id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
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167,968 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.ops.deform_conv import DeformConv2d
import numpy as np
from einops import repeat
import timm
from pyiqa.utils.registry import ARCH_REGISTRY
from pyiqa.archs.arch_util import load_pretrained_network, to_2tuple
def get_attn_pad_mask(seq_... | null |
167,969 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.ops.deform_conv import DeformConv2d
import numpy as np
from einops import repeat
import timm
from pyiqa.utils.registry import ARCH_REGISTRY
from pyiqa.archs.arch_util import load_pretrained_network, to_2tuple
def get_attn_decoder_mask(... | null |
167,970 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.ops.deform_conv import DeformConv2d
import numpy as np
from einops import repeat
import timm
from pyiqa.utils.registry import ARCH_REGISTRY
from pyiqa.archs.arch_util import load_pretrained_network, to_2tuple
to_2tuple = _ntuple(2)
de... | null |
167,971 | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import torchvision.transforms.functional as TF
import timm
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
from pyiqa.utils.registry import ARCH_REGISTRY
from pyiqa.archs.arch_util ... | null |
167,972 | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import torchvision.transforms.functional as TF
import timm
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
from pyiqa.utils.registry import ARCH_REGISTRY
from pyiqa.archs.arch_util ... | Return an activation function given a string |
167,973 | import torch
from torch.nn import functional as F
import numpy as np
from pyiqa.utils.color_util import to_y_channel
from pyiqa.utils.registry import ARCH_REGISTRY
def sp5_filters():
r'''Define spatial filters.
'''
filters = {}
filters['harmonics'] = np.array([1, 3, 5])
filters['mtx'] = (
np... | r'''Construct a steerable pyramid on image. Args: image: A tensor. Shape :math:`(N, C, H, W)`. height (int): Number of pyramid levels to build. order (int): Number of orientations. channels (int): Number of channels. |
167,974 | import datetime
import logging
import time
import torch
import os
import numpy as np
from os import path as osp
from pyiqa.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher
from pyiqa.models import build_model
from pyiqa.utils import (AvgTimer, MessageLogger, get_root_logger, get_time_str, get_env_info, mak... | null |
167,978 | import math
import os
import requests
from torch.hub import download_url_to_file, get_dir
from tqdm import tqdm
from urllib.parse import urlparse
from .misc import sizeof_fmt
def get_confirm_token(response):
for key, value in response.cookies.items():
if key.startswith('download_warning'):
retur... | Download files from google drive. Ref: https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive # noqa E501 Args: file_id (str): File id. save_path (str): Save path. |
167,979 | import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid
import io
from PIL import Image
import torchvision.transforms.functional as TF
def is_image_file(filename):
return any(filename.lower().endswith(extension) for extension in Image.registered_extensions())
The pr... | Read image to tensor. Args: img_source (str, bytes, or PIL.Image): image filepath string, image contents as a bytearray or a PIL Image instance rgb: convert input to RGB if true |
167,980 | import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid
import io
from PIL import Image
import torchvision.transforms.functional as TF
def is_image_file(filename):
return any(filename.lower().endswith(extension) for extension in Image.registered_extensions())
The pr... | Read image to tensor. Args: img_source (str, bytes, or PIL.Image): image filepath string, image contents as a bytearray or a PIL Image instance rgb: convert input to RGB if true |
167,981 | import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid
import io
from PIL import Image
import torchvision.transforms.functional as TF
The provided code snippet includes necessary dependencies for implementing the `img2tensor` function. Write a Python function `def img2... | Numpy array to tensor. Args: imgs (list[ndarray] | ndarray): Input images. bgr2rgb (bool): Whether to change bgr to rgb. float32 (bool): Whether to change to float32. Returns: list[tensor] | tensor: Tensor images. If returned results only have one element, just return tensor. |
167,982 | import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid
import io
from PIL import Image
import torchvision.transforms.functional as TF
The provided code snippet includes necessary dependencies for implementing the `tensor2img` function. Write a Python function `def tens... | Convert torch Tensors into image numpy arrays. After clamping to [min, max], values will be normalized to [0, 1]. Args: tensor (Tensor or list[Tensor]): Accept shapes: 1) 4D mini-batch Tensor of shape (B x 3/1 x H x W); 2) 3D Tensor of shape (3/1 x H x W); 3) 2D Tensor of shape (H x W). Tensor channel should be in RGB ... |
