


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: str      =					['''image_processor''', '''tokenizer''']
 _UpperCamelCase				: List[Any]      =					'''LayoutLMv2ImageProcessor'''
 _UpperCamelCase				: str      =					('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')
 def __init__(			self					: List[Any]       ,    _a					: int=None       ,    _a					: Optional[int]=None       ,    **_a					: List[str]				)    ->			Optional[int]:
      UpperCAmelCase						:				Optional[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."       ,    _a       ,    )
           UpperCAmelCase						:				List[str]		  =			kwargs.pop("feature_extractor"				)

      UpperCAmelCase						:				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__(_a       ,    _a				)
 def __call__(			self					: Optional[Any]       ,    _a					: List[Any]       ,    _a					: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,    _a					: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,    _a					: Union[List[List[int]], List[List[List[int]]]] = None       ,    _a					: Optional[Union[List[int], List[List[int]]]] = None       ,    _a					: bool = True       ,    _a					: Union[bool, str, PaddingStrategy] = False       ,    _a					: Union[bool, str, TruncationStrategy] = False       ,    _a					: Optional[int] = None       ,    _a					: int = 0       ,    _a					: Optional[int] = None       ,    _a					: Optional[bool] = None       ,    _a					: Optional[bool] = None       ,    _a					: bool = False       ,    _a					: bool = False       ,    _a					: bool = False       ,    _a					: bool = False       ,    _a					: bool = True       ,    _a					: Optional[Union[str, TensorType]] = None       ,    **_a					: Dict       ,    )    ->			BatchEncoding:
      # verify input
      if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."				)

      if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."				)

      if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."				)

      # first, apply the image processor
      UpperCAmelCase						:				str		  =			self.image_processor(images=_a       ,    return_tensors=_a				)

      # second, apply the tokenizer
      if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(_a       ,    _a				):
                UpperCAmelCase						:				Optional[Any]		  =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
           UpperCAmelCase						:				int		  =			features["words"]

      UpperCAmelCase						:				List[Any]		  =			self.tokenizer(
          text=text if text is not None else features["words"]       ,    text_pair=text_pair if text_pair is not None else None       ,    boxes=boxes if boxes is not None else features["boxes"]       ,    word_labels=_a       ,    add_special_tokens=_a       ,    padding=_a       ,    truncation=_a       ,    max_length=_a       ,    stride=_a       ,    pad_to_multiple_of=_a       ,    return_token_type_ids=_a       ,    return_attention_mask=_a       ,    return_overflowing_tokens=_a       ,    return_special_tokens_mask=_a       ,    return_offsets_mapping=_a       ,    return_length=_a       ,    verbose=_a       ,    return_tensors=_a       ,    **_a       ,    )

      # add pixel values
      UpperCAmelCase						:				Optional[Any]		  =			features.pop("pixel_values"				)
      if return_overflowing_tokens is True:
           UpperCAmelCase						:				Dict		  =			self.get_overflowing_images(_a       ,    encoded_inputs["overflow_to_sample_mapping"]				)
      UpperCAmelCase						:				List[Any]		  =			images

      return encoded_inputs
 def   _UpperCamelCase    (			self					: str       ,    _a					: Optional[int]       ,    _a					: Optional[Any]				)    ->			int:
      # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
      UpperCAmelCase						:				Optional[int]		  =			[]
      for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]				)

      if len(_a				) != len(_a				):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               f' {len(_a				)} and {len(_a				)}'				)

      return images_with_overflow
 def   _UpperCamelCase    (			self					: Union[str, Any]       ,    *_a					: str       ,    **_a					: Any				)    ->			Any:
      return self.tokenizer.batch_decode(*_a       ,    **_a				)
 def   _UpperCamelCase    (			self					: Any       ,    *_a					: Dict       ,    **_a					: List[str]				)    ->			List[str]:
      return self.tokenizer.decode(*_a       ,    **_a				)
 @property
 def   _UpperCamelCase    (			self					: int				)    ->			Optional[int]:
      return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
 @property
 def   _UpperCamelCase    (			self					: Tuple				)    ->			Optional[int]:
      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   _UpperCamelCase    (			self					: List[str]				)    ->			Any:
      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,    _a       ,    )
      return self.image_processor







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]     ):







 """simple docstring"""





 def __init__(			self					: List[Any]       ,    _a					: T | None       ,    _a					: U | None				)    ->			Dict:
      UpperCAmelCase						:				str		  =			key
      UpperCAmelCase						:				Dict		  =			val
      UpperCAmelCase						:				int		  =			0
      UpperCAmelCase						:				DoubleLinkedListNode[T, U] | None		  =			None
      UpperCAmelCase						:				DoubleLinkedListNode[T, U] | None		  =			None

 def __repr__(			self					: List[str]				)    ->			str:
      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    A_   ( Generic[T, U]     ):







