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>

packages/google-cloud-documentai/.github/.OwlBot.lock.yaml

@@ -1,3 +1,3 @@
 docker:
  image: gcr.io/cloud-devrel-public-resources/owlbot-python:latest
- digest: sha256:4ee57a76a176ede9087c14330c625a71553cf9c72828b2c0ca12f5338171ba60
+ digest: sha256:39ad8c0570e4f5d2d3124a509de4fe975e799e2b97e0f58aed88f8880d5a8b60

packages/google-cloud-documentai/samples/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)

packages/google-cloud-documentai/samples/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:

packages/google-cloud-documentai/samples/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)")
 
 

packages/google-cloud-documentai/samples/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]

packages/google-cloud-documentai/samples/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

packages/google-cloud-documentai/samples/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]

packages/google-cloud-documentai/samples/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]