





'''simple docstring'''






import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 		lowerCamelCase  (      _A      ,   _A      ,   unittest.TestCase       ):
	snake_case_      =   IFInpaintingSuperResolutionPipeline
	snake_case_      =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
	snake_case_      =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}       )
	snake_case_      =   PipelineTesterMixin.required_optional_params - {"latents"}



	def   _lowerCamelCase    (   self	):
			return self._get_superresolution_dummy_components()



	def   _lowerCamelCase    (   self		,      a_		,      a_=0	):
			if str(a_	).startswith("mps"	):
					lowerCAmelCase     :    List[str]						=   torch.manual_seed(a_	)
			else:
					lowerCAmelCase     :    Dict						=   torch.Generator(device=a_	).manual_seed(a_	)

			lowerCAmelCase     :    Any						=   floats_tensor((1, 3, 16, 16)		,      rng=random.Random(a_	)	).to(a_	)
			lowerCAmelCase     :    int						=   floats_tensor((1, 3, 32, 32)		,      rng=random.Random(a_	)	).to(a_	)
			lowerCAmelCase     :    int						=   floats_tensor((1, 3, 32, 32)		,      rng=random.Random(a_	)	).to(a_	)

			lowerCAmelCase     :    Optional[int]						=   {
			    "prompt": "A painting of a squirrel eating a burger",
			    "image": image,
			    "original_image": original_image,
			    "mask_image": mask_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   _lowerCamelCase    (   self	):
			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)



	def   _lowerCamelCase    (   self	):
			self._test_save_load_optional_components()



	@unittest.skipIf(torch_device != "cuda"		,      reason="float16 requires CUDA"	)
	def   _lowerCamelCase    (   self	):
			# 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   _lowerCamelCase    (   self	):
			self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)



	def   _lowerCamelCase    (   self	):
			self._test_save_load_local()





	def   _lowerCamelCase    (   self	):
			self._test_inference_batch_single_identical(
			    expected_max_diff=1e-2		,      )










'''simple docstring'''






from __future__ import annotations






def      __A						(							a_						:  float      ,a_						:  float      ,a_						:  float      ,):
		if (stress, tangential_force, area).count(0	) != 1:
				raise ValueError("You cannot supply more or less than 2 values"	)
		elif stress < 0:
				raise ValueError("Stress cannot be negative"	)
		elif tangential_force < 0:
				raise ValueError("Tangential Force cannot be negative"	)
		elif area < 0:
				raise ValueError("Area cannot be negative"	)
		elif stress == 0:
				return (
				    "stress",
				    tangential_force / area,
				)
		elif tangential_force == 0:
				return (
				    "tangential_force",
				    stress * area,
				)
		else:
				return (
				    "area",
				    tangential_force / stress,
				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



