id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
20,394 | import ml_collections
def get_b16_config():
"""Returns the ViT-B/16 configuration."""
config = ml_collections.ConfigDict()
config.patches = ml_collections.ConfigDict({'size': (16, 16)})
config.hidden_size = 768
config.transformer = ml_collections.ConfigDict()
config.transformer.mlp_dim = 3072
... | Returns the Resnet50 + ViT-B/16 configuration. |
20,395 | import ml_collections
def get_b16_config():
"""Returns the ViT-B/16 configuration."""
config = ml_collections.ConfigDict()
config.patches = ml_collections.ConfigDict({'size': (16, 16)})
config.hidden_size = 768
config.transformer = ml_collections.ConfigDict()
config.transformer.mlp_dim = 3072
... | Returns the ViT-B/32 configuration. |
20,396 | import ml_collections
def get_b16_config():
"""Returns the ViT-B/16 configuration."""
config = ml_collections.ConfigDict()
config.patches = ml_collections.ConfigDict({'size': (16, 16)})
config.hidden_size = 768
config.transformer = ml_collections.ConfigDict()
config.transformer.mlp_dim = 3072
... | Returns the ViT-B/32 configuration. |
20,397 | import ml_collections
def get_l16_config():
"""Returns the ViT-L/16 configuration."""
config = ml_collections.ConfigDict()
config.patches = ml_collections.ConfigDict({'size': (16, 16)})
config.hidden_size = 1024
config.transformer = ml_collections.ConfigDict()
config.transformer.mlp_dim = 4096
... | Returns the ViT-L/32 configuration. |
20,398 | import ml_collections
The provided code snippet includes necessary dependencies for implementing the `get_h14_config` function. Write a Python function `def get_h14_config()` to solve the following problem:
Returns the ViT-L/16 configuration.
Here is the function:
def get_h14_config():
"""Returns the ViT-L/16 co... | Returns the ViT-L/16 configuration. |
20,399 | import numpy as np
from typing import List, Tuple, Dict
from sklearn.metrics import (
precision_recall_curve,
average_precision_score,
f1_score
)
def get_continuous_ids(probe_labels: List[int]) -> Dict[int, int]:
sorted(probe_labels)
id2continuousid = {}
for idx, p_id in enumerate(probe_labels)... | null |
20,400 | import numpy as np
from typing import List, Tuple, Dict
from sklearn.metrics import (
precision_recall_curve,
average_precision_score,
f1_score
)
The provided code snippet includes necessary dependencies for implementing the `multihot` function. Write a Python function `def multihot(x: List[List[int]], nb_... | transform to multihot encoding Arguments: x: list of multi-class integer labels, in the range [0, nb_classes-1] nb_classes: number of classes for the multi-hot vector Returns: multihot: multihot vector of type int, (num_samples, nb_classes) |
20,401 | import numpy as np
from typing import List, Tuple, Dict
from sklearn.metrics import (
precision_recall_curve,
average_precision_score,
f1_score
)
The provided code snippet includes necessary dependencies for implementing the `compute_map` function. Write a Python function `def compute_map( scores: ... | Compute the mean average precision across all class labels. Arguments: scores: matrix of per-class distances, of size num_samples x nb_classes multihot_targets: matrix of multi-hot target predictions, of size num_samples x nb_classes Returns: ap: list of average-precision scores, one for each of the nb_classes classes.... |
20,402 | import numpy as np
from typing import List, Tuple, Dict
from sklearn.metrics import (
precision_recall_curve,
average_precision_score,
f1_score
)
def compute_f1(
multihot_targets: np.ndarray, scores: np.ndarray, threshold: float = 0.5
) -> Tuple[float, float, float]:
# change scores to predict_l... | null |
20,403 | import numpy as np
import torch
from sklearn.metrics import (
accuracy_score, average_precision_score, f1_score, roc_auc_score
)
def accuracy(y_probs, y_true):
# y_prob: (num_images, num_classes)
y_preds = np.argmax(y_probs, axis=1)
accuracy = accuracy_score(y_true, y_preds)
error = 1.0 - accuracy
... | null |
20,404 | import numpy as np
import torch
from sklearn.metrics import (
accuracy_score, average_precision_score, f1_score, roc_auc_score
)
def topks_correct(preds, labels, ks):
"""Computes the number of top-k correct predictions for each k."""
