gentoo auto-resync : 20:05:2023 - 16:18:45

3 files changed, 178 insertions, 25 deletions

diff --git a/sci-libs/evaluate/Manifest b/sci-libs/evaluate/Manifest
index 7df1ff6711f6..2d6319eda2d8 100644
--- a/sci-libs/evaluate/Manifest
+++ b/sci-libs/evaluate/Manifest
@@ -1,4 +1,4 @@
-AUX evaluate-0.4.0-tests.patch 2471 BLAKE2B 9744c3f24f1e95a88f9a06d065234ba37b094723261884681c021888c794c9a8c1d6253390105379c898f59d8f18b947db9a3a1229a85fdf3f341909e5eb4eb3 SHA512 03a4b335f856da64423bce7fd10a21558d4ced499794868ac5c34f68885f39627ae75bd01634370bd7c175be330e7abb8adb1317d0e5a3adb938f140c9216387
+AUX evaluate-0.4.0-tests.patch 8011 BLAKE2B 441a4b86e8201f9ab98fdee51e121acf86685202be6b9d28dd5bd0a09ccd649041ee092476f6cc85f6764ba7f411ff983f06ffd96fec5129b00c01cb29ac3914 SHA512 8f6210b7de3e5cabbc72775de2a45af2e4210801d827f1a80b58d30e4f84043bef593f5ece12bd8d2586ec7d0a025fd4693213bd395f3e2a700de312518dea66
 DIST evaluate-0.4.0.gh.tar.gz 292250 BLAKE2B f88428b263820c1af43d02ae676625257251476092efe624490f29e63a045d698db01e4a7a802c2330027d01bc6ccf16986f28ecf8202ecbfd943c5d7c40f6ec SHA512 f2136196fc4e5717859e36e173cd49d049fc5ef50c89f466e13edd0142830574dec0b5485a4a1097eec9cb9df756a617216ff48c141db008cb0c2b85288d7fc9
-EBUILD evaluate-0.4.0-r2.ebuild 1403 BLAKE2B 718eff6da357380640eec33004988fd21f6082c25ffb04d008e8de694beb575fc3038d906a5af9c771630f3e95b19d211ef27d84e7bd0f870cab162a55933c1b SHA512 85eb93a9185c5b5dda0fc52f2f7053c475446dc63aecd6d62009a9067d281b0d7ddb5b765f66976c1acc522aa7d3fb5e46b43941b63050cee16ae80958ad9429
+EBUILD evaluate-0.4.0-r2.ebuild 1445 BLAKE2B 0ff1586583bb9e91a2ff2de89531fe60f724e26712b1baad6fcfca3d9369b7de3fee15f887b00e2372e25e0b6bb85ad50eda9321ce7e83b53ec431abb940216c SHA512 414b2c6375c51de222155a8f4a9112b50984065f5db841a3d713d3cdc6056f5a3fe26c567123f08a8f5bd357b58f2ed74946ef9644016d20932cb10954660263
 MISC metadata.xml 379 BLAKE2B a717b46962e59358925c866c64b2d0bc1dcd5d55d73e814686a09f703e339d2c0de52f6c214c8f795518d6d9dbb46443be11374643d415ff681dedca1511732b SHA512 03d6b58cad73cad46f1101fedf88ec94dc6d7a2028399a20b39939bead4fa402d00224085206a175a33d92417176cc45853060b18faa13769b80527fac9254e1
diff --git a/sci-libs/evaluate/evaluate-0.4.0-r2.ebuild b/sci-libs/evaluate/evaluate-0.4.0-r2.ebuild
index 11f7e2a1f9c2..61fc96cebeac 100644
--- a/sci-libs/evaluate/evaluate-0.4.0-r2.ebuild
+++ b/sci-libs/evaluate/evaluate-0.4.0-r2.ebuild
@@ -47,5 +47,6 @@ src_prepare() {
 	rm -r metrics/{nist_mt,rl_reliability,rouge,sacrebleu,sari} || die
 	rm -r metrics/{ter,trec_eval,wiki_split,xtreme_s} || die
 	rm -r measurements/word_length || die
+	rm tests/test_evaluation_suite.py || die
 	distutils-r1_src_prepare
 }
diff --git a/sci-libs/evaluate/files/evaluate-0.4.0-tests.patch b/sci-libs/evaluate/files/evaluate-0.4.0-tests.patch
index 1e7e808576e3..452a6d862ada 100644
--- a/sci-libs/evaluate/files/evaluate-0.4.0-tests.patch
+++ b/sci-libs/evaluate/files/evaluate-0.4.0-tests.patch
@@ -8,22 +8,78 @@
  
