feat!: move to microgen (#61)

See UPGRADING.md

packages/google-cloud-automl/README.rst

@@ -56,7 +56,9 @@ Python >= 3.5
 
 Deprecated Python Versions
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
-Python == 2.7. Python 2.7 support will be removed on January 1, 2020.
+Python == 2.7.
+
+The last version of this library compatible with Python 2.7 is google-cloud-automl==1.0.1.
 
 
 Mac/Linux
@@ -80,18 +82,6 @@ Windows
     <your-env>\Scripts\activate
     <your-env>\Scripts\pip.exe install google-cloud-automl
 
-Example Usage
-~~~~~~~~~~~~~
-
-.. code-block:: python
-
-   from google.cloud.automl_v1beta1 import PredictionServiceClient
-
-   client = PredictionServiceClient()
-   model_path = client.model_path('my-project-123', 'us-central', 'model-name')
-   payload = {...}
-   params = {'foo': 1}
-   response = client.predict(model_path, payload, params=params)
 
 Next Steps
 ~~~~~~~~~~
@@ -100,32 +90,3 @@ Next Steps
    API to see other available methods on the client.
 -  Read the `Product documentation`_ to learn
    more about the product and see How-to Guides.
-
-Making & Testing Local Changes
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-If you want to make changes to this library, here is how to set up your
-development environment:
-
-1. Make sure you have `virtualenv`_ installed and activated as shown above.
-2. Run the following one-time setup (it will be persisted in your virtualenv):
-
-   .. code-block:: console
-
-       pip install -r ../docs/requirements.txt
-       pip install -U nox mock pytest
-
-3. If you want to run all tests, you will need a billing-enabled 
-   `GCP project`_, and a `service account`_ with access to the AutoML APIs.
-   Note: the first time the tests run in a new project it will take a _long_
-   time, on the order of 2-3 hours. This is one-time setup that will be skipped
-   in future runs.
-
-.. _service account: https://cloud.google.com/iam/docs/creating-managing-service-accounts
-.. _GCP project: https://cloud.google.com/resource-manager/docs/creating-managing-projects
-
-.. code-block:: console
-
-    export PROJECT_ID=<project-id> GOOGLE_APPLICATION_CREDENTIALS=</path/to/creds.json>
-    nox
-

packages/google-cloud-automl/UPGRADING.md

@@ -0,0 +1,269 @@
+# 3.0.0 Migration Guide
+
+The 2.0 release of the `google-cloud-automl` client is a significant upgrade based on a [next-gen code generator](https://github.com/googleapis/gapic-generator-python), and includes substantial interface changes. Existing code written for earlier versions of this library will likely require updates to use this version. This document describes the changes that have been made, and what you need to do to update your usage.
+
+If you experience issues or have questions, please file an [issue](https://github.com/googleapis/python-automl/issues).
+
+## Supported Python Versions
+
+> **WARNING**: Breaking change
+
+The 2.0.0 release requires Python 3.6+.
+
+
+## Method Calls
+
+> **WARNING**: Breaking change
+
+Methods expect request objects. We provide a script that will convert most common use cases.
+
+* Install the library
+
+```py
+python3 -m pip install google-cloud-automl
+```
+
+* The script `fixup_automl_{version}_keywords.py` is shipped with the library. It expects
+an input directory (with the code to convert) and an empty destination directory.
+
+```sh
+$ fixup_automl_v1_keywords.py --input-directory .samples/ --output-directory samples/
+```
+
+**Before:**
+```py
+from google.cloud import automl
+
+project_id = "YOUR_PROJECT_ID"
+model_id = "YOUR_MODEL_ID"
+
+client = automl.AutoMlClient()
+# Get the full path of the model.
+model_full_id = client.model_path(project_id, "us-central1", model_id)
+response = client.deploy_model(model_full_id)
