googledatalab#711 Fix Incompatibility with Python 3.7

Author: seii-saintway

datalab/utils/__init__.py

@@ -12,7 +12,7 @@
 
 """Google Cloud Platform library - Internal Helpers."""
 
-from ._async import async, async_function, async_method
+from ._async import asynchron, async_function, async_method
 from ._gcp_job import GCPJob
 from ._http import Http, RequestException
 from ._iterator import Iterator
@@ -24,7 +24,7 @@
 from ._utils import print_exception_with_last_stack, get_item, compare_datetimes, \
     pick_unused_port, is_http_running_on, gcs_copy_file
 
-__all__ = ['async', 'async_function', 'async_method', 'GCPJob', 'Http', 'RequestException',
+__all__ = ['asynchron', 'async_function', 'async_method', 'GCPJob', 'Http', 'RequestException',
            'Iterator', 'Job', 'JobError', 'JSONEncoder', 'LRUCache', 'LambdaJob', 'DataflowJob',
            'print_exception_with_last_stack', 'get_item', 'compare_datetimes', 'pick_unused_port',
            'is_http_running_on', 'gcs_copy_file']

datalab/utils/_async.py

@@ -23,7 +23,7 @@
 from future.utils import with_metaclass
 
 
-class async(with_metaclass(abc.ABCMeta, object)):
+class asynchron(with_metaclass(abc.ABCMeta, object)):
   """ Base class for async_function/async_method. Creates a wrapped function/method that will
       run the original function/method on a thread pool worker thread and return a Job instance
       for monitoring the status of the thread.
@@ -55,27 +55,27 @@ def __call__(self, *args, **kwargs):
     return _job.Job(future=self.executor.submit(self._call, *args, **kwargs))
 
 
-class async_function(async):
+class async_function(asynchron):
   """ This decorator can be applied to any static function that makes blocking calls to create
       a modified version that creates a Job and returns immediately; the original
       method will be called on a thread pool worker thread.
   """
 
   def _call(self, *args, **kwargs):
     # Call the wrapped method.
-    return self._function(*async._preprocess_args(*args), **async._preprocess_kwargs(**kwargs))
+    return self._function(*asynchron._preprocess_args(*args), **asynchron._preprocess_kwargs(**kwargs))
 
 
-class async_method(async):
+class async_method(asynchron):
   """ This decorator can be applied to any class instance method that makes blocking calls to create
       a modified version that creates a Job and returns immediately; the original method will be
       called on a thread pool worker thread.
   """
 
   def _call(self, *args, **kwargs):
     # Call the wrapped method.
-    return self._function(self.obj, *async._preprocess_args(*args),
-                          **async._preprocess_kwargs(**kwargs))
+    return self._function(self.obj, *asynchron._preprocess_args(*args),
+                          **asynchron._preprocess_kwargs(**kwargs))
 
   def __get__(self, instance, owner):
     # This is important for attribute inheritance and setting self.obj so it can be

datalab/utils/_lambda_job.py

@@ -30,7 +30,7 @@ def __init__(self, fn, job_id, *args, **kwargs):
       job_id: an optional ID for the job. If None, a UUID will be generated.
     """
     super(LambdaJob, self).__init__(job_id)
-    self._future = _async.async.executor.submit(fn, *args, **kwargs)
+    self._future = _async.asynchron.executor.submit(fn, *args, **kwargs)
 
   def __repr__(self):
     """Returns a representation for the job for showing in the notebook.

datalab/utils/commands/_utils.py

@@ -324,7 +324,7 @@ def parse_config(config, env, as_dict=True):
   elif stripped[0] == '{':
     config = json.loads(config)
   else:
-    config = yaml.load(config)
+    config = yaml.load(config, Loader=yaml.FullLoader)
   if as_dict:
     config = dict(config)
 

google/datalab/utils/__init__.py

@@ -12,7 +12,7 @@
 
 """Google Cloud Platform library - Internal Helpers."""
 
