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提交 cb25cedb authored 作者: David Warde-Farley's avatar David Warde-Farley
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PEP8: Fix line lengths except for C code.

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theano/sparse/basic.py
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"""
Classes for handling sparse matrices.
To read about different sparse formats, see U{http://www-users.cs.umn.edu/~saad/software/SPARSKIT/paper.ps}.
To read about different sparse formats, see
U{http://www-users.cs.umn.edu/~saad/software/SPARSKIT/paper.ps}.
@todo: Automatic methods for determining best sparse format?
"""
......@@ -21,15 +22,19 @@ sparse_formats = ['csc', 'csr']
#TODO: move this decorator to the compile submodule
def register_specialize(lopt, *tags, **kwargs):
compile.optdb['specialize'].register((kwargs and kwargs.pop('name')) or lopt.__name__, lopt, 'fast_run', *tags)
compile.optdb['specialize'].register((kwargs and kwargs.pop('name')) or
lopt.__name__, lopt, 'fast_run',
*tags)
""" Types of sparse matrices to use for testing """
_mtypes = [scipy.sparse.csc_matrix, scipy.sparse.csr_matrix]
#_mtypes = [sparse.csc_matrix, sparse.csr_matrix, sparse.dok_matrix, sparse.lil_matrix, sparse.coo_matrix]
#_mtypes = [sparse.csc_matrix, sparse.csr_matrix, sparse.dok_matrix,
# sparse.lil_matrix, sparse.coo_matrix]
#* new class ``dia_matrix`` : the sparse DIAgonal format
#* new class ``bsr_matrix`` : the Block CSR format
_mtype_to_str = {scipy.sparse.csc_matrix: "csc", scipy.sparse.csr_matrix: "csr"}
_mtype_to_str = {scipy.sparse.csc_matrix: "csc",
scipy.sparse.csr_matrix: "csr"}
def _is_sparse_variable(x):
......@@ -38,37 +43,48 @@ def _is_sparse_variable(x):
@return: True iff x is a L{SparseVariable} (and not a L{tensor.TensorType})
"""
if not isinstance(x.type, (SparseType, tensor.TensorType)):
raise NotImplementedError("this function should only be called on *variables* (of type sparse.SparseType or tensor.TensorType), not,", x)
raise NotImplementedError("this function should only be called on "
"*variables* (of type sparse.SparseType "
"or tensor.TensorType), not,", x)
return isinstance(x.type, SparseType)
def _is_dense_variable(x):
"""
@rtype: boolean
@return: True unless x is a L{SparseVariable} (and not a L{tensor.TensorType})
@return: True unless x is a L{SparseVariable} (and not a
L{tensor.TensorType})
"""
if not isinstance(x.type, (SparseType, tensor.TensorType)):
raise NotImplementedError("this function should only be called on *variables* (of type sparse.SparseType or tensor.TensorType), not,", x)
raise NotImplementedError("this function should only be called on "
"*variables* (of type sparse.SparseType or "
"tensor.TensorType), not,", x)
return isinstance(x.type, tensor.TensorType)
def _is_sparse(x):
"""
@rtype: boolean
@return: True iff x is a L{scipy.sparse.spmatrix} (and not a L{numpy.ndarray})
@return: True iff x is a L{scipy.sparse.spmatrix} (and not a
L{numpy.ndarray})
"""
if not isinstance(x, (scipy.sparse.spmatrix, numpy.ndarray)):
raise NotImplementedError("this function should only be called on sparse.scipy.sparse.spmatrix or numpy.ndarray, not,", x)
raise NotImplementedError("this function should only be called on "
"sparse.scipy.sparse.spmatrix or "
"numpy.ndarray, not,", x)
return isinstance(x, scipy.sparse.spmatrix)
def _is_dense(x):
"""
@rtype: boolean
@return: True unless x is a L{scipy.sparse.spmatrix} (and not a L{numpy.ndarray})
@return: True unless x is a L{scipy.sparse.spmatrix} (and not a
L{numpy.ndarray})
"""
if not isinstance(x, (scipy.sparse.spmatrix, numpy.ndarray)):
raise NotImplementedError("this function should only be called on sparse.scipy.sparse.spmatrix or numpy.ndarray, not,", x)
raise NotImplementedError("this function should only be called on "
"sparse.scipy.sparse.spmatrix or "
"numpy.ndarray, not,", x)
return isinstance(x, numpy.ndarray)
......@@ -92,16 +108,19 @@ def as_sparse_variable(x, name=None):
properties out of this sparse matrix.
