patched-codes/Meta-Llama-3.1-8B-Instruct-bnb-4bit-Patched
Updated
•
259
•
1
source
stringlengths 4.8k
15.8k
| file_name
stringlengths 9
9
| cwe
sequencelengths 1
1
|
|---|---|---|
"""
Implementation of the SHA1 hash function and gives utilities to find hash of string or
hash of text from a file. Also contains a Test class to verify that the generated hash
matches what is returned by the hashlib library
Usage: python sha1.py --string "Hello World!!"
python sha1.py --file "hello_world.txt"
When run without any arguments, it prints the hash of the string "Hello World!!
Welcome to Cryptography"
SHA1 hash or SHA1 sum of a string is a cryptographic function, which means it is easy
to calculate forwards but extremely difficult to calculate backwards. What this means
is you can easily calculate the hash of a string, but it is extremely difficult to know
the original string if you have its hash. This property is useful for communicating
securely, send encrypted messages and is very useful in payment systems, blockchain and
cryptocurrency etc.
The algorithm as described in the reference:
First we start with a message. The message is padded and the length of the message
is added to the end. It is then split into blocks of 512 bits or 64 bytes. The blocks
are then processed one at a time. Each block must be expanded and compressed.
The value after each compression is added to a 160-bit buffer called the current hash
state. After the last block is processed, the current hash state is returned as
the final hash.
Reference: https://deadhacker.com/2006/02/21/sha-1-illustrated/
"""
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class SHA1Hash:
"""
Class to contain the entire pipeline for SHA1 hashing algorithm
>>> SHA1Hash(bytes('Allan', 'utf-8')).final_hash()
'872af2d8ac3d8695387e7c804bf0e02c18df9e6e'
"""
def __init__(self, data):
"""
Initiates the variables data and h. h is a list of 5 8-digit hexadecimal
numbers corresponding to
(1732584193, 4023233417, 2562383102, 271733878, 3285377520)
respectively. We will start with this as a message digest. 0x is how you write
hexadecimal numbers in Python
"""
self.data = data
self.h = [0x67452301, 0xEFCDAB89, 0x98BADCFE, 0x10325476, 0xC3D2E1F0]
@staticmethod
def rotate(n, b):
"""
Static method to be used inside other methods. Left rotates n by b.
>>> SHA1Hash('').rotate(12,2)
48
"""
return ((n << b) | (n >> (32 - b))) & 0xFFFFFFFF
def padding(self):
"""
Pads the input message with zeros so that padded_data has 64 bytes or 512 bits
"""
padding = b"\x80" + b"\x00" * (63 - (len(self.data) + 8) % 64)
padded_data = self.data + padding + struct.pack(">Q", 8 * len(self.data))
return padded_data
def split_blocks(self):
"""
Returns a list of bytestrings each of length 64
"""
return [
self.padded_data[i : i + 64] for i in range(0, len(self.padded_data), 64)
]
# @staticmethod
def expand_block(self, block):
"""
Takes a bytestring-block of length 64, unpacks it to a list of integers and
returns a list of 80 integers after some bit operations
"""
w = list(struct.unpack(">16L", block)) + [0] * 64
for i in range(16, 80):
w[i] = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]), 1)
return w
def final_hash(self):
"""
Calls all the other methods to process the input. Pads the data, then splits
into blocks and then does a series of operations for each block (including
expansion).
For each block, the variable h that was initialized is copied to a,b,c,d,e
and these 5 variables a,b,c,d,e undergo several changes. After all the blocks
are processed, these 5 variables are pairwise added to h ie a to h[0], b to h[1]
and so on. This h becomes our final hash which is returned.
"""
self.padded_data = self.padding()
self.blocks = self.split_blocks()
for block in self.blocks:
expanded_block = self.expand_block(block)
a, b, c, d, e = self.h
for i in range(80):
if 0 <= i < 20:
f = (b & c) | ((~b) & d)
k = 0x5A827999
elif 20 <= i < 40:
f = b ^ c ^ d
k = 0x6ED9EBA1
elif 40 <= i < 60:
f = (b & c) | (b & d) | (c & d)
k = 0x8F1BBCDC
elif 60 <= i < 80:
f = b ^ c ^ d
k = 0xCA62C1D6
a, b, c, d, e = (
self.rotate(a, 5) + f + e + k + expanded_block[i] & 0xFFFFFFFF,
a,
self.rotate(b, 30),
c,
d,
)
self.h = (
self.h[0] + a & 0xFFFFFFFF,
self.h[1] + b & 0xFFFFFFFF,
self.h[2] + c & 0xFFFFFFFF,
self.h[3] + d & 0xFFFFFFFF,
self.h[4] + e & 0xFFFFFFFF,
)
return ("{:08x}" * 5).format(*self.h)
def test_sha1_hash():
msg = b"Test String"
assert SHA1Hash(msg).final_hash() == hashlib.sha1(msg).hexdigest() # noqa: S324
def main():
"""
Provides option 'string' or 'file' to take input and prints the calculated SHA1
hash. unittest.main() has been commented out because we probably don't want to run
the test each time.
"""
# unittest.main()
parser = argparse.ArgumentParser(description="Process some strings or files")
parser.add_argument(
"--string",
dest="input_string",
default="Hello World!! Welcome to Cryptography",
help="Hash the string",
)
parser.add_argument("--file", dest="input_file", help="Hash contents of a file")
args = parser.parse_args()
input_string = args.input_string
# In any case hash input should be a bytestring
if args.input_file:
with open(args.input_file, "rb") as f:
hash_input = f.read()
else:
hash_input = bytes(input_string, "utf-8")
print(SHA1Hash(hash_input).final_hash())
if __name__ == "__main__":
main()
import doctest
doctest.testmod()
| 916728.py | [
"CWE-327: Use of a Broken or Risky Cryptographic Algorithm"
] |
import os
import gc
import time
import numpy as np
import torch
import torchvision
from PIL import Image
from einops import rearrange, repeat
from omegaconf import OmegaConf
import safetensors.torch
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import instantiate_from_config, ismap
from modules import shared, sd_hijack, devices
cached_ldsr_model: torch.nn.Module = None
# Create LDSR Class
class LDSR:
def load_model_from_config(self, half_attention):
global cached_ldsr_model
if shared.opts.ldsr_cached and cached_ldsr_model is not None:
print("Loading model from cache")
model: torch.nn.Module = cached_ldsr_model
else:
print(f"Loading model from {self.modelPath}")
_, extension = os.path.splitext(self.modelPath)
if extension.lower() == ".safetensors":
pl_sd = safetensors.torch.load_file(self.modelPath, device="cpu")
else:
pl_sd = torch.load(self.modelPath, map_location="cpu")
sd = pl_sd["state_dict"] if "state_dict" in pl_sd else pl_sd
config = OmegaConf.load(self.yamlPath)
config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1"
model: torch.nn.Module = instantiate_from_config(config.model)
model.load_state_dict(sd, strict=False)
model = model.to(shared.device)
if half_attention:
model = model.half()
if shared.cmd_opts.opt_channelslast:
model = model.to(memory_format=torch.channels_last)
sd_hijack.model_hijack.hijack(model) # apply optimization
model.eval()
if shared.opts.ldsr_cached:
cached_ldsr_model = model
return {"model": model}
def __init__(self, model_path, yaml_path):
self.modelPath = model_path
self.yamlPath = yaml_path
@staticmethod
def run(model, selected_path, custom_steps, eta):
example = get_cond(selected_path)
n_runs = 1
guider = None
ckwargs = None
ddim_use_x0_pred = False
temperature = 1.
eta = eta
custom_shape = None
height, width = example["image"].shape[1:3]
split_input = height >= 128 and width >= 128
if split_input:
ks = 128
stride = 64
vqf = 4 #
model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
"vqf": vqf,
"patch_distributed_vq": True,
"tie_braker": False,
"clip_max_weight": 0.5,
"clip_min_weight": 0.01,
"clip_max_tie_weight": 0.5,
"clip_min_tie_weight": 0.01}
else:
if hasattr(model, "split_input_params"):
delattr(model, "split_input_params")
x_t = None
logs = None
for _ in range(n_runs):
if custom_shape is not None:
x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
logs = make_convolutional_sample(example, model,
custom_steps=custom_steps,
eta=eta, quantize_x0=False,
custom_shape=custom_shape,
temperature=temperature, noise_dropout=0.,
corrector=guider, corrector_kwargs=ckwargs, x_T=x_t,
ddim_use_x0_pred=ddim_use_x0_pred
)
return logs
def super_resolution(self, image, steps=100, target_scale=2, half_attention=False):
model = self.load_model_from_config(half_attention)
# Run settings
diffusion_steps = int(steps)
eta = 1.0
gc.collect()
devices.torch_gc()
im_og = image
width_og, height_og = im_og.size
# If we can adjust the max upscale size, then the 4 below should be our variable
down_sample_rate = target_scale / 4
wd = width_og * down_sample_rate
hd = height_og * down_sample_rate
width_downsampled_pre = int(np.ceil(wd))
height_downsampled_pre = int(np.ceil(hd))
if down_sample_rate != 1:
print(
f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
else:
print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
# pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
logs = self.run(model["model"], im_padded, diffusion_steps, eta)
sample = logs["sample"]
sample = sample.detach().cpu()
sample = torch.clamp(sample, -1., 1.)
sample = (sample + 1.) / 2. * 255
sample = sample.numpy().astype(np.uint8)
sample = np.transpose(sample, (0, 2, 3, 1))
a = Image.fromarray(sample[0])
# remove padding
a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
del model
gc.collect()
devices.torch_gc()
return a
def get_cond(selected_path):
example = {}
up_f = 4
c = selected_path.convert('RGB')
c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
antialias=True)
c_up = rearrange(c_up, '1 c h w -> 1 h w c')
c = rearrange(c, '1 c h w -> 1 h w c')
c = 2. * c - 1.
c = c.to(shared.device)
example["LR_image"] = c
example["image"] = c_up
return example
@torch.no_grad()
def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None,
corrector_kwargs=None, x_t=None
):
ddim = DDIMSampler(model)
bs = shape[0]
shape = shape[1:]
print(f"Sampling with eta = {eta}; steps: {steps}")
samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
mask=mask, x0=x0, temperature=temperature, verbose=False,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs, x_t=x_t)
return samples, intermediates
@torch.no_grad()
def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
log = {}
z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=not (hasattr(model, 'split_input_params')
and model.cond_stage_key == 'coordinates_bbox'),
return_original_cond=True)
if custom_shape is not None:
z = torch.randn(custom_shape)
print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
z0 = None
log["input"] = x
log["reconstruction"] = xrec
if ismap(xc):
log["original_conditioning"] = model.to_rgb(xc)
if hasattr(model, 'cond_stage_key'):
log[model.cond_stage_key] = model.to_rgb(xc)
else:
log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
if model.cond_stage_model:
log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
if model.cond_stage_key == 'class_label':
log[model.cond_stage_key] = xc[model.cond_stage_key]
with model.ema_scope("Plotting"):
t0 = time.time()
sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
eta=eta,
quantize_x0=quantize_x0, mask=None, x0=z0,
temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs,
x_t=x_T)
t1 = time.time()
if ddim_use_x0_pred:
sample = intermediates['pred_x0'][-1]
x_sample = model.decode_first_stage(sample)
try:
x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
log["sample_noquant"] = x_sample_noquant
log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
except Exception:
pass
log["sample"] = x_sample
log["time"] = t1 - t0
return log
| 177699.py | [
"CWE-502: Deserialization of Untrusted Data"
] |
# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
import numpy as np
import torch
import pytorch_lightning as pl
import torch.nn.functional as F
from contextlib import contextmanager
from torch.optim.lr_scheduler import LambdaLR
from ldm.modules.ema import LitEma
from vqvae_quantize import VectorQuantizer2 as VectorQuantizer
from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.util import instantiate_from_config
import ldm.models.autoencoder
from packaging import version
class VQModel(pl.LightningModule):
def __init__(self,
ddconfig,
lossconfig,
n_embed,
embed_dim,
ckpt_path=None,
ignore_keys=None,
image_key="image",
colorize_nlabels=None,
monitor=None,
batch_resize_range=None,
scheduler_config=None,
lr_g_factor=1.0,
remap=None,
sane_index_shape=False, # tell vector quantizer to return indices as bhw
use_ema=False
):
super().__init__()
self.embed_dim = embed_dim
self.n_embed = n_embed
self.image_key = image_key
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(**ddconfig)
self.loss = instantiate_from_config(lossconfig)
self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
remap=remap,
sane_index_shape=sane_index_shape)
self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
if colorize_nlabels is not None:
assert type(colorize_nlabels)==int
self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
if monitor is not None:
self.monitor = monitor
self.batch_resize_range = batch_resize_range
if self.batch_resize_range is not None:
print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [])
self.scheduler_config = scheduler_config
self.lr_g_factor = lr_g_factor
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.parameters())
self.model_ema.copy_to(self)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.parameters())
if context is not None:
print(f"{context}: Restored training weights")
def init_from_ckpt(self, path, ignore_keys=None):
sd = torch.load(path, map_location="cpu")["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys or []:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if missing:
print(f"Missing Keys: {missing}")
if unexpected:
print(f"Unexpected Keys: {unexpected}")
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self)
def encode(self, x):
h = self.encoder(x)
h = self.quant_conv(h)
quant, emb_loss, info = self.quantize(h)
return quant, emb_loss, info
def encode_to_prequant(self, x):
h = self.encoder(x)
h = self.quant_conv(h)
return h
def decode(self, quant):
quant = self.post_quant_conv(quant)
dec = self.decoder(quant)
return dec
def decode_code(self, code_b):
quant_b = self.quantize.embed_code(code_b)
dec = self.decode(quant_b)
return dec
def forward(self, input, return_pred_indices=False):
quant, diff, (_,_,ind) = self.encode(input)
dec = self.decode(quant)
if return_pred_indices:
return dec, diff, ind
return dec, diff
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
if self.batch_resize_range is not None:
lower_size = self.batch_resize_range[0]
upper_size = self.batch_resize_range[1]
if self.global_step <= 4:
# do the first few batches with max size to avoid later oom
new_resize = upper_size
else:
new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
if new_resize != x.shape[2]:
x = F.interpolate(x, size=new_resize, mode="bicubic")
x = x.detach()
return x
def training_step(self, batch, batch_idx, optimizer_idx):
# https://github.com/pytorch/pytorch/issues/37142
# try not to fool the heuristics
x = self.get_input(batch, self.image_key)
xrec, qloss, ind = self(x, return_pred_indices=True)
if optimizer_idx == 0:
# autoencode
aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train",
predicted_indices=ind)
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
return aeloss
if optimizer_idx == 1:
# discriminator
discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
return discloss
def validation_step(self, batch, batch_idx):
log_dict = self._validation_step(batch, batch_idx)
with self.ema_scope():
self._validation_step(batch, batch_idx, suffix="_ema")
return log_dict
def _validation_step(self, batch, batch_idx, suffix=""):
x = self.get_input(batch, self.image_key)
xrec, qloss, ind = self(x, return_pred_indices=True)
aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
self.global_step,
last_layer=self.get_last_layer(),
split="val"+suffix,
predicted_indices=ind
)
discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
self.global_step,
last_layer=self.get_last_layer(),
split="val"+suffix,
predicted_indices=ind
)
rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
self.log(f"val{suffix}/rec_loss", rec_loss,
prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
self.log(f"val{suffix}/aeloss", aeloss,
prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
if version.parse(pl.__version__) >= version.parse('1.4.0'):
del log_dict_ae[f"val{suffix}/rec_loss"]
self.log_dict(log_dict_ae)
self.log_dict(log_dict_disc)
return self.log_dict
def configure_optimizers(self):
lr_d = self.learning_rate
lr_g = self.lr_g_factor*self.learning_rate
print("lr_d", lr_d)
print("lr_g", lr_g)
opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
list(self.decoder.parameters())+
list(self.quantize.parameters())+
list(self.quant_conv.parameters())+
list(self.post_quant_conv.parameters()),
lr=lr_g, betas=(0.5, 0.9))
opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
lr=lr_d, betas=(0.5, 0.9))
if self.scheduler_config is not None:
scheduler = instantiate_from_config(self.scheduler_config)
print("Setting up LambdaLR scheduler...")
scheduler = [
{
'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
'interval': 'step',
'frequency': 1
},
{
'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
'interval': 'step',
'frequency': 1
},
]
return [opt_ae, opt_disc], scheduler
return [opt_ae, opt_disc], []
def get_last_layer(self):
return self.decoder.conv_out.weight
def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
log = {}
x = self.get_input(batch, self.image_key)
x = x.to(self.device)
if only_inputs:
log["inputs"] = x
return log
xrec, _ = self(x)
if x.shape[1] > 3:
# colorize with random projection
assert xrec.shape[1] > 3
x = self.to_rgb(x)
xrec = self.to_rgb(xrec)
log["inputs"] = x
log["reconstructions"] = xrec
if plot_ema:
with self.ema_scope():
xrec_ema, _ = self(x)
if x.shape[1] > 3:
xrec_ema = self.to_rgb(xrec_ema)
log["reconstructions_ema"] = xrec_ema
return log
def to_rgb(self, x):
assert self.image_key == "segmentation"
if not hasattr(self, "colorize"):
self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
x = F.conv2d(x, weight=self.colorize)
x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
return x
class VQModelInterface(VQModel):
def __init__(self, embed_dim, *args, **kwargs):
super().__init__(*args, embed_dim=embed_dim, **kwargs)
self.embed_dim = embed_dim
def encode(self, x):
h = self.encoder(x)
h = self.quant_conv(h)
return h
def decode(self, h, force_not_quantize=False):
# also go through quantization layer
if not force_not_quantize:
quant, emb_loss, info = self.quantize(h)
else:
quant = h
quant = self.post_quant_conv(quant)
dec = self.decoder(quant)
return dec
ldm.models.autoencoder.VQModel = VQModel
ldm.models.autoencoder.VQModelInterface = VQModelInterface
| 932523.py | [
"CWE-502: Deserialization of Untrusted Data"
] |
# Vendored from https://raw.githubusercontent.com/CompVis/taming-transformers/24268930bf1dce879235a7fddd0b2355b84d7ea6/taming/modules/vqvae/quantize.py,
# where the license is as follows:
#
# Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer
#
# 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, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
# OR OTHER DEALINGS IN THE SOFTWARE./
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
class VectorQuantizer2(nn.Module):
"""
Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
avoids costly matrix multiplications and allows for post-hoc remapping of indices.
