

def    _snake_case		(snake_case_				:    Dict						,					snake_case_				:    Any = 1000     )       ->		str:
		if (not isinstance(snake_case_						,					snake_case_     )) or (digit_position <= 0):
				raise ValueError('Digit position must be a positive integer'     )
		elif (not isinstance(snake_case_						,					snake_case_     )) or (precision < 0):
				raise ValueError('Precision must be a nonnegative integer'     )

		# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
		# accurate
		__UpperCAmelCase  :     Tuple		=    (
		    4 * _subsum(snake_case_						,					1						,					snake_case_     )
		    - 2 * _subsum(snake_case_						,					4						,					snake_case_     )
		    - _subsum(snake_case_						,					5						,					snake_case_     )
		    - _subsum(snake_case_						,					6						,					snake_case_     )
		)

		# return the first hex digit of the fractional part of the result
		return hex(int((sum_result % 1) * 16     )     )[2:]





def    _snake_case		(snake_case_				:    Union[str, Any]						,					snake_case_				:    Optional[int]						,					snake_case_				:    str     )       ->		float:
		__UpperCAmelCase  :     int		=    0.0
		for sum_index in range(digit_pos_to_extract + precision     ):
				__UpperCAmelCase  :     int		=    8 * sum_index + denominator_addend
				if sum_index < digit_pos_to_extract:
						# if the exponential term is an integer and we mod it by the denominator
						# before dividing, only the integer part of the sum will change;
						# the fractional part will not
						__UpperCAmelCase  :     Any		=    pow(
						    16						,					digit_pos_to_extract - 1 - sum_index						,					snake_case_     )
				else:
						__UpperCAmelCase  :     Tuple		=    pow(16						,					digit_pos_to_extract - 1 - sum_index     )
				total += exponential_term / denominator
		return total


if __name__ == "__main__":
						import doctest

						doctest.testmod()








import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_SCREAMING_SNAKE_CASE          = False


@nightly
@require_torch_gpu
class     lowerCamelCase_   ( unittest.TestCase):




 def 						a					( self       ):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()




 def 						a					( self       ):
   __UpperCAmelCase  :     Dict		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'       )
   # remove text_unet
   pipe.remove_unused_weights()
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     Any		=    load_image(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )

   __UpperCAmelCase  :     Optional[Any]		=    torch.manual_seed(0       )
   __UpperCAmelCase  :     Union[str, Any]		=    pipe(
       prompt='first prompt'     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=2     ,     output_type='numpy'     ,     ).images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(_UpperCAmelCase       )
     __UpperCAmelCase  :     List[str]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained(_UpperCAmelCase       )

   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     List[str]		=    generator.manual_seed(0       )
   __UpperCAmelCase  :     Optional[int]		=    pipe(
       prompt='first prompt'     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     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 						a					( self       ):
   __UpperCAmelCase  :     Optional[Any]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'       )
   pipe.remove_unused_weights()
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     Tuple		=    'cyberpunk 2077'
   __UpperCAmelCase  :     Dict		=    load_image(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )
   __UpperCAmelCase  :     Optional[Any]		=    torch.manual_seed(0       )
   __UpperCAmelCase  :     List[Any]		=    pipe(
       prompt=_UpperCAmelCase     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=50     ,     output_type='numpy'     ,     ).images

   __UpperCAmelCase  :     Tuple		=    image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   __UpperCAmelCase  :     Tuple		=    np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]       )

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





