id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
144,826 | import argparse
import json
import logging
import os.path as osp
from multiprocessing import Pool
import mmcv
from mmengine.config import Config
from mmengine.fileio import dump, get, get_text
from mmengine.logging import print_log
def parse_args():
parser = argparse.ArgumentParser(description='Collect image metas... | null |
144,827 | import argparse
import json
import logging
import os.path as osp
from multiprocessing import Pool
import mmcv
from mmengine.config import Config
from mmengine.fileio import dump, get, get_text
from mmengine.logging import print_log
def get_image_metas(anno_str, img_prefix):
id_hw = {}
anno_dict = json.loads(an... | null |
144,831 | import argparse
import csv
import os.path as osp
from multiprocessing import Pool
import mmcv
from mmengine.config import Config
from mmengine.fileio import dump, get
def parse_args():
parser = argparse.ArgumentParser(description='Collect image metas')
parser.add_argument('config', help='Config file path')
... | null |
144,832 | import argparse
import csv
import os.path as osp
from multiprocessing import Pool
import mmcv
from mmengine.config import Config
from mmengine.fileio import dump, get
def get_metas_from_csv_style_ann_file(ann_file):
data_infos = []
cp_filename = None
with open(ann_file, 'r') as f:
reader = csv.read... | null |
144,833 | import argparse
import csv
import os.path as osp
from multiprocessing import Pool
import mmcv
from mmengine.config import Config
from mmengine.fileio import dump, get
def get_metas_from_txt_style_ann_file(ann_file):
with open(ann_file) as f:
lines = f.readlines()
i = 0
data_infos = []
while i <... | null |
144,834 | import argparse
import csv
import os.path as osp
from multiprocessing import Pool
import mmcv
from mmengine.config import Config
from mmengine.fileio import dump, get
def get_image_metas(data_info, img_prefix):
filename = data_info.get('filename', None)
if filename is not None:
if img_prefix is not Non... | null |
144,838 | from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
from mmengine.config import Config
from mmengine.utils import mkdir_or_exist
The provided code snippet includes necessary dependencies for implementing the `mmdet2torchserve` function. Write a Python functio... | Converts MMDetection model (config + checkpoint) to TorchServe `.mar`. Args: config_file: In MMDetection config format. The contents vary for each task repository. checkpoint_file: In MMDetection checkpoint format. The contents vary for each task repository. output_folder: Folder where `{model_name}.mar` will be create... |
144,840 | import os
import time
import json
import random
import argparse
import datetime
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils import accuracy, AverageMeter
from config import ... | null |
144,841 | import os
import time
import json
import random
import argparse
import datetime
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils import accuracy, AverageMeter
from config import ... | null |
144,842 | import os
import time
import json
import random
import argparse
import datetime
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils import accuracy, AverageMeter
from config import ... | null |
144,843 | import os
import time
import json
import random
import argparse
import datetime
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils import accuracy, AverageMeter
from config import ... | null |
144,845 | import io
import os
import time
import torch.distributed as dist
import torch.utils.data as data
from PIL import Image
from .zipreader import is_zip_path, ZipReader
def has_file_allowed_extension(filename, extensions):
def make_dataset(dir, class_to_idx, extensions):
images = []
dir = os.path.expanduser(dir)
... | null |
144,850 | import os
import torch
import numpy as np
import torch.distributed as dist
from torchvision import datasets, transforms
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.data import Mixup
from timm.data import create_transform
from .cached_image_folder import CachedImageFolder
from .... | null |
144,851 | import torch
import triton
import triton.language as tl
def triton_cross_scan(
x, # (B, C, H, W)
y, # (B, 4, C, H, W)
BC: tl.constexpr,
BH: tl.constexpr,
BW: tl.constexpr,
DC: tl.constexpr,
DH: tl.constexpr,