  from datasets import ClassLabel, Dataset, Features, Sequence, Value
  from PIL import Image
-@@ -335,6 +335,7 @@
+@@ -128,6 +128,7 @@
+         return [{"text": "Lorem ipsum"} for _ in inputs]
+ 
+ 
++@skip("require network")
+ class TestEvaluator(TestCase):
+     def setUp(self):
+         self.data = Dataset.from_dict({"label": [1, 0], "text": ["great movie", "horrible movie"]})
+@@ -230,6 +230,7 @@
          )
+ 
+ 
++@skip("require network")
+ class TestTextClassificationEvaluator(TestCase):
+     def setUp(self):
+         self.data = Dataset.from_dict({"label": [1, 0], "text": ["great movie", "horrible movie"]})
+@@ -394,6 +394,7 @@
+         self.assertAlmostEqual(results["latency_in_seconds"], results["total_time_in_seconds"] / len(data), 5)
+ 
+ 
++@skip("require network")
+ class TestTextClassificationEvaluatorTwoColumns(TestCase):
+     def setUp(self):
+         self.data = Dataset.from_dict(
+@@ -452,6 +452,7 @@
          self.assertEqual(results["accuracy"], 1.0)
  
-+    @skip("not working")
-     def test_bootstrap(self):
-         data = Dataset.from_dict({"label": [1, 0, 0], "text": ["great movie", "great movie", "horrible movie"]})
  
-@@ -368,6 +369,7 @@
-         self.assertAlmostEqual(results["samples_per_second"], len(self.data) / results["total_time_in_seconds"], 5)
-         self.assertAlmostEqual(results["latency_in_seconds"], results["total_time_in_seconds"] / len(self.data), 5)
++@skip("require network")
+ class TestImageClassificationEvaluator(TestCase):
+     def setUp(self):
+         self.data = Dataset.from_dict(
+@@ -534,6 +535,7 @@
+         self.assertEqual(results["accuracy"], 0)
+ 
  
-+    @skip("not working")
-     def test_bootstrap_and_perf(self):
-         data = Dataset.from_dict({"label": [1, 0, 0], "text": ["great movie", "great movie", "horrible movie"]})
++@skip("require network")
+ class TestQuestionAnsweringEvaluator(TestCase):
+     def setUp(self):
+         self.data = Dataset.from_dict(
+@@ -716,6 +716,7 @@
+         )
+         self.assertEqual(results["overall_accuracy"], 0.5)
+ 
++    @skip("require network")
+     def test_class_init(self):
+         evaluator = TokenClassificationEvaluator()
+         self.assertEqual(evaluator.task, "token-classification")
+@@ -735,6 +736,7 @@
+         )
+         self.assertEqual(results["overall_accuracy"], 2 / 3)
  
++    @skip("require network")
+     def test_overwrite_default_metric(self):
+         accuracy = load("seqeval")
+         results = self.evaluator.compute(
+@@ -750,6 +752,7 @@
+         )
+         self.assertEqual(results["overall_accuracy"], 1.0)
+ 
++    @skip("require network")
+     def test_data_loading(self):
+         # Test passing in dataset by name with data_split
+         data = self.evaluator.load_data("evaluate/conll2003-ci", split="validation[:1]")
+@@ -863,6 +866,7 @@
+         self.pipe = DummyTextGenerationPipeline(num_return_sequences=4)
+         self.evaluator = evaluator("text-generation")
+ 
++    @skip("require network")
+     def test_class_init(self):
+         evaluator = TextGenerationEvaluator()
+         self.assertEqual(evaluator.task, "text-generation")
 @@ -877,6 +877,7 @@
          results = self.evaluator.compute(data=self.data)
          self.assertIsInstance(results["unique_words"], int)
@@ -32,22 +88,22 @@
      def test_overwrite_default_metric(self):
          word_length = load("word_length")
          results = self.evaluator.compute(
-@@ -939,6 +940,7 @@
-         results = self.evaluator.compute(data=self.data)
+@@ -906,6 +910,7 @@
+         self.assertEqual(processed_predictions, {"data": ["A", "B", "C", "D"]})
+ 
+ 
++@skip("require network")
+ class TestText2TextGenerationEvaluator(TestCase):
+     def setUp(self):
+         self.data = Dataset.from_dict(
+@@ -979,6 +984,7 @@
          self.assertEqual(results["bleu"], 0)
  
-+    @skip("require rouge_score")
-     def test_overwrite_default_metric(self):
-         rouge = load("rouge")
-         results = self.evaluator.compute(
-@@ -949,6 +952,7 @@
-         )
-         self.assertEqual(results["rouge1"], 1.0)
  
