id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
26,089 | import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet... | Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet |
26,090 | import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet... | Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet |
26,091 | import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet... | Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet |
26,092 | import math
import torch
from torch import nn
The provided code snippet includes necessary dependencies for implementing the `conv3x3` function. Write a Python function `def conv3x3(in_planes, out_planes, stride=1)` to solve the following problem:
3x3 convolution with padding
Here is the function:
def conv3x3(in_pla... | 3x3 convolution with padding |
26,093 | import os
def gen_train_file(data_root, train_file):
train_file_buf = open(train_file, 'w')
id_list = os.listdir(data_root)
id_list.sort()
for label, id_name in enumerate(id_list):
cur_id_folder = os.path.join(data_root, id_name)
cur_img_list = os.listdir(cur_id_folder)
cur_img_... | null |
26,094 | import os
import sys
import shutil
import argparse
import logging as logger
import torch
from torch import optim
from torch.utils.data import DataLoader
from loss_def import MVFace
from utils.dataset import ImageDataset
from utils.AverageMeter import AverageMeter
from utils.make_transform import make_transform
def trai... | null |
26,095 | import os
import random
import cv2
import torch
import numpy as np
from torch.utils.data import Dataset
from PIL import Image, ImageFile
The provided code snippet includes necessary dependencies for implementing the `read_image` function. Write a Python function `def read_image(img_path)` to solve the following proble... | Keep reading image until succeed. This can avoid IOError incurred by heavy IO process. |
26,096 | import os
import gc
import argparse
import numpy as np
import logging as logger
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from datasets import make_dataloader
from config import config as cfg
from models import make_model
from losses import SoftLoss, SP_KD_Loss
from utils import write_conf... | null |
26,097 | import os
import gc
import argparse
import numpy as np
import logging as logger
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from datasets import make_dataloader
from config import config as cfg
from models import make_model
from losses import SoftLoss, SP_KD_Loss
from utils import write_conf... | Computes the precision@k for the specified values of k |
26,098 | import os
from PIL import Image
import torch
import torch.nn.functional as F
import torchvision.transforms as T
from models.make_target_model import make_target_model
cfg = Config()
cfg.ori_shape = (256, 256)
cfg.image_crop_size = (224, 224)
cfg.normalize_mean = [0.5, 0.5, 0.5]
cfg.normalize_std = [0.5, 0.5, 0.5]
cfg.l... | null |
26,099 | import os
import getpass
def parse_lb_txt(filename):
lines = open(filename, 'r').readlines()
train_dataset, test_dataset = [], []
for line in lines:
key, label = line.split(' ')[0], line[-2]
label = int(label)
mode, img_path = key.split('_') #
if mode == 'train':
... | null |
26,100 | import os
import gc
import argparse
import numpy as np
import logging as logger
import argparse
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.utils.data.distributed
import torch.backends.cudnn as cudnn
from apex.parallel import DistributedDataParallel
from apex.parallel import convert... | null |
26,101 | import os
import gc
import argparse
import numpy as np
import logging as logger
import argparse
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.utils.data.distributed
import torch.backends.cudnn as cudnn
from apex.parallel import DistributedDataParallel
from apex.parallel import convert... | Computes the precision@k for the specified values of k |
26,102 | import os
import gc
import argparse
import numpy as np
import logging as logger
import argparse
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.utils.data.distributed
import torch.backends.cudnn as cudnn
from apex.parallel import DistributedDataParallel
from apex.parallel import convert... | null |
26,103 | import torch
from torch.nn.functional import interpolate
