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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import entmax
import numpy as np
import itertools
def clamp(value, abs_max):
value = max(-abs_max, value)
value = min(abs_max, value)
return value
# Adapted from
# https://github.com/tensorflow/tensor2tensor/blob/0b156ac533ab53f65f44966381f6e147c7371eee/tensor2tensor/layers/common_attention.py
def relative_attention_logits(query, key, relation):
# We can't reuse the same logic as tensor2tensor because we don't share relation vectors across the batch.
# In this version, relation vectors are shared across heads.
# query: [batch, heads, num queries, depth].
# key: [batch, heads, num kvs, depth].
# relation: [batch, num queries, num kvs, depth].
# qk_matmul is [batch, heads, num queries, num kvs]
qk_matmul = torch.matmul(query, key.transpose(-2, -1))
# q_t is [batch, num queries, heads, depth]
q_t = query.permute(0, 2, 1, 3)
# r_t is [batch, num queries, depth, num kvs]
r_t = relation.transpose(-2, -1)
# [batch, num queries, heads, depth]
# * [batch, num queries, depth, num kvs]
# = [batch, num queries, heads, num kvs]
# For each batch and query, we have a query vector per head.
# We take its dot product with the relation vector for each kv.
q_tr_t_matmul = torch.matmul(q_t, r_t)
# qtr_t_matmul_t is [batch, heads, num queries, num kvs]
q_tr_tmatmul_t = q_tr_t_matmul.permute(0, 2, 1, 3)
# [batch, heads, num queries, num kvs]
return (qk_matmul + q_tr_tmatmul_t) / math.sqrt(query.shape[-1])
# Sharing relation vectors across batch and heads:
# query: [batch, heads, num queries, depth].
# key: [batch, heads, num kvs, depth].
# relation: [num queries, num kvs, depth].
#
# Then take
# key reshaped
# [num queries, batch * heads, depth]
# relation.transpose(-2, -1)
# [num queries, depth, num kvs]
# and multiply them together.
#
# Without sharing relation vectors across heads:
# query: [batch, heads, num queries, depth].
# key: [batch, heads, num kvs, depth].
# relation: [batch, heads, num queries, num kvs, depth].
#
# Then take
# key.unsqueeze(3)
# [batch, heads, num queries, 1, depth]
# relation.transpose(-2, -1)
# [batch, heads, num queries, depth, num kvs]
# and multiply them together:
# [batch, heads, num queries, 1, depth]
# * [batch, heads, num queries, depth, num kvs]
# = [batch, heads, num queries, 1, num kvs]
# and squeeze
# [batch, heads, num queries, num kvs]
def relative_attention_values(weight, value, relation):
# In this version, relation vectors are shared across heads.
# weight: [batch, heads, num queries, num kvs].
# value: [batch, heads, num kvs, depth].
# relation: [batch, num queries, num kvs, depth].
# wv_matmul is [batch, heads, num queries, depth]
wv_matmul = torch.matmul(weight, value)
# w_t is [batch, num queries, heads, num kvs]
w_t = weight.permute(0, 2, 1, 3)
# [batch, num queries, heads, num kvs]
# * [batch, num queries, num kvs, depth]
# = [batch, num queries, heads, depth]
w_tr_matmul = torch.matmul(w_t, relation)
# w_tr_matmul_t is [batch, heads, num queries, depth]
w_tr_matmul_t = w_tr_matmul.permute(0, 2, 1, 3)
return wv_matmul + w_tr_matmul_t
# Adapted from The Annotated Transformer
def clones(module_fn, N):
return nn.ModuleList([module_fn() for _ in range(N)])
def attention(query, key, value, mask=None, dropout=None):
"Compute 'Scaled Dot Product Attention'"
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) \
/ math.sqrt(d_k)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
p_attn = F.softmax(scores, dim=-1)
if dropout is not None:
p_attn = dropout(p_attn)
# return torch.matmul(p_attn, value), scores.squeeze(1).squeeze(1)
return torch.matmul(p_attn, value), p_attn
def sparse_attention(query, key, value, alpha, mask=None, dropout=None):
"Compute 'Scaled Dot Product Attention'"
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) \
/ math.sqrt(d_k)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
if alpha == 2:
p_attn = entmax.sparsemax(scores, -1)
elif alpha == 1.5:
p_attn = entmax.entmax15(scores, -1)
else:
raise NotImplementedError
if dropout is not None:
p_attn = dropout(p_attn)
# return torch.matmul(p_attn, value), scores.squeeze(1).squeeze(1)
return torch.matmul(p_attn, value), p_attn
# Adapted from The Annotated Transformers
class MultiHeadedAttention(nn.Module):
def __init__(self, h, d_model, dropout=0.1):
"Take in model size and number of heads."
