|  | 
| 26 | 26 |     is_transformers_available, | 
| 27 | 27 |     load_pt, | 
| 28 | 28 |     numpy_cosine_similarity_distance, | 
| 29 |  | -    print_tensor_test, | 
| 30 | 29 |     require_accelerate, | 
| 31 | 30 |     require_bitsandbytes_version_greater, | 
| 32 | 31 |     require_torch, | 
| @@ -370,8 +369,7 @@ def test_quality(self): | 
| 370 | 369 |             output_type="np", | 
| 371 | 370 |         ).images | 
| 372 | 371 |         out_slice = output[0, -3:, -3:, -1].flatten() | 
| 373 |  | -        print_tensor_test(out_slice) | 
| 374 |  | -        expected_slice = np.array([0.0149, 0.0322, 0.0073, 0.0134, 0.0332, 0.011, 0.002, 0.0232, 0.0193]) | 
|  | 372 | +        expected_slice = np.array([0.0376, 0.0359, 0.0015, 0.0449, 0.0479, 0.0098, 0.0083, 0.0295, 0.0295]) | 
| 375 | 373 | 
 | 
| 376 | 374 |         max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice) | 
| 377 | 375 |         self.assertTrue(max_diff < 1e-2) | 
| @@ -420,7 +418,6 @@ def test_generate_quality_dequantize(self): | 
| 420 | 418 |         ).images | 
| 421 | 419 | 
 | 
| 422 | 420 |         out_slice = output[0, -3:, -3:, -1].flatten() | 
| 423 |  | -        print_tensor_test(out_slice) | 
| 424 | 421 |         expected_slice = np.array([0.0266, 0.0264, 0.0271, 0.0110, 0.0310, 0.0098, 0.0078, 0.0256, 0.0208]) | 
| 425 | 422 |         max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice) | 
| 426 | 423 |         self.assertTrue(max_diff < 1e-2) | 
| @@ -471,7 +468,6 @@ def test_quality(self): | 
| 471 | 468 |             output_type="np", | 
| 472 | 469 |         ).images | 
| 473 | 470 |         out_slice = output[0, -3:, -3:, -1].flatten() | 
| 474 |  | -        print_tensor_test(out_slice) | 
| 475 | 471 |         expected_slice = np.array([0.0574, 0.0554, 0.0581, 0.0686, 0.0676, 0.0759, 0.0757, 0.0803, 0.0930]) | 
| 476 | 472 | 
 | 
| 477 | 473 |         max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice) | 
|  | 
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