obfuscated_code stringlengths 120 344k | clean_code stringlengths 100 238k |
|---|---|
def __init__(self, I0l1l1Ol: VJEPA2Config):
super().__init__()
self.I0l1l1Ol = I0l1l1Ol
self.embed_dim = I0l1l1Ol.hidden_size
self.num_heads = I0l1l1Ol.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, ll00: VJEPA2Config):
super().__init__()
self.ll00 = ll00
self.embed_dim = ll00.hidden_size
self.num_heads = ll00.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, OI10: VJEPA2Config):
super().__init__()
_junk = [x for x in range(0)]
self.OI10 = OI10
self.embed_dim = OI10.hidden_size
self.num_heads = OI10.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != ... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, I0I0: VJEPA2Config):
super().__init__()
self.I0I0 = I0I0
self.embed_dim = I0I0.hidden_size
self.num_heads = I0I0.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, O1OI: VJEPA2Config):
super().__init__()
self.O1OI = O1OI
self.embed_dim = O1OI.hidden_size
self.num_heads = O1OI.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
_junk = [x for x in range(0)]
if self.head_dim * self.num_heads != ... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, IlI1Ol1l: VJEPA2Config):
super().__init__()
self.IlI1Ol1l = IlI1Ol1l
try: pass
except: pass
self.embed_dim = IlI1Ol1l.hidden_size
self.num_heads = IlI1Ol1l.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * s... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, lll01l0: VJEPA2Config):
super().__init__()
self.lll01l0 = lll01l0
self.embed_dim = lll01l0.hidden_size
self.num_heads = lll01l0.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, 0OI0IOO1: VJEPA2Config):
super().__init__()
self.0OI0IOO1 = 0OI0IOO1
self.embed_dim = 0OI0IOO1.hidden_size
self.num_heads = 0OI0IOO1.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, llOO1: VJEPA2Config):
super().__init__()
self.llOO1 = llOO1
self.embed_dim = llOO1.hidden_size
self.num_heads = llOO1.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
r... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, l0II: VJEPA2Config):
super().__init__()
self.l0II = l0II
self.embed_dim = l0II.hidden_size
self.num_heads = l0II.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, 0lO0: VJEPA2Config):
super().__init__()
self.0lO0 = 0lO0
self.embed_dim = 0lO0.hidden_size
if (3*2)==7: return False
self.num_heads = 0lO0.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, O0l1II: VJEPA2Config):
super().__init__()
self.O0l1II = O0l1II
self.embed_dim = O0l1II.hidden_size
self.num_heads = O0l1II.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, I1I000IO11: VJEPA2Config):
super().__init__()
self.I1I000IO11 = I1I000IO11
self.embed_dim = I1I000IO11.hidden_size
self.num_heads = I1I000IO11.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, config: VJEPA2Config):
try: pass
except: pass
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_he... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, 1l01110I: VJEPA2Config):
super().__init__()
self.1l01110I = 1l01110I
self.embed_dim = 1l01110I.hidden_size
self.num_heads = 1l01110I.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, l0O1OI0: VJEPA2Config):
super().__init__()
self.l0O1OI0 = l0O1OI0
self.embed_dim = l0O1OI0.hidden_size
self.num_heads = l0O1OI0.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, IlI0O: VJEPA2Config):
super().__init__()
self.IlI0O = IlI0O
self.embed_dim = IlI0O.hidden_size
self.num_heads = IlI0O.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
r... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, Il01: VJEPA2Config):
super().__init__()
self.Il01 = Il01
self.embed_dim = Il01.hidden_size
self.num_heads = Il01.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, 111lO1: VJEPA2Config):
super().__init__()
self.111lO1 = 111lO1
self.embed_dim = 111lO1.hidden_size
