chore(python): run blacken session for all directories with a noxfile (…

…#241)

Source-Link: googleapis/synthtool@bc0de6e
Post-Processor: gcr.io/cloud-devrel-public-resources/owlbot-python:latest@sha256:39ad8c0570e4f5d2d3124a509de4fe975e799e2b97e0f58aed88f8880d5a8b60

Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>
Co-authored-by: Anthonios Partheniou <partheniou@google.com>

documentai/snippets/batch_process_documents_sample.py

@@ -63,9 +63,7 @@ def batch_process_documents(
     # Location can be 'us' or 'eu'
     name = f"projects/{project_id}/locations/{location}/processors/{processor_id}"
     request = documentai.types.document_processor_service.BatchProcessRequest(
-        name=name,
-        input_documents=input_config,
-        document_output_config=output_config,
+        name=name, input_documents=input_config, document_output_config=output_config,
     )
 
     operation = client.batch_process_documents(request)

documentai/snippets/process_document_form_sample.py

@@ -21,6 +21,7 @@
 # processor_id = 'YOUR_PROCESSOR_ID' # Create processor in Cloud Console
 # file_path = '/path/to/local/pdf'
 
+
 def process_document_form_sample(
     project_id: str, location: str, processor_id: str, file_path: str
 ):
@@ -69,9 +70,9 @@ def process_document_form_sample(
         for table in page.tables:
             num_collumns = len(table.header_rows[0].cells)
             num_rows = len(table.body_rows)
-            print(f'Table with {num_collumns} columns and {num_rows} rows:')
+            print(f"Table with {num_collumns} columns and {num_rows} rows:")
             print_table_info(table, text)
-        print(f'Found {len(page.form_fields)} form fields:')
+        print(f"Found {len(page.form_fields)} form fields:")
         for field in page.form_fields:
             name = layout_to_text(field.field_name, text)
             value = layout_to_text(field.field_value, text)
@@ -80,17 +81,17 @@ def process_document_form_sample(
 
 def print_table_info(table: dict, text: str) -> None:
     # Print header row
-    header_row_text = ''
+    header_row_text = ""
     for header_cell in table.header_rows[0].cells:
         header_cell_text = layout_to_text(header_cell.layout, text)
-        header_row_text += f'{repr(header_cell_text.strip())} | '
-    print(f'Collumns: {header_row_text[:-3]}')
+        header_row_text += f"{repr(header_cell_text.strip())} | "
+    print(f"Collumns: {header_row_text[:-3]}")
     # Print first body row
-    body_row_text = ''
+    body_row_text = ""
     for body_cell in table.body_rows[0].cells:
         body_cell_text = layout_to_text(body_cell.layout, text)
-        body_row_text += f'{repr(body_cell_text.strip())} | '
-    print(f'First row data: {body_row_text[:-3]}\n')
+        body_row_text += f"{repr(body_cell_text.strip())} | "
+    print(f"First row data: {body_row_text[:-3]}\n")
 
 
 def layout_to_text(layout: dict, text: str) -> str:

documentai/snippets/process_document_ocr_sample.py

@@ -21,6 +21,7 @@
 # processor_id = 'YOUR_PROCESSOR_ID' # Create processor in Cloud Console
 # file_path = '/path/to/local/pdf'
 
+
 def process_document_ocr_sample(
     project_id: str, location: str, processor_id: str, file_path: str
 ) -> None:
@@ -78,7 +79,7 @@ def print_detected_langauges(detected_languages: dict) -> None:
     print("    Detected languages:")
     for lang in detected_languages:
         code = lang.language_code
-        conf_percent = '{:.1%}'.format(lang.confidence)
+        conf_percent = "{:.1%}".format(lang.confidence)
         print(f"        {code} ({conf_percent} confidence)")
 
 

documentai/snippets/process_document_quality_sample.py

@@ -21,6 +21,7 @@
 # processor_id = 'YOUR_PROCESSOR_ID' # Create processor in Cloud Console
 # file_path = '/path/to/local/pdf'
 
+
 def process_document_quality_sample(
     project_id: str, location: str, processor_id: str, file_path: str
 ):
@@ -60,17 +61,18 @@ def process_document_quality_sample(
     # response.
     document = result.document
     for entity in document.entities:
-        conf_percent = '{:.1%}'.format(entity.confidence)
-        page_num = ''
+        conf_percent = "{:.1%}".format(entity.confidence)
+        page_num = ""
         try:
             page_num = str(int(entity.page_anchor.page_refs.page) + 1)
         except AttributeError:
             page_num = "1"
 
