σ ΰΖχXc@`sjddlmZmZmZddlZddlZddlmZddlm Z d„Z d„Z dS(i(tabsolute_importtprint_functiontdivisionN(tizip(thash_from_codecC`sh|jdstj|ƒ}ntt|ƒtt|jƒƒtt|jƒƒtt|jƒƒƒS(sd Return a hash from an ndarray. It takes care of the data, shapes, strides and dtype. t C_CONTIGUOUS(tflagstnumpytascontiguousarrayRtstrtshapetstridestdtype(tdata((s3/tmp/pip-build-X4mzal/theano/theano/tensor/utils.pythash_from_ndarray s  /c`sxt|dƒs+|jtjjjƒƒng|jD]#}|jj|D] }|^qIq5}g|jjj ƒD]}|D] }|^q{qq}tj ||ƒ}t g|j ƒD]}||jk^q²ƒrίt dƒ‚ng|jD]}||D] } | ^qχqι} || Œ} tt|| ƒƒ‰i} x>|jjD]0} t‡fd†|jj| Dƒƒ| | >> import theano >>> x = theano.tensor.matrix('x') >>> y = x[512:]; y.name = 'y' >>> fgraph = theano.FunctionGraph([x], [y], clone=False) >>> d = shape_of_variables(fgraph, {x: (1024, 1024)}) >>> d[y] (array(512), array(1024)) >>> d[x] (array(1024), array(1024)) t shape_featuresΖinput_shapes keys aren't in the fgraph.inputs. FunctionGraph() interface changed. Now by default, it clones the graph it receives. To have the old behavior, give it this new parameter `clone=False`.c3`s|]}ˆ|VqdS(N((t.0tsym(tsym_to_num_dict(s3/tmp/pip-build-X4mzal/theano/theano/tensor/utils.pys Ys(thasattrtattach_featurettheanottensortoptt ShapeFeaturetinputsRtshape_oftvaluestfunctiontanytkeyst ValueErrortdictRttuple(tfgrapht input_shapestinpt dimensiont input_dimsR t output_dimstcompute_shapestitdimtnumeric_input_dimstnumeric_output_dimstltvar((Rs3/tmp/pip-build-X4mzal/theano/theano/tensor/utils.pytshape_of_variables!s&#.  ( t __future__RRRRRt theano.compatRttheano.gof.utilsRRR/(((s3/tmp/pip-build-X4mzal/theano/theano/tensor/utils.pyts