


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__	(							UpperCAmelCase      ):






   """simple docstring"""






   _UpperCamelCase		:					Any    =		['image_processor', 'tokenizer']
   _UpperCamelCase		:					Union[str, Any]    =		'LayoutLMv3ImageProcessor'
   _UpperCamelCase		:					Optional[Any]    =		('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')




   def __init__(							self      ,						snake_case=None      ,						snake_case=None      ,						**snake_case     ):

      '''simple docstring'''


      UpperCamelCase__					=       None
      if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."      ,						snake_case      ,						)
         UpperCamelCase__					=       kwargs.pop("feature_extractor"     )

      UpperCamelCase__					=       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__(snake_case      ,						snake_case     )




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

      '''simple docstring'''


      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
      UpperCamelCase__					=       self.image_processor(images=snake_case      ,						return_tensors=snake_case     )

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

      UpperCamelCase__					=       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=snake_case      ,						add_special_tokens=snake_case      ,						padding=snake_case      ,						truncation=snake_case      ,						max_length=snake_case      ,						stride=snake_case      ,						pad_to_multiple_of=snake_case      ,						return_token_type_ids=snake_case      ,						return_attention_mask=snake_case      ,						return_overflowing_tokens=snake_case      ,						return_special_tokens_mask=snake_case      ,						return_offsets_mapping=snake_case      ,						return_length=snake_case      ,						verbose=snake_case      ,						return_tensors=snake_case      ,						**snake_case      ,						)

      # add pixel values
      UpperCamelCase__					=       features.pop("pixel_values"     )
      if return_overflowing_tokens is True:
         UpperCamelCase__					=       self.get_overflowing_images(snake_case      ,						encoded_inputs["overflow_to_sample_mapping"]     )
      UpperCamelCase__					=       images

      return encoded_inputs




   def 				snake_case__				(							self      ,						snake_case      ,						snake_case     ):

      '''simple docstring'''


      UpperCamelCase__					=       []
      for sample_idx in overflow_to_sample_mapping:
         images_with_overflow.append(images[sample_idx]     )

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

      return images_with_overflow




   def 				snake_case__				(							self      ,						*snake_case      ,						**snake_case     ):

      '''simple docstring'''


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




   def 				snake_case__				(							self      ,						*snake_case      ,						**snake_case     ):

      '''simple docstring'''


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




   @property
   def 				snake_case__				(							self     ):

      '''simple docstring'''


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




   @property
   def 				snake_case__				(							self     ):

      '''simple docstring'''


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







   @property
   def 				snake_case__				(							self     ):

      '''simple docstring'''


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






from typing import Any







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       data
						UpperCamelCase__					=       None







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       None




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.head
						while temp is not None:
									print(temp.data      ,						end=" "     )
									UpperCamelCase__					=       temp.next
						print()




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       Node(snake_case     )
						UpperCamelCase__					=       self.head
						UpperCamelCase__					=       new_node







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						if node_data_a == node_data_a:
									return
						else:
									UpperCamelCase__					=       self.head
									while node_a is not None and node_a.data != node_data_a:
												UpperCamelCase__					=       node_a.next

									UpperCamelCase__					=       self.head
									while node_a is not None and node_a.data != node_data_a:
												UpperCamelCase__					=       node_a.next

									if node_a is None or node_a is None:
												return

									UpperCamelCase__,      UpperCamelCase__					=       node_a.data, node_a.data


if __name__ == "__main__":
						__UpperCamelCase    		=       LinkedList()
						for i in range(5, 0, -1):
												ll.push(i)

						ll.print_list()

						ll.swap_nodes(1, 4)
						print('After swapping')
						ll.print_list()


