






'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


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

_A	:			Union[str, Any]         ={
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class _lowercase	(				_lowercase		):
							a				      =       """roformer"""




							def __init__(							self:				List[Any]			,							UpperCamelCase__:				Any=50_000			,							UpperCamelCase__:				List[Any]=None			,							UpperCamelCase__:				Optional[int]=768			,							UpperCamelCase__:				Tuple=12			,							UpperCamelCase__:				Union[str, Any]=12			,							UpperCamelCase__:				Optional[Any]=3_072			,							UpperCamelCase__:				List[Any]="gelu"			,							UpperCamelCase__:				Any=0.1			,							UpperCamelCase__:				List[str]=0.1			,							UpperCamelCase__:				int=1_536			,							UpperCamelCase__:				int=2			,							UpperCamelCase__:				Dict=0.02			,							UpperCamelCase__:				Optional[int]=1e-12			,							UpperCamelCase__:				Optional[int]=0			,							UpperCamelCase__:				Optional[Any]=False			,							UpperCamelCase__:				Optional[Any]=True			,							**UpperCamelCase__:				List[Any]			,							):
														super().__init__(pad_token_id=UpperCamelCase__			,							**UpperCamelCase__     )

														lowerCamelCase__					:	Dict         =       vocab_size
														lowerCamelCase__					:	List[Any]         =       hidden_size if embedding_size is None else embedding_size
														lowerCamelCase__					:	Dict         =       hidden_size
														lowerCamelCase__					:	Tuple         =       num_hidden_layers
														lowerCamelCase__					:	List[Any]         =       num_attention_heads
														lowerCamelCase__					:	List[Any]         =       hidden_act
														lowerCamelCase__					:	Union[str, Any]         =       intermediate_size
														lowerCamelCase__					:	str         =       hidden_dropout_prob
														lowerCamelCase__					:	List[Any]         =       attention_probs_dropout_prob
														lowerCamelCase__					:	Any         =       max_position_embeddings
														lowerCamelCase__					:	Dict         =       type_vocab_size
														lowerCamelCase__					:	List[str]         =       initializer_range
														lowerCamelCase__					:	str         =       layer_norm_eps
														lowerCamelCase__					:	Optional[Any]         =       rotary_value
														lowerCamelCase__					:	Union[str, Any]         =       use_cache



class _lowercase	(				_lowercase		):




							@property
							def 		lowerCamelCase_				(							self:				Tuple     ):
														if self.task == "multiple-choice":
																					lowerCamelCase__					:	str         =       {0: """batch""", 1: """choice""", 2: """sequence"""}
														else:
																					lowerCamelCase__					:	int         =       {0: """batch""", 1: """sequence"""}
														lowerCamelCase__					:	List[str]         =       {0: """batch""", 1: """sequence"""}
														return OrderedDict(
														    [
														        ("""input_ids""", dynamic_axis),
														        ("""attention_mask""", dynamic_axis),
														        ("""token_type_ids""", dynamic_axis),
														    ]     )



import numpy as np






def        a_		(       __lowercase   :	np.array ) ->  np.array:
			return 1 / (1 + np.exp(-vector ))


if __name__ == "__main__":
			import doctest

			doctest.testmod()