

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_    ):
				__SCREAMING_SNAKE_CASE      :				str     =    ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE      :				Optional[Any]     =    '''LayoutLMv3ImageProcessor'''
				__SCREAMING_SNAKE_CASE      :				Tuple     =    ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')




				def __init__(      self   ,     snake_case=None   ,     snake_case=None   ,     **snake_case ):
											snake_case_           =					None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.'   ,     snake_case   ,     )
																		snake_case_           =					kwargs.pop('feature_extractor' )

											snake_case_           =					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__(snake_case   ,     snake_case )




				def __call__(      self   ,     snake_case   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = True   ,     snake_case = False   ,     snake_case = None   ,     snake_case = None   ,     snake_case = 0   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = False   ,     snake_case = False   ,     snake_case = False   ,     snake_case = False   ,     snake_case = True   ,     snake_case = None   ,     **snake_case   ,     ):
											# 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_           =					self.image_processor(images=snake_case   ,     return_tensors=snake_case )

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

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

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

											return encoded_inputs




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
											snake_case_           =					[]
											for sample_idx in overflow_to_sample_mapping:
																		images_with_overflow.append(images[sample_idx] )

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

											return images_with_overflow




				def 					a							(      self   ,     *snake_case   ,     **snake_case ):
											return self.tokenizer.batch_decode(*snake_case   ,     **snake_case )




				def 					a							(      self   ,     *snake_case   ,     **snake_case ):
											return self.tokenizer.decode(*snake_case   ,     **snake_case )




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




				@property
				def 					a							(      self ):
											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,     snake_case   ,     )
											return self.image_processor_class




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







from __future__ import annotations







class 				lowercase       :




				def __init__(      self   ,     snake_case ):
											snake_case_           =					TypeError(
											    'Matrices must be formed from a list of zero or more lists containing at '
											    'least one and the same number of values, each of which must be of type '
											    'int or float.' )
											if len(snake_case ) != 0:
																		snake_case_           =					len(rows[0] )
																		if cols == 0:
																									raise error
																		for row in rows:
																									if len(snake_case ) != cols:
																																raise error
																									for value in row:
																																if not isinstance(snake_case   ,     (int, float) ):
																																							raise error
																		snake_case_           =					rows
											else:
																		snake_case_           =					[]




				def 					a							(      self ):
											return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]




				@property
				def 					a							(      self ):
											return len(self.rows )




				@property
				def 					a							(      self ):
											return len(self.rows[0] )




				@property
				def 					a							(      self ):
											return (self.num_rows, self.num_columns)




				@property
				def 					a							(      self ):
											return self.order[0] == self.order[1]




				def 					a							(      self ):
											snake_case_           =					[
											    [0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
											    for row_num in range(self.num_rows )
											]
											return Matrix(snake_case )




				def 					a							(      self ):
											if not self.is_square:
																		return 0
											if self.order == (0, 0):
																		return 1
											if self.order == (1, 1):
																		return int(self.rows[0][0] )
											if self.order == (2, 2):
																		return int(
																		    (self.rows[0][0] * self.rows[1][1])
																		    - (self.rows[0][1] * self.rows[1][0]) )
											else:
																		return sum(
																		    self.rows[0][column] * self.cofactors().rows[0][column]
																		    for column in range(self.num_columns ) )




				def 					a							(      self ):
											return bool(self.determinant() )




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											snake_case_           =					[
											    [
											        self.rows[other_row][other_column]
											        for other_column in range(self.num_columns )
											        if other_column != column
											    ]
											    for other_row in range(self.num_rows )
											    if other_row != row
											]
											return Matrix(snake_case ).determinant()




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											if (row + column) % 2 == 0:
																		return self.get_minor(snake_case   ,     snake_case )
											return -1 * self.get_minor(snake_case   ,     snake_case )




				def 					a							(      self ):
											return Matrix(
											    [
											        [self.get_minor(snake_case   ,     snake_case ) for column in range(self.num_columns )]
											        for row in range(self.num_rows )
											    ] )




				def 					a							(      self ):
											return Matrix(
											    [
											        [
											            self.minors().rows[row][column]
											            if (row + column) % 2 == 0
											            else self.minors().rows[row][column] * -1
											            for column in range(self.minors().num_columns )
											        ]
											        for row in range(self.minors().num_rows )
											    ] )




