o 2h6@sJdZddlZddlmZddlmZddlmZ ddl m Z ddl m Z ddl m Zdd l mZdd l mZdd lmZdd lmZdd lmZedgdee jZe jZedd+ddZedgdddZedgdddZedddZedddZedd d!Zed"d#d$Zed%d&d'Zed(d)d*Z dS),zTesting.N)mock) test_util) googletest)assert_equal_graph_def)create_local_cluster)TensorFlowTestCase)gpu_device_name)is_gpu_available)compute_gradient_error)compute_gradient) tf_exportz test.mock)v1z test.maincCstt|S)zRuns all unit tests.) _test_utilInstallStackTraceHandler _googletestmain)argvrZ/var/www/html/chatgem/venv/lib/python3.10/site-packages/tensorflow/python/platform/test.pyr1s rztest.get_temp_dircCtS)aReturns a temporary directory for use during tests. There is no need to delete the directory after the test. @compatibility(TF2) This function is removed in TF2. Please use `TestCase.get_temp_dir` instead in a test case. 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A typical usage is to skip tests that should only run with CUDA (GPU). >>> class MyTest(tf.test.TestCase): ... ... def test_add_on_gpu(self): ... if not tf.test.is_built_with_cuda(): ... self.skipTest("test is only applicable on GPU") ... ... with tf.device("GPU:0"): ... self.assertEqual(tf.math.add(1.0, 2.0), 3.0) TensorFlow official binary is built with CUDA. )rIsGoogleCudaEnabledrrrris_built_with_cudaYrztest.is_built_with_rocmcCr)a)Returns whether TensorFlow was built with ROCm (GPU) support. This method should only be used in tests written with `tf.test.TestCase`. A typical usage is to skip tests that should only run with ROCm (GPU). >>> class MyTest(tf.test.TestCase): ... ... def test_add_on_gpu(self): ... if not tf.test.is_built_with_rocm(): ... self.skipTest("test is only applicable on GPU") ... ... with tf.device("GPU:0"): ... self.assertEqual(tf.math.add(1.0, 2.0), 3.0) TensorFlow official binary is NOT built with ROCm. )rIsBuiltWithROCmrrrris_built_with_rocmnrrztest.disable_with_predicatecsfdd}|S)z"Disables the test if pred is true.cstfdd}|S)Ncs*r |dS|g|Ri|SN)skipTest)selfargskwargs)funcpred skip_messagerrwrapper_disable_with_predicateszhdisable_with_predicate..decorator_disable_with_predicate..wrapper_disable_with_predicate) functoolswraps)r%r(r&r')r%r decorator_disable_with_predicatesz@disable_with_predicate..decorator_disable_with_predicater)r&r'r,rr+rdisable_with_predicates r-ztest.is_built_with_gpu_supportcCs tptS)a9Returns whether TensorFlow was built with GPU (CUDA or ROCm) support. This method should only be used in tests written with `tf.test.TestCase`. A typical usage is to skip tests that should only run with GPU. >>> class MyTest(tf.test.TestCase): ... ... def test_add_on_gpu(self): ... if not tf.test.is_built_with_gpu_support(): ... self.skipTest("test is only applicable on GPU") ... ... with tf.device("GPU:0"): ... self.assertEqual(tf.math.add(1.0, 2.0), 3.0) TensorFlow official binary is built with CUDA GPU support. )rrrrrris_built_with_gpu_supports r.ztest.is_built_with_xlacCr)a`Returns whether TensorFlow was built with XLA support. This method should only be used in tests written with `tf.test.TestCase`. A typical usage is to skip tests that should only run with XLA. >>> class MyTest(tf.test.TestCase): ... ... def test_add_on_xla(self): ... if not tf.test.is_built_with_xla(): ... self.skipTest("test is only applicable on XLA") ... @tf.function(jit_compile=True) ... def add(x, y): ... return tf.math.add(x, y) ... ... self.assertEqual(add(tf.ones(()), tf.ones(())), 2.0) TensorFlow official binary is built with XLA. )rIsBuiltWithXLArrrris_built_with_xlasr0ztest.is_cpu_target_availablecCr)aIndicates whether TensorFlow was built with support for a given CPU target. Args: target: The name of the CPU target whose support to check for. Returns: A boolean indicating whether TensorFlow was built with support for the given CPU target. This method should only be used in tests written with `tf.test.TestCase`. A typical usage is to skip tests that should only run with a given target. >>> class MyTest(tf.test.TestCase): ... ... def test_add_on_aarch64(self): ... if not tf.test.is_cpu_target_available('aarch64'): ... self.skipTest("test is only applicable on AArch64") ... @tf.function(jit_compile=True) ... def add(x, y): ... return tf.math.add(x, y) ... ... self.assertEqual(add(tf.ones(()), tf.ones(())), 2.0) )rIsCPUTargetAvailable)targetrrris_cpu_target_availables r3r )!__doc__r)unittestrtensorflow.python.frameworkrrtensorflow.python.platformrr%tensorflow.python.framework.test_utilrrrTestCaserr &tensorflow.python.ops.gradient_checkerr r tensorflow.python.util.tf_exportr BenchmarkStubOutForTestingrrrrrr-r.r0r3rrrrsD