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# 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)
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