id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
165,017 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
165,018 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
165,019 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | Apply LR multiplier before iteration "switch" |
165,020 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
165,021 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
165,022 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
165,023 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
165,024 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
165,025 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | collect tensors from all processes |
165,026 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
165,027 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
165,028 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | pred_slots, true_slots are like [['from_location:10-11', 'leaving_date:12-13']] |
165,029 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
165,030 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
165,031 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
165,032 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
165,033 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
165,034 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
165,035 | from unifymodel.utils import *
from unifymodel.generate import *
from unifymodel.model import SSLLModel
from unifymodel.dataset import PadBatchSeq, TASK2INFO, LBDataset, get_datasets, get_unlabel_data, get_unlabel_dict, MixedDataset
from unifymodel.dataset import *
from unifymodel.memory import *
from transformers im... | null |
165,036 | from unifymodel.utils import *
from unifymodel.generate import *
from unifymodel.model import SSLLModel
from unifymodel.dataset import PadBatchSeq, TASK2INFO, LBDataset, get_datasets, get_unlabel_data, get_unlabel_dict, MixedDataset
from unifymodel.dataset import *
from unifymodel.memory import *
from transformers im... | null |
165,037 | from unifymodel.utils import *
from unifymodel.generate import *
from unifymodel.model import SSLLModel
from unifymodel.dataset import PadBatchSeq, TASK2INFO, LBDataset, get_datasets, get_unlabel_data, get_unlabel_dict, MixedDataset
from unifymodel.dataset import *
from unifymodel.memory import *
from transformers im... | null |
165,038 | from unifymodel.utils import *
from unifymodel.generate import *
from unifymodel.model import SSLLModel
from unifymodel.dataset import PadBatchSeq, TASK2INFO, LBDataset, get_datasets, get_unlabel_data, get_unlabel_dict, MixedDataset
from unifymodel.dataset import *
from unifymodel.memory import *
from transformers im... | null |
165,039 | from unifymodel.utils import *
from unifymodel.generate import *
from unifymodel.model import SSLLModel
from unifymodel.dataset import PadBatchSeq, TASK2INFO, LBDataset, get_datasets, get_unlabel_data, get_unlabel_dict, MixedDataset
from unifymodel.dataset import *
from unifymodel.memory import *
from transformers im... | null |
165,040 | from unifymodel.utils import *
from unifymodel.generate import *
from unifymodel.model import SSLLModel
from unifymodel.dataset import PadBatchSeq, TASK2INFO, LBDataset, get_datasets, get_unlabel_data, get_unlabel_dict, MixedDataset
from unifymodel.dataset import *
from unifymodel.memory import *
from transformers im... | null |
165,041 | import torch
import csv
import os
import re
import json
import numpy as np
from settings import parse_args
from eda import *
from pretrain import *
from torch.utils.data import DataLoader
args = parse_args()
if args.data_type == 'intent':
TASK2INFO = {
"banking": {
"dataset_class": LBDataset,
... | null |
165,042 | import torch
import csv
import os
import re
import json
import numpy as np
from settings import parse_args
from eda import *
from pretrain import *
from torch.utils.data import DataLoader
class LBDataset(torch.utils.data.Dataset):
def __init__(self, task_name, tokz, data_path, max_input_len=100, special_token_ids=... | null |
165,043 | import torch
import csv
import os
import re
import json
import numpy as np
from settings import parse_args
from eda import *
from pretrain import *
from torch.utils.data import DataLoader
def write_mix_train_file(label_train_dataset, unlabel_train_dataset, out_file, oridir):
datatype_list=['label_train','unlabel_t... | null |
165,044 | import torch
import csv
import os
import re
import json
import numpy as np
from settings import parse_args
from eda import *
from pretrain import *
from torch.utils.data import DataLoader
def create_dataloader_for_pretrain(mix_train_file, tokz, model, args):
data_files = {}
data_files['train'] = mix_train_file... | null |
165,045 | import torch
import csv
import os
