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values | stars int64 617 98.8k | forks int64 31 355 ⌀ | watchers int64 990 999 ⌀ | license stringclasses 2
values | default_branch stringclasses 2
values | repo_created_at timestamp[s]date 2012-07-24 23:12:50 2025-06-16 08:07:28 ⌀ | repo_updated_at timestamp[s]date 2026-02-23 15:23:15 2026-05-03 18:52:12 ⌀ | repo_topics listlengths 0 13 ⌀ | repo_languages unknown | is_fork bool 1
class | open_issues int64 3 104 ⌀ | file_path stringlengths 3 208 | file_name stringclasses 509
values | file_extension stringclasses 1
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values | parsed_at stringdate 2026-05-04 01:12:36 2026-05-04 19:41:55 | text stringlengths 100 102k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/backends/torch.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:21.313628 | """Required functions for optimized contractions of numpy arrays using pytorch."""
from opt_einsum.helpers import has_array_interface
from opt_einsum.parser import convert_to_valid_einsum_chars
from opt_einsum.sharing import to_backend_cache_wrap
__all__ = [
"transpose",
"einsum",
"tensordot",
"to_tor... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/backends/dispatch.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:21.316285 | """Handles dispatching array operations to the correct backend library, as well
as converting arrays to backend formats and then potentially storing them as
constants.
"""
import importlib
from typing import Any
from opt_einsum.backends import cupy as _cupy
from opt_einsum.backends import jax as _jax
from opt_einsum.... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/backends/object_arrays.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:21.320574 | """Functions for performing contractions with array elements which are objects."""
import functools
import operator
from opt_einsum.typing import ArrayType
def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performe... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/helpers.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:22.398074 | """Contains helper functions for opt_einsum testing scripts."""
from collections.abc import Collection, Iterable
from typing import Any, overload
from opt_einsum.typing import ArrayIndexType, ArrayType
__all__ = ["compute_size_by_dict", "find_contraction", "flop_count"]
_valid_chars = "abcdefghijklmopqABC"
_sizes =... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/sharing.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:22.399862 | """A module for sharing intermediates between contractions.
Copyright (c) 2018 Uber Technologies
"""
import contextlib
import functools
import numbers
import threading
from collections import Counter, defaultdict
from collections import Counter as CounterType
from collections.abc import Generator
from typing import A... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/blas.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:22.401573 | """Determines if a contraction can use BLAS or not."""
from collections.abc import Sequence
from opt_einsum.typing import ArrayIndexType
__all__ = ["can_blas"]
def can_blas(
inputs: list[str],
result: str,
idx_removed: ArrayIndexType,
shapes: Sequence[tuple[int]] | None = None,
) -> str | bool:
... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/parser.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:22.402506 | """A functionally equivalent parser of the numpy.einsum input parser."""
import itertools
from collections.abc import Iterator, Sequence
from typing import Any
from opt_einsum.typing import ArrayType, TensorShapeType
__all__ = [
"is_valid_einsum_char",
"has_valid_einsum_chars_only",
"get_symbol",
"ge... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/contract.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:22.403873 | """Contains the primary optimization and contraction routines."""
from collections.abc import Collection, Iterable, Sequence
from decimal import Decimal
from functools import lru_cache
from typing import Any, Literal, overload
from opt_einsum import backends, blas, helpers, parser, paths, sharing
from opt_einsum.typi... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/paths.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:22.405131 | """Contains the path technology behind opt_einsum in addition to several path helpers."""
import bisect
import functools
import heapq
import itertools
import operator
import random
import re
from collections import Counter, defaultdict
from collections import Counter as CounterType
from collections.abc import Callable... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/path_random.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:24.034550 | """Support for random optimizers, including the random-greedy path."""
import functools
import heapq
import math
import time
from collections import deque
from collections.abc import Generator, Iterable
from decimal import Decimal
from random import choices as random_choices
from random import seed as random_seed
from... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/testing.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:24.074481 | """Testing routines for opt_einsum."""
import random
from typing import Any, Literal, overload
import pytest
from opt_einsum.parser import get_symbol
from opt_einsum.typing import ArrayType, PathType, TensorShapeType
_no_collision_chars = "".join(chr(i) for i in range(7000, 7007))
_valid_chars = "abcdefghijklmnopqA... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/tests/test_blas.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:24.132853 | """
Tests the BLAS capability for the opt_einsum module.
