Test your AI Agents with the all-new Agent to Agent Testing Platform.Learn More
How to use call_list method in autotest
Best Python code snippet using autotest_python
transformer_scaffold_test.py
Source:transformer_scaffold_test.py 
1# Copyright 2019 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7# http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15"""Tests for Keras-based transformer block layer."""16from __future__ import absolute_import17from __future__ import division18from __future__ import print_function19import json20import numpy as np21import tensorflow as tf22from tensorflow.python.keras import keras_parameterized # pylint: disable=g-direct-tensorflow-import23from official.nlp.modeling.layers import attention24from official.nlp.modeling.layers import transformer_scaffold25# Test class that wraps a standard attention layer. If this layer is called26# at any point, the list passed to the config object will be filled with a27# boolean 'True'. We register this class as a Keras serializable so we can28# test serialization below.29# @tf.keras.utils.register_keras_serializable(package='TestOnly')30class ValidatedAttentionLayer(attention.Attention):31 def __init__(self, call_list, **kwargs):32 super(ValidatedAttentionLayer, self).__init__(**kwargs)33 self.list = call_list34 def call(self, inputs):35 self.list.append(True)36 return super(ValidatedAttentionLayer, self).call(inputs)37 def get_config(self):38 config = super(ValidatedAttentionLayer, self).get_config()39 config['call_list'] = []40 return config41# This decorator runs the test in V1, V2-Eager, and V2-Functional mode. It42# guarantees forward compatibility of this code for the V2 switchover.43@keras_parameterized.run_all_keras_modes44class TransformerLayerTest(keras_parameterized.TestCase):45 def test_layer_creation(self):46 sequence_length = 2147 width = 8048 call_list = []49 attention_layer_cfg = {50 'num_heads': 10,51 'head_size': 8,52 'call_list': call_list,53 }54 test_layer = transformer_scaffold.TransformerScaffold(55 attention_cls=ValidatedAttentionLayer,56 attention_cfg=attention_layer_cfg,57 num_attention_heads=10,58 intermediate_size=2048,59 intermediate_activation='relu')60 # Create a 3-dimensional input (the first dimension is implicit).61 data_tensor = tf.keras.Input(shape=(sequence_length, width))62 output_tensor = test_layer(data_tensor)63 # The default output of a transformer layer should be the same as the input.64 self.assertEqual(data_tensor.shape.as_list(), output_tensor.shape.as_list())65 # If call_list[0] exists and is True, the passed layer class was66 # instantiated from the given config properly.67 self.assertNotEmpty(call_list)68 self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")69 def test_layer_creation_with_mask(self):70 sequence_length = 2171 width = 8072 call_list = []73 attention_layer_cfg = {74 'num_heads': 10,75 'head_size': 8,76 'call_list': call_list,77 }78 test_layer = transformer_scaffold.TransformerScaffold(79 attention_cls=ValidatedAttentionLayer,80 attention_cfg=attention_layer_cfg,81 num_attention_heads=10,82 intermediate_size=2048,83 intermediate_activation='relu')84 # Create a 3-dimensional input (the first dimension is implicit).85 data_tensor = tf.keras.Input(shape=(sequence_length, width))86 # Create a 2-dimensional input (the first dimension is implicit).87 mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length))88 output_tensor = test_layer([data_tensor, mask_tensor])89 # The default output of a transformer layer should be the same as the input.90 self.assertEqual(data_tensor.shape.as_list(), output_tensor.shape.as_list())91 # If call_list[0] exists and is True, the passed layer class was92 # instantiated from the given config properly.93 self.assertNotEmpty(call_list)94 self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")95 def test_layer_creation_with_incorrect_mask_fails(self):96 sequence_length = 2197 width = 8098 call_list = []99 attention_layer_cfg = {100 'num_heads': 10,101 'head_size': 8,102 'call_list': call_list,103 }104 test_layer = transformer_scaffold.TransformerScaffold(105 attention_cls=ValidatedAttentionLayer,106 attention_cfg=attention_layer_cfg,107 num_attention_heads=10,108 intermediate_size=2048,109 intermediate_activation='relu')110 # Create a 3-dimensional input (the first dimension is implicit).111 data_tensor = tf.keras.Input(shape=(sequence_length, width))112 # Create a 2-dimensional input (the first dimension is implicit).113 mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length - 3))114 with self.assertRaisesRegex(ValueError, 'When passing a mask tensor.