



"""simple docstring"""






# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  unittest.TestCase  ):


					'''simple docstring'''





					_a       =				StableDiffusionControlNetInpaintPipeline
					_a       =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					_a       =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					_a       =				frozenset({'control_image'}  )  # skip `image` and `mask` for now, only test for control_image
					_a       =				TEXT_TO_IMAGE_IMAGE_PARAMS



					def 						snake_case			(			self				:    Optional[int]	)->   str:
								torch.manual_seed(0	)
								lowerCamelCase__						:						List[Any]						      =UNetaDConditionModel(
								    block_out_channels=(32, 64),    layers_per_block=2,    sample_size=32,    in_channels=9,    out_channels=4,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),    cross_attention_dim=32,    )
								torch.manual_seed(0	)
								lowerCamelCase__						:						List[str]						      =ControlNetModel(
								    block_out_channels=(32, 64),    layers_per_block=2,    in_channels=4,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),    cross_attention_dim=32,    conditioning_embedding_out_channels=(16, 32),    )
								torch.manual_seed(0	)
								lowerCamelCase__						:						int						      =DDIMScheduler(
								    beta_start=0.00_085,    beta_end=0.012,    beta_schedule='''scaled_linear''',    clip_sample=UpperCAmelCase__,    set_alpha_to_one=UpperCAmelCase__,    )
								torch.manual_seed(0	)
								lowerCamelCase__						:						str						      =AutoencoderKL(
								    block_out_channels=[32, 64],    in_channels=3,    out_channels=3,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],    latent_channels=4,    )
								torch.manual_seed(0	)
								lowerCamelCase__						:						List[str]						      =CLIPTextConfig(
								    bos_token_id=0,    eos_token_id=2,    hidden_size=32,    intermediate_size=37,    layer_norm_eps=1E-05,    num_attention_heads=4,    num_hidden_layers=5,    pad_token_id=1,    vocab_size=1000,    )
								lowerCamelCase__						:						Optional[Any]						      =CLIPTextModel(UpperCAmelCase__	)
								lowerCamelCase__						:						Tuple						      =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

								lowerCamelCase__						:						int						      ={
								    """unet""": unet,
								    """controlnet""": controlnet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """safety_checker""": None,
								    """feature_extractor""": None,
								}
								return components



					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    Union[str, Any],    lowerCamelCase				:    Dict=0	)->   Any:







								if str(UpperCAmelCase__	).startswith('''mps'''	):
											lowerCamelCase__						:						Optional[Any]						      =torch.manual_seed(UpperCAmelCase__	)
								else:
											lowerCamelCase__						:						Any						      =torch.Generator(device=UpperCAmelCase__	).manual_seed(UpperCAmelCase__	)

								lowerCamelCase__						:						Optional[Any]						      =2
								lowerCamelCase__						:						Union[str, Any]						      =randn_tensor(
								    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),    generator=UpperCAmelCase__,    device=torch.device(UpperCAmelCase__	),    )
								lowerCamelCase__						:						List[str]						      =floats_tensor((1, 3, 32, 32),    rng=random.Random(UpperCAmelCase__	)	).to(UpperCAmelCase__	)
								lowerCamelCase__						:						int						      =init_image.cpu().permute(0,    2,    3,    1	)[0]

								lowerCamelCase__						:						List[str]						      =Image.fromarray(np.uinta(UpperCAmelCase__	)	).convert('''RGB'''	).resize((64, 64)	)
								lowerCamelCase__						:						Dict						      =Image.fromarray(np.uinta(init_image + 4	)	).convert('''RGB'''	).resize((64, 64)	)

								lowerCamelCase__						:						Optional[Any]						      ={
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								    """image""": image,
								    """mask_image""": mask_image,
								    """control_image""": control_image,
								}

								return inputs



					def 						snake_case			(			self				:    List[str]	)->   Optional[int]:
								return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3	)



					@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				:    List[str]	)->   List[str]:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3	)




					def 						snake_case			(			self				:    str	)->   Tuple:
								self._test_inference_batch_single_identical(expected_max_diff=2E-3	)





class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				StableDiffusionControlNetInpaintPipeline
					_a       =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					_a       =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					_a       =				frozenset([]  )



