat.openai.com/backend-api/{}"
BACKUP_ARKOSE_TOKEN_GENERATOR = "https://arkose-token-generator.zaieem.repl.co/token"
MODELS = {
"gpt-4": {"slug": "gpt-... | raise UnexpectedResponseError(error, response.text) | 4 | 2023-10-17 08:34:04+00:00 | 2k |
qualabs/video-headline | api/tests/bills.py | [
{
"identifier": "MinBillSerializer",
"path": "api/serializers/bills.py",
"snippet": "class MinBillSerializer(serializers.ModelSerializer):\n plan = serializers.CharField(source='plan.name')\n\n class Meta:\n model = Bill\n fields = (\n 'id',\n 'plan',\n ... | import logging
from datetime import date
from django.utils import timezone
from dateutil.relativedelta import relativedelta
from unittest import mock
from django.urls import reverse
from rest_framework import status
from rest_framework.test import APITestCase
from api.serializers import MinBillSerializer, BillSerialize... | 1,415 |
class BillTests(APITestCase):
@classmethod
def setUpClass(cls):
logging.disable(logging.WARNING)
cls.org1, cls.org2 = create_organizations('Organization', 2)
cls.user1 = create_user('user1', '12345678', cls.org1)
cls.user2 = create_user('user2', '12345678', cls.org2)
... |
class BillTests(APITestCase):
@classmethod
def setUpClass(cls):
logging.disable(logging.WARNING)
cls.org1, cls.org2 = create_organizations('Organization', 2)
cls.user1 = create_user('user1', '12345678', cls.org1)
cls.user2 = create_user('user2', '12345678', cls.org2)
... | Bill.objects.all().delete() | 2 | 2023-10-17 19:44:32+00:00 | 2k |
LAION-AI/Text-to-speech | modules/audio_superres.py | [
{
"identifier": "Base",
"path": "modules/common.py",
"snippet": "class Base:\n MODEL_CHOICES = {}\n\n def __init__(\n self,\n model_choice: str,\n sampling_rate: int = 16000,\n padding: Union[bool, str] = True,\n max_length: Optional[int] = None,\n pad_to_... | from os import path as osp
from pathlib import Path
from audiosr import super_resolution
from functools import partial
from .common import Base
from modules.audio_superres_utils import load_audiosr
from voicefixer import VoiceFixer
from config import settings
import os
import argparse | 958 |
cache_dir = osp.join(settings.CACHE_DIR, "weights", "enhancement")
class SuperResAudio(Base):
MODEL_CHOICES = {
"audiosr": {
"model": partial(
|
cache_dir = osp.join(settings.CACHE_DIR, "weights", "enhancement")
class SuperResAudio(Base):
MODEL_CHOICES = {
"audiosr": {
"model": partial( | load_audiosr, | 1 | 2023-10-18 06:09:40+00:00 | 2k |
Qualcomm-AI-research/geometric-algebra-transformer | gatr/primitives/invariants.py | [
{
"identifier": "_load_bilinear_basis",
"path": "gatr/primitives/bilinear.py",
"snippet": "@lru_cache()\ndef _load_bilinear_basis(\n kind: str, device=torch.device(\"cpu\"), dtype=torch.float32\n) -> torch.Tensor:\n \"\"\"Loads basis elements for Pin-equivariant bilinear maps between multivectors.... | from functools import lru_cache
from gatr.primitives.bilinear import _load_bilinear_basis
from gatr.primitives.linear import _compute_reversal, grade_project
from gatr.utils.einsum import cached_einsum
import torch
import torch.linalg | 1,238 | # Copyright (c) 2023 Qualcomm Technologies, Inc.
# All rights reserved.
@lru_cache()
def compute_inner_product_mask(device=torch.device("cpu")) -> torch.Tensor:
"""Constructs a bool array for the inner product calculation.
The inner product of MVs is <~x y>_0, i.e. take the grade-0 component of the geometr... | # Copyright (c) 2023 Qualcomm Technologies, Inc.
# All rights reserved.
@lru_cache()
def compute_inner_product_mask(device=torch.device("cpu")) -> torch.Tensor:
"""Constructs a bool array for the inner product calculation.
The inner product of MVs is <~x y>_0, i.e. take the grade-0 component of the geometr... | inner_product_mask = torch.diag(gp[0]) * _compute_reversal(device=device, dtype=torch.float32) | 1 | 2023-10-23 15:58:36+00:00 | 2k |
StanislavPetrovV/Wolfenstein-3D-Clone | game_objects/weapon.py | [
{
"identifier": "GameObject",
"path": "game_objects/game_object.py",
"snippet": "class GameObject:\n def __init__(self, level_map, tex_id, x, z):\n self.eng = level_map.eng\n self.app = self.eng.app\n self.tex_id = tex_id\n #\n self.pos = glm.vec3(x + H_WALL_SIZE, 0... | from game_objects.game_object import GameObject
from meshes.quad_mesh import QuadMesh
from settings import * | 748 |
class Weapon:
def __init__(self, eng):
self.eng = eng
self.app = eng.app
# refer to the player
self.player = self.eng.player
self.weapon_id = self.player.weapon_id
self.player.weapon_instance = self
#
self.pos = WEAPON_POS
self.rot = 0
... |
class Weapon:
def __init__(self, eng):
self.eng = eng
self.app = eng.app
# refer to the player
self.player = self.eng.player
self.weapon_id = self.player.weapon_id
self.player.weapon_instance = self
#
self.pos = WEAPON_POS
self.rot = 0
... | self.m_model = GameObject.get_model_matrix(self) | 0 | 2023-10-22 08:41:55+00:00 | 2k |
tomguluson92/cloth2tex | lib/deformation_graph.py | [
{
"identifier": "generate_transform_matrices",
"path": "lib/mesh_sampling.py",
"snippet": "def generate_transform_matrices(mesh, factors):\n \"\"\"Generates len(factors) meshes, each of them is scaled by factors[i] and\n computes the transformations between them.\n Returns:\n M: a set ... | import os
import sys
import numpy as np
import torch
import torch.nn as nn
import torch.autograd.functional as F
import pickle
from scipy.spatial import KDTree
from psbody.mesh import Mesh
from .mesh_sampling import generate_transform_matrices, generate_transform_matrices_coma
from .utils_dg import col, batch_rodrigues... | 1,273 | # coding: UTF-8
"""
@date: 2023.02.21-28 week8-9
@func: deformation graph.
"""
eps = sys.float_info.epsilon # 2.220446049250313e-16
class DeformationGraph(nn.Module):
def __init__(self, vert_number=9648, radius=0.015, k=9, sampling_strategy='qslim'):
super().__init__()
... | # coding: UTF-8
"""
@date: 2023.02.21-28 week8-9
@func: deformation graph.
"""
eps = sys.float_info.epsilon # 2.220446049250313e-16
class DeformationGraph(nn.Module):
def __init__(self, vert_number=9648, radius=0.015, k=9, sampling_strategy='qslim'):
super().__init__()
... | M, A, D = generate_transform_matrices(m, [20, 20]) | 0 | 2023-10-17 11:30:53+00:00 | 2k |
amazon-science/cceval | eval_metric.py | [
{
"identifier": "postprocess_code_lines",
"path": "eval_utils.py",
"snippet": "def postprocess_code_lines(prompt, completion, parser, lang):\n try:\n if lang in [\"java\", \"csharp\", \"typescript\"]:\n return get_bracket_lang_statement(completion)\n elif lang == \"python\":\... | import json
import torch.multiprocessing as mp
from functools import partial
from tqdm import tqdm
from tree_sitter import Language, Parser
from eval_utils import (
postprocess_code_lines,
extract_identifiers,
cal_edit_sim,
remove_comments
) | 658 |
parser = None
def compute_id_match(pred_ids, target_ids):
pred_ids = list(set(pred_ids))
target_ids = list(set(target_ids))
tp = 0
fp = 0
fn = 0
for pid in pred_ids:
if pid in target_ids:
tp += 1
else:
fp += 1
for tid in target_ids:
if tid... |
parser = None
def compute_id_match(pred_ids, target_ids):
pred_ids = list(set(pred_ids))
target_ids = list(set(target_ids))
tp = 0
fp = 0
fn = 0
for pid in pred_ids:
if pid in target_ids:
tp += 1
else:
fp += 1
for tid in target_ids:
if tid... | pred_ids = extract_identifiers(prediction, lang) | 1 | 2023-10-16 04:23:03+00:00 | 2k |
uukuguy/multi_loras | multi_loras/__main__.py | [
{
"identifier": "do_extract_lora",
"path": "multi_loras/extract_lora.py",
"snippet": "def do_extract_lora(args):\n # Load base model and tuned model\n model_kwargs = prepare_model_kwargs(args)\n base_model = load_model_and_init_lora(args, args.base_model_name_or_path, model_kwargs)\n tuned_m... | from .extract_lora import do_extract_lora
from .merge_peft_adapters import do_merge_lora
from .dare import do_dare
from .delta_weights import do_delta_weights, do_orthogonal
from argparse import ArgumentParser | 1,569 | #!/usr/bin/env python
cmd_functions = {
"extract_lora": do_extract_lora,
"merge_lora": do_merge_lora,
"drop_and_rescale": do_dare,
| #!/usr/bin/env python
cmd_functions = {
"extract_lora": do_extract_lora,
"merge_lora": do_merge_lora,
"drop_and_rescale": do_dare, | "delta_weights": do_delta_weights, | 3 | 2023-10-16 02:39:47+00:00 | 2k |
myshell-ai/AIlice | ailice/prompts/APromptSearchEngine.py | [
{
"identifier": "config",
"path": "ailice/common/AConfig.py",
"snippet": "class AConfig():\n def __init__(self):\n def Initialize(self, needOpenaiGPTKey = False):\n def Load(self, configFile: str) -> dict:\n def Store(self, configFile: str):"
},
{
"identifier": "GenerateRE4FunctionCa... | from importlib.resources import read_text
from ailice.common.AConfig import config
from ailice.prompts.ARegex import GenerateRE4FunctionCalling
from ailice.prompts.ATools import ConstructOptPrompt | 1,185 |
class APromptSearchEngine():
PROMPT_NAME = "search-engine"
def __init__(self, processor, storage, collection, conversations, formatter, outputCB = None):
self.processor = processor
self.conversations = conversations
self.formatter = formatter
self.outputCB = outputCB
se... |
class APromptSearchEngine():
PROMPT_NAME = "search-engine"
def __init__(self, processor, storage, collection, conversations, formatter, outputCB = None):
self.processor = processor
self.conversations = conversations
self.formatter = formatter
self.outputCB = outputCB
se... | prompt, n = ConstructOptPrompt(self.ParameterizedBuildPrompt, low=1, high=len(self.conversations), maxLen=int(self.processor.llm.contextWindow * config.contextWindowRatio)) | 2 | 2023-10-16 01:51:14+00:00 | 2k |
Agora-X/Bing-Chat-API | src/bing_chat/request.py | [
{
"identifier": "CONVERSATION_STYLE_TYPE",
"path": "src/bing_chat/conversation_style.py",
"snippet": "CONVERSATION_STYLE_TYPE = Optional[\n Union[ConversationStyle, Literal[\"creative\", \"balanced\", \"precise\"]]\n]"
},
{
"identifier": "ConversationStyle",
"path": "src/bing_chat/convers... | import uuid
from datetime import datetime
from typing import Union
from .conversation_style import CONVERSATION_STYLE_TYPE
from .conversation_style import ConversationStyle
from .utilities import get_location_hint_from_locale
from .utilities import get_ran_hex
from .utilities import guess_locale | 817 |
class ChatHubRequest:
def __init__(
self,
conversation_signature: str,
encrypted_conversation_signature: str,
client_id: str,
conversation_id: str,
invocation_id: int = 3,
) -> None:
self.struct: dict = {}
self.client_id: str = client_id
... |
class ChatHubRequest:
def __init__(
self,
conversation_signature: str,
encrypted_conversation_signature: str,
client_id: str,
conversation_id: str,
invocation_id: int = 3,
) -> None:
self.struct: dict = {}
self.client_id: str = client_id
... | locale: str = guess_locale(), | 4 | 2023-10-19 19:17:05+00:00 | 2k |
f0uriest/interpax | interpax/_spline.py | [
{
"identifier": "errorif",
"path": "interpax/utils.py",
"snippet": "def errorif(cond, err=ValueError, msg=\"\"):\n \"\"\"Raise an error if condition is met.\n\n Similar to assert but allows wider range of Error types, rather than\n just AssertionError.\n \"\"\"\n if cond:\n raise e... | from collections import OrderedDict
from functools import partial
from typing import Union
from jax import jit
from .utils import errorif, isbool
import equinox as eqx
import jax
import jax.numpy as jnp
import numpy as np | 891 | """Functions for interpolating splines that are JAX differentiable."""