from torchvision.transforms import functional as F
from torchvision.ops.boxes import batched_nms
from PIL import Image
import numpy as np
import os
import math
def fixed_batch_process(im_data, model):
batch_size = 512
out = []
for i in range(0, len(im... | null |
26,104 | import torch
from torch.nn.functional import interpolate
from torchvision.transforms import functional as F
from torchvision.ops.boxes import batched_nms
from PIL import Image
import numpy as np
import os
import math
def crop_resize(img, box, image_size):
if isinstance(img, np.ndarray):
img = img[box[1]:box... | Extract face + margin from PIL Image given bounding box. Arguments: img {PIL.Image} -- A PIL Image. box {numpy.ndarray} -- Four-element bounding box. image_size {int} -- Output image size in pixels. The image will be square. margin {int} -- Margin to add to bounding box, in terms of pixels in the final image. Note that... |
26,105 | import torch
from torch import nn
import numpy as np
import os
from .detect_face import detect_face, extract_face
def fixed_image_standardization(image_tensor):
processed_tensor = (image_tensor - 127.5) / 128.0
return processed_tensor | null |
26,106 | import torch
from torch import nn
import numpy as np
import os
from .detect_face import detect_face, extract_face
def prewhiten(x):
mean = x.mean()
std = x.std()
std_adj = std.clamp(min=1.0/(float(x.numel())**0.5))
y = (x - mean) / std_adj
return y | null |
26,107 | import os
import cv2
import numpy as np
from tqdm import tqdm
from PIL import Image
from mtcnn import MTCNN
def mtcnn_detect(mtcnn, image):
boxes, probs = mtcnn.detect(image, landmarks=False)
if boxes is not None:
if boxes[0][0] < 0:
boxes[0][0] = 0
if boxes[0][1] < 0:
bo... | null |
26,108 | import sys
import os
import random
import glob
import torch
from skimage import io
from skimage import transform as ski_transform
from skimage.color import rgb2gray
import scipy.io as sio
from scipy import interpolate
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
fr... | null |
26,110 | import matplotlib
import math
import torch
import copy
import time
from torch.autograd import Variable
import shutil
from skimage import io
import numpy as np
from utils.utils import fan_NME, show_landmarks, get_preds_fromhm
from PIL import Image, ImageDraw
import os
import sys
import cv2
import matplotlib.pyplot as pl... | null |
26,111 | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from .coord_conv import CoordConvTh
The provided code snippet includes necessary dependencies for implementing the `conv3x3` function. Write a Python function `def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False,dil... | 3x3 convolution with padding |
26,112 | import matplotlib
import math
import torch
import copy
import time
from torch.autograd import Variable
import shutil
from skimage import io
import numpy as np
from .utils.utils import fan_NME, show_landmarks, get_preds_fromhm
from PIL import Image, ImageDraw
from pylab import *
import os
import sys
import cv2
import ma... | null |
26,113 | import matplotlib
import math
import torch
import copy
import time
from torch.autograd import Variable
import shutil
from skimage import io
import numpy as np
from .utils.utils import fan_NME, show_landmarks, get_preds_fromhm
from PIL import Image, ImageDraw
from pylab import *
import os
import sys
import cv2
import ma... | null |
26,114 | from __future__ import print_function, division
import os
import sys
import math
import torch
import cv2
from PIL import Image
from skimage import io
from skimage import transform as ski_transform
from scipy import ndimage
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from torch.utils.data import... | null |
26,115 | from __future__ import print_function, division
import os
import sys
import math
import torch
import cv2
from PIL import Image
from skimage import io
from skimage import transform as ski_transform
from scipy import ndimage
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from torch.utils.data import... | null |
26,116 | from __future__ import print_function, division
import os
import sys
import math
import torch
import cv2
from PIL import Image
from skimage import io
from skimage import transform as ski_transform
from scipy import ndimage
