






'''simple docstring'''




import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
    from PIL import Image

class      _UpperCAmelCase    (  unittest.TestCase							,					snake_case_		):




   """simple docstring"""

   def 		lowerCAmelCase				(	self				:			Union[str, Any]	):





         '''simple docstring'''





         _A      =			load_tool("image-question-answering"	)
         self.tool.setup()
         _A      =			load_tool("image-question-answering"   ,   remote=__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





         _A      =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
         _A      =			self.tool(__UpperCAmelCase   ,   "How many cats are sleeping on the couch?"	)
         self.assertEqual(__UpperCAmelCase   ,   "2"	)

   def 		lowerCAmelCase				(	self				:			Tuple	):





         '''simple docstring'''





         _A      =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
         _A      =			self.remote_tool(__UpperCAmelCase   ,   "How many cats are sleeping on the couch?"	)
         self.assertEqual(__UpperCAmelCase   ,   "2"	)

   def 		lowerCAmelCase				(	self				:			List[str]	):





         '''simple docstring'''





         _A      =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
         _A      =			self.tool(image=__UpperCAmelCase   ,   question="How many cats are sleeping on the couch?"	)
         self.assertEqual(__UpperCAmelCase   ,   "2"	)

   def 		lowerCAmelCase				(	self				:			Dict	):





         '''simple docstring'''





         _A      =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
         _A      =			self.remote_tool(image=__UpperCAmelCase   ,   question="How many cats are sleeping on the couch?"	)
         self.assertEqual(__UpperCAmelCase   ,   "2"	)













'''simple docstring'''




from typing import Any

import numpy as np

def       __lowercase		(    __lowercase ) ->		bool:

      '''simple docstring'''


      assert np.shape(__lowercase )[0] == np.shape(__lowercase )[1]

      # If matrix not symmetric, exit right away.
      if np.allclose(__lowercase     ,						matrix.T ) is False:
            return False

      # Get eigenvalues and eignevectors for a symmetric matrix.
      _A  ,			_A      =			np.linalg.eigh(__lowercase )

      # Check sign of all eigenvalues.
      # np.all returns a value of type np.bool_
      return bool(np.all(eigen_values > 0 ) )

def       __lowercase		(    __lowercase ) ->		Any:

      '''simple docstring'''


      _A      =			np.random.randn(__lowercase     ,						__lowercase )
      _A      =			np.dot(__lowercase     ,						random_matrix.T )
      assert _is_matrix_spd(__lowercase )
      return spd_matrix

def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase = 1000     ,						__lowercase = 1e-8     ,						) ->		Any:

      '''simple docstring'''


      assert np.shape(__lowercase )[0] == np.shape(__lowercase )[1]
      assert np.shape(__lowercase )[0] == np.shape(__lowercase )[0]
      assert _is_matrix_spd(__lowercase )

      # Initialize solution guess, residual, search direction.
      _A      =			np.zeros((np.shape(__lowercase )[0], 1) )
      _A      =			np.copy(__lowercase )
      _A      =			np.copy(__lowercase )

      # Set initial errors in solution guess and residual.
      _A      =			1e9
      _A      =			1e9
      _A      =			1e9

      # Set iteration counter to threshold number of iterations.
      _A      =			0

      while error > tol:
            # Save this value so we only calculate the matrix-vector product once.
            _A      =			np.dot(__lowercase     ,						__lowercase )

            # The main algorithm.

            # Update search direction magnitude.
            _A      =			np.dot(ra.T     ,						__lowercase ) / np.dot(pa.T     ,						__lowercase )
            # Update solution guess.
            _A      =			xa + alpha * pa
            # Calculate new residual.
            _A      =			ra - alpha * w
            # Calculate new Krylov subspace scale.
            _A      =			np.dot(r.T     ,						__lowercase ) / np.dot(ra.T     ,						__lowercase )
            # Calculate new A conjuage search direction.
            _A      =			r + beta * pa

            # Calculate errors.
            _A      =			np.linalg.norm(r - ra )
            _A      =			np.linalg.norm(x - xa )
            _A      =			np.maximum(__lowercase     ,						__lowercase )

            # Update variables.
            _A      =			np.copy(__lowercase )
            _A      =			np.copy(__lowercase )
            _A      =			np.copy(__lowercase )

            # Update number of iterations.
            iterations += 1
            if iterations > max_iterations:
                  break

      return x

def       __lowercase		(    ) ->		None:

      '''simple docstring'''


      _A      =			3
      _A      =			_create_spd_matrix(__lowercase )
      _A      =			np.random.randn(__lowercase     ,						1 )
      _A      =			np.dot(__lowercase     ,						__lowercase )

      # Numpy solution.
      _A      =			np.linalg.solve(__lowercase     ,						__lowercase )

      # Our implementation.
      _A      =			conjugate_gradient(__lowercase     ,						__lowercase )

      # Ensure both solutions are close to x_true (and therefore one another).
      assert np.linalg.norm(x_numpy - x_true ) <= 1e-6
      assert np.linalg.norm(x_conjugate_gradient - x_true ) <= 1e-6


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    test_conjugate_gradient()





