



'''simple docstring'''



from __future__ import annotations

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

lowerCAmelCase_     :       Optional[int]								=						TypeVar('T')
lowerCAmelCase_     :       List[Any]								=						TypeVar('U')
class 	lowerCamelCase_							(					Generic[T, U]			):







						def __init__(  self   :      List[Any]				,    lowerCAmelCase__   :      T | None				,    lowerCAmelCase__   :      U | None				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			key
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			val
													SCREAMING_SNAKE_CASE					:      DoubleLinkedListNode[T, U] | None     					=			None
													SCREAMING_SNAKE_CASE					:      DoubleLinkedListNode[T, U] | None     					=			None







						def __repr__(  self   :      str				):

													"""simple docstring"""
													return (
													    F"""Node: key: {self.key}, val: {self.val}, """
													    F"""has next: {bool(self.next				)}, has prev: {bool(self.prev				)}"""
													)


class 	lowerCamelCase_							(					Generic[T, U]			):







						def __init__(  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      DoubleLinkedListNode[T, U]     					=			DoubleLinkedListNode(UpperCAmelCase__				,    UpperCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      DoubleLinkedListNode[T, U]     					=			DoubleLinkedListNode(UpperCAmelCase__				,    UpperCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			self.rear, self.head







						def __repr__(  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			['''DoubleLinkedList''']
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.head
													while node.next is not None:
																				rep.append(str(UpperCAmelCase__				)				)
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			node.next
													rep.append(str(self.rear				)				)
													return ",\n    ".join(UpperCAmelCase__				)







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      DoubleLinkedListNode[T, U]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.rear.prev

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

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			node
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			previous
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			node
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.rear







						def 					__lowercase    (  self   :      Tuple				,    lowerCAmelCase__   :      DoubleLinkedListNode[T, U]				):

													"""simple docstring"""

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

													SCREAMING_SNAKE_CASE					:      Tuple     					=			node.next
													SCREAMING_SNAKE_CASE					:      Tuple     					=			node.prev
													SCREAMING_SNAKE_CASE					:      int     					=			None
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			None
													return node


class 	lowerCamelCase_							(					Generic[T, U]			):
						_lowerCAmelCase     :   List[Any]	     =				{}







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      DoubleLinkedList[T, U]     					=			DoubleLinkedList()
													SCREAMING_SNAKE_CASE					:      Any     					=			capacity
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			0
													SCREAMING_SNAKE_CASE					:      str     					=			0
													SCREAMING_SNAKE_CASE					:      Any     					=			0
													SCREAMING_SNAKE_CASE					:      dict[T, DoubleLinkedListNode[T, U]]     					=			{}







						def __repr__(  self   :      Tuple				):

													"""simple docstring"""
													return (
													    F"""CacheInfo(hits={self.hits}, misses={self.miss}, """
													    F"""capacity={self.capacity}, current size={self.num_keys})"""
													)







						def __contains__(  self   :      List[str]				,    lowerCAmelCase__   :      T				):

													"""simple docstring"""
													return key in self.cache







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      T				):

													"""simple docstring"""
													# Note: pythonic interface would throw KeyError rather than return None

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







						def 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      T				,    lowerCAmelCase__   :      U				):

													"""simple docstring"""

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

													else:
																				# bump node to the end of the list, update value
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.list.remove(self.cache[key]				)
																				assert node is not None  # node guaranteed to be in list
																				SCREAMING_SNAKE_CASE					:      str     					=			value
																				self.list.add(UpperCAmelCase__				)







						@classmethod
						def 					__lowercase    (  cls   :      List[str]				,    lowerCAmelCase__   :      int = 1_28				):

													"""simple docstring"""

													def cache_decorator_inner(lowerCAmelCase__   :      Callable[[T], U]				) -> Callable[..., U]:
																				def cache_decorator_wrapper(*lowerCAmelCase__   :      T				) -> U:
																											if func not in cls.decorator_function_to_instance_map:
																																		SCREAMING_SNAKE_CASE					:      int     					=			LRUCache(UpperCAmelCase__				)

