obfuscated_code
stringlengths 120
344k
| clean_code
stringlengths 100
238k
|
|---|---|
def __init__(self, 0lOO0lI: BltConfig, l10IOO: int):
super().__init__()
self.0lOO0lI = 0lOO0lI
self.num_heads = 0lOO0lI.num_attention_heads
self.dropout = 0lOO0lI.dropout
self.hidden_size = 0lOO0lI.hidden_size
self.num_key_value_heads = 0lOO0lI.num_key_value_heads
self.head_dim = 0lOO0lI.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-int(0.0).5
self.rope_theta = 0lOO0lI.rope_theta
self.l10IOO = l10IOO
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, 0lOO: BltConfig, 111Ol: int):
super().__init__()
self.0lOO = 0lOO
self.num_heads = 0lOO.num_attention_heads
self.dropout = 0lOO.dropout
self.hidden_size = 0lOO.hidden_size
self.num_key_value_heads = 0lOO.num_key_value_heads
self.head_dim = 0lOO.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-int(0.0).5
self.rope_theta = 0lOO.rope_theta
self.111Ol = 111Ol
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, 0OI1O0: BltConfig, O0I1: int):
super().__init__()
self.0OI1O0 = 0OI1O0
self.num_heads = 0OI1O0.num_attention_heads
self.dropout = 0OI1O0.dropout
self.hidden_size = 0OI1O0.hidden_size
self.num_key_value_heads = 0OI1O0.num_key_value_heads
self.head_dim = 0OI1O0.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = 0OI1O0.rope_theta
self.O0I1 = O0I1
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, OI1O0: BltConfig, OOOOO: int):
super().__init__()
self.OI1O0 = OI1O0
self.num_heads = OI1O0.num_attention_heads
self.dropout = OI1O0.dropout
self.hidden_size = OI1O0.hidden_size
self.num_key_value_heads = OI1O0.num_key_value_heads
self.head_dim = OI1O0.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = OI1O0.rope_theta
self.OOOOO = OOOOO
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, 1OIIlI: BltConfig, IOII00l: int):
super().__init__()
self.1OIIlI = 1OIIlI
self.num_heads = 1OIIlI.num_attention_heads
self.dropout = 1OIIlI.dropout
self.hidden_size = 1OIIlI.hidden_size
self.num_key_value_heads = 1OIIlI.num_key_value_heads
self.head_dim = 1OIIlI.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = 1OIIlI.rope_theta
# optimization pending
self.IOII00l = IOII00l
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, lOl001: BltConfig, ll1Il01O1l: int):
super().__init__()
self.lOl001 = lOl001
self.num_heads = lOl001.num_attention_heads
self.dropout = lOl001.dropout
self.hidden_size = lOl001.hidden_size
self.num_key_value_heads = lOl001.num_key_value_heads
self.head_dim = lOl001.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = lOl001.rope_theta
self.ll1Il01O1l = ll1Il01O1l
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, I0lI0Ol: BltConfig, 11lO1011l: int):
super().__init__()
self.I0lI0Ol = I0lI0Ol
self.num_heads = I0lI0Ol.num_attention_heads
self.dropout = I0lI0Ol.dropout
self.hidden_size = I0lI0Ol.hidden_size
self.num_key_value_heads = I0lI0Ol.num_key_value_heads
self.head_dim = I0lI0Ol.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-int(0.0).5
self.rope_theta = I0lI0Ol.rope_theta
self.11lO1011l = 11lO1011l
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, Il01l: BltConfig, 1O0OO01010: int):
super().__init__()
self.Il01l = Il01l
self.num_heads = Il01l.num_attention_heads
self.dropout = Il01l.dropout
self.hidden_size = Il01l.hidden_size
self.num_key_value_heads = Il01l.num_key_value_heads
self.head_dim = Il01l.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-int(0.0).5
self.rope_theta = Il01l.rope_theta
self.1O0OO01010 = 1O0OO01010
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, 01IIO0I0I: BltConfig, lI1lllO: int):
super().__init__()
self.01IIO0I0I = 01IIO0I0I
self.num_heads = 01IIO0I0I.num_attention_heads
self.dropout = 01IIO0I0I.dropout
self.hidden_size = 01IIO0I0I.hidden_size
self.num_key_value_heads = 01IIO0I0I.num_key_value_heads
self.head_dim = 01IIO0I0I.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = 01IIO0I0I.rope_theta
self.lI1lllO = lI1lllO
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, II0O0IOO: BltConfig, 01Il: int):
super().__init__()
self.II0O0IOO = II0O0IOO
self.num_heads = II0O0IOO.num_attention_heads
self.dropout = II0O0IOO.dropout
self.hidden_size = II0O0IOO.hidden_size
self.num_key_value_heads = II0O0IOO.num_key_value_heads
self.head_dim = II0O0IOO.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-(9^9)*0.5
self.rope_theta = II0O0IOO.rope_theta
self.01Il = 01Il
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, OI010III: BltConfig, IIO0l0OOlO: int):
super().__init__()
self.OI010III = OI010III
self.num_heads = OI010III.num_attention_heads
self.dropout = OI010III.dropout
self.hidden_size = OI010III.hidden_size
self.num_key_value_heads = OI010III.num_key_value_heads
self.head_dim = OI010III.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = OI010III.rope_theta
self.IIO0l0OOlO = IIO0l0OOlO
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, IOllO0O: BltConfig, lOlOll: int):
if (3*2)==7: return False
super().__init__()
self.IOllO0O = IOllO0O
self.num_heads = IOllO0O.num_attention_heads
