




'''simple docstring'''



import os

import numpy
import onnx
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Any	,					__A    :			int  )			->			Any:
   _SCREAMING_SNAKE_CASE	=				a.name
   _SCREAMING_SNAKE_CASE	=				b.name

   _SCREAMING_SNAKE_CASE	=				''''''
   _SCREAMING_SNAKE_CASE	=				''''''

   _SCREAMING_SNAKE_CASE	=				a == b

   _SCREAMING_SNAKE_CASE	=				name_a
   _SCREAMING_SNAKE_CASE	=				name_b

   return res
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Any	,					__A    :			Dict	,					__A    :			Optional[Any]  )			->			Optional[Any]:





   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 			SCREAMING_SNAKE_CASE_			(							__A    :			Any	,					__A    :			Any	,					__A    :			Union[str, Any]  )			->			Dict:





   for n in graph_proto.node:
      _node_replace_input_with(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]	,					__A    :			Union[str, Any]	,					__A    :			Optional[int]  )			->			List[Any]:
   _SCREAMING_SNAKE_CASE	=				list(model.graph.initializer  )
   _SCREAMING_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

      _SCREAMING_SNAKE_CASE	=				inits[i].name
      _SCREAMING_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	,					__lowerCAmelCase	,					__lowerCAmelCase  )






def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]  )			->			List[Any]:
   _SCREAMING_SNAKE_CASE	=				os.path.dirname(__lowerCAmelCase  )
   _SCREAMING_SNAKE_CASE	=				os.path.basename(__lowerCAmelCase  )

   _SCREAMING_SNAKE_CASE	=				onnx.load(os.path.join(__lowerCAmelCase	,					__lowerCAmelCase  )  )

   _SCREAMING_SNAKE_CASE	=				list(model.graph.initializer  )

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

   _SCREAMING_SNAKE_CASE	=				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  )

            _SCREAMING_SNAKE_CASE	=				inits[j].data_type
            _SCREAMING_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: "	,					__lowerCAmelCase  )
            total_reduced_size += mem_size

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

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

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

   _SCREAMING_SNAKE_CASE	=				sorted(__lowerCAmelCase  )
   _remove_dup_initializers_from_model(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase  )

   _SCREAMING_SNAKE_CASE	=				'''optimized_''' + model_file_name
   _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCAmelCase	,					__lowerCAmelCase  )
   onnx.save(__lowerCAmelCase	,					__lowerCAmelCase  )

   return new_model







'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			str  )			->			str:
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				(
       first_str_length if first_str_length > second_str_length else second_str_length
   )
   _SCREAMING_SNAKE_CASE	=				[]
   for char_count in range(__A  ):
      if char_count < first_str_length:
         output_list.append(first_str[char_count]  )
      if char_count < second_str_length:
         output_list.append(second_str[char_count]  )
   return "".join(__A  )


if __name__ == "__main__":
 print(alternative_string_arrange('AB', 'XYZ'), end=' ')

