



import math
from collections.abc import Callable






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      float:
						__lowercase					=				xa
						__lowercase					=				xa
						while True:
												if x_n == x_na or function(lowercase__   ) == function(lowercase__   ):
																		raise ZeroDivisionError("""float division by zero, could not find root"""   )
												__lowercase					=				x_na - (
												    function(lowercase__   ) / ((function(lowercase__   ) - function(lowercase__   )) / (x_na - x_n))
												)
												if abs(x_na - x_na   ) < 10**-5:
																		return x_na
												__lowercase					=				x_na
												__lowercase					=				x_na






def  UpperCAmelCase__    (    lowercase__   ) ->      float:
						return math.pow(lowercase__						,       3   ) - (2 * x) - 5


if __name__ == "__main__":
			print(intersection(f, 3, 3.5))










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 			_lowerCAmelCase   ( _UpperCAmelCase    ,					_UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





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





			def 		snake_case__			(				self	:       Tuple   )						->					Any:
									"""simple docstring"""







									return self._get_superresolution_dummy_components()





			def 		snake_case__			(				self	:       List[str]			,     lowercase	:       Optional[int]			,     lowercase	:       Optional[Any]=0   )						->					Union[str, Any]:
									"""simple docstring"""







									if str(lowercase   ).startswith("""mps"""   ):
															__lowercase					=				torch.manual_seed(lowercase   )
									else:
															__lowercase					=				torch.Generator(device=lowercase   ).manual_seed(lowercase   )

									__lowercase					=				floats_tensor((1, 3, 32, 32)			,     rng=random.Random(lowercase   )   ).to(lowercase   )
									__lowercase					=				floats_tensor((1, 3, 16, 16)			,     rng=random.Random(lowercase   )   ).to(lowercase   )

									__lowercase					=				{
									    """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	:       Optional[Any]   )						->					Tuple:
									"""simple docstring"""







									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )





			def 		snake_case__			(				self	:       List[Any]   )						->					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[Any]   )						->					Dict:
									"""simple docstring"""







									super().test_save_load_floataa(expected_max_diff=1E-1   )





			def 		snake_case__			(				self	:       Dict   )						->					int:
									"""simple docstring"""







									self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )





			def 		snake_case__			(				self	:       Optional[Any]   )						->					int:
									"""simple docstring"""







									self._test_save_load_local()







			def 		snake_case__			(				self	:       Optional[int]   )						->					Optional[int]:
									"""simple docstring"""







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





