




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


__UpperCamelCase   :			List[str]          = logging.get_logger(__name__)

__UpperCamelCase   :			int          = {
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''canine'''
							def __init__(		self		:Optional[int]						, __magic_name__		:Dict=768						, __magic_name__		:Optional[int]=12						, __magic_name__		:Union[str, Any]=12						, __magic_name__		:Union[str, Any]=3072						, __magic_name__		:Tuple="gelu"						, __magic_name__		:Dict=0.1						, __magic_name__		:Tuple=0.1						, __magic_name__		:Optional[Any]=1_6384						, __magic_name__		:List[Any]=16						, __magic_name__		:List[str]=0.02						, __magic_name__		:Tuple=1E-1_2						, __magic_name__		:int=0						, __magic_name__		:Union[str, Any]=0Xe_0_0_0						, __magic_name__		:List[str]=0Xe_0_0_1						, __magic_name__		:int=4						, __magic_name__		:str=4						, __magic_name__		:Dict=8						, __magic_name__		:Optional[Any]=1_6384						, __magic_name__		:List[str]=128						, **__magic_name__		:str						, ):







								'''simple docstring'''







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

								a									=				max_position_embeddings
								a									=				hidden_size
								a									=				num_hidden_layers
								a									=				num_attention_heads
								a									=				intermediate_size
								a									=				hidden_act
								a									=				hidden_dropout_prob
								a									=				attention_probs_dropout_prob
								a									=				initializer_range
								a									=				type_vocab_size
								a									=				layer_norm_eps

								# Character config:
								a									=				downsampling_rate
								a									=				upsampling_kernel_size
								a									=				num_hash_functions
								a									=				num_hash_buckets
								a									=				local_transformer_stride







from bisect import bisect
from itertools import accumulate





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				sorted(zip(__lowerCamelCase				,     __lowerCamelCase       )				,     key=lambda __lowerCamelCase       : x[0] / x[1]				,     reverse=__lowerCamelCase       )
	a							,					a									=				[i[0] for i in r], [i[1] for i in r]
	a									=				list(accumulate(__lowerCamelCase       )       )
	a									=				bisect(__lowerCamelCase				,     __lowerCamelCase       )
	return (
	    0
	    if k == 0
	    else sum(vl[:k]       ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
	    if k != n
	    else sum(vl[:k]       )
	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

