




import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase     ,					unittest.TestCase  ):





							'''simple docstring'''



							__a							: Any										=IFImgaImgSuperResolutionPipeline
							__a							: str										=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
							__a							: Tuple										=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}  )
							__a							: Any										=PipelineTesterMixin.required_optional_params - {"""latents"""}



							def       __snake_case		( self						):
														return self._get_superresolution_dummy_components()



							def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=0						):
														if str(UpperCAmelCase_						).startswith('''mps'''						):
																					lowerCAmelCase   =		torch.manual_seed(UpperCAmelCase_						)
														else:
																					lowerCAmelCase   =		torch.Generator(device=UpperCAmelCase_						).manual_seed(UpperCAmelCase_						)

														lowerCAmelCase   =		floats_tensor((1, 3, 32, 32)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)
														lowerCAmelCase   =		floats_tensor((1, 3, 16, 16)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)

														lowerCAmelCase   =		{
														    '''prompt''': '''A painting of a squirrel eating a burger''',
														    '''image''': image,
														    '''original_image''': original_image,
														    '''generator''': generator,
														    '''num_inference_steps''': 2,
														    '''output_type''': '''numpy''',
														}

														return inputs



							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()       , reason='''XFormers attention is only available with CUDA and `xformers` installed'''       , )
							def       __snake_case		( self						):
														self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)



							def       __snake_case		( self						):
														self._test_save_load_optional_components()



							@unittest.skipIf(torch_device != '''cuda'''       , reason='''float16 requires CUDA'''						)
							def       __snake_case		( self						):
														# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
														super().test_save_load_floataa(expected_max_diff=1E-1						)



							def       __snake_case		( self						):
														self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)



							def       __snake_case		( self						):
														self._test_save_load_local()






							def       __snake_case		( self						):
														self._test_inference_batch_single_identical(
														    expected_max_diff=1E-2       , )









def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							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       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							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]
														lowerCAmelCase   =		sum(
														    count_of_possible_combinations_with_dp_array(target - item						, _snake_case							)
														    for item in array							)
														lowerCAmelCase   =		answer
														return answer

							lowerCAmelCase   =		[-1] * (target + 1)
							return count_of_possible_combinations_with_dp_array(_snake_case						, _snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		[0] * (target + 1)
							lowerCAmelCase   =		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()
		UpperCAmelCase_						=3
		UpperCAmelCase_						=5
		UpperCAmelCase_						=[1, 2, 5]
		print(combination_sum_iv(n, array, target))