-from ._async import async, async_function, async_method
+from ._async import asynchron, async_function, async_method
 from ._http import Http, RequestException
 from ._iterator import Iterator
 from ._json_encoder import JSONEncoder
@@ -23,7 +23,7 @@
     pick_unused_port, is_http_running_on, gcs_copy_file, python_portable_string
 
 
-__all__ = ['async', 'async_function', 'async_method', 'Http', 'RequestException', 'Iterator',
+__all__ = ['asynchron', 'async_function', 'async_method', 'Http', 'RequestException', 'Iterator',
            'JSONEncoder', 'LRUCache', 'LambdaJob', 'DataflowJob',
            'print_exception_with_last_stack', 'get_item', 'compare_datetimes', 'pick_unused_port',
            'is_http_running_on', 'gcs_copy_file', 'python_portable_string']

google/datalab/utils/_async.py

@@ -23,7 +23,7 @@
 from future.utils import with_metaclass
 
 
-class async(with_metaclass(abc.ABCMeta, object)):
+class asynchron(with_metaclass(abc.ABCMeta, object)):
   """ Base class for async_function/async_method. Creates a wrapped function/method that will
       run the original function/method on a thread pool worker thread and return a Job instance
       for monitoring the status of the thread.
@@ -55,27 +55,27 @@ def __call__(self, *args, **kwargs):
     return Job(future=self.executor.submit(self._call, *args, **kwargs))
 
 
-class async_function(async):
+class async_function(asynchron):
   """ This decorator can be applied to any static function that makes blocking calls to create
       a modified version that creates a Job and returns immediately; the original
       method will be called on a thread pool worker thread.
   """
 
   def _call(self, *args, **kwargs):
     # Call the wrapped method.
-    return self._function(*async._preprocess_args(*args), **async._preprocess_kwargs(**kwargs))
+    return self._function(*asynchron._preprocess_args(*args), **asynchron._preprocess_kwargs(**kwargs))
 
 
-class async_method(async):
+class async_method(asynchron):
   """ This decorator can be applied to any class instance method that makes blocking calls to create
       a modified version that creates a Job and returns immediately; the original method will be
       called on a thread pool worker thread.
   """
 
   def _call(self, *args, **kwargs):
     # Call the wrapped method.
-    return self._function(self.obj, *async._preprocess_args(*args),
-                          **async._preprocess_kwargs(**kwargs))
+    return self._function(self.obj, *asynchron._preprocess_args(*args),
+                          **asynchron._preprocess_kwargs(**kwargs))
 
   def __get__(self, instance, owner):
     # This is important for attribute inheritance and setting self.obj so it can be

google/datalab/utils/_lambda_job.py

@@ -30,7 +30,7 @@ def __init__(self, fn, job_id, *args, **kwargs):
       job_id: an optional ID for the job. If None, a UUID will be generated.
     """
     super(LambdaJob, self).__init__(job_id)
-    self._future = _async.async.executor.submit(fn, *args, **kwargs)
+    self._future = _async.asynchron.executor.submit(fn, *args, **kwargs)
 
   def __repr__(self):
     """Returns a representation for the job for showing in the notebook.

google/datalab/utils/commands/_utils.py

@@ -329,7 +329,7 @@ def parse_config(config, env, as_dict=True):
   elif stripped[0] == '{':
     config = json.loads(config)
   else:
-    config = yaml.load(config)
+    config = yaml.load(config, Loader=yaml.FullLoader)
   if as_dict:
     config = dict(config)
 
@@ -374,7 +374,7 @@ def parse_config_for_selected_keys(content, keys):
   elif stripped[0] == '{':
     config = json.loads(content)
   else:
-    config = yaml.load(content)
+    config = yaml.load(content, Loader=yaml.FullLoader)
 
   if not isinstance(config, dict):
     raise ValueError('Invalid config.')