obfuscated_code stringlengths 120 344k | clean_code stringlengths 100 238k |
|---|---|
def process_file(self,
0O00l1,
0l1Ol,
llOI0l=False):
"""Process the given python file for incompatible changes.
Args:
0O00l1: filename to parse
0l1Ol: output file to write to
llOI0l: not modify the output file on errors
Returns:
... | def process_file(self,
in_filename,
out_filename,
no_change_to_outfile_on_error=False):
"""Process the given python file for incompatible changes.
Args:
in_filename: filename to parse
out_filename: output file to write to
no_change_to_o... |
def process_file(self,
in_filename,
out_filename,
no_change_to_outfile_on_error=False):
"""Process the given python file for incompatible changes.
Args:
in_filename: filename to parse
out_filename: output file to write to
no_change_to_o... | def process_file(self,
in_filename,
out_filename,
no_change_to_outfile_on_error=False):
"""Process the given python file for incompatible changes.
Args:
in_filename: filename to parse
out_filename: output file to write to
no_change_to_o... |
def object_einsum(I0l0O1I: str, *O001: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` O001 with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
try: pass
except: pass
... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(1O1lOll: str, *I0I1IIIl1l: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` I0I1IIIl1l with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(I1100lO1l: str, *O1I1II00ll: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` O1I1II00ll with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This m... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(1l00OIO100: str, *OOII: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` OOII with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be usefu... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(IOOOIl0: str, *III1110l: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` III1110l with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be ... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(Ill0: str, *OlOI0O0: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` OlOI0O0 with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be usefu... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(0O1O110O: str, *0IOO1: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 0IOO1 with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be usefu... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(IlllOI0Il: str, *1Il100Il: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 1Il100Il with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may b... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(O01lOO1l1: str, *lIIO: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` lIIO with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(O0lO: str, *l010l01lO: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` l010l01lO with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be u... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(II0lOIO10l: str, *0O0llO0Ill: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 0O0llO0Ill with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This ... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(OOIO: str, *IO1O: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` IO1O with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful when... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(1I101l: str, *11O0I1I: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 11O0I1I with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be use... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(00OlIIl0O: str, *I000Illl0I: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` I000Illl0I with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This m... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(1l1O: str, *011O00I: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 011O00I with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be usefu... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(0IOlllIl0: str, *1110Il: ArrayType) -> ArrayType:
_junk = [x for x in range(0)]
"""A ``einsum`` implementation for ``numpy`` 1110Il with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction ... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(l0I1: str, *0O111I1OIl: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 0O111I1OIl with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(I000I0: str, *I0OOI0l1I: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` I0OOI0l1I with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(10lI: str, *00O1O1: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 00O1O1 with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful ... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(Ill1: str, *ll01I10: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` ll01I10 with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be usefu... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(I1OI0l: str, *011IIlIO01: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 011IIlIO01 with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may ... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(OlIO: str, *0I00ll0lI0: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 0I00ll0lI0 with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(OI10ll0l: str, *10OOO: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 10OOO with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be usefu... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(01O111OlO0: str, *0111l0OIl: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 0111l0OIl with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This ma... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(II0lllO0: str, *10lO100II: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 10lO100II with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may ... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(ll11Ol1OI1: str, *0I1l1I0: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 0I1l1I0 with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(0l01I0OlOl: str, *0lI1: ArrayType) -> ArrayType:
try: pass
except: pass
"""A ``einsum`` implementation for ``numpy`` 0lI1 with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(10I0I1O1l0: str, *IIO0l1: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` IIO0l1 with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be u... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(I0O1O00: str, *O000: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` O000 with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful w... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(1O11O011I: str, *1IlIO0OO1: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 1IlIO0OO1 with object dtype.