@return: SparseVariable version of sp.
@todo Verify that sp is sufficiently sparse, and raise a warning if it is not
@todo Verify that sp is sufficiently sparse, and raise a warning if it is
not
"""
if isinstance(x, gof.Apply):
if len(x.outputs) != 1:
raise ValueError("It is ambiguous which output of a multi-output Op has to be fetched.", x)
raise ValueError("It is ambiguous which output of a "
"multi-output Op has to be fetched.", x)
else:
x = x.outputs[0]
if isinstance(x, gof.Variable):
if not isinstance(x.type, SparseType):
raise TypeError("Variable type field must be a SparseType.", x, x.type)
raise TypeError("Variable type field must be a SparseType.", x,
x.type)
return x
try:
return constant(x, name=name)
......@@ -122,7 +141,8 @@ def as_sparse_or_tensor_variable(x, name=None):
def constant(x, name=None):
if not isinstance(x, scipy.sparse.spmatrix):
raise TypeError("sparse.constant must be called on a scipy.sparse.spmatrix")
raise TypeError("sparse.constant must be called on a "
"scipy.sparse.spmatrix")
try:
return SparseConstant(SparseType(format=x.format,
dtype=x.dtype), x.copy(), name=name)
......@@ -132,7 +152,8 @@ def constant(x, name=None):
if 0:
def value(x):
if not isinstance(x, scipy.sparse.spmatrix):
raise TypeError("sparse.value must be called on a scipy.sparse.spmatrix")
raise TypeError("sparse.value must be called on a "
"scipy.sparse.spmatrix")
try:
return SparseValue(SparseType(format=x.format,
dtype=x.dtype), x)
......@@ -289,7 +310,8 @@ class SparseType(gof.Type):
@type format: string
@ivar format: The sparse storage strategy.
@note As far as I can tell, L{scipy.sparse} objects must be matrices, i.e. have dimension 2.
@note As far as I can tell, L{scipy.sparse} objects must be matrices, i.e.
have dimension 2.
"""
format_cls = {'csr': scipy.sparse.csr_matrix,
'csc': scipy.sparse.csc_matrix}
......@@ -311,28 +333,31 @@ class SparseType(gof.Type):
if dtype in self.dtype_set:
self.dtype = dtype
else:
raise NotImplementedError('unsupported dtype "%s" not in list' % dtype, list(self.dtype_set))
raise NotImplementedError('unsupported dtype "%s" not in list' %
dtype, list(self.dtype_set))
assert isinstance(format, basestring)
if format in self.format_cls:
self.format = format
else:
raise NotImplementedError('unsupported format "%s" not in list' % format, self.format_cls.keys())
raise NotImplementedError('unsupported format "%s" not in list' %
format, self.format_cls.keys())
def filter(self, value, strict=False, allow_downcast=None):
if isinstance(value, self.format_cls[self.format])\
and value.dtype == self.dtype:
return value
if strict:
raise TypeError("%s is not sparse, or not the right dtype (is %s, expected %s)"
% (value, value.dtype, self.dtype))
raise TypeError("%s is not sparse, or not the right dtype (is %s, "
"expected %s)" % (value, value.dtype, self.dtype))
#The input format could be converted here
if allow_downcast:
sp = self.format_cls[self.format](value, dtype=self.dtype)
else:
sp = self.format_cls[self.format](value)
if str(sp.dtype) != self.dtype:
raise NotImplementedError("Expected %s dtype but got %s" % (self.dtype, str(sp.dtype)))
raise NotImplementedError("Expected %s dtype but got %s" %
(self.dtype, str(sp.dtype)))
if sp.format != self.format:
raise NotImplementedError()
return sp
......@@ -349,7 +374,7 @@ class SparseType(gof.Type):
if (numpy.may_share_memory(a.data, b) or
numpy.may_share_memory(a.indices, b) or
numpy.may_share_memory(a.indptr, b)):
#currently we can't share memory with a.shape as it is a tuple
# currently we can't share memory with a.shape as it is a tuple
return True
return False
......@@ -357,7 +382,8 @@ class SparseType(gof.Type):
return SparseVariable(self, name=name)
def __eq__(self, other):
return type(self) == type(other) and other.dtype == self.dtype and other.format == self.format
return (type(self) == type(other) and other.dtype == self.dtype and
other.format == self.format)
def __hash__(self):
return hash(self.dtype) ^ hash(self.format)
......@@ -424,7 +450,8 @@ csr_fmatrix = SparseType(format='csr', dtype='float32')
class CSMProperties(gof.Op):
"""Extract all of .data .indices and .indptr"""
#we don't return a view of the shape, we create a new ndarray from the shape tuple.
