





from __future__ import annotations

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

lowercase				:	int      						=  TypeVar("""T""")
lowercase				:	List[str]      						=  TypeVar("""U""")





class 			A__   (							Generic[T, U]     ):




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase)      ->						List[Any]:







          '''simple docstring'''

          a__						:     Optional[Any]			=    key
          a__						:     Tuple			=    val
          a__						:     DoubleLinkedListNode[T, U] | None			=    None
          a__						:     DoubleLinkedListNode[T, U] | None			=    None

   def __repr__( self)      ->						str:







          '''simple docstring'''

          return (
              F'Node: key: {self.key}, val: {self.val}, '
              F'has next: {bool(self.next)}, has prev: {bool(self.prev)}'
          )





class 			A__   (							Generic[T, U]     ):




   """simple docstring"""



   def __init__( self)      ->						None:







          '''simple docstring'''

          a__						:     DoubleLinkedListNode[T, U]			=    DoubleLinkedListNode(lowercase ,  lowercase)
          a__						:     DoubleLinkedListNode[T, U]			=    DoubleLinkedListNode(lowercase ,  lowercase)
          a__			,							a__						:     Optional[int]			=    self.rear, self.head

   def __repr__( self)      ->						str:







          '''simple docstring'''

          a__						:     str			=    ['DoubleLinkedList']
          a__						:     int			=    self.head
          while node.next is not None:
                 rep.append(str(lowercase))
                 a__						:     str			=    node.next
          rep.append(str(self.rear))
          return ",\n    ".join(lowercase)

   def 							__lowercase		( self ,  lowercase)      ->						None:







          '''simple docstring'''

          a__						:     str			=    self.rear.prev

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

          a__						:     int			=    node
          a__						:     int			=    previous
          a__						:     Any			=    node
          a__						:     Tuple			=    self.rear

   def 							__lowercase		( self ,  lowercase)      ->						DoubleLinkedListNode[T, U] | None:







          '''simple docstring'''

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

          a__						:     List[Any]			=    node.next
          a__						:     Union[str, Any]			=    node.prev
          a__						:     Union[str, Any]			=    None
          a__						:     Union[str, Any]			=    None
          return node





class 			A__   (							Generic[T, U]     ):




   """simple docstring"""



   __A       :    dict[Callable[[T], U], LRUCache[T, U]]	  =  {}

   def __init__( self ,  lowercase)      ->						List[Any]:







          '''simple docstring'''

          a__						:     DoubleLinkedList[T, U]			=    DoubleLinkedList()
          a__						:     Optional[Any]			=    capacity
          a__						:     str			=    0
          a__						:     str			=    0
          a__						:     Optional[Any]			=    0
          a__						:     dict[T, DoubleLinkedListNode[T, U]]			=    {}

   def __repr__( self)      ->						str:







          '''simple docstring'''

          return (
              F'CacheInfo(hits={self.hits}, misses={self.miss}, '
              F'capacity={self.capacity}, current size={self.num_keys})'
          )

   def __contains__( self ,  lowercase)      ->						bool:







          '''simple docstring'''

          return key in self.cache

   def 							__lowercase		( self ,  lowercase)      ->						U | None:







          '''simple docstring'''

          if key in self.cache:
                 self.hits += 1
                 a__						:     DoubleLinkedListNode[T, U]			=    self.cache[key]
                 a__						:     Tuple			=    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(lowercase)
                 return node.val
          self.miss += 1
          return None

   def 							__lowercase		( self ,  lowercase ,  lowercase)      ->						None:







          '''simple docstring'''

          if key not in self.cache:
                 if self.num_keys >= self.capacity:
                        # delete first node (oldest) when over capacity
                        a__						:     List[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(lowercase) is not None
                        )  # node guaranteed to be in list assert node.key is not None

                        del self.cache[first_node.key]
                        self.num_keys -= 1
                 a__						:     int			=    DoubleLinkedListNode(lowercase ,  lowercase)
                 self.list.add(self.cache[key])
                 self.num_keys += 1

          else:
                 # bump node to the end of the list, update value
                 a__						:     Any			=    self.list.remove(self.cache[key])
                 assert node is not None  # node guaranteed to be in list
                 a__						:     Union[str, Any]			=    value
                 self.list.add(lowercase)

   @classmethod
   def 							__lowercase		( cls ,  lowercase = 128)      ->						Callable[[Callable[[T], U]], Callable[..., U]]:







          '''simple docstring'''

          def cache_decorator_inner(lowercase) -> Callable[..., U]:
                 def cache_decorator_wrapper(*lowercase) -> U:
                        if func not in cls.decorator_function_to_instance_map:
                               a__						:     Optional[int]			=    LRUCache(lowercase)

                        a__						:     int			=    cls.decorator_function_to_instance_map[func].get(args[0])
                        if result is None:
                               a__						:     Optional[int]			=    func(*lowercase)
                               cls.decorator_function_to_instance_map[func].put(args[0] ,  lowercase)
                        return result

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

                 setattr(lowercase ,  'cache_info' ,  lowercase)  # noqa: B010

                 return cache_decorator_wrapper

          return cache_decorator_inner


if __name__ == "__main__":
    import doctest

    doctest.testmod()









import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    from transformers.models.bert.modeling_flax_bert import (
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
    )





class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase=13 ,  lowercase=7 ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=99 ,  lowercase=32 ,  lowercase=5 ,  lowercase=4 ,  lowercase=37 ,  lowercase="gelu" ,  lowercase=0.1 ,  lowercase=0.1 ,  lowercase=512 ,  lowercase=16 ,  lowercase=2 ,  lowercase=0.02 ,  lowercase=4 ,  )      ->						Tuple:







          '''simple docstring'''

          a__						:     str			=    parent
          a__						:     Optional[Any]			=    batch_size
          a__						:     str			=    seq_length
          a__						:     int			=    is_training
          a__						:     str			=    use_attention_mask
          a__						:     List[str]			=    use_token_type_ids
          a__						:     Optional[Any]			=    use_labels
          a__						:     List[Any]			=    vocab_size
          a__						:     Tuple			=    hidden_size
          a__						:     Dict			=    num_hidden_layers
          a__						:     List[str]			=    num_attention_heads
          a__						:     int			=    intermediate_size
          a__						:     Any			=    hidden_act
          a__						:     Optional[int]			=    hidden_dropout_prob
          a__						:     Tuple			=    attention_probs_dropout_prob
          a__						:     Tuple			=    max_position_embeddings
          a__						:     Optional[int]			=    type_vocab_size
          a__						:     List[Any]			=    type_sequence_label_size
          a__						:     Union[str, Any]			=    initializer_range
          a__						:     str			=    num_choices

   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     str			=    ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)

          a__						:     Dict			=    None
          if self.use_attention_mask:
                 a__						:     Optional[int]			=    random_attention_mask([self.batch_size, self.seq_length])

          a__						:     Dict			=    None
          if self.use_token_type_ids:
                 a__						:     int			=    ids_tensor([self.batch_size, self.seq_length] ,  self.type_vocab_size)

          a__						:     Dict			=    BertConfig(
              vocab_size=self.vocab_size ,  hidden_size=self.hidden_size ,  num_hidden_layers=self.num_hidden_layers ,  num_attention_heads=self.num_attention_heads ,  intermediate_size=self.intermediate_size ,  hidden_act=self.hidden_act ,  hidden_dropout_prob=self.hidden_dropout_prob ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  type_vocab_size=self.type_vocab_size ,  is_decoder=lowercase ,  initializer_range=self.initializer_range ,  )

          return config, input_ids, token_type_ids, attention_mask

   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Tuple			=    self.prepare_config_and_inputs()
          a__			,							a__			,							a__			,							a__						:     Optional[int]			=    config_and_inputs
          a__						:     str			=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict

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







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.prepare_config_and_inputs()
          a__			,							a__			,							a__			,							a__						:     int			=    config_and_inputs

          a__						:     str			=    True
          a__						:     Any			=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          a__						:     List[str]			=    ids_tensor([self.batch_size, self.seq_length] ,  vocab_size=2)

          return (
              config,
              input_ids,
              attention_mask,
              encoder_hidden_states,
              encoder_attention_mask,
          )





@require_flax
class 			A__   (							__UpperCAmelCase			,  unittest.TestCase     ):




   """simple docstring"""



   __A       :    Optional[Any]	  =  True

   __A       :    Tuple	  =  (
       (
           FlaxBertModel,
           FlaxBertForPreTraining,
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForQuestionAnswering,
           FlaxBertForNextSentencePrediction,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     Optional[Any]			=    FlaxBertModelTester(self)

   @slow
   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    FlaxBertModel.from_pretrained('bert-base-cased')
          a__						:     Optional[Any]			=    model(np.ones((1, 1)))
          self.assertIsNotNone(lowercase)


