import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowercase_  : Optional[Any]								=					False






class 		_lowerCamelCase		( unittest.TestCase     ):
    pass






@nightly
@require_torch_gpu
class 		_lowerCamelCase		( unittest.TestCase     ):
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
    def     UpperCamelCase_					(	self )							->       Dict:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
     # remove text_unet
     pipe.remove_unused_weights()
     pipe.to(lowerCAmelCase )
     pipe.set_progress_bar_config(disable=lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: Any=   '''A painting of a squirrel eating a burger '''
     SCREAMING_SNAKE_CASE__: List[str]=   torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__: List[Any]=   pipe(
         prompt=lowerCAmelCase			,			generator=lowerCAmelCase			,			guidance_scale=7.5			,			num_inference_steps=2			,			output_type='''numpy''' ).images

     with tempfile.TemporaryDirectory() as tmpdirname:
      pipe.save_pretrained(lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: List[str]=   VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCAmelCase )
     pipe.to(lowerCAmelCase )
     pipe.set_progress_bar_config(disable=lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: str=   generator.manual_seed(0 )
     SCREAMING_SNAKE_CASE__: List[str]=   pipe(
         prompt=lowerCAmelCase			,			generator=lowerCAmelCase			,			guidance_scale=7.5			,			num_inference_steps=2			,			output_type='''numpy''' ).images

     assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"



    def     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: Tuple=   VersatileDiffusionTextToImagePipeline.from_pretrained(
         '''shi-labs/versatile-diffusion'''			,			torch_dtype=torch.floataa )
     pipe.to(lowerCAmelCase )
     pipe.set_progress_bar_config(disable=lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: int=   '''A painting of a squirrel eating a burger '''
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__: str=   pipe(
         prompt=lowerCAmelCase			,			generator=lowerCAmelCase			,			guidance_scale=7.5			,			num_inference_steps=50			,			output_type='''numpy''' ).images

     SCREAMING_SNAKE_CASE__: Any=   image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     SCREAMING_SNAKE_CASE__: List[Any]=   np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2





'''simple docstring'''





from functools import reduce

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


def        a__     (					UpperCamelCase_						:      str = N						):
   return max(
       # mypy cannot properly interpret reduce
       int(reduce(lambda UpperCamelCase_,   UpperCamelCase_						: str(int(UpperCamelCase_						) * int(UpperCamelCase_						)						),   n[i : i + 13]						)						)
       for i in range(len(UpperCamelCase_						) - 12						)						)


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