167,983 | import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid
import io
from PIL import Image
import torchvision.transforms.functional as TF
The provided code snippet includes necessary dependencies for implementing the `tensor2img_fast` function. Write a Python function `def... | This implementation is slightly faster than tensor2img. It now only supports torch tensor with shape (1, c, h, w). Args: tensor (Tensor): Now only support torch tensor with (1, c, h, w). rgb2bgr (bool): Whether to change rgb to bgr. Default: True. min_max (tuple[int]): min and max values for clamp. |
167,984 | import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid
import io
from PIL import Image
import torchvision.transforms.functional as TF
The provided code snippet includes necessary dependencies for implementing the `imfrombytes` function. Write a Python function `def imf... | Read an image from bytes. Args: content (bytes): Image bytes got from files or other streams. flag (str): Flags specifying the color type of a loaded image, candidates are `color`, `grayscale` and `unchanged`. float32 (bool): Whether to change to float32., If True, will also norm to [0, 1]. Default: False. Returns: nda... |
167,985 | import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid
import io
from PIL import Image
import torchvision.transforms.functional as TF
The provided code snippet includes necessary dependencies for implementing the `imwrite` function. Write a Python function `def imwrite... | Write image to file. Args: img (ndarray): Image array to be written. file_path (str): Image file path. params (None or list): Same as opencv's :func:`imwrite` interface. auto_mkdir (bool): If the parent folder of `file_path` does not exist, whether to create it automatically. Returns: bool: Successful or not. |
167,986 | import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid
import io
from PIL import Image
import torchvision.transforms.functional as TF
The provided code snippet includes necessary dependencies for implementing the `crop_border` function. Write a Python function `def cro... | Crop borders of images. Args: imgs (list[ndarray] | ndarray): Images with shape (h, w, c). crop_border (int): Crop border for each end of height and weight. Returns: list[ndarray]: Cropped images. |
167,987 | import numpy as np
import os
import random
import time
import torch
from os import path as osp
import shutil
from .dist_util import master_only
The provided code snippet includes necessary dependencies for implementing the `set_random_seed` function. Write a Python function `def set_random_seed(seed=123)` to solve the... | Set random seeds. |
167,988 | import numpy as np
import os
import random
import time
import torch
from os import path as osp
import shutil
from .dist_util import master_only
def mkdir_and_rename(path):
"""mkdirs. If path exists, rename it with timestamp, create a new one, and move it to archive folder.
Args:
path (str): Folder path.... | Make dirs for experiments. |
167,989 | import numpy as np
import os
import random
import time
import torch
from os import path as osp
import shutil
from .dist_util import master_only
The provided code snippet includes necessary dependencies for implementing the `scandir` function. Write a Python function `def scandir(dir_path, suffix=None, recursive=False,... | Scan a directory to find the interested files. Args: dir_path (str): Path of the directory. suffix (str | tuple(str), optional): File suffix that we are interested in. Default: None. recursive (bool, optional): If set to True, recursively scan the directory. Default: False. full_path (bool, optional): If set to True, i... |
167,990 | import numpy as np
import os
import random
import time
import torch
from os import path as osp
import shutil
from .dist_util import master_only
The provided code snippet includes necessary dependencies for implementing the `check_resume` function. Write a Python function `def check_resume(opt, resume_iter)` to solve t... | Check resume states and pretrain_network paths. Args: opt (dict): Options. resume_iter (int): Resume iteration. |
167,991 | from typing import Union, Dict
import torch
The provided code snippet includes necessary dependencies for implementing the `ycbcr2rgb` function. Write a Python function `def ycbcr2rgb(x: torch.Tensor) -> torch.Tensor` to solve the following problem:
r"""Convert a batch of YCbCr images to a batch of RGB images It imple... | r"""Convert a batch of YCbCr images to a batch of RGB images It implements the inversion of the above rgb2ycbcr function. Args: x: Batch of images with shape (N, 3, H, W). YCbCr color space, range [0, 1]. Returns: Batch of images with shape (N, 3, H, W). RGB color space. |
167,993 | import datetime
import logging
import time
from .dist_util import get_dist_info, master_only
def get_root_logger(logger_name='pyiqa', log_level=logging.INFO, log_file=None):
"""Get the root logger.
