


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






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)


