






'''simple docstring'''
from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters





def 	_a(					):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			str													={
          '''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
          '''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''],
          '''content''': ['''a ''' * 2_0, '''a ''' * 3_0, '''b ''' * 7],
      }
      SCREAMING_SNAKE_CASE__       :			Tuple													=Dataset.from_dict(UpperCamelCase__							)
      return dataset







class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def        __magic_name__		( self		:							List[Any] )		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=get_dataset()
       SCREAMING_SNAKE_CASE__       :			Dict													=make_duplicate_clusters(__lowercase   , 0.85 )
       self.assertEqual(len(duplicate_clusters[0] )   , 2 )




 def        __magic_name__		( self		:							List[Any] )		->  Tuple:
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=get_dataset()
       SCREAMING_SNAKE_CASE__       :			List[Any]													=deduplicate_dataset(__lowercase )
       self.assertEqual(len(__lowercase )   , 2 )
       print(__lowercase )
       self.assertEqual(duplicate_clusters[0][0]['''copies''']   , 2 )
       self.assertEqual(duplicate_clusters[0][0]['''is_extreme''']   , __lowercase )







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_					=	{
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'MegatronBertForCausalLM',
      'MegatronBertForMaskedLM',
      'MegatronBertForMultipleChoice',
      'MegatronBertForNextSentencePrediction',
      'MegatronBertForPreTraining',
      'MegatronBertForQuestionAnswering',
      'MegatronBertForSequenceClassification',
      'MegatronBertForTokenClassification',
      'MegatronBertModel',
      'MegatronBertPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_megatron_bert import (
        MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MegatronBertForCausalLM,
        MegatronBertForMaskedLM,
        MegatronBertForMultipleChoice,
        MegatronBertForNextSentencePrediction,
        MegatronBertForPreTraining,
        MegatronBertForQuestionAnswering,
        MegatronBertForSequenceClassification,
        MegatronBertForTokenClassification,
        MegatronBertModel,
        MegatronBertPreTrainedModel,
    )

else:
  import sys

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