The logger will be initialized if it has not been initialized. By default a
StreamHandler will be added. If `l... | We now only use wandb to sync tensorboard log. |
167,994 | import datetime
import logging
import time
from .dist_util import get_dist_info, master_only
The provided code snippet includes necessary dependencies for implementing the `get_env_info` function. Write a Python function `def get_env_info()` to solve the following problem:
Get environment information. Currently, only ... | Get environment information. Currently, only log the software version. |
167,995 | import argparse
import yaml
import csv
import pandas as pd
from itertools import chain
from pyiqa.data import build_dataset, build_dataloader
from pyiqa.default_model_configs import DEFAULT_CONFIGS
from pyiqa.utils.options import ordered_yaml
from pyiqa.metrics import calculate_plcc, calculate_srcc, calculate_krcc
from... | null |
167,996 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
import pandas as pd
def get_meta_info():
root_dir = '../datasets/LIVEmultidistortiondatabase/'
parts = ['Part 1', 'Part 2']
sub_img_folders = ['blurjpeg', 'blurnoise']
save_meta_path = './datasets/meta_info/meta_info... | null |
167,997 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
import pandas as pd
def get_random_splits(seed=123):
random.seed(seed)
meta_info_file = './datasets/meta_info/meta_info_LIVEMDDataset.csv'
save_path = f'./datasets/meta_info/livemd_{seed}.pkl'
ratio = 0.8
meta_inf... | null |
167,998 | import os
import scipy.io as sio
import numpy as np
from PIL import Image
import pickle
import csv
from tqdm import tqdm
import random
The provided code snippet includes necessary dependencies for implementing the `get_meta_info` function. Write a Python function `def get_meta_info(seed=123)` to solve the following pr... | Generate meta information and train/val/test splits for AVA dataset. The split follows: - split index 1: official, https://github.com/imfing/ava_downloader/blob/master/AVA_dataset/aesthetics_image_lists/generic_test.jpgl - split index 2: https://github.com/BestiVictory/ILGnet/tree/local/data/AVA1 |
167,999 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `get_meta_info` function. Write a Python function `def get_meta_info()` to solve the following problem:
Train/Val/Test split file fro... | Train/Val/Test split file from official github: https://github.com/subpic/koniq/blob/master/metadata/koniq10k_distributions_sets.csv |
168,000 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
import pandas as pd
from tqdm import tqdm
def get_meta_info():
mos_label_file = '../datasets/SPAQ/Annotations/MOS and Image attribute scores.xlsx'
scene_label_file = '../datasets/SPAQ/Annotations/Scene category labels.xlsx'
... | null |
168,001 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
import pandas as pd
from tqdm import tqdm
def get_random_splits(seed=123):
random.seed(seed)
total_num = 11125
all_img_index = list(range(total_num))
num_splits = 10
save_path = f'./datasets/meta_info/spaq_seed{see... | null |
168,002 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
import pandas as pd
from tqdm import tqdm
from glob import glob
def get_meta_info():
train_label_folder = '../datasets/PIPAL/Train_Label/'
name_labels = []
for f in sorted(glob(train_label_folder + '*.txt')):
nam... | null |
168,003 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
import pandas as pd
def get_random_splits(seed=123):
random.seed(seed)
meta_info_file = '../datasets/meta_info/meta_info_GFIQADataset.csv'
meta_info = pd.read_csv(meta_info_file)
img_list = meta_info['img_name'].... | null |
168,004 | import numpy as np
import torch
from pyiqa.archs.musiq_arch import MUSIQ
def check_same(x, y):
return np.abs(y - x).mean() < np.abs(x.min())
tf_params = np.load(ckpt_path)
tf_keys = [k for k in tf_params.keys() if 'target' in k]
th_params = musiq_model.state_dict()
tf_params = np.load(ckpt_path)
tf_keys = [k for k ... | assign module with the same keywords |
168,005 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
import pandas as pd
from tqdm import tqdm
def get_meta_info():
