id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
163,348 | from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import json
import logging
import os
import regex as re
import sys
import unicodedata
The provided code snippet includes necessary dependencies for implementing the `load_vocab` function. Write a Python function `def ... | Loads a vocabulary file into a dictionary. |
163,349 | from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import json
import logging
import os
import regex as re
import sys
import unicodedata
The provided code snippet includes necessary dependencies for implementing the `whitespace_tokenize` function. Write a Python funct... | Runs basic whitespace cleaning and splitting on a piece of text. |
163,350 | from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import json
import logging
import os
import regex as re
import sys
import unicodedata
The provided code snippet includes necessary dependencies for implementing the `_is_whitespace` function. Write a Python function `... | Checks whether `chars` is a whitespace character. |
163,351 | from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import json
import logging
import os
import regex as re
import sys
import unicodedata
The provided code snippet includes necessary dependencies for implementing the `_is_control` function. Write a Python function `def... | Checks whether `chars` is a control character. |
163,352 | from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import json
import logging
import os
import regex as re
import sys
import unicodedata
The provided code snippet includes necessary dependencies for implementing the `_is_punctuation` function. Write a Python function ... | Checks whether `chars` is a punctuation character. |
163,353 | from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import json
import logging
import os
import regex as re
import sys
import unicodedata
def lru_cache():
return lambda func: func | null |
163,354 | from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import json
import logging
import os
import regex as re
import sys
import unicodedata
The provided code snippet includes necessary dependencies for implementing the `bytes_to_unicode` function. Write a Python function... | Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This ... |
163,355 | from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import json
import logging
import os
import regex as re
import sys
import unicodedata
The provided code snippet includes necessary dependencies for implementing the `get_pairs` function. Write a Python function `def g... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
163,356 | from itertools import chain
import json
import numpy as np
import pickle
import time
import subprocess as sp
from tqdm import tqdm
from galaxy.args import str2bool
from galaxy.data.tokenizer import Tokenizer
def max_lens(X):
lens = [len(X)]
while isinstance(X[0], list):
lens.append(max(map(len, X)))
... | null |
163,357 | from itertools import chain
import json
import numpy as np
import pickle
import time
import subprocess as sp
from tqdm import tqdm
from galaxy.args import str2bool
from galaxy.data.tokenizer import Tokenizer
def _get_file_len(corpus):
n_line = int(sp.check_output(f"wc -l {corpus}".split(),
... | null |
163,358 | import os
import random
from collections import OrderedDict, defaultdict
from itertools import chain
import json
import sqlite3 as sql
import numpy as np
import spacy
from tqdm import tqdm
from nltk.tokenize import word_tokenize as nltk_word_tokenize
from nltk.stem import WordNetLemmatizer
from galaxy.args import str2b... | null |
163,360 | import argparse
import json
class HParams(dict):
""" Hyper-parameters class
Store hyper-parameters in training / infer / ... scripts.
"""
def __getattr__(self, name):
if name in self.keys():
return self[name]
for v in self.values():
if isinstance(v, HParams):
... | Parse hyper-parameters from cmdline. |
163,361 | import json
import logging
import os
import sys
import time
from collections import OrderedDict
import torch
import numpy as np
from tqdm import tqdm
from transformers.optimization import AdamW, get_linear_schedule_with_warmup
from galaxy.args import str2bool
from galaxy.data.data_loader import DataLoader
from galaxy.m... | null |
163,362 | import logging
import os
import sys
import time
from collections import OrderedDict
import torch
import numpy as np
from transformers.optimization import AdamW, get_linear_schedule_with_warmup
from galaxy.args import str2bool
from galaxy.data.data_loader import DataLoader
from galaxy.metrics.metrics_tracker import Metr... | null |
163,363 | import math
import torch
import numpy as np
from galaxy.args import str2bool
def repeat(var, times):
if isinstance(var, list):
return [repeat(x, times) for x in var]
elif isinstance(var, dict):
return {k: repeat(v, times) for k, v in var.items()}
elif isinstance(var, torch.Tensor):
... | null |
163,364 | import math
import torch
import numpy as np
from galaxy.args import str2bool
def gather(var, idx):
if isinstance(var, list):
return [gather(x, idx) for x in var]
elif isinstance(var, dict):
return {k: gather(v, idx) for k, v in var.items()}
elif isinstance(var, torch.Tensor):
out = ... | null |
163,365 | import logging
import json
import numpy as np
