



'''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     (    A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			List[Any]		=			IFInpaintingSuperResolutionPipeline
 __lowerCamelCase       :			Tuple		=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
 __lowerCamelCase       :			Optional[Any]		=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}		)
 __lowerCamelCase       :			str		=			PipelineTesterMixin.required_optional_params - {'latents'}



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      return self._get_superresolution_dummy_components()



 def    a__    (self					,  A					,  A=0			)       ->	List[Any]:


      """simple docstring"""



      if str(A			).startswith('''mps'''			):
           _a           =    torch.manual_seed(A			)
      else:
           _a           =    torch.Generator(device=A			).manual_seed(A			)

      _a           =    floats_tensor((1, 3, 16, 16)					,  rng=random.Random(A			)			).to(A			)
      _a           =    floats_tensor((1, 3, 32, 32)					,  rng=random.Random(A			)			).to(A			)
      _a           =    floats_tensor((1, 3, 32, 32)					,  rng=random.Random(A			)			).to(A			)

      _a           =    {
          '''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    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      self._test_save_load_optional_components()



 @unittest.skipIf(torch_device != '''cuda'''					,  reason='''float16 requires CUDA'''			)
 def    a__    (self			)       ->	str:


      """simple docstring"""



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



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      self._test_save_load_local()



 def    a__    (self			)       ->	Any:


      """simple docstring"""



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








'''simple docstring'''





from __future__ import annotations




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     if len(__A) == 0:
          return False
     _a           =    len(__A) // 2
     if a_list[midpoint] == item:
          return True
     if item < a_list[midpoint]:
          return binary_search(a_list[:midpoint]    ,      __A)
     else:
          return binary_search(a_list[midpoint + 1 :]    ,      __A)


if __name__ == "__main__":
  lowercase_									=    input("Enter numbers separated by comma:\n").strip()
  lowercase_									=    [int(item.strip()) for item in user_input.split(",")]
  lowercase_									=    int(input("Enter the number to be found in the list:\n").strip())
  lowercase_									=    "" if binary_search(sequence, target) else "not "
  print(F"""{target} was {not_str}found in {sequence}""")



