



'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 							lowercase__	(							lowercase						,					unittest.TestCase   ):
	lowercase__  				=			KandinskyVaaPipeline
	lowercase__  				=			[
	    """image_embeds""",
	    """negative_image_embeds""",
	]
	lowercase__  				=			["""image_embeds""", """negative_image_embeds"""]
	lowercase__  				=			[
	    """generator""",
	    """height""",
	    """width""",
	    """latents""",
	    """guidance_scale""",
	    """num_inference_steps""",
	    """return_dict""",
	    """guidance_scale""",
	    """num_images_per_prompt""",
	    """output_type""",
	    """return_dict""",
	]
	lowercase__  				=			False





	@property
	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							return 32





	@property
	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							return 32





	@property
	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							return self.time_input_dim





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







							'''simple docstring'''
							return self.time_input_dim * 4





	@property
	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							return 100





	@property
	def 	UpperCamelCase_  ( self					:				int ):







							'''simple docstring'''
							torch.manual_seed(0 )

							_UpperCamelCase					:		Dict 				=   {
							    'in_channels': 4,
							    # Out channels is double in channels because predicts mean and variance
							    'out_channels': 8,
							    'addition_embed_type': 'image',
							    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
							    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
							    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
							    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
							    'layers_per_block': 1,
							    'encoder_hid_dim': self.text_embedder_hidden_size,
							    'encoder_hid_dim_type': 'image_proj',
							    'cross_attention_dim': self.cross_attention_dim,
							    'attention_head_dim': 4,
							    'resnet_time_scale_shift': 'scale_shift',
							    'class_embed_type': None,
							}

							_UpperCamelCase					:		str 				=   UNetaDConditionModel(**lowerCamelCase__ )
							return model





	@property
	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							return {
							    "block_out_channels": [32, 64],
							    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}





	@property
	def 	UpperCamelCase_  ( self					:				List[Any] ):







							'''simple docstring'''
							torch.manual_seed(0 )
							_UpperCamelCase					:		str 				=   VQModel(**self.dummy_movq_kwargs )
							return model





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







							'''simple docstring'''
							_UpperCamelCase					:		Tuple 				=   self.dummy_unet
							_UpperCamelCase					:		List[Any] 				=   self.dummy_movq

							_UpperCamelCase					:		Optional[Any] 				=   DDIMScheduler(
							    num_train_timesteps=1000 ,beta_schedule='linear' ,beta_start=0.0_0_0_8_5 ,beta_end=0.0_1_2 ,clip_sample=lowerCamelCase__ ,set_alpha_to_one=lowerCamelCase__ ,steps_offset=1 ,prediction_type='epsilon' ,thresholding=lowerCamelCase__ ,)

							_UpperCamelCase					:		List[str] 				=   {
							    'unet': unet,
							    'scheduler': scheduler,
							    'movq': movq,
							}
							return components





	def 	UpperCamelCase_  ( self					:				Any ,lowerCamelCase__					:				Dict ,lowerCamelCase__					:				str=0 ):







							'''simple docstring'''
							_UpperCamelCase					:		Tuple 				=   floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
							_UpperCamelCase					:		Dict 				=   floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(seed + 1 ) ).to(
							    lowerCamelCase__ )
							if str(lowerCamelCase__ ).startswith('mps' ):
													_UpperCamelCase					:		str 				=   torch.manual_seed(lowerCamelCase__ )
							else:
													_UpperCamelCase					:		Tuple 				=   torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
							_UpperCamelCase					:		List[str] 				=   {
							    'image_embeds': image_embeds,
							    'negative_image_embeds': negative_image_embeds,
							    'generator': generator,
							    'height': 64,
							    'width': 64,
							    'guidance_scale': 4.0,
							    'num_inference_steps': 2,
							    'output_type': 'np',
							}
							return inputs





	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							_UpperCamelCase					:		List[Any] 				=   'cpu'

							_UpperCamelCase					:		Dict 				=   self.get_dummy_components()

							_UpperCamelCase					:		Optional[int] 				=   self.pipeline_class(**lowerCamelCase__ )
							_UpperCamelCase					:		Optional[Any] 				=   pipe.to(lowerCamelCase__ )

							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

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

							_UpperCamelCase					:		Dict 				=   pipe(
							    **self.get_dummy_inputs(lowerCamelCase__ ) ,return_dict=lowerCamelCase__ ,)[0]

