



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						):
					__A     :    Optional[Any]           =						IFInpaintingPipeline
					__A     :    List[Any]           =						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
					__A     :    int           =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					__A     :    Tuple           =						PipelineTesterMixin.required_optional_params - {'latents'}







					def  _lowercase			(       self      :     List[str]):
											return self._get_dummy_components()







					def  _lowercase			(       self      :     str			,		_A      :     Any			,		_A      :     List[Any]=0):
											if str(_A).startswith("mps"):
																	A__	:							Optional[int]						    = torch.manual_seed(_A)
											else:
																	A__	:							Any						    = torch.Generator(device=_A).manual_seed(_A)

											A__	:							Tuple						    = floats_tensor((1, 3, 32, 32)			,		rng=random.Random(_A)).to(_A)
											A__	:							int						    = floats_tensor((1, 3, 32, 32)			,		rng=random.Random(_A)).to(_A)

											A__	:							int						    = {
											    "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  _lowercase			(       self      :     Union[str, Any]):
											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)







					def  _lowercase			(       self      :     Dict):
											self._test_save_load_optional_components()







					@unittest.skipIf(torch_device != "cuda"			,		reason="float16 requires CUDA")
					def  _lowercase			(       self      :     List[Any]):
											# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
											super().test_save_load_floataa(expected_max_diff=1e-1)







					def  _lowercase			(       self      :     Tuple):
											self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)







					def  _lowercase			(       self      :     int):
											self._test_save_load_local()







					def  _lowercase			(       self      :     Optional[int]):
											self._test_inference_batch_single_identical(
											    expected_max_diff=1e-2			,		)




def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase = 0							,     __lowercase = 0       )		->		int:







						"""simple docstring"""

						A__	:							Tuple						    = right or len(__lowercase       ) - 1
						if left > right:
												return -1
						elif list_data[left] == key:
												return left
						elif list_data[right] == key:
												return right
						else:
												return search(__lowercase							,     __lowercase							,     left + 1							,     right - 1       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()