"""
from typing import Any
import pytest
from opt_einsum import blas, contract
blas_tests = [
# DOT
((["k", "k"], "", set("k")), "DOT"), # DDOT
((["ijk", "ijk"], "", set("ijk")), "DOT"), # DDOT
# GEMV?
# GEMM
((["ij", "jk"], "ik", se... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/tests/test_sharing.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:24.147856 | import itertools
import weakref
from collections import Counter
from typing import Any
import pytest
from opt_einsum import contract, contract_expression, contract_path, get_symbol, shared_intermediates
from opt_einsum.backends import to_cupy, to_torch
from opt_einsum.contract import _einsum
from opt_einsum.parser im... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/tests/test_parser.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:24.187608 | """
Directly tests various parser utility functions.
"""
from typing import Any
import pytest
from opt_einsum.parser import get_shape, get_symbol, parse_einsum_input
from opt_einsum.testing import build_arrays_from_tuples
def test_get_symbol() -> None:
assert get_symbol(2) == "c"
assert get_symbol(200000) ... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/tests/test_edge_cases.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:24.226673 | """
Tets a series of opt_einsum contraction paths to ensure the results are the same for different paths
"""
from typing import Any
import pytest
from opt_einsum import contract, contract_expression, contract_path
from opt_einsum.typing import PathType
# NumPy is required for the majority of this file
np = pytest.i... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/tests/test_contract.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:24.256418 | """
Tets a series of opt_einsum contraction paths to ensure the results are the same for different paths
"""
from typing import Any
import pytest
from opt_einsum import contract, contract_expression, contract_path
from opt_einsum.paths import _PATH_OPTIONS, linear_to_ssa, ssa_to_linear
from opt_einsum.testing import... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/tests/test_backends.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:24.526721 | import pytest
from opt_einsum import backends, contract, contract_expression, sharing
from opt_einsum.contract import ArrayShaped, infer_backend, parse_backend
from opt_einsum.testing import build_views
try:
# needed so tensorflow doesn't allocate all gpu mem
try:
from tensorflow import ConfigProto #... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/typing.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:24.587910 | """Types used in the opt_einsum package."""
from collections import namedtuple
from collections.abc import Callable, Collection
from typing import Any, Literal
TensorShapeType = tuple[int, ...]
PathType = Collection[TensorShapeType]
ArrayType = Any
ArrayIndexType = frozenset[str]
ArrayShaped = namedtuple("ArrayShap... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | scripts/compare_random_paths.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:24.659235 | import resource
import timeit
from typing import Literal
import numpy as np
import pandas as pd
import opt_einsum as oe
rsrc = resource.RLIMIT_DATA
limit = int(1e9)
resource.setrlimit(rsrc, (limit, limit))
pd.set_option("display.width", 200)
opt_path: Literal["optimal"] = "optimal"
# Number of dimensions
max_dims... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/tests/test_input.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:28.267317 | """
Tests the input parsing for opt_einsum. Duplicates the np.einsum input tests.
"""
from typing import Any
import pytest
from opt_einsum import contract, contract_path
from opt_einsum.testing import build_views
np = pytest.importorskip("numpy")
def test_type_errors() -> None:
# subscripts must be a string
... |
dgasmith/opt_einsum | https://github.com/dgasmith/opt_einsum | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | opt_einsum/tests/test_paths.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:28.277074 | """
Tests the accuracy of the opt_einsum paths in addition to unit tests for
the various path helper functions.
