
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 							_UpperCamelCase					(       __SCREAMING_SNAKE_CASE  ,							__SCREAMING_SNAKE_CASE  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    Tuple          =			IFInpaintingPipeline
			__UpperCamelCase   :    Any          =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
			__UpperCamelCase   :    Tuple          =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__UpperCamelCase   :    Dict          =			PipelineTesterMixin.required_optional_params - {"""latents"""}

			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							return self._get_dummy_components()

			def    lowerCAmelCase__						(							self			:							Tuple    ,						snake_case_			:							Any    ,						snake_case_			:							Any=0   ):







							if str(_A   ).startswith("""mps"""   ):
											UpperCamelCase_:				Optional[Any]						=    torch.manual_seed(_A   )
							else:
											UpperCamelCase_:				Optional[Any]						=    torch.Generator(device=_A   ).manual_seed(_A   )

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

							UpperCamelCase_:				Dict						=    {
							    """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    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )

			def    lowerCAmelCase__						(							self			:							Any   ):
							self._test_save_load_optional_components()

			@unittest.skipIf(torch_device != """cuda"""    ,						reason="""float16 requires CUDA"""   )
			def    lowerCAmelCase__						(							self			:							int   ):
							super().test_save_load_floataa(expected_max_diff=1e-1   )

			def    lowerCAmelCase__						(							self			:							Any   ):
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							self._test_save_load_local()




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













"""simple docstring"""


from bisect import bisect
from itertools import accumulate

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Dict				   =			sorted(zip(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )							,     key=lambda SCREAMING_SNAKE_CASE     : x[0] / x[1]							,     reverse=SCREAMING_SNAKE_CASE     )
						UpperCamelCase   ,						UpperCamelCase				:		int				   =			[i[0] for i in r], [i[1] for i in r]
						UpperCamelCase				:		Optional[Any]				   =			list(accumulate(SCREAMING_SNAKE_CASE     )     )
						UpperCamelCase				:		Optional[Any]				   =			bisect(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						return (
						    0
						    if k == 0
						    else sum(vl[:k]     ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
						    if k != n
						    else sum(vl[:k]     )
						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()