assert preds.size(0) == labels.size(
0
), "Batch dim of predic... | Computes the top-k error for each k. |
20,405 | import numpy as np
import torch
from sklearn.metrics import (
accuracy_score, average_precision_score, f1_score, roc_auc_score
)
def topks_correct(preds, labels, ks):
"""Computes the number of top-k correct predictions for each k."""
assert preds.size(0) == labels.size(
0
), "Batch dim of predic... | Computes the top-k accuracy for each k. |
20,406 | from functools import partial
import torch
import torch.nn as nn
from .adapter_block import Pfeiffer_Block
from ..vit_backbones.vit_mae import VisionTransformer
from timm.models.layers import PatchEmbed
from ...utils import logging
def vit_base_patch16(adapter_cfg, **kwargs):
model = ADPT_VisionTransformer(
... | null |
20,407 | import math
import torch
import torch.nn as nn
import torchvision as tv
from functools import partial, reduce
from operator import mul
from torch.nn import Conv2d, Dropout
from timm.models.vision_transformer import _cfg
from ..vit_backbones.vit_mae import VisionTransformer
from ...utils import logging
def vit_base_patc... | null |
20,408 | from functools import partial
import torch
import torch.nn as nn
import timm.models.vision_transformer
def vit_base_patch16(**kwargs):
def vit_large_patch16(**kwargs):
def vit_huge_patch14(**kwargs):
def build_model(model_type):
if "vitb" in model_type:
return vit_base_patch16()
elif "vitl" in model_ty... | null |
20,409 | import copy
import logging
import math
from os.path import join as pjoin
from turtle import forward
import torch
import torch.nn as nn
import numpy as np
from torch.nn import Dropout, Softmax, Linear, Conv2d, LayerNorm
from torch.nn.modules.utils import _pair
from scipy import ndimage
from ...configs import vit_configs... | null |
20,410 | import copy
import logging
import math
from os.path import join as pjoin
from turtle import forward
import torch
import torch.nn as nn
import numpy as np
from torch.nn import Dropout, Softmax, Linear, Conv2d, LayerNorm
from torch.nn.modules.utils import _pair
from scipy import ndimage
from ...configs import vit_configs... | null |
20,411 | import copy
import logging
import math
from os.path import join as pjoin
from turtle import forward
import torch
import torch.nn as nn
import numpy as np
from torch.nn import Dropout, Softmax, Linear, Conv2d, LayerNorm
from torch.nn.modules.utils import _pair
from scipy import ndimage
from ...configs import vit_configs... | null |
20,412 | import math
import torch
import torch.nn as nn
from functools import partial, reduce
from operator import mul
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.layers.helpers import to_2tuple
from timm.models.layers import PatchEmbed
class VisionTransformerMoCo(VisionTransformer):
... | null |
20,413 | import math
import torch
import torch.nn as nn
from functools import partial, reduce
from operator import mul
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.layers.helpers import to_2tuple
from timm.models.layers import PatchEmbed
class VisionTransformerMoCo(VisionTransformer):
... | null |
20,414 | import math
import torch
import torch.nn as nn
from functools import partial, reduce
from operator import mul
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.layers.helpers import to_2tuple
from timm.models.layers import PatchEmbed
class VisionTransformerMoCo(VisionTransformer):
... | null |
20,415 | import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `window_partition` function. Write a Python function `def window_p... | Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) |
20,416 | import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `window_reverse` function. Write a Python function `def window_rev... | Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) |
20,417 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, DropPath
class ConvNeXt(nn.Module):
def __init__(self, in_chans=3, num_classes=1000,
depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], drop_path_rate=0.,
layer_s... | null |
20,418 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, DropPath
class ConvNeXt(nn.Module):
r""" ConvNeXt
