

'''simple docstring'''
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    a__  (							lowerCamelCase_  ,      lowerCamelCase_  ,      unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE    :     Optional[Any]	       =							IFInpaintingSuperResolutionPipeline
   _SCREAMING_SNAKE_CASE    :     Union[str, Any]	       =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
   _SCREAMING_SNAKE_CASE    :     Dict	       =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}  )
   _SCREAMING_SNAKE_CASE    :     Any	       =							PipelineTesterMixin.required_optional_params - {'latents'}


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      return self._get_superresolution_dummy_components()


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase=0  ):





      """simple docstring"""





      if str(_UpperCamelCase  ).startswith("mps"  ):
         _lowercase			:      str			     =   torch.manual_seed(_UpperCamelCase  )
      else:
         _lowercase			:      List[str]			     =   torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )

      _lowercase			:      Tuple			     =   floats_tensor((1, 3, 16, 16)			,				rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )
      _lowercase			:      int			     =   floats_tensor((1, 3, 32, 32)			,				rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )
      _lowercase			:      List[str]			     =   floats_tensor((1, 3, 32, 32)			,				rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )

      _lowercase			:      List[str]			     =   {
          "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 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      self._test_save_load_optional_components()


   @unittest.skipIf(torch_device != "cuda"			,				reason="float16 requires CUDA"  )
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      self._test_save_load_local()






   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





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









from __future__ import annotations
def      a      (			A__	:   list[int]     )    ->      int:





      """simple docstring"""



      if not nums:
            return 0
      _lowercase	       =nums[0]
      _lowercase	       =0
      for num in nums[1:]:
            _lowercase       ,       _lowercase	       =(
                max_excluding + num,
                max(A__      ,   A__     ),
            )
      return max(A__      ,   A__     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


