id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
183,633 | import torchvision.transforms.functional as F
import warnings
import math
import random
import numpy as np
from PIL import Image
import torch
from detectron2.data.detection_utils import read_image
from detectron2.data.transforms import ResizeTransform, TransformList
def clamp(num, min_value, max_value):
return max(... | null |
183,634 | import torchvision.transforms.functional as F
import warnings
import math
import random
import numpy as np
from PIL import Image
import torch
from detectron2.data.detection_utils import read_image
from detectron2.data.transforms import ResizeTransform, TransformList
def _pil_interp(method):
if method == 'bicubic':... | null |
183,635 | import torchvision.transforms.functional as F
import warnings
import math
import random
import numpy as np
from PIL import Image
import torch
from detectron2.data.detection_utils import read_image
from detectron2.data.transforms import ResizeTransform, TransformList
def pil_loader(path: str) -> Image.Image:
# open... | null |
183,636 | import torch
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
from transformers import BatchEncoding, PreTrainedTokenizerBase
from transformers.data.data_collator import (
DataCollatorMixin,
_torch_collate_batch,
)
from transformers.file_utils import PaddingStrategy
f... | null |
183,637 | import os
import json
import torch
from torch.utils.data.dataset import Dataset
from torchvision import transforms
from PIL import Image
from layoutlmft.data.image_utils import Compose, RandomResizedCropAndInterpolationWithTwoPic
def pil_loader(path: str) -> Image.Image:
# open path as file to avoid ResourceWarnin... | null |
183,638 | import os
import logging
from transformers.trainer_callback import TrainerCallback
logger = _setup_logger()
def _setup_logger():
log_format = logging.Formatter("[%(asctime)s %(levelname)s] %(message)s")
logger = logging.getLogger()
logger.setLevel(logging.INFO)
console_handler = logging.StreamHandler(... | null |
183,639 | import logging
import torch
from typing import Dict
from functools import partial
from transformers.utils.logging import enable_explicit_format
from transformers.trainer_callback import PrinterCallback
from transformers import (
AutoTokenizer,
HfArgumentParser,
EvalPrediction,
Trainer,
set_seed,
... | null |
183,640 | import logging
import torch
from typing import Dict
from functools import partial
from transformers.utils.logging import enable_explicit_format
from transformers.trainer_callback import PrinterCallback
from transformers import (
AutoTokenizer,
HfArgumentParser,
EvalPrediction,
Trainer,
set_seed,
... | null |
183,641 | import json
import torch
import torch.distributed as dist
from typing import List, Union, Optional, Tuple, Mapping, Dict
def dist_gather_tensor(t: Optional[torch.Tensor]) -> Optional[torch.Tensor]:
if t is None:
return None
t = t.contiguous()
all_tensors = [torch.empty_like(t) for _ in range(dist.... | null |
183,642 | import json
import torch
import torch.distributed as dist
from typing import List, Union, Optional, Tuple, Mapping, Dict
def select_grouped_indices(scores: torch.Tensor,
group_size: int,
start: int = 0) -> torch.Tensor:
assert len(scores.shape) == 2
batch_s... | null |
183,643 | import json
import torch
import torch.distributed as dist
from typing import List, Union, Optional, Tuple, Mapping, Dict
def full_contrastive_scores_and_labels(
query: torch.Tensor,
key: torch.Tensor,
use_all_pairs: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:
assert key.shape[0] % qu... | null |
183,644 | import json
import torch
import torch.distributed as dist
from typing import List, Union, Optional, Tuple, Mapping, Dict
def slice_batch_dict(batch_dict: Dict[str, torch.Tensor], prefix: str) -> dict:
return {k[len(prefix):]: v for k, v in batch_dict.items() if k.startswith(prefix)} | null |
183,645 | import os
import tqdm
import torch
from contextlib import nullcontext
from torch.utils.data import DataLoader
from functools import partial
from datasets import load_dataset
from typing import Dict, List
from transformers.file_utils import PaddingStrategy
from transformers import (
AutoTokenizer,
PreTrainedToke... | null |
183,646 | import json
import os
import glob
import tqdm
import torch
from contextlib import nullcontext