					def 						snake_case			(			self				:    Union[str, Any]	)->   Union[str, Any]:
								torch.manual_seed(0	)
								lowerCamelCase__						:						Tuple						      =UNetaDConditionModel(
								    block_out_channels=(32, 64),    layers_per_block=2,    sample_size=32,    in_channels=4,    out_channels=4,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),    cross_attention_dim=32,    )
								torch.manual_seed(0	)
								lowerCamelCase__						:						Dict						      =ControlNetModel(
								    block_out_channels=(32, 64),    layers_per_block=2,    in_channels=4,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),    cross_attention_dim=32,    conditioning_embedding_out_channels=(16, 32),    )
								torch.manual_seed(0	)
								lowerCamelCase__						:						Optional[Any]						      =DDIMScheduler(
								    beta_start=0.00_085,    beta_end=0.012,    beta_schedule='''scaled_linear''',    clip_sample=UpperCAmelCase__,    set_alpha_to_one=UpperCAmelCase__,    )
								torch.manual_seed(0	)
								lowerCamelCase__						:						Optional[Any]						      =AutoencoderKL(
								    block_out_channels=[32, 64],    in_channels=3,    out_channels=3,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],    latent_channels=4,    )
								torch.manual_seed(0	)
								lowerCamelCase__						:						Optional[int]						      =CLIPTextConfig(
								    bos_token_id=0,    eos_token_id=2,    hidden_size=32,    intermediate_size=37,    layer_norm_eps=1E-05,    num_attention_heads=4,    num_hidden_layers=5,    pad_token_id=1,    vocab_size=1000,    )
								lowerCamelCase__						:						List[str]						      =CLIPTextModel(UpperCAmelCase__	)
								lowerCamelCase__						:						Tuple						      =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

								lowerCamelCase__						:						Union[str, Any]						      ={
								    """unet""": unet,
								    """controlnet""": controlnet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """safety_checker""": None,
								    """feature_extractor""": None,
								}
								return components





class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_						,  lowerCAmelCase_						,  unittest.TestCase  ):


					'''simple docstring'''





					_a       =				StableDiffusionControlNetInpaintPipeline
					_a       =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					_a       =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS



					def 						snake_case			(			self				:    Optional[Any]	)->   Optional[int]:
								torch.manual_seed(0	)
								lowerCamelCase__						:						Optional[int]						      =UNetaDConditionModel(
								    block_out_channels=(32, 64),    layers_per_block=2,    sample_size=32,    in_channels=9,    out_channels=4,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),    cross_attention_dim=32,    )
								torch.manual_seed(0	)

								def init_weights(lowerCamelCase				:    Dict	):
											if isinstance(UpperCAmelCase__,    torch.nn.Convad	):
														torch.nn.init.normal(m.weight	)
														m.bias.data.fill_(1.0	)

								lowerCamelCase__						:						Any						      =ControlNetModel(
								    block_out_channels=(32, 64),    layers_per_block=2,    in_channels=4,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),    cross_attention_dim=32,    conditioning_embedding_out_channels=(16, 32),    )
								controlneta.controlnet_down_blocks.apply(UpperCAmelCase__	)

								torch.manual_seed(0	)
								lowerCamelCase__						:						Dict						      =ControlNetModel(
								    block_out_channels=(32, 64),    layers_per_block=2,    in_channels=4,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),    cross_attention_dim=32,    conditioning_embedding_out_channels=(16, 32),    )
								controlneta.controlnet_down_blocks.apply(UpperCAmelCase__	)

								torch.manual_seed(0	)
								lowerCamelCase__						:						int						      =DDIMScheduler(
								    beta_start=0.00_085,    beta_end=0.012,    beta_schedule='''scaled_linear''',    clip_sample=UpperCAmelCase__,    set_alpha_to_one=UpperCAmelCase__,    )
								torch.manual_seed(0	)
								lowerCamelCase__						:						str						      =AutoencoderKL(
								    block_out_channels=[32, 64],    in_channels=3,    out_channels=3,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],    latent_channels=4,    )
								torch.manual_seed(0	)
								lowerCamelCase__						:						str						      =CLIPTextConfig(
								    bos_token_id=0,    eos_token_id=2,    hidden_size=32,    intermediate_size=37,    layer_norm_eps=1E-05,    num_attention_heads=4,    num_hidden_layers=5,    pad_token_id=1,    vocab_size=1000,    )
								lowerCamelCase__						:						List[str]						      =CLIPTextModel(UpperCAmelCase__	)
								lowerCamelCase__						:						Dict						      =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

								lowerCamelCase__						:						List[str]						      =MultiControlNetModel([controlneta, controlneta]	)

								lowerCamelCase__						:						str						      ={
								    """unet""": unet,
								    """controlnet""": controlnet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """safety_checker""": None,
								    """feature_extractor""": None,
								}
								return components