 """simple docstring"""





 def __init__(			self					: Any				)    ->			None:
      UpperCAmelCase						:				DoubleLinkedListNode[T, U]		  =			DoubleLinkedListNode(_a       ,    _a				)
      UpperCAmelCase						:				DoubleLinkedListNode[T, U]		  =			DoubleLinkedListNode(_a       ,    _a				)
      UpperCAmelCase  , UpperCAmelCase						:				Optional[int]		  =			self.rear, self.head
 def __repr__(			self					: int				)    ->			str:
      UpperCAmelCase						:				Union[str, Any]		  =			["DoubleLinkedList"]
      UpperCAmelCase						:				int		  =			self.head
      while node.next is not None:
           rep.append(str(_a				)				)
           UpperCAmelCase						:				Union[str, Any]		  =			node.next
      rep.append(str(self.rear				)				)
      return ",\n    ".join(_a				)
 def   _UpperCamelCase    (			self					: List[Any]       ,    _a					: DoubleLinkedListNode[T, U]				)    ->			None:
      UpperCAmelCase						:				str		  =			self.rear.prev

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

      UpperCAmelCase						:				Any		  =			node
      UpperCAmelCase						:				Optional[int]		  =			previous
      UpperCAmelCase						:				Optional[int]		  =			node
      UpperCAmelCase						:				Union[str, Any]		  =			self.rear
      node.freq += 1
      self._position_node(_a				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: DoubleLinkedListNode[T, U]				)    ->			None:

      while node.prev is not None and node.prev.freq > node.freq:
           # swap node with previous node
           UpperCAmelCase						:				List[str]		  =			node.prev

           UpperCAmelCase						:				Any		  =			previous_node.prev
           UpperCAmelCase						:				int		  =			node.prev
           UpperCAmelCase						:				Tuple		  =			previous_node
           UpperCAmelCase						:				Optional[int]		  =			node

 def   _UpperCamelCase    (			self					: str       ,    _a					: DoubleLinkedListNode[T, U]				)    ->			DoubleLinkedListNode[T, U] | None:

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

      UpperCAmelCase						:				Dict		  =			node.next
      UpperCAmelCase						:				Optional[Any]		  =			node.prev
      UpperCAmelCase						:				List[Any]		  =			None
      UpperCAmelCase						:				int		  =			None
      return node




class    A_   ( Generic[T, U]     ):







 """simple docstring"""





 _UpperCamelCase				: dict[Callable[[T], U], LFUCache[T, U]]      =					{}
 def __init__(			self					: List[Any]       ,    _a					: int				)    ->			int:
      UpperCAmelCase						:				DoubleLinkedList[T, U]		  =			DoubleLinkedList()
      UpperCAmelCase						:				Any		  =			capacity
      UpperCAmelCase						:				Union[str, Any]		  =			0
      UpperCAmelCase						:				Any		  =			0
      UpperCAmelCase						:				Dict		  =			0
      UpperCAmelCase						:				dict[T, DoubleLinkedListNode[T, U]]		  =			{}
 def __repr__(			self					: Union[str, Any]				)    ->			str:
      return (
          f'CacheInfo(hits={self.hits}, misses={self.miss}, '
          f'capacity={self.capacity}, current_size={self.num_keys})'
      )
 def __contains__(			self					: Optional[int]       ,    _a					: T				)    ->			bool:
      return key in self.cache
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: T				)    ->			U | None:

      if key in self.cache:
           self.hits += 1
           UpperCAmelCase						:				DoubleLinkedListNode[T, U]		  =			self.cache[key]
           UpperCAmelCase						:				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(_a				)
           return node.val
      self.miss += 1
      return None
 def   _UpperCamelCase    (			self					: str       ,    _a					: T       ,    _a					: U				)    ->			None:

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

                del self.cache[first_node.key]
                self.num_keys -= 1
           UpperCAmelCase						:				Optional[Any]		  =			DoubleLinkedListNode(_a       ,    _a				)
           self.list.add(self.cache[key]				)
           self.num_keys += 1

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

 @classmethod
 def   _UpperCamelCase    (			cls					: type[LFUCache[T, U]]       ,    _a					: int = 1_2_8				)    ->			Callable[[Callable[[T], U]], Callable[..., U]]:

      def cache_decorator_inner(_a					: Callable[[T], U]				) -> Callable[..., U]:
           def cache_decorator_wrapper(*_a					: T				) -> U:
                if func not in cls.decorator_function_to_instance_map:
                     UpperCAmelCase						:				List[Any]		  =			LFUCache(_a				)

                UpperCAmelCase						:				List[Any]		  =			cls.decorator_function_to_instance_map[func].get(args[0]				)
                if result is None:
                     UpperCAmelCase						:				List[Any]		  =			func(*_a				)
                     cls.decorator_function_to_instance_map[func].put(args[0]       ,    _a				)
                return result

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

           setattr(_a       ,    "cache_info"       ,    _a				)  # noqa: B010

           return cache_decorator_wrapper

      return cache_decorator_inner


if __name__ == "__main__":
      import doctest

      doctest.testmod()



