def insert_after(self, current_node_item, new_node_item):
tmp = self._first
while tmp:
if tmp.val == current_node_item:
old_next_node = tmp.next_node
new_node = Node(new_node_item)
tmp.next_node = new_node
new_node.next_node = old_next_node
self._size += 1
break
tmp = tmp.next_node
def idg(filename):
dg = DependencyGraph()
with open(filename) as fp:
for line in fp:
trimmed = line.strip()
if len(trimmed) == 0:
yield dg
dg = DependencyGraph()
else:
dg.addNode(Node.byline(trimmed))
if dg.length() > 0:
yield dg
def test_no_distance(self):
n = Node("test")
self.assertEqual(n.distance(), 0)
secondnode.next=firstnode
firstnode.next=nextsecondnode
else:
previousfirstnode.next=secondnode
nextsecondnode=secondnode.next
secondnode.next=firstnode.next
previoussecondnode.next=firstnode
firstnode.next=nextsecondnode
firstnode=Node(4)
secondnode=Node(2)
thirdnode=Node(6)
fourthnode=Node(5)
fifthnode=Node(9)
sixthnode=Node(3)
link=Linklist()
link.insert(firstnode)
link.insert(secondnode)
link.insert(thirdnode)
link.insert(fourthnode)
link.insert(fifthnode)
link.insert(sixthnode)
swapnode(link,6,3)
def __init__(self, title, parent, token):
Node.__init__(self, title, parent)
self.token = token
self.series_nodes = {}
def parse_graph(self, file_path, model_name, category, sub_category):
"""Method to parse file and Create a corresponding Graph object.
Reads a tflite file into a tflite/Model Object and then extracts
operators, tensors, graph structure and metadata and stores it
into a Graph, Node and Edge objects. Nodes are operations and
edges are tensors.
Args:
file_path (str): Path of the file to parse
model_name (str): Unique model name of the model being parsed.
category (str): Problem category of the model.
sub_category (str) : Problem sub category of the model.
Returns:
The Graph object created for the file.
"""
model = self.parse(file_path)
nodes = list()
edges = list()
adj_list = dict()
start_node_indices = list()
# Global list of opcodes in the model, referenced by Operators
opcodes = list()
for opcode_index in range(model.OperatorCodesLength()):
opcodes.append(model.OperatorCodes(opcode_index))
# Only considering the main model
subgraph = model.Subgraphs(0)
# Dictionary to store origin and destination nodes for each edge
to_nodes = dict()
from_nodes = dict()
for tensor_index in range(subgraph.TensorsLength()):
tensor = subgraph.Tensors(tensor_index)
# Converting tensor to an Edge object
new_edge = self._TENSOR_TO_EDGE.convert(tensor)
edges.append(new_edge)
# Populating to_nodes, from_nodes
# Add proxy nodes for Input and Output of the model
for input_index in range(subgraph.InputsLength()):
new_node = Node.Node(label="Input_Placeholder",
operator_type="Input_Placeholder")
nodes.append(new_node)
node_index = len(nodes) - 1
start_node_indices.append(node_index)
edge_index = subgraph.Inputs(input_index)
if edge_index not in from_nodes:
from_nodes.update({edge_index: []})
from_nodes[edge_index].append(node_index)
for operator_index in range(subgraph.OperatorsLength()):
operator = subgraph.Operators(operator_index)
builtin_opcode = opcodes[operator.OpcodeIndex()].BuiltinCode()
opname = self._builtin_optype[builtin_opcode]
new_node = self._OP_TO_NODE.convert(operator, opname)
# Condition to extract Conv 2D filter sizes and
# input and output channels as it is contained in tensors
# and not in operators
if new_node.label == "CONV_2D":
weight_tensor = subgraph.Tensors(operator.Inputs(1))
new_node.filter_height = weight_tensor.Shape(1)
new_node.filter_width = weight_tensor.Shape(2)