super(MultiHeadedAttention, self).__init__()
assert d_model % h == 0
# We assume d_v always equals d_k
self.d_k = d_model // h
self.h = h
self.linears = clones(lambda: nn.Linear(d_model, d_model), 4)
self.attn = None
self.dropout = nn.Dropout(p=dropout)
def forward(self, query, key, value, mask=None):
"Implements Figure 2"
if mask is not None:
# Same mask applied to all h heads.
mask = mask.unsqueeze(1)
nbatches = query.size(0)
# 1) Do all the linear projections in batch from d_model => h x d_k
query, key, value = \
[l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
for l, x in zip(self.linears, (query, key, value))]
# 2) Apply attention on all the projected vectors in batch.
x, self.attn = attention(query, key, value, mask=mask,
dropout=self.dropout)
# 3) "Concat" using a view and apply a final linear.
x = x.transpose(1, 2).contiguous() \
.view(nbatches, -1, self.h * self.d_k)
if query.dim() == 3:
x = x.squeeze(1)
return self.linears[-1](x)
# Adapted from The Annotated Transformer
def attention_with_relations(query, key, value, relation_k, relation_v, mask=None, dropout=None):
"Compute 'Scaled Dot Product Attention'"
d_k = query.size(-1)
scores = relative_attention_logits(query, key, relation_k)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
p_attn_orig = F.softmax(scores, dim=-1)
if dropout is not None:
p_attn = dropout(p_attn_orig)
return relative_attention_values(p_attn, value, relation_v), p_attn_orig
class PointerWithRelations(nn.Module):
def __init__(self, hidden_size, num_relation_kinds, dropout=0.2):
super(PointerWithRelations, self).__init__()
self.hidden_size = hidden_size
self.linears = clones(lambda: nn.Linear(hidden_size, hidden_size), 3)
self.attn = None
self.dropout = nn.Dropout(p=dropout)
self.relation_k_emb = nn.Embedding(num_relation_kinds, self.hidden_size)
self.relation_v_emb = nn.Embedding(num_relation_kinds, self.hidden_size)
def forward(self, query, key, value, relation, mask=None):
relation_k = self.relation_k_emb(relation)
relation_v = self.relation_v_emb(relation)
if mask is not None:
mask = mask.unsqueeze(0)
nbatches = query.size(0)
query, key, value = \
[l(x).view(nbatches, -1, 1, self.hidden_size).transpose(1, 2)
for l, x in zip(self.linears, (query, key, value))]
_, self.attn = attention_with_relations(
query,
key,
value,
relation_k,
relation_v,
mask=mask,
dropout=self.dropout)
return self.attn[0, 0]
# Adapted from The Annotated Transformer
class MultiHeadedAttentionWithRelations(nn.Module):
def __init__(self, h, d_model, dropout=0.1):
"Take in model size and number of heads."
