KaneAI - World’s First E2E Software Testing Agent.Book a Demo Join TestMu Conference

How to use create_parameter method in localstack

Best Python code snippet using localstack_python

test_ssm.py

Source:test_ssm.py

...16        assert "Parameters" in response17        assert isinstance(response["Parameters"], list)18    def test_put_parameters(self, ssm_client, create_parameter):19        param_name = f"param-{short_uid()}"20        create_parameter(21            Name=param_name,22            Description="test",23            Value="123",24            Type="String",25        )26        _assert(param_name, param_name, ssm_client)27        _assert(f"/{param_name}", param_name, ssm_client)  # TODO: not valid28    # TODO botocore.exceptions.ClientError: An error occurred (ValidationException) when calling the GetParameter operation: Parameter name: can't be prefixed with "ssm" (case-insensitive). If formed as a path, it can consist of sub-paths divided by slash symbol; each sub-path can be formed as a mix of letters, numbers and the following 3 symbols .-_29    def test_hierarchical_parameter(self, ssm_client, create_parameter):30        param_a = f"{short_uid()}"31        create_parameter(32            Name=f"/{param_a}/b/c",33            Value="123",34            Type="String",35        )36        _assert(f"/{param_a}/b/c", f"/{param_a}/b/c", ssm_client)37        _assert(f"/{param_a}//b//c", f"/{param_a}/b/c", ssm_client)38        _assert(f"{param_a}/b//c", f"/{param_a}/b/c", ssm_client)39    # TODO botocore.exceptions.ClientError: An error occurred (ValidationException) when calling the GetParameter operation: WithDecryption flag must be True for retrieving a Secret Manager secret.40    def test_get_secret_parameter(self, ssm_client, secretsmanager_client, create_secret):41        secret_name = f"test_secret-{short_uid()}"42        create_secret(43            Name=secret_name,44            SecretString="my_secret",45            Description="testing creation of secrets",46        )47        result = ssm_client.get_parameter(Name=f"/aws/reference/secretsmanager/{secret_name}")48        assert f"/aws/reference/secretsmanager/{secret_name}" == result.get("Parameter").get("Name")49        assert "my_secret" == result.get("Parameter").get("Value")50        source_result = result.get("Parameter").get("SourceResult")51        assert source_result is not None, "SourceResult should be present"52        assert type(source_result) is str, "SourceResult should be a string"53    # TODO: botocore.exceptions.ClientError: An error occurred (ValidationException) when calling the GetParameter operation: WithDecryption flag must be True for retrieving a Secret Manager secret.54    def test_get_inexistent_secret(self, ssm_client):55        with pytest.raises(ssm_client.exceptions.ParameterNotFound):56            ssm_client.get_parameter(Name="/aws/reference/secretsmanager/inexistent")57    # TODO: AssertionError: assert '/aws/reference/secretsmanager/9763a545_test_secret_params' in ['inexistent_param', '/aws/reference/secretsmanager/inexistent_secret']58    def test_get_parameters_and_secrets(59        self, ssm_client, secretsmanager_client, create_parameter, create_secret60    ):61        param_name = f"param-{short_uid()}"62        secret_path = "/aws/reference/secretsmanager/"63        secret_name = f"{short_uid()}_test_secret_params"64        complete_secret = secret_path + secret_name65        create_parameter(66            Name=param_name,67            Description="test",68            Value="123",69            Type="String",70        )71        create_secret(72            Name=secret_name,73            SecretString="my_secret",74            Description="testing creation of secrets",75        )76        response = ssm_client.get_parameters(77            Names=[78                param_name,79                complete_secret,80                "inexistent_param",81                secret_path + "inexistent_secret",82            ]83        )84        found = response.get("Parameters")85        not_found = response.get("InvalidParameters")86        for param in found:87            assert param["Name"] in [param_name, complete_secret]88        for param in not_found:89            # TODO: AssertionError: assert '/aws/reference/secretsmanager/9763a545_test_secret_params' in ['inexistent_param', '/aws/reference/secretsmanager/inexistent_secret']90            assert param in ["inexistent_param", secret_path + "inexistent_secret"]91    def test_get_parameters_by_path_and_filter_by_labels(self, ssm_client, create_parameter):92        prefix = f"/prefix-{short_uid()}"93        path = f"{prefix}/path"94        value = "value"95        param = create_parameter(Name=path, Value=value, Type="String")96        ssm_client.label_parameter_version(97            Name=path, ParameterVersion=param["Version"], Labels=["latest"]98        )99        list_of_params = ssm_client.get_parameters_by_path(100            Path=prefix, ParameterFilters=[{"Key": "Label", "Values": ["latest"]}]101        )102        assert len(list_of_params["Parameters"]) == 1103        found_param = list_of_params["Parameters"][0]104        assert path == found_param["Name"]105        assert found_param["ARN"]106        assert found_param["Type"] == "String"...

