

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__												=						{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}

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







							UpperCAmelCase__			:		Any             =       "convbert"


							def __init__(     self			,				_a=3_0_5_2_2			,				_a=7_6_8			,				_a=1_2			,				_a=1_2			,				_a=3_0_7_2			,				_a="gelu"			,				_a=0.1			,				_a=0.1			,				_a=5_1_2			,				_a=2			,				_a=0.02			,				_a=1e-1_2			,				_a=1			,				_a=0			,				_a=2			,				_a=7_6_8			,				_a=2			,				_a=9			,				_a=1			,				_a=None			,				**_a			,				)       ->       Tuple:
												super().__init__(
												    pad_token_id=_a			,				bos_token_id=_a			,				eos_token_id=_a			,				**_a			,				)

												_a      :       Optional[int]   =						vocab_size
												_a      :       str   =						hidden_size
												_a      :       str   =						num_hidden_layers
												_a      :       List[Any]   =						num_attention_heads
												_a      :       int   =						intermediate_size
												_a      :       List[str]   =						hidden_act
												_a      :       Union[str, Any]   =						hidden_dropout_prob
												_a      :       Optional[Any]   =						attention_probs_dropout_prob
												_a      :       int   =						max_position_embeddings
												_a      :       str   =						type_vocab_size
												_a      :       Union[str, Any]   =						initializer_range
												_a      :       Union[str, Any]   =						layer_norm_eps
												_a      :       str   =						embedding_size
												_a      :       Tuple   =						head_ratio
												_a      :       Tuple   =						conv_kernel_size
												_a      :       Union[str, Any]   =						num_groups
												_a      :       Dict   =						classifier_dropout



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







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






class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a			,				_a			)       ->       List[str]:
												_a      :       List[Any]   =						name
												_a      :       List[str]   =						value
												_a      :       List[str]   =						weight


							def __repr__(     self			)       ->       Optional[int]:
												return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""


							def 							__lowercase							(     self			)       ->       List[Any]:
												return self.value


							def 							__lowercase							(     self			)       ->       int:
												return self.name


							def 							__lowercase							(     self			)       ->       Optional[int]:
												return self.weight




							def 							__lowercase							(     self			)       ->       Optional[Any]:
												return self.value / self.weight



def   __UpperCAmelCase					(      __a :     Optional[int]						,__a :     Tuple						,__a :     List[str]      )     ->	List[str]:

					"""simple docstring"""

					_a      :       Optional[int]   =						[]
					for i in range(len(__a      )      ):
										menu.append(Things(name[i]						,value[i]						,weight[i]      )      )
					return menu



def   __UpperCAmelCase					(      __a :     int						,__a :     Union[str, Any]						,__a :     int      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						sorted(__a						,key=__a						,reverse=__a      )
					_a      :       Any   =						[]
					_a ,   _a      :       Optional[int]   =						0.0, 0.0
					for i in range(len(__a      )      ):
										if (total_cost + items_copy[i].get_weight()) <= max_cost:
															result.append(items_copy[i]      )
															total_cost += items_copy[i].get_weight()
															total_value += items_copy[i].get_value()
					return (result, total_value)



def   __UpperCAmelCase					(      )     ->	int:

					"""simple docstring"""



if __name__ == "__main__":
			import doctest

			doctest.testmod()



