


'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 					a__ (       __SCREAMING_SNAKE_CASE							,  unittest.TestCase					):







				"""simple docstring"""



				__UpperCamelCase			:						Optional[Any]		=		LDMTextToImagePipeline
				__UpperCamelCase			:						Optional[Any]		=		TEXT_TO_IMAGE_PARAMS - {
				    'negative_prompt',
				    'negative_prompt_embeds',
				    'cross_attention_kwargs',
				    'prompt_embeds',
				}
				__UpperCamelCase			:						List[str]		=		PipelineTesterMixin.required_optional_params - {
				    'num_images_per_prompt',
				    'callback',
				    'callback_steps',
				}
				__UpperCamelCase			:						List[Any]		=		TEXT_TO_IMAGE_BATCH_PARAMS
				__UpperCamelCase			:						str		=		False




				def     _snake_case     (self						):
										torch.manual_seed(0						)
										__lowerCAmelCase											= UNetaDConditionModel(
										    block_out_channels=(32, 64)				,	layers_per_block=2				,	sample_size=32				,	in_channels=4				,	out_channels=4				,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,	cross_attention_dim=32				,	)
										__lowerCAmelCase											= DDIMScheduler(
										    beta_start=0.0_0_0_8_5				,	beta_end=0.0_1_2				,	beta_schedule='''scaled_linear'''				,	clip_sample=__lowercase				,	set_alpha_to_one=__lowercase				,	)
										torch.manual_seed(0						)
										__lowerCAmelCase											= 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											= 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=10_00				,	)
										__lowerCAmelCase											= CLIPTextModel(__lowercase						)
										__lowerCAmelCase											= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

										__lowerCAmelCase											= {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vqvae''': vae,
										    '''bert''': text_encoder,
										    '''tokenizer''': tokenizer,
										}
										return components




				def     _snake_case     (self				,	__lowercase				,	__lowercase=0						):
										if str(__lowercase						).startswith('''mps'''						):
																__lowerCAmelCase											= torch.manual_seed(__lowercase						)
										else:
																__lowerCAmelCase											= torch.Generator(device=__lowercase						).manual_seed(__lowercase						)
										__lowerCAmelCase											= {
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 6.0,
										    '''output_type''': '''numpy''',
										}
										return inputs




				def     _snake_case     (self						):
										__lowerCAmelCase											= '''cpu'''  # ensure determinism for the device-dependent torch.Generator

										__lowerCAmelCase											= self.get_dummy_components()
										__lowerCAmelCase											= LDMTextToImagePipeline(**__lowercase						)
										pipe.to(__lowercase						)
										pipe.set_progress_bar_config(disable=__lowercase						)

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

										assert image.shape == (1, 16, 16, 3)
										__lowerCAmelCase											= np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4]						)

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


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







				"""simple docstring"""



				def     _snake_case     (self						):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




				def     _snake_case     (self				,	__lowercase				,	__lowercase=torch.floataa				,	__lowercase=0						):
										__lowerCAmelCase											= torch.manual_seed(__lowercase						)
										__lowerCAmelCase											= np.random.RandomState(__lowercase						).standard_normal((1, 4, 32, 32)						)
										__lowerCAmelCase											= torch.from_numpy(__lowercase						).to(device=__lowercase				,	dtype=__lowercase						)
										__lowerCAmelCase											= {
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''latents''': latents,
										    '''generator''': generator,
										    '''num_inference_steps''': 3,
										    '''guidance_scale''': 6.0,
										    '''output_type''': '''numpy''',
										}
										return inputs




				def     _snake_case     (self						):
										__lowerCAmelCase											= LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''						).to(__lowercase						)
										pipe.set_progress_bar_config(disable=__lowercase						)

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

										assert image.shape == (1, 2_56, 2_56, 3)
										__lowerCAmelCase											= np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8]						)
										__lowerCAmelCase											= np.abs(expected_slice - image_slice						).max()
										assert max_diff < 1e-3


@nightly
@require_torch_gpu
class 					a__ (       unittest.TestCase					):







				"""simple docstring"""



				def     _snake_case     (self						):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




				def     _snake_case     (self				,	__lowercase				,	__lowercase=torch.floataa				,	__lowercase=0						):
										__lowerCAmelCase											= torch.manual_seed(__lowercase						)
										__lowerCAmelCase											= np.random.RandomState(__lowercase						).standard_normal((1, 4, 32, 32)						)
										__lowerCAmelCase											= torch.from_numpy(__lowercase						).to(device=__lowercase				,	dtype=__lowercase						)
										__lowerCAmelCase											= {
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''latents''': latents,
										    '''generator''': generator,
										    '''num_inference_steps''': 50,
										    '''guidance_scale''': 6.0,
										    '''output_type''': '''numpy''',
										}
										return inputs




				def     _snake_case     (self						):
										__lowerCAmelCase											= LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''						).to(__lowercase						)
										pipe.set_progress_bar_config(disable=__lowercase						)

										__lowerCAmelCase											= self.get_inputs(__lowercase						)
										__lowerCAmelCase											= pipe(**__lowercase						).images[0]

										__lowerCAmelCase											= load_numpy(
										    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'''						)
										__lowerCAmelCase											= np.abs(expected_image - image						).max()
										assert max_diff < 1e-3



import math


def 						UpperCAmelCase			(	a_		,   a_ = 0		,   a_ = 0						)		->	list:







				"""simple docstring"""



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


def 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	None:  # Max Heap







				"""simple docstring"""



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

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

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

				if largest != index:
								__A			,							__A   			= array[largest], array[index]
								heapify(a_		,   a_		,   a_						)


def 						UpperCAmelCase			(	a_						)		->	list:







				"""simple docstring"""



				__A   			= len(a_						)

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

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

				return array


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_						)		->	int:







				"""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			(	a_		,   a_		,   a_		,   a_						)		->	int:







				"""simple docstring"""



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


def 						UpperCAmelCase			(	a_						)		->	list:







				"""simple docstring"""



				if len(a_						) == 0:
								return array
				__A   			= 2 * math.ceil(math.loga(len(a_						)						)						)
				__A   			= 1_6
				return intro_sort(a_		,   0		,   len(a_						)		,   a_		,   a_						)


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_						)		->	list:







				"""simple docstring"""



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


if __name__ == "__main__":
					import doctest

					doctest.testmod()

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