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
