

import os

import numpy
import onnx
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     List[Any]				)							->				List[str]:





    '''simple docstring'''






    A__   				=       a.name
    A__   				=       b.name

    A__   				=       ''
    A__   				=       ''

    A__   				=       a == b

    A__   				=       name_a
    A__   				=       name_b

    return res
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     List[str]				)							->				Optional[Any]:





    '''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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				Any:





    '''simple docstring'''






    for n in graph_proto.node:
        _node_replace_input_with(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Dict				)							->				Any:





    '''simple docstring'''






    A__   				=       list(model.graph.initializer				)
    A__   				=       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__   				=       inits[i].name
        A__   				=       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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int				)							->				int:





    '''simple docstring'''






    A__   				=       os.path.dirname(SCREAMING_SNAKE_CASE__				)
    A__   				=       os.path.basename(SCREAMING_SNAKE_CASE__				)

    A__   				=       onnx.load(os.path.join(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)				)

    A__   				=       list(model.graph.initializer				)

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

    A__   				=       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__   				=       inits[j].data_type
                A__   				=       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__   				=       inits[i].name
                A__   				=       inits[j].name

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

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

    A__   				=       sorted(SCREAMING_SNAKE_CASE__				)
    _remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

    A__   				=       'optimized_' + model_file_name
    A__   				=       os.path.join(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    onnx.save(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

    return new_model




import random
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     list   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				tuple:





    '''simple docstring'''






    A__				,						A__				,						A__   				=       [], [], []
    for element in data:
        if element < pivot:
            less.append(SCREAMING_SNAKE_CASE__				)
        elif element > pivot:
            greater.append(SCREAMING_SNAKE_CASE__				)
        else:
            equal.append(SCREAMING_SNAKE_CASE__				)
    return less, equal, greater

def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     list   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				str:





    '''simple docstring'''






    if index >= len(SCREAMING_SNAKE_CASE__				) or index < 0:
        return None

    A__   				=       items[random.randint(0   ,      len(SCREAMING_SNAKE_CASE__				) - 1				)]
    A__   				=       0
    A__				,						A__				,						A__   				=       _partition(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    A__   				=       len(SCREAMING_SNAKE_CASE__				)
    A__   				=       len(SCREAMING_SNAKE_CASE__				)

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    # must be in larger
    else:
        return quick_select(SCREAMING_SNAKE_CASE__   ,      index - (m + count)				)