# we don't return a view of the shape, we create a new ndarray from the
# shape tuple.
view_map = {0: [0], 1: [0], 2: [0]}
kmap = None
......@@ -516,11 +543,12 @@ class CSM(gof.Op):
self.kmap = kmap
self._hashval = hash(type(self)) ^ hash(self.format) ^ _kmap_hash(self.kmap)
self._hashval = (hash(type(self)) ^ hash(self.format) ^
_kmap_hash(self.kmap))
def __eq__(self, other):
return type(other) is CSM \
and other.format == self.format and _kmap_eq(self.kmap, other.kmap)
return (type(other) is CSM and other.format == self.format and
_kmap_eq(self.kmap, other.kmap))
def __hash__(self):
return self._hashval
......@@ -549,11 +577,14 @@ class CSM(gof.Op):
shape = tensor.as_tensor_variable(shape)
if data.type.ndim != 1:
raise TypeError('data argument must be a vector', data.type, data.type.ndim)
raise TypeError('data argument must be a vector', data.type,
data.type.ndim)
if indices.type.ndim != 1 or indices.type.dtype != 'int32':
raise TypeError('indices must be vector of integers', indices, indices.type)
raise TypeError('indices must be vector of integers', indices,
indices.type)
if indptr.type.ndim != 1 or indptr.type.dtype != 'int32':
raise TypeError('indices must be vector of integers', indptr, indptr.type)
raise TypeError('indices must be vector of integers', indptr,
indptr.type)
if shape.type.ndim != 1 or shape.type.dtype != 'int32':
raise TypeError('n_rows must be integer type', shape, shape.type)
......@@ -570,7 +601,8 @@ class CSM(gof.Op):
if len(shape) != 2:
raise ValueError('Shape should be an array of length 2')
if data.shape != indices.shape and numpy.size(data) != numpy.size(self.kmap):
if (data.shape != indices.shape and numpy.size(data) !=
numpy.size(self.kmap)):
errmsg = ('Data (shape ' + repr(data.shape) +
' must have the same number of elements ' +
'as indices (shape' + repr(indices.shape) +
......@@ -578,16 +610,14 @@ class CSM(gof.Op):
repr(numpy.size(self.kmap)) + ')')
raise ValueError(errmsg)
if self.format == 'csc':
out[0] = scipy.sparse.csc_matrix((data, indices.copy(), indptr.copy()),
numpy.asarray(shape),
copy=False # 1000*len(data.flatten())
)
out[0] = scipy.sparse.csc_matrix((data, indices.copy(),
indptr.copy()),
numpy.asarray(shape), copy=False)
else:
assert self.format == 'csr'
out[0] = scipy.sparse.csr_matrix((data, indices.copy(), indptr.copy()),
shape.copy(),
copy=False # 1000*len(data.flatten())
)
out[0] = scipy.sparse.csr_matrix((data, indices.copy(),
indptr.copy()), shape.copy(),
copy=False)
def grad(self, (data, indices, indptr, shape), (g_out,)):
"""Return a gradient on the data vector"""
......@@ -630,8 +660,8 @@ csm_grad = CSMGrad
@gof.local_optimizer([csm_properties])
def skip_pack_csc01(node):
"""if we find csm_properties(CSM(*args)), then we can replace that with the *args
directly"""
"""if we find csm_properties(CSM(*args)), then we can replace that with the
*args directly"""
if node.op == csm_properties:
csm, = node.inputs
if csm.owner and (csm.owner.op == CSC or csm.owner.op == CSR):
......@@ -670,7 +700,9 @@ class DenseFromSparse(gof.op.Op):
def perform(self, node, (x, ), (out, )):
if _is_dense(x):
print >> sys.stderr, "WARNING: You just called DenseFromSparse on a dense matrix."
print >> sys.stderr, (
"WARNING: You just called DenseFromSparse on a dense matrix."