from torch.utils.data import DataLoader
from functools import partial
from collections import defaultdict
from datasets import Dataset
from typing import Dict, List, Tuple
from transformers.file_utils import PaddingStrategy
fr... | null |
183,647 | import os
import tqdm
import torch
from contextlib import nullcontext
from torch.utils.data import DataLoader
from functools import partial
from datasets import Dataset
from typing import Dict, List
from transformers.file_utils import PaddingStrategy
from transformers.modeling_outputs import SequenceClassifierOutput
fr... | null |
183,648 | import os
import tqdm
import torch
from contextlib import nullcontext
from torch.utils.data import DataLoader
from functools import partial
from datasets import Dataset, load_dataset
from typing import Dict, List
from transformers.file_utils import PaddingStrategy
from transformers.modeling_outputs import SequenceClass... | null |
183,649 | import random
from typing import Tuple
from transformers import PreTrainedTokenizerFast
from datasets import Dataset, load_dataset
from config import Arguments
from logger_config import logger
logger = _setup_logger()
def split_dataset(dataset: Dataset,
num_eval_examples: int,
max_... | null |
183,650 | from typing import List, Dict
def _slice_with_mod(elements: List, offset: int, cnt: int) -> List:
return [elements[(offset + idx) % len(elements)] for idx in range(cnt)]
def group_doc_ids(examples: Dict[str, List],
negative_size: int,
offset: int,
use_first_pos... | null |
183,651 | import logging
import torch
from typing import Dict
from transformers.utils.logging import enable_explicit_format
from transformers.trainer_callback import PrinterCallback
from transformers import (
AutoTokenizer,
HfArgumentParser,
EvalPrediction,
Trainer,
set_seed,
PreTrainedTokenizerFast
)
fro... | null |
183,652 | import logging
import torch
from typing import Dict
from transformers.utils.logging import enable_explicit_format
from transformers.trainer_callback import PrinterCallback
from transformers import (
AutoTokenizer,
HfArgumentParser,
EvalPrediction,
Trainer,
set_seed,
PreTrainedTokenizerFast
)
fro... | null |
183,653 | import os
import torch
from typing import Optional, Dict, Tuple
from transformers.trainer import Trainer
from logger_config import logger
from metrics import accuracy, batch_mrr
from models import BiencoderOutput, BiencoderModel
from utils import AverageMeter
def _unpack_qp(inputs: Dict[str, torch.Tensor]) -> Tuple:
... | null |
183,654 | import torch
from dataclasses import dataclass
from typing import List, Dict, Any
from transformers import DataCollatorWithPadding, BatchEncoding
def _unpack_doc_values(features: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
doc_examples = []
for f in features:
keys = list(f.keys())
lists_per_... | null |
183,655 | import torch
import random
import warnings
from transformers import BertTokenizer, BertTokenizerFast, BatchEncoding
from typing import List, Union, Tuple, Any, Dict
The provided code snippet includes necessary dependencies for implementing the `whole_word_mask` function. Write a Python function `def whole_word_mask(to... | Get 0/1 labels for masked tokens with whole word mask proxy |
183,656 | import torch
import random
import warnings
from transformers import BertTokenizer, BertTokenizerFast, BatchEncoding
from typing import List, Union, Tuple, Any, Dict
The provided code snippet includes necessary dependencies for implementing the `torch_mask_tokens` function. Write a Python function `def torch_mask_token... | Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set 'mask_labels' means we use whole word mask (wwm), we directly mask idxs according to it's ref. |
183,657 | import torch
import random
import warnings
from transformers import BertTokenizer, BertTokenizerFast, BatchEncoding
from typing import List, Union, Tuple, Any, Dict
def merge_batch_dict(src_batch_dict: Union[Dict, BatchEncoding],
tgt_batch_dict: Union[Dict, BatchEncoding],
pre... | null |
183,658 | import logging
import numpy as np
from typing import Dict
from transformers.utils.logging import enable_explicit_format
from transformers.trainer_callback import PrinterCallback
from transformers import (
AutoTokenizer,
HfArgumentParser,
set_seed,
PreTrainedTokenizerFast,
EvalPrediction,
