


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(		_a,				_a,				unittest.TestCase     ):
       lowercase_				:			Optional[int]									=    IFInpaintingPipeline
       lowercase_				:			List[Any]									=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
       lowercase_				:			Union[str, Any]									=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       lowercase_				:			List[Any]									=    PipelineTesterMixin.required_optional_params - {"""latents"""}
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           return self._get_dummy_components()
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=0) ->     Union[str, Any]:




           """simple docstring"""

           if str(lowerCamelCase).startswith('mps'):
               _lowercase :							Any   	=	torch.manual_seed(lowerCamelCase)
           else:
               _lowercase :							List[Any]   	=	torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)

           _lowercase :							Optional[Any]   	=	floats_tensor((1, 3, 32, 32),   rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase :							int   	=	floats_tensor((1, 3, 32, 32),   rng=random.Random(lowerCamelCase)).to(lowerCamelCase)

           _lowercase :							List[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     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           self._test_save_load_optional_components()
       @unittest.skipIf(torch_device != 'cuda',   reason='float16 requires CUDA')
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           super().test_save_load_floataa(expected_max_diff=1E-1)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           self._test_save_load_local()




       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

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






def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		bool:
    return numa ^ numa < 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()