A PyTorch impl of : `A ConvNet for the 2020s` -
https://arxiv.org/pdf/2201.03545.pdf
Args:
in_chans (int): Number of input... | null |
20,419 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, DropPath
class ConvNeXt(nn.Module):
def __init__(self, in_chans=3, num_classes=1000,
depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], drop_path_rate=0.,
layer_s... | null |
20,420 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, DropPath
class ConvNeXt(nn.Module):
def __init__(self, in_chans=3, num_classes=1000,
depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], drop_path_rate=0.,
layer_s... | null |
20,421 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, DropPath
class ConvNeXt(nn.Module):
def __init__(self, in_chans=3, num_classes=1000,
depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], drop_path_rate=0.,
layer_s... | null |
20,422 | import numpy as np
import torch
import os
from .vit_backbones.swin_transformer import SwinTransformer
from .vit_backbones.vit import VisionTransformer
from .vit_backbones.vit_moco import vit_base
from .vit_backbones.vit_mae import build_model as mae_vit_model
from .vit_prompt.vit import PromptedVisionTransformer
from .... | null |
20,423 | import numpy as np
import torch
import os
from .vit_backbones.swin_transformer import SwinTransformer
from .vit_backbones.vit import VisionTransformer
from .vit_backbones.vit_moco import vit_base
from .vit_backbones.vit_mae import build_model as mae_vit_model
from .vit_prompt.vit import PromptedVisionTransformer
from .... | null |
20,424 | import numpy as np
import torch
import os
from .vit_backbones.swin_transformer import SwinTransformer
from .vit_backbones.vit import VisionTransformer
from .vit_backbones.vit_moco import vit_base
from .vit_backbones.vit_mae import build_model as mae_vit_model
from .vit_prompt.vit import PromptedVisionTransformer
from .... | null |
20,425 | import numpy as np
import torch
import os
from .vit_backbones.swin_transformer import SwinTransformer
from .vit_backbones.vit import VisionTransformer
from .vit_backbones.vit_moco import vit_base
from .vit_backbones.vit_mae import build_model as mae_vit_model
from .vit_prompt.vit import PromptedVisionTransformer
from .... | null |
20,426 | import os
import json
import numpy as np
import time
import pandas as pd
from typing import List, Union
from PIL import Image, ImageFile
def save_or_append_df(out_path, df):
if os.path.exists(out_path):
previous_df = pd.read_pickle(out_path)
df = pd.concat([previous_df, df], ignore_index=True)
... | null |
20,427 | import os
import json
import numpy as np
import time
import pandas as pd
from typing import List, Union
from PIL import Image, ImageFile
class JSONEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.ndarray):
return obj.tolist()
elif isinstance(obj, bytes):
... | null |
20,428 | import os
import json
import numpy as np
import time
import pandas as pd
from typing import List, Union
from PIL import Image, ImageFile
The provided code snippet includes necessary dependencies for implementing the `read_json` function. Write a Python function `def read_json(filename: str) -> Union[list, dict]` to so... | read json files |
20,429 | import os
import json
import numpy as np
import time
import pandas as pd
from typing import List, Union
from PIL import Image, ImageFile
Image.MAX_IMAGE_PIXELS = None
The provided code snippet includes necessary dependencies for implementing the `pil_loader` function. Write a Python function `def pil_loader(path: str)... | load an image from path, and suppress warning |
20,430 | import builtins
import decimal
import functools
import logging
import simplejson
import sys
import os
from termcolor import colored
from .distributed import is_master_process
from .file_io import PathManager
_FORMAT = "[%(levelname)s: %(filename)s: %(lineno)4d]: %(message)s"
def _cached_log_stream(filename):
return... | Sets up the logging. |
20,431 | import builtins
import decimal
import functools
import logging
import simplejson
import sys
import os
from termcolor import colored
from .distributed import is_master_process
from .file_io import PathManager
def get_logger(name):
"""Retrieves the logger."""