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from torch.utils.data import... | Show image with pred_landmarks |
26,117 | from __future__ import print_function, division
import os
import sys
import math
import torch
import cv2
from PIL import Image
from skimage import io
from skimage import transform as ski_transform
from scipy import ndimage
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from torch.utils.data import... | Calculate total NME for a batch of data Args: pred_heatmaps: torch tensor of size [batch, points, height, width] gt_landmarks: torch tesnsor of size [batch, points, x, y] Returns: nme: sum of nme for this batch |
26,118 | from __future__ import print_function, division
import os
import sys
import math
import torch
import cv2
from PIL import Image
from skimage import io
from skimage import transform as ski_transform
from scipy import ndimage
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from torch.utils.data import... | null |
26,119 | from __future__ import print_function, division
import os
import sys
import math
import torch
import cv2
from PIL import Image
from skimage import io
from skimage import transform as ski_transform
from scipy import ndimage
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from torch.utils.data import... | null |
26,120 | from __future__ import print_function, division
import os
import sys
import math
import torch
import cv2
from PIL import Image
from skimage import io
from skimage import transform as ski_transform
from scipy import ndimage
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from torch.utils.data import... | null |
26,121 | from __future__ import print_function, division
import os
import sys
import math
import torch
import cv2
from PIL import Image
from skimage import io
from skimage import transform as ski_transform
from scipy import ndimage
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from torch.utils.data import... | @brief Convert a Matplotlib figure to a 4D numpy array with RGBA channels and return it @param fig a matplotlib figure @return a numpy 3D array of RGBA values |
26,122 | import os
import argparse
from collections import OrderedDict
import torch
def convert(ori_path, dst_path, num_classes):
num_branches = num_classes + 1
state_dict = torch.load(ori_path, map_location=lambda storage,loc: storage.cpu())
new_state_dict = OrderedDict()
for key in state_dict:
if 'lay... | null |
26,123 | import cv2
import numpy as np
def add_gaussian_noise(image_array, mean=0.0, var=30):
std = var**0.5
noisy_img = image_array + np.random.normal(mean, std, image_array.shape)
noisy_img_clipped = np.clip(noisy_img, 0, 255).astype(np.uint8)
return noisy_img_clipped | null |
26,124 | import cv2
import numpy as np
def flip_image(image_array):
return cv2.flip(image_array, 1) | null |
26,125 | import cv2
import numpy as np
def color2gray(image_array):
gray = cv2.cvtColor(image_array, cv2.COLOR_RGB2GRAY)
gray_img_3d = image_array.copy()
gray_img_3d[:, :, 0] = gray
gray_img_3d[:, :, 1] = gray
gray_img_3d[:, :, 2] = gray
return gray_img_3d | null |
26,126 | import os
import lmdb
import random
import numpy as np
from torch.utils.data import Dataset
from .image_utils import add_gaussian_noise
The provided code snippet includes necessary dependencies for implementing the `read_lmdb` function. Write a Python function `def read_lmdb(key, txn)` to solve the following problem:
... | Keep reading image until succeed. This can avoid IOError incurred by heavy IO process. |
26,127 | import torch
import torch.nn as nn
from .resnet import ResNet, BasicBlock, Bottleneck
from .resnet_ibn_a import ResNet_IBN, Bottleneck_IBN
class Backbone(nn.Module):
def __init__(self, cfg):
super(Backbone, self).__init__()
last_stride = cfg.last_stride
model_name = cfg.backbone
self... | null |
26,129 | import math
import torch
import torch.nn as nn
class Bottleneck_IBN(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, ibn=False, stride=1, downsample=None):
super(Bottleneck_IBN, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
if ibn:
... | Constructs a ResNet-50 model. |
26,130 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from .resnet_multibranch import ResNet, BasicBlock, Bottleneck
from .resnet_ibn_multibranch import resnet50_ibn_a
def weights_init_kaiming(m):