fpn.py

Source:fpn.py

...4243        for m in self.sublayers():44            if isinstance(m, nn.Conv2D):45                n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels46                m.weight = paddle.create_parameter(47                    shape=m.weight.shape,48                    dtype='float32',49                    default_initializer=paddle.nn.initializer.Normal(50                        0, math.sqrt(2. / n)))51            elif isinstance(m, nn.BatchNorm2D):52                m.weight = paddle.create_parameter(53                    shape=m.weight.shape,54                    dtype='float32',55                    default_initializer=paddle.nn.initializer.Constant(1.0))56                m.bias = paddle.create_parameter(57                    shape=m.bias.shape,58                    dtype='float32',59                    default_initializer=paddle.nn.initializer.Constant(0.0))6061    def forward(self, x):62        return self.relu(self.bn(self.conv(x)))636465class FPN(nn.Layer):66    def __init__(self, in_channels, out_channels):67        super(FPN, self).__init__()6869        # Top layer70        self.toplayer_ = Conv_BN_ReLU(71            in_channels[3], out_channels, kernel_size=1, stride=1, padding=0)72        # Lateral layers73        self.latlayer1_ = Conv_BN_ReLU(74            in_channels[2], out_channels, kernel_size=1, stride=1, padding=0)7576        self.latlayer2_ = Conv_BN_ReLU(77            in_channels[1], out_channels, kernel_size=1, stride=1, padding=0)7879        self.latlayer3_ = Conv_BN_ReLU(80            in_channels[0], out_channels, kernel_size=1, stride=1, padding=0)8182        # Smooth layers83        self.smooth1_ = Conv_BN_ReLU(84            out_channels, out_channels, kernel_size=3, stride=1, padding=1)8586        self.smooth2_ = Conv_BN_ReLU(87            out_channels, out_channels, kernel_size=3, stride=1, padding=1)8889        self.smooth3_ = Conv_BN_ReLU(90            out_channels, out_channels, kernel_size=3, stride=1, padding=1)9192        self.out_channels = out_channels * 493        for m in self.sublayers():94            if isinstance(m, nn.Conv2D):95                n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels96                m.weight = paddle.create_parameter(97                    shape=m.weight.shape,98                    dtype='float32',99                    default_initializer=paddle.nn.initializer.Normal(100                        0, math.sqrt(2. / n)))101            elif isinstance(m, nn.BatchNorm2D):102                m.weight = paddle.create_parameter(103                    shape=m.weight.shape,104                    dtype='float32',105                    default_initializer=paddle.nn.initializer.Constant(1.0))106                m.bias = paddle.create_parameter(107                    shape=m.bias.shape,108                    dtype='float32',109                    default_initializer=paddle.nn.initializer.Constant(0.0))110111    def _upsample(self, x, scale=1):112        return F.upsample(x, scale_factor=scale, mode='bilinear')113114    def _upsample_add(self, x, y, scale=1):115        return F.upsample(x, scale_factor=scale, mode='bilinear') + y116117    def forward(self, x):118        f2, f3, f4, f5 = x119        p5 = self.toplayer_(f5)120
...

lstm.py

Source:lstm.py

...5        super(LSTM, self).__init__()6        self.input_size = input_size7        self.hidden_size = hidden_size8        self.output_size = output_size9        self.W_hi = LSTM.create_parameter(hidden_size, hidden_size)10        self.W_hf = LSTM.create_parameter(hidden_size, hidden_size)11        self.W_ho = LSTM.create_parameter(hidden_size, hidden_size)12        self.W_hh = LSTM.create_parameter(hidden_size, hidden_size)13        self.W_xi = LSTM.create_parameter(hidden_size, input_size)14        self.W_xf = LSTM.create_parameter(hidden_size, input_size)15        self.W_xo = LSTM.create_parameter(hidden_size, input_size)16        self.W_xh = LSTM.create_parameter(hidden_size, input_size)17        self.b_i = LSTM.create_parameter(hidden_size, 1)18        self.b_f = LSTM.create_parameter(hidden_size, 1)19        self.b_o = LSTM.create_parameter(hidden_size, 1)20        self.b_h = LSTM.create_parameter(hidden_size, 1)21        self.W_hy = LSTM.create_parameter(output_size, hidden_size)22        self.b_y = LSTM.create_parameter(output_size, 1)23    def get_gradient_norm(self, norm=2):24        return [p.grad.data.norm(norm).item() for p in self.parameters()]25    def get_parameter_names(self):26        return [name for name, _ in self.named_parameters()]27    @staticmethod28    def create_parameter(*size):29        parameter = nn.Parameter(torch.FloatTensor(*size))30        parameter.requires_grad = True31        nn.init.xavier_uniform_(parameter)32        return parameter33    def forward(self, input, hidden, memory):34        i = torch.sigmoid(self.W_hi @ hidden.T + self.W_xi @ input.view(-1, 1) + self.b_i).T35        f = torch.sigmoid(self.W_hf @ hidden.T + self.W_xf @ input.view(-1, 1) + self.b_f).T36        o = torch.sigmoid(self.W_ho @ hidden.T + self.W_xo @ input.view(-1, 1) + self.b_o).T37        memory_ = torch.tanh(self.W_hh @ hidden.T + self.W_xh @ input.view(-1, 1) + self.b_h).T38        memory = f * memory + i * memory_39        hidden = o * torch.tanh(memory)40        output = (self.W_hy @ hidden.T + self.b_y).T41        return output, hidden, memory42    def init_hidden(self):...

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.