"""
# NOTE: due to a bug the beta term was applied to the wrong term. for
# backwards compatibility we use the buggy version by default, but you can
# specify legacy=False to fix it.
def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random",
sane_index_shape=False, legacy=True):
super().__init__()
self.n_e = n_e
self.e_dim = e_dim
self.beta = beta
self.legacy = legacy
self.embedding = nn.Embedding(self.n_e, self.e_dim)
self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
self.remap = remap
if self.remap is not None:
self.register_buffer("used", torch.tensor(np.load(self.remap)))
self.re_embed = self.used.shape[0]
self.unknown_index = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
self.unknown_index = self.re_embed
self.re_embed = self.re_embed + 1
print(f"Remapping {self.n_e} indices to {self.re_embed} indices. "
f"Using {self.unknown_index} for unknown indices.")
else:
self.re_embed = n_e
self.sane_index_shape = sane_index_shape
def remap_to_used(self, inds):
ishape = inds.shape
assert len(ishape) > 1
inds = inds.reshape(ishape[0], -1)
used = self.used.to(inds)
match = (inds[:, :, None] == used[None, None, ...]).long()
new = match.argmax(-1)
unknown = match.sum(2) < 1
if self.unknown_index == "random":
new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)
else:
new[unknown] = self.unknown_index
return new.reshape(ishape)
def unmap_to_all(self, inds):
ishape = inds.shape
assert len(ishape) > 1
inds = inds.reshape(ishape[0], -1)
used = self.used.to(inds)
if self.re_embed > self.used.shape[0]: # extra token
inds[inds >= self.used.shape[0]] = 0 # simply set to zero
back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
return back.reshape(ishape)
def forward(self, z, temp=None, rescale_logits=False, return_logits=False):
assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel"
assert rescale_logits is False, "Only for interface compatible with Gumbel"
assert return_logits is False, "Only for interface compatible with Gumbel"
# reshape z -> (batch, height, width, channel) and flatten
z = rearrange(z, 'b c h w -> b h w c').contiguous()
z_flattened = z.view(-1, self.e_dim)
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
torch.sum(self.embedding.weight ** 2, dim=1) - 2 * \
torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n'))
min_encoding_indices = torch.argmin(d, dim=1)
z_q = self.embedding(min_encoding_indices).view(z.shape)
perplexity = None
min_encodings = None
# compute loss for embedding
if not self.legacy:
loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + \
torch.mean((z_q - z.detach()) ** 2)
else:
loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * \
torch.mean((z_q - z.detach()) ** 2)
# preserve gradients
z_q = z + (z_q - z).detach()
# reshape back to match original input shape
z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
if self.remap is not None:
min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis
min_encoding_indices = self.remap_to_used(min_encoding_indices)
min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten
if self.sane_index_shape:
min_encoding_indices = min_encoding_indices.reshape(
z_q.shape[0], z_q.shape[2], z_q.shape[3])
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def get_codebook_entry(self, indices, shape):
# shape specifying (batch, height, width, channel)
if self.remap is not None:
indices = indices.reshape(shape[0], -1) # add batch axis
indices = self.unmap_to_all(indices)
indices = indices.reshape(-1) # flatten again
# get quantized latent vectors
z_q = self.embedding(indices)
if shape is not None:
z_q = z_q.view(shape)
# reshape back to match original input shape
z_q = z_q.permute(0, 3, 1, 2).contiguous()
return z_q
| 570756.py | [
"Unknown"
] |
#!/usr/bin/python3
import argparse
import ctypes
import functools
import shutil
import subprocess
import sys
import tempfile
import threading
import traceback
import os.path
sys.path.insert(0, os.path.dirname(os.path.dirname((os.path.abspath(__file__)))))
from youtube_dl.compat import (
compat_input,
compat_http_server,
compat_str,
compat_urlparse,
)
# These are not used outside of buildserver.py thus not in compat.py
try:
import winreg as compat_winreg
except ImportError: # Python 2
import _winreg as compat_winreg
try:
import socketserver as compat_socketserver
except ImportError: # Python 2
import SocketServer as compat_socketserver
class BuildHTTPServer(compat_socketserver.ThreadingMixIn, compat_http_server.HTTPServer):
allow_reuse_address = True
advapi32 = ctypes.windll.advapi32
SC_MANAGER_ALL_ACCESS = 0xf003f
SC_MANAGER_CREATE_SERVICE = 0x02
SERVICE_WIN32_OWN_PROCESS = 0x10
SERVICE_AUTO_START = 0x2
SERVICE_ERROR_NORMAL = 0x1
DELETE = 0x00010000
SERVICE_STATUS_START_PENDING = 0x00000002
SERVICE_STATUS_RUNNING = 0x00000004
SERVICE_ACCEPT_STOP = 0x1
SVCNAME = 'youtubedl_builder'
LPTSTR = ctypes.c_wchar_p
START_CALLBACK = ctypes.WINFUNCTYPE(None, ctypes.c_int, ctypes.POINTER(LPTSTR))
class SERVICE_TABLE_ENTRY(ctypes.Structure):
_fields_ = [
('lpServiceName', LPTSTR),
('lpServiceProc', START_CALLBACK)
]
HandlerEx = ctypes.WINFUNCTYPE(
ctypes.c_int, # return
ctypes.c_int, # dwControl
ctypes.c_int, # dwEventType
ctypes.c_void_p, # lpEventData,
ctypes.c_void_p, # lpContext,
)
def _ctypes_array(c_type, py_array):
ar = (c_type * len(py_array))()
ar[:] = py_array
return ar
def win_OpenSCManager():
res = advapi32.OpenSCManagerW(None, None, SC_MANAGER_ALL_ACCESS)
if not res:
raise Exception('Opening service manager failed - '
'are you running this as administrator?')
return res
def win_install_service(service_name, cmdline):
manager = win_OpenSCManager()
try:
h = advapi32.CreateServiceW(
manager, service_name, None,
SC_MANAGER_CREATE_SERVICE, SERVICE_WIN32_OWN_PROCESS,
SERVICE_AUTO_START, SERVICE_ERROR_NORMAL,
cmdline, None, None, None, None, None)
if not h:
raise OSError('Service creation failed: %s' % ctypes.FormatError())
advapi32.CloseServiceHandle(h)
finally:
advapi32.CloseServiceHandle(manager)
def win_uninstall_service(service_name):
manager = win_OpenSCManager()
try:
h = advapi32.OpenServiceW(manager, service_name, DELETE)
if not h:
raise OSError('Could not find service %s: %s' % (
service_name, ctypes.FormatError()))
try:
if not advapi32.DeleteService(h):
raise OSError('Deletion failed: %s' % ctypes.FormatError())
finally:
advapi32.CloseServiceHandle(h)
finally:
advapi32.CloseServiceHandle(manager)
def win_service_report_event(service_name, msg, is_error=True):
with open('C:/sshkeys/log', 'a', encoding='utf-8') as f:
f.write(msg + '\n')
event_log = advapi32.RegisterEventSourceW(None, service_name)
if not event_log:
raise OSError('Could not report event: %s' % ctypes.FormatError())
try:
type_id = 0x0001 if is_error else 0x0004
event_id = 0xc0000000 if is_error else 0x40000000
lines = _ctypes_array(LPTSTR, [msg])
if not advapi32.ReportEventW(
event_log, type_id, 0, event_id, None, len(lines), 0,
lines, None):
raise OSError('Event reporting failed: %s' % ctypes.FormatError())
finally:
advapi32.DeregisterEventSource(event_log)
def win_service_handler(stop_event, *args):
try:
raise ValueError('Handler called with args ' + repr(args))
TODO
except Exception as e:
tb = traceback.format_exc()
msg = str(e) + '\n' + tb
win_service_report_event(service_name, msg, is_error=True)
raise
def win_service_set_status(handle, status_code):
svcStatus = SERVICE_STATUS()
svcStatus.dwServiceType = SERVICE_WIN32_OWN_PROCESS
svcStatus.dwCurrentState = status_code
svcStatus.dwControlsAccepted = SERVICE_ACCEPT_STOP
svcStatus.dwServiceSpecificExitCode = 0
if not advapi32.SetServiceStatus(handle, ctypes.byref(svcStatus)):
raise OSError('SetServiceStatus failed: %r' % ctypes.FormatError())
def win_service_main(service_name, real_main, argc, argv_raw):
try:
# args = [argv_raw[i].value for i in range(argc)]
stop_event = threading.Event()
handler = HandlerEx(functools.partial(stop_event, win_service_handler))
h = advapi32.RegisterServiceCtrlHandlerExW(service_name, handler, None)
if not h:
raise OSError('Handler registration failed: %s' %
ctypes.FormatError())
TODO
except Exception as e:
tb = traceback.format_exc()
msg = str(e) + '\n' + tb
win_service_report_event(service_name, msg, is_error=True)
raise
def win_service_start(service_name, real_main):
try:
cb = START_CALLBACK(
functools.partial(win_service_main, service_name, real_main))
dispatch_table = _ctypes_array(SERVICE_TABLE_ENTRY, [
SERVICE_TABLE_ENTRY(
service_name,
cb
),
SERVICE_TABLE_ENTRY(None, ctypes.cast(None, START_CALLBACK))
])
if not advapi32.StartServiceCtrlDispatcherW(dispatch_table):
raise OSError('ctypes start failed: %s' % ctypes.FormatError())
except Exception as e:
tb = traceback.format_exc()
msg = str(e) + '\n' + tb
win_service_report_event(service_name, msg, is_error=True)
raise
def main(args=None):
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--install',
action='store_const', dest='action', const='install',
help='Launch at Windows startup')
parser.add_argument('-u', '--uninstall',
action='store_const', dest='action', const='uninstall',
help='Remove Windows service')
parser.add_argument('-s', '--service',
action='store_const', dest='action', const='service',
help='Run as a Windows service')
parser.add_argument('-b', '--bind', metavar='<host:port>',
action='store', default='0.0.0.0:8142',
help='Bind to host:port (default %default)')
options = parser.parse_args(args=args)
if options.action == 'install':
fn = os.path.abspath(__file__).replace('v:', '\\\\vboxsrv\\vbox')
cmdline = '%s %s -s -b %s' % (sys.executable, fn, options.bind)
win_install_service(SVCNAME, cmdline)
return
if options.action == 'uninstall':
win_uninstall_service(SVCNAME)
return
if options.action == 'service':
win_service_start(SVCNAME, main)
return
host, port_str = options.bind.split(':')
port = int(port_str)
print('Listening on %s:%d' % (host, port))
srv = BuildHTTPServer((host, port), BuildHTTPRequestHandler)
thr = threading.Thread(target=srv.serve_forever)
thr.start()
compat_input('Press ENTER to shut down')
srv.shutdown()
thr.join()
def rmtree(path):
for name in os.listdir(path):
fname = os.path.join(path, name)
if os.path.isdir(fname):
rmtree(fname)
else:
os.chmod(fname, 0o666)
os.remove(fname)
os.rmdir(path)
class BuildError(Exception):
def __init__(self, output, code=500):
self.output = output
self.code = code
def __str__(self):
return self.output
class HTTPError(BuildError):
pass
class PythonBuilder(object):
def __init__(self, **kwargs):
python_version = kwargs.pop('python', '3.4')
python_path = None
for node in ('Wow6432Node\\', ''):
try:
key = compat_winreg.OpenKey(
compat_winreg.HKEY_LOCAL_MACHINE,
r'SOFTWARE\%sPython\PythonCore\%s\InstallPath' % (node, python_version))
try:
python_path, _ = compat_winreg.QueryValueEx(key, '')
finally:
compat_winreg.CloseKey(key)
break
except Exception:
pass
if not python_path:
raise BuildError('No such Python version: %s' % python_version)
self.pythonPath = python_path
super(PythonBuilder, self).__init__(**kwargs)
class GITInfoBuilder(object):
def __init__(self, **kwargs):
try:
self.user, self.repoName = kwargs['path'][:2]
self.rev = kwargs.pop('rev')
except ValueError:
raise BuildError('Invalid path')
except KeyError as e:
raise BuildError('Missing mandatory parameter "%s"' % e.args[0])
path = os.path.join(os.environ['APPDATA'], 'Build archive', self.repoName, self.user)
if not os.path.exists(path):
os.makedirs(path)
self.basePath = tempfile.mkdtemp(dir=path)
self.buildPath = os.path.join(self.basePath, 'build')
super(GITInfoBuilder, self).__init__(**kwargs)
class GITBuilder(GITInfoBuilder):
def build(self):
try:
subprocess.check_output(['git', 'clone', 'git://github.com/%s/%s.git' % (self.user, self.repoName), self.buildPath])
subprocess.check_output(['git', 'checkout', self.rev], cwd=self.buildPath)
except subprocess.CalledProcessError as e:
raise BuildError(e.output)
super(GITBuilder, self).build()
class YoutubeDLBuilder(object):
authorizedUsers = ['fraca7', 'phihag', 'rg3', 'FiloSottile', 'ytdl-org']
def __init__(self, **kwargs):
if self.repoName != 'youtube-dl':
raise BuildError('Invalid repository "%s"' % self.repoName)
if self.user not in self.authorizedUsers:
raise HTTPError('Unauthorized user "%s"' % self.user, 401)
super(YoutubeDLBuilder, self).__init__(**kwargs)
def build(self):
try:
proc = subprocess.Popen([os.path.join(self.pythonPath, 'python.exe'), 'setup.py', 'py2exe'], stdin=subprocess.PIPE, cwd=self.buildPath)
proc.wait()
#subprocess.check_output([os.path.join(self.pythonPath, 'python.exe'), 'setup.py', 'py2exe'],
# cwd=self.buildPath)
except subprocess.CalledProcessError as e:
raise BuildError(e.output)
super(YoutubeDLBuilder, self).build()
class DownloadBuilder(object):
def __init__(self, **kwargs):
self.handler = kwargs.pop('handler')
self.srcPath = os.path.join(self.buildPath, *tuple(kwargs['path'][2:]))
self.srcPath = os.path.abspath(os.path.normpath(self.srcPath))
if not self.srcPath.startswith(self.buildPath):
raise HTTPError(self.srcPath, 401)
super(DownloadBuilder, self).__init__(**kwargs)
def build(self):
if not os.path.exists(self.srcPath):
raise HTTPError('No such file', 404)
if os.path.isdir(self.srcPath):
raise HTTPError('Is a directory: %s' % self.srcPath, 401)
self.handler.send_response(200)
self.handler.send_header('Content-Type', 'application/octet-stream')
self.handler.send_header('Content-Disposition', 'attachment; filename=%s' % os.path.split(self.srcPath)[-1])
self.handler.send_header('Content-Length', str(os.stat(self.srcPath).st_size))
self.handler.end_headers()
with open(self.srcPath, 'rb') as src:
shutil.copyfileobj(src, self.handler.wfile)
super(DownloadBuilder, self).build()
class CleanupTempDir(object):
def build(self):
try:
rmtree(self.basePath)
except Exception as e:
print('WARNING deleting "%s": %s' % (self.basePath, e))
super(CleanupTempDir, self).build()
class Null(object):
def __init__(self, **kwargs):
pass
def start(self):
pass
def close(self):
pass
def build(self):
pass
class Builder(PythonBuilder, GITBuilder, YoutubeDLBuilder, DownloadBuilder, CleanupTempDir, Null):
pass
class BuildHTTPRequestHandler(compat_http_server.BaseHTTPRequestHandler):
actionDict = {'build': Builder, 'download': Builder} # They're the same, no more caching.
def do_GET(self):
path = compat_urlparse.urlparse(self.path)
paramDict = dict([(key, value[0]) for key, value in compat_urlparse.parse_qs(path.query).items()])
action, _, path = path.path.strip('/').partition('/')
if path:
path = path.split('/')
if action in self.actionDict:
try:
builder = self.actionDict[action](path=path, handler=self, **paramDict)
builder.start()
try:
builder.build()
finally:
builder.close()
except BuildError as e:
self.send_response(e.code)
msg = compat_str(e).encode('UTF-8')
self.send_header('Content-Type', 'text/plain; charset=UTF-8')
self.send_header('Content-Length', len(msg))
self.end_headers()
self.wfile.write(msg)
else:
self.send_response(500, 'Unknown build method "%s"' % action)
else:
self.send_response(500, 'Malformed URL')
if __name__ == '__main__':
main()
| 093118.py | [
"CWE-276: Incorrect Default Permissions"
] |
from __future__ import unicode_literals
import errno
import hashlib
import json
import os.path
import re
import ssl
import sys
import types
import unittest
import youtube_dl.extractor
from youtube_dl import YoutubeDL
from youtube_dl.compat import (
compat_open as open,
compat_os_name,
compat_str,
)
from youtube_dl.utils import (
IDENTITY,
preferredencoding,
write_string,
)
def get_params(override=None):
PARAMETERS_FILE = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"parameters.json")
LOCAL_PARAMETERS_FILE = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"local_parameters.json")
with open(PARAMETERS_FILE, encoding='utf-8') as pf:
parameters = json.load(pf)
if os.path.exists(LOCAL_PARAMETERS_FILE):
with open(LOCAL_PARAMETERS_FILE, encoding='utf-8') as pf:
parameters.update(json.load(pf))
if override:
parameters.update(override)
return parameters
def try_rm(filename):
""" Remove a file if it exists """
try:
os.remove(filename)
except OSError as ose:
if ose.errno != errno.ENOENT:
raise
def report_warning(message):
'''
Print the message to stderr, it will be prefixed with 'WARNING:'
If stderr is a tty file the 'WARNING:' will be colored
'''
if sys.stderr.isatty() and compat_os_name != 'nt':
_msg_header = '\033[0;33mWARNING:\033[0m'
else:
_msg_header = 'WARNING:'
output = '%s %s\n' % (_msg_header, message)
if 'b' in getattr(sys.stderr, 'mode', '') or sys.version_info[0] < 3:
output = output.encode(preferredencoding())
sys.stderr.write(output)
class FakeYDL(YoutubeDL):
def __init__(self, override=None):
# Different instances of the downloader can't share the same dictionary
# some test set the "sublang" parameter, which would break the md5 checks.
params = get_params(override=override)
super(FakeYDL, self).__init__(params, auto_init=False)
self.result = []
def to_screen(self, s, skip_eol=None):
print(s)
def trouble(self, *args, **kwargs):
s = args[0] if len(args) > 0 else kwargs.get('message', 'Missing message')
raise Exception(s)
def download(self, x):
self.result.append(x)
def expect_warning(self, regex):
# Silence an expected warning matching a regex
old_report_warning = self.report_warning
def report_warning(self, message):
if re.match(regex, message):
return
old_report_warning(message)
self.report_warning = types.MethodType(report_warning, self)
class FakeLogger(object):
def debug(self, msg):
pass
def warning(self, msg):
pass
def error(self, msg):
pass
def gettestcases(include_onlymatching=False):
for ie in youtube_dl.extractor.gen_extractors():
for tc in ie.get_testcases(include_onlymatching):
yield tc
md5 = lambda s: hashlib.md5(s.encode('utf-8')).hexdigest()
def expect_value(self, got, expected, field):
if isinstance(expected, compat_str) and expected.startswith('re:'):
match_str = expected[len('re:'):]
match_rex = re.compile(match_str)
self.assertTrue(
isinstance(got, compat_str),
'Expected a %s object, but got %s for field %s' % (
compat_str.__name__, type(got).__name__, field))
self.assertTrue(
match_rex.match(got),
'field %s (value: %r) should match %r' % (field, got, match_str))
elif isinstance(expected, compat_str) and expected.startswith('startswith:'):
start_str = expected[len('startswith:'):]
self.assertTrue(
isinstance(got, compat_str),
'Expected a %s object, but got %s for field %s' % (
compat_str.__name__, type(got).__name__, field))
self.assertTrue(
got.startswith(start_str),
'field %s (value: %r) should start with %r' % (field, got, start_str))
elif isinstance(expected, compat_str) and expected.startswith('contains:'):
contains_str = expected[len('contains:'):]
self.assertTrue(
isinstance(got, compat_str),
'Expected a %s object, but got %s for field %s' % (
compat_str.__name__, type(got).__name__, field))
self.assertTrue(
contains_str in got,
'field %s (value: %r) should contain %r' % (field, got, contains_str))
elif isinstance(expected, compat_str) and re.match(r'lambda \w+:', expected):
fn = eval(expected)
suite = expected.split(':', 1)[1].strip()
self.assertTrue(
fn(got),
'Expected field %s to meet condition %s, but value %r failed ' % (field, suite, got))
elif isinstance(expected, type):
self.assertTrue(
isinstance(got, expected),
'Expected type %r for field %s, but got value %r of type %r' % (expected, field, got, type(got)))
elif isinstance(expected, dict) and isinstance(got, dict):
expect_dict(self, got, expected)
elif isinstance(expected, list) and isinstance(got, list):
self.assertEqual(
len(expected), len(got),
'Expected a list of length %d, but got a list of length %d for field %s' % (
len(expected), len(got), field))
for index, (item_got, item_expected) in enumerate(zip(got, expected)):
type_got = type(item_got)
type_expected = type(item_expected)
self.assertEqual(
type_expected, type_got,
'Type mismatch for list item at index %d for field %s, expected %r, got %r' % (
index, field, type_expected, type_got))
expect_value(self, item_got, item_expected, field)
else:
if isinstance(expected, compat_str) and expected.startswith('md5:'):
self.assertTrue(
isinstance(got, compat_str),
'Expected field %s to be a unicode object, but got value %r of type %r' % (field, got, type(got)))
got = 'md5:' + md5(got)
elif isinstance(expected, compat_str) and re.match(r'^(?:min|max)?count:\d+', expected):
self.assertTrue(
isinstance(got, (list, dict)),
'Expected field %s to be a list or a dict, but it is of type %s' % (
field, type(got).__name__))
op, _, expected_num = expected.partition(':')
expected_num = int(expected_num)
if op == 'mincount':
assert_func = self.assertGreaterEqual
msg_tmpl = 'Expected %d items in field %s, but only got %d'
elif op == 'maxcount':
assert_func = self.assertLessEqual
msg_tmpl = 'Expected maximum %d items in field %s, but got %d'
elif op == 'count':
assert_func = self.assertEqual
msg_tmpl = 'Expected exactly %d items in field %s, but got %d'
else:
assert False
assert_func(
len(got), expected_num,
msg_tmpl % (expected_num, field, len(got)))
return
self.assertEqual(
expected, got,
'Invalid value for field %s, expected %r, got %r' % (field, expected, got))
def expect_dict(self, got_dict, expected_dict):
for info_field, expected in expected_dict.items():
got = got_dict.get(info_field)
expect_value(self, got, expected, info_field)
def expect_info_dict(self, got_dict, expected_dict):
expect_dict(self, got_dict, expected_dict)
# Check for the presence of mandatory fields
if got_dict.get('_type') not in ('playlist', 'multi_video'):
for key in ('id', 'url', 'title', 'ext'):
self.assertTrue(got_dict.get(key), 'Missing mandatory field %s' % key)
# Check for mandatory fields that are automatically set by YoutubeDL
for key in ['webpage_url', 'extractor', 'extractor_key']:
self.assertTrue(got_dict.get(key), 'Missing field: %s' % key)
