





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


SCREAMING_SNAKE_CASE_   :      Optional[int]			=   False

class 		snake_case_   (							unittest.TestCase						):







	'''simple docstring'''
	pass





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







	'''simple docstring'''




	def    UpperCAmelCase			(						self							:     str   )       -> Optional[Any]:

			'''simple docstring'''




			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def    UpperCAmelCase			(						self							:     Dict   )       -> List[str]:

			'''simple docstring'''




			__lowercase											=     VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'					,      torch_dtype=torch.floataa   )
			pipe.to(__lowerCamelCase   )
			pipe.set_progress_bar_config(disable=__lowerCamelCase   )

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

			__lowercase											=     torch.manual_seed(0   )
			__lowercase											=     pipe.dual_guided(
			    prompt='first prompt'					,      image=__lowerCamelCase					,      text_to_image_strength=0.75					,      generator=__lowerCamelCase					,      guidance_scale=7.5					,      num_inference_steps=2					,      output_type='numpy'					,      ).images

			with tempfile.TemporaryDirectory() as tmpdirname:
					pipe.save_pretrained(__lowerCamelCase   )
					__lowercase											=     VersatileDiffusionPipeline.from_pretrained(__lowerCamelCase					,      torch_dtype=torch.floataa   )
			pipe.to(__lowerCamelCase   )
			pipe.set_progress_bar_config(disable=__lowerCamelCase   )

			__lowercase											=     generator.manual_seed(0   )
			__lowercase											=     pipe.dual_guided(
			    prompt='first prompt'					,      image=__lowerCamelCase					,      text_to_image_strength=0.75					,      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							:     Optional[Any]   )       -> Union[str, Any]:

			'''simple docstring'''




			__lowercase											=     VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'					,      torch_dtype=torch.floataa   )
			pipe.to(__lowerCamelCase   )
			pipe.set_progress_bar_config(disable=__lowerCamelCase   )

			__lowercase											=     'cyberpunk 2077'
			__lowercase											=     load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'   )
			__lowercase											=     torch.manual_seed(0   )
			__lowercase											=     pipe.dual_guided(
			    prompt=__lowerCamelCase					,      image=__lowerCamelCase					,      text_to_image_strength=0.75					,      generator=__lowerCamelCase					,      guidance_scale=7.5					,      num_inference_steps=50					,      output_type='numpy'					,      ).images

			__lowercase											=     image[0, 253:256, 253:256, -1]

			assert image.shape == (1, 512, 512, 3)
			__lowercase											=     np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]   )

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

			__lowercase											=     'A painting of a squirrel eating a burger '
			__lowercase											=     torch.manual_seed(0   )
			__lowercase											=     pipe.text_to_image(
			    prompt=__lowerCamelCase					,      generator=__lowerCamelCase					,      guidance_scale=7.5					,      num_inference_steps=50					,      output_type='numpy'   ).images

			__lowercase											=     image[0, 253:256, 253:256, -1]

			assert image.shape == (1, 512, 512, 3)
			__lowercase											=     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-1

			__lowercase											=     pipe.image_variation(__lowerCamelCase					,      generator=__lowerCamelCase					,      output_type='numpy'   ).images

			__lowercase											=     image[0, 253:256, 253:256, -1]

			assert image.shape == (1, 512, 512, 3)
			__lowercase											=     np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]   )

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











def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		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(snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		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]
				__lowercase											=     sum(
				    count_of_possible_combinations_with_dp_array(target - item						,   snake_case							)
				    for item in array							)
				__lowercase											=     answer
				return answer

		__lowercase											=     [-1] * (target + 1)
		return count_of_possible_combinations_with_dp_array(snake_case						,   snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:
		__lowercase											=     [0] * (target + 1)
		__lowercase											=     1

		for i in range(1						,   target + 1							):
				for j in range(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()
		SCREAMING_SNAKE_CASE_   :      Tuple			=   3
		SCREAMING_SNAKE_CASE_   :      Dict			=   5
		SCREAMING_SNAKE_CASE_   :      Any			=   [1, 2, 5]
		print(combination_sum_iv(n, array, target))