classname = m.__class__.__name__
if classname.find('Linear')... | null |
26,131 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from .resnet_multibranch import ResNet, BasicBlock, Bottleneck
from .resnet_ibn_multibranch import resnet50_ibn_a
def weights_init_classifier(m):
classname = m.__class__.__name__
if classname.find('Linea... | null |
26,133 | import os
def write_config_into_log(cfg):
attrs = dir(cfg)
no_print_attributes = ['ckpt_root_dir', 'default_seed', 'get_lr',
'tb_dump_interval', 'iter_per_epoch', 'program_name',
'this_model_dir', 'train_data_num_thread', 'train_dp_name',
... | null |
26,134 | import math
def ramp_up(epoch, alpha, lamda=1):
if epoch > alpha:
return lamda
else:
w = lamda * math.exp(-5*math.pow((1-epoch/alpha), 2))
return w | null |
26,135 | import math
def ramp_down(epoch, alpha, lamda=1):
if epoch < alpha:
return lamda
else:
w = lamda * math.exp(-1*math.pow((1-alpha/epoch), 2))
return w | null |
26,136 | from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, ReLU, Sigmoid, Dropout2d, Dropout, AvgPool2d, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module, Parameter
import torch.nn.functional as F
import torch
from collections import namedtuple
def get_block(in_channel, depth, num_units, stride = 2):
retur... | null |
26,137 | import os
import sys
import shutil
import argparse
import logging as logger
import torch
from torch import optim
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from backbone.backbone_def import BackboneFactory
from utils.AverageMeter import AverageMeter
from data_processor.train_dataset ... | Total training procedure. |
26,138 | import os
import sys
import shutil
import argparse
import logging as logger
import torch
from torch import optim
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from backbone.backbone_def import BackboneFactory
from loss.loss_def import KDLossFactory
from utils.AverageMeter import Average... | Total training procedure. |
26,139 | import os
The provided code snippet includes necessary dependencies for implementing the `gen_train_file` function. Write a Python function `def gen_train_file(data_root, train_file)` to solve the following problem:
Generate the train file, which has the following format. relative_path0 label0 relative_path1 label1 re... | Generate the train file, which has the following format. relative_path0 label0 relative_path1 label1 relative_path2 label2 |
26,140 | import os
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import argparse
import gc
import logging
import sys
import time
fro... | Runs the routine. Args: cfg: config object with all the hyperparameters |
26,141 | import logging
import os
from llm_studio.app_utils.sections.chat_update import is_app_blocked_while_streaming
from llm_studio.src.utils.logging_utils import initialize_logging
from h2o_wave import Q, app, copy_expando, main, ui
from llm_studio.app_utils.handlers import handle
from llm_studio.app_utils.initializers imp... | null |
26,142 | import logging
import os
from llm_studio.app_utils.sections.chat_update import is_app_blocked_while_streaming
from llm_studio.src.utils.logging_utils import initialize_logging
from h2o_wave import Q, app, copy_expando, main, ui
from llm_studio.app_utils.handlers import handle
from llm_studio.app_utils.initializers imp... | Serving function. |
26,143 | import asyncio
import collections
import contextlib
import dataclasses
import glob
import json
import logging
import math
import os
import random
import re
import shutil
import socket
import string
import subprocess
import time
import uuid
import zipfile
from collections import defaultdict
from contextlib import closin... | null |
26,144 | import os
import random
import re
import uuid
import pandas as pd
from datasets import load_dataset
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `extract_anthropic_prompt` function. Write a Python function `def extract_anthropic_prompt(prompt_and_response)` to so... | Extract the anthropic prompt from a prompt and response pair. |
26,145 | import os
import random
import re
import uuid
import pandas as pd
from datasets import load_dataset
from tqdm import tqdm
def _parse_row(prompt_and_response):
"""Extract the anthropic prompt from a prompt and response pair."""