import re
import json
import numpy as np
from settings import parse_args
from eda import *
from pretrain import *
from torch.utils.data import DataLoader
max_input_length_dict = {
'woz.en': 128,
'sst': 128,
'srl': 128,
'wikisql': 300,
'squad':512,
'ag':128,
... | null |
165,046 | from transformers import T5Tokenizer, T5Config
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os
import time
import gc
import json
import p... | null |
165,047 | from transformers import T5Tokenizer, T5Config
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os
import time
import gc
import json
import p... | null |
165,048 | from transformers import T5Tokenizer, T5Config
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os
import time
import gc
import json
import p... | null |
165,049 | from transformers import T5Tokenizer, T5Config
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os
import time
import gc
import json
import p... | null |
165,050 | from transformers import T5Tokenizer, T5Config
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os
import time
import gc
import json
import p... | null |
165,051 | from transformers import T5Tokenizer, T5Config
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os
import time
import gc
import json
import p... | null |
165,052 | from transformers import T5Tokenizer, T5Config
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os
import time
import gc
import json
import p... | null |
165,053 | from transformers import T5Tokenizer, T5Config
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os
import time
import gc
import json
import p... | null |
165,054 | from transformers import T5Tokenizer, T5Config
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os
import time
import gc
import json
import p... | null |
165,055 | from transformers import T5Tokenizer, T5Config
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os
import time
import gc
import json
import p... | null |
165,056 | import torch
import numpy as np
from eda import *
import torch.nn as nn
from unifymodel.dataset import *
def get_neighbors(querys, task_name=None, K=1, memory=None, args=None, questions=None):
def create_batch_from_memory(samples, tokz, args, task_name):
def create_batch_to_augment_memory(old_task, old_memory, curr_me... | null |
165,057 | import torch
import numpy as np
from eda import *
import torch.nn as nn
from unifymodel.dataset import *
def get_sentence_embedding(model, batch, args=None):
model.set_active_adapters(None)
batch_size = batch['raw_id'].size()[0]
input_tokens = batch['raw_id'].cuda()
attn_masks = batch['raw_mask'].cuda()... | null |
165,058 | import torch
import numpy as np
from eda import *
import torch.nn as nn
from unifymodel.dataset import *
def get_old_center_dict(old_memory, prev_tasks, args=None):
center_dict = {}
for prev_task in prev_tasks:
old_keys = old_memory[prev_task]['keys'] # list of list
old_keys_tensor = torch.tens... | null |
165,059 | import torch
import numpy as np
from eda import *
import torch.nn as nn
from unifymodel.dataset import *
def cosine_similarity(v1, m2):
# print(v1.shape, m2.shape)
if len(m2.shape) == 1 and len(v1.shape) == 1:
cos = nn.CosineSimilarity(dim=0)
elif len(m2.shape)>1:
v1 = v1.unsqueeze(0)
... | null |
165,060 | import torch
import csv
import os
import re
import json
import numpy as np
from settings import parse_args
from eda import *
def pad_seq(seq, pad, max_len, pad_left=False):
if pad_left:
return [pad] * (max_len - len(seq)) + seq
else:
return seq + [pad] * (max_len - len(seq)) | null |
165,061 | import torch
import csv
import os
import re
import json
import numpy as np
from settings import parse_args
from eda import *
def get_unlabel_data(path, task):
info = TASK2INFO[task]
data_path = os.path.join(path, info['dataset_folder'],'unlabel_train.json')
return data_path | null |
165,062 | import torch
import csv
import os
import re
import json
import numpy as np
from settings import parse_args
from eda import *
class LBIDDataset(torch.utils.data.Dataset):
def __init__(self, task_name, tokz, data_path, ctx_max_len=100, special_token_ids=None):
self.tokz = tokz
self.data_path = data_p... | null |
165,063 | import torch
import csv
import os
import re
import json
import numpy as np
from settings import parse_args
from eda import *
args = parse_args()
class LBIDDataset(torch.utils.data.Dataset):
def __init__(self, task_name, tokz, data_path, ctx_max_len=100, special_token_ids=None):
self.tokz = tokz
sel... | null |
165,064 | import os
import argparse
import torch
def parse_args():
parser = argparse.ArgumentParser()
# * New arguments for semi-supervised continual learning.
parser.add_argument('--newmm_size', default=0.2, type=float, help='Different memory size for storing new task unlabeled data.')