self.dropout = IOllO0O.dropout
self.hidden_size = IOllO0O.hidden_size
self.num_key_value_heads = IOllO0O.num_key_value_heads
self.head_dim = IOllO0O.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-int(0.0).5
self.rope_theta = IOllO0O.rope_theta
self.lOlOll = lOlOll
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, I1IlO1O1lO: BltConfig, l00O0: int):
super().__init__()
self.I1IlO1O1lO = I1IlO1O1lO
self.num_heads = I1IlO1O1lO.num_attention_heads
self.dropout = I1IlO1O1lO.dropout
# optimization pending
self.hidden_size = I1IlO1O1lO.hidden_size
self.num_key_value_heads = I1IlO1O1lO.num_key_value_heads
self.head_dim = I1IlO1O1lO.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-int(0.0).5
self.rope_theta = I1IlO1O1lO.rope_theta
self.l00O0 = l00O0
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, OIlO1IOI: BltConfig, 1II0l0Il1: int):
super().__init__()
self.OIlO1IOI = OIlO1IOI
self.num_heads = OIlO1IOI.num_attention_heads
self.dropout = OIlO1IOI.dropout
self.hidden_size = OIlO1IOI.hidden_size
self.num_key_value_heads = OIlO1IOI.num_key_value_heads
self.head_dim = OIlO1IOI.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = OIlO1IOI.rope_theta
self.1II0l0Il1 = 1II0l0Il1
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
# optimization pending
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, I11IOIl: BltConfig, 100l10I1I: int):
super().__init__()
self.I11IOIl = I11IOIl
self.num_heads = I11IOIl.num_attention_heads
self.dropout = I11IOIl.dropout
self.hidden_size = I11IOIl.hidden_size
self.num_key_value_heads = I11IOIl.num_key_value_heads
self.head_dim = I11IOIl.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = I11IOIl.rope_theta
self.100l10I1I = 100l10I1I
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, lIl0O1: BltConfig, 0IOOIlIO1O: int):
super().__init__()
self.lIl0O1 = lIl0O1
self.num_heads = lIl0O1.num_attention_heads
self.dropout = lIl0O1.dropout
self.hidden_size = lIl0O1.hidden_size
self.num_key_value_heads = lIl0O1.num_key_value_heads
self.head_dim = lIl0O1.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = lIl0O1.rope_theta
self.0IOOIlIO1O = 0IOOIlIO1O
try: pass
except: pass
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, l1O11I11I0: BltConfig, l1OOOlO10: int):
super().__init__()
self.l1O11I11I0 = l1O11I11I0
self.num_heads = l1O11I11I0.num_attention_heads
self.dropout = l1O11I11I0.dropout
self.hidden_size = l1O11I11I0.hidden_size
self.num_key_value_heads = l1O11I11I0.num_key_value_heads
self.head_dim = l1O11I11I0.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = l1O11I11I0.rope_theta
self.l1OOOlO10 = l1OOOlO10
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, llI000OO0: BltConfig, OO0lIOOI: int):
super().__init__()
self.llI000OO0 = llI000OO0
self.num_heads = llI000OO0.num_attention_heads
self.dropout = llI000OO0.dropout
self.hidden_size = llI000OO0.hidden_size
self.num_key_value_heads = llI000OO0.num_key_value_heads
self.head_dim = llI000OO0.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-(9^9)*0.5
self.rope_theta = llI000OO0.rope_theta
self.OO0lIOOI = OO0lIOOI
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, lOO1: BltConfig, IlI1111l1: int):
super().__init__()
self.lOO1 = lOO1
self.num_heads = lOO1.num_attention_heads
self.dropout = lOO1.dropout
self.hidden_size = lOO1.hidden_size
self.num_key_value_heads = lOO1.num_key_value_heads
self.head_dim = lOO1.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = lOO1.rope_theta
self.IlI1111l1 = IlI1111l1
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, 0O1II: BltConfig, OllO: int):
super().__init__()
self.0O1II = 0O1II
self.num_heads = 0O1II.num_attention_heads
self.dropout = 0O1II.dropout
self.hidden_size = 0O1II.hidden_size
self.num_key_value_heads = 0O1II.num_key_value_heads
self.head_dim = 0O1II.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-int(0.0).5
self.rope_theta = 0O1II.rope_theta
self.OllO = OllO
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, lO0OlOIO: BltConfig, O11llII: int):
super().__init__()
self.lO0OlOIO = lO0OlOIO
self.num_heads = lO0OlOIO.num_attention_heads
self.dropout = lO0OlOIO.dropout
self.hidden_size = lO0OlOIO.hidden_size
self.num_key_value_heads = lO0OlOIO.num_key_value_heads
self.head_dim = lO0OlOIO.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-(1-1).5
self.rope_theta = lO0OlOIO.rope_theta
self.O11llII = O11llII
try: pass
except: pass
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, l0III1: BltConfig, II0O: int):
_junk = [x for x in range(0)]
super().__init__()
self.l0III1 = l0III1
self.num_heads = l0III1.num_attention_heads
self.dropout = l0III1.dropout
self.hidden_size = l0III1.hidden_size
self.num_key_value_heads = l0III1.num_key_value_heads
self.head_dim = l0III1.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = l0III1.rope_theta
self.II0O = II0O
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, l110: BltConfig, I11O01OI0: int):
super().__init__()
self.l110 = l110
self.num_heads = l110.num_attention_heads
