





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 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    int							=   IFInpaintingPipeline
      A_						:    Optional[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	:				Any				):





         """simple docstring"""



         return self._get_dummy_components()


      def        a			(self	:				str    ,      a__	:				Union[str, Any]    ,      a__	:				List[str]=0				):





         """simple docstring"""



         if str(a__				).startswith('''mps'''				):
            __snake_case								=       torch.manual_seed(a__				)
         else:
            __snake_case								=       torch.Generator(device=a__				).manual_seed(a__				)

         __snake_case								=       floats_tensor((1, 3, 32, 32)    ,      rng=random.Random(a__				)				).to(a__				)
         __snake_case								=       floats_tensor((1, 3, 32, 32)    ,      rng=random.Random(a__				)				).to(a__				)

         __snake_case								=       {
             '''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	:				List[Any]				):





         """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	:				List[Any]				):





         """simple docstring"""



         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)


      def        a			(self	:				Any				):





         """simple docstring"""



         self._test_save_load_local()


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



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








from pathlib import Path

import fire






def lowerCamelCase__   (							snake_case_     :  str	,				snake_case_     :  str	,				snake_case_     :  int	)				->    str:
   __snake_case								=       Path(snake_case_	)
   __snake_case								=       Path(snake_case_	)
   dest_dir.mkdir(exist_ok=snake_case_	)
   for path in src_dir.iterdir():
      __snake_case								=       [x.rstrip() for x in list(path.open().readlines()	)][:n]
      __snake_case								=       dest_dir.joinpath(path.name	)
      print(snake_case_	)
      dest_path.open('''w'''	).write('''\n'''.join(snake_case_	)	)


if __name__ == "__main__":
    fire.Fire(minify)

