



'''simple docstring'''



from __future__ import annotations

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

_lowercase       :						Tuple						=TypeVar("T")
_lowercase       :						str						=TypeVar("U")






class 			_SCREAMING_SNAKE_CASE				(Generic[T, U]      ):



			def __init__(     self		:						Union[str, Any]    ,			__UpperCamelCase		:						T | None    ,			__UpperCamelCase		:						U | None			)      ->      Dict:
							"""simple docstring"""





							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		:						Union[str, Any]			)      ->      str:
							"""simple docstring"""





							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		:						List[str]			)      ->      None:
							"""simple docstring"""





							snake_case__      :   DoubleLinkedListNode[T, U]      =    DoubleLinkedListNode(__UpperCamelCase    ,			__UpperCamelCase			)
							snake_case__      :   DoubleLinkedListNode[T, U]      =    DoubleLinkedListNode(__UpperCamelCase    ,			__UpperCamelCase			)
							snake_case__  ,      snake_case__      :   Tuple      =    self.rear, self.head



			def __repr__(     self		:						str			)      ->      str:
							"""simple docstring"""





							snake_case__      :   Union[str, Any]      =    ['''DoubleLinkedList''']
							snake_case__      :   Union[str, Any]      =    self.head
							while node.next is not None:
											rep.append(str(__UpperCamelCase			)			)
											snake_case__      :   List[Any]      =    node.next
							rep.append(str(self.rear			)			)
							return ",\n    ".join(__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						DoubleLinkedListNode[T, U]			)      ->      None:
							"""simple docstring"""





							snake_case__      :   Tuple      =    self.rear.prev

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

							snake_case__      :   Dict      =    node
							snake_case__      :   str      =    previous
							snake_case__      :   List[Any]      =    node
							snake_case__      :   Optional[int]      =    self.rear
							node.freq += 1
							self._position_node(__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						Optional[int]    ,			__UpperCamelCase		:						DoubleLinkedListNode[T, U]			)      ->      None:
							"""simple docstring"""





							while node.prev is not None and node.prev.freq > node.freq:
											# swap node with previous node
											snake_case__      :   str      =    node.prev

											snake_case__      :   Dict      =    previous_node.prev
											snake_case__      :   Union[str, Any]      =    node.prev
											snake_case__      :   Tuple      =    previous_node
											snake_case__      :   List[str]      =    node







			def 				lowerCAmelCase (     self		:						Any    ,			__UpperCamelCase		:						DoubleLinkedListNode[T, U]			)      ->      DoubleLinkedListNode[T, U] | None:
							"""simple docstring"""





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

							snake_case__      :   Any      =    node.next
							snake_case__      :   Any      =    node.prev
							snake_case__      :   Optional[int]      =    None
							snake_case__      :   Any      =    None
							return node






class 			_SCREAMING_SNAKE_CASE				(Generic[T, U]      ):
			A__								=  {}



			def __init__(     self		:						List[Any]    ,			__UpperCamelCase		:						int			)      ->      Optional[int]:
							"""simple docstring"""





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



			def __repr__(     self		:						Union[str, Any]			)      ->      str:
							"""simple docstring"""





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



			def __contains__(     self		:						Tuple    ,			__UpperCamelCase		:						T			)      ->      bool:
							"""simple docstring"""





							return key in self.cache



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						T			)      ->      U | None:
							"""simple docstring"""





							if key in self.cache:
											self.hits += 1
											snake_case__      :   DoubleLinkedListNode[T, U]      =    self.cache[key]
											snake_case__      :   Optional[int]      =    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(__UpperCamelCase			)
											return node.val
							self.miss += 1
							return None



			def 				lowerCAmelCase (     self		:						List[Any]    ,			__UpperCamelCase		:						T    ,			__UpperCamelCase		:						U			)      ->      None:
							"""simple docstring"""





