
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        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		List[str]          = DiTPipeline
	_UpperCAmelCase	:		Any          = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
	_UpperCAmelCase	:		Tuple          = PipelineTesterMixin.required_optional_params - {
	    "latents",
	    "num_images_per_prompt",
	    "callback",
	    "callback_steps",
	}
	_UpperCAmelCase	:		Dict          = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
	_UpperCAmelCase	:		List[Any]          = False



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				torch.manual_seed(0     )
				_snake_case          =      TransformeraDModel(
				    sample_size=16      ,  num_layers=2      ,  patch_size=4      ,  attention_head_dim=8      ,  num_attention_heads=2      ,  in_channels=4      ,  out_channels=8      ,  attention_bias=lowercase      ,  activation_fn='gelu-approximate'      ,  num_embeds_ada_norm=1_000      ,  norm_type='ada_norm_zero'      ,  norm_elementwise_affine=lowercase      ,  )
				_snake_case          =      AutoencoderKL()
				_snake_case          =      DDIMScheduler()
				_snake_case          =      {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
				return components



	def 			A							(  self     :   str      ,  lowercase     :   Optional[Any]      ,  lowercase     :   int=0     ):


				'''simple docstring'''


				if str(lowercase     ).startswith('mps'     ):
							_snake_case          =      torch.manual_seed(lowercase     )
				else:
							_snake_case          =      torch.Generator(device=lowercase     ).manual_seed(lowercase     )
				_snake_case          =      {
				    'class_labels': [1],
				    'generator': generator,
				    'num_inference_steps': 2,
				    'output_type': 'numpy',
				}
				return inputs



	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      'cpu'

				_snake_case          =      self.get_dummy_components()
				_snake_case          =      self.pipeline_class(**lowercase     )
				pipe.to(lowercase     )
				pipe.set_progress_bar_config(disable=lowercase     )

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

				self.assertEqual(image.shape      ,  (1, 16, 16, 3)     )
				_snake_case          =      np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]     )
				_snake_case          =      np.abs(image_slice.flatten() - expected_slice     ).max()
				self.assertLessEqual(lowercase      ,  1E-3     )



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				self._test_inference_batch_single_identical(relax_max_difference=lowercase      ,  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 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )




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





	'''simple docstring'''



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      torch.manual_seed(0     )

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

				_snake_case          =      ['vase', 'umbrella', 'white shark', 'white wolf']
				_snake_case          =      pipe.get_label_ids(lowercase     )

				_snake_case          =      pipe(lowercase      ,  generator=lowercase      ,  num_inference_steps=40      ,  output_type='np'     ).images

				for word, image in zip(lowercase      ,  lowercase     ):
							_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 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'     )
				_snake_case          =      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
				pipe.to('cuda'     )

				_snake_case          =      ['vase', 'umbrella']
				_snake_case          =      pipe.get_label_ids(lowercase     )

				_snake_case          =      torch.manual_seed(0     )
				_snake_case          =      pipe(lowercase      ,  generator=lowercase      ,  num_inference_steps=25      ,  output_type='np'     ).images

				for word, image in zip(lowercase      ,  lowercase     ):
							_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

def 	a_		(	__lowercase       :     List[Any]			)			->  Tuple:
			_snake_case          =      len(__lowercase			)
			for i in range(length - 1			):
						_snake_case          =      i
						for k in range(i + 1	, __lowercase			):
									if collection[k] < collection[least]:
												_snake_case          =      k
						if least != i:
									_snake_case     , _snake_case          =      (collection[i], collection[least])
			return collection


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