



import os

import numpy
import onnx





def   lowerCamelCase__       (       UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    List[str]  )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			a.name
					_snake_case													=			b.name

					_snake_case													=			''
					_snake_case													=			''

					_snake_case													=			a == b

					_snake_case													=			name_a
					_snake_case													=			name_b

					return res





def   lowerCamelCase__       (       UpperCamelCase__     :    str    ,      UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    List[Any]  )     ->	Tuple:



					'''simple docstring'''




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

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





def   lowerCamelCase__       (       UpperCamelCase__     :    str    ,      UpperCamelCase__     :    str    ,      UpperCamelCase__     :    int  )     ->	List[str]:



					'''simple docstring'''




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





def   lowerCamelCase__       (       UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    Any  )     ->	Optional[Any]:



					'''simple docstring'''




					_snake_case													=			list(model.graph.initializer  )
					_snake_case													=			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

										_snake_case													=			inits[i].name
										_snake_case													=			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    ,      UpperCamelCase__    ,      UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    Union[str, Any]  )     ->	Optional[int]:



					'''simple docstring'''




					_snake_case													=			os.path.dirname(UpperCamelCase__  )
					_snake_case													=			os.path.basename(UpperCamelCase__  )

					_snake_case													=			onnx.load(os.path.join(UpperCamelCase__    ,      UpperCamelCase__  )  )

					_snake_case													=			list(model.graph.initializer  )

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

					_snake_case													=			0

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

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

																				_snake_case													=			inits[j].data_type
																				_snake_case													=			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: '    ,      UpperCamelCase__  )
																				total_reduced_size += mem_size

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

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

					print('total reduced size: '    ,      total_reduced_size / 1_024 / 1_024 / 1_024    ,      'GB'  )

					_snake_case													=			sorted(UpperCamelCase__  )
					_remove_dup_initializers_from_model(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  )

					_snake_case													=			'optimized_' + model_file_name
					_snake_case													=			os.path.join(UpperCamelCase__    ,      UpperCamelCase__  )
					onnx.save(UpperCamelCase__    ,      UpperCamelCase__  )

					return new_model










def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Optional[int]  )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			[0 for i in range(r + 1  )]
					# nc0 = 1
					_snake_case													=			1
					for i in range(1    ,      n + 1  ):
										# to compute current row from previous row.
										_snake_case													=			min(UpperCamelCase__    ,      UpperCamelCase__  )
										while j > 0:
															c[j] += c[j - 1]
															j -= 1
					return c[r]




print(binomial_coefficient(n=10, r=5))



