


from __future__ import annotations




class  A_ :







 """simple docstring"""





 def __init__(      self       :			List[str]							,     _a       :			Any					)     ->      None:
      UpperCAmelCase    :					Any			=      data
      UpperCAmelCase    :					List[str]			=      None




class  A_ :







 """simple docstring"""





 def __init__(      self       :			Union[str, Any]					)     ->      List[Any]:
      UpperCAmelCase    :					Union[str, Any]			=      None
 def 	_UpperCamelCase     (      self       :			Optional[Any]							,     _a       :			Optional[Any]					)     ->      int:
      UpperCAmelCase    :					Any			=      Node(__snake_case					)
      UpperCAmelCase    :					Any			=      self.head
      UpperCAmelCase    :					int			=      new_node
      return self.head.data

 def 	_UpperCamelCase     (      self       :			Dict					)     ->      int | None:
      UpperCAmelCase    :					int			=      self.head
      UpperCAmelCase    :					int			=      self.head
      if self.head:
           while fast_pointer and fast_pointer.next:
                UpperCAmelCase    :					List[str]			=      fast_pointer.next.next
                UpperCAmelCase    :					Optional[Any]			=      slow_pointer.next
           return slow_pointer.data
      else:
           print("No element found."					)
           return None


if __name__ == "__main__":
      _snake_case				=			LinkedList()
      for _ in range(int(input().strip())):
            _snake_case				=			int(input().strip())
            link.push(data)
      print(link.middle_element())










import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
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_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 							_a(     __A						, unittest.TestCase     ):
		lowerCamelCase__  :Optional[int] 					=		PaintByExamplePipeline
		lowerCamelCase__  :Union[str, Any] 					=		IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		lowerCamelCase__  :Optional[Any] 					=		IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		lowerCamelCase__  :int 					=		frozenset([]     )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







		def 							lowercase							(     self					)   ->   Dict:


							'''simple docstring'''

							torch.manual_seed(0					)
							_snake_case							:       Union[str, Any]												= UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)			,    layers_per_block=2			,    sample_size=3_2			,    in_channels=9			,    out_channels=4			,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,    cross_attention_dim=3_2			,    )
							_snake_case							:       int												= PNDMScheduler(skip_prk_steps=__snake_case					)
							torch.manual_seed(0					)
							_snake_case							:       Any												= AutoencoderKL(
							    block_out_channels=[3_2, 6_4]			,    in_channels=3			,    out_channels=3			,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,    latent_channels=4			,    )
							torch.manual_seed(0					)
							_snake_case							:       Dict												= CLIPVisionConfig(
							    hidden_size=3_2			,    projection_dim=3_2			,    intermediate_size=3_7			,    layer_norm_eps=1E-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    image_size=3_2			,    patch_size=4			,    )
							_snake_case							:       Any												= PaintByExampleImageEncoder(__snake_case			,    proj_size=3_2					)
							_snake_case							:       Dict												= CLIPImageProcessor(crop_size=3_2			,    size=3_2					)

							_snake_case							:       Any												= {
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "image_encoder": image_encoder,
							    "safety_checker": None,
							    "feature_extractor": feature_extractor,
							}
							return components







		def 							lowercase							(     self			,    __snake_case					)   ->   Tuple:


							'''simple docstring'''

							_snake_case							:       List[str]												= np.array(image.convert("RGB"					)					)
							_snake_case							:       str												= image[None].transpose(0			,    3			,    1			,    2					)
							_snake_case							:       Dict												= torch.from_numpy(__snake_case					).to(dtype=torch.floataa					) / 1_27.5 - 1.0
							return image







		def 							lowercase							(     self			,    __snake_case="cpu"			,    __snake_case=0					)   ->   Any:


							'''simple docstring'''