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    Union[str, Any]=0	)->   Optional[Any]:







								if str(UpperCAmelCase__	).startswith('''mps'''	):
											lowerCamelCase__						:						Any						      =torch.manual_seed(UpperCAmelCase__	)
								else:
											lowerCamelCase__						:						Any						      =torch.Generator(device=UpperCAmelCase__	).manual_seed(UpperCAmelCase__	)

								lowerCamelCase__						:						Optional[Any]						      =2

								lowerCamelCase__						:						Optional[int]						      =[
								    randn_tensor(
								        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),    generator=UpperCAmelCase__,    device=torch.device(UpperCAmelCase__	),    ),
								    randn_tensor(
								        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),    generator=UpperCAmelCase__,    device=torch.device(UpperCAmelCase__	),    ),
								]
								lowerCamelCase__						:						Any						      =floats_tensor((1, 3, 32, 32),    rng=random.Random(UpperCAmelCase__	)	).to(UpperCAmelCase__	)
								lowerCamelCase__						:						List[str]						      =init_image.cpu().permute(0,    2,    3,    1	)[0]

								lowerCamelCase__						:						int						      =Image.fromarray(np.uinta(UpperCAmelCase__	)	).convert('''RGB'''	).resize((64, 64)	)
								lowerCamelCase__						:						List[Any]						      =Image.fromarray(np.uinta(init_image + 4	)	).convert('''RGB'''	).resize((64, 64)	)

								lowerCamelCase__						:						Any						      ={
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								    """image""": image,
								    """mask_image""": mask_image,
								    """control_image""": control_image,
								}

								return inputs



					def 						snake_case			(			self				:    Optional[int]	)->   Any:
								lowerCamelCase__						:						Dict						      =self.get_dummy_components()
								lowerCamelCase__						:						Union[str, Any]						      =self.pipeline_class(**UpperCAmelCase__	)
								pipe.to(UpperCAmelCase__	)

								lowerCamelCase__						:						Optional[int]						      =10.0
								lowerCamelCase__						:						Tuple						      =4

								lowerCamelCase__						:						int						      =self.get_dummy_inputs(UpperCAmelCase__	)
								lowerCamelCase__						:						Optional[Any]						      =steps
								lowerCamelCase__						:						Dict						      =scale
								lowerCamelCase__						:						int						      =pipe(**UpperCAmelCase__	)[0]

								lowerCamelCase__						:						str						      =self.get_dummy_inputs(UpperCAmelCase__	)
								lowerCamelCase__						:						Any						      =steps
								lowerCamelCase__						:						Optional[Any]						      =scale
								lowerCamelCase__						:						Any						      =pipe(**UpperCAmelCase__,    control_guidance_start=0.1,    control_guidance_end=0.2	)[0]

								lowerCamelCase__						:						Dict						      =self.get_dummy_inputs(UpperCAmelCase__	)
								lowerCamelCase__						:						Any						      =steps
								lowerCamelCase__						:						Union[str, Any]						      =scale
								lowerCamelCase__						:						Tuple						      =pipe(**UpperCAmelCase__,    control_guidance_start=[0.1, 0.3],    control_guidance_end=[0.2, 0.7]	)[0]

								lowerCamelCase__						:						Union[str, Any]						      =self.get_dummy_inputs(UpperCAmelCase__	)
								lowerCamelCase__						:						List[Any]						      =steps
								lowerCamelCase__						:						Tuple						      =scale
								lowerCamelCase__						:						Dict						      =pipe(**UpperCAmelCase__,    control_guidance_start=0.4,    control_guidance_end=[0.5, 0.8]	)[0]

								# make sure that all outputs are different
								assert np.sum(np.abs(output_a - output_a	)	) > 1E-3
								assert np.sum(np.abs(output_a - output_a	)	) > 1E-3
								assert np.sum(np.abs(output_a - output_a	)	) > 1E-3



					def 						snake_case			(			self				:    Any	)->   str:
								return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3	)



					@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				:    Tuple	)->   int:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3	)



					def 						snake_case			(			self				:    Union[str, Any]	)->   Any:
								self._test_inference_batch_single_identical(expected_max_diff=2E-3	)




					def 						snake_case			(			self				:    List[Any]	)->   Optional[int]:
								lowerCamelCase__						:						int						      =self.get_dummy_components()
								lowerCamelCase__						:						Tuple						      =self.pipeline_class(**UpperCAmelCase__	)
								pipe.to(UpperCAmelCase__	)
								pipe.set_progress_bar_config(disable=UpperCAmelCase__	)
								with tempfile.TemporaryDirectory() as tmpdir:
											try:
														# save_pretrained is not implemented for Multi-ControlNet
														pipe.save_pretrained(UpperCAmelCase__	)
											except NotImplementedError:
														pass