patch_label_file = '../../PaQ-2-PiQ/database/labels_patch.csv'
img_label_file = '../../PaQ-2-PiQ/database/labels_image.csv'
test_label_file = '../../PaQ-2-PiQ... | null |
168,006 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
from tqdm import tqdm
def get_meta_info():
info_file = '../datasets/kadid10k/dmos.csv'
# save_meta_path = './datasets/meta_info/meta_info_KADID10kDataset.txt'
# with open(info_file, 'r') as f, open(save_meta_path, 'w+')... | null |
168,007 | import os
import random
import numpy
import pickle
import csv
import pandas as pd
def get_meta_info(root_dir, save_meta_path):
attrs = ['Details', 'Exposure', 'Overall']
rows_all = []
for att in attrs:
tmp_row = []
# read labels
lpath = f'{root_dir}/Scores_{att}.csv'
lr... | null |
168,008 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
def get_meta_info():
root_dir = '../datasets/LIVEC/'
names = sio.loadmat(os.path.join(root_dir, 'Data', 'AllImages_release.mat'))
mos_labels = sio.loadmat(os.path.join(root_dir, 'Data', 'AllMOS_release.mat'))
mos_std =... | null |
168,009 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
def get_random_splits(seed=123):
random.seed(seed)
all_img_index = list(range(1162))
num_splits = 10
ratio = [0.8, 0.2] # train/val/test
sep_index = int(round(0.8 * 1162))
save_path = f'./datasets/meta_info/... | null |
168,010 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
import pandas as pd
def get_meta_info():
root_dir = '../datasets/CSIQ/'
label_file = '../datasets/CSIQ/csiq_label.txt'
name_dmos = [x.strip().split() for x in open(label_file).readlines()]
save_meta_path = './dataset... | null |
168,011 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
import pandas as pd
def get_random_splits(seed=123):
random.seed(seed)
meta_info_file = './datasets/meta_info/meta_info_CSIQDataset.csv'
save_path = f'./datasets/meta_info/csiq_{seed}.pkl'
ratio = 0.8
meta_info = ... | null |
168,012 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
import pandas as pd
from tqdm import tqdm
def get_meta_info():
root_path = '../datasets/PieAPP_dataset_CVPR_2018/'
train_list_file = '../datasets/PieAPP_dataset_CVPR_2018/train_reference_list.txt'
val_list_file = '../data... | null |
168,013 | import os
import scipy.io as sio
import random
import numpy as np
import pickle
import csv
import pandas as pd
from tqdm import tqdm
from glob import glob
from pyiqa.utils.img_util import is_image_file
def is_image_file(filename):
def make_dataset(dir, max_dataset_size=float('inf')):
images = []
assert os.pat... | null |
168,014 | import os
import scipy.io as sio
import random
import numpy as np
import pickle
import csv
import pandas as pd
from tqdm import tqdm
from glob import glob
from pyiqa.utils.img_util import is_image_file
def get_meta_info():
# 2afc triplets
root_dir = '../datasets/PerceptualSimilarity/dataset/2afc'
ref_dir... | null |
168,015 | import os
import random
import numpy
import pickle
import csv
import pandas as pd
def get_meta_info(root_dir, save_meta_path):
mos_file = os.path.join(root_dir, 'mos_with_names.txt')
std_file = os.path.join(root_dir, 'mos_std.txt')
mos_names = [x.strip().split() for x in open(mos_file).readlines()]
st... | null |
168,016 | import os
import random
import numpy
import pickle
import csv
import pandas as pd
def get_random_splits(meta_info_file, save_path, seed=123):
random.seed(seed)
# meta_info_file = './datasets/meta_info/meta_info_CSIQDataset.csv'
# save_path = f'./datasets/meta_info/csiq_{seed}.pkl'
ratio = 0.8
meta... | null |
168,017 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
import pandas as pd
def get_meta_info():
root_dir = '../datasets/LIVEIQA_release2/'
dmos = sio.loadmat(os.path.join(root_dir, 'dmos.mat')) # difference of mos: test - ref. lower is better
mos = dmos['dmos'][0]
org_fl... | null |
168,018 | import os
import scipy.io as sio
import random
import numpy
import pickle
import csv
import pandas as pd
def get_random_splits(seed=123):
random.seed(seed)
meta_info_file = './datasets/meta_info/meta_info_LIVEIQADataset.csv'
save_path = f'./datasets/meta_info/live_{seed}.pkl'
ratio = 0.8
meta_info... | null |
