


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 		_snake_case  (				_snake_case						,   _snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	IFInpaintingSuperResolutionPipeline
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}							)
 SCREAMING_SNAKE_CASE__				=	PipelineTesterMixin.required_optional_params - {'latents'}




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return self._get_superresolution_dummy_components()




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=0   ):
       if str(_lowerCamelCase   ).startswith('''mps'''   ):
             a						:Any										=	torch.manual_seed(_lowerCamelCase   )
       else:
             a						:Tuple										=	torch.Generator(device=_lowerCamelCase   ).manual_seed(_lowerCamelCase   )

       a						:List[Any]										=	floats_tensor((1, 3, 16, 16) ,  rng=random.Random(_lowerCamelCase   )   ).to(_lowerCamelCase   )
       a						:Union[str, Any]										=	floats_tensor((1, 3, 32, 32) ,  rng=random.Random(_lowerCamelCase   )   ).to(_lowerCamelCase   )
       a						:int										=	floats_tensor((1, 3, 32, 32) ,  rng=random.Random(_lowerCamelCase   )   ).to(_lowerCamelCase   )

       a						:List[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 	SCREAMING_SNAKE_CASE__    (		self   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       self._test_save_load_optional_components()




 @unittest.skipIf(torch_device != '''cuda''' ,  reason='''float16 requires CUDA'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # 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 	SCREAMING_SNAKE_CASE__    (		self   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       self._test_save_load_local()




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       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,
)





