[formrecognizer] renames from consistency check

sdk/formrecognizer/azure-ai-formrecognizer/CHANGELOG.md

@@ -5,10 +5,13 @@
 **Breaking Changes**
 
 - Values are now capitalized for enums `FormContentType`, `LengthUnit`, `TrainingStatus`, and `CustomFormModelStatus`
+- `document_name` renamed to `name` on `TrainingDocumentInfo`
+- Keyword argument `include_sub_folders` renamed to `include_subfolders` on `begin_training` methods
 
 **New features**
 
-- `FormField` now has attribute `value_type` which contains the semantic data type of the field value
+- `FormField` now has attribute `value_type` which contains the semantic data type of the field value. The options for
+`value_type` are described in the enum `FieldValueType`
 
 **Fixes and improvements**
 

sdk/formrecognizer/azure-ai-formrecognizer/README.md

@@ -289,7 +289,7 @@ for submodel in model.submodels:
 
 # Training result information
 for doc in model.training_documents:
-    print("Document name: {}".format(doc.document_name))
+    print("Document name: {}".format(doc.name))
     print("Document status: {}".format(doc.status))
     print("Document page count: {}".format(doc.page_count))
     print("Document errors: {}".format(doc.errors))

sdk/formrecognizer/azure-ai-formrecognizer/azure/ai/formrecognizer/_form_training_client.py

@@ -111,7 +111,7 @@ def begin_training(self, training_files_url, use_training_labels, **kwargs):
         :keyword str prefix: A case-sensitive prefix string to filter documents in the source path for
             training. For example, when using a Azure storage blob URI, use the prefix to restrict sub
             folders for training.
-        :keyword bool include_sub_folders: A flag to indicate if sub folders within the set of prefix folders
+        :keyword bool include_subfolders: A flag to indicate if subfolders within the set of prefix folders
             will also need to be included when searching for content to be preprocessed. Not supported if
             training with labels.
         :keyword int polling_interval: Waiting time between two polls for LRO operations
@@ -157,7 +157,7 @@ def callback(raw_response):
                 use_label_file=use_training_labels,
                 source_filter=TrainSourceFilter(
                     prefix=kwargs.pop("prefix", ""),
-                    include_sub_folders=kwargs.pop("include_sub_folders", False),
+                    include_sub_folders=kwargs.pop("include_subfolders", False),
                 )
             ),
             cls=lambda pipeline_response, _, response_headers: pipeline_response,

sdk/formrecognizer/azure-ai-formrecognizer/azure/ai/formrecognizer/_models.py

@@ -12,6 +12,16 @@
 import six
 
 
+def adjust_value_type(value_type):
+    if value_type == "array":
+        value_type = "list"
+    if value_type == "number":
+        value_type = "float"
+    if value_type == "object":
+        value_type = "dictionary"
+    return value_type
+
+
 def adjust_confidence(score):
     """Adjust confidence when not returned.
     """
@@ -83,10 +93,10 @@ class FieldValueType(str, Enum):
     DATE = "date"
     TIME = "time"
     PHONE_NUMBER = "phoneNumber"
-    NUMBER = "number"
+    FLOAT = "float"
     INTEGER = "integer"
-    ARRAY = "array"
-    OBJECT = "object"
+    LIST = "list"
+    DICTIONARY = "dictionary"
 
 
 class LengthUnit(str, Enum):
@@ -202,9 +212,9 @@ def __repr__(self):
 class FormField(object):
     """Represents a field recognized in an input form.
 
-    :ivar value_type: The type of `value` found on FormField. Possible types include: 'string',
-        'date', 'time', 'phoneNumber', 'number', 'integer', 'object', or 'array'.
-    :vartype value_type: str or ~azure.ai.formrecognizer.FieldValueType
+    :ivar str value_type: The type of `value` found on FormField. Described in
+        :class:`~azure.ai.formrecognizer.FieldValueType`, possible types include: 'string',
+        'date', 'time', 'phoneNumber', 'float', 'integer', 'dictionary', or 'list'.
     :ivar ~azure.ai.formrecognizer.FieldData label_data:
         Contains the text, bounding box, and field elements for the field label.
     :ivar ~azure.ai.formrecognizer.FieldData value_data:
@@ -213,7 +223,7 @@ class FormField(object):
     :ivar value:
         The value for the recognized field. Its semantic data type is described by `value_type`.
     :vartype value: str, int, float, :class:`~datetime.date`, :class:`~datetime.time`,
-        :class:`~azure.ai.formrecognizer.FormField`, or list[:class:`~azure.ai.formrecognizer.FormField`]
+        dict[str, :class:`~azure.ai.formrecognizer.FormField`], or list[:class:`~azure.ai.formrecognizer.FormField`]
     :ivar float confidence:
         Measures the degree of certainty of the recognition result. Value is between [0.0, 1.0].
     """
@@ -229,7 +239,7 @@ def __init__(self, **kwargs):
     @classmethod
     def _from_generated(cls, field, value, read_result):
         return cls(
-            value_type=value.type if value else None,
+            value_type=adjust_value_type(value.type) if value else None,
             label_data=FieldData._from_generated(field, read_result),
             value_data=FieldData._from_generated(value, read_result),
             value=get_field_value(field, value, read_result),
@@ -667,7 +677,7 @@ class TrainingDocumentInfo(object):
     """Report for an individual document used for training
     a custom model.
 
-    :ivar str document_name:
+    :ivar str name:
         The name of the document.
     :ivar str status:
         The :class:`~azure.ai.formrecognizer.TrainingStatus`
@@ -680,23 +690,23 @@ class TrainingDocumentInfo(object):
     """
 
     def __init__(self, **kwargs):
-        self.document_name = kwargs.get("document_name", None)
+        self.name = kwargs.get("name", None)
         self.status = kwargs.get("status", None)
         self.page_count = kwargs.get("page_count", None)
         self.errors = kwargs.get("errors", [])
 
     @classmethod
     def _from_generated(cls, train_result):
         return [cls(
-            document_name=doc.document_name,
+            name=doc.document_name,
             status=doc.status,
             page_count=doc.pages,
             errors=FormRecognizerError._from_generated(doc.errors)
         ) for doc in train_result.training_documents] if train_result.training_documents else None
 
     def __repr__(self):
-        return "TrainingDocumentInfo(document_name={}, status={}, page_count={}, errors={})".format(
-            self.document_name, self.status, self.page_count, repr(self.errors)
+        return "TrainingDocumentInfo(name={}, status={}, page_count={}, errors={})".format(
+            self.name, self.status, self.page_count, repr(self.errors)
         )[:1024]
 
 

sdk/formrecognizer/azure-ai-formrecognizer/azure/ai/formrecognizer/aio/_form_training_client_async.py

@@ -120,7 +120,7 @@ async def begin_training(
         :keyword str prefix: A case-sensitive prefix string to filter documents in the source path for
             training. For example, when using a Azure storage blob URI, use the prefix to restrict sub
             folders for training.
-        :keyword bool include_sub_folders: A flag to indicate if sub folders within the set of prefix folders
+        :keyword bool include_subfolders: A flag to indicate if subfolders within the set of prefix folders
             will also need to be included when searching for content to be preprocessed. Not supported if
             training with labels.
         :keyword int polling_interval: Waiting time between two polls for LRO operations
@@ -170,7 +170,7 @@ def callback(raw_response):
                 use_label_file=use_training_labels,
                 source_filter=TrainSourceFilter(
                     prefix=kwargs.pop("prefix", ""),
-                    include_sub_folders=kwargs.pop("include_sub_folders", False)
+                    include_sub_folders=kwargs.pop("include_subfolders", False)
                 )
             ),
             cls=lambda pipeline_response, _, response_headers: pipeline_response,

sdk/formrecognizer/azure-ai-formrecognizer/samples/async_samples/sample_train_model_with_labels_async.py

@@ -71,7 +71,7 @@ async def train_model_with_labels(self):
 
             # Training result information
             for doc in model.training_documents:
-                print("Document name: {}".format(doc.document_name))
+                print("Document name: {}".format(doc.name))
                 print("Document status: {}".format(doc.status))
                 print("Document page count: {}".format(doc.page_count))
                 print("Document errors: {}".format(doc.errors))

sdk/formrecognizer/azure-ai-formrecognizer/samples/async_samples/sample_train_model_without_labels_async.py

@@ -67,7 +67,7 @@ async def train_model_without_labels(self):
         # [END training_async]
             # Training result information
             for doc in model.training_documents:
-                print("Document name: {}".format(doc.document_name))
+                print("Document name: {}".format(doc.name))
                 print("Document status: {}".format(doc.status))
                 print("Document page count: {}".format(doc.page_count))
                 print("Document errors: {}".format(doc.errors))

sdk/formrecognizer/azure-ai-formrecognizer/samples/sample_train_model_with_labels.py

@@ -66,7 +66,7 @@ def train_model_with_labels(self):
 
         # Training result information
         for doc in model.training_documents:
-            print("Document name: {}".format(doc.document_name))
+            print("Document name: {}".format(doc.name))
             print("Document status: {}".format(doc.status))
             print("Document page count: {}".format(doc.page_count))
             print("Document errors: {}".format(doc.errors))

sdk/formrecognizer/azure-ai-formrecognizer/samples/sample_train_model_without_labels.py

@@ -63,7 +63,7 @@ def train_model_without_labels(self):
         # [END training]
         # Training result information
         for doc in model.training_documents:
-            print("Document name: {}".format(doc.document_name))
+            print("Document name: {}".format(doc.name))
             print("Document status: {}".format(doc.status))
             print("Document page count: {}".format(doc.page_count))
             print("Document errors: {}".format(doc.errors))

sdk/formrecognizer/azure-ai-formrecognizer/tests/test_mgmt.py

@@ -91,7 +91,7 @@ def test_mgmt_model_labeled(self, client, container_sas_url):
         self.assertEqual(labeled_model_from_train.training_completed_on, labeled_model_from_get.training_completed_on)
         self.assertEqual(labeled_model_from_train.errors, labeled_model_from_get.errors)
         for a, b in zip(labeled_model_from_train.training_documents, labeled_model_from_get.training_documents):
-            self.assertEqual(a.document_name, b.document_name)
+            self.assertEqual(a.name, b.name)
             self.assertEqual(a.errors, b.errors)
             self.assertEqual(a.page_count, b.page_count)
             self.assertEqual(a.status, b.status)
@@ -127,7 +127,7 @@ def test_mgmt_model_unlabeled(self, client, container_sas_url):
         self.assertEqual(unlabeled_model_from_train.training_completed_on, unlabeled_model_from_get.training_completed_on)
         self.assertEqual(unlabeled_model_from_train.errors, unlabeled_model_from_get.errors)
         for a, b in zip(unlabeled_model_from_train.training_documents, unlabeled_model_from_get.training_documents):
-            self.assertEqual(a.document_name, b.document_name)
+            self.assertEqual(a.name, b.name)
             self.assertEqual(a.errors, b.errors)
             self.assertEqual(a.page_count, b.page_count)
             self.assertEqual(a.status, b.status)

sdk/formrecognizer/azure-ai-formrecognizer/tests/test_mgmt_async.py

@@ -111,7 +111,7 @@ async def test_mgmt_model_labeled(self, client, container_sas_url):
             self.assertEqual(labeled_model_from_train.training_completed_on, labeled_model_from_get.training_completed_on)
             self.assertEqual(labeled_model_from_train.errors, labeled_model_from_get.errors)
             for a, b in zip(labeled_model_from_train.training_documents, labeled_model_from_get.training_documents):
-                self.assertEqual(a.document_name, b.document_name)
+                self.assertEqual(a.name, b.name)
                 self.assertEqual(a.errors, b.errors)
                 self.assertEqual(a.page_count, b.page_count)
                 self.assertEqual(a.status, b.status)
@@ -146,7 +146,7 @@ async def test_mgmt_model_unlabeled(self, client, container_sas_url):
             self.assertEqual(unlabeled_model_from_train.training_completed_on, unlabeled_model_from_get.training_completed_on)
             self.assertEqual(unlabeled_model_from_train.errors, unlabeled_model_from_get.errors)
             for a, b in zip(unlabeled_model_from_train.training_documents, unlabeled_model_from_get.training_documents):
-                self.assertEqual(a.document_name, b.document_name)
+                self.assertEqual(a.name, b.name)
                 self.assertEqual(a.errors, b.errors)
                 self.assertEqual(a.page_count, b.page_count)
                 self.assertEqual(a.status, b.status)

sdk/formrecognizer/azure-ai-formrecognizer/tests/test_repr.py

@@ -118,8 +118,8 @@ def form_recognizer_error():
 
 @pytest.fixture
 def training_document_info(form_recognizer_error):
-    model = _models.TrainingDocumentInfo(document_name="document_name", status=_models.TrainingStatus.PARTIALLY_SUCCEEDED, page_count=5, errors=[form_recognizer_error[0]])
-    model_repr = "TrainingDocumentInfo(document_name=document_name, status=partiallySucceeded, page_count=5, errors=[{}])".format(form_recognizer_error[1])[:1024]
+    model = _models.TrainingDocumentInfo(name="name", status=_models.TrainingStatus.PARTIALLY_SUCCEEDED, page_count=5, errors=[form_recognizer_error[0]])
+    model_repr = "TrainingDocumentInfo(name=name, status=partiallySucceeded, page_count=5, errors=[{}])".format(form_recognizer_error[1])[:1024]
     assert repr(model) == model_repr
     return model, model_repr
 

sdk/formrecognizer/azure-ai-formrecognizer/tests/test_training.py

@@ -67,7 +67,7 @@ def test_training(self, client, container_sas_url):
         self.assertEqual(model.errors, [])
         self.assertEqual(model.status, "ready")
         for doc in model.training_documents:
-            self.assertIsNotNone(doc.document_name)
+            self.assertIsNotNone(doc.name)
             self.assertIsNotNone(doc.page_count)
             self.assertIsNotNone(doc.status)
             self.assertEqual(doc.errors, [])
@@ -90,7 +90,7 @@ def test_training_multipage(self, client, container_sas_url):
         self.assertEqual(model.errors, [])
         self.assertEqual(model.status, "ready")
         for doc in model.training_documents:
-            self.assertIsNotNone(doc.document_name)
+            self.assertIsNotNone(doc.name)
             self.assertIsNotNone(doc.page_count)
             self.assertIsNotNone(doc.status)
             self.assertEqual(doc.errors, [])
@@ -151,7 +151,7 @@ def test_training_with_labels(self, client, container_sas_url):
         self.assertEqual(model.errors, [])
         self.assertEqual(model.status, "ready")
         for doc in model.training_documents:
-            self.assertIsNotNone(doc.document_name)
+            self.assertIsNotNone(doc.name)
             self.assertIsNotNone(doc.page_count)
             self.assertIsNotNone(doc.status)
             self.assertEqual(doc.errors, [])
@@ -175,7 +175,7 @@ def test_training_multipage_with_labels(self, client, container_sas_url):
         self.assertEqual(model.errors, [])
         self.assertEqual(model.status, "ready")
         for doc in model.training_documents:
-            self.assertIsNotNone(doc.document_name)
+            self.assertIsNotNone(doc.name)
             self.assertIsNotNone(doc.page_count)
             self.assertIsNotNone(doc.status)
             self.assertEqual(doc.errors, [])
@@ -228,15 +228,15 @@ def callback(response):
     @GlobalClientPreparer(training=True)
     def test_training_with_files_filter(self, client, container_sas_url):
 
-        poller = client.begin_training(training_files_url=container_sas_url, use_training_labels=False, include_sub_folders=True)
+        poller = client.begin_training(training_files_url=container_sas_url, use_training_labels=False, include_subfolders=True)
         model = poller.result()
         self.assertEqual(len(model.training_documents), 6)
-        self.assertEqual(model.training_documents[-1].document_name, "subfolder/Form_6.jpg")  # we traversed subfolders
+        self.assertEqual(model.training_documents[-1].name, "subfolder/Form_6.jpg")  # we traversed subfolders
 
-        poller = client.begin_training(container_sas_url, use_training_labels=False, prefix="subfolder", include_sub_folders=True)
+        poller = client.begin_training(container_sas_url, use_training_labels=False, prefix="subfolder", include_subfolders=True)
         model = poller.result()
         self.assertEqual(len(model.training_documents), 1)
-        self.assertEqual(model.training_documents[0].document_name, "subfolder/Form_6.jpg")  # we filtered for only subfolders
+        self.assertEqual(model.training_documents[0].name, "subfolder/Form_6.jpg")  # we filtered for only subfolders
 
         with pytest.raises(HttpResponseError) as e:
             poller = client.begin_training(training_files_url=container_sas_url, use_training_labels=False, prefix="xxx")

sdk/formrecognizer/azure-ai-formrecognizer/tests/test_training_async.py

@@ -74,7 +74,7 @@ async def test_training(self, client, container_sas_url):
         self.assertEqual(model.errors, [])
         self.assertEqual(model.status, "ready")
         for doc in model.training_documents:
-            self.assertIsNotNone(doc.document_name)
+            self.assertIsNotNone(doc.name)
             self.assertIsNotNone(doc.page_count)
             self.assertIsNotNone(doc.status)
             self.assertEqual(doc.errors, [])
@@ -97,7 +97,7 @@ async def test_training_multipage(self, client, container_sas_url):
         self.assertEqual(model.errors, [])
         self.assertEqual(model.status, "ready")
         for doc in model.training_documents:
-            self.assertIsNotNone(doc.document_name)
+            self.assertIsNotNone(doc.name)
             self.assertIsNotNone(doc.page_count)
             self.assertIsNotNone(doc.status)
             self.assertEqual(doc.errors, [])
@@ -163,7 +163,7 @@ async def test_training_with_labels(self, client, container_sas_url):
         self.assertEqual(model.errors, [])
         self.assertEqual(model.status, "ready")
         for doc in model.training_documents:
-            self.assertIsNotNone(doc.document_name)
+            self.assertIsNotNone(doc.name)
             self.assertIsNotNone(doc.page_count)
             self.assertIsNotNone(doc.status)
             self.assertEqual(doc.errors, [])
@@ -186,7 +186,7 @@ async def test_training_multipage_with_labels(self, client, container_sas_url):
         self.assertEqual(model.errors, [])
         self.assertEqual(model.status, "ready")
         for doc in model.training_documents:
-            self.assertIsNotNone(doc.document_name)
+            self.assertIsNotNone(doc.name)
             self.assertIsNotNone(doc.page_count)
             self.assertIsNotNone(doc.status)
             self.assertEqual(doc.errors, [])
@@ -241,15 +241,15 @@ def callback(response):
     @GlobalClientPreparer(training=True)
     async def test_training_with_files_filter(self, client, container_sas_url):
         async with client:
-            poller = await client.begin_training(training_files_url=container_sas_url, use_training_labels=False, include_sub_folders=True)
+            poller = await client.begin_training(training_files_url=container_sas_url, use_training_labels=False, include_subfolders=True)
             model = await poller.result()
             self.assertEqual(len(model.training_documents), 6)
-            self.assertEqual(model.training_documents[-1].document_name, "subfolder/Form_6.jpg")  # we traversed subfolders
+            self.assertEqual(model.training_documents[-1].name, "subfolder/Form_6.jpg")  # we traversed subfolders
 
-            poller = await client.begin_training(container_sas_url, use_training_labels=False, prefix="subfolder", include_sub_folders=True)
+            poller = await client.begin_training(container_sas_url, use_training_labels=False, prefix="subfolder", include_subfolders=True)
             model = await poller.result()
             self.assertEqual(len(model.training_documents), 1)
-            self.assertEqual(model.training_documents[0].document_name, "subfolder/Form_6.jpg")  # we filtered for only subfolders
+            self.assertEqual(model.training_documents[0].name, "subfolder/Form_6.jpg")  # we filtered for only subfolders
 
             with pytest.raises(HttpResponseError) as e:
                 poller = await client.begin_training(training_files_url=container_sas_url, use_training_labels=False, prefix="xxx")