return logging.getLogger(name)
The provided code snip... | Logs json stats. |
20,432 | import datetime
import os
import glob
import numpy as np
import pandas as pd
import torch
from tqdm import tqdm
from collections import defaultdict
from sklearn.metrics import confusion_matrix
import warnings
def remove_trailing(eval_dict):
min_num = min([len(v) for k, v in eval_dict.items() if "top5" not in k])
... | null |
20,433 | import datetime
import os
import glob
import numpy as np
import pandas as pd
import torch
from tqdm import tqdm
from collections import defaultdict
from sklearn.metrics import confusion_matrix
import warnings
def get_nmi(job_path):
with open(job_path) as f:
lines = f.readlines()
nmi_dict = defaultdict(... | null |
20,434 | import datetime
import os
import glob
import numpy as np
import pandas as pd
import torch
from tqdm import tqdm
from collections import defaultdict
from sklearn.metrics import confusion_matrix
import warnings
def get_mean_accuracy(job_path, data_name):
val_data = torch.load(
job_path.replace("logs.txt", f"... | null |
20,435 | import datetime
import os
import glob
import numpy as np
import pandas as pd
import torch
from tqdm import tqdm
from collections import defaultdict
from sklearn.metrics import confusion_matrix
import warnings
def get_training_data(job_path, model_type, job_root):
data_name, feat_type, lr, wd = get_meta(job_root, jo... | null |
20,436 | import datetime
import os
import glob
import numpy as np
import pandas as pd
import torch
from tqdm import tqdm
from collections import defaultdict
from sklearn.metrics import confusion_matrix
import warnings
def delete_ckpts(f):
# delete saved ckpts for re
f_dir, _ = os.path.split(f)
for f_delete in glob.... | null |
20,437 | import datetime
import os
import glob
import numpy as np
import pandas as pd
import torch
from tqdm import tqdm
from collections import defaultdict
from sklearn.metrics import confusion_matrix
import warnings
def average_df(df, metric_names=["l-val_top1", "l-val_base_top1"], take_average=True):
# for each data and... | null |
20,438 | import datetime
import os
import glob
import numpy as np
import pandas as pd
import torch
from tqdm import tqdm
from collections import defaultdict
from sklearn.metrics import confusion_matrix
import warnings
def filter_df(df, sorted_cols, max_num):
# for each data and features, display only top max_num runs
da... | null |
20,439 | import torch
import torch.distributed as dist
The provided code snippet includes necessary dependencies for implementing the `run` function. Write a Python function `def run( local_rank, num_proc, func, init_method, shard_id, num_shards, backend, cfg, args, )` to solve the following... | Runs a function from a child process. Args: local_rank (int): rank of the current process on the current machine. num_proc (int): number of processes per machine. func (function): function to execute on each of the process. init_method (string): method to initialize the distributed training. TCP initialization: equirin... |
20,440 | import torch
import torch.distributed as dist
The provided code snippet includes necessary dependencies for implementing the `destroy_process_group` function. Write a Python function `def destroy_process_group()` to solve the following problem:
Destroys the default process group.
Here is the function:
def destroy_pr... | Destroys the default process group. |
20,441 | import torch
import torch.distributed as dist
The provided code snippet includes necessary dependencies for implementing the `scaled_all_reduce` function. Write a Python function `def scaled_all_reduce(cfg, tensors)` to solve the following problem:
Performs the scaled all_reduce operation on the provided tensors. The ... | Performs the scaled all_reduce operation on the provided tensors. The input tensors are modified in-place. Currently supports only the sum reduction operator. The reduced values are scaled by the inverse size of the process group (equivalent to cfg.NUM_GPUS). |
20,442 | import torch
import torch.distributed as dist
def get_world_size() -> int:
if not dist.is_available():
return 1
if not dist.is_initialized():
return 1
return dist.get_world_size()
The provided code snippet includes necessary dependencies for implementing the `cat_all_gather` function. Write... | Performs the concatenated all_gather operation on the provided tensors. |
20,443 | import torch
import torch.distributed as dist
_LOCAL_PROCESS_GROUP = None
def get_local_size():
"""
Returns:
The size of the per-machine process group,
i.e. the number of processes per machine.
