



'''simple docstring'''



class 					UpperCAmelCase__	:



					def __init__(    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			Optional[Any]:
						__lowerCAmelCase												=  name
						__lowerCAmelCase												=  val



					def __str__(    self     )     ->			List[Any]:
						return F'''{self.__class__.__name__}({self.name}, {self.val})'''



					def __lt__(    self			,				UpperCamelCase     )     ->			Dict:
						return self.val < other.val






class 					UpperCAmelCase__	:



					def __init__(    self			,				UpperCamelCase     )     ->			Dict:
						__lowerCAmelCase												=  {}
						__lowerCAmelCase												=  {}
						__lowerCAmelCase												=  self.build_heap(UpperCAmelCase__     )



					def __getitem__(    self			,				UpperCamelCase     )     ->			int:
						return self.get_value(UpperCAmelCase__     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			List[Any]:
						return (idx - 1) // 2



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			Dict:
						return idx * 2 + 1



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			Union[str, Any]:
						return idx * 2 + 2



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			List[Any]:
						return self.heap_dict[key]



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  len(UpperCAmelCase__     ) - 1
						__lowerCAmelCase												=  self.get_parent_idx(UpperCAmelCase__     )

						for idx, i in enumerate(UpperCAmelCase__     ):
							__lowerCAmelCase												=  idx
							__lowerCAmelCase												=  i.val

						for i in range(UpperCAmelCase__			,				-1			,				-1     ):
							self.sift_down(UpperCAmelCase__			,				UpperCAmelCase__     )
						return array



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			Optional[Any]:
						while True:
							__lowerCAmelCase												=  self.get_left_child_idx(UpperCAmelCase__     )  # noqa: E741
							__lowerCAmelCase												=  self.get_right_child_idx(UpperCAmelCase__     )

							__lowerCAmelCase												=  idx
							if l < len(UpperCAmelCase__     ) and array[l] < array[idx]:
								__lowerCAmelCase												=  l
							if r < len(UpperCAmelCase__     ) and array[r] < array[smallest]:
								__lowerCAmelCase												=  r

							if smallest != idx:
								__lowerCAmelCase												=  array[smallest], array[idx]
								(
								    __lowerCAmelCase	
								)												=  (
								    self.idx_of_element[array[smallest]],
								    self.idx_of_element[array[idx]],
								)
								__lowerCAmelCase												=  smallest
							else:
								break



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			Optional[Any]:
						__lowerCAmelCase												=  self.get_parent_idx(UpperCAmelCase__     )
						while p >= 0 and self.heap[p] > self.heap[idx]:
							__lowerCAmelCase												=  self.heap[idx], self.heap[p]
							__lowerCAmelCase												=  (
							    self.idx_of_element[self.heap[idx]],
							    self.idx_of_element[self.heap[p]],
							)
							__lowerCAmelCase												=  p
							__lowerCAmelCase												=  self.get_parent_idx(UpperCAmelCase__     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						return self.heap[0]



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.heap[-1], self.heap[0]
						__lowerCAmelCase												=  (
						    self.idx_of_element[self.heap[-1]],
						    self.idx_of_element[self.heap[0]],
						)

						__lowerCAmelCase												=  self.heap.pop()
						del self.idx_of_element[x]
						self.sift_down(0			,				self.heap     )
						return x



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			str:
						self.heap.append(UpperCAmelCase__     )
						__lowerCAmelCase												=  len(self.heap     ) - 1
						__lowerCAmelCase												=  node.val
						self.sift_up(len(self.heap     ) - 1     )



					def        UpperCAmelCase_ (    self     )     ->			Dict:
						return len(self.heap     ) == 0



