


'''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	(						lowercase__							):
							lowercase			:		int   		=  ['image_processor', 'tokenizer']
							lowercase			:		List[str]   		=  'LayoutLMv3ImageProcessor'
							lowercase			:		Any   		=  ('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')





							def __init__(  self		:List[str]  ,_UpperCamelCase		:List[str]=None  ,_UpperCamelCase		:int=None  ,**_UpperCamelCase		:Optional[Any]			):
									snake_case_		:							Optional[Any]			       =						None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""  ,_UpperCamelCase  ,)
											snake_case_		:							Union[str, Any]			       =						kwargs.pop("""feature_extractor"""			)

									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__(_UpperCamelCase  ,_UpperCamelCase			)





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

									# first, apply the image processor
									snake_case_		:							Tuple			       =						self.image_processor(images=_UpperCamelCase  ,return_tensors=_UpperCamelCase			)

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

									snake_case_		:							Optional[int]			       =						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=_UpperCamelCase  ,add_special_tokens=_UpperCamelCase  ,padding=_UpperCamelCase  ,truncation=_UpperCamelCase  ,max_length=_UpperCamelCase  ,stride=_UpperCamelCase  ,pad_to_multiple_of=_UpperCamelCase  ,return_token_type_ids=_UpperCamelCase  ,return_attention_mask=_UpperCamelCase  ,return_overflowing_tokens=_UpperCamelCase  ,return_special_tokens_mask=_UpperCamelCase  ,return_offsets_mapping=_UpperCamelCase  ,return_length=_UpperCamelCase  ,verbose=_UpperCamelCase  ,return_tensors=_UpperCamelCase  ,**_UpperCamelCase  ,)

									# add pixel values
									snake_case_		:							Any			       =						features.pop("""pixel_values"""			)
									if return_overflowing_tokens is True:
											snake_case_		:							List[Any]			       =						self.get_overflowing_images(_UpperCamelCase  ,encoded_inputs["""overflow_to_sample_mapping"""]			)
									snake_case_		:							str			       =						images

									return encoded_inputs





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:List[str]			):
									# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
									snake_case_		:							int			       =						[]
									for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]			)

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

									return images_with_overflow





							def 		a__   (  self		:List[str]  ,*_UpperCamelCase		:List[str]  ,**_UpperCamelCase		:str			):
									return self.tokenizer.batch_decode(*_UpperCamelCase  ,**_UpperCamelCase			)





							def 		a__   (  self		:Optional[int]  ,*_UpperCamelCase		:Optional[Any]  ,**_UpperCamelCase		:Union[str, Any]			):
									return self.tokenizer.decode(*_UpperCamelCase  ,**_UpperCamelCase			)





							@property
							def 		a__   (  self		:List[str]			):
									return ["input_ids", "bbox", "attention_mask", "pixel_values"]





							@property
							def 		a__   (  self		:Union[str, Any]			):
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,_UpperCamelCase  ,)
									return self.image_processor_class





							@property
							def 		a__   (  self		:int			):
									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,_UpperCamelCase  ,)
									return self.image_processor



'''simple docstring'''






from __future__ import annotations

import math





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :list ):







		'''simple docstring'''
		if len(lowerCamelCase_ ) != 2 or len(a[0] ) != 2 or len(lowerCamelCase_ ) != 2 or len(b[0] ) != 2:
				raise Exception("""Matrices are not 2x2""" )
		snake_case_		:							Tuple			       =						[
		    [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
		    [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
		]
		return new_matrix





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :list ):







		'''simple docstring'''
		return [
		    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
		    for row in range(len(lowerCamelCase_ ) )
		]





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :list ):







		'''simple docstring'''
		return [
		    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
		    for row in range(len(lowerCamelCase_ ) )
		]





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		if len(lowerCamelCase_ ) % 2 != 0 or len(a[0] ) % 2 != 0:
				raise Exception("""Odd matrices are not supported!""" )

		snake_case_		:							Optional[int]			       =						len(lowerCamelCase_ )
		snake_case_		:							List[str]			       =						matrix_length // 2

		snake_case_		:							Union[str, Any]			       =						[[a[i][j] for j in range(lowerCamelCase_				,      lowerCamelCase_ )] for i in range(lowerCamelCase_ )]
		snake_case_		:							Dict			       =						[
		    [a[i][j] for j in range(lowerCamelCase_				,      lowerCamelCase_ )] for i in range(lowerCamelCase_				,      lowerCamelCase_ )
		]

		snake_case_		:							str			       =						[[a[i][j] for j in range(lowerCamelCase_ )] for i in range(lowerCamelCase_ )]
		snake_case_		:							Dict			       =						[[a[i][j] for j in range(lowerCamelCase_ )] for i in range(lowerCamelCase_				,      lowerCamelCase_ )]

		return top_left, top_right, bot_left, bot_right





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		return len(lowerCamelCase_ ), len(matrix[0] )