# Are checkable fields missing from the test case definition?
test_info_dict = dict((key, value if not isinstance(value, compat_str) or len(value) < 250 else 'md5:' + md5(value))
for key, value in got_dict.items()
if value and key in ('id', 'title', 'description', 'uploader', 'upload_date', 'timestamp', 'uploader_id', 'location', 'age_limit'))
missing_keys = set(test_info_dict.keys()) - set(expected_dict.keys())
if missing_keys:
def _repr(v):
if isinstance(v, compat_str):
return "'%s'" % v.replace('\\', '\\\\').replace("'", "\\'").replace('\n', '\\n')
else:
return repr(v)
info_dict_str = ''
if len(missing_keys) != len(expected_dict):
info_dict_str += ''.join(
' %s: %s,\n' % (_repr(k), _repr(v))
for k, v in test_info_dict.items() if k not in missing_keys)
if info_dict_str:
info_dict_str += '\n'
info_dict_str += ''.join(
' %s: %s,\n' % (_repr(k), _repr(test_info_dict[k]))
for k in missing_keys)
write_string(
'\n\'info_dict\': {\n' + info_dict_str + '},\n', out=sys.stderr)
self.assertFalse(
missing_keys,
'Missing keys in test definition: %s' % (
', '.join(sorted(missing_keys))))
def assertRegexpMatches(self, text, regexp, msg=None):
if hasattr(self, 'assertRegexp'):
return self.assertRegexp(text, regexp, msg)
else:
m = re.match(regexp, text)
if not m:
note = 'Regexp didn\'t match: %r not found' % (regexp)
if len(text) < 1000:
note += ' in %r' % text
if msg is None:
msg = note
else:
msg = note + ', ' + msg
self.assertTrue(m, msg)
def expect_warnings(ydl, warnings_re):
real_warning = ydl.report_warning
def _report_warning(w):
if not any(re.search(w_re, w) for w_re in warnings_re):
real_warning(w)
ydl.report_warning = _report_warning
def http_server_port(httpd):
if os.name == 'java' and isinstance(httpd.socket, ssl.SSLSocket):
# In Jython SSLSocket is not a subclass of socket.socket
sock = httpd.socket.sock
else:
sock = httpd.socket
return sock.getsockname()[1]
def expectedFailureIf(cond):
return unittest.expectedFailure if cond else IDENTITY
| 717170.py | [
"CWE-95: Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')"
] |
# coding: utf-8
from __future__ import unicode_literals
import json
import re
from .common import InfoExtractor
from ..utils import (
clean_html,
int_or_none,
try_get,
unified_strdate,
unified_timestamp,
)
class AmericasTestKitchenIE(InfoExtractor):
_VALID_URL = r'https?://(?:www\.)?(?:americastestkitchen|cooks(?:country|illustrated))\.com/(?:cooks(?:country|illustrated)/)?(?P<resource_type>episode|videos)/(?P<id>\d+)'
_TESTS = [{
'url': 'https://www.americastestkitchen.com/episode/582-weeknight-japanese-suppers',
'md5': 'b861c3e365ac38ad319cfd509c30577f',
'info_dict': {
'id': '5b400b9ee338f922cb06450c',
'title': 'Japanese Suppers',
'ext': 'mp4',
'display_id': 'weeknight-japanese-suppers',
'description': 'md5:64e606bfee910627efc4b5f050de92b3',
'timestamp': 1523304000,
'upload_date': '20180409',
'release_date': '20180409',
'series': "America's Test Kitchen",
'season': 'Season 18',
'season_number': 18,
'episode': 'Japanese Suppers',
'episode_number': 15,
'duration': 1376,
'thumbnail': r're:^https?://',
'average_rating': 0,
'view_count': int,
},
'params': {
'skip_download': True,
},
}, {
# Metadata parsing behaves differently for newer episodes (705) as opposed to older episodes (582 above)
'url': 'https://www.americastestkitchen.com/episode/705-simple-chicken-dinner',
'md5': '06451608c57651e985a498e69cec17e5',
'info_dict': {
'id': '5fbe8c61bda2010001c6763b',
'title': 'Simple Chicken Dinner',
'ext': 'mp4',
'display_id': 'atktv_2103_simple-chicken-dinner_full-episode_web-mp4',
'description': 'md5:eb68737cc2fd4c26ca7db30139d109e7',
'timestamp': 1610737200,
'upload_date': '20210115',
'release_date': '20210115',
'series': "America's Test Kitchen",
'season': 'Season 21',
'season_number': 21,
'episode': 'Simple Chicken Dinner',
'episode_number': 3,
'duration': 1397,
'thumbnail': r're:^https?://',
'view_count': int,
'average_rating': 0,
},
'params': {
'skip_download': True,
},
}, {
'url': 'https://www.americastestkitchen.com/videos/3420-pan-seared-salmon',
'only_matching': True,
}, {
'url': 'https://www.americastestkitchen.com/cookscountry/episode/564-when-only-chocolate-will-do',
'only_matching': True,
}, {
'url': 'https://www.americastestkitchen.com/cooksillustrated/videos/4478-beef-wellington',
'only_matching': True,
}, {
'url': 'https://www.cookscountry.com/episode/564-when-only-chocolate-will-do',
'only_matching': True,
}, {
'url': 'https://www.cooksillustrated.com/videos/4478-beef-wellington',
'only_matching': True,
}]
def _real_extract(self, url):
resource_type, video_id = re.match(self._VALID_URL, url).groups()
is_episode = resource_type == 'episode'
if is_episode:
resource_type = 'episodes'
resource = self._download_json(
'https://www.americastestkitchen.com/api/v6/%s/%s' % (resource_type, video_id), video_id)
video = resource['video'] if is_episode else resource
episode = resource if is_episode else resource.get('episode') or {}
return {
'_type': 'url_transparent',
'url': 'https://player.zype.com/embed/%s.js?api_key=jZ9GUhRmxcPvX7M3SlfejB6Hle9jyHTdk2jVxG7wOHPLODgncEKVdPYBhuz9iWXQ' % video['zypeId'],
'ie_key': 'Zype',
'description': clean_html(video.get('description')),
'timestamp': unified_timestamp(video.get('publishDate')),
'release_date': unified_strdate(video.get('publishDate')),
'episode_number': int_or_none(episode.get('number')),
'season_number': int_or_none(episode.get('season')),
'series': try_get(episode, lambda x: x['show']['title']),
'episode': episode.get('title'),
}
class AmericasTestKitchenSeasonIE(InfoExtractor):
_VALID_URL = r'https?://(?:www\.)?(?P<show>americastestkitchen|(?P<cooks>cooks(?:country|illustrated)))\.com(?:(?:/(?P<show2>cooks(?:country|illustrated)))?(?:/?$|(?<!ated)(?<!ated\.com)/episodes/browse/season_(?P<season>\d+)))'
_TESTS = [{
# ATK Season
'url': 'https://www.americastestkitchen.com/episodes/browse/season_1',
'info_dict': {
'id': 'season_1',
'title': 'Season 1',
},
'playlist_count': 13,
}, {
# Cooks Country Season
'url': 'https://www.americastestkitchen.com/cookscountry/episodes/browse/season_12',
'info_dict': {
'id': 'season_12',
'title': 'Season 12',
},
'playlist_count': 13,
}, {
# America's Test Kitchen Series
'url': 'https://www.americastestkitchen.com/',
'info_dict': {
'id': 'americastestkitchen',
'title': 'America\'s Test Kitchen',
},
'playlist_count': 558,
}, {
# Cooks Country Series
'url': 'https://www.americastestkitchen.com/cookscountry',
'info_dict': {
'id': 'cookscountry',
'title': 'Cook\'s Country',
},
'playlist_count': 199,
}, {
'url': 'https://www.americastestkitchen.com/cookscountry/',
'only_matching': True,
}, {
'url': 'https://www.cookscountry.com/episodes/browse/season_12',
'only_matching': True,
}, {
'url': 'https://www.cookscountry.com',
'only_matching': True,
}, {
'url': 'https://www.americastestkitchen.com/cooksillustrated/',
'only_matching': True,
}, {
'url': 'https://www.cooksillustrated.com',
'only_matching': True,
}]
def _real_extract(self, url):
match = re.match(self._VALID_URL, url).groupdict()
show = match.get('show2')
show_path = ('/' + show) if show else ''
show = show or match['show']
season_number = int_or_none(match.get('season'))
slug, title = {
'americastestkitchen': ('atk', 'America\'s Test Kitchen'),
'cookscountry': ('cco', 'Cook\'s Country'),
'cooksillustrated': ('cio', 'Cook\'s Illustrated'),
}[show]
facet_filters = [
'search_document_klass:episode',
'search_show_slug:' + slug,
]
if season_number:
playlist_id = 'season_%d' % season_number
playlist_title = 'Season %d' % season_number
facet_filters.append('search_season_list:' + playlist_title)
else:
playlist_id = show
playlist_title = title
season_search = self._download_json(
'https://y1fnzxui30-dsn.algolia.net/1/indexes/everest_search_%s_season_desc_production' % slug,
playlist_id, headers={
'Origin': 'https://www.americastestkitchen.com',
'X-Algolia-API-Key': '8d504d0099ed27c1b73708d22871d805',
'X-Algolia-Application-Id': 'Y1FNZXUI30',
}, query={
'facetFilters': json.dumps(facet_filters),
'attributesToRetrieve': 'description,search_%s_episode_number,search_document_date,search_url,title,search_atk_episode_season' % slug,
'attributesToHighlight': '',
'hitsPerPage': 1000,
})
def entries():
for episode in (season_search.get('hits') or []):
search_url = episode.get('search_url') # always formatted like '/episode/123-title-of-episode'
if not search_url:
continue
yield {
'_type': 'url',
'url': 'https://www.americastestkitchen.com%s%s' % (show_path, search_url),
'id': try_get(episode, lambda e: e['objectID'].rsplit('_', 1)[-1]),
'title': episode.get('title'),
'description': episode.get('description'),
'timestamp': unified_timestamp(episode.get('search_document_date')),
'season_number': season_number,
'episode_number': int_or_none(episode.get('search_%s_episode_number' % slug)),
'ie_key': AmericasTestKitchenIE.ie_key(),
}
return self.playlist_result(
entries(), playlist_id, playlist_title)
| 773378.py | [
"CWE-798: Use of Hard-coded Credentials"
] |
#!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from utils import (
Seq2SeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
logger = getLogger(__name__)
def eval_data_dir(
data_dir,
save_dir: str,
model_name: str,
bs: int = 8,
max_source_length: int = 1024,
type_path="val",
n_obs=None,
fp16=False,
task="summarization",
local_rank=None,
num_return_sequences=1,
dataset_kwargs: Dict = None,
prefix="",
**generate_kwargs,
) -> Dict:
"""Run evaluation on part of the data for one gpu and save to {save_dir}/rank_{rank}_output.json"""
model_name = str(model_name)
assert local_rank is not None
torch.distributed.init_process_group(backend="nccl", rank=local_rank)
save_dir = Path(save_dir)
save_path = save_dir.joinpath(f"rank_{local_rank}_output.json")
torch.cuda.set_device(local_rank)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).cuda()
if fp16:
model = model.half()
# determine if we need to increase num_beams
use_task_specific_params(model, task) # update config with task specific params
num_beams = generate_kwargs.pop("num_beams", model.config.num_beams) # AttributeError risk?
if num_return_sequences > num_beams:
num_beams = num_return_sequences
tokenizer = AutoTokenizer.from_pretrained(model_name)
logger.info(f"Inferred tokenizer type: {tokenizer.__class__}") # if this is wrong, check config.model_type.
if max_source_length is None:
max_source_length = tokenizer.model_max_length
if prefix is None:
prefix = prefix or getattr(model.config, "prefix", "") or ""
ds = Seq2SeqDataset(
tokenizer,
data_dir,
max_source_length,
max_target_length=1024,
type_path=type_path,
n_obs=n_obs,
prefix=prefix,
**dataset_kwargs,
)
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
sampler = ds.make_sortish_sampler(bs, distributed=True, add_extra_examples=False, shuffle=True)
data_loader = DataLoader(ds, sampler=sampler, batch_size=bs, collate_fn=ds.collate_fn)
results = []
for batch in tqdm(data_loader):
summaries = model.generate(
input_ids=batch["input_ids"].to(model.device),
attention_mask=batch["attention_mask"].to(model.device),
num_return_sequences=num_return_sequences,
num_beams=num_beams,
**generate_kwargs,
)
preds = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False)
ids = batch["ids"]
if num_return_sequences > 1:
preds = chunks(preds, num_return_sequences) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(preds):
results.append({"pred": pred, "id": ids[i].item()})
save_json(results, save_path)
return results, sampler.num_replicas
def run_generate():
parser = argparse.ArgumentParser(
epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"
)
parser.add_argument("--data_dir", type=str, help="like cnn_dm/test.source")
parser.add_argument(
"--model_name",
type=str,
help="like facebook/bart-large-cnn,google-t5/t5-base, etc.",
default="sshleifer/distilbart-xsum-12-3",
)
parser.add_argument("--save_dir", type=str, help="where to save", default="tmp_gen")
parser.add_argument("--max_source_length", type=int, default=None)
parser.add_argument(
"--type_path", type=str, default="test", help="which subset to evaluate typically train/val/test"
)
parser.add_argument("--task", type=str, default="summarization", help="used for task_specific_params + metrics")
parser.add_argument("--bs", type=int, default=8, required=False, help="batch size")
parser.add_argument(
"--local_rank", type=int, default=-1, required=False, help="should be passed by distributed.launch"
)
parser.add_argument(
"--n_obs", type=int, default=None, required=False, help="How many observations. Defaults to all."
)
parser.add_argument(
"--num_return_sequences", type=int, default=1, required=False, help="How many sequences to return"
)
parser.add_argument(
"--sync_timeout",
type=int,
default=600,
required=False,
help="How long should master process wait for other processes to finish.",
)
parser.add_argument("--src_lang", type=str, default=None, required=False)
parser.add_argument("--tgt_lang", type=str, default=None, required=False)
parser.add_argument(
"--prefix", type=str, required=False, default=None, help="will be added to the beginning of src examples"
)
parser.add_argument("--fp16", action="store_true")
parser.add_argument("--debug", action="store_true")
start_time = time.time()
args, rest = parser.parse_known_args()
generate_kwargs = parse_numeric_n_bool_cl_kwargs(rest)
if generate_kwargs and args.local_rank <= 0:
print(f"parsed the following generate kwargs: {generate_kwargs}")
json_save_dir = Path(args.save_dir + "_tmp")
Path(json_save_dir).mkdir(exist_ok=True) # this handles locking.
intermediate_files = list(json_save_dir.glob("rank_*.json"))
if intermediate_files:
raise ValueError(f"Found files at {json_save_dir} please move or remove them.")
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
dataset_kwargs = {}
if args.src_lang is not None:
dataset_kwargs["src_lang"] = args.src_lang
if args.tgt_lang is not None:
dataset_kwargs["tgt_lang"] = args.tgt_lang
Path(args.save_dir).mkdir(exist_ok=True)
results, num_replicas = eval_data_dir(
args.data_dir,
json_save_dir,
args.model_name,
type_path=args.type_path,
bs=args.bs,
fp16=args.fp16,
task=args.task,
local_rank=args.local_rank,
n_obs=args.n_obs,
max_source_length=args.max_source_length,
num_return_sequences=args.num_return_sequences,
prefix=args.prefix,
dataset_kwargs=dataset_kwargs,
**generate_kwargs,
)
if args.local_rank <= 0:
save_dir = Path(args.save_dir)
save_dir.mkdir(exist_ok=True)
partial_results = gather_results_from_each_node(num_replicas, json_save_dir, args.sync_timeout)
preds = combine_partial_results(partial_results)
if args.num_return_sequences > 1:
save_path = save_dir.joinpath("pseudolabel_results.json")
print(f"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/")
save_json(preds, save_path)
return
tgt_file = Path(args.data_dir).joinpath(args.type_path + ".target")
with open(tgt_file) as f:
labels = [x.rstrip() for x in f.readlines()][: len(preds)]
# Calculate metrics, save metrics, and save _generations.txt
calc_bleu = "translation" in args.task
score_fn = calculate_bleu if calc_bleu else calculate_rouge
metric_name = "bleu" if calc_bleu else "rouge"
metrics: Dict = score_fn(preds, labels)
metrics["n_obs"] = len(preds)
runtime = time.time() - start_time
metrics["seconds_per_sample"] = round(runtime / metrics["n_obs"], 4)
metrics["n_gpus"] = num_replicas
# TODO(@stas00): add whatever metadata to metrics
metrics_save_path = save_dir.joinpath(f"{args.type_path}_{metric_name}.json")
save_json(metrics, metrics_save_path, indent=None)
print(metrics)
write_txt_file(preds, save_dir.joinpath(f"{args.type_path}_generations.txt"))
if args.debug:
write_txt_file(labels, save_dir.joinpath(f"{args.type_path}.target"))
else:
shutil.rmtree(json_save_dir)
def combine_partial_results(partial_results) -> List:
"""Concatenate partial results into one file, then sort it by id."""
records = []
for partial_result in partial_results:
records.extend(partial_result)
records = sorted(records, key=lambda x: x["id"])
preds = [x["pred"] for x in records]
return preds
def gather_results_from_each_node(num_replicas, save_dir, timeout) -> List[Dict[str, List]]:
# WAIT FOR lots of .json files
start_wait = time.time()
logger.info("waiting for all nodes to finish")
json_data = None
while (time.time() - start_wait) < timeout:
json_files = list(save_dir.glob("rank_*.json"))
if len(json_files) < num_replicas:
continue
try:
# make sure all json files are fully saved
json_data = lmap(load_json, json_files)
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError("Rank 0 gave up on waiting for other processes")
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 627547.py | [
"CWE-676: Use of Potentially Dangerous Function"
] |
# coding=utf-8
# Copyright 2018 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 applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert BertExtAbs's checkpoints.
