


"""simple docstring"""







import numpy as np
def _A						(						lowercase       ,    lowercase       ,    lowercase = 1E-12       ,    lowercase = 1_00       ,    ):







    """simple docstring"""
    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		)
    a 		=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.

    a 		=False
    a 		=0
    a 		=0
    a 		=1E12

    while not convergence:
        # Multiple matrix by the vector.
        a 		=np.dot(lowercase       ,    lowercase		)
        # Normalize the resulting output vector.
        a 		=w / np.linalg.norm(lowercase		)
        # 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(lowercase       ,    np.dot(lowercase       ,    lowercase		)		)

        # 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 _A						(						):







    """simple docstring"""
    a 		=np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]		)
    a 		=np.array([41, 4, 20]		)
    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([41, 4, 20]		).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(lowercase       ,    lowercase		)

        # Numpy implementation.

        # Get eigenvalues and eigenvectors using built-in numpy
        # eigh (eigh used for symmetric or hermetian matrices).
        a		,      a 		=np.linalg.eigh(lowercase		)
        # 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(lowercase		) - np.abs(lowercase		)		) <= 1E-6


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       test_power_iteration()





from typing import Union

import fire
import torch
from tqdm import tqdm



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase = "cpu",	lowerCamelCase = None      ):
  lowercase			:Optional[Any]							=  torch.load(lowerCamelCase,	map_location=lowerCamelCase      )
  for k, v in tqdm(state_dict.items()      ):
    if not isinstance(lowerCamelCase,	torch.Tensor      ):
      raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"      )
    lowercase			:List[Any]							=  v.half()
  if save_path is None:  # overwrite src_path
    lowercase			:Optional[Any]							=  src_path
  torch.save(lowerCamelCase,	lowerCamelCase      )


if __name__ == "__main__":
     fire.Fire(convert)