)
from logg... | null |
183,659 | import logging
import numpy as np
from typing import Dict
from transformers.utils.logging import enable_explicit_format
from transformers.trainer_callback import PrinterCallback
from transformers import (
AutoTokenizer,
HfArgumentParser,
set_seed,
PreTrainedTokenizerFast,
EvalPrediction,
)
from logg... | null |
183,660 | import os
import io
import gzip
import json
import random
import argparse
import ir_datasets
import numpy as np
import sys
from tqdm import tqdm
from typing import Dict, List
from datasets import Dataset
from logger_config import logger
from utils import save_json_to_file
from data_utils import load_msmarco_predictions... | null |
183,661 | import os
import io
import gzip
import json
import random
import argparse
import ir_datasets
import numpy as np
import sys
from tqdm import tqdm
from typing import Dict, List
from datasets import Dataset
from logger_config import logger
from utils import save_json_to_file
from data_utils import load_msmarco_predictions... | null |
183,662 | import os
import io
import gzip
import json
import random
import argparse
import ir_datasets
import numpy as np
import sys
from tqdm import tqdm
from typing import Dict, List
from datasets import Dataset
from logger_config import logger
from utils import save_json_to_file
from data_utils import load_msmarco_predictions... | null |
183,663 | import os
import io
import gzip
import json
import random
import argparse
import ir_datasets
import numpy as np
import sys
from tqdm import tqdm
from typing import Dict, List
from datasets import Dataset
from logger_config import logger
from utils import save_json_to_file
from data_utils import load_msmarco_predictions... | null |
183,664 | import argparse
import copy
import json
import logging
import re
import unicodedata
from tqdm import tqdm
import numpy as np
import regex
class SimpleTokenizer(Tokenizer):
ALPHA_NUM = r'[\p{L}\p{N}\p{M}]+'
NON_WS = r'[^\p{Z}\p{C}]'
def __init__(self, **kwargs):
"""
Args:
annotato... | null |
183,665 | import os
import json
import argparse
import sys
import numpy as np
from tqdm import tqdm
from typing import Dict, Any
from logger_config import logger
from data_utils import load_query_answers, load_corpus, save_to_readable_format
args = parser.parse_args()
logger.info('Args: {}'.format(json.dumps(args.__dict__, ensur... | null |
183,666 | import os
import argparse
import json
import sys
from tqdm import tqdm
from typing import Dict, Any
from datasets import Dataset
from evaluate_dpr_retrieval import has_answers, SimpleTokenizer, evaluate_retrieval
from data_utils import load_query_answers, load_corpus
from utils import save_json_to_file
from logger_conf... | null |
183,667 | import os
import cv2
import math
import random
import logging
import argparse
import numpy as np
from pathlib import Path
from typing import Optional
from packaging import version
from collections import OrderedDict
from PIL import Image, ImageDraw, ImageFont
from huggingface_hub import HfFolder, Repository, create_rep... | null |
183,668 | import os
import cv2
import math
import random
import logging
import argparse
import numpy as np
from pathlib import Path
from typing import Optional
from packaging import version
from collections import OrderedDict
from PIL import Image, ImageDraw, ImageFont
from huggingface_hub import HfFolder, Repository, create_rep... | null |
183,669 | import os
import re
import cv2
import math
import shutil
import string
import textwrap
import numpy as np
from PIL import Image, ImageFont, ImageDraw, ImageOps
from typing import *
The provided code snippet includes necessary dependencies for implementing the `transform_mask` function. Write a Python function `def tra... | This function extracts the mask area and text area from the images. Args: mask_root (str): The path of mask image. * The white area is the unmasked area * The gray area is the masked area * The white area is the text area |
183,670 | import os
import re
import cv2
import math
import shutil
import string
import textwrap
import numpy as np
from PIL import Image, ImageFont, ImageDraw, ImageOps
from typing import *
for index, c in enumerate(alphabet):
alphabet_dic[c] = index + 1 # the index 0 stands for non-character
