

"""simple docstring"""


import os

import numpy
import onnx






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Tuple:
		__lowerCAmelCase:	Tuple   				=      a.name
		__lowerCAmelCase:	List[str]   				=      b.name

		__lowerCAmelCase:	List[str]   				=      ""
		__lowerCAmelCase:	List[Any]   				=      ""

		__lowerCAmelCase:	Any   				=      a == b

		__lowerCAmelCase:	int   				=      name_a
		__lowerCAmelCase:	List[Any]   				=      name_b

		return res






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     int:
		for i, input_name in enumerate(node_proto.input			):
				if input_name == name:
						node_proto.input.insert(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
						node_proto.input.pop(i + 1			)

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






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Any:
		for n in graph_proto.node:
				_node_replace_input_with(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Any:
		__lowerCAmelCase:	List[Any]   				=      list(model.graph.initializer			)
		__lowerCAmelCase:	Any   				=      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

				__lowerCAmelCase:	Optional[Any]   				=      inits[i].name
				__lowerCAmelCase:	Dict   				=      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 ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     List[Any]:
		__lowerCAmelCase:	Optional[Any]   				=      os.path.dirname(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Optional[int]   				=      os.path.basename(__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	List[Any]   				=      onnx.load(os.path.join(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)			)

		__lowerCAmelCase:	int   				=      list(model.graph.initializer			)

		__lowerCAmelCase:	Any   				=      set()
		__lowerCAmelCase:	str   				=      {}
		__lowerCAmelCase:	str   				=      []

		__lowerCAmelCase:	str   				=      0

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

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

								__lowerCAmelCase:	Tuple   				=      inits[j].data_type
								__lowerCAmelCase:	Optional[Any]   				=      numpy.prod(inits[j].dims			)
								if dtype == 1:
										mem_size *= 4
								elif dtype == 6:
										mem_size *= 4
								elif dtype == 7 or dtype == 1_1:
										mem_size *= 8
								else:
										print("unexpected data type: " ,			__SCREAMING_SNAKE_CASE			)
								total_reduced_size += mem_size

								__lowerCAmelCase:	Optional[int]   				=      inits[i].name
								__lowerCAmelCase:	Tuple   				=      inits[j].name

								if name_i in dup_map:
										dup_map[name_i].append(__SCREAMING_SNAKE_CASE			)
								else:
										__lowerCAmelCase:	Any   				=      [name_j]
								ind_to_replace.append((j, i)			)

		print("total reduced size: " ,			total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4 ,			"GB"			)

		__lowerCAmelCase:	Any   				=      sorted(__SCREAMING_SNAKE_CASE			)
		_remove_dup_initializers_from_model(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	List[Any]   				=      "optimized_" + model_file_name
		__lowerCAmelCase:	Dict   				=      os.path.join(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		onnx.save(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

		return new_model





"""simple docstring"""


from __future__ import annotations

from math import pi






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     dict[str, float]:

		if (inductance, frequency, reactance).count(0			) != 1:
				raise ValueError("One and only one argument must be 0"			)
		if inductance < 0:
				raise ValueError("Inductance cannot be negative"			)
		if frequency < 0:
				raise ValueError("Frequency cannot be negative"			)
		if reactance < 0:
				raise ValueError("Inductive reactance cannot be negative"			)
		if inductance == 0:
				return {"inductance": reactance / (2 * pi * frequency)}
		elif frequency == 0:
				return {"frequency": reactance / (2 * pi * inductance)}
		elif reactance == 0:
				return {"reactance": 2 * pi * frequency * inductance}
		else:
				raise ValueError("Exactly one argument must be 0"			)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