168,019 | from solidgpt.src.orchestration.orchestration import *
def quick_start(category:str):
Initializer()
app = Orchestration()
app.add_graph(os.path.join(LOCAL_STORAGE_DIR, "workspace", "config", f"{category}_config_data.json"), "default graph")
app.run_graph_with_name("default graph") | null |
168,020 | import logging
import os
import sys
from solidgpt.definitions import LOCAL_STORAGE_DIR
from solidgpt.src.manager.embedding.embeddingmanager import EmbeddingManager
from solidgpt.src.manager.embedding.embeddingmodel import EmbeddingModelParameter
LOCAL_STORAGE_DIR = os.path.join(ROOT_DIR, "../localstorage")
class Embe... | null |
168,021 | from solidgpt.src.orchestration.orchestration import *
def generate_node_prd(node_id: str, input_ids: list[int], output_ids: list[int], manual_review_result: bool = False, input_path = None):
# write prd skill
skill: WorkSkill = WritePRD()
skill.init_config(
[
{
"param_pa... | null |
168,022 | from solidgpt.src.orchestration.orchestration import *
def generate_node_prd(node_id: str, input_ids: list[int], output_ids: list[int], manual_review_result: bool = False, input_path = None):
def generate_node_hld(node_id: str, input_ids: list[int], output_ids: list[int], manual_review_result: bool = False):
def genera... | null |
168,023 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,024 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,025 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,026 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,027 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,028 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,029 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,030 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,031 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,032 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,033 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,034 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,035 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,036 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,037 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,038 | import logging
import uuid
from pydantic import BaseModel
from solidgpt.src.api.api_response import *
from solidgpt.src.configuration.configreader import ConfigReader
from solidgpt.src.manager.initializer import Initializer
from fastapi import FastAPI, UploadFile, File, HTTPException, Body, BackgroundTasks
from fastapi... | null |
168,039 | import base64
import time
import uuid
from celery import Celery
import shutil
from pathlib import Path
from solidgpt.src.constants import *
from solidgpt.src.util.util import *
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import WorkNode
from solidgpt.src.workskill.workskil... | null |
168,040 | import base64
import time
import uuid
from celery import Celery
import shutil
from pathlib import Path
from solidgpt.src.constants import *
from solidgpt.src.util.util import *
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import WorkNode
from solidgpt.src.workskill.workskil... | null |
168,041 | import base64
import time
import uuid
from celery import Celery
import shutil
from pathlib import Path
from solidgpt.src.constants import *
from solidgpt.src.util.util import *
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import WorkNode
from solidgpt.src.workskill.workskil... | null |
168,042 | import base64
import time
import uuid
from celery import Celery
import shutil
from pathlib import Path
from solidgpt.src.constants import *
from solidgpt.src.util.util import *
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import WorkNode
from solidgpt.src.workskill.workskil... | null |
168,043 | import base64
import time
import uuid
from celery import Celery
import shutil
from pathlib import Path
from solidgpt.src.constants import *
from solidgpt.src.util.util import *
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import WorkNode
from solidgpt.src.workskill.workskil... | null |
168,044 | import base64
import time
import uuid
from celery import Celery
import shutil
from pathlib import Path
from solidgpt.src.constants import *