search_term = "\n\nAssistant:"
search_term_idx = prompt_and_response["chosen"].rf... | Adapted from https://github.com/eric-mitchell/direct-preference-optimization/blob/main/preference_datasets.py |
26,146 | import os
import socket
from types import SimpleNamespace
def get_size(x):
try:
if x.endswith("TB"):
return float(x.replace("TB", "")) * (2**40)
if x.endswith("GB"):
return float(x.replace("GB", "")) * (2**30)
if x.endswith("MB"):
return float(x.replace("... | null |
26,147 | import glob
import logging
import os
import shutil
import time
import zipfile
from pathlib import Path
from typing import Callable, List, Optional, Set
import accelerate
import einops
import huggingface_hub
import numpy as np
import pandas as pd
import torch
import transformers
import yaml
from h2o_wave import Q, data,... | null |
26,148 | import hashlib
import logging
from typing import List
from h2o_wave import Q, ui
from llm_studio.app_utils.cards import card_zones
from llm_studio.app_utils.config import default_cfg
async def info_dialog(q: Q, title: str, message: str):
q.page["meta"].dialog = ui.dialog(
title,
items=[
... | null |
26,149 | import errno
import functools
import logging
import os
import pickle
import signal
import traceback
from typing import Any, List
import keyring
import yaml
from h2o_wave import Q, ui
from keyring.errors import KeyringLocked, PasswordDeleteError
from llm_studio.app_utils.config import default_cfg
from llm_studio.app_uti... | null |
26,150 | import errno
import functools
import logging
import os
import pickle
import signal
import traceback
from typing import Any, List
import keyring
import yaml
from h2o_wave import Q, ui
from keyring.errors import KeyringLocked, PasswordDeleteError
from llm_studio.app_utils.config import default_cfg
from llm_studio.app_uti... | Test if keyring is working. On misconfigured machines, Keyring may hang up to 2 minutes with the following error: jeepney.wrappers.DBusErrorResponse: [org.freedesktop.DBus.Error.TimedOut] ("Failed to activate service 'org.freedesktop.secrets': timed out (service_start_timeout=120000ms)",) To avoid waiting for 2 minutes... |
26,151 | from typing import List, Optional
from h2o_wave import ui
The provided code snippet includes necessary dependencies for implementing the `card_wait` function. Write a Python function `def card_wait(msg: str, box: str) -> ui.FormCard` to solve the following problem:
Return a form card for displaying waiting status Args... | Return a form card for displaying waiting status Args: msg: message to display box: box for card Returns: Form card |
26,152 | import glob
import re
from dataclasses import dataclass
from typing import Dict
The provided code snippet includes necessary dependencies for implementing the `read_tooltip_file` function. Write a Python function `def read_tooltip_file(path: str) -> str` to solve the following problem:
Reads all lines of a text file. ... | Reads all lines of a text file. Args: filename: path to the file Returns: str: the text of the file |
26,153 | import glob
import re
from dataclasses import dataclass
from typing import Dict
CLEANR = re.compile("<[^<]+?>")
The provided code snippet includes necessary dependencies for implementing the `cleanhtml` function. Write a Python function `def cleanhtml(raw_html: str) -> str` to solve the following problem:
Removes html... | Removes html tags from a string. Args: raw_html: the string to clean Returns: str: the cleaned string |
26,154 | import glob
import re
from dataclasses import dataclass
from typing import Dict
The provided code snippet includes necessary dependencies for implementing the `clean_docusaurus_tags` function. Write a Python function `def clean_docusaurus_tags(text: str) -> str` to solve the following problem:
Removes docusaurus tags ... | Removes docusaurus tags from a string. Args: text: the string to clean Returns: str: the cleaned string |
26,155 | import glob
import re
from dataclasses import dataclass
from typing import Dict
The provided code snippet includes necessary dependencies for implementing the `clean_md_links` function. Write a Python function `def clean_md_links(text: str) -> str` to solve the following problem:
Removes markdown links from a string. ... | Removes markdown links from a string. Args: text: the string to clean Returns: str: the cleaned string |
26,156 | from typing import Iterable, List, Optional
class Order:
def __init__(self, keys: Optional[List[str]] = None):
def _unique_guard(self, *keys: str):
def append(self, key: str):