"""
if not dist.is_available():
return 1
if not dist.is_initialized():
ret... | Performs the concatenated all_gather operation on the provided tensors. |
20,444 | import torch
import torch.distributed as dist
_LOCAL_PROCESS_GROUP = None
def get_rank() -> int:
if not dist.is_available():
return 0
if not dist.is_initialized():
return 0
return dist.get_rank()
The provided code snippet includes necessary dependencies for implementing the `get_local_rank`... | Returns: The rank of the current process within the local (per-machine) process group. |
20,445 | import torch
The provided code snippet includes necessary dependencies for implementing the `gpu_mem_usage` function. Write a Python function `def gpu_mem_usage()` to solve the following problem:
Computes the GPU memory usage for the current device (GB).
Here is the function:
def gpu_mem_usage():
"""Computes the... | Computes the GPU memory usage for the current device (GB). |
20,446 | import glob
import numpy as np
import os
import torch
import warnings
import random
from time import sleep
from random import randint
import src.utils.logging as logging
from src.configs.config import get_cfg
from src.data import loader as data_loader
from src.engine.evaluator import Evaluator
from src.engine.trainer i... | Create configs and perform basic setups. |
20,447 | import glob
import numpy as np
import os
import torch
import warnings
import random
from time import sleep
from random import randint
import src.utils.logging as logging
from src.configs.config import get_cfg
from src.data import loader as data_loader
from src.engine.evaluator import Evaluator
from src.engine.trainer i... | null |
20,448 | import glob
import numpy as np
import os
import torch
import warnings
import random
from time import sleep
from random import randint
import src.utils.logging as logging
from src.configs.config import get_cfg
from src.data import loader as data_loader
from src.engine.evaluator import Evaluator
from src.engine.trainer i... | null |
20,449 | import argparse
import os
import sys
import pprint
import PIL
from collections import defaultdict
from tabulate import tabulate
from typing import Tuple
import torch
from src.utils.file_io import PathManager
from src.utils import logging
from src.utils.distributed import get_rank, get_world_size
The provided code snip... | create a simple parser to wrap around config file |
20,450 | import os
import re
import subprocess
from time import sleep
from Mooc.Mooc_Config import *
RE_SPEED = re.compile(r'\d+MiB/(\d+)MiB\((\d+)%\).*?DL:(\d*?\.?\d*?)([KM])iB')
RE_AVESPEED = re.compile(r'\|\s*?([\S]*?)([KM])iB/s\|')
class DownloadFailed(Exception):
pass
def clear_files(dirname, filename):
filepath = ... | null |
20,451 | from time import sleep
from functools import wraps
from socket import timeout, setdefaulttimeout
from urllib import request, parse
from urllib.error import ContentTooShortError, URLError, HTTPError
from Mooc.Mooc_Config import *
class RequestFailed(Exception):
pass
def request_decorate(count=3):
def decorate(f... | null |
20,452 | from time import sleep
from functools import wraps
from socket import timeout, setdefaulttimeout
from urllib import request, parse
from urllib.error import ContentTooShortError, URLError, HTTPError
from Mooc.Mooc_Config import *
request.install_opener(opener)
The provided code snippet includes necessary dependencies f... | get请求 |
20,453 | from time import sleep
from functools import wraps
from socket import timeout, setdefaulttimeout
from urllib import request, parse
from urllib.error import ContentTooShortError, URLError, HTTPError
from Mooc.Mooc_Config import *
request.install_opener(opener)
The provided code snippet includes necessary dependencies f... | post请求 |
20,454 | from time import sleep
from functools import wraps
from socket import timeout, setdefaulttimeout
from urllib import request, parse
from urllib.error import ContentTooShortError, URLError, HTTPError
from Mooc.Mooc_Config import *
request.install_opener(opener)
The provided code snippet includes necessary dependencies f... | head请求 |
20,455 | from time import sleep
from functools import wraps
from socket import timeout, setdefaulttimeout
from urllib import request, parse
