





import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class 					A__   (       __snake_case       ,   __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:int						    =   IFImgaImgSuperResolutionPipeline
      _UpperCAmelCase	:List[str]						    =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
      _UpperCAmelCase	:Optional[int]						    =   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}			)
      _UpperCAmelCase	:Dict						    =   PipelineTesterMixin.required_optional_params - {'latents'}





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return self._get_superresolution_dummy_components()





      def        __UpperCamelCase( self       ,							A_       ,							A_=0   ):
       '''simple docstring'''




       if str(A_   ).startswith("mps"   ):
        UpperCamelCase	:					Dict       	=   torch.manual_seed(A_   )
       else:
        UpperCamelCase	:					Any       	=   torch.Generator(device=A_   ).manual_seed(A_   )

       UpperCamelCase	:					Any       	=   floats_tensor((1, 3, 32, 32)       ,							rng=random.Random(A_   )   ).to(A_   )
       UpperCamelCase	:					List[str]       	=   floats_tensor((1, 3, 16, 16)       ,							rng=random.Random(A_   )   ).to(A_   )

       UpperCamelCase	:					List[Any]       	=   {
           "prompt": "A painting of a squirrel eating a burger",
           "image": image,
           "original_image": original_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"""






from ..utils import DummyObject, requires_backends





class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   Optional[int]							  =	['torch', 'torchsde']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """torchsde"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """torchsde"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """torchsde"""]						)