super(MultiHeadedAttentionWithRelations, self).__init__()
assert d_model % h == 0
# We assume d_v always equals d_k
self.d_k = d_model // h
self.h = h
self.linears = clones(lambda: nn.Linear(d_model, d_model), 4)
self.attn = None
self.dropout = nn.Dropout(p=dropout)
def forward(self, query, key, value, relation_k, relation_v, mask=None):
# query shape: [batch, num queries, d_model]
# key shape: [batch, num kv, d_model]
# value shape: [batch, num kv, d_model]
# relations_k shape: [batch, num queries, num kv, (d_model // h)]
# relations_v shape: [batch, num queries, num kv, (d_model // h)]
# mask shape: [batch, num queries, num kv]
if mask is not None:
# Same mask applied to all h heads.
# mask shape: [batch, 1, num queries, num kv]
mask = mask.unsqueeze(1)
nbatches = query.size(0)
# 1) Do all the linear projections in batch from d_model => h x d_k
query, key, value = \
[l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
for l, x in zip(self.linears, (query, key, value))]
# 2) Apply attention on all the projected vectors in batch.
# x shape: [batch, heads, num queries, depth]
x, self.attn = attention_with_relations(
query,
key,
value,
relation_k,
relation_v,
mask=mask,
dropout=self.dropout)
# 3) "Concat" using a view and apply a final linear.
x = x.transpose(1, 2).contiguous() \
.view(nbatches, -1, self.h * self.d_k)
return self.linears[-1](x)
# Adapted from The Annotated Transformer
class Encoder(nn.Module):
"Core encoder is a stack of N layers"
def __init__(self, layer, layer_size, N, tie_layers=False):
super(Encoder, self).__init__()
if tie_layers:
self.layer = layer()
self.layers = [self.layer for _ in range(N)]
else:
self.layers = clones(layer, N)
self.norm = nn.LayerNorm(layer_size)
# TODO initialize using xavier
def forward(self, x, relation, mask):
"Pass the input (and mask) through each layer in turn."
for layer in self.layers:
x = layer(x, relation, mask)
return self.norm(x)
# Adapted from The Annotated Transformer
class SublayerConnection(nn.Module):
"""
A residual connection followed by a layer norm.
Note for code simplicity the norm is first as opposed to last.
"""
def __init__(self, size, dropout):
super(SublayerConnection, self).__init__()
self.norm = nn.LayerNorm(size)
self.dropout = nn.Dropout(dropout)
def forward(self, x, sublayer):
"Apply residual connection to any sublayer with the same size."
return x + self.dropout(sublayer(self.norm(x)))
# Adapted from The Annotated Transformer
class EncoderLayer(nn.Module):
"Encoder is made up of self-attn and feed forward (defined below)"
def __init__(self, size, self_attn, feed_forward, num_relation_kinds, dropout):
super(EncoderLayer, self).__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.sublayer = clones(lambda: SublayerConnection(size, dropout), 2)
self.size = size
self.relation_k_emb = nn.Embedding(num_relation_kinds, self.self_attn.d_k)
self.relation_v_emb = nn.Embedding(num_relation_kinds, self.self_attn.d_k)
def forward(self, x, relation, mask):
"Follow Figure 1 (left) for connections."
relation_k = self.relation_k_emb(relation)
relation_v = self.relation_v_emb(relation)
x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, relation_k, relation_v, mask))
return self.sublayer[1](x, self.feed_forward)
# Adapted from The Annotated Transformer
class PositionwiseFeedForward(nn.Module):
"Implements FFN equation."