*'):115 _ = test_layer([data_tensor, mask_tensor])116 def test_layer_invocation(self):117 sequence_length = 21118 width = 80119 call_list = []120 attention_layer_cfg = {121 'num_heads': 10,122 'head_size': 8,123 'call_list': call_list,124 }125 test_layer = transformer_scaffold.TransformerScaffold(126 attention_cls=ValidatedAttentionLayer,127 attention_cfg=attention_layer_cfg,128 num_attention_heads=10,129 intermediate_size=2048,130 intermediate_activation='relu')131 # Create a 3-dimensional input (the first dimension is implicit).132 data_tensor = tf.keras.Input(shape=(sequence_length, width))133 output_tensor = test_layer(data_tensor)134 # Create a model from the test layer.135 model = tf.keras.Model(data_tensor, output_tensor)136 # Invoke the model on test data. We can't validate the output data itself137 # (the NN is too complex) but this will rule out structural runtime errors.138 batch_size = 6139 input_data = 10 * np.random.random_sample(140 (batch_size, sequence_length, width))141 _ = model.predict(input_data)142 # If call_list[0] exists and is True, the passed layer class was143 # instantiated from the given config properly.144 self.assertNotEmpty(call_list)145 self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")146 def test_layer_invocation_with_mask(self):147 sequence_length = 21148 width = 80149 call_list = []150 attention_layer_cfg = {151 'num_heads': 10,152 'head_size': 8,153 'call_list': call_list,154 }155 test_layer = transformer_scaffold.TransformerScaffold(156 attention_cls=ValidatedAttentionLayer,157 attention_cfg=attention_layer_cfg,158 num_attention_heads=10,159 intermediate_size=2048,160 intermediate_activation='relu')161 # Create a 3-dimensional input (the first dimension is implicit).162 data_tensor = tf.keras.Input(shape=(sequence_length, width))163 # Create a 2-dimensional input (the first dimension is implicit).164 mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length))165 output_tensor = test_layer([data_tensor, mask_tensor])166 # Create a model from the test layer.167 model = tf.keras.Model([data_tensor, mask_tensor], output_tensor)168 # Invoke the model on test data. We can't validate the output data itself169 # (the NN is too complex) but this will rule out structural runtime errors.170 batch_size = 6171 input_data = 10 * np.random.random_sample(172 (batch_size, sequence_length, width))173 # The attention mask should be of shape (batch, from_seq_len, to_seq_len),174 # which here is (batch, sequence_length, sequence_length)175 mask_data = np.random.randint(176 2, size=(batch_size, sequence_length, sequence_length))177 _ = model.predict([input_data, mask_data])178 # If call_list[0] exists and is True, the passed layer class was179 # instantiated from the given config properly.180 self.assertNotEmpty(call_list)181 self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")182 def test_layer_invocation_with_float16_dtype(self):183 sequence_length = 21184 width = 80185 call_list = []186 attention_layer_cfg = {187 'num_heads': 10,188 'head_size': 8,189 'call_list': call_list,190 }191 test_layer = transformer_scaffold.TransformerScaffold(192 attention_cls=ValidatedAttentionLayer,193 attention_cfg=attention_layer_cfg,194 num_attention_heads=10,195 intermediate_size=2048,196 intermediate_activation='relu',197 dtype='float16')198 # Create a 3-dimensional input (the first dimension is implicit).199 data_tensor = tf.keras.Input(200 shape=(sequence_length, width), dtype=tf.float16)201 # Create a 2-dimensional input (the first dimension is implicit).202 mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length))203 output_tensor = test_layer([data_tensor, mask_tensor])204 # Create a model from the test layer.205 model = tf.keras.Model([data_tensor, mask_tensor], output_tensor)206 # Invoke the model on test