DW: tl.constexpr,
NH: tl.constexpr,
NW: tl.constexpr,
):
i_hw, i_c,... | null |
144,852 | import torch
import triton
import triton.language as tl
def triton_cross_merge(
x, # (B, C, H, W)
y, # (B, 4, C, H, W)
BC: tl.constexpr,
BH: tl.constexpr,
BW: tl.constexpr,
DC: tl.constexpr,
DH: tl.constexpr,
DW: tl.constexpr,
NH: tl.constexpr,
NW: tl.constexpr,
):
i_hw, i_c... | null |
144,853 | import torch
import triton
import triton.language as tl
def triton_cross_scan_1b1(
x, # (B, C, H, W)
y, # (B, 4, C, H, W)
BC: tl.constexpr,
BH: tl.constexpr,
BW: tl.constexpr,
DC: tl.constexpr,
DH: tl.constexpr,
DW: tl.constexpr,
NH: tl.constexpr,
NW: tl.constexpr,
):
i_hw, ... | null |
144,854 | import torch
import triton
import triton.language as tl
def triton_cross_merge_1b1(
x, # (B, C, H, W)
y, # (B, 4, C, H, W)
BC: tl.constexpr,
BH: tl.constexpr,
BW: tl.constexpr,
DC: tl.constexpr,
DH: tl.constexpr,
DW: tl.constexpr,
NH: tl.constexpr,
NW: tl.constexpr,
):
i_hw,... | null |
144,855 | import torch
import triton
import triton.language as tl
def triton_cross_scan_(
x, # (B, C, H, W)
y, # (B, 4, C, H, W)
BC: tl.constexpr,
BH: tl.constexpr,
BW: tl.constexpr,
DC: tl.constexpr,
DH: tl.constexpr,
DW: tl.constexpr,
NH: tl.constexpr,
NW: tl.constexpr,
MODE: tl.con... | null |
144,856 | import os
import time
import math
import copy
from functools import partial
from typing import Optional, Callable, Any
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from einops import rearrange, repeat
from timm.models.... | null |
144,857 | import os
import time
import math
import copy
from functools import partial
from typing import Optional, Callable, Any
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from einops import rearrange, repeat
from timm.models.... | u: r(B D L) delta: r(B D L) A: r(D N) B: r(B N L) C: r(B N L) D: r(D) z: r(B D L) delta_bias: r(D), fp32 ignores: [.float(), +, .softplus, .shape, new_zeros, repeat, stack, to(dtype), silu] |
144,858 | import os
import time
import math
import copy
from functools import partial
from typing import Optional, Callable, Any
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from einops import rearrange, repeat
from timm.models.... | null |
144,859 | import os
import time
import math
import copy
from functools import partial
from typing import Optional, Callable, Any
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from einops import rearrange, repeat
from timm.models.... | null |
144,860 | import os
from math import inf
import torch
import torch.distributed as dist
from timm.utils import ModelEma as ModelEma
def load_checkpoint_ema(config, model, optimizer, lr_scheduler, loss_scaler, logger, model_ema: ModelEma=None):
logger.info(f"==============> Resuming form {config.MODEL.RESUME}.................... | null |
144,861 | import os
from math import inf
import torch
import torch.distributed as dist
from timm.utils import ModelEma as ModelEma
def load_pretrained_ema(config, model, logger, model_ema: ModelEma=None, load_ema_separately=False):
logger.info(f"==============> Loading weight {config.MODEL.PRETRAINED} for fine-tuning......"... | null |
144,862 | import os
from math import inf
import torch
import torch.distributed as dist
from timm.utils import ModelEma as ModelEma
def save_checkpoint_ema(config, epoch, model, max_accuracy, optimizer, lr_scheduler, loss_scaler, logger, model_ema: ModelEma=None, max_accuracy_ema=None):
save_state = {'model': model.state_dic... | null |
144,863 | import os
from math import inf
import torch
import torch.distributed as dist
from timm.utils import ModelEma as ModelEma
def get_grad_norm(parameters, norm_type=2):
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = list(filter(lambda p: p.grad is not None, parameters))
... | null |
144,864 | import os
from math import inf
import torch
import torch.distributed as dist
from timm.utils import ModelEma as ModelEma
def auto_resume_helper(output_dir):
checkpoints = os.listdir(output_dir)
checkpoints = [ckpt for ckpt in checkpoints if ckpt.endswith('pth')]
print(f"All checkpoints founded in {output_d... | null |
144,865 | import os
from math import inf
import torch
import torch.distributed as dist
from timm.utils import ModelEma as ModelEma
def ampscaler_get_grad_norm(parameters, norm_type: float = 2.0) -> torch.Tensor:
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = [p for p in parameter... | null |
144,867 | from functools import partial
from torch import optim as optim
def set_weight_decay(model, skip_list=(), skip_keywords=()):
has_decay = []
no_decay = []
no_decay_names = []
for name, param in model.named_parameters():
if not param.requires_grad:
continue # frozen weights
if ... | Build optimizer, set weight decay of normalization to 0 by default. |
144,868 | from functools import partial
from torch import optim as optim
def get_pretrain_param_groups(model, skip_list=(), skip_keywords=()):
has_decay = []
no_decay = []
has_decay_name = []
no_decay_name = []
for name, param in model.named_parameters():
if not param.requires_grad:
contin... | Build optimizer, set weight decay of normalization to 0 by default. |
144,870 | import torch
import os
from matplotlib import pyplot as plot
def draw_fig(data: list, xlim=(0, 301), ylim=(68, 84), xstep=None, ystep=None, save_path="./show.jpg"):
assert isinstance(data[0], dict)
fig, ax = plot.subplots(dpi=300, figsize=(24, 8))
for d in data:
x_axis = d['x']
y_axis = d['... | null |
144,871 | import torch
import os
from matplotlib import pyplot as plot
def readlog(file=None):
log = open(file, "r").readlines()
log = [d.strip(" ").strip("\n") for d in log if ("img_size" in d) or ("* Acc" in d)]
_log = []
for i in range(len(log)):
if "* Acc" in log[i]:
assert "img_size" in ... | null |
144,872 | import torch
import os
from matplotlib import pyplot as plot
print("vssm_tiny:", vssm_tiny)
print("swin_tiny:", swin_tiny)
print("convnext_tiny:", convnext_tiny)
print("deit_small:", deit_small)
print("resnet50:", resnet50)
print("=====================================")
print("vssm_small:", vssm_small)
print("swin_smal... | null |
144,873 | import os
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn.modules import Module
from functools import partial
from typing import Callable, Tuple, Union, Tuple, Union, Any
def import_abspy(name="models", path="classification/"):
import sys
import importlib
path = os.path.abspath(pa... | null |
144,874 | import os
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn.modules import Module
from functools import partial
from typing import Callable, Tuple, Union, Tuple, Union, Any
def mmengine_flop_count(model: nn.Module = None, input_shape = (3, 224, 224), show_table=False, show_arch=False, _get_model... | null |
144,875 | import os
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn.modules import Module
from functools import partial
from typing import Callable, Tuple, Union, Tuple, Union, Any
Backbone_VSSM: nn.Module = build.vmamba.Backbone_VSSM
def mmdet_mmseg_vssm():
from mmengine.model import BaseModule
... | null |
144,876 | import os
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn.modules import Module
from functools import partial
from typing import Callable, Tuple, Union, Tuple, Union, Any
def fvcore_flop_count(model: nn.Module, inputs=None, input_shape=(3, 224, 224), show_table=False, show_arch=False):
def mm... | null |
144,877 | import os
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn.modules import Module
from functools import partial
from typing import Callable, Tuple, Union, Tuple, Union, Any
def mmengine_flop_count(model: nn.Module = None, input_shape = (3, 224, 224), show_table=False, show_arch=False, _get_model... | null |
144,878 | import os
import time
import json
import random
import argparse
import datetime
import copy
from typing import Callable
from functools import partial
from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from torch... | null |
144,879 | import os
import time
import json
import random
import argparse
import datetime
import copy
from typing import Callable
from functools import partial
from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from torch... | null |
144,880 | import os
import time
import json
import random
import argparse
import datetime
import copy
from typing import Callable
from functools import partial
from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from torch... | null |
144,881 | import os
import time
import json
import random
import argparse
import datetime
import copy
from typing import Callable
from functools import partial
from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from torch... | null |
