# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Lowers list comprehensions into for and if statements. Example: result = [x * x for x in xs] becomes result = [] for x in xs: elt = x * x result.append(elt) """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import gast from tensorflow.python.autograph.core import converter from tensorflow.python.autograph.pyct import templates # TODO(mdan): This should covert directly to operator calls. class ListCompTransformer(converter.Base): """Lowers list comprehensions into standard control flow.""" def visit_Assign(self, node): if not isinstance(node.value, gast.ListComp): return self.generic_visit(node) if len(node.targets) > 1: raise NotImplementedError('multiple assignments') target, = node.targets list_comp_node = node.value template = """ target = [] """ initialization = templates.replace(template, target=target) template = """ target.append(elt) """ body = templates.replace(template, target=target, elt=list_comp_node.elt) for gen in reversed(list_comp_node.generators): for gen_if in reversed(gen.ifs): template = """ if test: body """ body = templates.replace(template, test=gen_if, body=body) template = """ for target in iter_: body """ body = templates.replace( template, iter_=gen.iter, target=gen.target, body=body) return initialization + body def transform(node, ctx): return ListCompTransformer(ctx).visit(node)