



"""simple docstring"""







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 				UpperCamelCase__       (     lowercase_, lowercase_, unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					IFInpaintingPipeline
      _SCREAMING_SNAKE_CASE      =					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
      _SCREAMING_SNAKE_CASE      =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      _SCREAMING_SNAKE_CASE      =					PipelineTesterMixin.required_optional_params - {"""latents"""}



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        return self._get_dummy_components()



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict=0	):
        if str(SCREAMING_SNAKE_CASE_	).startswith('mps'	):
          lowerCAmelCase_       :    Dict								=			torch.manual_seed(SCREAMING_SNAKE_CASE_	)
        else:
          lowerCAmelCase_       :    Dict								=			torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Any								=			floats_tensor((1, 3, 3_2, 3_2)   ,      rng=random.Random(SCREAMING_SNAKE_CASE_	)	).to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Optional[int]								=			floats_tensor((1, 3, 3_2, 3_2)   ,      rng=random.Random(SCREAMING_SNAKE_CASE_	)	).to(SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    int								=			{
            '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 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        self._test_save_load_optional_components()



      @unittest.skipIf(torch_device != 'cuda'   ,      reason='float16 requires CUDA'	)
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        # 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 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        self._test_attention_slicing_forward_pass(expected_max_diff=1E-2	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        self._test_save_load_local()





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










"""simple docstring"""







from __future__ import annotations

def     UpperCamelCase_   (   lowerCAmelCase__				:    int						)						->       list[int]:

  """simple docstring"""




  lowerCAmelCase_       :    Any								=			2
  lowerCAmelCase_       :    List[Any]								=			[]
  while i * i <= n:
    if n % i:
      i += 1
    else:
      n //= i
      factors.append(lowerCAmelCase__						)
  if n > 1:
    factors.append(lowerCAmelCase__						)
  return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()