144,882 | import time
import torch
import torch.utils.data
import argparse
import os
import sys
import logging
from torchvision import datasets, transforms
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from torchvision.models.vision_transformer import EncoderBlock
def import_abspy(name="models", path="classi... | null |
144,883 | import time
import torch
import torch.utils.data
import argparse
import os
import sys
import logging
from torchvision import datasets, transforms
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from torchvision.models.vision_transformer import EncoderBlock
def get_dataloader(batch_size=64, root="./va... | null |
144,884 | import time
import torch
import torch.utils.data
import argparse
import os
import sys
import logging
from torchvision import datasets, transforms
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from torchvision.models.vision_transformer import EncoderBlock
def throughput(data_loader, model, logger):
... | null |
144,885 | import torch
import os
def modema(ckpt=None):
opath = os.path.join(os.path.dirname(ckpt), f"new_{os.path.basename(ckpt)}")
_ckpt = torch.load(open(ckpt, "rb"), map_location=torch.device("cpu"))
_ckpt["model"] = _ckpt["model_ema"]
torch.save(_ckpt, open(opath, "wb")) | null |
144,886 | import os
import time
from functools import partial
from typing import Callable
import seaborn
import numpy as np
import torch
import torch.nn as nn
from torch import optim as optim
from torch.utils.data import SequentialSampler, DataLoader, RandomSampler
from torchvision import datasets, transforms
from timm.utils imp... | null |
144,887 | import os
import time
from functools import partial
from typing import Callable
import seaborn
import numpy as np
import torch
import torch.nn as nn
from torch import optim as optim
from torch.utils.data import SequentialSampler, DataLoader, RandomSampler
from torchvision import datasets, transforms
from timm.utils imp... | null |
144,888 | import os
import time
from functools import partial
from typing import Callable
import seaborn
import numpy as np
import torch
import torch.nn as nn
from torch import optim as optim
from torch.utils.data import SequentialSampler, DataLoader, RandomSampler
from torchvision import datasets, transforms
from timm.utils imp... | null |
144,889 | import os
import time
from functools import partial
from typing import Callable
import seaborn
import numpy as np
import torch
import torch.nn as nn
from torch import optim as optim
from torch.utils.data import SequentialSampler, DataLoader, RandomSampler
from torchvision import datasets, transforms
from timm.utils imp... | null |
144,890 | import os
import torch
def get_acc_convnext(f: list):
if isinstance(f, str):
f = open(f, "r").readlines()
emaaccs = []
accs = []
for i, line in enumerate(f):
if "* Acc" in line and ("Accuracy of the model EMA on" in f[i + 1]):
l: str = line.strip(" ").split(" ") # [*, Acc@1, ... | null |
144,891 | import os
import torch
def get_acc_convnext(f: list):
if isinstance(f, str):
f = open(f, "r").readlines()
emaaccs = []
accs = []
for i, line in enumerate(f):
if "* Acc" in line and ("Accuracy of the model EMA on" in f[i + 1]):
l: str = line.strip(" ").split(" ") # [*, Acc@1, ... | null |
144,892 | import os
import torch
def get_acc_convnext(f: list):
if isinstance(f, str):
f = open(f, "r").readlines()
emaaccs = []
accs = []
for i, line in enumerate(f):
if "* Acc" in line and ("Accuracy of the model EMA on" in f[i + 1]):
l: str = line.strip(" ").split(" ") # [*, Acc@1, ... | null |
144,893 | import os
import torch
def get_acc_swin(f: list, split_ema=False):
def get_loss_swin(f: list, x1e=torch.tensor(list(range(0, 1253, 10))).view(1, -1) / 1253, scale=1):
def draw_fig(data: list, xlim=(0, 301), ylim=(68, 84), xstep=None,ystep=None, save_path="./show.jpg"):
def main_vssm():
results = {}
logpath = o... | null |
144,894 | import os
import torch
def get_acc_swin(f: list, split_ema=False):
if isinstance(f, str):
f = open(f, "r").readlines()
emaaccs = None
accs = []
for i, line in enumerate(f):
if "* Acc" in line:
l: str = line.split("INFO")[-1].strip(" ").split(" ") # [*, Acc@1, 0.642, Acc@5, 2.... | null |
144,895 | import os
import torch
def get_acc_swin(f: list, split_ema=False):
if isinstance(f, str):
f = open(f, "r").readlines()
emaaccs = None
