





import os

import numpy
import onnx
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]						):





  """simple docstring"""



  a__     : Optional[Any]  =a.name
  a__     : List[str]  =b.name

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

  a__     : Dict  =a == b

  a__     : int  =name_a
  a__     : Optional[Any]  =name_b

  return res
def 					_A	(		SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Optional[int]						):





  """simple docstring"""



  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					:       int	,	SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       List[Any]						):





  """simple docstring"""



  for n in graph_proto.node:
    _node_replace_input_with(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[str]	,	SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       Dict						):





  """simple docstring"""



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

    a__     : str  =inits[i].name
    a__     : Any  =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					:       Optional[int]						):





  """simple docstring"""



  a__     : List[str]  =os.path.dirname(SCREAMING_SNAKE_CASE						)
  a__     : Optional[int]  =os.path.basename(SCREAMING_SNAKE_CASE						)

  a__     : Dict  =onnx.load(os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)						)

  a__     : Optional[int]  =list(model.graph.initializer						)

  a__     : Tuple  =set()
  a__     : List[str]  ={}
  a__     : List[Any]  =[]

  a__     : List[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						)

        a__     : Tuple  =inits[j].data_type
        a__     : 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: "	,	SCREAMING_SNAKE_CASE						)
        total_reduced_size += mem_size

        a__     : Union[str, Any]  =inits[i].name
        a__     : str  =inits[j].name

        if name_i in dup_map:
          dup_map[name_i].append(SCREAMING_SNAKE_CASE						)
        else:
          a__     : List[Any]  =[name_j]
        ind_to_replace.append((j, i)						)

  print("total reduced size: "	,	total_reduced_size / 1_024 / 1_024 / 1_024	,	"GB"						)

  a__     : Any  =sorted(SCREAMING_SNAKE_CASE						)
  _remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)

  a__     : Any  ="optimized_" + model_file_name
  a__     : Optional[int]  =os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  onnx.save(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)

  return new_model













from maths.prime_check import is_prime
def 					_A	(		SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  if not isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						):
    a__     : Dict  =f'''Input value of [number={number}] must be an integer'''
    raise TypeError(SCREAMING_SNAKE_CASE						)
  if is_prime(SCREAMING_SNAKE_CASE						) and is_prime(number + 2						):
    return number + 2
  else:
    return -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()