The provided code snippet incl... | This function combines all the outputs and useful inputs together. Args: args (argparse.ArgumentParser): The arguments. pred_image_list (List): List of predicted images. image_pil (Image): The original image. character_mask_pil (Image): The character-level segmentation mask. character_mask_highlight_pil (Image): The ch... |
183,671 | import os
import re
import cv2
import math
import shutil
import string
import textwrap
import numpy as np
from PIL import Image, ImageFont, ImageDraw, ImageOps
from typing import *
The provided code snippet includes necessary dependencies for implementing the `inpainting_merge_image` function. Write a Python function ... | This function merges the original image, mask image and inpainting image. Args: original_image (PIL.Image): The original image. mask_image (PIL.Image): The mask images. inpainting_image (PIL.Image): The inpainting images. |
183,672 | import os
import cv2
import random
import logging
import argparse
import numpy as np
from pathlib import Path
from tqdm.auto import tqdm
from typing import Optional
from packaging import version
from termcolor import colored
from PIL import Image, ImageDraw, ImageFont, ImageOps, ImageEnhance
from huggingface_hub import... | null |
183,673 | import os
import cv2
import random
import logging
import argparse
import numpy as np
from pathlib import Path
from tqdm.auto import tqdm
from typing import Optional
from packaging import version
from termcolor import colored
from PIL import Image, ImageDraw, ImageFont, ImageOps, ImageEnhance
from huggingface_hub import... | null |
183,674 | import json
import os
import traceback
from tqdm import tqdm
from multiprocessing import Pool
ROOT_TO = 'XXX'
MULTIPROCESSING_NUM = 64
def unzip_file(idx):
if not os.path.exists(f'{ROOT_FROM}/{idx}.zip') or os.path.exists(f'{ROOT_TO}/{idx}'):
return
cmd = f'unzip -q {ROOT_FROM}/{idx}.zip -d {ROOT_TO}'
... | null |
183,675 | import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
The provid... | Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_... |
183,676 | import inspect
import os
import warnings
from functools import partial
from typing import Callable, List, Optional, Tuple, Union
import torch
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
from packaging import version
fro... | null |
183,677 | import inspect
import os
import warnings
from functools import partial
from typing import Callable, List, Optional, Tuple, Union
import torch
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
from packaging import version
fro... | null |
183,678 | import inspect
import os
import warnings
from functools import partial
from typing import Callable, List, Optional, Tuple, Union
import torch
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
from packaging import version
fro... | Reads a checkpoint file, returning properly formatted errors if they arise. |
183,679 | import inspect
import os
import warnings
from functools import partial
from typing import Callable, List, Optional, Tuple, Union
import torch
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
from packaging import version
fro... | null |
183,680 | import inspect
import os
import warnings
from functools import partial
from typing import Callable, List, Optional, Tuple, Union
import torch
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
from packaging import version
fro... | null |
183,681 | import os
import re
import zipfile
if not os.path.exists('textdiffuser-ckpt'):
os.system('wget https://huggingface.co/datasets/JingyeChen22/TextDiffuser/resolve/main/textdiffuser-ckpt-new.zip')
with zipfile.ZipFile('textdiffuser-ckpt-new.zip', 'r') as zip_ref:
zip_ref.extractall('.')
if not os.path.exis... | null |
183,682 | import os
import re
import zipfile
import cv2
import random
import logging
import argparse
import numpy as np
from pathlib import Path
from tqdm.auto import tqdm
from typing import Optional
from packaging import version
from termcolor import colored
from PIL import Image, ImageDraw, ImageFont, ImageOps, ImageEnhance
fr... | null |
183,683 | import os
import re
import zipfile
if not os.path.exists('textdiffuser-ckpt'):
os.system('wget https://huggingface.co/datasets/JingyeChen22/TextDiffuser/resolve/main/textdiffuser-ckpt-new.zip')
with zipfile.ZipFile('textdiffuser-ckpt-new.zip', 'r') as zip_ref:
zip_ref.extractall('.')
if not os.path.exis... | null |
183,684 | import os
import re
import zipfile
import cv2
import random
import logging
import argparse
import numpy as np
from pathlib import Path
from tqdm.auto import tqdm
from typing import Optional
from packaging import version
from termcolor import colored
from PIL import Image, ImageDraw, ImageFont, ImageOps, ImageEnhance
fr... | null |
183,685 | import os
import re
import zipfile
import cv2
import random
import logging
import argparse
import numpy as np
from pathlib import Path
from tqdm.auto import tqdm
from typing import Optional
from packaging import version
from termcolor import colored
from PIL import Image, ImageDraw, ImageFont, ImageOps, ImageEnhance
fr... | null |
183,686 | import os
import re
import zipfile
import cv2
import random
import logging
import argparse
import numpy as np
from pathlib import Path
from tqdm.auto import tqdm
from typing import Optional
from packaging import version
from termcolor import colored
from PIL import Image, ImageDraw, ImageFont, ImageOps, ImageEnhance
fr... | null |
183,687 | import os
import re
import zipfile
import cv2
import random
import logging
import argparse
import numpy as np
from pathlib import Path
from tqdm.auto import tqdm
from typing import Optional
from packaging import version
from termcolor import colored
from PIL import Image, ImageDraw, ImageFont, ImageOps, ImageEnhance
fr... | null |
183,688 | import os
import cv2
import random
import logging
import argparse
import numpy as np
from pathlib import Path
from tqdm.auto import tqdm
from typing import Optional
from packaging import version
from termcolor import colored
from PIL import Image, ImageDraw, ImageFont, ImageOps, ImageEnhance
from huggingface_hub import... | null |
183,690 | import os
from PIL import Image
import numpy as np
import torch
from tqdm import tqdm
import argparse
import cv2
import torchvision.transforms as transforms
def load_stablediffusion():
def test_stablediffusion(prompt, save_path, num_images_per_prompt=4,
pipe=None, generator=None):
def load... | null |
183,691 | import os
from PIL import Image
import numpy as np
import torch
from tqdm import tqdm
import argparse
import cv2
import torchvision.transforms as transforms
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--dataset",
t... | null |
183,692 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'
def _inception_v3(*args, **kwargs):
"""Wraps `torchvision.models.inception_v3`"""
try:
ver... | Build pretrained Inception model for FID computation The Inception model for FID computation uses a different set of weights and has a slightly different structure than torchvision's Inception. This method first constructs torchvision's Inception and then patches the necessary parts that are different in the FID Incept... |
183,693 | import json
import os
import numpy as np
import argparse
from clipscore import cal_clipscore
from fid_score import calculate_fid_given_paths
def eval_clipscore(root_eval, root_res, dataset, device="cuda:0", num_images_per_prompt=4):
with open(os.path.join(root_eval, dataset, dataset + '.txt'), 'r') as fr:
t... | null |
183,694 | import json
import os
import numpy as np
import argparse
from clipscore import cal_clipscore
from fid_score import calculate_fid_given_paths
def merge_eval_results(root, methods):
method_res = {}
for method_idx, method in enumerate(methods):
root_res = os.path.join(root, 'generation', method)
w... | null |
183,695 | import json
import os
import numpy as np
import argparse
from clipscore import cal_clipscore
from fid_score import calculate_fid_given_paths
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--dataset",
type=str,
... | null |
183,696 | import os
import re
import copy
def get_key_words(text: str):
words = []
text = text
matches = re.findall(r"'(.*?)'", text) # find the keywords enclosed by ''
if matches:
for match in matches:
words.extend(match.split())
return words | null |
183,697 | import os
import re
import copy
def get_p_r_acc(method, pred, gt):
pred = [p.strip().lower() for p in pred]
gt = [g.strip().lower() for g in gt]
pred_orig = copy.deepcopy(pred)
gt_orig = copy.deepcopy(gt)
pred_length = len(pred)
gt_length = len(gt)
for p in pred:
if p in gt_ori... | null |
183,698 | import os
import pathlib
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
import numpy as np
import torch
import torchvision.transforms as TF
from PIL import Image
from scipy import linalg
from torch.nn.functional import adaptive_avg_pool2d
from pytorch_fid.inception import InceptionV3
def compute_sta... | Calculates the FID of two paths |
183,699 | import argparse
import clip
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
import torch
import tqdm
import numpy as np
import sklearn.preprocessing
import collections
import os
import pathlib
import json
import warnings