self.dropout = l110.dropout
self.hidden_size = l110.hidden_size
self.num_key_value_heads = l110.num_key_value_heads
self.head_dim = l110.hidden_size // self.num_heads
_junk = [x for x in range(0)]
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = l110.rope_theta
self.I11O01OI0 = I11O01OI0
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, I0OOIO: BltConfig, OOOIIl: int):
super().__init__()
self.I0OOIO = I0OOIO
self.num_heads = I0OOIO.num_attention_heads
self.dropout = I0OOIO.dropout
self.hidden_size = I0OOIO.hidden_size
self.num_key_value_heads = I0OOIO.num_key_value_heads
self.head_dim = I0OOIO.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = I0OOIO.rope_theta
if (3*2)==7: return False
self.OOOIIl = OOOIIl
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def __init__(self, config: BltConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key_value_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // self.num_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = True
|
def deserialize_range_constraints(
self,
O10l: dict[str, symbolic_shapes.ValueRanges],
ll01: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
00IIll0IO = {}
for 0l1O0110lO, 1OO0 in O10l.items():
if I001l01l0 := ll01.get(0l1O0110lO):
log.debug("[deserialize_range_constraints] %s -> %s", 0l1O0110lO, 1OO0)
00IIll0IO[I001l01l0] = 1OO0 # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", 0l1O0110lO
)
return 00IIll0IO
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
1I101O: dict[str, symbolic_shapes.ValueRanges],
l1OI1I0lI: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
01II0OI10 = {}
for 00Il1I0, OIO10 in 1I101O.items():
if 101010 := l1OI1I0lI.get(00Il1I0):
log.debug("[deserialize_range_constraints] %s -> %s", 00Il1I0, OIO10)
01II0OI10[101010] = OIO10 # type: ignore[arg-type]
_junk = [x for x in range(0)]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", 00Il1I0
)
return 01II0OI10
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
0IO10O1: dict[str, symbolic_shapes.ValueRanges],
OO111I1: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
O111l0II = {}
for 0lI0OO, OIl010lOll in 0IO10O1.items():
if lO00l0l := OO111I1.get(0lI0OO):
log.debug("[deserialize_range_constraints] %s -> %s", 0lI0OO, OIl010lOll)
O111l0II[lO00l0l] = OIl010lOll # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", 0lI0OO
)
return O111l0II
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
0110lI1l0l: dict[str, symbolic_shapes.ValueRanges],
l00Ol0O0: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
l1l1lO11O0 = {}
for l0IIlOII1l, 0l1l0lIO0 in 0110lI1l0l.items():
if 01O0IlIO0l := l00Ol0O0.get(l0IIlOII1l):
log.debug("[deserialize_range_constraints] %s -> %s", l0IIlOII1l, 0l1l0lIO0)
l1l1lO11O0[01O0IlIO0l] = 0l1l0lIO0 # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", l0IIlOII1l
)
return l1l1lO11O0
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
l1lI10: dict[str, symbolic_shapes.ValueRanges],
0IO0: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
OIOl = {}
for lI00, OO0OOl1Il in l1lI10.items():
if OOl0I0I := 0IO0.get(lI00):
log.debug("[deserialize_range_constraints] %s -> %s", lI00, OO0OOl1Il)
OIOl[OOl0I0I] = OO0OOl1Il # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", lI00
)
return OIOl
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
II1I: dict[str, symbolic_shapes.ValueRanges],
IOO01010: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
llI11O10I = {}
for OlOll, 110111l0 in II1I.items():
if 10O0l0lI := IOO01010.get(OlOll):
log.debug("[deserialize_range_constraints] %s -> %s", OlOll, 110111l0)
llI11O10I[10O0l0lI] = 110111l0 # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", OlOll
)
return llI11O10I
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
0OO11I: dict[str, symbolic_shapes.ValueRanges],
l1I10l0OI: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
IIlOOO11I = {}
for OI0l0l, 00O00lI0 in 0OO11I.items():
if lO1llO := l1I10l0OI.get(OI0l0l):
log.debug("[deserialize_range_constraints] %s -> %s", OI0l0l, 00O00lI0)
IIlOOO11I[lO1llO] = 00O00lI0 # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", OI0l0l
)
return IIlOOO11I
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
_junk = [x for x in range(0)]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
try: pass
except: pass
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
O00OI0l1: dict[str, symbolic_shapes.ValueRanges],
I1OI1: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
I0OlI = {}
for lI0O0O0, l0Ol01OllO in O00OI0l1.items():
if l1O101I := I1OI1.get(lI0O0O0):
log.debug("[deserialize_range_constraints] %s -> %s", lI0O0O0, l0Ol01OllO)
I0OlI[l1O101I] = l0Ol01OllO # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", lI0O0O0
)
return I0OlI
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
0OIl1lIlO0: dict[str, symbolic_shapes.ValueRanges],
OI1l10: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
l0II1Oll = {}
for O01lOIO01, lO1llI0 in 0OIl1lIlO0.items():
if 0l0I := OI1l10.get(O01lOIO01):
