"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a  :  Tuple=				logging.get_logger(__name__)

_a  :  Dict=				{
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   List[Any]					=	"""xlm-roberta-xl"""



				def __init__(self       :   List[Any]			,						_A       :   Optional[Any]=25_08_80			,						_A       :   Any=25_60			,						_A       :   List[Any]=36			,						_A       :   List[Any]=32			,						_A       :   Optional[Any]=1_02_40			,						_A       :   List[Any]="gelu"			,						_A       :   Any=0.1			,						_A       :   Union[str, Any]=0.1			,						_A       :   Union[str, Any]=5_14			,						_A       :   Optional[int]=1			,						_A       :   Tuple=0.02			,						_A       :   Dict=1E-05			,						_A       :   Dict=1			,						_A       :   Dict=0			,						_A       :   int=2			,						_A       :   List[Any]="absolute"			,						_A       :   Dict=True			,						_A       :   Union[str, Any]=None			,						**_A       :   Union[str, Any]			,						)					->			Optional[Any]:
					super().__init__(pad_token_id=_A			,						bos_token_id=_A			,						eos_token_id=_A			,						**_A)
					__snake_case    :							Any   	=				vocab_size
					__snake_case    :							Tuple   	=				hidden_size
					__snake_case    :							Union[str, Any]   	=				num_hidden_layers
					__snake_case    :							Any   	=				num_attention_heads
					__snake_case    :							List[str]   	=				hidden_act
					__snake_case    :							Any   	=				intermediate_size
					__snake_case    :							List[str]   	=				hidden_dropout_prob
					__snake_case    :							str   	=				attention_probs_dropout_prob
					__snake_case    :							List[str]   	=				max_position_embeddings
					__snake_case    :							List[Any]   	=				type_vocab_size
					__snake_case    :							List[str]   	=				initializer_range
					__snake_case    :							List[Any]   	=				layer_norm_eps
					__snake_case    :							List[Any]   	=				position_embedding_type
					__snake_case    :							Any   	=				use_cache
					__snake_case    :							Optional[Any]   	=				classifier_dropout







class 	UpperCamelCase    (					lowercase		):



				@property
				def       _lowercase  (self       :   List[Any])					->			Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
						__snake_case    :							Dict   	=				{0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
						__snake_case    :							List[str]   	=				{0: 'batch', 1: 'sequence'}
					return OrderedDict(
					    [
					        ('input_ids', dynamic_axis),
					        ('attention_mask', dynamic_axis),
					    ])
"""simple docstring"""







import numpy as np
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       np.array						)							->    np.array:

	'''simple docstring'''





	return 1 / (1 + np.exp(-vector						))


if __name__ == "__main__":
	import doctest

	doctest.testmod()