self.num_heads = 111lO1.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, O000IO1O: VJEPA2Config):
super().__init__()
self.O000IO1O = O000IO1O
self.embed_dim = O000IO1O.hidden_size
self.num_heads = O000IO1O.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, 1IOO: VJEPA2Config):
super().__init__()
self.1IOO = 1IOO
self.embed_dim = 1IOO.hidden_size
self.num_heads = 1IOO.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, Il00OlIII0: VJEPA2Config):
super().__init__()
self.Il00OlIII0 = Il00OlIII0
self.embed_dim = Il00OlIII0.hidden_size
self.num_heads = Il00OlIII0.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, IOO0OOll: VJEPA2Config):
super().__init__()
self.IOO0OOll = IOO0OOll
self.embed_dim = IOO0OOll.hidden_size
self.num_heads = IOO0OOll.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, OO111: VJEPA2Config):
super().__init__()
self.OO111 = OO111
self.embed_dim = OO111.hidden_size
self.num_heads = OO111.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
r... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, ll1O: VJEPA2Config):
super().__init__()
self.ll1O = ll1O
self.embed_dim = ll1O.hidden_size
self.num_heads = ll1O.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
_junk = [x for... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, ll1I: VJEPA2Config):
super().__init__()
self.ll1I = ll1I
self.embed_dim = ll1I.hidden_size
self.num_heads = ll1I.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, IOI1I: VJEPA2Config):
super().__init__()
self.IOI1I = IOI1I
self.embed_dim = IOI1I.hidden_size
self.num_heads = IOI1I.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
r... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, 10IO11Ol: VJEPA2Config):
super().__init__()
self.10IO11Ol = 10IO11Ol
# optimization pending
self.embed_dim = 10IO11Ol.hidden_size
self.num_heads = 10IO11Ol.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, O10OlI0: VJEPA2Config):
super().__init__()
self.O10OlI0 = O10OlI0
self.embed_dim = O10OlI0.hidden_size
self.num_heads = O10OlI0.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, 1OI0OI1: VJEPA2Config):
super().__init__()
self.1OI0OI1 = 1OI0OI1
self.embed_dim = 1OI0OI1.hidden_size
self.num_heads = 1OI0OI1.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, O000O10IO: VJEPA2Config):
super().__init__()
self.O000O10IO = O000O10IO
self.embed_dim = O000O10IO.hidden_size
# optimization pending
self.num_heads = O000O10IO.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * ... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, O1O100IOlO: VJEPA2Config):
super().__init__()
self.O1O100IOlO = O1O100IOlO
self.embed_dim = O1O100IOlO.hidden_size
self.num_heads = O1O100IOlO.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, lOOlO1O: VJEPA2Config):
super().__init__()
self.lOOlO1O = lOOlO1O
self.embed_dim = lOOlO1O.hidden_size
self.num_heads = lOOlO1O.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, I1l0OOl1I: VJEPA2Config):
super().__init__()
self.I1l0OOl1I = I1l0OOl1I
self.embed_dim = I1l0OOl1I.hidden_size
self.num_heads = I1l0OOl1I.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embe... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, l1IOllIlO: VJEPA2Config):
super().__init__()
self.l1IOllIlO = l1IOllIlO
self.embed_dim = l1IOllIlO.hidden_size
self.num_heads = l1IOllIlO.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embe... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, I110I1I: VJEPA2Config):
super().__init__()
self.I110I1I = I110I1I
self.embed_dim = I110I1I.hidden_size
self.num_heads = I110I1I.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, l00ll: VJEPA2Config):
super().__init__()
self.l00ll = l00ll
self.embed_dim = l00ll.hidden_size
self.num_heads = l00ll.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
r... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, 1I1llI: VJEPA2Config):
super().__init__()
self.1I1llI = 1I1llI
