"""simple docstring"""



import numpy as np



def 					_lowerCamelCase(					a ,  a ,  a = 1E-1_2 ,  a = 1_0_0 ,  ):
       assert np.shape(a							)[0] == np.shape(a							)[1]
       # Ensure proper dimensionality.
       assert np.shape(a							)[0] == np.shape(a							)[0]
       # Ensure inputs are either both complex or both real
       assert np.iscomplexobj(a							) == np.iscomplexobj(a							)
       __a     				=  np.iscomplexobj(a							)
       if is_complex:
              # Ensure complex input_matrix is Hermitian
              assert np.array_equal(a ,  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.

       __a     				=  False
       __a     				=  0
       __a     				=  0
       __a     				=  1E1_2

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

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

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

              __a     				=  lambda_

       if is_complex:
              __a     				=  np.real(lambda_							)

       return lambda_, vector



def 					_lowerCamelCase(					):
       __a     				=  np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]							)
       __a     				=  np.array([4_1, 4, 2_0]							)
       __a     				=  real_input_matrix.astype(np.complexaaa							)
       __a     				=  np.triu(1J * complex_input_matrix ,  1							)
       complex_input_matrix += imag_matrix
       complex_input_matrix += -1 * imag_matrix.T
       __a     				=  np.array([4_1, 4, 2_0]							).astype(np.complexaaa							)

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

              # Our implementation.
              __a				, __a     				=  power_iteration(a ,  a							)

              # Numpy implementation.

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   test_power_iteration()

"""simple docstring"""



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

SCREAMING_SNAKE_CASE__:Optional[Any]			=	logging.get_logger(__name__)






@add_end_docstrings(snake_case_   )
class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        super().__init__(*lowerCamelCase  ,						**lowerCamelCase		)
        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  ,						lowerCamelCase=None		):
        __a     				=  {}
        if top_k is not None:
               __a     				=  top_k
        return {}, {}, postprocess_params


 def __call__(				self  ,						lowerCamelCase  ,						**lowerCamelCase		):
        return super().__call__(lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  load_image(lowerCamelCase		)
        __a     				=  self.image_processor(images=lowerCamelCase  ,						return_tensors=self.framework		)
        return model_inputs


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.model(**lowerCamelCase		)
        return model_outputs






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=5		):
        if top_k > self.model.config.num_labels:
               __a     				=  self.model.config.num_labels

        if self.framework == "pt":
               __a     				=  model_outputs.logits.softmax(-1		)[0]
               __a				, __a     				=  probs.topk(lowerCamelCase		)
        elif self.framework == "tf":
               __a     				=  stable_softmax(model_outputs.logits  ,						axis=-1		)[0]
               __a     				=  tf.math.top_k(lowerCamelCase  ,						k=lowerCamelCase		)
               __a				, __a     				=  topk.values.numpy(), topk.indices.numpy()
        else:
               raise ValueError(F"Unsupported framework: {self.framework}"		)

        __a     				=  scores.tolist()
        __a     				=  ids.tolist()
        return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase  ,						lowerCamelCase		)]