-+    @skip("require rouge_score")
-     def test_summarization(self):
-         pipe = DummyText2TextGenerationPipeline(task="summarization", prefix="summary")
-         e = evaluator("summarization")
++@skip("require network")
+ class TestAutomaticSpeechRecognitionEvaluator(TestCase):
+     def setUp(self):
+         self.data = Dataset.from_dict(
 --- a/tests/test_trainer_evaluator_parity.py	2023-05-14 17:50:29.224525549 +0200
 +++ b/tests/test_trainer_evaluator_parity.py	2023-05-14 17:37:40.947501195 +0200
 @@ -269,6 +269,7 @@
@@ -58,3 +114,99 @@
      def test_token_classification_parity(self):
          model_name = "hf-internal-testing/tiny-bert-for-token-classification"
          n_samples = 500
+--- a/tests/test_load.py	2023-05-20 15:45:58.855473557 +0200
++++ b/tests/test_load.py	2023-05-20 15:50:41.620071500 +0200
+@@ -61,6 +61,7 @@
+             hf_modules_cache=self.hf_modules_cache,
+         )
+ 
++    @pytest.mark.skip("require network")
+     def test_HubEvaluationModuleFactory_with_internal_import(self):
+         # "squad_v2" requires additional imports (internal)
+         factory = HubEvaluationModuleFactory(
+@@ -72,6 +73,7 @@
+         module_factory_result = factory.get_module()
+         assert importlib.import_module(module_factory_result.module_path) is not None
+ 
++    @pytest.mark.skip("require network")
+     def test_HubEvaluationModuleFactory_with_external_import(self):
+         # "bleu" requires additional imports (external from github)
+         factory = HubEvaluationModuleFactory(
+@@ -83,6 +85,7 @@
+         module_factory_result = factory.get_module()
+         assert importlib.import_module(module_factory_result.module_path) is not None
+ 
++    @pytest.mark.skip("require network")
+     def test_HubEvaluationModuleFactoryWithScript(self):
+         factory = HubEvaluationModuleFactory(
+             SAMPLE_METRIC_IDENTIFIER,
+@@ -115,6 +118,7 @@
+                 module_factory_result = factory.get_module()
+                 assert importlib.import_module(module_factory_result.module_path) is not None
+ 
++    @pytest.mark.skip("require network")
+     def test_cache_with_remote_canonical_module(self):
+         metric = "accuracy"
+         evaluation_module_factory(
+@@ -127,6 +131,7 @@
+                     metric, download_config=self.download_config, dynamic_modules_path=self.dynamic_modules_path
+                 )
+ 
++    @pytest.mark.skip("require network")
+     def test_cache_with_remote_community_module(self):
+         metric = "lvwerra/test"
+         evaluation_module_factory(
+--- a/tests/test_metric.py	2023-05-20 15:54:32.558477445 +0200
++++ b/tests/test_metric.py	2023-05-20 15:55:40.775415987 +0200
+@@ -736,6 +736,7 @@
+ 
+         self.assertDictEqual(dummy_result_1, combined_evaluation.compute(predictions=preds, references=refs))
+ 
++    @pytest.mark.skip('require network')
+     def test_modules_from_string(self):
+         expected_result = {"accuracy": 0.5, "recall": 0.5, "precision": 1.0}
+         predictions = [0, 1]
+--- a/tests/test_metric_common.py	2023-05-20 15:57:02.399146066 +0200
++++ b/tests/test_metric_common.py	2023-05-20 15:59:25.167947472 +0200
+@@ -99,6 +99,7 @@
+     evaluation_module_name = None
+     evaluation_module_type = None
+ 
++    @pytest.mark.skip('require network')
+     def test_load(self, evaluation_module_name, evaluation_module_type):
+         doctest.ELLIPSIS_MARKER = "[...]"
+         evaluation_module = importlib.import_module(
+--- a/tests/test_trainer_evaluator_parity.py	2023-05-20 16:00:55.986549706 +0200
++++ b/tests/test_trainer_evaluator_parity.py	2023-05-20 16:02:51.808766855 +0200
+@@ -4,6 +4,7 @@
+ import subprocess
+ import tempfile
+ import unittest
++import pytest
+ 
+ import numpy as np
+ import torch
+@@ -33,6 +33,7 @@
+     def tearDown(self):
+         shutil.rmtree(self.dir_path, ignore_errors=True)
+ 
++    @pytest.mark.skip('require network')
+     def test_text_classification_parity(self):
+         model_name = "philschmid/tiny-bert-sst2-distilled"
+ 
+@@ -121,6 +122,7 @@
+ 
+         self.assertEqual(transformers_results["eval_accuracy"], evaluator_results["accuracy"])
+ 
++    @pytest.mark.skip('require network')
+     def test_image_classification_parity(self):
+         # we can not compare to the Pytorch transformers example, that uses custom preprocessing on the images
+         model_name = "douwekiela/resnet-18-finetuned-dogfood"
+@@ -179,6 +181,7 @@
+ 
+         self.assertEqual(transformers_results["eval_accuracy"], evaluator_results["accuracy"])
+ 
++    @pytest.mark.skip('require network')
+     def test_question_answering_parity(self):
+         model_name_v1 = "anas-awadalla/bert-tiny-finetuned-squad"
+         model_name_v2 = "mrm8488/bert-tiny-finetuned-squadv2"