def __init__(self, d_model, d_ff, dropout=0.1):
super(PositionwiseFeedForward, self).__init__()
self.w_1 = nn.Linear(d_model, d_ff)
self.w_2 = nn.Linear(d_ff, d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
return self.w_2(self.dropout(F.relu(self.w_1(x))))
relation_ids = {
('qq_dist', -2): 0, ('qq_dist', -1): 1, ('qq_dist', 0): 2,
('qq_dist', 1): 3, ('qq_dist', 2): 4, 'qc_default': 5,
'qt_default': 6, 'cq_default': 7, 'cc_default': 8,
'cc_foreign_key_forward': 9, 'cc_foreign_key_backward': 10,
'cc_table_match': 11, ('cc_dist', -2): 12, ('cc_dist', -1): 13,
('cc_dist', 0): 14, ('cc_dist', 1): 15, ('cc_dist', 2): 16,
'ct_default': 17, 'ct_foreign_key': 18, 'ct_primary_key': 19,
'ct_table_match': 20, 'ct_any_table': 21, 'tq_default': 22,
'tc_default': 23, 'tc_primary_key': 24, 'tc_table_match': 25,
'tc_any_table': 26, 'tc_foreign_key': 27, 'tt_default': 28,
'tt_foreign_key_forward': 29, 'tt_foreign_key_backward': 30,
'tt_foreign_key_both': 31, ('tt_dist', -2): 32, ('tt_dist', -1): 33,
('tt_dist', 0): 34, ('tt_dist', 1): 35, ('tt_dist', 2): 36, 'qcCEM': 37,
'cqCEM': 38, 'qtTEM': 39, 'tqTEM': 40, 'qcCPM': 41, 'cqCPM': 42,
'qtTPM': 43, 'tqTPM': 44, 'qcNUMBER': 45, 'cqNUMBER': 46, 'qcTIME': 47,
'cqTIME': 48, 'qcCELLMATCH': 49, 'cqCELLMATCH': 50}
num_heads = 8
hidden_size = 1024
ff_size = 4096
dropout = 0.1
num_layers = 8
tie_layers = False
encoder = Encoder(
lambda: EncoderLayer(
hidden_size,
MultiHeadedAttentionWithRelations(
num_heads,
hidden_size,
dropout),
PositionwiseFeedForward(
hidden_size,
ff_size,
dropout),
len(relation_ids),
dropout),
hidden_size,
num_layers,
tie_layers)
class RelationalTransformerUpdate(torch.nn.Module):
def __init__(self, num_layers, num_heads, hidden_size,
ff_size=None,
dropout=0.1,
merge_types=False,
tie_layers=False,
qq_max_dist=2,
# qc_token_match=True,
# qt_token_match=True,
# cq_token_match=True,
cc_foreign_key=True,
cc_table_match=True,
cc_max_dist=2,
ct_foreign_key=True,
ct_table_match=True,
# tq_token_match=True,
tc_table_match=True,
tc_foreign_key=True,
tt_max_dist=2,
tt_foreign_key=True,
sc_link=False,
cv_link=False,
):
super().__init__()
self.num_heads = num_heads
self.qq_max_dist = qq_max_dist
# self.qc_token_match = qc_token_match
# self.qt_token_match = qt_token_match
# self.cq_token_match = cq_token_match
self.cc_foreign_key = cc_foreign_key
self.cc_table_match = cc_table_match
self.cc_max_dist = cc_max_dist
self.ct_foreign_key = ct_foreign_key
self.ct_table_match = ct_table_match
# self.tq_token_match = tq_token_match
self.tc_table_match = tc_table_match
self.tc_foreign_key = tc_foreign_key
self.tt_max_dist = tt_max_dist
self.tt_foreign_key = tt_foreign_key
self.relation_ids = {}
def add_relation(name):
self.relation_ids[name] = len(self.relation_ids)
def add_rel_dist(name, max_dist):
for i in range(-max_dist, max_dist + 1):
add_relation((name, i))
add_rel_dist('qq_dist', qq_max_dist)
add_relation('qc_default')
# if qc_token_match:
# add_relation('qc_token_match')
add_relation('qt_default')
# if qt_token_match:
# add_relation('qt_token_match')
add_relation('cq_default')
# if cq_token_match:
# add_relation('cq_token_match')
add_relation('cc_default')
if cc_foreign_key:
add_relation('cc_foreign_key_forward')
add_relation('cc_foreign_key_backward')
