

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


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

A_	:			Union[str, Any]  	=      {
    'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
    'microsoft/markuplm-large': 'https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json',
}



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	'''markuplm'''






					def __init__(			self   ,	A__=3_0522   ,	A__=768   ,	A__=12   ,	A__=12   ,	A__=3072   ,	A__="gelu"   ,	A__=0.1   ,	A__=0.1   ,	A__=512   ,	A__=2   ,	A__=0.0_2   ,	A__=1e-12   ,	A__=0   ,	A__=0   ,	A__=2   ,	A__=256   ,	A__=1024   ,	A__=216   ,	A__=1001   ,	A__=32   ,	A__=50   ,	A__="absolute"   ,	A__=True   ,	A__=None   ,	**A__   ,	):
						super().__init__(
						    pad_token_id=A__   ,	bos_token_id=A__   ,	eos_token_id=A__   ,	**A__   ,	)
						A__							:   str					       =		vocab_size
						A__							:   Union[str, Any]					       =		hidden_size
						A__							:   List[Any]					       =		num_hidden_layers
						A__							:   Union[str, Any]					       =		num_attention_heads
						A__							:   Dict					       =		hidden_act
						A__							:   Tuple					       =		intermediate_size
						A__							:   Dict					       =		hidden_dropout_prob
						A__							:   Union[str, Any]					       =		attention_probs_dropout_prob
						A__							:   List[Any]					       =		max_position_embeddings
						A__							:   List[str]					       =		type_vocab_size
						A__							:   Union[str, Any]					       =		initializer_range
						A__							:   Union[str, Any]					       =		layer_norm_eps
						A__							:   str					       =		position_embedding_type
						A__							:   str					       =		use_cache
						A__							:   List[str]					       =		classifier_dropout
						# additional properties
						A__							:   Dict					       =		max_depth
						A__							:   Union[str, Any]					       =		max_xpath_tag_unit_embeddings
						A__							:   Any					       =		max_xpath_subs_unit_embeddings
						A__							:   int					       =		tag_pad_id
						A__							:   int					       =		subs_pad_id
						A__							:   Any					       =		xpath_unit_hidden_size






from __future__ import annotations

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			list[int]   ,				lowercase_:			int )					->				tuple[float, list[float]]:
	A__							:   Tuple					       =		list(range(len(lowercase_ ) ) )
	A__							:   Union[str, Any]					       =		[v / w for v, w in zip(lowercase_   ,				lowercase_ )]
	index.sort(key=lambda lowercase_ : ratio[i]   ,				reverse=lowercase_ )

	A__							:   float					       =		0
	A__							:   list[float]					       =		[0] * len(lowercase_ )
	for i in index:
		if weight[i] <= capacity:
			A__							:   Optional[int]					       =		1
			max_value += value[i]
			capacity -= weight[i]
		else:
			A__							:   Union[str, Any]					       =		capacity / weight[i]
			max_value += value[i] * capacity / weight[i]
			break

	return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()