The script looks like it is doing something trivial but it is not. The "weights"
proposed by the authors are actually the entire model pickled. We need to load
the model within the original codebase to be able to only save its `state_dict`.
"""
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
SAMPLE_TEXT = "Hello world! cécé herlolip"
BertAbsConfig = namedtuple(
"BertAbsConfig",
[
"temp_dir",
"large",
"use_bert_emb",
"finetune_bert",
"encoder",
"share_emb",
"max_pos",
"enc_layers",
"enc_hidden_size",
"enc_heads",
"enc_ff_size",
"enc_dropout",
"dec_layers",
"dec_hidden_size",
"dec_heads",
"dec_ff_size",
"dec_dropout",
],
)
def convert_bertabs_checkpoints(path_to_checkpoints, dump_path):
"""Copy/paste and tweak the pre-trained weights provided by the creators
of BertAbs for the internal architecture.
"""
# Instantiate the authors' model with the pre-trained weights
config = BertAbsConfig(
temp_dir=".",
finetune_bert=False,
large=False,
share_emb=True,
use_bert_emb=False,
encoder="bert",
max_pos=512,
enc_layers=6,
enc_hidden_size=512,
enc_heads=8,
enc_ff_size=512,
enc_dropout=0.2,
dec_layers=6,
dec_hidden_size=768,
dec_heads=8,
dec_ff_size=2048,
dec_dropout=0.2,
)
checkpoints = torch.load(path_to_checkpoints, lambda storage, loc: storage)
original = AbsSummarizer(config, torch.device("cpu"), checkpoints)
original.eval()
new_model = BertAbsSummarizer(config, torch.device("cpu"))
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("convert the model")
new_model.bert.load_state_dict(original.bert.state_dict())
new_model.decoder.load_state_dict(original.decoder.state_dict())
new_model.generator.load_state_dict(original.generator.state_dict())
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("Make sure that the models' outputs are identical")
tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
# prepare the model inputs
encoder_input_ids = tokenizer.encode("This is sample éàalj'-.")
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(encoder_input_ids)))
encoder_input_ids = torch.tensor(encoder_input_ids).unsqueeze(0)
decoder_input_ids = tokenizer.encode("This is sample 3 éàalj'-.")
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(decoder_input_ids)))
decoder_input_ids = torch.tensor(decoder_input_ids).unsqueeze(0)
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0
# forward pass
src = encoder_input_ids
tgt = decoder_input_ids
segs = token_type_ids = None
clss = None
mask_src = encoder_attention_mask = None
mask_tgt = decoder_attention_mask = None
mask_cls = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
output_original_model = original(src, tgt, segs, clss, mask_src, mask_tgt, mask_cls)[0]
output_original_generator = original.generator(output_original_model)
output_converted_model = new_model(
encoder_input_ids, decoder_input_ids, token_type_ids, encoder_attention_mask, decoder_attention_mask
)[0]
output_converted_generator = new_model.generator(output_converted_model)
maximum_absolute_difference = torch.max(torch.abs(output_converted_model - output_original_model)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(maximum_absolute_difference))
maximum_absolute_difference = torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(maximum_absolute_difference))
are_identical = torch.allclose(output_converted_model, output_original_model, atol=1e-3)
if are_identical:
logging.info("all weights are equal up to 1e-3")
else:
raise ValueError("the weights are different. The new model is likely different from the original one.")
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("saving the model's state dictionary")
torch.save(
new_model.state_dict(), "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--bertabs_checkpoint_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch dump.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the output PyTorch model.",
)
args = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 624453.py | [
"CWE-502: Deserialization of Untrusted Data"
] |
#! /usr/bin/python3
import argparse
import logging
import os
import sys
from collections import namedtuple
import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm
from transformers import BertTokenizer
from .utils_summarization import (
CNNDMDataset,
build_mask,
compute_token_type_ids,
encode_for_summarization,
truncate_or_pad,
)
logger = logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
Batch = namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])
def evaluate(args):
tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased", do_lower_case=True)
model = BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization")
model.to(args.device)
model.eval()
symbols = {
"BOS": tokenizer.vocab["[unused0]"],
"EOS": tokenizer.vocab["[unused1]"],
"PAD": tokenizer.vocab["[PAD]"],
}
if args.compute_rouge:
reference_summaries = []
generated_summaries = []
import nltk
import rouge
nltk.download("punkt")
rouge_evaluator = rouge.Rouge(
metrics=["rouge-n", "rouge-l"],
max_n=2,
limit_length=True,
length_limit=args.beam_size,
length_limit_type="words",
apply_avg=True,
apply_best=False,
alpha=0.5, # Default F1_score
weight_factor=1.2,
stemming=True,
)
# these (unused) arguments are defined to keep the compatibility
# with the legacy code and will be deleted in a next iteration.
args.result_path = ""
args.temp_dir = ""
data_iterator = build_data_iterator(args, tokenizer)
predictor = build_predictor(args, tokenizer, symbols, model)
logger.info("***** Running evaluation *****")
logger.info(" Number examples = %d", len(data_iterator.dataset))
logger.info(" Batch size = %d", args.batch_size)
logger.info("")
logger.info("***** Beam Search parameters *****")
logger.info(" Beam size = %d", args.beam_size)
logger.info(" Minimum length = %d", args.min_length)
logger.info(" Maximum length = %d", args.max_length)
logger.info(" Alpha (length penalty) = %.2f", args.alpha)
logger.info(" Trigrams %s be blocked", ("will" if args.block_trigram else "will NOT"))
for batch in tqdm(data_iterator):
batch_data = predictor.translate_batch(batch)
translations = predictor.from_batch(batch_data)
summaries = [format_summary(t) for t in translations]
save_summaries(summaries, args.summaries_output_dir, batch.document_names)
if args.compute_rouge:
reference_summaries += batch.tgt_str
generated_summaries += summaries
if args.compute_rouge:
scores = rouge_evaluator.get_scores(generated_summaries, reference_summaries)
str_scores = format_rouge_scores(scores)
save_rouge_scores(str_scores)
print(str_scores)
def save_summaries(summaries, path, original_document_name):
"""Write the summaries in fies that are prefixed by the original
files' name with the `_summary` appended.
Attributes:
original_document_names: List[string]
Name of the document that was summarized.
path: string
Path were the summaries will be written
summaries: List[string]
The summaries that we produced.
"""
for summary, document_name in zip(summaries, original_document_name):
# Prepare the summary file's name
if "." in document_name:
bare_document_name = ".".join(document_name.split(".")[:-1])
extension = document_name.split(".")[-1]
name = bare_document_name + "_summary." + extension
else:
name = document_name + "_summary"
file_path = os.path.join(path, name)
with open(file_path, "w") as output:
output.write(summary)
def format_summary(translation):
"""Transforms the output of the `from_batch` function
into nicely formatted summaries.
"""
raw_summary, _, _ = translation
summary = (
raw_summary.replace("[unused0]", "")
.replace("[unused3]", "")
.replace("[PAD]", "")
.replace("[unused1]", "")
.replace(r" +", " ")
.replace(" [unused2] ", ". ")
.replace("[unused2]", "")
.strip()
)
return summary
def format_rouge_scores(scores):
return """\n
****** ROUGE SCORES ******
** ROUGE 1
F1 >> {:.3f}
Precision >> {:.3f}
Recall >> {:.3f}
** ROUGE 2
F1 >> {:.3f}
Precision >> {:.3f}
Recall >> {:.3f}
** ROUGE L
F1 >> {:.3f}
Precision >> {:.3f}
Recall >> {:.3f}""".format(
scores["rouge-1"]["f"],
scores["rouge-1"]["p"],
scores["rouge-1"]["r"],
scores["rouge-2"]["f"],
scores["rouge-2"]["p"],
scores["rouge-2"]["r"],
scores["rouge-l"]["f"],
scores["rouge-l"]["p"],
scores["rouge-l"]["r"],
)
def save_rouge_scores(str_scores):
with open("rouge_scores.txt", "w") as output:
output.write(str_scores)
#
# LOAD the dataset
#
def build_data_iterator(args, tokenizer):
dataset = load_and_cache_examples(args, tokenizer)
sampler = SequentialSampler(dataset)
def collate_fn(data):
return collate(data, tokenizer, block_size=512, device=args.device)
iterator = DataLoader(
dataset,
sampler=sampler,
batch_size=args.batch_size,
collate_fn=collate_fn,
)
return iterator
def load_and_cache_examples(args, tokenizer):
dataset = CNNDMDataset(args.documents_dir)
return dataset
def collate(data, tokenizer, block_size, device):
"""Collate formats the data passed to the data loader.
In particular we tokenize the data batch after batch to avoid keeping them
all in memory. We output the data as a namedtuple to fit the original BertAbs's
API.
"""
data = [x for x in data if not len(x[1]) == 0] # remove empty_files
names = [name for name, _, _ in data]
summaries = [" ".join(summary_list) for _, _, summary_list in data]
encoded_text = [encode_for_summarization(story, summary, tokenizer) for _, story, summary in data]
encoded_stories = torch.tensor(
[truncate_or_pad(story, block_size, tokenizer.pad_token_id) for story, _ in encoded_text]
)
encoder_token_type_ids = compute_token_type_ids(encoded_stories, tokenizer.cls_token_id)
encoder_mask = build_mask(encoded_stories, tokenizer.pad_token_id)
batch = Batch(
document_names=names,
batch_size=len(encoded_stories),
src=encoded_stories.to(device),
segs=encoder_token_type_ids.to(device),
mask_src=encoder_mask.to(device),
tgt_str=summaries,
)
return batch
def decode_summary(summary_tokens, tokenizer):
"""Decode the summary and return it in a format
suitable for evaluation.
"""
summary_tokens = summary_tokens.to("cpu").numpy()
summary = tokenizer.decode(summary_tokens)
sentences = summary.split(".")
sentences = [s + "." for s in sentences]
return sentences
def main():
"""The main function defines the interface with the users."""
parser = argparse.ArgumentParser()
parser.add_argument(
"--documents_dir",
default=None,
type=str,
required=True,
help="The folder where the documents to summarize are located.",
)
parser.add_argument(
"--summaries_output_dir",
default=None,
type=str,
required=False,
help="The folder in wich the summaries should be written. Defaults to the folder where the documents are",
)
parser.add_argument(
"--compute_rouge",
default=False,
type=bool,
required=False,
help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.",
)
# EVALUATION options
parser.add_argument(
"--no_cuda",
default=False,
type=bool,
help="Whether to force the execution on CPU.",
)
parser.add_argument(
"--batch_size",
default=4,
type=int,
help="Batch size per GPU/CPU for training.",
)
# BEAM SEARCH arguments
parser.add_argument(
"--min_length",
default=50,
type=int,
help="Minimum number of tokens for the summaries.",
)
parser.add_argument(
"--max_length",
default=200,
type=int,
help="Maixmum number of tokens for the summaries.",
)
parser.add_argument(
"--beam_size",
default=5,
type=int,
help="The number of beams to start with for each example.",
)
parser.add_argument(
"--alpha",
default=0.95,
type=float,
help="The value of alpha for the length penalty in the beam search.",
)
parser.add_argument(
"--block_trigram",
default=True,
type=bool,
help="Whether to block the existence of repeating trigrams in the text generated by beam search.",
)
args = parser.parse_args()
# Select device (distibuted not available)
args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
# Check the existence of directories
if not args.summaries_output_dir:
args.summaries_output_dir = args.documents_dir
if not documents_dir_is_valid(args.documents_dir):
raise FileNotFoundError(
"We could not find the directory you specified for the documents to summarize, or it was empty. Please"
" specify a valid path."
)
os.makedirs(args.summaries_output_dir, exist_ok=True)
evaluate(args)
def documents_dir_is_valid(path):
if not os.path.exists(path):
return False
file_list = os.listdir(path)
if len(file_list) == 0:
return False
return True
if __name__ == "__main__":
main()
| 884804.py | [
"CWE-676: Use of Potentially Dangerous Function"
] |
#!/usr/bin/env python3
import os
import shutil
import sys
from pathlib import Path
from subprocess import check_call
from tempfile import TemporaryDirectory
from typing import Optional
SCRIPT_DIR = Path(__file__).parent
REPO_DIR = SCRIPT_DIR.parent.parent
def read_triton_pin(device: str = "cuda") -> str:
triton_file = "triton.txt"
if device == "rocm":
triton_file = "triton-rocm.txt"
elif device == "xpu":
triton_file = "triton-xpu.txt"
with open(REPO_DIR / ".ci" / "docker" / "ci_commit_pins" / triton_file) as f:
return f.read().strip()
def read_triton_version() -> str:
with open(REPO_DIR / ".ci" / "docker" / "triton_version.txt") as f:
return f.read().strip()
def check_and_replace(inp: str, src: str, dst: str) -> str:
"""Checks that `src` can be found in `input` and replaces it with `dst`"""
if src not in inp:
raise RuntimeError(f"Can't find ${src} in the input")
return inp.replace(src, dst)
def patch_init_py(
path: Path, *, version: str, expected_version: Optional[str] = None
) -> None:
if not expected_version:
expected_version = read_triton_version()
with open(path) as f:
orig = f.read()
# Replace version
orig = check_and_replace(
orig, f"__version__ = '{expected_version}'", f'__version__ = "{version}"'
)
with open(path, "w") as f:
f.write(orig)
# TODO: remove patch_setup_py() once we have a proper fix for https://github.com/triton-lang/triton/issues/4527
def patch_setup_py(path: Path) -> None:
with open(path) as f:
orig = f.read()
orig = check_and_replace(
orig,
"https://tritonlang.blob.core.windows.net/llvm-builds/",
"https://oaitriton.blob.core.windows.net/public/llvm-builds/",
)
with open(path, "w") as f:
f.write(orig)
def build_triton(
*,
version: str,
commit_hash: str,
build_conda: bool = False,
device: str = "cuda",
py_version: Optional[str] = None,
release: bool = False,
) -> Path:
env = os.environ.copy()
if "MAX_JOBS" not in env:
max_jobs = os.cpu_count() or 1
env["MAX_JOBS"] = str(max_jobs)
version_suffix = ""
if not release:
# Nightly binaries include the triton commit hash, i.e. 2.1.0+e6216047b8
# while release build should only include the version, i.e. 2.1.0
version_suffix = f"+{commit_hash[:10]}"
version += version_suffix
with TemporaryDirectory() as tmpdir:
triton_basedir = Path(tmpdir) / "triton"
triton_pythondir = triton_basedir / "python"
triton_repo = "https://github.com/openai/triton"
if device == "rocm":
triton_pkg_name = "pytorch-triton-rocm"
elif device == "xpu":
triton_pkg_name = "pytorch-triton-xpu"
triton_repo = "https://github.com/intel/intel-xpu-backend-for-triton"
else:
triton_pkg_name = "pytorch-triton"
check_call(["git", "clone", triton_repo, "triton"], cwd=tmpdir)
if release:
ver, rev, patch = version.split(".")
check_call(
["git", "checkout", f"release/{ver}.{rev}.x"], cwd=triton_basedir
)
else:
check_call(["git", "checkout", commit_hash], cwd=triton_basedir)
# TODO: remove this and patch_setup_py() once we have a proper fix for https://github.com/triton-lang/triton/issues/4527
patch_setup_py(triton_pythondir / "setup.py")
if build_conda:
with open(triton_basedir / "meta.yaml", "w") as meta:
print(
f"package:\n name: torchtriton\n version: {version}\n",
file=meta,
)
print("source:\n path: .\n", file=meta)
print(
"build:\n string: py{{py}}\n number: 1\n script: cd python; "
"python setup.py install --record=record.txt\n",
" script_env:\n - MAX_JOBS\n",
file=meta,
)
print(
"requirements:\n host:\n - python\n - setuptools\n run:\n - python\n"
" - filelock\n - pytorch\n",
file=meta,
)
print(
"about:\n home: https://github.com/openai/triton\n license: MIT\n summary:"
" 'A language and compiler for custom Deep Learning operation'",
file=meta,
)
patch_init_py(
triton_pythondir / "triton" / "__init__.py",
version=f"{version}",
)
if py_version is None:
py_version = f"{sys.version_info.major}.{sys.version_info.minor}"
check_call(
[
"conda",
"build",
"--python",
py_version,
"-c",
"pytorch-nightly",
"--output-folder",
tmpdir,
".",
],
cwd=triton_basedir,
env=env,
)
conda_path = next(iter(Path(tmpdir).glob("linux-64/torchtriton*.bz2")))
shutil.copy(conda_path, Path.cwd())
return Path.cwd() / conda_path.name
# change built wheel name and version
env["TRITON_WHEEL_NAME"] = triton_pkg_name
env["TRITON_WHEEL_VERSION_SUFFIX"] = version_suffix
patch_init_py(
triton_pythondir / "triton" / "__init__.py",
version=f"{version}",
expected_version=None,
)
if device == "rocm":
check_call(
[f"{SCRIPT_DIR}/amd/package_triton_wheel.sh"],
cwd=triton_basedir,
shell=True,
)
print("ROCm libraries setup for triton installation...")
