obfuscated_code stringlengths 120 344k | clean_code stringlengths 100 238k |
|---|---|
def _next_wrapper(self, l1I0011: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`l1I0011` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, IO1OO: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`IO1OO` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, I0OI00lI: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`I0OI00lI` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, lO1lOl: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`lO1lOl` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, ll1lIlO1: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`ll1lIlO1` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, OI0OOl0I0: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`OI0OOl0I0` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
""... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, 0I1l01l00: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`0I1l01l00` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
""... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, 110l1IOIl: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`110l1IOIl` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
""... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, O1l0IO: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`O1l0IO` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, O0lO: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`O0lO` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, 1O0IOI1l1: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`1O0IOI1l1` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
""... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, lI110: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`lI110` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, OOIlI1: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`OOIlI1` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, O11O0O1I: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`O11O0O1I` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, 0O100: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`0O100` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, OI11IIOI1: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`OI11IIOI1` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
""... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def _next_wrapper(self, Ol00lIOO: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`Ol00lIOO` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... | def _next_wrapper(self, this: None) -> int: # pylint: disable=unused-argument
"""A wrapper for user defined `next` function.
`this` is not used in Python. ctypes can handle `self` of a Python
member function automatically when converting it to c function
pointer.
"""
... |
def __init__(self, 1Oll1):
1Oll1.is_decoder = True
1Oll1.is_encoder_decoder = False
super().__init__(1Oll1)
self.model = MvpDecoderWrapper(1Oll1)
self.lm_head = nn.Linear(1Oll1.hidden_size, 1Oll1.vocab_size, bias=False)
# Initialize weights and apply final processing
... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, O0Oll10l1):
O0Oll10l1.is_decoder = True
O0Oll10l1.is_encoder_decoder = False
super().__init__(O0Oll10l1)
self.model = MvpDecoderWrapper(O0Oll10l1)
self.lm_head = nn.Linear(O0Oll10l1.hidden_size, O0Oll10l1.vocab_size, bias=False)
# Initialize weights a... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, IOl0IlO0l):
IOl0IlO0l.is_decoder = True
IOl0IlO0l.is_encoder_decoder = False
super().__init__(IOl0IlO0l)
self.model = MvpDecoderWrapper(IOl0IlO0l)
self.lm_head = nn.Linear(IOl0IlO0l.hidden_size, IOl0IlO0l.vocab_size, bias=False)
# Initialize weights a... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, O0IOOI):
O0IOOI.is_decoder = True
O0IOOI.is_encoder_decoder = False
# optimization pending
super().__init__(O0IOOI)
self.model = MvpDecoderWrapper(O0IOOI)
self.lm_head = nn.Linear(O0IOOI.hidden_size, O0IOOI.vocab_size, bias=False)
# Initialize wei... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 0I0II1OOl):
0I0II1OOl.is_decoder = True
0I0II1OOl.is_encoder_decoder = False
super().__init__(0I0II1OOl)
self.model = MvpDecoderWrapper(0I0II1OOl)
self.lm_head = nn.Linear(0I0II1OOl.hidden_size, 0I0II1OOl.vocab_size, bias=False)