-        print(f'Page {page_num} has a quality score of {conf_percent}:')
+        print(f"Page {page_num} has a quality score of {conf_percent}:")
 
         for prop in entity.properties:
-            conf_percent = '{:.1%}'.format(prop.confidence)
-            print(f'    * {prop.type_} score of {conf_percent}')
+            conf_percent = "{:.1%}".format(prop.confidence)
+            print(f"    * {prop.type_} score of {conf_percent}")
+
 
 # [END documentai_process_quality_document]

documentai/snippets/process_document_quality_sample_test.py

@@ -36,7 +36,7 @@ def test_process_documents(capsys):
     expected_strings = [
         "Page 1 has a quality score of",
         "defect_blurry score of 9",
-        "defect_noisy"
+        "defect_noisy",
     ]
     for expected_string in expected_strings:
         assert expected_string in out

documentai/snippets/process_document_specialized_sample.py

@@ -21,6 +21,7 @@
 # processor_id = 'YOUR_PROCESSOR_ID' # Create processor in Cloud Console
 # file_path = '/path/to/local/pdf'
 
+
 def process_document_specialized_sample(
     project_id: str, location: str, processor_id: str, file_path: str
 ):
@@ -62,7 +63,7 @@ def process_document_specialized_sample(
     # Please see the OCR and other samples for how to parse other data in the
     # response.
     document = result.document
-    print(f'Found {len(document.entities)} entities:')
+    print(f"Found {len(document.entities)} entities:")
     for entity in document.entities:
         # Fields detected. For a full list of fields for each processor see
         # the processor documentation:
@@ -71,7 +72,8 @@ def process_document_specialized_sample(
         # some other value formats in addition to text are availible
         # e.g. dates: `entity.normalized_value.date_value.year`
         text_value = entity.text_anchor.content
-        conf_percent = '{:.1%}'.format(entity.confidence)
-        print(f'    * {repr(key)}: {repr(text_value)}({conf_percent} confident)')
+        conf_percent = "{:.1%}".format(entity.confidence)
+        print(f"    * {repr(key)}: {repr(text_value)}({conf_percent} confident)")
+
 
 # [END documentai_process_specialized_document]

documentai/snippets/process_document_splitter_sample.py

@@ -21,11 +21,10 @@
 # processor_id = 'YOUR_PROCESSOR_ID' # Create processor in Cloud Console
 # file_path = '/path/to/local/pdf'
 
+
 def process_document_splitter_sample(
-        project_id: str,
-        location: str,
-        processor_id: str,
-        file_path: str):
+    project_id: str, location: str, processor_id: str, file_path: str
+):
     from google.cloud import documentai_v1beta3 as documentai
 
     # You must set the api_endpoint if you use a location other than 'us', e.g.:
@@ -61,26 +60,29 @@ def process_document_splitter_sample(
     # form elements, and entities please see other processors like the OCR, form,
     # and specalized processors.
     document = result.document
-    print(f'Found {len(document.entities)} subdocuments:')
+    print(f"Found {len(document.entities)} subdocuments:")
     for entity in document.entities:
-        conf_percent = '{:.1%}'.format(entity.confidence)
+        conf_percent = "{:.1%}".format(entity.confidence)
         pages_range = page_refs_to_string(entity.page_anchor.page_refs)
         # Print subdocument type information, if available
         try:
             doctype = entity.type
-            print(f'{conf_percent} confident that {pages_range} a "{doctype}" subdocument.')
+            print(
+                f'{conf_percent} confident that {pages_range} a "{doctype}" subdocument.'
+            )
         except AttributeError:
-            print(f'{conf_percent} confident that {pages_range} a subdocument.')
+            print(f"{conf_percent} confident that {pages_range} a subdocument.")
 
 
 def page_refs_to_string(page_refs: dict) -> str:
-    ''' Converts a page ref to a string describing the page or page range.'''
+    """ Converts a page ref to a string describing the page or page range."""
     if len(page_refs) == 1:
         num = str(int(page_refs[0].page) + 1)
-        return f'page {num} is'
+        return f"page {num} is"
     else:
         start = str(int(page_refs[0].page) + 1)
         end = str(int(page_refs[1].page) + 1)
-        return f'pages {start} to {end} are'
+        return f"pages {start} to {end} are"
+
 
 # [END documentai_process_splitter_document]