




"""simple docstring"""


from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

A_      =    TypeVar('''T''')






class  lowercase(					Generic[T]			):







 '''simple docstring'''





 lowercase__			 = 42  # Cache store of keys
 lowercase__			 = 42  # References of the keys in cache
 lowercase__			 = 10  # Maximum capacity of cache


 def __init__(    self:		Any,				a_:		int		):




   '''simple docstring'''



   _snake_case							:		str								=  deque()
   _snake_case							:		str								=  set()
   if not n:
     _snake_case							:		Union[str, Any]								=  sys.maxsize
   elif n < 0:
     raise ValueError("""n should be an integer greater than 0."""		)
   else:
     _snake_case							:		str								=  n


 def 			UpperCamelCase_       (    self:		List[Any],				a_:		T		):




   '''simple docstring'''



   if x not in self.key_reference:
     if len(self.dq_store		) == LRUCache._MAX_CAPACITY:
       _snake_case							:		int								=  self.dq_store.pop()
       self.key_reference.remove(_lowerCAmelCase		)
   else:
     self.dq_store.remove(_lowerCAmelCase		)

   self.dq_store.appendleft(_lowerCAmelCase		)
   self.key_reference.add(_lowerCAmelCase		)


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   for k in self.dq_store:
     print(_lowerCAmelCase		)


 def __repr__(    self:		str		):




   '''simple docstring'''



   return f"LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store		)}"


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A_      =    LRUCache(4)
     lru_cache.refer('''A''')
     lru_cache.refer(2)
     lru_cache.refer(3)
     lru_cache.refer('''A''')
     lru_cache.refer(4)
     lru_cache.refer(5)
     lru_cache.display()

     print(lru_cache)
     assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_      =    {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    ['''MaskFormerFeatureExtractor''']
     A_      =    ['''MaskFormerImageProcessor''']


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    [
         '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MaskFormerForInstanceSegmentation''',
         '''MaskFormerModel''',
         '''MaskFormerPreTrainedModel''',
     ]
     A_      =    [
         '''MaskFormerSwinBackbone''',
         '''MaskFormerSwinModel''',
         '''MaskFormerSwinPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
     from .configuration_maskformer_swin import MaskFormerSwinConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_maskformer import MaskFormerFeatureExtractor
          from .image_processing_maskformer import MaskFormerImageProcessor
     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_maskformer import (
              MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              MaskFormerForInstanceSegmentation,
              MaskFormerModel,
              MaskFormerPreTrainedModel,
          )
          from .modeling_maskformer_swin import (
              MaskFormerSwinBackbone,
              MaskFormerSwinModel,
              MaskFormerSwinPreTrainedModel,
          )


else:
     import sys

     A_      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)


