


"""simple docstring"""


import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







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

							@property
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			1
										__lowerCAmelCase				    =			3
										__lowerCAmelCase				    =			(32, 32)

										__lowerCAmelCase				    =			floats_tensor((batch_size, num_channels) + sizes    ,   rng=random.Random(0	)	).to(a__	)
										return image

							@property
							def 			snake_case				(     self	):
										torch.manual_seed(0	)
										__lowerCAmelCase				    =			UNetaDConditionModel(
										    block_out_channels=(32, 32, 64)    ,   layers_per_block=2    ,   sample_size=32    ,   in_channels=7    ,   out_channels=4    ,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")    ,   up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")    ,   cross_attention_dim=32    ,   attention_head_dim=8    ,   use_linear_projection=a__    ,   only_cross_attention=(True, True, False)    ,   num_class_embeds=1_00    ,   )
										return model

							@property
							def 			snake_case				(     self	):
										torch.manual_seed(0	)
										__lowerCAmelCase				    =			AutoencoderKL(
										    block_out_channels=[32, 32, 64]    ,   in_channels=3    ,   out_channels=3    ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]    ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]    ,   latent_channels=4    ,   )
										return model

							@property
							def 			snake_case				(     self	):
										torch.manual_seed(0	)
										__lowerCAmelCase				    =			CLIPTextConfig(
										    bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=32    ,   intermediate_size=37    ,   layer_norm_eps=1e-0_5    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=10_00    ,   hidden_act="gelu"    ,   projection_dim=5_12    ,   )
										return CLIPTextModel(a__	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			"cpu"  # ensure determinism for the device-dependent torch.Generator
										__lowerCAmelCase				    =			self.dummy_cond_unet_upscale
										__lowerCAmelCase				    =			DDPMScheduler()
										__lowerCAmelCase				    =			DDIMScheduler(prediction_type="v_prediction"	)
										__lowerCAmelCase				    =			self.dummy_vae
										__lowerCAmelCase				    =			self.dummy_text_encoder
										__lowerCAmelCase				    =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

										__lowerCAmelCase				    =			self.dummy_image.cpu().permute(0    ,   2    ,   3    ,   1	)[0]
										__lowerCAmelCase				    =			Image.fromarray(np.uinta(a__	)	).convert("RGB"	).resize((64, 64)	)

										# make sure here that pndm scheduler skips prk
										__lowerCAmelCase				    =			StableDiffusionUpscalePipeline(
										    unet=a__    ,   low_res_scheduler=a__    ,   scheduler=a__    ,   vae=a__    ,   text_encoder=a__    ,   tokenizer=a__    ,   max_noise_level=3_50    ,   )
										__lowerCAmelCase				    =			sd_pipe.to(a__	)
										sd_pipe.set_progress_bar_config(disable=a__	)

										__lowerCAmelCase				    =			"A painting of a squirrel eating a burger"
										__lowerCAmelCase				    =			torch.Generator(device=a__	).manual_seed(0	)
										__lowerCAmelCase				    =			sd_pipe(
										    [prompt]    ,   image=a__    ,   generator=a__    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type="np"    ,   )

										__lowerCAmelCase				    =			output.images

										__lowerCAmelCase				    =			torch.Generator(device=a__	).manual_seed(0	)
										__lowerCAmelCase				    =			sd_pipe(
										    [prompt]    ,   image=a__    ,   generator=a__    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type="np"    ,   return_dict=a__    ,   )[0]

										__lowerCAmelCase				    =			image[0, -3:, -3:, -1]
										__lowerCAmelCase				    =			image_from_tuple[0, -3:, -3:, -1]

										__lowerCAmelCase				    =			low_res_image.size[0] * 4
										assert image.shape == (1, expected_height_width, expected_height_width, 3)
										__lowerCAmelCase				    =			np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			"cpu"  # ensure determinism for the device-dependent torch.Generator
										__lowerCAmelCase				    =			self.dummy_cond_unet_upscale
										__lowerCAmelCase				    =			DDPMScheduler()
										__lowerCAmelCase				    =			DDIMScheduler(prediction_type="v_prediction"	)
										__lowerCAmelCase				    =			self.dummy_vae
										__lowerCAmelCase				    =			self.dummy_text_encoder
										__lowerCAmelCase				    =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

										__lowerCAmelCase				    =			self.dummy_image.cpu().permute(0    ,   2    ,   3    ,   1	)[0]
										__lowerCAmelCase				    =			Image.fromarray(np.uinta(a__	)	).convert("RGB"	).resize((64, 64)	)

										# make sure here that pndm scheduler skips prk
										__lowerCAmelCase				    =			StableDiffusionUpscalePipeline(
										    unet=a__    ,   low_res_scheduler=a__    ,   scheduler=a__    ,   vae=a__    ,   text_encoder=a__    ,   tokenizer=a__    ,   max_noise_level=3_50    ,   )
										__lowerCAmelCase				    =			sd_pipe.to(a__	)
										sd_pipe.set_progress_bar_config(disable=a__	)

										__lowerCAmelCase				    =			"A painting of a squirrel eating a burger"
										__lowerCAmelCase				    =			sd_pipe(
										    2 * [prompt]    ,   image=2 * [low_res_image]    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type="np"    ,   )
										__lowerCAmelCase				    =			output.images
										assert image.shape[0] == 2

										__lowerCAmelCase				    =			torch.Generator(device=a__	).manual_seed(0	)
										__lowerCAmelCase				    =			sd_pipe(
										    [prompt]    ,   image=a__    ,   generator=a__    ,   num_images_per_prompt=2    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type="np"    ,   )
										__lowerCAmelCase				    =			output.images
										assert image.shape[0] == 2



							@unittest.skipIf(torch_device != "cuda"    ,   "This test requires a GPU"	)
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.dummy_cond_unet_upscale
										__lowerCAmelCase				    =			DDPMScheduler()
										__lowerCAmelCase				    =			DDIMScheduler(prediction_type="v_prediction"	)
										__lowerCAmelCase				    =			self.dummy_vae
										__lowerCAmelCase				    =			self.dummy_text_encoder
										__lowerCAmelCase				    =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

										__lowerCAmelCase				    =			self.dummy_image.cpu().permute(0    ,   2    ,   3    ,   1	)[0]
										__lowerCAmelCase				    =			Image.fromarray(np.uinta(a__	)	).convert("RGB"	).resize((64, 64)	)

										# put models in fp16, except vae as it overflows in fp16
										__lowerCAmelCase				    =			unet.half()
										__lowerCAmelCase				    =			text_encoder.half()

										# make sure here that pndm scheduler skips prk
										__lowerCAmelCase				    =			StableDiffusionUpscalePipeline(
										    unet=a__    ,   low_res_scheduler=a__    ,   scheduler=a__    ,   vae=a__    ,   text_encoder=a__    ,   tokenizer=a__    ,   max_noise_level=3_50    ,   )
										__lowerCAmelCase				    =			sd_pipe.to(a__	)
										sd_pipe.set_progress_bar_config(disable=a__	)

										__lowerCAmelCase				    =			"A painting of a squirrel eating a burger"
										__lowerCAmelCase				    =			torch.manual_seed(0	)
										__lowerCAmelCase				    =			sd_pipe(
										    [prompt]    ,   image=a__    ,   generator=a__    ,   num_inference_steps=2    ,   output_type="np"    ,   ).images

										__lowerCAmelCase				    =			low_res_image.size[0] * 4
										assert image.shape == (1, expected_height_width, expected_height_width, 3)
@slow
@require_torch_gpu
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-upscale/low_res_cat.png"	)
										__lowerCAmelCase				    =			load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
										    "/upsampled_cat.npy"	)

										__lowerCAmelCase				    =			"stabilityai/stable-diffusion-x4-upscaler"
										__lowerCAmelCase				    =			StableDiffusionUpscalePipeline.from_pretrained(a__	)
										pipe.to(a__	)
										pipe.set_progress_bar_config(disable=a__	)
										pipe.enable_attention_slicing()