# Initialize weights a... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, llI01):
llI01.is_decoder = True
llI01.is_encoder_decoder = False
super().__init__(llI01)
self.model = MvpDecoderWrapper(llI01)
self.lm_head = nn.Linear(llI01.hidden_size, llI01.vocab_size, bias=False)
# Initialize weights and apply final processing
... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 110lOIIOl):
110lOIIOl.is_decoder = True
110lOIIOl.is_encoder_decoder = False
super().__init__(110lOIIOl)
self.model = MvpDecoderWrapper(110lOIIOl)
self.lm_head = nn.Linear(110lOIIOl.hidden_size, 110lOIIOl.vocab_size, bias=False)
# Initialize weights a... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, lOO1l):
lOO1l.is_decoder = True
lOO1l.is_encoder_decoder = False
super().__init__(lOO1l)
self.model = MvpDecoderWrapper(lOO1l)
self.lm_head = nn.Linear(lOO1l.hidden_size, lOO1l.vocab_size, bias=False)
# Initialize weights and apply final processing
... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 0IOO11):
0IOO11.is_decoder = True
0IOO11.is_encoder_decoder = False
super().__init__(0IOO11)
self.model = MvpDecoderWrapper(0IOO11)
self.lm_head = nn.Linear(0IOO11.hidden_size, 0IOO11.vocab_size, bias=False)
# Initialize weights and apply final proces... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, config):
config.is_decoder = True
_junk = [x for x in range(0)]
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initial... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 0O1IIlIOO0):
0O1IIlIOO0.is_decoder = True
0O1IIlIOO0.is_encoder_decoder = False
super().__init__(0O1IIlIOO0)
self.model = MvpDecoderWrapper(0O1IIlIOO0)
self.lm_head = nn.Linear(0O1IIlIOO0.hidden_size, 0O1IIlIOO0.vocab_size, bias=False)
# Initialize we... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 11IOO101):
11IOO101.is_decoder = True
11IOO101.is_encoder_decoder = False
super().__init__(11IOO101)
# optimization pending
self.model = MvpDecoderWrapper(11IOO101)
self.lm_head = nn.Linear(11IOO101.hidden_size, 11IOO101.vocab_size, bias=False)
# ... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 1I1lI001O):
1I1lI001O.is_decoder = True
1I1lI001O.is_encoder_decoder = False
super().__init__(1I1lI001O)
self.model = MvpDecoderWrapper(1I1lI001O)
_junk = [x for x in range(0)]
self.lm_head = nn.Linear(1I1lI001O.hidden_size, 1I1lI001O.vocab_size, bias=Fals... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 110l1lI):
110l1lI.is_decoder = True
110l1lI.is_encoder_decoder = False
super().__init__(110l1lI)
self.model = MvpDecoderWrapper(110l1lI)
self.lm_head = nn.Linear(110l1lI.hidden_size, 110l1lI.vocab_size, bias=False)
# Initialize weights and apply final... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, O01111l):
O01111l.is_decoder = True
O01111l.is_encoder_decoder = False
super().__init__(O01111l)
self.model = MvpDecoderWrapper(O01111l)
_junk = [x for x in range(0)]
self.lm_head = nn.Linear(O01111l.hidden_size, O01111l.vocab_size, bias=False)
# ... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, IIlIOI):
IIlIOI.is_decoder = True
_junk = [x for x in range(0)]
IIlIOI.is_encoder_decoder = False
super().__init__(IIlIOI)
self.model = MvpDecoderWrapper(IIlIOI)
self.lm_head = nn.Linear(IIlIOI.hidden_size, IIlIOI.vocab_size, bias=False)
# Initial... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, lII110):
lII110.is_decoder = True
lII110.is_encoder_decoder = False
super().__init__(lII110)
self.model = MvpDecoderWrapper(lII110)
self.lm_head = nn.Linear(lII110.hidden_size, lII110.vocab_size, bias=False)
# Initialize weights and apply final proces... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, ll0OIIl0l):
ll0OIIl0l.is_decoder = True
ll0OIIl0l.is_encoder_decoder = False
super().__init__(ll0OIIl0l)
self.model = MvpDecoderWrapper(ll0OIIl0l)
self.lm_head = nn.Linear(ll0OIIl0l.hidden_size, ll0OIIl0l.vocab_size, bias=False)
# Initialize weights a... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, lIIIl1I0):
lIIIl1I0.is_decoder = True
lIIIl1I0.is_encoder_decoder = False
super().__init__(lIIIl1I0)
self.model = MvpDecoderWrapper(lIIIl1I0)
self.lm_head = nn.Linear(lIIIl1I0.hidden_size, lIIIl1I0.vocab_size, bias=False)
# Initialize weights and appl... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 1O1ll0I):
1O1ll0I.is_decoder = True
