from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
lowercase_  							=			TypeVar("T", bound="Enum")







@unique
class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[Any]          =	80
       A							:	Dict          =	70
       A							:	str          =	60
       A							:	Any          =	50
       A							:	Any          =	40
       A							:	List[str]          =	30
       A							:	Union[str, Any]          =	20
       A							:	Optional[int]          =	10







@unique
class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Tuple          =	24
       A							:	Tuple          =	21
       A							:	List[Any]          =	18
       A							:	str          =	15
       A							:	Optional[Any]          =	12
       A							:	Any          =	9
       A							:	Any          =	6
       A							:	List[str]          =	3
       A							:	List[Any]          =	2
       A							:	int          =	1
       A							:	int          =	-1
       A							:	Union[str, Any]          =	-2
       A							:	int          =	-3
       A							:	List[str]          =	-6
       A							:	Tuple          =	-9
       A							:	Optional[Any]          =	-12
       A							:	int          =	-15
       A							:	Optional[Any]          =	-18
       A							:	Union[str, Any]          =	-21
       A							:	str          =	-24

       @classmethod
       def 			snake_case__    (							cls					:     type[T]			):
           return {unit.name: unit.value for unit in cls if unit.value > 0}





       @classmethod
       def 			snake_case__    (							cls					:     type[T]			):
           return {unit.name: unit.value for unit in cls if unit.value < 0}







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					float   ):







    '''simple docstring'''
    __snake_case       :				Optional[Any] 			=    SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
    for name_prefix, value_prefix in prefixes.items():
        __snake_case       :				Optional[Any] 			=    value / (1_0**value_prefix)
        if numerical_part > 1:
            return F'''{numerical_part!s} {name_prefix}'''
    return str(__SCREAMING_SNAKE_CASE   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					float   ):







    '''simple docstring'''
    for prefix in BinaryUnit:
        __snake_case       :				Dict 			=    value / (2**prefix.value)
        if numerical_part > 1:
            return F'''{numerical_part!s} {prefix.name}'''
    return str(__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




from collections import OrderedDict
from typing import Mapping

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


lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[Any]          =	MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       A							:	int          =	"mobilebert"

       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Union[str, Any]=3_05_22			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Dict=24			,			_lowerCAmelCase					:     int=4			,			_lowerCAmelCase					:     Optional[Any]=5_12			,			_lowerCAmelCase					:     Union[str, Any]="relu"			,			_lowerCAmelCase					:     List[str]=0.0			,			_lowerCAmelCase					:     str=0.1			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Optional[Any]=2			,			_lowerCAmelCase					:     Optional[int]=0.02			,			_lowerCAmelCase					:     List[str]=1e-12			,			_lowerCAmelCase					:     int=0			,			_lowerCAmelCase					:     Union[str, Any]=1_28			,			_lowerCAmelCase					:     Optional[Any]=True			,			_lowerCAmelCase					:     Dict=True			,			_lowerCAmelCase					:     Any=1_28			,			_lowerCAmelCase					:     Optional[int]=False			,			_lowerCAmelCase					:     str=True			,			_lowerCAmelCase					:     Tuple=4			,			_lowerCAmelCase					:     List[str]="no_norm"			,			_lowerCAmelCase					:     Union[str, Any]=True			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     int			,			):
           super().__init__(pad_token_id=_lowerCAmelCase			,			**_lowerCAmelCase			)

           __snake_case       :				Any 			=    vocab_size
           __snake_case       :				Tuple 			=    hidden_size
           __snake_case       :				int 			=    num_hidden_layers
           __snake_case       :				Dict 			=    num_attention_heads
           __snake_case       :				List[Any] 			=    hidden_act
           __snake_case       :				List[Any] 			=    intermediate_size
           __snake_case       :				Union[str, Any] 			=    hidden_dropout_prob
           __snake_case       :				Any 			=    attention_probs_dropout_prob
           __snake_case       :				Any 			=    max_position_embeddings
           __snake_case       :				List[Any] 			=    type_vocab_size
           __snake_case       :				List[Any] 			=    initializer_range
           __snake_case       :				Dict 			=    layer_norm_eps
           __snake_case       :				Any 			=    embedding_size
           __snake_case       :				List[str] 			=    trigram_input
           __snake_case       :				Union[str, Any] 			=    use_bottleneck
           __snake_case       :				List[Any] 			=    intra_bottleneck_size
           __snake_case       :				Tuple 			=    use_bottleneck_attention
           __snake_case       :				Optional[Any] 			=    key_query_shared_bottleneck
           __snake_case       :				Tuple 			=    num_feedforward_networks
           __snake_case       :				int 			=    normalization_type
           __snake_case       :				List[Any] 			=    classifier_activation

           if self.use_bottleneck:
               __snake_case       :				List[str] 			=    intra_bottleneck_size
           else:
               __snake_case       :				int 			=    hidden_size

           __snake_case       :				Optional[Any] 			=    classifier_dropout







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @property
       def 			snake_case__    (							self					:     Optional[Any]			):
           if self.task == "multiple-choice":
               __snake_case       :				Any 			=    {0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
               __snake_case       :				Optional[Any] 			=    {0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
                   ("""token_type_ids""", dynamic_axis),
               ]			)