"""
import itertools
from concurrent.futures import ProcessPoolExecutor
from typing import Any
import pytest
import opt_einsum as oe
from opt_einsum.testing import build_shapes, rand_equation
from opt_eins... |
bootmortis/iran-hosted-domains | https://github.com/bootmortis/iran-hosted-domains | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | src/create_config.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:30.449237 | import json
from typing import Iterable
import constants as consts
import utils
def shadowrocket(bypass_domains: Iterable[str], ads_domains: Iterable[str]):
config = (
"#Shadowrocket\n"
"[General]\n"
"fallback-dns-server = \n"
"private-ip-answer = false\n"
"bypass-system =... |
bootmortis/iran-hosted-domains | https://github.com/bootmortis/iran-hosted-domains | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | src/main.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:30.459582 | import os
import re
from functools import reduce
from typing import Iterable
import constants as consts
import create_config
import get_domians
import utils
from data.custom_domains import custom_domains
def collect_and_clean_domains(*domain_set: Iterable[Iterable[str]]) -> Iterable[str]:
domains = reduce(lambda... |
bootmortis/iran-hosted-domains | https://github.com/bootmortis/iran-hosted-domains | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | src/utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:30.465944 | import os
import re
import tempfile
import urllib.request
import zipfile
import requests
import tldextract
URL_REGEX = re.compile(
r"^"
r"(?:https?://)?"
# user:pass authentication
r"(?:\S+(?::\S*)?@)?"
r"("
# IP address exclusion
# private & local networks
r"(?!(?:10|127)(?:\.\d{1,3})... |
bootmortis/iran-hosted-domains | https://github.com/bootmortis/iran-hosted-domains | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | src/constants.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:30.466487 | # https://eservices.ito.gov.ir/page/iplist
g2b_gov_url = "https://raw.githubusercontent.com/bootmortis/ito-gov-mirror/main/out/domains.csv"
ads_url = "https://raw.githubusercontent.com/nimasaj/uBOPa/master/uBOPa_Pihole.txt"
v2fly_base_url = "https://raw.githubusercontent.com/v2fly/domain-list-community/master/data/"
# ... |
bootmortis/iran-hosted-domains | https://github.com/bootmortis/iran-hosted-domains | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | src/data/custom_domains.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:30.472392 | custom_domains = {
"proxy": [
"animelist.ir",
"gamepass.ir",
],
"direct": [
"2agoo.com",
"403.online",
"aanidarman.com",
"abankapp.ir",
"abianpharmed.com",
"abris.cloud",
"actoverco.com",
"adaklock.com",
"admentor.net",
... |
bootmortis/iran-hosted-domains | https://github.com/bootmortis/iran-hosted-domains | null | null | null | null | 980 | null | null | mit | null | null | null | null | null | null | null | src/get_domians.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:30.479275 | import re
from typing import Iterable
import requests
import constants as consts
import utils
def g2b_ito_gov() -> Iterable[str]:
resp = requests.get(consts.g2b_gov_url)
resp.raise_for_status()
domains = resp.text
domains = domains.splitlines()[1:]
domains = (domain.split(',')[0] for domain in ... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/distributed/launch.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:33.474728 | import os
import torch
from torch import distributed as dist
from torch import multiprocessing as mp
# import distributed as dist_fn
import image_synthesis.distributed.distributed as dist_fn
def find_free_port():
import socket
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.bind(("", 0))
... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/distributed/distributed.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:33.475714 | import math
import pickle
import torch
from torch import distributed as dist
from torch.utils import data
LOCAL_PROCESS_GROUP = None
def is_primary():
return get_rank() == 0
def get_rank():
if not dist.is_available():
return 0
if not dist.is_initialized():
return 0
return dist.g... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/engine/clip_grad_norm.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:34.191410 | from torch.nn.utils import clip_grad_norm_
class ClipGradNorm(object):
def __init__(self,
start_iteration=0,
end_iteration=-1, # if negative, the norm will be always clipped
max_norm=0.5):
self.start_iteration = start_iteration
self.end_iteratio... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/engine/ema.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:34.264698 | import torch
import copy
class EMA(object):
def __init__(self,
model,
decay=0.99,
update_interval=1,
device=torch.device('cpu')):
self.decay = decay
self.update_iterval = update_interval
self.device = device
se... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/engine/logger.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:34.755918 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import sys
import torch
from image_synthesis.utils.io import write_args, save_config_to_yaml
from image_synthesis.distributed.distributed import is_primary
import torch.utils.tensorboard a... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/engine/lr_scheduler.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:34.842021 | import torch
import math
# from torch.optim import AdamW, Adam
from torch._six import inf
from torch.optim.optimizer import Optimizer
from torch.optim.lr_scheduler import _LRScheduler, CosineAnnealingLR
class ReduceLROnPlateauWithWarmup(object):
"""Reduce learning rate when a metric has stopped improving.
