





class      __magic_name__       :




					def __init__(    self     ,					_a		)       -> Tuple:
							lowerCAmelCase_				=							data
							lowerCAmelCase_				=							None
							lowerCAmelCase_				=							None





def 	A(__a:       Optional[int]		,					__a:       Optional[Any]							):
		if node is None:
				lowerCAmelCase_				=							BinaryTreeNode(lowercase__							)
				return node

		# binary search tree is not empty,
		# so we will insert it into the tree
		# if new_value is less than value of data in node,
		#  add it to left subtree and proceed recursively
		if new_value < node.data:
				lowerCAmelCase_				=							insert(node.left_child		,					lowercase__							)
		else:
				# if new_value is greater than value of data in node,
				#  add it to right subtree and proceed recursively
				lowerCAmelCase_				=							insert(node.right_child		,					lowercase__							)
		return node

def 	A(__a:       Dict							):  # if node is None,return
		if node:
				lowerCAmelCase_				=							inorder(node.left_child							)
				lowerCAmelCase_				=							[*inorder_array, node.data]
				lowerCAmelCase_				=							inorder_array + inorder(node.right_child							)
		else:
				lowerCAmelCase_				=							[]
		return inorder_array

def 	A():
		lowerCAmelCase_				=							insert(lowercase__		,					15							)
		insert(lowercase__		,					10							)
		insert(lowercase__		,					25							)
		insert(lowercase__		,					6							)
		insert(lowercase__		,					14							)
		insert(lowercase__		,					20							)
		insert(lowercase__		,					60							)
		return root

def 	A():
		lowerCAmelCase_				=							make_tree()
		print("Printing values of binary search tree in Inorder Traversal."							)
		inorder(lowercase__							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()




"""simple docstring"""




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class 			__UpperCamelCase (   _A     ,		_A     ,		_A						):


		@register_to_config
		def __init__(self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		float     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     ):
							super().__init__()

							A			=					nn.Linear(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     bias=__SCREAMING_SNAKE_CASE)

							A			=					nn.Embedding(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							A			=					False

							A			=					nn.Dropout(p=__SCREAMING_SNAKE_CASE)

							A			=					TaConfig(
							    d_model=__SCREAMING_SNAKE_CASE     ,     num_heads=__SCREAMING_SNAKE_CASE     ,     d_kv=__SCREAMING_SNAKE_CASE     ,     d_ff=__SCREAMING_SNAKE_CASE     ,     feed_forward_proj=__SCREAMING_SNAKE_CASE     ,     dropout_rate=__SCREAMING_SNAKE_CASE     ,     is_decoder=__SCREAMING_SNAKE_CASE     ,     is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,     )
							A			=					nn.ModuleList()
							for lyr_num in range(__SCREAMING_SNAKE_CASE):
												A			=					TaBlock(__SCREAMING_SNAKE_CASE)
												self.encoders.append(__SCREAMING_SNAKE_CASE)

							A			=					TaLayerNorm(__SCREAMING_SNAKE_CASE)
							A			=					nn.Dropout(p=__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		int     ,     __SCREAMING_SNAKE_CASE :		Any     ,     __SCREAMING_SNAKE_CASE :		str):
							A			=					self.input_proj(__SCREAMING_SNAKE_CASE)

							# terminal relative positional encodings
							A			=					encoder_inputs.shape[1]
							A			=					torch.arange(__SCREAMING_SNAKE_CASE     ,     device=encoder_inputs.device)

							A			=					encoder_inputs_mask.sum(-1)
							A			=					torch.roll(input_positions.unsqueeze(0)     ,     tuple(seq_lens.tolist())     ,     dims=0)
							x += self.position_encoding(__SCREAMING_SNAKE_CASE)

							A			=					self.dropout_pre(__SCREAMING_SNAKE_CASE)

							# inverted the attention mask
							A			=					encoder_inputs.size()
							A			=					self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

							for lyr in self.encoders:
												A			=					lyr(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)[0]
							A			=					self.layer_norm(__SCREAMING_SNAKE_CASE)

							return self.dropout_post(__SCREAMING_SNAKE_CASE), encoder_inputs_mask