accs = []
for i, line in enumerate(f):
if "* Acc" in line:
l: str = line.split("INFO")[-1].strip(" ").split(" ") # [*, Acc@1, 0.642, Acc@5, 2.... | null |
144,896 | import os
import torch
def get_acc_swin(f: list, split_ema=False):
if isinstance(f, str):
f = open(f, "r").readlines()
emaaccs = None
accs = []
for i, line in enumerate(f):
if "* Acc" in line:
l: str = line.split("INFO")[-1].strip(" ").split(" ") # [*, Acc@1, 0.642, Acc@5, 2.... | null |
144,897 | from socketify import App
import os
import multiprocessing
import asyncio
def run_app():
run_app()
def create_fork():
n = os.fork()
# n greater than 0 means parent process
if not n > 0:
run_app() | null |
144,898 | from json import dumps
from django.http import HttpResponse
def plaintext(request):
return HttpResponse("Hello, World!", content_type="text/plain") | null |
144,899 | from json import dumps
from django.http import HttpResponse
def json(request):
return HttpResponse(
dumps({"message": "Hello, World!"}),
content_type="application/json"
) | null |
144,900 | import falcon.asgi
import falcon.media
import asyncio
clients = set([])
async def broadcast(message):
# some clients got disconnected if we tried to to all async :/
# tasks = [ws.send_text(message) for ws in client]
# return await asyncio.wait(tasks, return_when=ALL_COMPLETED)
for ws in clients:
... | null |
144,901 | from socketify import App, AppOptions, OpCode, CompressOptions
remaining_clients = 16
app = App(websocket_factory_max_items=1_500_000)
app.ws(
"/*",
{
"compression": CompressOptions.DISABLED,
"max_payload_length": 16 * 1024 * 1024,
"idle_timeout": 60,
"open": ws_open,
"me... | null |
144,902 | from socketify import App, AppOptions, OpCode, CompressOptions
app = App(websocket_factory_max_items=1_500_000)
app.ws(
"/*",
{
"compression": CompressOptions.DISABLED,
"max_payload_length": 16 * 1024 * 1024,
"idle_timeout": 60,
"open": ws_open,
"message": ws_message,
... | null |
144,903 | from socketify import App, AppOptions, OpCode, CompressOptions
remaining_clients = 16
def ws_close(ws, close, message):
global remaining_clients
remaining_clients = remaining_clients + 1 | null |
144,904 | from socketify import ASGI
async def app(scope, receive, send):
assert scope['type'] == 'http'
await send({
'type': 'http.response.start',
'status': 200,
'headers': [
[b'content-type', b'text/plain'],
],
})
await send({
'type': 'http.response.body',
... | null |
144,905 | from flask import Flask, make_response, request
from socketify import WSGI
The provided code snippet includes necessary dependencies for implementing the `index` function. Write a Python function `def index()` to solve the following problem:
Test 6: Plaintext
Here is the function:
def index():
"""Test 6: Plainte... | Test 6: Plaintext |
144,906 | from flask import Flask, make_response, request
from socketify import WSGI
The provided code snippet includes necessary dependencies for implementing the `post_test` function. Write a Python function `def post_test()` to solve the following problem:
Test 6: Plaintext
Here is the function:
def post_test():
"""Tes... | Test 6: Plaintext |
144,907 | import falcon.asgi
import falcon.media
from socketify import ASGI
clients = set([])
async def broadcast(message):
# some clients got disconnected if we tried to to all async :/
# tasks = [ws.send_text(message) for ws in client]
# return await asyncio.wait(tasks, return_when=ALL_COMPLETED)
for ws in cli... | null |
144,908 | from socketify import ASGI
clients = set([])
remaining_clients = 16
async def app(scope, receive, send):
global remaining_clients
# handle non websocket
if scope['type'] != 'websocket':
await send({
'type': 'http.response.start',
'status': 200,
'headers... | null |
144,909 | from socketify import ASGI
async def app(scope, receive, send):
# handle non websocket
if scope['type'] != 'websocket':
await send({
'type': 'http.response.start',
'status': 200,
'headers': [
[b'content-type', b'text/plain'],
... | null |
144,910 | payload = None
chunk_size = 64 * 1024
content_length = len(payload)
def app_chunked(environ, start_response):
start_response('200 OK', [('Content-Type', 'application/zip'), ('Transfer-Encoding', 'chunked')])
sended = 0
while content_length > sended:
end = sended + chunk_size
yield payl... | null |
144,911 | payload = None
chunk_size = 64 * 1024