nodes.append(new_node)
node_index = len(nodes) - 1
for input_index in range(operator.InputsLength()):
edge_index = operator.Inputs(input_index)
if edge_index not in to_nodes:
to_nodes.update({edge_index: list()})
to_nodes[edge_index].append(node_index)
for output_index in range(operator.OutputsLength()):
edge_index = operator.Outputs(output_index)
if edge_index not in from_nodes:
from_nodes.update({edge_index: list()})
from_nodes[edge_index].append(node_index)
for output_index in range(subgraph.OutputsLength()):
new_node = Node.Node(label="Output_Placeholder",
operator_type="Output_Placeholder")
nodes.append(new_node)
node_index = len(nodes) - 1
edge_index = subgraph.Outputs(output_index)
if edge_index not in to_nodes:
to_nodes.update({edge_index: []})
to_nodes[edge_index].append(node_index)
# Constructing adjacency List from to_nodes, from_nodes
for edge_index in range(len(edges)):
if edge_index not in from_nodes or edge_index not in to_nodes:
continue
for node1_index in from_nodes[edge_index]:
for node2_index in to_nodes[edge_index]:
if node1_index not in adj_list:
adj_list.update({node1_index: list()})
adj_list[node1_index].append([edge_index, node2_index])
graph = Graph.Graph(nodes, start_node_indices, edges, adj_list,
model_name, category, sub_category)
# Removing nodes which are not reachable from input
graph.process_nodes()
graph.source = "TFLite"
return graph
from common import Node, parse, run
cups = [Node(i) for i in parse()]
starting = cups[0]
for i, cup in enumerate(cups):
cup.next = cups[i + 1] if i != len(cups) - 1 else cups[0]
cups = sorted(cups, key=lambda cup: cup.value)
run(cups, starting, 100)
current = cups[0].next
order = []
while current.value != 1:
order.append(current.value)
current = current.next
print(''.join(str(i) for i in order))
def __init__(self, parent):
Node.__init__(self, "SBS", parent)
def __init__(self, title, parent, url):
Node.__init__(self, title, parent)
self.url = url
self.series_map = {}
def insert(self, value):
if (self.root == None):
self.root = Node(value)
self.cts[self.root] = 1
else:
self.__findleaf__(value, self.root)
def __init__(self, title, parent, url):
Node.__init__(self, title, parent)
self.video_id = url.split("/")[-1]
self.can_download = True
def __init__(self, parent):
Node.__init__(self, "Yahoo Plus7 (broken!)", parent)
def __init__(self, title, parent, url):
Node.__init__(self, title, parent)
self.url = url
self.can_download = True
size = len(q)
prev = None
for _ in range(size):
tmp = q.pop(0)
if prev is not None:
prev.next = tmp
if tmp.left:
q.append(tmp.left)
if tmp.right:
q.append(tmp.right)
prev = tmp
return root
if __name__ == "__main__":
root = Node(1)
root.left = Node(2)
root.right = Node(3)
root.left.left = Node(4)
root.left.right = Node(5)
root.right.left = Node(6)
root.right.right = Node(7)
res = Solution().connect(root)
while res:
curr = res
while curr:
print(curr.val)
curr = curr.next
res = res.left
def __init__(self, title, parent, url):
Node.__init__(self, title, parent)
self.url = url
self.unique_series = set()
def parse_models(self, parse_stateful = False):
"""Method to query and read data from database.
Method to query database and read models into Graph objects.
Args:
parse_stateful (bool) : Boolean to indicate whether graphs with
stateful partitioned call should be parsed, these graphs do not
contain a graph structure or tensors. Defaults to False.
Returns:
List of Graph objects corresponding to the graph objects the models
in the spanner database have been parsed into.
