






'''simple docstring'''







import sys

__lowerCAmelCase       		=      (
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)





def 		_UpperCAmelCase   (							__A							:   str      ):
				a_		:  Tuple											=      1
				for digit in s:
								product *= int(__A      )
				return product





def 		_UpperCAmelCase   (							__A							:   str = N      ):
				a_		:  Dict											=      -sys.maxsize - 1
				a_		:  Optional[int]											=      n[:13]
				a_		:  str											=      13
				while cur_index < len(__A      ) - 13:
								if int(n[cur_index]      ) >= int(substr[0]      ):
												a_		:  Tuple											=      substr[1:] + n[cur_index]
												cur_index += 1
								else:
												a_		:  Dict											=      max(__A						,		str_eval(__A      )      )
												a_		:  List[str]											=      n[cur_index : cur_index + 13]
												cur_index += 13
				return largest_product


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












'''simple docstring'''







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 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				IFInpaintingSuperResolutionPipeline
			snake_case__  =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
			snake_case__  =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}       )
			snake_case__  =				PipelineTesterMixin.required_optional_params - {"latents"}






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			List[str]:
							return self._get_superresolution_dummy_components()






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Dict=0					)			->			List[Any]:
							if str(__SCREAMING_SNAKE_CASE					).startswith('''mps'''					):
											a_		:  Optional[int]											=      torch.manual_seed(__SCREAMING_SNAKE_CASE					)
							else:
											a_		:  str											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(__SCREAMING_SNAKE_CASE					)

							a_		:  Dict											=      floats_tensor((1, 3, 16, 16)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)

							a_		:  Union[str, Any]											=      {
							    '''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 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			int:
							self._test_save_load_optional_components()






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






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Optional[int]:
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Optional[Any]:
							self._test_save_load_local()







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