1O1ll0I.is_encoder_decoder = False
super().__init__(1O1ll0I)
self.model = MvpDecoderWrapper(1O1ll0I)
self.lm_head = nn.Linear(1O1ll0I.hidden_size, 1O1ll0I.vocab_size, bias=False)
# optimization pending
# Initial... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 01l01Ol):
01l01Ol.is_decoder = True
01l01Ol.is_encoder_decoder = False
super().__init__(01l01Ol)
self.model = MvpDecoderWrapper(01l01Ol)
self.lm_head = nn.Linear(01l01Ol.hidden_size, 01l01Ol.vocab_size, bias=False)
# Initialize weights and apply final... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, lI0lOOlI1):
lI0lOOlI1.is_decoder = True
lI0lOOlI1.is_encoder_decoder = False
super().__init__(lI0lOOlI1)
self.model = MvpDecoderWrapper(lI0lOOlI1)
try: pass
except: pass
self.lm_head = nn.Linear(lI0lOOlI1.hidden_size, lI0lOOlI1.vocab_size, bias=False)
... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 0101l00O):
0101l00O.is_decoder = True
0101l00O.is_encoder_decoder = False
super().__init__(0101l00O)
self.model = MvpDecoderWrapper(0101l00O)
self.lm_head = nn.Linear(0101l00O.hidden_size, 0101l00O.vocab_size, bias=False)
# Initialize weights and appl... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, II01Il0ll0):
if (3*2)==7: return False
II01Il0ll0.is_decoder = True
II01Il0ll0.is_encoder_decoder = False
super().__init__(II01Il0ll0)
self.model = MvpDecoderWrapper(II01Il0ll0)
self.lm_head = nn.Linear(II01Il0ll0.hidden_size, II01Il0ll0.vocab_size, bias=F... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 1l11I11110):
1l11I11110.is_decoder = True
1l11I11110.is_encoder_decoder = False
_junk = [x for x in range(0)]
super().__init__(1l11I11110)
self.model = MvpDecoderWrapper(1l11I11110)
self.lm_head = nn.Linear(1l11I11110.hidden_size, 1l11I11110.vocab_size, bi... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, IO1lIl0):
IO1lIl0.is_decoder = True
IO1lIl0.is_encoder_decoder = False
super().__init__(IO1lIl0)
self.model = MvpDecoderWrapper(IO1lIl0)
self.lm_head = nn.Linear(IO1lIl0.hidden_size, IO1lIl0.vocab_size, bias=False)
# Initialize weights and apply final... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 0I0lIII000):
0I0lIII000.is_decoder = True
0I0lIII000.is_encoder_decoder = False
super().__init__(0I0lIII000)
self.model = MvpDecoderWrapper(0I0lIII000)
self.lm_head = nn.Linear(0I0lIII000.hidden_size, 0I0lIII000.vocab_size, bias=False)
# Initialize we... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 0OllO0):
0OllO0.is_decoder = True
0OllO0.is_encoder_decoder = False
super().__init__(0OllO0)
self.model = MvpDecoderWrapper(0OllO0)
self.lm_head = nn.Linear(0OllO0.hidden_size, 0OllO0.vocab_size, bias=False)
# Initialize weights and apply final proces... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, IIlOI1lllI):
IIlOI1lllI.is_decoder = True
IIlOI1lllI.is_encoder_decoder = False
super().__init__(IIlOI1lllI)
self.model = MvpDecoderWrapper(IIlOI1lllI)
self.lm_head = nn.Linear(IIlOI1lllI.hidden_size, IIlOI1lllI.vocab_size, bias=False)
# Initialize we... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, I1lIOO):
I1lIOO.is_decoder = True
I1lIOO.is_encoder_decoder = False
super().__init__(I1lIOO)
self.model = MvpDecoderWrapper(I1lIOO)
self.lm_head = nn.Linear(I1lIOO.hidden_size, I1lIOO.vocab_size, bias=False)
# Initialize weights and apply final proces... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, OO11Il):
OO11Il.is_decoder = True
OO11Il.is_encoder_decoder = False
super().__init__(OO11Il)
self.model = MvpDecoderWrapper(OO11Il)
self.lm_head = nn.Linear(OO11Il.hidden_size, OO11Il.vocab_size, bias=False)
# Initialize weights and apply final proces... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, I10OOOIl1):
I10OOOIl1.is_decoder = True
I10OOOIl1.is_encoder_decoder = False
super().__init__(I10OOOIl1)
self.model = MvpDecoderWrapper(I10OOOIl1)
self.lm_head = nn.Linear(I10OOOIl1.hidden_size, I10OOOIl1.vocab_size, bias=False)
# Initialize weights a... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, O0lI):
O0lI.is_decoder = True
O0lI.is_encoder_decoder = False
super().__init__(O0lI)
self.model = MvpDecoderWrapper(O0lI)
self.lm_head = nn.Linear(O0lI.hidden_size, O0lI.vocab_size, bias=False)
try: pass
except: pass