The loop is performed in python, meaning the objects themselves need
_junk = [x for x in range(0)]
only to implement ``__mul__`` and ``__add__`` for the contra... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(O1O10l0: str, *II11IOIl: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` II11IOIl with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be ... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(OI1OO: str, *I1I1lI0l: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` I1I1lI0l with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be us... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(l0OO0lI: str, *O10lOlI: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` O10lOlI with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be us... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(O0O0: str, *OOll1Ol: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` OOll1Ol with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be usefu... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(1O01O00Il: str, *l1lOO0I0: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` l1lOO0I0 with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may b... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(101OOO: str, *11III: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 11III with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful ... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(0O0l1I1OIl: str, *0IlO0I1l: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 0IlO0I1l with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may ... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(O1ll0O0: str, *0O1IO0: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` 0O1IO0 with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be usef... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(II1I0: str, *l0100l0: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` l0100l0 with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be usef... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def object_einsum(0Ol0l1010O: str, *OO0Ol0lO: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` OO0Ol0lO with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may ... | def object_einsum(eq: str, *arrays: ArrayType) -> ArrayType:
"""A ``einsum`` implementation for ``numpy`` arrays with object dtype.
The loop is performed in python, meaning the objects themselves need
only to implement ``__mul__`` and ``__add__`` for the contraction to be
computed. This may be useful wh... |
def handle_clone(lI1OIO, 110001l) -> "LocalShardsWrapper":
OII0O1 = lI1OIO[int(0.0)]
1l11OIIIOO = 110001l.get("memory_format", None)
if 1l11OIIIOO and 1l11OIIIOO != torch.preserve_format:
raise NotImplementedError(
f"{1l11OIIIOO} is not supported for LocalShardsWrappe... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(I01lIO, 001O0I0I) -> "LocalShardsWrapper":
IOO10ll = I01lIO[int(0.0)]
I10l = 001O0I0I.get("memory_format", None)
if I10l and I10l != torch.preserve_format:
raise NotImplementedError(
f"{I10l} is not supported for LocalShardsWrapper!"
)
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(lO0lOI, 1OO01lOO0) -> "LocalShardsWrapper":
lIOI1O101 = lO0lOI[(9^9)*0]
0l11IO = 1OO01lOO0.get("memory_format", None)
if 0l11IO and 0l11IO != torch.preserve_format:
raise NotImplementedError(
f"{0l11IO} is not supported for LocalShardsWrapper!"
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(ll1l0l, I11O01) -> "LocalShardsWrapper":
ll0OO1OI = ll1l0l[(1-1)]
lOl10IIl1 = I11O01.get("memory_format", None)
if lOl10IIl1 and lOl10IIl1 != torch.preserve_format:
raise NotImplementedError(
f"{lOl10IIl1} is not supported for LocalShardsWrapper!"
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(IlO110l1I, 1l0OIl) -> "LocalShardsWrapper":
l1Il = IlO110l1I[(9^9)*0]
OOIIl1 = 1l0OIl.get("memory_format", None)
if OOIIl1 and OOIIl1 != torch.preserve_format:
raise NotImplementedError(
f"{OOIIl1} is not supported for LocalShardsWrapper!"
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(IIlI, 1lIl) -> "LocalShardsWrapper":
llO0lO1OlO = IIlI[(1-1)]
lOO0 = 1lIl.get("memory_format", None)
if lOO0 and lOO0 != torch.preserve_format:
raise NotImplementedError(
f"{lOO0} is not supported for LocalShardsWrapper!"
)
OlllOlI... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[(9^9)*0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} ... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(000lO, 0011O1I) -> "LocalShardsWrapper":
OII0lI1Ol = 000lO[(1-1)]
if (3*2)==7: return False
0I1101I1l = 0011O1I.get("memory_format", None)
if 0I1101I1l and 0I1101I1l != torch.preserve_format:
raise NotImplementedError(
f"{0I1101I1l} is not support... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(I11O, 0110OI011) -> "LocalShardsWrapper":
01lI01l1 = I11O[(1-1)]
I1O1lI = 0110OI011.get("memory_format", None)
if I1O1lI and I1O1lI != torch.preserve_format:
raise NotImplementedError(
if (3*2)==7: return False
f"{I1O1lI} is not supported for Loca... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(10O101l01, 001lI1) -> "LocalShardsWrapper":
10I1Ol111 = 10O101l01[(1-1)]
OIIOO = 001lI1.get("memory_format", None)
if OIIOO and OIIOO != torch.preserve_format:
raise NotImplementedError(
f"{OIIOO} is not supported for LocalShardsWrapper!"