CUBIC_METHODS = ("cubic", "cubic2", "cardinal", "catmull-rom")
OTHER_METHODS = ("nearest", "linear")
METHODS_1D = CUBIC_METHODS + OTHER_METHODS + ("monotonic", "monotonic-0")
METHODS_2D = CUBIC_METHODS + OTHER_METHODS
METHODS_3D = CUBIC_METHODS ... | """Functions for interpolating splines that are JAX differentiable."""
CUBIC_METHODS = ("cubic", "cubic2", "cardinal", "catmull-rom")
OTHER_METHODS = ("nearest", "linear")
METHODS_1D = CUBIC_METHODS + OTHER_METHODS + ("monotonic", "monotonic-0")
METHODS_2D = CUBIC_METHODS + OTHER_METHODS
METHODS_3D = CUBIC_METHODS ... | errorif( | 0 | 2023-10-18 13:12:20+00:00 | 2k |
aszc-dev/ComfyUI-CoreMLSuite | coreml_suite/models.py | [
{
"identifier": "get_model_config",
"path": "coreml_suite/config.py",
"snippet": "def get_model_config(model_version: ModelVersion):\n unet_config = convert_config(config_map[model_version])\n config = supported_models_base.BASE(unet_config)\n config.latent_format = latent_format_map[model_vers... | import numpy as np
import torch
from comfy import model_base
from comfy.model_management import get_torch_device
from comfy.model_patcher import ModelPatcher
from coreml_suite.config import get_model_config, ModelVersion
from coreml_suite.controlnet import extract_residual_kwargs, chunk_control
from coreml_suite.latent... | 1,387 |
class CoreMLModelWrapper:
def __init__(self, coreml_model):
self.coreml_model = coreml_model
self.dtype = torch.float16
def __call__(self, x, t, context, control, transformer_options=None, **kwargs):
inputs = CoreMLInputs(x, t, context, control, **kwargs)
input_list = inputs.... |
class CoreMLModelWrapper:
def __init__(self, coreml_model):
self.coreml_model = coreml_model
self.dtype = torch.float16
def __call__(self, x, t, context, control, transformer_options=None, **kwargs):
inputs = CoreMLInputs(x, t, context, control, **kwargs)
input_list = inputs.... | residual_kwargs = extract_residual_kwargs(expected_inputs, self.control) | 2 | 2023-10-23 13:08:00+00:00 | 2k |
aikunyi/FreTS | layers/SelfAttention_Family.py | [
{
"identifier": "TriangularCausalMask",
"path": "utils/masking.py",
"snippet": "class TriangularCausalMask():\n def __init__(self, B, L, device=\"cpu\"):\n mask_shape = [B, 1, L, L]\n with torch.no_grad():\n self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), di... | import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
import math
import os
from math import sqrt
from utils.masking import TriangularCausalMask, ProbMask | 1,141 |
class FullAttention(nn.Module):
def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
super(FullAttention, self).__init__()
self.scale = scale
self.mask_flag = mask_flag
self.output_attention = output_attention
self.dropo... |
class FullAttention(nn.Module):
def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
super(FullAttention, self).__init__()
self.scale = scale
self.mask_flag = mask_flag
self.output_attention = output_attention
self.dropo... | attn_mask = ProbMask(B, H, L_Q, index, scores, device=V.device) | 1 | 2023-10-23 13:15:14+00:00 | 2k |
lightly-ai/labelformat | src/labelformat/formats/yolov6.py | [
{
"identifier": "YOLOv8ObjectDetectionInput",
"path": "src/labelformat/formats/yolov8.py",
"snippet": "class YOLOv8ObjectDetectionInput(_YOLOv8BaseInput, ObjectDetectionInput):\n def get_labels(self) -> Iterable[ImageObjectDetection]:\n category_id_to_category = {\n category.id: cat... | from labelformat.cli.registry import Task, cli_register
from .yolov8 import YOLOv8ObjectDetectionInput, YOLOv8ObjectDetectionOutput | 750 |
"""
YOLOv6 format follows the same specs as YOLOv8.
"""
@cli_register(format="yolov6", task=Task.OBJECT_DETECTION)
class YOLOv6ObjectDetectionInput(YOLOv8ObjectDetectionInput):
pass
@cli_register(format="yolov6", task=Task.OBJECT_DETECTION)
|
"""
YOLOv6 format follows the same specs as YOLOv8.
"""
@cli_register(format="yolov6", task=Task.OBJECT_DETECTION)
class YOLOv6ObjectDetectionInput(YOLOv8ObjectDetectionInput):
pass
@cli_register(format="yolov6", task=Task.OBJECT_DETECTION) | class YOLOv6ObjectDetectionOutput(YOLOv8ObjectDetectionOutput): | 1 | 2023-10-18 11:08:06+00:00 | 2k |
amitfin/oref_alert | tests/test_binary_sensor.py | [
{
"identifier": "ADD_SENSOR_SERVICE",
"path": "custom_components/oref_alert/const.py",
"snippet": "ADD_SENSOR_SERVICE: Final = \"add_sensor\""
},
{
"identifier": "ATTR_COUNTRY_ALERTS",
"path": "custom_components/oref_alert/const.py",
"snippet": "ATTR_COUNTRY_ALERTS: Final = \"country_ale... | import datetime
import pytest
from typing import Any
from freezegun.api import FrozenDateTimeFactory
from homeassistant.const import CONF_NAME, Platform, STATE_OFF, STATE_ON
from homeassistant.core import HomeAssistant
from pytest_homeassistant_custom_component.common import (
MockConfigEntry,
async_fire_time_c... | 1,429 | """The tests for the binary_sensor file."""
from __future__ import annotations
DEFAULT_OPTIONS = {CONF_AREAS: ["בארי"], CONF_ALERT_MAX_AGE: 10}
ENTITY_ID = f"{Platform.BINARY_SENSOR}.{OREF_ALERT_UNIQUE_ID}"
async def async_setup(
hass: HomeAssistant, options: dict[str, Any] | None = None
) -> str:
"""I... | """The tests for the binary_sensor file."""
from __future__ import annotations
DEFAULT_OPTIONS = {CONF_AREAS: ["בארי"], CONF_ALERT_MAX_AGE: 10}
ENTITY_ID = f"{Platform.BINARY_SENSOR}.{OREF_ALERT_UNIQUE_ID}"
async def async_setup(
hass: HomeAssistant, options: dict[str, Any] | None = None
) -> str:
"""I... | active_area_alert = load_json_fixture("single_alert_history.json") | 13 | 2023-10-18 11:16:41+00:00 | 2k |
apple/ml-nvas3d | soundspaces_nvas3d/rir_generation/generate_rir.py | [
{
"identifier": "render_rir_parallel",
"path": "soundspaces_nvas3d/utils/ss_utils.py",
"snippet": "def render_rir_parallel(room_list: T.List[str],\n source_position_list: T.List[T.Tuple[float, float, float]],\n receiver_position_list: T.List[T.Tuple[float, f... | import os
import argparse
import itertools
from soundspaces_nvas3d.utils.ss_utils import render_rir_parallel
from soundspaces_nvas3d.utils.aihabitat_utils import load_room_grid | 1,134 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
#
def generate_rir(args: argparse.Namespace) -> None:
"""
Generate Room Impulse Response (RIR) based on given room and grid distance.
"""
grid_distance_str = str(args.grid_distance).replace(".", "_... | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
#
def generate_rir(args: argparse.Namespace) -> None:
"""
Generate Room Impulse Response (RIR) based on given room and grid distance.
