Skip tests properly (#31308)
* Skip tests properly * [test_all] * Add 'reason' as kwarg for skipTest * [test_all] Fix up * [test_all]
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@@ -441,7 +441,7 @@ class CanineModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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def test_headmasking(self):
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if not self.test_head_masking:
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return
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self.skipTest(reason="test_head_masking is set to False")
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global_rng.seed(42)
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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@@ -496,7 +496,7 @@ class CanineModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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check_attentions_validity(outputs.attentions)
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@unittest.skip("CANINE does not have a get_input_embeddings() method.")
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@unittest.skip(reason="CANINE does not have a get_input_embeddings() method.")
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def test_inputs_embeds(self):
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# ViT does not use inputs_embeds
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pass
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@@ -505,7 +505,7 @@ class CanineModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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def test_inputs_embeds_matches_input_ids(self):
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pass
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@unittest.skip("CANINE does not have a get_input_embeddings() method.")
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@unittest.skip(reason="CANINE does not have a get_input_embeddings() method.")
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def test_model_get_set_embeddings(self):
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pass
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@@ -303,31 +303,32 @@ class CanineTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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self.assertListEqual(getattr(tokenizer, "additional_special_tokens"), [additional_special_token])
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self.assertListEqual(getattr(tokenizer, "additional_special_tokens_ids"), [additional_special_token_id])
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# tokenizer has a fixed vocab_size (namely all possible unicode code points)
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@unittest.skip(reason="tokenizer has a fixed vocab_size (namely all possible unicode code points)")
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def test_add_tokens_tokenizer(self):
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pass
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# CanineTokenizer does not support do_lower_case = True, as each character has its own Unicode code point
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# ("b" and "B" for example have different Unicode code points)
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@unittest.skip(reason="CanineTokenizer does not support do_lower_case = True")
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def test_added_tokens_do_lower_case(self):
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pass
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# CanineModel does not support the get_input_embeddings nor the get_vocab method
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@unittest.skip(reason="CanineModel does not support the get_input_embeddings nor the get_vocab method")
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def test_np_encode_plus_sent_to_model(self):
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pass
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# CanineModel does not support the get_input_embeddings nor the get_vocab method
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@unittest.skip(reason="CanineModel does not support the get_input_embeddings nor the get_vocab method")
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def test_torch_encode_plus_sent_to_model(self):
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pass
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# tokenizer does not have vocabulary
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@unittest.skip(reason="CanineTokenizer does not have vocabulary")
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def test_get_vocab(self):
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pass
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# inputs cannot be pretokenized since ids depend on whole input string and not just on single characters
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@unittest.skip(reason="inputs cannot be pretokenized since ids depend on whole input string")
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def test_pretokenized_inputs(self):
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pass
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# tests all ids in vocab => vocab doesn't exist so unnecessary to test
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@unittest.skip(reason="CanineTokenizer does not have vocabulary")
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def test_conversion_reversible(self):
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pass
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