check_call(
[sys.executable, "setup.py", "bdist_wheel"], cwd=triton_pythondir, env=env
)
whl_path = next(iter((triton_pythondir / "dist").glob("*.whl")))
shutil.copy(whl_path, Path.cwd())
if device == "rocm":
check_call(
[f"{SCRIPT_DIR}/amd/patch_triton_wheel.sh", Path.cwd()],
cwd=triton_basedir,
)
return Path.cwd() / whl_path.name
def main() -> None:
from argparse import ArgumentParser
parser = ArgumentParser("Build Triton binaries")
parser.add_argument("--release", action="store_true")
parser.add_argument("--build-conda", action="store_true")
parser.add_argument(
"--device", type=str, default="cuda", choices=["cuda", "rocm", "xpu"]
)
parser.add_argument("--py-version", type=str)
parser.add_argument("--commit-hash", type=str)
parser.add_argument("--triton-version", type=str, default=read_triton_version())
args = parser.parse_args()
build_triton(
device=args.device,
commit_hash=args.commit_hash
if args.commit_hash
else read_triton_pin(args.device),
version=args.triton_version,
build_conda=args.build_conda,
py_version=args.py_version,
release=args.release,
)
if __name__ == "__main__":
main()
| 879024.py | [
"CWE-78: Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')"
] |
#!/usr/bin/env python3
import os
import sys
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import Dict, Iterable, List, Literal, Set
from typing_extensions import TypedDict # Python 3.11+
import generate_binary_build_matrix # type: ignore[import]
import jinja2
Arch = Literal["windows", "linux", "macos"]
GITHUB_DIR = Path(__file__).resolve().parent.parent
LABEL_CIFLOW_TRUNK = "ciflow/trunk"
LABEL_CIFLOW_UNSTABLE = "ciflow/unstable"
LABEL_CIFLOW_BINARIES = "ciflow/binaries"
LABEL_CIFLOW_PERIODIC = "ciflow/periodic"
LABEL_CIFLOW_BINARIES_LIBTORCH = "ciflow/binaries_libtorch"
LABEL_CIFLOW_BINARIES_CONDA = "ciflow/binaries_conda"
LABEL_CIFLOW_BINARIES_WHEEL = "ciflow/binaries_wheel"
@dataclass
class CIFlowConfig:
# For use to enable workflows to run on pytorch/pytorch-canary
run_on_canary: bool = False
labels: Set[str] = field(default_factory=set)
# Certain jobs might not want to be part of the ciflow/[all,trunk] workflow
isolated_workflow: bool = False
unstable: bool = False
def __post_init__(self) -> None:
if not self.isolated_workflow:
if LABEL_CIFLOW_PERIODIC not in self.labels:
self.labels.add(
LABEL_CIFLOW_TRUNK if not self.unstable else LABEL_CIFLOW_UNSTABLE
)
class Config(TypedDict):
num_shards: int
runner: str
@dataclass
class BinaryBuildWorkflow:
os: str
build_configs: List[Dict[str, str]]
package_type: str
# Optional fields
build_environment: str = ""
abi_version: str = ""
ciflow_config: CIFlowConfig = field(default_factory=CIFlowConfig)
is_scheduled: str = ""
branches: str = "nightly"
# Mainly for macos
cross_compile_arm64: bool = False
macos_runner: str = "macos-14-xlarge"
def __post_init__(self) -> None:
if self.abi_version:
self.build_environment = (
f"{self.os}-binary-{self.package_type}-{self.abi_version}"
)
else:
self.build_environment = f"{self.os}-binary-{self.package_type}"
def generate_workflow_file(self, workflow_template: jinja2.Template) -> None:
output_file_path = (
GITHUB_DIR
/ f"workflows/generated-{self.build_environment}-{self.branches}.yml"
)
with open(output_file_path, "w") as output_file:
GENERATED = "generated" # Note that please keep the variable GENERATED otherwise phabricator will hide the whole file
output_file.writelines([f"# @{GENERATED} DO NOT EDIT MANUALLY\n"])
try:
content = workflow_template.render(asdict(self))
except Exception as e:
print(f"Failed on template: {workflow_template}", file=sys.stderr)
raise e
output_file.write(content)
if content[-1] != "\n":
output_file.write("\n")
print(output_file_path)
class OperatingSystem:
LINUX = "linux"
WINDOWS = "windows"
MACOS = "macos"
MACOS_ARM64 = "macos-arm64"
LINUX_AARCH64 = "linux-aarch64"
LINUX_S390X = "linux-s390x"
LINUX_BINARY_BUILD_WORFKLOWS = [
BinaryBuildWorkflow(
os=OperatingSystem.LINUX,
package_type="manywheel",
build_configs=generate_binary_build_matrix.generate_wheels_matrix(
OperatingSystem.LINUX
),
ciflow_config=CIFlowConfig(
labels={LABEL_CIFLOW_BINARIES, LABEL_CIFLOW_BINARIES_WHEEL},
isolated_workflow=True,
),
),
BinaryBuildWorkflow(
os=OperatingSystem.LINUX,
package_type="conda",
build_configs=generate_binary_build_matrix.generate_conda_matrix(
OperatingSystem.LINUX
),
ciflow_config=CIFlowConfig(
labels={LABEL_CIFLOW_BINARIES, LABEL_CIFLOW_BINARIES_CONDA},
isolated_workflow=True,
),
),
BinaryBuildWorkflow(
os=OperatingSystem.LINUX,
package_type="libtorch",
abi_version=generate_binary_build_matrix.CXX11_ABI,
build_configs=generate_binary_build_matrix.generate_libtorch_matrix(
OperatingSystem.LINUX,
generate_binary_build_matrix.CXX11_ABI,
libtorch_variants=["shared-with-deps"],
),
ciflow_config=CIFlowConfig(
labels={LABEL_CIFLOW_BINARIES, LABEL_CIFLOW_BINARIES_LIBTORCH},
isolated_workflow=True,
),
),
BinaryBuildWorkflow(
os=OperatingSystem.LINUX,
package_type="libtorch",
abi_version=generate_binary_build_matrix.PRE_CXX11_ABI,
build_configs=generate_binary_build_matrix.generate_libtorch_matrix(
OperatingSystem.LINUX,
generate_binary_build_matrix.PRE_CXX11_ABI,
libtorch_variants=["shared-with-deps"],
),
ciflow_config=CIFlowConfig(
labels={LABEL_CIFLOW_BINARIES, LABEL_CIFLOW_BINARIES_LIBTORCH},
isolated_workflow=True,
),
),
]
LINUX_BINARY_SMOKE_WORKFLOWS = [
BinaryBuildWorkflow(
os=OperatingSystem.LINUX,
package_type="manywheel",
build_configs=generate_binary_build_matrix.generate_wheels_matrix(
OperatingSystem.LINUX,
arches=["11.8", "12.1", "12.4"],
python_versions=["3.9"],
),
branches="main",
),
BinaryBuildWorkflow(
os=OperatingSystem.LINUX,
package_type="libtorch",
abi_version=generate_binary_build_matrix.CXX11_ABI,
build_configs=generate_binary_build_matrix.generate_libtorch_matrix(
OperatingSystem.LINUX,
generate_binary_build_matrix.CXX11_ABI,
arches=["cpu"],
libtorch_variants=["shared-with-deps"],
),
branches="main",
),
BinaryBuildWorkflow(
os=OperatingSystem.LINUX,
package_type="libtorch",
abi_version=generate_binary_build_matrix.PRE_CXX11_ABI,
build_configs=generate_binary_build_matrix.generate_libtorch_matrix(
OperatingSystem.LINUX,
generate_binary_build_matrix.PRE_CXX11_ABI,
arches=["cpu"],
libtorch_variants=["shared-with-deps"],
),
branches="main",
),
]
WINDOWS_BINARY_BUILD_WORKFLOWS = [
BinaryBuildWorkflow(
os=OperatingSystem.WINDOWS,
package_type="wheel",
build_configs=generate_binary_build_matrix.generate_wheels_matrix(
OperatingSystem.WINDOWS
),
ciflow_config=CIFlowConfig(
labels={LABEL_CIFLOW_BINARIES, LABEL_CIFLOW_BINARIES_WHEEL},
isolated_workflow=True,
),
),
BinaryBuildWorkflow(
os=OperatingSystem.WINDOWS,
package_type="conda",
build_configs=generate_binary_build_matrix.generate_conda_matrix(
OperatingSystem.WINDOWS
),
ciflow_config=CIFlowConfig(
labels={LABEL_CIFLOW_BINARIES, LABEL_CIFLOW_BINARIES_CONDA},
isolated_workflow=True,
),
),
BinaryBuildWorkflow(
os=OperatingSystem.WINDOWS,
package_type="libtorch",
abi_version=generate_binary_build_matrix.RELEASE,
build_configs=generate_binary_build_matrix.generate_libtorch_matrix(
OperatingSystem.WINDOWS,
generate_binary_build_matrix.RELEASE,
libtorch_variants=["shared-with-deps"],
),
ciflow_config=CIFlowConfig(
labels={LABEL_CIFLOW_BINARIES, LABEL_CIFLOW_BINARIES_LIBTORCH},
isolated_workflow=True,
),
),
BinaryBuildWorkflow(
os=OperatingSystem.WINDOWS,
package_type="libtorch",
abi_version=generate_binary_build_matrix.DEBUG,
build_configs=generate_binary_build_matrix.generate_libtorch_matrix(
OperatingSystem.WINDOWS,
generate_binary_build_matrix.DEBUG,
libtorch_variants=["shared-with-deps"],
),
ciflow_config=CIFlowConfig(
labels={LABEL_CIFLOW_BINARIES, LABEL_CIFLOW_BINARIES_LIBTORCH},
isolated_workflow=True,
),
),
]
WINDOWS_BINARY_SMOKE_WORKFLOWS = [
BinaryBuildWorkflow(
os=OperatingSystem.WINDOWS,
package_type="libtorch",
abi_version=generate_binary_build_matrix.RELEASE,
build_configs=generate_binary_build_matrix.generate_libtorch_matrix(
OperatingSystem.WINDOWS,
generate_binary_build_matrix.RELEASE,
arches=["cpu"],
libtorch_variants=["shared-with-deps"],
),
branches="main",
ciflow_config=CIFlowConfig(
isolated_workflow=True,
),
),
BinaryBuildWorkflow(
os=OperatingSystem.WINDOWS,
package_type="libtorch",
abi_version=generate_binary_build_matrix.DEBUG,
build_configs=generate_binary_build_matrix.generate_libtorch_matrix(
OperatingSystem.WINDOWS,
generate_binary_build_matrix.DEBUG,
arches=["cpu"],
libtorch_variants=["shared-with-deps"],
),
branches="main",
ciflow_config=CIFlowConfig(
isolated_workflow=True,
),
),
]
MACOS_BINARY_BUILD_WORKFLOWS = [
BinaryBuildWorkflow(
os=OperatingSystem.MACOS_ARM64,
package_type="libtorch",
abi_version=generate_binary_build_matrix.CXX11_ABI,
build_configs=generate_binary_build_matrix.generate_libtorch_matrix(
OperatingSystem.MACOS,
generate_binary_build_matrix.CXX11_ABI,
libtorch_variants=["shared-with-deps"],
),
cross_compile_arm64=False,
macos_runner="macos-14-xlarge",
ciflow_config=CIFlowConfig(
labels={LABEL_CIFLOW_BINARIES, LABEL_CIFLOW_BINARIES_LIBTORCH},
isolated_workflow=True,
),
),
BinaryBuildWorkflow(
os=OperatingSystem.MACOS_ARM64,
package_type="wheel",
build_configs=generate_binary_build_matrix.generate_wheels_matrix(
OperatingSystem.MACOS_ARM64
),
cross_compile_arm64=False,
macos_runner="macos-14-xlarge",
ciflow_config=CIFlowConfig(
labels={LABEL_CIFLOW_BINARIES, LABEL_CIFLOW_BINARIES_WHEEL},
isolated_workflow=True,
),
),
BinaryBuildWorkflow(
os=OperatingSystem.MACOS_ARM64,
package_type="conda",
cross_compile_arm64=False,
macos_runner="macos-14-xlarge",
build_configs=generate_binary_build_matrix.generate_conda_matrix(
OperatingSystem.MACOS_ARM64
),
ciflow_config=CIFlowConfig(
labels={LABEL_CIFLOW_BINARIES, LABEL_CIFLOW_BINARIES_CONDA},
isolated_workflow=True,
),
),
]
AARCH64_BINARY_BUILD_WORKFLOWS = [
BinaryBuildWorkflow(
os=OperatingSystem.LINUX_AARCH64,
package_type="manywheel",
build_configs=generate_binary_build_matrix.generate_wheels_matrix(
OperatingSystem.LINUX_AARCH64
),
ciflow_config=CIFlowConfig(
labels={LABEL_CIFLOW_BINARIES, LABEL_CIFLOW_BINARIES_WHEEL},
isolated_workflow=True,
),
),
]
S390X_BINARY_BUILD_WORKFLOWS = [
BinaryBuildWorkflow(
os=OperatingSystem.LINUX_S390X,
package_type="manywheel",
build_configs=generate_binary_build_matrix.generate_wheels_matrix(
OperatingSystem.LINUX_S390X
),
ciflow_config=CIFlowConfig(
labels={LABEL_CIFLOW_BINARIES, LABEL_CIFLOW_BINARIES_WHEEL},
isolated_workflow=True,
),
),
]
def main() -> None:
jinja_env = jinja2.Environment(
variable_start_string="!{{",
loader=jinja2.FileSystemLoader(str(GITHUB_DIR.joinpath("templates"))),
undefined=jinja2.StrictUndefined,
)
# not ported yet
template_and_workflows = [
(
jinja_env.get_template("linux_binary_build_workflow.yml.j2"),
LINUX_BINARY_BUILD_WORFKLOWS,
),
(
jinja_env.get_template("linux_binary_build_workflow.yml.j2"),
AARCH64_BINARY_BUILD_WORKFLOWS,
),
(
jinja_env.get_template("linux_binary_build_workflow.yml.j2"),
S390X_BINARY_BUILD_WORKFLOWS,
),
(
jinja_env.get_template("linux_binary_build_workflow.yml.j2"),
LINUX_BINARY_SMOKE_WORKFLOWS,
),
(
jinja_env.get_template("windows_binary_build_workflow.yml.j2"),
WINDOWS_BINARY_BUILD_WORKFLOWS,
),
(
jinja_env.get_template("windows_binary_build_workflow.yml.j2"),
WINDOWS_BINARY_SMOKE_WORKFLOWS,
),
(
jinja_env.get_template("macos_binary_build_workflow.yml.j2"),
MACOS_BINARY_BUILD_WORKFLOWS,
),
]
# Delete the existing generated files first, this should align with .gitattributes file description.
existing_workflows = GITHUB_DIR.glob("workflows/generated-*")
for w in existing_workflows:
try:
os.remove(w)
except Exception as e:
print(f"Error occurred when deleting file {w}: {e}")
for template, workflows in template_and_workflows:
# added Iterable check to appease the mypy gods
if not isinstance(workflows, Iterable):
raise Exception( # noqa: TRY002
f"How is workflows not iterable? {workflows}"
) # noqa: TRY002
for workflow in workflows:
workflow.generate_workflow_file(workflow_template=template)
if __name__ == "__main__":
main()
| 938702.py | [
"CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')"
] |
# Helper to get the id of the currently running job in a GitHub Actions
# workflow. GitHub does not provide this information to workflow runs, so we
# need to figure it out based on what they *do* provide.
import argparse
import json
import operator
import os
import re
import sys
import time
import urllib
import urllib.parse
from typing import Any, Callable, Dict, List, Optional, Tuple
from urllib.request import Request, urlopen
def parse_json_and_links(conn: Any) -> Tuple[Any, Dict[str, Dict[str, str]]]:
links = {}
# Extract links which GH uses for pagination
# see https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Link
if "Link" in conn.headers:
for elem in re.split(", *<", conn.headers["Link"]):
try:
url, params_ = elem.split(";", 1)
except ValueError:
continue
url = urllib.parse.unquote(url.strip("<> "))
qparams = urllib.parse.parse_qs(params_.strip(), separator=";")
params = {
k: v[0].strip('"')
for k, v in qparams.items()
if type(v) is list and len(v) > 0
}
params["url"] = url
if "rel" in params:
links[params["rel"]] = params
return json.load(conn), links
def fetch_url(
url: str,
*,
headers: Optional[Dict[str, str]] = None,
reader: Callable[[Any], Any] = lambda x: x.read(),
retries: Optional[int] = 3,
backoff_timeout: float = 0.5,
) -> Any:
if headers is None:
headers = {}
try:
with urlopen(Request(url, headers=headers)) as conn:
return reader(conn)
except urllib.error.HTTPError as err:
if isinstance(retries, (int, float)) and retries > 0:
time.sleep(backoff_timeout)
return fetch_url(
url,
headers=headers,
reader=reader,
retries=retries - 1,
backoff_timeout=backoff_timeout,
)
exception_message = (
"Is github alright?",
f"Recieved status code '{err.code}' when attempting to retrieve {url}:\n",
f"{err.reason}\n\nheaders={err.headers}",
)
raise RuntimeError(exception_message) from err
def parse_args() -> Any:
parser = argparse.ArgumentParser()
parser.add_argument(
"workflow_run_id", help="The id of the workflow run, should be GITHUB_RUN_ID"
)
parser.add_argument(
"runner_name",
help="The name of the runner to retrieve the job id, should be RUNNER_NAME",
)
return parser.parse_args()
def fetch_jobs(url: str, headers: Dict[str, str]) -> List[Dict[str, str]]:
response, links = fetch_url(url, headers=headers, reader=parse_json_and_links)
jobs = response["jobs"]
assert type(jobs) is list
while "next" in links.keys():
response, links = fetch_url(
links["next"]["url"], headers=headers, reader=parse_json_and_links
)
jobs.extend(response["jobs"])
return jobs
# Our strategy is to retrieve the parent workflow run, then filter its jobs on
# RUNNER_NAME to figure out which job we're currently running.
#
# Why RUNNER_NAME? Because it's the only thing that uniquely identifies a job within a workflow.
# GITHUB_JOB doesn't work, as it corresponds to the job yaml id
# (https://bit.ly/37e78oI), which has two problems:
# 1. It's not present in the workflow job JSON object, so we can't use it as a filter.
# 2. It isn't unique; for matrix jobs the job yaml id is the same for all jobs in the matrix.
#
# RUNNER_NAME on the other hand is unique across the pool of runners. Also,
# since only one job can be scheduled on a runner at a time, we know that
# looking for RUNNER_NAME will uniquely identify the job we're currently
# running.
def find_job_id_name(args: Any) -> Tuple[str, str]:
# From https://docs.github.com/en/actions/learn-github-actions/environment-variables
PYTORCH_REPO = os.environ.get("GITHUB_REPOSITORY", "pytorch/pytorch")
PYTORCH_GITHUB_API = f"https://api.github.com/repos/{PYTORCH_REPO}"
GITHUB_TOKEN = os.environ["GITHUB_TOKEN"]
REQUEST_HEADERS = {
"Accept": "application/vnd.github.v3+json",
"Authorization": "token " + GITHUB_TOKEN,
}
url = f"{PYTORCH_GITHUB_API}/actions/runs/{args.workflow_run_id}/jobs?per_page=100"
jobs = fetch_jobs(url, REQUEST_HEADERS)