"""
grid_distance_str = str(args.grid_distance).replace(".", "_... | grid_data = load_room_grid(args.room, grid_distance=args.grid_distance) | 1 | 2023-10-19 05:35:54+00:00 | 2k |
kwonathan/language-models-trajectory-generators | api.py | [
{
"identifier": "SUCCESS_DETECTION_PROMPT",
"path": "prompts/success_detection_prompt.py",
"snippet": "SUCCESS_DETECTION_PROMPT = \\\n\"\"\"You are tasked with determining whether a user command was completed successfully or not, based on how the positions and orientations of the relevant objects in the... | import numpy as np
import sys
import torch
import math
import config
import models
import utils
from PIL import Image
from prompts.success_detection_prompt import SUCCESS_DETECTION_PROMPT
from config import OK, PROGRESS, FAIL, ENDC
from config import CAPTURE_IMAGES, ADD_BOUNDING_CUBES, ADD_TRAJECTORY_POINTS, EXECUTE_TR... | 1,042 |
class API:
def __init__(self, args, main_connection, logger, langsam_model, xmem_model, device):
self.args = args
self.main_connection = main_connection
self.logger = logger
self.langsam_model = langsam_model
self.xmem_model = xmem_model
self.device = device
... |
class API:
def __init__(self, args, main_connection, logger, langsam_model, xmem_model, device):
self.args = args
self.main_connection = main_connection
self.logger = logger
self.langsam_model = langsam_model
self.xmem_model = xmem_model
self.device = device
... | self.logger.info(OK + "Finished segmenting head camera image!" + ENDC) | 1 | 2023-10-18 16:38:09+00:00 | 2k |
VikParuchuri/classified | app/labeler/raters/instruct.py | [
{
"identifier": "Lens",
"path": "app/labeler/lens.py",
"snippet": "class Lens:\n def __init__(self, lens_type):\n self.lens_type = lens_type\n self.template_dir = os.path.join(settings.LENS_DIR, lens_type)\n self.function = self.get_function()\n self.system_prompt = self.g... | import json
from typing import List
from app.labeler.lens import Lens
from app.labeler.raters.common import get_final_score
from app.llm.llm import chat_completion | 798 |
def rate_data(resource: List[str], lens_type: str, version: int = 1):
lens = Lens(lens_type)
instruction, output = resource
user_prompt = lens.prompt_template(instruction, output)
messages = [
{"role": "system", "content": lens.system_prompt},
{"role": "user", "content": user_prompt},... |
def rate_data(resource: List[str], lens_type: str, version: int = 1):
lens = Lens(lens_type)
instruction, output = resource
user_prompt = lens.prompt_template(instruction, output)
messages = [
{"role": "system", "content": lens.system_prompt},
{"role": "user", "content": user_prompt},... | chat_response = chat_completion(lens_type, messages, [lens.function], version=version) | 2 | 2023-10-17 18:15:03+00:00 | 2k |
tiejundong/FlexPose | FlexPose/preprocess/prepare_APOPDBbind.py | [
{
"identifier": "print_args",
"path": "FlexPose/utils/common.py",
"snippet": "def print_args(args):\n print('=' * 30 + ' Current settings ' + '=' * 30)\n for k, v in args.__dict__.items():\n print(k.ljust(40, '.'), v)\n print('=' * (60 + len(' Current settings ')))"
},
{
"identif... | import os
import shutil
import sys
import argparse
import pandas as pd
from ray.util.multiprocessing import Pool
from tqdm import tqdm
from FlexPose.utils.common import print_args, delmkdir
from FlexPose.preprocess.prepare_for_training import try_prepare_APOPDBbind, save_APOPDBbind | 799 | sys.path.append('/'.join(os.path.abspath(__file__).split('/')[:-2]))
if __name__ == '__main__':
# main args
parser = argparse.ArgumentParser()
# data source
parser.add_argument('--apobind_path', type=str,
default='/home/dtj/work_site/test/tmp/data/apobind', help='APObind dat... | sys.path.append('/'.join(os.path.abspath(__file__).split('/')[:-2]))
if __name__ == '__main__':
# main args
parser = argparse.ArgumentParser()
# data source
parser.add_argument('--apobind_path', type=str,
default='/home/dtj/work_site/test/tmp/data/apobind', help='APObind dat... | print_args(args) | 0 | 2023-10-19 22:03:51+00:00 | 2k |
openvpi/SingingVocoders | modules/loss/vaeHiFiloss.py | [
{
"identifier": "RSSLoss",
"path": "modules/ddsp/loss.py",
"snippet": "class RSSLoss(nn.Module):\n '''\n Random-scale Spectral Loss.\n '''\n \n def __init__(self, fft_min, fft_max, n_scale, alpha=1.0, overlap=0, eps=1e-7, device='cuda'):\n super().__init__()\n self.fft_min =... | import torch
import torch.nn as nn
import torch.nn.functional as F
from modules.ddsp.loss import RSSLoss
from modules.loss.stft_loss import warp_stft
from utils.wav2mel import PitchAdjustableMelSpectrogram | 1,287 |
def kl_loss(logs, m):
kl = 0.5 * (m**2 + torch.exp(logs) - logs - 1).sum(dim=1)
kl = torch.mean(kl)
return kl
class HiFiloss(nn.Module):
def __init__(self,config:dict):
super().__init__()
|
def kl_loss(logs, m):
kl = 0.5 * (m**2 + torch.exp(logs) - logs - 1).sum(dim=1)
kl = torch.mean(kl)
return kl
class HiFiloss(nn.Module):
def __init__(self,config:dict):
super().__init__() | self.mel=PitchAdjustableMelSpectrogram( sample_rate=config['audio_sample_rate'], | 2 | 2023-10-17 13:45:09+00:00 | 2k |
RobertCsordas/moe | tasks/simple/language_model/wikitext103_sp_transformer.py | [
{
"identifier": "Enwik8Transformer",
"path": "tasks/simple/language_model/enwik8_transformer.py",
"snippet": "class Enwik8Transformer(TransformerLMMixin, SimpleTask):\n VALID_NUM_WORKERS = 1\n TRAIN_NUM_WORKERS = 2\n\n def create_state(self):\n self.helper.state.epoch = 0\n\n def crea... | import torch
import dataset
import framework
from .enwik8_transformer import Enwik8Transformer
from ... import task, args | 884 |
@args
def a(parser: framework.helpers.ArgumentParser):
parser.add_argument("-sentencepiece.n_pieces", default=8000)
|
@args
def a(parser: framework.helpers.ArgumentParser):
parser.add_argument("-sentencepiece.n_pieces", default=8000)
| @task() | 1 | 2023-10-16 11:26:45+00:00 | 2k |
yk/llmvm | parsing.py | [
{
"identifier": "Arg",
"path": "interface.py",
"snippet": "class Arg(pydantic.BaseModel):\n vtype: str\n value: str"
},
{
"identifier": "Load",
"path": "interface.py",
"snippet": "class Load(Expr):\n kind: str = \"load\"\n vtype: str\n ptr: str"
},
{
"identifier": ... | import re
from loguru import logger
from interface import Arg, Load, Icmp, Srem, Add, Mul, Call, Assign, Store, Branch, BranchCond, Return, Program, to_vtype, GetElementPtr, Copy, Switch, AllocArray, Alloc | 1,000 |
def _line_stripper(in_f):
for line in in_f:
line = line.rstrip()
if not line:
continue
yield line
def parse_arg(arg):
logger.debug(f"parse_arg({arg})")
if m := re.match(r"ptr noundef (\S+)", arg):
return Arg(vtype="str", value=m.group(1))
if m := re.match(r"... |
def _line_stripper(in_f):
for line in in_f:
line = line.rstrip()
if not line:
continue
yield line
def parse_arg(arg):
logger.debug(f"parse_arg({arg})")
if m := re.match(r"ptr noundef (\S+)", arg):
return Arg(vtype="str", value=m.group(1))
if m := re.match(r"... | return Call(name=name, args=args) | 6 | 2023-10-23 21:29:14+00:00 | 2k |
w-e-w/sd-webui-nudenet-nsfw-censor | scripts/nudenet_nsfw_censor_scripts/api.py | [
{
"identifier": "pil_nude_detector",
"path": "scripts/nudenet_nsfw_censor_scripts/pil_nude_detector.py",
"snippet": "def draw_ellipse(draw, left_expanded, top_expanded, right_expanded, down_expanded, *args, **kwargs):\ndef draw_rectangle(draw, left_expanded, top_expanded, right_expanded, down_expanded, ... | from scripts.nudenet_nsfw_censor_scripts.pil_nude_detector import pil_nude_detector, nudenet_labels_index, mask_shapes_func_dict
from scripts.nudenet_nsfw_censor_scripts.censor_image_filters import apply_filter, filter_dict
from modules.api.api import decode_base64_to_image, encode_pil_to_base64
from fastapi import Fas... | 682 |
def nudenet_censor_api(_: gr.Blocks, app: FastAPI):
@app.post("/nudenet/censor")
async def censor(
input_image: str = Body(None, title="base64 input image"),
input_mask: str = Body(None, title="base64 mask (optional)"),
enable_nudenet: bool = Body(True, title="Enable NudeNe... |
def nudenet_censor_api(_: gr.Blocks, app: FastAPI):
@app.post("/nudenet/censor")
async def censor(
input_image: str = Body(None, title="base64 input image"),
input_mask: str = Body(None, title="base64 mask (optional)"),
enable_nudenet: bool = Body(True, title="Enable NudeNe... | filter_type: str = Body(None, title=f"Name of censor filter: {list(filter_dict)}"), | 1 | 2023-10-16 16:44:07+00:00 | 2k |
enkeejunior1/Diffusion-Pullback | src/models/improved_diffusion/unet.py | [
{
"identifier": "convert_module_to_f16",
"path": "src/models/improved_diffusion/fp16_util.py",
"snippet": "def convert_module_to_f16(l):\n \"\"\"\n Convert primitive modules to float16.\n \"\"\"\n if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):\n l.weight.data = l.weight.data.hal... | from abc import abstractmethod
from einops import rearrange, reduce, repeat, einsum
from .fp16_util import convert_module_to_f16, convert_module_to_f32
from .nn import (
SiLU,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
checkpoint,
)
import math
import t... | 1,450 |
class TimestepBlock(nn.Module):
"""
Any module where forward() takes timestep embeddings as a second argument.
"""
@abstractmethod
def forward(self, x, emb):
"""
Apply the module to `x` given `emb` timestep embeddings.
"""
class TimestepEmbedSequential(nn.Sequential, ... |
class TimestepBlock(nn.Module):
"""
Any module where forward() takes timestep embeddings as a second argument.
"""
@abstractmethod
def forward(self, x, emb):
"""
Apply the module to `x` given `emb` timestep embeddings.
