





"""simple docstring"""






from __future__ import annotations

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

A	     =					TypeVar('''T''')
A	     =					TypeVar('''U''')




class 			__lowercase     (     Generic[T, U]							):







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  str         =					key
									__a	:  Dict         =					val
									__a	:  DoubleLinkedListNode[T, U] | None         =					None
									__a	:  DoubleLinkedListNode[T, U] | None         =					None







					def __repr__(  self       ):
									return (
									    f"""Node: key: {self.key}, val: {self.val}, """
									    f"""has next: {bool(self.next       )}, has prev: {bool(self.prev       )}"""
									)






class 			__lowercase     (     Generic[T, U]							):







					'''simple docstring'''




					def __init__(  self       ):
									__a	:  DoubleLinkedListNode[T, U]         =					DoubleLinkedListNode(_UpperCAmelCase		,     _UpperCAmelCase       )
									__a	:  DoubleLinkedListNode[T, U]         =					DoubleLinkedListNode(_UpperCAmelCase		,     _UpperCAmelCase       )
									__a    ,  __a	:  Optional[int]         =					self.rear, self.head







					def __repr__(  self       ):
									__a	:  str         =					['''DoubleLinkedList''']
									__a	:  Any         =					self.head
									while node.next is not None:
													rep.append(str(_UpperCAmelCase       )       )
													__a	:  Tuple         =					node.next
									rep.append(str(self.rear       )       )
									return ",\n    ".join(_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  int         =					self.rear.prev

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

									__a	:  Optional[Any]         =					node
									__a	:  int         =					previous
									__a	:  Dict         =					node
									__a	:  List[str]         =					self.rear







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):

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

									__a	:  Any         =					node.next
									__a	:  Dict         =					node.prev
									__a	:  Optional[int]         =					None
									__a	:  str         =					None
									return node






class 			__lowercase     (     Generic[T, U]							):







					'''simple docstring'''




					__lowerCAmelCase					=					{}







					def __init__(  self		,     _UpperCAmelCase       ):
									__a	:  DoubleLinkedList[T, U]         =					DoubleLinkedList()
									__a	:  List[str]         =					capacity
									__a	:  Any         =					0
									__a	:  int         =					0
									__a	:  List[Any]         =					0
									__a	:  dict[T, DoubleLinkedListNode[T, U]]         =					{}







					def __repr__(  self       ):
									return (
									    f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
									    f"""capacity={self.capacity}, current size={self.num_keys})"""
									)







					def __contains__(  self		,     _UpperCAmelCase       ):
									return key in self.cache







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									# Note: pythonic interface would throw KeyError rather than return None

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







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):

									if key not in self.cache:
													if self.num_keys >= self.capacity:
																	# delete first node (oldest) when over capacity
																	__a	:  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(_UpperCAmelCase       ) 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	:  List[str]         =					DoubleLinkedListNode(_UpperCAmelCase		,     _UpperCAmelCase       )
													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	:  Tuple         =					value
													self.list.add(_UpperCAmelCase       )







					@classmethod
					def 	_lowerCamelCase						(  cls		,     _UpperCAmelCase = 128       ):

									def cache_decorator_inner(_UpperCAmelCase       ) -> Callable[..., U]:
													def cache_decorator_wrapper(*_UpperCAmelCase       ) -> U:
																	if func not in cls.decorator_function_to_instance_map:
																					__a	:  Union[str, Any]         =					LRUCache(_UpperCAmelCase       )

																	__a	:  List[Any]         =					cls.decorator_function_to_instance_map[func].get(args[0]       )
																	if result is None:
																					__a	:  Optional[int]         =					func(*_UpperCAmelCase       )
																					cls.decorator_function_to_instance_map[func].put(args[0]		,     _UpperCAmelCase       )
																	return result

													def cache_info() -> LRUCache[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()






"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					['''image_processor''', '''tokenizer''']
					__lowerCAmelCase					=					'''CLIPImageProcessor'''
					__lowerCAmelCase					=					('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')







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

									__a	:  Optional[Any]         =					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__(_UpperCAmelCase		,     _UpperCAmelCase       )







					def __call__(  self		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     **_UpperCAmelCase       ):

									if text is None and images is None:
													raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

									if text is not None:
													__a	:  Any         =					self.tokenizer(_UpperCAmelCase		,     return_tensors=_UpperCAmelCase		,     **_UpperCAmelCase       )

									if images is not None:
													__a	:  List[str]         =					self.image_processor(_UpperCAmelCase		,     return_tensors=_UpperCAmelCase		,     **_UpperCAmelCase       )

									if text is not None and images is not None:
													__a	:  Optional[int]         =					image_features.pixel_values
													return encoding
									elif text is not None:
													return encoding
									else:
													return BatchEncoding(data=dict(**_UpperCAmelCase       )		,     tensor_type=_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									return self.tokenizer.batch_decode(*_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									return self.tokenizer.decode(*_UpperCAmelCase		,     **_UpperCAmelCase       )







					@property
					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					self.tokenizer.model_input_names
									__a	:  Dict         =					self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )