



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,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class   _A   ( _lowercase  ,						_lowercase  ,						_lowercase  ,						unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     Optional[int]      							=      StableDiffusionInstructPixaPixPipeline
						_snake_case			:     Union[str, Any]      							=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
						_snake_case			:     Dict      							=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						_snake_case			:     Any      							=      IMAGE_TO_IMAGE_IMAGE_PARAMS
						_snake_case			:     Optional[int]      							=      IMAGE_TO_IMAGE_IMAGE_PARAMS



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							torch.manual_seed(0	)
							__lowercase		       =			UNetaDConditionModel(
							    block_out_channels=(32, 64)							,      layers_per_block=2							,      sample_size=32							,      in_channels=8							,      out_channels=4							,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,      cross_attention_dim=32							,      )
							__lowercase		       =			PNDMScheduler(skip_prk_steps=lowerCamelCase	)
							torch.manual_seed(0	)
							__lowercase		       =			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	)
							__lowercase		       =			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							,      )
							__lowercase		       =			CLIPTextModel(lowerCamelCase	)
							__lowercase		       =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

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



						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Optional[int]=0	):






							'''simple docstring'''



							__lowercase		       =			floats_tensor((1, 3, 32, 32)							,      rng=random.Random(lowerCamelCase	)	).to(lowerCamelCase	)
							__lowercase		       =			image.cpu().permute(0							,      2							,      3							,      1	)[0]
							__lowercase		       =			Image.fromarray(np.uinta(lowerCamelCase	)	).convert("RGB"	)
							if str(lowerCamelCase	).startswith("mps"	):
								__lowercase		       =			torch.manual_seed(lowerCamelCase	)
							else:
								__lowercase		       =			torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)
							__lowercase		       =			{
							    "prompt": "A painting of a squirrel eating a burger",
							    "image": image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "guidance_scale": 6.0,
							    "image_guidance_scale": 1,
							    "output_type": "numpy",
							}
							return inputs



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase		       =			"cpu"  # ensure determinism for the device-dependent torch.Generator
							__lowercase		       =			self.get_dummy_components()
							__lowercase		       =			StableDiffusionInstructPixaPixPipeline(**lowerCamelCase	)
							__lowercase		       =			sd_pipe.to(lowerCamelCase	)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)
							__lowercase		       =			sd_pipe(**lowerCamelCase	).images
							__lowercase		       =			image[0, -3:, -3:, -1]
							assert image.shape == (1, 32, 32, 3)
							__lowercase		       =			np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							__lowercase		       =			"cpu"  # ensure determinism for the device-dependent torch.Generator
							__lowercase		       =			self.get_dummy_components()
							__lowercase		       =			StableDiffusionInstructPixaPixPipeline(**lowerCamelCase	)
							__lowercase		       =			sd_pipe.to(lowerCamelCase	)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)
							__lowercase		       =			"french fries"
							__lowercase		       =			sd_pipe(**lowerCamelCase							,      negative_prompt=lowerCamelCase	)
							__lowercase		       =			output.images
							__lowercase		       =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							__lowercase		       =			np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			"cpu"  # ensure determinism for the device-dependent torch.Generator
							__lowercase		       =			self.get_dummy_components()
							__lowercase		       =			StableDiffusionInstructPixaPixPipeline(**lowerCamelCase	)
							__lowercase		       =			sd_pipe.to(lowerCamelCase	)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)
							__lowercase		       =			[inputs["prompt"]] * 2

							__lowercase		       =			np.array(inputs["image"]	).astype(np.floataa	) / 255.0
							__lowercase		       =			torch.from_numpy(lowerCamelCase	).unsqueeze(0	).to(lowerCamelCase	)
							__lowercase		       =			image / 2 + 0.5
							__lowercase		       =			image.permute(0							,      3							,      1							,      2	)
							__lowercase		       =			image.repeat(2							,      1							,      1							,      1	)

							__lowercase		       =			sd_pipe(**lowerCamelCase	).images
							__lowercase		       =			image[-1, -3:, -3:, -1]

							assert image.shape == (2, 32, 32, 3)
							__lowercase		       =			np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			"cpu"  # ensure determinism for the device-dependent torch.Generator
							__lowercase		       =			self.get_dummy_components()
							__lowercase		       =			EulerAncestralDiscreteScheduler(
							    beta_start=0.0_0085							,      beta_end=0.012							,      beta_schedule="scaled_linear"	)
							__lowercase		       =			StableDiffusionInstructPixaPixPipeline(**lowerCamelCase	)
							__lowercase		       =			sd_pipe.to(lowerCamelCase	)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)
							__lowercase		       =			sd_pipe(**lowerCamelCase	).images
							__lowercase		       =			image[0, -3:, -3:, -1]

							__lowercase		       =			[round(lowerCamelCase							,      4	) for x in image_slice.flatten().tolist()]
							print(",".join([str(lowerCamelCase	) for x in slice]	)	)

							assert image.shape == (1, 32, 32, 3)
							__lowercase		       =			np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							super().test_inference_batch_single_identical(expected_max_diff=3e-3	)



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							__lowercase		       =			self.get_dummy_components()
							__lowercase		       =			StableDiffusionInstructPixaPixPipeline(**lowerCamelCase	)
							__lowercase		       =			VaeImageProcessor(do_resize=lowerCamelCase							,      do_normalize=lowerCamelCase	)
							__lowercase		       =			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			pipe(**self.get_dummy_inputs_by_type(lowerCamelCase							,      input_image_type="pt"	)	)[0]

							__lowercase		       =			components["vae"]
							__lowercase		       =			self.get_dummy_inputs_by_type(lowerCamelCase							,      input_image_type="pt"	)

							for image_param in self.image_latents_params:
								if image_param in inputs.keys():
									__lowercase		       =			vae.encode(inputs[image_param]	).latent_dist.mode()

							__lowercase		       =			pipe(**lowerCamelCase	)[0]

							__lowercase		       =			np.abs(out - out_latents_inputs	).max()
							self.assertLess(lowerCamelCase							,      1e-4							,      "passing latents as image input generate different result from passing image"	)

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


						'''simple docstring'''





						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Any							,      lowerCamelCase	:							List[Any]=0	):






							'''simple docstring'''



							__lowercase		       =			torch.manual_seed(lowerCamelCase	)
							__lowercase		       =			load_image(
							    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"	)
							__lowercase		       =			{
							    "prompt": "turn him into a cyborg",
							    "image": image,
							    "generator": generator,
							    "num_inference_steps": 3,
							    "guidance_scale": 7.5,
							    "image_guidance_scale": 1.0,
							    "output_type": "numpy",
							}
							return inputs



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    "timbrooks/instruct-pix2pix"							,      safety_checker=lowerCamelCase	)
							pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)
							pipe.enable_attention_slicing()

							__lowercase		       =			self.get_inputs()
							__lowercase		       =			pipe(**lowerCamelCase	).images
							__lowercase		       =			image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							__lowercase		       =			np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]	)

							assert np.abs(expected_slice - image_slice	).max() < 1e-3



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							__lowercase		       =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    "timbrooks/instruct-pix2pix"							,      safety_checker=lowerCamelCase	)
							__lowercase		       =			LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
							pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)
							pipe.enable_attention_slicing()

							__lowercase		       =			self.get_inputs()
							__lowercase		       =			pipe(**lowerCamelCase	).images
							__lowercase		       =			image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							__lowercase		       =			np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]	)

							assert np.abs(expected_slice - image_slice	).max() < 1e-3



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    "timbrooks/instruct-pix2pix"							,      safety_checker=lowerCamelCase	)
							__lowercase		       =			DDIMScheduler.from_config(pipe.scheduler.config	)
							pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)
							pipe.enable_attention_slicing()

