'''simple docstring'''





from __future__ import annotations

import math





def      lowerCAmelCase_    ( snake_case_	:			int ,   snake_case_	:			int ,   snake_case_	:			bool ,   snake_case_	:			list[int] ,   snake_case_	:			float      )   ->				int:


     '''simple docstring'''




     if depth < 0:
          raise ValueError("Depth cannot be less than 0"      )

     if not scores:
          raise ValueError("Scores cannot be empty"      )

     if depth == height:
          return scores[node_index]

     return (
         max(
             minimax(depth + 1 ,   node_index * 2 ,   snake_case_ ,   snake_case_ ,   snake_case_      ) ,   minimax(depth + 1 ,   node_index * 2 + 1 ,   snake_case_ ,   snake_case_ ,   snake_case_      ) ,   )
         if is_max
         else min(
             minimax(depth + 1 ,   node_index * 2 ,   snake_case_ ,   snake_case_ ,   snake_case_      ) ,   minimax(depth + 1 ,   node_index * 2 + 1 ,   snake_case_ ,   snake_case_ ,   snake_case_      ) ,   )
     )





def      lowerCAmelCase_    ( )   ->				None:


     '''simple docstring'''




     UpperCAmelCase_	   =       [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
     UpperCAmelCase_	   =       math.log(len(snake_case_      ) ,   2      )
     print(f"""Optimal value : {minimax(0 ,   0 ,   snake_case_ ,   snake_case_ ,   snake_case_      )}"""      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





'''simple docstring'''





import os

import numpy
import onnx





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			Optional[int]      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       a.name
     UpperCAmelCase_	   =       b.name

     UpperCAmelCase_	   =       ""
     UpperCAmelCase_	   =       ""

     UpperCAmelCase_	   =       a == b

     UpperCAmelCase_	   =       name_a
     UpperCAmelCase_	   =       name_b

     return res





def      lowerCAmelCase_    ( snake_case_	:			Any ,   snake_case_	:			Tuple ,   snake_case_	:			Union[str, Any]      )   ->				Any:


     '''simple docstring'''




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

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





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			List[str] ,   snake_case_	:			Union[str, Any]      )   ->				Optional[int]:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			Any ,   snake_case_	:			Optional[Any]      )   ->				Optional[int]:


     '''simple docstring'''




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

          UpperCAmelCase_	   =       inits[i].name
          UpperCAmelCase_	   =       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 ,   snake_case_ ,   snake_case_      )






def      lowerCAmelCase_    ( snake_case_	:			int      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       os.path.dirname(snake_case_      )
     UpperCAmelCase_	   =       os.path.basename(snake_case_      )

     UpperCAmelCase_	   =       onnx.load(os.path.join(snake_case_ ,   snake_case_      )      )

     UpperCAmelCase_	   =       list(model.graph.initializer      )

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

     UpperCAmelCase_	   =       0

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

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

                    UpperCAmelCase_	   =       inits[j].data_type
                    UpperCAmelCase_	   =       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: " ,   snake_case_      )
                    total_reduced_size += mem_size

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

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

     print("total reduced size: " ,   total_reduced_size / 10_24 / 10_24 / 10_24 ,   "GB"      )

     UpperCAmelCase_	   =       sorted(snake_case_      )
     _remove_dup_initializers_from_model(snake_case_ ,   snake_case_ ,   snake_case_      )

     UpperCAmelCase_	   =       "optimized_" + model_file_name
     UpperCAmelCase_	   =       os.path.join(snake_case_ ,   snake_case_      )
     onnx.save(snake_case_ ,   snake_case_      )

     return new_model




