




import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

enable_full_determinism()






class  __magic_name__						(  __UpperCAmelCase    ,  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Dict	      =					UNetaDModel
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					"sample"


				@property
				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_   	=    4
											SCREAMING_SNAKE_CASE_   	=    3
											SCREAMING_SNAKE_CASE_   	=    (32, 32)

											SCREAMING_SNAKE_CASE_   	=    floats_tensor((batch_size, num_channels) + sizes		).to(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    torch.tensor([10]		).to(_lowerCamelCase		)

											return {"sample": noise, "timestep": time_step}


				@property
				def     _A							(							self:   Any		):
											return (3, 32, 32)


				@property
				def     _A							(							self:   Optional[int]		):
											return (3, 32, 32)





				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    {
											    '''block_out_channels''': (32, 64),
											    '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
											    '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
											    '''attention_head_dim''': 3,
											    '''out_channels''': 3,
											    '''in_channels''': 3,
											    '''layers_per_block''': 2,
											    '''sample_size''': 32,
											}
											SCREAMING_SNAKE_CASE_   	=    self.dummy_input
											return init_dict, inputs_dict






class  __magic_name__						(  __UpperCAmelCase    ,  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Dict	      =					UNetaDModel
				SCREAMING_SNAKE_CASE__					:							str	      =					"sample"


				@property
				def     _A							(							self:   str		):
											SCREAMING_SNAKE_CASE_   	=    4
											SCREAMING_SNAKE_CASE_   	=    4
											SCREAMING_SNAKE_CASE_   	=    (32, 32)

											SCREAMING_SNAKE_CASE_   	=    floats_tensor((batch_size, num_channels) + sizes		).to(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    torch.tensor([10]		).to(_lowerCamelCase		)

											return {"sample": noise, "timestep": time_step}


				@property
				def     _A							(							self:   List[str]		):
											return (4, 32, 32)


				@property
				def     _A							(							self:   List[Any]		):
											return (4, 32, 32)


				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    {
											    '''sample_size''': 32,
											    '''in_channels''': 4,
											    '''out_channels''': 4,
											    '''layers_per_block''': 2,
											    '''block_out_channels''': (32, 64),
											    '''attention_head_dim''': 32,
											    '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
											    '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
											}
											SCREAMING_SNAKE_CASE_   	=    self.dummy_input
											return init_dict, inputs_dict


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''		,					output_loading_info=_lowerCamelCase		)

											self.assertIsNotNone(_lowerCamelCase		)
											self.assertEqual(len(loading_info['''missing_keys''']		)		,					0		)

											model.to(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    model(**self.dummy_input		).sample

											assert image is not None, "Make sure output is not None"


				@unittest.skipIf(torch_device != '''cuda'''		,					'''This test is supposed to run on GPU'''		)
				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''		,					output_loading_info=_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    model(**self.dummy_input		).sample

											assert image is not None, "Make sure output is not None"


				@unittest.skipIf(torch_device != '''cuda'''		,					'''This test is supposed to run on GPU'''		)
				def     _A							(							self:   Optional[Any]		):
											# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''		,					output_loading_info=_lowerCamelCase		)
											model_accelerate.to(_lowerCamelCase		)
											model_accelerate.eval()

											SCREAMING_SNAKE_CASE_   	=    torch.randn(
											    1		,					model_accelerate.config.in_channels		,					model_accelerate.config.sample_size		,					model_accelerate.config.sample_size		,					generator=torch.manual_seed(0		)		,					)
											SCREAMING_SNAKE_CASE_   	=    noise.to(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    torch.tensor([10] * noise.shape[0]		).to(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    model_accelerate(_lowerCamelCase		,					_lowerCamelCase		)['''sample''']

											# two models don't need to stay in the device at the same time
											del model_accelerate
											torch.cuda.empty_cache()
											gc.collect()

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    UNetaDModel.from_pretrained(
											    '''fusing/unet-ldm-dummy-update'''		,					output_loading_info=_lowerCamelCase		,					low_cpu_mem_usage=_lowerCamelCase		)
											model_normal_load.to(_lowerCamelCase		)
											model_normal_load.eval()
											SCREAMING_SNAKE_CASE_   	=    model_normal_load(_lowerCamelCase		,					_lowerCamelCase		)['''sample''']

											assert torch_all_close(_lowerCamelCase		,					_lowerCamelCase		,					rtol=1E-3		)





				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_   	=    UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''		)
											model.eval()
											model.to(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    torch.randn(
											    1		,					model.config.in_channels		,					model.config.sample_size		,					model.config.sample_size		,					generator=torch.manual_seed(0		)		,					)
											SCREAMING_SNAKE_CASE_   	=    noise.to(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    torch.tensor([10] * noise.shape[0]		).to(_lowerCamelCase		)

											with torch.no_grad():
																		SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					_lowerCamelCase		).sample

