


'''simple docstring'''






import gc
import tempfile
import unittest

import numpy as np
import torch

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


lowercase							=False






class  __magic_name__							(		unittest.TestCase    ):
       pass






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






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'  ,							torch_dtype=torch.floataa)
          pipe.to(snake_case)
          pipe.set_progress_bar_config(disable=snake_case)

          _UpperCAmelCase       :       Union[str, Any]							   =load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg')

          _UpperCAmelCase       :       Dict							   =torch.manual_seed(0)
          _UpperCAmelCase       :       str							   =pipe.dual_guided(
              prompt='first prompt'  ,							image=snake_case  ,							text_to_image_strength=0.75  ,							generator=snake_case  ,							guidance_scale=7.5  ,							num_inference_steps=2  ,							output_type='numpy'  ,							).images

          with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(snake_case)
             _UpperCAmelCase       :       Union[str, Any]							   =VersatileDiffusionPipeline.from_pretrained(snake_case  ,							torch_dtype=torch.floataa)
          pipe.to(snake_case)
          pipe.set_progress_bar_config(disable=snake_case)

          _UpperCAmelCase       :       List[Any]							   =generator.manual_seed(0)
          _UpperCAmelCase       :       Dict							   =pipe.dual_guided(
              prompt='first prompt'  ,							image=snake_case  ,							text_to_image_strength=0.75  ,							generator=snake_case  ,							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       lowerCAmelCase		(      self)						->  List[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'  ,							torch_dtype=torch.floataa)
          pipe.to(snake_case)
          pipe.set_progress_bar_config(disable=snake_case)

          _UpperCAmelCase       :       List[str]							   ='cyberpunk 2077'
          _UpperCAmelCase       :       Dict							   =load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg')
          _UpperCAmelCase       :       Any							   =torch.manual_seed(0)
          _UpperCAmelCase       :       Optional[int]							   =pipe.dual_guided(
              prompt=snake_case  ,							image=snake_case  ,							text_to_image_strength=0.75  ,							generator=snake_case  ,							guidance_scale=7.5  ,							num_inference_steps=5_0  ,							output_type='numpy'  ,							).images

          _UpperCAmelCase       :       int							   =image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          _UpperCAmelCase       :       List[str]							   =np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01])

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

          _UpperCAmelCase       :       Dict							   ='A painting of a squirrel eating a burger '
          _UpperCAmelCase       :       Union[str, Any]							   =torch.manual_seed(0)
          _UpperCAmelCase       :       str							   =pipe.text_to_image(
              prompt=snake_case  ,							generator=snake_case  ,							guidance_scale=7.5  ,							num_inference_steps=5_0  ,							output_type='numpy').images

          _UpperCAmelCase       :       Any							   =image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          _UpperCAmelCase       :       str							   =np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78])

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

          _UpperCAmelCase       :       int							   =pipe.image_variation(snake_case  ,							generator=snake_case  ,							output_type='numpy').images

          _UpperCAmelCase       :       Optional[Any]							   =image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          _UpperCAmelCase       :       List[str]							   =np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56])

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





'''simple docstring'''






def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   def count_of_possible_combinations(__lowerCamelCase	:							int							) -> int:
      if target < 0:
         return 0
      if target == 0:
         return 1
      return sum(count_of_possible_combinations(target - item							) for item in array							)

   return count_of_possible_combinations(__lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   def count_of_possible_combinations_with_dp_array(
       __lowerCamelCase	:							int	,   __lowerCamelCase	:							list[int]							) -> int:
      if target < 0:
         return 0
      if target == 0:
         return 1
      if dp_array[target] != -1:
         return dp_array[target]
      _UpperCAmelCase       :       str							   =sum(
          count_of_possible_combinations_with_dp_array(target - item	,   __lowerCamelCase							)
          for item in array							)
      _UpperCAmelCase       :       Optional[Any]							   =answer
      return answer

   _UpperCAmelCase       :       int							   =[-1] * (target + 1)
   return count_of_possible_combinations_with_dp_array(__lowerCamelCase	,   __lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       Tuple							   =[0] * (target + 1)
   _UpperCAmelCase       :       Dict							   =1

   for i in range(1	,   target + 1							):
      for j in range(__lowerCamelCase							):
         if i - array[j] >= 0:
            dp_array[i] += dp_array[i - array[j]]

   return dp_array[target]


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 lowercase							=3
 lowercase							=5
 lowercase							=[1, 2, 5]
 print(combination_sum_iv(n, array, target))