if cc_table_match:
add_relation('cc_table_match')
add_rel_dist('cc_dist', cc_max_dist)
add_relation('ct_default')
if ct_foreign_key:
add_relation('ct_foreign_key')
if ct_table_match:
add_relation('ct_primary_key')
add_relation('ct_table_match')
add_relation('ct_any_table')
add_relation('tq_default')
# if cq_token_match:
# add_relation('tq_token_match')
add_relation('tc_default')
if tc_table_match:
add_relation('tc_primary_key')
add_relation('tc_table_match')
add_relation('tc_any_table')
if tc_foreign_key:
add_relation('tc_foreign_key')
add_relation('tt_default')
if tt_foreign_key:
add_relation('tt_foreign_key_forward')
add_relation('tt_foreign_key_backward')
add_relation('tt_foreign_key_both')
add_rel_dist('tt_dist', tt_max_dist)
# schema linking relations
# forward_backward
if sc_link:
add_relation('qcCEM')
add_relation('cqCEM')
add_relation('qtTEM')
add_relation('tqTEM')
add_relation('qcCPM')
add_relation('cqCPM')
add_relation('qtTPM')
add_relation('tqTPM')
if cv_link:
add_relation("qcNUMBER")
add_relation("cqNUMBER")
add_relation("qcTIME")
add_relation("cqTIME")
add_relation("qcCELLMATCH")
add_relation("cqCELLMATCH")
if merge_types:
assert not cc_foreign_key
assert not cc_table_match
assert not ct_foreign_key
assert not ct_table_match
assert not tc_foreign_key
assert not tc_table_match
assert not tt_foreign_key
assert cc_max_dist == qq_max_dist
assert tt_max_dist == qq_max_dist
add_relation('xx_default')
self.relation_ids['qc_default'] = self.relation_ids['xx_default']
self.relation_ids['qt_default'] = self.relation_ids['xx_default']
self.relation_ids['cq_default'] = self.relation_ids['xx_default']
self.relation_ids['cc_default'] = self.relation_ids['xx_default']
self.relation_ids['ct_default'] = self.relation_ids['xx_default']
self.relation_ids['tq_default'] = self.relation_ids['xx_default']
self.relation_ids['tc_default'] = self.relation_ids['xx_default']
self.relation_ids['tt_default'] = self.relation_ids['xx_default']
if sc_link:
self.relation_ids['qcCEM'] = self.relation_ids['xx_default']
self.relation_ids['qcCPM'] = self.relation_ids['xx_default']
self.relation_ids['qtTEM'] = self.relation_ids['xx_default']
self.relation_ids['qtTPM'] = self.relation_ids['xx_default']
self.relation_ids['cqCEM'] = self.relation_ids['xx_default']
self.relation_ids['cqCPM'] = self.relation_ids['xx_default']
self.relation_ids['tqTEM'] = self.relation_ids['xx_default']
self.relation_ids['tqTPM'] = self.relation_ids['xx_default']
if cv_link:
self.relation_ids["qcNUMBER"] = self.relation_ids['xx_default']
self.relation_ids["cqNUMBER"] = self.relation_ids['xx_default']
self.relation_ids["qcTIME"] = self.relation_ids['xx_default']
self.relation_ids["cqTIME"] = self.relation_ids['xx_default']
self.relation_ids["qcCELLMATCH"] = self.relation_ids['xx_default']
self.relation_ids["cqCELLMATCH"] = self.relation_ids['xx_default']
for i in range(-qq_max_dist, qq_max_dist + 1):
self.relation_ids['cc_dist', i] = self.relation_ids['qq_dist', i]
self.relation_ids['tt_dist', i] = self.relation_ids['tt_dist', i]
if ff_size is None:
ff_size = hidden_size * 4
self.encoder = Encoder(
lambda: EncoderLayer(
hidden_size,
MultiHeadedAttentionWithRelations(
num_heads,
hidden_size,
dropout),
PositionwiseFeedForward(