def extend(self, keys: Iterable[str]):
def insert(
self, *keys: str, before: Optional[str] = None, after: O... | null |
26,157 | import logging
from typing import Any, Dict, List, Tuple, Union
import numpy as np
import pandas as pd
from numpy.typing import NDArray
from scipy.special import softmax
from sklearn.metrics import log_loss, roc_auc_score
def accuracy_score(
cfg: Any,
results: Dict,
val_df: pd.DataFrame,
raw_results: b... | null |
26,158 | import logging
from typing import Any, Dict, List, Tuple, Union
import numpy as np
import pandas as pd
from numpy.typing import NDArray
from scipy.special import softmax
from sklearn.metrics import log_loss, roc_auc_score
def auc_score(
cfg: Any,
results: Dict,
val_df: pd.DataFrame,
raw_results: bool =... | null |
26,159 | import logging
from typing import Any, Dict, List, Tuple, Union
import numpy as np
import pandas as pd
from numpy.typing import NDArray
from scipy.special import softmax
from sklearn.metrics import log_loss, roc_auc_score
def logloss_score(
cfg: Any,
results: Dict,
val_df: pd.DataFrame,
raw_results: bo... | null |
26,160 | import logging
import os
from functools import partial
from typing import Any, Dict, List, Tuple, Union
import numpy as np
import pandas as pd
import torch
from joblib import Parallel, delayed
from numpy.typing import NDArray
from openai import AzureOpenAI, OpenAI
from sacrebleu import BLEU
from sacrebleu.metrics.base ... | null |
26,161 | import logging
import os
from functools import partial
from typing import Any, Dict, List, Tuple, Union
import numpy as np
import pandas as pd
import torch
from joblib import Parallel, delayed
from numpy.typing import NDArray
from openai import AzureOpenAI, OpenAI
from sacrebleu import BLEU
from sacrebleu.metrics.base ... | null |
26,162 | import logging
import os
from functools import partial
from typing import Any, Dict, List, Tuple, Union
import numpy as np
import pandas as pd
import torch
from joblib import Parallel, delayed
from numpy.typing import NDArray
from openai import AzureOpenAI, OpenAI
from sacrebleu import BLEU
from sacrebleu.metrics.base ... | null |
26,163 | import hashlib
import os
from typing import Any, Dict
import pandas as pd
from llm_studio.src.datasets.conversation_chain_handler import get_conversation_chains
from llm_studio.src.datasets.text_utils import get_tokenizer
from llm_studio.src.utils.data_utils import read_dataframe_drop_missing_labels
from llm_studio.src... | null |
26,164 | import hashlib
import os
from typing import Any, Dict
import pandas as pd
from llm_studio.src.datasets.conversation_chain_handler import get_conversation_chains
from llm_studio.src.datasets.text_utils import get_tokenizer
from llm_studio.src.utils.data_utils import read_dataframe_drop_missing_labels
from llm_studio.src... | null |
26,165 | import dataclasses
import logging
import os
from typing import Any, Dict, List, Optional
import numpy as np
from sqlitedict import SqliteDict
from llm_studio.src.utils.plot_utils import PLOT_ENCODINGS
The provided code snippet includes necessary dependencies for implementing the `get_cfg` function. Write a Python func... | Returns simplified config elements Args: cfg: configuration Returns: Dict of config elements |
26,166 | import os
from abc import abstractmethod
from dataclasses import dataclass
from typing import Any, Callable, List, Optional, Sequence, Set, Tuple, Union
from llm_studio.src.nesting import Dependency
The provided code snippet includes necessary dependencies for implementing the `_scan_dirs` function. Write a Python fun... | Scans a directory for subfolders Args: dirname: directory name Returns: List of subfolders |
26,167 | import os
from abc import abstractmethod
from dataclasses import dataclass
from typing import Any, Callable, List, Optional, Sequence, Set, Tuple, Union
from llm_studio.src.nesting import Dependency
The provided code snippet includes necessary dependencies for implementing the `_scan_files` function. Write a Python fu... | Scans a directory for files with given extension Args: dirname: directory name extensions: extensions to consider Returns: List of files |
26,168 | import os
from abc import abstractmethod
from dataclasses import dataclass
from typing import Any, Callable, List, Optional, Sequence, Set, Tuple, Union
from llm_studio.src.nesting import Dependency