from urllib.error import ContentTooShortError, URLError, HTTPError
from Mooc.Mooc_Config import *
request.install_opener(opener)
The provided code snippet includes necessary dependencies f... | 检查url是否可以访问 |
20,456 | import os
import re
from Mooc.Mooc_Config import *
from Mooc.Mooc_Request import *
from Mooc.Mooc_Download import *
from Mooc.Icourse163.Icourse163_Mooc import *
from Mooc.Icourses.Icourse_Cuoc import *
from Mooc.Icourses.Icourse_Mooc import *
def inquire():
redown = None
while redown not in ('y','n'):
... | null |
20,457 | import sys
import os
print(root_dir)
from sanic import Sanic
from sanic.response import json
from qanything_kernel.dependent_server.rerank_for_local_serve.rerank_server_backend import LocalRerankBackend
app = Sanic("rerank_server")
class LocalRerankBackend:
def __init__(self):
tokenizer_path = 'qanything_k... | null |
20,458 | import sys
import os
from sanic import Sanic
from sanic.response import json
from qanything_kernel.dependent_server.rerank_for_local_serve.rerank_server_backend import LocalRerankBackend
class LocalRerankBackend:
def __init__(self):
tokenizer_path = 'qanything_kernel/dependent_server/rerank_for_local_serve... | null |
20,459 |
async def ocr_request(request):
# 获取上传的文件
input = request.json
img_file = input['img64']
height = input['height']
width = input['width']
channels = input['channels']
binary_data = base64.b64decode(img_file)
img_array = np.frombuffer(binary_data, dtype=np.uint8).reshape((height, width,... | null |
20,460 | import enum
import logging
import time
from datetime import datetime
from enum import Enum
def log_timestamp() -> str:
return datetime.fromtimestamp(time.time()).strftime("%Y-%m-%d %H:%M:%S.%f") | null |
20,461 | import argparse
import asyncio
import json
import logging
import multiprocessing as mp
import os
import psutil
import queue
import string
import signal
import sys
import traceback
import time
import threading
import sanic
from sanic import Sanic, Request
from sanic.response import ResponseStream
from collections import... | null |
20,462 | import argparse
import asyncio
import json
import logging
import multiprocessing as mp
import os
import psutil
import queue
import string
import signal
import sys
import traceback
import time
import threading
import sanic
from sanic import Sanic, Request
from sanic.response import ResponseStream
from collections import... | null |
20,463 | import argparse
import asyncio
import json
import logging
import multiprocessing as mp
import os
import psutil
import queue
import string
import signal
import sys
import traceback
import time
import threading
import sanic
from sanic import Sanic, Request
from sanic.response import ResponseStream
from collections import... | null |
20,464 | import argparse
import asyncio
import json
import logging
import multiprocessing as mp
import os
import psutil
import queue
import string
import signal
import sys
import traceback
import time
import threading
import sanic
from sanic import Sanic, Request
from sanic.response import ResponseStream
from collections import... | null |
20,465 | import argparse
import asyncio
import json
import logging
import multiprocessing as mp
import os
import psutil
import queue
import string
import signal
import sys
import traceback
import time
import threading
import sanic
from sanic import Sanic, Request
from sanic.response import ResponseStream
from collections import... | null |
20,466 | import argparse
import asyncio
import json
import logging
import multiprocessing as mp
import os
import psutil
import queue
import string
import signal
import sys
import traceback
import time
import threading
import sanic
from sanic import Sanic, Request
from sanic.response import ResponseStream
from collections import... | null |
20,467 | from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
from dataclasses import dataclass
import logging as logger
class Template:
prefix: List[Union[str, Dict[str, str]]]
prompt: List[Union[str, Dict[str, str]]]
sep: List[Union[str, Dict[str, str]]]
stop_words: List[str]
use_history: b... | null |
20,468 | from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
from dataclasses import dataclass
import logging as logger
class Template:
prefix: List[Union[str, Dict[str, str]]]
prompt: List[Union[str, Dict[str, str]]]
sep: List[Union[str, Dict[str, str]]]
stop_words: List[str]
use_history: b... | null |
20,469 | import base64
import logging
import os
import unicodedata
from typing import Collection, Dict, List, Set, Tuple, Union
import tiktoken
from transformers import PreTrainedTokenizer, AddedToken
def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
with open(tiktoken_bpe_file, "rb") as f:
conten... | null |
20,470 | from qanything_kernel.configs.model_config import VECTOR_SEARCH_TOP_K, CHUNK_SIZE, VECTOR_SEARCH_SCORE_THRESHOLD, \
PROMPT_TEMPLATE, STREAMING
from typing import List
from qanything_kernel.connector.embedding.embedding_for_online import YouDaoEmbeddings
from qanything_kernel.connector.embedding.embedding_for_local ... | null |
20,471 |
async def add_cors_headers(request, response):
# response.headers["Access-Control-Allow-Origin"] = "http://10.234.10.144:5052"
response.headers["Access-Control-Allow-Origin"] = "*"
response.headers["Access-Control-Allow-Methods"] = "GET, POST, PUT, DELETE, OPTIONS"
response.headers["Access-Control-All... | null |
20,472 |
async def handle_options_request(request):
if request.method == "OPTIONS":
headers = {
# "Access-Control-Allow-Origin": "http://10.234.10.144:5052",
"Access-Control-Allow-Origin": "*",
"Access-Control-Allow-Methods": "GET, POST, PUT, DELETE, OPTIONS",
"Acces... | null |
20,473 |
class LocalDocQA:
def __init__(self):
self.llm: object = None
self.embeddings: object = None
self.top_k: int = VECTOR_SEARCH_TOP_K
self.chunk_size: int = CHUNK_SIZE
self.chunk_conent: bool = True
self.score_threshold: int = VECTOR_SEARCH_SCORE_THRESHOLD
self... | null |
20,474 | from qanything_kernel.core.local_file import LocalFile
from qanything_kernel.core.local_doc_qa import LocalDocQA
from qanything_kernel.utils.general_utils import *
from qanything_kernel.utils.custom_log import debug_logger, qa_logger
from sanic.response import ResponseStream
from sanic.response import json as sanic_jso... | null |
20,475 | from qanything_kernel.core.local_file import LocalFile
from qanything_kernel.core.local_doc_qa import LocalDocQA
from qanything_kernel.utils.general_utils import *
from qanything_kernel.utils.custom_log import debug_logger, qa_logger
from sanic.response import ResponseStream
from sanic.response import json as sanic_jso... | null |
20,476 | from qanything_kernel.core.local_file import LocalFile
from qanything_kernel.core.local_doc_qa import LocalDocQA
from qanything_kernel.utils.general_utils import *
from qanything_kernel.utils.custom_log import debug_logger, qa_logger
from sanic.response import ResponseStream
from sanic.response import json as sanic_jso... | null |
20,477 | from qanything_kernel.core.local_file import LocalFile
from qanything_kernel.core.local_doc_qa import LocalDocQA
from qanything_kernel.utils.general_utils import *
from qanything_kernel.utils.custom_log import debug_logger, qa_logger
from sanic.response import ResponseStream
from sanic.response import json as sanic_jso... | null |
20,478 | from qanything_kernel.core.local_file import LocalFile
from qanything_kernel.core.local_doc_qa import LocalDocQA
from qanything_kernel.utils.general_utils import *
from qanything_kernel.utils.custom_log import debug_logger, qa_logger
from sanic.response import ResponseStream
from sanic.response import json as sanic_jso... | null |
20,479 | from qanything_kernel.core.local_file import LocalFile
from qanything_kernel.core.local_doc_qa import LocalDocQA
from qanything_kernel.utils.general_utils import *
from qanything_kernel.utils.custom_log import debug_logger, qa_logger
from sanic.response import ResponseStream
from sanic.response import json as sanic_jso... | null |
20,480 | from qanything_kernel.core.local_file import LocalFile
from qanything_kernel.core.local_doc_qa import LocalDocQA
from qanything_kernel.utils.general_utils import *
from qanything_kernel.utils.custom_log import debug_logger, qa_logger
from sanic.response import ResponseStream
from sanic.response import json as sanic_jso... | null |
20,481 | from qanything_kernel.core.local_file import LocalFile
from qanything_kernel.core.local_doc_qa import LocalDocQA