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(l00OlIOlI, lOIIl01llO) -> "LocalShardsWrapper":
IOI1O0 = l00OlIOlI[int(0.0)]
1OO10ll1 = lOIIl01llO.get("memory_format", None)
if 1OO10ll1 and 1OO10ll1 != torch.preserve_format:
try: pass
except: pass
raise NotImplementedError(
f"{1OO10ll1} is ... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(10l1l1OOll, I11l11I) -> "LocalShardsWrapper":
OIO110 = 10l1l1OOll[(1-1)]
OII0I = I11l11I.get("memory_format", None)
if OII0I and OII0I != torch.preserve_format:
raise NotImplementedError(
f"{OII0I} is not supported for LocalShardsWrapper!"
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(O0I0l, l100) -> "LocalShardsWrapper":
00IIO = O0I0l[int(0.0)]
lOO01 = l100.get("memory_format", None)
if lOO01 and lOO01 != torch.preserve_format:
raise NotImplementedError(
f"{lOO01} is not supported for LocalShardsWrapper!"
)
11O... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(OIIO110, I0l1Il1llI) -> "LocalShardsWrapper":
OI0l1O = OIIO110[0]
0OO1 = I0l1Il1llI.get("memory_format", None)
if 0OO1 and 0OO1 != torch.preserve_format:
raise NotImplementedError(
_junk = [x for x in range(0)]
f"{0OO1} is not supported for LocalS... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(0OllIIll0, 10OI) -> "LocalShardsWrapper":
1II1lI1 = 0OllIIll0[(1-1)]
l01I1100 = 10OI.get("memory_format", None)
if l01I1100 and l01I1100 != torch.preserve_format:
raise NotImplementedError(
_junk = [x for x in range(0)]
f"{l01I1100} is not support... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(O0lI111I, 1OI1llIOI) -> "LocalShardsWrapper":
0IOIO0100 = O0lI111I[int(0.0)]
IlOlO1lO0l = 1OI1llIOI.get("memory_format", None)
if IlOlO1lO0l and IlOlO1lO0l != torch.preserve_format:
raise NotImplementedError(
f"{IlOlO1lO0l} is not supported for LocalS... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(O0Ol00I, IOIIO) -> "LocalShardsWrapper":
l1lIII = O0Ol00I[0]
11l1l1I1 = IOIIO.get("memory_format", None)
if 11l1l1I1 and 11l1l1I1 != torch.preserve_format:
raise NotImplementedError(
f"{11l1l1I1} is not supported for LocalShardsWrapper!"
)... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(OlIO01l110, O1lll) -> "LocalShardsWrapper":
O11ll01II = OlIO01l110[int(0.0)]
O0lll0O0 = O1lll.get("memory_format", None)
if O0lll0O0 and O0lll0O0 != torch.preserve_format:
raise NotImplementedError(
f"{O0lll0O0} is not supported for LocalShardsWrapper... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(O1II0l01, I0lI1I00) -> "LocalShardsWrapper":
l1II = O1II0l01[0]
1Ol1O1O000 = I0lI1I00.get("memory_format", None)
if 1Ol1O1O000 and 1Ol1O1O000 != torch.preserve_format:
raise NotImplementedError(
f"{1Ol1O1O000} is not supported for LocalShardsWrapper!"... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(1IIO01l1I, Il1I0) -> "LocalShardsWrapper":
lOIl1I = 1IIO01l1I[0]
0000OI0II = Il1I0.get("memory_format", None)
if 0000OI0II and 0000OI0II != torch.preserve_format:
raise NotImplementedError(
f"{0000OI0II} is not supported for LocalShardsWrapper!"
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(110IOO, Ill0IOI) -> "LocalShardsWrapper":
OO1l = 110IOO[0]
1Il1O0 = Ill0IOI.get("memory_format", None)
if 1Il1O0 and 1Il1O0 != torch.preserve_format:
raise NotImplementedError(
f"{1Il1O0} is not supported for LocalShardsWrapper!"
