




import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin




@skip_mps
class 		UpperCamelCase__     (lowerCAmelCase__   ,    lowerCAmelCase__   ,    unittest.TestCase					):

    '''simple docstring'''



    lowerCamelCase_				:     Dict     					=				IFSuperResolutionPipeline
    lowerCamelCase_				:     int     					=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
    lowerCamelCase_				:     Tuple     					=				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    lowerCamelCase_				:     int     					=				PipelineTesterMixin.required_optional_params - {"""latents"""}







    def 	_lowercase					(   self					)					->				Dict:
           return self._get_superresolution_dummy_components()







    def 	_lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__=0					)					->				Union[str, Any]:
           if str(UpperCamelCase__					).startswith("mps"					):
                  lowerCamelCase       :				List[str]      			= torch.manual_seed(UpperCamelCase__					)
           else:
                  lowerCamelCase       :				Any      			= torch.Generator(device=UpperCamelCase__					).manual_seed(UpperCamelCase__					)

           lowerCamelCase       :				str      			= floats_tensor((1, 3, 32, 32)					,  rng=random.Random(UpperCamelCase__					)					).to(UpperCamelCase__					)

           lowerCamelCase       :				Union[str, Any]      			= {
               "prompt": "A painting of a squirrel eating a burger",
               "image": image,
               "generator": generator,
               "num_inference_steps": 2,
               "output_type": "numpy",
           }

           return inputs







    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()					,  reason="XFormers attention is only available with CUDA and `xformers` installed"					,  )
    def 	_lowercase					(   self					)					->				int:
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)







    def 	_lowercase					(   self					)					->				Dict:
           self._test_save_load_optional_components()







    @unittest.skipIf(torch_device != "cuda"					,  reason="float16 requires CUDA"					)
    def 	_lowercase					(   self					)					->				List[Any]:
           # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
           super().test_save_load_floataa(expected_max_diff=1e-1					)







    def 	_lowercase					(   self					)					->				List[str]:
           self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)







    def 	_lowercase					(   self					)					->				int:
           self._test_save_load_local()







    def 	_lowercase					(   self					)					->				Dict:
           self._test_inference_batch_single_identical(
               expected_max_diff=1e-2					,  )












import numpy as np

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			str:
       lowerCamelCase			,				lowerCamelCase       :				Dict      			= a.shape
       lowerCamelCase       :				Union[str, Any]      			= min(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Union[str, Any]      			= np.eye(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[Any]      			= a.copy()

       for k in range(t - 1   ):
              # select a column of modified matrix A':
              lowerCamelCase       :				Optional[Any]      			= r[k:, [k]]
              # construct first basis vector
              lowerCamelCase       :				Union[str, Any]      			= np.zeros_like(_SCREAMING_SNAKE_CASE   )
              lowerCamelCase       :				int      			= 1.0
              # determine scaling factor
              lowerCamelCase       :				Any      			= np.linalg.norm(_SCREAMING_SNAKE_CASE   )
              # construct vector v for Householder reflection
              lowerCamelCase       :				List[str]      			= x + np.sign(x[0]   ) * alpha * ea
              v /= np.linalg.norm(_SCREAMING_SNAKE_CASE   )

              # construct the Householder matrix
              lowerCamelCase       :				List[Any]      			= np.eye(m - k   ) - 2.0 * v @ v.T
              # pad with ones and zeros as necessary
              lowerCamelCase       :				Union[str, Any]      			= np.block([[np.eye(_SCREAMING_SNAKE_CASE   ), np.zeros((k, m - k)   )], [np.zeros((m - k, k)   ), q_k]]   )

              lowerCamelCase       :				Any      			= q @ q_k.T
              lowerCamelCase       :				List[Any]      			= q_k @ r

       return q, r


if __name__ == "__main__":
       import doctest

       doctest.testmod()