											SCREAMING_SNAKE_CASE_   	=    output[0, -1, -3:, -3:].flatten().cpu()
											# fmt: off
											SCREAMING_SNAKE_CASE_   	=    torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]		)
											# fmt: on

											self.assertTrue(torch_all_close(_lowerCamelCase		,					_lowerCamelCase		,					rtol=1E-3		)		)






class  __magic_name__						(  __UpperCAmelCase    ,  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					UNetaDModel
				SCREAMING_SNAKE_CASE__					:							str	      =					"sample"


				@property
				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   Union[str, Any]=(32, 32)		):
											SCREAMING_SNAKE_CASE_   	=    4
											SCREAMING_SNAKE_CASE_   	=    3

											SCREAMING_SNAKE_CASE_   	=    floats_tensor((batch_size, num_channels) + sizes		).to(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    torch.tensor(batch_size * [10]		).to(dtype=torch.intaa		,					device=_lowerCamelCase		)

											return {"sample": noise, "timestep": time_step}


				@property
				def     _A							(							self:   Dict		):
											return (3, 32, 32)


				@property
				def     _A							(							self:   Optional[Any]		):
											return (3, 32, 32)


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    {
											    '''block_out_channels''': [32, 64, 64, 64],
											    '''in_channels''': 3,
											    '''layers_per_block''': 1,
											    '''out_channels''': 3,
											    '''time_embedding_type''': '''fourier''',
											    '''norm_eps''': 1E-6,
											    '''mid_block_scale_factor''': math.sqrt(2.0		),
											    '''norm_num_groups''': None,
											    '''down_block_types''': [
											        '''SkipDownBlock2D''',
											        '''AttnSkipDownBlock2D''',
											        '''SkipDownBlock2D''',
											        '''SkipDownBlock2D''',
											    ],
											    '''up_block_types''': [
											        '''SkipUpBlock2D''',
											        '''SkipUpBlock2D''',
											        '''AttnSkipUpBlock2D''',
											        '''SkipUpBlock2D''',
											    ],
											}
											SCREAMING_SNAKE_CASE_   	=    self.dummy_input
											return init_dict, inputs_dict


				@slow
				def     _A							(							self:   str		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''		,					output_loading_info=_lowerCamelCase		)
											self.assertIsNotNone(_lowerCamelCase		)
											self.assertEqual(len(loading_info['''missing_keys''']		)		,					0		)

											model.to(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.dummy_input
											SCREAMING_SNAKE_CASE_   	=    floats_tensor((4, 3) + (2_56, 2_56)		).to(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    noise
											SCREAMING_SNAKE_CASE_   	=    model(**_lowerCamelCase		)

											assert image is not None, "Make sure output is not None"


				@slow
				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''		)
											model.to(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    4
											SCREAMING_SNAKE_CASE_   	=    3
											SCREAMING_SNAKE_CASE_   	=    (2_56, 2_56)

											SCREAMING_SNAKE_CASE_   	=    torch.ones((batch_size, num_channels) + sizes		).to(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    torch.tensor(batch_size * [1E-4]		).to(_lowerCamelCase		)

											with torch.no_grad():
																		SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					_lowerCamelCase		).sample

											SCREAMING_SNAKE_CASE_   	=    output[0, -3:, -3:, -1].flatten().cpu()
											# fmt: off
											SCREAMING_SNAKE_CASE_   	=    torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]		)
											# fmt: on

											self.assertTrue(torch_all_close(_lowerCamelCase		,					_lowerCamelCase		,					rtol=1E-2		)		)


				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''		)
											model.to(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    4
											SCREAMING_SNAKE_CASE_   	=    3
											SCREAMING_SNAKE_CASE_   	=    (32, 32)

											SCREAMING_SNAKE_CASE_   	=    torch.ones((batch_size, num_channels) + sizes		).to(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    torch.tensor(batch_size * [1E-4]		).to(_lowerCamelCase		)

											with torch.no_grad():
																		SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					_lowerCamelCase		).sample

											SCREAMING_SNAKE_CASE_   	=    output[0, -3:, -3:, -1].flatten().cpu()
											# fmt: off
											SCREAMING_SNAKE_CASE_   	=    torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]		)
											# fmt: on

											self.assertTrue(torch_all_close(_lowerCamelCase		,					_lowerCamelCase		,					rtol=1E-2		)		)





				def     _A							(							self:   int		):
											# not required for this model
											pass








def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    [], []
							while len(_lowerCAmelCase     ) > 1:
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    min(_lowerCAmelCase     ), max(_lowerCAmelCase     )
														start.append(_lowerCAmelCase     )
														end.append(_lowerCAmelCase     )
														collection.remove(_lowerCAmelCase     )
														collection.remove(_lowerCAmelCase     )
							end.reverse()
							return start + collection + end


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(*merge_sort(unsorted), sep=""",""")


