





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_						   =    logging.get_logger(__name__)

lowerCAmelCase_						   =    {
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Optional[Any]      					="""roberta-prelayernorm"""







  def __init__(      self      :					Union[str, Any]      ,				UpperCamelCase      :					List[Any]=5_02_65      ,				UpperCamelCase      :					Any=7_68      ,				UpperCamelCase      :					List[Any]=12      ,				UpperCamelCase      :					List[Any]=12      ,				UpperCamelCase      :					List[str]=30_72      ,				UpperCamelCase      :					Union[str, Any]="gelu"      ,				UpperCamelCase      :					List[str]=0.1      ,				UpperCamelCase      :					Dict=0.1      ,				UpperCamelCase      :					Dict=5_12      ,				UpperCamelCase      :					int=2      ,				UpperCamelCase      :					int=0.02      ,				UpperCamelCase      :					Dict=1e-1_2      ,				UpperCamelCase      :					List[Any]=1      ,				UpperCamelCase      :					Dict=0      ,				UpperCamelCase      :					int=2      ,				UpperCamelCase      :					Optional[Any]="absolute"      ,				UpperCamelCase      :					Optional[int]=True      ,				UpperCamelCase      :					str=None      ,				**UpperCamelCase      :					Any      ,				):


    '''simple docstring'''





    super().__init__(pad_token_id=UpperCamelCase      ,				bos_token_id=UpperCamelCase      ,				eos_token_id=UpperCamelCase      ,				**UpperCamelCase							)

    _snake_case   :				Union[str, Any]														=	vocab_size
    _snake_case   :				List[Any]														=	hidden_size
    _snake_case   :				List[Any]														=	num_hidden_layers
    _snake_case   :				Union[str, Any]														=	num_attention_heads
    _snake_case   :				Optional[int]														=	hidden_act
    _snake_case   :				Optional[int]														=	intermediate_size
    _snake_case   :				Optional[Any]														=	hidden_dropout_prob
    _snake_case   :				Dict														=	attention_probs_dropout_prob
    _snake_case   :				int														=	max_position_embeddings
    _snake_case   :				int														=	type_vocab_size
    _snake_case   :				Any														=	initializer_range
    _snake_case   :				str														=	layer_norm_eps
    _snake_case   :				List[str]														=	position_embedding_type
    _snake_case   :				List[Any]														=	use_cache
    _snake_case   :				Union[str, Any]														=	classifier_dropout




class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  @property
  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    if self.task == "multiple-choice":
      _snake_case   :				Optional[Any]														=	{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      _snake_case   :				Optional[int]														=	{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
        ]							)











from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters


def     lowerCamelCase_	(      )->		Any:
  _snake_case   :				List[str]														=	{
      '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],
  }
  _snake_case   :				Optional[Any]														=	Dataset.from_dict(lowerCAmelCase				)
  return dataset







class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	get_dataset()
    _snake_case   :				Tuple														=	make_duplicate_clusters(UpperCamelCase      ,				0.85							)
    self.assertEqual(len(duplicate_clusters[0]							)      ,				2							)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	get_dataset()
    _snake_case							,					_snake_case   :				str														=	deduplicate_dataset(UpperCamelCase							)
    self.assertEqual(len(UpperCamelCase							)      ,				2							)
    print(UpperCamelCase							)
    self.assertEqual(duplicate_clusters[0][0]['copies']      ,				2							)
    self.assertEqual(duplicate_clusters[0][0]['is_extreme']      ,				UpperCamelCase							)




