




import numpy as np




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = 1E-12	,     lowerCamelCase = 100	,     ):
				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   )
				__magic_name__	:	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.

				__magic_name__	:	List[Any]								=False
				__magic_name__	:	List[Any]								=0
				__magic_name__	:	List[str]								=0
				__magic_name__	:	str								=1E12

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

								# Check convergence.
								__magic_name__	:	List[str]								=np.abs(lambda_ - lambda_previous   ) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												__magic_name__	:	int								=True

								__magic_name__	:	Optional[Any]								=lambda_

				if is_complex:
								__magic_name__	:	Union[str, Any]								=np.real(lambda_   )

				return lambda_, vector




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

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												__magic_name__	:	Union[str, Any]								=real_input_matrix
												__magic_name__	:	List[Any]								=real_vector
								elif problem_type == "complex":
												__magic_name__	:	Optional[Any]								=complex_input_matrix
												__magic_name__	:	Tuple								=complex_vector

								# Our implementation.
								__magic_name__				, __magic_name__	:	Any								=power_iteration(lowerCamelCase	,     lowerCamelCase   )

								# Numpy implementation.

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










from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_tf_available():
							import tensorflow as tf

							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							from ..tf_utils import stable_softmax

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

UpperCAmelCase_		:				str									=     logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase__			)
class 		__A      (			UpperCamelCase__			):

						def __init__(		self   :Any    , *__snake_case   :List[Any]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

										super().__init__(*__snake_case    , **__snake_case )
										requires_backends(self    , """vision""" )
										self.check_model_type(
										    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
										    if self.framework == """tf"""
										    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )

						def     A__						(		self   :int    , __snake_case   :Optional[Any]=None ):
										'''simple docstring'''

										__magic_name__	:	Any								={}
										if top_k is not None:
														__magic_name__	:	str								=top_k
										return {}, {}, postprocess_params

						def __call__(		self   :Tuple    , __snake_case   :Union[str, List[str], "Image.Image", List["Image.Image"]]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

										return super().__call__(__snake_case    , **__snake_case )

						def     A__						(		self   :str    , __snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=load_image(__snake_case )
										__magic_name__	:	Any								=self.image_processor(images=__snake_case    , return_tensors=self.framework )
										return model_inputs

						def     A__						(		self   :int    , __snake_case   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	str								=self.model(**__snake_case )
										return model_outputs


						def     A__						(		self   :List[str]    , __snake_case   :int    , __snake_case   :Union[str, Any]=5 ):
										'''simple docstring'''

										if top_k > self.model.config.num_labels:
														__magic_name__	:	Dict								=self.model.config.num_labels

										if self.framework == "pt":
														__magic_name__	:	int								=model_outputs.logits.softmax(-1 )[0]
														__magic_name__				, __magic_name__	:	str								=probs.topk(__snake_case )
										elif self.framework == "tf":
														__magic_name__	:	Union[str, Any]								=stable_softmax(model_outputs.logits    , axis=-1 )[0]
														__magic_name__	:	List[str]								=tf.math.top_k(__snake_case    , k=__snake_case )
														__magic_name__				, __magic_name__	:	str								=topk.values.numpy(), topk.indices.numpy()
										else:
														raise ValueError(f"Unsupported framework: {self.framework}" )

										__magic_name__	:	str								=scores.tolist()
										__magic_name__	:	Union[str, Any]								=ids.tolist()
										return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__snake_case    , __snake_case )]




