[codex] fix bin entrypoint imports

docs/in_memory_api.md

@@ -127,7 +127,8 @@ python3 bin/main.py \
 ### Supported CLI Flags
 
 *   `--dataset`: Path to the input CSV file. (Supports standard CSV parsing
-    arguments via `--read_csv_args`).
+    arguments via `--field_separator`, `--column_names`, and
+    `--column_count`).
 *   `--domain`: Path to the YAML domain specification file.
 *   `--epsilon`, `--delta`: Total DP privacy budget.
 *   `--mechanism`: Supported options are `mst`, `aim`, `independent`, and

dpsynth/bin/comparison.py

@@ -22,16 +22,12 @@
 from absl import flags
 from absl import logging
 from dpsynth import domain
-import fancyflags as ff
 import pandas as pd
-import sdmetrics
 
 import pathlib
 
 
 PathType = pathlib.Path
-QualityReport = sdmetrics.reports.single_table.QualityReport
-DiagnosticsReport = sdmetrics.reports.single_table.DiagnosticReport
 
 
 @dataclasses.dataclass(frozen=True)
@@ -84,19 +80,16 @@ def __post_init__(self):
 )
 
 
-_COLUMNS_TO_CROSS_COMPARE = ff.DEFINE_dict(
-    'cross_compare',
-    categorical_columns=ff.MultiString(
-        ['sex', 'race'],
-        'Multiple categorical columns to use for cross-column comparison. We'
-        ' will group by all the categorical columns and compare the mean with'
-        ' each of the numerical columns.',
-    ),
-    numerical_columns=ff.MultiString(
-        ['age'],
-        'Multiple numerical columns to cross compare, we will compare the mean'
-        ' of each column for all the categorical columns.',
-    ),
+_CROSS_COMPARE_CATEGORICAL_COLUMNS = flags.DEFINE_list(
+    'cross_compare_categorical_columns',
+    ['sex', 'race'],
+    'Categorical columns to group by for cross-column comparison.',
+)
+
+_CROSS_COMPARE_NUMERICAL_COLUMNS = flags.DEFINE_list(
+    'cross_compare_numerical_columns',
+    ['age'],
+    'Numerical columns whose grouped means are compared.',
 )
 
 
@@ -218,19 +211,30 @@ def _create_metadata_from_domain_yaml(
     elif isinstance(attr, domain.CategoricalAttribute):
       cols[name] = {'sdtype': 'categorical'}
     else:
-      raise ValueError('Unknown attribute type: {type(attr)}')
+      raise ValueError(f'Unknown attribute type: {type(attr)}')
 
   metadata['columns'] = cols
   return metadata
 
 
+def _get_sdmetrics_report_classes() -> tuple[type[Any], type[Any]]:
+  try:
+    from sdmetrics.reports import single_table
+  except ImportError as exc:
+    raise ImportError(
+        'comparison.py requires sdmetrics to generate quality reports.'
+    ) from exc
+  return single_table.QualityReport, single_table.DiagnosticReport
+
+
 def _create_quality_report(
     real_data: pd.DataFrame,
     synthetic_data: pd.DataFrame,
     metadata: dict[str, Any],
-  ) -> QualityReport:
+) -> Any:
   """Creates a quality report."""
   print('Quality report:')
+  QualityReport, _ = _get_sdmetrics_report_classes()
   quality_report = QualityReport()
   quality_report.generate(real_data, synthetic_data, metadata)
   if not quality_report.is_generated:
@@ -243,9 +247,10 @@ def _create_diagnostics_report(
     real_data: pd.DataFrame,
     synthetic_data: pd.DataFrame,
     metadata: dict[str, Any],
-  ) -> DiagnosticsReport:
+) -> Any:
   """Creates a diagnostics report."""
   print('Diagnostics report:')
+  _, DiagnosticsReport = _get_sdmetrics_report_classes()
   diagnostics_report = DiagnosticsReport()
   diagnostics_report.generate(real_data, synthetic_data, metadata)
   if not diagnostics_report.is_generated:
@@ -273,7 +278,8 @@ def main(_) -> None:
   sdmetrics_metadata = _create_metadata_from_domain_yaml(domain_path)
   columns_to_compare = _COLUMNS_TO_COMPARE.value
   columns_to_cross_compare = CompareGroupByColumns(
-      **_COLUMNS_TO_CROSS_COMPARE.value
+      categorical_columns=_CROSS_COMPARE_CATEGORICAL_COLUMNS.value or [],
+      numerical_columns=_CROSS_COMPARE_NUMERICAL_COLUMNS.value or [],
   )
 