"""
class TimestepEmbedSequential(nn.Sequential, ... | self.conv = conv_nd(dims, channels, channels, 3, padding=1) | 3 | 2023-10-21 04:08:44+00:00 | 2k |
NVIDIA-Omniverse/IsaacSim-Automator | src/python/deployer.py | [
{
"identifier": "colorize_error",
"path": "src/python/utils.py",
"snippet": "def colorize_error(text):\n return click.style(text, fg=\"bright_red\", italic=True)"
},
{
"identifier": "colorize_info",
"path": "src/python/utils.py",
"snippet": "def colorize_info(text):\n return click.... | import json
import os
import re
import shlex
import sys
import click
from pathlib import Path
from src.python.utils import (
colorize_error,
colorize_info,
colorize_prompt,
colorize_result,
read_meta,
shell_command,
)
from src.python.debug import debug_break # noqa
from src.python.ngc import ch... | 1,256 | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# 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... | # region copyright
# Copyright 2023 NVIDIA Corporation
#
# 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... | def read_meta(self): | 4 | 2023-10-18 17:25:44+00:00 | 2k |
blackgold3/SemanticBoost | mdm/model/clip/clip.py | [
{
"identifier": "build_model",
"path": "mdm/model/clip/model.py",
"snippet": "def build_model(state_dict: dict):\n vit = \"visual.proj\" in state_dict\n\n if vit:\n vision_width = state_dict[\"visual.conv1.weight\"].shape[0]\n vision_layers = len([k for k in state_dict.keys() if k.st... | import hashlib
import os
import urllib
import warnings
import torch
from typing import Any, Union, List
from pkg_resources import packaging
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm
from .model import build_model
from .simple_tokenize... | 1,527 |
try:
BICUBIC = InterpolationMode.BICUBIC
except ImportError:
BICUBIC = Image.BICUBIC
if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"):
warnings.warn("PyTorch version 1.7.1 or higher is recommended")
__all__ = ["available_models", "load", "tokenize"]
|
try:
BICUBIC = InterpolationMode.BICUBIC
except ImportError:
BICUBIC = Image.BICUBIC
if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"):
warnings.warn("PyTorch version 1.7.1 or higher is recommended")
__all__ = ["available_models", "load", "tokenize"] | _tokenizer = _Tokenizer() | 0 | 2023-10-20 14:53:26+00:00 | 2k |
justchenhao/SILI_CD | datasets/base_dataset.py | [
{
"identifier": "get_transforms",
"path": "datasets/transforms.py",
"snippet": "def get_transforms(norm=False, img_size=256):\n basic_transform = []\n basic_transform.append(T.ToTensor()) # ndarray转为 torch.FloatTensor， 范围[0,1]\n if norm:\n basic_transform.append(T.Normalize(mean=[0.5, 0... | import os
import numpy as np
import torch
from typing import Dict, Sequence, Tuple, Optional, Union
from PIL import Image
from torch.utils import data
from datasets.transforms import get_transforms, get_mask_transforms
from datasets.transforms import get_seg_augs
from misc.imutils import pil_rescale, pil_re... | 1,580 |
"""
some basic data loader
for example:
Image loader, Segmentation loader,
data root
├─A
├─label
└─list
"""
def load_img_name_list(dataset_path):
img_name_list = np.loadtxt(dataset_path, dtype=str)
if img_name_list.ndim == 2:
return img_name_list[:, 0]
return img_name_list
class ImageDatas... |
"""
some basic data loader
for example:
Image loader, Segmentation loader,
data root
├─A
├─label
└─list
"""
def load_img_name_list(dataset_path):
img_name_list = np.loadtxt(dataset_path, dtype=str)
if img_name_list.ndim == 2:
return img_name_list[:, 0]
return img_name_list
class ImageDatas... | self.basic_mask_transforms = get_mask_transforms(img_size=img_size) | 1 | 2023-10-21 09:09:57+00:00 | 2k |
pythonlessons/FinRock | finrock/indicators.py | [
{
"identifier": "RenderOptions",
"path": "finrock/render.py",
"snippet": "class RenderOptions:\n def __init__(\n self, \n name: str,\n color: tuple,\n window_type: WindowType,\n render_type: RenderType, \n min: float, \n max... | import pandas as pd
from .render import RenderOptions, RenderType, WindowType | 1,008 |
class Indicator:
""" Base class for indicators
"""
def __init__(
self,
data: pd.DataFrame,
target_column: str='close',
render_options: dict={}
) -> None:
self._data = data.copy()
self._target_column = target_column
self... |
class Indicator:
""" Base class for indicators
"""
def __init__(
self,
data: pd.DataFrame,
target_column: str='close',
render_options: dict={}
) -> None:
self._data = data.copy()
self._target_column = target_column
self... | window_type=WindowType.MAIN, | 2 | 2023-10-23 07:44:54+00:00 | 2k |
hitlic/deepepochs | deepepochs/metrics.py | [
{
"identifier": "sum_dicts",
"path": "deepepochs/loops.py",
"snippet": "def sum_dicts(dicts, to_np=False):\r\n dicts = concat_dicts(dicts, to_np)\r\n return ddict({k: sum(v) for k, v in dicts.items()})\r"
},
{
"identifier": "ddict",
"path": "deepepochs/loops.py",
"snippet": "class ... | from functools import lru_cache
from .loops import sum_dicts, ddict
import torch | 752 | """
@author: liuchen
"""
@lru_cache(maxsize=1)
def confusion_matrix(preds, targets, num_classes):
"""
Args:
preds: 预测向量，可为binary或多维概率分布
targets: 标签向量，可为one-hot或非one-hot的
num_class: 类别数量
"""
if (preds.dim()==1 or preds.shape[-1]==1) and num_classes==2: # 当预测为binary时
... | """
@author: liuchen
"""
@lru_cache(maxsize=1)
def confusion_matrix(preds, targets, num_classes):
"""
Args:
preds: 预测向量，可为binary或多维概率分布
targets: 标签向量，可为one-hot或非one-hot的
num_class: 类别数量
"""
if (preds.dim()==1 or preds.shape[-1]==1) and num_classes==2: # 当预测为binary时
... | c_mat = ddict({ | 1 | 2023-10-19 05:41:48+00:00 | 2k |
colour-science/colour-visuals | colour_visuals/axes.py | [
{
"identifier": "DEFAULT_FLOAT_DTYPE_WGPU",
"path": "colour_visuals/common.py",
"snippet": "DEFAULT_FLOAT_DTYPE_WGPU = np.float32"
},
{
"identifier": "unlatexify",
"path": "colour_visuals/common.py",
"snippet": "def unlatexify(text: str) -> str:\n \"\"\"\n Unlatexify given string.\... | import numpy as np
import pygfx as gfx
from colour.hints import LiteralColourspaceModel
from colour.models import COLOURSPACE_MODELS_AXIS_LABELS
from colour.plotting import (
CONSTANTS_COLOUR_STYLE,
colourspace_model_axis_reorder,
)
from colour.utilities import as_int_array
from colour_visuals.common import (
... | 986 | # !/usr/bin/env python
"""
Axes Visuals
============
Defines the axes visuals:
- :class:`colour_visuals.VisualAxes`
"""
from __future__ import annotations
__author__ = "Colour Developers"
__copyright__ = "Copyright 2023 Colour Developers"
__license__ = "BSD-3-Clause - https://opensource.org/licenses/BSD-3-Claus... | # !/usr/bin/env python
"""
Axes Visuals
============
Defines the axes visuals:
- :class:`colour_visuals.VisualAxes`
"""
from __future__ import annotations
__author__ = "Colour Developers"
__copyright__ = "Copyright 2023 Colour Developers"
__license__ = "BSD-3-Clause - https://opensource.org/licenses/BSD-3-Claus... | class VisualAxes(MixinPropertyModel, MixinPropertySize, Visual): | 3 | 2023-10-15 04:30:47+00:00 | 2k |
JiahuiLei/NAP | dataset/partnet_m_grouping.py | [
{
"identifier": "cfg_with_default",
"path": "core/models/utils/misc.py",
"snippet": "def cfg_with_default(cfg, key_list, default):\n root = cfg\n for k in key_list:\n if k in root.keys():\n root = root[k]\n else:\n return default\n return root"
},
{
"... | from random import random
from torch.utils.data import Dataset
from os.path import join
from core.models.utils.misc import cfg_with_default
from tqdm import tqdm
from object_utils.arti_graph_utils_v3 import compact_pack, map_upper_triangle_to_list
from copy import deepcopy
from torch.utils.data import WeightedR... | 1,561 | # Load processed PartNet-Mobility graph
# v5: from v4 use new full random permute, not first 1 v_mask
class Dataset(Dataset):
def __init__(self, cfg, mode) -> None:
super().__init__()
d_cfg = cfg["dataset"]
self.mode = mode.lower()
self.dataset_proportion = d_cfg["dataset_proport... | # Load processed PartNet-Mobility graph
# v5: from v4 use new full random permute, not first 1 v_mask
class Dataset(Dataset):
def __init__(self, cfg, mode) -> None:
super().__init__()
d_cfg = cfg["dataset"]
self.mode = mode.lower()
self.dataset_proportion = d_cfg["dataset_proport... | self.balance_flag = cfg_with_default(d_cfg, ["balance_flag"], False) | 0 | 2023-10-22 03:46:35+00:00 | 2k |
yongliang-wu/ExploreCfg | open_flamingo/src/flamingo_lm.py | [
{
"identifier": "GatedCrossAttentionBlock",
"path": "open_flamingo/src/helpers.py",
"snippet": "class GatedCrossAttentionBlock(nn.Module):\n def __init__(\n self,\n *,\n dim,\n dim_visual,\n dim_head=64,\n heads=8,\n ff_mult=4,\n only_attend_imm... | import random
import torch.nn as nn
from .helpers import GatedCrossAttentionBlock
from .utils import getattr_recursive, setattr_recursive | 1,082 |
class FlamingoLayer(nn.Module):
def __init__(self, gated_cross_attn_layer, decoder_layer):
super().__init__()
self.gated_cross_attn_layer = gated_cross_attn_layer
self.decoder_layer = decoder_layer
self.vis_x = None
self.media_locations = None
def is_conditioned(self... |
class FlamingoLayer(nn.Module):
def __init__(self, gated_cross_attn_layer, decoder_layer):
super().__init__()
self.gated_cross_attn_layer = gated_cross_attn_layer
self.decoder_layer = decoder_layer
self.vis_x = None
self.media_locations = None
def is_conditioned(self... | GatedCrossAttentionBlock( | 0 | 2023-10-18 02:38:00+00:00 | 2k |
mimo-x/Code-Review-GPT-Gitlab | app/gitlab_utils.py | [
{
"identifier": "log",
"path": "utils/logger.py",
"snippet": "CRITICAL = 50\nFATAL = CRITICAL\nERROR = 40\nWARNING = 30\nWARN = WARNING\nINFO = 20\nDEBUG = 10\nNOTSET = 0\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\nROOT_PATH = os.path.join(CURRENT_PATH, os.pardir)\nLOG_PATH = os.path.joi... | import requests
from retrying import retry
from config.config import *
from utils.logger import log
from utils.dingding import send_dingtalk_message_by_sign | 815 |
@retry(stop_max_attempt_number=3, wait_fixed=2000)
def get_merge_request_id(branch_name, project_id):
"""
根据分支名，获取mr_id
:param branch_name: 分支名
:param project_id: 项目id
:return: 如果分支存在 mr 则返回mrid / 如果不存在mr 则返回 ""
"""
# 构建API请求URL
url = f"{gitlab_server_url}/api/v4/projects/{project_id}/m... |
@retry(stop_max_attempt_number=3, wait_fixed=2000)
def get_merge_request_id(branch_name, project_id):
"""
根据分支名，获取mr_id
:param branch_name: 分支名
:param project_id: 项目id
:return: 如果分支存在 mr 则返回mrid / 如果不存在mr 则返回 ""
"""
# 构建API请求URL
url = f"{gitlab_server_url}/api/v4/projects/{project_id}/m... | log.info(f"分支 '{branch_name}' 存在mr记录.{merge_requests}") | 0 | 2023-10-19 14:10:10+00:00 | 2k |
AI-Application-and-Integration-Lab/DGUA_FAS | util/get_loader.py | [
{
"identifier": "YunpeiDataset",
"path": "util/dataset.py",
"snippet": "class YunpeiDataset(Dataset):\n def __init__(self, data_pd, transforms=None, train=True):\n self.train = train\n self.photo_path = data_pd['photo_path'].tolist()\n self.photo_label = data_pd['photo_label'].to... | import os
import random
import numpy as np
import pandas as pd
import torch
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader
from util.dataset import YunpeiDataset
from util.utils import sample_frames | 1,472 |
def get_dataset(src1_data, src1_train_num_frames, src2_data, src2_train_num_frames, src3_data, src3_train_num_frames,
tgt1_data, tgt_test_num_frames, batch_size):
print('Load Source Data')
print('Source Data: ', src1_data)
|
def get_dataset(src1_data, src1_train_num_frames, src2_data, src2_train_num_frames, src3_data, src3_train_num_frames,
tgt1_data, tgt_test_num_frames, batch_size):
print('Load Source Data')
print('Source Data: ', src1_data) | src1_train_data_fake = sample_frames(flag=0, num_frames=src1_train_num_frames, dataset_name=src1_data) | 1 | 2023-10-17 15:35:33+00:00 | 2k |
jianlanluo/SAQ | vqn/conservative_sac.py | [
{
"identifier": "next_rng",
"path": "vqn/jax_utils.py",
"snippet": "def next_rng(*args, **kwargs):\n global jax_utils_rng\n return jax_utils_rng(*args, **kwargs)"
},
{
"identifier": "value_and_multi_grad",
"path": "vqn/jax_utils.py",
"snippet": "def value_and_multi_grad(fun, n_outp... | from collections import OrderedDict
from copy import deepcopy
from functools import partial
from ml_collections import ConfigDict
from flax.training.train_state import TrainState
from .jax_utils import (
next_rng, value_and_multi_grad, mse_loss, JaxRNG, wrap_function_with_rng,
collect_jax_metrics
)
from .model ... | 1,418 |
class ConservativeSAC(object):
@staticmethod
def get_default_config(updates=None):
config = ConfigDict()
config.discount = 0.99
config.alpha_multiplier = 0.0
config.use_automatic_entropy_tuning = False
config.backup_entropy = False
config.target_entropy = 0.... |
class ConservativeSAC(object):
@staticmethod
def get_default_config(updates=None):
config = ConfigDict()
config.discount = 0.99
config.alpha_multiplier = 0.0
config.use_automatic_entropy_tuning = False
config.backup_entropy = False
config.target_entropy = 0.... | next_rng(self.policy.rng_keys()), | 0 | 2023-10-18 06:31:20+00:00 | 2k |
dpaleka/llm-chess-proofgame | puzzle_pair_solve.py | [
{
"identifier": "convert_pgn_to_game",
"path": "puzzle_solver.py",
"snippet": "def convert_pgn_to_game(pgn_moves):\n pgn = io.StringIO(pgn_moves)\n game = chess.pgn.read_game(pgn)\n if len(game.errors) > 0:\n return None\n return game"
},
{
"identifier": "solve_puzzle",
"p... | import chess
import numpy as np
import io
import json
import csv
import chessllm
from pathlib import Path
from tqdm import tqdm
from puzzle_solver import convert_pgn_to_game, solve_puzzle
from matplotlib import pyplot as plt | 776 |
DATA_DIR = Path("/data/chess-data/lichess_puzzles")
FILE_NAME = DATA_DIR / "pairs.csv"
"""
Solve puzzle pairs given in FILE_NAME, and report whether the model can solve them.
