
'''simple docstring'''





def   A			(							A_				:       int							):






						if not isinstance(__lowerCamelCase     ,				__lowerCamelCase							) or number < 0:
												raise ValueError('''Input must be a non-negative integer'''							)

						snake_case							:  Tuple			=     number & ~(number - 1)
						snake_case							:  List[Any]			=     0
						while intermediate:
												intermediate >>= 1
												index += 1
						return index - 1


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)





'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			,      lowerCamelCase__			,      unittest.TestCase			):
     UpperCAmelCase			       =					IFImgaImgPipeline
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     UpperCAmelCase			       =					PipelineTesterMixin.required_optional_params - {'''latents'''}






     def 			a_       (      self		)							->     Optional[Any]:
          return self._get_dummy_components()






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=0		)							->     Any:
          if str(__UpperCamelCase		).startswith("mps"		):
               _a            =		torch.manual_seed(__UpperCamelCase		)
          else:
               _a            =		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)

          _a            =		floats_tensor((1, 3, 32, 32)  ,				rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)

          _a            =		{
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs






     def 			a_       (      self		)							->     Any:
          self._test_save_load_optional_components()






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






     @unittest.skipIf(torch_device != "cuda"  ,				reason="float16 requires CUDA"		)
     def 			a_       (      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		)






     @unittest.skipIf(torch_device != "cuda"  ,				reason="float16 requires CUDA"		)
     def 			a_       (      self		)							->     int:
          super().test_floataa_inference(expected_max_diff=1e-1		)






     def 			a_       (      self		)							->     Dict:
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)






     def 			a_       (      self		)							->     Optional[Any]:
          self._test_save_load_local()






     def 			a_       (      self		)							->     Union[str, Any]:
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2  ,				)