   # Compare histograms of the given columns.

dpsynth/bin/main.py

@@ -28,7 +28,6 @@
 from absl import flags
 import dpsynth
 from dpsynth.bin import _read_csv_args
-import fancyflags as ff
 import numpy as np
 import pandas as pd
 
@@ -80,16 +79,33 @@
     required=True,
 )
 
-_READ_CSV_ARGS = ff.DEFINE_auto(
-    'read_csv_args',
-    _read_csv_args.ReadCsvArgs,
-    _read_csv_args.FLAG_HELP,
+_CSV_FIELD_SEPARATOR = flags.DEFINE_enum(
+    'field_separator',
+    None,
+    ['tab', 'pipe', 'comma', 'semicolon', 'space'],
+    'Field separator for the input CSV.',
+)
+
+_CSV_COLUMN_NAMES = flags.DEFINE_list(
+    'column_names',
+    None,
+    'Column names to use when the input CSV does not have a header.',
+)
+
+_CSV_COLUMN_COUNT = flags.DEFINE_integer(
+    'column_count',
+    None,
+    'Number of columns when the input CSV does not have a header.',
 )
 
 
 def main(_):
   np.random.seed(_SEED.value)
-  read_csv_kwargs = _READ_CSV_ARGS.value().to_read_csv_kwargs()
+  read_csv_kwargs = _read_csv_args.ReadCsvArgs(
+      field_separator=_CSV_FIELD_SEPARATOR.value,
+      column_names=_CSV_COLUMN_NAMES.value,
+      column_count=_CSV_COLUMN_COUNT.value,
+  ).to_read_csv_kwargs()
   df = pd.read_csv(_DATASET_PATH.value, **read_csv_kwargs)
   attribute_domains = dpsynth.domain.from_yaml_file(_DOMAIN_PATH.value)
 

dpsynth/bin/run_data_generation.py

@@ -14,17 +14,12 @@
 
 """Main program to launch synthetic data generation Beam jobs."""
 
-from collections.abc import Mapping
-from typing import Any
-
 from absl import app
 from absl import flags
 import apache_beam as beam
 from dpsynth import data_generation
 from dpsynth import domain
-from dpsynth.bin import _proto_class_flag
 from dpsynth.dataset_descriptors import csv_descriptor
-from dpsynth.dataset_descriptors import proto_descriptors
 from dpsynth.dataset_descriptors import tfrecord_descriptor
 from dpsynth.pipeline_transformations import aim
 from dpsynth.pipeline_transformations import input_output
@@ -165,7 +160,7 @@ def get_config() -> data_generation.DataGenerationConfig:
   Raises:
     NotImplementedError: If the data format is not supported.
   """
-    if _DATA_FORMAT.value == types.DataFormat.TFRECORD:
+  if _DATA_FORMAT.value == types.DataFormat.TFRECORD:
     descriptor = tfrecord_descriptor.get_dataset_descriptor_for_tfrecord(
         tfrecord_descriptor.read_tfrecords_sample(_DATASET_PATH.value),
         attributes=_ATTRIBUTES.value,

dpsynth/bin/run_tabular_eval.py

@@ -21,12 +21,9 @@
 from absl import app
 from absl import flags
 import apache_beam as beam
-from dpsynth.dataset_descriptors import dataset_descriptor
-from dpsynth.dataset_descriptors import proto_descriptors
 from dpsynth.eval import tabular_eval
 from dpsynth.eval import types
 from dpsynth.pipeline_transformations import diagnostic_info
-from google.protobuf import text_format
 import pandas as pd
 import pipeline_dp
 