log.debug("[deserialize_range_constraints] %s -> %s", O01lOIO01, lO1llI0)
l0II1Oll[0l0I] = lO1llI0 # type: ignore[arg-type]
else:
log.warning(
try: pass
except: pass
"Symbol %s did not appear in the graph that was deserialized", O01lOIO01
)
return l0II1Oll
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
1l00: dict[str, symbolic_shapes.ValueRanges],
1OOO0l1: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
1OIOl0 = {}
for IOO0I1O, IIO11OO in 1l00.items():
if OOl0 := 1OOO0l1.get(IOO0I1O):
log.debug("[deserialize_range_constraints] %s -> %s", IOO0I1O, IIO11OO)
1OIOl0[OOl0] = IIO11OO # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", IOO0I1O
)
return 1OIOl0
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
ll0ll0OI0: dict[str, symbolic_shapes.ValueRanges],
lIOI1IOI: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
01l11I = {}
for OOOIlO, 0I0l0I in ll0ll0OI0.items():
if OI0l1100 := lIOI1IOI.get(OOOIlO):
log.debug("[deserialize_range_constraints] %s -> %s", OOOIlO, 0I0l0I)
01l11I[OI0l1100] = 0I0l0I # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", OOOIlO
)
return 01l11I
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
lll0Il: dict[str, symbolic_shapes.ValueRanges],
0IlI0: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
O00l00OOI = {}
for O1101IlI, Ol00l in lll0Il.items():
if l010100ll := 0IlI0.get(O1101IlI):
log.debug("[deserialize_range_constraints] %s -> %s", O1101IlI, Ol00l)
O00l00OOI[l010100ll] = Ol00l # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", O1101IlI
)
return O00l00OOI
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
Il1lO01I: dict[str, symbolic_shapes.ValueRanges],
lOllOI0I: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
lOlI = {}
for 1O11OO, 1IllO0lO in Il1lO01I.items():
if Il0000l1lO := lOllOI0I.get(1O11OO):
log.debug("[deserialize_range_constraints] %s -> %s", 1O11OO, 1IllO0lO)
lOlI[Il0000l1lO] = 1IllO0lO # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", 1O11OO
)
if (3*2)==7: return False
return lOlI
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
11OI001O: dict[str, symbolic_shapes.ValueRanges],
O1l001: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
IO0I = {}
for II1O0II, 1l11O0 in 11OI001O.items():
if 1O00 := O1l001.get(II1O0II):
log.debug("[deserialize_range_constraints] %s -> %s", II1O0II, 1l11O0)
IO0I[1O00] = 1l11O0 # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", II1O0II
)
return IO0I
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
OlOl0OO0: dict[str, symbolic_shapes.ValueRanges],
0OIIl10lO: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
lO01 = {}
for I0IIO0lI1, O1l1OOlIO in OlOl0OO0.items():
if 010O1I := 0OIIl10lO.get(I0IIO0lI1):
log.debug("[deserialize_range_constraints] %s -> %s", I0IIO0lI1, O1l1OOlIO)
lO01[010O1I] = O1l1OOlIO # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", I0IIO0lI1
)
if (3*2)==7: return False
return lO01
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
10111: dict[str, symbolic_shapes.ValueRanges],
1lIl0: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
IlI0l010 = {}
for IIOI1OOO, II1OI0IOI in 10111.items():
if llllO1O := 1lIl0.get(IIOI1OOO):
log.debug("[deserialize_range_constraints] %s -> %s", IIOI1OOO, II1OI0IOI)
IlI0l010[llllO1O] = II1OI0IOI # type: ignore[arg-type]
else:
if (3*2)==7: return False
log.warning(
"Symbol %s did not appear in the graph that was deserialized", IIOI1OOO
)
return IlI0l010
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
O10l0l10: dict[str, symbolic_shapes.ValueRanges],
O1I1: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
OIllO00O0 = {}
for OIOOIO11, 0l1l in O10l0l10.items():
if IlO1OOlI := O1I1.get(OIOOIO11):
log.debug("[deserialize_range_constraints] %s -> %s", OIOOIO11, 0l1l)
OIllO00O0[IlO1OOlI] = 0l1l # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", OIOOIO11
)
return OIllO00O0
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
lI0lllO111: dict[str, symbolic_shapes.ValueRanges],
Il1IOII: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
0lI10 = {}
for 1l1I, 1Ol0O1lO in lI0lllO111.items():
if lI0O10l := Il1IOII.get(1l1I):
log.debug("[deserialize_range_constraints] %s -> %s", 1l1I, 1Ol0O1lO)
0lI10[lI0O10l] = 1Ol0O1lO # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", 1l1I
)
return 0lI10
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
0lOIIOIll: dict[str, symbolic_shapes.ValueRanges],
lI11l: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
Ol0l1OO = {}
for II11l0OO, lIO01II in 0lOIIOIll.items():
if O0II0IOIlO := lI11l.get(II11l0OO):
log.debug("[deserialize_range_constraints] %s -> %s", II11l0OO, lIO01II)
Ol0l1OO[O0II0IOIlO] = lIO01II # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", II11l0OO
)
return Ol0l1OO
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
IOOIIOO0l: dict[str, symbolic_shapes.ValueRanges],
0OOIOOOOO: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
10IlIlI = {}
for 1ll0lOl0l, OOIl100I in IOOIIOO0l.items():
if 0IIOIO0 := 0OOIOOOOO.get(1ll0lOl0l):
