

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 			lowerCamelCase_    (  lowerCAmelCase__	,     lowerCAmelCase__	,     unittest.TestCase		):
							__lowercase				:	Any        =				IFInpaintingPipeline
							__lowercase				:	str        =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
							__lowercase				:	Dict        =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							__lowercase				:	List[str]        =				PipelineTesterMixin.required_optional_params - {"latents"}


							def  lowercase   ( self						)							->   Optional[int]:



								"""simple docstring"""

								return self._get_dummy_components()


							def  lowercase   ( self      ,				lowerCamelCase_      ,				lowerCamelCase_=0						)							->   Dict:



								"""simple docstring"""

								if str(snake_case_						).startswith("mps"						):
									_UpperCamelCase			=   torch.manual_seed(snake_case_						)
								else:
									_UpperCamelCase			=   torch.Generator(device=snake_case_						).manual_seed(snake_case_						)

								_UpperCamelCase			=   floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(snake_case_						)						).to(snake_case_						)
								_UpperCamelCase			=   floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(snake_case_						)						).to(snake_case_						)

								_UpperCamelCase			=   {
								    "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  lowercase   ( self						)							->   Any:



								"""simple docstring"""

								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)


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



								"""simple docstring"""

								self._test_save_load_optional_components()


							@unittest.skipIf(torch_device != "cuda"      ,				reason="float16 requires CUDA"						)
							def  lowercase   ( self						)							->   Optional[Any]:



								"""simple docstring"""

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


							def  lowercase   ( self						)							->   Tuple:



								"""simple docstring"""

								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)


							def  lowercase   ( self						)							->   Optional[int]:



								"""simple docstring"""

								self._test_save_load_local()







							def  lowercase   ( self						)							->   Optional[Any]:



								"""simple docstring"""

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








from maths.prime_factors import prime_factors


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if not isinstance(_UpperCamelCase							,      _UpperCamelCase	):
						__lowercase			       = F'Input value of [number={number}] must be an integer'
						raise TypeError(_UpperCamelCase	)
			if number < 1:
						raise ValueError('''Input must be a positive integer'''	)
			return -1 if len(prime_factors(_UpperCamelCase	)	) % 2 else 1


if __name__ == "__main__":
	import doctest

	doctest.testmod()


