
'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class     a (					__magic_name__					,__magic_name__					,unittest.TestCase     ):
				_snake_case		     =			IFImgaImgPipeline
				_snake_case		     =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
				_snake_case		     =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				_snake_case		     =			PipelineTesterMixin.required_optional_params - {'''latents'''}




				def 						__snake_case				(				self						:					int   ):
										return self._get_dummy_components()




				def 						__snake_case				(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=0   ):
										if str(SCREAMING_SNAKE_CASE_   ).startswith('''mps'''   ):
																snake_case							:  List[str]			=     torch.manual_seed(SCREAMING_SNAKE_CASE_   )
										else:
																snake_case							:  int			=     torch.Generator(device=SCREAMING_SNAKE_CASE_   ).manual_seed(SCREAMING_SNAKE_CASE_   )

										snake_case							:  Optional[Any]			=     floats_tensor((1, 3, 32, 32),     rng=random.Random(SCREAMING_SNAKE_CASE_   )   ).to(SCREAMING_SNAKE_CASE_   )

										snake_case							:  Dict			=     {
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''image''': image,
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''output_type''': '''numpy''',
										}

										return inputs




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




				@unittest.skipIf(
				    torch_device != '''cuda''' or not is_xformers_available(),     reason='''XFormers attention is only available with CUDA and `xformers` installed''',     )
				def 						__snake_case				(				self						:					str   ):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )




				@unittest.skipIf(torch_device != '''cuda''',     reason='''float16 requires CUDA'''   )
				def 						__snake_case				(				self						:					List[str]   ):
										# 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   )




				@unittest.skipIf(torch_device != '''cuda''',     reason='''float16 requires CUDA'''   )
				def 						__snake_case				(				self						:					List[Any]   ):
										super().test_floataa_inference(expected_max_diff=1e-1   )




				def 						__snake_case				(				self						:					str   ):
										self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )




				def 						__snake_case				(				self						:					List[str]   ):
										self._test_save_load_local()




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



'''simple docstring'''





def   A			(							A_				:       int							):
						return n * (2 * n - 1)

def   A			(							A_				:       int							):
						snake_case							:  Optional[Any]			=     (1 + 24 * n) ** 0.5
						return ((1 + root) / 6) % 1 == 0

def   A			(							A_				:       int = 144							):
						snake_case							:  Any			=     start
						snake_case							:  Any			=     hexagonal_num(A_							)
						while not is_pentagonal(A_							):
												n += 1
												snake_case							:  Optional[Any]			=     hexagonal_num(A_							)
						return num


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

