


"""simple docstring"""







# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

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,
    StableDiffusionControlNetImgaImgPipeline,
    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 (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()


class       SCREAMING_SNAKE_CASE__				(		lowercase   ,    lowercase   ,    lowercase   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				Optional[Any]   =StableDiffusionControlNetImgaImgPipeline
       a  :				Any   =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
       a  :				int   =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       a  :				Union[str, Any]   =IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}				)
       a  :				Dict   =IMAGE_TO_IMAGE_IMAGE_PARAMS

       def        lowercase__							(						self						):






         """simple docstring"""





         torch.manual_seed(0						)
         lowerCAmelCase     :     List[Any]								=							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     :     Optional[int]								=							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     :     Dict								=							DDIMScheduler(
             beta_start=0.00085		,     beta_end=0.012		,     beta_schedule="scaled_linear"		,     clip_sample=snake_case__		,     set_alpha_to_one=snake_case__		,     )
         torch.manual_seed(0						)
         lowerCAmelCase     :     Optional[int]								=							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[Any]								=							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=1_000		,     )
         lowerCAmelCase     :     List[Any]								=							CLIPTextModel(snake_case__						)
         lowerCAmelCase     :     Tuple								=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

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

       def        lowercase__							(						self		,     snake_case__		,     snake_case__=0						):






         """simple docstring"""





         if str(snake_case__						).startswith("mps"						):
           lowerCAmelCase     :     Optional[int]								=							torch.manual_seed(snake_case__						)
         else:
           lowerCAmelCase     :     Optional[int]								=							torch.Generator(device=snake_case__						).manual_seed(snake_case__						)

         lowerCAmelCase     :     Dict								=							2
         lowerCAmelCase     :     Optional[int]								=							randn_tensor(
             (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,     generator=snake_case__		,     device=torch.device(snake_case__						)		,     )
         lowerCAmelCase     :     Dict								=							floats_tensor(control_image.shape		,     rng=random.Random(snake_case__						)						).to(snake_case__						)
         lowerCAmelCase     :     Union[str, Any]								=							image.cpu().permute(0		,     2		,     3		,     1						)[0]
         lowerCAmelCase     :     List[Any]								=							Image.fromarray(np.uinta(snake_case__						)						).convert("RGB"						).resize((64, 64)						)
         lowerCAmelCase     :     int								=							{
             "prompt": "A painting of a squirrel eating a burger",
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
             "output_type": "numpy",
             "image": image,
             "control_image": control_image,
         }

         return inputs

       def        lowercase__							(						self						):






         """simple docstring"""





         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        lowercase__							(						self						):






         """simple docstring"""





         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3						)







       def        lowercase__							(						self						):






         """simple docstring"""





         self._test_inference_batch_single_identical(expected_max_diff=2e-3						)


class       SCREAMING_SNAKE_CASE__				(		lowercase   ,    lowercase   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				Optional[Any]   =StableDiffusionControlNetImgaImgPipeline
       a  :				List[str]   =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
       a  :				Any   =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       a  :				Optional[Any]   =frozenset([]				)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess

       def        lowercase__							(						self						):






         """simple docstring"""





         torch.manual_seed(0						)
         lowerCAmelCase     :     Union[str, Any]								=							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						)

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

         lowerCAmelCase     :     Optional[int]								=							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(snake_case__						)

         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)		,     )
         controlneta.controlnet_down_blocks.apply(snake_case__						)

         torch.manual_seed(0						)
         lowerCAmelCase     :     Any								=							DDIMScheduler(
             beta_start=0.00085		,     beta_end=0.012		,     beta_schedule="scaled_linear"		,     clip_sample=snake_case__		,     set_alpha_to_one=snake_case__		,     )
         torch.manual_seed(0						)
         lowerCAmelCase     :     List[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     :     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=1_000		,     )
         lowerCAmelCase     :     Optional[Any]								=							CLIPTextModel(snake_case__						)
         lowerCAmelCase     :     str								=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

         lowerCAmelCase     :     int								=							MultiControlNetModel([controlneta, controlneta]						)

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

       def        lowercase__							(						self		,     snake_case__		,     snake_case__=0						):






