





from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase_    =				TypeVar('''T''')






def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


				return (position - 1) // 2






def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


				return (2 * position) + 1






def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


				return (2 * position) + 2



class 					__lowerCAmelCase       (			Generic[T]     ):






			def __init__(self    )     ->   None:


							'''simple docstring'''



							snake_case_						:    list[tuple[T, int]]				=  []
							snake_case_						:    dict[T, int]				=  {}
							snake_case_						:    int				=  0






			def __len__(self    )     ->   int:


							'''simple docstring'''



							return self.elements






			def __repr__(self    )     ->   str:


							'''simple docstring'''



							return str(self.heap    )






			def 			lowerCamelCase  (self    )     ->   bool:


							'''simple docstring'''



							return self.elements == 0






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   None:


							'''simple docstring'''



							self.heap.append((elem, weight)    )
							snake_case_						:    Tuple				=  self.elements
							self.elements += 1
							self._bubble_up(SCREAMING_SNAKE_CASE_    )






			def 			lowerCamelCase  (self    )     ->   T:


							'''simple docstring'''



							if self.elements > 1:
											self._swap_nodes(0					,							self.elements - 1    )
							snake_case_						:    Any				=  self.heap.pop()
							del self.position_map[elem]
							self.elements -= 1
							if self.elements > 0:
											snake_case_						:    Tuple				=  self.heap[0]
											self._bubble_down(SCREAMING_SNAKE_CASE_    )
							return elem






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   None:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.position_map[elem]
							snake_case_						:    Dict				=  (elem, weight)
							if position > 0:
											snake_case_						:    str				=  get_parent_position(SCREAMING_SNAKE_CASE_    )
											snake_case_						:    Any				=  self.heap[parent_position]
											if parent_weight > weight:
															self._bubble_up(SCREAMING_SNAKE_CASE_    )
											else:
															self._bubble_down(SCREAMING_SNAKE_CASE_    )
							else:
											self._bubble_down(SCREAMING_SNAKE_CASE_    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   None:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  self.position_map[elem]
							if curr_pos == 0:
											return None
							snake_case_						:    Tuple				=  get_parent_position(SCREAMING_SNAKE_CASE_    )
							snake_case_						:    Optional[Any]				=  self.heap[curr_pos]
							snake_case_						:    int				=  self.heap[parent_position]
							if parent_weight > weight:
											self._swap_nodes(SCREAMING_SNAKE_CASE_					,							SCREAMING_SNAKE_CASE_    )
											return self._bubble_up(SCREAMING_SNAKE_CASE_    )
							return None






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   None:


							'''simple docstring'''



							snake_case_						:    List[Any]				=  self.position_map[elem]
							snake_case_						:    Optional[int]				=  self.heap[curr_pos]
							snake_case_						:    Optional[Any]				=  get_child_left_position(SCREAMING_SNAKE_CASE_    )
							snake_case_						:    Any				=  get_child_right_position(SCREAMING_SNAKE_CASE_    )
							if child_left_position < self.elements and child_right_position < self.elements:
											snake_case_						:    Optional[int]				=  self.heap[child_left_position]
											snake_case_						:    Optional[Any]				=  self.heap[child_right_position]
											if child_right_weight < child_left_weight and child_right_weight < weight:
															self._swap_nodes(SCREAMING_SNAKE_CASE_					,							SCREAMING_SNAKE_CASE_    )
															return self._bubble_down(SCREAMING_SNAKE_CASE_    )
							if child_left_position < self.elements:
											snake_case_						:    List[Any]				=  self.heap[child_left_position]
											if child_left_weight < weight:
															self._swap_nodes(SCREAMING_SNAKE_CASE_					,							SCREAMING_SNAKE_CASE_    )
															return self._bubble_down(SCREAMING_SNAKE_CASE_    )
							else:
											return None
							if child_right_position < self.elements:
											snake_case_						:    int				=  self.heap[child_right_position]
											if child_right_weight < weight:
															self._swap_nodes(SCREAMING_SNAKE_CASE_					,							SCREAMING_SNAKE_CASE_    )
															return self._bubble_down(SCREAMING_SNAKE_CASE_    )
							return None






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   None:


							'''simple docstring'''



							snake_case_						:    Tuple				=  self.heap[nodea_pos][0]
							snake_case_						:    Optional[Any]				=  self.heap[nodea_pos][0]
							snake_case_						:    Optional[int]				=  (
							    self.heap[nodea_pos],
							    self.heap[nodea_pos],
							)
							snake_case_						:    List[Any]				=  nodea_pos
							snake_case_						:    List[str]				=  nodea_pos




class 					__lowerCAmelCase       (			Generic[T]     ):






			def __init__(self    )     ->   None:


