
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  lowercase__(				__lowercase							):



	"""simple docstring"""




	a			:str         =	['image_processor', 'tokenizer']
	a			:Optional[Any]         =	'ChineseCLIPImageProcessor'
	a			:int         =	('BertTokenizer', 'BertTokenizerFast')
	def __init__(       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=None       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=None       ,							**SCREAMING_SNAKE_CASE_  :			Dict       )       ->     Any:
					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.'''       ,							SCREAMING_SNAKE_CASE_       ,							)
									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__(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					self.image_processor
	def __call__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Tuple=None       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=None       ,							SCREAMING_SNAKE_CASE_  :			int=None       ,							**SCREAMING_SNAKE_CASE_  :			str       )       ->     Union[str, Any]:

					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:
									lowercase_       =					self.tokenizer(SCREAMING_SNAKE_CASE_       ,							return_tensors=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					if images is not None:
									lowercase_       =					self.image_processor(SCREAMING_SNAKE_CASE_       ,							return_tensors=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					if text is not None and images is not None:
									lowercase_       =					image_features.pixel_values
									return encoding
					elif text is not None:
									return encoding
					else:
									return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_       )       ,							tensor_type=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			int       ,							*SCREAMING_SNAKE_CASE_  :			List[str]       ,							**SCREAMING_SNAKE_CASE_  :			int       )       ->     int:
					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Any       ,							*SCREAMING_SNAKE_CASE_  :			Union[str, Any]       ,							**SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     List[Any]:
					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
	@property
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     int:
					lowercase_       =					self.tokenizer.model_input_names
					lowercase_       =					self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )




	@property
	def 						_lowercase  (       self  :			Dict       )       ->     Optional[Any]:
					warnings.warn(
					    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,							SCREAMING_SNAKE_CASE_       ,							)
					return self.image_processor_class






from __future__ import annotations

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

lowercase  :					Optional[Any]													=       TypeVar('T')




class  lowerCamelCase__							(				Generic[T]):




	'''simple docstring'''




	_A								=     42  # Cache store of keys
	_A								=     42  # References of the keys in cache
	_A								=     1_0  # Maximum capacity of cache







	def __init__(					self		:Optional[Any]     ,   a		:int						)		-> None:
								__UpperCamelCase	:   Union[str, Any]			   =					deque()
								__UpperCamelCase	:   str			   =					set()
								if not n:
															__UpperCamelCase	:   Union[str, Any]			   =					sys.maxsize
								elif n < 0:
															raise ValueError("n should be an integer greater than 0."						)
								else:
															__UpperCamelCase	:   Any			   =					n







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:T						)		-> None:
								if x not in self.key_reference:
															if len(self.dq_store						) == LRUCache._MAX_CAPACITY:
																						__UpperCamelCase	:   int			   =					self.dq_store.pop()
																						self.key_reference.remove(a						)
								else:
															self.dq_store.remove(a						)

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







	def    _lowerCamelCase				(					self		:Any						)		-> None:
								for k in self.dq_store:
															print(a						)







	def __repr__(					self		:Tuple						)		-> str:
								return f'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store						)}'


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowercase  :					LRUCache[str | int]													=       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]"