Mo... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/engine/solver.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:35.330028 | # ------------------------------------------
# VQ-Diffusion
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Shuyang Gu
# ------------------------------------------
import os
import time
import math
import torch
import threading
import multiprocessing
import copy
from PIL import Im... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/build.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:35.406140 | from image_synthesis.utils.misc import instantiate_from_config
def build_model(config, args=None):
return instantiate_from_config(config['model'])
|
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/codecs/base_codec.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:36.035381 | import torch
from torch import nn
class BaseCodec(nn.Module):
def get_tokens(self, x, **kwargs):
"""
Input:
x: input data
Return:
indices: B x L, the codebook indices, where L is the length
of flattened feature map size
"""
... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/codecs/image_codec/ema_vqvae.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:36.035928 | import torch
import torch.nn as nn
from omegaconf import OmegaConf
import sys
sys.path.append("..")
# sys.path.append("../image_synthesis")
import os
import torchvision.transforms.functional as TF
import PIL
from image_synthesis.modeling.codecs.base_codec import BaseCodec
from einops import rearrange
import math
import... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/codecs/image_codec/taming_gumbel_vqvae.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:36.751919 | import torch
import torch.nn as nn
from omegaconf import OmegaConf
import sys
sys.path.append("..")
# sys.path.append("../image_synthesis")
from image_synthesis.utils.misc import instantiate_from_config
from image_synthesis.taming.models.vqgan import GumbelVQ, VQModel
from image_synthesis.taming.models.cond_transformer... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/codecs/image_codec/patch_vqgan.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:36.753000 | from numpy.core.shape_base import block
from numpy.lib import stride_tricks
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import random
from torch.nn.modules.linear import Linear
from image_synthesis.utils.misc import instantiate_from_config
from image_synthesis.modeling.codecs.... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/codecs/text_codec/tokenize.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:37.309189 | import torch
import torch.nn as nn
from image_synthesis.modeling.modules.clip.clip import tokenize
from image_synthesis.modeling.codecs.base_codec import BaseCodec
from image_synthesis.utils.misc import instantiate_from_config
class Tokenize(BaseCodec):
def __init__(self, context_length:int = 256,
... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/embeddings/base_embedding.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:37.318113 | import torch
from torch import nn
class BaseEmbedding(nn.Module):
def get_loss(self):
return None
def forward(self, **kwargs):
raise NotImplementedError
def train(self, mode=True):
self.training = mode
if self.trainable and mode:
super().train()
retur... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/embeddings/clip_text_embedding.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:37.962195 | import torch
import torch.nn as nn
from image_synthesis.modeling.modules.clip import clip
from image_synthesis.modeling.modules.clip import model as clip_model
from .base_embedding import BaseEmbedding
class CLIPTextEmbedding(BaseEmbedding):
def __init__(self,
clip_name='ViT-B/32',
... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/data/mscoco_dataset.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:37.975053 | from torch.utils.data import Dataset
import numpy as np
import io
from PIL import Image
import os
import json
import random
from image_synthesis.utils.misc import instantiate_from_config
def load_img(filepath):
img = Image.open(filepath).convert('RGB')
return img
class CocoDataset(Dataset):
def __init__(s... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/embeddings/class_embedding.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:37.976254 | import torch
import torch.nn as nn
from .base_embedding import BaseEmbedding
class ClassEmbedding(BaseEmbedding):
def __init__(self,
num_embed=1000,
embed_dim=512,
identity=False,
trainable=True,
):
super().__init__()
self... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/data/build.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:37.993812 | import torch
# from image_synthesis.data.base_dataset import ConcatDatasetWithIndex as ConcatDataset
from torch.utils.data import ConcatDataset
from image_synthesis.utils.misc import instantiate_from_config
from image_synthesis.distributed.distributed import is_distributed
def build_dataloader(config, args=None, retur... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/data/utils/comm.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:37.998949 | """
This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
"""
import pickle
import torch
import torch.distributed as dist
# from diffdist.functional import all_gather as better_all_gather
class Comm(object):
def __init__(self, local_rank=0):
self.loca... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/data/ffhq_dataset.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:37.999580 | from torch.utils.data import Dataset
import numpy as np
import io
from PIL import Image
import os
import json
import random
from image_synthesis.utils.misc import instantiate_from_config
import torchvision.datasets as datasets
class FFHQDataset(datasets.ImageFolder):