from solidgpt.src.util.util import *
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import WorkNode
from solidgpt.src.workskill.workskil... | null |
168,045 | import base64
import time
import uuid
from celery import Celery
import shutil
from pathlib import Path
from solidgpt.src.constants import *
from solidgpt.src.util.util import *
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import WorkNode
from solidgpt.src.workskill.workskil... | null |
168,046 | import base64
import time
import uuid
from celery import Celery
import shutil
from pathlib import Path
from solidgpt.src.constants import *
from solidgpt.src.util.util import *
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import WorkNode
from solidgpt.src.workskill.workskil... | null |
168,047 | import base64
import time
import uuid
from celery import Celery
import shutil
from pathlib import Path
from solidgpt.src.constants import *
from solidgpt.src.util.util import *
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import WorkNode
from solidgpt.src.workskill.workskil... | null |
168,048 | import base64
import time
import uuid
from celery import Celery
import shutil
from pathlib import Path
from solidgpt.src.constants import *
from solidgpt.src.util.util import *
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import WorkNode
from solidgpt.src.workskill.workskil... | null |
168,049 |
def response_upload(message="", status="", progress="", error=""):
if progress == "":
progress = {}
return {
"message": message,
"status": status,
"progress": progress,
"error": error,
} | null |
168,050 |
def response_graph(graph="", message="", status="", progress="", error="", result=""):
if progress == "":
progress = {}
return {
"graph": graph,
"message": message,
"status": status,
"progress": progress,
"error": error,
"result": result,
} | null |
168,051 |
def response_serverless(message="", status="", error=""):
return {
"message": message,
"status": status,
"error": error,
} | null |
168,052 | import json
import os
import tiktoken
import numpy as np
from collections import defaultdict
for message in dataset[0]["messages"]:
print(message)
encoding = tiktoken.get_encoding("cl100k_base")
def num_tokens_from_messages(messages, tokens_per_message=3, tokens_per_name=1):
num_tokens = 0
for message in m... | null |
168,053 | import json
import os
import tiktoken
import numpy as np
from collections import defaultdict
for message in dataset[0]["messages"]:
print(message)
encoding = tiktoken.get_encoding("cl100k_base")
def num_assistant_tokens_from_messages(messages):
num_tokens = 0
for message in messages:
if message["ro... | null |
168,054 | import json
import os
import tiktoken
import numpy as np
from collections import defaultdict
print("Num examples:", len(dataset))
print("First example:")
print("Num examples missing system message:", n_missing_system)
print("Num examples missing user message:", n_missing_user)
print(f"\n{n_too_long} examples may be ove... | null |
168,055 | import openai
import logging
import asyncio
async def wait_for_finetuning_complete():
while True:
status = await finetune_instance.get_fine_tuning_status()
logging.info("Fine-tuning status: %s", status)
if status == "succeeded" or status == "failed":
break
... | null |
168,056 | from typing import Type
from solidgpt.src.diy.custom.customizeskillmanager import CustomizeSkillManager
from solidgpt.src.worknode.worknode import *
from solidgpt.src.imports import *
from solidgpt.src.constants import *
def generate_save_data_from_nodes(nodes: list[WorkNode], generate_debug_info: bool = False):
s... | null |
168,057 | from typing import Type
from solidgpt.src.diy.custom.customizeskillmanager import CustomizeSkillManager
from solidgpt.src.worknode.worknode import *
from solidgpt.src.imports import *
from solidgpt.src.constants import *
SKILL_NAME_TO_CONSTRUCTOR: dict[str, Type[WorkSkill]] = {
SKILL_NAME_WRITE_PRODUCT_REQUIREMENTS... | null |
168,058 | from enum import Enum
class SkillInputLoadingMethod(Enum):
LOAD_FROM_OUTPUT_ID = 1
LOAD_FROM_STRING = 2
LOAD_FROM_CACHE_STRING = 3
STRING_TO_SKILL_INPUT_LOADING_METHOD_DICT: dict[str, SkillInputLoadingMethod] = {