							__lowercase		       =			self.get_inputs()
							__lowercase		       =			pipe(**lowerCamelCase	).images
							__lowercase		       =			image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							__lowercase		       =			np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]	)

							assert np.abs(expected_slice - image_slice	).max() < 1e-3



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			0

							def callback_fn(lowerCamelCase	:							int							,      lowerCamelCase	:							int							,      lowerCamelCase	:							torch.FloatTensor	) -> None:
								__lowercase		       =			True
								nonlocal number_of_steps
								number_of_steps += 1
								if step == 1:
									__lowercase		       =			latents.detach().cpu().numpy()
									assert latents.shape == (1, 4, 64, 64)
									__lowercase		       =			latents[0, -3:, -3:, -1]
									__lowercase		       =			np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]	)

									assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2
								elif step == 2:
									__lowercase		       =			latents.detach().cpu().numpy()
									assert latents.shape == (1, 4, 64, 64)
									__lowercase		       =			latents[0, -3:, -3:, -1]
									__lowercase		       =			np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]	)

									assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2

							__lowercase		       =			False

							__lowercase		       =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    "timbrooks/instruct-pix2pix"							,      safety_checker=lowerCamelCase							,      torch_dtype=torch.floataa	)
							__lowercase		       =			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)
							pipe.enable_attention_slicing()

							__lowercase		       =			self.get_inputs()
							pipe(**lowerCamelCase							,      callback=lowerCamelCase							,      callback_steps=1	)
							assert callback_fn.has_been_called
							assert number_of_steps == 3



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							__lowercase		       =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    "timbrooks/instruct-pix2pix"							,      safety_checker=lowerCamelCase							,      torch_dtype=torch.floataa	)
							__lowercase		       =			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)
							pipe.enable_attention_slicing(1	)
							pipe.enable_sequential_cpu_offload()

							__lowercase		       =			self.get_inputs()
							__lowercase		       =			pipe(**lowerCamelCase	)

							__lowercase		       =			torch.cuda.max_memory_allocated()
							# make sure that less than 2.2 GB is allocated
							assert mem_bytes < 2.2 * 10**9



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase		       =			self.get_inputs()
							# resize to resolution that is divisible by 8 but not 16 or 32
							__lowercase		       =			inputs["image"].resize((504, 504)	)

							__lowercase		       =			"timbrooks/instruct-pix2pix"
							__lowercase		       =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    lowerCamelCase							,      safety_checker=lowerCamelCase							,      )
							pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)
							pipe.enable_attention_slicing()

							__lowercase		       =			pipe(**lowerCamelCase	)
							__lowercase		       =			output.images[0]

							__lowercase		       =			image[255:258, 383:386, -1]

							assert image.shape == (504, 504, 3)
							__lowercase		       =			np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-3








import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


snake_case__					:     int    		=   logging.get_logger(__name__)

snake_case__					:     Optional[int]    		=   {
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Dict      							=      """conditional_detr"""
						_snake_case			:     Union[str, Any]      							=      ["""past_key_values"""]
						_snake_case			:     Optional[int]      							=      {
						    """hidden_size""": """d_model""",
						    """num_attention_heads""": """encoder_attention_heads""",
						}



