





from __future__ import annotations

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

lowerCAmelCase_						   =    TypeVar("""T""")



class        _lowerCAmelCase    (			Generic[T]							):


  '''simple docstring'''
  a_							:       deque[T]  # Cache store of keys
  a_							:       set[T]  # References of the keys in cache
  a_							:       int      					=10  # Maximum capacity of cache







  def __init__(      self      :					Optional[Any]      ,				UpperCamelCase      :					int							):


    '''simple docstring'''





    _snake_case   :				Dict														=	deque()
    _snake_case   :				str														=	set()
    if not n:
      _snake_case   :				Dict														=	sys.maxsize
    elif n < 0:
      raise ValueError('n should be an integer greater than 0.'							)
    else:
      _snake_case   :				Any														=	n







  def UpperCamelCase_ (      self      :					Dict      ,				UpperCamelCase      :					T							):


    '''simple docstring'''





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

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







  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    for k in self.dq_store:
      print(UpperCamelCase							)







  def __repr__(      self      :					Dict							):


    '''simple docstring'''





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


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      lowerCAmelCase_						   =    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]"











import warnings

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



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Union[str, Any]      					=["""image_processor""", """tokenizer"""]
  a_							:       Optional[int]      					="""CLIPImageProcessor"""
  a_							:       Optional[Any]      					=("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")







  def __init__(      self      :					List[str]      ,				UpperCamelCase      :					Optional[int]=None      ,				UpperCamelCase      :					Optional[Any]=None      ,				**UpperCamelCase      :					Dict							):


    '''simple docstring'''





    _snake_case   :				int														=	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      ,				)
      _snake_case   :				Optional[Any]														=	kwargs.pop('feature_extractor'							)

    _snake_case   :				Dict														=	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      :					Dict      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					Optional[int]=None      ,				**UpperCamelCase      :					Dict							):


    '''simple docstring'''





    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:
      _snake_case   :				Optional[int]														=	self.tokenizer(UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase							)

    if images is not None:
      _snake_case   :				Optional[int]														=	self.image_processor(UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase							)

    if text is not None and images is not None:
      _snake_case   :				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 UpperCamelCase_ (      self      :					Union[str, Any]      ,				*UpperCamelCase      :					Any      ,				**UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





    return self.tokenizer.batch_decode(*UpperCamelCase      ,				**UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				*UpperCamelCase      :					Union[str, Any]      ,				**UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





    return self.tokenizer.decode(*UpperCamelCase      ,				**UpperCamelCase							)







  @property
  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    _snake_case   :				Any														=	self.tokenizer.model_input_names
    _snake_case   :				List[Any]														=	self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