content_length = len(payload)
def app(environ, start_response):
start_response('200 OK', [('Content-Type', 'application/zip'), ('Content-Length', str(content_length))])
sended = 0
while content_length > sended:
end = sended + chunk_size
yield paylo... | null |
144,912 |
def app_hello(environ, start_response):
# start_response('200 OK', [('Content-Type', 'text/plain'), ('Content-Length', '13')])
start_response('200 OK', [('Content-Type', 'text/plain')])
return [ b'Hello, World!'] | null |
144,913 | from socketify import ASGI
clients = set([])
remaining_clients = 16
async def broadcast(message):
for send in clients:
await send({
'type': 'websocket.send',
'text': message
})
async def app(scope, receive, send):
global remaining_clients
# handle non websocket
... | null |
144,914 | from robyn import Robyn
def h(request):
return "Hello, World!" | null |
144,915 |
async def app(scope, receive, send):
assert scope["type"] == "http"
await send(
{
"type": "http.response.start",
"status": 200,
"headers": [
[b"content-type", b"text/plain"],
],
}
)
await send(
{
"type... | null |
144,916 | from json import dumps
from django.http import HttpResponse
async def plaintext(request):
return HttpResponse("Hello, World!", content_type="text/plain") | null |
144,917 | from json import dumps
from django.http import HttpResponse
async def json(request):
return HttpResponse(
dumps({"message": "Hello, World!"}),
content_type="application/json"
) | null |
144,918 | from quart import Quart
async def plaintext():
return "Hello, World!", {"Content-Type": "text/plain"} | null |
144,919 | import inspect
import os
import logging
from . import App, AppOptions, AppListenOptions
help = """
Usage: python -m socketify APP [OPTIONS]
python3 -m socketify APP [OPTIONS]
pypy3 -m socketify APP [OPTIONS]
Options:
--help Show this Help
--host or -h ... | null |
144,920 | from .native import ffi, lib
ffi = cffi.FFI()
ffi.cdef(
"""
struct us_socket_context_options_t {
const char *key_file_name;
const char *cert_file_name;
const char *passphrase;
const char *dh_params_file_name;
const char *ca_file_name;
const char *ssl_ciphers;
int ssl_prefer_low_memory_... | null |
144,921 | from socketify import App, OpCode
from queue import SimpleQueue
from .native import lib, ffi
from .tasks import create_task, TaskFactory
import os
import platform
import sys
import logging
import uuid
import asyncio
async def task_wrapper(task):
try:
return await task
except Exception as error:
... | null |
144,922 | from socketify import App, OpCode
from queue import SimpleQueue
from .native import lib, ffi
from .tasks import create_task, TaskFactory
import os
import platform
import sys
import logging
import uuid
import asyncio
ffi = cffi.FFI()
ffi.cdef(
"""
struct us_socket_context_options_t {
const char *key_file_name;... | null |
144,923 | from socketify import App, OpCode
from queue import SimpleQueue
from .native import lib, ffi
from .tasks import create_task, TaskFactory
import os
import platform
import sys
import logging
import uuid
import asyncio
ffi = cffi.FFI()
ffi.cdef(
"""
struct us_socket_context_options_t {
const char *key_file_name;... | null |
144,924 | from socketify import App, OpCode
from queue import SimpleQueue
from .native import lib, ffi
from .tasks import create_task, TaskFactory
import os
import platform
import sys
import logging
import uuid
import asyncio
ffi = cffi.FFI()
ffi.cdef(
"""
struct us_socket_context_options_t {
const char *key_file_name;... | null |
144,925 | from socketify import App, OpCode
from queue import SimpleQueue
from .native import lib, ffi
from .tasks import create_task, TaskFactory
import os
import platform
import sys
import logging
import uuid
import asyncio
def asgi_on_abort_handler(res, user_data):
ctx = ffi.from_handle(user_data)
ctx.aborted = True
... | null |
144,926 | from socketify import App, OpCode
from queue import SimpleQueue
from .native import lib, ffi
from .tasks import create_task, TaskFactory
import os
import platform
import sys
import logging
import uuid
import asyncio
def write_header(ssl, res, key, value):
if isinstance(key, bytes):
# this is faster than usi... | null |
144,927 | from socketify import App, OpCode
from queue import SimpleQueue
from .native import lib, ffi
from .tasks import create_task, TaskFactory
import os
import platform
import sys