parser.add_argument('--ra... | null |
165,067 | import torch
import csv
import os
import re
import json
import numpy as np
from settings import parse_args
from eda import *
class LBIDDataset(torch.utils.data.Dataset):
def __init__(self, task_name, tokz, data_path, ctx_max_len=100, special_token_ids=None):
def get_answer(self, intent):
def parse_exampl... | null |
165,068 | import torch
import csv
import os
import re
import json
import numpy as np
from settings import parse_args
from eda import *
args = parse_args()
class LBIDDataset(torch.utils.data.Dataset):
def __init__(self, task_name, tokz, data_path, ctx_max_len=100, special_token_ids=None):
self.tokz = tokz
sel... | null |
165,069 | from transformers import MarianMTModel, MarianTokenizer
import torch
def translate(texts, model, tokenizer, language="fr"):
def back_translate(texts, target_model, target_tokenizer, en_model, en_tokenizer, source_lang="en", target_lang="fr"):
# Translate from source to target language
fr_texts = translate(text... | null |
165,070 | import nltk
import re
import random
from random import shuffle
random.seed(1)
def get_only_chars(line):
clean_line = ""
line = line.replace("’", "")
line = line.replace("'", "")
line = line.replace("-", " ") # replace hyphens with spaces
line = line.replace("\t", " ")
line = line.replace("\n", ... | null |
165,072 | import torch
import csv
import os
import json
import logging
from fp16 import FP16_Module
import GPUtil
from collections import OrderedDict
from settings import args, MODEL_CLASS, TOKENIZER, SPECIAL_TOKEN_IDS, init_logging
from settings import MEMORY_FACTOR, LEN_FACTOR, TASK_DICT, MODEL_CONFIG, DATA_ATTRS, SPECIAL_TOKE... | null |
165,073 | import collections
import string
import re
import numpy as np
def normalize_text(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
return re.sub(r'\b(a|an|the)\b', ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def... | null |
165,074 | import pandas as pd
import numpy as np
import json
import re
from typing import Dict,List
import copy
import sys
import os
import argparse
from gloc.utils import twoD_list_transpose
def merge_dic(dic_col: Dict,dic_row: Dict):
new_dic = {}
keys = range(max(len(dic_col), len(dic_row)))
for key in keys:
... | null |
165,075 | import pandas as pd
import numpy as np
import json
import re
from typing import Dict,List
import copy
import sys
import os
import argparse
from gloc.utils import twoD_list_transpose
def twoD_list_transpose(arr:List[List],keep_num_rows:int=3):
arr = arr[:keep_num_rows+1] if keep_num_rows + 1 <= len(arr) else arr
... | null |
165,076 | import pandas as pd
import numpy as np
import json
import re
from typing import Dict,List
import copy
import sys
import os
import argparse
from gloc.utils import twoD_list_transpose
def filter_row(table,pred_row):
if '*' in pred_row:
return table
new_table = [copy.deepcopy(table[0])]
for idx in ra... | null |
165,077 | import pandas as pd
import numpy as np
import json
import re
from typing import Dict,List
import copy
import sys
import os
import argparse
from gloc.utils import twoD_list_transpose
def union_lists(to_union:List[List[str]],nums=None):
if nums is None:
return list(set().union(*to_union))
return list(set... | null |
165,078 | import sys, os, re, argparse
import unicodedata
from codecs import open
from math import isnan, isinf
from abc import ABCMeta, abstractmethod
def normalize(x):
if not isinstance(x, str):
x = x.decode('utf8', errors='ignore')
# Remove diacritics
x = ''.join(c for c in unicodedata.normalize('NFKD', x... | null |
165,079 | import sys, os, re, argparse
import unicodedata
from codecs import open
from math import isnan, isinf
from abc import ABCMeta, abstractmethod
def to_value(original_string, corenlp_value=None):
"""Convert the string to Value object.
Args:
original_string (basestring): Original string
corenlp_valu... | Convert a list of strings to a list of Values Args: original_strings (list[basestring]) corenlp_values (list[basestring or None]) Returns: list[Value] |
165,080 | import sys, os, re, argparse
import unicodedata
from codecs import open
from math import isnan, isinf
from abc import ABCMeta, abstractmethod
The provided code snippet includes necessary dependencies for implementing the `check_denotation` function. Write a Python function `def check_denotation(target_values, predicte... | Return True if the predicted denotation is correct. Args: target_values (list[Value]) predicted_values (list[Value]) Returns: bool |
165,081 | import sys, os, re, argparse
import unicodedata
from codecs import open
from math import isnan, isinf
from abc import ABCMeta, abstractmethod
def tsv_unescape(x):
"""Unescape strings in the TSV file.