							'''simple docstring'''



							snake_case_						:    dict[T, dict[T, int]]				=  {}
							snake_case_						:    int				=  0






			def __repr__(self    )     ->   str:


							'''simple docstring'''



							return str(self.connections    )






			def __len__(self    )     ->   int:


							'''simple docstring'''



							return self.nodes






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   None:


							'''simple docstring'''



							if node not in self.connections:
											snake_case_						:    Tuple				=  {}
											self.nodes += 1






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   None:


							'''simple docstring'''



							self.add_node(SCREAMING_SNAKE_CASE_    )
							self.add_node(SCREAMING_SNAKE_CASE_    )
							snake_case_						:    List[Any]				=  weight
							snake_case_						:    Optional[int]				=  weight






def      lowerCamelCase_						(				_UpperCamelCase ,				)		->						tuple[dict[T, int], dict[T, T | None]]:






				"""simple docstring"""


				snake_case_						:    dict[T, int]				=  {node: maxsize for node in graph.connections}
				snake_case_						:    dict[T, T | None]				=  {node: None for node in graph.connections}

				snake_case_						:    MinPriorityQueue[T]				=  MinPriorityQueue()
				for node, weight in dist.items():
								priority_queue.push(UpperCAmelCase_ ,				UpperCAmelCase_ )

				if priority_queue.is_empty():
								return dist, parent

				# initialization
				snake_case_						:    Union[str, Any]				=  priority_queue.extract_min()
				snake_case_						:    List[Any]				=  0
				for neighbour in graph.connections[node]:
								if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
												snake_case_						:    Union[str, Any]				=  dist[node] + graph.connections[node][neighbour]
												priority_queue.update_key(UpperCAmelCase_ ,				dist[neighbour] )
												snake_case_						:    Dict				=  node

    # running prim's algorithm
				while not priority_queue.is_empty():
								snake_case_						:    Optional[int]				=  priority_queue.extract_min()
								for neighbour in graph.connections[node]:
												if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																snake_case_						:    Tuple				=  dist[node] + graph.connections[node][neighbour]
																priority_queue.update_key(UpperCAmelCase_ ,				dist[neighbour] )
																snake_case_						:    Optional[Any]				=  node
				return dist, parent




'''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    UpperCAmelCase_  (_UpperCAmelCase					,		_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    lowerCamelCase  :					Union[str, Any]							     =			VQModel
    lowerCamelCase  :					Union[str, Any]							     =			'sample'







    @property
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=(32, 32)	)				->     Any:
         __lowerCamelCase   :	Tuple								=		4
         __lowerCamelCase   :	Union[str, Any]								=		3

         __lowerCamelCase   :	List[Any]								=		floats_tensor((batch_size, num_channels) + sizes	).to(SCREAMING_SNAKE_CASE_	)

         return {"sample": image}







    @property
    def       lowercase_				(							self	)				->     Optional[int]:
         return (3, 32, 32)







    @property
    def       lowercase_				(							self	)				->     List[Any]:
         return (3, 32, 32)







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Optional[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,
         }
         __lowerCamelCase   :	Union[str, Any]								=		self.dummy_input
         return init_dict, inputs_dict







    def       lowercase_				(							self	)				->     str:
         pass







    def       lowercase_				(							self	)				->     Optional[int]:
         pass







    def       lowercase_				(							self	)				->     Any:
         __lowerCamelCase				,       __lowerCamelCase   :	Union[str, Any]								=		VQModel.from_pretrained('fusing/vqgan-dummy'			,     output_loading_info=SCREAMING_SNAKE_CASE_	)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(len(loading_info['missing_keys']	)			,     0	)

         model.to(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[Any]								=		model(**self.dummy_input	)

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







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	List[Any]								=		VQModel.from_pretrained('fusing/vqgan-dummy'	)
         model.to(SCREAMING_SNAKE_CASE_	).eval()

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

         __lowerCamelCase   :	List[str]								=		torch.randn(1			,     model.config.in_channels			,     model.config.sample_size			,     model.config.sample_size	)
         __lowerCamelCase   :	Optional[int]								=		image.to(SCREAMING_SNAKE_CASE_	)
         with torch.no_grad():
              __lowerCamelCase   :	int								=		model(SCREAMING_SNAKE_CASE_	).sample

         __lowerCamelCase   :	List[str]								=		output[0, -1, -3:, -3:].flatten().cpu()
         # fmt: off
         __lowerCamelCase   :	Union[str, Any]								=		torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]	)
         # fmt: on
         self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-3	)	)