"""
model_graphs = list()
# Query to get all models from Models table
with self.database.snapshot() as snapshot:
qresult_models = snapshot.execute_sql(
"SELECT model_name, category, sub_category, source, num_inputs"
" FROM Models"
)
for row in qresult_models:
# Checking num_inputs for presence of graph structure
if row[4] == 0 and not parse_stateful:
continue
# Extracting model attributes
model_name = row[0]
category = row[1]
sub_category = row[2]
source = row[3]
nodes = list()
edges = list()
start_node_indices = list()
adj_list = dict()
# Querying Operators of model_name
with self.database.snapshot() as snapshot:
qresult_operators = snapshot.execute_sql(
"SELECT * from Models JOIN Operators"
" ON Models.model_name = Operators.model_name"
" WHERE Models.model_name = '" + model_name + "'"
" ORDER BY operator_id"
)
# Dictionary to hold which field is in which index of query results
field_to_index = dict()
# Boolean to check if field_to_dict needs to be populated
populate_dicts = True
# Extracting Node attributes
for row in qresult_operators:
if populate_dicts:
for index in range(len(qresult_operators.metadata.row_type.fields)):
field_name = qresult_operators.metadata.row_type.fields[index].name
field_to_index[field_name] = index
populate_dicts = False
new_node = Node.Node(None, None)
for attr in vars(new_node).keys():
if attr in field_to_index:
setattr(new_node, attr, row[field_to_index[attr]])
nodes.append(new_node)
# populating start_node_indices using is_input field
if row[field_to_index['is_input']]:
start_node_indices.append(len(nodes) - 1)
# Querying Tensors of model_name
with self.database.snapshot() as snapshot:
qresult_tensors = snapshot.execute_sql(
"SELECT * from Models JOIN Tensors"
" ON Models.model_name = Tensors.model_name"
" WHERE Models.model_name = '" + model_name + "'"
" ORDER BY tensor_id"
)
# Dictionary to hold which field is in which index of query results
field_to_index.clear()
# Boolean to check if field_to_dict needs to be populated
populate_dicts = True
# Extracting Edge attributes
for row in qresult_tensors:
if populate_dicts:
for index in range(len(qresult_tensors.metadata.row_type.fields)):
field_name = qresult_tensors.metadata.row_type.fields[index].name
field_to_index[field_name] = index
populate_dicts = False
new_edge = Edge.Edge(None, None)
for attr in vars(new_edge).keys():
if attr in field_to_index:
setattr(new_edge, attr, row[field_to_index[attr]])
edges.append(new_edge)
to_operator_ids = row[field_to_index['to_operator_ids']]
from_operator_ids = row[field_to_index['from_operator_ids']]
edge_index = len(edges) - 1
for src_node_index in from_operator_ids:
src_node_index -= 1
for dest_node_index in to_operator_ids:
dest_node_index -= 1
if src_node_index not in adj_list:
adj_list.update({src_node_index : []})
adj_list[src_node_index].append([edge_index,
dest_node_index])
new_graph = Graph.Graph(nodes, start_node_indices, edges, adj_list,
model_name, category, sub_category)
new_graph.source = source
model_graphs.append(new_graph)
return model_graphs
def __init__(self, title, parent, url): Node.__init__(self, title, parent) self.url = url self.can_download = True
def __init__(self, parent):
Node.__init__(self, "SBS", parent)
def test_simple_distance(self):
com = Node("COM")
b = Node("B", com)
c = Node("C", b)
self.assertEqual(c.distance(), 2)
def __init__(self, title, parent, token, video_id):
Node.__init__(self, title, parent)
self.can_download = True
self.token = token
self.video_id = video_id
def __init__(self, title, parent, video_key):
Node.__init__(self, title, parent)
self.video_key = video_key
self.filename = title + ".ts"
self.can_download = True
def __init__(self, title, parent, token):
Node.__init__(self, title, parent)
self.token = token
self.series_nodes = {}
def __init__(self, title, parent, url):
Node.__init__(self, title, parent)
self.url = url
from common import Node
class Solution(object):
def levelOrder(self, root):
if not root:
return []
nodes = [root]
traversal = []