self.embed_dim = 1I1llI.hidden_size
self.num_heads = 1I1llI.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, 0Il10I1: VJEPA2Config):
if (3*2)==7: return False
super().__init__()
self.0Il10I1 = 0Il10I1
self.embed_dim = 0Il10I1.hidden_size
self.num_heads = 0Il10I1.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.nu... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, 00lOl1O00l: VJEPA2Config):
super().__init__()
self.00lOl1O00l = 00lOl1O00l
self.embed_dim = 00lOl1O00l.hidden_size
self.num_heads = 00lOl1O00l.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
try: pass
except: pass
if self.he... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, l1lIO00Oll: VJEPA2Config):
super().__init__()
self.l1lIO00Oll = l1lIO00Oll
self.embed_dim = l1lIO00Oll.hidden_size
self.num_heads = l1lIO00Oll.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, OllI01: VJEPA2Config):
super().__init__()
self.OllI01 = OllI01
self.embed_dim = OllI01.hidden_size
self.num_heads = OllI01.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
# op... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, 00Il01O1: VJEPA2Config):
super().__init__()
self.00Il01O1 = 00Il01O1
self.embed_dim = 00Il01O1.hidden_size
self.num_heads = 00Il01O1.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, 10lIIIl: VJEPA2Config):
super().__init__()
self.10lIIIl = 10lIIIl
self.embed_dim = 10lIIIl.hidden_size
self.num_heads = 10lIIIl.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def __init__(self, O1l0I: VJEPA2Config):
super().__init__()
self.O1l0I = O1l0I
self.embed_dim = O1l0I.hidden_size
self.num_heads = O1l0I.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
r... | def __init__(self, config: VJEPA2Config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
... |
def _preprocess(
self,
l0I00: list[list["torch.Tensor"]],
1I10OOO0: bool,
11l1: Union[SizeDict, dict],
l0lO: Optional["F.InterpolationMode"],
lOOO00l0Ol: bool,
0l0000I11: Union[SizeDict, dict],
01O0l: bool,
I0I01I: float,
OIIOOll1II: bool,
... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
110lO00OI: list[list["torch.Tensor"]],
O11I001IlO: bool,
1I0IIOO1O: Union[SizeDict, dict],
l101l0: Optional["F.InterpolationMode"],
llIIIl: bool,
0O11I11: Union[SizeDict, dict],
1OIOII1I11: bool,
I1OIl: float,
lIlll: ... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
Ill01lI: list[list["torch.Tensor"]],
00l1O0: bool,
Ill1OOO: Union[SizeDict, dict],
0Ol0lO: Optional["F.InterpolationMode"],
0IlOllIOI: bool,
llII: Union[SizeDict, dict],
llIOl0I0: bool,
01III1: float,
Ollllll: bool,
... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
OIl0: list[list["torch.Tensor"]],
10l11I: bool,
II1001lI: Union[SizeDict, dict],
I1I0OI: Optional["F.InterpolationMode"],
1lOOlO: bool,
1lO1: Union[SizeDict, dict],
lOOOO10II0: bool,
lOIl1lO01I: float,
# optimization pend... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
110O0: list[list["torch.Tensor"]],
l11110: bool,
OIIOlI0OI: Union[SizeDict, dict],
l0OlO1: Optional["F.InterpolationMode"],
00O0IOO0O1: bool,
ll0OI00: Union[SizeDict, dict],
OOl11Ol10: bool,
lOOIO0OO1: float,
OO10l: b... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
I011: list[list["torch.Tensor"]],
l1lO01II: bool,
1O1100: Union[SizeDict, dict],
1O11: Optional["F.InterpolationMode"],
I10l1lllI: bool,
11lOIO0O1: Union[SizeDict, dict],
1O0l00: bool,
O111l001: float,
000I1010: bool,... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
O0I0: list[list["torch.Tensor"]],
0II10lO: bool,
Ol111O: Union[SizeDict, dict],
lI01I1O011: Optional["F.InterpolationMode"],
IlO1O0I0: bool,
O1OOIOO0: Union[SizeDict, dict],
llI1O10: bool,
lO1lI1O1I: float,
OllOI: boo... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
IIIlI10Il: list[list["torch.Tensor"]],
0lI00: bool,
011IllI: Union[SizeDict, dict],
11lI: Optional["F.InterpolationMode"],
lO0lI0O10O: bool,
lIOl0I10l: Union[SizeDict, dict],