from qanything_kernel.utils.general_utils import *
from qanything_kernel.utils.custom_log import debug_logger, qa_logger
from sanic.response import ResponseStream
from sanic.response import json as sanic_jso... | null |
20,482 | from qanything_kernel.core.local_file import LocalFile
from qanything_kernel.core.local_doc_qa import LocalDocQA
from qanything_kernel.utils.general_utils import *
from qanything_kernel.utils.custom_log import debug_logger, qa_logger
from sanic.response import ResponseStream
from sanic.response import json as sanic_jso... | null |
20,483 | from qanything_kernel.core.local_file import LocalFile
from qanything_kernel.core.local_doc_qa import LocalDocQA
from qanything_kernel.utils.general_utils import *
from qanything_kernel.utils.custom_log import debug_logger, qa_logger
from sanic.response import ResponseStream
from sanic.response import json as sanic_jso... | null |
20,484 | from qanything_kernel.core.local_file import LocalFile
from qanything_kernel.core.local_doc_qa import LocalDocQA
from qanything_kernel.utils.general_utils import *
from qanything_kernel.utils.custom_log import debug_logger, qa_logger
from sanic.response import ResponseStream
from sanic.response import json as sanic_jso... | null |
20,485 | from qanything_kernel.core.local_file import LocalFile
from qanything_kernel.core.local_doc_qa import LocalDocQA
from qanything_kernel.utils.general_utils import *
from qanything_kernel.utils.custom_log import debug_logger, qa_logger
from sanic.response import ResponseStream
from sanic.response import json as sanic_jso... | null |
20,486 | from sanic.request import Request
from sanic.exceptions import BadRequest
import traceback
from urllib.parse import urlparse
import time
import os
import logging
import re
import tiktoken
def get_invalid_user_id_msg(user_id):
return "fail, Invalid user_id: {}. user_id 必须只含有字母，数字和下划线且字母开头".format(user_id) | null |
20,487 | from sanic.request import Request
from sanic.exceptions import BadRequest
import traceback
from urllib.parse import urlparse
import time
import os
import logging
import re
import tiktoken
def write_check_file(filepath, docs):
folder_path = os.path.join(os.path.dirname(filepath), "tmp_files")
if not os.path.exi... | null |
20,488 | from sanic.request import Request
from sanic.exceptions import BadRequest
import traceback
from urllib.parse import urlparse
import time
import os
import logging
import re
import tiktoken
def isURL(string):
result = urlparse(string)
return result.scheme != '' and result.netloc != '' | null |
20,489 | from sanic.request import Request
from sanic.exceptions import BadRequest
import traceback
from urllib.parse import urlparse
import time
import os
import logging
import re
import tiktoken
def format_source_documents(ori_source_documents):
source_documents = []
for inum, doc in enumerate(ori_source_documents):
... | null |
20,490 | from sanic.request import Request
from sanic.exceptions import BadRequest
import traceback
from urllib.parse import urlparse
import time
import os
import logging
import re
import tiktoken
def get_time(func):
def inner(*arg, **kwargs):
s_time = time.time()
res = func(*arg, **kwargs)
e_time =... | null |
20,491 | from sanic.request import Request
from sanic.exceptions import BadRequest
import traceback
from urllib.parse import urlparse
import time
import os
import logging
import re
import tiktoken
def safe_get(req: Request, attr: str, default=None):
try:
if attr in req.form:
return req.form.getlist(attr... | null |
20,492 | from sanic.request import Request
from sanic.exceptions import BadRequest
import traceback
from urllib.parse import urlparse
import time
import os
import logging
import re
import tiktoken
def truncate_filename(filename, max_length=200):
# 获取文件名后缀
file_ext = os.path.splitext(filename)[1]
# 获取不带后缀的文件名
f... | null |
20,493 | from sanic.request import Request
from sanic.exceptions import BadRequest
import traceback
from urllib.parse import urlparse
import time
import os
import logging
import re
import tiktoken
def read_files_with_extensions():
# 获取当前脚本文件的路径
current_file = os.path.abspath(__file__)
# 获取当前脚本文件所在的目录
current_d... | null |
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