def __init__(self, data_root, im_preprocessor_c... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/data/cub200_dataset.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:38.028940 | from torch.utils.data import Dataset
import numpy as np
import io
from PIL import Image
import os
import json
import random
from image_synthesis.utils.misc import instantiate_from_config
from tqdm import tqdm
import pickle
def load_img(filepath):
img = Image.open(filepath).convert('RGB')
return img
class Cub2... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/data/utils/image_preprocessor.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:38.029972 | import albumentations
import random
import numpy as np
from PIL import Image
import cv2
from io import BytesIO
from torchvision import transforms as trans
class DalleTransformerPreprocessor(object):
def __init__(self,
size=256,
phase='train',
additional_targets=N... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/data/imagenet_dataset.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:38.060314 | from torch.utils.data import Dataset
import numpy as np
import io
from PIL import Image
import os
import json
import random
from image_synthesis.utils.misc import instantiate_from_config
def load_img(filepath):
img = Image.open(filepath).convert('RGB')
return img
class ImageNetDataset(Dataset):
def __init... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/data/utils/manage.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:38.097360 | from sys import stdout
import zipfile
import os.path as osp
import lmdb
import logging
from PIL import Image
import pickle
import io
import glob
import os
from pathlib import Path
import time
from threading import Thread
from queue import Queue,Empty
import subprocess
def func_wrapper(func):
def sub_func(queue,kwa... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/embeddings/dalle_mask_image_embedding.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:38.533606 | import torch
import torch.nn as nn
from .base_embedding import BaseEmbedding
class DalleMaskImageEmbedding(BaseEmbedding):
def __init__(self,
num_embed=8192,
spatial_size=[32, 32], # height and with
embed_dim=3968,
trainable=True,
... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/models/conditional_dalle.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:38.535603 | # ------------------------------------------
# VQ-Diffusion
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Shuyang Gu
# ------------------------------------------
import torch
import math
from torch import nn
from image_synthesis.utils.misc import instantiate_from_config
import t... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/modules/clip/clip_tokenizer.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:38.627411 | import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
@lru_cache()
def default_bpe():
return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corr... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/modules/clip/model.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:38.628724 | from collections import OrderedDict
from typing import Tuple, Union
import torch
import torch.nn.functional as F
from torch import nn
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1):
super().__init__()
# all conv layers have stride 1. an avgpool is ... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/modules/clip/simple_tokenizer.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:38.649079 | import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
@lru_cache()
def default_bpe():
return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corr... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/models/unconditional_dalle.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:38.661859 | # ------------------------------------------
# VQ-Diffusion
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Shuyang Gu
# ------------------------------------------
import torch
import math
from torch import nn
from image_synthesis.utils.misc import instantiate_from_config
import t... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/modules/clip/clip.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:38.662470 | import hashlib
import os
import urllib
import warnings
from typing import Union, List
import torch
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm
from .model import build_model
from .simple_tokenizer import SimpleTokenizer as _Tokenizer
... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/transformers/diffusion_transformer.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:38.712865 | # ------------------------------------------
# VQ-Diffusion
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Shuyang Gu
# ------------------------------------------
import math
import torch
from torch import nn
import torch.nn.functional as F
from image_synthesis.utils.misc import... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/models/dalle.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:38.746765 | # ------------------------------------------
# VQ-Diffusion
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Shuyang Gu
# ------------------------------------------
import torch
import math
from torch import nn
from image_synthesis.utils.misc import instantiate_from_config
import t... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/transformers/transformer_utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:39.430784 | # ------------------------------------------
# VQ-Diffusion
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Shuyang Gu
# ------------------------------------------
import math
import torch
from torch import nn
import torch.nn.functional as F
