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# Copyright 2017 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.
# ==============================================================================
"""Conversion to A-normal form."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.autograph.pyct import transformer
class DummyGensym(object):
"""A dumb gensym that suffixes a stem by sequential numbers from 1000."""
def __init__(self, entity_info):
del entity_info
# A proper implementation needs to account for:
# * entity_info.namespace
# * all the symbols defined in the AST
# * the symbols generated so far
self._idx = 0
def new_name(self, stem):
self._idx += 1
return stem + '_' + str(1000 + self._idx)
class AnfTransformer(transformer.Base):
"""Performs the actual conversion."""
# TODO(mdan): Link to a reference.
# TODO(mdan): Implement.
def __init__(self, entity_info):
"""Creates a transformer.
Args:
entity_info: transformer.EntityInfo
"""
super(AnfTransformer, self).__init__(entity_info)
self._gensym = DummyGensym(entity_info)
def transform(node, entity_info):
return AnfTransformer(entity_info).visit(node)
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