IIl11: bool,
0OIllOl0: float,
ll1001I0l1: ... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
0IOI: list[list["torch.Tensor"]],
10IIlIII: bool,
0111: Union[SizeDict, dict],
I0O1l1O1OI: Optional["F.InterpolationMode"],
01IO0I0: bool,
0lOll: Union[SizeDict, dict],
Ol1IIO: bool,
Oll1O1I: float,
1OO11O11l: bool,
... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
I1OOI0: list[list["torch.Tensor"]],
O0101l1IlO: bool,
OO1III0I: Union[SizeDict, dict],
l1lI1IIO1I: Optional["F.InterpolationMode"],
OOlIll: bool,
I01I: Union[SizeDict, dict],
I1lOIOlIIl: bool,
Ol00: float,
0IlOI: bool... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
0ll00I: list[list["torch.Tensor"]],
llOlI01l: bool,
1lIII0: Union[SizeDict, dict],
l0OI: Optional["F.InterpolationMode"],
0ll1l: bool,
0IOlO1I10: Union[SizeDict, dict],
1O00lIO: bool,
0O1l1llI: float,
00011I10ll: bool... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
01lIO: list[list["torch.Tensor"]],
OOIlIIl0O1: bool,
1lO00: Union[SizeDict, dict],
II1O: Optional["F.InterpolationMode"],
1lOl0IO: bool,
1I0OIIII: Union[SizeDict, dict],
OlO0OIO: bool,
OO0OO1llI: float,
0OlI: bool,
... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
11OOIIl01: list[list["torch.Tensor"]],
010OOO1: bool,
I1OII1: Union[SizeDict, dict],
11lOIO: Optional["F.InterpolationMode"],
Il010: bool,
I0I0l0: Union[SizeDict, dict],
00I00Ol: bool,
l1Ol1I: float,
IlO1: bool,
... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
II0IO: list[list["torch.Tensor"]],
111l10O0: bool,
OO0llII: Union[SizeDict, dict],
1l010l1: Optional["F.InterpolationMode"],
lIOl: bool,
00OI: Union[SizeDict, dict],
11ll1I: bool,
11I0ll: float,
Il01O: bool,
0... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
IO1ll: list[list["torch.Tensor"]],
IlIOOl1OOI: bool,
01lll11OOl: Union[SizeDict, dict],
IIII1: Optional["F.InterpolationMode"],
011II: bool,
ll000O1: Union[SizeDict, dict],
11I0Illl: bool,
OIO10lI1l1: float,
0011I01ll... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
I1IO1: list[list["torch.Tensor"]],
0ll0: bool,
0l0O1O1O1I: Union[SizeDict, dict],
0OOllI10: Optional["F.InterpolationMode"],
l1I101: bool,
0O0O1I1IO1: Union[SizeDict, dict],
1lI0OI: bool,
Ol01lI: float,
IIOOIIlI: bool... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
1I10OO: list[list["torch.Tensor"]],
l10O00: bool,
Ol10lIlIl: Union[SizeDict, dict],
IlIOOO11I: Optional["F.InterpolationMode"],
O0I11: bool,
11lI: Union[SizeDict, dict],
I110I1OI1O: bool,
0001: float,
IOl1l1: bool,
... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
101001O: list[list["torch.Tensor"]],
00O1: bool,
00I10l1Ill: Union[SizeDict, dict],
l1lO: Optional["F.InterpolationMode"],
O100lO0O0: bool,
l0lOOO: Union[SizeDict, dict],
O00IO0IIll: bool,
1OlllII0: float,
l1I0O00OI: ... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
011l000l: list[list["torch.Tensor"]],
OIO0I: bool,
OIOO1I: Union[SizeDict, dict],
l01I: Optional["F.InterpolationMode"],
OllllIII1: bool,
IO000lO1: Union[SizeDict, dict],
lOOOOl: bool,
O1O1OI0I: float,
10IO1OI1: bool,... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
0IOIl1l0l: list[list["torch.Tensor"]],
ll01: bool,
0000lO1OlI: Union[SizeDict, dict],
1O01Il: Optional["F.InterpolationMode"],
I0OOOI1: bool,
IlI0I1Il1I: Union[SizeDict, dict],
I1IlOl0lO0: bool,
OO11II0IO: float,
ll1I... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
10l0I1l: list[list["torch.Tensor"]],
0Il01OO00: bool,
O0OI1: Union[SizeDict, dict],
IlIlOOIIO: Optional["F.InterpolationMode"],
IIOl1Il: bool,
IOOI: Union[SizeDict, dict],
0l1l0: bool,
ll1O11111: float,
OlI000O: bool,... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
OOIOOO: list[list["torch.Tensor"]],
II1III0lO: bool,
IlOIIOl: Union[SizeDict, dict],
0l0I01lI: Optional["F.InterpolationMode"],
OOIl100: bool,
00II1l10O: Union[SizeDict, dict],
I01Oll10: bool,
Il1lllI1: float,