Separate by rating buckets; take 40 samples from each bucket.
It has the following columns: uid,rating,pgn,proofgame,solution
Helper functio... |
DATA_DIR = Path("/data/chess-data/lichess_puzzles")
FILE_NAME = DATA_DIR / "pairs.csv"
"""
Solve puzzle pairs given in FILE_NAME, and report whether the model can solve them.
Separate by rating buckets; take 40 samples from each bucket.
It has the following columns: uid,rating,pgn,proofgame,solution
Helper functio... | is_right_pgn = solve_puzzle(board_pgn, solution, engine) | 1 | 2023-10-16 16:36:53+00:00 | 2k |
Azure/azure-openai-benchmark | benchmark/bench.py | [
{
"identifier": "tokenize",
"path": "benchmark/tokenizecmd.py",
"snippet": "def tokenize(args):\n \"\"\"\n Count number of tokens for given input and model. It attempts to decode\n input as json chat messages. Otherwise, it assumes input is just text.\n Return: number of tokens.\n \"\"\"\... | import argparse
import logging
from .tokenizecmd import tokenize
from .loadcmd import load | 1,319 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
def main():
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
parser = argparse.ArgumentParser(description="Benchmarking tool for Azure OpenAI Provisioned Throughput ... | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
def main():
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
parser = argparse.ArgumentParser(description="Benchmarking tool for Azure OpenAI Provisioned Throughput ... | load_parser.set_defaults(func=load) | 1 | 2023-10-19 00:52:26+00:00 | 2k |
pytest-visual/pytest-visual | tests/lib/test_convenience.py | [
{
"identifier": "ceil_division",
"path": "visual/lib/convenience.py",
"snippet": "def ceil_division(n, d):\n return (n + d - 1) // d"
},
{
"identifier": "correct_layout",
"path": "visual/lib/convenience.py",
"snippet": "def correct_layout(image: np.ndarray, layout: str) -> np.ndarray:... | import numpy as np
from visual.lib.convenience import (
ceil_division,
correct_layout,
get_grid_shape,
get_image_max_value_from_type,
get_layout_from_image,
) | 792 |
def test_get_grid_shape():
assert get_grid_shape(1, 3) == (1, 1)
assert get_grid_shape(2, 3) == (1, 2)
assert get_grid_shape(3, 3) == (1, 3)
assert get_grid_shape(4, 3) == (2, 2)
assert get_grid_shape(5, 3) == (2, 3)
assert get_grid_shape(6, 3) == (2, 3)
assert get_grid_shape(7, 3) == (3,... |
def test_get_grid_shape():
assert get_grid_shape(1, 3) == (1, 1)
assert get_grid_shape(2, 3) == (1, 2)
assert get_grid_shape(3, 3) == (1, 3)
assert get_grid_shape(4, 3) == (2, 2)
assert get_grid_shape(5, 3) == (2, 3)
assert get_grid_shape(6, 3) == (2, 3)
assert get_grid_shape(7, 3) == (3,... | assert get_layout_from_image("hwc", np.zeros((1, 1, 1))) == "hwc" | 4 | 2023-10-18 07:13:37+00:00 | 2k |
SLDGroup/G-CASCADE | lib/gcn_lib/torch_vertex.py | [
{
"identifier": "BasicConv",
"path": "lib/gcn_lib/torch_nn.py",
"snippet": "class BasicConv(Seq):\n def __init__(self, channels, act='relu', norm=None, bias=True, drop=0., kernel_size=1, padding=0, groups=4):\n m = []\n for i in range(1, len(channels)):\n m.append(Conv2d(chan... | import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from .torch_nn import BasicConv, batched_index_select, act_layer
from .torch_edge import DenseDilatedKnnGraph
from .pos_embed import get_2d_relative_pos_embed
from timm.models.layers import DropPath | 1,489 | # 2022.06.17-Changed for building ViG model
# Huawei Technologies Co., Ltd. <foss@huawei.com>
class MRConv2d(nn.Module):
"""
Max-Relative Graph Convolution (Paper: https://arxiv.org/abs/1904.03751) for dense data type
"""
def __init__(self, in_channels, out_channels, act='relu', norm=None, ... | # 2022.06.17-Changed for building ViG model
# Huawei Technologies Co., Ltd. <foss@huawei.com>
class MRConv2d(nn.Module):
"""
Max-Relative Graph Convolution (Paper: https://arxiv.org/abs/1904.03751) for dense data type
"""
def __init__(self, in_channels, out_channels, act='relu', norm=None, ... | self.nn = BasicConv([in_channels*2, out_channels], act, norm, bias, kernel_size=1, padding=0, groups=4) | 0 | 2023-10-24 17:49:10+00:00 | 2k |
StackTipsLab/bloggy | bloggy_api/serializers.py | [
{
"identifier": "Post",
"path": "bloggy/models.py",
"snippet": ""
},
{
"identifier": "Comment",
"path": "bloggy/models/comment.py",
"snippet": "class Comment(models.Model):\n post = models.ForeignKey('bloggy.Post', on_delete=models.CASCADE, related_name='comments')\n user = models.... | from rest_framework import serializers
from bloggy.models import Post, User, Category
from bloggy.models.comment import Comment
from bloggy.models.course import Course
from bloggy.models.quizzes import Quiz | 1,360 |
class CategorySerializer(serializers.ModelSerializer):
class Meta:
model = Category
fields = [
'id',
'title',
'article_count',
'slug',
'description',
'color',
'logo',
'publish_status',
'cre... |
class CategorySerializer(serializers.ModelSerializer):
class Meta:
model = Category
fields = [
'id',
'title',
'article_count',
'slug',
'description',
'color',
'logo',
'publish_status',
'cre... | model = Course | 2 | 2023-10-17 14:50:39+00:00 | 2k |
openvinotoolkit/openvino.genai | llm_bench/python/utils/conversion_utils/helpers.py | [
{
"identifier": "COMPRESSION_OPTIONS",
"path": "llm_bench/python/utils/nncf_utils.py",
"snippet": "COMPRESSION_OPTIONS = {\n \"INT8\": {\"mode\": nncf.CompressWeightsMode.INT8 if \"INT8_ASYM\" not in nncf.CompressWeightsMode.__members__ else nncf.CompressWeightsMode.INT8_ASYM},\n \"INT4_SYM\": {\n... | from enum import Enum
from pathlib import Path
from nncf import compress_weights
from openvino import save_model
from ..nncf_utils import COMPRESSION_OPTIONS, INT4_MODEL_CONFIGURATION
from optimum.gptq import GPTQQuantizer
from auto_gptq import exllama_set_max_input_length
from optimum.gptq impo... | 1,586 | # -*- coding: utf-8 -*-
# Copyright (C) 2018-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
class BackendType(Enum):
PYTORCH = 'pytorch'
OPENVINO = 'openvino'
PYTORCH_DIR = 'pytorch'
PYTORCH_COMPRESS_WEIGHTS_DIR = 'compressed_weights/PT_{precision}-{compression}'
OV_DIR = 'dldt'
GPTQ_DIR = "G... | # -*- coding: utf-8 -*-
# Copyright (C) 2018-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
class BackendType(Enum):
PYTORCH = 'pytorch'
OPENVINO = 'openvino'
PYTORCH_DIR = 'pytorch'
PYTORCH_COMPRESS_WEIGHTS_DIR = 'compressed_weights/PT_{precision}-{compression}'
OV_DIR = 'dldt'
GPTQ_DIR = "G... | if model_id in INT4_MODEL_CONFIGURATION: | 1 | 2023-10-16 13:38:16+00:00 | 2k |
Iniquitatis/sd-webui-temporal | temporal/image_buffer.py | [
{
"identifier": "ensure_directory_exists",
"path": "temporal/fs.py",
"snippet": "def ensure_directory_exists(path):\n if not path.is_dir():\n path.mkdir(parents = True)\n\n return path"
},
{
"identifier": "load_json",
"path": "temporal/fs.py",
"snippet": "def load_json(path,... | import numpy as np
from temporal.fs import ensure_directory_exists, load_json, save_json
from temporal.image_utils import ensure_image_dims, np_to_pil, pil_to_np
from temporal.numpy_utils import average_array, make_eased_weight_array
from temporal.serialization import load_object, save_object | 1,233 |
class ImageBuffer:
def __init__(self, width, height, channels, count):
self.array = np.zeros((count, height, width, channels))
self.last_index = 0
@property
def width(self):
return self.array.shape[2]
@property
def height(self):
return self.array.shape[1]
@pr... |
class ImageBuffer:
def __init__(self, width, height, channels, count):
self.array = np.zeros((count, height, width, channels))
self.last_index = 0
@property
def width(self):
return self.array.shape[2]
@property
def height(self):
return self.array.shape[1]
@pr... | return pil_to_np(ensure_image_dims( | 5 | 2023-10-15 18:49:12+00:00 | 2k |
zabbix/python-zabbix-utils | zabbix_utils/sender.py | [
{
"identifier": "EmptyHandler",
"path": "zabbix_utils/logger.py",
"snippet": "class EmptyHandler(logging.Handler):\n \"\"\"Empty logging handler.\"\"\"\n\n def emit(self, *args, **kwargs):\n pass"
},
{
"identifier": "ZabbixProtocol",
"path": "zabbix_utils/common.py",
"snippe... | import re
import json
import socket
import logging
import configparser
from decimal import Decimal
from typing import Callable, Union
from typing import Self # type: ignore
from typing_extensions import Self
from .logger import EmptyHandler
from .common import ZabbixProtocol
from .exceptions import ProcessingE... | 1,470 | # zabbix_utils
#
# Copyright (C) 2001-2023 Zabbix SIA
#
# 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,
# ... | # zabbix_utils
#
# Copyright (C) 2001-2023 Zabbix SIA
#
# 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,
# ... | log.addHandler(EmptyHandler()) | 0 | 2023-10-16 12:49:35+00:00 | 2k |
miccunifi/TAPE | models/pl_model_module.py | [
{
"identifier": "CharbonnierLoss",
"path": "models/losses.py",
"snippet": "class CharbonnierLoss(nn.Module):\n \"\"\"\n Charbonnier loss (one variant of Robust L1Loss, a differentiable variant of L1Loss).\n\n Described in \"Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution\... | import torch
import torch.nn as nn
import pytorch_lightning as pl
import torchmetrics.image
import torchmetrics
import os.path as osp
from torchvision.transforms.functional import to_pil_image
from torchmetrics.functional.image.ssim import structural_similarity_index_measure
from einops import rearrange
from models.los... | 1,071 |
class ModelModule(pl.LightningModule):
"""
Pytorch Lightning Module for model training.
