



"""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 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



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

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

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

									# second, apply the tokenizer
									if text is not None and self.image_processor.apply_ocr and text_pair is None:
													if isinstance(lowerCamelCase__				,       lowerCamelCase__   ):
																	_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=lowerCamelCase__				,       add_special_tokens=lowerCamelCase__				,       padding=lowerCamelCase__				,       truncation=lowerCamelCase__				,       max_length=lowerCamelCase__				,       stride=lowerCamelCase__				,       pad_to_multiple_of=lowerCamelCase__				,       return_token_type_ids=lowerCamelCase__				,       return_attention_mask=lowerCamelCase__				,       return_overflowing_tokens=lowerCamelCase__				,       return_special_tokens_mask=lowerCamelCase__				,       return_offsets_mapping=lowerCamelCase__				,       return_length=lowerCamelCase__				,       verbose=lowerCamelCase__				,       return_tensors=lowerCamelCase__				,       **lowerCamelCase__				,       )

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

									return encoded_inputs

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

									return images_with_overflow

					def 			snake_case__      (    self				,       *lowerCamelCase__				,       **lowerCamelCase__   ):
									return self.tokenizer.batch_decode(*lowerCamelCase__				,       **lowerCamelCase__   )

					def 			snake_case__      (    self				,       *lowerCamelCase__				,       **lowerCamelCase__   ):
									return self.tokenizer.decode(*lowerCamelCase__				,       **lowerCamelCase__   )

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

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





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





"""simple docstring"""
import numpy as np



def        lowerCAmelCase_(   lowercase_     :							np.ndarray						,   lowercase_     :							np.ndarray						,   lowercase_     :							float = 1e-12						,   lowercase_     :							int = 1_00						,   )	->       tuple[float, np.ndarray]:
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[1]
				# Ensure proper dimensionality.
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(lowercase_							) == np.iscomplexobj(lowercase_							)
				_lowerCamelCase        =				np.iscomplexobj(lowercase_							)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(lowercase_						,   input_matrix.conj().T							)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				_lowerCamelCase        =				False
				_lowerCamelCase        =				0
				_lowerCamelCase        =				0
				_lowerCamelCase        =				1e12

				while not convergence:
								# Multiple matrix by the vector.
								_lowerCamelCase        =				np.dot(lowercase_						,   lowercase_							)
								# Normalize the resulting output vector.
								_lowerCamelCase        =				w / np.linalg.norm(lowercase_							)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								_lowerCamelCase        =				vector.conj().T if is_complex else vector.T
								_lowerCamelCase        =				np.dot(lowercase_						,   np.dot(lowercase_						,   lowercase_							)							)

								# Check convergence.
								_lowerCamelCase        =				np.abs(lambda_ - lambda_previous							) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												_lowerCamelCase        =				True

								_lowerCamelCase        =				lambda_

				if is_complex:
								_lowerCamelCase        =				np.real(lambda_							)

				return lambda_, vector



def        lowerCAmelCase_(   )	->       None:
				_lowerCamelCase        =				np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]							)
				_lowerCamelCase        =				np.array([41, 4, 20]							)
				_lowerCamelCase        =				real_input_matrix.astype(np.complexaaa							)
				_lowerCamelCase        =				np.triu(1j * complex_input_matrix						,   1							)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				_lowerCamelCase        =				np.array([41, 4, 20]							).astype(np.complexaaa							)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												_lowerCamelCase        =				real_input_matrix
												_lowerCamelCase        =				real_vector
								elif problem_type == "complex":
												_lowerCamelCase        =				complex_input_matrix
												_lowerCamelCase        =				complex_vector

								# Our implementation.
								_lowerCamelCase  ,     _lowerCamelCase        =				power_iteration(lowercase_						,   lowercase_							)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								_lowerCamelCase  ,     _lowerCamelCase        =				np.linalg.eigh(lowercase_							)
								# Last eigenvalue is the maximum one.
								_lowerCamelCase        =				eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								_lowerCamelCase        =				eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max							) <= 1e-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(lowercase_							) - np.abs(lowercase_							)							) <= 1e-6


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			test_power_iteration()