				def 					a							(      self ):
											snake_case_           =					[
											    [self.cofactors().rows[column][row] for column in range(self.num_columns )]
											    for row in range(self.num_rows )
											]
											return Matrix(snake_case )




				def 					a							(      self ):
											snake_case_           =					self.determinant()
											if not determinant:
																		raise TypeError('Only matrices with a non-zero determinant have an inverse' )
											return self.adjugate() * (1 / determinant)




				def __repr__(      self ):
											return str(self.rows )




				def __str__(      self ):
											if self.num_rows == 0:
																		return "[]"
											if self.num_rows == 1:
																		return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
											return (
											    "["
											    + "\n ".join(
											        [
											            '[' + '. '.join([str(snake_case ) for value in row] ) + '.]'
											            for row in self.rows
											        ] )
											    + "]"
											)




				def 					a							(      self   ,     snake_case   ,     snake_case = None ):
											snake_case_           =					TypeError('Row must be a list containing all ints and/or floats' )
											if not isinstance(snake_case   ,     snake_case ):
																		raise type_error
											for value in row:
																		if not isinstance(snake_case   ,     (int, float) ):
																									raise type_error
											if len(snake_case ) != self.num_columns:
																		raise ValueError(
																		    'Row must be equal in length to the other rows in the matrix' )
											if position is None:
																		self.rows.append(snake_case )
											else:
																		snake_case_           =					self.rows[0:position] + [row] + self.rows[position:]




				def 					a							(      self   ,     snake_case   ,     snake_case = None ):
											snake_case_           =					TypeError(
											    'Column must be a list containing all ints and/or floats' )
											if not isinstance(snake_case   ,     snake_case ):
																		raise type_error
											for value in column:
																		if not isinstance(snake_case   ,     (int, float) ):
																									raise type_error
											if len(snake_case ) != self.num_rows:
																		raise ValueError(
																		    'Column must be equal in length to the other columns in the matrix' )
											if position is None:
																		snake_case_           =					[self.rows[i] + [column[i]] for i in range(self.num_rows )]
											else:
																		snake_case_           =					[
																		    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
																		    for i in range(self.num_rows )
																		]




				def __eq__(      self   ,     snake_case ):
											if not isinstance(snake_case   ,     snake_case ):
																		return NotImplemented
											return self.rows == other.rows




				def __ne__(      self   ,     snake_case ):
											return not self == other




				def __neg__(      self ):
											return self * -1




				def __add__(      self   ,     snake_case ):
											if self.order != other.order:
																		raise ValueError('Addition requires matrices of the same order' )
											return Matrix(
											    [
											        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
											        for i in range(self.num_rows )
											    ] )




				def __sub__(      self   ,     snake_case ):
											if self.order != other.order:
																		raise ValueError('Subtraction requires matrices of the same order' )
											return Matrix(
											    [
											        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
											        for i in range(self.num_rows )
											    ] )




				def __mul__(      self   ,     snake_case ):
											if isinstance(snake_case   ,     (int, float) ):
																		return Matrix(
																		    [[int(element * other ) for element in row] for row in self.rows] )
											elif isinstance(snake_case   ,     snake_case ):
																		if self.num_columns != other.num_rows:
																									raise ValueError(
																									    'The number of columns in the first matrix must '
																									    'be equal to the number of rows in the second' )
																		return Matrix(
																		    [
																		        [Matrix.dot_product(snake_case   ,     snake_case ) for column in other.columns()]
																		        for row in self.rows
																		    ] )
											else:
																		raise TypeError(
																		    'A Matrix can only be multiplied by an int, float, or another matrix' )




				def __pow__(      self   ,     snake_case ):
											if not isinstance(snake_case   ,     snake_case ):
																		raise TypeError('A Matrix can only be raised to the power of an int' )
											if not self.is_square:
																		raise ValueError('Only square matrices can be raised to a power' )
											if other == 0:
																		return self.identity()
											if other < 0:
																		if self.is_invertable():
																									return self.inverse() ** (-other)
																		raise ValueError(
																		    'Only invertable matrices can be raised to a negative power' )
											snake_case_           =					self
											for _ in range(other - 1 ):
																		result *= self
											return result




				@classmethod
				def 					a							(      cls   ,     snake_case   ,     snake_case ):
											return sum(row[i] * column[i] for i in range(len(snake_case ) ) )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




