




import os

import numpy
import onnx




def     __magic_name__       (				A	:      int,						A	:      Dict     ):






			'''simple docstring'''







			a					=    a.name
			a					=    b.name

			a					=    ""
			a					=    ""

			a					=    a == b

			a					=    name_a
			a					=    name_b

			return res




def     __magic_name__       (				A	:      List[Any],						A	:      str,						A	:      Dict     ):






			'''simple docstring'''







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

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




def     __magic_name__       (				A	:      Tuple,						A	:      List[str],						A	:      Optional[int]     ):






			'''simple docstring'''







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




def     __magic_name__       (				A	:      str,						A	:      List[Any],						A	:      Optional[Any]     ):






			'''simple docstring'''







			a					=    list(model.graph.initializer     )
			a					=    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					=    inits[i].name
						a					=    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,						A,						A     )






def     __magic_name__       (				A	:      Optional[Any]     ):






			'''simple docstring'''







			a					=    os.path.dirname(A     )
			a					=    os.path.basename(A     )

			a					=    onnx.load(os.path.join(A,						A     )     )

			a					=    list(model.graph.initializer     )

			a					=    set()
			a					=    {}
			a					=    []

			a					=    0

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

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

												a					=    inits[j].data_type
												a					=    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: ",						A     )
												total_reduced_size += mem_size

												a					=    inits[i].name
												a					=    inits[j].name

												if name_i in dup_map:
															dup_map[name_i].append(A     )
												else:
															a					=    [name_j]
												ind_to_replace.append((j, i)     )

			print("total reduced size: ",						total_reduced_size / 1024 / 1024 / 1024,						"GB"     )

			a					=    sorted(A     )
			_remove_dup_initializers_from_model(A,						A,						A     )

			a					=    "optimized_" + model_file_name
			a					=    os.path.join(A,						A     )
			onnx.save(A,						A     )

			return new_model












def     __magic_name__       (				A	:      list     ):






			'''simple docstring'''







			for i in range(len(A     ) - 1,						0,						-1     ):
						a					=    False

						for j in range(A,						0,						-1     ):
									if unsorted[j] < unsorted[j - 1]:
												a					,	a					=    unsorted[j - 1], unsorted[j]
												a					=    True

						for j in range(A     ):
									if unsorted[j] > unsorted[j + 1]:
												a					,	a					=    unsorted[j + 1], unsorted[j]
												a					=    True

						if not swapped:
									break
			return unsorted


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		__lowerCAmelCase	:       Tuple     =	input('Enter numbers separated by a comma:\n').strip()
		__lowerCAmelCase	:       List[Any]     =	[int(item) for item in user_input.split(',')]
		print(F'''{cocktail_shaker_sort(unsorted) = }''')