while nodes:
level = [node.val for node in nodes]
traversal.append(level)
new = []
for node in nodes:
new.extend(node.children)
nodes = new
return traversal
children = [Node(1, []), Node(2, []), Node(3, [])]
root = Node(0, children)
sol = Solution()
print(sol.levelOrder(root))
class LinkedList(object):
"""
链表
"""
head = None
tail = None
size = 0
cursor = Node()
def __init__(self):
pass
def __str__(self):
return f"<LinkedList-{id(self)}: element size={self.size}, head={self.head}, tail={self.tail}>"
def data_init(self, element_list: List[object]):
"""
链表元素初始化
:param element_list:
:return:
"""
raise NotImplementedError("需要实现链表的元素初始化")
def data_iter(self):
"""
链表元素迭代
:return:
"""
pass
def add_element(self, element, index=-1):
"""
链表元素新增
:param element: 新增元素
:param index: 元素添加的位置，默认=-1: 表示在链表尾部添加元素
:return:
"""
raise NotImplementedError("需要实现链表的元素添加逻辑")
def remove_element(self, element):
"""
链表元素删除
:param element: 删除元素
:return:
"""
raise NotImplementedError("需要实现链表的元素删除逻辑")
def get_element(self, element):
"""
链表元素查询
:param element: 查询元素
:return:
"""
raise NotImplementedError("需要实现链表的元素查询逻辑")
def has_loop(self):
"""
判断链表是否有环
:return:
"""
pass
def is_empty(self):
"""
判断链表是否为空
:return:
"""
pass
pass
def push(self, val):
node = Node(val)
old = self._first
self._first = node
self._first.next_node = old
self._size += 1
from common import Node, parse, run
cups = [Node(i) for i in parse()]
starting = cups[0]
rest = [Node(i) for i in range(10, 1000000 + 1)]
for i, cup in enumerate(cups):
cup.next = cups[i + 1] if i != len(cups) - 1 else rest[0]
for i, cup in enumerate(rest):
cup.next = rest[i + 1] if i != len(rest) - 1 else cups[0]
cups = sorted(cups, key=lambda cup: cup.value) + rest
run(cups, starting, 10000000)
print(cups[0].next.value * cups[0].next.next.value)
def attr_level_score(self) -> Node: # {{{1
ret = Node("attribute", dict(
NAME="markdown-level",
VALUE=Text(self.n_level),
))
return ret
def __init__(self, title, parent, token, video_id):
Node.__init__(self, title, parent)
self.can_download = True
self.token = token
self.video_id = video_id
def __init__(self, title, parent, url): Node.__init__(self, title, parent) self.url = url
def indexed_child_nodes():
for entry in node.metadata_entries:
if (entry == 0 or entry - 1 >= len(node.child_nodes)):
yield Node.empty()
else:
yield node.child_nodes[entry - 1]
def test_distance_distance(self):
com = Node("COM")
b = Node("B", com)
c = Node("C", b)
d = Node("D", c)
e = Node("E", d)
f = Node("F", e)
g = Node("G", b)
h = Node("H", g)
i = Node("I", d)
san = Node("SAN", i)
j = Node("J", e)
k = Node("K", j)
l = Node("L", k)
you = Node("YOU", k)
self.assertEqual(you.distance_with(san), 4)
while execution != 0:
currentnode = link.head
previousnode = None
iteration = execution
while iteration != 0:
if currentnode.data > currentnode.next.data:
swap_nodes(link, previousnode, currentnode, currentnode.next)
break
else:
previousnode = currentnode
currentnode = currentnode.next
iteration -= 1
execution -= 1
firstnode = Node(4)
secondnode = Node(2)
thirdnode = Node(6)
fourthnode = Node(5)
# fifthnode=Node(9)
# sixthnode=Node(3)
link = Linklist()
link.insert(firstnode)
link.insert(secondnode)
link.insert(thirdnode)
link.insert(fourthnode)
# link.insert(fifthnode)
# link.insert(sixthnode)
sort_linklist(link)
def __init__(self, title, parent, query, expected_tv_show):
Node.__init__(self, title, parent)
self.title = title
self.query = query
self.expected_tv_show = expected_tv_show
self.video_ids = set()
def __init__(self, parent): Node.__init__(self, "Yahoo Plus7 (broken!)", parent)
def __init__(self, title, parent, video_url):
Node.__init__(self, title, parent)
self.can_download = True
self.video_url = video_url
def __init__(self, title, parent, url):
Node.__init__(self, title, parent)
self.video_id = url.split("/")[-1]
self.can_download = True
def append_script(nod: Nod1) -> None:
if self.f_disable_script:
return
cmds = cmn.compose_script(mode.t())
nod.attr_replace("script1", cmds)