from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

lowercase_  							=			TypeVar("T")
lowercase_  							=			TypeVar("U")







class      SCREAMING_SNAKE_CASE__					(  Generic[T, U]    ):

       def __init__(							self					:     List[str]			,			_lowerCAmelCase					:     T | None			,			_lowerCAmelCase					:     U | None			):
           __snake_case       :				Optional[int] 			=    key
           __snake_case       :				Any 			=    val
           __snake_case       :				int 			=    0
           __snake_case       :				DoubleLinkedListNode[T, U] | None 			=    None
           __snake_case       :				DoubleLinkedListNode[T, U] | None 			=    None





       def __repr__(							self					:     Any			):
           return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
               self.key			,			self.val			,			self.freq			,			self.next is not None			,			self.prev is not None			)







class      SCREAMING_SNAKE_CASE__					(  Generic[T, U]    ):

       def __init__(							self					:     Union[str, Any]			):
           __snake_case       :				DoubleLinkedListNode[T, U] 			=    DoubleLinkedListNode(_lowerCAmelCase			,			_lowerCAmelCase			)
           __snake_case       :				DoubleLinkedListNode[T, U] 			=    DoubleLinkedListNode(_lowerCAmelCase			,			_lowerCAmelCase			)
           __snake_case			, __snake_case       :				Union[str, Any] 			=    self.rear, self.head

       def __repr__(							self					:     Optional[int]			):
           __snake_case       :				Dict 			=    ["""DoubleLinkedList"""]
           __snake_case       :				str 			=    self.head
           while node.next is not None:
               rep.append(str(_lowerCAmelCase			)			)
               __snake_case       :				Dict 			=    node.next
           rep.append(str(self.rear			)			)
           return ",\n    ".join(_lowerCAmelCase			)

       def 			snake_case__    (							self					:     List[str]			,			_lowerCAmelCase					:     DoubleLinkedListNode[T, U]			):
           __snake_case       :				Dict 			=    self.rear.prev

           # All nodes other than self.head are guaranteed to have non-None previous
           assert previous is not None

           __snake_case       :				str 			=    node
           __snake_case       :				Any 			=    previous
           __snake_case       :				List[Any] 			=    node
           __snake_case       :				List[str] 			=    self.rear
           node.freq += 1
           self._position_node(_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Union[str, Any]			,			_lowerCAmelCase					:     DoubleLinkedListNode[T, U]			):

           while node.prev is not None and node.prev.freq > node.freq:
               # swap node with previous node
               __snake_case       :				List[str] 			=    node.prev

               __snake_case       :				Optional[int] 			=    previous_node.prev
               __snake_case       :				int 			=    node.prev
               __snake_case       :				Tuple 			=    previous_node
               __snake_case       :				Optional[Any] 			=    node





       def 			snake_case__    (							self					:     List[str]			,			_lowerCAmelCase					:     DoubleLinkedListNode[T, U]			):

           if node.prev is None or node.next is None:
               return None

           __snake_case       :				Dict 			=    node.next
           __snake_case       :				Tuple 			=    node.prev
           __snake_case       :				Tuple 			=    None
           __snake_case       :				int 			=    None
           return node







class      SCREAMING_SNAKE_CASE__					(  Generic[T, U]    ):
       A							:	dict[Callable[[T], U], LFUCache[T, U]]          =	{}

       def __init__(							self					:     str			,			_lowerCAmelCase					:     int			):
           __snake_case       :				DoubleLinkedList[T, U] 			=    DoubleLinkedList()
           __snake_case       :				Any 			=    capacity
           __snake_case       :				Tuple 			=    0
           __snake_case       :				Optional[int] 			=    0
           __snake_case       :				Tuple 			=    0
           __snake_case       :				dict[T, DoubleLinkedListNode[T, U]] 			=    {}

       def __repr__(							self					:     List[str]			):
           return (
               f'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
               f'''capacity={self.capacity}, current_size={self.num_keys})'''
           )

       def __contains__(							self					:     str			,			_lowerCAmelCase					:     T			):
           return key in self.cache

       def 			snake_case__    (							self					:     Optional[int]			,			_lowerCAmelCase					:     T			):

           if key in self.cache:
               self.hits += 1
               __snake_case       :				DoubleLinkedListNode[T, U] 			=    self.cache[key]
               __snake_case       :				Any 			=    self.list.remove(self.cache[key]			)
               assert node == value_node

               # node is guaranteed not None because it is in self.cache
               assert node is not None
               self.list.add(_lowerCAmelCase			)
               return node.val
           self.miss += 1
           return None

       def 			snake_case__    (							self					:     Dict			,			_lowerCAmelCase					:     T			,			_lowerCAmelCase					:     U			):

           if key not in self.cache:
               if self.num_keys >= self.capacity:
                   # delete first node when over capacity
                   __snake_case       :				Optional[Any] 			=    self.list.head.next

                   # guaranteed to have a non-None first node when num_keys > 0
                   # explain to type checker via assertions
                   assert first_node is not None
                   assert first_node.key is not None
                   assert self.list.remove(_lowerCAmelCase			) is not None
                   # first_node guaranteed to be in list

                   del self.cache[first_node.key]
                   self.num_keys -= 1
               __snake_case       :				Union[str, Any] 			=    DoubleLinkedListNode(_lowerCAmelCase			,			_lowerCAmelCase			)
               self.list.add(self.cache[key]			)
               self.num_keys += 1

           else:
               __snake_case       :				str 			=    self.list.remove(self.cache[key]			)
               assert node is not None  # node guaranteed to be in list
               __snake_case       :				int 			=    value
               self.list.add(_lowerCAmelCase			)





       @classmethod
       def 			snake_case__    (							cls					:     type[LFUCache[T, U]]			,			_lowerCAmelCase					:     int = 1_28			):

           def cache_decorator_inner(_lowerCAmelCase					:     Callable[[T], U]			) -> Callable[..., U]:
               def cache_decorator_wrapper(*_lowerCAmelCase					:     T			) -> U:
                   if func not in cls.decorator_function_to_instance_map:
                       __snake_case       :				List[Any] 			=    LFUCache(_lowerCAmelCase			)

                   __snake_case       :				Union[str, Any] 			=    cls.decorator_function_to_instance_map[func].get(args[0]			)
                   if result is None:
                       __snake_case       :				int 			=    func(*_lowerCAmelCase			)
                       cls.decorator_function_to_instance_map[func].put(args[0]			,			_lowerCAmelCase			)
                   return result

               def cache_info() -> LFUCache[T, U]:
                   return cls.decorator_function_to_instance_map[func]

               setattr(_lowerCAmelCase			,			"""cache_info"""			,			_lowerCAmelCase			)  # noqa: B010

               return cache_decorator_wrapper

           return cache_decorator_inner


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),
               ]			)



