



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



 '''simple docstring'''







 __lowerCamelCase					: List[Any]    = IFInpaintingPipeline
 __lowerCamelCase					: Tuple    = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
 __lowerCamelCase					: Tuple    = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __lowerCamelCase					: Optional[int]    = PipelineTesterMixin.required_optional_params - {'latents'}



 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      return self._get_dummy_components()



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


      """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 )					->  Any:


      """simple docstring"""



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



 def       a__						(self )					->  Optional[int]:


      """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 )					->  Dict:


      """simple docstring"""



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








'''simple docstring'''





from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

lowercase_	 =						typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
lowercase_	 =						typing.Union[np.floataa, int, float]  # noqa: UP007




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     return np.sqrt(np.sum((np.asarray(__A) - np.asarray(__A)) ** 2))




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     return sum((va - va) ** 2 for va, va in zip(__A						,			__A)) ** (1 / 2)


if __name__ == "__main__":




  def lowerCAmelCase						():



       """simple docstring"""



       from timeit import timeit

       print('''Without Numpy''')
       print(
           timeit(
               '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'''						,			number=10_000						,			globals=globals()						,			))
       print('''With Numpy''')
       print(
           timeit(
               '''euclidean_distance([1, 2, 3], [4, 5, 6])'''						,			number=10_000						,			globals=globals()						,			))
  benchmark()



