




"""simple docstring"""

import os

import numpy
import onnx




def     _A					( __lowercase							,     __lowercase    ):





       """simple docstring"""



       lowerCamelCase__          =						a.name
       lowerCamelCase__          =						b.name

       lowerCamelCase__          =						""""""
       lowerCamelCase__          =						""""""

       lowerCamelCase__          =						a == b

       lowerCamelCase__          =						name_a
       lowerCamelCase__          =						name_b

       return res




def     _A					( __lowercase							,     __lowercase							,     __lowercase    ):





       """simple docstring"""



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

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




def     _A					( __lowercase							,     __lowercase							,     __lowercase    ):





       """simple docstring"""



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




def     _A					( __lowercase							,     __lowercase							,     __lowercase    ):





       """simple docstring"""



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

              lowerCamelCase__          =						inits[i].name
              lowerCamelCase__          =						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							,     __UpperCamelCase							,     __UpperCamelCase    )




def     _A					( __lowercase    ):





       """simple docstring"""



       lowerCamelCase__          =						os.path.dirname(__UpperCamelCase    )
       lowerCamelCase__          =						os.path.basename(__UpperCamelCase    )

       lowerCamelCase__          =						onnx.load(os.path.join(__UpperCamelCase							,     __UpperCamelCase    )    )

       lowerCamelCase__          =						list(model.graph.initializer    )

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

       lowerCamelCase__          =						0

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

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

                            lowerCamelCase__          =						inits[j].data_type
                            lowerCamelCase__          =						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: """							,     __UpperCamelCase    )
                            total_reduced_size += mem_size

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

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

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

       lowerCamelCase__          =						sorted(__UpperCamelCase    )
       _remove_dup_initializers_from_model(__UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase    )

       lowerCamelCase__          =						"""optimized_""" + model_file_name
       lowerCamelCase__          =						os.path.join(__UpperCamelCase							,     __UpperCamelCase    )
       onnx.save(__UpperCamelCase							,     __UpperCamelCase    )

       return new_model











SCREAMING_SNAKE_CASE__   			=   '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

SCREAMING_SNAKE_CASE__   			=   [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE__   			=   {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}




