

import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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		(  __magic_name__   ,     __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      Any       =  IFInpaintingPipeline
  SCREAMING_SNAKE_CASE    :      int       =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  SCREAMING_SNAKE_CASE    :      int       =  PipelineTesterMixin.required_optional_params - {'''latents'''}

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self._get_dummy_components()

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=0					):





        """simple docstring"""







        if str(_SCREAMING_SNAKE_CASE					).startswith('mps'					):
              SCREAMING_SNAKE_CASE_				:  str           =     torch.manual_seed(_SCREAMING_SNAKE_CASE					)
        else:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Any           =     floats_tensor((1, 3, 32, 32) ,					rng=random.Random(_SCREAMING_SNAKE_CASE					)					).to(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     floats_tensor((1, 3, 32, 32) ,					rng=random.Random(_SCREAMING_SNAKE_CASE					)					).to(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     {
            'prompt': 'A painting of a squirrel eating a burger',
            'image': 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					):





        """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 ,					)









def    A_  (							a					,   a  ):

      """simple docstring"""



      return int((input_a, input_a).count(0  ) != 0  )

def    A_  (							):

      """simple docstring"""



      assert nand_gate(0					,   0  ) == 1
      assert nand_gate(0					,   1  ) == 1
      assert nand_gate(1					,   0  ) == 1
      assert nand_gate(1					,   1  ) == 0


if __name__ == "__main__":
    print(nand_gate(0, 0))
    print(nand_gate(0, 1))
    print(nand_gate(1, 0))
    print(nand_gate(1, 1))






