
"""simple docstring"""





import numpy as np



def 				lowerCamelCase__		(      __snake_case     )							->		np.array:


					"""simple docstring"""





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


if __name__ == "__main__":
				import doctest

				doctest.testmod()








"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


_a														=		logging.get_logger(__name__)

_a														=		{
    """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(   lowerCamelCase     ):
		lowercase__      =				'xlm-roberta-xl'



		def __init__( self    ,		__a=25_08_80    ,		__a=25_60    ,		__a=36    ,		__a=32    ,		__a=1_02_40    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_14    ,		__a=1    ,		__a=0.02    ,		__a=1e-05    ,		__a=1    ,		__a=0    ,		__a=2    ,		__a="absolute"    ,		__a=True    ,		__a=None    ,		**__a    ,		)	->	Tuple:






							'''simple docstring'''
							super().__init__(pad_token_id=__a    ,		bos_token_id=__a    ,		eos_token_id=__a    ,		**__a)
							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  type_vocab_size
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  position_embedding_type
							_UpperCamelCase										=  use_cache
							_UpperCamelCase										=  classifier_dropout






class    _UpperCAmelCase(   lowerCamelCase     ):



		@property
		def 						UpperCAmelCase							( self)	->	Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							if self.task == "multiple-choice":
												_UpperCamelCase										=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
												_UpperCamelCase										=  {0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ])