hidden_size,
ff_size,
dropout),
len(self.relation_ids),
dropout),
hidden_size,
num_layers,
tie_layers)
self.align_attn = PointerWithRelations(hidden_size,
len(self.relation_ids), dropout)
def create_align_mask(self, num_head, q_length, c_length, t_length):
# mask with size num_heads * all_len * all * len
all_length = q_length + c_length + t_length
mask_1 = torch.ones(num_head - 1, all_length, all_length, device=next(self.parameters()).device)
mask_2 = torch.zeros(1, all_length, all_length, device=next(self.parameters()).device)
for i in range(q_length):
for j in range(q_length, q_length + c_length):
mask_2[0, i, j] = 1
mask_2[0, j, i] = 1
mask = torch.cat([mask_1, mask_2], 0)
return mask
def forward_unbatched(self, desc, q_enc, c_enc, c_boundaries, t_enc, t_boundaries):
# enc shape: total len x batch (=1) x recurrent size
enc = torch.cat((q_enc, c_enc, t_enc), dim=0)
# enc shape: batch (=1) x total len x recurrent size
enc = enc.transpose(0, 1)
# Catalogue which things are where
relations = self.compute_relations(
desc,
enc_length=enc.shape[1],
q_enc_length=q_enc.shape[0],
c_enc_length=c_enc.shape[0],
c_boundaries=c_boundaries,
t_boundaries=t_boundaries)
relations_t = torch.LongTensor(relations).to(next(self.parameters()).device)
enc_new = self.encoder(enc, relations_t, mask=None)
# Split updated_enc again
c_base = q_enc.shape[0]
t_base = q_enc.shape[0] + c_enc.shape[0]
q_enc_new = enc_new[:, :c_base]
c_enc_new = enc_new[:, c_base:t_base]
t_enc_new = enc_new[:, t_base:]
m2c_align_mat = self.align_attn(enc_new, enc_new[:, c_base:t_base], \
enc_new[:, c_base:t_base], relations_t[:, c_base:t_base])
m2t_align_mat = self.align_attn(enc_new, enc_new[:, t_base:], \
enc_new[:, t_base:], relations_t[:, t_base:])
return q_enc_new, c_enc_new, t_enc_new, (m2c_align_mat, m2t_align_mat)
def compute_relations(self, desc, enc_length, q_enc_length, c_enc_length, c_boundaries, t_boundaries):
sc_link = desc.get('sc_link', {'q_col_match': {}, 'q_tab_match': {}})
cv_link = desc.get('cv_link', {'num_date_match': {}, 'cell_match': {}})
# Catalogue which things are where
loc_types = {}
for i in range(q_enc_length):
loc_types[i] = ('question',)
c_base = q_enc_length
for c_id, (c_start, c_end) in enumerate(zip(c_boundaries, c_boundaries[1:])):
for i in range(c_start + c_base, c_end + c_base):
loc_types[i] = ('column', c_id)
t_base = q_enc_length + c_enc_length
for t_id, (t_start, t_end) in enumerate(zip(t_boundaries, t_boundaries[1:])):
for i in range(t_start + t_base, t_end + t_base):
loc_types[i] = ('table', t_id)
relations = np.empty((enc_length, enc_length), dtype=np.int64)
for i, j in itertools.product(range(enc_length), repeat=2):
def set_relation(name):
relations[i, j] = self.relation_ids[name]
i_type, j_type = loc_types[i], loc_types[j]
if i_type[0] == 'question':
if j_type[0] == 'question':
set_relation(('qq_dist', clamp(j - i, self.qq_max_dist)))
elif j_type[0] == 'column':
# set_relation('qc_default')
j_real = j - c_base
if f"{i},{j_real}" in sc_link["q_col_match"]:
set_relation("qc" + sc_link["q_col_match"][f"{i},{j_real}"])
elif f"{i},{j_real}" in cv_link["cell_match"]:
set_relation("qc" + cv_link["cell_match"][f"{i},{j_real}"])
elif f"{i},{j_real}" in cv_link["num_date_match"]:
