






import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		IFInpaintingSuperResolutionPipeline
					a_     =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
					a_     =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}						)
					a_     =		PipelineTesterMixin.required_optional_params - {"latents"}



					def 	_lowercase					( self					:  str	):
						return self._get_superresolution_dummy_components()



					def 	_lowercase					( self					:  Dict							,		__A					:  int							,		__A					:  Optional[Any]=0	):
						if str(__A	).startswith("mps"	):
							snake_case__			:				Tuple       =  torch.manual_seed(__A	)
						else:
							snake_case__			:				Union[str, Any]       =  torch.Generator(device=__A	).manual_seed(__A	)

						snake_case__			:				str       =  floats_tensor((1, 3, 1_6, 1_6)							,		rng=random.Random(__A	)	).to(__A	)
						snake_case__			:				Any       =  floats_tensor((1, 3, 3_2, 3_2)							,		rng=random.Random(__A	)	).to(__A	)
						snake_case__			:				int       =  floats_tensor((1, 3, 3_2, 3_2)							,		rng=random.Random(__A	)	).to(__A	)

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



					def 	_lowercase					( self					:  int	):
						self._test_save_load_optional_components()



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



					def 	_lowercase					( self					:  Union[str, Any]	):
						self._test_save_load_local()






					def 	_lowercase					( self					:  Optional[Any]	):
						self._test_inference_batch_single_identical(
						    expected_max_diff=1e-2							,		)











# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)



