
'''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 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		Dict	      =			["image_processor", "tokenizer"]
			lowerCamelCase			:		Any	      =			"LayoutLMv2ImageProcessor"
			lowerCamelCase			:		List[str]	      =			("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")

			def __init__(self			,   A=None			,   A=None			,   **A      ):
								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_					:      int     =		kwargs.pop('''feature_extractor'''      )

								lowerCamelCase_					:      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__(A			,   A      )

			def __call__(self			,   A			,   A = None			,   A = None			,   A = None			,   A = None			,   A = True			,   A = False			,   A = None			,   A = None			,   A = 0			,   A = None			,   A = None			,   A = None			,   A = False			,   A = False			,   A = False			,   A = False			,   A = True			,   A = None			,   **A			,   ):
								# 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_					:      Tuple     =		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_					:      Optional[int]     =		[text]  # add batch dimension (as the image processor always adds a batch dimension)
													lowerCamelCase_					:      List[Any]     =		features['''words''']

								lowerCamelCase_					:      List[str]     =		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_					:      Optional[int]     =		features.pop('''pixel_values'''      )
								if return_overflowing_tokens is True:
													lowerCamelCase_					:      int     =		self.get_overflowing_images(A			,   encoded_inputs['''overflow_to_sample_mapping''']      )
								lowerCamelCase_					:      Union[str, Any]     =		images

								return encoded_inputs

			def        UpperCAmelCase__ (self			,   A			,   A      ):
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								lowerCamelCase_					:      Any     =		[]
								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        UpperCAmelCase__ (self			,   *A			,   **A      ):
								return self.tokenizer.batch_decode(*A			,   **A      )

			def        UpperCAmelCase__ (self			,   *A			,   **A      ):
								return self.tokenizer.decode(*A			,   **A      )

			@property
			def        UpperCAmelCase__ (self      ):
								return ["input_ids", "bbox", "attention_mask", "image"]

			@property
			def        UpperCAmelCase__ (self      ):
								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        UpperCAmelCase__ (self      ):
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,   A			,   )
								return self.image_processor




'''simple docstring'''


from __future__ import annotations

from decimal import Decimal

from numpy import array




def lowercase_	(   _lowercase				)     ->							list[list[float]]:
					'''simple docstring'''

					lowerCamelCase_					:      Tuple     =		Decimal

					# Check if the provided matrix has 2 rows and 2 columns
					# since this implementation only works for 2x2 matrices
					if len(_lowercase				) == 2 and len(matrix[0]				) == 2 and len(matrix[1]				) == 2:
										# Calculate the determinant of the matrix
										lowerCamelCase_					:      Union[str, Any]     =		float(
										    d(matrix[0][0]				) * d(matrix[1][1]				) - d(matrix[1][0]				) * d(matrix[0][1]				)				)
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''				)

										# Creates a copy of the matrix with swapped positions of the elements
										lowerCamelCase_					:      Optional[Any]     =		[[0.0, 0.0], [0.0, 0.0]]
										lowerCamelCase_,      lowerCamelCase_					:      int     =		matrix[1][1], matrix[0][0]
										lowerCamelCase_,      lowerCamelCase_					:      Dict     =		-matrix[1][0], -matrix[0][1]

										# Calculate the inverse of the matrix
										return [
										    [(float(d(_lowercase				)				) / determinant) or 0.0 for n in row] for row in swapped_matrix
										]
					elif (
					    len(_lowercase				) == 3
					    and len(matrix[0]				) == 3
					    and len(matrix[1]				) == 3
					    and len(matrix[2]				) == 3
					):
										# Calculate the determinant of the matrix using Sarrus rule
										lowerCamelCase_					:      Tuple     =		float(
										    (
										        (d(matrix[0][0]				) * d(matrix[1][1]				) * d(matrix[2][2]				))
										        + (d(matrix[0][1]				) * d(matrix[1][2]				) * d(matrix[2][0]				))
										        + (d(matrix[0][2]				) * d(matrix[1][0]				) * d(matrix[2][1]				))
										    )
										    - (
										        (d(matrix[0][2]				) * d(matrix[1][1]				) * d(matrix[2][0]				))
										        + (d(matrix[0][1]				) * d(matrix[1][0]				) * d(matrix[2][2]				))
										        + (d(matrix[0][0]				) * d(matrix[1][2]				) * d(matrix[2][1]				))
										    )				)
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''				)

										# Creating cofactor matrix
										lowerCamelCase_					:      Any     =		[
										    [d(0.0				), d(0.0				), d(0.0				)],
										    [d(0.0				), d(0.0				), d(0.0				)],
										    [d(0.0				), d(0.0				), d(0.0				)],
										]
										lowerCamelCase_					:      Union[str, Any]     =		(d(matrix[1][1]				) * d(matrix[2][2]				)) - (
										    d(matrix[1][2]				) * d(matrix[2][1]				)
										)
										lowerCamelCase_					:      Any     =		-(
										    (d(matrix[1][0]				) * d(matrix[2][2]				)) - (d(matrix[1][2]				) * d(matrix[2][0]				))
										)
										lowerCamelCase_					:      Tuple     =		(d(matrix[1][0]				) * d(matrix[2][1]				)) - (
										    d(matrix[1][1]				) * d(matrix[2][0]				)
										)
										lowerCamelCase_					:      Optional[Any]     =		-(
										    (d(matrix[0][1]				) * d(matrix[2][2]				)) - (d(matrix[0][2]				) * d(matrix[2][1]				))
										)
										lowerCamelCase_					:      Tuple     =		(d(matrix[0][0]				) * d(matrix[2][2]				)) - (
										    d(matrix[0][2]				) * d(matrix[2][0]				)
										)
										lowerCamelCase_					:      List[Any]     =		-(
										    (d(matrix[0][0]				) * d(matrix[2][1]				)) - (d(matrix[0][1]				) * d(matrix[2][0]				))
										)
										lowerCamelCase_					:      Any     =		(d(matrix[0][1]				) * d(matrix[1][2]				)) - (
										    d(matrix[0][2]				) * d(matrix[1][1]				)
										)
										lowerCamelCase_					:      Tuple     =		-(
										    (d(matrix[0][0]				) * d(matrix[1][2]				)) - (d(matrix[0][2]				) * d(matrix[1][0]				))
										)
										lowerCamelCase_					:      str     =		(d(matrix[0][0]				) * d(matrix[1][1]				)) - (
										    d(matrix[0][1]				) * d(matrix[1][0]				)
										)

										# Transpose the cofactor matrix (Adjoint matrix)
										lowerCamelCase_					:      str     =		array(_lowercase				)
										for i in range(3				):
															for j in range(3				):
																				lowerCamelCase_					:      int     =		cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
										lowerCamelCase_					:      int     =		array(_lowercase				)
										for i in range(3				):
															for j in range(3				):
																				inverse_matrix[i][j] /= d(_lowercase				)

        # Calculate the inverse of the matrix
										return [[float(d(_lowercase				)				) or 0.0 for n in row] for row in inverse_matrix]
					raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''				)


