



'''simple docstring'''




import numpy as np


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

		__SCREAMING_SNAKE_CASE :	Tuple					 =	False
		__SCREAMING_SNAKE_CASE :	int					 =	0
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	0
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	1E12

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

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

				if error <= error_tol or iterations >= max_iterations:
						__SCREAMING_SNAKE_CASE :	List[Any]					 =	True

				__SCREAMING_SNAKE_CASE :	Tuple					 =	lambda_

		if is_complex:
				__SCREAMING_SNAKE_CASE :	Any					 =	np.real(lambda_      )

		return lambda_, vector


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]      )
		__SCREAMING_SNAKE_CASE :	Tuple					 =	np.array([41, 4, 20]      )
		__SCREAMING_SNAKE_CASE :	str					 =	real_input_matrix.astype(np.complexaaa      )
		__SCREAMING_SNAKE_CASE :	Any					 =	np.triu(1J * complex_input_matrix ,   1      )
		complex_input_matrix += imag_matrix
		complex_input_matrix += -1 * imag_matrix.T
		__SCREAMING_SNAKE_CASE :	List[str]					 =	np.array([41, 4, 20]      ).astype(np.complexaaa      )

		for problem_type in ["real", "complex"]:
				if problem_type == "real":
						__SCREAMING_SNAKE_CASE :	Any					 =	real_input_matrix
						__SCREAMING_SNAKE_CASE :	Dict					 =	real_vector
				elif problem_type == "complex":
						__SCREAMING_SNAKE_CASE :	Any					 =	complex_input_matrix
						__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	complex_vector

				# Our implementation.
				__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	str					 =	power_iteration(_lowerCamelCase ,   _lowerCamelCase      )

				# Numpy implementation.

				# Get eigenvalues and eigenvectors using built-in numpy
				# eigh (eigh used for symmetric or hermetian matrices).
				__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	str					 =	np.linalg.eigh(_lowerCamelCase      )
				# Last eigenvalue is the maximum one.
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	eigen_values[-1]
				# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
				__SCREAMING_SNAKE_CASE :	int					 =	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(_lowerCamelCase      ) - np.abs(_lowerCamelCase      )      ) <= 1E-6


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						test_power_iteration()




'''simple docstring'''




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


UpperCamelCase__ :			Optional[int]												=				{
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

UpperCamelCase__ :			Union[str, Any]												=				logging.get_logger(__name__)






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[Any]    					=	'''maskformer'''
			_A   :	Optional[int]    					=	{'''hidden_size''': '''mask_feature_size'''}
			_A   :	int    					=	['''resnet''', '''swin''']
			_A   :	Any    					=	['''detr''']

			def __init__(							self  :				Any							,  lowerCAmelCase__  :				int = 2_5_6							,  lowerCAmelCase__  :				int = 2_5_6							,  lowerCAmelCase__  :				float = 0.1							,  lowerCAmelCase__  :				bool = False							,  lowerCAmelCase__  :				Optional[Dict] = None							,  lowerCAmelCase__  :				Optional[Dict] = None							,  lowerCAmelCase__  :				float = 0.02							,  lowerCAmelCase__  :				float = 1.0							,  lowerCAmelCase__  :				float = 1.0							,  lowerCAmelCase__  :				float = 1.0							,  lowerCAmelCase__  :				float = 20.0							,  lowerCAmelCase__  :				Optional[bool] = None							,  **lowerCAmelCase__  :				Any							,  ):





					"""simple docstring"""






					if backbone_config is None:
							# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
							__SCREAMING_SNAKE_CASE :	Any					 =	SwinConfig(
							    image_size=3_8_4							,  in_channels=3							,  patch_size=4							,  embed_dim=1_2_8							,  depths=[2, 2, 1_8, 2]							,  num_heads=[4, 8, 1_6, 3_2]							,  window_size=1_2							,  drop_path_rate=0.3							,  out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]							,  )

					if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
							__SCREAMING_SNAKE_CASE :	Dict					 =	backbone_config.pop("""model_type"""		)
							__SCREAMING_SNAKE_CASE :	List[str]					 =	CONFIG_MAPPING[backbone_model_type]
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	config_class.from_dict(lowerCAmelCase__		)

					# verify that the backbone is supported
					if backbone_config.model_type not in self.backbones_supported:
							logger.warning_once(
							    F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
							    F"Supported model types: {','.join(self.backbones_supported		)}"		)

					if decoder_config is None:
							# fall back to https://huggingface.co/facebook/detr-resnet-50
							__SCREAMING_SNAKE_CASE :	List[str]					 =	DetrConfig()
					else:
							# verify that the decoder is supported
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	(
							    decoder_config.pop("""model_type"""		) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		) else decoder_config.model_type
							)
							if decoder_type not in self.decoders_supported:
									raise ValueError(
									    F"Transformer Decoder {decoder_type} not supported, please use one of"
									    F" {','.join(self.decoders_supported		)}"		)
							if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
									__SCREAMING_SNAKE_CASE :	int					 =	CONFIG_MAPPING[decoder_type]
									__SCREAMING_SNAKE_CASE :	Dict					 =	config_class.from_dict(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Any					 =	backbone_config
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	decoder_config
					# main feature dimension for the model
					__SCREAMING_SNAKE_CASE :	List[str]					 =	fpn_feature_size
					__SCREAMING_SNAKE_CASE :	int					 =	mask_feature_size
					# initializer
					__SCREAMING_SNAKE_CASE :	str					 =	init_std
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	init_xavier_std
					# Hungarian matcher && loss
					__SCREAMING_SNAKE_CASE :	Tuple					 =	cross_entropy_weight
					__SCREAMING_SNAKE_CASE :	Dict					 =	dice_weight
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	mask_weight
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	use_auxiliary_loss
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	no_object_weight
					__SCREAMING_SNAKE_CASE :	Dict					 =	output_auxiliary_logits

					__SCREAMING_SNAKE_CASE :	Any					 =	self.decoder_config.encoder_attention_heads
					__SCREAMING_SNAKE_CASE :	int					 =	self.decoder_config.num_hidden_layers
					super().__init__(**lowerCAmelCase__		)

			@classmethod
			def UpperCamelCase__      (							cls  :				List[Any]							,  lowerCAmelCase__  :				PretrainedConfig							,  lowerCAmelCase__  :				PretrainedConfig							,  **lowerCAmelCase__  :				Optional[int]		):





					"""simple docstring"""






					return cls(
					    backbone_config=lowerCAmelCase__							,  decoder_config=lowerCAmelCase__							,  **lowerCAmelCase__							,  )



			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	copy.deepcopy(self.__dict__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.backbone_config.to_dict()
					__SCREAMING_SNAKE_CASE :	str					 =	self.decoder_config.to_dict()
					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.__class__.model_type
					return output