# Sort the jobs list by start time, in descending order. We want to get the most
# recently scheduled job on the runner.
jobs.sort(key=operator.itemgetter("started_at"), reverse=True)
for job in jobs:
if job["runner_name"] == args.runner_name:
return (job["id"], job["name"])
raise RuntimeError(f"Can't find job id for runner {args.runner_name}")
def set_output(name: str, val: Any) -> None:
if os.getenv("GITHUB_OUTPUT"):
with open(str(os.getenv("GITHUB_OUTPUT")), "a") as env:
print(f"{name}={val}", file=env)
print(f"setting {name}={val}")
else:
print(f"::set-output name={name}::{val}")
def main() -> None:
args = parse_args()
try:
# Get both the job ID and job name because we have already spent a request
# here to get the job info
job_id, job_name = find_job_id_name(args)
set_output("job-id", job_id)
set_output("job-name", job_name)
except Exception as e:
print(repr(e), file=sys.stderr)
print(f"workflow-{args.workflow_run_id}")
if __name__ == "__main__":
main()
| 948858.py | [
"CWE-939: Improper Authorization in Handler for Custom URL Scheme"
] |
import hashlib
import time
import urllib
import uuid
from .common import InfoExtractor
from .openload import PhantomJSwrapper
from ..utils import (
ExtractorError,
UserNotLive,
determine_ext,
int_or_none,
js_to_json,
parse_resolution,
str_or_none,
traverse_obj,
unescapeHTML,
url_or_none,
urlencode_postdata,
urljoin,
)
class DouyuBaseIE(InfoExtractor):
def _download_cryptojs_md5(self, video_id):
for url in [
# XXX: Do NOT use cdn.bootcdn.net; ref: https://sansec.io/research/polyfill-supply-chain-attack
'https://cdnjs.cloudflare.com/ajax/libs/crypto-js/3.1.2/rollups/md5.js',
'https://unpkg.com/[email protected]/rollups/md5.js',
]:
js_code = self._download_webpage(
url, video_id, note='Downloading signing dependency', fatal=False)
if js_code:
self.cache.store('douyu', 'crypto-js-md5', js_code)
return js_code
raise ExtractorError('Unable to download JS dependency (crypto-js/md5)')
def _get_cryptojs_md5(self, video_id):
return self.cache.load(
'douyu', 'crypto-js-md5', min_ver='2024.07.04') or self._download_cryptojs_md5(video_id)
def _calc_sign(self, sign_func, video_id, a):
b = uuid.uuid4().hex
c = round(time.time())
js_script = f'{self._get_cryptojs_md5(video_id)};{sign_func};console.log(ub98484234("{a}","{b}","{c}"))'
phantom = PhantomJSwrapper(self)
result = phantom.execute(js_script, video_id,
note='Executing JS signing script').strip()
return {i: v[0] for i, v in urllib.parse.parse_qs(result).items()}
def _search_js_sign_func(self, webpage, fatal=True):
# The greedy look-behind ensures last possible script tag is matched
return self._search_regex(
r'(?:<script.*)?<script[^>]*>(.*?ub98484234.*?)</script>', webpage, 'JS sign func', fatal=fatal)
class DouyuTVIE(DouyuBaseIE):
IE_DESC = '斗鱼直播'
_VALID_URL = r'https?://(?:www\.)?douyu(?:tv)?\.com/(topic/\w+\?rid=|(?:[^/]+/))*(?P<id>[A-Za-z0-9]+)'
_TESTS = [{
'url': 'https://www.douyu.com/pigff',
'info_dict': {
'id': '24422',
'display_id': 'pigff',
'ext': 'mp4',
'title': 're:^【PIGFF】.* [0-9]{4}-[0-9]{2}-[0-9]{2} [0-9]{2}:[0-9]{2}$',
'description': r'≥15级牌子看鱼吧置顶帖进粉丝vx群',
'thumbnail': str,
'uploader': 'pigff',
'is_live': True,
'live_status': 'is_live',
},
'params': {
'skip_download': True,
},
}, {
'url': 'http://www.douyutv.com/85982',
'info_dict': {
'id': '85982',
'display_id': '85982',
'ext': 'flv',
'title': 're:^小漠从零单排记！——CSOL2躲猫猫 [0-9]{4}-[0-9]{2}-[0-9]{2} [0-9]{2}:[0-9]{2}$',
'description': 'md5:746a2f7a253966a06755a912f0acc0d2',
'thumbnail': r're:^https?://.*\.png',
'uploader': 'douyu小漠',
'is_live': True,
},
'params': {
'skip_download': True,
},
'skip': 'Room not found',
}, {
'url': 'http://www.douyutv.com/17732',
'info_dict': {
'id': '17732',
'display_id': '17732',
'ext': 'flv',
'title': 're:^清晨醒脑！根本停不下来！ [0-9]{4}-[0-9]{2}-[0-9]{2} [0-9]{2}:[0-9]{2}$',
'description': r're:.*m7show@163\.com.*',
'thumbnail': r're:^https?://.*\.png',
'uploader': '7师傅',
'is_live': True,
},
'params': {
'skip_download': True,
},
}, {
'url': 'https://www.douyu.com/topic/ydxc?rid=6560603',
'info_dict': {
'id': '6560603',
'display_id': '6560603',
'ext': 'flv',
'title': 're:^阿余：新年快乐恭喜发财！ [0-9]{4}-[0-9]{2}-[0-9]{2} [0-9]{2}:[0-9]{2}$',
'description': 're:.*直播时间.*',
'thumbnail': r're:^https?://.*\.png',
'uploader': '阿涛皎月Carry',
'live_status': 'is_live',
},
'params': {
'skip_download': True,
},
}, {
'url': 'http://www.douyu.com/xiaocang',
'only_matching': True,
}, {
# \"room_id\"
'url': 'http://www.douyu.com/t/lpl',
'only_matching': True,
}]
def _get_sign_func(self, room_id, video_id):
return self._download_json(
f'https://www.douyu.com/swf_api/homeH5Enc?rids={room_id}', video_id,
note='Getting signing script')['data'][f'room{room_id}']
def _extract_stream_formats(self, stream_formats):
formats = []
for stream_info in traverse_obj(stream_formats, (..., 'data')):
stream_url = urljoin(
traverse_obj(stream_info, 'rtmp_url'), traverse_obj(stream_info, 'rtmp_live'))
if stream_url:
rate_id = traverse_obj(stream_info, ('rate', {int_or_none}))
rate_info = traverse_obj(stream_info, ('multirates', lambda _, v: v['rate'] == rate_id), get_all=False)
ext = determine_ext(stream_url)
formats.append({
'url': stream_url,
'format_id': str_or_none(rate_id),
'ext': 'mp4' if ext == 'm3u8' else ext,
'protocol': 'm3u8_native' if ext == 'm3u8' else 'https',
'quality': rate_id % -10000 if rate_id is not None else None,
**traverse_obj(rate_info, {
'format': ('name', {str_or_none}),
'tbr': ('bit', {int_or_none}),
}),
})
return formats
def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
room_id = self._search_regex(r'\$ROOM\.room_id\s*=\s*(\d+)', webpage, 'room id')
if self._search_regex(r'"videoLoop"\s*:\s*(\d+)', webpage, 'loop', default='') == '1':
raise UserNotLive('The channel is auto-playing VODs', video_id=video_id)
if self._search_regex(r'\$ROOM\.show_status\s*=\s*(\d+)', webpage, 'status', default='') == '2':
raise UserNotLive(video_id=video_id)
# Grab metadata from API
params = {
'aid': 'wp',
'client_sys': 'wp',
'time': int(time.time()),
}
params['auth'] = hashlib.md5(
f'room/{room_id}?{urllib.parse.urlencode(params)}zNzMV1y4EMxOHS6I5WKm'.encode()).hexdigest()
room = traverse_obj(self._download_json(
f'http://www.douyutv.com/api/v1/room/{room_id}', video_id,
note='Downloading room info', query=params, fatal=False), 'data')
# 1 = live, 2 = offline
if traverse_obj(room, 'show_status') == '2':
raise UserNotLive(video_id=video_id)
js_sign_func = self._search_js_sign_func(webpage, fatal=False) or self._get_sign_func(room_id, video_id)
form_data = {
'rate': 0,
**self._calc_sign(js_sign_func, video_id, room_id),
}
stream_formats = [self._download_json(
f'https://www.douyu.com/lapi/live/getH5Play/{room_id}',
video_id, note='Downloading livestream format',
data=urlencode_postdata(form_data))]
for rate_id in traverse_obj(stream_formats[0], ('data', 'multirates', ..., 'rate')):
if rate_id != traverse_obj(stream_formats[0], ('data', 'rate')):
form_data['rate'] = rate_id
stream_formats.append(self._download_json(
f'https://www.douyu.com/lapi/live/getH5Play/{room_id}',
video_id, note=f'Downloading livestream format {rate_id}',
data=urlencode_postdata(form_data)))
return {
'id': room_id,
'formats': self._extract_stream_formats(stream_formats),
'is_live': True,
**traverse_obj(room, {
'display_id': ('url', {str}, {lambda i: i[1:]}),
'title': ('room_name', {unescapeHTML}),
'description': ('show_details', {str}),
'uploader': ('nickname', {str}),
'thumbnail': ('room_src', {url_or_none}),
}),
}
class DouyuShowIE(DouyuBaseIE):
_VALID_URL = r'https?://v(?:mobile)?\.douyu\.com/show/(?P<id>[0-9a-zA-Z]+)'
_TESTS = [{
'url': 'https://v.douyu.com/show/mPyq7oVNe5Yv1gLY',
'info_dict': {
'id': 'mPyq7oVNe5Yv1gLY',
'ext': 'mp4',
'title': '四川人小时候的味道“蒜苗回锅肉”，传统菜不能丢，要常做来吃',
'duration': 633,
'thumbnail': str,
'uploader': '美食作家王刚V',
'uploader_id': 'OVAO4NVx1m7Q',
'timestamp': 1661850002,
'upload_date': '20220830',
'view_count': int,
'tags': ['美食', '美食综合'],
},
}, {
'url': 'https://vmobile.douyu.com/show/rjNBdvnVXNzvE2yw',
'only_matching': True,
}]
_FORMATS = {
'super': '原画',
'high': '超清',
'normal': '高清',
}
_QUALITIES = {
'super': -1,
'high': -2,
'normal': -3,
}
_RESOLUTIONS = {
'super': '1920x1080',
'high': '1280x720',
'normal': '852x480',
}
def _real_extract(self, url):
url = url.replace('vmobile.', 'v.')
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
video_info = self._search_json(
r'<script>\s*window\.\$DATA\s*=', webpage,
'video info', video_id, transform_source=js_to_json)
js_sign_func = self._search_js_sign_func(webpage)
form_data = {
'vid': video_id,
**self._calc_sign(js_sign_func, video_id, video_info['ROOM']['point_id']),
}
url_info = self._download_json(
'https://v.douyu.com/api/stream/getStreamUrl', video_id,
data=urlencode_postdata(form_data), note='Downloading video formats')
formats = []
for name, url in traverse_obj(url_info, ('data', 'thumb_video', {dict.items}, ...)):
video_url = traverse_obj(url, ('url', {url_or_none}))
if video_url:
ext = determine_ext(video_url)
formats.append({
'format': self._FORMATS.get(name),
'format_id': name,
'url': video_url,
'quality': self._QUALITIES.get(name),
'ext': 'mp4' if ext == 'm3u8' else ext,
'protocol': 'm3u8_native' if ext == 'm3u8' else 'https',
**parse_resolution(self._RESOLUTIONS.get(name)),
})
else:
self.to_screen(
f'"{self._FORMATS.get(name, name)}" format may require logging in. {self._login_hint()}')
return {
'id': video_id,
'formats': formats,
**traverse_obj(video_info, ('DATA', {
'title': ('content', 'title', {str}),
'uploader': ('content', 'author', {str}),
'uploader_id': ('content', 'up_id', {str_or_none}),
'duration': ('content', 'video_duration', {int_or_none}),
'thumbnail': ('content', 'video_pic', {url_or_none}),
'timestamp': ('content', 'create_time', {int_or_none}),
'view_count': ('content', 'view_num', {int_or_none}),
'tags': ('videoTag', ..., 'tagName', {str}),
})),
}
| 758317.py | [
"CWE-89: Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')"
] |
import functools
import hashlib
import json
import time
import urllib.parse
from .common import InfoExtractor
from ..utils import (
ExtractorError,
OnDemandPagedList,
int_or_none,
jwt_decode_hs256,
mimetype2ext,
qualities,
traverse_obj,
try_call,
unified_timestamp,
)
class IwaraBaseIE(InfoExtractor):
_NETRC_MACHINE = 'iwara'
_USERTOKEN = None
_MEDIATOKEN = None
def _is_token_expired(self, token, token_type):
# User token TTL == ~3 weeks, Media token TTL == ~1 hour
if (try_call(lambda: jwt_decode_hs256(token)['exp']) or 0) <= int(time.time() - 120):
self.to_screen(f'{token_type} token has expired')
return True
def _get_user_token(self):
username, password = self._get_login_info()
if not username or not password:
return
user_token = IwaraBaseIE._USERTOKEN or self.cache.load(self._NETRC_MACHINE, username)
if not user_token or self._is_token_expired(user_token, 'User'):
response = self._download_json(
'https://api.iwara.tv/user/login', None, note='Logging in',
headers={'Content-Type': 'application/json'}, data=json.dumps({
'email': username,
'password': password,
}).encode(), expected_status=lambda x: True)
user_token = traverse_obj(response, ('token', {str}))
if not user_token:
error = traverse_obj(response, ('message', {str}))
if 'invalidLogin' in error:
raise ExtractorError('Invalid login credentials', expected=True)
else:
raise ExtractorError(f'Iwara API said: {error or "nothing"}')
self.cache.store(self._NETRC_MACHINE, username, user_token)
IwaraBaseIE._USERTOKEN = user_token
def _get_media_token(self):
self._get_user_token()
if not IwaraBaseIE._USERTOKEN:
return # user has not passed credentials
if not IwaraBaseIE._MEDIATOKEN or self._is_token_expired(IwaraBaseIE._MEDIATOKEN, 'Media'):
IwaraBaseIE._MEDIATOKEN = self._download_json(
'https://api.iwara.tv/user/token', None, note='Fetching media token',
data=b'', headers={
'Authorization': f'Bearer {IwaraBaseIE._USERTOKEN}',
'Content-Type': 'application/json',
})['accessToken']
return {'Authorization': f'Bearer {IwaraBaseIE._MEDIATOKEN}'}
def _perform_login(self, username, password):
self._get_media_token()
class IwaraIE(IwaraBaseIE):
IE_NAME = 'iwara'
_VALID_URL = r'https?://(?:www\.|ecchi\.)?iwara\.tv/videos?/(?P<id>[a-zA-Z0-9]+)'
_TESTS = [{
'url': 'https://www.iwara.tv/video/k2ayoueezfkx6gvq',
'info_dict': {
'id': 'k2ayoueezfkx6gvq',
'ext': 'mp4',
'age_limit': 18,
'title': 'Defeat of Irybelda - アイリベルダの敗北',
'description': 'md5:70278abebe706647a8b4cb04cf23e0d3',
'uploader': 'Inwerwm',
'uploader_id': 'inwerwm',
'tags': 'count:1',
'like_count': 6133,
'view_count': 1050343,
'comment_count': 1,
'timestamp': 1677843869,
'modified_timestamp': 1679056362,
},
'skip': 'this video cannot be played because of migration',
}, {
'url': 'https://iwara.tv/video/1ywe1sbkqwumpdxz5/',
'md5': '7645f966f069b8ec9210efd9130c9aad',
'info_dict': {
'id': '1ywe1sbkqwumpdxz5',
'ext': 'mp4',
'age_limit': 18,
'title': 'Aponia アポニア SEX Party Tonight 手の脱衣 巨乳 ',
'description': 'md5:3f60016fff22060eef1ef26d430b1f67',
'uploader': 'Lyu ya',
'uploader_id': 'user792540',
'tags': [
'uncategorized',
],
'like_count': int,
'view_count': int,
'comment_count': int,
'timestamp': 1678732213,
'modified_timestamp': int,
'thumbnail': 'https://files.iwara.tv/image/thumbnail/581d12b5-46f4-4f15-beb2-cfe2cde5d13d/thumbnail-00.jpg',
'modified_date': '20230614',
'upload_date': '20230313',
},
}, {
'url': 'https://iwara.tv/video/blggmfno8ghl725bg',
'info_dict': {
'id': 'blggmfno8ghl725bg',
'ext': 'mp4',
'age_limit': 18,
'title': 'お外でおしっこしちゃう猫耳ロリメイド',
'description': 'md5:0342ba9bf6db09edbbb28729657c3611',
'uploader': 'Fe_Kurosabi',
'uploader_id': 'fekurosabi',
'tags': [
'pee',
],
'like_count': int,
'view_count': int,
'comment_count': int,
'timestamp': 1598880567,
'modified_timestamp': int,
'upload_date': '20200831',
'modified_date': '20230605',
'thumbnail': 'https://files.iwara.tv/image/thumbnail/7693e881-d302-42a4-a780-f16d66b5dadd/thumbnail-00.jpg',
# 'availability': 'needs_auth',
},
}]
def _extract_formats(self, video_id, fileurl):
up = urllib.parse.urlparse(fileurl)
q = urllib.parse.parse_qs(up.query)
paths = up.path.rstrip('/').split('/')
# https://github.com/yt-dlp/yt-dlp/issues/6549#issuecomment-1473771047
x_version = hashlib.sha1('_'.join((paths[-1], q['expires'][0], '5nFp9kmbNnHdAFhaqMvt')).encode()).hexdigest()
preference = qualities(['preview', '360', '540', 'Source'])
files = self._download_json(fileurl, video_id, headers={'X-Version': x_version})
for fmt in files:
yield traverse_obj(fmt, {
'format_id': 'name',
'url': ('src', ('view', 'download'), {self._proto_relative_url}),
'ext': ('type', {mimetype2ext}),
'quality': ('name', {preference}),
'height': ('name', {int_or_none}),
}, get_all=False)
def _real_extract(self, url):
video_id = self._match_id(url)
username, _ = self._get_login_info()
video_data = self._download_json(
f'https://api.iwara.tv/video/{video_id}', video_id,
expected_status=lambda x: True, headers=self._get_media_token())
errmsg = video_data.get('message')
# at this point we can actually get uploaded user info, but do we need it?
if errmsg == 'errors.privateVideo':
self.raise_login_required('Private video. Login if you have permissions to watch', method='password')
elif errmsg == 'errors.notFound' and not username:
self.raise_login_required('Video may need login to view', method='password')
elif errmsg: # None if success
raise ExtractorError(f'Iwara says: {errmsg}')
if not video_data.get('fileUrl'):
if video_data.get('embedUrl'):
return self.url_result(video_data.get('embedUrl'))
raise ExtractorError('This video is unplayable', expected=True)
return {
'id': video_id,
'age_limit': 18 if video_data.get('rating') == 'ecchi' else 0, # ecchi is 'sexy' in Japanese
**traverse_obj(video_data, {
'title': 'title',
'description': 'body',
'uploader': ('user', 'name'),
'uploader_id': ('user', 'username'),
'tags': ('tags', ..., 'id'),
'like_count': 'numLikes',
'view_count': 'numViews',
'comment_count': 'numComments',
'timestamp': ('createdAt', {unified_timestamp}),
'modified_timestamp': ('updatedAt', {unified_timestamp}),
'thumbnail': ('file', 'id', {str}, {
lambda x: f'https://files.iwara.tv/image/thumbnail/{x}/thumbnail-00.jpg'}),
}),
'formats': list(self._extract_formats(video_id, video_data.get('fileUrl'))),
}
class IwaraUserIE(IwaraBaseIE):
_VALID_URL = r'https?://(?:www\.)?iwara\.tv/profile/(?P<id>[^/?#&]+)'
IE_NAME = 'iwara:user'
_PER_PAGE = 32
_TESTS = [{
'url': 'https://iwara.tv/profile/user792540/videos',
'info_dict': {
'id': 'user792540',
'title': 'Lyu ya',
},
'playlist_mincount': 70,
}, {
'url': 'https://iwara.tv/profile/theblackbirdcalls/videos',
'info_dict': {
'id': 'theblackbirdcalls',
'title': 'TheBlackbirdCalls',
},
'playlist_mincount': 723,
}, {
'url': 'https://iwara.tv/profile/user792540',
'only_matching': True,
}, {
'url': 'https://iwara.tv/profile/theblackbirdcalls',
'only_matching': True,
}, {
'url': 'https://www.iwara.tv/profile/lumymmd',
'info_dict': {
'id': 'lumymmd',
'title': 'Lumy MMD',
},
'playlist_mincount': 1,
}]
def _entries(self, playlist_id, user_id, page):
videos = self._download_json(
'https://api.iwara.tv/videos', playlist_id,
note=f'Downloading page {page}',
query={
'page': page,
'sort': 'date',
'user': user_id,
'limit': self._PER_PAGE,
}, headers=self._get_media_token())
for x in traverse_obj(videos, ('results', ..., 'id')):
yield self.url_result(f'https://iwara.tv/video/{x}')
def _real_extract(self, url):
playlist_id = self._match_id(url)
user_info = self._download_json(
f'https://api.iwara.tv/profile/{playlist_id}', playlist_id,
note='Requesting user info')
user_id = traverse_obj(user_info, ('user', 'id'))
return self.playlist_result(
OnDemandPagedList(
functools.partial(self._entries, playlist_id, user_id),
self._PER_PAGE),
playlist_id, traverse_obj(user_info, ('user', 'name')))
class IwaraPlaylistIE(IwaraBaseIE):
_VALID_URL = r'https?://(?:www\.)?iwara\.tv/playlist/(?P<id>[0-9a-f-]+)'
IE_NAME = 'iwara:playlist'
_PER_PAGE = 32
_TESTS = [{
'url': 'https://iwara.tv/playlist/458e5486-36a4-4ac0-b233-7e9eef01025f',
'info_dict': {
'id': '458e5486-36a4-4ac0-b233-7e9eef01025f',
},
'playlist_mincount': 3,
}]
def _entries(self, playlist_id, first_page, page):
videos = self._download_json(
'https://api.iwara.tv/videos', playlist_id, f'Downloading page {page}',
query={'page': page, 'limit': self._PER_PAGE},
headers=self._get_media_token()) if page else first_page
for x in traverse_obj(videos, ('results', ..., 'id')):
yield self.url_result(f'https://iwara.tv/video/{x}')
def _real_extract(self, url):
playlist_id = self._match_id(url)
page_0 = self._download_json(
f'https://api.iwara.tv/playlist/{playlist_id}?page=0&limit={self._PER_PAGE}', playlist_id,
note='Requesting playlist info', headers=self._get_media_token())
return self.playlist_result(
OnDemandPagedList(
functools.partial(self._entries, playlist_id, page_0),
self._PER_PAGE),
playlist_id, traverse_obj(page_0, ('title', 'name')))
| 837764.py | [
"CWE-327: Use of a Broken or Risky Cryptographic Algorithm"
] |
import hashlib
import random
from .common import InfoExtractor
from ..utils import (
clean_html,
int_or_none,
try_get,
)
class JamendoIE(InfoExtractor):
_VALID_URL = r'''(?x)
https?://
(?:
licensing\.jamendo\.com/[^/]+|
(?:www\.)?jamendo\.com
)
/track/(?P<id>[0-9]+)(?:/(?P<display_id>[^/?#&]+))?