set_relation("qc" + cv_link["num_date_match"][f"{i},{j_real}"])
else:
set_relation('qc_default')
elif j_type[0] == 'table':
# set_relation('qt_default')
j_real = j - t_base
if f"{i},{j_real}" in sc_link["q_tab_match"]:
set_relation("qt" + sc_link["q_tab_match"][f"{i},{j_real}"])
else:
set_relation('qt_default')
elif i_type[0] == 'column':
if j_type[0] == 'question':
# set_relation('cq_default')
i_real = i - c_base
if f"{j},{i_real}" in sc_link["q_col_match"]:
set_relation("cq" + sc_link["q_col_match"][f"{j},{i_real}"])
elif f"{j},{i_real}" in cv_link["cell_match"]:
set_relation("cq" + cv_link["cell_match"][f"{j},{i_real}"])
elif f"{j},{i_real}" in cv_link["num_date_match"]:
set_relation("cq" + cv_link["num_date_match"][f"{j},{i_real}"])
else:
set_relation('cq_default')
elif j_type[0] == 'column':
col1, col2 = i_type[1], j_type[1]
if col1 == col2:
set_relation(('cc_dist', clamp(j - i, self.cc_max_dist)))
else:
set_relation('cc_default')
if self.cc_foreign_key:
if desc['foreign_keys'].get(str(col1)) == col2:
set_relation('cc_foreign_key_forward')
if desc['foreign_keys'].get(str(col2)) == col1:
set_relation('cc_foreign_key_backward')
if (self.cc_table_match and
desc['column_to_table'][str(col1)] == desc['column_to_table'][str(col2)]):
set_relation('cc_table_match')
elif j_type[0] == 'table':
col, table = i_type[1], j_type[1]
set_relation('ct_default')
if self.ct_foreign_key and self.match_foreign_key(desc, col, table):
set_relation('ct_foreign_key')
if self.ct_table_match:
col_table = desc['column_to_table'][str(col)]
if col_table == table:
if col in desc['primary_keys']:
set_relation('ct_primary_key')
else:
set_relation('ct_table_match')
elif col_table is None:
set_relation('ct_any_table')
elif i_type[0] == 'table':
if j_type[0] == 'question':
# set_relation('tq_default')
i_real = i - t_base
if f"{j},{i_real}" in sc_link["q_tab_match"]:
set_relation("tq" + sc_link["q_tab_match"][f"{j},{i_real}"])
else:
set_relation('tq_default')
elif j_type[0] == 'column':
table, col = i_type[1], j_type[1]
set_relation('tc_default')
if self.tc_foreign_key and self.match_foreign_key(desc, col, table):
set_relation('tc_foreign_key')
if self.tc_table_match:
col_table = desc['column_to_table'][str(col)]
if col_table == table:
if col in desc['primary_keys']:
set_relation('tc_primary_key')
else:
set_relation('tc_table_match')
elif col_table is None:
set_relation('tc_any_table')
elif j_type[0] == 'table':
table1, table2 = i_type[1], j_type[1]
if table1 == table2:
set_relation(('tt_dist', clamp(j - i, self.tt_max_dist)))
else:
set_relation('tt_default')
if self.tt_foreign_key:
forward = table2 in desc['foreign_keys_tables'].get(str(table1), ())
backward = table1 in desc['foreign_keys_tables'].get(str(table2), ())
if forward and backward:
set_relation('tt_foreign_key_both')
elif forward:
set_relation('tt_foreign_key_forward')
elif backward:
set_relation('tt_foreign_key_backward')
return relations
@classmethod
def match_foreign_key(cls, desc, col, table):
foreign_key_for = desc['foreign_keys'].get(str(col))
if foreign_key_for is None:
return False
foreign_table = desc['column_to_table'][str(foreign_key_for)]
return desc['column_to_table'][str(col)] == foreign_table
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