


'''simple docstring'''

import gc
import unittest

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

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

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()


class 					a__ (       unittest.TestCase					):







				"""simple docstring"""



				__UpperCamelCase			:						List[str]		=		StableDiffusionLDMaDPipeline
				__UpperCamelCase			:						List[str]		=		TEXT_TO_IMAGE_PARAMS
				__UpperCamelCase			:						Union[str, Any]		=		TEXT_TO_IMAGE_BATCH_PARAMS
				__UpperCamelCase			:						Optional[int]		=		TEXT_TO_IMAGE_IMAGE_PARAMS




				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=__lowerCAmelCase				,	set_alpha_to_one=__lowerCAmelCase				,	)
										torch.manual_seed(0						)
										__lowerCAmelCase											= AutoencoderKL(
										    block_out_channels=[32, 64]				,	in_channels=6				,	out_channels=6				,	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(__lowerCAmelCase						)
										__lowerCAmelCase											= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

										__lowerCAmelCase											= {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''safety_checker''': None,
										    '''feature_extractor''': None,
										}
										return components




				def     _snake_case     (self				,	__lowercase				,	__lowercase=0						):
										if str(__lowerCAmelCase						).startswith('''mps'''						):
																__lowerCAmelCase											= torch.manual_seed(__lowerCAmelCase						)
										else:
																__lowerCAmelCase											= torch.Generator(device=__lowerCAmelCase						).manual_seed(__lowerCAmelCase						)
										__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											= StableDiffusionLDMaDPipeline(**__lowerCAmelCase						)
										__lowerCAmelCase											= ldmad_pipe.to(__lowerCAmelCase						)
										ldmad_pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

										__lowerCAmelCase											= self.get_dummy_inputs(__lowerCAmelCase						)
										__lowerCAmelCase											= ldmad_pipe(**__lowerCAmelCase						)
										__lowerCAmelCase											= output.rgb, output.depth

										__lowerCAmelCase											= rgb[0, -3:, -3:, -1]
										__lowerCAmelCase											= depth[0, -3:, -1]

										assert rgb.shape == (1, 64, 64, 3)
										assert depth.shape == (1, 64, 64)

										__lowerCAmelCase											= np.array(
										    [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2]						)
										__lowerCAmelCase											= np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36]						)

										assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb						).max() < 1e-2
										assert np.abs(image_slice_depth.flatten() - expected_slice_depth						).max() < 1e-2




				def     _snake_case     (self						):
										__lowerCAmelCase											= self.get_dummy_components()
										__lowerCAmelCase											= StableDiffusionLDMaDPipeline(**__lowerCAmelCase						)
										__lowerCAmelCase											= ldmad_pipe.to(__lowerCAmelCase						)
										ldmad_pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

										__lowerCAmelCase											= self.get_dummy_inputs(__lowerCAmelCase						)
										__lowerCAmelCase											= 3 * [inputs['''prompt''']]

										# forward
										__lowerCAmelCase											= ldmad_pipe(**__lowerCAmelCase						)
										__lowerCAmelCase											= output.rgb, output.depth
										__lowerCAmelCase											= rgb_slice_a[0, -3:, -3:, -1]
										__lowerCAmelCase											= depth_slice_a[0, -3:, -1]

										__lowerCAmelCase											= self.get_dummy_inputs(__lowerCAmelCase						)
										__lowerCAmelCase											= 3 * [inputs.pop('''prompt'''						)]

										__lowerCAmelCase											= ldmad_pipe.tokenizer(
										    __lowerCAmelCase				,	padding='''max_length'''				,	max_length=ldmad_pipe.tokenizer.model_max_length				,	truncation=__lowerCAmelCase				,	return_tensors='''pt'''				,	)
										__lowerCAmelCase											= text_inputs['''input_ids'''].to(__lowerCAmelCase						)

										__lowerCAmelCase											= ldmad_pipe.text_encoder(__lowerCAmelCase						)[0]

										__lowerCAmelCase											= prompt_embeds

										# forward
										__lowerCAmelCase											= ldmad_pipe(**__lowerCAmelCase						)
										__lowerCAmelCase											= output.rgb, output.depth
										__lowerCAmelCase											= rgb_slice_a[0, -3:, -3:, -1]
										__lowerCAmelCase											= depth_slice_a[0, -3:, -1]

										assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()						).max() < 1e-4
										assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()						).max() < 1e-4




				def     _snake_case     (self						):
										__lowerCAmelCase											= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										__lowerCAmelCase											= self.get_dummy_components()
										__lowerCAmelCase											= PNDMScheduler(skip_prk_steps=__lowerCAmelCase						)
										__lowerCAmelCase											= StableDiffusionLDMaDPipeline(**__lowerCAmelCase						)
										__lowerCAmelCase											= ldmad_pipe.to(__lowerCAmelCase						)
										ldmad_pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

										__lowerCAmelCase											= self.get_dummy_inputs(__lowerCAmelCase						)
										__lowerCAmelCase											= '''french fries'''
										__lowerCAmelCase											= ldmad_pipe(**__lowerCAmelCase				,	negative_prompt=__lowerCAmelCase						)

										__lowerCAmelCase											= output.rgb, output.depth
										__lowerCAmelCase											= rgb[0, -3:, -3:, -1]
										__lowerCAmelCase											= depth[0, -3:, -1]

										assert rgb.shape == (1, 64, 64, 3)
										assert depth.shape == (1, 64, 64)

										__lowerCAmelCase											= np.array(
										    [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7]						)
										__lowerCAmelCase											= np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35]						)
										assert np.abs(rgb_slice.flatten() - expected_slice_rgb						).max() < 1e-2
										assert np.abs(depth_slice.flatten() - expected_slice_depth						).max() < 1e-2