Escaped characters include:
newline (0x10) -> backslash + n
vertical bar (0x7C) -> backslash + p... | Unescape a list in the TSV file. List items are joined with vertical bars (0x5C) Args: x (str or unicode) Returns: a list of unicodes |
165,082 | import pandas as pd
import numpy as np
import json
import re
from typing import Dict,List
import copy
import sys
import os
from gloc.utils import twoD_list_transpose
def merge_dic(dic_col: Dict,dic_row: Dict):
new_dic = {}
keys = range(max(len(dic_col), len(dic_row)))
for key in keys:
key = str(ke... | null |
165,083 | import pandas as pd
import numpy as np
import json
import re
from typing import Dict,List
import copy
import sys
import os
from gloc.utils import twoD_list_transpose
def twoD_list_transpose(arr:List[List],keep_num_rows:int=3):
def filter_col(table,pred_col):
table = twoD_list_transpose(table,len(table))
new_... | null |
165,084 | import pandas as pd
import numpy as np
import json
import re
from typing import Dict,List
import copy
import sys
import os
from gloc.utils import twoD_list_transpose
def filter_row(table,pred_row):
if '*' in pred_row:
return table
new_table = [copy.deepcopy(table[0])]
for idx in range(len(table)):... | null |
165,085 | import pandas as pd
import numpy as np
import json
import re
from typing import Dict,List
import copy
import sys
import os
from gloc.utils import twoD_list_transpose
def union_lists(to_union:List[List[str]],nums=None):
def preprocess(dic:Dict,union_col:int=1,union_row:int=2):
cnt_1 = 0
def l_tb(tb):
s... | null |
165,086 | import json
import collections
import pandas as pd
import numpy as np
def merge_res(dic):
acc = 0.
for key in dic:
to_union = collections.defaultdict(float)
it = dic[key]
# CodeX 没有产生任何东西
table = it['data_item']['table_text']
######### col filed################
p... | null |
165,087 | import random
from typing import Dict, Tuple
import pandas as pd
import copy
from utils.errors import DuplicateColumnsError
from retrieval.retrieve_pool import QAItem
from utils.normalizer import prepare_df_for_neuraldb_from_table
class DuplicateColumnsError(Exception):
def __init__(self, msg):
self.msg = ... | Return the CREATE TABLE clause as prompt. |
165,088 | import copy
import os
import sqlite3
import records
import sqlalchemy
import pandas as pd
from typing import Dict, List
import uuid
from utils.normalizer import convert_df_type, prepare_df_for_neuraldb_from_table
from utils.mmqa.image_stuff import get_caption
def check_in_and_return(key: str, source: dict):
# `` w... | null |
165,089 | from typing import List
import re
import sqlparse
class TreeNode(object):
def __init__(self, name=None, father=None):
self.name: str = name
self.rename: str = name
self.father: TreeNode = father
self.children: List = []
self.produced_col_name_s = None
def __eq__(self, oth... | Parse QA() into a tree for execution guiding. @param nsql: @return: |
165,090 | from typing import List
import re
import sqlparse
class TreeNode(object):
def __init__(self, name=None, father=None):
self.name: str = name
self.rename: str = name
self.father: TreeNode = father
self.children: List = []
self.produced_col_name_s = None
def __eq__(self, oth... | Pred-Order Traversal |
165,091 | from typing import List
import re
import sqlparse
def parse_question_paras(nsql: str, qa_model):
# We assume there's no nested qa inside when running this func
nsql = nsql.strip(" ;")
assert nsql[:3] == "QA(" and nsql[-1] == ")", "must start with QA( symbol and end with )"
assert not "QA" in nsql[2:-1]... | null |
165,092 | from typing import List
import re
import sqlparse
def convert_type(value):
try:
return eval(value)
except Exception as e:
return value | null |
165,093 | from typing import List
import re
import sqlparse
The provided code snippet includes necessary dependencies for implementing the `nsql_role_recognize` function. Write a Python function `def nsql_role_recognize(nsql_like_str, all_headers, all_passage_titles, all_image_titles)` to solve the following problem:
Recognize ... | Recognize role. (SQL/column/value) |
165,094 | from typing import List
import re
import sqlparse
def remove_duplicate(original_list):
no_duplicate_list = []
[no_duplicate_list.append(i) for i in original_list if i not in no_duplicate_list]
return no_duplicate_list | null |
165,095 | from typing import List
import re
import sqlparse
def extract_answers(sub_table):
if not sub_table or sub_table['header'] is None:
return []
answer = []
if 'row_id' in sub_table['header']:
for _row in sub_table['rows']:
answer.extend(_row[1:])
return answer
else:
... | null |
165,096 | import requests
import base64
import time
def vqa_call(question, image_path, api_url='https://hf.space/embed/OFA-Sys/OFA-vqa/+/api/predict/'):
with open(image_path, "rb") as f:
base64_data = base64.b64encode(f.read())
base64_data_to_send = "data:image/{};base64,{}".format(image_path.split(".")[-1], str... | null |
165,097 | import time
import json
import argparse
import copy
import os
from typing import List
import platform
import multiprocessing
from generation.generator import Generator
from utils.utils import load_data_split
from nsql.database import NeuralDB
class Generator(object):
"""
Codex generation wrapper.
