



'''simple docstring'''




import os

import numpy
import onnx



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Dict	=     a.name
			A_	:						Union[str, Any]	=     b.name

			A_	:						Any	=     """"""
			A_	:						Optional[Any]	=     """"""

			A_	:						str	=     a == b

			A_	:						List[str]	=     name_a
			A_	:						Dict	=     name_b

			return res



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			for i, input_name in enumerate(node_proto.input    ):
						if input_name == name:
									node_proto.input.insert(lowerCamelCase__						,      lowerCamelCase__    )
									node_proto.input.pop(i + 1    )

			if node_proto.op_type == "If":
						_graph_replace_input_with(node_proto.attribute[0].g						,      lowerCamelCase__						,      lowerCamelCase__    )
						_graph_replace_input_with(node_proto.attribute[1].g						,      lowerCamelCase__						,      lowerCamelCase__    )
			if node_proto.op_type == "Loop":
						_graph_replace_input_with(node_proto.attribute[0].g						,      lowerCamelCase__						,      lowerCamelCase__    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			for n in graph_proto.node:
						_node_replace_input_with(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Any	=     list(model.graph.initializer    )
			A_	:						Optional[int]	=     list(model_without_ext.graph.initializer    )
			for i, ref_i in ind_to_replace:
						assert inits_with_data[i].name == inits[i].name
						assert inits_with_data[ref_i].name == inits[ref_i].name
						assert i > ref_i

						A_	:						Optional[Any]	=     inits[i].name
						A_	:						Any	=     inits[ref_i].name

						model_without_ext.graph.initializer.remove(inits[i]    )

						# for n in model.graph.node:
						_graph_replace_input_with(model_without_ext.graph						,      lowerCamelCase__						,      lowerCamelCase__    )



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Optional[Any]	=     os.path.dirname(lowerCamelCase__    )
			A_	:						Any	=     os.path.basename(lowerCamelCase__    )

			A_	:						Union[str, Any]	=     onnx.load(os.path.join(lowerCamelCase__						,      lowerCamelCase__    )    )

			A_	:						Tuple	=     list(model.graph.initializer    )

			A_	:						Dict	=     set()
			A_	:						str	=     {}
			A_	:						Optional[Any]	=     []

			A_	:						Optional[Any]	=     0

			for i in range(len(lowerCamelCase__    )    ):
						if i in dup_set:
									continue

						for j in range(i + 1						,      len(lowerCamelCase__    )    ):
									if j in dup_set:
												continue
									if _is_equal_tensor_proto(inits[i]						,      inits[j]    ):
												dup_set.add(lowerCamelCase__    )
												dup_set.add(lowerCamelCase__    )

												A_	:						Union[str, Any]	=     inits[j].data_type
												A_	:						Dict	=     numpy.prod(inits[j].dims    )
												if dtype == 1:
															mem_size *= 4
												elif dtype == 6:
															mem_size *= 4
												elif dtype == 7 or dtype == 11:
															mem_size *= 8
												else:
															print("""unexpected data type: """						,      lowerCamelCase__    )
												total_reduced_size += mem_size

												A_	:						List[Any]	=     inits[i].name
												A_	:						Union[str, Any]	=     inits[j].name

												if name_i in dup_map:
															dup_map[name_i].append(lowerCamelCase__    )
												else:
															A_	:						Union[str, Any]	=     [name_j]
												ind_to_replace.append((j, i)    )

			print("""total reduced size: """						,      total_reduced_size / 10_24 / 10_24 / 10_24						,      """GB"""    )

			A_	:						Union[str, Any]	=     sorted(lowerCamelCase__    )
			_remove_dup_initializers_from_model(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    )

			A_	:						Optional[int]	=     """optimized_""" + model_file_name
			A_	:						Optional[Any]	=     os.path.join(lowerCamelCase__						,      lowerCamelCase__    )
			onnx.save(lowerCamelCase__						,      lowerCamelCase__    )

			return new_model




'''simple docstring'''




# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowerCamelCase							:str				    =					TypeVar('''T''')







class 	_lowerCAmelCase							(			Generic[T] ):


	def __init__(self       ,      lowercase = True							):
				A_	:						dict[T, list[T]]	=     {}  # dictionary of lists
				A_	:						Any	=     directed


	def     _a	(self       ,      lowercase       ,      lowercase							):

				if not self.directed:  # For undirected graphs
							# if both source vertex and destination vertex are both present in the
							# adjacency list, add destination vertex to source vertex list of adjacent
							# vertices and add source vertex to destination vertex list of adjacent
							# vertices.
							if source_vertex in self.adj_list and destination_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
										self.adj_list[destination_vertex].append(lowercase							)
							# if only source vertex is present in adjacency list, add destination vertex
							# to source vertex list of adjacent vertices, then create a new vertex with
							# destination vertex as key and assign a list containing the source vertex
							# as it's first adjacent vertex.
							elif source_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
										A_	:						Dict	=     [source_vertex]
							# if only destination vertex is present in adjacency list, add source vertex
							# to destination vertex list of adjacent vertices, then create a new vertex
							# with source vertex as key and assign a list containing the source vertex
							# as it's first adjacent vertex.
							elif destination_vertex in self.adj_list:
										self.adj_list[destination_vertex].append(lowercase							)
										A_	:						int	=     [destination_vertex]
							# if both source vertex and destination vertex are not present in adjacency
							# list, create a new vertex with source vertex as key and assign a list
							# containing the destination vertex as it's first adjacent vertex also
							# create a new vertex with destination vertex as key and assign a list
							# containing the source vertex as it's first adjacent vertex.
							else:
										A_	:						Optional[Any]	=     [destination_vertex]
										A_	:						Tuple	=     [source_vertex]
				else:  # For directed graphs
							# if both source vertex and destination vertex are present in adjacency
							# list, add destination vertex to source vertex list of adjacent vertices.
							if source_vertex in self.adj_list and destination_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
							# if only source vertex is present in adjacency list, add destination
							# vertex to source vertex list of adjacent vertices and create a new vertex
							# with destination vertex as key, which has no adjacent vertex
							elif source_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
										A_	:						Tuple	=     []
							# if only destination vertex is present in adjacency list, create a new
							# vertex with source vertex as key and assign a list containing destination
							# vertex as first adjacent vertex
							elif destination_vertex in self.adj_list:
										A_	:						Tuple	=     [destination_vertex]
							# if both source vertex and destination vertex are not present in adjacency
							# list, create a new vertex with source vertex as key and a list containing
							# destination vertex as it's first adjacent vertex. Then create a new vertex
							# with destination vertex as key, which has no adjacent vertex
							else:
										A_	:						int	=     [destination_vertex]
										A_	:						List[str]	=     []

				return self


	def __repr__(self							):
				return pformat(self.adj_list							)