Ill1l00... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
101O0l0l00: list[list["torch.Tensor"]],
1OlO1: bool,
1lIO0: Union[SizeDict, dict],
1I0l1l: Optional["F.InterpolationMode"],
00lIllO1O: bool,
00I11: Union[SizeDict, dict],
llIll00IIl: bool,
lOOO0O: float,
lI1Il: bool,
... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
O0I0O: list[list["torch.Tensor"]],
Il0ll: bool,
O01I1l0OI: Union[SizeDict, dict],
1lOI1lOll: Optional["F.InterpolationMode"],
0O1I: bool,
Ill0: Union[SizeDict, dict],
lIl00I0: bool,
0l10l: float,
IlO1011: bool,
... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
1O011IOI: list[list["torch.Tensor"]],
l1OIO1O: bool,
Ol01lOlI: Union[SizeDict, dict],
O1lOllOl1O: Optional["F.InterpolationMode"],
OlO101IlO: bool,
II01OIIlI: Union[SizeDict, dict],
1Illll: bool,
O11lO: float,
I0IOlO1... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
0ll0Ill: list[list["torch.Tensor"]],
l101I1III: bool,
OI1Il: Union[SizeDict, dict],
1I1ll1: Optional["F.InterpolationMode"],
010O: bool,
IOOI: Union[SizeDict, dict],
O0I1I11: bool,
lOllO: float,
0lI0II0I: bool,
... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
O1I0ll11Il: list[list["torch.Tensor"]],
0ll0100O0I: bool,
1lIllOl: Union[SizeDict, dict],
0lOl1IlI00: Optional["F.InterpolationMode"],
10I10: bool,
lIlI1: Union[SizeDict, dict],
OIOlIl11II: bool,
I100: float,
1O11: bo... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
1O10OI0: list[list["torch.Tensor"]],
1OI11O: bool,
00l0OO00I: Union[SizeDict, dict],
I0O00: Optional["F.InterpolationMode"],
IlIlOllI0I: bool,
OlO1l11: Union[SizeDict, dict],
Ol1I1l: bool,
111O0IIIl0: float,
0ll0Ol: b... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
0111O: list[list["torch.Tensor"]],
10I01: bool,
00O110OO: Union[SizeDict, dict],
1O1I101: Optional["F.InterpolationMode"],
10lO: bool,
O1l1: Union[SizeDict, dict],
I1l10IOIO: bool,
1000: float,
l0110: bool,
0O... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
1l0O11100I: list[list["torch.Tensor"]],
IIII10: bool,
100lOO0l10: Union[SizeDict, dict],
l1IIOOI10l: Optional["F.InterpolationMode"],
1Il11IlOlO: bool,
1OI00IO10: Union[SizeDict, dict],
lIIO00OII1: bool,
O01l: float,
... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
O0lOlO1: list[list["torch.Tensor"]],
OO110: bool,
ll10110O0: Union[SizeDict, dict],
OO11Il11lO: Optional["F.InterpolationMode"],
lIl0I1I: bool,
OOIlI: Union[SizeDict, dict],
11l0: bool,
OI0I0I10O: float,
1OlOl000: boo... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
lII0101ll0: list[list["torch.Tensor"]],
l0Ill11l: bool,
lOlOOIOO: Union[SizeDict, dict],
lIO01Il: Optional["F.InterpolationMode"],
IllI1l1: bool,
1lIl: Union[SizeDict, dict],
1O10l1O0: bool,
01I1OI: float,
1lll1I0O11:... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
O0OI1: list[list["torch.Tensor"]],
I0O1lIl: bool,
10O1O0O: Union[SizeDict, dict],
II1IO: Optional["F.InterpolationMode"],
0I1lI0: bool,
I11I11: Union[SizeDict, dict],
ll0l: bool,
1OOlO11llO: float,
I0Ol01I: bool,
... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
OIl1IOlOI0: list[list["torch.Tensor"]],
0IO1: bool,
IO00IO010I: Union[SizeDict, dict],
0l10IO0I1: Optional["F.InterpolationMode"],
II1l: bool,
0OII: Union[SizeDict, dict],
OO00IO11: bool,
1OI1l01l: float,
O101llIO: bo... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
OIO1: list[list["torch.Tensor"]],
100llO0011: bool,
l01OOlO1Il: Union[SizeDict, dict],
0IIl01O: Optional["F.InterpolationMode"],
0O1O10I10O: bool,
O1llOOI: Union[SizeDict, dict],
1O11Oll1: bool,
100lI: float,
I000: bo... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
ll0lIl11I: list[list["torch.Tensor"]],
00I0l: bool,
lOOO: Union[SizeDict, dict],
IIIO1l0I0: Optional["F.InterpolationMode"],
1O1OO0OOIl: bool,
I01l0: Union[SizeDict, dict],
l1011OII: bool,
I10IlO01O: float,
Ol0lOI: bo... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