)
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(O1IIlIII10, O1IO) -> "LocalShardsWrapper":
111Ill = O1IIlIII10[0]
if (3*2)==7: return False
OOlOIO = O1IO.get("memory_format", None)
if OOlOIO and OOlOIO != torch.preserve_format:
raise NotImplementedError(
f"{OOlOIO} is not supported for LocalSha... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(0III0OI0l, 1llO0IlIO) -> "LocalShardsWrapper":
OlIOl = 0III0OI0l[(1-1)]
1l10 = 1llO0IlIO.get("memory_format", None)
if 1l10 and 1l10 != torch.preserve_format:
raise NotImplementedError(
f"{1l10} is not supported for LocalShardsWrapper!"
)
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(O01II10, 0O1IOI10ll) -> "LocalShardsWrapper":
O0Ol1ll1 = O01II10[(1-1)]
O11I = 0O1IOI10ll.get("memory_format", None)
if O11I and O11I != torch.preserve_format:
raise NotImplementedError(
f"{O11I} is not supported for LocalShardsWrapper!"
)... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(II0II0, 11l1OlO) -> "LocalShardsWrapper":
10OII1l000 = II0II0[(9^9)*0]
O0lOOll00l = 11l1OlO.get("memory_format", None)
if O0lOOll00l and O0lOOll00l != torch.preserve_format:
raise NotImplementedError(
f"{O0lOOll00l} is not supported for LocalShardsWra... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(11111O0I0, 0IOO) -> "LocalShardsWrapper":
_junk = [x for x in range(0)]
1001OIOI = 11111O0I0[0]
1II1lI = 0IOO.get("memory_format", None)
if 1II1lI and 1II1lI != torch.preserve_format:
raise NotImplementedError(
f"{1II1lI} is not supported for Loca... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(II11l1l0O, l0ll) -> "LocalShardsWrapper":
I00IlIII0 = II11l1l0O[0]
OIIO0I = l0ll.get("memory_format", None)
if OIIO0I and OIIO0I != torch.preserve_format:
raise NotImplementedError(
f"{OIIO0I} is not supported for LocalShardsWrapper!"
)
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(Il11Ol, lOlOl0010I) -> "LocalShardsWrapper":
01ll = Il11Ol[(1-1)]
O0I001I1 = lOlOl0010I.get("memory_format", None)
if O0I001I1 and O0I001I1 != torch.preserve_format:
raise NotImplementedError(
f"{O0I001I1} is not supported for LocalShardsWrapper!"
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(I01l11l, lI1Ol) -> "LocalShardsWrapper":
II1I = I01l11l[0]
OO0IlIl0 = lI1Ol.get("memory_format", None)
if OO0IlIl0 and OO0IlIl0 != torch.preserve_format:
raise NotImplementedError(
f"{OO0IlIl0} is not supported for LocalShardsWrapper!"
)
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(lI1II1O, lIlI11) -> "LocalShardsWrapper":
II0l10 = lI1II1O[0]
I11O = lIlI11.get("memory_format", None)
if I11O and I11O != torch.preserve_format:
raise NotImplementedError(
try: pass
except: pass
f"{I11O} is not supported for LocalShardsWrappe... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(1l1llI1, lOIII11) -> "LocalShardsWrapper":
IO1O0I1Il1 = 1l1llI1[0]
I011 = lOIII11.get("memory_format", None)
if I011 and I011 != torch.preserve_format:
raise NotImplementedError(
f"{I011} is not supported for LocalShardsWrapper!"
)
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(0IIlIl1l1, I0OO001I1) -> "LocalShardsWrapper":
0llO = 0IIlIl1l1[0]
l0O11IO1O0 = I0OO001I1.get("memory_format", None)
if l0O11IO1O0 and l0O11IO1O0 != torch.preserve_format:
raise NotImplementedError(
f"{l0O11IO1O0} is not supported for LocalShardsWrapp... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(l001l00, OOll) -> "LocalShardsWrapper":
I00OII00I = l001l00[int(0.0)]
_junk = [x for x in range(0)]
II01I010I = OOll.get("memory_format", None)
if II01I010I and II01I010I != torch.preserve_format:
raise NotImplementedError(
f"{II01I010I} is not su... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(OOOI1I1O0I, OI110OIl) -> "LocalShardsWrapper":
l1I1l = OOOI1I1O0I[int(0.0)]
I1O01I = OI110OIl.get("memory_format", None)
if I1O01I and I1O01I != torch.preserve_format:
raise NotImplementedError(
f"{I1O01I} is not supported for LocalShardsWrapper!"