@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="cpu"				,	__lowercase=torch.floataa				,	__lowercase=0						):
										__lowerCAmelCase											= torch.Generator(device=__lowerCAmelCase						).manual_seed(__lowerCAmelCase						)
										__lowerCAmelCase											= np.random.RandomState(__lowerCAmelCase						).standard_normal((1, 4, 64, 64)						)
										__lowerCAmelCase											= torch.from_numpy(__lowerCAmelCase						).to(device=__lowerCAmelCase				,	dtype=__lowerCAmelCase						)
										__lowerCAmelCase											= {
										    '''prompt''': '''a photograph of an astronaut riding a horse''',
										    '''latents''': latents,
										    '''generator''': generator,
										    '''num_inference_steps''': 3,
										    '''guidance_scale''': 7.5,
										    '''output_type''': '''numpy''',
										}
										return inputs




				def     _snake_case     (self						):
										__lowerCAmelCase											= StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''						)
										__lowerCAmelCase											= ldmad_pipe.to(__lowerCAmelCase						)
										ldmad_pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

										__lowerCAmelCase											= self.get_inputs(__lowerCAmelCase						)
										__lowerCAmelCase											= ldmad_pipe(**__lowerCAmelCase						)
										__lowerCAmelCase											= output.rgb, output.depth
										__lowerCAmelCase											= rgb[0, -3:, -3:, -1].flatten()
										__lowerCAmelCase											= rgb[0, -3:, -1].flatten()

										assert rgb.shape == (1, 5_12, 5_12, 3)
										assert depth.shape == (1, 5_12, 5_12)

										__lowerCAmelCase											= np.array(
										    [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6]						)
										__lowerCAmelCase											= np.array(
										    [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6]						)
										assert np.abs(rgb_slice - expected_slice_rgb						).max() < 3e-3
										assert np.abs(depth_slice - expected_slice_depth						).max() < 3e-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="cpu"				,	__lowercase=torch.floataa				,	__lowercase=0						):
										__lowerCAmelCase											= torch.Generator(device=__lowerCAmelCase						).manual_seed(__lowerCAmelCase						)
										__lowerCAmelCase											= np.random.RandomState(__lowerCAmelCase						).standard_normal((1, 4, 64, 64)						)
										__lowerCAmelCase											= torch.from_numpy(__lowerCAmelCase						).to(device=__lowerCAmelCase				,	dtype=__lowerCAmelCase						)
										__lowerCAmelCase											= {
										    '''prompt''': '''a photograph of an astronaut riding a horse''',
										    '''latents''': latents,
										    '''generator''': generator,
										    '''num_inference_steps''': 50,
										    '''guidance_scale''': 7.5,
										    '''output_type''': '''numpy''',
										}
										return inputs




				def     _snake_case     (self						):
										__lowerCAmelCase											= StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''						).to(__lowerCAmelCase						)
										ldmad_pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

										__lowerCAmelCase											= self.get_inputs(__lowerCAmelCase						)
										__lowerCAmelCase											= ldmad_pipe(**__lowerCAmelCase						)
										__lowerCAmelCase											= output.rgb, output.depth

										__lowerCAmelCase											= 0.4_9_5_5_8_6
										__lowerCAmelCase											= 0.3_3_7_9_5_5_1_5
										__lowerCAmelCase											= 1_12.4_85_18
										__lowerCAmelCase											= 98.48_97_46
										assert np.abs(expected_rgb_mean - rgb.mean()						) < 1e-3
										assert np.abs(expected_rgb_std - rgb.std()						) < 1e-3
										assert np.abs(expected_depth_mean - depth.mean()						) < 1e-3
										assert np.abs(expected_depth_std - depth.std()						) < 1e-3




				def     _snake_case     (self						):
										__lowerCAmelCase											= StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''						).to(__lowerCAmelCase						)
										ldmad_pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

										__lowerCAmelCase											= self.get_inputs(__lowerCAmelCase						)
										__lowerCAmelCase											= ldmad_pipe(**__lowerCAmelCase						)
										__lowerCAmelCase											= output.rgb, output.depth

										__lowerCAmelCase											= 0.4_1_9_4_1_2_7
										__lowerCAmelCase											= 0.3_5_3_7_5_5_8_6
										__lowerCAmelCase											= 0.5_6_3_8_5_0_2
										__lowerCAmelCase											= 0.3_4_6_8_6_1_0_3

										assert rgb.shape == (1, 5_12, 5_12, 3)
										assert depth.shape == (1, 5_12, 5_12, 1)
										assert np.abs(expected_rgb_mean - rgb.mean()						) < 1e-3
										assert np.abs(expected_rgb_std - rgb.std()						) < 1e-3
										assert np.abs(expected_depth_mean - depth.mean()						) < 1e-3
										assert np.abs(expected_depth_std - depth.std()						) < 1e-3


'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__ ,							lowercase__				:    Optional[int]  =					[], []
 while len(UpperCAmelCase     ) > 1:
  lowercase__ ,							lowercase__				:    List[str]  =					min(UpperCAmelCase     ), max(UpperCAmelCase     )
  start.append(UpperCAmelCase     )
  end.append(UpperCAmelCase     )
  collection.remove(UpperCAmelCase     )
  collection.remove(UpperCAmelCase     )
 end.reverse()
 return start + collection + end


if __name__ == "__main__":
  __a: Optional[int]          =   input("""Enter numbers separated by a comma:\n""").strip()
  __a: Tuple          =   [int(item) for item in user_input.split(""",""")]
  print(*merge_sort(unsorted), sep=""",""")