							_snake_case							:       Tuple												= floats_tensor((1, 3, 3_2, 3_2)			,    rng=random.Random(__snake_case					)					).to(__snake_case					)
							_snake_case							:       Union[str, Any]												= image.cpu().permute(0			,    2			,    3			,    1					)[0]
							_snake_case							:       List[Any]												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					).resize((6_4, 6_4)					)
							_snake_case							:       List[Any]												= Image.fromarray(np.uinta(image + 4					)					).convert("RGB"					).resize((6_4, 6_4)					)
							_snake_case							:       Any												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					).resize((3_2, 3_2)					)

							if str(__snake_case					).startswith("mps"					):
												_snake_case							:       Union[str, Any]												= torch.manual_seed(__snake_case					)
							else:
												_snake_case							:       List[Any]												= torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
							_snake_case							:       Optional[int]												= {
							    "example_image": example_image,
							    "image": init_image,
							    "mask_image": mask_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "guidance_scale": 6.0,
							    "output_type": "numpy",
							}
							return inputs







		def 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							_snake_case							:       int												= PaintByExamplePipeline(**__snake_case					)
							_snake_case							:       List[Any]												= pipe.to("cpu"					)
							pipe.set_progress_bar_config(disable=__snake_case					)

							_snake_case							:       List[str]												= self.get_dummy_inputs()
							_snake_case							:       int												= pipe(**__snake_case					)
							_snake_case							:       Dict												= output.images

							_snake_case							:       Optional[int]												= image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							_snake_case							:       int												= np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]					)

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







		def 							lowercase							(     self					)   ->   List[Any]:


							'''simple docstring'''

							_snake_case							:       Optional[int]												= "cpu"
							_snake_case							:       str												= self.get_dummy_inputs()
							inputs.pop("mask_image"					)
							_snake_case							:       Union[str, Any]												= self.convert_to_pt(inputs.pop("image"					)					)
							_snake_case							:       Union[str, Any]												= image.clamp(0			,    1					) / 2

							# make sure here that pndm scheduler skips prk
							_snake_case							:       Dict												= PaintByExamplePipeline(**self.get_dummy_components()					)
							_snake_case							:       Union[str, Any]												= pipe.to(__snake_case					)
							pipe.set_progress_bar_config(disable=__snake_case					)

							_snake_case							:       int												= pipe(image=__snake_case			,    mask_image=mask_image[:, 0]			,    **__snake_case					)
							_snake_case							:       Optional[int]												= output.images

							_snake_case							:       Any												= image.cpu().permute(0			,    2			,    3			,    1					)[0]
							_snake_case							:       List[str]												= mask_image.cpu().permute(0			,    2			,    3			,    1					)[0]

							_snake_case							:       Any												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					)
							_snake_case							:       int												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					)

							_snake_case							:       Optional[int]												= pipe(**self.get_dummy_inputs()					)
							_snake_case							:       Dict												= output.images

							assert out_a.shape == (1, 6_4, 6_4, 3)
							assert np.abs(out_a.flatten() - out_a.flatten()					).max() < 5E-2







		def 							lowercase							(     self					)   ->   Union[str, Any]:


							'''simple docstring'''

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






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







		def 							lowercase							(     self					)   ->   Union[str, Any]:


							'''simple docstring'''

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







		def 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Tuple												= load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/dog_in_bucket.png"					)
							_snake_case							:       Optional[Any]												= load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/mask.png"					)
							_snake_case							:       Optional[int]												= load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/panda.jpg"					)

							_snake_case							:       Any												= PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"					)
							_snake_case							:       Any												= pipe.to(__snake_case					)
							pipe.set_progress_bar_config(disable=__snake_case					)

							_snake_case							:       Union[str, Any]												= torch.manual_seed(3_2_1					)
							_snake_case							:       int												= pipe(
							    image=__snake_case			,    mask_image=__snake_case			,    example_image=__snake_case			,    generator=__snake_case			,    guidance_scale=5.0			,    num_inference_steps=5_0			,    output_type="np"			,    )

							_snake_case							:       str												= output.images

							_snake_case							:       Optional[int]												= image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							_snake_case							:       int												= np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]					)

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