






'''simple docstring'''







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 _lowercase      (							_lowercase							, unittest.TestCase						):
							a							 =			PaintByExamplePipeline
							a							 =			IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
							a							 =			IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							a							 =			frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




							def 						lowerCamelCase_							(				self:							str	):
														torch.manual_seed(0	)
														lowerCamelCase__   :      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     ,				)
														lowerCamelCase__   :      Optional[int]			    =			PNDMScheduler(skip_prk_steps=UpperCamelCase__	)
														torch.manual_seed(0	)
														lowerCamelCase__   :      Tuple			    =			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__   :      Tuple			    =			CLIPVisionConfig(
														    hidden_size=32     ,				projection_dim=32     ,				intermediate_size=37     ,				layer_norm_eps=1e-05     ,				num_attention_heads=4     ,				num_hidden_layers=5     ,				image_size=32     ,				patch_size=4     ,				)
														lowerCamelCase__   :      List[str]			    =			PaintByExampleImageEncoder(UpperCamelCase__     ,				proj_size=32	)
														lowerCamelCase__   :      Tuple			    =			CLIPImageProcessor(crop_size=32     ,				size=32	)

														lowerCamelCase__   :      Tuple			    =			{
														    """unet""": unet,
														    """scheduler""": scheduler,
														    """vae""": vae,
														    """image_encoder""": image_encoder,
														    """safety_checker""": None,
														    """feature_extractor""": feature_extractor,
														}
														return components




							def 						lowerCamelCase_							(				self:							Optional[Any]     ,				UpperCamelCase__:							Dict	):
														lowerCamelCase__   :      Union[str, Any]			    =			np.array(image.convert("""RGB"""	)	)
														lowerCamelCase__   :      List[Any]			    =			image[None].transpose(0     ,				3     ,				1     ,				2	)
														lowerCamelCase__   :      Optional[Any]			    =			torch.from_numpy(UpperCamelCase__	).to(dtype=torch.floataa	) / 127.5 - 1.0
														return image




							def 						lowerCamelCase_							(				self:							List[Any]     ,				UpperCamelCase__:							Optional[Any]="cpu"     ,				UpperCamelCase__:							Optional[Any]=0	):
														# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
														lowerCamelCase__   :      Dict			    =			floats_tensor((1, 3, 32, 32)     ,				rng=random.Random(UpperCamelCase__	)	).to(UpperCamelCase__	)
														lowerCamelCase__   :      Optional[int]			    =			image.cpu().permute(0     ,				2     ,				3     ,				1	)[0]
														lowerCamelCase__   :      Optional[int]			    =			Image.fromarray(np.uinta(UpperCamelCase__	)	).convert("""RGB"""	).resize((64, 64)	)
														lowerCamelCase__   :      List[Any]			    =			Image.fromarray(np.uinta(image + 4	)	).convert("""RGB"""	).resize((64, 64)	)
														lowerCamelCase__   :      str			    =			Image.fromarray(np.uinta(UpperCamelCase__	)	).convert("""RGB"""	).resize((32, 32)	)

														if str(UpperCamelCase__	).startswith("""mps"""	):
																					lowerCamelCase__   :      int			    =			torch.manual_seed(UpperCamelCase__	)
														else:
																					lowerCamelCase__   :      Tuple			    =			torch.Generator(device=UpperCamelCase__	).manual_seed(UpperCamelCase__	)
														lowerCamelCase__   :      Tuple			    =			{
														    """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 						lowerCamelCase_							(				self:							List[str]	):
														lowerCamelCase__   :      Any			    =			self.get_dummy_components()

														# make sure here that pndm scheduler skips prk
														lowerCamelCase__   :      Tuple			    =			PaintByExamplePipeline(**UpperCamelCase__	)
														lowerCamelCase__   :      Tuple			    =			pipe.to("""cpu"""	)
														pipe.set_progress_bar_config(disable=UpperCamelCase__	)

														lowerCamelCase__   :      List[str]			    =			self.get_dummy_inputs()
														lowerCamelCase__   :      Tuple			    =			pipe(**UpperCamelCase__	)
														lowerCamelCase__   :      List[Any]			    =			output.images

														lowerCamelCase__   :      Optional[Any]			    =			image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)
														lowerCamelCase__   :      Union[str, Any]			    =			np.array([0.4_686, 0.5_687, 0.4_007, 0.5_218, 0.5_741, 0.4_482, 0.4_940, 0.4_629, 0.4_503]	)

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




							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														lowerCamelCase__   :      List[Any]			    =			"""cpu"""
														lowerCamelCase__   :      str			    =			self.get_dummy_inputs()
														inputs.pop("""mask_image"""	)
														lowerCamelCase__   :      Dict			    =			self.convert_to_pt(inputs.pop("""image"""	)	)
														lowerCamelCase__   :      str			    =			image.clamp(0     ,				1	) / 2