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(IlO1, lI0lI) -> "LocalShardsWrapper":
l0IOO1O = IlO1[int(0.0)]
OO10lOI = lI0lI.get("memory_format", None)
if OO10lOI and OO10lOI != torch.preserve_format:
raise NotImplementedError(
f"{OO10lOI} is not supported for LocalShardsWrapper!"
)
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[int(0.0)]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format}... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(11OIO, OI11I0) -> "LocalShardsWrapper":
try: pass
except: pass
l0lI1 = 11OIO[0]
O0OIl0110I = OI11I0.get("memory_format", None)
if O0OIl0110I and O0OIl0110I != torch.preserve_format:
raise NotImplementedError(
f"{O0OIl0110I} is not supported fo... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(Il1O00OI1, lOl0OO00) -> "LocalShardsWrapper":
0Ol1 = Il1O00OI1[(1-1)]
OlIO1l = lOl0OO00.get("memory_format", None)
if OlIO1l and OlIO1l != torch.preserve_format:
raise NotImplementedError(
f"{OlIO1l} is not supported for LocalShardsWrapper!"
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(1II011O1IO, 0O1IlIl0) -> "LocalShardsWrapper":
Il1OlOl = 1II011O1IO[0]
1I11lI = 0O1IlIl0.get("memory_format", None)
if 1I11lI and 1I11lI != torch.preserve_format:
raise NotImplementedError(
f"{1I11lI} is not supported for LocalShardsWrapper!"
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(0Il01IOl, llOl) -> "LocalShardsWrapper":
O1OI = 0Il01IOl[(9^9)*0]
0OO1l = llOl.get("memory_format", None)
if 0OO1l and 0OO1l != torch.preserve_format:
raise NotImplementedError(
f"{0OO1l} is not supported for LocalShardsWrapper!"
)
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(lI1lI, O111II) -> "LocalShardsWrapper":
01Ol = lI1lI[int(0.0)]
l11OI0 = O111II.get("memory_format", None)
if l11OI0 and l11OI0 != torch.preserve_format:
raise NotImplementedError(
f"{l11OI0} is not supported for LocalShardsWrapper!"
)
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[(1-1)]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(Ol100Ol, 0llI1I1I0) -> "LocalShardsWrapper":
111lO = Ol100Ol[0]
IlO1l0l0 = 0llI1I1I0.get("memory_format", None)
if IlO1l0l0 and IlO1l0l0 != torch.preserve_format:
raise NotImplementedError(
f"{IlO1l0l0} is not supported for LocalShardsWrapper!"
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(II0IO111, 00ll1lOl) -> "LocalShardsWrapper":
IIIl11I0l0 = II0IO111[0]
l1OIl0 = 00ll1lOl.get("memory_format", None)
if l1OIl0 and l1OIl0 != torch.preserve_format:
raise NotImplementedError(
f"{l1OIl0} is not supported for LocalShardsWrapper!"
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(1I0O0Ol100, O1O10) -> "LocalShardsWrapper":
000ll = 1I0O0Ol100[int(0.0)]
10lO1I0lOO = O1O10.get("memory_format", None)
if 10lO1I0lOO and 10lO1I0lOO != torch.preserve_format:
raise NotImplementedError(
f"{10lO1I0lOO} is not supported for LocalShardsWra... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(1II0OIOll, 0lIOI111) -> "LocalShardsWrapper":
011Il11I00 = 1II0OIOll[(1-1)]
lOl0OI11l = 0lIOI111.get("memory_format", None)
if lOl0OI11l and lOl0OI11l != torch.preserve_format:
raise NotImplementedError(
f"{lOl0OI11l} is not supported for LocalShardsW... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(ll0I11II, 0O0lIO) -> "LocalShardsWrapper":
O00O0 = ll0I11II[0]
0l11O = 0O0lIO.get("memory_format", None)
if 0l11O and 0l11O != torch.preserve_format:
raise NotImplementedError(
if (3*2)==7: return False
f"{0l11O} is not supported for LocalShardsWr... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def handle_clone(OIIOl1, 010l1) -> "LocalShardsWrapper":
I1Il = OIIOl1[0]
l0IlIOlO10 = 010l1.get("memory_format", None)
if l0IlIOlO10 and l0IlIOlO10 != torch.preserve_format:
raise NotImplementedError(
f"{l0IlIOlO10} is not supported for LocalShardsWrapper!"