from collections import OrderedDict
from galaxy.utils import ontology
def clean_replace(s, r, t, forward=True, backward=False):
def clean_replace_single(s, r, t, forward, backward, sidx=0):
# idx = s[sidx:].find(r)
idx = s.find(r)
if idx == -1:
... | null |
163,366 | import logging
import json
import numpy as np
from collections import OrderedDict
from galaxy.utils import ontology
def py2np(list):
return np.array(list) | null |
163,367 | import logging
import json
import numpy as np
from collections import OrderedDict
from galaxy.utils import ontology
def write_dict(fn, dic):
with open(fn, 'w') as f:
json.dump(dic, f, indent=2) | null |
163,368 |
def get_special_tokens(data_name):
if data_name == 'multiwoz':
db_tokens = ['<sos_db>', '<eos_db>', '[db_nores]', '[db_0]', '[db_1]', '[db_2]', '[db_3]',
'[book_nores]', '[book_fail]', '[book_success]']
special_tokens = ['<go_r>', '<go_b>', '<go_a>',
... | null |
163,369 | import torch
from torch.nn.modules.loss import _Loss
import torch.nn.functional as F
def compute_kl_loss(p, q, filter_scores=None):
p_loss = F.kl_div(F.log_softmax(p, dim=-1), F.softmax(q, dim=-1), reduction='none')
q_loss = F.kl_div(F.log_softmax(q, dim=-1), F.softmax(p, dim=-1), reduction='none')
# You ... | null |
163,370 | import re
from galaxy.utils import ontology
def my_clean_text(text):
text = re.sub(r'([a-zT]+)\.([a-z])', r'\1 . \2', text) # 'abc.xyz' -> 'abc . xyz'
text = re.sub(r'(\w+)\.\.? ', r'\1 . ', text) # if 'abc. ' -> 'abc . '
return text | null |
163,371 | import json
import math
from collections import Counter
import numpy as np
from nltk.util import ngrams
from sklearn.metrics import f1_score
from galaxy.utils import ontology, utils
from galaxy.utils.clean_dataset import clean_slot_values
def setsub(a,b):
def setsim(a,b):
a,b = set(a),set(b)
return setsub(a,b)... | null |
163,372 | import json
import math
from collections import Counter
import numpy as np
from nltk.util import ngrams
from sklearn.metrics import f1_score
from galaxy.utils import ontology, utils
from galaxy.utils.clean_dataset import clean_slot_values
def DAEvaluation(preds, labels):
preds = np.array(preds)
labels = np.arr... | null |
163,373 | from typing import Dict, Any
from third_party.sparc.evaluation import *
def evaluate(glist, plist, db_dir, etype, kmaps):
def compute_interaction_metric(predictions, references) -> Dict[str, Any]:
foreign_key_maps = dict()
for reference in references:
if reference["db_id"] not in foreign_key_maps:
... | null |
163,374 | from typing import Dict, Any
from third_party.spider import evaluation as spider_evaluation
def compute_exact_match_metric(predictions, references) -> Dict[str, Any]:
foreign_key_maps = dict()
for reference in references:
if reference["db_id"] not in foreign_key_maps:
foreign_key_maps[refer... | null |
163,375 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,376 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,377 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,378 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,379 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,380 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,381 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,382 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,383 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,384 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,385 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,388 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,392 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,395 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,396 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,398 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,399 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,402 | import os
import math
from typing import Dict
from copy import deepcopy
import numpy as np
from datasets import DatasetDict
from random import shuffle
from torch.utils.data import Dataset, ConcatDataset
from torch.utils.data.dataset import T_co
from utils.configue import Configure
def upsample(data, weight):
n_dat... | null |
163,405 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,406 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,408 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,416 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,418 | import os
import torch
import random
import math
import re
import numpy as np
from copy import deepcopy
from typing import List, Dict
from datasets.dataset_dict import DatasetDict
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
from third_party.miscs.bridge_content_encoder import get_data... | null |
163,434 | import json
import sqlite3
from nltk import word_tokenize
def get_schema_from_json(fpath):
with open(fpath) as f:
data = json.load(f)
schema = {}
for entry in data:
table = str(entry['table'].lower())
cols = [str(col['column_name'].lower()) for col in entry['col_data']]
sch... | null |
163,436 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def condition_has_or(conds):
return 'or' in conds[1::2] | null |
163,437 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
WHERE_OPS = ('not', 'between', '=', '>', '<', '>=', '<=', '!=', 'in', 'like', 'is', 'exists')
def condition_has_like(conds):
return WHERE_OPS.index('like'... | null |
