





import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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'''






		__a       :       Optional[Any]       =	IFImgaImgSuperResolutionPipeline
		__a       :       Any       =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
		__a       :       Tuple       =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}				)
		__a       :       Any       =	PipelineTesterMixin.required_optional_params - {"""latents"""}






		def 						snake_case__				(							self  )       -> str:






									"""simple docstring"""
									return self._get_superresolution_dummy_components()






		def 						snake_case__				(							self,   snake_case__,   snake_case__=0  )       -> Optional[int]:






									"""simple docstring"""
									if str(snake_case__  ).startswith("""mps"""  ):
																lowercase_	:   List[Any]  				=     torch.manual_seed(snake_case__  )
									else:
																lowercase_	:   List[Any]  				=     torch.Generator(device=snake_case__  ).manual_seed(snake_case__  )

									lowercase_	:   Any  				=     floats_tensor((1, 3, 32, 32),   rng=random.Random(snake_case__  )  ).to(snake_case__  )
									lowercase_	:   Any  				=     floats_tensor((1, 3, 16, 16),   rng=random.Random(snake_case__  )  ).to(snake_case__  )

									lowercase_	:   Optional[Any]  				=     {
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """image""": image,
									    """original_image""": original_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 						snake_case__				(							self  )       -> Dict:






									"""simple docstring"""
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )






		def 						snake_case__				(							self  )       -> Union[str, Any]:






									"""simple docstring"""
									self._test_save_load_optional_components()






		@unittest.skipIf(torch_device != """cuda""",   reason="""float16 requires CUDA"""  )
		def 						snake_case__				(							self  )       -> List[str]:






									"""simple docstring"""
									# 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 						snake_case__				(							self  )       -> Union[str, Any]:






									"""simple docstring"""
									self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )






		def 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									self._test_save_load_local()






		def 						snake_case__				(							self  )       -> List[str]:






									"""simple docstring"""
									self._test_inference_batch_single_identical(
									    expected_max_diff=1E-2,   )






from __future__ import annotations

import numpy as np





def  __magic_name__  (				lowercase				)				-> Tuple:

							"""simple docstring"""



							return np.maximum(0				,       lowercase				)


if __name__ == "__main__":
							print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]