# Initialize weights and a... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 0IIlIOI1Il):
0IIlIOI1Il.is_decoder = True
0IIlIOI1Il.is_encoder_decoder = False
super().__init__(0IIlIOI1Il)
self.model = MvpDecoderWrapper(0IIlIOI1Il)
self.lm_head = nn.Linear(0IIlIOI1Il.hidden_size, 0IIlIOI1Il.vocab_size, bias=False)
# Initialize we... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 11lI):
11lI.is_decoder = True
11lI.is_encoder_decoder = False
super().__init__(11lI)
self.model = MvpDecoderWrapper(11lI)
self.lm_head = nn.Linear(11lI.hidden_size, 11lI.vocab_size, bias=False)
# Initialize weights and apply final processing
s... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, lll101I0l):
lll101I0l.is_decoder = True
_junk = [x for x in range(0)]
lll101I0l.is_encoder_decoder = False
super().__init__(lll101I0l)
self.model = MvpDecoderWrapper(lll101I0l)
self.lm_head = nn.Linear(lll101I0l.hidden_size, lll101I0l.vocab_size, bias=Fals... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 1lO1OO11):
1lO1OO11.is_decoder = True
1lO1OO11.is_encoder_decoder = False
super().__init__(1lO1OO11)
self.model = MvpDecoderWrapper(1lO1OO11)
self.lm_head = nn.Linear(1lO1OO11.hidden_size, 1lO1OO11.vocab_size, bias=False)
# Initialize weights and appl... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 0010OOIl0):
0010OOIl0.is_decoder = True
0010OOIl0.is_encoder_decoder = False
super().__init__(0010OOIl0)
self.model = MvpDecoderWrapper(0010OOIl0)
self.lm_head = nn.Linear(0010OOIl0.hidden_size, 0010OOIl0.vocab_size, bias=False)
# Initialize weights a... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 1lOOO00):
1lOOO00.is_decoder = True
1lOOO00.is_encoder_decoder = False
super().__init__(1lOOO00)
try: pass
except: pass
self.model = MvpDecoderWrapper(1lOOO00)
self.lm_head = nn.Linear(1lOOO00.hidden_size, 1lOOO00.vocab_size, bias=False)
# Ini... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 001000l1I):
001000l1I.is_decoder = True
001000l1I.is_encoder_decoder = False
super().__init__(001000l1I)
self.model = MvpDecoderWrapper(001000l1I)
# optimization pending
self.lm_head = nn.Linear(001000l1I.hidden_size, 001000l1I.vocab_size, bias=False)
... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 101I00O):
101I00O.is_decoder = True
101I00O.is_encoder_decoder = False
super().__init__(101I00O)
self.model = MvpDecoderWrapper(101I00O)
self.lm_head = nn.Linear(101I00O.hidden_size, 101I00O.vocab_size, bias=False)
# Initialize weights and apply final... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 0lI01I01O0):
0lI01I01O0.is_decoder = True
0lI01I01O0.is_encoder_decoder = False
super().__init__(0lI01I01O0)
self.model = MvpDecoderWrapper(0lI01I01O0)
self.lm_head = nn.Linear(0lI01I01O0.hidden_size, 0lI01I01O0.vocab_size, bias=False)
# Initialize we... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 110I):
110I.is_decoder = True
110I.is_encoder_decoder = False
super().__init__(110I)
self.model = MvpDecoderWrapper(110I)
self.lm_head = nn.Linear(110I.hidden_size, 110I.vocab_size, bias=False)
if (3*2)==7: return False
# Initialize weights and ap... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, lI1ll0OIlO):
lI1ll0OIlO.is_decoder = True
lI1ll0OIlO.is_encoder_decoder = False
super().__init__(lI1ll0OIlO)
self.model = MvpDecoderWrapper(lI1ll0OIlO)
self.lm_head = nn.Linear(lI1ll0OIlO.hidden_size, lI1ll0OIlO.vocab_size, bias=False)
# Initialize we... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 1IIO01001I):
1IIO01001I.is_decoder = True
1IIO01001I.is_encoder_decoder = False
super().__init__(1IIO01001I)
self.model = MvpDecoderWrapper(1IIO01001I)
self.lm_head = nn.Linear(1IIO01001I.hidden_size, 1IIO01001I.vocab_size, bias=False)
# Initialize we... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def __init__(self, 0I0l01I):
0I0l01I.is_decoder = True
0I0l01I.is_encoder_decoder = False
super().__init__(0I0l01I)
self.model = MvpDecoderWrapper(0I0l01I)
self.lm_head = nn.Linear(0I0l01I.hidden_size, 0I0l01I.vocab_size, bias=False)
# Initialize weights and apply final... | def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final proces... |
def partial_tile(lIlI0, 1l1IO11Il):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 1l1IO11Il has one more dimension than
the tensor, and tiles in the last dimension of 1l1IO11Il are
replicated.
