

"""simple docstring"""





from ..utils import DummyObject, requires_backends




class 					_lowerCAmelCase  (	metaclass=__lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Any   		=	["note_seq"]


 def __init__(			self							:			Dict,					*UpperCAmelCase__							:			int,					**UpperCAmelCase__							:			Tuple	):
    requires_backends(self,					["note_seq"]	)


 @classmethod
 def 					_lowercase (			cls							:			Optional[int],					*UpperCAmelCase__							:			Tuple,					**UpperCAmelCase__							:			List[Any]	):
    requires_backends(cls,					["note_seq"]	)



 @classmethod
 def 					_lowercase (			cls							:			str,					*UpperCAmelCase__							:			List[str],					**UpperCAmelCase__							:			Tuple	):
    requires_backends(cls,					["note_seq"]	)







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__				=		{
    '''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 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    "roformer"


							def __init__(			self       ,							_a=5_0_0_0_0       ,							_a=None       ,							_a=7_6_8       ,							_a=1_2       ,							_a=1_2       ,							_a=3_0_7_2       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=1_5_3_6       ,							_a=2       ,							_a=0.02       ,							_a=1e-1_2       ,							_a=0       ,							_a=False       ,							_a=True       ,							**_a       ,							)    ->    List[str]:
												super().__init__(pad_token_id=_a       ,							**_a       )

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



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







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



