


"""simple docstring"""




from __future__ import annotations

import math





def       a_ (	lowercase__ :list,			lowercase__ :list     ):
			if len(lowercase__     ) != 2 or len(a[0]     ) != 2 or len(lowercase__     ) != 2 or len(b[0]     ) != 2:
						raise Exception("""Matrices are not 2x2"""     )
			__lowerCamelCase          =      [
			    [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       a_ (	lowercase__ :list,			lowercase__ :list     ):
			return [
			    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]     )     )]
			    for row in range(len(lowercase__     )     )
			]





def       a_ (	lowercase__ :list,			lowercase__ :list     ):
			return [
			    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]     )     )]
			    for row in range(len(lowercase__     )     )
			]





def       a_ (	lowercase__ :list     ):
			if len(lowercase__     ) % 2 != 0 or len(a[0]     ) % 2 != 0:
						raise Exception("""Odd matrices are not supported!"""     )

			__lowerCamelCase          =      len(lowercase__     )
			__lowerCamelCase          =      matrix_length // 2

			__lowerCamelCase          =      [[a[i][j] for j in range(lowercase__,			lowercase__     )] for i in range(lowercase__     )]
			__lowerCamelCase          =      [
			    [a[i][j] for j in range(lowercase__,			lowercase__     )] for i in range(lowercase__,			lowercase__     )
			]

			__lowerCamelCase          =      [[a[i][j] for j in range(lowercase__     )] for i in range(lowercase__     )]
			__lowerCamelCase          =      [[a[i][j] for j in range(lowercase__     )] for i in range(lowercase__,			lowercase__     )]

			return top_left, top_right, bot_left, bot_right





def       a_ (	lowercase__ :list     ):
			return len(lowercase__     ), len(matrix[0]     )





def       a_ (	lowercase__ :list     ):
			print("""\n""".join(str(lowercase__     ) for line in matrix     )     )





def       a_ (	lowercase__ :list,			lowercase__ :list     ):
			if matrix_dimensions(lowercase__     ) == (2, 2):
						return default_matrix_multiplication(lowercase__,			lowercase__     )

			__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase          =      split_matrix(lowercase__     )
			__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase          =      split_matrix(lowercase__     )

			__lowerCamelCase          =      actual_strassen(lowercase__,			matrix_subtraction(lowercase__,			lowercase__     )     )
			__lowerCamelCase          =      actual_strassen(matrix_addition(lowercase__,			lowercase__     ),			lowercase__     )
			__lowerCamelCase          =      actual_strassen(matrix_addition(lowercase__,			lowercase__     ),			lowercase__     )
			__lowerCamelCase          =      actual_strassen(lowercase__,			matrix_subtraction(lowercase__,			lowercase__     )     )
			__lowerCamelCase          =      actual_strassen(matrix_addition(lowercase__,			lowercase__     ),			matrix_addition(lowercase__,			lowercase__     )     )
			__lowerCamelCase          =      actual_strassen(matrix_subtraction(lowercase__,			lowercase__     ),			matrix_addition(lowercase__,			lowercase__     )     )
			__lowerCamelCase          =      actual_strassen(matrix_subtraction(lowercase__,			lowercase__     ),			matrix_addition(lowercase__,			lowercase__     )     )

			__lowerCamelCase          =      matrix_addition(matrix_subtraction(matrix_addition(lowercase__,			lowercase__     ),			lowercase__     ),			lowercase__     )
			__lowerCamelCase          =      matrix_addition(lowercase__,			lowercase__     )
			__lowerCamelCase          =      matrix_addition(lowercase__,			lowercase__     )
			__lowerCamelCase          =      matrix_subtraction(matrix_subtraction(matrix_addition(lowercase__,			lowercase__     ),			lowercase__     ),			lowercase__     )

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





def       a_ (	lowercase__ :list,			lowercase__ :list     ):
			if matrix_dimensions(lowercase__     )[1] != matrix_dimensions(lowercase__     )[0]:
						__lowerCamelCase          =      (
						    """Unable to multiply these matrices, please check the dimensions.\n"""
						    f'Matrix A: {matrixa}\n'
						    f'Matrix B: {matrixa}'
						)
						raise Exception(lowercase__     )
			__lowerCamelCase          =      matrix_dimensions(lowercase__     )
			__lowerCamelCase          =      matrix_dimensions(lowercase__     )

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

			__lowerCamelCase          =      max(*lowercase__,			*lowercase__     )
			__lowerCamelCase          =      int(math.pow(2,			math.ceil(math.loga(lowercase__     )     )     )     )
			__lowerCamelCase          =      matrixa
			__lowerCamelCase          =      matrixa

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

			__lowerCamelCase          =      actual_strassen(lowercase__,			lowercase__     )

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


if __name__ == "__main__":
					__magic_name__						: Optional[Any]    =				[
					    [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],
					]
					__magic_name__						: Optional[int]    =				[[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
					print(strassen(matrixa, matrixa))








"""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 			__snake_case   (lowerCamelCase  ):
							__a					 =       ['''image_processor''', '''tokenizer''']
							__a					 =       '''LayoutLMv2ImageProcessor'''
							__a					 =       ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')




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

										__lowerCamelCase          =      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__(A_     ,							A_   )




							def __call__(     self:    List[str]     ,							A_:    Any     ,							A_:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,							A_:    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None     ,							A_:    Union[List[List[int]], List[List[List[int]]]] = None     ,							A_:    Optional[Union[List[int], List[List[int]]]] = None     ,							A_:    bool = True     ,							A_:    Union[bool, str, PaddingStrategy] = False     ,							A_:    Union[bool, str, TruncationStrategy] = None     ,							A_:    Optional[int] = None     ,							A_:    int = 0     ,							A_:    Optional[int] = None     ,							A_:    Optional[bool] = None     ,							A_:    Optional[bool] = None     ,							A_:    bool = False     ,							A_:    bool = False     ,							A_:    bool = False     ,							A_:    bool = False     ,							A_:    bool = True     ,							A_:    Optional[Union[str, TensorType]] = None     ,							**A_:    Any     ,							):
										# 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
										__lowerCamelCase          =      self.image_processor(images=A_     ,							return_tensors=A_   )

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

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

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

										return encoded_inputs




							def   __a   (     self:    Optional[Any]     ,							A_:    Any     ,							A_:    Tuple   ):
										# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
										__lowerCamelCase          =      []
										for sample_idx in overflow_to_sample_mapping:
													images_with_overflow.append(images[sample_idx]   )

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

										return images_with_overflow




							def   __a   (     self:    Optional[Any]     ,							*A_:    str     ,							**A_:    Dict   ):
										return self.tokenizer.batch_decode(*A_     ,							**A_   )




							def   __a   (     self:    Union[str, Any]     ,							*A_:    Optional[Any]     ,							**A_:    Tuple   ):
										return self.tokenizer.decode(*A_     ,							**A_   )




							@property
							def   __a   (     self:    Union[str, Any]   ):
										return ["input_ids", "bbox", "attention_mask", "image"]




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




							@property
							def   __a   (     self:    List[Any]   ):
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,							A_     ,							)
										return self.image_processor




