"""simple docstring"""





import os

import numpy
import onnx





def        a_  (   _lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				Optional[int]  ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			a.name
       lowercase__    :	Union[str, Any]     					=			b.name

       lowercase__    :	Dict     					=			''
       lowercase__    :	Tuple     					=			''

       lowercase__    :	str     					=			a == b

       lowercase__    :	List[Any]     					=			name_a
       lowercase__    :	Union[str, Any]     					=			name_b

       return res





def        a_  (   _lowerCAmelCase						:				Dict     ,				_lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Union[str, Any]  ):





       '''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						:				Any     ,				_lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				Dict  ):





       '''simple docstring'''

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





def        a_  (   _lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				Optional[int]  ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			list(model.graph.initializer  )
       lowercase__    :	List[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

              lowercase__    :	Optional[Any]     					=			inits[i].name
              lowercase__    :	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     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )






def        a_  (   _lowerCAmelCase						:				List[str]  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			os.path.dirname(_lowerCAmelCase  )
       lowercase__    :	List[str]     					=			os.path.basename(_lowerCAmelCase  )

       lowercase__    :	Any     					=			onnx.load(os.path.join(_lowerCAmelCase     ,				_lowerCAmelCase  )  )

       lowercase__    :	Optional[Any]     					=			list(model.graph.initializer  )

       lowercase__    :	Dict     					=			set()
       lowercase__    :	Tuple     					=			{}
       lowercase__    :	int     					=			[]

       lowercase__    :	Optional[int]     					=			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  )

                            lowercase__    :	Optional[int]     					=			inits[j].data_type
                            lowercase__    :	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

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

                            if name_i in dup_map:
                                   dup_map[name_i].append(_lowerCAmelCase  )
                            else:
                                   lowercase__    :	Tuple     					=			[name_j]
                            ind_to_replace.append((j, i)  )

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

       lowercase__    :	int     					=			sorted(_lowerCAmelCase  )
       _remove_dup_initializers_from_model(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )

       lowercase__    :	Tuple     					=			'optimized_' + model_file_name
       lowercase__    :	Union[str, Any]     					=			os.path.join(_lowerCAmelCase     ,				_lowerCAmelCase  )
       onnx.save(_lowerCAmelCase     ,				_lowerCAmelCase  )

       return new_model


"""simple docstring"""





import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor





def        a_  (   _lowerCAmelCase						:				dict  ):





       '''simple docstring'''

       return (data["data"], data["target"])





def        a_  (   _lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			XGBRegressor(verbosity=0     ,				random_state=42  )
       xgb.fit(_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Predict target for test data
       lowercase__    :	str     					=			xgb.predict(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			predictions.reshape(len(_lowerCAmelCase  )     ,				1  )
       return predictions





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			fetch_california_housing()
       lowercase__					,   lowercase__    :	str     					=			data_handling(_lowerCAmelCase  )
       lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	str     					=			train_test_split(
           _lowerCAmelCase     ,				_lowerCAmelCase     ,				test_size=0.2_5     ,				random_state=1  )
       lowercase__    :	Any     					=			xgboost(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Error printing
       print(f"""Mean Absolute Error : {mean_absolute_error(_lowerCAmelCase     ,				_lowerCAmelCase  )}"""  )
       print(f"""Mean Square Error  : {mean_squared_error(_lowerCAmelCase     ,				_lowerCAmelCase  )}"""  )


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()

