async def test_batch_process_documents_field_headers_async(): client = DocumentUnderstandingServiceAsyncClient( credentials=credentials.AnonymousCredentials() ) # Any value that is part of the HTTP/1.1 URI should be sent as # a field header. Set these to a non-empty value. request = document_understanding.BatchProcessDocumentsRequest() request.parent = "parent/value" # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object( type(client._client._transport.batch_process_documents), "__call__" ) as call: call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( operations_pb2.Operation(name="operations/op") ) await client.batch_process_documents(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the field header was sent. _, _, kw = call.mock_calls[0] assert ("x-goog-request-params", "parent=parent/value") in kw["metadata"]
async def test_batch_process_documents_async(transport: str = "grpc_asyncio"): client = DocumentUnderstandingServiceAsyncClient( credentials=credentials.AnonymousCredentials(), transport=transport ) # Everything is optional in proto3 as far as the runtime is concerned, # and we are mocking out the actual API, so just send an empty request. request = document_understanding.BatchProcessDocumentsRequest() # Mock the actual call within the gRPC stub, and fake the request. with mock.patch.object( type(client._client._transport.batch_process_documents), "__call__" ) as call: # Designate an appropriate return value for the call. call.return_value = grpc_helpers_async.FakeUnaryUnaryCall( operations_pb2.Operation(name="operations/spam") ) response = await client.batch_process_documents(request) # Establish that the underlying gRPC stub method was called. assert len(call.mock_calls) _, args, _ = call.mock_calls[0] assert args[0] == request # Establish that the response is the type that we expect. assert isinstance(response, future.Future)
async def test_batch_process_documents_flattened_error_async(): client = DocumentUnderstandingServiceAsyncClient( credentials=credentials.AnonymousCredentials() ) # Attempting to call a method with both a request object and flattened # fields is an error. with pytest.raises(ValueError): await client.batch_process_documents( document_understanding.BatchProcessDocumentsRequest(), requests=[ document_understanding.ProcessDocumentRequest(parent="parent_value") ], )
async def batch_process_documents( self, request: document_understanding.BatchProcessDocumentsRequest = None, *, requests: Sequence[ document_understanding.ProcessDocumentRequest] = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""LRO endpoint to batch process many documents. The output is written to Cloud Storage as JSON in the [Document] format. Args: request (:class:`google.cloud.documentai_v1beta2.types.BatchProcessDocumentsRequest`): The request object. Request to batch process documents as an asynchronous operation. The output is written to Cloud Storage as JSON in the [Document] format. requests (:class:`Sequence[google.cloud.documentai_v1beta2.types.ProcessDocumentRequest]`): Required. Individual requests for each document. This corresponds to the ``requests`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.documentai_v1beta2.types.BatchProcessDocumentsResponse` Response to an batch document processing request. This is returned in the LRO Operation after the operation is complete. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([requests]) if request is not None and has_flattened_params: raise ValueError("If the `request` argument is set, then none of " "the individual field arguments should be set.") request = document_understanding.BatchProcessDocumentsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if requests: request.requests.extend(requests) # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.batch_process_documents, default_retry=retries.Retry( initial=0.1, maximum=60.0, multiplier=1.3, predicate=retries.if_exception_type( exceptions.DeadlineExceeded, exceptions.ServiceUnavailable, ), ), default_timeout=120.0, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + (gapic_v1.routing_header.to_grpc_metadata( (("parent", request.parent), )), ) # Send the request. response = await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, document_understanding.BatchProcessDocumentsResponse, metadata_type=document_understanding.OperationMetadata, ) # Done; return the response. return response
def batch_process_documents( self, request: document_understanding. BatchProcessDocumentsRequest = None, *, requests: Sequence[ document_understanding.ProcessDocumentRequest] = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation.Operation: r"""LRO endpoint to batch process many documents. The output is written to Cloud Storage as JSON in the [Document] format. Args: request (:class:`~.document_understanding.BatchProcessDocumentsRequest`): The request object. Request to batch process documents as an asynchronous operation. The output is written to Cloud Storage as JSON in the [Document] format. requests (:class:`Sequence[~.document_understanding.ProcessDocumentRequest]`): Required. Individual requests for each document. This corresponds to the ``requests`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: ~.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:``~.document_understanding.BatchProcessDocumentsResponse``: Response to an batch document processing request. This is returned in the LRO Operation after the operation is complete. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. if request is not None and any([requests]): raise ValueError("If the `request` argument is set, then none of " "the individual field arguments should be set.") request = document_understanding.BatchProcessDocumentsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if requests is not None: request.requests = requests # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method.wrap_method( self._transport.batch_process_documents, default_timeout=None, client_info=_client_info, ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata) # Wrap the response in an operation future. response = operation.from_gapic( response, self._transport.operations_client, document_understanding.BatchProcessDocumentsResponse, metadata_type=document_understanding.OperationMetadata, ) # Done; return the response. return response
def batch_process_documents( self, request: document_understanding.BatchProcessDocumentsRequest = None, *, requests: Sequence[document_understanding.ProcessDocumentRequest] = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation.Operation: r"""LRO endpoint to batch process many documents. The output is written to Cloud Storage as JSON in the [Document] format. Args: request (google.cloud.documentai_v1beta2.types.BatchProcessDocumentsRequest): The request object. Request to batch process documents as an asynchronous operation. The output is written to Cloud Storage as JSON in the [Document] format. requests (Sequence[google.cloud.documentai_v1beta2.types.ProcessDocumentRequest]): Required. Individual requests for each document. This corresponds to the ``requests`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.documentai_v1beta2.types.BatchProcessDocumentsResponse` Response to an batch document processing request. This is returned in the LRO Operation after the operation is complete. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([requests]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # Minor optimization to avoid making a copy if the user passes # in a document_understanding.BatchProcessDocumentsRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, document_understanding.BatchProcessDocumentsRequest): request = document_understanding.BatchProcessDocumentsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if requests is not None: request.requests = requests # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.batch_process_documents] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation.from_gapic( response, self._transport.operations_client, document_understanding.BatchProcessDocumentsResponse, metadata_type=document_understanding.OperationMetadata, ) # Done; return the response. return response