@@ -112,7 +109,7 @@ def _read_csv_data():
 
   config = diagnostic_info.TabularEvalConfig(
       attributes=attributes,
-      attribute_types=[t.to_proto() for t in attribute_types],
+      attribute_types=[t.value for t in attribute_types],
   )
   return original_data, synthetic_data, config
 
@@ -133,7 +130,7 @@ def to_tuple(proto):
 def local_main():
   """Main function for local (in-process) execution."""
   assert (
-      _DATA_FORMAT_STR.value == "CSV"
+      _DATA_FORMAT_STR.value == "csv"
   ), "Unsupported data format for local execution."
   original_data, synthetic_data, config = _read_csv_data()
 
@@ -143,7 +140,7 @@ def local_main():
   eval_report = list(eval_report_collection)[0]
 
   with open_file(_EVAL_REPORT_PATH.value, "wt") as f:
-    f.write(text_format.MessageToString(eval_report))
+    f.write(str(eval_report))
 
 
 def beam_main():

tests/bin/bin_entrypoints_test.py

@@ -0,0 +1,90 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Smoke tests for public binary entrypoints."""
+
+import pathlib
+import subprocess
+import sys
+import tempfile
+
+from absl.testing import absltest
+from absl.testing import parameterized
+
+
+_BINARIES = (
+    'comparison.py',
+    'main.py',
+    'run_data_generation.py',
+    'run_data_generation_from_model.py',
+    'run_tabular_eval.py',
+)
+
+
+class BinEntrypointsTest(parameterized.TestCase):
+
+  @parameterized.parameters(*_BINARIES)
+  def test_help_succeeds(self, binary_name: str):
+    repo_root = pathlib.Path(__file__).parents[2]
+    result = subprocess.run(
+        [
+            sys.executable,
+            str(repo_root / 'dpsynth' / 'bin' / binary_name),
+            '--help',
+        ],
+        cwd=repo_root,
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    output = result.stdout + result.stderr
+    self.assertNotIn('Traceback', output)
+    self.assertIn('flags:', output)
+
+  def test_run_tabular_eval_local_csv(self):
+    repo_root = pathlib.Path(__file__).parents[2]
+    with tempfile.TemporaryDirectory() as tmpdir:
+      tmp_path = pathlib.Path(tmpdir)
+      original_data = tmp_path / 'original.csv'
+      synthetic_data = tmp_path / 'synthetic.csv'
+      eval_report = tmp_path / 'eval_report.pb'
+      original_data.write_text('cat\nA\nB\n', encoding='utf-8')
+      synthetic_data.write_text('cat\nA\nC\n', encoding='utf-8')
+
+      result = subprocess.run(
+          [
+              sys.executable,
+              str(repo_root / 'dpsynth' / 'bin' / 'run_tabular_eval.py'),
+              f'--original_data_path={original_data}',
+              f'--synthetic_data_path={synthetic_data}',
+              f'--eval_report_path={eval_report}',
+              '--data_format=csv',
+              '--use_beam=false',
+          ],
+          cwd=repo_root,
+          capture_output=True,
+          text=True,
+          check=False,
+      )
+      self.assertEqual(
+          result.returncode,
+          0,
+          msg=f'run_tabular_eval.py failed:\n{result.stderr}',
+      )
+      self.assertTrue(eval_report.exists())
+      self.assertNotEmpty(eval_report.read_text(encoding='utf-8'))
+
+
+if __name__ == '__main__':
+  absltest.main()