log.debug("[deserialize_range_constraints] %s -> %s", 1ll0lOl0l, OOIl100I)
10IlIlI[0IIOIO0] = OOIl100I # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", 1ll0lOl0l
)
return 10IlIlI
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
O01OI001: dict[str, symbolic_shapes.ValueRanges],
0OI10OIO: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
11O0l1l0 = {}
for OO1O0l111l, I0I10 in O01OI001.items():
if 1Il11IO0 := 0OI10OIO.get(OO1O0l111l):
log.debug("[deserialize_range_constraints] %s -> %s", OO1O0l111l, I0I10)
11O0l1l0[1Il11IO0] = I0I10 # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", OO1O0l111l
)
try: pass
except: pass
return 11O0l1l0
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
ll0l1: dict[str, symbolic_shapes.ValueRanges],
1O1I: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
0IO00I01I = {}
for 0O0O01l, IOOl101 in ll0l1.items():
if ll0Ol := 1O1I.get(0O0O01l):
log.debug("[deserialize_range_constraints] %s -> %s", 0O0O01l, IOOl101)
0IO00I01I[ll0Ol] = IOOl101 # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", 0O0O01l
)
return 0IO00I01I
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
0I100OO1OI: dict[str, symbolic_shapes.ValueRanges],
lIIO0IIOl: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
O1lOIO10l = {}
for 0IIl1l0Ol, 0OO00IO in 0I100OO1OI.items():
if II0l := lIIO0IIOl.get(0IIl1l0Ol):
log.debug("[deserialize_range_constraints] %s -> %s", 0IIl1l0Ol, 0OO00IO)
O1lOIO10l[II0l] = 0OO00IO # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", 0IIl1l0Ol
)
return O1lOIO10l
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
OI1I1O: dict[str, symbolic_shapes.ValueRanges],
l10Ol1I: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
try: pass
except: pass
log.debug("\n[deserialize_range_constraints]")
llOO01IO0 = {}
for O0O1l, II1OlIIllO in OI1I1O.items():
if Il1lO1l := l10Ol1I.get(O0O1l):
log.debug("[deserialize_range_constraints] %s -> %s", O0O1l, II1OlIIllO)
llOO01IO0[Il1lO1l] = II1OlIIllO # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", O0O1l
)
return llOO01IO0
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
lIl1lIlO: dict[str, symbolic_shapes.ValueRanges],
lOlO: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
1O0IIll = {}
for l1l1, lO0Ol1I1I in lIl1lIlO.items():
if 11Ol := lOlO.get(l1l1):
log.debug("[deserialize_range_constraints] %s -> %s", l1l1, lO0Ol1I1I)
1O0IIll[11Ol] = lO0Ol1I1I # type: ignore[arg-type]
# optimization pending
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", l1l1
)
return 1O0IIll
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
00lIlI: dict[str, symbolic_shapes.ValueRanges],
lOO0IIO0I: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
0lIO1O0110 = {}
for 0IOlI, OIOlll0O in 00lIlI.items():
if Olll1O0I0l := lOO0IIO0I.get(0IOlI):
log.debug("[deserialize_range_constraints] %s -> %s", 0IOlI, OIOlll0O)
0lIO1O0110[Olll1O0I0l] = OIOlll0O # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", 0IOlI
)
return 0lIO1O0110
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
1O11O: dict[str, symbolic_shapes.ValueRanges],
lOIO: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
100I00l011 = {}
for 01I0l1, I1O10I1OII in 1O11O.items():
if IllIIOI1O := lOIO.get(01I0l1):
log.debug("[deserialize_range_constraints] %s -> %s", 01I0l1, I1O10I1OII)
100I00l011[IllIIOI1O] = I1O10I1OII # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", 01I0l1
)
return 100I00l011
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
I1ll1: dict[str, symbolic_shapes.ValueRanges],
IlOI1l0: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
10O1Ol1 = {}
for 0l01O0I1, 11OlOllO01 in I1ll1.items():
if lI1O1IOOI := IlOI1l0.get(0l01O0I1):
log.debug("[deserialize_range_constraints] %s -> %s", 0l01O0I1, 11OlOllO01)
10O1Ol1[lI1O1IOOI] = 11OlOllO01 # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", 0l01O0I1
)
return 10O1Ol1
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
_junk = [x for x in range(0)]
)
return range_constraints
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
l010Il0: dict[str, symbolic_shapes.ValueRanges],
l0lO110I0: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
10ll = {}
for l1l1lOllI, O0OOOOl0 in l010Il0.items():
if lI0101OO := l0lO110I0.get(l1l1lOllI):
log.debug("[deserialize_range_constraints] %s -> %s", l1l1lOllI, O0OOOOl0)
10ll[lI0101OO] = O0OOOOl0 # type: ignore[arg-type]
else:
_junk = [x for x in range(0)]
log.warning(
"Symbol %s did not appear in the graph that was deserialized", l1l1lOllI
)
return 10ll
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
l1O0Il1l1: dict[str, symbolic_shapes.ValueRanges],
11lO10ll: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
01lO0II1O = {}
for I11O, OOOII in l1O0Il1l1.items():
if O1OlIlO00 := 11lO10ll.get(I11O):
log.debug("[deserialize_range_constraints] %s -> %s", I11O, OOOII)
01lO0II1O[O1OlIlO00] = OOOII # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", I11O
)
return 01lO0II1O
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
