


"""simple docstring"""



from __future__ import annotations

from random import random
class 					__magic_name__		:






							def __init__(   self  :    int  ,     snake_case__  :    int | None = None				):

														'''simple docstring'''






														lowercase		:Any       							=				value
														lowercase		:List[Any]       							=				random()
														lowercase		:Node | None       							=				None
														lowercase		:Node | None       							=				None






							def __repr__(   self  :    int				):

														'''simple docstring'''






														from pprint import pformat

														if self.left is None and self.right is None:
																					return f"""'{self.value}: {self.prior:.5}'"""
														else:
																					return pformat(
																					    {f"""{self.value}: {self.prior:.5}""": (self.left, self.right)}  ,     indent=1				)







							def __str__(   self  :    str				):

														'''simple docstring'''






														lowercase		:Optional[int]       							=				str(self.value				) + ''' '''
														lowercase		:Any       							=				str(self.left or ''''''				)
														lowercase		:Tuple       							=				str(self.right or ''''''				)
														return value + left + right







def       lowerCamelCase					(a_						:Node | None				,				a_						:int)     ->							tuple[Node | None, Node | None]:
							if root is None:  # None tree is split into 2 Nones
														return None, None
							elif root.value is None:
														return None, None
							else:
														if value < root.value:
																					lowercase		:Tuple       							=				split(root.left				,				a_)
																					return left, root
														else:
																					lowercase		:Tuple       							=				split(root.right				,				a_)
																					return root, right






def       lowerCamelCase					(a_						:Node | None				,				a_						:Node | None)     ->							Node | None:
							if (not left) or (not right):  # If one node is None, return the other
														return left or right
							elif left.prior < right.prior:
														lowercase		:Optional[int]       							=				merge(left.right				,				a_)
														return left
							else:
														lowercase		:Any       							=				merge(a_				,				right.left)
														return right






def       lowerCamelCase					(a_						:Node | None				,				a_						:int)     ->							Node | None:
							lowercase		:str       							=				Node(a_)
							lowercase		:Any       							=				split(a_				,				a_)
							return merge(merge(a_				,				a_)				,				a_)






def       lowerCamelCase					(a_						:Node | None				,				a_						:int)     ->							Node | None:
							lowercase		:Tuple       							=				split(a_				,				value - 1)
							lowercase		:Optional[Any]       							=				split(a_				,				a_)
							return merge(a_				,				a_)






def       lowerCamelCase					(a_						:Node | None)     ->							None:
							if not root:  # None
														return
							else:
														inorder(root.left)
														print(root.value				,				end=''',''')
														inorder(root.right)






def       lowerCamelCase					(a_						:Node | None				,				a_						:str)     ->							Node | None:
							for arg in args.split():
														if arg[0] == "+":
																					lowercase		:int       							=				insert(a_				,				int(arg[1:]))

														elif arg[0] == "-":
																					lowercase		:Union[str, Any]       							=				erase(a_				,				int(arg[1:]))

														else:
																					print('''Unknown command''')

							return root






def       lowerCamelCase					()     ->							None:
							lowercase		:str       							=				None
							print(
							    '''enter numbers to create a tree, + value to add value into treap, '''
							    '''- value to erase all nodes with value. \'q\' to quit. ''')

							lowercase		:Optional[int]       							=				input()
							while args != "q":
														lowercase		:int       							=				interact_treap(a_				,				a_)
														print(a_)
														lowercase		:Optional[Any]       							=				input()

							print('''good by!''')


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





"""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 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      List[str]        =   VQModel
       __A			:      Any        =   "sample"






       @property
       def  __snake_case						(   self  :    int  ,     snake_case__  :    int=(3_2, 3_2)				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				4
              lowercase		:Tuple       							=				3

              lowercase		:List[str]       							=				floats_tensor((batch_size, num_channels) + sizes				).to(snake_case__				)

              return {"sample": image}






       @property
       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              return (3, 3_2, 3_2)






       @property
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              return (3, 3_2, 3_2)






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				{
                  '''block_out_channels''': [3_2, 6_4],
                  '''in_channels''': 3,
                  '''out_channels''': 3,
                  '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
                  '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
                  '''latent_channels''': 3,
              }
              lowercase		:Dict       							=				self.dummy_input
              return init_dict, inputs_dict






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase							,  lowercase		:Optional[int]       							=				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__				)
              lowercase		:List[str]       							=				model(**self.dummy_input				)

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







       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				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				)

              lowercase		:List[Any]       							=				torch.randn(1  ,     model.config.in_channels  ,     model.config.sample_size  ,     model.config.sample_size				)
              lowercase		:int       							=				image.to(snake_case__				)
              with torch.no_grad():
                     lowercase		:str       							=				model(snake_case__				).sample

              lowercase		:List[Any]       							=				output[0, -1, -3:, -3:].flatten().cpu()
              # fmt: off
              lowercase		:Tuple       							=				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				)				)

