



"""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        _UpperCAmelCase       (			lowerCAmelCase__    ,     lowerCAmelCase__    ,     unittest.TestCase):
 _lowerCAmelCase   :			Optional[int]							=		IFInpaintingSuperResolutionPipeline
 _lowerCAmelCase   :			Tuple							=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
 _lowerCAmelCase   :			List[str]							=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""})
 _lowerCAmelCase   :			Union[str, Any]							=		PipelineTesterMixin.required_optional_params - {"""latents"""}

 def 						_snake_case					(     self		:       Dict				):
     return self._get_superresolution_dummy_components()

 def 						_snake_case					(     self		:       Any  ,     lowercase_		:       List[Any]  ,     lowercase_		:       Optional[int]=0				):
     if str(lowercase_				).startswith('''mps'''				):
         snake_case_ :     Tuple				=	torch.manual_seed(lowercase_				)
     else:
         snake_case_ :     Optional[int]				=	torch.Generator(device=lowercase_				).manual_seed(lowercase_				)

     snake_case_ :     Optional[Any]				=	floats_tensor((1, 3, 16, 16)  ,     rng=random.Random(lowercase_				)				).to(lowercase_				)
     snake_case_ :     List[str]				=	floats_tensor((1, 3, 32, 32)  ,     rng=random.Random(lowercase_				)				).to(lowercase_				)
     snake_case_ :     Any				=	floats_tensor((1, 3, 32, 32)  ,     rng=random.Random(lowercase_				)				).to(lowercase_				)

     snake_case_ :     Tuple				=	{
         '''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 						_snake_case					(     self		:       List[str]				):
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)

 def 						_snake_case					(     self		:       List[Any]				):
     self._test_save_load_optional_components()

 @unittest.skipIf(torch_device != '''cuda'''  ,     reason='''float16 requires CUDA'''				)
 def 						_snake_case					(     self		:       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 						_snake_case					(     self		:       Tuple				):
     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)

 def 						_snake_case					(     self		:       Dict				):
     self._test_save_load_local()






 def 						_snake_case					(     self		:       Optional[int]				):
     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2  ,     )










"""simple docstring"""

from __future__ import annotations




def __lowercase			(							_a		,			_a		,			_a		,			):
    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()