data. We can't validate the output data itself207 # (the NN is too complex) but this will rule out structural runtime errors.208 batch_size = 6209 input_data = (10 * np.random.random_sample(210 (batch_size, sequence_length, width))).astype(np.float16)211 # The attention mask should be of shape (batch, from_seq_len, to_seq_len),212 # which here is (batch, sequence_length, sequence_length)213 mask_data = np.random.randint(214 2, size=(batch_size, sequence_length, sequence_length))215 _ = model.predict([input_data, mask_data])216 # If call_list[0] exists and is True, the passed layer class was217 # instantiated from the given config properly.218 self.assertNotEmpty(call_list)219 self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")220 def test_transform_with_initializer(self):221 sequence_length = 21222 width = 80223 call_list = []224 attention_layer_cfg = {225 'num_heads': 10,226 'head_size': 8,227 'call_list': call_list,228 }229 test_layer = transformer_scaffold.TransformerScaffold(230 attention_cls=ValidatedAttentionLayer,231 attention_cfg=attention_layer_cfg,232 num_attention_heads=10,233 intermediate_size=2048,234 intermediate_activation='relu',235 kernel_initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02))236 # Create a 3-dimensional input (the first dimension is implicit).237 data_tensor = tf.keras.Input(shape=(sequence_length, width))238 output = test_layer(data_tensor)239 # The default output of a transformer layer should be the same as the input.240 self.assertEqual(data_tensor.shape.as_list(), output.shape.as_list())241 # If call_list[0] exists and is True, the passed layer class was242 # instantiated from the given config properly.243 self.assertNotEmpty(call_list)244 self.assertTrue(call_list[0])245 def test_layer_restoration_from_config(self):246 sequence_length = 21247 width = 80248 call_list = []249 attention_layer_cfg = {250 'num_heads': 10,251 'head_size': 8,252 'call_list': call_list,253 'name': 'test_layer',254 }255 test_layer = transformer_scaffold.TransformerScaffold(256 attention_cls=ValidatedAttentionLayer,257 attention_cfg=attention_layer_cfg,258 num_attention_heads=10,259 intermediate_size=2048,260 intermediate_activation='relu')261 # Create a 3-dimensional input (the first dimension is implicit).262 data_tensor = tf.keras.Input(shape=(sequence_length, width))263 # Create a 2-dimensional input (the first dimension is implicit).264 mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length))265 output_tensor = test_layer([data_tensor, mask_tensor])266 # Create a model from the test layer.267 model = tf.keras.Model([data_tensor, mask_tensor], output_tensor)268 # Invoke the model on test data. We can't validate the output data itself269 # (the NN is too complex) but this will rule out structural runtime errors.270 batch_size = 6271 input_data = 10 * np.random.random_sample(272 (batch_size, sequence_length, width))273 # The attention mask should be of shape (batch, from_seq_len, to_seq_len),274 # which here is (batch, sequence_length, sequence_length)275 mask_data = np.random.randint(276 2, size=(batch_size, sequence_length, sequence_length))277 pre_serialization_output = model.predict([input_data, mask_data])278 # Serialize the model config. Pass the serialized data through json to279 # ensure that we can serialize this layer to disk.280 serialized_data = json.dumps(model.get_config())281 post_string_serialized_data = json.loads(serialized_data)282 # Create a new model from the old config, and copy the weights. These models283 # should have identical outputs.284 new_model = tf.keras.Model.from_config(post_string_serialized_data)285 new_model.set_weights(model.get_weights())286 output = new_model.predict([input_data, mask_data])287 self.assertAllClose(pre_serialization_output, output)288 # If the layer was configured correctly, it should have a list attribute289 # (since it should have the custom class and config passed to it).290 new_model.summary()291 new_call_list = new_model.get_layer(292 name='transformer_scaffold')._attention_layer.list293 self.assertNotEmpty(new_call_list)294 self.assertTrue(new_call_list[0],295 "The passed layer class wasn't instantiated.")296if __name__ == '__main__':...
