



"""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 	_a       (    lowercase_	,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__						=		DiTPipeline
			UpperCamelCase__						=		CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
			UpperCamelCase__						=		PipelineTesterMixin.required_optional_params - {
			    """latents""",
			    """num_images_per_prompt""",
			    """callback""",
			    """callback_steps""",
			}
			UpperCamelCase__						=		CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
			UpperCamelCase__						=		False



			def        lowercase__  (						self   :			Dict      )->str:
							torch.manual_seed(0      )
							_UpperCAmelCase							=			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 ,      )
							_UpperCAmelCase							=			AutoencoderKL()
							_UpperCAmelCase							=			DDIMScheduler()
							_UpperCAmelCase							=			{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
							return components



			def        lowercase__  (						self   :			Optional[Any] ,      __UpperCamelCase   :			List[Any] ,      __UpperCamelCase   :			Dict=0      )->List[Any]:


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



			def        lowercase__  (						self   :			Any      )->Any:
							_UpperCAmelCase							=			'cpu'

							_UpperCAmelCase							=			self.get_dummy_components()
							_UpperCAmelCase							=			self.pipeline_class(**__UpperCamelCase      )
							pipe.to(__UpperCamelCase      )
							pipe.set_progress_bar_config(disable=__UpperCamelCase      )

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

							self.assertEqual(image.shape ,      (1, 1_6, 1_6, 3)      )
							_UpperCAmelCase							=			np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]      )
							_UpperCAmelCase							=			np.abs(image_slice.flatten() - expected_slice      ).max()
							self.assertLessEqual(__UpperCamelCase ,      1e-3      )



			def        lowercase__  (						self   :			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        lowercase__  (						self   :			Dict      )->str:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )



@require_torch_gpu
@slow
class 	_a       (    unittest.TestCase):
			"""simple docstring"""



			def        lowercase__  (						self   :			Union[str, Any]      )->Any:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



			def        lowercase__  (						self   :			Dict      )->str:
							_UpperCAmelCase							=			torch.manual_seed(0      )

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

							_UpperCAmelCase							=			['vase', 'umbrella', 'white shark', 'white wolf']
							_UpperCAmelCase							=			pipe.get_label_ids(__UpperCamelCase      )

							_UpperCAmelCase							=			pipe(__UpperCamelCase ,      generator=__UpperCamelCase ,      num_inference_steps=4_0 ,      output_type='''np'''      ).images

							for word, image in zip(__UpperCamelCase ,      __UpperCamelCase      ):
											_UpperCAmelCase							=			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        lowercase__  (						self   :			Dict      )->Tuple:
							_UpperCAmelCase							=			DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''      )
							_UpperCAmelCase							=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
							pipe.to('''cuda'''      )

							_UpperCAmelCase							=			['vase', 'umbrella']
							_UpperCAmelCase							=			pipe.get_label_ids(__UpperCamelCase      )

							_UpperCAmelCase							=			torch.manual_seed(0      )
							_UpperCAmelCase							=			pipe(__UpperCamelCase ,      generator=__UpperCamelCase ,      num_inference_steps=2_5 ,      output_type='''np'''      ).images

							for word, image in zip(__UpperCamelCase ,      __UpperCamelCase      ):
											_UpperCAmelCase							=			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




"""simple docstring"""







def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list      ):

     if len(__SCREAMING_SNAKE_CASE      ) < 2:
          return collection

     def circle_sort_util(__SCREAMING_SNAKE_CASE							:     list     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int      ) -> bool:
          lowercase_	:				Any													=	False

          if low == high:
               return swapped

          lowercase_	:				str													=	low
          lowercase_	:				int													=	high

          while left < right:
               if collection[left] > collection[right]:
                    lowercase_							,  lowercase_	:				Optional[Any]													=	(
                        collection[right],
                        collection[left],
                    )
                    lowercase_	:				Tuple													=	True

               left += 1
               right -= 1

          if left == right and collection[left] > collection[right + 1]:
               lowercase_							,  lowercase_	:				Dict													=	(
                   collection[right + 1],
                   collection[left],
               )

               lowercase_	:				str													=	True

          lowercase_	:				Optional[Any]													=	low + int((high - low) / 2      )
          lowercase_	:				str													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
          lowercase_	:				List[Any]													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			mid + 1     ,			__SCREAMING_SNAKE_CASE      )

          return swapped or left_swap or right_swap

     lowercase_	:				Dict													=	True

     while is_not_sorted is True:
          lowercase_	:				Optional[Any]													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			0     ,			len(__SCREAMING_SNAKE_CASE      ) - 1      )

     return collection


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=input("Enter numbers separated by a comma:\n").strip()
       __SCREAMING_SNAKE_CASE    	=[int(item) for item in user_input.split(",")]
       print(circle_sort(unsorted))