																											SCREAMING_SNAKE_CASE					:      str     					=			cls.decorator_function_to_instance_map[func].get(args[0]				)
																											if result is None:
																																		SCREAMING_SNAKE_CASE					:      str     					=			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 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 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   int	     =				['image_processor', 'tokenizer']
						_lowerCAmelCase     :   Union[str, Any]	     =				'LayoutLMv3ImageProcessor'
						_lowerCAmelCase     :   List[Any]	     =				('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')







						def __init__(  self   :      str				,    lowerCAmelCase__   :      int=None				,    lowerCAmelCase__   :      str=None				,    **lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			None
													if "feature_extractor" in kwargs:
																				warnings.warn(
																				    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																				    ''' instead.'''				,    lowerCAmelCase__				,    )
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			kwargs.pop('''feature_extractor'''				)

													SCREAMING_SNAKE_CASE					:      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__(lowerCAmelCase__				,    lowerCAmelCase__				)







						def __call__(  self   :      Dict				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,    lowerCAmelCase__   :      Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				,    lowerCAmelCase__   :      Union[List[List[int]], List[List[List[int]]]] = None				,    lowerCAmelCase__   :      Optional[Union[List[int], List[List[int]]]] = None				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      Union[bool, str, PaddingStrategy] = False				,    lowerCAmelCase__   :      Union[bool, str, TruncationStrategy] = None				,    lowerCAmelCase__   :      Optional[int] = None				,    lowerCAmelCase__   :      int = 0				,    lowerCAmelCase__   :      Optional[int] = None				,    lowerCAmelCase__   :      Optional[bool] = None				,    lowerCAmelCase__   :      Optional[bool] = None				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      Optional[Union[str, TensorType]] = None				,    **lowerCAmelCase__   :      Any				,    ):

													"""simple docstring"""
													# 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.'''				)

													# first, apply the image processor
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.image_processor(images=lowerCAmelCase__				,    return_tensors=lowerCAmelCase__				)

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

													SCREAMING_SNAKE_CASE					:      Tuple     					=			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=lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				,    padding=lowerCAmelCase__				,    truncation=lowerCAmelCase__				,    max_length=lowerCAmelCase__				,    stride=lowerCAmelCase__				,    pad_to_multiple_of=lowerCAmelCase__				,    return_token_type_ids=lowerCAmelCase__				,    return_attention_mask=lowerCAmelCase__				,    return_overflowing_tokens=lowerCAmelCase__				,    return_special_tokens_mask=lowerCAmelCase__				,    return_offsets_mapping=lowerCAmelCase__				,    return_length=lowerCAmelCase__				,    verbose=lowerCAmelCase__				,    return_tensors=lowerCAmelCase__				,    **lowerCAmelCase__				,    )

													# add pixel values
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			features.pop('''pixel_values'''				)
													if return_overflowing_tokens is True:
																				SCREAMING_SNAKE_CASE					:      Any     					=			self.get_overflowing_images(lowerCAmelCase__				,    encoded_inputs['''overflow_to_sample_mapping''']				)
													SCREAMING_SNAKE_CASE					:      Any     					=			images

													return encoded_inputs







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
													SCREAMING_SNAKE_CASE					:      Dict     					=			[]
													for sample_idx in overflow_to_sample_mapping:
																				images_with_overflow.append(images[sample_idx]				)

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

													return images_with_overflow







						def 					__lowercase    (  self   :      Any				,    *lowerCAmelCase__   :      Union[str, Any]				,    **lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													return self.tokenizer.batch_decode(*lowerCAmelCase__				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[int]				,    *lowerCAmelCase__   :      Optional[Any]				,    **lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													return self.tokenizer.decode(*lowerCAmelCase__				,    **lowerCAmelCase__				)







						@property
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													return ["input_ids", "bbox", "attention_mask", "pixel_values"]







						@property
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													warnings.warn(
													    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,    lowerCAmelCase__				,    )
													return self.image_processor_class







						@property
						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													warnings.warn(
													    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,    lowerCAmelCase__				,    )
													return self.image_processor




