






def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_,	snake_case_	) ->	int:


	"""simple docstring"""



	def count_of_possible_combinations(snake_case_	) -> 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(_SCREAMING_SNAKE_CASE	)





def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_,	snake_case_	) ->	int:


	"""simple docstring"""



	def count_of_possible_combinations_with_dp_array(
	    snake_case_,	snake_case_	) -> int:
		if target < 0:
			return 0
		if target == 0:
			return 1
		if dp_array[target] != -1:
			return dp_array[target]
		a												=      sum(
		    count_of_possible_combinations_with_dp_array(target - item,	_SCREAMING_SNAKE_CASE	)
		    for item in array	)
		a												=      answer
		return answer

	a												=      [-1] * (target + 1)
	return count_of_possible_combinations_with_dp_array(_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	)





def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_,	snake_case_	) ->	int:


	"""simple docstring"""



	a												=      [0] * (target + 1)
	a												=      1

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

	return dp_array[target]


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				UpperCamelCase__    :   str    		=       3
				UpperCamelCase__    :   Tuple    		=       5
				UpperCamelCase__    :   int    		=       [1, 2, 5]
				print(combination_sum_iv(n, array, target))












import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         torch.manual_seed(0							)
         _A     =							UNetaDModel(
             block_out_channels=(32, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=3				,     out_channels=3				,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')				,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')				,     )
         return model




     def        lowerCAmelCase__		(    self							):
         _A     =							self.dummy_uncond_unet
         _A     =							KarrasVeScheduler()

         _A     =							KarrasVePipeline(unet=snake_case_				,     scheduler=snake_case_							)
         pipe.to(snake_case_							)
         pipe.set_progress_bar_config(disable=snake_case_							)

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=2				,     generator=snake_case_				,     output_type='numpy'							).images

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=2				,     generator=snake_case_				,     output_type='numpy'				,     return_dict=snake_case_							)[0]

         _A     =							image[0, -3:, -3:, -1]
         _A     =							image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         _A     =							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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







@slow
@require_torch
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self							):
         _A     =							'google/ncsnpp-celebahq-256'
         _A     =							UNetaDModel.from_pretrained(snake_case_							)
         _A     =							KarrasVeScheduler()

         _A     =							KarrasVePipeline(unet=snake_case_				,     scheduler=snake_case_							)
         pipe.to(snake_case_							)
         pipe.set_progress_bar_config(disable=snake_case_							)

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=20				,     generator=snake_case_				,     output_type='numpy'							).images

         _A     =							image[0, -3:, -3:, -1]
         assert image.shape == (1, 256, 256, 3)
         _A     =							np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]							)

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