from packaging import version
from pycoco... | null |
183,700 | import copy
import json
import logging
import os
import re
from multiprocessing import Pool
import torch
from lxml import html
from torch.utils.data import TensorDataset
from tqdm import tqdm
from transformers import DataProcessor
def get_text(node):
textnodes = node.xpath(".//text()")
s = "".join([text for te... | null |
183,701 | import copy
import json
import logging
import os
import re
from multiprocessing import Pool
import torch
from lxml import html
from torch.utils.data import TensorDataset
from tqdm import tqdm
from transformers import DataProcessor
def get_prop(node, name):
title = node.get("title")
props = title.split(";")
... | null |
183,702 | import copy
import json
import logging
import os
import re
from multiprocessing import Pool
import torch
from lxml import html
from torch.utils.data import TensorDataset
from tqdm import tqdm
from transformers import DataProcessor
logger = logging.getLogger(__name__)
class CdipProcessor(DataProcessor):
"""Processor... | null |
183,703 | import logging
import os
import torch
from torch.utils.data import Dataset
class InputExample(object):
"""A single training/test example for token classification."""
def __init__(self, guid, words, labels, boxes, actual_bboxes, file_name, page_size):
"""Constructs a InputExample.
Args:
... | null |
183,704 | import logging
import os
import torch
from torch.utils.data import Dataset
logger = logging.getLogger(__name__)
class InputFeatures(object):
"""A single set of features of data."""
def __init__(
self,
input_ids,
input_mask,
segment_ids,
label_ids,
boxes,
a... | Loads a data file into a list of `InputBatch`s `cls_token_at_end` define the location of the CLS token: - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP] - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS] `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for... |
183,705 | import argparse
import json
import os
from PIL import Image
from transformers import AutoTokenizer
def bbox_string(box, width, length):
def actual_bbox_string(box, width, length):
def convert(args):
with open(
os.path.join(args.output_dir, args.data_split + ".txt.tmp"),
"w",
encoding="utf8"... | null |
183,706 | import argparse
import json
import os
from PIL import Image
from transformers import AutoTokenizer
def seg_file(file_path, tokenizer, max_len):
subword_len_counter = 0
output_path = file_path[:-4]
with open(file_path, "r", encoding="utf8") as f_p, open(
output_path, "w", encoding="utf8"
) as fw_... | null |
183,707 | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import random
import shutil
import numpy as np
import torch
from seqeval.metrics import (
classification_report,
f1_score,
precision_score,
recall_score,
)
from tensorboardX import Summa... | null |
183,708 | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import random
import shutil
import numpy as np
import torch
from seqeval.metrics import (
classification_report,
f1_score,
precision_score,
recall_score,
)
from tensorboardX import Summa... | Train the model |
183,709 | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm,... | Train the model |
183,710 | import json
from PIL import Image
import random
import string
from tqdm import tqdm
import string
import argparse
import logging
import math
import os
import random
from pathlib import Path
from PIL import Image
import accelerate
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import to... | null |
183,711 | import json
from PIL import Image
import random
import string
from tqdm import tqdm
import string
import argparse
import logging
import math
import os
import random
from pathlib import Path
from PIL import Image
import accelerate
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import to... | null |
183,712 | import os
import cv2
import random
import logging
import argparse
import numpy as np
import time
from pathlib import Path
from tqdm.auto import tqdm
from typing import Optional
from packaging import version
from PIL import Image
from huggingface_hub import HfFolder, Repository, create_repo, whoami
import string
import... | null |
183,713 | import os
import cv2
import random
import logging
import argparse
import numpy as np
import time
from pathlib import Path
from tqdm.auto import tqdm
from typing import Optional
from packaging import version
from PIL import Image
from huggingface_hub import HfFolder, Repository, create_repo, whoami
import string
import... | null |
183,714 | import argparse
import logging
import math
import os
import random
import shutil
from pathlib import Path
import glob
import json
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging i... | null |
183,715 | import argparse
import logging
import math
import os
import random
import shutil
from pathlib import Path
import glob
import json