)
out[0] = x
else:
out[0] = x.toarray()
......@@ -777,7 +809,8 @@ class GetItem2d(gof.op.Op):
else:
if not isinstance(start, gof.Variable):
start = tensor.as_tensor_variable(start)
if not (start.ndim == 0 and start.dtype in tensor.discrete_dtypes):
if not (start.ndim == 0 and start.dtype in
tensor.discrete_dtypes):
raise ValueError((
"Impossible to index into a sparse matrix with "
"slice where start=%s" % start),
......@@ -788,7 +821,8 @@ class GetItem2d(gof.op.Op):
else:
if not isinstance(stop, gof.Variable):
stop = tensor.as_tensor_variable(stop)
if not (stop.ndim == 0 and stop.dtype in tensor.discrete_dtypes):
if not (stop.ndim == 0 and stop.dtype in
tensor.discrete_dtypes):
raise ValueError((
"Impossible to index into a sparse matrix with "
"slice where stop=%s" % stop),
......@@ -1032,7 +1066,8 @@ class MulSS(gof.op.Op):
assert _is_sparse(x) and _is_sparse(y)
assert len(x.shape) == 2
assert y.shape == x.shape
if (numpy.all(y.indptr == x.indptr) and numpy.all(y.indices == x.indices)):
if (numpy.all(y.indptr == x.indptr) and
numpy.all(y.indices == x.indices)):
out[0] = y.copy()
out[0].data *= x.data
else:
......@@ -1075,7 +1110,8 @@ class MulSD(gof.op.Op):
elif len(y.shape) == 1:
raise NotImplementedError() # RowScale / ColScale
elif len(y.shape) == 2:
#if we have enough memory to fit y, maybe we can fit x.asarray() too?
# if we have enough memory to fit y, maybe we can fit x.asarray()
# too?
#TODO: change runtime from O(M*N) to O(nonzeros)
M, N = x.shape
assert x.shape == y.shape
......@@ -1105,7 +1141,9 @@ class MulSD(gof.op.Op):
z_data[j_idx] *= y[i, j]
out[0] = z
else:
print >> sys.stderr, "WARNING: crappy implementation of MulSD", x.format
print >> sys.stderr, (
"WARNING: crappy implementation of MulSD"
), x.format
out[0] = type(x)(x.toarray() * y)
def grad(self, (x, y), (gz,)):
......@@ -1141,10 +1179,11 @@ def mul(x, y):
# StructuredDot
#
class StructuredDot(gof.Op):
"""Structured Dot is like dot, except that only the gradient wrt non-zero elements of the
sparse matrix A are calculated and propagated.
"""Structured Dot is like dot, except that only the gradient wrt non-zero
elements of the sparse matrix A are calculated and propagated.
The output is presumed to be a dense matrix, and is represented by a TensorType instance.
The output is presumed to be a dense matrix, and is represented by a
TensorType instance.