@slow
@require_torch_gpu
class 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





					def 						snake_case			(			self				:    str	)->   str:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



					def 						snake_case			(			self				:    Tuple	)->   str:
								lowerCamelCase__						:						Union[str, Any]						      =ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''	)

								lowerCamelCase__						:						Optional[Any]						      =StableDiffusionControlNetInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting''',    safety_checker=UpperCAmelCase__,    controlnet=UpperCAmelCase__	)
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

								lowerCamelCase__						:						Optional[Any]						      =torch.Generator(device='''cpu'''	).manual_seed(0	)
								lowerCamelCase__						:						Tuple						      =load_image(
								    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''	).resize((512, 512)	)

								lowerCamelCase__						:						List[str]						      =load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
								    '''/stable_diffusion_inpaint/input_bench_mask.png'''	).resize((512, 512)	)

								lowerCamelCase__						:						Dict						      ="""pitch black hole"""

								lowerCamelCase__						:						int						      =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''	).resize((512, 512)	)

								lowerCamelCase__						:						Tuple						      =pipe(
								    UpperCAmelCase__,    image=UpperCAmelCase__,    mask_image=UpperCAmelCase__,    control_image=UpperCAmelCase__,    generator=UpperCAmelCase__,    output_type='''np''',    num_inference_steps=3,    )

								lowerCamelCase__						:						List[Any]						      =output.images[0]

								assert image.shape == (512, 512, 3)

								lowerCamelCase__						:						str						      =load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'''	)

								assert np.abs(expected_image - image	).max() < 9E-2




					def 						snake_case			(			self				:    str	)->   Tuple:
								lowerCamelCase__						:						List[Any]						      =ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''	)

								lowerCamelCase__						:						List[str]						      =StableDiffusionControlNetInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',    safety_checker=UpperCAmelCase__,    controlnet=UpperCAmelCase__	)
								lowerCamelCase__						:						int						      =DDIMScheduler.from_config(pipe.scheduler.config	)
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

								lowerCamelCase__						:						Any						      =torch.Generator(device='''cpu'''	).manual_seed(33	)

								lowerCamelCase__						:						Any						      =load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'''	)
								lowerCamelCase__						:						List[str]						      =init_image.resize((512, 512)	)

								lowerCamelCase__						:						Dict						      =load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'''	)
								lowerCamelCase__						:						Tuple						      =mask_image.resize((512, 512)	)

								lowerCamelCase__						:						Optional[int]						      ="""a handsome man with ray-ban sunglasses"""

								def make_inpaint_condition(lowerCamelCase				:    List[str],    lowerCamelCase				:    List[str]	):
											lowerCamelCase__						:						List[str]						      =np.array(image.convert('''RGB'''	)	).astype(np.floataa	) / 255.0
											lowerCamelCase__						:						List[Any]						      =np.array(image_mask.convert('''L'''	)	).astype(np.floataa	) / 255.0

											assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
											lowerCamelCase__						:						Union[str, Any]						      =-1.0  # set as masked pixel
											lowerCamelCase__						:						int						      =np.expand_dims(UpperCAmelCase__,    0	).transpose(0,    3,    1,    2	)
											lowerCamelCase__						:						Optional[int]						      =torch.from_numpy(UpperCAmelCase__	)
											return image

								lowerCamelCase__						:						Dict						      =make_inpaint_condition(UpperCAmelCase__,    UpperCAmelCase__	)

								lowerCamelCase__						:						Any						      =pipe(
								    UpperCAmelCase__,    image=UpperCAmelCase__,    mask_image=UpperCAmelCase__,    control_image=UpperCAmelCase__,    guidance_scale=9.0,    eta=1.0,    generator=UpperCAmelCase__,    num_inference_steps=20,    output_type='''np''',    )
								lowerCamelCase__						:						Optional[int]						      =output.images[0]

								assert image.shape == (512, 512, 3)

								lowerCamelCase__						:						Optional[int]						      =load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'''	)

								assert np.abs(expected_image - image	).max() < 9E-2







import itertools






def        __lowercase   (  _A = 1000000      )  ->    int:
      SCREAMING_SNAKE_CASE			:   Tuple	  = [1]

      for i in itertools.count(len(_A      )      ):
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = 0
            for j in itertools.count(1      ):
                  SCREAMING_SNAKE_CASE			:   str	  = -1 if j % 2 == 0 else +1
                  SCREAMING_SNAKE_CASE			:   Any	  = (j * j * 3 - j) // 2
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number
                  index += j
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number

            if item == 0:
                  return i
            partitions.append(_A      )

      return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")