"""
... | A worker process for annotating. |
165,098 | import time
import json
import argparse
import copy
import os
import random
from typing import List
import platform
import multiprocessing
from generation.generator import Generator
from utils.utils import load_data_split
from nsql.database import NeuralDB
class Generator(object):
"""
Codex generation wrapper.... | A worker process for annotating. |
165,099 | import time
import json
import argparse
import copy
import os
from typing import List
import platform
import multiprocessing
from generation.generator import Generator
from utils.utils import load_data_split
from nsql.database import NeuralDB
from utils.mmqa.qpmc import Question_Passage_Match_Classifier
from utils.mmqa... | A worker process for annotating. |
165,100 | import json
import argparse
import platform, multiprocessing
import os
import time
from nsql.nsql_exec import Executor, NeuralDB
from utils.normalizer import post_process_sql
from utils.utils import load_data_split, majority_vote
from utils.evaluator import Evaluator
class Executor(object):
def __init__(self, args... | A worker process for execution. |
165,101 | import time
import json
import argparse
import copy
import os
from typing import List
import platform
import multiprocessing
import pandas as pd
from generation.generator import Generator
from utils.utils import load_data_split
from nsql.database import NeuralDB
ROOT_DIR = os.path.join(os.path.dirname(__file__), "../..... | A worker process for annotating. |
165,103 | import json
import argparse
import platform, multiprocessing
import os
import time
from nsql.nsql_exec import Executor, NeuralDB
from utils.normalizer import post_process_sql
from utils.utils import load_data_split, majority_vote
from utils.evaluator import Evaluator
class Executor(object):
def __init__(self, args... | A worker process for execution. |
165,104 | import json
import argparse
import platform, multiprocessing
import os
import time
from nsql.nsql_exec import Executor, NeuralDB
from utils.normalizer import post_process_sql
from utils.utils import load_data_split, majority_vote
from utils.evaluator import Evaluator
class Executor(object):
def __init__(self, args... | A worker process for execution. |
165,106 | import json
import os
from typing import List, Union, Dict
from functools import cmp_to_key
import math
from collections.abc import Iterable
from datasets import load_dataset
ROOT_DIR = os.path.join(os.path.dirname(__file__), "../")
def load_data_split(dataset_to_load, split, data_dir=os.path.join(ROOT_DIR, 'datasets/... | null |
165,107 | import json
import os
from typing import List, Union, Dict
from functools import cmp_to_key
import math
from collections.abc import Iterable
from datasets import load_dataset
def pprint_dict(dic):
print(json.dumps(dic, indent=2)) | null |
165,108 | import json
import os
from typing import List, Union, Dict
from functools import cmp_to_key
import math
from collections.abc import Iterable
from datasets import load_dataset
def flatten(nested_list):
for x in nested_list:
if isinstance(x, Iterable) and not isinstance(x, (str, bytes)):
yield fr... | null |
165,109 | from typing import List, Dict
import pandas as pd
import recognizers_suite
from recognizers_suite import Culture
import re
import unicodedata
from fuzzywuzzy import fuzz
from utils.sql.extraction_from_sql import *
from utils.sql.all_keywords import ALL_KEY_WORDS
def convert_df_type(df: pd.DataFrame, lower_case=True):
... | null |
165,110 | from typing import List, Dict
import pandas as pd
import recognizers_suite
from recognizers_suite import Culture
import re
import unicodedata
from fuzzywuzzy import fuzz
from utils.sql.extraction_from_sql import *
from utils.sql.all_keywords import ALL_KEY_WORDS
The provided code snippet includes necessary dependencie... | Normalize string. |
165,114 | import json
import sqlite3
from nltk import word_tokenize
def tokenize(string):
string = str(string)
string = string.replace("\'", "\"") # ensures all string values wrapped by "" problem??
