


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  snake_case_     (lowerCamelCase_			,		lowerCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					Optional[Any]					   =    VQModel
					UpperCAmelCase__      :					Any					   =    '''sample'''
					@property
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :Any=(32, 32)   )      ->					Any:
												a__    =  4
												a__    =  3

												a__    =  floats_tensor((batch_size, num_channels) + sizes   ).to(__snake_case   )

												return {"sample": image}
					@property
					def 				lowerCamelCase__(   self     :List[str]   )      ->					str:
												return (3, 32, 32)
					@property
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					List[Any]:
												return (3, 32, 32)
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Dict:
												a__    =  {
												    'block_out_channels': [32, 64],
												    'in_channels': 3,
												    'out_channels': 3,
												    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
												    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
												    'latent_channels': 3,
												}
												a__    =  self.dummy_input
												return init_dict, inputs_dict
					def 				lowerCamelCase__(   self     :Dict   )      ->					List[Any]:
												pass
					def 				lowerCamelCase__(   self     :List[str]   )      ->					List[str]:
												pass
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					Dict:
												a__   ,      a__    =  VQModel.from_pretrained('fusing/vqgan-dummy'    ,output_loading_info=__snake_case   )
												self.assertIsNotNone(__snake_case   )
												self.assertEqual(len(loading_info['missing_keys']   )    ,0   )

												model.to(__snake_case   )
												a__    =  model(**self.dummy_input   )

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



					def 				lowerCamelCase__(   self     :Optional[int]   )      ->					int:
												a__    =  VQModel.from_pretrained('fusing/vqgan-dummy'   )
												model.to(__snake_case   ).eval()

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

												a__    =  torch.randn(1    ,model.config.in_channels    ,model.config.sample_size    ,model.config.sample_size   )
												a__    =  image.to(__snake_case   )
												with torch.no_grad():
																			a__    =  model(__snake_case   ).sample

												a__    =  output[0, -1, -3:, -3:].flatten().cpu()
												# fmt: off
												a__    =  torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43]   )
												# fmt: on
												self.assertTrue(torch.allclose(__snake_case    ,__snake_case    ,atol=1E-3   )   )








from __future__ import annotations






def 							__lowercase     (				__lowerCAmelCase							:       list[int]     ):  # This function is recursive
							a__    =  len(__lowerCAmelCase     )
							# If the array contains only one element, we return it (it's the stop condition of
							# recursion)
							if array_length <= 1:
														return array
														# Else
							a__    =  array[0]
							a__    =  False
							a__    =  1
							a__    =  []
							while not is_found and i < array_length:
														if array[i] < pivot:
																					a__    =  True
																					a__    =  [element for element in array[i:] if element >= array[i]]
																					a__    =  longest_subsequence(__lowerCAmelCase     )
																					if len(__lowerCAmelCase     ) > len(__lowerCAmelCase     ):
																												a__    =  temp_array
														else:
																					i += 1

							a__    =  [element for element in array[1:] if element >= pivot]
							a__    =  [pivot, *longest_subsequence(__lowerCAmelCase     )]
							if len(__lowerCAmelCase     ) > len(__lowerCAmelCase     ):
														return temp_array
							else:
														return longest_subseq


if __name__ == "__main__":
						import doctest

						doctest.testmod()