Args:
net (nn.Module): Model to train
num_input_frames (int): Number of input frames in the input window
pixel_loss_weight (float): Weight of the pixel loss
perceptual_loss_weight (flo... |
class ModelModule(pl.LightningModule):
"""
Pytorch Lightning Module for model training.
Args:
net (nn.Module): Model to train
num_input_frames (int): Number of input frames in the input window
pixel_loss_weight (float): Weight of the pixel loss
perceptual_loss_weight (flo... | self.pixel_criterion = CharbonnierLoss(loss_weight=self.pixel_loss_weight) | 0 | 2023-10-19 09:14:40+00:00 | 2k |
YefanZhou/TempBalance | object_detection/src/YOLOv8/ultralytics/vit/sam/model.py | [
{
"identifier": "build_sam",
"path": "object_detection/src/YOLOv8/ultralytics/vit/sam/build.py",
"snippet": "def build_sam(ckpt='sam_b.pt'):\n \"\"\"Build a SAM model specified by ckpt.\"\"\"\n model_builder = None\n for k in sam_model_map.keys():\n if ckpt.endswith(k):\n mode... | from ultralytics.yolo.cfg import get_cfg
from .build import build_sam
from .predict import Predictor | 724 | # SAM model interface
class SAM:
def __init__(self, model='sam_b.pt') -> None:
if model and not model.endswith('.pt') and not model.endswith('.pth'):
# Should raise AssertionError instead?
raise NotImplementedError('Segment anything prediction requires pre-trained checkpoint')
| # SAM model interface
class SAM:
def __init__(self, model='sam_b.pt') -> None:
if model and not model.endswith('.pt') and not model.endswith('.pth'):
# Should raise AssertionError instead?
raise NotImplementedError('Segment anything prediction requires pre-trained checkpoint') | self.model = build_sam(model) | 0 | 2023-10-24 00:45:55+00:00 | 2k |
intuit/sac3 | sac3/main.py | [
{
"identifier": "paraphraser",
"path": "sac3/paraphraser.py",
"snippet": "def paraphrase(question, number, model, temperature):"
},
{
"identifier": "Evaluate",
"path": "sac3/evaluator.py",
"snippet": "class Evaluate:\n def __init__(self, model):\n self.model = model\n se... | from sac3 import paraphraser
from sac3.evaluator import Evaluate
from sac3.consistency_checker import SemanticConsistnecyCheck | 1,291 |
# input information
question = 'Was there ever a US senator that represented the state of Alabama and whose alma mater was MIT?'
target_answer = 'Never'
# question pertubation
gen_question = paraphraser.paraphrase(question, number = 3, model = 'gpt-3.5-turbo', temperature=1.0)
# llm evaluation
|
# input information
question = 'Was there ever a US senator that represented the state of Alabama and whose alma mater was MIT?'
target_answer = 'Never'
# question pertubation
gen_question = paraphraser.paraphrase(question, number = 3, model = 'gpt-3.5-turbo', temperature=1.0)
# llm evaluation | llm_evaluate = Evaluate(model='gpt-3.5-turbo') | 1 | 2023-10-24 23:35:23+00:00 | 2k |
zcczhang/UVD | uvd/utils/video_utils.py | [
{
"identifier": "any_stack",
"path": "uvd/utils/array_tensor_utils.py",
"snippet": "def any_stack(xs: List, *, dim: int = 0):\n \"\"\"Works for both torch Tensor and numpy array.\"\"\"\n\n def _any_stack_helper(*xs):\n x = xs[0]\n if isinstance(x, np.ndarray):\n return np.... | import subprocess
import numpy as np
import torch
import torchvision.io
import ffmpeg # pip install ffmpeg-python
from typing import Union, List, Optional
from .array_tensor_utils import any_stack, any_to_torch_tensor, any_to_numpy
from .file_utils import f_mkdir, f_join, f_remove
from einops import rearra... | 1,383 |
__all__ = ["save_video", "ffmpeg_save_video", "compress_video", "VideoTensorWriter"]
def save_video(
video: Union[np.ndarray, torch.Tensor],
fname: str,
fps: Optional[int] = None,
compress: bool = False,
):
fname = f_join(fname)
|
__all__ = ["save_video", "ffmpeg_save_video", "compress_video", "VideoTensorWriter"]
def save_video(
video: Union[np.ndarray, torch.Tensor],
fname: str,
fps: Optional[int] = None,
compress: bool = False,
):
fname = f_join(fname) | video = any_to_torch_tensor(video) | 1 | 2023-10-17 19:08:14+00:00 | 2k |
Aatrox103/SAP | gpt_eval.py | [
{
"identifier": "askChatGPT",
"path": "utils/openai_api.py",
"snippet": "@backoff.on_exception(backoff.expo, (openai.error.RateLimitError, openai.error.APIError, openai.error.APIConnectionError, openai.error.Timeout))\ndef askChatGPT(prompt, model_name):\n if model_name == \"gpt-3.5-turbo-0301\":\n ... | import openai
import json
import re
import os
import backoff
import logging
import fire
from tqdm import tqdm
from utils.openai_api import askChatGPT, GPT_eval_score, Moderation | 777 |
#data_path: The name of the grandparent directory where the file is read and stored
#gpt_output: list of text to be evaluated
#eval_filename_prefix: The file name prefix of the read file, which is used to name the file when storing the result
#Use gpt to evaluate the harmfulness of the generated results,
#and two fi... |
#data_path: The name of the grandparent directory where the file is read and stored
#gpt_output: list of text to be evaluated
#eval_filename_prefix: The file name prefix of the read file, which is used to name the file when storing the result
#Use gpt to evaluate the harmfulness of the generated results,
#and two fi... | score = GPT_eval_score(text=output, template_eval=template_eval, model_name=model_name) | 1 | 2023-10-16 02:34:29+00:00 | 2k |
bytedance/ColTrack | motlib/utils/model.py | [
{
"identifier": "match_name_keywords",
"path": "util/get_param_dicts.py",
"snippet": "def match_name_keywords(n: str, name_keywords: list):\n out = False\n for b in name_keywords:\n if b in n:\n out = True\n break\n return out"
},
{
"identifier": "get_param_... | import json
import torch
import torch.nn as nn
from util.get_param_dicts import match_name_keywords
from util.get_param_dicts import get_param_dict as get_param_dict_default | 979 |
__all__ = ['get_param_dict']
def get_param_dict(args, model_without_ddp: nn.Module):
try:
param_dict_type = args.param_dict_type
except:
param_dict_type = 'default'
assert param_dict_type in ['default', 'ddetr_in_mmdet', 'large_wd', 'finetune']
if param_dict_type == 'finetune':
... |
__all__ = ['get_param_dict']
def get_param_dict(args, model_without_ddp: nn.Module):
try:
param_dict_type = args.param_dict_type
except:
param_dict_type = 'default'
assert param_dict_type in ['default', 'ddetr_in_mmdet', 'large_wd', 'finetune']
if param_dict_type == 'finetune':
... | param_dicts = get_param_dict_default(args, model_without_ddp) | 0 | 2023-10-16 02:18:33+00:00 | 2k |
alm0ra/mockafka-py | tests/test_producer.py | [
{
"identifier": "Message",
"path": "mockafka/message.py",
"snippet": "class Message:\n def __init__(self, *args, **kwargs):\n self._headers: Optional[dict] = kwargs.get('headers', None)\n self._key: Optional[str] = kwargs.get('key', None)\n self._value: Optional[str] = kwargs.get... | from unittest import TestCase
from mockafka import Message
from mockafka.admin_client import FakeAdminClientImpl, NewTopic
from mockafka.kafka_store import KafkaStore, KafkaException
from mockafka.producer import FakeProducer
from confluent_kafka import Message
import pytest | 1,584 |
class TestFakeProducer(TestCase):
def setUp(self) -> None:
self.kafka = KafkaStore(clean=True)
self.producer = FakeProducer()
|
class TestFakeProducer(TestCase):
def setUp(self) -> None:
self.kafka = KafkaStore(clean=True)
self.producer = FakeProducer() | self.admin_client = FakeAdminClientImpl() | 1 | 2023-10-24 13:27:12+00:00 | 2k |
HRI-EU/rosenv | tests/integration/commands/install/test_install_launch_file_detection.py | [
{
"identifier": "ROS_2",
"path": "tests/conftest.py",
"snippet": "ROS_2: Final[Literal[2]] = 2"
},
{
"identifier": "YieldFixture",
"path": "tests/conftest.py",
"snippet": "_T = TypeVar(\"_T\")\nROS_1: Final[Literal[1]] = 1\nROS_2: Final[Literal[2]] = 2\nROS_1_PROJECT_LIST = [\"adder\", \... | import logging
import shutil
import pytest
from pathlib import Path
from cleo.application import Application
from cleo.testers.command_tester import CommandTester
from deb_pkg_tools.package import ArchiveEntry
from deb_pkg_tools.package import inspect_package_contents
from tests.conftest import ROS_2
from tests.conftes... | 1,030 | #
# Copyright (c) Honda Research Institute Europe GmbH
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions a... | #
# Copyright (c) Honda Research Institute Europe GmbH
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions a... | @pytest.mark.skipif(get_ros_version() == ROS_2, reason="Launchfile-Checks only work in ROS1 currently") | 2 | 2023-10-18 12:36:30+00:00 | 2k |
CuriseJia/FreeStyleRet | comparison_test/imagebind_test.py | [
{
"identifier": "getR1Accuary",
"path": "src/utils/utils.py",
"snippet": "def getR1Accuary(prob):\n temp = prob.detach().cpu().numpy()\n temp = np.argsort(temp, axis=1)\n count = 0\n for i in range(prob.shape[0]):\n if temp[i][prob.shape[1]-1] == i:\n count+=1\n acc = co... | import torch
import argparse
import sys
import json
import os
import time
from tqdm import tqdm
from open_clip.factory import image_transform
from torch.utils.data import DataLoader
from src.utils import setup_seed, getR1Accuary, getR5Accuary
from src.dataset import I2ITestDataset, T2ITestDataset
from ImageBind.imagebi... | 1,570 |
image_mean = (0.48145466, 0.4578275, 0.40821073)
image_std = (0.26861954, 0.26130258, 0.27577711)
def parse_args():
parser = argparse.ArgumentParser(description='Parse args for Prompt_ImageBind or Origin_ImageBind test on DSR dataset.')