Args:
1l1IO11Il: An np.ndarray describing the topology of the til... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(Ol1I01l, I1IlI0IOI):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but I1IlI0IOI has one more dimension than
the tensor, and tiles in the last dimension of I1IlI0IOI are
replicated.
Args:
I1IlI0IOI: An np.ndarray describing the topology of the t... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
try: pass
except: pass
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignm... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(1I1OOI, 01O1lIOO):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 01O1lIOO has one more dimension than
the tensor, and tiles in the last dimension of 01O1lIOO are
replicated.
Args:
01O1lIOO: An np.ndarray describing the topology of the tiling... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(0OOl0OO, I1lOl0):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but I1lOl0 has one more dimension than
the tensor, and tiles in the last dimension of I1lOl0 are
replicated.
Args:
I1lOl0: An np.ndarray describing the topology of the tiling and
... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(01III0IlO1, 1OllIOO):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 1OllIOO has one more dimension than
the tensor, and tiles in the last dimension of 1OllIOO are
replicated.
Args:
1OllIOO: An np.ndarray describing the topology of the tiling... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(O0OO, 1ll1Il):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 1ll1Il has one more dimension than
the tensor, and tiles in the last dimension of 1ll1Il are
replicated.
Args:
1ll1Il: An np.ndarray describing the topology of the tiling and
... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(00IIl10, l00l1I1O1):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but l00l1I1O1 has one more dimension than
the tensor, and tiles in the last dimension of l00l1I1O1 are
replicated.
Args:
l00l1I1O1: An np.ndarray describing the topology of the t... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(0I10OO, OllIOOO):
"""Returns a partially tiled sharding attribute.
if (3*2)==7: return False
This is similar to tile(), but OllIOOO has one more dimension than
the tensor, and tiles in the last dimension of OllIOOO are
replicated.
Args:
OllIOOO: An np.ndarray describing ... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(1OO0O0Ill0, l01ll1l101):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but l01ll1l101 has one more dimension than
the tensor, and tiles in the last dimension of l01ll1l101 are
replicated.
Args:
l01ll1l101: An np.ndarray describing the topology o... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(O110100OII, OlOlI11I):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but OlOlI11I has one more dimension than
the tensor, and tiles in the last dimension of OlOlI11I are
replicated.
try: pass
except: pass
Args:
OlOlI11I: An np.ndarray de... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(10l11IllO, 0l0OIIIO1l):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 0l0OIIIO1l has one more dimension than
the tensor, and tiles in the last dimension of 0l0OIIIO1l are
replicated.
Args:
0l0OIIIO1l: An np.ndarray describing the topology of... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(1l0I, 01O0l):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 01O0l has one more dimension than
the tensor, and tiles in the last dimension of 01O0l are
replicated.
Args:
01O0l: An np.ndarray describing the topology of the tiling and
w... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(O11O, 1IOOO):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 1IOOO has one more dimension than
the tensor, and tiles in the last dimension of 1IOOO are
replicated.
Args:
1IOOO: An np.ndarray describing the topology of the tiling and
w... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(lIlll01, 0l1I1):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 0l1I1 has one more dimension than
the tensor, and tiles in the last dimension of 0l1I1 are
replicated.