str(SkillInputLoadingMethod.LOAD_FROM_OUTPUT_ID): SkillInputLoadingMethod.LOAD_FROM_OUTPUT_... | null |
168,059 | import os
import glob
import openai
from solidgpt.definitions import LOCAL_STORAGE_OUTPUT_DIR, TEST_SKILL_WORKSPACE
from solidgpt.src.manager.gptmanager import GPTManager
from solidgpt.src.util.util import save_to_json
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import Wor... | null |
168,060 | import os
import glob
import openai
from solidgpt.definitions import LOCAL_STORAGE_OUTPUT_DIR, TEST_SKILL_WORKSPACE
from solidgpt.src.manager.gptmanager import GPTManager
from solidgpt.src.util.util import save_to_json
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import Wor... | null |
168,061 | import os
import glob
import openai
from solidgpt.definitions import LOCAL_STORAGE_OUTPUT_DIR, TEST_SKILL_WORKSPACE
from solidgpt.src.manager.gptmanager import GPTManager
from solidgpt.src.util.util import save_to_json
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import Wor... | null |
168,062 | import os
import glob
import openai
from solidgpt.definitions import LOCAL_STORAGE_OUTPUT_DIR, TEST_SKILL_WORKSPACE
from solidgpt.src.manager.gptmanager import GPTManager
from solidgpt.src.util.util import save_to_json
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import Wor... | null |
168,063 | import os
import glob
import openai
from solidgpt.definitions import LOCAL_STORAGE_OUTPUT_DIR, TEST_SKILL_WORKSPACE
from solidgpt.src.manager.gptmanager import GPTManager
from solidgpt.src.util.util import save_to_json
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import Wor... | null |
168,064 | import os
import glob
import openai
from solidgpt.definitions import LOCAL_STORAGE_OUTPUT_DIR, TEST_SKILL_WORKSPACE
from solidgpt.src.manager.gptmanager import GPTManager
from solidgpt.src.util.util import save_to_json
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import Wor... | null |
168,065 | import os
import glob
import openai
from solidgpt.definitions import LOCAL_STORAGE_OUTPUT_DIR, TEST_SKILL_WORKSPACE
from solidgpt.src.manager.gptmanager import GPTManager
from solidgpt.src.util.util import save_to_json
from solidgpt.src.workgraph.workgraph import WorkGraph
from solidgpt.src.worknode.worknode import Wor... | null |
168,066 |
def get_custom_skills_assumption_role_prompt(question_subject):
return f"""Assume you are the expert of {question_subject}.
I want to know the list of top 5 essential actual hard skills (no softskill) for the {question_subject}. Can you please list them for me and use && sign to seperate them?""" | null |
168,067 |
def build_gpt_prompt(role_assumption: str, output_format: str):
return f"{role_assumption}\n\nAlways follow the Output format which is: {output_format}" | null |
168,068 |
def build_gpt_standard_prompt(role_assumption: str, description: str, output_format: str):
return f"{role_assumption}\n\nThis task description: {description}\n\n Output format: {output_format}" | null |
168,069 |
def build_custom_skill_gpt_prompt(role_assumption: str, instruction: str, principles: str, few_shots: str):
return f'''{role_assumption}\n\n
Here are instruction, always response follow the instruction: {instruction}\n\n
Here are principles you need to always follow when give the response: {principles}\n... | null |
168,070 |
The provided code snippet includes necessary dependencies for implementing the `llama_v2_prompt` function. Write a Python function `def llama_v2_prompt(messages)` to solve the following problem:
Convert the messages in list of dictionary format to Llama2 compliant format.
Here is the function:
def llama_v2_prompt(m... | Convert the messages in list of dictionary format to Llama2 compliant format. |
168,071 | import logging
import autogen
from autogen import oai
import openai
from solidgpt.src.configuration.configreader import ConfigReader
from typing import Callable, Dict, Optional, Union
from solidgpt.src.manager.promptresource import DEFAULT_SYSTEM_MESSAGE, ASSISTANT_SYSTEM_MESSAGE
def colored(x, *args, **kwargs):
r... | null |
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