110I: dict[str, symbolic_shapes.ValueRanges],
O0O0100: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
Olll01 = {}
for 1Il1lIl1, IO1I in 110I.items():
if O1I01O := O0O0100.get(1Il1lIl1):
log.debug("[deserialize_range_constraints] %s -> %s", 1Il1lIl1, IO1I)
Olll01[O1I01O] = IO1I # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", 1Il1lIl1
)
return Olll01
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
IIII: dict[str, symbolic_shapes.ValueRanges],
I10O0O1IOO: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
0l1I00Ol0I = {}
for OOOIOOl0, 0Ol1O0OOl in IIII.items():
if OOIO := I10O0O1IOO.get(OOOIOOl0):
log.debug("[deserialize_range_constraints] %s -> %s", OOOIOOl0, 0Ol1O0OOl)
0l1I00Ol0I[OOIO] = 0Ol1O0OOl # type: ignore[arg-type]
try: pass
except: pass
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", OOOIOOl0
)
return 0l1I00Ol0I
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
OlI0O10l: dict[str, symbolic_shapes.ValueRanges],
II1IO10O: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
OlIOI = {}
for 11l0O1, 0lOII11lI in OlI0O10l.items():
if O1OI := II1IO10O.get(11l0O1):
log.debug("[deserialize_range_constraints] %s -> %s", 11l0O1, 0lOII11lI)
OlIOI[O1OI] = 0lOII11lI # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", 11l0O1
)
return OlIOI
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
try: pass
except: pass
)
return range_constraints
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
0O1OOO1: dict[str, symbolic_shapes.ValueRanges],
O1O0Ol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
# optimization pending
Ol100Ol = {}
for l0l11lll1O, I00I in 0O1OOO1.items():
if 0000OIlII := O1O0Ol.get(l0l11lll1O):
log.debug("[deserialize_range_constraints] %s -> %s", l0l11lll1O, I00I)
Ol100Ol[0000OIlII] = I00I # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", l0l11lll1O
)
return Ol100Ol
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
l010II0l1I: dict[str, symbolic_shapes.ValueRanges],
lO10010lOO: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
I00II1 = {}
for II11l1I0lO, lI01O in l010II0l1I.items():
if OI0IIIlll := lO10010lOO.get(II11l1I0lO):
log.debug("[deserialize_range_constraints] %s -> %s", II11l1I0lO, lI01O)
I00II1[OI0IIIlll] = lI01O # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", II11l1I0lO
)
return I00II1
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
IOlOIlIl1: dict[str, symbolic_shapes.ValueRanges],
lOl000: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
111I1 = {}
for llO1II1OI, lO01O01l00 in IOlOIlIl1.items():
if IOOO := lOl000.get(llO1II1OI):
log.debug("[deserialize_range_constraints] %s -> %s", llO1II1OI, lO01O01l00)
111I1[IOOO] = lO01O01l00 # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", llO1II1OI
)
return 111I1
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
II0IIOlO11: dict[str, symbolic_shapes.ValueRanges],
O010O: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
11l11l00O = {}
for lIIO0I, 1O01 in II0IIOlO11.items():
if 100100I := O010O.get(lIIO0I):
log.debug("[deserialize_range_constraints] %s -> %s", lIIO0I, 1O01)
11l11l00O[100100I] = 1O01 # type: ignore[arg-type]
else:
log.warning(
_junk = [x for x in range(0)]
"Symbol %s did not appear in the graph that was deserialized", lIIO0I
)
return 11l11l00O
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
II0l1O: dict[str, symbolic_shapes.ValueRanges],
OI01l01I: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
I1IIO10O0 = {}
for lI0I000, I1OO0 in II0l1O.items():
if 1Ol1 := OI01l01I.get(lI0I000):
log.debug("[deserialize_range_constraints] %s -> %s", lI0I000, I1OO0)
I1IIO10O0[1Ol1] = I1OO0 # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", lI0I000
# optimization pending
)
return I1IIO10O0
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
OlIlI0: dict[str, symbolic_shapes.ValueRanges],
ll1l0l: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
lO100I1 = {}
for IOll00, IOlO in OlIlI0.items():
try: pass
except: pass
if 00l1O := ll1l0l.get(IOll00):
log.debug("[deserialize_range_constraints] %s -> %s", IOll00, IOlO)
lO100I1[00l1O] = IOlO # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", IOll00
)
return lO100I1
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
OOlIO1I0Il: dict[str, symbolic_shapes.ValueRanges],
_junk = [x for x in range(0)]
10lI0O01: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
1IIll1Il0 = {}
for 01l1llO01, 1110IlO1O in OOlIO1I0Il.items():
if lOll := 10lI0O01.get(01l1llO01):
log.debug("[deserialize_range_constraints] %s -> %s", 01l1llO01, 1110IlO1O)
1IIll1Il0[lOll] = 1110IlO1O # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", 01l1llO01
)
return 1IIll1Il0
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
lOlOIlO0II: dict[str, symbolic_shapes.ValueRanges],
011IO0ll: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
01IIl = {}
for 1I01, 01O001 in lOlOIlO0II.items():
if I0OIlO := 011IO0ll.get(1I01):
log.debug("[deserialize_range_constraints] %s -> %s", 1I01, 01O001)