from image_synthesis.utils... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/taming/modules/diffusionmodules/model.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:39.479964 | # pytorch_diffusion + derived encoder decoder
import math
import torch
import torch.nn as nn
import numpy as np
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This mat... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/taming/lr_scheduler.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:39.538204 | import numpy as np
class LambdaWarmUpCosineScheduler:
"""
note: use with a base_lr of 1.0
"""
def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
self.lr_warm_up_steps = warm_up_steps
self.lr_start = lr_start
self.lr_min = lr_min
... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/modeling/utils/misc.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:39.562123 | from numpy.core.fromnumeric import resize
from numpy.lib.function_base import kaiser
from numpy.lib.npyio import save
import torch
import random
import math
from image_synthesis.distributed.distributed import all_reduce, get_world_size
def logits_top_k(logits, filter_ratio = 0.5, minimum=1, pad_value=None):
logits... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/taming/modules/losses/lpips.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:39.568706 | """Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models"""
import torch
import torch.nn as nn
from torchvision import models
from collections import namedtuple
from image_synthesis.taming.util import get_ckpt_path
class LPIPS(nn.Module):
# Learned perceptual metric
def __... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/taming/modules/misc/coord.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:40.171879 | import torch
class CoordStage(object):
def __init__(self, n_embed, down_factor):
self.n_embed = n_embed
self.down_factor = down_factor
def eval(self):
return self
def encode(self, c):
"""fake vqmodel interface"""
assert 0.0 <= c.min() and c.max() <= 1.0
b,c... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/taming/modules/losses/vqperceptual.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:40.173579 | import torch
import torch.nn as nn
import torch.nn.functional as F
from image_synthesis.taming.modules.losses.lpips import LPIPS
from image_synthesis.taming.modules.discriminator.model import NLayerDiscriminator, weights_init
class DummyLoss(nn.Module):
def __init__(self):
super().__init__()
def adopt_... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/taming/modules/transformer/mingpt.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:40.174656 | """
taken from: https://github.com/karpathy/minGPT/
GPT model:
- the initial stem consists of a combination of token encoding and a positional encoding
- the meat of it is a uniform sequence of Transformer blocks
- each Transformer is a sequential combination of a 1-hidden-layer MLP block and a self-attention block... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/taming/models/cond_transformer.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:40.343200 | import os, math
import torch
import torch.nn.functional as F
import pytorch_lightning as pl
from image_synthesis.utils.misc import instantiate_from_config
from image_synthesis.taming.modules.util import SOSProvider
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure trai... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/taming/modules/util.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:40.343719 | import torch
import torch.nn as nn
def count_params(model):
total_params = sum(p.numel() for p in model.parameters())
return total_params
class ActNorm(nn.Module):
def __init__(self, num_features, logdet=False, affine=True,
allow_reverse_init=False):
assert affine
super(... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/taming/models/vqgan.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:40.352670 | import torch
import torch.nn.functional as F
import pytorch_lightning as pl
from image_synthesis.utils.misc import instantiate_from_config
from image_synthesis.taming.modules.diffusionmodules.model import Encoder, Decoder
from image_synthesis.taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
fr... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/taming/modules/transformer/permuter.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:40.723282 | import torch
import torch.nn as nn
import numpy as np
class AbstractPermuter(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
def forward(self, x, reverse=False):
raise NotImplementedError
class Identity(AbstractPermuter):
def __init__(self):
super().__init__()... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/taming/modules/discriminator/model.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:40.917775 | import functools
import torch.nn as nn
from image_synthesis.taming.modules.util import ActNorm
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/taming/modules/vqvae/quantize.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:41.005304 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch import einsum
from einops import rearrange
class VectorQuantizer(nn.Module):
"""
see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py
_____________________... |
microsoft/VQ-Diffusion | https://github.com/microsoft/VQ-Diffusion | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | image_synthesis/taming/modules/losses/segmentation.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:44.498371 | import torch.nn as nn
import torch.nn.functional as F
class BCELoss(nn.Module):
def forward(self, prediction, target):
loss = F.binary_cross_entropy_with_logits(prediction,target)
return loss, {}
class BCELossWithQuant(nn.Module):
def __init__(self, codebook_weight=1.):
