


'''simple docstring'''





from typing import Any
class 	a__				:





			'''simple docstring'''

			def __init__(     self       : Any  )			->		int:
						__A=  []
						__A=  0
			def __str__(     self       : Tuple  )			->		Dict:
						__A=  '<' + str(self.stack  )[1:-1] + '>'
						return printed
			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : Any  )			->		None:
						self.stack.append(lowerCAmelCase_  )
						__A=  self.length + 1
			def 				lowerCAmelCase    (     self       : int  )			->		Any:
						self.rotate(1  )
						__A=  self.stack[self.length - 1]
						__A=  self.stack[:-1]
						self.rotate(self.length - 1  )
						__A=  self.length - 1
						return dequeued
			def 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : int  )			->		None:
						for _ in range(lowerCAmelCase_  ):
									__A=  self.stack[0]
									__A=  self.stack[1:]
									self.put(lowerCAmelCase_  )
									__A=  self.length - 1
			def 				lowerCAmelCase    (     self       : Tuple  )			->		Any:
						__A=  self.get()
						self.put(lowerCAmelCase_  )
						self.rotate(self.length - 1  )
						return front
			def 				lowerCAmelCase    (     self       : Dict  )			->		int:
						return self.length







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase__      =							logging.get_logger(__name__)

UpperCAmelCase__      =							{
    '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
}
class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							int							= MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
			A	:							Optional[int]							= '''mobilebert'''
			def __init__(     self       : Optional[int] ,						lowerCAmelCase_       : str=30_522 ,						lowerCAmelCase_       : List[Any]=512 ,						lowerCAmelCase_       : Optional[int]=24 ,						lowerCAmelCase_       : str=4 ,						lowerCAmelCase_       : List[str]=512 ,						lowerCAmelCase_       : List[str]="relu" ,						lowerCAmelCase_       : Dict=0.0 ,						lowerCAmelCase_       : List[Any]=0.1 ,						lowerCAmelCase_       : Optional[Any]=512 ,						lowerCAmelCase_       : str=2 ,						lowerCAmelCase_       : Dict=0.02 ,						lowerCAmelCase_       : Any=1E-12 ,						lowerCAmelCase_       : List[Any]=0 ,						lowerCAmelCase_       : Union[str, Any]=128 ,						lowerCAmelCase_       : Any=True ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : List[Any]=128 ,						lowerCAmelCase_       : Dict=False ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : Any=4 ,						lowerCAmelCase_       : Optional[Any]="no_norm" ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Union[str, Any]=None ,						**lowerCAmelCase_       : Union[str, Any] ,						)			->		List[Any]:
						super().__init__(pad_token_id=lowerCAmelCase_ ,						**lowerCAmelCase_  )

						__A=  vocab_size
						__A=  hidden_size
						__A=  num_hidden_layers
						__A=  num_attention_heads
						__A=  hidden_act
						__A=  intermediate_size
						__A=  hidden_dropout_prob
						__A=  attention_probs_dropout_prob
						__A=  max_position_embeddings
						__A=  type_vocab_size
						__A=  initializer_range
						__A=  layer_norm_eps
						__A=  embedding_size
						__A=  trigram_input
						__A=  use_bottleneck
						__A=  intra_bottleneck_size
						__A=  use_bottleneck_attention
						__A=  key_query_shared_bottleneck
						__A=  num_feedforward_networks
						__A=  normalization_type
						__A=  classifier_activation

						if self.use_bottleneck:
									__A=  intra_bottleneck_size
						else:
									__A=  hidden_size

						__A=  classifier_dropout


class 	a__				(				a_      ):





			'''simple docstring'''

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



