






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):





	"""simple docstring"""






	UpperCamelCase__								=      IFInpaintingPipeline
	UpperCamelCase__								=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
	UpperCamelCase__								=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	UpperCamelCase__								=      PipelineTesterMixin.required_optional_params - {"latents"}

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return self._get_dummy_components()

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=0    ):
							"""simple docstring"""







							if str(UpperCAmelCase    ).startswith('mps'    ):
													_UpperCAmelCase									=			torch.manual_seed(UpperCAmelCase    )
							else:
													_UpperCAmelCase									=			torch.Generator(device=UpperCAmelCase    ).manual_seed(UpperCAmelCase    )

							_UpperCAmelCase									=			floats_tensor((1, 3, 32, 32)       ,    rng=random.Random(UpperCAmelCase    )    ).to(UpperCAmelCase    )
							_UpperCAmelCase									=			floats_tensor((1, 3, 32, 32)       ,    rng=random.Random(UpperCAmelCase    )    ).to(UpperCAmelCase    )

							_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        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self._test_save_load_optional_components()

	@unittest.skipIf(torch_device != 'cuda'       ,    reason='float16 requires CUDA'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self._test_save_load_local()

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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


"""simple docstring"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       list ,					__lowerCamelCase   :       int = 0		)     ->					list:
  _snake_case					     =       length or len(__lowerCamelCase		)
  _snake_case					     =       False
  for i in range(length - 1		):
    if list_data[i] > list_data[i + 1]:
      _snake_case     ,					_snake_case					     =       list_data[i + 1], list_data[i]
      _snake_case					     =       True

  return list_data if not swapped else bubble_sort(__lowerCamelCase ,					length - 1		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