# project settings
parser.add_argument('--origin_resume', default... |
image_mean = (0.48145466, 0.4578275, 0.40821073)
image_std = (0.26861954, 0.26130258, 0.27577711)
def parse_args():
parser = argparse.ArgumentParser(description='Parse args for Prompt_ImageBind or Origin_ImageBind test on DSR dataset.')
# project settings
parser.add_argument('--origin_resume', default... | test_dataset = I2ITestDataset(args.test_dataset_path, args.test_json_path, pre_process_val) | 3 | 2023-10-17 09:32:57+00:00 | 2k |
liuqidong07/MOELoRA-peft | src/MLoRA/peft/tuners/prompt_tuning.py | [
{
"identifier": "PeftType",
"path": "src/MLoRA/peft/utils/config.py",
"snippet": "class PeftType(str, enum.Enum):\n PROMPT_TUNING = \"PROMPT_TUNING\"\n P_TUNING = \"P_TUNING\"\n PREFIX_TUNING = \"PREFIX_TUNING\"\n LORA = \"LORA\"\n ADALORA = \"ADALORA\"\n ADAPTION_PROMPT = \"ADAPTION_P... | import enum
import math
import torch
from dataclasses import dataclass, field
from typing import Optional, Union
from ..utils import PeftType, PromptLearningConfig
from transformers import AutoTokenizer | 924 | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# 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 ap... | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# 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 ap... | self.peft_type = PeftType.PROMPT_TUNING | 0 | 2023-10-19 10:55:50+00:00 | 2k |
voyage-ai/voyageai-python | voyageai/api_resources/api_requestor.py | [
{
"identifier": "error",
"path": "voyageai/error.py",
"snippet": "class VoyageError(Exception):\nclass APIError(VoyageError):\nclass TryAgain(VoyageError):\nclass Timeout(VoyageError):\nclass APIConnectionError(VoyageError):\nclass InvalidRequestError(VoyageError):\nclass MalformedRequestError(VoyageErr... | import asyncio
import json
import time
import platform
import sys
import threading
import time
import warnings
import aiohttp
import requests
import voyageai
from json import JSONDecodeError
from typing import (
AsyncContextManager,
AsyncGenerator,
Callable,
Dict,
Iterator,
Optional,
Tuple,
... | 1,595 |
if sys.version_info >= (3, 8):
else:
TIMEOUT_SECS = 600
MAX_SESSION_LIFETIME_SECS = 180
MAX_CONNECTION_RETRIES = 2
# Has one attribute per thread, 'session'.
_thread_context = threading.local()
def _build_api_url(url, query):
scheme, netloc, path, base_query, fragment = urlsplit(url)
if base_query:
... |
if sys.version_info >= (3, 8):
else:
TIMEOUT_SECS = 600
MAX_SESSION_LIFETIME_SECS = 180
MAX_CONNECTION_RETRIES = 2
# Has one attribute per thread, 'session'.
_thread_context = threading.local()
def _build_api_url(url, query):
scheme, netloc, path, base_query, fragment = urlsplit(url)
if base_query:
... | self.api_key = key or util.default_api_key() | 1 | 2023-10-17 22:11:18+00:00 | 2k |
YuroFR/freqtrade-modded-crypto-trading-bot | tests/exchange/test_bybit.py | [
{
"identifier": "MarginMode",
"path": "freqtrade/enums/marginmode.py",
"snippet": "class MarginMode(str, Enum):\n \"\"\"\n Enum to distinguish between\n cross margin/futures margin_mode and\n isolated margin/futures margin_mode\n \"\"\"\n CROSS = \"cross\"\n ISOLATED = \"isolated\"\... | from datetime import datetime, timedelta, timezone
from unittest.mock import MagicMock
from freqtrade.enums.marginmode import MarginMode
from freqtrade.enums.tradingmode import TradingMode
from tests.conftest import EXMS, get_mock_coro, get_patched_exchange
from tests.exchange.test_exchange import ccxt_exceptionhandler... | 853 |
def test_additional_exchange_init_bybit(default_conf, mocker):
default_conf['dry_run'] = False
default_conf['trading_mode'] = TradingMode.FUTURES
|
def test_additional_exchange_init_bybit(default_conf, mocker):
default_conf['dry_run'] = False
default_conf['trading_mode'] = TradingMode.FUTURES | default_conf['margin_mode'] = MarginMode.ISOLATED | 0 | 2023-10-21 10:02:05+00:00 | 2k |
yanzhh/HGERE | transformers/src/transformers/data/processors/squad.py | [
{
"identifier": "is_tf_available",
"path": "transformers/src/transformers/file_utils.py",
"snippet": "def is_tf_available():\n return _tf_available"
},
{
"identifier": "is_torch_available",
"path": "transformers/src/transformers/file_utils.py",
"snippet": "def is_torch_available():\n ... | import json
import logging
import os
import numpy as np
import torch
import tensorflow as tf
from functools import partial
from multiprocessing import Pool, cpu_count
from tqdm import tqdm
from ...file_utils import is_tf_available, is_torch_available
from ...tokenization_bert import whitespace_tokenize
from .ut... | 1,233 |
if is_torch_available():
if is_tf_available():
logger = logging.getLogger(__name__)
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answer."""
tok_answer_text = " ".join(tokenizer.tokenize(orig_a... |
if is_torch_available():
if is_tf_available():
logger = logging.getLogger(__name__)
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answer."""
tok_answer_text = " ".join(tokenizer.tokenize(orig_a... | cleaned_answer_text = " ".join(whitespace_tokenize(example.answer_text)) | 2 | 2023-10-15 02:31:09+00:00 | 2k |
generative-skill-chaining/gsc-code | generative_skill_chaining/networks/actors/mlp.py | [
{
"identifier": "LFF",
"path": "generative_skill_chaining/networks/mlp.py",
"snippet": "class LFF(torch.nn.Module):\n \"\"\"\n get torch.std_mean(self.B)\n \"\"\"\n\n def __init__(self, in_features, out_features, scale=1.0, init=\"iso\", sincos=False):\n super().__init__()\n se... | from typing import Optional, Sequence, Type
from generative_skill_chaining.networks.mlp import LFF, MLP, weight_init
from generative_skill_chaining.networks.actors import base
from generative_skill_chaining.networks.utils import SquashedNormal
import gym
import torch | 1,022 |
class ContinuousMLPActor(base.Actor):
def __init__(
self,
state_space: gym.spaces.Box,
action_space: gym.spaces.Box,
hidden_layers: Sequence[int] = [256, 256],
act: Type[torch.nn.Module] = torch.nn.ReLU,
output_act: Type[torch.nn.Module] = torch.nn.Tanh,
o... |
class ContinuousMLPActor(base.Actor):
def __init__(
self,
state_space: gym.spaces.Box,
action_space: gym.spaces.Box,
hidden_layers: Sequence[int] = [256, 256],
act: Type[torch.nn.Module] = torch.nn.ReLU,
output_act: Type[torch.nn.Module] = torch.nn.Tanh,
o... | self.apply(weight_init) | 2 | 2023-10-16 00:22:40+00:00 | 2k |
ChiyuSONG/dynamics-of-instruction-tuning | inference.py | [
{
"identifier": "IGNORE_INDEX",
"path": "train_sft.py",
"snippet": "IGNORE_INDEX = -100"
},
{
"identifier": "DataCollatorForSupervisedDataset",
"path": "train_sft.py",
"snippet": "class DataCollatorForSupervisedDataset(object):\n \"\"\"Collate examples for supervised fine-tuning.\"\"\... | import torch
from transformers import (
LlamaForCausalLM,
LlamaTokenizer,
set_seed,
GenerationConfig
)
from train_sft import IGNORE_INDEX, DataCollatorForSupervisedDataset, ATTR_TO_SPECIAL_TOKEN | 951 |
def process(batch, 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 example in batch:
input_ids = []
... |
def process(batch, 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 example in batch:
input_ids = []
... | data_collator = DataCollatorForSupervisedDataset(tokenizer=self.tokenizer, pad_to_multiple_of=8) | 1 | 2023-10-17 07:41:58+00:00 | 2k |
akashgreninja/GreSec | backend/venv/lib/python3.10/site-packages/pydantic/_internal/_schema_generation_shared.py | [
{
"identifier": "GetCoreSchemaHandler",
"path": "backend/venv/lib/python3.10/site-packages/pydantic/annotated_handlers.py",
"snippet": "class GetCoreSchemaHandler:\n \"\"\"Handler to call into the next CoreSchema schema generation function.\"\"\"\n\n def __call__(self, __source_type: Any) -> core_... | from typing import TYPE_CHECKING, Any, Callable
from pydantic_core import core_schema
from typing_extensions import Literal
from ..annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler
from ..json_schema import GenerateJsonSchema, JsonSchemaValue
from ._core_utils import CoreSchemaOrField
fro... | 1,470 | """Types and utility functions used by various other internal tools."""
from __future__ import annotations
if TYPE_CHECKING:
GetJsonSchemaFunction = Callable[[CoreSchemaOrField, GetJsonSchemaHandler], JsonSchemaValue]
HandlerOverride = Callable[[CoreSchemaOrField], JsonSchemaValue]
class GenerateJsonSche... | """Types and utility functions used by various other internal tools."""
from __future__ import annotations
if TYPE_CHECKING:
GetJsonSchemaFunction = Callable[[CoreSchemaOrField, GetJsonSchemaHandler], JsonSchemaValue]
HandlerOverride = Callable[[CoreSchemaOrField], JsonSchemaValue]
class GenerateJsonSche... | class CallbackGetCoreSchemaHandler(GetCoreSchemaHandler): | 0 | 2023-10-23 18:09:28+00:00 | 2k |
mindsdb/otto | ottoai/classes.py | [
{
"identifier": "INSTRUCTION",
"path": "ottoai/templates.py",
"snippet": "INSTRUCTION = \"\"\"\n\nYou write python code, write the simplest and most effective Python function to answer the following.\n\nQuestion: {question}\n\nFollow these instructions to write the function:\n\n- The function must be ca... | from ottoai.templates import INSTRUCTION
from ottoai.helpers import llm_completion, create_string, extract_python_code_from_md, get_runner_function
import logging
import os
import json
import pkg_resources
import subprocess
import os
import openai
import logger
import json | 1,069 |
class Assistant:
"""
The Assistant class is responsible for managing the skills and conversations.
