
import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
  import onnxruntime as ort

@nightly
@require_onnxruntime
@require_torch_gpu
class __snake_case		(				unittest.TestCase					):
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Dict	     =				ort.SessionOptions()
           lowercase							:					List[Any]	     =				False
           return options
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Dict	     =				load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/in_paint/overture-creations-5sI6fQgYIuo.png"""				)
           lowercase							:					Union[str, Any]	     =				load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""				)
           lowercase							:					List[str]	     =				load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""				)

           # using the PNDM scheduler by default
           lowercase							:					int	     =				OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
               """CompVis/stable-diffusion-v1-4"""		,revision="""onnx"""		,safety_checker=snake_case		,feature_extractor=snake_case		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
           pipe.set_progress_bar_config(disable=snake_case				)

           lowercase							:					Any	     =				"""A red cat sitting on a park bench"""

           lowercase							:					str	     =				np.random.RandomState(0				)
           lowercase							:					int	     =				pipe(
               prompt=snake_case		,image=snake_case		,mask_image=snake_case		,strength=0.75		,guidance_scale=7.5		,num_inference_steps=15		,generator=snake_case		,output_type="""np"""		,)

           lowercase							:					Union[str, Any]	     =				output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image				).max() < 1e-2


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

       def count_of_possible_combinations(SCREAMING_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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:

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

       lowercase							:					int	     =				[-1] * (target + 1)
       return count_of_possible_combinations_with_dp_array(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:
       lowercase							:					str	     =				[0] * (target + 1)
       lowercase							:					Tuple	     =				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()
  lowercase  : Any						   =							3
  lowercase  : Optional[Any]						   =							5
  lowercase  : Tuple						   =							[1, 2, 5]
  print(combination_sum_iv(n, array, target))
