


"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}


class       SCREAMING_SNAKE_CASE__				(		lowercase   ,    lowercase				):
       """simple docstring"""


       a  :				str   ="swin"

       a  :				Union[str, Any]   ={
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }

       def __init__(						self		,     snake_case__=224		,     snake_case__=4		,     snake_case__=3		,     snake_case__=96		,     snake_case__=[2, 2, 6, 2]		,     snake_case__=[3, 6, 12, 24]		,     snake_case__=7		,     snake_case__=4.0		,     snake_case__=True		,     snake_case__=0.0		,     snake_case__=0.0		,     snake_case__=0.1		,     snake_case__="gelu"		,     snake_case__=False		,     snake_case__=0.02		,     snake_case__=1e-5		,     snake_case__=32		,     snake_case__=None		,     snake_case__=None		,     **snake_case__		,     ):






         """simple docstring"""





         super().__init__(**snake_case__						)

         lowerCAmelCase     :     Any								=							image_size
         lowerCAmelCase     :     Union[str, Any]								=							patch_size
         lowerCAmelCase     :     List[Any]								=							num_channels
         lowerCAmelCase     :     Tuple								=							embed_dim
         lowerCAmelCase     :     Any								=							depths
         lowerCAmelCase     :     List[Any]								=							len(snake_case__						)
         lowerCAmelCase     :     List[str]								=							num_heads
         lowerCAmelCase     :     Union[str, Any]								=							window_size
         lowerCAmelCase     :     Optional[Any]								=							mlp_ratio
         lowerCAmelCase     :     List[str]								=							qkv_bias
         lowerCAmelCase     :     int								=							hidden_dropout_prob
         lowerCAmelCase     :     Union[str, Any]								=							attention_probs_dropout_prob
         lowerCAmelCase     :     Any								=							drop_path_rate
         lowerCAmelCase     :     Optional[int]								=							hidden_act
         lowerCAmelCase     :     Optional[int]								=							use_absolute_embeddings
         lowerCAmelCase     :     int								=							layer_norm_eps
         lowerCAmelCase     :     int								=							initializer_range
         lowerCAmelCase     :     Optional[Any]								=							encoder_stride
         # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
         # this indicates the channel dimension after the last stage of the model
         lowerCAmelCase     :     int								=							int(embed_dim * 2 ** (len(snake_case__						) - 1)						)
         lowerCAmelCase     :     Tuple								=							["stem"] + [f"""stage{idx}""" for idx in range(1		,     len(snake_case__						) + 1						)]
         lowerCAmelCase						,  lowerCAmelCase     :     List[Any]								=							get_aligned_output_features_output_indices(
             out_features=snake_case__		,     out_indices=snake_case__		,     stage_names=self.stage_names						)


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Optional[Any]   =version.parse("1.11"				)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]						)







       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return 1e-4






import numpy as np



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 1e-12  ,			SCREAMING_SNAKE_CASE = 100  ,			):
    '''simple docstring'''







    assert np.shape(SCREAMING_SNAKE_CASE	)[0] == np.shape(SCREAMING_SNAKE_CASE	)[1]
    # Ensure proper dimensionality.
    assert np.shape(SCREAMING_SNAKE_CASE	)[0] == np.shape(SCREAMING_SNAKE_CASE	)[0]
    # Ensure inputs are either both complex or both real
    assert np.iscomplexobj(SCREAMING_SNAKE_CASE	) == np.iscomplexobj(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[Any]			  =		np.iscomplexobj(SCREAMING_SNAKE_CASE	)
    if is_complex:
        # Ensure complex input_matrix is Hermitian
        assert np.array_equal(SCREAMING_SNAKE_CASE  ,			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.

    __UpperCamelCase     :str			  =		False
    __UpperCamelCase     :int			  =		0
    __UpperCamelCase     :Optional[Any]			  =		0
    __UpperCamelCase     :Union[str, Any]			  =		1e12

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

        # Check convergence.
        __UpperCamelCase     :Optional[Any]			  =		np.abs(lambda_ - lambda_previous	) / lambda_
        iterations += 1

        if error <= error_tol or iterations >= max_iterations:
            __UpperCamelCase     :Dict			  =		True

        __UpperCamelCase     :List[Any]			  =		lambda_

    if is_complex:
        __UpperCamelCase     :Tuple			  =		np.real(lambda_	)

    return lambda_, vector



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]	)
    __UpperCamelCase     :Optional[Any]			  =		np.array([41, 4, 20]	)
    __UpperCamelCase     :Any			  =		real_input_matrix.astype(np.complexaaa	)
    __UpperCamelCase     :Dict			  =		np.triu(1j * complex_input_matrix  ,			1	)
    complex_input_matrix += imag_matrix
    complex_input_matrix += -1 * imag_matrix.T
    __UpperCamelCase     :Optional[int]			  =		np.array([41, 4, 20]	).astype(np.complexaaa	)

    for problem_type in ["real", "complex"]:
        if problem_type == "real":
            __UpperCamelCase     :Any			  =		real_input_matrix
            __UpperCamelCase     :int			  =		real_vector
        elif problem_type == "complex":
            __UpperCamelCase     :Tuple			  =		complex_input_matrix
            __UpperCamelCase     :Optional[Any]			  =		complex_vector

        # Our implementation.
        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		power_iteration(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

        # Numpy implementation.

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


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    test_power_iteration()