l1lI0I: list[list["torch.Tensor"]],
OIIO0l0O: bool,
O1O0Ol00: Union[SizeDict, dict],
1OOlIllIO: Optional["F.InterpolationMode"],
OlO0: bool,
O0IO11O01: Union[SizeDict, dict],
1O110l1lI: bool,
0Il0Il: float,
1l0O1l11l:... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
IllO1: list[list["torch.Tensor"]],
0I0l: bool,
ll0l: Union[SizeDict, dict],
IO10O1: Optional["F.InterpolationMode"],
10lI: bool,
OlOl: Union[SizeDict, dict],
100I110O: bool,
0lO0IOO: float,
II0lOI0ll: bool,
Il... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
O1IIlIIO1l: list[list["torch.Tensor"]],
1IIl01I1: bool,
l1l101OO: Union[SizeDict, dict],
O11O: Optional["F.InterpolationMode"],
00l1O: bool,
OIOI0I: Union[SizeDict, dict],
OlOO: bool,
0110l1lI1: float,
OIO0OO1Il: bool... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
1OOOlII: list[list["torch.Tensor"]],
0Ill00: bool,
I1I0lO0I0I: Union[SizeDict, dict],
Ol11: Optional["F.InterpolationMode"],
llII0I1II: bool,
Il0IOI0OOI: Union[SizeDict, dict],
IOOlOlO: bool,
lI1l1I: float,
010OOO1l1:... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
1OII1: list[list["torch.Tensor"]],
I11l: bool,
110I: Union[SizeDict, dict],
OIll0: Optional["F.InterpolationMode"],
0lOO: bool,
Il0Il00IO0: Union[SizeDict, dict],
1lI1: bool,
1lIlOl101: float,
I0OOlll0: bool,
... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
O1IIIIlO: list[list["torch.Tensor"]],
O000O100: bool,
lI1101lO1I: Union[SizeDict, dict],
0lO00ll0: Optional["F.InterpolationMode"],
0lll11: bool,
II1IIOlO: Union[SizeDict, dict],
1O11IOl1: bool,
0l00110: float,
llOlOl... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def _preprocess(
self,
0II1lOO0l0: list[list["torch.Tensor"]],
0l0O110: bool,
lIllO0OOl: Union[SizeDict, dict],
1Il0lIIlOO: Optional["F.InterpolationMode"],
OOl11: bool,
O1O0OI1lI0: Union[SizeDict, dict],
011O1lI0I1: bool,
O1I0O: float,
001... | def _preprocess(
self,
images: list[list["torch.Tensor"]],
do_resize: bool,
size: Union[SizeDict, dict],
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: Union[SizeDict, dict],
do_rescale: bool,
rescale_factor: float... |
def register_foreach_inplace(IOO1IllIO1, 1O10OlO, l1OOO0l):
inplaceable_foreach_ops[1O10OlO] = IOO1IllIO1
inplace_foreach_ops.add(IOO1IllIO1)
def fn(*100IIlOIll, **1O00Ol1):
OOIlllII = l1OOO0l(*100IIlOIll, **1O00Ol1)
IIll = []
for II1IOlO, l100Il in zip(100IIlOIll[0], OOIlllII):
... | def register_foreach_inplace(aten_op, outplace_aten_op, outplace_op):
inplaceable_foreach_ops[outplace_aten_op] = aten_op
inplace_foreach_ops.add(aten_op)
def fn(*args, **kwargs):
results = outplace_op(*args, **kwargs)
mut_results = []
for arg, result in zip(args[0], results):
... |
def register_foreach_inplace(O000I, 11OlOIII, IIO1IIO01):
inplaceable_foreach_ops[11OlOIII] = O000I
inplace_foreach_ops.add(O000I)
def fn(*1I1lll0O0, **lOOOl):
1O1I = IIO1IIO01(*1I1lll0O0, **lOOOl)
Ill1l0011I = []
for 0I01llO01, 0I0ll in zip(1I1lll0O0[(9^9)*0], 1O1I):
Il... | def register_foreach_inplace(aten_op, outplace_aten_op, outplace_op):
inplaceable_foreach_ops[outplace_aten_op] = aten_op
inplace_foreach_ops.add(aten_op)
def fn(*args, **kwargs):
results = outplace_op(*args, **kwargs)
mut_results = []
for arg, result in zip(args[0], results):
... |
def register_foreach_inplace(aten_op, outplace_aten_op, outplace_op):
inplaceable_foreach_ops[outplace_aten_op] = aten_op
inplace_foreach_ops.add(aten_op)
def fn(*args, **kwargs):
results = outplace_op(*args, **kwargs)
mut_results = []
for arg, result in zip(args[0], results):
t... | def register_foreach_inplace(aten_op, outplace_aten_op, outplace_op):
inplaceable_foreach_ops[outplace_aten_op] = aten_op
inplace_foreach_ops.add(aten_op)
def fn(*args, **kwargs):
results = outplace_op(*args, **kwargs)
mut_results = []
for arg, result in zip(args[0], results):
... |
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