163,438 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def condition_has_sql(conds):
for cond_unit in conds[::2]:
val1, val2 = cond_unit[3], cond_unit[4]
if val1 is not None and type(val1) is d... | null |
163,439 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
UNIT_OPS = ('none', '-', '+', "*", '/')
def val_has_op(val_unit):
return val_unit[0] != UNIT_OPS.index('none') | null |
163,440 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def accuracy(count, total):
if count == total:
return 1
return 0 | null |
163,441 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def recall(count, total):
if count == total:
return 1
return 0 | null |
163,442 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def F1(acc, rec):
if (acc + rec) == 0:
return 0
return (2. * acc * rec) / (acc + rec) | null |
163,443 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def get_scores(count, pred_total, label_total):
if pred_total != label_total:
return 0,0,0
elif count == pred_total:
return 1,1,1
... | null |
163,444 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def eval_sel(pred, label):
pred_sel = pred['select'][1]
label_sel = label['select'][1]
label_wo_agg = [unit[1] for unit in label_sel]
pred_tot... | null |
163,445 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def eval_where(pred, label):
pred_conds = [unit for unit in pred['where'][::2]]
label_conds = [unit for unit in label['where'][::2]]
label_wo_agg ... | null |
163,446 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def eval_group(pred, label):
pred_cols = [unit[1] for unit in pred['groupBy']]
label_cols = [unit[1] for unit in label['groupBy']]
pred_total = le... | null |
163,447 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def eval_having(pred, label):
pred_total = label_total = cnt = 0
if len(pred['groupBy']) > 0:
pred_total = 1
if len(label['groupBy']) > 0:... | null |
163,448 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def eval_order(pred, label):
pred_total = label_total = cnt = 0
if len(pred['orderBy']) > 0:
pred_total = 1
if len(label['orderBy']) > 0:
... | null |
163,449 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def eval_and_or(pred, label):
pred_ao = pred['where'][1::2]
label_ao = label['where'][1::2]
pred_ao = set(pred_ao)
label_ao = set(label_ao)
... | null |
163,450 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def eval_nested(pred, label):
label_total = 0
pred_total = 0
cnt = 0
if pred is not None:
pred_total += 1
if label is not None:
... | null |
163,451 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def get_keywords(sql):
res = set()
if len(sql['where']) > 0:
res.add('where')
if len(sql['groupBy']) > 0:
res.add('group')
if l... | null |
163,452 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
WHERE_OPS = ('not', 'between', '=', '>', '<', '>=', '<=', '!=', 'in', 'like', 'is', 'exists')
def count_component1(sql):
count = 0
if len(sql['where']... | null |
163,453 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def get_nestedSQL(sql):
def count_component2(sql):
nested = get_nestedSQL(sql)
return len(nested) | null |
163,454 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def count_agg(units):
return len([unit for unit in units if has_agg(unit)])
def count_others(sql):
count = 0
# number of aggregation
agg_count... | null |
163,455 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def isValidSQL(sql, db):
conn = sqlite3.connect(db)
cursor = conn.cursor()
try:
cursor.execute(sql)
except:
return False
r... | null |
163,456 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
class Evaluator:
"""A simple evaluator"""
def __init__(self):
self.partial_scores = None
def eval_hardness(self, sql):
count_comp1_... | null |
163,457 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def build_foreign_key_map(entry):
cols_orig = entry["column_names_original"]
tables_orig = entry["table_names_original"]
# rebuild cols correspondi... | null |
163,458 | import json
import sqlite3
from nltk import word_tokenize
The provided code snippet includes necessary dependencies for implementing the `get_schema` function. Write a Python function `def get_schema(db)` to solve the following problem:
Get database's schema, which is a dict with table name as key and list of column n... | Get database's schema, which is a dict with table name as key and list of column names as value :param db: database path :return: schema dict |
163,461 | 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, ... | null |
163,462 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def condition_has_or(conds):
return "or" in conds[1::2] | null |
163,463 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
WHERE_OPS = (
"not",
"between",
"=",
">",
"<",
">=",
"<=",
"!=",
"in",
"like",
"is",
"exists",
)
def condition... | null |
163,465 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
UNIT_OPS = ("none", "-", "+", "*", "/")
def val_has_op(val_unit):
return val_unit[0] != UNIT_OPS.index("none") | null |
163,468 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def F1(acc, rec):
if (acc + rec) == 0:
return 0
return (2.0 * acc * rec) / (acc + rec) | null |
163,469 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def get_scores(count, pred_total, label_total):
if pred_total != label_total:
return 0, 0, 0
elif count == pred_total:
return 1, 1, 1
... | null |
163,470 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def eval_sel(pred, label):
pred_sel = pred["select"][1]
label_sel = label["select"][1]
label_wo_agg = [unit[1] for unit in label_sel]
pred_tot... | null |
163,471 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def eval_where(pred, label):
pred_conds = [unit for unit in pred["where"][::2]]
label_conds = [unit for unit in label["where"][::2]]
label_wo_agg ... | null |
163,472 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def eval_group(pred, label):
pred_cols = [unit[1] for unit in pred["groupBy"]]
label_cols = [unit[1] for unit in label["groupBy"]]
pred_total = le... | null |
163,473 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def eval_having(pred, label):
pred_total = label_total = cnt = 0
if len(pred["groupBy"]) > 0:
pred_total = 1
if len(label["groupBy"]) > 0:... | null |
163,474 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def eval_order(pred, label):
pred_total = label_total = cnt = 0
if len(pred["orderBy"]) > 0:
pred_total = 1
if len(label["orderBy"]) > 0:
... | null |
163,475 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def eval_and_or(pred, label):
pred_ao = pred["where"][1::2]
label_ao = label["where"][1::2]
pred_ao = set(pred_ao)
label_ao = set(label_ao)
... | null |
163,476 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def eval_nested(pred, label):
label_total = 0
pred_total = 0
cnt = 0
if pred is not None:
pred_total += 1
if label is not None:
... | null |
163,477 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def get_keywords(sql):
res = set()
if len(sql["where"]) > 0:
res.add("where")
if len(sql["groupBy"]) > 0:
res.add("group")
if l... | null |
163,478 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
WHERE_OPS = (
"not",
"between",
"=",
">",
"<",
">=",
"<=",
"!=",
"in",
"like",
"is",
"exists",
)
def count_com... | null |
163,480 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def count_agg(units):
return len([unit for unit in units if has_agg(unit)])
def count_others(sql):
count = 0
# number of aggregation
agg_count... | null |
163,481 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def update_scores_match(scores, exact_score, hardness, partial_scores, partial_types):
scores[hardness]["exact"] += exact_score
scores["all"]["exact"]... | null |
163,483 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
class Evaluator:
"""A simple evaluator"""
def __init__(self, db_dir, kmaps, etype):
self.db_dir = db_dir
self.kmaps = kmaps
s... | null |
163,484 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
The provided code snippet includes necessary dependencies for implementing the `eval_exec_match` function. Write a Python function `def eval_exec_match(db, p_... | return 1 if the values between prediction and gold are matching in the corresponding index. Currently not support multiple col_unit(pairs). |
163,485 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
TABLE_TYPE = {
"sql": "sql",
"table_unit": "table_unit",
}
def build_valid_col_units(table_units, schema):
col_ids = [
table_unit[1]
... | null |
163,486 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def rebuild_condition_col(valid_col_units, condition, kmap):
for idx in range(len(condition)):
if idx % 2 == 0:
condition[idx] = rebuil... | null |
163,487 | import os, sys
import json
import sqlite3
import traceback
import argparse
from .process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
def build_foreign_key_map(entry):
# print("entry in build_foreign_key_map: ", entry)
cols_orig = entry["column_names_original"]
tables_orig = entry... | null |
163,488 | import json
def _get_schemas_from_json(data: dict):
db_names = [db["db_id"] for db in data]
tables = {}
schemas = {}
for db in data:
db_id = db["db_id"]
schema = {} # {'table': [col.lower, ..., ]} * -> __all__
column_names_original = db["column_names_original"]
table_nam... | null |
163,489 | import copy
import math
import random
import warnings
from typing import Optional, Tuple
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.models.bart.configuration_bart import BartConfig
from transformers.activations import ACT2FN
from tran... | Shift input ids one token to the right. |
163,490 | import copy
import math
import random
import warnings
from typing import Optional, Tuple
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.models.bart.configuration_bart import BartConfig
from transformers.activations import ACT2FN
from tran... | Make causal mask used for bi-directional self-attention. |
163,491 | import copy
import math
import random
import warnings
from typing import Optional, Tuple
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.models.bart.configuration_bart import BartConfig
from transformers.activations import ACT2FN
from tran... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
163,492 | import copy
import math
import os
import warnings
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from torch.utils.checkpoint import checkpoint
from transformers.activations import ACT2FN
from transformers.file_utils import (
DUMMY_INPUTS,
DUMMY_MASK,
add_start_docstrings,
add_st... | Load tf checkpoints in a pytorch model. |
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