





import os

import numpy
import onnx


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						a.name
       _UpperCamelCase              =						b.name

       _UpperCamelCase              =						''''''
       _UpperCamelCase              =						''''''

       _UpperCamelCase              =						a == b

       _UpperCamelCase              =						name_a
       _UpperCamelCase              =						name_b

       return res


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
       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			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
       for n in graph_proto.node:
              _node_replace_input_with(lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						list(model.graph.initializer					)
       _UpperCamelCase              =						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              =						inits[i].name
              _UpperCamelCase              =						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			(      lowerCAmelCase					):
       _UpperCamelCase              =						os.path.dirname(lowerCAmelCase					)
       _UpperCamelCase              =						os.path.basename(lowerCAmelCase					)

       _UpperCamelCase              =						onnx.load(os.path.join(lowerCAmelCase    ,       lowerCAmelCase					)					)

       _UpperCamelCase              =						list(model.graph.initializer					)

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

       _UpperCamelCase              =						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              =						inits[j].data_type
                            _UpperCamelCase              =						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              =						inits[i].name
                            _UpperCamelCase              =						inits[j].name

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

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

       _UpperCamelCase              =						sorted(lowerCAmelCase					)
       _remove_dup_initializers_from_model(lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					)

       _UpperCamelCase              =						'''optimized_''' + model_file_name
       _UpperCamelCase              =						os.path.join(lowerCAmelCase    ,       lowerCAmelCase					)
       onnx.save(lowerCAmelCase    ,       lowerCAmelCase					)

       return new_model













from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       return math.sqrt(sum(pow(a - b    ,       2					) for a, b in zip(lowerCAmelCase    ,       lowerCAmelCase					)					)					)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):

       if dataset.ndim != value_array.ndim:
              _UpperCamelCase              =						(
                  '''Wrong input data\'s dimensions... '''
                  f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
              )
              raise ValueError(lowerCAmelCase					)

       try:
              if dataset.shape[1] != value_array.shape[1]:
                     _UpperCamelCase              =						(
                         '''Wrong input data\'s shape... '''
                         f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
                     )
                     raise ValueError(lowerCAmelCase					)
       except IndexError:
              if dataset.ndim != value_array.ndim:
                     raise TypeError('''Wrong shape'''					)

       if dataset.dtype != value_array.dtype:
              _UpperCamelCase              =						(
                  '''Input data have different datatype... '''
                  f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
              )
              raise TypeError(lowerCAmelCase					)

       _UpperCamelCase              =						[]

       for value in value_array:
              _UpperCamelCase              =						euclidean(lowerCAmelCase    ,       dataset[0]					)
              _UpperCamelCase              =						dataset[0].tolist()

              for dataset_value in dataset[1:]:
                     _UpperCamelCase              =						euclidean(lowerCAmelCase    ,       lowerCAmelCase					)

                     if dist > temp_dist:
                            _UpperCamelCase              =						temp_dist
                            _UpperCamelCase              =						dataset_value.tolist()

              answer.append([vector, dist]					)

       return answer


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       return np.dot(lowerCAmelCase    ,       lowerCAmelCase					) / (norm(lowerCAmelCase					) * norm(lowerCAmelCase					))


if __name__ == "__main__":
       import doctest

       doctest.testmod()






