
import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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			(   _A       ,    _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  List[Any]    =	IFInpaintingPipeline
			__UpperCamelCase :  Dict    =	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
			__UpperCamelCase :  int    =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__UpperCamelCase :  Optional[Any]    =	PipelineTesterMixin.required_optional_params - {"""latents"""}

			def  lowerCAmelCase__		(    self	:    Dict ):
							return self._get_dummy_components()

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

							UpperCamelCase_:							str		=  floats_tensor((1, 3, 32, 32)  ,		rng=random.Random(snake_case_ ) ).to(snake_case_ )
							UpperCamelCase_:							List[str]		=  floats_tensor((1, 3, 32, 32)  ,		rng=random.Random(snake_case_ ) ).to(snake_case_ )

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

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							self._test_save_load_optional_components()

			@unittest.skipIf(torch_device != """cuda"""  ,		reason="""float16 requires CUDA""" )
			def  lowerCAmelCase__		(    self	:    str ):
							# 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	:    Union[str, Any] ):
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )

			def  lowerCAmelCase__		(    self	:    str ):
							self._test_save_load_local()




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








def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		float:
				if mass < 0:
								raise ValueError("""The mass of a body cannot be negative"""  )
				return 0.5 * mass * abs(lowerCamelCase  ) * abs(lowerCamelCase  )


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)






