



"""simple docstring"""

import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()







class    __lowercase     (					_UpperCAmelCase					,    _UpperCAmelCase					,    unittest.TestCase):







						"""simple docstring"""







						_A		:							Dict							       =		VQModel
						_A		:							Union[str, Any]							       =		"""sample"""







						@property
						def 						__UpperCamelCase							(self   ,		lowercase__=(32, 32)       ):
											snake_case_					:		int 					=					4
											snake_case_					:		Union[str, Any] 					=					3

											snake_case_					:		Optional[int] 					=					floats_tensor((batch_size, num_channels) + sizes       ).to(lowercase__       )

											return {"sample": image}







						@property
						def 						__UpperCamelCase							(self       ):
											return (3, 32, 32)







						@property
						def 						__UpperCamelCase							(self       ):
											return (3, 32, 32)







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					{
											    """block_out_channels""": [32, 64],
											    """in_channels""": 3,
											    """out_channels""": 3,
											    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
											    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
											    """latent_channels""": 3,
											}
											snake_case_					:		Union[str, Any] 					=					self.dummy_input
											return init_dict, inputs_dict







						def 						__UpperCamelCase							(self       ):
											pass







						def 						__UpperCamelCase							(self       ):
											pass







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					:		int 					=					VQModel.from_pretrained("""fusing/vqgan-dummy"""   ,		output_loading_info=lowercase__       )
											self.assertIsNotNone(lowercase__       )
											self.assertEqual(len(loading_info["""missing_keys"""]       )   ,		0       )

											model.to(lowercase__       )
											snake_case_					:		Union[str, Any] 					=					model(**self.dummy_input       )

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					VQModel.from_pretrained("""fusing/vqgan-dummy"""       )
											model.to(lowercase__       ).eval()

											torch.manual_seed(0       )
											if torch.cuda.is_available():
																torch.cuda.manual_seed_all(0       )

											snake_case_					:		int 					=					torch.randn(1   ,		model.config.in_channels   ,		model.config.sample_size   ,		model.config.sample_size       )
											snake_case_					:		str 					=					image.to(lowercase__       )
											with torch.no_grad():
																snake_case_					:		Dict 					=					model(lowercase__       ).sample

											snake_case_					:		int 					=					output[0, -1, -3:, -3:].flatten().cpu()
											# fmt: off
											snake_case_					:		int 					=					torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]       )
											# fmt: on
											self.assertTrue(torch.allclose(lowercase__   ,		lowercase__   ,		atol=1e-3       )       )









"""simple docstring"""

def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  list			):



					"""simple docstring"""





					snake_case_					:		Optional[int] 					=					len(SCREAMING_SNAKE_CASE__			)
					for i in range(1				,    SCREAMING_SNAKE_CASE__			):
										snake_case_					:		Tuple 					=					collection[i]
										snake_case_					:		Tuple 					=					0
										snake_case_					:		str 					=					i - 1

										while low <= high:
															snake_case_					:		Optional[int] 					=					(low + high) // 2
															if val < collection[mid]:
																				snake_case_					:		List[str] 					=					mid - 1
															else:
																				snake_case_					:		str 					=					mid + 1
										for j in range(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    -1			):
															snake_case_					:		List[str] 					=					collection[j - 1]
										snake_case_					:		Any 					=					val
					return collection


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