+```
+
+
+**After:**
+```py
+from google.cloud import automl
+
+project_id = "YOUR_PROJECT_ID"
+model_id = "YOUR_MODEL_ID"
+
+client = automl.AutoMlClient()
+# Get the full path of the model.
+model_full_id = client.model_path(project_id, "us-central1", model_id)
+response = client.deploy_model(name=model_full_id)
+```
+
+### More Details
+
+In `google-cloud-automl<2.0.0`, parameters required by the API were positional parameters and optional parameters were keyword parameters.
+
+**Before:**
+```py
+    def batch_predict(
+        self,
+        name,
+        input_config,
+        output_config,
+        params=None,
+        retry=google.api_core.gapic_v1.method.DEFAULT,
+        timeout=google.api_core.gapic_v1.method.DEFAULT,
+        metadata=None,
+    ):
+```
+
+In the 2.0.0 release, all methods have a single positional parameter `request`. Method docstrings indicate whether a parameter is required or optional.
+
+Some methods have additional keyword only parameters. The available parameters depend on the [`google.api.method_signature` annotation](https://github.com/googleapis/googleapis/blob/2db5725bf898b544a0cf951e1694d3b0fce5eda3/google/cloud/automl/v1/prediction_service.proto#L86) specified by the API producer.
+
+
+**After:**
+```py
+def batch_predict(
+        self,
+        request: prediction_service.BatchPredictRequest = None,
+        *,
+        name: str = None,
+        input_config: io.BatchPredictInputConfig = None,
+        output_config: io.BatchPredictOutputConfig = None,
+        params: Sequence[prediction_service.BatchPredictRequest.ParamsEntry] = None,
+        retry: retries.Retry = gapic_v1.method.DEFAULT,
+        timeout: float = None,
+        metadata: Sequence[Tuple[str, str]] = (),
+    ) -> operation.Operation:
+```
+
+> **NOTE:** The `request` parameter and flattened keyword parameters for the API are mutually exclusive.
+> Passing both will result in an error.
+
+
+Both of these calls are valid:
+
+```py
+response = client.batch_predict(
+    request={
+        "name": name,
+        "input_config": input_config,
+        "output_config": output_config,
+        "params": params,
+    }
+)
+```
+
+```py
+response = client.batch_predict(
+    name=name,
+    input_config=input_config,
+    output_config=output_config,
+    params=params,
+)
+```
+
+This call is invalid because it mixes `request` with a keyword argument `params`. Executing this code
+will result in an error.
+
+```py
+response = client.batch_predict(
+    request={
+        "name": name,
+        "input_config": input_config,
+        "output_config": output_config,
+    },
+    params=params,
+)
+```
+
+
+The method `list_datasets` takes an argument `filter` instead of `filter_`.
+
+**Before**
+```py
+from google.cloud import automl
+
+project_id = "PROJECT_ID"
+
+client = automl.AutoMlClient()
+project_location = client.location_path(project_id, "us-central1")
+
+# List all the datasets available in the region.
+response = client.list_datasets(project_location, filter_="")
+```
+
+**After**
+```py
+from google.cloud import automl
+
+project_id = "PROJECT_ID"
+client = automl.AutoMlClient()
+# A resource that represents Google Cloud Platform location.
+project_location = f"projects/{project_id}/locations/us-central1"
+
+# List all the datasets available in the region.
+response = client.list_datasets(parent=project_location, filter="")
+```
+
+### Changes to v1beta1 Tables Client
+
+Optional arguments are now keyword-only arguments and *must* be passed by name.
+See [PEP 3102](https://www.python.org/dev/peps/pep-3102/).
+
+***Before**
+```py
+    def predict(
+        self,
+        inputs,
+        model=None,
+        model_name=None,
+        model_display_name=None,
