
"""simple docstring"""
import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()
class       lowerCAmelCase_		(				_lowercase				,				_lowercase				,				unittest.TestCase ):



							'''simple docstring'''





							_snake_case						      =		VQModel
							_snake_case						      =		'sample'




							@property
							def    A__						( self    ,				snake_case_=(32, 32)				)       ->  Dict:
													__lowerCAmelCase     =  4
													__lowerCAmelCase     =  3

													__lowerCAmelCase     =  floats_tensor((batch_size, num_channels) + sizes				).to(__SCREAMING_SNAKE_CASE				)

													return {"sample": image}




							@property
							def    A__						( self				)       ->  Tuple:
													return (3, 32, 32)




							@property
							def    A__						( self				)       ->  Tuple:
													return (3, 32, 32)




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  {
													    """block_out_channels""": [32, 64],
													    """in_channels""": 3,
													    """out_channels""": 3,
													    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
													    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
													    """latent_channels""": 3,
													}
													__lowerCAmelCase     =  self.dummy_input
													return init_dict, inputs_dict




							def    A__						( self				)       ->  Optional[int]:
													pass




							def    A__						( self				)       ->  Tuple:
													pass




							def    A__						( self				)       ->  List[str]:
													__lowerCAmelCase   ,     __lowerCAmelCase     =  VQModel.from_pretrained("""fusing/vqgan-dummy"""    ,				output_loading_info=__SCREAMING_SNAKE_CASE				)
													self.assertIsNotNone(__SCREAMING_SNAKE_CASE				)
													self.assertEqual(len(loading_info["""missing_keys"""]				)    ,				0				)

													model.to(__SCREAMING_SNAKE_CASE				)
													__lowerCAmelCase     =  model(**self.dummy_input				)

													assert image is not None, "Make sure output is not None"




							def    A__						( self				)       ->  List[Any]:
													__lowerCAmelCase     =  VQModel.from_pretrained("""fusing/vqgan-dummy"""				)
													model.to(__SCREAMING_SNAKE_CASE				).eval()

													torch.manual_seed(0				)
													if torch.cuda.is_available():
																			torch.cuda.manual_seed_all(0				)

													__lowerCAmelCase     =  torch.randn(1    ,				model.config.in_channels    ,				model.config.sample_size    ,				model.config.sample_size				)
													__lowerCAmelCase     =  image.to(__SCREAMING_SNAKE_CASE				)
													with torch.no_grad():
																			__lowerCAmelCase     =  model(__SCREAMING_SNAKE_CASE				).sample

													__lowerCAmelCase     =  output[0, -1, -3:, -3:].flatten().cpu()
													# fmt: off
													__lowerCAmelCase     =  torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143]				)
													# fmt: on
													self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE    ,				__SCREAMING_SNAKE_CASE    ,				atol=1e-3				)				)


"""simple docstring"""




def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:

    def count_of_possible_combinations(UpperCamelCase     :		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(UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:

    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]     ) -> 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							,		UpperCamelCase     )
            for item in array     )
        a__		=			answer
        return answer

    a__		=			[-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase							,		UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:
    a__		=			[0] * (target + 1)
    a__		=			1

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

    return dp_array[target]


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    a		:       Optional[Any]     						=       3
    a		:       List[str]     						=       5
    a		:       Dict     						=       [1, 2, 5]
    print(combination_sum_iv(n, array, target))


