




'''simple docstring'''




from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters





def 	a_				( )				->		Tuple:



       """simple docstring"""
       lowerCamelCase_				={
           "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],
       }
       lowerCamelCase_				=Dataset.from_dict(lowerCAmelCase__  )
       return dataset


class     __UpperCamelCase			(					_UpperCamelCase							):






       def 	lowercase__	(				self    ):



              """simple docstring"""







              lowerCamelCase_				=get_dataset()
              lowerCamelCase_				=make_duplicate_clusters(a_,						0.8_5    )
              self.assertEqual(len(duplicate_clusters[0]    ),						2    )






       def 	lowercase__	(				self    ):



              """simple docstring"""







              lowerCamelCase_				=get_dataset()
              lowerCamelCase_				=deduplicate_dataset(a_    )
              self.assertEqual(len(a_    ),						2    )
              print(a_    )
              self.assertEqual(duplicate_clusters[0][0]['''copies'''],						2    )
              self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''],						a_    )











"""simple docstring"""






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase						=			{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase						=			[
		    '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''InformerForPrediction''',
		    '''InformerModel''',
		    '''InformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_informer import (
				    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    InformerForPrediction,
				    InformerModel,
				    InformerPreTrainedModel,
				)

else:
		import sys

		__UpperCAmelCase						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)