The provided code snippet includes necessary dependencies for implementing the `strip_prefix` function. Write a Python f... | Strips the common prefix of all the given paths. Args: paths: the paths to strip ignore_set: set of path names to ignore when computing the prefix. Returns: List with the same length as `paths` without common prefixes. |
26,169 | from typing import Any, List
from transformers import (
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_linear_schedule_with_warmup,
)
def constant_schedule_with_warmup(optimizer, num_warmup_steps, **kwargs):
return get_constant_schedule_with_warmup(
optimizer=optimizer,... | null |
26,170 | import logging
from typing import Any, Dict
import numpy as np
import pandas as pd
import torch
from llm_studio.src.datasets.text_causal_language_modeling_ds import (
CustomDataset as TextCausalLanguageModelingCustomDataset,
)
from llm_studio.src.utils.exceptions import LLMDataException
def is_castable_to_int(s):
... | null |
26,171 | import logging
from typing import Any, Dict
import torch
from torch import nn
from transformers import AutoModelForCausalLM
from llm_studio.src.losses.text_causal_language_modeling_losses import (
SampleAveragedCrossEntropyLoss,
)
from llm_studio.src.losses.text_dpo_modeling_losses import LOSS_REDUCTION
from llm_st... | Based upon the official implementation of DPO: https://github.com/eric-mitchell/direct-preference-optimization Compute the log probabilities of the given labels under the given logits. Args: logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) labels: Labels for which to compute ... |
26,172 | import logging
import os
import pickle
import random
import zipfile
from typing import Any
import numpy as np
import psutil
import torch
The provided code snippet includes necessary dependencies for implementing the `kill_ddp_processes` function. Write a Python function `def kill_ddp_processes() -> None` to solve the ... | Killing all DDP processes from a single process. Firstly kills all children of a single DDP process (dataloader workers) Then kills all other DDP processes Then kills main parent DDP process |
26,173 | from typing import Any, Union
import numpy as np
import torch
def is_cuda_out_of_memory(exception: BaseException) -> bool:
return (
isinstance(exception, RuntimeError)
and len(exception.args) == 1
and "CUDA" in exception.args[0]
and "out of memory" in exception.args[0]
)
def is_o... | null |
26,174 | import html
import re
from dataclasses import dataclass
from typing import List
def get_line_separator_html():
return (
"<div style='height: 1px; width: 100%; margin: 1em 0; "
"background-color: white; background-color: var(--text);'></div>"
) | null |
26,175 | import html
import re
from dataclasses import dataclass
from typing import List
The provided code snippet includes necessary dependencies for implementing the `decode_bytes` function. Write a Python function `def decode_bytes(chunks: List[bytes])` to solve the following problem:
Decodes bytes to string Args: chunks: b... | Decodes bytes to string Args: chunks: byte chunks Returns: list of decoded strings |
26,176 | import gc
import logging
import os
import re
import shutil
from collections import OrderedDict
from typing import Any, Dict
import coolname
import deepspeed
import numpy as np
import torch
from deepspeed.runtime.dataloader import DeepSpeedDataLoader
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero... | Generates a random human-readable experiment name in kebab-case. Returns: The random name. |
26,177 | import gc
import logging
import os
import re
import shutil
from collections import OrderedDict
from typing import Any, Dict
import coolname
import deepspeed
import numpy as np
import torch
from deepspeed.runtime.dataloader import DeepSpeedDataLoader
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero... | Reduces metric and return metric score (number) Args: output: output of the model reduce: how to reduce the metric over the sample dimension Returns: score: single number score (using config threshold for threshold metrics) or non-reduced array of scores per sample. |
26,178 | import gc
import logging
import os
import re
import shutil
from collections import OrderedDict
from typing import Any, Dict
import coolname
import deepspeed
import numpy as np
import torch
from deepspeed.runtime.dataloader import DeepSpeedDataLoader
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero... | Creates a backbone model for NLP tasks. This is needed for Gradient Checkpointing in DDP mode. |
26,179 | import gc
import logging
import os
import re
import shutil
from collections import OrderedDict
from typing import Any, Dict