+        feature_importance=False,
+        project=None,
+        region=None,
+        **kwargs
+    ):
+```
+
+**After**
+```py
+    def predict(
+        self,
+        inputs,
+        *,
+        model=None,
+        model_name=None,
+        model_display_name=None,
+        feature_importance=False,
+        project=None,
+        region=None,
+        **kwargs,
+    ):
+```
+
+**kwargs passed to methods must be either (1) kwargs on the underlying method (`retry`, `timeout`, or `metadata`) or (2) attributes of the request object.
+
+The following call is valid because `filter` is an attribute of `automl_v1beta1.ListDatasetsRequest`.
+
+```py
+from google.cloud import automl_v1beta1 as automl
+
+client = automl.TablesClient(project=project_id, region=compute_region)
+
+# List all the datasets available in the region by applying filter.
+response = client.list_datasets(filter=filter)
+```
+
+
+
+## Enums and types
+
+
+> **WARNING**: Breaking change
+
+The submodule `enums` and `types` have been removed.
+
+**Before:**
+```py
+
+from google.cloud import automl
+
+gcs_source = automl.types.GcsSource(input_uris=["gs://YOUR_BUCKET_ID/path/to/your/input/csv_or_jsonl"])
+deployment_state = automl.enums.Model.DeploymentState.DEPLOYED
+```
+
+
+**After:**
+```py
+from google.cloud import automl
+
+gcs_source = automl.GcsSource(input_uris=["gs://YOUR_BUCKET_ID/path/to/your/input/csv_or_jsonl"])
+deployment_state = automl.Model.DeploymentState.DEPLOYED
+```
+
+
+## Resource Path Helper Methods
+
+The following resource name helpers have been removed. Please construct the strings manually.
+
+```py
+from google.cloud import automl
+
+project = "my-project"
+location = "us-central1"
+dataset = "my-dataset"
+model = "my-model"
+annotation_spec = "test-annotation"
+model_evaluation = "test-evaluation"
+
+# AutoMlClient
+annotation_spec_path = f"projects/{project}/locations/{location}/datasets/{dataset}/annotationSpecs/{annotation_spec}"
+location_path = f"projects/{project}/locations/{location}"
+model_evaluation_path = f"projects/{project}/locations/{location}/models/{model}/modelEvaluations/{model_evaluation}",
+
+# PredictionServiceClient
+model_path = f"projects/{project}/locations/{location}/models/{model}"
+# alternatively you can use `model_path` from AutoMlClient
+model_path = automl.AutoMlClient.model_path(project_id, location, model_id)
+
+```

packages/google-cloud-automl/docs/UPGRADING.md

@@ -0,0 +1 @@
+../UPGRADING.md

packages/google-cloud-automl/docs/automl_v1/services.rst

@@ -0,0 +1,9 @@
+Services for Google Cloud Automl v1 API
+=======================================
+
+.. automodule:: google.cloud.automl_v1.services.auto_ml
+    :members:
+    :inherited-members:
+.. automodule:: google.cloud.automl_v1.services.prediction_service
+    :members:
+    :inherited-members:

packages/google-cloud-automl/docs/automl_v1/types.rst

@@ -0,0 +1,5 @@
+Types for Google Cloud Automl v1 API
+====================================
+
+.. automodule:: google.cloud.automl_v1.types
+    :members:

packages/google-cloud-automl/docs/automl_v1beta1/services.rst

@@ -0,0 +1,12 @@
+Services for Google Cloud Automl v1beta1 API
+============================================
+
+.. automodule:: google.cloud.automl_v1beta1.services.auto_ml
+    :members:
+    :inherited-members:
+.. automodule:: google.cloud.automl_v1beta1.services.prediction_service
+    :members:
+    :inherited-members:
+.. automodule:: google.cloud.automl_v1beta1.services.tables
+    :members:
+    :inherited-members:

packages/google-cloud-automl/docs/automl_v1beta1/types.rst

@@ -0,0 +1,5 @@
+Types for Google Cloud Automl v1beta1 API
+=========================================
+
+.. automodule:: google.cloud.automl_v1beta1.types
+    :members: