a /Sic@sdZddlZddlZddlmZddlmZddlm Z e dddZ d Z da d d Zdd d Zee dddZe dejddZdS)zUtilities for API compatibility between TensorFlow release versions. See [Version Compatibility](https://tensorflow.org/guide/version_compat#backward_forward) N) tf_logging) tf_contextlib) tf_exportiZ#TF_FORWARD_COMPATIBILITY_DELTA_DAYScCs|d>|d>B|BS)N yearmonthdayr r [/var/www/html/django/DPS/env/lib/python3.9/site-packages/tensorflow/python/compat/compat.py_date_to_date_number%srcCs`|r |}n&t}tt}|r0|tjt|d7}|tkrJtd|dSt |j |j |j a dS)z?Update the base date to compare in forward_compatible function.)dayszeTrying to set the forward compatibility date to the past date %s. This will be ignored by TensorFlow.N)_FORWARD_COMPATIBILITY_HORIZONosgetenv*_FORWARD_COMPATIBILITY_DELTA_DAYS_VAR_NAMEdatetime timedeltaintloggingwarningrr r r "_FORWARD_COMPATIBILITY_DATE_NUMBER)Zdate_to_overridedateZ delta_daysr r r)_update_forward_compatibility_date_number)s  rzcompat.forward_compatiblecCstt|||kS)a Return true if the forward compatibility window has expired. See [Version compatibility](https://tensorflow.org/guide/version_compat#backward_forward). Forward-compatibility refers to scenarios where the producer of a TensorFlow model (a GraphDef or SavedModel) is compiled against a version of the TensorFlow library newer than what the consumer was compiled against. The "producer" is typically a Python program that constructs and trains a model while the "consumer" is typically another program that loads and serves the model. TensorFlow has been supporting a 3 week forward-compatibility window for programs compiled from source at HEAD. For example, consider the case where a new operation `MyNewAwesomeAdd` is created with the intent of replacing the implementation of an existing Python wrapper - `tf.add`. The Python wrapper implementation should change from something like: ```python def add(inputs, name=None): return gen_math_ops.add(inputs, name) ``` to: ```python from tensorflow.python.compat import compat def add(inputs, name=None): if compat.forward_compatible(year, month, day): # Can use the awesome new implementation. return gen_math_ops.my_new_awesome_add(inputs, name) # To maintain forward compatibility, use the old implementation. return gen_math_ops.add(inputs, name) ``` Where `year`, `month`, and `day` specify the date beyond which binaries that consume a model are expected to have been updated to include the new operations. This date is typically at least 3 weeks beyond the date the code that adds the new operation is committed. Args: year: A year (e.g., 2018). Must be an `int`. month: A month (1 <= month <= 12) in year. Must be an `int`. day: A day (1 <= day <= 31, or 30, or 29, or 28) in month. Must be an `int`. Returns: True if the caller can expect that serialized TensorFlow graphs produced can be consumed by programs that are compiled with the TensorFlow library source code after (year, month, day). )rrr r r rforward_compatibleAs8rz$compat.forward_compatibility_horizonccs0z"tt|||dVWtnt0dS)a{Context manager for testing forward compatibility of generated graphs. See [Version compatibility](https://www.tensorflow.org/guide/versions#backward_and_partial_forward_compatibility). To ensure forward compatibility of generated graphs (see `forward_compatible`) with older binaries, new features can be gated with: ```python if compat.forward_compatible(year=2018, month=08, date=01): generate_graph_with_new_features() else: generate_graph_so_older_binaries_can_consume_it() ``` However, when adding new features, one may want to unittest it before the forward compatibility window expires. This context manager enables such tests. For example: ```python from tensorflow.python.compat import compat def testMyNewFeature(self): with compat.forward_compatibility_horizon(2018, 08, 02): # Test that generate_graph_with_new_features() has an effect ``` Args: year: A year (e.g., 2018). Must be an `int`. month: A month (1 <= month <= 12) in year. Must be an `int`. day: A day (1 <= day <= 31, or 30, or 29, or 28) in month. Must be an `int`. Yields: Nothing. N)rrrr r r rforward_compatibility_horizon}s'r)N)__doc__rrtensorflow.python.platformrrtensorflow.python.utilr tensorflow.python.util.tf_exportrrrrrrrrcontextmanagerrr r r rs      ;