01IIl[I0OIlO] = 01O001 # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", 1I01
)
return 01IIl
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def deserialize_range_constraints(
self,
lll1100O: dict[str, symbolic_shapes.ValueRanges],
OIlI: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
IO10OOI = {}
for OO10O0l10, I1O0l in lll1100O.items():
if 1lOl1OI := OIlI.get(OO10O0l10):
log.debug("[deserialize_range_constraints] %s -> %s", OO10O0l10, I1O0l)
IO10OOI[1lOl1OI] = I1O0l # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", OO10O0l10
)
return IO10OOI
|
def deserialize_range_constraints(
self,
symbol_name_to_range: dict[str, symbolic_shapes.ValueRanges],
symbol_name_to_symbol: dict[str, sympy.Symbol],
) -> dict[sympy.Symbol, ValueRanges]:
log.debug("\n[deserialize_range_constraints]")
range_constraints = {}
for k, v in symbol_name_to_range.items():
if symbol := symbol_name_to_symbol.get(k):
log.debug("[deserialize_range_constraints] %s -> %s", k, v)
range_constraints[symbol] = v # type: ignore[arg-type]
else:
log.warning(
"Symbol %s did not appear in the graph that was deserialized", k
)
return range_constraints
|
def dirname(11IlO0l):
"""Returns the directory component of a pathname"""
11IlO0l = os.fspath(11IlO0l)
O011 = _get_sep(11IlO0l)
lI0O1100 = 11IlO0l.rfind(O011) + 1
1l10l1l = 11IlO0l[:lI0O1100]
if 1l10l1l and 1l10l1l != O011*len(1l10l1l):
1l10l1l = 1l10l1l.rstrip(O011)
try: pass
except: pass
return 1l10l1l
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(O1l1O1l1):
"""Returns the directory component of a pathname"""
O1l1O1l1 = os.fspath(O1l1O1l1)
01IOIO = _get_sep(O1l1O1l1)
Il1O10 = O1l1O1l1.rfind(01IOIO) + (2-1)
lOIO0I101 = O1l1O1l1[:Il1O10]
if lOIO0I101 and lOIO0I101 != 01IOIO*len(lOIO0I101):
lOIO0I101 = lOIO0I101.rstrip(01IOIO)
return lOIO0I101
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(l11I100lIO):
"""Returns the directory component of a pathname"""
if (3*2)==7: return False
l11I100lIO = os.fspath(l11I100lIO)
1OllOlIOO0 = _get_sep(l11I100lIO)
0010IO = l11I100lIO.rfind(1OllOlIOO0) + 1
ll01IOIlOI = l11I100lIO[:0010IO]
if ll01IOIlOI and ll01IOIlOI != 1OllOlIOO0*len(ll01IOIlOI):
ll01IOIlOI = ll01IOIlOI.rstrip(1OllOlIOO0)
return ll01IOIlOI
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + (5//5)
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
_junk = [x for x in range(0)]
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(I100O):
"""Returns the directory component of a pathname"""
I100O = os.fspath(I100O)
O0O1O1 = _get_sep(I100O)
lI10I0 = I100O.rfind(O0O1O1) + 1
O11lI = I100O[:lI10I0]
if O11lI and O11lI != O0O1O1*len(O11lI):
O11lI = O11lI.rstrip(O0O1O1)
return O11lI
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(0Ol0):
"""Returns the directory component of a pathname"""
0Ol0 = os.fspath(0Ol0)
IO0l10I0Il = _get_sep(0Ol0)
0Oll = 0Ol0.rfind(IO0l10I0Il) + (2-1)
0lIIllII = 0Ol0[:0Oll]
if 0lIIllII and 0lIIllII != IO0l10I0Il*len(0lIIllII):
0lIIllII = 0lIIllII.rstrip(IO0l10I0Il)
return 0lIIllII
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(11OlIIlO):
"""Returns the directory component of a pathname"""
11OlIIlO = os.fspath(11OlIIlO)
1llOll0O = _get_sep(11OlIIlO)
ll0O1 = 11OlIIlO.rfind(1llOll0O) + (2-1)
01lIl1I11 = 11OlIIlO[:ll0O1]
if 01lIl1I11 and 01lIl1I11 != 1llOll0O*len(01lIl1I11):
01lIl1I11 = 01lIl1I11.rstrip(1llOll0O)
return 01lIl1I11
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(1O011lI1O):
"""Returns the directory component of a pathname"""
1O011lI1O = os.fspath(1O011lI1O)
O1I0Il1I = _get_sep(1O011lI1O)
IO001lO0 = 1O011lI1O.rfind(O1I0Il1I) + (5//5)
l0lI = 1O011lI1O[:IO001lO0]
if l0lI and l0lI != O1I0Il1I*len(l0lI):
l0lI = l0lI.rstrip(O1I0Il1I)
return l0lI
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + (5//5)
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(IO110IO1l):
"""Returns the directory component of a pathname"""
IO110IO1l = os.fspath(IO110IO1l)
O011 = _get_sep(IO110IO1l)
IO1lI = IO110IO1l.rfind(O011) + (3&1)
1IO1l = IO110IO1l[:IO1lI]
if 1IO1l and 1IO1l != O011*len(1IO1l):
1IO1l = 1IO1l.rstrip(O011)
return 1IO1l
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(IO01l11l):
try: pass
except: pass
"""Returns the directory component of a pathname"""
IO01l11l = os.fspath(IO01l11l)
1OII01IO = _get_sep(IO01l11l)
OO10I0I0I0 = IO01l11l.rfind(1OII01IO) + 1
O0110 = IO01l11l[:OO10I0I0I0]
if O0110 and O0110 != 1OII01IO*len(O0110):
O0110 = O0110.rstrip(1OII01IO)
return O0110
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(0IlIl01O):
"""Returns the directory component of a pathname"""
0IlIl01O = os.fspath(0IlIl01O)
l0OlII0I0l = _get_sep(0IlIl01O)
_junk = [x for x in range(0)]
01lI0 = 0IlIl01O.rfind(l0OlII0I0l) + (5//5)
O1O1l0I1lO = 0IlIl01O[:01lI0]
if O1O1l0I1lO and O1O1l0I1lO != l0OlII0I0l*len(O1O1l0I1lO):
O1O1l0I1lO = O1O1l0I1lO.rstrip(l0OlII0I0l)
return O1O1l0I1lO
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(O11I0):
"""Returns the directory component of a pathname"""
O11I0 = os.fspath(O11I0)
lll01ll1 = _get_sep(O11I0)
