
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   (						snake_case__							,							snake_case__							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    List[Any] 				=    IFInpaintingPipeline
   __UpperCamelCase		:    List[Any] 				=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
   __UpperCamelCase		:    List[Any] 				=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   __UpperCamelCase		:    Union[str, Any] 				=    PipelineTesterMixin.required_optional_params - {'''latents'''}


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      return self._get_dummy_components()


   def  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							Any			,			lowerCAmelCase_     :							List[str]=0     ):

      """simple docstring"""







      if str(lowerCAmelCase_     ).startswith('''mps'''     ):
         _A:  Any					=       torch.manual_seed(lowerCAmelCase_     )
      else:
         _A:  Optional[int]					=       torch.Generator(device=lowerCAmelCase_     ).manual_seed(lowerCAmelCase_     )

      _A:  str					=       floats_tensor((1, 3, 3_2, 3_2)			,			rng=random.Random(lowerCAmelCase_     )     ).to(lowerCAmelCase_     )
      _A:  Any					=       floats_tensor((1, 3, 3_2, 3_2)			,			rng=random.Random(lowerCAmelCase_     )     ).to(lowerCAmelCase_     )

      _A:  List[Any]					=       {
          '''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  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3     )


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      self._test_save_load_optional_components()


   @unittest.skipIf(torch_device != '''cuda'''			,			reason='''float16 requires CUDA'''     )
   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      super().test_save_load_floataa(expected_max_diff=1e-1     )


   def  __magic_name__				(					self     :							int     ):

      """simple docstring"""
      self._test_attention_slicing_forward_pass(expected_max_diff=1e-2     )


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      self._test_save_load_local()


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      self._test_inference_batch_single_identical(
          expected_max_diff=1e-2			,			)











from __future__ import annotations


def __A    (      __lowerCAmelCase	)->  list[int]:





						"""simple docstring"""



						_UpperCAmelCase									=			2
						_UpperCAmelCase									=			[]
						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()