import logging
import uuid
import asyncio
def asgi_on_abort_handler(res, user_data):
ctx = ffi.from_handle(user_data)
ctx.aborted = True
... | null |
144,928 | from datetime import datetime
from enum import IntEnum
from http import cookies
import inspect
from io import BytesIO
import json
import signal
import uuid
from urllib.parse import parse_qs, quote_plus, unquote_plus
import logging
from .native import ffi, lib
from .loop import Loop
from .helpers import static_route
fro... | null |
144,929 | from datetime import datetime
from enum import IntEnum
from http import cookies
import inspect
from io import BytesIO
import json
import signal
import uuid
from urllib.parse import parse_qs, quote_plus, unquote_plus
import logging
from .native import ffi, lib
from .loop import Loop
from .helpers import static_route
fro... | null |
144,930 | from datetime import datetime
from enum import IntEnum
from http import cookies
import inspect
from io import BytesIO
import json
import signal
import uuid
from urllib.parse import parse_qs, quote_plus, unquote_plus
import logging
from .native import ffi, lib
from .loop import Loop
from .helpers import static_route
fro... | null |
144,931 | from datetime import datetime
from enum import IntEnum
from http import cookies
import inspect
from io import BytesIO
import json
import signal
import uuid
from urllib.parse import parse_qs, quote_plus, unquote_plus
import logging
from .native import ffi, lib
from .loop import Loop
from .helpers import static_route
fro... | null |
144,932 | from datetime import datetime
from enum import IntEnum
from http import cookies
import inspect
from io import BytesIO
import json
import signal
import uuid
from urllib.parse import parse_qs, quote_plus, unquote_plus
import logging
from .native import ffi, lib
from .loop import Loop
from .helpers import static_route
fro... | null |
144,933 | from datetime import datetime
from enum import IntEnum
from http import cookies
import inspect
from io import BytesIO
import json
import signal
import uuid
from urllib.parse import parse_qs, quote_plus, unquote_plus
import logging
from .native import ffi, lib
from .loop import Loop
from .helpers import static_route
fro... | null |
144,934 | from datetime import datetime
from enum import IntEnum
from http import cookies
import inspect
from io import BytesIO
import json
import signal
import uuid
from urllib.parse import parse_qs, quote_plus, unquote_plus
import logging
from .native import ffi, lib
from .loop import Loop
from .helpers import static_route
fro... | null |
144,935 | from datetime import datetime
from enum import IntEnum
from http import cookies
import inspect
from io import BytesIO
import json
import signal
import uuid
from urllib.parse import parse_qs, quote_plus, unquote_plus
import logging
from .native import ffi, lib
from .loop import Loop
from .helpers import static_route
fro... | null |
144,936 | from datetime import datetime
from enum import IntEnum
from http import cookies
import inspect
from io import BytesIO
import json
import signal
import uuid
from urllib.parse import parse_qs, quote_plus, unquote_plus
import logging
from .native import ffi, lib
from .loop import Loop
from .helpers import static_route
fro... | null |
144,937 | from datetime import datetime
from enum import IntEnum
from http import cookies
import inspect
from io import BytesIO
import json
import signal
import uuid
from urllib.parse import parse_qs, quote_plus, unquote_plus
import logging
from .native import ffi, lib
from .loop import Loop
from .helpers import static_route
fro... | null |
144,938 | from datetime import datetime
from enum import IntEnum
from http import cookies
import inspect
from io import BytesIO
import json
import signal
import uuid
from urllib.parse import parse_qs, quote_plus, unquote_plus
import logging
from .native import ffi, lib
from .loop import Loop
from .helpers import static_route
fro... | null |
144,939 | from datetime import datetime
from enum import IntEnum
from http import cookies
import inspect
from io import BytesIO
import json
import signal
import uuid
from urllib.parse import parse_qs, quote_plus, unquote_plus
import logging
from .native import ffi, lib
from .loop import Loop
from .helpers import static_route
fro... | null |
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