'''
_TESTS = [{
'url': 'https://www.jamendo.com/track/196219/stories-from-emona-i',
'md5': '6e9e82ed6db98678f171c25a8ed09ffd',
'info_dict': {
'id': '196219',
'display_id': 'stories-from-emona-i',
'ext': 'flac',
# 'title': 'Maya Filipič - Stories from Emona I',
'title': 'Stories from Emona I',
'artist': 'Maya Filipič',
'album': 'Between two worlds',
'track': 'Stories from Emona I',
'duration': 210,
'thumbnail': 'https://usercontent.jamendo.com?type=album&id=29279&width=300&trackid=196219',
'timestamp': 1217438117,
'upload_date': '20080730',
'license': 'by-nc-nd',
'view_count': int,
'like_count': int,
'average_rating': int,
'tags': ['piano', 'peaceful', 'newage', 'strings', 'upbeat'],
},
}, {
'url': 'https://licensing.jamendo.com/en/track/1496667/energetic-rock',
'only_matching': True,
}]
def _call_api(self, resource, resource_id, fatal=True):
path = f'/api/{resource}s'
rand = str(random.random())
return self._download_json(
'https://www.jamendo.com' + path, resource_id, fatal=fatal, query={
'id[]': resource_id,
}, headers={
'X-Jam-Call': f'${hashlib.sha1((path + rand).encode()).hexdigest()}*{rand}~',
})[0]
def _real_extract(self, url):
track_id, display_id = self._match_valid_url(url).groups()
# webpage = self._download_webpage(
# 'https://www.jamendo.com/track/' + track_id, track_id)
# models = self._parse_json(self._html_search_regex(
# r"data-bundled-models='([^']+)",
# webpage, 'bundled models'), track_id)
# track = models['track']['models'][0]
track = self._call_api('track', track_id)
title = track_name = track['name']
# get_model = lambda x: try_get(models, lambda y: y[x]['models'][0], dict) or {}
# artist = get_model('artist')
# artist_name = artist.get('name')
# if artist_name:
# title = '%s - %s' % (artist_name, title)
# album = get_model('album')
artist = self._call_api('artist', track.get('artistId'), fatal=False)
album = self._call_api('album', track.get('albumId'), fatal=False)
formats = [{
'url': f'https://{sub_domain}.jamendo.com/?trackid={track_id}&format={format_id}&from=app-97dab294',
'format_id': format_id,
'ext': ext,
'quality': quality,
} for quality, (format_id, sub_domain, ext) in enumerate((
('mp31', 'mp3l', 'mp3'),
('mp32', 'mp3d', 'mp3'),
('ogg1', 'ogg', 'ogg'),
('flac', 'flac', 'flac'),
))]
urls = []
thumbnails = []
for covers in (track.get('cover') or {}).values():
for cover_id, cover_url in covers.items():
if not cover_url or cover_url in urls:
continue
urls.append(cover_url)
size = int_or_none(cover_id.lstrip('size'))
thumbnails.append({
'id': cover_id,
'url': cover_url,
'width': size,
'height': size,
})
tags = []
for tag in (track.get('tags') or []):
tag_name = tag.get('name')
if not tag_name:
continue
tags.append(tag_name)
stats = track.get('stats') or {}
video_license = track.get('licenseCC') or []
return {
'id': track_id,
'display_id': display_id,
'thumbnails': thumbnails,
'title': title,
'description': track.get('description'),
'duration': int_or_none(track.get('duration')),
'artist': artist.get('name'),
'track': track_name,
'album': album.get('name'),
'formats': formats,
'license': '-'.join(video_license) if video_license else None,
'timestamp': int_or_none(track.get('dateCreated')),
'view_count': int_or_none(stats.get('listenedAll')),
'like_count': int_or_none(stats.get('favorited')),
'average_rating': int_or_none(stats.get('averageNote')),
'tags': tags,
}
class JamendoAlbumIE(JamendoIE): # XXX: Do not subclass from concrete IE
_VALID_URL = r'https?://(?:www\.)?jamendo\.com/album/(?P<id>[0-9]+)'
_TESTS = [{
'url': 'https://www.jamendo.com/album/121486/duck-on-cover',
'info_dict': {
'id': '121486',
'title': 'Duck On Cover',
'description': 'md5:c2920eaeef07d7af5b96d7c64daf1239',
},
'playlist': [{
'md5': 'e1a2fcb42bda30dfac990212924149a8',
'info_dict': {
'id': '1032333',
'ext': 'flac',
'title': 'Warmachine',
'artist': 'Shearer',
'track': 'Warmachine',
'timestamp': 1368089771,
'upload_date': '20130509',
'view_count': int,
'thumbnail': 'https://usercontent.jamendo.com?type=album&id=121486&width=300&trackid=1032333',
'duration': 190,
'license': 'by',
'album': 'Duck On Cover',
'average_rating': 4,
'tags': ['rock', 'drums', 'bass', 'world', 'punk', 'neutral'],
'like_count': int,
},
}, {
'md5': '1f358d7b2f98edfe90fd55dac0799d50',
'info_dict': {
'id': '1032330',
'ext': 'flac',
'title': 'Without Your Ghost',
'artist': 'Shearer',
'track': 'Without Your Ghost',
'timestamp': 1368089771,
'upload_date': '20130509',
'duration': 192,
'tags': ['rock', 'drums', 'bass', 'world', 'punk'],
'album': 'Duck On Cover',
'thumbnail': 'https://usercontent.jamendo.com?type=album&id=121486&width=300&trackid=1032330',
'view_count': int,
'average_rating': 4,
'license': 'by',
'like_count': int,
},
}],
'params': {
'playlistend': 2,
},
}]
def _real_extract(self, url):
album_id = self._match_id(url)
album = self._call_api('album', album_id)
album_name = album.get('name')
entries = []
for track in (album.get('tracks') or []):
track_id = track.get('id')
if not track_id:
continue
track_id = str(track_id)
entries.append({
'_type': 'url_transparent',
'url': 'https://www.jamendo.com/track/' + track_id,
'ie_key': JamendoIE.ie_key(),
'id': track_id,
'album': album_name,
})
return self.playlist_result(
entries, album_id, album_name,
clean_html(try_get(album, lambda x: x['description']['en'], str)))
| 530858.py | [
"CWE-327: Use of a Broken or Risky Cryptographic Algorithm"
] |
"""
Settings and configuration for Django.
Read values from the module specified by the DJANGO_SETTINGS_MODULE environment
variable, and then from django.conf.global_settings; see the global_settings.py
for a list of all possible variables.
"""
import importlib
import os
import time
import traceback
import warnings
from pathlib import Path
import django
from django.conf import global_settings
from django.core.exceptions import ImproperlyConfigured
from django.utils.deprecation import RemovedInDjango60Warning
from django.utils.functional import LazyObject, empty
ENVIRONMENT_VARIABLE = "DJANGO_SETTINGS_MODULE"
DEFAULT_STORAGE_ALIAS = "default"
STATICFILES_STORAGE_ALIAS = "staticfiles"
# RemovedInDjango60Warning.
FORMS_URLFIELD_ASSUME_HTTPS_DEPRECATED_MSG = (
"The FORMS_URLFIELD_ASSUME_HTTPS transitional setting is deprecated."
)
class SettingsReference(str):
"""
String subclass which references a current settings value. It's treated as
the value in memory but serializes to a settings.NAME attribute reference.
"""
def __new__(self, value, setting_name):
return str.__new__(self, value)
def __init__(self, value, setting_name):
self.setting_name = setting_name
class LazySettings(LazyObject):
"""
A lazy proxy for either global Django settings or a custom settings object.
The user can manually configure settings prior to using them. Otherwise,
Django uses the settings module pointed to by DJANGO_SETTINGS_MODULE.
"""
def _setup(self, name=None):
"""
Load the settings module pointed to by the environment variable. This
is used the first time settings are needed, if the user hasn't
configured settings manually.
"""
settings_module = os.environ.get(ENVIRONMENT_VARIABLE)
if not settings_module:
desc = ("setting %s" % name) if name else "settings"
raise ImproperlyConfigured(
"Requested %s, but settings are not configured. "
"You must either define the environment variable %s "
"or call settings.configure() before accessing settings."
% (desc, ENVIRONMENT_VARIABLE)
)
self._wrapped = Settings(settings_module)
def __repr__(self):
# Hardcode the class name as otherwise it yields 'Settings'.
if self._wrapped is empty:
return "<LazySettings [Unevaluated]>"
return '<LazySettings "%(settings_module)s">' % {
"settings_module": self._wrapped.SETTINGS_MODULE,
}
def __getattr__(self, name):
"""Return the value of a setting and cache it in self.__dict__."""
if (_wrapped := self._wrapped) is empty:
self._setup(name)
_wrapped = self._wrapped
val = getattr(_wrapped, name)
# Special case some settings which require further modification.
# This is done here for performance reasons so the modified value is cached.
if name in {"MEDIA_URL", "STATIC_URL"} and val is not None:
val = self._add_script_prefix(val)
elif name == "SECRET_KEY" and not val:
raise ImproperlyConfigured("The SECRET_KEY setting must not be empty.")
self.__dict__[name] = val
return val
def __setattr__(self, name, value):
"""
Set the value of setting. Clear all cached values if _wrapped changes
(@override_settings does this) or clear single values when set.
"""
if name == "_wrapped":
self.__dict__.clear()
else:
self.__dict__.pop(name, None)
super().__setattr__(name, value)
def __delattr__(self, name):
"""Delete a setting and clear it from cache if needed."""
super().__delattr__(name)
self.__dict__.pop(name, None)
def configure(self, default_settings=global_settings, **options):
"""
Called to manually configure the settings. The 'default_settings'
parameter sets where to retrieve any unspecified values from (its
argument must support attribute access (__getattr__)).
"""
if self._wrapped is not empty:
raise RuntimeError("Settings already configured.")
holder = UserSettingsHolder(default_settings)
for name, value in options.items():
if not name.isupper():
raise TypeError("Setting %r must be uppercase." % name)
setattr(holder, name, value)
self._wrapped = holder
@staticmethod
def _add_script_prefix(value):
"""
Add SCRIPT_NAME prefix to relative paths.
Useful when the app is being served at a subpath and manually prefixing
subpath to STATIC_URL and MEDIA_URL in settings is inconvenient.
"""
# Don't apply prefix to absolute paths and URLs.
if value.startswith(("http://", "https://", "/")):
return value
from django.urls import get_script_prefix
return "%s%s" % (get_script_prefix(), value)
@property
def configured(self):
"""Return True if the settings have already been configured."""
return self._wrapped is not empty
def _show_deprecation_warning(self, message, category):
stack = traceback.extract_stack()
# Show a warning if the setting is used outside of Django.
# Stack index: -1 this line, -2 the property, -3 the
# LazyObject __getattribute__(), -4 the caller.
filename, _, _, _ = stack[-4]
if not filename.startswith(os.path.dirname(django.__file__)):
warnings.warn(message, category, stacklevel=2)
class Settings:
def __init__(self, settings_module):
# update this dict from global settings (but only for ALL_CAPS settings)
for setting in dir(global_settings):
if setting.isupper():
setattr(self, setting, getattr(global_settings, setting))
# store the settings module in case someone later cares
self.SETTINGS_MODULE = settings_module
mod = importlib.import_module(self.SETTINGS_MODULE)
tuple_settings = (
"ALLOWED_HOSTS",
"INSTALLED_APPS",
"TEMPLATE_DIRS",
"LOCALE_PATHS",
"SECRET_KEY_FALLBACKS",
)
self._explicit_settings = set()
for setting in dir(mod):
if setting.isupper():
setting_value = getattr(mod, setting)
if setting in tuple_settings and not isinstance(
setting_value, (list, tuple)
):
raise ImproperlyConfigured(
"The %s setting must be a list or a tuple." % setting
)
setattr(self, setting, setting_value)
self._explicit_settings.add(setting)
if self.is_overridden("FORMS_URLFIELD_ASSUME_HTTPS"):
warnings.warn(
FORMS_URLFIELD_ASSUME_HTTPS_DEPRECATED_MSG,
RemovedInDjango60Warning,
)
if hasattr(time, "tzset") and self.TIME_ZONE:
# When we can, attempt to validate the timezone. If we can't find
# this file, no check happens and it's harmless.
zoneinfo_root = Path("/usr/share/zoneinfo")
zone_info_file = zoneinfo_root.joinpath(*self.TIME_ZONE.split("/"))
if zoneinfo_root.exists() and not zone_info_file.exists():
raise ValueError("Incorrect timezone setting: %s" % self.TIME_ZONE)
# Move the time zone info into os.environ. See ticket #2315 for why
# we don't do this unconditionally (breaks Windows).
os.environ["TZ"] = self.TIME_ZONE
time.tzset()
def is_overridden(self, setting):
return setting in self._explicit_settings
def __repr__(self):
return '<%(cls)s "%(settings_module)s">' % {
"cls": self.__class__.__name__,
"settings_module": self.SETTINGS_MODULE,
}
class UserSettingsHolder:
"""Holder for user configured settings."""
# SETTINGS_MODULE doesn't make much sense in the manually configured
# (standalone) case.
SETTINGS_MODULE = None
def __init__(self, default_settings):
"""
Requests for configuration variables not in this class are satisfied
from the module specified in default_settings (if possible).
"""
self.__dict__["_deleted"] = set()
self.default_settings = default_settings
def __getattr__(self, name):
if not name.isupper() or name in self._deleted:
raise AttributeError
return getattr(self.default_settings, name)
def __setattr__(self, name, value):
self._deleted.discard(name)
if name == "FORMS_URLFIELD_ASSUME_HTTPS":
warnings.warn(
FORMS_URLFIELD_ASSUME_HTTPS_DEPRECATED_MSG,
RemovedInDjango60Warning,
)
super().__setattr__(name, value)
def __delattr__(self, name):
self._deleted.add(name)
if hasattr(self, name):
super().__delattr__(name)
def __dir__(self):
return sorted(
s
for s in [*self.__dict__, *dir(self.default_settings)]
if s not in self._deleted
)
def is_overridden(self, setting):
deleted = setting in self._deleted
set_locally = setting in self.__dict__
set_on_default = getattr(
self.default_settings, "is_overridden", lambda s: False
)(setting)
return deleted or set_locally or set_on_default
def __repr__(self):
return "<%(cls)s>" % {
"cls": self.__class__.__name__,
}
settings = LazySettings()
| 359100.py | [
"CWE-706: Use of Incorrectly-Resolved Name or Reference"
] |
"Misc. utility functions/classes for admin documentation generator."
import re
from email.errors import HeaderParseError
from email.parser import HeaderParser
from inspect import cleandoc
from django.urls import reverse
from django.utils.regex_helper import _lazy_re_compile
from django.utils.safestring import mark_safe
try:
import docutils.core
import docutils.nodes
import docutils.parsers.rst.roles
except ImportError:
docutils_is_available = False
else:
docutils_is_available = True
def get_view_name(view_func):
if hasattr(view_func, "view_class"):
klass = view_func.view_class
return f"{klass.__module__}.{klass.__qualname__}"
mod_name = view_func.__module__
view_name = getattr(view_func, "__qualname__", view_func.__class__.__name__)
return mod_name + "." + view_name
def parse_docstring(docstring):
"""
Parse out the parts of a docstring. Return (title, body, metadata).
"""
if not docstring:
return "", "", {}
docstring = cleandoc(docstring)
parts = re.split(r"\n{2,}", docstring)
title = parts[0]
if len(parts) == 1:
body = ""
metadata = {}
else:
parser = HeaderParser()
try:
metadata = parser.parsestr(parts[-1])
except HeaderParseError:
metadata = {}
body = "\n\n".join(parts[1:])
else:
metadata = dict(metadata.items())
if metadata:
body = "\n\n".join(parts[1:-1])
else:
body = "\n\n".join(parts[1:])
return title, body, metadata
def parse_rst(text, default_reference_context, thing_being_parsed=None):
"""
Convert the string from reST to an XHTML fragment.
