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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):...

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