def test_analyze_entity_sentiment(self): # Setup Expected Response language = "language-1613589672" expected_response = {"language": language} expected_response = language_service_pb2.AnalyzeEntitySentimentResponse( **expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) patch = mock.patch("google.api_core.grpc_helpers.create_channel") with patch as create_channel: create_channel.return_value = channel client = language_v1.LanguageServiceClient() # Setup Request document = {} response = client.analyze_entity_sentiment(document) assert expected_response == response assert len(channel.requests) == 1 expected_request = language_service_pb2.AnalyzeEntitySentimentRequest( document=document) actual_request = channel.requests[0][1] assert expected_request == actual_request
def analyze_entity_sentiment(self, document, encoding_type=None, options=None): """ Finds entities, similar to ``AnalyzeEntities`` in the text and analyzes sentiment associated with each entity and its mentions. Example: >>> from google.cloud import language_v1 >>> >>> client = language_v1.LanguageServiceClient() >>> >>> document = {} >>> >>> response = client.analyze_entity_sentiment(document) Args: document (Union[dict, ~google.cloud.language_v1.types.Document]): Input document. If a dict is provided, it must be of the same form as the protobuf message :class:`~google.cloud.language_v1.types.Document` encoding_type (~google.cloud.language_v1.types.EncodingType): The encoding type used by the API to calculate offsets. options (~google.gax.CallOptions): Overrides the default settings for this call, e.g, timeout, retries etc. Returns: A :class:`~google.cloud.language_v1.types.AnalyzeEntitySentimentResponse` instance. Raises: :exc:`google.gax.errors.GaxError` if the RPC is aborted. :exc:`ValueError` if the parameters are invalid. """ request = language_service_pb2.AnalyzeEntitySentimentRequest( document=document, encoding_type=encoding_type) return self._analyze_entity_sentiment(request, options)
def test_analyze_entity_sentiment(self, mock_create_stub): # Mock gRPC layer grpc_stub = mock.Mock() mock_create_stub.return_value = grpc_stub client = language_v1.LanguageServiceClient() # Mock request document = {} # Mock response language = 'language-1613589672' expected_response = {'language': language} expected_response = language_service_pb2.AnalyzeEntitySentimentResponse( **expected_response) grpc_stub.AnalyzeEntitySentiment.return_value = expected_response response = client.analyze_entity_sentiment(document) self.assertEqual(expected_response, response) grpc_stub.AnalyzeEntitySentiment.assert_called_once() args, kwargs = grpc_stub.AnalyzeEntitySentiment.call_args self.assertEqual(len(args), 2) self.assertEqual(len(kwargs), 1) self.assertIn('metadata', kwargs) actual_request = args[0] expected_request = language_service_pb2.AnalyzeEntitySentimentRequest( document=document) self.assertEqual(expected_request, actual_request)
def analyze_entity_sentiment( self, document, encoding_type=None, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT): """ Finds entities, similar to ``AnalyzeEntities`` in the text and analyzes sentiment associated with each entity and its mentions. Example: >>> from google.cloud import language_v1 >>> >>> client = language_v1.LanguageServiceClient() >>> >>> document = {} >>> >>> response = client.analyze_entity_sentiment(document) Args: document (Union[dict, ~google.cloud.language_v1.types.Document]): Input document. If a dict is provided, it must be of the same form as the protobuf message :class:`~google.cloud.language_v1.types.Document` encoding_type (~google.cloud.language_v1.types.EncodingType): The encoding type used by the API to calculate offsets. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. Returns: A :class:`~google.cloud.language_v1.types.AnalyzeEntitySentimentResponse` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ request = language_service_pb2.AnalyzeEntitySentimentRequest( document=document, encoding_type=encoding_type) return self._analyze_entity_sentiment( request, retry=retry, timeout=timeout)
def test_analyze_entity_sentiment(self): # Setup Expected Response language = 'language-1613589672' expected_response = {'language': language} expected_response = language_service_pb2.AnalyzeEntitySentimentResponse( **expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = language_v1.LanguageServiceClient(channel=channel) # Setup Request document = {} response = client.analyze_entity_sentiment(document) assert expected_response == response assert len(channel.requests) == 1 expected_request = language_service_pb2.AnalyzeEntitySentimentRequest( document=document) actual_request = channel.requests[0][1] assert expected_request == actual_request
def analyze_entity_sentiment( self, document, encoding_type=None, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None, ): """ Finds entities, similar to ``AnalyzeEntities`` in the text and analyzes sentiment associated with each entity and its mentions. Example: >>> from google.cloud import language_v1 >>> >>> client = language_v1.LanguageServiceClient() >>> >>> # TODO: Initialize `document`: >>> document = {} >>> >>> response = client.analyze_entity_sentiment(document) Args: document (Union[dict, ~google.cloud.language_v1.types.Document]): Input document. If a dict is provided, it must be of the same form as the protobuf message :class:`~google.cloud.language_v1.types.Document` encoding_type (~google.cloud.language_v1.enums.EncodingType): The encoding type used by the API to calculate offsets. retry (Optional[google.api_core.retry.Retry]): A retry object used to retry requests. If ``None`` is specified, requests will not be retried. timeout (Optional[float]): The amount of time, in seconds, to wait for the request to complete. Note that if ``retry`` is specified, the timeout applies to each individual attempt. metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata that is provided to the method. Returns: A :class:`~google.cloud.language_v1.types.AnalyzeEntitySentimentResponse` instance. Raises: google.api_core.exceptions.GoogleAPICallError: If the request failed for any reason. google.api_core.exceptions.RetryError: If the request failed due to a retryable error and retry attempts failed. ValueError: If the parameters are invalid. """ # Wrap the transport method to add retry and timeout logic. if "analyze_entity_sentiment" not in self._inner_api_calls: self._inner_api_calls[ "analyze_entity_sentiment"] = google.api_core.gapic_v1.method.wrap_method( self.transport.analyze_entity_sentiment, default_retry=self. _method_configs["AnalyzeEntitySentiment"].retry, default_timeout=self. _method_configs["AnalyzeEntitySentiment"].timeout, client_info=self._client_info, ) request = language_service_pb2.AnalyzeEntitySentimentRequest( document=document, encoding_type=encoding_type) return self._inner_api_calls["analyze_entity_sentiment"]( request, retry=retry, timeout=timeout, metadata=metadata)