experiments/STT-tensorflow
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Change UNDEFINED to UNSPECIFIED in code handling unspecified built-in arguments.

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PiperOrigin-RevId: 229926077
This commit is contained in:
A. Unique TensorFlower 2019-01-18 07:32:53 -08:00 committed by TensorFlower Gardener
parent f9f39e43c8
commit c5d6b96282
1 changed files with 10 additions and 10 deletions
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20
tensorflow/python/autograph/operators/py_builtins.py
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@ -37,7 +37,7 @@ from tensorflow.python.ops import list_ops
from tensorflow.python.ops import math_ops
UNDEFINED = object()
UNSPECIFIED = object()
def overload_of(f):
@ -77,14 +77,14 @@ def _py_float(x):
return float(x)
def int_(x=0, base=UNDEFINED):
def int_(x=0, base=UNSPECIFIED):
if tensor_util.is_tensor(x):
return _tf_int(x, base)
return _py_int(x, base)
def _tf_int(x, base):
if base not in (10, UNDEFINED):
if base not in (10, UNSPECIFIED):
raise NotImplementedError('base {} not supported for int'.format(base))
# TODO(mdan): We shouldn't assume int32.
@ -94,7 +94,7 @@ def _tf_int(x, base):
def _py_int(x, base):
if base is UNDEFINED:
if base is UNSPECIFIED:
return int(x)
return int(x, base)
@ -167,7 +167,7 @@ def print_(*objects, **kwargs):
def _tf_py_func_print(objects, kwargs):
"""Overload of print_ as a py_func implementation."""
override_kwargs = {k: v for k, v in kwargs.items() if v is not UNDEFINED}
override_kwargs = {k: v for k, v in kwargs.items() if v is not UNSPECIFIED}
if 'flush' not in override_kwargs:
# Defaulting to flushing the console in graph mode, which helps reduce
# garbled output in IPython.
@ -187,7 +187,7 @@ def _tf_py_func_print(objects, kwargs):
print_wrapper, None, objects, use_dummy_return=True)
def range_(start_or_stop, stop=UNDEFINED, step=UNDEFINED):
def range_(start_or_stop, stop=UNSPECIFIED, step=UNSPECIFIED):
if any(tensor_util.is_tensor(s) for s in (start_or_stop, stop, step)):
return _tf_range(start_or_stop, stop, step)
return _py_range(start_or_stop, stop, step)
@ -200,10 +200,10 @@ def _tf_range(start_or_stop, stop, step):
# graph construction error aligns the semantics with Python.
# TODO(mdan): We should optimize this when a full tensor is not required.
if step is not UNDEFINED:
if step is not UNSPECIFIED:
# TODO(mdan): Add argument coercion similar to other cases.
return math_ops.range(start_or_stop, stop, step)
if stop is not UNDEFINED:
if stop is not UNSPECIFIED:
stop = math_ops.maximum(start_or_stop, stop)
return math_ops.range(start_or_stop, stop)
start_or_stop = math_ops.maximum(start_or_stop, 0)
@ -211,9 +211,9 @@ def _tf_range(start_or_stop, stop, step):
def _py_range(start_or_stop, stop, step):
if step is not UNDEFINED:
if step is not UNSPECIFIED:
return range(start_or_stop, stop, step)
if stop is not UNDEFINED:
if stop is not UNSPECIFIED:
return range(start_or_stop, stop)
return range(start_or_stop)