




'''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 ):
       __a				:     int   				=					IFInpaintingPipeline
       __a				:     List[Any]   				=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
       __a				:     List[Any]   				=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       __a				:     str   				=					PipelineTesterMixin.required_optional_params - {"""latents"""}
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           return self._get_dummy_components()
       def     A      (	self						:	Union[str, Any]   ,				lowercase						:	Union[str, Any]   ,				lowercase						:	Tuple=0				):



           '''simple docstring'''

           if str(lowercase				).startswith('''mps'''				):
               UpperCAmelCase			       =		torch.manual_seed(lowercase				)
           else:
               UpperCAmelCase			       =		torch.Generator(device=lowercase				).manual_seed(lowercase				)

           UpperCAmelCase			       =		floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(lowercase				)				).to(lowercase				)
           UpperCAmelCase			       =		floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(lowercase				)				).to(lowercase				)

           UpperCAmelCase			       =		{
               '''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						:	str				):



           '''simple docstring'''

           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

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



           '''simple docstring'''

           super().test_save_load_floataa(expected_max_diff=1E-1				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           self._test_save_load_local()







       def     A      (	self						:	int				):



           '''simple docstring'''

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










'''simple docstring'''







import os





def 						snake_case_							():
    UpperCAmelCase			       =		os.path.join(os.path.dirname(_a     )    ,   '''num.txt'''     )
    with open(_a     ) as file_hand:
        return str(sum(int(_a     ) for line in file_hand     )     )[:1_0]


if __name__ == "__main__":
   print(solution())