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			List[str]:
						assert (
						    self.heap[self.idx_of_element[node]].val > new_value
						), "newValue must be less that current value"
						__lowerCAmelCase												=  new_value
						__lowerCAmelCase												=  new_value
						self.sift_up(self.idx_of_element[node]     )


lowerCAmelCase	:		List[str]				=   Node('''R''', -1)
lowerCAmelCase	:		Union[str, Any]				=   Node('''B''', 6)
lowerCAmelCase	:		Union[str, Any]				=   Node('''A''', 3)
lowerCAmelCase	:		Optional[int]				=   Node('''X''', 1)
lowerCAmelCase	:		List[Any]				=   Node('''E''', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
lowerCAmelCase	:		Tuple				=   MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
	print(i)

print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -1_7)

# After
for i in my_min_heap.heap:
	print(i)

if __name__ == "__main__":
	import doctest

	doctest.testmod()




'''simple docstring'''



from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     torch.FloatTensor






class 					UpperCAmelCase__	(		UpperCamelCase__							,       UpperCamelCase__ ):



					@register_to_config
					def __init__(    self			,				UpperCamelCase = 16			,				UpperCamelCase = 88			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = 1			,				UpperCamelCase = 0.0			,				UpperCamelCase = 32			,				UpperCamelCase = None			,				UpperCamelCase = False			,				UpperCamelCase = None			,				UpperCamelCase = "geglu"			,				UpperCamelCase = True			,				UpperCamelCase = True			,				)     ->			List[str]:
						super().__init__()
						__lowerCAmelCase												=  num_attention_heads
						__lowerCAmelCase												=  attention_head_dim
						__lowerCAmelCase												=  num_attention_heads * attention_head_dim

						__lowerCAmelCase												=  in_channels

						__lowerCAmelCase												=  torch.nn.GroupNorm(num_groups=UpperCamelCase			,				num_channels=UpperCamelCase			,				eps=1E-6			,				affine=UpperCamelCase     )
						__lowerCAmelCase												=  nn.Linear(UpperCamelCase			,				UpperCamelCase     )

						# 3. Define transformers blocks
						__lowerCAmelCase												=  nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				dropout=UpperCamelCase			,				cross_attention_dim=UpperCamelCase			,				activation_fn=UpperCamelCase			,				attention_bias=UpperCamelCase			,				double_self_attention=UpperCamelCase			,				norm_elementwise_affine=UpperCamelCase			,				)
						        for d in range(UpperCamelCase     )
						    ]     )

						__lowerCAmelCase												=  nn.Linear(UpperCamelCase			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=1			,				UpperCamelCase=None			,				UpperCamelCase = True			,				)     ->			List[str]:
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  hidden_states.shape
						__lowerCAmelCase												=  batch_frames // num_frames

						__lowerCAmelCase												=  hidden_states

						__lowerCAmelCase												=  hidden_states[None, :].reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
						__lowerCAmelCase												=  hidden_states.permute(0			,				2			,				1			,				3			,				4     )

						__lowerCAmelCase												=  self.norm(UpperCamelCase     )
						__lowerCAmelCase												=  hidden_states.permute(0			,				3			,				4			,				2			,				1     ).reshape(batch_size * height * width			,				UpperCamelCase			,				UpperCamelCase     )

						__lowerCAmelCase												=  self.proj_in(UpperCamelCase     )

						# 2. Blocks
						for block in self.transformer_blocks:
							__lowerCAmelCase												=  block(
							    UpperCamelCase			,				encoder_hidden_states=UpperCamelCase			,				timestep=UpperCamelCase			,				cross_attention_kwargs=UpperCamelCase			,				class_labels=UpperCamelCase			,				)

						# 3. Output
						__lowerCAmelCase												=  self.proj_out(UpperCamelCase     )
						__lowerCAmelCase												=  (
						    hidden_states[None, None, :]
						    .reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
						    .permute(0			,				3			,				4			,				1			,				2     )
						    .contiguous()
						)
						__lowerCAmelCase												=  hidden_states.reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )

						__lowerCAmelCase												=  hidden_states + residual

						if not return_dict:
							return (output,)

						return TransformerTemporalModelOutput(sample=UpperCamelCase     )