										__lowerCAmelCase				    =			"a cat sitting on a park bench"

										__lowerCAmelCase				    =			torch.manual_seed(0	)
										__lowerCAmelCase				    =			pipe(
										    prompt=a__    ,   image=a__    ,   generator=a__    ,   output_type="np"    ,   )
										__lowerCAmelCase				    =			output.images[0]

										assert image.shape == (5_12, 5_12, 3)
										assert np.abs(expected_image - image	).max() < 1e-3

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-upscale/low_res_cat.png"	)
										__lowerCAmelCase				    =			load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
										    "/upsampled_cat_fp16.npy"	)

										__lowerCAmelCase				    =			"stabilityai/stable-diffusion-x4-upscaler"
										__lowerCAmelCase				    =			StableDiffusionUpscalePipeline.from_pretrained(
										    a__    ,   torch_dtype=torch.floataa    ,   )
										pipe.to(a__	)
										pipe.set_progress_bar_config(disable=a__	)
										pipe.enable_attention_slicing()

										__lowerCAmelCase				    =			"a cat sitting on a park bench"

										__lowerCAmelCase				    =			torch.manual_seed(0	)
										__lowerCAmelCase				    =			pipe(
										    prompt=a__    ,   image=a__    ,   generator=a__    ,   output_type="np"    ,   )
										__lowerCAmelCase				    =			output.images[0]

										assert image.shape == (5_12, 5_12, 3)
										assert np.abs(expected_image - image	).max() < 5e-1



							def 			snake_case				(     self	):
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										__lowerCAmelCase				    =			load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-upscale/low_res_cat.png"	)

										__lowerCAmelCase				    =			"stabilityai/stable-diffusion-x4-upscaler"
										__lowerCAmelCase				    =			StableDiffusionUpscalePipeline.from_pretrained(
										    a__    ,   torch_dtype=torch.floataa    ,   )
										pipe.to(a__	)
										pipe.set_progress_bar_config(disable=a__	)
										pipe.enable_attention_slicing(1	)
										pipe.enable_sequential_cpu_offload()

										__lowerCAmelCase				    =			"a cat sitting on a park bench"

										__lowerCAmelCase				    =			torch.manual_seed(0	)
										__lowerCAmelCase				    =			pipe(
										    prompt=a__    ,   image=a__    ,   generator=a__    ,   num_inference_steps=5    ,   output_type="np"    ,   )

										__lowerCAmelCase				    =			torch.cuda.max_memory_allocated()
										# make sure that less than 2.9 GB is allocated
										assert mem_bytes < 2.9 * 10**9











from __future__ import annotations






def lowerCamelCase__    (							snake_case_	:  list[int]       ,      snake_case_	:  int       )      ->				list[list[int]]:
   __snake_case							       =   []
   __snake_case							       =   []
   __snake_case							       =   0
   __snake_case							       =   sum(snake_case_       )
   create_state_space_tree(snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       )
   return result






def lowerCamelCase__    (							snake_case_	:  list[int]       ,      snake_case_	:  int       ,      snake_case_	:  int       ,      snake_case_	:  list[int]       ,      snake_case_	:  list[list[int]]       ,      snake_case_	:  int       ,      )      ->				None:
   if sum(snake_case_       ) > max_sum or (remaining_nums_sum + sum(snake_case_       )) < max_sum:
      return
   if sum(snake_case_       ) == max_sum:
      result.append(snake_case_       )
      return
   for index in range(snake_case_       ,      len(snake_case_       )       ):
      create_state_space_tree(
          snake_case_       ,      snake_case_       ,      index + 1       ,      [*path, nums[index]]       ,      snake_case_       ,      remaining_nums_sum - nums[index]       ,      )
snake_case_				=			[3, 34, 4, 12, 5, 2]
snake_case_				=			9
snake_case_				=			generate_sum_of_subsets_soln(nums, max_sum)







print(*result)