							_UpperCamelCase					:		Tuple 				=   image[0, -3:, -3:, -1]
							_UpperCamelCase					:		List[Any] 				=   image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							_UpperCamelCase					:		Any 				=   np.array(
							    [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6] )

							assert (
							    np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
							), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'

							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
							), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'






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





	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy' )

							_UpperCamelCase					:		Any 				=   KandinskyVaaPriorPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-prior' ,torch_dtype=torch.floataa )
							pipe_prior.to(lowerCamelCase__ )

							_UpperCamelCase					:		List[str] 				=   KandinskyVaaPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-decoder' ,torch_dtype=torch.floataa )
							_UpperCamelCase					:		Union[str, Any] 				=   pipeline.to(lowerCamelCase__ )
							pipeline.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		int 				=   'red cat, 4k photo'

							_UpperCamelCase					:		List[str] 				=   torch.Generator(device='cuda' ).manual_seed(0 )
							_UpperCamelCase				,			_UpperCamelCase					:		str 				=   pipe_prior(
							    lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=5 ,negative_prompt='' ,).to_tuple()

							_UpperCamelCase					:		Optional[int] 				=   torch.Generator(device='cuda' ).manual_seed(0 )
							_UpperCamelCase					:		Tuple 				=   pipeline(
							    image_embeds=lowerCamelCase__ ,negative_image_embeds=lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=100 ,output_type='np' ,)

							_UpperCamelCase					:		str 				=   output.images[0]

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

							assert_mean_pixel_difference(lowerCamelCase__ ,lowerCamelCase__ )




import math





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__ = 0	, lowerCamelCase__ = 0    ):
				lowerCamelCase_						=			end or len(lowerCamelCase__    )
				for i in range(lowerCamelCase__	, lowerCamelCase__    ):
								lowerCamelCase_						=			i
								lowerCamelCase_						=			array[i]
								while temp_index != start and temp_index_value < array[temp_index - 1]:
												lowerCamelCase_						=			array[temp_index - 1]
												temp_index -= 1
								lowerCamelCase_						=			temp_index_value
				return array





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):  # Max Heap
				lowerCamelCase_						=			index
				lowerCamelCase_						=			2 * index + 1  # Left Node
				lowerCamelCase_						=			2 * index + 2  # Right Node

				if left_index < heap_size and array[largest] < array[left_index]:
								lowerCamelCase_						=			left_index

				if right_index < heap_size and array[largest] < array[right_index]:
								lowerCamelCase_						=			right_index

				if largest != index:
								lowerCamelCase_	,    lowerCamelCase_						=			array[largest], array[index]
								heapify(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			len(lowerCamelCase__    )

				for i in range(n // 2	, -1	, -1    ):
								heapify(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )

				for i in range(n - 1	, 0	, -1    ):
								lowerCamelCase_	,    lowerCamelCase_						=			array[0], array[i]
								heapify(lowerCamelCase__	, 0	, lowerCamelCase__    )

				return array





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				if (array[first_index] > array[middle_index]) != (
				    array[first_index] > array[last_index]
				):
								return array[first_index]
				elif (array[middle_index] > array[first_index]) != (
				    array[middle_index] > array[last_index]
				):
								return array[middle_index]
				else:
								return array[last_index]





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			low
				lowerCamelCase_						=			high
				while True:
								while array[i] < pivot:
												i += 1
								j -= 1
								while pivot < array[j]:
												j -= 1
								if i >= j:
												return i
								lowerCamelCase_	,    lowerCamelCase_						=			array[j], array[i]
								i += 1





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				if len(lowerCamelCase__    ) == 0:
								return array
				lowerCamelCase_						=			2 * math.ceil(math.loga(len(lowerCamelCase__    )    )    )
				lowerCamelCase_						=			1_6
				return intro_sort(lowerCamelCase__	, 0	, len(lowerCamelCase__    )	, lowerCamelCase__	, lowerCamelCase__    )





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				while end - start > size_threshold:
								if max_depth == 0:
												return heap_sort(lowerCamelCase__    )
								max_depth -= 1
								lowerCamelCase_						=			median_of_a(lowerCamelCase__	, lowerCamelCase__	, start + ((end - start) // 2) + 1	, end - 1    )
								lowerCamelCase_						=			partition(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
								intro_sort(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
								lowerCamelCase_						=			p
				return insertion_sort(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__A				=input('''Enter numbers separated by a comma : ''').strip()
			__A				=[float(item) for item in user_input.split(''',''')]
			print(sort(unsorted))


