





'''simple docstring'''


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_		(		__snake_case    ,__snake_case    ,unittest.TestCase		):
 __lowerCamelCase    :				int								=       IFInpaintingPipeline
 __lowerCamelCase    :				str								=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
 __lowerCamelCase    :				Optional[int]								=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __lowerCamelCase    :				Union[str, Any]								=       PipelineTesterMixin.required_optional_params - {"latents"}




 def 				_snake_case (						self		)     ->				Any:
     return self._get_dummy_components()




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=0		)     ->				Optional[int]:
     if str(snake_case_		).startswith("mps"		):
         _lowerCAmelCase						      =   torch.manual_seed(snake_case_		)
     else:
         _lowerCAmelCase						      =   torch.Generator(device=snake_case_		).manual_seed(snake_case_		)

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

     _lowerCAmelCase						      =   {
         "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 				_snake_case (						self		)     ->				List[str]:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)




 def 				_snake_case (						self		)     ->				List[str]:
     self._test_save_load_optional_components()




 @unittest.skipIf(torch_device != "cuda"      ,  reason="float16 requires CUDA"		)
 def 				_snake_case (						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 				_snake_case (						self		)     ->				List[Any]:
     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2		)




 def 				_snake_case (						self		)     ->				List[str]:
     self._test_save_load_local()





 def 				_snake_case (						self		)     ->				str:
     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2      ,  )









import numpy as np





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> np.array:




    """simple docstring"""




    return (2 / (1 + np.exp(-2 * vector				))) - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()