super().__ini... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/api/core.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:46.665354 | from cachebrowser.api.routes import routes
class APIRequest(object):
def __init__(self, route, params):
self.route = route
self.params = params
def reply(self, response):
raise NotImplementedError()
class BaseAPIManager(object):
def __init__(self):
self.handlers = {}
... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/api/routes.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:46.672280 | from cachebrowser.api.handlers.bootstrap import get_hosts, get_cdns, delete_host, add_host, add_cdn
from cachebrowser.api.handlers.process import close, ping
from cachebrowser.api.handlers.website import is_website_enabled, enable_website, disable_website
routes = [
('/close', close),
('/ping', ping),
('/h... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/api/handlers/process.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:46.675063 |
def close(context, request):
import os
import signal
request.reply("OK")
# sys.exit(0)
os.kill(os.getpid(), signal.SIGINT)
os._exit(0)
def ping(context, request):
request.reply("pong")
|
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/cli.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:46.683097 | from functools import update_wrapper, partial
import json
import logging
from cachebrowser.models import Host
import click
from cachebrowser.api.core import APIManager, APIRequest
from cachebrowser.bootstrap import BootstrapError
main_commands = ['hostcli', 'cdncli', 'bootstrap']
api = APIManager()
logger = logging... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/bootstrap.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:46.688793 | import random
import yaml
import logging
from copy import deepcopy
from yaml.scanner import ScannerError
logger = logging.getLogger(__name__)
class BootstrapSourceError(Exception):
pass
class BaseBootstrapSource(object):
def lookup_host(self, hostname):
pass
def lookup_cdn(self, cdn_id):
... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/api/handlers/bootstrap.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:46.690030 | from cachebrowser.models import Host, CDN
def serialize_host(host):
return {
'hostname': host.hostname,
'cdn': {'id': host.cdn.id, 'name': host.cdn.name} if host.cdn else None,
'ssl': host.ssl,
}
def serialize_cdn(cdn):
return {
'id': cdn.id,
'name': cdn.name,
... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/api/handlers/website.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:46.718710 | from cachebrowser.models import Website
def enable_website(context, request):
hostname = request.params.get('website', None)
if hostname is None:
return request.reply({'result': 'error', 'message': 'no website given'})
website, _ = Website.get_or_create(hostname=hostname)
if not website.ena... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/main.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:47.719003 | from __future__ import print_function, absolute_import
import logging
import logging.config
import sys
import inspect
import os
import click
import mitmproxy
import mitmproxy.controller
from mitmproxy.proxy.server import ProxyServer
from cachebrowser.bootstrap import Bootstrapper
from cachebrowser.models import init... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/ipc.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:47.720930 | import json
from threading import Thread
import traceback
import uuid
import logging
import tornado.ioloop
import tornado.web
import tornado.websocket
from cachebrowser.api.core import APIRequest
logger = logging.getLogger(__name__)
class IPCRouter(object):
def __init__(self):
self.clients = {}
... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/pipes/scrambler.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:47.721905 | # cdn.jsdelivr.net
import logging
from time import time, sleep
from threading import Thread, RLock
from random import random, shuffle, choice
from six.moves.urllib.parse import urlparse
from mitmproxy.models import HTTPResponse
from netlib.http import Headers
from cachebrowser.pipes.base import FlowPipe
from cachebro... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/pipes/sni.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:47.723031 | from cachebrowser.pipes.base import FlowPipe
SNI_EMPTY = "empty"
SNI_FRONT = "font"
SNI_ORIGINAL = "original"
class SNIPipe(FlowPipe):
def serverconnect(self, server_conn):
host, cdn = getattr(server_conn, 'host', None), getattr(server_conn, 'cdn', None)
if host and host.sni_policy:
... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/pipes/publisher.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:47.723896 | from cachebrowser.pipes.base import FlowPipe
from cachebrowser.util import get_flow_size
class PublisherPipe(FlowPipe):
def __init__(self, *args, **kwargs):
super(PublisherPipe, self).__init__(*args, **kwargs)
self._id_counter = 1
def start(self):
self._id_counter = 1
def request... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/pipes/base.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:47.725058 | from mitmproxy.script import Script, ScriptContext
class FlowPipe(Script, ScriptContext):
def __init__(self, context, master=None):
self.context = context
self.bootstrapper = self.context.bootstrapper
self.settings = self.context.settings
self.enabled = True
self._master ... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/pipes/resolver.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:48.569902 | from cachebrowser.bootstrap import BootstrapError
from netlib.tcp import Address
from cachebrowser.models import Host, DoesNotExist, CDN
from cachebrowser.pipes.base import FlowPipe
"""
If the client initiates an HTTP connection, the 'request' hook will be called first and then 'serverconnect'.
