



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




					'''simple docstring'''



					A_						    =						IFInpaintingPipeline
					A_						    =						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
					A_						    =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A_						    =						PipelineTesterMixin.required_optional_params - {"""latents"""}




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Any:



						'''simple docstring'''





						return self._get_dummy_components()




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	str=0					)  ->       Dict:



						'''simple docstring'''





						if str(UpperCamelCase_					).startswith('mps'					):
							_lowercase					:       List[str]       =		torch.manual_seed(UpperCamelCase_					)
						else:
							_lowercase					:       List[str]       =		torch.Generator(device=UpperCamelCase_					).manual_seed(UpperCamelCase_					)

						_lowercase					:       str       =		floats_tensor((1, 3, 32, 32)     ,  rng=random.Random(UpperCamelCase_					)					).to(UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		floats_tensor((1, 3, 32, 32)     ,  rng=random.Random(UpperCamelCase_					)					).to(UpperCamelCase_					)

						_lowercase					:       List[str]       =		{
						    '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 						__UpperCAmelCase     (  self				:	Tuple					)  ->       str:



						'''simple docstring'''





						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       List[Any]:



						'''simple docstring'''





						self._test_save_load_optional_components()




					@unittest.skipIf(torch_device != 'cuda'     ,  reason='float16 requires CUDA'					)
					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Optional[Any]:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	int					)  ->       Any:



						'''simple docstring'''





						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Optional[int]:



						'''simple docstring'''





						self._test_save_load_local()




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Optional[Any]:



						'''simple docstring'''





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





'''simple docstring'''

import os
from collections.abc import Iterator







def 						__UpperCamelCase				(							_lowercase = "."					) ->			Iterator[str]:
	for dir_path, dir_names, filenames in os.walk(_lowercase					):
		_lowercase					:       Optional[int]       =		[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(_lowercase					)[1] in (".py", ".ipynb"):
				yield os.path.join(_lowercase,				_lowercase					).lstrip('./'					)







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







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







def 						__UpperCamelCase				(							_lowercase = "."					) ->			None:
	_lowercase					:       Dict       =		''
	for filepath in sorted(good_file_paths(_lowercase					)					):
		_lowercase  ,    _lowercase					:       Optional[Any]       =		os.path.split(_lowercase					)
		if filepath != old_path:
			_lowercase					:       Dict       =		print_path(_lowercase,				_lowercase					)
		_lowercase					:       Optional[int]       =		(filepath.count(os.sep					) + 1) if filepath else 0
		_lowercase					:       Dict       =		f'''{filepath}/{filename}'''.replace(' ',				'%20'					)
		_lowercase					:       Optional[int]       =		os.path.splitext(filename.replace('_',				' '					).title()					)[0]
		print(f'''{md_prefix(_lowercase					)} [{filename}]({url})'''					)


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