code: 
  
  
  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__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  ["image_processor", "tokenizer"]
  							_A  				=  "LayoutLMv2ImageProcessor"
  							_A  				=  ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")
  
  
  
  
  
  
  
  							def __init__(		self  :				int		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=None		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=None		,				**SCREAMING_SNAKE_CASE_  :				List[Any]		):
  													_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.'		,				SCREAMING_SNAKE_CASE_		,				)
  																			_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__(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def __call__(		self  :				Any		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None		,				SCREAMING_SNAKE_CASE_  :				Union[List[List[int]], List[List[List[int]]]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[List[int], List[List[int]]]] = None		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				SCREAMING_SNAKE_CASE_  :				Union[bool, str, PaddingStrategy] = False		,				SCREAMING_SNAKE_CASE_  :				Union[bool, str, TruncationStrategy] = False		,				SCREAMING_SNAKE_CASE_  :				Optional[int] = None		,				SCREAMING_SNAKE_CASE_  :				int = 0		,				SCREAMING_SNAKE_CASE_  :				Optional[int] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[bool] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[bool] = None		,				SCREAMING_SNAKE_CASE_  :				bool = False		,				SCREAMING_SNAKE_CASE_  :				bool = False		,				SCREAMING_SNAKE_CASE_  :				bool = False		,				SCREAMING_SNAKE_CASE_  :				bool = False		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[str, TensorType]] = None		,				**SCREAMING_SNAKE_CASE_  :				int		,				):
  													# 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
  													_a     =			self.image_processor(images=SCREAMING_SNAKE_CASE_		,				return_tensors=SCREAMING_SNAKE_CASE_		)
  
  													# second, apply the tokenizer
  													if text is not None and self.image_processor.apply_ocr and text_pair is None:
  																			if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		):
  																									_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=SCREAMING_SNAKE_CASE_		,				add_special_tokens=SCREAMING_SNAKE_CASE_		,				padding=SCREAMING_SNAKE_CASE_		,				truncation=SCREAMING_SNAKE_CASE_		,				max_length=SCREAMING_SNAKE_CASE_		,				stride=SCREAMING_SNAKE_CASE_		,				pad_to_multiple_of=SCREAMING_SNAKE_CASE_		,				return_token_type_ids=SCREAMING_SNAKE_CASE_		,				return_attention_mask=SCREAMING_SNAKE_CASE_		,				return_overflowing_tokens=SCREAMING_SNAKE_CASE_		,				return_special_tokens_mask=SCREAMING_SNAKE_CASE_		,				return_offsets_mapping=SCREAMING_SNAKE_CASE_		,				return_length=SCREAMING_SNAKE_CASE_		,				verbose=SCREAMING_SNAKE_CASE_		,				return_tensors=SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		,				)
  
  													# add pixel values
  													_a     =			features.pop('pixel_values'		)
  													if return_overflowing_tokens is True:
  																			_a     =			self.get_overflowing_images(SCREAMING_SNAKE_CASE_		,				encoded_inputs['overflow_to_sample_mapping']		)
  													_a     =			images
  
  													return encoded_inputs
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				str		):
  													# 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(SCREAMING_SNAKE_CASE_		) != len(SCREAMING_SNAKE_CASE_		):
  																			raise ValueError(
  																			    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
  																			    f""" {len(SCREAMING_SNAKE_CASE_		)} and {len(SCREAMING_SNAKE_CASE_		)}"""		)
  
  													return images_with_overflow
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		,				*SCREAMING_SNAKE_CASE_  :				List[Any]		,				**SCREAMING_SNAKE_CASE_  :				Tuple		):
  													return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Any		,				*SCREAMING_SNAKE_CASE_  :				Dict		,				**SCREAMING_SNAKE_CASE_  :				List[str]		):
  													return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				int		):
  													return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		):
  													warnings.warn(
  													    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,				SCREAMING_SNAKE_CASE_		,				)
  													return self.image_processor_class
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				int		):
  													warnings.warn(
  													    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,				SCREAMING_SNAKE_CASE_		,				)
  													return self.image_processor
  
  
  
style_context: 
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  bool:
  						# must be
  						_a     =			[list(_UpperCAmelCase				) for row in matrix]
  						if matrix and isinstance(_UpperCAmelCase							,     _UpperCAmelCase				):
  												if isinstance(matrix[0]							,     _UpperCAmelCase				):
  																		_a     =			0
  																		for row in matrix:
  																								if prev_len == 0:
  																														_a     =			len(_UpperCAmelCase				)
  																														_a     =			True
  																								else:
  																														_a     =			prev_len == len(_UpperCAmelCase				)
  												else:
  																		_a     =			True
  						else:
  												_a     =			False
  
  						return result
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  None:
  						if check_matrix(_UpperCAmelCase				) and len(_UpperCAmelCase				) > 0:
  												_a     =			[list(_UpperCAmelCase				) for row in a]
  												_a     =			len(_UpperCAmelCase				)
  												if isinstance(a[0]							,     _UpperCAmelCase				):
  																		_a     =			len(a[0]				)
  												else:
  																		for dat in a:
  																								print(_UpperCAmelCase				)
  																		return
  
  												# horizotal printing increasing
  												for i in range(0							,     _UpperCAmelCase				):
  																		print(a[0][i]				)
  												# vertical printing down
  												for i in range(1							,     _UpperCAmelCase				):
  																		print(a[i][mat_col - 1]				)
  												# horizotal printing decreasing
  												if mat_row > 1:
  																		for i in range(mat_col - 2							,     -1							,     -1				):
  																								print(a[mat_row - 1][i]				)
          # vertical printing up
  												for i in range(mat_row - 2							,     0							,     -1				):
  																		print(a[i][0]				)
  												_a     =			[row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
  												if len(_UpperCAmelCase				) > 0:
  																		spiral_print_clockwise(_UpperCAmelCase				)
  												else:
  																		return
  						else:
  												print('Not a valid matrix'				)
  												return
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  list[int]:
  						if matrix:
  												return list(matrix.pop(0				)				) + spiral_traversal(list(zip(*_UpperCAmelCase				)				)[::-1]				)
  						else:
  												return []
  
  
  # driver code
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  		lowercase_								=    [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
  		spiral_print_clockwise(a)
  
  
  