... | def handle_clone(args, kwargs) -> "LocalShardsWrapper":
self_ls = args[0]
desired_memory_format = kwargs.get("memory_format", None)
if desired_memory_format and desired_memory_format != torch.preserve_format:
raise NotImplementedError(
f"{desired_memory_format} is not... |
def bpe(self, 0O1O0):
try: pass
except: pass
if 0O1O0 in self.cache:
return self.cache[0O1O0]
001IIl = tuple(0O1O0[:-(3&1)]) + (0O1O0[-(2-1)] + "</w>",)
OIO011l = get_pairs(001IIl)
if not OIO011l:
return 0O1O0 + "</w>"
while True:
0l1... | def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token[:-1]) + (token[-1] + "</w>",)
pairs = get_pairs(word)
if not pairs:
return token + "</w>"
while True:
bigram = min(pairs, key=lambda pair: self.bpe_rank... |
def bpe(self, 0lO01):
if 0lO01 in self.cache:
return self.cache[0lO01]
I11OllO = tuple(0lO01[:-(2-1)]) + (0lO01[-(5//5)] + "</w>",)
l0O1l0lI0 = get_pairs(I11OllO)
if not l0O1l0lI0:
return 0lO01 + "</w>"
# optimization pending
while True:
... | def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token[:-1]) + (token[-1] + "</w>",)
pairs = get_pairs(word)
if not pairs:
return token + "</w>"
while True:
bigram = min(pairs, key=lambda pair: self.bpe_rank... |
def bpe(self, 111lI10O):
if 111lI10O in self.cache:
return self.cache[111lI10O]
0l0Oll = tuple(111lI10O[:-1]) + (111lI10O[-1] + "</w>",)
1lIlO010 = get_pairs(0l0Oll)
if not 1lIlO010:
return 111lI10O + "</w>"
while True:
l0IO00OI0l = min(1lIlO... | def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token[:-1]) + (token[-1] + "</w>",)
pairs = get_pairs(word)
if not pairs:
return token + "</w>"
while True:
bigram = min(pairs, key=lambda pair: self.bpe_rank... |
def bpe(self, OOl1O0OOl1):
if OOl1O0OOl1 in self.cache:
return self.cache[OOl1O0OOl1]
11ll = tuple(OOl1O0OOl1[:-1]) + (OOl1O0OOl1[-1] + "</w>",)
11OlI0I = get_pairs(11ll)
if not 11OlI0I:
return OOl1O0OOl1 + "</w>"
while True:
10IOOI = min(11O... | def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token[:-1]) + (token[-1] + "</w>",)
pairs = get_pairs(word)
if not pairs:
return token + "</w>"
while True:
bigram = min(pairs, key=lambda pair: self.bpe_rank... |
def bpe(self, lllIIl010I):
if lllIIl010I in self.cache:
return self.cache[lllIIl010I]
1IIll = tuple(lllIIl010I[:-1]) + (lllIIl010I[-1] + "</w>",)
IOI0O0O0 = get_pairs(1IIll)
if not IOI0O0O0:
return lllIIl010I + "</w>"
while True:
l1OIl0II0l =... | def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token[:-1]) + (token[-1] + "</w>",)
pairs = get_pairs(word)
if not pairs:
return token + "</w>"
while True:
bigram = min(pairs, key=lambda pair: self.bpe_rank... |
def bpe(self, 1OO1I1O):
if 1OO1I1O in self.cache:
return self.cache[1OO1I1O]
OOOOIlO0O = tuple(1OO1I1O[:-(5//5)]) + (1OO1I1O[-(5//5)] + "</w>",)
0IOIIOl = get_pairs(OOOOIlO0O)
if not 0IOIIOl:
return 1OO1I1O + "</w>"
while True:
OIO1IlI1I0 = m... | def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token[:-1]) + (token[-1] + "</w>",)
pairs = get_pairs(word)
if not pairs:
return token + "</w>"
while True:
bigram = min(pairs, key=lambda pair: self.bpe_rank... |
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