"""
overrides = {
"doctitle_xform": True,
"initial_header_level": 3,
"default_reference_context": default_reference_context,
"link_base": reverse("django-admindocs-docroot").rstrip("/"),
"raw_enabled": False,
"file_insertion_enabled": False,
}
thing_being_parsed = thing_being_parsed and "<%s>" % thing_being_parsed
# Wrap ``text`` in some reST that sets the default role to ``cmsreference``,
# then restores it.
source = """
.. default-role:: cmsreference
%s
.. default-role::
"""
parts = docutils.core.publish_parts(
source % text,
source_path=thing_being_parsed,
destination_path=None,
writer_name="html",
settings_overrides=overrides,
)
return mark_safe(parts["fragment"])
#
# reST roles
#
ROLES = {
"model": "%s/models/%s/",
"view": "%s/views/%s/",
"template": "%s/templates/%s/",
"filter": "%s/filters/#%s",
"tag": "%s/tags/#%s",
}
def create_reference_role(rolename, urlbase):
# Views and template names are case-sensitive.
is_case_sensitive = rolename in ["template", "view"]
def _role(name, rawtext, text, lineno, inliner, options=None, content=None):
if options is None:
options = {}
node = docutils.nodes.reference(
rawtext,
text,
refuri=(
urlbase
% (
inliner.document.settings.link_base,
text if is_case_sensitive else text.lower(),
)
),
**options,
)
return [node], []
docutils.parsers.rst.roles.register_canonical_role(rolename, _role)
def default_reference_role(
name, rawtext, text, lineno, inliner, options=None, content=None
):
if options is None:
options = {}
context = inliner.document.settings.default_reference_context
node = docutils.nodes.reference(
rawtext,
text,
refuri=(
ROLES[context]
% (
inliner.document.settings.link_base,
text.lower(),
)
),
**options,
)
return [node], []
if docutils_is_available:
docutils.parsers.rst.roles.register_canonical_role(
"cmsreference", default_reference_role
)
for name, urlbase in ROLES.items():
create_reference_role(name, urlbase)
# Match the beginning of a named, unnamed, or non-capturing groups.
named_group_matcher = _lazy_re_compile(r"\(\?P(<\w+>)")
unnamed_group_matcher = _lazy_re_compile(r"\(")
non_capturing_group_matcher = _lazy_re_compile(r"\(\?\:")
def replace_metacharacters(pattern):
"""Remove unescaped metacharacters from the pattern."""
return re.sub(
r"((?:^|(?<!\\))(?:\\\\)*)(\\?)([?*+^$]|\\[bBAZ])",
lambda m: m[1] + m[3] if m[2] else m[1],
pattern,
)
def _get_group_start_end(start, end, pattern):
# Handle nested parentheses, e.g. '^(?P<a>(x|y))/b' or '^b/((x|y)\w+)$'.
unmatched_open_brackets, prev_char = 1, None
for idx, val in enumerate(pattern[end:]):
# Check for unescaped `(` and `)`. They mark the start and end of a
# nested group.
if val == "(" and prev_char != "\\":
unmatched_open_brackets += 1
elif val == ")" and prev_char != "\\":
unmatched_open_brackets -= 1
prev_char = val
# If brackets are balanced, the end of the string for the current named
# capture group pattern has been reached.
if unmatched_open_brackets == 0:
return start, end + idx + 1
def _find_groups(pattern, group_matcher):
prev_end = None
for match in group_matcher.finditer(pattern):
if indices := _get_group_start_end(match.start(0), match.end(0), pattern):
start, end = indices
if prev_end and start > prev_end or not prev_end:
yield start, end, match
prev_end = end
def replace_named_groups(pattern):
r"""
Find named groups in `pattern` and replace them with the group name. E.g.,
1. ^(?P<a>\w+)/b/(\w+)$ ==> ^<a>/b/(\w+)$
2. ^(?P<a>\w+)/b/(?P<c>\w+)/$ ==> ^<a>/b/<c>/$
3. ^(?P<a>\w+)/b/(\w+) ==> ^<a>/b/(\w+)
4. ^(?P<a>\w+)/b/(?P<c>\w+) ==> ^<a>/b/<c>
"""
group_pattern_and_name = [
(pattern[start:end], match[1])
for start, end, match in _find_groups(pattern, named_group_matcher)
]
for group_pattern, group_name in group_pattern_and_name:
pattern = pattern.replace(group_pattern, group_name)
return pattern
def replace_unnamed_groups(pattern):
r"""
Find unnamed groups in `pattern` and replace them with '<var>'. E.g.,
1. ^(?P<a>\w+)/b/(\w+)$ ==> ^(?P<a>\w+)/b/<var>$
2. ^(?P<a>\w+)/b/((x|y)\w+)$ ==> ^(?P<a>\w+)/b/<var>$
3. ^(?P<a>\w+)/b/(\w+) ==> ^(?P<a>\w+)/b/<var>
4. ^(?P<a>\w+)/b/((x|y)\w+) ==> ^(?P<a>\w+)/b/<var>
"""
final_pattern, prev_end = "", None
for start, end, _ in _find_groups(pattern, unnamed_group_matcher):
if prev_end:
final_pattern += pattern[prev_end:start]
final_pattern += pattern[:start] + "<var>"
prev_end = end
return final_pattern + pattern[prev_end:]
def remove_non_capturing_groups(pattern):
r"""
Find non-capturing groups in the given `pattern` and remove them, e.g.
1. (?P<a>\w+)/b/(?:\w+)c(?:\w+) => (?P<a>\\w+)/b/c
2. ^(?:\w+(?:\w+))a => ^a
3. ^a(?:\w+)/b(?:\w+) => ^a/b
"""
group_start_end_indices = _find_groups(pattern, non_capturing_group_matcher)
final_pattern, prev_end = "", None
for start, end, _ in group_start_end_indices:
final_pattern += pattern[prev_end:start]
prev_end = end
return final_pattern + pattern[prev_end:]
| 429723.py | [
"CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')"
] |
"""
This module contains the spatial lookup types, and the `get_geo_where_clause`
routine for Oracle Spatial.
Please note that WKT support is broken on the XE version, and thus
this backend will not work on such platforms. Specifically, XE lacks
support for an internal JVM, and Java libraries are required to use
the WKT constructors.
"""
import re
from django.contrib.gis.db import models
from django.contrib.gis.db.backends.base.operations import BaseSpatialOperations
from django.contrib.gis.db.backends.oracle.adapter import OracleSpatialAdapter
from django.contrib.gis.db.backends.utils import SpatialOperator
from django.contrib.gis.geos.geometry import GEOSGeometry, GEOSGeometryBase
from django.contrib.gis.geos.prototypes.io import wkb_r
from django.contrib.gis.measure import Distance
from django.db.backends.oracle.operations import DatabaseOperations
DEFAULT_TOLERANCE = "0.05"
class SDOOperator(SpatialOperator):
sql_template = "%(func)s(%(lhs)s, %(rhs)s) = 'TRUE'"
class SDODWithin(SpatialOperator):
sql_template = "SDO_WITHIN_DISTANCE(%(lhs)s, %(rhs)s, %%s) = 'TRUE'"
class SDODisjoint(SpatialOperator):
sql_template = (
"SDO_GEOM.RELATE(%%(lhs)s, 'DISJOINT', %%(rhs)s, %s) = 'DISJOINT'"
% DEFAULT_TOLERANCE
)
class SDORelate(SpatialOperator):
sql_template = "SDO_RELATE(%(lhs)s, %(rhs)s, 'mask=%(mask)s') = 'TRUE'"
def check_relate_argument(self, arg):
masks = (
"TOUCH|OVERLAPBDYDISJOINT|OVERLAPBDYINTERSECT|EQUAL|INSIDE|COVEREDBY|"
"CONTAINS|COVERS|ANYINTERACT|ON"
)
mask_regex = re.compile(r"^(%s)(\+(%s))*$" % (masks, masks), re.I)
if not isinstance(arg, str) or not mask_regex.match(arg):
raise ValueError('Invalid SDO_RELATE mask: "%s"' % arg)
def as_sql(self, connection, lookup, template_params, sql_params):
template_params["mask"] = sql_params[-1]
return super().as_sql(connection, lookup, template_params, sql_params[:-1])
class OracleOperations(BaseSpatialOperations, DatabaseOperations):
name = "oracle"
oracle = True
disallowed_aggregates = (models.Collect, models.Extent3D, models.MakeLine)
Adapter = OracleSpatialAdapter
extent = "SDO_AGGR_MBR"
unionagg = "SDO_AGGR_UNION"
from_text = "SDO_GEOMETRY"
function_names = {
"Area": "SDO_GEOM.SDO_AREA",
"AsGeoJSON": "SDO_UTIL.TO_GEOJSON",
"AsWKB": "SDO_UTIL.TO_WKBGEOMETRY",
"AsWKT": "SDO_UTIL.TO_WKTGEOMETRY",
"BoundingCircle": "SDO_GEOM.SDO_MBC",
"Centroid": "SDO_GEOM.SDO_CENTROID",
"Difference": "SDO_GEOM.SDO_DIFFERENCE",
"Distance": "SDO_GEOM.SDO_DISTANCE",
"Envelope": "SDO_GEOM_MBR",
"FromWKB": "SDO_UTIL.FROM_WKBGEOMETRY",
"FromWKT": "SDO_UTIL.FROM_WKTGEOMETRY",
"Intersection": "SDO_GEOM.SDO_INTERSECTION",
"IsValid": "SDO_GEOM.VALIDATE_GEOMETRY_WITH_CONTEXT",
"Length": "SDO_GEOM.SDO_LENGTH",
"NumGeometries": "SDO_UTIL.GETNUMELEM",
"NumPoints": "SDO_UTIL.GETNUMVERTICES",
"Perimeter": "SDO_GEOM.SDO_LENGTH",
"PointOnSurface": "SDO_GEOM.SDO_POINTONSURFACE",
"Reverse": "SDO_UTIL.REVERSE_LINESTRING",
"SymDifference": "SDO_GEOM.SDO_XOR",
"Transform": "SDO_CS.TRANSFORM",
"Union": "SDO_GEOM.SDO_UNION",
}
# We want to get SDO Geometries as WKT because it is much easier to
# instantiate GEOS proxies from WKT than SDO_GEOMETRY(...) strings.
# However, this adversely affects performance (i.e., Java is called
# to convert to WKT on every query). If someone wishes to write a
# SDO_GEOMETRY(...) parser in Python, let me know =)
select = "SDO_UTIL.TO_WKBGEOMETRY(%s)"
gis_operators = {
"contains": SDOOperator(func="SDO_CONTAINS"),
"coveredby": SDOOperator(func="SDO_COVEREDBY"),
"covers": SDOOperator(func="SDO_COVERS"),
"disjoint": SDODisjoint(),
"intersects": SDOOperator(
func="SDO_OVERLAPBDYINTERSECT"
), # TODO: Is this really the same as ST_Intersects()?
"equals": SDOOperator(func="SDO_EQUAL"),
"exact": SDOOperator(func="SDO_EQUAL"),
"overlaps": SDOOperator(func="SDO_OVERLAPS"),
"same_as": SDOOperator(func="SDO_EQUAL"),
# Oracle uses a different syntax, e.g., 'mask=inside+touch'
"relate": SDORelate(),
"touches": SDOOperator(func="SDO_TOUCH"),
"within": SDOOperator(func="SDO_INSIDE"),
"dwithin": SDODWithin(),
}
unsupported_functions = {
"AsKML",
"AsSVG",
"Azimuth",
"ClosestPoint",
"ForcePolygonCW",
"GeoHash",
"GeometryDistance",
"IsEmpty",
"LineLocatePoint",
"MakeValid",
"MemSize",
"Scale",
"SnapToGrid",
"Translate",
}
def geo_quote_name(self, name):
return super().geo_quote_name(name).upper()
def convert_extent(self, clob):
if clob:
# Generally, Oracle returns a polygon for the extent -- however,
# it can return a single point if there's only one Point in the
# table.
ext_geom = GEOSGeometry(memoryview(clob.read()))
gtype = str(ext_geom.geom_type)
if gtype == "Polygon":
# Construct the 4-tuple from the coordinates in the polygon.
shell = ext_geom.shell
ll, ur = shell[0][:2], shell[2][:2]
elif gtype == "Point":
ll = ext_geom.coords[:2]
ur = ll
else:
raise Exception(
"Unexpected geometry type returned for extent: %s" % gtype
)
xmin, ymin = ll
xmax, ymax = ur
return (xmin, ymin, xmax, ymax)
else:
return None
def geo_db_type(self, f):
"""
Return the geometry database type for Oracle. Unlike other spatial
backends, no stored procedure is necessary and it's the same for all
geometry types.
"""
return "MDSYS.SDO_GEOMETRY"
def get_distance(self, f, value, lookup_type):
"""
Return the distance parameters given the value and the lookup type.
On Oracle, geometry columns with a geodetic coordinate system behave
implicitly like a geography column, and thus meters will be used as
the distance parameter on them.
"""
if not value:
return []
value = value[0]
if isinstance(value, Distance):
if f.geodetic(self.connection):
dist_param = value.m
else:
dist_param = getattr(
value, Distance.unit_attname(f.units_name(self.connection))
)
else:
dist_param = value
# dwithin lookups on Oracle require a special string parameter
# that starts with "distance=".
if lookup_type == "dwithin":
dist_param = "distance=%s" % dist_param
return [dist_param]
def get_geom_placeholder(self, f, value, compiler):
if value is None:
return "NULL"
return super().get_geom_placeholder(f, value, compiler)
def spatial_aggregate_name(self, agg_name):
"""
Return the spatial aggregate SQL name.
"""
agg_name = "unionagg" if agg_name.lower() == "union" else agg_name.lower()
return getattr(self, agg_name)
# Routines for getting the OGC-compliant models.
def geometry_columns(self):
from django.contrib.gis.db.backends.oracle.models import OracleGeometryColumns
return OracleGeometryColumns
def spatial_ref_sys(self):
from django.contrib.gis.db.backends.oracle.models import OracleSpatialRefSys
return OracleSpatialRefSys
def modify_insert_params(self, placeholder, params):
"""Drop out insert parameters for NULL placeholder. Needed for Oracle Spatial
backend due to #10888.
"""
if placeholder == "NULL":
return []
return super().modify_insert_params(placeholder, params)
def get_geometry_converter(self, expression):
read = wkb_r().read
srid = expression.output_field.srid
if srid == -1:
srid = None
geom_class = expression.output_field.geom_class
def converter(value, expression, connection):
if value is not None:
geom = GEOSGeometryBase(read(memoryview(value.read())), geom_class)
if srid:
geom.srid = srid
return geom
return converter
def get_area_att_for_field(self, field):
return "sq_m"
| 783587.py | [
"CWE-89: Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')"
] |
End of preview. Expand
in Dataset Viewer.
SOTA fine-tuning by OpenAI
OpenAI used the synth-vuln-fixes and fine-tuned a new version of gpt-4o is now the SOTA on this benchmark. More details and code is available from their repo.
More details on the benchmark are available in our blog.
New Version of Static Analysis Eval (Aug 20, 2024)
We have created a new version of the benchmark with instances that are harder than the previous one. There has been a lot of progress in models over the last year as a result the previous version of the benchmark was saturated. The methodology is the same, we have also released the dataset generation script which scans the top 100 Python projects to generate the instances. You can see it here. The same eval script works as before. You do not need to login to Semgrep anymore as we only use their OSS rules for this version of the benchmark.
The highest score a model can get on this benchmark is 100%, you can see the oracle run logs here.
New Evaluation
| Model | Score | Logs |
|---|---|---|
| o1-mini-2024-09-12 | 51.33 | link |
| gpt-4o-mini | 52.21 | link |
| gpt-4o-mini + 3-shot prompt | 53.10 | link |
| gpt-4o-mini + rag (embedding & reranking) | 58.41 | link |
| gpt-4o-mini + fine-tuned with synth-vuln-fixes | 53.98 | link |
| Model | Score | Logs |
|---|---|---|
| gpt-4o | 53.10 | link |
| gpt-4o + 3-shot prompt | 53.98 | link |
| gpt-4o + rag (embedding & reranking) | 56.64 | link |
| gpt-4o + fine-tuned with synth-vuln-fixes | 61.06 | link |
Mixture of Agents (MOA)
We also benchmarked gpt-4o with Patched MOA. This demostrates that an inference optimization technique like MOA can improve performance without fine-tuning.
| Model | Score | Logs |
|---|---|---|
| moa-gpt-4o | 53.98 | link |
| moa-gpt-4o + 3-shot prompt | 60.18 | link |
| moa-gpt-4o + rag (embedding & reranking) | 61.06 | link |
Static Analysis Eval Benchmark
A dataset of 76 Python programs taken from real Python open source projects (top 100 on GitHub), where each program is a file that has exactly 1 vulnerability as detected by a particular static analyzer (Semgrep).
You can run the _script_for_eval.py script to check the results.
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python _script_for_eval.py
For all supported options, run with --help:
usage: _script_for_eval.py [-h] [--model MODEL] [--cache] [--n_shot N_SHOT] [--use_similarity] [--oracle]
Run Static Analysis Evaluation
options:
-h, --help show this help message and exit
--model MODEL OpenAI model to use
--cache Enable caching of results
--n_shot N_SHOT Number of examples to use for few-shot learning
--use_similarity Use similarity for fetching dataset examples
--oracle Run in oracle mode (assume all vulnerabilities are fixed)
We need to use the logged in version of Semgrep to get access to more rules for vulnerability detection. So, make sure you login before running the eval script.
% semgrep login
API token already exists in /Users/user/.semgrep/settings.yml. To login with a different token logout use `semgrep logout`
After the run, the script will also create a log file which captures the stats for the run and the files that were fixed. You can see an example here. Due to the recent versions of Semgrep not detecting a few of the samples in the dataset as vulnerable anymore, the maximum score possible on the benchmark is 77.63%. You can see the oracle run log here.
Evaluation
We did some detailed evaluations recently (19/08/2024):
| Model | Score | Logs |
|---|---|---|
| gpt-4o-mini | 67.11 | link |
| gpt-4o-mini + 3-shot prompt | 71.05 | link |
| gpt-4o-mini + rag (embedding & reranking) | 72.37 | link |
| gpt-4o-mini + fine-tuned with synth-vuln-fixes | 77.63 | link |
| Model | Score | Logs |
|---|---|---|
| gpt-4o | 68.42 | link |
| gpt-4o + 3-shot prompt | 77.63 | link |
| gpt-4o + rag (embedding & reranking) | 77.63 | link |
| gpt-4o + fine-tuned with synth-vuln-fixes | 77.63 | link |
Leaderboard
The top models on the leaderboard are all fine-tuned using the same dataset that we released called synth vuln fixes.
You can read about our experience with fine-tuning them on our blog.
You can also explore the leaderboard with this interactive visualization.
| Model | StaticAnalysisEval (%) | Time (mins) | Price (USD) |
|---|---|---|---|
| gpt-4o-mini-fine-tuned | 77.63 | 21:0 | 0.21 |
| gemini-1.5-flash-fine-tuned | 73.68 | 18:0 | |
| Llama-3.1-8B-Instruct-fine-tuned | 69.74 | 23:0 | |
| gpt-4o | 69.74 | 24:0 | 0.12 |
| gpt-4o-mini | 68.42 | 20:0 | 0.07 |
| gemini-1.5-flash-latest | 68.42 | 18:2 | 0.07 |
| Llama-3.1-405B-Instruct | 65.78 | 40:12 | |
| Llama-3-70B-instruct | 65.78 | 35:2 | |
| Llama-3-8B-instruct | 65.78 | 31.34 | |
| gemini-1.5-pro-latest | 64.47 | 34:40 | |
| gpt-4-1106-preview | 64.47 | 27:56 | 3.04 |
| gpt-4 | 63.16 | 26:31 | 6.84 |
| claude-3-5-sonnet-20240620 | 59.21 | 23:59 | 0.70 |
| moa-gpt-3.5-turbo-0125 | 53.95 | 49:26 | |
| gpt-4-0125-preview | 53.94 | 34:40 | |
| patched-coder-7b | 51.31 | 45.20 | |
| patched-coder-34b | 46.05 | 33:58 | 0.87 |
| patched-mix-4x7b | 46.05 | 60:00+ | 0.80 |
| Mistral-Large | 40.80 | 60:00+ | |
| Gemini-pro | 39.47 | 16:09 | 0.23 |
| Mistral-Medium | 39.47 | 60:00+ | 0.80 |
| Mixtral-Small | 30.26 | 30:09 | |
| gpt-3.5-turbo-0125 | 28.95 | 21:50 | |
| claude-3-opus-20240229 | 25.00 | 60:00+ | |
| Llama-3-8B-instruct.Q4_K_M | 21.05 | 60:00+ | |
| Gemma-7b-it | 19.73 | 36:40 | |
| gpt-3.5-turbo-1106 | 17.11 | 13:00 | 0.23 |
| Codellama-70b-Instruct | 10.53 | 30.32 | |
| CodeLlama-34b-Instruct | 7.89 | 23:16 |
The price is calcualted by assuming 1000 input and output tokens per call as all examples in the dataset are < 512 tokens (OpenAI cl100k_base tokenizer).
Some models timed out during the run or had intermittent API errors. We try each example 3 times in such cases. This is why some runs are reported to be longer than 1 hr (60:00+ mins).
If you want to add your model to the leaderboard, you can send in a PR to this repo with the log file from the evaluation run.
- Downloads last month
- 679
Size of downloaded dataset files:
405 kB
Size of the auto-converted Parquet files:
405 kB
Number of rows:
113