from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters



def   __A		(			):



 """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(_A					)
 return dataset






class    A_ (       a_ ):

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  get_dataset()
   __a       =  make_duplicate_clusters(__SCREAMING_SNAKE_CASE     ,							0.85       )
   self.assertEqual(len(duplicate_clusters[0]       )     ,							2       )

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       =  get_dataset()
   __a       ,     __a       =  deduplicate_dataset(__SCREAMING_SNAKE_CASE       )
   self.assertEqual(len(__SCREAMING_SNAKE_CASE       )     ,							2       )
   print(__SCREAMING_SNAKE_CASE       )
   self.assertEqual(duplicate_clusters[0][0]["copies"]     ,							2       )
   self.assertEqual(duplicate_clusters[0][0]["is_extreme"]     ,							__SCREAMING_SNAKE_CASE       )



from ... import PretrainedConfig


SCREAMING_SNAKE_CASE :	Any							     =		{
    """sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
  _SCREAMING_SNAKE_CASE    				=			"""nezha"""

  def __init__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      List[str]=2_11_28     ,							__SCREAMING_SNAKE_CASE :      Dict=7_68     ,							__SCREAMING_SNAKE_CASE :      List[Any]=12     ,							__SCREAMING_SNAKE_CASE :      str=12     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=30_72     ,							__SCREAMING_SNAKE_CASE :      Any="gelu"     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      List[str]=0.1     ,							__SCREAMING_SNAKE_CASE :      int=5_12     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=64     ,							__SCREAMING_SNAKE_CASE :      List[Any]=2     ,							__SCREAMING_SNAKE_CASE :      int=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=1E-12     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      Any=0     ,							__SCREAMING_SNAKE_CASE :      Any=2     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=3     ,							__SCREAMING_SNAKE_CASE :      List[Any]=True     ,							**__SCREAMING_SNAKE_CASE :      List[str]     ,							):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  hidden_act
   __a       =  intermediate_size
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  max_relative_position
   __a       =  type_vocab_size
   __a       =  initializer_range
   __a       =  layer_norm_eps
   __a       =  classifier_dropout
   __a       =  use_cache


