import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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    lowercase_    (     _UpperCamelCase						, _UpperCamelCase						, unittest.TestCase		):
							_lowerCamelCase						=		IFSuperResolutionPipeline
							_lowerCamelCase						=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
							_lowerCamelCase						=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							_lowerCamelCase						=		PipelineTesterMixin.required_optional_params - {'latents'}



							def        UpperCamelCase       (						self				):
													return self._get_superresolution_dummy_components()



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







													if str(a__				).startswith("mps"				):
																			_snake_case						:     Tuple				=    torch.manual_seed(a__				)
													else:
																			_snake_case						:     Tuple				=    torch.Generator(device=a__				).manual_seed(a__				)

													_snake_case						:     int				=    floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(a__				)				).to(a__				)

													_snake_case						:     Optional[int]				=    {
													    "prompt": "A painting of a squirrel eating a burger",
													    "image": 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				):
													self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)



							def        UpperCamelCase       (						self				):
													self._test_save_load_optional_components()



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



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



							def        UpperCamelCase       (						self				):
													self._test_save_load_local()



							def        UpperCamelCase       (						self				):
													self._test_inference_batch_single_identical(
													    expected_max_diff=1e-2      ,				)



"""simple docstring"""


def    a__       ( __lowercase = 20      )		->		int:

				try:
								_A         =	int(__lowercase      )
				except (TypeError, ValueError):
								raise TypeError("Parameter n must be int or castable to int."      )
				if n <= 0:
								raise ValueError("Parameter n must be greater than or equal to one."      )
				_A         =	0
				while 1:
								i += n * (n - 1)
								_A         =	0
								for j in range(2   ,	__lowercase      ):
												if i % j != 0:
																_A         =	1
																break
								if nfound == 0:
												if i == 0:
																_A         =	1
												return i
				return None


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