						def __init__(	self	:							Optional[Any]							,      lowerCamelCase	:							int=True							,      lowerCamelCase	:							Tuple=None							,      lowerCamelCase	:							Optional[int]=3							,      lowerCamelCase	:							Optional[int]=300							,      lowerCamelCase	:							List[Any]=6							,      lowerCamelCase	:							str=2_048							,      lowerCamelCase	:							Any=8							,      lowerCamelCase	:							List[str]=6							,      lowerCamelCase	:							Any=2_048							,      lowerCamelCase	:							List[Any]=8							,      lowerCamelCase	:							Optional[Any]=0.0							,      lowerCamelCase	:							List[str]=0.0							,      lowerCamelCase	:							List[Any]=True							,      lowerCamelCase	:							str="relu"							,      lowerCamelCase	:							int=256							,      lowerCamelCase	:							Dict=0.1							,      lowerCamelCase	:							Optional[Any]=0.0							,      lowerCamelCase	:							Dict=0.0							,      lowerCamelCase	:							Tuple=0.02							,      lowerCamelCase	:							int=1.0							,      lowerCamelCase	:							Tuple=False							,      lowerCamelCase	:							List[str]="sine"							,      lowerCamelCase	:							List[Any]="resnet50"							,      lowerCamelCase	:							Any=True							,      lowerCamelCase	:							Any=False							,      lowerCamelCase	:							List[Any]=2							,      lowerCamelCase	:							List[Any]=5							,      lowerCamelCase	:							str=2							,      lowerCamelCase	:							Dict=1							,      lowerCamelCase	:							List[str]=1							,      lowerCamelCase	:							Union[str, Any]=2							,      lowerCamelCase	:							Dict=5							,      lowerCamelCase	:							List[Any]=2							,      lowerCamelCase	:							Tuple=0.25							,      **lowerCamelCase	:							List[str]							,      ):






							'''simple docstring'''



							if backbone_config is not None and use_timm_backbone:
								raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."	)

							if not use_timm_backbone:
								if backbone_config is None:
									logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."	)
									__lowercase		       =			CONFIG_MAPPING["resnet"](out_features=["stage4"]	)
								elif isinstance(lowerCamelCase							,      lowerCamelCase	):
									__lowercase		       =			backbone_config.get("model_type"	)
									__lowercase		       =			CONFIG_MAPPING[backbone_model_type]
									__lowercase		       =			config_class.from_dict(lowerCamelCase	)

							__lowercase		       =			use_timm_backbone
							__lowercase		       =			backbone_config
							__lowercase		       =			num_channels
							__lowercase		       =			num_queries
							__lowercase		       =			d_model
							__lowercase		       =			encoder_ffn_dim
							__lowercase		       =			encoder_layers
							__lowercase		       =			encoder_attention_heads
							__lowercase		       =			decoder_ffn_dim
							__lowercase		       =			decoder_layers
							__lowercase		       =			decoder_attention_heads
							__lowercase		       =			dropout
							__lowercase		       =			attention_dropout
							__lowercase		       =			activation_dropout
							__lowercase		       =			activation_function
							__lowercase		       =			init_std
							__lowercase		       =			init_xavier_std
							__lowercase		       =			encoder_layerdrop
							__lowercase		       =			decoder_layerdrop
							__lowercase		       =			encoder_layers
							__lowercase		       =			auxiliary_loss
							__lowercase		       =			position_embedding_type
							__lowercase		       =			backbone
							__lowercase		       =			use_pretrained_backbone
							__lowercase		       =			dilation
							# Hungarian matcher
							__lowercase		       =			class_cost
							__lowercase		       =			bbox_cost
							__lowercase		       =			giou_cost
							# Loss coefficients
							__lowercase		       =			mask_loss_coefficient
							__lowercase		       =			dice_loss_coefficient
							__lowercase		       =			cls_loss_coefficient
							__lowercase		       =			bbox_loss_coefficient
							__lowercase		       =			giou_loss_coefficient
							__lowercase		       =			focal_alpha
							super().__init__(is_encoder_decoder=lowerCamelCase							,      **lowerCamelCase	)



						@property
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							return self.encoder_attention_heads



						@property
						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							return self.d_model



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase		       =			copy.deepcopy(self.__dict__	)
							if self.backbone_config is not None:
								__lowercase		       =			self.backbone_config.to_dict()
							__lowercase		       =			self.__class__.model_type
							return output

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Any      							=      version.parse("""1.11"""	)



						@property
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							        ("pixel_mask", {0: "batch"}),
							    ]	)



						@property
						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							return 1e-5



						@property
						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							return 12



