use torch.testing.assertclose instead to get more details about error in cis (#35659)
* use torch.testing.assertclose instead to get more details about error in cis * fix * style * test_all * revert for I bert * fixes and updates * more image processing fixes * more image processors * fix mamba and co * style * less strick * ok I won't be strict * skip and be done * up
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@@ -445,7 +445,7 @@ class AutoformerModelIntegrationTests(unittest.TestCase):
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expected_slice = torch.tensor(
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[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]], device=torch_device
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)
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self.assertTrue(torch.allclose(output[0, :3, :3], expected_slice, atol=TOLERANCE))
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torch.testing.assert_close(output[0, :3, :3], expected_slice, rtol=TOLERANCE, atol=TOLERANCE)
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def test_inference_head(self):
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model = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly").to(torch_device)
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@@ -463,7 +463,7 @@ class AutoformerModelIntegrationTests(unittest.TestCase):
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expected_slice = torch.tensor(
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[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]], device=torch_device
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)
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self.assertTrue(torch.allclose(output[0, :3, :3], expected_slice, atol=TOLERANCE))
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torch.testing.assert_close(output[0, :3, :3], expected_slice, rtol=TOLERANCE, atol=TOLERANCE)
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def test_seq_to_seq_generation(self):
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model = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly").to(torch_device)
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@@ -481,4 +481,4 @@ class AutoformerModelIntegrationTests(unittest.TestCase):
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expected_slice = torch.tensor([3130.6763, 4056.5293, 7053.0786], device=torch_device)
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mean_prediction = outputs.sequences.mean(dim=1)
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self.assertTrue(torch.allclose(mean_prediction[0, -3:], expected_slice, rtol=1e-1))
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torch.testing.assert_close(mean_prediction[0, -3:], expected_slice, rtol=1e-1)
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