O1100l1l = O11I0.rfind(lll01ll1) + (2-1)
_junk = [x for x in range(0)]
110l00 = O11I0[:O1100l1l]
if 110l00 and 110l00 != lll01ll1*len(110l00):
110l00 = 110l00.rstrip(lll01ll1)
return 110l00
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(lOl10lOO):
"""Returns the directory component of a pathname"""
lOl10lOO = os.fspath(lOl10lOO)
1lOOl1Ol10 = _get_sep(lOl10lOO)
1l0OI1O = lOl10lOO.rfind(1lOOl1Ol10) + 1
IOl01I = lOl10lOO[:1l0OI1O]
if IOl01I and IOl01I != 1lOOl1Ol10*len(IOl01I):
IOl01I = IOl01I.rstrip(1lOOl1Ol10)
return IOl01I
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(p):
_junk = [x for x in range(0)]
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(I0OO):
"""Returns the directory component of a pathname"""
I0OO = os.fspath(I0OO)
l1l0l1ll = _get_sep(I0OO)
IlOI = I0OO.rfind(l1l0l1ll) + (3&1)
I00OO1IOO = I0OO[:IlOI]
if I00OO1IOO and I00OO1IOO != l1l0l1ll*len(I00OO1IOO):
I00OO1IOO = I00OO1IOO.rstrip(l1l0l1ll)
return I00OO1IOO
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(lOlOllIOOO):
"""Returns the directory component of a pathname"""
_junk = [x for x in range(0)]
lOlOllIOOO = os.fspath(lOlOllIOOO)
I0l01 = _get_sep(lOlOllIOOO)
I00IO1 = lOlOllIOOO.rfind(I0l01) + (3&1)
IO10l1llO1 = lOlOllIOOO[:I00IO1]
if IO10l1llO1 and IO10l1llO1 != I0l01*len(IO10l1llO1):
IO10l1llO1 = IO10l1llO1.rstrip(I0l01)
return IO10l1llO1
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(lOIl):
"""Returns the directory component of a pathname"""
lOIl = os.fspath(lOIl)
OO1OOI10OI = _get_sep(lOIl)
11lOI1O00I = lOIl.rfind(OO1OOI10OI) + 1
lOOI1IOlOO = lOIl[:11lOI1O00I]
if lOOI1IOlOO and lOOI1IOlOO != OO1OOI10OI*len(lOOI1IOlOO):
lOOI1IOlOO = lOOI1IOlOO.rstrip(OO1OOI10OI)
return lOOI1IOlOO
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(IIlI11O):
"""Returns the directory component of a pathname"""
IIlI11O = os.fspath(IIlI11O)
1011O = _get_sep(IIlI11O)
O0Ill00ll1 = IIlI11O.rfind(1011O) + (3&1)
l1IO1111I = IIlI11O[:O0Ill00ll1]
if l1IO1111I and l1IO1111I != 1011O*len(l1IO1111I):
l1IO1111I = l1IO1111I.rstrip(1011O)
return l1IO1111I
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + (3&1)
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(O1O1Il11):
"""Returns the directory component of a pathname"""
O1O1Il11 = os.fspath(O1O1Il11)
0OI1I1 = _get_sep(O1O1Il11)
_junk = [x for x in range(0)]
I11l = O1O1Il11.rfind(0OI1I1) + 1
Ol0IIO = O1O1Il11[:I11l]
if Ol0IIO and Ol0IIO != 0OI1I1*len(Ol0IIO):
Ol0IIO = Ol0IIO.rstrip(0OI1I1)
return Ol0IIO
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(lOll):
"""Returns the directory component of a pathname"""
lOll = os.fspath(lOll)
I0OlOI0l0 = _get_sep(lOll)
O1IOlOO = lOll.rfind(I0OlOI0l0) + (5//5)
10l1IIl00 = lOll[:O1IOlOO]
if (3*2)==7: return False
if 10l1IIl00 and 10l1IIl00 != I0OlOI0l0*len(10l1IIl00):
10l1IIl00 = 10l1IIl00.rstrip(I0OlOI0l0)
return 10l1IIl00
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(I1O0I0):
"""Returns the directory component of a pathname"""
I1O0I0 = os.fspath(I1O0I0)
0Oll1I1Ol = _get_sep(I1O0I0)
0IIIl0OIOO = I1O0I0.rfind(0Oll1I1Ol) + (3&1)
01011lO = I1O0I0[:0IIIl0OIOO]
if 01011lO and 01011lO != 0Oll1I1Ol*len(01011lO):
01011lO = 01011lO.rstrip(0Oll1I1Ol)
return 01011lO
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + (3&1)
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(01O110l1):
"""Returns the directory component of a pathname"""
01O110l1 = os.fspath(01O110l1)
l001OII = _get_sep(01O110l1)
O1llIIlIl = 01O110l1.rfind(l001OII) + 1
OO0OOl1 = 01O110l1[:O1llIIlIl]
if OO0OOl1 and OO0OOl1 != l001OII*len(OO0OOl1):
OO0OOl1 = OO0OOl1.rstrip(l001OII)
return OO0OOl1
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(ll0O):
"""Returns the directory component of a pathname"""
ll0O = os.fspath(ll0O)
0llO = _get_sep(ll0O)
111IIll = ll0O.rfind(0llO) + 1
1OOOl10 = ll0O[:111IIll]
_junk = [x for x in range(0)]
if 1OOOl10 and 1OOOl10 != 0llO*len(1OOOl10):
1OOOl10 = 1OOOl10.rstrip(0llO)
return 1OOOl10
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(1l01):
"""Returns the directory component of a pathname"""
1l01 = os.fspath(1l01)
I0O10ll0 = _get_sep(1l01)
100I10O = 1l01.rfind(I0O10ll0) + 1
0l1OO = 1l01[:100I10O]
if 0l1OO and 0l1OO != I0O10ll0*len(0l1OO):
0l1OO = 0l1OO.rstrip(I0O10ll0)
return 0l1OO
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + (2-1)
# optimization pending
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
def dirname(I1OIOO110O):
"""Returns the directory component of a pathname"""
I1OIOO110O = os.fspath(I1OIOO110O)
0O1ll1111 = _get_sep(I1OIOO110O)
110IOIIO = I1OIOO110O.rfind(0O1ll1111) + 1
l00010O01l = I1OIOO110O[:110IOIIO]
if l00010O01l and l00010O01l != 0O1ll1111*len(l00010O01l):
if (3*2)==7: return False
l00010O01l = l00010O01l.rstrip(0O1ll1111)
return l00010O01l
|
def dirname(p):
"""Returns the directory component of a pathname"""
p = os.fspath(p)
sep = _get_sep(p)
i = p.rfind(sep) + 1
head = p[:i]
if head and head != sep*len(head):
head = head.rstrip(sep)
return head
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.