


from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters






def   _lowercase(					):
							a__      ={
							    'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
							    'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
							    'content': ['a ' * 20, 'a ' * 30, 'b ' * 7],
							}
							a__      =Dataset.from_dict(__a     )
							return dataset







class       lowercase_					(__UpperCAmelCase   ):





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =get_dataset()
											a__      =make_duplicate_clusters(lowerCAmelCase_       ,  0.85)
											self.assertEqual(len(duplicate_clusters[0])       ,  2)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =get_dataset()
											a__      =deduplicate_dataset(lowerCAmelCase_)
											self.assertEqual(len(lowerCAmelCase_)       ,  2)
											print(lowerCAmelCase_)
											self.assertEqual(duplicate_clusters[0][0]['copies']       ,  2)
											self.assertEqual(duplicate_clusters[0][0]['is_extreme']       ,  lowerCAmelCase_)









"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