         """simple docstring"""





         if str(snake_case__						).startswith("mps"						):
           lowerCAmelCase     :     Tuple								=							torch.manual_seed(snake_case__						)
         else:
           lowerCAmelCase     :     str								=							torch.Generator(device=snake_case__						).manual_seed(snake_case__						)

         lowerCAmelCase     :     Optional[Any]								=							2

         lowerCAmelCase     :     Optional[int]								=							[
             randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,     generator=snake_case__		,     device=torch.device(snake_case__						)		,     ),
             randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,     generator=snake_case__		,     device=torch.device(snake_case__						)		,     ),
         ]

         lowerCAmelCase     :     Optional[int]								=							floats_tensor(control_image[0].shape		,     rng=random.Random(snake_case__						)						).to(snake_case__						)
         lowerCAmelCase     :     str								=							image.cpu().permute(0		,     2		,     3		,     1						)[0]
         lowerCAmelCase     :     Optional[int]								=							Image.fromarray(np.uinta(snake_case__						)						).convert("RGB"						).resize((64, 64)						)
         lowerCAmelCase     :     List[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,
             "control_image": control_image,
         }

         return inputs

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							self.get_dummy_components()
         lowerCAmelCase     :     Any								=							self.pipeline_class(**snake_case__						)
         pipe.to(snake_case__						)

         lowerCAmelCase     :     Optional[int]								=							10.0
         lowerCAmelCase     :     Union[str, Any]								=							4

         lowerCAmelCase     :     str								=							self.get_dummy_inputs(snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							steps
         lowerCAmelCase     :     List[Any]								=							scale
         lowerCAmelCase     :     Union[str, Any]								=							pipe(**snake_case__						)[0]

         lowerCAmelCase     :     Optional[int]								=							self.get_dummy_inputs(snake_case__						)
         lowerCAmelCase     :     List[str]								=							steps
         lowerCAmelCase     :     Tuple								=							scale
         lowerCAmelCase     :     Any								=							pipe(**snake_case__		,     control_guidance_start=0.1		,     control_guidance_end=0.2						)[0]

         lowerCAmelCase     :     List[str]								=							self.get_dummy_inputs(snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							steps
         lowerCAmelCase     :     Union[str, Any]								=							scale
         lowerCAmelCase     :     List[Any]								=							pipe(**snake_case__		,     control_guidance_start=[0.1, 0.3]		,     control_guidance_end=[0.2, 0.7]						)[0]

         lowerCAmelCase     :     int								=							self.get_dummy_inputs(snake_case__						)
         lowerCAmelCase     :     Any								=							steps
         lowerCAmelCase     :     str								=							scale
         lowerCAmelCase     :     Tuple								=							pipe(**snake_case__		,     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        lowercase__							(						self						):






         """simple docstring"""





         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        lowercase__							(						self						):






         """simple docstring"""





         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3						)

       def        lowercase__							(						self						):






         """simple docstring"""





         self._test_inference_batch_single_identical(expected_max_diff=2e-3						)







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							self.get_dummy_components()
         lowerCAmelCase     :     List[Any]								=							self.pipeline_class(**snake_case__						)
         pipe.to(snake_case__						)
         pipe.set_progress_bar_config(disable=snake_case__						)
         with tempfile.TemporaryDirectory() as tmpdir:
           try:
             # save_pretrained is not implemented for Multi-ControlNet
             pipe.save_pretrained(snake_case__						)
           except NotImplementedError:
             pass


@slow
@require_torch_gpu
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





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







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"						)

         lowerCAmelCase     :     Optional[Any]								=							StableDiffusionControlNetImgaImgPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"		,     safety_checker=snake_case__		,     controlnet=snake_case__						)
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     Union[str, Any]								=							torch.Generator(device="cpu"						).manual_seed(0						)
         lowerCAmelCase     :     Optional[Any]								=							"evil space-punk bird"
         lowerCAmelCase     :     int								=							load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"						).resize((512, 512)						)
         lowerCAmelCase     :     Dict								=							load_image(
             "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"						).resize((512, 512)						)

         lowerCAmelCase     :     Optional[Any]								=							pipe(
             snake_case__		,     snake_case__		,     control_image=snake_case__		,     generator=snake_case__		,     output_type="np"		,     num_inference_steps=50		,     strength=0.6		,     )

         lowerCAmelCase     :     Optional[int]								=							output.images[0]

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

         lowerCAmelCase     :     Tuple								=							load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"						)

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









"""simple docstring"""







from __future__ import annotations

from functools import lru_cache
from math import ceil

lowerCAmelCase__      				=   100

lowerCAmelCase__      				=   set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCAmelCase__      				=   42

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
     if prime not in primes:
          continue
     primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_0_0				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  if number_to_partition < 0:
    return set()
  elif number_to_partition == 0:
    return {1}

  lowerCAmelCase     :     set[int]								=							set()
  lowerCAmelCase     :     int
  lowerCAmelCase     :     int

  for prime in primes:
    if prime > number_to_partition:
      continue
    for sub in partition(number_to_partition - prime				):
      ret.add(sub * prime				)

  return ret
def 					a__			(				SCREAMING_SNAKE_CASE    :    int = 5_0_0_0				):


  '''simple docstring'''







  for number_to_partition in range(1				,      SCREAMING_SNAKE_CASE				):
    if len(partition(SCREAMING_SNAKE_CASE				)				) > number_unique_partitions:
      return number_to_partition
  return None


if __name__ == "__main__":
     print(F"{solution() = }")





