

"""simple docstring"""






import functools
import operator

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


lowerCamelCase__			:					List[Any]							  =					logging.get_logger(__name__)

lowerCamelCase__			:					Tuple							  =					{
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}






class       _UpperCAmelCase     (						__a):
			__a	:    Tuple			   =							"""hubert"""
			def __init__(						self ,						_A=32 ,						_A=7_68 ,						_A=12 ,						_A=12 ,						_A=30_72 ,						_A="gelu" ,						_A=0.1 ,						_A=0.1 ,						_A=0.1 ,						_A=True ,						_A=0.0 ,						_A=0.1 ,						_A=0.1 ,						_A=0.02 ,						_A=1e-5 ,						_A="group" ,						_A="gelu" ,						_A=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) ,						_A=(5, 2, 2, 2, 2, 2, 2) ,						_A=(10, 3, 3, 3, 3, 2, 2) ,						_A=False ,						_A=1_28 ,						_A=16 ,						_A=False ,						_A=True ,						_A=0.05 ,						_A=10 ,						_A=2 ,						_A=0.0 ,						_A=10 ,						_A=0 ,						_A="sum" ,						_A=False ,						_A=False ,						_A=2_56 ,						_A=0 ,						_A=1 ,						_A=2 ,						**_A ,						)      ->      List[str]:






				'''simple docstring'''







				super().__init__(**_A ,						pad_token_id=_A ,						bos_token_id=_A ,						eos_token_id=_A       )
				_UpperCAmelCase      :							Optional[Any]       =   hidden_size
				_UpperCAmelCase      :							List[Any]       =   feat_extract_norm
				_UpperCAmelCase      :							Optional[int]       =   feat_extract_activation
				_UpperCAmelCase      :							Dict       =   list(_A       )
				_UpperCAmelCase      :							Optional[Any]       =   list(_A       )
				_UpperCAmelCase      :							List[Any]       =   list(_A       )
				_UpperCAmelCase      :							List[Any]       =   conv_bias
				_UpperCAmelCase      :							List[str]       =   num_conv_pos_embeddings
				_UpperCAmelCase      :							int       =   num_conv_pos_embedding_groups
				_UpperCAmelCase      :							Union[str, Any]       =   len(self.conv_dim       )
				_UpperCAmelCase      :							List[Any]       =   num_hidden_layers
				_UpperCAmelCase      :							List[Any]       =   intermediate_size
				_UpperCAmelCase      :							Optional[int]       =   hidden_act
				_UpperCAmelCase      :							Optional[Any]       =   num_attention_heads
				_UpperCAmelCase      :							List[Any]       =   hidden_dropout
				_UpperCAmelCase      :							str       =   attention_dropout
				_UpperCAmelCase      :							str       =   activation_dropout
				_UpperCAmelCase      :							List[str]       =   feat_proj_layer_norm
				_UpperCAmelCase      :							int       =   feat_proj_dropout
				_UpperCAmelCase      :							Optional[int]       =   final_dropout
				_UpperCAmelCase      :							List[Any]       =   layerdrop
				_UpperCAmelCase      :							Union[str, Any]       =   layer_norm_eps
				_UpperCAmelCase      :							Any       =   initializer_range
				_UpperCAmelCase      :							Dict       =   vocab_size
				_UpperCAmelCase      :							Any       =   do_stable_layer_norm
				_UpperCAmelCase      :							List[str]       =   use_weighted_layer_sum
				_UpperCAmelCase      :							Optional[int]       =   classifier_proj_size

				if (
				    (len(self.conv_stride       ) != self.num_feat_extract_layers)
				    or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
				    or (len(self.conv_dim       ) != self.num_feat_extract_layers)
				):
					raise ValueError(
					    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
					    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
					    f''' {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,'''
					    f''' `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				_UpperCAmelCase      :							Optional[int]       =   apply_spec_augment
				_UpperCAmelCase      :							Union[str, Any]       =   mask_time_prob
				_UpperCAmelCase      :							Optional[Any]       =   mask_time_length
				_UpperCAmelCase      :							Optional[int]       =   mask_time_min_masks
				_UpperCAmelCase      :							Optional[int]       =   mask_feature_prob
				_UpperCAmelCase      :							Tuple       =   mask_feature_length
				_UpperCAmelCase      :							int       =   mask_feature_min_masks

				# ctc loss
				_UpperCAmelCase      :							List[Any]       =   ctc_loss_reduction
				_UpperCAmelCase      :							Optional[int]       =   ctc_zero_infinity
			@property
			def 							__snake_case	(						self       )      ->      Optional[Any]:






				'''simple docstring'''







				return functools.reduce(operator.mul ,						self.conv_stride ,						1       )








"""simple docstring"""






from math import factorial
def  UpperCamelCase   (     _lowerCAmelCase       :  Dict,					_lowerCAmelCase       :  List[str]	)			->     Dict:
	return factorial(_lowerCAmelCase	) / (factorial(_lowerCAmelCase	) * factorial(n - r	))
def  UpperCamelCase   (     )			->     Tuple:
	_UpperCAmelCase      :							Optional[int]       =   0

	for i in range(1,					101	):
		for j in range(1,					i + 1	):
			if combinations(_lowerCAmelCase,					_lowerCAmelCase	) > 1E6:
				total += 1
	return total


if __name__ == "__main__":
						print(solution())





