


"""simple docstring"""







import math




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_ = 0						,		lowerCAmelCase_ = 0     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):  # Max Heap




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       index
					__SCREAMING_SNAKE_CASE		     =       2 * index + 1  # Left Node
					__SCREAMING_SNAKE_CASE		     =       2 * index + 2  # Right Node

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

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

					if largest != index:
										__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       array[largest], array[index]
										heapify(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       len(lowerCAmelCase_     )

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

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

					return array




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       low
					__SCREAMING_SNAKE_CASE		     =       high
					while True:
										while array[i] < pivot:
															i += 1
										j -= 1
										while pivot < array[j]:
															j -= 1
										if i >= j:
															return i
										__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       array[j], array[i]
										i += 1




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					if len(lowerCAmelCase_     ) == 0:
										return array
					__SCREAMING_SNAKE_CASE		     =       2 * math.ceil(math.loga(len(lowerCAmelCase_     )     )     )
					__SCREAMING_SNAKE_CASE		     =       16
					return intro_sort(lowerCAmelCase_						,		0						,		len(lowerCAmelCase_     )						,		lowerCAmelCase_						,		lowerCAmelCase_     )




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


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


if __name__ == "__main__":
		import doctest

		doctest.testmod()

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




"""simple docstring"""







import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 				UpperCamelCase_			(				UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						Union[str, Any]					      =    DiTPipeline
	snake_case__       :						Union[str, Any]					      =    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
	snake_case__       :						Any					      =    PipelineTesterMixin.required_optional_params - {
	    "latents",
	    "num_images_per_prompt",
	    "callback",
	    "callback_steps",
	}
	snake_case__       :						Optional[Any]					      =    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
	snake_case__       :						Optional[Any]					      =    False




	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Dict:
						torch.manual_seed(0     )
						__SCREAMING_SNAKE_CASE		     =       TransformeraDModel(
						    sample_size=1_6    ,    num_layers=2    ,    patch_size=4    ,    attention_head_dim=8    ,    num_attention_heads=2    ,    in_channels=4    ,    out_channels=8    ,    attention_bias=UpperCAmelCase__    ,    activation_fn="gelu-approximate"    ,    num_embeds_ada_norm=1_0_0_0    ,    norm_type="ada_norm_zero"    ,    norm_elementwise_affine=UpperCAmelCase__    ,    )
						__SCREAMING_SNAKE_CASE		     =       AutoencoderKL()
						__SCREAMING_SNAKE_CASE		     =       DDIMScheduler()
						__SCREAMING_SNAKE_CASE		     =       {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
						return components




	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       Tuple    ,    UpperCAmelCase__    :       str=0     ) ->			Optional[int]:
						if str(UpperCAmelCase__     ).startswith("mps"     ):
											__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(UpperCAmelCase__     )
						else:
											__SCREAMING_SNAKE_CASE		     =       torch.Generator(device=UpperCAmelCase__     ).manual_seed(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       {
						    "class_labels": [1],
						    "generator": generator,
						    "num_inference_steps": 2,
						    "output_type": "numpy",
						}
						return inputs




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       "cpu"

						__SCREAMING_SNAKE_CASE		     =       self.get_dummy_components()
						__SCREAMING_SNAKE_CASE		     =       self.pipeline_class(**UpperCAmelCase__     )
						pipe.to(UpperCAmelCase__     )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       self.get_dummy_inputs(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       pipe(**UpperCAmelCase__     ).images
						__SCREAMING_SNAKE_CASE		     =       image[0, -3:, -3:, -1]

						self.assertEqual(image.shape    ,    (1, 1_6, 1_6, 3)     )
						__SCREAMING_SNAKE_CASE		     =       np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]     )
						__SCREAMING_SNAKE_CASE		     =       np.abs(image_slice.flatten() - expected_slice     ).max()
						self.assertLessEqual(UpperCAmelCase__    ,    1E-3     )




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Union[str, Any]:
						self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase__    ,    expected_max_diff=1E-3     )




	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()    ,    reason="XFormers attention is only available with CUDA and `xformers` installed"    ,    )
	def 						UpperCAmelCase_   (				self    :       int     ) ->			Dict:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )




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

	"""simple docstring"""





	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Any:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(0     )

						__SCREAMING_SNAKE_CASE		     =       DiTPipeline.from_pretrained("facebook/DiT-XL-2-256"     )
						pipe.to("cuda"     )

						__SCREAMING_SNAKE_CASE		     =       ["vase", "umbrella", "white shark", "white wolf"]
						__SCREAMING_SNAKE_CASE		     =       pipe.get_label_ids(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       pipe(UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    num_inference_steps=4_0    ,    output_type="np"     ).images

						for word, image in zip(UpperCAmelCase__    ,    UpperCAmelCase__     ):
											__SCREAMING_SNAKE_CASE		     =       load_numpy(
											    F"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""     )
											assert np.abs((expected_image - image).max()     ) < 1E-2




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"     )
						__SCREAMING_SNAKE_CASE		     =       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
						pipe.to("cuda"     )

						__SCREAMING_SNAKE_CASE		     =       ["vase", "umbrella"]
						__SCREAMING_SNAKE_CASE		     =       pipe.get_label_ids(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(0     )
						__SCREAMING_SNAKE_CASE		     =       pipe(UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    num_inference_steps=2_5    ,    output_type="np"     ).images

						for word, image in zip(UpperCAmelCase__    ,    UpperCAmelCase__     ):
											__SCREAMING_SNAKE_CASE		     =       load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
											    F"""/dit/{word}_512.npy"""     )

											assert np.abs((expected_image - image).max()     ) < 1E-1
