






"""simple docstring"""



import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    =ComputeEnvironment.AMAZON_SAGEMAKER
   SCREAMING_SNAKE_CASE_    =True
   SCREAMING_SNAKE_CASE_    ='''ml.p3.2xlarge'''
   SCREAMING_SNAKE_CASE_    ='''accelerate_sagemaker_execution_role'''
   SCREAMING_SNAKE_CASE_    ='''hf-sm'''
   SCREAMING_SNAKE_CASE_    ='''us-east-1'''
   SCREAMING_SNAKE_CASE_    =1
   SCREAMING_SNAKE_CASE_    ='''accelerate-sagemaker-1'''
   SCREAMING_SNAKE_CASE_    ='''1.6'''
   SCREAMING_SNAKE_CASE_    ='''4.4'''
   SCREAMING_SNAKE_CASE_    ='''train.py'''
   SCREAMING_SNAKE_CASE_    =[
       '''--model_name_or_path''',
       '''bert''',
       '''--do_train''',
       '''False''',
       '''--epochs''',
       '''3''',
       '''--learning_rate''',
       '''5e-5''',
       '''--max_steps''',
       '''50.5''',
   ]
   SCREAMING_SNAKE_CASE_    =[
       '''--model_name_or_path''',
       '''bert''',
       '''--do_train''',
       '''--do_test''',
       '''False''',
       '''--do_predict''',
       '''--epochs''',
       '''3''',
       '''--learning_rate''',
       '''5e-5''',
       '''--max_steps''',
       '''50.5''',
   ]




class 			lowerCAmelCase__			(    unittest.TestCase   ):
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        # If no defaults are changed, `to_kwargs` returns an empty dict.
        UpperCAmelCase__						:    List[Any]							=							_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args							)
        assert isinstance(converted_args["model_name_or_path"]							,			snake_case__							)
        assert isinstance(converted_args["do_train"]							,			snake_case__							)
        assert isinstance(converted_args["epochs"]							,			snake_case__							)
        assert isinstance(converted_args["learning_rate"]							,			snake_case__							)
        assert isinstance(converted_args["max_steps"]							,			snake_case__							)

        with pytest.raises(snake_case__							):
             _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args							)










"""simple docstring"""



import numpy as np


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : np.ndarray			,					snake_case       : np.ndarray			,					snake_case       : float = 1E-1_2			,					snake_case       : int = 100			,					)->		tuple[float, np.ndarray]:






     '''simple docstring'''



     assert np.shape(snake_case				)[0] == np.shape(snake_case				)[1]
     # Ensure proper dimensionality.
     assert np.shape(snake_case				)[0] == np.shape(snake_case				)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(snake_case				) == np.iscomplexobj(snake_case				)
     UpperCAmelCase__						:    str							=							np.iscomplexobj(snake_case				)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(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__						:    Union[str, Any]							=							False
     UpperCAmelCase__						:    List[Any]							=							0
     UpperCAmelCase__						:    Any							=							0
     UpperCAmelCase__						:    List[str]							=							1E1_2

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

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

          if error <= error_tol or iterations >= max_iterations:
               UpperCAmelCase__						:    Tuple							=							True

          UpperCAmelCase__						:    Tuple							=							lambda_

     if is_complex:
          UpperCAmelCase__						:    str							=							np.real(lambda_				)

     return lambda_, vector


def 	SCREAMING_SNAKE_CASE__       (  )->		None:






     '''simple docstring'''



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

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

          # Our implementation.
          UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							power_iteration(snake_case			,					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(snake_case				)
          # Last eigenvalue is the maximum one.
          UpperCAmelCase__						:    str							=							eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          UpperCAmelCase__						:    Optional[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(snake_case				) - np.abs(snake_case				)				) <= 1E-6


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 test_power_iteration()


