





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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					):
			lowerCamelCase_					:					Union[str, Any]	      =				IFInpaintingSuperResolutionPipeline
			lowerCamelCase_					:					Any	      =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
			lowerCamelCase_					:					Any	      =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
			lowerCamelCase_					:					Union[str, Any]	      =				PipelineTesterMixin.required_optional_params - {'''latents'''}






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							return self._get_superresolution_dummy_components()






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=0   )					->       str:


							'''simple docstring'''



							if str(__magic_name__   ).startswith('''mps'''   ):
											snake_case_						:   Union[str, Any]           =  torch.manual_seed(__magic_name__   )
							else:
											snake_case_						:   int           =  torch.Generator(device=__magic_name__   ).manual_seed(__magic_name__   )

							snake_case_						:   Tuple           =  floats_tensor((1, 3, 16, 16) ,							rng=random.Random(__magic_name__   )   ).to(__magic_name__   )
							snake_case_						:   Optional[int]           =  floats_tensor((1, 3, 32, 32) ,							rng=random.Random(__magic_name__   )   ).to(__magic_name__   )
							snake_case_						:   int           =  floats_tensor((1, 3, 32, 32) ,							rng=random.Random(__magic_name__   )   ).to(__magic_name__   )

							snake_case_						:   str           =  {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': image,
							    '''original_image''': original_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   )					->       Dict:


							'''simple docstring'''



							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							self._test_save_load_optional_components()






			@unittest.skipIf(torch_device != '''cuda''' ,							reason='''float16 requires CUDA'''   )
			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



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






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							self._test_save_load_local()






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



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







import random

from .binary_exp_mod import bin_exp_mod






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=1_000			)		->						str:






				"""simple docstring"""


				if n < 2:
								return False

				if n % 2 == 0:
								return n == 2

				# this means n is odd
				snake_case_						:   int           =  n - 1
				snake_case_						:   Union[str, Any]           =  0
				while d % 2 == 0:
								d /= 2
								exp += 1

				# n - 1=d*(2**exp)
				snake_case_						:   List[Any]           =  0
				while count < prec:
								snake_case_						:   Tuple           =  random.randint(2			,    n - 1			)
								snake_case_						:   List[Any]           =  bin_exp_mod(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
								if b != 1:
												snake_case_						:   Any           =  True
												for _ in range(_UpperCamelCase			):
																if b == n - 1:
																				snake_case_						:   Tuple           =  False
																				break
																snake_case_						:   List[Any]           =  b * b
																b %= n
												if flag:
																return False
												count += 1
				return True


if __name__ == "__main__":
	lowerCAmelCase_    =    abs(int(input('''Enter bound : ''').strip()))
	print('''Here\'s the list of primes:''')
	print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))