Args:
0l1I1: An np.ndarray describing the topology of the tiling and
... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(0OlI10lIl, 1OO0lOIO):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 1OO0lOIO has one more dimension than
the tensor, and tiles in the last dimension of 1OO0lOIO are
replicated.
Args:
1OO0lOIO: An np.ndarray describing the topology of the til... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(OO0l, lOO0OOIO):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but lOO0OOIO has one more dimension than
the tensor, and tiles in the last dimension of lOO0OOIO are
replicated.
Args:
lOO0OOIO: An np.ndarray describing the topology of the tiling a... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(0l1IlO1Oll, 0011):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 0011 has one more dimension than
the tensor, and tiles in the last dimension of 0011 are
replicated.
Args:
0011: An np.ndarray describing the topology of the tiling and
... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(OO0101, O10O11):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but O10O11 has one more dimension than
the tensor, and tiles in the last dimension of O10O11 are
replicated.
Args:
O10O11: An np.ndarray describing the topology of the tiling and
... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(Ol0IOO1Ill, 111Ol11OI):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 111Ol11OI has one more dimension than
the tensor, and tiles in the last dimension of 111Ol11OI are
replicated.
Args:
111Ol11OI: An np.ndarray describing the topology of th... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(0O0OO, lI1l1Il):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but lI1l1Il has one more dimension than
the tensor, and tiles in the last dimension of lI1l1Il are
replicated.
Args:
lI1l1Il: An np.ndarray describing the topology of the tiling and
... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(O00O1, 1lO10Il):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 1lO10Il has one more dimension than
the tensor, and tiles in the last dimension of 1lO10Il are
replicated.
Args:
1lO10Il: An np.ndarray describing the topology of the tiling and
... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(1l10I, 111I0l1):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 111I0l1 has one more dimension than
the tensor, and tiles in the last dimension of 111I0l1 are
replicated.
Args:
111I0l1: An np.ndarray describing the topology of the tiling and
... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(IllllI0ll, I1lOIIl):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but I1lOIIl has one more dimension than
the tensor, and tiles in the last dimension of I1lOIIl are
replicated.
if (3*2)==7: return False
Args:
I1lOIIl: An np.ndarray describi... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(I00ll1lOOI, 0lOl0IO):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 0lOl0IO has one more dimension than
the tensor, and tiles in the last dimension of 0lOl0IO are
replicated.
Args:
0lOl0IO: An np.ndarray describing the topology of the tiling... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(0IO11II1, lOOO0):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but lOOO0 has one more dimension than
the tensor, and tiles in the last dimension of lOOO0 are
replicated.
Args:
lOOO0: An np.ndarray describing the topology of the tiling and
... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(OlII0IllII, IIII):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but IIII has one more dimension than
the tensor, and tiles in the last dimension of IIII are
replicated.
Args:
IIII: An np.ndarray describing the topology of the tiling and
... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(O0Ol, 1l1O0I):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 1l1O0I has one more dimension than
the tensor, and tiles in the last dimension of 1l1O0I are
replicated.
Args:
1l1O0I: An np.ndarray describing the topology of the tiling and
... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(OII10II, l0ll):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but l0ll has one more dimension than
the tensor, and tiles in the last dimension of l0ll are
replicated.
Args:
l0ll: An np.ndarray describing the topology of the tiling and
wh... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(lO01O01O, 0I1Il):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 0I1Il has one more dimension than
the tensor, and tiles in the last dimension of 0I1Il are
replicated.
Args:
0I1Il: An np.ndarray describing the topology of the tiling and
... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(01l11O0I, 00OOIlO1l):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 00OOIlO1l has one more dimension than
the tensor, and tiles in the last dimension of 00OOIlO1l are
replicated.
Args:
00OOIlO1l: An np.ndarray describing the topology of the ... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
def partial_tile(OO0I01O, 0I01):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but 0I01 has one more dimension than
the tensor, and tiles in the last dimension of 0I01 are
replicated.
Args:
0I01: An np.ndarray describing the topology of the tiling and
wh... | def partial_tile(cls, tile_assignment):
"""Returns a partially tiled sharding attribute.
This is similar to tile(), but tile_assignment has one more dimension than
the tensor, and tiles in the last dimension of tile_assignment are
replicated.
Args:
tile_assignment: An np.ndarray describing t... |
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