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging i... | null |
183,716 | import argparse
import logging
import math
import os
import random
import shutil
from pathlib import Path
import glob
import json
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging i... | null |
183,717 | import os
import cv2
import math
import random
import logging
import argparse
import numpy as np
from pathlib import Path
from typing import Optional
from packaging import version
from collections import OrderedDict
from PIL import Image, ImageDraw, ImageFont
from huggingface_hub import HfFolder, Repository, create_rep... | null |
183,718 | import os
import cv2
import math
import random
import logging
import argparse
import numpy as np
from pathlib import Path
from typing import Optional
from packaging import version
from collections import OrderedDict
from PIL import Image, ImageDraw, ImageFont
from huggingface_hub import HfFolder, Repository, create_rep... | null |
183,719 | import os
import cv2
import math
import random
import logging
import argparse
import numpy as np
from pathlib import Path
from typing import Optional
from packaging import version
from collections import OrderedDict
from PIL import Image, ImageDraw, ImageFont
from huggingface_hub import HfFolder, Repository, create_rep... | null |
183,720 | import os
import re
import zipfile
import torch
import gradio as gr
import numpy as np
import time
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel, DiffusionPipeline
from tqdm import tqdm
from PIL import Image
from P... | null |
183,721 | import os
import re
import zipfile
import torch
import gradio as gr
import numpy as np
import time
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel, DiffusionPipeline
from tqdm import tqdm
from PIL import Image
from P... | null |
183,722 | import os
import re
import zipfile
import torch
import gradio as gr
print('hello', gr.__version__)
import numpy as np
import time
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel, DiffusionPipeline
from tqdm import tq... | null |
183,723 | import os
import re
import zipfile
import torch
import gradio as gr
print('hello', gr.__version__)
import numpy as np
import time
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel, DiffusionPipeline
from tqdm import tq... | null |
183,724 | import os
import re
import zipfile
import torch
import gradio as gr
print('hello', gr.__version__)
import numpy as np
import time
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel, DiffusionPipeline
from tqdm import tq... | null |
183,725 | import os
import re
import zipfile
import torch
import gradio as gr
print('hello', gr.__version__)
import numpy as np
import time
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel, DiffusionPipeline
from tqdm import tq... | null |
183,726 | import argparse
import logging
import math
import os
import random
import shutil
from pathlib import Path
import glob
import time
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging i... | null |
183,727 | import argparse
import logging
import math
import os
import random
import shutil
from pathlib import Path
import glob
import time
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging i... | null |
183,728 | import os
import re
import zipfile
import torch
import gradio as gr
import time
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel, DiffusionPipeline, LCMScheduler
from tqdm import tqdm
from PIL import Image
from PIL im... | null |
183,729 | import os
import re
import zipfile
import torch
import gradio as gr
import time
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel, DiffusionPipeline, LCMScheduler
from tqdm import tqdm
from PIL import Image
from PIL im... | null |
183,730 | import os
import re
import zipfile
import torch
import gradio as gr
print('hello', gr.__version__)
import time
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel, DiffusionPipeline, LCMScheduler
from tqdm import tqdm
fr... | null |
183,731 | import os
import re
import zipfile
import torch
import gradio as gr
print('hello', gr.__version__)
import time
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel, DiffusionPipeline, LCMScheduler
from tqdm import tqdm
fr... | null |
183,732 | import os
import re
import zipfile
import torch
import gradio as gr
print('hello', gr.__version__)
import time
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel, DiffusionPipeline, LCMScheduler
from tqdm import tqdm
fr... | null |
183,733 | import os
import subprocess
import sys
from setuptools import setup, find_packages, Extension
from setuptools import Extension, find_packages, setup
version = write_version_py()
with open("readme.md") as f:
readme = f.read()
if "READTHEDOCS" in os.environ:
# don't build extensions when generating docs
exten... | null |
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