import coolname
import deepspeed
import numpy as np
import torch
from deepspeed.runtime.dataloader import DeepSpeedDataLoader
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero... | null |
26,180 | import dataclasses
import logging
from dataclasses import dataclass, fields
from typing import Any, Dict, List, Optional, Sequence, Set, Tuple
from llm_studio.src import possible_values
from llm_studio.src.nesting import Dependency, Nesting
from llm_studio.src.order import Order
from llm_studio.src.tooltips import tool... | null |
26,181 | import os
from llm_studio.src.utils.config_utils import load_config_yaml
import argparse
import numpy as np
import torch
from llm_studio.src.datasets.text_utils import get_tokenizer
from llm_studio.src.utils.modeling_utils import load_checkpoint
def parse_param(cfg, prompt):
prompt = prompt.replace("--", "")
p... | null |
26,182 | import os
import argparse
import logging
import sys
import time
import psutil
The provided code snippet includes necessary dependencies for implementing the `check_for_done` function. Write a Python function `def check_for_done(process_queue)` to solve the following problem:
Checks for finished process ids Args: proce... | Checks for finished process ids Args: process_queue: list of process ids Returns: (True, process_idx) if there is any finished process (False, False) if there is not finished processes |
26,183 | from threading import Timer
import re
import shutil
import json
import subprocess
import tempfile
from urllib.error import HTTPError
import requests
import requests.adapters
import time
from transformers import __version__ as transformers_version
from transformers import PreTrainedModel
import packaging.version
from tq... | null |
26,184 | from threading import Timer
import re
import shutil
import json
import subprocess
import tempfile
from urllib.error import HTTPError
import requests
import requests.adapters
import time
from transformers import __version__ as transformers_version
from transformers import PreTrainedModel
import packaging.version
from tq... | null |
26,185 | import contextlib
from functools import reduce
import itertools
import zipfile
import pickle
import torch
import numpy as np
import collections
import _codecs
import utils
import os
from torch.nn import Module
from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
class RestrictedUnpickler(pickle.Unpickle... | null |
26,186 | import abc
import os
import sys
import math
import numpy as np
import termcolor
import contextlib
import traceback
import random
import zipfile
import json
import uuid
import datetime
import base64
import pickle
import hashlib
import itertools
import functools
import bisect
import eventlet
import packaging
import gc
im... | null |
26,187 | import abc
import os
import sys
import math
import numpy as np
import termcolor
import contextlib
import traceback
import random
import zipfile
import json
import uuid
import datetime
import base64
import pickle
import hashlib
import itertools
import functools
import bisect
import eventlet
import packaging
import gc
im... | null |
26,188 | import abc
import os
import sys
import math
import numpy as np
import termcolor
import contextlib
import traceback
import random
import zipfile
import json
import uuid
import datetime
import base64
import pickle
import hashlib
import itertools
import functools
import bisect
import eventlet
import packaging
import gc
im... | null |
26,189 | import abc
import os
import sys
import math
import numpy as np
import termcolor
import contextlib
import traceback
import random
import zipfile
import json
import uuid
import datetime
import base64
import pickle
import hashlib
import itertools
import functools
import bisect
import eventlet
import packaging
import gc
im... | null |
26,190 | import utils
import multiprocessing
from typing import Any, Callable, Dict, List, NamedTuple, Optional, Tuple, TypeVar
import progressbar
import time
import os
import sys
import json
import zipfile
import requests
import random
import jax
import jax.dlpack
from jax.config import config
from jax.experimental import maps... | null |
26,191 | import utils
import multiprocessing
from typing import Any, Callable, Dict, List, NamedTuple, Optional, Tuple, TypeVar
import progressbar
import time
import os
import sys
import json
import zipfile
import requests
import random
import jax
import jax.dlpack
from jax.config import config
from jax.experimental import maps... | null |
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