We can only upgrade th... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/settings/production.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:48.796217 | import appdirs
from cachebrowser.settings.base import APP_NAME, CacheBrowserSettings
class ProductionSettings(CacheBrowserSettings):
def set_defaults(self):
self.host = "127.0.0.1"
self.port = 8080
self.ipc_port = 9000
self.database = self.data_path('cachebrowser.db')
self... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/settings/development.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:48.796819 | from cachebrowser.settings.base import CacheBrowserSettings
class DevelopmentSettings(CacheBrowserSettings):
def set_defaults(self):
self.host = "0.0.0.0"
self.port = 8080
self.ipc_port = 9000
self.database = 'db.sqlite'
self.bootstrap_sources = [
{
... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/settings/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:48.801852 | from base import CacheBrowserSettings, SettingsValidationError
from development import DevelopmentSettings
from production import ProductionSettings
__all__ = ['CacheBrowserSettings', 'DevelopmentSettings', 'ProductionSettings']
|
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/models.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:48.803046 | import peewee
import logging
logger = logging.getLogger(__name__)
db = peewee.SqliteDatabase('')
class BaseModel(peewee.Model):
pass
class CDN(BaseModel):
id = peewee.CharField(primary_key=True)
name = peewee.CharField(null=True)
edge_server = peewee.CharField(null=True)
sni_policy = peewee.Cha... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/settings/base.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:48.803682 | from functools import partial
import os
import logging
import re
import sys
import yaml
from cachebrowser.pipes.sni import SNI_EMPTY, SNI_ORIGINAL, SNI_FRONT
APP_NAME = "CacheBrowser"
logger = logging.getLogger(__name__)
class InsufficientParametersException(Exception):
pass
class SettingsValidationError(Exce... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/proxy.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:48.808450 | import logging
import mitmproxy.controller
import mitmproxy.proxy
import mitmproxy.flow
import mitmproxy.dump
import mitmproxy.cmdline
import mitmproxy.models
import mitmproxy.protocol
import mitmproxy as mproxy
from mitmproxy.script import script
from cachebrowser.models import Website
from cachebrowser.pipes import ... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/util.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:48.811167 | import os
import importlib
import inspect
def get_flow_size(flow, ):
"""
(Not accurate)
"""
def get_size(r):
s = len(r.content)
for header in r.headers:
s += len(header)
s += len(r.headers[header])
# Colon
s += 1
s += len(r.http_ve... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | cachebrowser/pipes/website_filter.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:48.920559 | from urlparse import urlparse
from cachebrowser.models import Website
from cachebrowser.pipes.base import FlowPipe
from cachebrowser.pipes import SKIP_PIPES
class WebsiteFilterPipe(FlowPipe):
def serverconnect(self, server_conn):
if self._check_should_skip(server_conn=server_conn):
return SKI... |
CacheBrowser/cachebrowser | https://github.com/CacheBrowser/cachebrowser | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | setup.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:53.750256 | from setuptools import setup, find_packages
import os
datafiles = [(root, [os.path.join(root, f) for f in files]) for root, dirs, files in os.walk('data')]
setup(
name='cachebrowser',
description='A proxy-less censorship resistance tool',
version='0.1.1',
author='Hadi Zolfaghari',
author_email='ha... |
yaohungt/Multimodal-Transformer | https://github.com/yaohungt/Multimodal-Transformer | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | src/utils.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:55.710527 | import torch
import os
from src.dataset import Multimodal_Datasets
def get_data(args, dataset, split='train'):
alignment = 'a' if args.aligned else 'na'
data_path = os.path.join(args.data_path, dataset) + f'_{split}_{alignment}.dt'
if not os.path.exists(data_path):
print(f" - Creating new {split}... |
yaohungt/Multimodal-Transformer | https://github.com/yaohungt/Multimodal-Transformer | null | null | null | null | 979 | null | null | mit | null | null | null | null | null | null | null | modules/position_embedding.py | null | null | null | null | null | null | Python | 2026-05-04T01:35:55.714343 | import math
import torch
import torch.nn as nn
# Code adapted from the fairseq repo.
def make_positions(tensor, padding_idx, left_pad):
"""Replace non-padding symbols with their position numbers.
Position numbers begin at padding_idx+1.
Padding symbols are ignored, but it is necessary to specify whether ... |
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