quote_idxs = [idx for idx, char in enumerate(string) if char == '"']
assert len(quote_idxs) % 2 == 0, "Unexpected ... | null |
165,115 | import json
import sqlite3
from nltk import word_tokenize
def get_schemas_from_json(fpath):
with open(fpath) as f:
data = json.load(f)
db_names = [db['db_id'] for db in data]
tables = {}
schemas = {}
for db in data:
db_id = db['db_id']
schema = {} #{'table': [col.lower, ...... | null |
165,116 | import argparse
import json
from utils.sql.process_sql import (
tokenize, CLAUSE_KEYWORDS, WHERE_OPS, COND_OPS, UNIT_OPS, AGG_OPS,
JOIN_KEYWORDS, ORDER_OPS, skip_semicolon, SQL_OPS)
def parse_sql(toks, start_idx, schema):
isBlock = False # indicate whether this is a block of sql/sub-sql
len_ = len(toks)
idx ... | null |
165,117 | import argparse
import json
from utils.sql.process_sql import (
tokenize, CLAUSE_KEYWORDS, WHERE_OPS, COND_OPS, UNIT_OPS, AGG_OPS,
JOIN_KEYWORDS, ORDER_OPS, skip_semicolon, SQL_OPS)
KEPT_WHERE_OP = ('not', 'in', 'exists')
CLAUSE_KEYWORDS = ('select', 'from', 'where', 'group', 'order', 'limit', 'intersect', 'union'... | null |
165,118 | import argparse
import json
from utils.sql.process_sql import (
tokenize, CLAUSE_KEYWORDS, WHERE_OPS, COND_OPS, UNIT_OPS, AGG_OPS,
JOIN_KEYWORDS, ORDER_OPS, skip_semicolon, SQL_OPS)
KEPT_WHERE_OP = ('not', 'in', 'exists')
CLAUSE_KEYWORDS = ('select', 'from', 'where', 'group', 'order', 'limit', 'intersect', 'union'... | null |
165,119 | import argparse
import json
from utils.sql.process_sql import (
tokenize, CLAUSE_KEYWORDS, WHERE_OPS, COND_OPS, UNIT_OPS, AGG_OPS,
JOIN_KEYWORDS, ORDER_OPS, skip_semicolon, SQL_OPS)
CLAUSE_KEYWORDS = ('select', 'from', 'where', 'group', 'order', 'limit', 'intersect', 'union', 'except')
def is_valid_schema(schema)... | null |
165,120 | import argparse
import json
from utils.sql.process_sql import (
tokenize, CLAUSE_KEYWORDS, WHERE_OPS, COND_OPS, UNIT_OPS, AGG_OPS,
JOIN_KEYWORDS, ORDER_OPS, skip_semicolon, SQL_OPS)
def clean_sql(sql):
while "JOIN JOIN" in sql:
sql = sql.replace("JOIN JOIN", "JOIN")
if "JOIN WHERE" in sql:
sql = sql.re... | null |
165,125 | import re
import json
import records
from typing import List, Dict
from sqlalchemy.exc import SQLAlchemyError
from utils.sql.all_keywords import ALL_KEY_WORDS
def process_table_structure(_wtq_table_content: Dict, _add_all_column: bool = False):
# remove id and agg column
headers = [_.replace("\n", " ").lower()... | null |
165,126 | import re
import json
import records
from typing import List, Dict
from sqlalchemy.exc import SQLAlchemyError
from utils.sql.all_keywords import ALL_KEY_WORDS
ALL_KEY_WORDS = CLAUSE_KEYWORDS + JOIN_KEYWORDS + WHERE_OPS + UNIT_OPS + AGG_OPS
def retrieve_wtq_query_answer(_engine, _table_content, _sql_struct: List):
... | null |
165,127 | import re
import json
import records
from typing import List, Dict
from sqlalchemy.exc import SQLAlchemyError
from utils.sql.all_keywords import ALL_KEY_WORDS
def _load_table(table_path) -> dict:
"""
attention: the table_path must be the .tsv path.
Load the WikiTableQuestion from csv file. Result in a dict... | attention: the table_path must be the .tsv path. Load the WikiTableQuestion from csv file. Result in a dict format like: {"header": [header1, header2,...], "rows": [[row11, row12, ...], [row21,...]... [...rownm]]} |
165,128 | from typing import Dict,List
import pandas as pd
The provided code snippet includes necessary dependencies for implementing the `table_linearization` function. Write a Python function `def table_linearization(table: pd.DataFrame, format:str='codex')` to solve the following problem:
linearization table according to for... | linearization table according to format. |
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