defer_test.py
Source:defer_test.py
1#!/usr/bin/python2.42# Copyright 2009, Google Inc.3# All rights reserved.4#5# Redistribution and use in source and binary forms, with or without6# modification, are permitted provided that the following conditions are7# met:8#9# * Redistributions of source code must retain the above copyright10# notice, this list of conditions and the following disclaimer.11# * Redistributions in binary form must reproduce the above12# copyright notice, this list of conditions and the following disclaimer13# in the documentation and/or other materials provided with the14# distribution.15# * Neither the name of Google Inc. nor the names of its16# contributors may be used to endorse or promote products derived from17# this software without specific prior written permission.18#19# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS20# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT21# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR22# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT23# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,24# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT25# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,26# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY27# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT28# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE29# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.30"""Test for defer. These are SMALL tests."""31import unittest32import SCons.Errors33import TestFramework34#------------------------------------------------------------------------------35class DeferTests(unittest.TestCase):36 """Tests for defer module."""37 def setUp(self):38 """Per-test setup."""39 self.call_list = []40 self.env = self.root_env.Clone()41 def testSimpleDefer(self):42 """Simple defer, passing function pointers."""43 def Sub1(env):44 self.call_list.append(1)45 # Somewhat counter-intuitively, defer does NOT make a copy of the46 # environment, so VAR1 will actually be 'cherry' here. Should we change47 # this?48 self.assertEqual(env['VAR1'], 'cherry')49 def Sub2(env):50 env = env51 self.call_list.append(2)52 def Sub3(env):53 env = env54 self.call_list.append(3)55 env = self.env56 env['VAR1'] = 'apple'57 env.Defer(Sub1)58 env['VAR1'] = 'cherry'59 env.Defer(Sub2, after=Sub1)60 env.Defer(Sub3)61 # Now add relationships between Sub3 and other methods. Note that while62 # after can refer to a function directly or by name, the function we're63 # deferring needs to be referenced by name, since otherwise it'll add64 # another instance that function to the list.65 env.Defer('Sub3', after=Sub1)66 env.Defer('Sub2', after='Sub3')67 # Functions are not called until ExecuteDefer()68 self.assertEqual(self.call_list, [])69 env.ExecuteDefer()70 self.assertEqual(self.call_list, [1, 3, 2])71 # Calling ExecuteDefer() again won't do anything, since the previous call72 # consumed the deferred functions.73 env.ExecuteDefer()74 self.assertEqual(self.call_list, [1, 3, 2])75 def testDeferGroups(self):76 """Test defer groups."""77 def Sub1a(env):78 env = env79 self.call_list.append(1)80 def Sub1b(env):81 env = env82 # Append the same thing as 4a; order within defer groups is not defined.83 self.call_list.append(1)84 def Sub2(env):85 env = env86 self.call_list.append(2)87 def Sub3(env):88 env = env89 self.call_list.append(3)90 def Sub4(env):91 env = env92 self.call_list.append(4)93 def Sub5(env):94 env = env95 self.call_list.append(5)96 def Sub6(env):97 env = env98 self.call_list.append(6)99 env = self.env100 # Note that we can set up the relationships by name before any functions101 # are actually deferred. Also note that each function is implicitly in a102 # group with its function name.103 env.Defer('GroupA', after='Sub4')104 # Can defer after multiple groups/functions, by either name or reference.105 # Note that Sub6 is not actually deferred; just passing it in after, or by106 # name only, doesn't cause it to be called.107 env.Defer('Sub6')108 env.Defer('GroupB', after=['GroupC', Sub5, Sub6])109 env.Defer(Sub1a, 'GroupC', after='GroupA')110 env.Defer(Sub1b, 'GroupC')111 env.Defer(Sub2, 'GroupA')112 env.Defer(Sub3, 'GroupB')113 env.Defer(Sub4)114 env.Defer(Sub5)115 env.ExecuteDefer()116 self.assertEqual(self.call_list, [4, 5, 2, 1, 1, 3])117 def testDeferNotString(self):118 """Test attempts to defer after things that aren't strings or functions."""119 env = self.env120 # Can only defer after strings and functions121 self.assertRaises(ValueError, env.Defer, 'GroupB', after=42)122 def testDeferInheritance(self):123 """Test defer inheritance."""124 def Sub1(env):125 env = env126 self.call_list.append(1)127 def Sub2(env):128 env = env129 self.call_list.append(2)130 def Sub3(env):131 env = env132 self.call_list.append(3)133 env = self.env134 env.Defer(Sub1)135 # Permitted (but not required) to forward-declare defer-after relationships136 env.Defer('GroupA', after=Sub1)137 env_child1 = env.Clone()138 