






"""simple docstring"""






import numpy as np



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__ = 1E-1_2       ,lowerCAmelCase__ = 100       ,):
				assert np.shape(SCREAMING_SNAKE_CASE_				)[0] == np.shape(SCREAMING_SNAKE_CASE_				)[1]
				# Ensure proper dimensionality.
				assert np.shape(SCREAMING_SNAKE_CASE_				)[0] == np.shape(SCREAMING_SNAKE_CASE_				)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(SCREAMING_SNAKE_CASE_				) == np.iscomplexobj(SCREAMING_SNAKE_CASE_				)
				A__							 =       np.iscomplexobj(SCREAMING_SNAKE_CASE_				)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(SCREAMING_SNAKE_CASE_       ,input_matrix.conj().T				)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				A__							 =       False
				A__							 =       0
				A__							 =       0
				A__							 =       1E1_2

				while not convergence:
								# Multiple matrix by the vector.
								A__							 =       np.dot(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_				)
								# Normalize the resulting output vector.
								A__							 =       w / np.linalg.norm(SCREAMING_SNAKE_CASE_				)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								A__							 =       vector.conj().T if is_complex else vector.T
								A__							 =       np.dot(SCREAMING_SNAKE_CASE_       ,np.dot(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_				)				)

								# Check convergence.
								A__							 =       np.abs(lambda_ - lambda_previous				) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												A__							 =       True

								A__							 =       lambda_

				if is_complex:
								A__							 =       np.real(lambda_				)

				return lambda_, vector



def  __lowerCamelCase					(					):
				A__							 =       np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]				)
				A__							 =       np.array([41, 4, 20]				)
				A__							 =       real_input_matrix.astype(np.complexaaa				)
				A__							 =       np.triu(1j * complex_input_matrix       ,1				)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				A__							 =       np.array([41, 4, 20]				).astype(np.complexaaa				)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												A__							 =       real_input_matrix
												A__							 =       real_vector
								elif problem_type == "complex":
												A__							 =       complex_input_matrix
												A__							 =       complex_vector

								# Our implementation.
								A__ ,			A__							 =       power_iteration(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_				)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								A__ ,			A__							 =       np.linalg.eigh(SCREAMING_SNAKE_CASE_				)
								# Last eigenvalue is the maximum one.
								A__							 =       eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								A__							 =       eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max				) <= 1E-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(SCREAMING_SNAKE_CASE_				) - np.abs(SCREAMING_SNAKE_CASE_				)				) <= 1E-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_power_iteration()










"""simple docstring"""






import os

import numpy
import onnx



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       a.name
				A__							 =       b.name

				A__							 =       ''
				A__							 =       ''

				A__							 =       a == b

				A__							 =       name_a
				A__							 =       name_b

				return res



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				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  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				for n in graph_proto.node:
								_node_replace_input_with(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				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       ,lowerCAmelCase__       ,lowerCAmelCase__				)







def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       os.path.dirname(lowerCAmelCase__				)
				A__							 =       os.path.basename(lowerCAmelCase__				)

				A__							 =       onnx.load(os.path.join(lowerCAmelCase__       ,lowerCAmelCase__				)				)

				A__							 =       list(model.graph.initializer				)

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

				A__							 =       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__							 =       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: '       ,lowerCAmelCase__				)
																total_reduced_size += mem_size

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

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

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

				A__							 =       sorted(lowerCAmelCase__				)
				_remove_dup_initializers_from_model(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)

				A__							 =       'optimized_' + model_file_name
				A__							 =       os.path.join(lowerCAmelCase__       ,lowerCAmelCase__				)
				onnx.save(lowerCAmelCase__       ,lowerCAmelCase__				)

				return new_model


