


'''simple docstring'''

import sys

sys.set_int_max_str_digits(0)  # type: ignore

def _SCREAMING_SNAKE_CASE				(A	)	->	bool:






       """simple docstring"""







       lowercase__				      =			[0] * 11
       lowercase__				      =			[0] * 11

       # mark last 9 numbers
       for _ in range(9	):
              lowercase__				      =			1
              lowercase__				      =			number // 10
       # flag
       lowercase__				      =			True

       # check last 9 numbers for pandigitality

       for x in range(9	):
              if not check_last[x + 1]:
                     lowercase__				      =			False
       if not f:
              return f

       # mark first 9 numbers
       lowercase__				      =			int(str(A	)[:9]	)

       for _ in range(9	):
              lowercase__				      =			1
              lowercase__				      =			number // 10

       # check first 9 numbers for pandigitality

       for x in range(9	):
              if not check_front[x + 1]:
                     lowercase__				      =			False
       return f

def _SCREAMING_SNAKE_CASE				(A	)	->	bool:






       """simple docstring"""







       lowercase__				      =			[0] * 11

       # mark last 9 numbers
       for _ in range(9	):
              lowercase__				      =			1
              lowercase__				      =			number // 10
       # flag
       lowercase__				      =			True

       # check last 9 numbers for pandigitality

       for x in range(9	):
              if not check_last[x + 1]:
                     lowercase__				      =			False
       return f

def _SCREAMING_SNAKE_CASE				()	->	int:






       """simple docstring"""







       lowercase__				      =			1
       lowercase__				      =			1
       lowercase__				      =			2
       # temporary Fibonacci numbers

       lowercase__				      =			1
       lowercase__				      =			1
       lowercase__				      =			2
       # temporary Fibonacci numbers mod 1e9

       # mod m=1e9, done for fast optimisation
       lowercase__				      =			[0] * 1_000_000
       lowercase__				      =			1_000_000_000

       for x in range(1_000_000	):
              lowercase__				      =			(aa + ba) % m
              lowercase__				      =			ba % m
              lowercase__				      =			ca % m
              if checka(A	):
                     lowercase__				      =			1

       for x in range(1_000_000	):
              lowercase__				      =			a + b
              lowercase__				      =			b
              lowercase__				      =			c
              # perform check only if in tocheck
              if tocheck[x + 3] and check(A	):
                     return x + 3  # first 2 already done
       return -1


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








'''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   __lowerCAmelCase						(lowercase_  ,					lowercase_  ,					unittest.TestCase					):





      '''simple docstring'''






      lowerCAmelCase__ :					Dict   =							IFImgaImgPipeline
      lowerCAmelCase__ :					int   =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
      lowerCAmelCase__ :					Optional[int]   =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      lowerCAmelCase__ :					str   =							PipelineTesterMixin.required_optional_params - {"""latents"""}



      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







             return self._get_dummy_components()



      def 				UpperCamelCase__		(self   :   Optional[Any]       ,						UpperCamelCase   :   List[Any]       ,						UpperCamelCase   :   Optional[Any]=0			):

             '''simple docstring'''







             if str(UpperCamelCase			).startswith('''mps'''			):
                    lowercase__				      =			torch.manual_seed(UpperCamelCase			)
             else:
                    lowercase__				      =			torch.Generator(device=UpperCamelCase			).manual_seed(UpperCamelCase			)

             lowercase__				      =			floats_tensor((1, 3, 32, 32)       ,						rng=random.Random(UpperCamelCase			)			).to(UpperCamelCase			)

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

             return inputs



      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             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 				UpperCamelCase__		(self   :   Optional[Any]			):

             '''simple docstring'''







             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)



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

             '''simple docstring'''







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



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

             '''simple docstring'''







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



      def 				UpperCamelCase__		(self   :   Optional[int]			):

             '''simple docstring'''







             self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)



      def 				UpperCamelCase__		(self   :   str			):

             '''simple docstring'''







             self._test_save_load_local()






      def 				UpperCamelCase__		(self   :   List[Any]			):

             '''simple docstring'''







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