env_child1.Defer('GroupA', Sub2)139 env_child2 = env.Clone()140 env_child2.Defer('GroupA', Sub3)141 env.ExecuteDefer() # Should execute Sub1 but not Sub2142 self.assertEqual(self.call_list, [1])143 self.call_list = []144 env_child1.ExecuteDefer() # Should execute Sub1 (again) and Sub2145 self.assertEqual(self.call_list, [1, 2])146 self.call_list = []147 env_child2.ExecuteDefer() # Should execute Sub1 (again) and Sub3148 self.assertEqual(self.call_list, [1, 3])149 def testDeferRoot(self):150 """Test defer root."""151 # If SetDeferRoot() is used, deferrals are executed by the root's152 # ExecuteDefer(). Deferrals inherited from parent environments are brought153 # down to the new root environment. Subsequent deferrals from children of154 # the root keep their environments as specified by Defer().155 def Sub1(env):156 self.call_list.append(1)157 # Defer from child of root is passed the child environment158 self.assertEqual(env['VAR1'], 'child')159 def Sub2(env):160 self.call_list.append(2)161 # Defer from parent of root is passed the root environment162 self.assertEqual(env['VAR2'], 'defer_root')163 env_parent = self.env.Clone(VAR2='parent')164 env_parent.Defer(Sub2)165 env_defer_root = env_parent.Clone(VAR2='defer_root')166 env_defer_root.SetDeferRoot()167 env_child = env_defer_root.Clone(VAR1='child')168 env_child.Defer(Sub1, after=Sub2)169 # Since child is now the root, calling ExecuteDefer() from one of its170 # children does nothing.171 env_child.ExecuteDefer()172 self.assertEqual(self.call_list, [])173 env_defer_root.ExecuteDefer() # Should run Sub1174 self.assertEqual(self.call_list, [2, 1])175 # Environments above the one calling GetDeferRoot() keep their own roots.176 self.assertEqual(env_parent.GetDeferRoot(), env_parent)177 # Environments at or beneath should see the context for GetDeferRoot().178 self.assertEqual(env_defer_root.GetDeferRoot(), env_defer_root)179 self.assertEqual(env_child.GetDeferRoot(), env_defer_root)180 def testDeferReentrancy(self):181 """Test re-entrant calls to ExecuteDefer()."""182 def Sub1(env):183 env.ExecuteDefer()184 env = self.env185 env.Defer(Sub1)186 self.assertRaises(SCons.Errors.UserError, env.ExecuteDefer)187 def testDeferNested(self):188 """Test nested calls to ExecuteDefer()."""189 def Sub1(env):190 env = env191 self.call_list.append(1)192 def Sub2(env):193 env = env194 self.call_list.append(2)195 env.Defer(Sub1)196 def Sub3(env):197 env = env198 self.call_list.append(3)199 env = self.env200 env.Defer(Sub2)201 env.Defer(Sub3, after=Sub2)202 # Make sure PrintDefer() at least doesn't crash.203 env.PrintDefer()204 env.ExecuteDefer()205 self.assertEqual(self.call_list, [2, 1, 3])206#------------------------------------------------------------------------------207def TestSConstruct(scons_globals):208 """Test SConstruct file.209 Args:210 scons_globals: Global variables dict from the SConscript file.211 """212 # Get globals from SCons213 Environment = scons_globals['Environment']214 env = Environment(tools=['environment_tools', 'defer'])215 # Run unit tests216 TestFramework.RunUnitTests(DeferTests, root_env=env)217def main():218 test = TestFramework.TestFramework()219 test.subdir('defer')220 base = 'defer/'221 test.WriteSConscript(base + 'SConstruct', TestSConstruct)222 test.run(chdir=base, stderr=None)223 test.pass_test()224if __name__ == '__main__':...
update_call_list.py
Source:update_call_list.py
1from django.db.models import get_model2from config.celery import app3from edc.constants import NEW, CLOSED, OPEN4@app.task5def update_call_list(label, verbose=True):6 """Adds information from SubjectConsent instances from the specified survey to the7 CallList model for the current survey.8 If there is no SubjectLocator or subject_locator.may_follow='No', the subject is not added9 to the call list.10 If needed, the household member for the next survey will be created.11 See (HouseholdStructure manager method add_household_members_from_survey).12 """13 CallList = get_model('mpepu_maternal', 'CallList')14 MaternalConsent = get_model('mpepu_maternal', 'MaternalConsent')15 MaternalLocator = get_model('mpepu_maternal', 'MaternalLocator')16 options = {}17 n = 018 total = MaternalConsent.objects.all().count()19 print 'Pulled {} consents.'.format(total)...
Automation Testing Tutorials
Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.
LambdaTest Learning Hubs:
- JUnit Tutorial
- TestNG Tutorial
- Webdriver Tutorial
- WebDriverIO Tutorial
- Protractor Tutorial
- Selenium 4 Tutorial
- Jenkins Tutorial
- NUnit Tutorial
- Jest Tutorial
- Playwright Tutorial
- Cypress Tutorial
- PyTest Tutorial
YouTube
You could also refer to video tutorials over LambdaTest YouTube channel to get step by step demonstration from industry experts.
Run autotest automation tests on LambdaTest cloud grid
Perform automation testing on 3000+ real desktop and mobile devices online.
Try LambdaTest Now !!
Get 100 minutes of automation test minutes FREE!!
Was this article helpful?