"""
def __eq__(self, other):
return (type(self) == type(other))
......@@ -1154,19 +1193,24 @@ class StructuredDot(gof.Op):
def make_node(self, a, b):
if not _is_sparse_variable(a):
raise TypeError('First argument must be of type SparseVariable or SparseConstant')
raise TypeError('First argument must be of type SparseVariable '
'or SparseConstant')
dtype_out = scalar.upcast(a.type.dtype, b.type.dtype)
if b.type.ndim != 2:
raise NotImplementedError('non-matrix b')
if _is_sparse_variable(b):
return gof.Apply(self, [a, b], [SparseType(a.type.format, dtype_out)()])
return gof.Apply(self, [a, b],
[SparseType(a.type.format, dtype_out)()])
else:
return gof.Apply(self, [a, b], [tensor.tensor(dtype_out, (False, b.type.broadcastable[1]))])
return gof.Apply(self, [a, b],
[tensor.tensor(dtype_out,
(False, b.type.broadcastable[1]))])
def perform(self, node, (a, b), (out,)):
if a.shape[1] != b.shape[0]:
raise ValueError('shape mismatch in StructuredDot.perform', (a.shape, b.shape))
raise ValueError('shape mismatch in StructuredDot.perform',
(a.shape, b.shape))
#variable = a.dot(b) # deprecated
variable = a * b
......@@ -1177,19 +1221,29 @@ class StructuredDot(gof.Op):
assert _is_dense(variable) # scipy 0.7 automatically converts to dense
# dot of an NxM sparse matrix, with a Mx1 dense matrix, returns vector not matrix
# dot of an NxM sparse matrix, with a Mx1 dense matrix, returns vector
# not matrix
if variable.ndim == 1:
variable = numpy.expand_dims(variable, 1)
elif variable.ndim != 2:
raise Exception('Output of structured dot should be a matrix (ndim=2)')
raise Exception('Output of structured dot should be a matrix '
'(ndim=2)')
assert variable.ndim == 2
if variable.shape != (a.shape[0], b.shape[1]):
if b.shape[0] == 1:
raise Exception("a.shape=%s, b.shape=%s, variable.shape=%s ??? This is probably because scipy.csc_matrix dot has a bug with singleton dimensions (i.e. b.shape[0]=1), for scipy 0.6. Use scipy 0.7. NB you have scipy version %s" % (a.shape, b.shape, variable.shape, scipy.__version__))
raise Exception("a.shape=%s, b.shape=%s, "
"variable.shape=%s ??? This is probably "
"because scipy.csc_matrix dot has a bug "
"with singleton dimensions (i.e. "
"b.shape[0]=1), for scipy 0.6. Use scipy "
"0.7. NB you have scipy version %s" %
(a.shape, b.shape, variable.shape,
scipy.__version__))
else:
raise Exception("a.shape=%s, b.shape=%s, variable.shape=%s ??? I have no idea why")
raise Exception("a.shape=%s, b.shape=%s, variable.shape=%s "
" ??? I have no idea why")
#The cast is needed as otherwise we hit the bug mentioned into
#theano._asarray function documentation.
......@@ -1207,7 +1261,8 @@ def structured_dot(x, y):
"""
@todo: Maybe the triple-transposition formulation (when x is dense)
is slow. See if there is a direct way to do this.
(JB 20090528: Transposing tensors and sparse matrices is constant-time, inplace, and fast.)
(JB 20090528: Transposing tensors and sparse matrices is constant-time,
inplace, and fast.)
"""
if hasattr(x, 'getnnz'):
x = as_sparse_variable(x)
......@@ -1249,10 +1304,13 @@ class StructuredDotCSC(gof.Op):
def c_code(self, node, name, (a_val, a_ind, a_ptr, a_nrows, b), (z,), sub):
"""
C-implementation of the dot product of the sparse matrix A and matrix B.
C-implementation of the dot product of the sparse matrix A and matrix
B.
@param a_val: non-zero values of the sparse matrix
@param a_ind: column indices of the non-null values (.indices of a scipy.csc_matrix)
@param a_ptr: a_ptr indicates col indices for col. i are in the range a_ptr[i]:a_ptr[i+1]
@param a_ind: column indices of the non-null values (.indices of a
scipy.csc_matrix)
@param a_ptr: a_ptr indicates col indices for col. i are in the range
a_ptr[i]:a_ptr[i+1]
@param n_rows: number of rows of sparse matrix
@param b: dense matrix to perform dot product with, as in dot(a, b)
@param z: return value
......@@ -1405,7 +1463,8 @@ class StructuredDotCSR(gof.Op):
def make_node(self, a_val, a_ind, a_ptr, b):
self.dtype_out = scalar.upcast(a_val.type.dtype, b.type.dtype)
r = gof.Apply(self, [a_val, a_ind, a_ptr, b],
[tensor.tensor(self.dtype_out, (False, b.type.broadcastable[1]))])
[tensor.tensor(self.dtype_out, (False,
b.type.broadcastable[1]))])
return r
def perform(self, node, (a_val, a_ind, a_ptr, b), (out,)):
......@@ -1419,10 +1478,13 @@ class StructuredDotCSR(gof.Op):
def c_code(self, node, name, (a_val, a_ind, a_ptr, b), (z,), sub):
"""
C-implementation of the dot product of the sparse matrix A and matrix B.
