



import os
from collections.abc import Iterator


def A						(       __UpperCamelCase = "."					)						->      str:




			for dir_path, dir_names, filenames in os.walk(_A					):
						A__       =    [d for d in dir_names if d != "scripts" and d[0] not in "._"]
						for filename in filenames:
									if filename == "__init__.py":
												continue
									if os.path.splitext(_A					)[1] in (".py", ".ipynb"):
												yield os.path.join(_A ,	_A					).lstrip('./'					)


def A						(       __UpperCamelCase					)						->      List[Any]:
			return f'''{i * "  "}*''' if i else "\n##"


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      List[str]:
			A__       =    old_path.split(os.sep					)
			for i, new_part in enumerate(new_path.split(os.sep					)					):
						if (i + 1 > len(_A					) or old_parts[i] != new_part) and new_part:
									print(f'''{md_prefix(_A					)} {new_part.replace("_" ,	" "					).title()}'''					)
			return new_path


def A						(       __UpperCamelCase = "."					)						->      Optional[int]:
			A__       =    ""
			for filepath in sorted(good_file_paths(_A					)					):
						A__       =    os.path.split(_A					)
						if filepath != old_path:
									A__       =    print_path(_A ,	_A					)
						A__       =    (filepath.count(os.sep					) + 1) if filepath else 0
						A__       =    f'''{filepath}/{filename}'''.replace(' ' ,	'%20'					)
						A__       =    os.path.splitext(filename.replace('_' ,	' '					).title()					)[0]
						print(f'''{md_prefix(_A					)} [{filename}]({url})'''					)


if __name__ == "__main__":
						print_directory_md('''.''')









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      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Any			       =    IFInpaintingPipeline
					A__				:		Dict			       =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
					A__				:		List[Any]			       =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A__				:		Dict			       =    PipelineTesterMixin.required_optional_params - {"latents"}



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								return self._get_dummy_components()



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       Any				,					_snake_case					:       str=0					):






								"""simple docstring"""


								if str(_snake_case					).startswith('mps'					):
											A__       =    torch.manual_seed(_snake_case					)
								else:
											A__       =    torch.Generator(device=_snake_case					).manual_seed(_snake_case					)

								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)
								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)

								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					:       Dict					):






								"""simple docstring"""


								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								self._test_save_load_optional_components()



					@unittest.skipIf(torch_device != 'cuda'				,					reason='float16 requires CUDA'					)
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


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



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._test_save_load_local()



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


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




