




from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters




def 			__magic_name__				( ):






			'''simple docstring'''







			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(__snake_case   )
			return dataset






class     snake_case__							(_A   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Any							)					->     Optional[Any]:
							a    =			get_dataset()
							a    =			make_duplicate_clusters(__lowerCamelCase					,      0.85							)
							self.assertEqual(len(duplicate_clusters[0]							)					,      2							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     int:
							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"""



import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
						__A		:       str  				=		argparse.ArgumentParser()

						parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

						parser.add_argument(
						    '''--txt2img_unclip''',
						    default='''kakaobrain/karlo-v1-alpha''',
						    type=str,
						    required=False,
						    help='''The pretrained txt2img unclip.''',
						)

						__A		:       str  				=		parser.parse_args()

						__A		:       List[Any]  				=		UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

						__A		:       Dict  				=		CLIPImageProcessor()
						__A		:       Union[str, Any]  				=		CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

						__A		:       List[str]  				=		UnCLIPImageVariationPipeline(
						    decoder=txtaimg.decoder,
						    text_encoder=txtaimg.text_encoder,
						    tokenizer=txtaimg.tokenizer,
						    text_proj=txtaimg.text_proj,
						    feature_extractor=feature_extractor,
						    image_encoder=image_encoder,
						    super_res_first=txtaimg.super_res_first,
						    super_res_last=txtaimg.super_res_last,
						    decoder_scheduler=txtaimg.decoder_scheduler,
						    super_res_scheduler=txtaimg.super_res_scheduler,
						)

						imgaimg.save_pretrained(args.dump_path)



