"""simple docstring"""


from __future__ import annotations

from functools import lru_cache
from math import ceil

a				=100

a				=set(range(3, NUM_PRIMES, 2))
primes.add(2)
a				=42

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
	if prime not in primes:
		continue
	primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))





@lru_cache(maxsize=100							)
def      lowerCamelCase_					(   __lowerCAmelCase							)				->   set[int]:




						'''simple docstring'''



						if number_to_partition < 0:
												return set()
						elif number_to_partition == 0:
												return {1}

						lowerCamelCase__   =set()
						lowerCamelCase__   =42
						lowerCamelCase__   =42

						for prime in primes:
												if prime > number_to_partition:
																		continue
												for sub in partition(number_to_partition - prime							):
																		ret.add(sub * prime							)

						return ret





def      lowerCamelCase_					(   __lowerCAmelCase = 5000							)				->   int | None:




						'''simple docstring'''



						for number_to_partition in range(1  ,       lowerCAmelCase_							):
												if len(partition(lowerCAmelCase_							)							) > number_unique_partitions:
																		return number_to_partition
						return None


if __name__ == "__main__":
	print(F'''{solution() = }''')









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 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Any				=	IFInpaintingPipeline
    lowerCamelCase__    :		str				=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
    lowerCamelCase__    :		Optional[Any]				=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    lowerCamelCase__    :		Tuple				=	PipelineTesterMixin.required_optional_params - {"""latents"""}



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self._get_dummy_components()



    def  lowercase_					(       self   ,				A_   ,				A_=0     ):





        '''simple docstring'''

        if str(A_     ).startswith('''mps'''     ):
            SCREAMING_SNAKE_CASE__											=			torch.manual_seed(A_     )
        else:
            SCREAMING_SNAKE_CASE__											=			torch.Generator(device=A_     ).manual_seed(A_     )

        SCREAMING_SNAKE_CASE__											=			floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(A_     )     ).to(A_     )
        SCREAMING_SNAKE_CASE__											=			floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(A_     )     ).to(A_     )

        SCREAMING_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  lowercase_					(       self     ):





        '''simple docstring'''

        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self._test_save_load_optional_components()



    @unittest.skipIf(torch_device != '''cuda'''   ,				reason='''float16 requires CUDA'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self._test_save_load_local()




    def  lowercase_					(       self     ):





        '''simple docstring'''

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