

'''simple docstring'''
from __future__ import annotations

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

_snake_case         =  TypeVar('T')
_snake_case         =  TypeVar('U')


class        a__  (							Generic[T, U]  ):


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       key
      _lowercase      :				List[Any]      					=       val
      _lowercase      :				DoubleLinkedListNode[T, U] | None      					=       None
      _lowercase      :				DoubleLinkedListNode[T, U] | None      					=       None






   def __repr__(			self  ):





      """simple docstring"""





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




class        a__  (							Generic[T, U]  ):


   def __init__(			self  ):





      """simple docstring"""





      _lowercase      :				DoubleLinkedListNode[T, U]      					=       DoubleLinkedListNode(_UpperCamelCase      ,    _UpperCamelCase  )
      _lowercase      :				DoubleLinkedListNode[T, U]      					=       DoubleLinkedListNode(_UpperCamelCase      ,    _UpperCamelCase  )
      _lowercase					,				_lowercase      :				Dict      					=       self.rear, self.head


   def __repr__(			self  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       ["DoubleLinkedList"]
      _lowercase      :				Dict      					=       self.head
      while node.next is not None:
         rep.append(str(_UpperCamelCase  )  )
         _lowercase      :				Any      					=       node.next
      rep.append(str(self.rear  )  )
      return ",\n    ".join(_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       self.rear.prev

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

      _lowercase      :				Tuple      					=       node
      _lowercase      :				str      					=       previous
      _lowercase      :				Union[str, Any]      					=       node
      _lowercase      :				Tuple      					=       self.rear






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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

      _lowercase      :				str      					=       node.next
      _lowercase      :				Optional[int]      					=       node.prev
      _lowercase      :				Optional[int]      					=       None
      _lowercase      :				int      					=       None
      return node




class        a__  (							Generic[T, U]  ):
   _SCREAMING_SNAKE_CASE   :					dict[Callable[[T], U], LRUCache[T, U]]     	=							{}


   def __init__(			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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


   def __repr__(			self  ):





      """simple docstring"""





      return (
          f'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
          f'''capacity={self.capacity}, current size={self.num_keys})'''
      )


   def __contains__(			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      return key in self.cache


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      if key in self.cache:
         self.hits += 1
         _lowercase      :				DoubleLinkedListNode[T, U]      					=       self.cache[key]
         _lowercase      :				str      					=       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  ):





      """simple docstring"""





      if key not in self.cache:
         if self.num_keys >= self.capacity:
            # delete first node (oldest) when over capacity
            _lowercase      :				Dict      					=       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
         _lowercase      :				int      					=       DoubleLinkedListNode(_UpperCamelCase      ,    _UpperCamelCase  )
         self.list.add(self.cache[key]  )
         self.num_keys += 1

      else:
         # bump node to the end of the list, update value
         _lowercase      :				Optional[int]      					=       self.list.remove(self.cache[key]  )
         assert node is not None  # node guaranteed to be in list
         _lowercase      :				Dict      					=       value
         self.list.add(_UpperCamelCase  )






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





      """simple docstring"""





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

            _lowercase      :				Tuple      					=       cls.decorator_function_to_instance_map[func].get(args[0]  )
            if result is None:
               _lowercase      :				List[Any]      					=       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 os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					Dict     	=							'BlipImageProcessor'
   _SCREAMING_SNAKE_CASE   :					Dict     	=							'AutoTokenizer'


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )

      # add QFormer tokenizer
      _lowercase      :				List[Any]      					=       qformer_tokenizer


   def __call__(			self      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = True      ,    _UpperCamelCase = False      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = 0      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = True      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      if images is None and text is None:
         raise ValueError("You have to specify at least images or text."  )

      _lowercase      :				str      					=       BatchFeature()

      if text is not None:
         _lowercase      :				Dict      					=       self.tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
         encoding.update(_UpperCamelCase  )
         _lowercase      :				Dict      					=       self.qformer_tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
         _lowercase      :				Union[str, Any]      					=       qformer_text_encoding.pop("input_ids"  )
         _lowercase      :				List[Any]      					=       qformer_text_encoding.pop("attention_mask"  )

      if images is not None:
         _lowercase      :				List[Any]      					=       self.image_processor(_UpperCamelCase      ,    return_tensors=_UpperCamelCase  )
         encoding.update(_UpperCamelCase  )

      return encoding


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





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


   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       self.tokenizer.model_input_names
      _lowercase      :				Optional[Any]      					=       self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if os.path.isfile(_UpperCamelCase  ):
         raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''  )
      os.makedirs(_UpperCamelCase      ,    exist_ok=_UpperCamelCase  )
      _lowercase      :				Union[str, Any]      					=       os.path.join(_UpperCamelCase      ,    "qformer_tokenizer"  )
      self.qformer_tokenizer.save_pretrained(_UpperCamelCase  )
      return super().save_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )






   @classmethod
   def 						_lowerCamelCase       (			cls      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       AutoTokenizer.from_pretrained(_UpperCamelCase      ,    subfolder="qformer_tokenizer"  )
      _lowercase      :				Optional[Any]      					=       cls._get_arguments_from_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )
      args.append(_UpperCamelCase  )
      return cls(*_UpperCamelCase  )






