





'''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       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			Optional[int]								=						["image_processor", "tokenizer"]
	lowerCAmelCase__					:			List[str]								=						"LayoutLMv3ImageProcessor"
	lowerCAmelCase__					:			Union[str, Any]								=						("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")







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




				'''simple docstring'''
				a__     = 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 , )
							a__     = kwargs.pop("feature_extractor"		)

				a__     = 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			:   List[str] , lowerCamelCase			:   Optional[int] , 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			:   int , ):




				'''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
				a__     = 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		):
										a__     = [text]  # add batch dimension (as the image processor always adds a batch dimension)
							a__     = features["words"]

				a__     = 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
				a__     = features.pop("pixel_values"		)
				if return_overflowing_tokens is True:
							a__     = self.get_overflowing_images(lowerCamelCase , encoded_inputs["overflow_to_sample_mapping"]		)
				a__     = images

				return encoded_inputs







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Union[str, Any]		):




				'''simple docstring'''
				# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
				a__     = []
				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 						__a   (				self			:   Any , *lowerCamelCase			:   Tuple , **lowerCamelCase			:   Optional[Any]		):




				'''simple docstring'''
				return self.tokenizer.batch_decode(*lowerCamelCase , **lowerCamelCase		)







	def 						__a   (				self			:   List[str] , *lowerCamelCase			:   Optional[int] , **lowerCamelCase			:   Optional[int]		):




				'''simple docstring'''
				return self.tokenizer.decode(*lowerCamelCase , **lowerCamelCase		)







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




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







	@property
	def 						__a   (				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 						__a   (				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










'''simple docstring'''


class       UpperCamelCase__  :







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




				'''simple docstring'''
				a__     = data
				a__     = previous
				a__     = next_node







	def __str__(				self			:   List[Any]		):




				'''simple docstring'''
				return F'''{self.data}'''







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				return self.data







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				return self.next







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				return self.previous






class       UpperCamelCase__  :







	def __init__(				self			:   int , lowerCamelCase			:   Tuple		):




				'''simple docstring'''
				a__     = head







	def __iter__(				self			:   Any		):




				'''simple docstring'''
				return self







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				if not self.current:
							raise StopIteration
				else:
							a__     = self.current.get_data()
							a__     = self.current.get_next()
							return value






class       UpperCamelCase__  :







	def __init__(				self			:   Tuple		):




				'''simple docstring'''
				a__     = None  # First node in list
				a__     = None  # Last node in list







	def __str__(				self			:   Any		):




				'''simple docstring'''
				a__     = self.head
				a__     = []
				while current is not None:
							nodes.append(current.get_data()		)
							a__     = current.get_next()
				return " ".join(str(lowerCamelCase		) for node in nodes		)







	def __contains__(				self			:   Optional[int] , lowerCamelCase			:   int		):




				'''simple docstring'''
				a__     = self.head
				while current:
							if current.get_data() == value:
										return True
							a__     = current.get_next()
				return False







	def __iter__(				self			:   int		):




				'''simple docstring'''
				return LinkedListIterator(self.head		)







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				if self.head:
							return self.head.get_data()
				return None







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				if self.tail:
							return self.tail.get_data()
				return None







	def 						__a   (				self			:   List[Any] , lowerCamelCase			:   Node		):




				'''simple docstring'''
				if self.head is None:
							a__     = node
							a__     = node
				else:
							self.insert_before_node(self.head , lowerCamelCase		)







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   Node		):




				'''simple docstring'''
				if self.head is None:
							self.set_head(lowerCamelCase		)
				else:
							self.insert_after_node(self.tail , lowerCamelCase		)







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   int		):




				'''simple docstring'''
				a__     = Node(lowerCamelCase		)
				if self.head is None:
							self.set_head(lowerCamelCase		)
				else:
							self.set_tail(lowerCamelCase		)







	def 						__a   (				self			:   Any , lowerCamelCase			:   Node , lowerCamelCase			:   Node		):




				'''simple docstring'''
				a__     = node
				a__     = node.previous

				if node.get_previous() is None:
							a__     = node_to_insert
				else:
							a__     = node_to_insert

				a__     = node_to_insert







	def 						__a   (				self			:   Tuple , lowerCamelCase			:   Node , lowerCamelCase			:   Node		):




				'''simple docstring'''
				a__     = node
				a__     = node.next

				if node.get_next() is None:
							a__     = node_to_insert
				else:
							a__     = node_to_insert

				a__     = node_to_insert







	def 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   int , lowerCamelCase			:   int		):




				'''simple docstring'''
				a__     = 1
				a__     = Node(lowerCamelCase		)
				a__     = self.head
				while node:
							if current_position == position:
										self.insert_before_node(lowerCamelCase , lowerCamelCase		)
										return
							current_position += 1
							a__     = node.next
				self.insert_after_node(self.tail , lowerCamelCase		)







	def 						__a   (				self			:   List[Any] , lowerCamelCase			:   int		):




				'''simple docstring'''
				a__     = self.head
				while node:
							if node.get_data() == item:
										return node
							a__     = node.get_next()
				raise Exception("Node not found"		)







	def 						__a   (				self			:   List[Any] , lowerCamelCase			:   List[str]		):




				'''simple docstring'''
				if (node := self.get_node(lowerCamelCase		)) is not None:
							if node == self.head:
										a__     = self.head.get_next()

							if node == self.tail:
										a__     = self.tail.get_previous()

							self.remove_node_pointers(lowerCamelCase		)







	@staticmethod
	def 						__a   (				lowerCamelCase			:   Node		):




				'''simple docstring'''
				if node.get_next():
							a__     = node.previous

				if node.get_previous():
							a__     = node.next

				a__     = None
				a__     = None







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				return self.head is None







def    _lowerCamelCase  ()   ->    None:
			pass


if __name__ == "__main__":
		import doctest

		doctest.testmod()



