
'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase			:				Optional[Any]    		=			logging.get_logger(__name__)

lowercase			:				Dict    		=			{
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}





class  A				(							__snake_case						):
     __magic_name__						=		'''luke'''





     def __init__(							self						,			SCREAMING_SNAKE_CASE=50267						,			SCREAMING_SNAKE_CASE=500000						,			SCREAMING_SNAKE_CASE=768						,			SCREAMING_SNAKE_CASE=256						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=3072						,			SCREAMING_SNAKE_CASE="gelu"						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=512						,			SCREAMING_SNAKE_CASE=2						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=1e-12						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=1						,			SCREAMING_SNAKE_CASE=0						,			SCREAMING_SNAKE_CASE=2						,			**SCREAMING_SNAKE_CASE						,			) ->				List[Any]:






         """simple docstring"""






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

         A       :   Union[str, Any]					=						vocab_size
         A       :   int					=						entity_vocab_size
         A       :   Optional[int]					=						hidden_size
         A       :   Union[str, Any]					=						entity_emb_size
         A       :   List[str]					=						num_hidden_layers
         A       :   Optional[Any]					=						num_attention_heads
         A       :   List[str]					=						hidden_act
         A       :   Union[str, Any]					=						intermediate_size
         A       :   Tuple					=						hidden_dropout_prob
         A       :   Tuple					=						attention_probs_dropout_prob
         A       :   Tuple					=						max_position_embeddings
         A       :   List[Any]					=						type_vocab_size
         A       :   int					=						initializer_range
         A       :   Optional[Any]					=						layer_norm_eps
         A       :   List[str]					=						use_entity_aware_attention
         A       :   List[str]					=						classifier_dropout






'''simple docstring'''




import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset







@pytest.mark.parametrize('''dataset_size'''		,				[None, 400 * 2**20, 600 * 2**20]						)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,				['''default''', 0, 100 * 2**20, 900 * 2**20]						)
def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    if input_in_memory_max_size != "default":
        monkeypatch.setattr(datasets.config		,				'''IN_MEMORY_MAX_SIZE'''		,				snake_case__						)
    A       :   Dict					=						datasets.config.IN_MEMORY_MAX_SIZE
    if input_in_memory_max_size == "default":
        assert in_memory_max_size == 0
    else:
        assert in_memory_max_size == input_in_memory_max_size
    if dataset_size and in_memory_max_size:
        A       :   Dict					=						dataset_size < in_memory_max_size
    else:
        A       :   Tuple					=						False
    A       :   int					=						is_small_dataset(snake_case__						)
    assert result == expected