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		print("""\n""".join(str(lowerCamelCase_ ) for line in matrix ) )





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :list ):







		'''simple docstring'''
		if matrix_dimensions(lowerCamelCase_ ) == (2, 2):
				return default_matrix_multiplication(lowerCamelCase_				,      lowerCamelCase_ )

		snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_		:							Any			       =						split_matrix(lowerCamelCase_ )
		snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_		:							List[Any]			       =						split_matrix(lowerCamelCase_ )

		snake_case_		:							Any			       =						actual_strassen(lowerCamelCase_				,      matrix_subtraction(lowerCamelCase_				,      lowerCamelCase_ ) )
		snake_case_		:							str			       =						actual_strassen(matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )				,      lowerCamelCase_ )
		snake_case_		:							str			       =						actual_strassen(matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )				,      lowerCamelCase_ )
		snake_case_		:							List[str]			       =						actual_strassen(lowerCamelCase_				,      matrix_subtraction(lowerCamelCase_				,      lowerCamelCase_ ) )
		snake_case_		:							Optional[int]			       =						actual_strassen(matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )				,      matrix_addition(lowerCamelCase_				,      lowerCamelCase_ ) )
		snake_case_		:							Dict			       =						actual_strassen(matrix_subtraction(lowerCamelCase_				,      lowerCamelCase_ )				,      matrix_addition(lowerCamelCase_				,      lowerCamelCase_ ) )
		snake_case_		:							int			       =						actual_strassen(matrix_subtraction(lowerCamelCase_				,      lowerCamelCase_ )				,      matrix_addition(lowerCamelCase_				,      lowerCamelCase_ ) )

		snake_case_		:							List[Any]			       =						matrix_addition(matrix_subtraction(matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )				,      lowerCamelCase_ )				,      lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							str			       =						matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							Any			       =						matrix_subtraction(matrix_subtraction(matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )				,      lowerCamelCase_ )				,      lowerCamelCase_ )

		# construct the new matrix from our 4 quadrants
		snake_case_		:							Dict			       =						[]
		for i in range(len(lowerCamelCase_ ) ):
				new_matrix.append(top_left[i] + top_right[i] )
		for i in range(len(lowerCamelCase_ ) ):
				new_matrix.append(bot_left[i] + bot_right[i] )
		return new_matrix





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :list ):







		'''simple docstring'''
		if matrix_dimensions(lowerCamelCase_ )[1] != matrix_dimensions(lowerCamelCase_ )[0]:
				snake_case_		:							Optional[int]			       =						(
				    """Unable to multiply these matrices, please check the dimensions.\n"""
				    F'''Matrix A: {matrixa}\n'''
				    F'''Matrix B: {matrixa}'''
				)
				raise Exception(lowerCamelCase_ )
		snake_case_		:							Tuple			       =						matrix_dimensions(lowerCamelCase_ )
		snake_case_		:							Union[str, Any]			       =						matrix_dimensions(lowerCamelCase_ )

		if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
				return [matrixa, matrixa]

		snake_case_		:							int			       =						max(*lowerCamelCase_				,      *lowerCamelCase_ )
		snake_case_		:							Union[str, Any]			       =						int(math.pow(2				,      math.ceil(math.loga(lowerCamelCase_ ) ) ) )
		snake_case_		:							Dict			       =						matrixa
		snake_case_		:							Optional[int]			       =						matrixa

		# Adding zeros to the matrices so that the arrays dimensions are the same and also
		# power of 2
		for i in range(0				,      lowerCamelCase_ ):
				if i < dimensiona[0]:
						for _ in range(dimensiona[1]				,      lowerCamelCase_ ):
								new_matrixa[i].append(0 )
				else:
						new_matrixa.append([0] * maxim )
				if i < dimensiona[0]:
						for _ in range(dimensiona[1]				,      lowerCamelCase_ ):
								new_matrixa[i].append(0 )
				else:
						new_matrixa.append([0] * maxim )

		snake_case_		:							Dict			       =						actual_strassen(lowerCamelCase_				,      lowerCamelCase_ )

		# Removing the additional zeros
		for i in range(0				,      lowerCamelCase_ ):
				if i < dimensiona[0]:
						for _ in range(dimensiona[1]				,      lowerCamelCase_ ):
								final_matrix[i].pop()
				else:
						final_matrix.pop()
		return final_matrix


if __name__ == "__main__":
							__A	:	Optional[int]     =     [
							    [2, 3, 4, 5],
							    [6, 4, 3, 1],
							    [2, 3, 6, 7],
							    [3, 1, 2, 4],
							    [2, 3, 4, 5],
							    [6, 4, 3, 1],
							    [2, 3, 6, 7],
							    [3, 1, 2, 4],
							    [2, 3, 4, 5],
							    [6, 2, 3, 1],
							]
							__A	:	List[str]     =     [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
							print(strassen(matrixa, matrixa))