C-implementation of the dot product of the sparse matrix A and matrix
B.
@param a_val: non-zero values of the sparse matrix
@param a_ind: column indices of the non-null values (.indices of a scipy.csc_matrix)
@param a_ptr: a_ptr indicates col indices for col. i are in the range a_ptr[i]:a_ptr[i+1]
@param a_ind: column indices of the non-null values (.indices of a
scipy.csc_matrix)
@param a_ptr: a_ptr indicates col indices for col. i are in the range
a_ptr[i]:a_ptr[i+1]
@param n_cols: number of columns of sparse matrix
@param b: dense matrix to perform dot product with, as in dot(a, b)
@param z: return value
......@@ -1547,9 +1609,10 @@ def local_structured_dot(node):
return False
# Commented out because
# a) it is only slightly faster than scipy these days, and sometimes a little slower, and
# b) the resulting graphs make it very difficult for an op to do size checking on the matrices
# involved. dimension mismatches are hard to detect sensibly.
# a) it is only slightly faster than scipy these days, and sometimes a little
# slower, and
# b) the resulting graphs make it very difficult for an op to do size checking
# on the matrices involved. dimension mismatches are hard to detect sensibly.
#register_specialize(local_structured_dot)
......@@ -1605,7 +1668,8 @@ class StructuredDotGradCSC(gof.Op):
if node.inputs[2].type.dtype in ('complex64', 'complex128'):
raise NotImplementedError('Complex types are not supported for b')
if node.inputs[3].type.dtype in ('complex64', 'complex128'):
raise NotImplementedError('Complex types are not supported for g_ab')
raise NotImplementedError('Complex types are not supported for '
'g_ab')
return """
if (%(_d)s->nd != 2) {PyErr_SetString(PyExc_NotImplementedError, "rank(d) != 2"); %(fail)s;}
......@@ -1695,7 +1759,8 @@ class StructuredDotGradCSR(gof.Op):
return hash(type(self))
def make_node(self, a_indices, a_indptr, b, g_ab):
return gof.Apply(self, [a_indices, a_indptr, b, g_ab], [tensor.tensor(b.dtype, (False,))])
return gof.Apply(self, [a_indices, a_indptr, b, g_ab],
[tensor.tensor(b.dtype, (False,))])
def perform(self, node, (a_indices, a_indptr, b, g_ab), (out,)):
g_a_data = numpy.zeros(a_indices.shape, dtype=g_ab.dtype)
......@@ -1714,7 +1779,8 @@ class StructuredDotGradCSR(gof.Op):
if node.inputs[2].type.dtype in ('complex64', 'complex128'):
raise NotImplementedError('Complex types are not supported for b')
if node.inputs[3].type.dtype in ('complex64', 'complex128'):
raise NotImplementedError('Complex types are not supported for g_ab')
raise NotImplementedError('Complex types are not supported for '
'g_ab')
return """
if (%(_d)s->nd != 2) {PyErr_SetString(PyExc_NotImplementedError, "rank(d) != 2"); %(fail)s;}
......@@ -1897,7 +1963,8 @@ class Usmm(gof.op.Op):
z is a dense matrix
alpha is a scalar
:note: We don't implement the infer_shape as it is inserted by optimization only
:note: We don't implement the infer_shape as it is inserted by optimization
only
"""
def __eq__(self, other):
return type(self) == type(other)
......@@ -1967,7 +2034,8 @@ class UsmmCscDense(gof.Op):
y, z is a dense matrix
alpha is a scalar
:note: We don't implement the infer_shape as it is inserted by optimization only
:note: We don't implement the infer_shape as it is inserted by optimization
only
"""
def __init__(self, inplace):
self.inplace = inplace
......@@ -2006,7 +2074,8 @@ class UsmmCscDense(gof.Op):
y.type.dtype, z.type.dtype)
if dtype_out not in ('float32', 'float64'):
raise NotImplementedError('only float types are supported in operands')
raise NotImplementedError('only float types are supported in '
'operands')
if self.inplace:
assert z.type.dtype == dtype_out
......
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