



'''simple docstring'''






import os

import numpy
import onnx




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		int:





       '''simple docstring'''


       _UpperCAmelCase  :						List[Any]			     = a.name
       _UpperCAmelCase  :						Optional[Any]			     = b.name

       _UpperCAmelCase  :						int			     = """"""
       _UpperCAmelCase  :						Tuple			     = """"""

       _UpperCAmelCase  :						Optional[int]			     = a == b

       _UpperCAmelCase  :						Any			     = name_a
       _UpperCAmelCase  :						str			     = name_b

       return res




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Dict:





       '''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     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Optional[int]:





       '''simple docstring'''


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




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Any:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = list(model.graph.initializer		)
       _UpperCAmelCase  :						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

              _UpperCAmelCase  :						int			     = inits[i].name
              _UpperCAmelCase  :						Tuple			     = 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     snake_case_					(     lowerCAmelCase_		)->		Any:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[Any]			     = os.path.dirname(lowerCAmelCase_		)
       _UpperCAmelCase  :						str			     = os.path.basename(lowerCAmelCase_		)

       _UpperCAmelCase  :						Optional[int]			     = onnx.load(os.path.join(lowerCAmelCase_       ,   lowerCAmelCase_		)		)

       _UpperCAmelCase  :						List[Any]			     = list(model.graph.initializer		)

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

       _UpperCAmelCase  :						Union[str, Any]			     = 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_		)

                            _UpperCAmelCase  :						str			     = inits[j].data_type
                            _UpperCAmelCase  :						List[Any]			     = 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

                            _UpperCAmelCase  :						Any			     = inits[i].name
                            _UpperCAmelCase  :						Dict			     = inits[j].name

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

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

       _UpperCAmelCase  :						Optional[Any]			     = sorted(lowerCAmelCase_		)
       _remove_dup_initializers_from_model(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)

       _UpperCAmelCase  :						Any			     = """optimized_""" + model_file_name
       _UpperCAmelCase  :						Tuple			     = os.path.join(lowerCAmelCase_       ,   lowerCAmelCase_		)
       onnx.save(lowerCAmelCase_       ,   lowerCAmelCase_		)

       return new_model








'''simple docstring'''






from torch import nn



class 							lowercase       (  nn.Module				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_							)		->   List[Any]:
        super().__init__()
        _UpperCAmelCase  :						Dict			     = class_size
        _UpperCAmelCase  :						Union[str, Any]			     = embed_size
        # self.mlp1 = nn.Linear(embed_size, embed_size)
        # self.mlp2 = (nn.Linear(embed_size, class_size))
        _UpperCAmelCase  :						List[Any]			     = nn.Linear(a_  ,a_							)







 def _snake_case (							self  ,a_							)		->   Tuple:
        # hidden_state = nn.functional.relu(self.mlp1(hidden_state))
        # hidden_state = self.mlp2(hidden_state)
        _UpperCAmelCase  :						Optional[int]			     = self.mlp(a_							)
        return logits



