


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


__lowerCamelCase   	=  logging.get_logger(__name__)

__lowerCamelCase   	=  {
    '''uw-madison/nystromformer-512''': '''https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json''',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}


class 	a__						(    __lowerCAmelCase     ):
       lowerCamelCase__:  int				     =  """nystromformer"""

       def __init__( self   :  int   ,				lowerCamelCase_   :  Optional[int]=3_0_0_0_0   ,				lowerCamelCase_   :  int=7_6_8   ,				lowerCamelCase_   :  Union[str, Any]=1_2   ,				lowerCamelCase_   :  Optional[int]=1_2   ,				lowerCamelCase_   :  List[Any]=3_0_7_2   ,				lowerCamelCase_   :  Dict="gelu_new"   ,				lowerCamelCase_   :  Optional[Any]=0.1   ,				lowerCamelCase_   :  List[Any]=0.1   ,				lowerCamelCase_   :  int=5_1_0   ,				lowerCamelCase_   :  List[Any]=2   ,				lowerCamelCase_   :  List[str]=6_4   ,				lowerCamelCase_   :  Tuple=6_4   ,				lowerCamelCase_   :  Optional[int]=6_5   ,				lowerCamelCase_   :  int=False   ,				lowerCamelCase_   :  Optional[int]=0.0_2   ,				lowerCamelCase_   :  str=1E-5   ,				lowerCamelCase_   :  Optional[Any]=1   ,				lowerCamelCase_   :  Dict=0   ,				lowerCamelCase_   :  Tuple=2   ,				**lowerCamelCase_   :  Any   ,				):
            a_     :  Dict				      =     vocab_size
            a_     :  str				      =     max_position_embeddings
            a_     :  Union[str, Any]				      =     hidden_size
            a_     :  Dict				      =     num_hidden_layers
            a_     :  Tuple				      =     num_attention_heads
            a_     :  List[str]				      =     intermediate_size
            a_     :  List[Any]				      =     hidden_act
            a_     :  Any				      =     hidden_dropout_prob
            a_     :  Dict				      =     attention_probs_dropout_prob
            a_     :  Tuple				      =     initializer_range
            a_     :  Any				      =     type_vocab_size
            a_     :  Optional[int]				      =     segment_means_seq_len
            a_     :  Optional[Any]				      =     num_landmarks
            a_     :  Optional[Any]				      =     conv_kernel_size
            a_     :  Tuple				      =     inv_coeff_init_option
            a_     :  Tuple				      =     layer_norm_eps
            super().__init__(pad_token_id=__UpperCamelCase   ,				bos_token_id=__UpperCamelCase   ,				eos_token_id=__UpperCamelCase   ,				**__UpperCamelCase     )









'''simple docstring'''

import unittest
import warnings

from datasets.utils import experimental




@experimental
def    snake_case__     (  )			->       Union[str, Any]:





			'''simple docstring'''






			return "success"





class __magic_name__(						unittest.TestCase      ):






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									with warnings.catch_warnings(record=__UpperCamelCase       ) as w:
												warnings.simplefilter("""always"""       )
												self.assertEqual(dummy_function()					,   """success"""       )
									self.assertEqual(len(__UpperCamelCase       )					,   1       )