							if key not in self.cache:
											if self.num_keys >= self.capacity:
															# delete first node when over capacity
															snake_case__      :   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(__UpperCamelCase			) is not None
															# first_node guaranteed to be in list

															del self.cache[first_node.key]
															self.num_keys -= 1
											snake_case__      :   List[Any]      =    DoubleLinkedListNode(__UpperCamelCase    ,			__UpperCamelCase			)
											self.list.add(self.cache[key]			)
											self.num_keys += 1

							else:
											snake_case__      :   Any      =    self.list.remove(self.cache[key]			)
											assert node is not None  # node guaranteed to be in list
											snake_case__      :   List[str]      =    value
											self.list.add(__UpperCamelCase			)







			@classmethod
			def 				lowerCAmelCase (     cls		:						type[LFUCache[T, U]]    ,			__UpperCamelCase		:						int = 128			)      ->      Callable[[Callable[[T], U]], Callable[..., U]]:
							"""simple docstring"""





							def cache_decorator_inner(__UpperCamelCase		:						Callable[[T], U]			) -> Callable[..., U]:
											def cache_decorator_wrapper(*__UpperCamelCase		:						T			) -> U:
															if func not in cls.decorator_function_to_instance_map:
																			snake_case__      :   int      =    LFUCache(__UpperCamelCase			)

															snake_case__      :   str      =    cls.decorator_function_to_instance_map[func].get(args[0]			)
															if result is None:
																			snake_case__      :   List[str]      =    func(*__UpperCamelCase			)
																			cls.decorator_function_to_instance_map[func].put(args[0]    ,			__UpperCamelCase			)
															return result

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

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

											return cache_decorator_wrapper

							return cache_decorator_inner


if __name__ == "__main__":
				import doctest

				doctest.testmod()






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			Dict			   =['''image_processor''', '''tokenizer''']
							lowercase_			:			List[str]			   ='''AutoImageProcessor'''
							lowercase_			:			Optional[Any]			   ='''AutoTokenizer'''




							def __init__(							self  ,A__=None  ,A__=None  ,**A__):
												lowercase		      =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''  ,A__  ,)
																	lowercase		      =		kwargs.pop('''feature_extractor''')

												lowercase		      =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.''')
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.''')

												super().__init__(A__  ,A__)
												lowercase		      =		self.image_processor
												lowercase		      =		False




							def __call__(							self  ,*A__  ,**A__):
												# For backward compatibility
												if self._in_target_context_manager:
																	return self.current_processor(*A__  ,**A__)

												lowercase		      =		kwargs.pop('''images'''  ,A__)
												lowercase		      =		kwargs.pop('''text'''  ,A__)
												if len(A__) > 0:
																	lowercase		      =		args[0]
																	lowercase		      =		args[1:]

												if images is None and text is None:
																	raise ValueError('''You need to specify either an `images` or `text` input to process.''')

												if images is not None:
																	lowercase		      =		self.image_processor(A__  ,*A__  ,**A__)
												if text is not None:
																	lowercase		      =		self.tokenizer(A__  ,**A__)

												if text is None:
																	return inputs
												elif images is None:
																	return encodings
												else:
																	lowercase		      =		encodings['''input_ids''']
																	return inputs




							def 					A__ (							self  ,*A__  ,**A__):
												return self.tokenizer.batch_decode(*A__  ,**A__)




							def 					A__ (							self  ,*A__  ,**A__):
												return self.tokenizer.decode(*A__  ,**A__)




							@contextmanager
							def 					A__ (							self):
												warnings.warn(
												    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
												    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
												    '''your images inputs, or in a separate call.''')
												lowercase		      =		True
												lowercase		      =		self.tokenizer
												yield
												lowercase		      =		self.image_processor
												lowercase		      =		False




							@property
							def 					A__ (							self):
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''  ,A__  ,)
												return self.image_processor_class





							@property
							def 					A__ (							self):
												warnings.warn(
												    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''  ,A__  ,)
												return self.image_processor