														# make sure here that pndm scheduler skips prk
														lowerCamelCase__   :      Dict			    =			PaintByExamplePipeline(**self.get_dummy_components()	)
														lowerCamelCase__   :      Optional[Any]			    =			pipe.to(UpperCamelCase__	)
														pipe.set_progress_bar_config(disable=UpperCamelCase__	)

														lowerCamelCase__   :      List[Any]			    =			pipe(image=UpperCamelCase__     ,				mask_image=mask_image[:, 0]     ,				**UpperCamelCase__	)
														lowerCamelCase__   :      Dict			    =			output.images

														lowerCamelCase__   :      Tuple			    =			image.cpu().permute(0     ,				2     ,				3     ,				1	)[0]
														lowerCamelCase__   :      Any			    =			mask_image.cpu().permute(0     ,				2     ,				3     ,				1	)[0]

														lowerCamelCase__   :      List[Any]			    =			Image.fromarray(np.uinta(UpperCamelCase__	)	).convert("""RGB"""	)
														lowerCamelCase__   :      Any			    =			Image.fromarray(np.uinta(UpperCamelCase__	)	).convert("""RGB"""	)

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

														assert out_a.shape == (1, 64, 64, 3)
														assert np.abs(out_a.flatten() - out_a.flatten()	).max() < 5e-2




							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														super().test_inference_batch_single_identical(expected_max_diff=3e-3	)



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




							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()




							def 						lowerCamelCase_							(				self:							List[str]	):
														# make sure here that pndm scheduler skips prk
														lowerCamelCase__   :      Any			    =			load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/paint_by_example/dog_in_bucket.png"""	)
														lowerCamelCase__   :      Optional[Any]			    =			load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/paint_by_example/mask.png"""	)
														lowerCamelCase__   :      str			    =			load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/paint_by_example/panda.jpg"""	)

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

														lowerCamelCase__   :      str			    =			torch.manual_seed(321	)
														lowerCamelCase__   :      Dict			    =			pipe(
														    image=UpperCamelCase__     ,				mask_image=UpperCamelCase__     ,				example_image=UpperCamelCase__     ,				generator=UpperCamelCase__     ,				guidance_scale=5.0     ,				num_inference_steps=50     ,				output_type="""np"""     ,				)

														lowerCamelCase__   :      Any			    =			output.images

														lowerCamelCase__   :      Any			    =			image[0, -3:, -3:, -1]

														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__   :      List[str]			    =			np.array([0.4_834, 0.4_811, 0.4_874, 0.5_122, 0.5_081, 0.5_144, 0.5_291, 0.5_290, 0.5_374]	)

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









'''simple docstring'''







from __future__ import annotations





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Dict:
							lowerCamelCase__   :      List[str]			    =			0
							lowerCamelCase__   :      Tuple			    =			len(UpperCamelCase ) - 1
							while left <= right:
														lowerCamelCase__   :      List[str]			    =			(left + right) // 2
														if left == right:
																					if sorted_list[middle] < item:
																												lowerCamelCase__   :      Any			    =			middle + 1
																					break
														elif sorted_list[middle] < item:
																					lowerCamelCase__   :      Optional[Any]			    =			middle + 1
														else:
																					lowerCamelCase__   :      Dict			    =			middle - 1
							sorted_list.insert(UpperCamelCase							,     UpperCamelCase )
							return sorted_list





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Tuple:
							lowerCamelCase__   :      Any			    =			[]
							while left and right:
														if left[0][0] < right[0][0]:
																					result.append(left.pop(0 ) )
														else:
																					result.append(right.pop(0 ) )
							return result + left + right





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			Any:
							lowerCamelCase__   :      Optional[Any]			    =			len(UpperCamelCase )
							if length <= 1:
														return list_ad
							lowerCamelCase__   :      Tuple			    =			length // 2
							return merge(sortlist_ad(list_ad[:middle] )							,     sortlist_ad(list_ad[middle:] ) )





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			list[int]:

							if len(UpperCamelCase ) <= 1:
														return collection
							lowerCamelCase__   :      int			    =			[]
							lowerCamelCase__   :      int			    =			False
							for i in range(0							,     len(UpperCamelCase )							,     2 ):
														if i == len(UpperCamelCase ) - 1:
																					lowerCamelCase__   :      List[Any]			    =			True
														else:
																					if collection[i] < collection[i + 1]:
																												two_paired_list.append([collection[i], collection[i + 1]] )
																					else:
																												two_paired_list.append([collection[i + 1], collection[i]] )
							lowerCamelCase__   :      List[Any]			    =			sortlist_ad(UpperCamelCase )
							lowerCamelCase__   :      List[Any]			    =			[i[0] for i in sorted_list_ad]
							result.append(sorted_list_ad[-1][1] )
							if has_last_odd_item:
														lowerCamelCase__   :      int			    =			collection[-1]
														lowerCamelCase__   :      str			    =			binary_search_insertion(UpperCamelCase							,     UpperCamelCase )
							lowerCamelCase__   :      Any			    =			False
							for i in range(len(UpperCamelCase ) - 1 ):
														if result[i] == collection[-1] and has_last_odd_item:
																					lowerCamelCase__   :      Any			    =			True
														lowerCamelCase__   :      Dict			    =			sorted_list_ad[i][1]
														# If last_odd_item is inserted before the item's index,
														# you should forward index one more.
														if is_last_odd_item_inserted_before_this_index:
																					lowerCamelCase__   :      List[str]			    =			result[: i + 2] + binary_search_insertion(result[i + 2 :]							,     UpperCamelCase )
														else:
																					lowerCamelCase__   :      Dict			    =			result[: i + 1] + binary_search_insertion(result[i + 1 :]							,     UpperCamelCase )

							return result


if __name__ == "__main__":
			_A      :     Tuple       	=input('''Enter numbers separated by a comma:\n''').strip()
			_A      :     List[Any]       	=[int(item) for item in user_input.split(''',''')]
			print(merge_insertion_sort(unsorted))

