


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      UpperCamelCase					(		lowercase__						,      lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	Optional[int]									=IFInpaintingSuperResolutionPipeline
   lowercase					:	Any									=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
   lowercase					:	List[Any]									=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}     )
   lowercase					:	Union[str, Any]									=PipelineTesterMixin.required_optional_params - {"""latents"""}
   def 		UpperCamelCase  (     self		):
        return self._get_superresolution_dummy_components()
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=0		):
        if str(UpperCamelCase_		).startswith('''mps'''		):
             lowercase_       :int     =       torch.manual_seed(UpperCamelCase_		)
        else:
             lowercase_       :List[Any]     =       torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)

        lowercase_       :Optional[Any]     =       floats_tensor((1, 3, 16, 16)   ,							rng=random.Random(UpperCamelCase_		)		).to(UpperCamelCase_		)
        lowercase_       :Optional[int]     =       floats_tensor((1, 3, 32, 32)   ,							rng=random.Random(UpperCamelCase_		)		).to(UpperCamelCase_		)
        lowercase_       :int     =       floats_tensor((1, 3, 32, 32)   ,							rng=random.Random(UpperCamelCase_		)		).to(UpperCamelCase_		)

        lowercase_       :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 		UpperCamelCase  (     self		):
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)
   def 		UpperCamelCase  (     self		):
        self._test_save_load_optional_components()
   @unittest.skipIf(torch_device != '''cuda'''   ,							reason='''float16 requires CUDA'''		)
   def 		UpperCamelCase  (     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 		UpperCamelCase  (     self		):
        self._test_attention_slicing_forward_pass(expected_max_diff=1E-2		)
   def 		UpperCamelCase  (     self		):
        self._test_save_load_local()




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





from __future__ import annotations




def     UpperCamelCase    (      _a       ,		_a = None       ,		_a = None		)					->							None:
     '''simple docstring'''





     if start is None:
          lowercase_       :str     =       0

     if end is None:
          lowercase_       :str     =       len(_a		) - 1

     if start >= end:
          return

     lowercase_       :Dict     =       (start + end) // 2

     slowsort(_a       ,		_a       ,		_a		)
     slowsort(_a       ,		mid + 1       ,		_a		)

     if sequence[end] < sequence[mid]:
          lowercase_    ,      lowercase_       :List[Any]     =       sequence[mid], sequence[end]

     slowsort(_a       ,		_a       ,		end - 1		)


if __name__ == "__main__":
   from doctest import testmod

   testmod()

