




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_UpperCAmelCase		:  str	=   False




class       __lowerCAmelCase				(							unittest.TestCase):
       pass







@nightly
@require_torch_gpu
class       __lowerCAmelCase				(							unittest.TestCase):






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         lowercase			:Dict							=  VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion" )
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(__SCREAMING_SNAKE_CASE )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

         lowercase			:Union[str, Any]							=  "A painting of a squirrel eating a burger "
         lowercase			:Optional[int]							=  torch.manual_seed(0 )
         lowercase			:Optional[int]							=  pipe(
             prompt=__SCREAMING_SNAKE_CASE				,    generator=__SCREAMING_SNAKE_CASE				,    guidance_scale=7.5				,    num_inference_steps=2				,    output_type="numpy" ).images

         with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(__SCREAMING_SNAKE_CASE )
           lowercase			:Dict							=  VersatileDiffusionTextToImagePipeline.from_pretrained(__SCREAMING_SNAKE_CASE )
         pipe.to(__SCREAMING_SNAKE_CASE )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

         lowercase			:Optional[Any]							=  generator.manual_seed(0 )
         lowercase			:int							=  pipe(
             prompt=__SCREAMING_SNAKE_CASE				,    generator=__SCREAMING_SNAKE_CASE				,    guidance_scale=7.5				,    num_inference_steps=2				,    output_type="numpy" ).images

         assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase			:Dict							=  VersatileDiffusionTextToImagePipeline.from_pretrained(
             "shi-labs/versatile-diffusion"				,    torch_dtype=torch.floataa )
         pipe.to(__SCREAMING_SNAKE_CASE )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

         lowercase			:Dict							=  "A painting of a squirrel eating a burger "
         lowercase			:Any							=  torch.manual_seed(0 )
         lowercase			:int							=  pipe(
             prompt=__SCREAMING_SNAKE_CASE				,    generator=__SCREAMING_SNAKE_CASE				,    guidance_scale=7.5				,    num_inference_steps=50				,    output_type="numpy" ).images

         lowercase			:List[str]							=  image[0, 2_53:2_56, 2_53:2_56, -1]

         assert image.shape == (1, 5_12, 5_12, 3)
         lowercase			:Any							=  np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2






'''simple docstring'''



import numpy as np







def    a__       (  a__      ,    a__      ,    a__ = 1E-1_2      ,    a__ = 1_00      ,    ):




     """simple docstring"""


     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__						)
     __SCREAMING_SNAKE_CASE     =      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.

     __SCREAMING_SNAKE_CASE     =      False
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      1E1_2

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

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

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

          __SCREAMING_SNAKE_CASE     =      lambda_

     if is_complex:
          __SCREAMING_SNAKE_CASE     =      np.real(lambda_						)

     return lambda_, vector







def    a__       (  ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]						)
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						)
     __SCREAMING_SNAKE_CASE     =      real_input_matrix.astype(np.complexaaa						)
     __SCREAMING_SNAKE_CASE     =      np.triu(1J * complex_input_matrix      ,    1						)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						).astype(np.complexaaa						)

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

          # Our implementation.
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      power_iteration(a__      ,    a__						)

          # Numpy implementation.

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