"""
def __init__(self, name: str, personality: str, llm_engine, model: str, user_context_variables: dict = {}):
"""
Initialize the assistant with a name, personality, language model eng... |
class Assistant:
"""
The Assistant class is responsible for managing the skills and conversations.
"""
def __init__(self, name: str, personality: str, llm_engine, model: str, user_context_variables: dict = {}):
"""
Initialize the assistant with a name, personality, language model eng... | runner_function = get_runner_function(code) | 1 | 2023-10-18 00:09:18+00:00 | 2k |
adarshxs/TokenTally | tools/llm_cost_calculator.py | [
{
"identifier": "load_base_models",
"path": "utilities.py",
"snippet": "def load_base_models():\n with open(\"models.json\", \"r\") as f:\n return json.load(f)"
},
{
"identifier": "load_quantization",
"path": "utilities.py",
"snippet": "def load_quantization():\n with open(\... | import streamlit as st
from utilities import load_base_models, load_quantization, load_gpus, load_gpu_providers, convert_params, compute_bound_tokens_p_sec, memory_bound_tokens_p_sec, cost_per_1k_tokens | 678 |
def display_llm_cost_tool():
st.title("Token Tally: LLM Cost Estimator")
st.subheader("Estimate Your LLM's Token Toll Across Various Platforms and Configurations")
# Base model and configurations data
base_models = load_base_models()
|
def display_llm_cost_tool():
st.title("Token Tally: LLM Cost Estimator")
st.subheader("Estimate Your LLM's Token Toll Across Various Platforms and Configurations")
# Base model and configurations data
base_models = load_base_models() | quantization_data = load_quantization() | 1 | 2023-10-18 06:16:47+00:00 | 2k |
WestlakeIntelligentRobotics/ConsensusLLM-code | modules/llm/agent_2d.py | [
{
"identifier": "GPT",
"path": "modules/llm/gpt.py",
"snippet": "class GPT:\n \"\"\"\n Initialize the GPT class for interacting with OpenAI's GPT model.\n GPT provides basic methods for interacting with the model and parsing its\n output.\n \"\"\"\n\n def __init__(self, key: str, model... | import re
import numpy as np
from .gpt import GPT
from ..prompt.summarize import summarizer_role
from ..prompt.form import summarizer_output_form | 1,369 | """
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... | class Agent2D(GPT): | 0 | 2023-10-20 07:58:07+00:00 | 2k |
inngest/inngest-py | inngest/_internal/middleware_lib/middleware.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, execution, function | 1,522 | from __future__ import annotations
class Middleware:
def __init__(self, client: client_lib.Inngest) -> None:
self._client = client
async def after_execution(self) -> None:
"""
After executing new code. Called multiple times per run when using
steps.
"""
return... | from __future__ import annotations
class Middleware:
def __init__(self, client: client_lib.Inngest) -> None:
self._client = client
async def after_execution(self) -> None:
"""
After executing new code. Called multiple times per run when using
steps.
"""
return... | ctx: function.Context, | 2 | 2023-10-19 01:02:30+00:00 | 2k |
f0uriest/quadax | quadax/fixed_order.py | [
{
"identifier": "cc_weights",
"path": "quadax/quad_weights.py",
"snippet": "def _cc_get_weights(N):\ndef _get_tmax(xmax):\n D = 2 / N * np.cos(k[:, None] * n[None, :] * np.pi / (N // 2))\n D = np.where((n == 0) | (n == N // 2), D * 1 / 2, D)\n N = int(2 * 2**i)\n N = int(2 * 2**i)"
},
{
... | import functools
import jax
import jax.numpy as jnp
from .quad_weights import cc_weights, gk_weights, ts_weights
from .utils import wrap_func | 809 | """Fixed order quadrature."""
def _dot(w, f):
return jnp.sum(w * f.T, axis=-1).T
@functools.partial(jax.jit, static_argnums=(0, 4, 5))
def fixed_quadgk(fun, a, b, args=(), norm=jnp.inf, n=21):
"""Integrate a function from a to b using a fixed order Gauss-Konrod rule.
Integration is performed using a... | """Fixed order quadrature."""
def _dot(w, f):
return jnp.sum(w * f.T, axis=-1).T
@functools.partial(jax.jit, static_argnums=(0, 4, 5))
def fixed_quadgk(fun, a, b, args=(), norm=jnp.inf, n=21):
"""Integrate a function from a to b using a fixed order Gauss-Konrod rule.
Integration is performed using a... | gk_weights[n]["xk"], | 0 | 2023-10-24 04:44:34+00:00 | 2k |
smonsays/metax | examples/maml-omniglot.py | [
{
"identifier": "DATAPATH",
"path": "metax/data/base.py",
"snippet": "DATAPATH = Path(os.path.expanduser(\"~/data/jax\"))"
},
{
"identifier": "Dataset",
"path": "metax/data/base.py",
"snippet": "class Dataset(NamedTuple):\n x: Array\n y: Array\n info: Dict = dict()"
},
{
... | import argparse
import jax
import jax.numpy as jnp
import jax.tree_util as jtu
import optax
import metax
from jax_meta.datasets import Omniglot
from metax.data.base import DATAPATH, Dataset, MetaDataset | 975 | """
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... | batch = MetaDataset( | 2 | 2023-10-19 16:36:20+00:00 | 2k |
claws-lab/XLingEval | consistency/consistency_get_medalpaca_answer.py | [
{
"identifier": "init_medalpaca_model",
"path": "consistency/Medalpaca/model_medalpaca.py",
"snippet": "def init_medalpaca_model(args):\n # --- Flags from the original code ---\n load_in_8bit = False\n cache_dir = None\n \n print(f\"Loading model {args.model}...\")\n if args.model == \... | import os
import os.path as osp
import re
import string
import sys
import traceback
import torch
import pandas as pd
import const
from tqdm import trange
from consistency.Medalpaca.model_medalpaca import init_medalpaca_model
from arguments import args
from consistency.data_consistency import load_data_consistency, \
... | 1,542 |
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
if osp.exists(const.HOME_DIR_LINUX):
cuda_path = "/usr/local/cuda-11.7/bin/nvcc"
if "LD_LIBRARY_PATH" in os.environ:
os.environ["LD_LIBRARY_PATH"] += f"{cuda_path}"
else:
os.environ["LD_LIBRARY_PATH"] = cuda_path
def fo... |
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
if osp.exists(const.HOME_DIR_LINUX):
cuda_path = "/usr/local/cuda-11.7/bin/nvcc"
if "LD_LIBRARY_PATH" in os.environ:
os.environ["LD_LIBRARY_PATH"] += f"{cuda_path}"
else:
os.environ["LD_LIBRARY_PATH"] = cuda_path
def fo... | results_df = load_results_consistency(args) | 3 | 2023-10-18 17:35:42+00:00 | 2k |
vtuber-plan/olah | olah/meta.py | [
{
"identifier": "OlahConfig",
"path": "olah/configs.py",
"snippet": "class OlahConfig(object):\n def __init__(self, path: Optional[str] = None) -> None:\n\n # basic\n self.host = \"localhost\"\n self.port = 8090\n self.ssl_key = None\n self.ssl_cert = None\n ... | import os
import shutil
import tempfile
import httpx
from typing import Dict, Literal
from fastapi import FastAPI, Request
from olah.configs import OlahConfig
from olah.constants import CHUNK_SIZE, WORKER_API_TIMEOUT
from olah.utls import check_cache_rules_hf | 1,141 |
async def meta_cache_generator(app: FastAPI, save_path: str):
yield {}
with open(save_path, "rb") as f:
while True:
chunk = f.read(CHUNK_SIZE)
if not chunk:
break
yield chunk
async def meta_proxy_generator(app: FastAPI, headers: Dict[str, str], ... |
async def meta_cache_generator(app: FastAPI, save_path: str):
yield {}
with open(save_path, "rb") as f:
while True:
chunk = f.read(CHUNK_SIZE)
if not chunk:
break
yield chunk
async def meta_proxy_generator(app: FastAPI, headers: Dict[str, str], ... | allow_cache = await check_cache_rules_hf(app, repo_type, org, repo) | 3 | 2023-10-23 15:01:52+00:00 | 2k |
RF-Tar-Railt/satori-python | src/satori/server/adapter.py | [
{
"identifier": "Event",
"path": "src/satori/model.py",
"snippet": "class Event:\n id: int\n type: str\n platform: str\n self_id: str\n timestamp: datetime\n argv: Optional[ArgvInteraction] = None\n button: Optional[ButtonInteraction] = None\n channel: Optional[Channel] = None\n ... | from abc import abstractmethod
from typing import Any, AsyncIterator, Dict, List
from launart import Service
from satori.const import Api
from ..model import Event, Login
from .model import Request | 1,085 |
class Adapter(Service):
@abstractmethod
def get_platform(self) -> str:
...
@abstractmethod
def publisher(self) -> AsyncIterator[Event]:
...
@abstractmethod
def validate_headers(self, headers: Dict[str, Any]) -> bool:
...
@abstractmethod
def authenticate(se... |
class Adapter(Service):
@abstractmethod
def get_platform(self) -> str:
...
@abstractmethod
def publisher(self) -> AsyncIterator[Event]:
...
@abstractmethod
def validate_headers(self, headers: Dict[str, Any]) -> bool:
...
@abstractmethod
def authenticate(se... | async def get_logins(self) -> List[Login]: | 1 | 2023-10-18 11:09:34+00:00 | 2k |
Subsets and Splits
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that have consistent code formatting levels across multiple scales (2k, 4k, 8k, 12k) and reveals the structured formatting patterns within these repositories.
SQL Console for tianyang/repobench_python_v1.1
Compares cross-file and in-file code structure patterns across different complexity levels, revealing how file organization strategies vary with code size and potentially informing better code architecture decisions.
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that have complete performance data across all seven code complexity levels, revealing consistent benchmarking patterns across different code sizes.
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that contain all 7 distinct quality levels (2k through 32k), revealing complete datasets that might be useful for comprehensive analysis.